System identification techniques for adaptive signal processing
NLOS identification and mitigation for localization based on UWB experimental data
NLOS Identification and Mitigation for Localization Based on UWB Experimental Data Stefano Maran`o,Student Member,IEEE,Wesley M.Gifford,Student Member,IEEE,Henk Wymeersch,Member,IEEE,Moe Z.Win,Fellow,IEEEAbstract—Sensor networks can benefit greatly from location-awareness,since it allows information gathered by the sensors to be tied to their physical locations.Ultra-wide bandwidth(UWB) transmission is a promising technology for location-aware sensor networks,due to its power efficiency,fine delay resolution,and robust operation in harsh environments.However,the presence of walls and other obstacles presents a significant challenge in terms of localization,as they can result in positively biased distance estimates.We have performed an extensive indoor measurement campaign with FCC-compliant UWB radios to quantify the effect of non-line-of-sight(NLOS)propagation.From these channel pulse responses,we extract features that are representative of the propagation conditions.We then develop classification and regression algorithms based on machine learning techniques, which are capable of:(i)assessing whether a signal was trans-mitted in LOS or NLOS conditions;and(ii)reducing ranging error caused by NLOS conditions.We evaluate the resulting performance through Monte Carlo simulations and compare with existing techniques.In contrast to common probabilistic approaches that require statistical models of the features,the proposed optimization-based approach is more robust against modeling errors.Index Terms—Localization,UWB,NLOS Identification,NLOS Mitigation,Support Vector Machine.I.I NTRODUCTIONL OCATION-AW ARENESS is fast becoming an essential aspect of wireless sensor networks and will enable a myr-iad of applications,in both the commercial and the military sectors[1],[2].Ultra-wide bandwidth(UWB)transmission [3]–[8]provides robust signaling[8],[9],as well as through-wall propagation and high-resolution ranging capabilities[10], [11].Therefore,UWB represents a promising technology for localization applications in harsh environments and accuracy-critical applications[10]–[15].In practical scenarios,however, a number of challenges remain before UWB localization and communication can be deployed.These include signal Manuscript received15May2009;revised15February2010.This research was supported,in part,by the National Science Foundation under grant ECCS-0901034,the Office of Naval Research Presidential Early Career Award for Scientists and Engineers(PECASE)N00014-09-1-0435,the Defense University Research Instrumentation Program under grant N00014-08-1-0826, and the MIT Institute for Soldier Nanotechnologies.S.Maran`o was with Laboratory for Information and Decision Systems (LIDS),Massachusetts Institute of Technology(MIT),and is now with the Swiss Seismological Service,ETH Z¨u rich,Z¨u rich,Switzerland(e-mail: stefano.marano@sed.ethz.ch).H.Wymeersch was with LIDS,MIT,and is now with Chalmers University of Technology,G¨o teborg,Sweden(e-mail:henkw@chalmers.se).Wesley M.Gifford and Moe Z.Win are with LIDS,MIT,Cambridge,MA 02139USA(e-mail:wgifford@,moewin@).Digital Object Identifier10.1109/JSAC.2010.100907.acquisition[16],multi-user interference[17],[18],multipath effects[19],[20],and non-line-of-sight(NLOS)propagation [10],[11].The latter issue is especially critical[10]–[15]for high-resolution localization systems,since NLOS propagation introduces positive biases in distance estimation algorithms, thus seriously affecting the localization performance.Typical harsh environments such as enclosed areas,urban canyons, or under tree canopies inherently have a high occurrence of NLOS situations.It is therefore critical to understand the impact of NLOS conditions on localization systems and to develop techniques that mitigate their effects.There are several ways to deal with ranging bias in NLOS conditions,which we classify as identification and mitigation. NLOS identification attempts to distinguish between LOS and NLOS conditions,and is commonly based on range estimates[21]–[23]or on the channel pulse response(CPR) [24],[25].Recent,detailed overviews of NLOS identification techniques can be found in[22],[26].NLOS mitigation goes beyond identification and attempts to counter the positive bias introduced in NLOS signals.Several techniques[27]–[31]rely on a number of redundant range estimates,both LOS and NLOS,in order to reduce the impact of NLOS range estimates on the estimated agent position.In[32]–[34]the geometry of the environment is explicitly taken into account to cope with NLOS situations.Other approaches,such as[35],attempt to detect the earliest path in the CPR in order to better estimate the TOA in NLOS prehensive overviews of NLOS mitigation techniques can be found in[26],[36]. The main drawbacks of existing NLOS identification and mitigation techniques are:(i)loss of information due to the direct use of ranges instead of the CPRs;(ii)latency incurred during the collection of range estimates to establish a history; and(iii)difficulty in determining the joint probability distribu-tions of the features required by many statistical approaches. In this paper,we consider an optimization-based approach. In particular,we propose the use of non-parametric ma-chine learning techniques to perform NLOS identification and NLOS mitigation.Hence,they do not require a statistical characterization of LOS and NLOS channels,and can perform identification and mitigation under a common framework.The main contributions of this paper are as follows:•characterization of differences in the CPRs under LOS and NLOS conditions based on an extensive indoor mea-surement campaign with FCC-compliant UWB radios;•determination of novel features extracted from the CPR that capture the salient properties in LOS and NLOS conditions;0733-8716/10/$25.00c 2010IEEE•demonstration that a support vector machine (SVM)clas-si fier can be used to distinguish between LOS and NLOS conditions,without the need for statistical modeling of the features under either condition;and•development of SVM regressor-based techniques to mit-igate the ranging bias in NLOS situations,again without the need for statistical modeling of the features under either condition.The remainder of the paper is organized as follows.In Section II,we introduce the system model,problem statement,and describe the effect of NLOS conditions on ranging.In Section III,we describe the equipment and methodologies of the LOS/NLOS measurement campaign and its contribu-tion to this work.The proposed techniques for identi fication and mitigation are described in Section IV,while different strategies for incorporating the proposed techniques within any localization system are discussed in Section V.Numerical performance results are provided in Section VI,and we draw our conclusions in Section VII.II.P ROBLEM S TATEMENT AND S YSTEM M ODEL In this section,we describe the ranging and localization algorithm,and demonstrate the need for NLOS identi fication and mitigation.A.Single-node LocalizationA network consists of two types of nodes:anchors are nodes with known positions,while agents are nodes with unknown positions.For notational convenience,we consider the point of view of a single agent,with unknown position p ,surrounded by N b anchors,with positions,p i ,i =1,...,N b .The distance between the agent and anchor i is d i = p −p i .The agent estimates the distance between itself and the anchors,using a ranging protocol.We denote the estimateddistance between the agent and anchor i by ˆdi ,the ranging error by εi =ˆdi −d i ,the estimate of the ranging error by ˆεi ,the channel condition between the agent and anchor i by λi ∈{LOS ,NLOS },and the estimate of the channelcondition by ˆλi .The mitigated distance estimate of d i is ˆd m i=ˆd i −ˆεi .The residual ranging error after mitigation is de fined as εm i =ˆd m i −d i .Given a set of at least three distance estimates,the agent will then determine its position.While there are numerous positioning algorithms,we focus on the least squares (LS)criterion,due to its simplicity and because it makes no assumptions regarding ranging errors.The agent can infer its position by minimizing the LS cost functionˆp=arg min p(p i ,ˆdi )∈Sˆd i − p −p i 2.(1)Note that we have introduced the concept of the set of useful neighbors S ,consisting of couplesp i ,ˆdi .The optimization problem (1)can be solved numerically using steepest descent.B.Sources of ErrorThe localization algorithm will lead to erroneous results when the ranging errors are large.In practice the estimated distances are not equal to the true distances,because of a number of effects including thermal noise,multipath propa-gation,interference,and ranging algorithm inaccuracies.Ad-ditionally,the direct path between requester and responder may be obstructed,leading to NLOS propagation.In NLOS conditions,the direct path is either attenuated due to through-material propagation,or completely blocked.In the former case,the distance estimates will be positively biased due to the reduced propagation speed (i.e.,less than the expected speed of light,c ).In the latter case the distance estimate is also positively biased,as it corresponds to a re flected path.These bias effects can be accounted for in either the ranging or localization phase.In the remainder of this paper,we focus on techniques that identify and mitigate the effects of NLOS signals during the ranging phase.In NLOS identi fication,the terms in (1)corre-sponding to NLOS distance estimates are omitted.In NLOS mitigation,the distance estimates corresponding to NLOS signals are corrected for improved accuracy.The localization algorithm can then adopt different strategies,depending on the quality and the quantity of available range estimates.III.E XPERIMENTAL A CTIVITIESThis section describes the UWB LOS/NLOS measurement campaign performed at the Massachusetts Institute of Tech-nology by the Wireless Communication and Network Sciences Laboratory during Fall 2007.A.OverviewThe aim of this experimental effort is to build a large database containing a variety of propagation conditions in the indoor of fice environment.The measurements were made using two FCC-compliant UWB radios.These radios repre-sent off-the-shelf transceivers and therefore an appropriate benchmark for developing techniques using currently available technology.The primary focus is to characterize the effects of obstructions.Thus,measurement positions (see Fig.1)were chosen such that half of the collected waveforms were cap-tured in NLOS conditions.The distance between transmitter and receiver varies widely,from roughly 0.6m up to 18m,to capture a variety of operating conditions.Several of fices,hallways,one laboratory,and a large lobby constitute the physical setting of this campaign.While the campaign was conducted in one particular indoor of fice envi-ronment,because of the large number of measurements and the variety of propagation scenarios encountered,we expect that our results are applicable in other of fice environments.The physical arrangement of the campaign is depicted in Fig.1.In each measurement location,the received waveform and the associated range estimate,as well as the actual distance are recorded.The waveforms are then post-processed in order to reduce dependencies on the speci fic algorithm and hardware,e.g.,on the leading edge detection (LED)algorithm embedded in the radios.Fig.1.Measurements were taken in clusters over several different rooms and hallways to capture different propagation conditions.B.Experimental ApparatusThe commercially-available radios used during the data collection process are capable of performing communications and ranging using UWB signals.The radio complies with the emission limit set forth by the FCC[37].Specifically, the10dB bandwidth spans from3.1GHz to6.3GHz.The radio is equipped with a bottom fed planar elliptical antenna. This type of dipole antenna is reported to be well matched and radiation efficient.Most importantly,it is omni-directional and thus suited for ad-hoc networks with arbitrary azimuthal orientation[38].Each radio is mounted on the top of a plastic cart at a height of90cm above the ground.The radios perform a round-trip time-of-arrival(RTOA)ranging protocol1and are capable of capturing waveforms while performing the ranging procedure.Each waveform r(t)captured at the receiving radio is sampled at41.3ps over an observation window of190ns.C.Measurement ArrangementMeasurements were taken at more than one hundred points in the considered area.A map,depicting the topological organization of the clusters within the building,is shown 1RTOA allows ranging between two radios without a common time reference;and thus alleviates the need for networksynchronization.Fig.2.The measurement setup for collecting waveforms between D675CA and H6around the corner of the WCNS Laboratory.in Fig.1,and a typical measurement scenario is shown in Fig.2.Points are placed randomly,but are restricted to areas which are accessible by the carts.The measurement points are grouped into non-overlapping clusters,i.e.,each point only belongs to a single cluster.Typically,a cluster corresponds to a room or a region of a hallway.Within each cluster,measurements between every possible pair of points were captured.When two