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理想RZRVEV三级培训讲义v2.5

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二、机器操作
1、进纸台的设置
厚纸、薄纸。滑动进纸导板时必须抬起锁定杆。
2、出纸台的设置
收起时侧导板和出纸挡板的安放方向与原来的机型不同。压纸器（出纸导臂）的设置
3、出纸跳翼的设置（RZ3XX、RV3XXX、RV5XXX）
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一、性能简介——规格
项目
RZ2XX、RV24XX RZ3XX、RV36XX
扫描制版幅面 B4
A3
印刷纸张尺寸 100X148mm —297X432mm （最大A3）
印刷纸张厚度 46g/m2—157g/m2
进纸容量
1000张，64g/m2
46g/m2 —210g/m2
分辨率制版时间
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300dpi
纸张尺寸检测传感器纸张宽度电位计
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七、第一进纸区—机构
2、升降系统
进纸压力杆
进纸盘按钮
上限传感器上限传动板
下限传感器
（从后侧观察）
纸张检测传感器
升降电机
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七、第一进纸区—机构
3、进纸盘安全机构
如果此两个安全开关中的任意一个处于闭合状态，升降电机停止运作
上限安全开关
下限安全开关
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七、第一进纸区—机构
中文触摸屏自动出纸跳翼标配接口 50组编程印刷

Festival Multisyn Voices for the 2007 Blizzard Challenge

Festival Multisyn Voices for the2007Blizzard Challenge Korin Richmond,Volker Strom,Robert Clark,Junichi Yamagishi and Sue Fitt Centre for Speech Technology ResearchUniversity of Edinburgh,Edinburgh,United Kingdom(korin|vstrom|robert|jyamagis|sue)@AbstractThis paper describes selected aspects of the Festival Mul-tisyn entry to the Blizzard Challenge2007.We provide an overview of the process of building the three required voices from the speech data provided.This paper focuses on new fea-tures of Multisyn which are currently under development and which have been employed in the system used for this Bliz-zard Challenge.These differences are the application of a more ﬂexible phonetic lattice representation during forced alignment labelling and the use of a pitch accent target cost component. Finally,we also examine aspects of the speech data provided for this year’s Blizzard Challenge and raise certain issues for discussion concerning the aim of comparing voices made with differing subsets of the data provided.1.IntroductionMultisyn is a waveform synthesis module which has recently been added to the Festival speech synthesis system[1].It pro-vides aﬂexible,general implementation of unit selection and a set of associated voice building tools.Strong emphasis is placed onﬂexibility as a research tool on one hand,and a high level of automation using default settings during“standard”voice build-ing on the other.This paper accompanies the Festival Multisyn entry to the Blizzard Challenge2007.Similar to the Blizzard Challenges of the previous two years([2,3]),the2007Blizzard Challenge required entrants to build three voices from the speech data pro-vided by speaker“EM001”,then submit a set of synthesised test sentences for evaluation.Theﬁrst voice,labelled voice“A”, used the entire voice database.Two smaller voices,“B”and“C”used subsections of the database.V oice“B”used the set of sen-tences from the ARCTIC database[4]which were recorded by the EM001speaker.For voice“C”,entrants were invited to per-form their own text selection on the voice database prompts to select a subset of sentences no larger than the ARCTIC data set in terms of total duration of speech in seconds.V oices“B”and “C”are intended as a means to compare different text selection algorithms,as well as to evaluate the performance of synthesis systems when using more limited amounts of speech data.Multisyn and the process of building voices for Multisyn is described in detail in[1].In addition,entrants to the Blizzard Challenge this year have been asked to provide a separate sys-tem description in the form of a template questionnaire.For the reader’s convenience this paper will provide a brief overview of Multisyn and the voices built.To limit redundancy,however,we will not repeat all details comprehensively.Instead,we aim to focus here on areas where the use of Multisyn differs from[1]. Those signiﬁcant differences are two-fold.First,we will intro-duce a new technique we have been developing to help in forced alignment labelling.Next,we describe a target cost component which uses a simple pitch accent prediction model.Finally,we will discuss our experience of building voice“C”,and highlight some issues we believe may complicate comparison of entrants’voices“B”and“C”.2.Multisyn voice buildingWe use our own Unisyn lexicon and phone set[5],so only used the prompts and associated waveﬁles from the distributed data, performing all other processing for voice building from scratch. Theﬁrst step of voice building involved some brief examina-tion of the text prompts toﬁnd missing words and to add some of them to our lexicon,ﬁx gross text normalisation problems and so on.Next,we used an automatic script to reduce the du-ration of any single silence found in a waveﬁle to a maximum of50msec.From this