clusters were within transmission range, every inter-cluster measurement was collected as well.Overall, more than one thousand unique point-to-point measurements were performed.For each pair of points,several received waveforms and distance estimates are recorded,along with the actual distance.During each measurement the radios remain stationary and care is taken to limit movement of other objects in the nearby surroundings.D.DatabaseUsing the measurements collected during the measurement phase,a database was created and used to develop and evaluate the proposed identification and mitigation techniques. It includes1024measurements consisting of512waveforms captured in the LOS condition and512waveforms captured in the NLOS condition.The term LOS is used to denote the existence of a visual path between transmitter and receiver,i.e., a measurement is labeled as LOS when the straight line be-tween the transmitting and receiving antenna is unobstructed. The ranging estimate was obtained by an RTOA algorithm embedded on the radio.The actual position of the radio during each measurement was manually recorded,and the ranging error was calculated with the aid of computer-aided design(CAD)software.The collected waveforms were then processed to align thefirst path in the delay domain using a simple threshold-based method.The alignment process creates a time reference independent of the LED algorithm embedded on the radio.IV.NLOS I DENTIFICATION AND M ITIGATIONThe collected measurement data illustrates that NLOS prop-agation conditions significantly impact ranging performance. For example,Fig.3shows the empirical CDFs of the ranging error over the ensemble of all measurements collected under the two different channel conditions.In LOS conditions a ranging error below one meter occurs in more than95%of the measurements.On the other hand,in NLOS conditions a ranging error below one meter occurs in less than30%of the measurements.Clearly,LOS and NLOS range estimates have very dif-ferent characteristics.In this section,we develop techniques to distinguish between LOS and NLOS situations,and to mitigate the positive biases present in NLOS range estimates. Our techniques are non-parametric,and rely on least-squares support-vector machines(LS-SVM)[39],[40].Wefirst de-scribe the features for distinguishing LOS and NLOS situa-tions,followed by a brief introduction to LS-SVM.We then describe how LS-SVM can be used for NLOS identification and mitigation in localization applications,without needing to determine parametric joint distributions of the features for both the LOS and NLOS conditions.A.Feature Selection for NLOS ClassificationWe have extracted a number of features,which we expect to capture the salient differences between LOS and NLOS signals,from every received waveform r(t).These featuresFig.3.CDF of the ranging error for the LOS and NLOS condition. were selected based on the following observations:(i)in NLOS conditions,signals are considerably more attenuated and have smaller energy and amplitude due to reflections or obstructions;(ii)in LOS conditions,the strongest path of the signal typically corresponds to thefirst path,while in NLOS conditions weak components typically precede the strongest path,resulting in a longer rise time;and(iii)the root-mean-square(RMS)delay spread,which captures the temporal dispersion of the signal’s energy,is larger for NLOS signals. Fig.4depicts two waveforms received in the LOS and NLOS condition supporting our observations.We also include some features that have been presented in the literature.Taking these considerations into account,the features we will consider are as follows:1)Energy of the received signal:E r=+∞−∞|r(t)|2dt(2) 2)Maximum amplitude of the received signal:r max=maxt|r(t)|(3) 3)Rise time:t rise=t H−t L(4) wheret L=min{t:|r(t)|≥ασn}t H=min{t:|r(t)|≥βr max},andσn is the standard deviation of the thermal noise.The values ofα>0and0<β≤1are chosen empirically in order to capture the rise time;in our case, we usedα=6andβ=0.6.4)Mean excess delay:τMED=+∞−∞tψ(t)dt(5) whereψ(t)=|r(t)|2/E r.Fig.4.In some situations there is a clear difference between LOS(upper waveform)and NLOS(lower waveform)signals.5)RMS delay spread:τRMS=+∞−∞(t−τMED)2ψ(t)dt(6)6)Kurtosis:κ=1σ4|r|TT|r(t)|−μ|r|4dt(7)whereμ|r|=1TT|r(t)|dtσ2|r|=1TT|r(t)|−μ|r|2dt.B.Least Squares SVMThe SVM is a supervised learning technique used both for classification and regression problems[41].It represents one of the most widely used classification techniques because of its robustness,its rigorous underpinning,the fact that it requires few user-defined parameters,and its superior performance compared to other techniques such as neural networks.LS-SVM is a low-complexity variation of the standard SVM, which has been applied successfully to classification and regression problems[39],[40].1)Classification:A linear classifier is a function R n→{−1,+1}of the forml(x)=sign[y(x)](8) withy(x)=w Tϕ(x)+b(9) whereϕ(·)is a predetermined function,and w and b are unknown parameters of the classifier.These parameters are de-termined based on the training set{x k,l k}N k=1,where x k∈R n and l k∈{−1,+1}are the inputs and labels,respectively.In the case where the two classes can be separated the SVM determines the separating hyperplane which maximizes the margin between the two classes.2Typically,most practical problems involve classes which are not separable.In this case,the SVM classifier is obtained by solving the following optimization problem:arg minw,b,ξ12w 2+γNk=1ξk(10)s.t.l k y(x k)≥1−ξk,∀k(11)ξk≥0,∀k,(12) where theξk are slack variables that allow the SVM to tolerate misclassifications andγcontrols the trade-off between minimizing training errors and model complexity.It can be shown that the Lagrangian dual is a quadratic program(QP) [40,eqn.2.26].To further simplify the problem,the LS-SVM replaces the inequality(11)by an equality:arg minw,b,e12w 2+γ12Nk=1e2k(13)s.t.l k y(x k)=1−e k,∀k.(14) Now,the Lagrangian dual is a linear program(LP)[40,eqn.3.5],which can be solved efficiently by standard optimization toolboxes.The resulting classifier can be written asl(x)=signNk=1αk l k K(x,x k)+b,(15)whereαk,the Lagrange multipliers,and b are found from the solution of the Lagrangian dual.The function K(x k,x l)=ϕ(x k)Tϕ(x l)is known as the kernel which enables the SVM to perform nonlinear classification.2)Regression:A linear regressor is a function R n→R of the formy(x)=w Tϕ(x)+b(16) whereϕ(·)is a predetermined function,and w and b are unknown parameters of the regressor.These parameters are determined based on the training set{x k,y k}N k=1,where x k∈R n and y k∈R are the inputs and outputs,respectively. The LS-SVM regressor is obtained by solving the following optimization problem:arg minw,b,e12w 2+γ12e 2(17)s.t.y k=y(x k)+e k,∀k,(18) whereγcontrols the trade-off between minimizing training errors and model complexity.Again,the Lagrangian dual is an LP[40,eqn.3.32],whose solution results in the following LS-SVM regressory(x)=Nk=1αk K(x,x k)+b.(19)2The margin is given by1/ w ,and is defined as the smallest distance between the decision boundary w Tϕ(x)+b=0and any of the training examplesϕ(x k).C.LS-SVM for NLOS Identi fication and MitigationWe now apply the non-parametric LS-SVM classi fier to NLOS identi fication,and the LS-SVM regressor to NLOS mitigation.We use 10-fold cross-validation 3to assess the performance of our features and the SVM.Not only are we interested in the performance of LS-SVM for certain features,but we are also interested in which subsets of the available features give the best performance.1)Classi fication:To distinguish between LOS and NLOS signals,we train an LS-SVM classi fier with inputs x k and corresponding labels l k =+1when λk =LOS and l k =−1when λk =NLOS .The input x k is composed of a subset of the features given in Section IV-A.A trade-off between classi fier complexity and performance can be made by using a different size feature subset.2)Regression:To mitigate the effect of NLOS propagation,we train an LS-SVM regressor with inputs x k and corre-sponding outputs y k =εk associated with the NLOS signals.Similar to the classi fication case,x k is composed of a subset of features,selected from those given in Section IV-A and therange estimate ˆdk .Again,the performance achieved by the regressor will depend on the size of the feature subset and the combination of features used.V.L OCALIZATION S TRATEGIESBased on the LS-SVM classi fier and regressor,we can develop the following localization strategies:(i)localization via identi fication ,where only classi fication is employed;(ii)localization via identi fication and mitigation ,where the re-ceived waveform is first classi fied and error mitigation is performed only on the range estimates from those signals identi fied as NLOS;and (iii)a hybrid approach which discards mitigated NLOS range estimates when a suf ficient number of LOS range estimates are present.A.Strategy 1:StandardIn the standard strategy,all the range estimates ˆdi from neighboring anchor nodes are used by the LS algorithm (1)for localization.In other words,S S =p i ,ˆd i :1≤i ≤N b .(20)B.Strategy 2:Identi ficationIn the second strategy,waveforms are classi fied as LOS or NLOS using the LS-SVM classi fier.Range estimates are used by the localization algorithm only if the associated waveform was classi fied as LOS,while range estimates from waveforms classi fied as NLOS are discarded:S I = p i ,ˆd i:1≤i ≤N b ,ˆλi =LOS .(21)Whenever the cardinality of S I is less than three,the agent isunable to localize.4In this case,we set the localization error to +∞.3InK -fold cross-validation,the dataset is randomly partitioned into K parts of approximately equal size,each containing 50%LOS and 50%NLOS waveforms.The SVM is trained on K −1parts and the performance is evaluated on the remaining part.This is done a total of K times,using each of the K parts exactly once for evaluation and K −1times for training.4Note that three is the minimum number of anchor nodes needed to localize in two-dimensions.TABLE IF ALSE ALARM PROBABILITY (P F ),MISSED DETECTION PROBABILITY (P M ),AND OVERALL ERROR PROBABILITY (P E )FOR DIFFERENT NLOSIDENTIFICATION TECHNIQUES .T HE SET F i IDENOTES THE SET OF i FEATURES WITH THE SMALLEST P E USING THE LS-SVM TECHNIQUE .Identi fication Technique P F P M P E Parametric technique given in [42]0.1840.1430.164LS-SVM using features from [42]0.1290.1520.141F 1I ={r max }0.1370.1230.130F 2I ={r max ,t rise }0.0920.1090.100F 3I ={E r ,t rise ,κ}0.0820.0900.086F 4I={E r ,r max ,t rise ,κ}0.0820.0900.086F 5I ={E r ,r max ,t rise ,τMED ,κ}0.0860.0900.088F 6I={E r ,r max ,t rise ,τMED ,τRMS ,κ}0.0920.0900.091C.Strategy 3:Identi fication and MitigationThis strategy is an extension to the previous strategy,wherethe received waveform is first classi fied as LOS or NLOS,and then the mitigation algorithm is applied to those signals with ˆλi =NLOS .For this case S IM =S I ∪S M ,where S M = p i ,ˆd m i :1≤i ≤N b ,ˆλi =NLOS ,(22)and the mitigated range estimate ˆd m iis described in Sec.II.This approach is motivated by the observation that mitigation is not necessary for range estimates associated with LOS waveforms,since their accuracy is suf ficiently high.D.Strategy 4:Hybrid Identi fication and Mitigation In the hybrid approach,range estimates are mitigated as in the previous strategy.However,mitigated range estimates are only used when less than three LOS anchors are available:5S H =S I if |S I |≥3S IMotherwise(23)This approach is motivated by the fact that mitigated range es-timates are often still less accurate than LOS range estimates.Hence,only LOS range estimates should be used,unless there is an insuf ficient number of them to make an unambiguous location estimate.VI.P ERFORMANCE E VALUATION AND D ISCUSSION In this section,we quantify the performance of the LS-SVM classi fier and regressor from Section IV,as well as the four localization strategies from Section V.We will first consider identi fication,then mitigation,and finally localization.For every technique,we will provide the relevant performance measures as well as the quantitative details of how the results were obtained.5Inpractice the angular separation of the anchors should be suf ficiently large to obtain an accurate estimate.If this is not the case,more than three anchors may be needed.TABLE IIM EAN AND RMS VALUES OF RRE FOR LS-SVM REGRESSION -BASEDMITIGATION .T HE SET F i MDENOTES THE SET OF i FEATURES WHICH ACHIEVES THE MINIMUM RMS RRE.Mitigation Technique with LS-SVM RegressionMean [m]RMS [m ]No Mitigation2.63223.589F 1M={ˆd}-0.0004 1.718F 2M ={κ,ˆd }-0.0042 1.572F 3M ={t rise ,κ,ˆd }0.0005 1.457F 4M ={t rise ,τMED ,κ,ˆd }0.0029 1.433F 5M={E r ,t rise ,τMED ,κ,ˆd }0.0131 1.425F 6M ={E r ,t rise ,τMED ,τRMS ,κ,ˆd }0.0181 1.419F 7M={E r ,r max ,t rise ,τMED ,τRMS ,κ,ˆd}0.01801.425A.LOS/NLOS Identi ficationIdenti fication results,showing the performance 6for eachfeature set size,are given in Table I.For the sake of compari-son,we also evaluate the performance of the parametric identi-fication technique from [42],which relies on three features:the mean excess delay,the RMS delay spread,and the kurtosis of the waveform.For fair comparison,these features are extracted from our database.The performance is measured in terms of the misclassi fication rate:P E =(P F +P M )/2,where P F is the false alarm probability (i.e.,deciding NLOS when the signal was LOS),and P M is the missed detection probability (i.e.,deciding LOS when the signal was NLOS).The table only lists the feature sets which achieved the minimum misclassi fication rate for each feature set size.We observe that the LS-SVM,using the three features from [42],reduces the false alarm probability compared to the parametric technique.It was shown in [43]that the features from [42],in fact,give rise to the worst performance among all possible sets of size three considered ing the features from Section IV-A and considering all feature set sizes,our results indicate that the feature set of size three,F 3I ={E r ,t rise ,κ},provides the best pared to the parametric technique,this set reduces both the false alarm and missed detection probabilities and achieves a correct classi fication rate of above 91%.In particular,among all feature sets of size three (results not shown,see [43]),there are seven sets that yield a P E of roughly 10%.All seven of these sets have t rise in common,while four have r max in common,indicating that these two features play an important role.Their importance is also corroborated by the presence of r max and t rise in the selected sets listed in Table I.For the remainder of this paper we will use the feature set F 3I for identi fication.B.NLOS MitigationMitigation results,showing the performance 7for different feature set sizes are given in Table II.The performance is measured in terms of the root mean square residual ranging6Wehave used an RBF kernel of the form K (x ,x k )=exp “− x −x k 2”and set γ=0.1.Features are first converted to the log domain in order to reduce the dynamic range.7Here we used a kernel given by K (x ,x k )=exp “− x −x k2/162”and set γ=10.Again,features are first converted to the log domain.Fig.5.CDF of the ranging error for the NLOS case,before and after mitigation.error (RMS RRE): 1/N N i =1(εm i )2.A detailed analysis ofthe experimental data indicates that large range estimates are likely to exhibit large positive ranging errors.This means that ˆditself is a useful feature,as con firmed by the presence of ˆdin all of the best feature sets listed in the table.Increasing the feature set size can further improve the RMS RRE.Thefeature set of size six,F 6M={E r ,t rise ,τMED ,τRMS ,κ,ˆd },offers the best performance.For the remainder of this paper,we will use this feature set for NLOS mitigation.Fig.5shows the CDF of the ranging error before and after mitigation using this feature set.We observe that without mitigation around 30%of the NLOS waveforms achieved an accuracy of less than one meter (|ε|<1).Whereas,after the mitigation process,60%of the cases have an accuracy less than 1m.C.Localization Performance1)Simulation Setup:We evaluate the localization perfor-mance for fixed number of anchors (N b )and a varying prob-ability of NLOS condition 0≤P NLOS ≤1.We place an agent at a position p =(0,0).For every anchor i (1≤i ≤N b ),we draw a waveform from the database:with probability P NLOS we draw from the NLOS database and with probability 1−P NLOS from the LOS database.The true distance d i corre-sponding to that waveform is then used to place the i th anchor at position p i =(d i sin(2π(i −1)/N b ),d i cos(2π(i −1)/N b )),while the estimated distance ˆdi is provided to the agent.This creates a scenario where the anchors are located at different distances from the agent with equal angular spacing.The agent estimates its position,based on a set of useful neighbors S ,using the LS algorithm from Section II.The arithmetic mean 8of the anchor positions is used as the initial estimate of the agent’s position.2)Performance Measure:To capture the accuracy and availability of localization,we introduce the notion of outage probability .For a certain scenario (N b and P NLOS )and a8Thisis a fair setting for the simulation,as all the strategies are initialized inthe same way.Indeed,despite the identical initialization,strategies converge to signi ficantly different final position estimates.In addition,we note that such an initial position estimate is always available to the agent.。
中国原子能科学研究院的AMS研究进展
2007年10月质谱学报21中国原子能科学研究院的AMS研究进展姜山，何明，胡跃明，袁坚（中国原子能科学研究院，北京 102413）Status of AMS at the China Institute of Atomic EnergyJIANG Shan, HE Ming, HU Y ue-ming, YUAN Jian(China Institute of Atomic Energy, Beijing 102413, China)Abstract: The first accelerator mass spectrometer (AMS) system in China was set up at the China Institute ofAtomic Energy in 1989. In the following years, long-lived nuclides 10Be, 26Al, 36Cl, 41Ca, 79Se and 129I were measured in geology, environment and biology samples. The newly development of the AMS measurements andapplications in recent years were introduced. The current projects include upgrading of AMS injection system forincreasing mass resolution, new particle identification techniques for separation of isobars, development of theAMS measurement methods for 99Tc, 93Zr, 151Sm and 182Hf analyses, and applications in biology, environmentand nuclear astrophysics.Key words: accelerator mass spectrometer; long-lived nuclides; measurement中图分类号：O657.63 文献标识码：A 文章编号：1004-2997（2007）增刊-21-03加速器质谱（accelerator mass spectrometry, 简称AMS）是20世纪70年代末基于粒子加速器技术和离子探测技术发展起来的一种质谱分析技术[1-2]。
先进过程控制参考书目
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机械工程及自动化专业外文翻译--应用坐标测量机的机器人运动学姿态的标定翻译
外文原文：Full-Pose Calibration of a Robot Manipulator Using a Coordinate-Measuring MachineThe work reported in this article addresses the kinematic calibration of a robot manipulator using a coordinate measuring m a c h i n e(C M M)w h i c h i s a b l e t o o b t a i n t h e f u l l p o s e o f t h e e n d-e f f e c t o r.A k i n e m a t i c m o d e l i s d e v e l o p e d f o r t h e manipulator, its relationship to the world coordinate frame and the tool. The derivation of the tool pose from experimental measurements is discussed, as is the identification methodolo gy.A complete simulation of the experiment is performed, allowing the observation strategy to be defined. The experimental work is described together with the parameter identification and accuracy verification. The principal conclusion is that the m et ho d is a ble t o calibrate the robot succes sful ly, with a resulting accuracy approaching that of its repeatability.Keywords: Robot calibration; Coordinate measurement; Parameter identification; Simulation study; Accuracy enhancement 1. IntroductionIt is wel l known tha t robo t manip ula tors t ypical ly ha ve reasonable repeatability (0.3 ram), yet exhibit poor accuracy(10.0m m).T h e p r o c e s s b y w h i c h r o b o t s m a y b e c a l i b r a t e di n o r d e r t o a c h i e v e a c c u r a c i e s a p p r o a c h i n g t h a t o f t h e m a n i p u l a t o r i s a l s o w e l l u n d e r s t o o d .I n t h e c a l i b r a t i o n process, several sequential steps enable the precise kinematic p ar am et er s o f th e m an ip u l ato r to b e ide nti fi ed,l ead ing t o improved accuracy. These steps may be described as follows: 1. A kinematic model of the manipulator and the calibration process itself is developed and is usually accomplished with s t a n d a r d k i n e m a t i c m o d e l l i n g t o o l s.T h e r e s u l t i n g m o d e l i s u s e d t o d e f i n e a n e r r o r q u a n t i t y b a s e d o n a n o m i n a l (m a n u f a c t u r e r's)k i n e m a t i c p a r a m e t e r s e t,a n d a n u n k n o w n, actual parameter set which is to be identified.2. Ex pe ri me n ta l m ea su re m e nts o f th e rob ot po se (p art ial o r complete) are taken in order to obtain data relating to the actual parameter set for the robot.3.The actual kinematic parameters are identified by systematicallyc h a n g i n g t h e n o m i n a l p a r a m e t e r s e t s o a s t o r ed u ce t h e e r r o r q u a n t i t y d ef i n e d i n t h e m o d e l l i ng ph a s e.O n e a p p r o a c h t o a c hi e v i n g t h i s i d e n t i f i c a t i o n i s d e t e r m i n i n g t h e a n a l y t i c a l d i f f e r e n t i a l r e l a t i o n s h i p b e t w e e n t h e p o s e v a r i a b l e s P a n d t h e k i n e m a t i c p a r a m e t e r s K i n t h e f o r m of a Jacobian,and then inverting the equation to calculate the deviation of t h e k i n e m a t i c p a r a m e t e r s f r o m t h e i r n o m i n a l v a l u e sAlternatively, the problem can be viewed as a multidimensional o p t i m i s a t i o n t a s k,i n w h i c h t h e k i n e m a t i c p a r a m e t e r set is changed in order to reduce some defined error function t o z e r o.T h i s i s a s t a n d a r d o p t i m i s a t i o n p r o b l e m a n d m a y be solved using well-known methods.4. The final step involves the incorporation of the identified k i n e m a t i c p a r a m e t e r s i n t h e c o n t r o l l e r o f t h e r o b o t a r m, the details of which are rather specific to the hardware of the system under study.This paper addresses the issue of gathering the experimental d a t a u s e d i n t h e c a l i b r a t i o n p r o c e s s.S e v e r a l m e t h o d s a r e available to perform this task, although they vary in complexity, c o s t a n d t h e t i m e t a k e n t o a c q u i r e t h e d a t a.E x a m p l e s o f s u c h t e c h n i q u e s i n c l u d e t h e u s e o f v i s u a l a n d a u t o m a t i c t h e o d o l i t e s,s e r v o c o n t r o l l e d l a s e r i n t e r f e r o m e t e r s, a c o u s t i c s e n s o r s a n d v i d u a l s e n s o r s .A n i d e a l m e a s u r i n g system would acquire the full pose of the manipulator (position and orientation), because this would incorporate the maximum information for each position of the arm. All of the methods m e n t i o n e d a b o v e u s e o n l y t h e p a r t i a l p o s e,r e q u i r i n g m o r ed a t a t o be t a k e nf o r t h e c a l i b r a t i o n p r o c e s s t o p r o c e e d.2. TheoryIn the method described in this paper, for each position in which the manipulator is placed, the full pose is measured, although several intermediate measurements have to be taken in order to arrive at the pose. The device used for the pose m e a s u r e m e n t i s a c o o r d i n a t e-m e a s u r i n g m a c h i n e(C M M), w h i c h i s a t h r e e-a x i s,p r i s m a t i c m e a s u r i n g s y s t e m w i t h a q u o t e d a c c u r a c y o f0.01r a m.T h e r o b o t m a n i p u l a t o r t o b e c a l i b r a t e d,a P U M A560,i s p l a c e d c l o s e t o t h e C M M,a n d a special end-effector is attached to the flange. Fig. 1 shows the arrangement of the various parts of the system. In this s e c t i o n t h e k i n e m a t i c m o d e l w i l l b e d e v e l o p e d,t h e p o s e estimation algorithms explained, and the parameter identification methodology outlined.2.1 Kinematic ParametersIn this section, the basic kinematic structure of the manipulator will be specified, its relation to a user-defined world coordinatesystem discussed, and the end-point toil modelled. From these m o d e l s,t h e k i n e m a t i c p a r a m e t e r s w h i c h m a y b e i d e n t i f i e d using the proposed technique will be specified, and a method f o r d e t e r m i n i n g t h o s e p a r a m e t e r s d e s c r i b e d. The fundamental modelling tool used to describe the spatial relationship between the various objects and locations in the m a n i p u l a t o r w o r k s p a c e i s t h e D e n a v i t-H a r t e n b e r g m e t h o d ,w i t h m o d i f i c a t i o n s p r o p o s e d b y H a y a t i,M o o r i n g a n d W u t o a c c o u n t f o r d i s p r o p o r t i o n a l m o d e l s w he n tw o co n se cu t iv e jo i n t a x e s ar e nom ina ll y p a r all el. A s s h o w n i n F i g.2,t h i s m e t h o d p l a c e s a c o o r d i n a t e f r a m e o neach object or manipulator link of interest, and the kinematics a r e d e f i n e d b y t h e h o m o g e n e o u s t r a n s f o r m a t i o n r e q u i r e d t o change one coordinate frame into the next. This transformation takes the familiar formT h e a b o v e e q u a t i o n m a y b e i n t e r p r e t e d a s a m e a n s t o t r a n s f o r m f r a m e n-1i n t o f r a m e n b y m e a n s o f f o u r o u t o f t h e f i v e o p e r a t i o n s i n d i c a t e d.I t i s k n o w n t h a t o n l y f o u r transformations are needed to locate a coordinate frame with r es pect to the p revious one. Whe n consecutiv e ax es are not parallel, the value of/3. is defined to be zero, while for the case when consecutive axes are parallel, d. is the variable chosen to be zero.W h e n c o o r d i n a t e f r a m e s a r e p l a c e d i n c o n f o r m a n c e w i t h the modified Denavit-Hartenberg method, the transformations given in the above equation will apply to all transforms of o n e f r a m e i n t o t h e n e x t,a n d t h e s e m a y b e w r i t t e n i n a g e n e r i c m a t r i x f o r m,w h e r e t h e e l e m e n t s o f t h e m a t r i x a r e functions of the kinematic parameters. These parameters are simply the variables of the transformations: the joint angle 0., the common normal offset d., the link length a., the angle o f tw is t a., a nd th e an g l e /3.. Th e mat rix f orm i s u suall y expressed as follows:For a serial linkage, such as a robot manipulator, a coordinate frame is attached to each consecutive link so that both the instantaneous position together with the invariant geometry a r e d e s c r i b e d b y t h e p r e v i o u s m a t r i x t r a n s f o r m a t i o n.'T h etransformation from the base link to the nth link will therefore be given byF i g.3s h o w s t h e P U M A m a n i p u l a t o r w i t h t h e D e n a v i t-H a r t e n b e r g f r a m e s a t t a c h e d t o e a c h l i n k,t o g e t h e r with world coordinate frame and a tool frame. The transformation f r o m t h e w o r l d f r a m e t o t h e b a s e f r a m e o f t h e manipulator needs to be considered carefully, since there are potential parameter dependencies if certain types of transforms a r e c h o s e n.C o n s i d e r F i g.4,w h i c h s h o w s t h e w o r l d f r a m e x w,y,,z,,t h e f r a m e X o,Y o,z0w h i c h i s d e f i n e d b y a D H t r a n s f o r m f r o m t h e w o r l d f r a m e t o t h e f i r s t j o i n t a x i s o f t h e m a n i p u l a t o r,f r a m e X b,Y b,Z b,w h i c h i s t h e P U M Amanufacturer's defined base frame, and frame xl, Yl, zl which is the second DH frame of the manipulator. We are interested i n d e t e r m i n i n g t h e m i n i m u m n u m b e r o f p a r a m e t e r s r e q u i r e d to move from the world frame to the frame x~, Yl, z~. There are two transformation paths that will accomplish this goal: P a t h1:A D H t r a n s f o r m f r o m x,,y,,z,,t o x0,Y o,z o i n v o l v i n g f o u r p a r a m e t e r s,f o l l o w e d b y a n o t h e r t r a n s f o r m f r o m x o,Y o,z0t o X b,Y b,Z b w h i c h w i l l i n v o l v e o n l y t w o parameters ~b' and d' in the transformFinally, another DH transform from xb, Yb, Zb to Xt, y~, Z~ w hi ch i nv ol v es f o ur p ar a m ete r s e xc ept t hat A01 a n d 4~' ar e b o t h a b o u t t h e a x i s z o a n d c a n n o t t h e r e f o r e b e i d e n t i f i e d independently, and Adl and d' are both along the axis zo and also cannot be identified independently. It requires, therefore, o nl y ei gh t i nd ep e nd en t k i nem a t ic p arame ter s to g o fr om th e world frame to the first frame of the PUMA using this path. Path 2: As an alternative, a transform may be defined directly from the world frame to the base frame Xb, Yb, Zb. Since this is a frame-to-frame transform it requires six parameters, such as the Euler form:T h e f o l l o w i n g D H t r a n s f o r m f r o m x b,Y b,z b t O X l,Y l,z l would involve four parameters, but A0~ may be resolved into 4~,, 0b, ~, and Ad~ resolved into Pxb, Pyb, Pzb, reducing theparameter count to two. It is seen that this path also requires e i g h t p a r a m e t e r s a s i n p a t h i,b u t a d i f f e r e n t s e t.E i t h e r o f t h e a b o v e m e t h o d s m a y b e u s e d t o m o v e f r o m t h e w o r l d f r a m e t o t h e s e c o n d f r a m e o f t h e P U M A.I n t h i s w o r k,t h e s e c o n d p a t h i s c h o s e n.T h e t o o l t r a n s f o r m i s a n E u l e r t r a n s f o r m w h i c h r e q u i r e s t h e s p e c i f i c a t i o n o f s i x parameters:T he total n umber of paramete rs u sed in the k inem atic model becomes 30, and their nominal values are defined in Table 1.2.2 Identification MethodologyThe kinematic parameter identification will be performed as a multidimensional minimisation process, since this avoids the calculation of the system Jacobian. The process is as follows: 1. Be gi n wi t h a g ue ss s e t of k in em atic par am ete r s, s uch a s the nominal set.2. Select an arbitrary set of joint angles for the PUMA.3. Calculate the pose of the PUMA end-effector.4.M e a s u r e t h e a c t u a l p o s e o f t h e P U M A e n d-e f f e c t o r f o r t he s am e se t o f j oi nt a n g les.In g enera l, th e m e a sur ed an d predicted pose will be different.5. Mo di fy t h e ki n em at ic p ara m e te rs in a n o rd erl y man ner i n o r d e r t o b e s t f i t(i n a l e a s t-s q u a r e s s e n s e)t h e m e a s u r e d pose to the predicted pose.The process is applied not to a single set of joint angles but to a number of joint angles. The total number of joint angles e t s r e q u i r e d,w h i c h a l s o e q u a l s t h e n u m b e r o f p h y s i c a l measurement made, must satisfyK p i s t h e n u m b e r o f k i n e m a t i c p a r a m e t e r s t o b e i d e n t i f i e d N i s t h e n u m b e r o f m e a s u r e m e n t s(p o s e s)t a k e n D r r e p r e s e n t s t h e n u m b e r o f d e g r e e s o f f r e e d o m p r e s e n t i n each measurement.In the system described in this paper, the number of degrees of freedom is given bysince full pose is measured. In practice, many more measurements s h o u l d b e t a k e n t o o f f s e t t h e e f f e c t o f n o i s e i n t h e e xp er im en ta l m ea s ur em en t s. T h e o pt imisa tio n pro c e dur e use d is known as ZXSSO, and is a standard library function in the IMSL package .2.3 Pose MeasurementIt is apparent from the above that a means to determine the f u l l p o s e o f t h e P U M A i s r e q u i r e d i n o r d e r t o p e r f o r m t h e calibration. This method will now be described in detail. The end-effector consists of an arrangement of five precisiontooling b a l l s a s s h o w n i n F i g. 5.C o n s i d e r t h e c o o r d i n a t e s o f the centre of each ball expressed in terms of the tool frame (Fig. 5) and the world coordinate frame, as shown in Fig. 6. T h e r e l a t i o n s h i p b e t w e e n t h e s e c o o r d i n a t e s m a y b e w r i t t e n as:w he re P i' i s t he 4 x 1 c o lum n ve ct or of th e coo r d ina tes o f the ith ball expressed with respect to the world frame, P~ is t he 4 x 1 c o lu mn ve ct or o f t h e c oo rdina tes o f t h e it h bal l expressed with respect to the tool frame, and T is the 4 • 4 h o m o g e n i o u s t r a n s f o r m f r o m t h e w o r l d f r a m e t o t h e t o o l frame.The n may be foun d, a n d use d as th e m easure d pose in t he calibration process. It is not quite that simple, however, since it is not possible to invert equation (11) to obtain T. The a bo ve proce ss is performed f or t he four ball s, A, B, C and D, and the positions ordered as:or in the form:S i n c e P',T a n d P a r e a l l n o w s q u a r e,t h e p o s e m a t r i x m a y be obtained by inversion:I n pr ac ti ce it m a y be d i f fic u l t fo r the CM M to a c ces s fou r b a i l s t o d e t e r m i n e P~w h e n t h e P U M A i s p l a c e d i n c e r t a i n configurations. Three balls are actually measured and a fourth ball is fictitiously located according to the vector cross product:R e g a r d i n g t h e d e t e r m i n a t i o n o f t h e c o o r d i n a t e s o f t h ec e n t r e o f a b a l l b a s ed o n me a s u r e d p o i n t s o n i t s s u rf a c e, n o a n a l y t i c a l p r o c e d u r e s a r e a v a i l a b l e.A n o t h e r n u m e r i c a l optimisation scheme was used for this purpose such that the penalty function:w a s m i n i m i s e d,w h e r e(u,v,w)a r e t h e c o o r d i n a t e s o f t h e c e n t r e o f t h e b a l l t o h e d e t e r m i n e d,(x/,y~,z~)a r e t h e coordinates of the ith point on the surface of the ball and r i s th e ba ll di am e te r. I n the t es ts perf orm ed, i t was foun d sufficient to measure only four points (i = 4) on the surface to determine the ball centre.中文译文：应用坐标测量机的机器人运动学姿态的标定这篇文章报到的是用于机器人运动学标定中能获得全部姿态的操作装置——坐标测量机（CMM）。
三线性系统辨识方法性能优化研究
三线性系统辨识方法性能优化研究在现代工业控制系统中，三线性系统的辨识是十分重要的一项任务。
三线性系统由三个输入变量和三个输出变量组成，其模型具有三个变量之间的非线性关系。
本文旨在探讨三线性系统辨识方法的性能优化问题。
一、引言三线性系统广泛应用于电力系统、化工过程以及自动控制等领域。
对于三线性系统的辨识方法，一般采用参数化模型，例如线性参数化模型、非线性参数化模型等。
然而现有的辨识方法在复杂系统中可能存在性能不足的问题。
因此，我们需要研究优化三线性系统辨识方法的性能，以提高辨识的准确性和稳定性。
二、三线性系统辨识方法概述在介绍性能优化之前，我们先回顾一下目前常用的三线性系统辨识方法。
常见的方法包括基于频域分析的系统辨识方法、基于参数估计的系统辨识方法以及基于优化算法的系统辨识方法等。
这些方法各有优缺点，需要根据具体问题进行选择。
三、性能指标分析为了评估三线性系统辨识方法的性能，我们需要定义一些指标对其进行量化分析。
常见的性能指标包括准确性、鲁棒性、计算效率等。
准确性指标反映了辨识结果与实际系统模型之间的接近程度；鲁棒性指标评估了辨识方法对测量误差、模型误差等不确定性因素的敏感程度；计算效率指标反映了辨识方法的运行速度。
四、基于优化算法的性能优化针对性能指标分析中的不足，我们可以借鉴优化算法来进行三线性系统辨识方法的性能优化。
例如，可以使用遗传算法、模拟退火算法等进行参数优化，以寻找最佳的辨识结果。
优化算法的核心思想是通过迭代搜索来找到全局最优解或接近最优解，从而提高辨识方法的准确性和鲁棒性。
五、实验与结果分析为了验证优化算法在三线性系统辨识中的性能优化效果，我们设计了一系列实验，并比较了优化前后的辨识结果。
实验结果显示，采用优化算法的辨识方法在准确性和鲁棒性方面均有显著提高。
六、结论本文以三线性系统辨识方法的性能优化为研究目标，通过分析性能指标和引入优化算法等手段，提出了一种性能优化的方法。
实验结果表明，该方法在提高辨识方法的准确性和鲁棒性方面具有一定的优势。
ieee conference on decision and control 检索
ieee conference on decision and control 检索IEEE conference on decision and control (CDC) is a prominent annual conference in the field of systems, control, and decision-making. Focusing on new theories, methodologies, and applications, CDC serves as a platform for researchers and practitioners to exchange ideas and present their latest findings. In this article, we will explore the significance of CDC, its history, and key topics covered in the conference.CDC has a long history dating back to 1962 when it was first held. Since then, it has grown into a prestigious conference attracting experts and professionals from academia, industry, and government organizations. The conference features a wide range of technical sessions, workshops, panel discussions, and keynote speeches by distinguished speakers. The topics covered in CDC are diverse and encompass various aspects of control and decision-making, including but not limited to:1. Control Theory: CDC provides a platform for discussing state-of-the-art control theory and its applications. Topics such as stability analysis, optimal control, robust control, adaptive control, and nonlinear control are frequently discussed in the conference.2. Decision Theory: Decision-making plays a crucial role in many fields such as finance, economics, and operations research. CDC explores decision models, algorithms, and methods for solving decision-making problems in uncertain and dynamic environments.3. System Identification and Estimation: Estimating system parameters and identifying system models are essential tasks incontrol systems. CDC focuses on techniques for system identification, parameter estimation, and model validation.4. Robotics and Automation: With advancements in robotics and automation, CDC features sessions and workshops dedicated to discussing topics related to autonomous systems, robot control, machine learning in robotics, and human-robot interaction.5. Networked Control Systems: Networked systems have become ubiquitous in various domains such as transportation, energy, and communication networks. CDC explores control methodologies for networked systems, including networked estimation, networked control, and distributed control.6. Game Theory and Multi-agent Systems: Game theory provides a mathematical framework for analyzing decision-making in multi-agent systems. CDC discusses game-theoretic approaches to system design, coordination, and cooperation in multi-agent systems.7. Cyber-Physical Systems (CPS): CDC addresses challenges and solutions related to integrating physical systems with computational elements. Topics include modeling and control of CPS, sensor networks, and real-time systems.In addition to the technical sessions, CDC also organizes tutorial sessions presented by experts in the field. These tutorials offer an opportunity for attendees to learn about the fundamentals and recent developments in specific areas of control and decision-making.CDC also hosts several competitions, such as the Student Best Paper Competition and the Student Poster Competition, to encourage young researchers to showcase their work and interact with peers and mentors.Apart from the technical program, CDC provides ample opportunities for networking and collaboration among participants. The conference attracts researchers, practitioners, and industry professionals from all over the world, providing a diverse and stimulating environment for knowledge sharing and collaboration. In conclusion, IEEE conference on decision and control (CDC) is a significant annual event in the field of systems, control, and decision-making. With its rich history and expansive technical program, CDC serves as a platform for researchers and practitioners to exchange ideas, present their latest findings, and foster collaboration in various areas of control and decision-making.。
System identification
早先的健康监测，主要应用于桥梁 • The Federal Highway Administration (FHWA, USA) was sponsoring a large program of research and development in new technologies for the nondestructive evaluation of highway bridges. 主要的目的是： • One of the two main objectives of the program is to develop new tools and techniques to solve specific problems. The other is to develop technologies for the quantitative assessment of the condition of bridges in support of bridge management and to investigate how best to incorporate quantitative condition information into bridge management systems.
①the laboratory and field testing research on the damage assessment; 实验室和现场 ②analytical developments of damage detection methods, including 损伤监测方法 (a) signature analysis and pattern recognition approaches, 信号分析和模态识别 (b) model updating and system identification approaches, 模型更新 (c) neural networks approaches; 神经网络 ③sensors and their optimum placements
调节系统参数辨识
[2] 戴义平，邓仁刚，刘炯，孙凯，王勇.基于遗传算法的汽轮 机数字电液调节系统的参数辨识研究.中国电机工程学报， 2002年7月第22卷第7期. [3] 戴义平，刘炯，刘朝.基于遗传算法的汽轮机DEH控制系统 的参数优化研究. 热能动力工程，2003年5月第18卷第3期. [4] C.Evans, P.J.Fleming, D.C.Hill, J.P.Norton, I.Pratt, D.Rees,
三、汽轮机调节系统参数辨识方法
遗传算法 频率特性法 最小二乘法 卡尔曼滤波法 相关分析法 分段线性多项式函数法 最大似然法 人工神经网络法
参考文献
[1] Arkov,C.Evans,P.J.Fleming,D.C.Hill,J.P.Norton,I.Pratt,D.Rees,
K.Rodrí guez-Vázquez.Application of system identification techniques to aircraft gas turbine engines. Control Engineering Practice 9(2001)135-148.
[5] 戴义平，俞茂铮，刘炯，王勇.基于相关分析的汽轮机调节 系统参数辨识研究. 汽轮机技术，2001年10月第43卷第5期
二、参数辨识的基本思想
系统参数辨识：求得由物 理定律确定的数学模型中 的未知参数，使该数学模 型等价于真实系统 基本过程
计算机专业英语第二版课后翻译答案
Unite 1Section A: 1、artificial intelligence 人工智能2、paper-tape reader 纸带阅读器3、Optical computer 光学计算机4、Neural network 神经网络5、Instruction set 指令集6、Parallel processing 并行处理器7、Difference engine差分机8、Versatile logical element 多用途逻辑元件9、Silicon substrate 硅衬底10、Vaccum tube 真空管11、数据的存储与管理the storage and management of data12、超大规模集成电路large-scale integrated circuit13、中央处理器central processing unit14、个人计算机personal computer15、模拟计算机analog computer16、数字计算机digital computer17、通用计算机general purpose computer18、处理器芯片processor chip19、操作指令operating instructions20、输入设备input devicesSection B1、artificial neural network 人工智能神经网络2、Computer architecture 计算机体系结构3、Robust computer program 健壮的计算机程序4、Human-computer interface 人机接口5、Knowledge representation 知识代表6、数值分析numerical analysis7、程序设计环境programming environment8、数据结构data structure9、存储和检索信息store and retrieve information10、虚拟现实virtual realityUnit 2Section A：1、function key 功能键2、V oice recognition module 声音识别调制器3、Touch-sensitive region 触敏扫描仪4、Address bus 地址总线5、Flatbed scanner 平板扫描仪6、Dot-matrix printer 矩阵式打印机7、Parallel connection 并行连接8、Cathode ray tube 阴极射线管9、Video game 电子游戏10、Audio signal 音频信号11、操作系统operating system12、液晶显示liquid crystal display13、喷墨打印机inkjet printer14、数据总线data bus15、串行连接serial connection16、易失性存储器volatile memory17、激光打印机laser printer18、磁盘存储器floppy disc19、基本输入输出系统basic input/output system20、视频显示器video displaySection B:1、interrupt handler 中断处理程序2、Virtual memory 虚拟内存3、Context switch 上下文转换4、Main memory 主存5、Bit pattern 位模式6、外围设备peripheral device7、进程表process table8、时间片time slice9、图形用户界面graphics user interface10、海量存储器mass storageUnit 3Section A:1、storage register 存储寄存器2、Function statement 函数语句3、Program statement 程序语句4、Object-oriented language 面向对象语言5、Assembly language 汇编语言6、Intermediate language 中间语言7、Relational language 关系语言8、Artificial language 人工语言9、Data declaration 数据声明10、SQL 结构化查询语言11、可执行程序executable program12、程序模块program module13、条件语句conditional statement14、赋值语句assignment statement15、逻辑语言logic statement16、机器语言machine language17、函数式语言functional language18、程序设计语言programming language19、运行计算机程序run a omputer program20、计算机程序员computer programmerSection B1、native code 本机代码2、Header file 头文件3、Multithreaded program 多线程程序4、Java-enabled browser 支持Java的浏览器5、Mallicious code6、机器码machine code7、汇编码assembly code8、特洛伊木马程序trojan9、软件包software package10、类层次class hierarchyUnit 4Section A1、inference engine 推理机2、System call 系统调用3、Compiled language 编译执行的语言4、Parellel computing 并行计算5、Pattern matching 模式匹配6、Memory location 存储单元7、Interpreter program 解释程序8、Library routine 库程序9、Intermediate program 中间程序10、Source file 源文件11、解释执行的语言interpreted language12、设备驱动程序device driver13、源程序source program14、调试程序debugger15、目标代码object code16、应用程序application program17、实用程序utility program18、逻辑程序logic program19、黑盒ink cartridge20、程序的存储与执行storage and execution of program Section B1、Messaging model 通信模式2、Common language runtime 通用语言运行时刻（环境）3、Hierarchical namespace 分层的名称空间4、Development community 开发社区5、CORBA 公共对象请求代理体系结构6、基本组件basic components7、元数据标记metadata token8、虚拟机VM virtual machine9、集成开发环境IDE（intergrated development environment）10、简单对象访问协议SOAP(simple object access protocol) Unit 5Section A1、system specification 系统规范2、Unit testing 单元测试3、Software life cycle 软件的生命周期4、System validation process 系统验证过程5、Evolutionary development process 进化发展过程6、Simple linear model 简单线性模型7、Program unit 程序单元8、Throwaway prototype 一次性使用原型9、Text formatting 文本格式10、System evolution 系统演变11、系统设计范例paradigm for system design12、需求分析与定义Requirements analysis and definition13、探索式编程方法exploratory programming approach14、系统文件编制system documentation15、瀑布模型waterfall model16、系统集成system integration17、商用现成软件commercial off-the-shelf software18、基于组件的软件工程component-based software engineering19、软件维护工具software maintenance tool20、软件复用software reuseSection B1、check box 复选框2、Structured design 结构化设计3、Building block 构建模块4、Database schema 数据库模式5、Radio button 单选按钮6、系统建模技术system modeling techniques7、模型驱动开发MDD(model-driven development)8、数据流程图data flow diagram9、下拉式菜单drop-down10、滚动条scroll barUnit 6Section A1、end user 终端用户2、Atomic operation 原子操作3、Database administrator 数据库管理员4、Relational database model 关系数据库模型5、Local data 本地数据6、Object-oriented database 面向对象的数据库7、Database management system 数据库管理系统8、Entity-relationship model 实体关系模型9、Distributed database 分布式数据库10、Flat file 展开文件11、二维表two-dimensional table12、数据属性data attributes13、数据库对象database object14、存储设备storage device15、数据类型data type16、数据插入与删除insertion and deletion17、层次数据库模型hierarchical18、数据库体系结构database architecture19、关系数据库管理系统ralational database management system20、全局控制总线global control busSection B1、nonvolatile storage system 易失性存储系统2、Equitment malfunction 设备故障3、Wound-wait protocol 损伤等待协议4、Exclusive lock 排它锁5、Database integrity 数据库完整性6、共享锁shared lock7、数据库实现database implementation8、级联回滚cascading rollback9、数据项data item10、分时操作系统time sharing operating system ;Unit 7Section A1、microwave radio 微波无线电2、digital television 数字电视3、DSL 数字用户线路4、analog transmission 模拟传输5、on-screen pointer 屏幕上的指针6、computer terminal 计算机终端7、radio telephone 无线电话8、cellular telephone 蜂窝电话，移动电话，手机9、decentralized network 分散型网络10、wire-based internal network 基于导线的内部网络，有线内部网11、光缆fiber-optic cable12、传真机fax machine13、线通信wireless communications14、点对点通信point-to-point communications15、调制电脉冲modulated electrical impulse16、通信卫星communication(s) satellite17、电报电键telegraph key18、传输媒体transmission medium (或media)19、无绳电话cordless telephone20、金属导体metal conductorSection B1、bit map 位图2、parallel port 并行端口3、direct memory access (DMA) 直接存储器存取4、universal serial bus 通用串行总线5、general-purpose register 通用寄存器6、电路板circuit board7、串行通信serial communication8、数码照相机digital camera9、存储映射输入/输出memory-mapped I/O10、有线电视cable televisionUnit 8Section A1、file server 文件服务器2、carrier sense 载波检测3、Protocol suite 协议族4、Peer-to-peer model 点对点模型5、bus topology network 总线拓扑网络6、inter-machine cooperation 计算机间合作7、Ethernet protocol collection 以太网协作集8、Proprietary network 专有网络9、utility package 实用软件包10、star network 星形网络11、局域网local area network (LAN)12、令牌环token ring13、无线网络wireless network14、封闭式网络closed network15、环形拓扑网络ring topology16、客户/服务机模型client/server model17、网络应用程序network application18、进程间通信interprocess communication19、打印服务机printer server20、广域网wide area networkSection B1、routing path 路由选择通路2、dual-ring topology 双环形拓扑结构3、extended star topology 扩展星形拓扑结构4、backbone network 基干网，骨干网5、mesh topology网络拓扑结构6、同轴电缆coaxial cable7、逻辑拓扑结构logical topology8、无冲突连网环境collision-free networking environment9、树形拓扑结构tree topology10、目的地节点destination nodeUnit 9Section A1、cell phone 蜂窝电话，移动电话，手机2、IP address 网际协议地址，IP地址3、autonomous system 自主系统4、dial-up connection 拨号连接5、network identifier 网络标识符6、binary notation 二进制记数法7、mnemonic name 助记名，缩写名8、Internet-wide directory system 因特网范围的目录系统9、name server 名称服务器10、Internet infrastructure 因特网基础结构11、助记地址mnemonic address12、网吧cyber cafe13、宽带因特网访问broadband Internet access14、顶级域名top-level domain (TLD)15、因特网编址Internet addressing16、点分十进制记数法dotted decimal notation17、因特网服务提供商Internet service provider (ISP)18、专用因特网连接dedicated Internet connection19、主机地址host address20、硬件与软件支持hardware and software support Section B1、incoming message 来报，到来的报文2、application layer 应用层3、utility software 实用软件4、sequence number （顺）序号，序列号5、remote login capabilities 远程登录能力6、端口号port number7、软件例程software routine8、传输层transport layer9、文件传送协议FTP（File Transfer Protocol）10、万维网浏览器Web browserUnit 10Section A1、mailing list 邮件发送清单，邮件列表2、proprietary software 专有软件3、cc line 抄送行4、bcc line 密送行5、forwarded e-mail messages 转发的电子邮件6、e-mail convention 电子邮件常规7、click on an icon 点击图标8、confidential document 密件，秘密文件9、classified information 密级信息10、recovered e-mail message 恢复的电子邮件11、常用情感符commonly used emoticon12、已删除电子邮件deleted e-mail13、电子系统electronic system14、附件行Attachments line15、版权法copyright law16、电子邮件网规e-mail netiquette17、信息高速公路information superhighway18、签名文件signature file19、电子数据表程序spreadsheet program20、文字处理软件word processorSection B1、web-authoring software 网络写作软件2、template generator 模版生成程序3、navigation page 导航页面4、corporate logo 公司标识5、splash page 醒目页面，过渡页6、导航条navigation bar7、节点页面node page8、网站地图site map9、可用性测试usability testing10、图形交换格式gif（Graphics Interchange Format）Unit 11Section A1、customized marketing strategy 定制的营销策略2、B2G transaction 企业对政府交易3、mobile telephone 移动电话4、dot-com bust 网络不景气5、smart card 智能卡，灵巧卡6、digital piracy 数字盗版7、dot-com boom 网络繁荣8、C2C transaction 消费者对消费者交易9、Web auction site 拍卖网站10、fingerprint reader 指纹读取器11、射频识别装置radio-frequency identification (RFID) device12、电子数据交换electronic data interchange (EDI)13、库存管理技术inventory management technology14、知识产权intellectual property15、条形码bar code16、货币兑换currency conversion17、电子图书electronic book18、视网膜扫描仪retina scanner19、个人数字助理personal digital assistant (PDA)20、企业对企业电子商务B2B electronic commerceSection B1、software suite 软件套件2、text box 文本框3、virtual checkout counter 虚拟付款台4、static catalog 静态目录5、browser session 浏览器会话期6、动态目录dynamic catalog7、购物车软件shopping cart software8、供应链supply chain9、企业资源计划软件enterprise resource planning (ERP) software10、税率tax rateUnit 12Section A1、encryption program 加密程序2、deletion command 删除命令3、authorized user 授权的用户4、backup copy 备份5、voltage surge 电压浪涌6、circuit breaker 断路器7、electronic component 电子元件（或部件）8、data-entry error 数据输入错误9、electronic break-in 电子入侵10、power line 电力线，输电线11、检测程序detection program12、电源power source13、破坏性计算机程序destructive computer program14、计算机病毒computer virus15、软件侵权software piracy16、硬盘驱动器hard-disk drive17、病毒检查程序virus checker18、主存储器primary storage19、电子公告板electronic bulletin board20、浪涌电压保护器surge protectorSection B1、phishing attack 网络钓鱼攻击2、graphics card 显（示）卡3、heuristic analysis 试探性分析4、infected file 被感染文件5、virus dictionary 病毒字典6、数据捕获data capture7、恶意软件malicious software8、病毒特征代码virus signature9、防病毒软件antivirus software10、内存驻留程序memory-resident program。
生物信息学主要英文术语及释义（续完）
⽣物信息学主要英⽂术语及释义（续完）These substitutions may be found in an amino acid substitution matrix such as the Dayhoff PAM and Henikoff BLOSUM matrices. Columns in the alignment that include gaps are not scored in the calculation. Perceptron（感知器，模拟⼈类视神经控制系统的图形识别机) A neural network in which input and output states are directly connected without intervening hidden layers. PHRED （⼀种⼴泛应⽤的原始序列分析程序，可以对序列的各个碱基进⾏识别和质量评价） A widely used computer program that analyses raw sequence to produce a 'base call' with an associated 'quality score' for each position in the sequence. A PHRED quality score of X corresponds to an error probability of approximately 10-X/10. Thus, a PHRED quality score of30 corresponds to 99.9% accuracy for the base call in the raw read. PHRAP （⼀种⼴泛应⽤的原始序列组装程序） A widely used computer program that assembles raw sequence into sequence contigs and assigns to each position in the sequence an associated 'quality score', on the basis of the PHRED scores of the raw sequence reads. A PHRAP quality score of X corresponds to an error probability of approximately 10-X/10. Thus, a PHRAP quality score of 30 corresponds to 99.9% accuracy for a base in the assembled sequence. Phylogenetic studies（系统发育研究） PIR （主要蛋⽩质序列数据库之⼀，翻译⾃GenBank） A database of translated GenBank nucleotide sequences. PIR is a redundant (see Redundancy) protein sequence database. The database is divided into four categories: PIR1 - Classified and annotated. PIR2 - Annotated. PIR3 -Unverified. PIR4 - Unencoded or untranslated. Poisson distribution（帕松分布） Used to predict the occurrence of infrequent events over a long period of time 143or when there are a large number of trials. In sequence analysis, it is used to calculate the chance that one pair of a large number of pairs of unrelated sequences may give a high local alignment score. Position-specific scoring matrix (PSSM)（特定位点记分矩阵，PSI-BLAST等搜索程序使⽤） The PSSM gives the log-odds score for finding a particular matching amino acid in a target sequence. Represents the variation found in the columns of an alignment of a set of related sequences. Each subsequent matrix column corresponds to the next column in the alignment and each row corresponds to a particular sequence character (one of four bases in DNA sequences or 20 amino acids in protein sequences). Matrix values are log odds scores obtained by dividing the counts of the residue in the alignment, dividing by the expected number of counts based on sequence composition, and converting the ratio to a log score. The matrix is moved along sequences to find similar regions by adding the matching log odds scores and looking for high values. There is no allowance for gaps. Also called a weight matrix or scoring matrix. Posterior (Bayesian analysis) A conditional probability based on prior knowledge and newly uated relationships among variables using Bayes rule. See also Bayes rule. Prior (Bayesian analysis) The expected distribution of a variable based on previous data. Profile（分布型） A matrix representation of a conserved region in a multiple sequence alignment that allows for gaps in the alignment. The rows include scores for matching sequential columns of the alignment to a test sequence. The columns include substitution scores for amino acids and gap penalties. See also PSSM. Profile hidden Markov model（分布型隐马尔可夫模型） A hidden Markov model of a conserved region in a multiple sequence alignment that includes gaps and may be used to search new sequences for similarity to the aligned sequences. Proteome（蛋⽩质组） The entire collection of proteins that are encoded by the genome of an organism. Initially the proteome is estimated by gene prediction and annotation methods but eventually will be revised as more information on the sequence of the expressed genes is obtained. Proteomics （蛋⽩质组学） Systematic analysis of protein expression_r of normal and diseased tissues that involves the separation, identification and characterization of all of the proteins in an organism. Pseudocounts Small number of counts that is added to the columns of a scoring matrix to increase the variability either to avoid zero counts or to add more variation than was found in the sequences used to produce the matrix. 144PSI-BLAST （BLAST系列程序之⼀） Position-Specific Iterative BLAST. An iterative search using the BLAST algorithm. A profile is built after the initial search, which is then used in subsequent searches. The process may be repeated, if desired with new sequences found in each cycle used to refine the profile. Details can be found in this discussion of PSI-BLAST. (Altschul et al.) PSSM （特定位点记分矩阵） See position-specific scoring matrix and profile. Public sequence databases （公共序列数据库，指GenBank、EMBL和DDBJ） The three coordinated international sequence databases: GenBank, the EMBL data library and DDBJ. Q20 (Quality score 20) A quality score of > or = 20 indicates that there is less than a 1 in 100 chance that the base call is incorrect. These are consequently high-quality bases. Specifically, the quality value "q" assigned to a basecall is defined as: q = -10 x log10(p) where p is the estimated error probability for that basecall. Note that high quality values correspond to low error probabilities, and conversely. Quality trimming This is an algorithm which uses a sliding window of 50 bases and trims from the 5' end of the read followed by the 3' end. With each window, the number of low quality (10 or less) bases is determined. If more than 5 bases are below the threshold quality, the window is incremented by one base and the process is repeated. When the low quality test fails, the position where it stopped is recorded. The parameters for window length low quality threshold and number of low quality bases tolerated are fixed. The positions of the 5' and 3' boundaries of the quality region are noted in the plot of quality values presented in the" Chromatogram Details" report. Query （待查序列/搜索序列） The input sequence (or other type of search term) with which all of the entries in a database are to be compared. Radiation hybrid (RH) map （辐射杂交图谱） A genome map in which STSs are positioned relative to one another on the basis of the frequency with which they are separated by radiation-induced breaks. The frequency is assayed by analysing a panel of human–hamster hybrid cell lines, each produced by lethally irradiating human cells and fusing them with recipient hamster cells such that each carries a collection of human chromosomal fragments. The unit of distance is centirays (cR), denoting a 1% chanceof a break occuring between two loci Raw Score （初值，指最初得到的联配值S） The score of an alignment, S, calculated as the sum of substitution and gap scores. Substitution scores are given by a look-up table (see PAM, BLOSUM). Gap scores are typically calculated as the sum of G, the gap opening penalty 145and L, the gap extension penalty. For a gap of length n, the gap cost would be G+Ln. The choice of gap costs, G and L is empirical, but it is customary to choose a high value for G (10-15)and a low value for L (1-2). Raw sequence （原始序列/读胶序列） Individual unassembled sequence reads, produced by sequencing of clones containing DNA inserts. Receiver operator characteristic The receiver operator characteristic (ROC) curve describes the probability that a test will correctly declare the condition present against the probability that the test will declare the condition present when actually absent. This is shown through a graph of the tesls sensitivity against one minus the test specificity for different possible threshold values. Redundancy （冗余） The presence of more than one identical item represents redundancy. In bioinformatics, the term is used with reference to the sequences in a sequence database. If a database is described as being redundant, more than one identical (redundant) sequence may be found. If the database is said to be non-redundant (nr), the database managers have attempted to reduce the redundancy. The term is ambiguous with reference to genetics, and as such, the degree of non-redundancy varies according to the database manager's interpretation of the term. One can argue whether or not two alleles of a locus defines the limit of redundancy, or whether the same locus in different, closely related organisms constitutes redundency. Non-redundant databases are, in some ways, superior, but are less complete. These factors should be taken into consideration when selecting a database to search. Regular expression_rs This computational tool provides a method for expressing the variations found in a set of related sequences including a range of choices at one position, insertions, repeats, and so on. For example, these expression_rs are used to characterize variations found in protein domains in the PROSITE catalog. Regularization A set of techniques for reducing data overfitting when training a model. See also Overfitting. Relational database（关系数据库）Organizes information into tables where each column represents the fields of informa-tion that can be stored in a single record. Each row in the table corresponds to a single record. A single database can have many tables and a query language is used to access the data. See also Object-oriented database. Scaffold （⽀架，由序列重叠群拼接⽽成） The result of connecting contigs by linking information from paired-end reads from plasmids, paired-end reads from BACs, known messenger RNAs or other sources. The contigs in a scaffold are ordered and oriented with respect to one another. 146 Scoring matrix（记分矩阵） See Position-specific scoring matrix. SEG （⼀种蛋⽩质程序低复杂性区段过滤程序） A program for filtering low complexity regions in amino acid sequences. Residues that have been masked are represented as "X" in an alignment. SEG filtering is performed by default in the blastp subroutine of BLAST 2.0. (Wootton and Federhen) Selectivity (in database similarity searches)（数据库相似性搜索的选择准确性） The ability of a search method to locate members of a protein family without making a false-positive classification of members of other families. Sensitivity (in database similarity searches)（数据库相似性搜索的灵敏性） The ability of a search method to locate as many members of a protein family as possi-ble, including distant members of limited sequence similarity. Sequence Tagged Site （序列标签位点） Short cDNA sequences of regions that have been physically mapped. STSs provide unique landmarks, or identifiers, throughout the genome. Useful as a framework for further sequencing. Significance（显著⽔平） A significant result is one that has not simply occurred by chance, and therefore is prob-ably true. Significance levels show how likely a result is due to chance, expressed as a probability. In sequence analysis, the significance of an alignment score may be calcu-lated as the chance that such a score would be found between random or unrelated sequences. See Expect value. Similarity score (sequence alignment) （相似性值） Similarity means the extent to which nucleotide or protein sequences are related. The extent of similarity between two sequences can be based on percent sequence identity and/or conservation. In BLAST similarity refers to a positive matrix score. The sum of the number of identical matches and conservative (high scoring) substitu-tions in a sequence alignment divided by the total number of aligned sequence charac-ters. Gaps are usually ignored. Simulated annealing A search algorithm that attempts to solve the problem of finding global extrema. The algorithm was inspired by the physical cooling process of metals and the freezing process in liquids where atoms slow down in movement and line up to form a crystal. The algorithm traverses the energy levels of a function, always accepting energy levels that are smaller than previous ones, but sometimes accepting energy levels that are greater, according to the Boltzmann probability distribution. Single-linkage cluster analysis An analysis of a group of related objects, e.g., similar proteins in different genomes to identify both close and more distant relationships, represented on a tree or dendogram. The method joins the most closely related pairs by the neighbor-joining algorithm by representing these pairs as outer branches on 147the tree. More distant objects are then pro-gressively added to lower tree branches. The method is also used to predict phylogenet-ic relationships by distance methods. See also Hierarchical clustering, Neighbor-joining method. Smith-Waterman algorithm（Smith-Waterman算法） Uses dynamic programming to find local alignments between sequences. The key fea-ture is that all negative scores calculated in the dynamic programming matrix are changed to zero in order to avoid extending poorly scoring alignments and to assist in identifying local alignments starting and stopping anywhere with the matrix. SNP （单核苷酸多态性） Single nucleotide polymorphism, or a single nucleotide position in the genome sequence for which two or more alternative alleles are present at appreciable frequency (traditionally, at least 1%) in the human population. Space or time complexity（时间或空间复杂性） An algorithms complexity is the maximum amount of computer memory or time required for the number of algorithmic steps to solve a problem. Specificity (in database similarity searches)（数据库相似性搜索的特异性） The ability of a search method to locate members of one protein family, including dis-tantly related members. SSR （简单序列重复） Simple sequence repeat, a sequence consisting largely of a tandem repeat of a specific k-mer (such as (CA)15). Many SSRs are polymorphic and have been widely used in genetic mapping. Stochastic context-free grammar A formal representation of groups of symbols in different parts of a sequence; i.e., not in the same context. An example is complementary regions in RNA that will form sec-ondary structures. The stochastic feature introduces variability into such regions. Stringency Refers to the minimum number of matches required within a window. See also Filtering. STS （序列标签位点的缩写） See Sequence Tagged Site Substitution （替换） The presence of a non-identical amino acid at a given position in an alignment. If the aligned residues have similar physico-chemical properties the substitution is said to be "conservative". Substitution Matrix （替换矩阵） A substitution matrix containing values proportional to the probability that amino acid i mutates into amino acid j for all pairs of amino acids. such matrices are constructed by assembling a large and diverse sample of verified pairwise alignments of amino acids. If the sample is large enough to be statistically significant, the resulting matrices should reflect the true probabilities of mutations occuring through a period of evolution. 148Sum of pairs method Sums the substitution scores of all possible pair-wise combinations of sequence charac-ters in one column of a multiple sequence alignment. SWISS-PROT （主要蛋⽩质序列数据库之⼀） A non-redundant (See Redundancy) protein sequence database. Thoroughly annotated and cross referenced. A subdivision is TrEMBL. Synteny The presence of a set of homologous genes in the same order on two genomes. Threading In protein structure prediction, the aligning of the sequence of a protein of unknown structure with a known three-dimensional structure to determine whether the amino acid sequence is spatially and chemically compatible with that structure. TrEMBL （蛋⽩质数据库之⼀，翻译⾃EMBL） A protein sequence database of Translated EMBL nucleotide sequences. Uncertainty（不确定性） From information theory, a logarithmic measure of the average number of choices that must be made for identification purposes. See also Information content. Unified Modeling Language (UML) A standard sanctioned by the Object Management Group that provides a formal nota-tion for describing object-oriented design. UniGene （⼈类基因数据库之⼀） Database of unique human genes, at NCBI. Entries are selected by near identical presence in GenBank and dbEST databases. The clusters of sequences produced are considered to represent a single gene. Unitary Matrix （⼀元矩阵） Also known as Identity Matrix.A scoring system in which only identical characters receive a positive score. URL（统⼀资源定位符） Uniform resource locator. Viterbi algorithm Calculates the optimal path of a sequence through a hidden Markov model of sequences using a dynamic programming algorithm. Weight matrix See Position-specifc scoring matrix.。
沈阳工业大学毕业论文模板及格式规范.doc
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示例如下：关键词：电动机；DSP；自适应控制；直接转矩控制AbstractBoth in model predictive control and system identification an optimization problem has to be solved. In model predictive control an optimal input signal over a certain future horizon is calculated on-line, while in system identification an optimal model is required. In both optimization problems it is important to choose the degrees of freedom wisely. The choice of the degrees of freedom is denoted with parametrization, i.e. input parametrization in predictive control and model parametrization in identification.For quadratic cost functions, which are common in both areas, and a linear parametrization the optimization problems are convex: a quadratic programming problem for predictive control and a linear least squares problem for identification. There is a wide variety of linear parametrizations that can be utilized. In this thesis it is investigate to what extend flexibility in the linear parametrization can contribute to the improvement of model predictive control and system identification techniques.Keywords: motor; DSP; adaptive control; direct torque controlI Abstract (II)第1章正文格式说明 (1)1.1 论文格式基本要求 (1)1.2 论文页眉页脚的编排 (1)1.3 论文正文格式 (1)1.4 章节标题格式 (2)1.5 各章之间的分隔符设置 (2)第2章图表及公式的格式说明 (3)2.1 图的格式说明 (3)2.1.1 图的格式示例 (3)2.1.2 图的格式描述 (3)2.2 表的格式说明 (4)2.2.1 表的格式示例 (4)2.2.2 表的格式描述 (5)2.3 公式的格式说明 (5)2.3.1 公式的格式示例 (5)2.3.2 公式的格式描述 (5)2.4 参考文献的格式说明 (6)2.4.1 参考文献在正文中引用的示例 (6)2.4.2 参考文献在正文中引用的书写格式 (6)2.4.3 参考文献的书写格式 (6)2.4.4 参考文献的书写格式示例 (7)2.5量和单位 (7)第3章结论 (8)参考文献 (9)致谢 (10)附录 (11)第1章正文格式说明“正文”不可省略。
FDA《行业指南：注射产品可见颗粒的检查》，中英文对照版来了！
FDA《⾏业指南：注射产品可见颗粒的检查》，中英⽂对照版来了！⾏业指南：注射剂可见异物的检查》草案，该⽂件已完成翻译，现12⽉14⽇，FDA发布了《⾏业指南：注射剂可见异物的检查分享给⼤家，如下：Inspection of Injectable Productsfor Visible Particulates注射产品中可见颗粒的检查Guidance for Industry⾏业指南TABLE OFCONTENTS⽬录I. INTRODUCTION介绍II. STATUTORY AND REGULATORY FRAMEWORK法律法规框架III. CLINICAL RISK OF VISIBLE PARTICULATES可见颗粒物的临床风险IV. QUALITY RISK ASSESSMENT质量风险评估V. VISUAL INSPECTION PROGRAM CONSIDERATIONS⽬视检查的程序考虑A. 100% Inspection100%检查1. Components and Container Closure Systems部件和容器密封系统2. Facility and Equipment设施和设备3. Process⼯艺4. Special Injectable Product Considerations特殊注射产品的考虑B. Statistical Sampling统计学抽样C. Training and Qualification培训和确认D. Quality Assurance Through a Life Cycle Approach通过⽣命周期⽅法实现质量保证E. Actions To Address Nonconformance解决不符合问题的措施VI. REFERENCES参考⽂献I. INTRODUCTION介绍Visibleparticulates in injectable products can jeopardize patient safety. Thisguidanceaddresses the development and implementation of a holistic, risk-basedapproach to visibleparticulate control that incorporates product development,manufacturing controls, visualinspection techniques, particulate identification,investigation, and corrective actions designedto assess, correct, and preventthe risk of visible particulate contamination.2The guidance alsoclarifies that meeting an applicable United StatesPharmacopeia (USP)3compendialstandardalone is not generally sufficient for meeting the current goodmanufacturing practice (CGMP)requirements for the manufacture of injectableproducts. The guidance does not coversubvisible particulates4 or physical defects that products are typicallyinspected for along withinspection for visible particulates (e.g., containerintegrity flaws, fill volume, appearance of lyophilized cake/suspensionsolids).注射产品中的可见颗粒物会危及患者安全。
read checksum error for16384b block
read checksum error for16384b block Checksum errors are a common occurrence when dealing with data transmission or storage. These errors can occur due to various factors including hardware malfunctions, software bugs, or even environmental interference. One specific example of a checksum error is when it is encountered for a 16384-byte block.In order to understand the root cause of this issue, let's first delve into what a checksum is and how it is calculated. A checksum is a value that is calculated from a set of data, such as a file or a block of memory. Its purpose is to ensure data integrity by evaluating the error rate during transmission or storage. When the data is received or read back, the checksum is recalculated, and any discrepancies between the original and the recalculated checksum indicate the presence of errors.A 16384-byte block is a sizable chunk of data, and checksum errors can have significant implications in terms of data corruption. To address this issue, we will explore the steps involved in diagnosing the cause of the checksum error and potential solutions to rectify it.Step 1: Identify the Source of the ErrorThe first step in tackling the checksum error is to identify the source of the problem. Since checksum errors can be caused by various factors, it is essential to investigate all possible contributors. One common cause of such errors is faulty hardware or transmission channels. Thus, checking and verifying the integrity of the hardware components involved, such as disk drives, network cables, or memory modules, is a critical initial step.Step 2: Check for External FactorsWhile hardware-related issues are often responsible for checksum errors, external factors can also play a role. Environmental factors, such as electromagnetic interference or power fluctuations, can introduce errors during data transmission. It is important to assess the environment where the error occurred and take proper precautions to mitigate potential sources of interference.Step 3: Review Software and FirmwareSoftware bugs or firmware inconsistencies are another potential cause of checksum errors. It is necessary to eliminate any software-related issues by reviewing the relevant programming codes and ensuring that all components are updated to the latest versions. This includes operating systems, device drivers, and anyother relevant software or firmware that might impact data transfer or storage.Step 4: Recalculate the ChecksumAt this stage, if the previous steps have not resolved the issue, it is imperative to recalculate the checksum of the 16384-byte block. This will help determine if the error is specific to the block or extends to other portions of the data. By recalculating the checksum and comparing it with the corrupted data, it is possible to pinpoint the exact location and nature of the error, which can be useful in troubleshooting and finding a suitable solution.Step 5: Implement Error Correction TechniquesAfter precisely identifying the nature of the checksum error, appropriate error correction techniques can be employed to rectify the problem. These techniques vary depending on the specific circumstances surrounding the error. For example, if the error is caused by faulty hardware, repairing or replacing the defective components may be necessary. In cases where the error is due to software or firmware issues, applying patches or updates might suffice.Step 6: Retest and MonitorOnce the potential solutions have been implemented, it is crucial to retest the system and monitor it for any recurring issues. This step is essential to ensure that the problem has been fully resolved and that the checksum error no longer occurs. System monitoring allows for the detection of any new errors that may arise, enabling prompt action to minimize their impact.In conclusion, encountering a checksum error for a 16384-byte block can be a perplexing issue with various potential causes. However, by following a systematic approach encompassing identification, elimination of external factors, review of software and firmware, checksum recalculation, implementation of error correction techniques, and diligent retesting, it is feasible to diagnose and resolve the problem. Data integrity is paramount in any system, and mitigating checksum errors ensures the reliability and accuracy of the transmitted and stored information.。
自动化专业英语讲义_学生用
自动化专业英语讲义(2010-2011第２学期)课前要求：通读课文1遍、将生词查出。
课后要求：独立完成作业。
预习下次课的内容成绩计算办法：平时成绩50%，期末考试成绩50%。
平时成绩由作业(约40分)和出勤(约10分)等决定，缺交作业、或者作业抄袭者被发现（包括将作业给别人抄袭者）本次记为0分。
作业要整洁。
作业必须按时完成，必须在上课前交作业！！请假者，请通过他人交作业。
答疑地点：杰德控制系统工程研究中心（一校西门南三层楼305室）答疑时间：每周工作日上午10:00～11:30、下午3:00～5:00(周四除外)之间,请先电话联系办公电话：2554参考书：《工业自动化专业外语》，王树青，韩建国编，化学工业出版社，2001年。
《自动化专业英语教程》王宏文，机械工业出版社，2007年。
《Modern Control System》，R.C. Dorf, R.H. Bishop，科学出版社，2004年。
《自动化专业英语》，李国厚，王春阳，北京大学出版社，2006年。
《自动化专业英语》，任金霞,任金霞,何小阳，华中科技大学出版社，2008年。
《自动化专业英语》，王旸,原驰,哈尔滨工业大学，2008年。
《自动化专业英语》，王建国,陈东淼，中国电力出版社，2005年。
《自动控制专业英语》，沈宏，电子工业出版社，2003年。
《自动控制专业英语》，李国厚，清华大学出版社，2005年。
《自动化专业英语》，戴文进，章卫国，武汉理工大学出版社，2006年。
《自动化与电子信息专业英语》，杨植新，周劲，孙江波，电子工业出版社，2009年。
No.1 Pronunciation of mathematical expressions; Major catalogue; Periodicals and journals1.1 Pronunciation of Mathematical ExpressionsThe pronunciations of the most common mathematical expressions are given in the list below. In general, the shortest versions are preferred (unless greater precision is necessary).3、Real numbers1+x x plus one 1-x x minus one 1±xx plus or minus one xyxy / x multiplied by y))((y x y x +- x minus y , x plus yyx x over y / x on y＝the equals signx x equals 5 / x is equal to 5=5x x (is) not equal to 55≠x≡x is equivalent to (or identical with) yyx≠y x is not equivalent to (or identical with) y yx>x is greater than yx≥x is greater than or equal to yyx<x is less than yyyx≤x is less than or equal to y<x zero is less than x is less than 10<1≤x zero is less than or equal to x is less than or equal to 1 10≤x mod x / modulus x / absolute value of x2x x squared / x (raised) to the power 23x x cubed4x x to the fourth / x to the power fournx x to the nth / x to the power nnx-x to the (power) minus nx(square) root x / the square root of x3x cube root (of) x4x fourth root (of) xn x n th root (of) x2)(y x +x plus y all squared2⎪⎪⎭⎫ ⎝⎛y x x over y all squared!n n factorial xˆ x hat x x bar x ~x tildei xxi / x subscript i / x suffix i / x sub ii xxi / x superscript i / x superfix i / x super i∑=ni ia1the sum from i equals one to n a i / the sum as i runs from 1 to n of the a i4、Linear algebraxthe norm (or modulus) of xOA / vector OAOAOA / the length of the segment OA T A A transpose / the transpose of A 1-AA inverse / the inverse of A5、Functions)(x ff x / f of x / the function f of xT S f →:a function f from S to Ty xx maps to y / x is sent (or mapped) to y)(x f 'f prime x / f dash x / the (first) derivative of f with respect to x )(x f ''f double –prime x / f double –dash x / the second derivative of f with respect to x)(x f ''' f triple –prime x / f triple –dash x / the third derivative of f with respect to x)()4(x ff four x / the fourth derivative of f with respect to xF x ∂∂ partial F on partial x / partial differential F on x1x f ∂∂ the partial (derivative) of f with respect to x 1212x f∂∂ the second partial (derivative) of f with respect to x 1⎰∞the integral from zero to infinitylim →xthe limit as x approaches zerolim +→xthe limit as x approaches zero from abovelim -→xthe limit as x approaches zero from belowy e loglog y to the base e / log to the base e of y / natural log (of) yy lnlog y to the base e / log to the base e of y / natural log (of) yIndividual mathematicians often have their own way of pronouncing mathematical expressions and in many cases there is no generally accepted “correct” pronunciation.Distinctions made in writing are often not made explicit in speech; thus the sounds fx may be interpreted as any of: fx ,,,,),(FX FX FX f x f x The difference is usually made clear by the context; it is only when confusionmay occur, or where he/she wishes to emphasize the point, that the mathematician will use the longer forms: fmultiplied by x , the function f of x , f subscript x , line FX , the length of the segment FX , vector FX .Similarly, a mathematician is unlikely to make any distinction in speech (except sometimes a difference in intonation or length of pauses) between pairs such as the following: )(z y x ++ and z y x ++)(b ax + andb ax +1-na and 1-n a6、其它数学符号和公式例子 4/5 four fifths / 4 on 5 0.025 zero point zero two five 38.49 thirty-eight point four nine 2%two per cent25 the second power of five / five to the power twoxthe square root of x7106⨯ six times the seventh power of ten +plus ; positive -minus ; negative ⨯ multiplied by ; times ÷; /divided by = is equal to ; equals( ) round brackets ; parentheses (parenthesis) i ; jimaginary unit!a factorial asin sine of xxarcsin arc sine of xx∏the product of the terms indicated∑the sum of the terms indicatedb'b primeb''b second primeb b sub two2"b b second prime sub mmdxdy/the first derivative of y with respect to x2/dx2d the second derivative of y with respect to xy⎰b a integral between limits a and bx x approaches to infinity∞→a=+a plus b is equal to cbc-a minus b equals ca=cbs=s equals v multiplied by tvt=v equals s divided by tv/ts⨯-+/)(a plus b minus c multiplied by d, all divided by e equals f a=dbefcC+=C over R equals G divided by the sum of one and H times G R1/()/GHG1.2 Major Catalogue根据国家教委1998年颁布的新专业目录（Major Catalogue），将原工业自动化（Industrial Automation）、自动控制（Automatic Control）、自动化（Automation）、电气技术（Electrical Technology）等专业合并统称为自动化专业（Automation）。
软件工程外文文献—软件稳定性模型SSM
外文原文Due to the instability of software systems produced over a period of time unlike other systems, it has become essential to research upon and ascertain the Stabilrty of software systems which determines other factors such as reliability, trustworthiness etc. Though theoretically there is no deterioration expected for a software product, it does owing to changes in software which involves re-engineering of the changed code. The re-engineering of the software product is not essential for smafl changes that would have been made in the code of the software modules.The idea behind this research work is to bring out the basic techniques used in Pattern identification of Real Time Computing Systems by using the Software Stability Model (SSM).A case study has been built for illustrating the application of SSM to real time computing systems that use Adaptive ReconfigurabIe controls. The "Control Software" that is being used in the real time computing system makes use of the properties of Adaptive Control [8]. Adaptive Reconfigurable Control finds applications in areas including real time systems such as Air Traffic Control Systems, Networked Multimedia Systems, Command Control Systems, Medical Critical Care systems, Real Time Operating Systems (RTOS) etc. In essence the n Control Software** is modeled as a "System" and hence the concepts of stability of a physical system (as defined in Control Engineering) are applied to that of the software used in real- time systems.Software failure happens in many real time systems such as transportation systems, medical systems, defense systems, etc. A software which is dynamically controlling the buffering functions of a database management system, or a software that uses the concept of caching for OS memory management are typical examples of software using algorithms with embedded control and adaptation. We have considered a Real Time Computing System that uses Adaptive Control Algorithms. The intuitive use of the stability concepts available in Control Engineering in Real Time Computing systems with the support of the Software Stabilrty Model(SSM) is the theme of this research work.For the real time system shown in fig I, using Re- configurable control, the control laws are stored in the Controller Database. The required n Control Law" would be chosen as required. The highlight of reconfigurable control is that the controller could be redesigned at runtime.The code level implementation of the control law as shown in [9], could be used in reconfigurable control, in order to produce stability and performance checks on the "control software** used. Adaptive components are included in Real r∏me Systems in order to cope up With changing environments .Fig I. Btock Diagram of a Real Time System with Adaptive Reconfigurab Ie Control In [3] the stability criteria for software has been stated as follows :" A system is said to be stable when little disturbances applied to the system have negligible effects on the system In case of model driven software system this implies the very small changes in the input model do not radically change the behavior of the system". This concept when extended to defining the term "controllability" in software engineering is to establish the fact that software systems will perform as per the g iven specifications when the inputs are changed. In a simikιr fashion the "observability*1 criteria for software systems could also be given. A software system is said to be "observable" if it becomes possible to obtain information about the state of the system at any point of time.M.E Fayad discusses the Software Stability Model (SSM) in [4] using EBT∕B0∕10. Enduring Business Themes (EBTs) remain constant for a given system. EBTs are so chosen that the objects will remain stable in order to make up the core of software systems. Business Objects (BOs) also remain stable, but the internal processes might exchange on a need basis. Therefore externally BOs remain as stable as EBls. Industrial Objects (IOs) are the objects that one designs in a classical object model An Industrial Object represents a physical entity.As defined by Fayad in SSM, The EB r Is are determined by answering the question :"What is this system for?'*, the BOs are deteπnined by answering the question :" Howdo the intangible conceptual themes map into more concrete objects?" The IOs are determined by answering the question :" What is the physical representation of the BOs? "[ 10].It is difficult for Software Engineers to identify the objects of Stabilrty in Real Time Domain Applications, who are not so much exposed to stability concepts. It is the control engineer who should be able to advice the software engineers in the identification of EBTs, BOs and IOs. The domain knowledge of the application is very essential to identify all the three objects clearly. Stability over time, Adaptability, Essentiality, Intuition, Explicitness, Commonality to the domain, Tangibility etc. are the identification criteria M.E. Fayad has suggested to identify the EBT/80/1O [5,7].As a software system is controlled by the control software programs running, it is important to analysis the reliability of such systems while code change happens. It is also inportant to identify the range over which the software systems would behave stable during such code changes. This in line with the principles involved in defining the Controllability and Observability criteria for systems that would serve as a strong methodology to support the stability of Software systems.As in any other physical system, stability is defined over the Control Gain Factor Kin the Loop Transfer Function of the system, there are ranges over which the control parameters of the Software systems would also behave stable. The core idea of this research is to do an in-depth analysis of the behavior of Software systems on code changes and determining the cond⅛ions under which the control parameters would make the Software System stable. This in turn suggests the design of appropriate controllers using software programs in order to keep the software system within a stable region.Computing System from the external entities. Ideally the Real Time Computing system is a MIMO (Mult⅛>le I叩Ul Multiple Output) system. The inputs thus received are being processed by the Conputing System based on the parameter settings made in the Real Time Computing System. The Algorrthms used in the control software of theReal Time Computing System will process the inputs (signals) received. Appropriate Computation using real-time data meeting the required conditions as defined by the control parameters of the real time applications are being handled by the Conputing System. The Output thus obtained is fed into the Transaction Processing System which mon⅛ors for example - the database operations that take place in the backend database server.The control signals coming out of the TP system are fed into the Adaptive Control Server that has the master control system The signals received from the TP system which refers to the Model Estimator and Controller Designer Database chooses the apt control values as defined by the control parameters of the particular Real Time Application. The reconfigurable control used in the Adaptive Control Server gives the advantage that the controller is being redesigned at runtime since both the Model Estimator and Controller Designer refer to their respective Databases and hence adaptive nature of the server helps the server act appropriately.The various EBTs, BOs and IOs have been identified for the Real Tune TP system as shown in Fig 3. Based on the concepts of software stability, the categorization of the objects viz., EBT, BO and IO has been done based on the degree of stability [6,10]. Computing, Monitoring and Controlling represent the EBls of the system. The main objective of the TP System is to perform the computation of the control parameters based on the sensor values inputted and algorithms implemented using the control software. The other objective of the TP system is to track the Transactions triggered and compare the actual values computed with the real time system parameter limits pre- defined.Based on the comparison, the Adaptive Control system in turn controls the TP system and the output is thus controlled by the values chosen from the Controller Database dynamically at run-time ." Computing" is an enduring theme since it remains stable extremely and internally as long as this system lasts .n Monrtoring" is an enduring theme that represents the process of conparing the actual values conputedwith the real time system parameter limits pre- defined, which also renns stable both externally and internally."Controlling" is an enduring theme that provides Adaptive control to the TP system at run-time which also remains stable both externally and internally. All the three EBls identified here, define the concept of the system since these are the main aims of TP SystemReal Time Control Parameters, Real-time system parameter limits and Adaptive Control parameters represent the BOs. Every BO identified here are externally stable and highly adaptable in ternally. For exaπple, Real time control parameters are the ones which are always the key control elements that are stable for a TP system, yet they can depend on the sensors, control Algorithms, process controllers etc.Fig 3 shows a stable model of a TP system. We now identify a domain pattern for the TP system based on the SSM, by extracting the EBTs and BOs of the stable model of TP system. We see that the chosen EBTs, BOs etc. based on the SSM is owing to the domain of the problem. Hence software stable models for other applications in the TP domain will have these objects as their EBTs and BOs. Fig 6 shows a domain pattern for the TP system. As defined in [10], the various elements of the stability model for the TP System has been discussed in this section.Intent: This pattern suggests the basic structure of any Transaction Processing (TP) System Context: There are many Online Transaction Processing Systems I Software that require TP function.Problem: Arrive at the stable objects which represent the basic structure of the TP System Forces: The pattern identified should depict the basic structure of the TP System. It has to be generic in nature so that it could be applied to any kind of TP systems. It is quite challenging to arrive at a pattern which will handle different types/ functions of TP systems.How far a real time (Transaction Processing) system is dependent on the input parameters defines the controllability of the TP system. How far the outputs are dependent on the varying system parameters determines the Observability of the TP System. The determining control parameters of the TP System uħimately determines the EBTζ80s and 10s. Therefore decision on the control parameters decides whether the TP systems is Controllable as well Observable. Since the Controllability and Observability are the key elements of stability, we thereby ensure determination of EB r Is,80s and K)s as well depend on the control parameters governing the Control Software being used in Adaptive Re-ConfigurabIe Control based Transaction Processing System. The TP System Pattern thus arrived gives a reliable system which is more stable.中文翻译由于与其他系统不同，在一段时间内产生的软件系统不稳定，因此研究和确定软件系统的稳定性变得至关重要，这些系统决定了其他因素，如可靠性，可信度等。
预防压力性损伤的制度流程
预防压力性损伤的制度流程英文回答：Preventing pressure injuries requires a systematic approach to ensure the well-being of individuals at risk. The process involves several steps and protocols toidentify, assess, and manage pressure-related risks. Hereis a brief overview of the institutional process for preventing pressure injuries:1. Identification: The first step is to identify individuals who are at risk of developing pressure injuries. This can be done through comprehensive assessments,including medical history, physical examination, and risk assessment scales. For example, a nurse may identify a patient with limited mobility, such as someone who is bedridden or uses a wheelchair, as being at risk.2. Risk assessment: Once individuals at risk are identified, a thorough risk assessment is conducted. Thisinvolves evaluating various factors that contribute to pressure injuries, such as immobility, malnutrition, incontinence, and sensory impairment. A standardized risk assessment scale, such as the Braden Scale, may be used to quantify the risk level. For instance, a patient with a low Braden score indicating high risk may require additional preventive measures.3. Prevention plan: Based on the risk assessment, a personalized prevention plan is developed for each individual. This plan includes interventions to reduce pressure, friction, and shear, as well as to maintain skin integrity. For example, a plan may include regular repositioning, using pressure-relieving devices, providing adequate nutrition, and promoting moisture control.4. Education and training: It is crucial to educate both healthcare professionals and individuals at risk about pressure injury prevention. This includes teaching proper techniques for repositioning, skin care, and the use of assistive devices. For instance, nurses can educatepatients on the importance of shifting their weight everyfew hours and using cushions to relieve pressure.5. Monitoring and evaluation: Regular monitoring and evaluation of the prevention plan are essential to ensure its effectiveness. This involves assessing the skin condition, reviewing the individual's response to interventions, and making necessary adjustments. For example, if a patient's skin shows signs of redness or breakdown, the prevention plan may need to be revised to provide more effective interventions.6. Communication and collaboration: Effective communication and collaboration among healthcare professionals, patients, and caregivers are vital in preventing pressure injuries. This includes sharing information, coordinating care, and addressing any concerns or challenges. For instance, a nurse may communicate with a physical therapist to develop a comprehensive plan for a patient's mobility and pressure relief.In conclusion, preventing pressure injuries requires a comprehensive and systematic approach that involvesidentification, risk assessment, personalized prevention plans, education, monitoring, and collaboration. By implementing these processes, healthcare institutions can effectively reduce the incidence of pressure injuries and promote the well-being of individuals at risk.中文回答：预防压力性损伤需要采取系统化的方法，以确保风险个体的健康。
质量体系流程培训英语作文
Quality management systems are essential for organizations to ensure they consistently meet customer and regulatory requirements.Training in quality system processes is crucial for employees to understand and implement these systems effectively. Here is an essay on the importance of quality system process training.Title:The Significance of Quality System Process TrainingIn the contemporary business landscape,the implementation of a robust quality management system QMS is indispensable for organizations aiming to excel in their respective industries.The ISO9001standard,for instance,provides a framework for establishing,implementing,maintaining,and continually improving a QMS.However, the true effectiveness of such a system hinges on the employees understanding and adherence to its processes.This essay will delve into the significance of training in quality system processes and how it contributes to organizational success. Understanding the Quality SystemThe first step in quality system process training is to ensure that employees have a comprehensive understanding of what a quality system entails.This includes the principles of quality management,the organizations quality policy,and the specific processes and procedures that are in place to meet quality objectives.Training should cover the following aspects:The importance of customer satisfaction and how the QMS is designed to meet and exceed customer expectations.The role of top management in creating and maintaining a qualityfocused culture.The significance of continuous improvement and its integration into daily operations. RoleSpecific TrainingEmployees must be trained according to their specific roles within the organization.For example,while a production manager may need to understand the entire scope of the QMS,an assembly line worker might only require training on the processes directly related to their tasks.Tailoring training to the needs of each role ensures that everyone is equipped with the knowledge necessary to contribute effectively to the QMS. Compliance and Regulatory RequirementsTraining should also focus on compliance with industryspecific regulations and standards. Employees must be aware of the legal and regulatory requirements that impact their workand understand how the QMS helps the organization meet these obligations.This includes training on:Relevant laws and regulations that apply to the industry.The role of documentation in demonstrating compliance.The process for identifying and addressing nonconformities.Risk ManagementA critical component of quality system process training is the ability to identify and manage risks.Employees should be trained to recognize potential risks in their work processes and understand the steps to mitigate these risks.This includes:The process for risk identification,assessment,and control.The importance of proactive risk management in preventing quality issues.The role of risk management in the overall QMS.Continuous ImprovementTraining should instill a culture of continuous improvement within the organization. Employees should be encouraged to seek out opportunities for process optimization and efficiency gains.Training in this area might include:Techniques for process mapping and analysis.Methods for identifying areas for improvement.The process for implementing and validating changes to the QMS.ConclusionIn conclusion,quality system process training is a vital investment for any organization seeking to maintain a competitive edge through highquality products and services.By providing employees with the knowledge and skills to effectively implement and improve the QMS,organizations can ensure that they are wellpositioned to meet customer expectations,comply with regulations,and achieve sustainable success in the marketplace.。
System Identification and Control
System Identification and Control System identification and control are critical elements in the field of engineering and technology. These concepts play a crucial role in understandingand manipulating complex systems, whether it's in the realm of mechanical, electrical, or chemical engineering. In this discussion, we will delve into the significance of system identification and control, exploring its applications, challenges, and future prospects. First and foremost, let's understand the fundamental concept of system identification. Essentially, system identification involves the process of building mathematical models to represent dynamic systems. These models are derived from observed data, allowing engineers to understand the behavior of the system and make predictions about its future performance. In essence, system identification provides a means to comprehend and quantify the dynamics of a system, which is essential for control and optimization. One of the key applications of system identification is in the field of control systems. Control systems are ubiquitous in modern technology, from regulating the temperature in a building to guiding a spacecraft. System identificationtechniques are used to develop accurate models of the systems being controlled, which in turn allows engineers to design and implement control strategies that ensure the system behaves in a desired manner. This is particularly crucial in industries such as aerospace, automotive, and manufacturing, where precise control of complex systems is paramount. Moreover, system identification plays a vitalrole in the development of autonomous systems, such as self-driving cars and unmanned aerial vehicles. By accurately identifying the dynamics of the environment and the system itself, engineers can create robust control algorithms that enable these autonomous systems to operate safely and efficiently. This has the potential to revolutionize transportation and logistics, making it safer and more reliable. However, despite its myriad applications, system identificationand control are not without challenges. One of the primary difficulties is the accurate modeling of complex, non-linear systems. Many real-world systems exhibit non-linear behavior, making it challenging to develop accurate mathematical models. This necessitates the use of advanced techniques such as neural networks and fuzzy logic to capture the intricacies of non-linear systems. Furthermore, the processof system identification often requires a significant amount of data, which may be difficult or expensive to obtain in some cases. This is particularly true for large-scale industrial systems, where conducting experiments to gather data may disrupt regular operations. Additionally, the presence of noise and uncertainties in the data further complicates the identification process, requiring engineers to employ sophisticated signal processing and estimation techniques. Looking ahead, the future of system identification and control holds great promise. With advancements in machine learning and artificial intelligence, there is potential to develop more robust and adaptive control systems. By integrating learning algorithms with system identification, it may be possible to create self-tuning controllers that can adapt to changing operating conditions and uncertainties in real-time. This could lead to more resilient and efficient systems across various industries. In conclusion, system identification and control are indispensable tools in the realm of engineering and technology. From enabling precise control of complex systems to driving the development of autonomous technologies, the impact of system identification is far-reaching. While challenges persist, ongoing research and technological advancements are paving the way for more sophisticated and adaptive control systems in the future. As we continue to unravel the complexities of dynamic systems, the role of system identification and controlwill only grow in significance.。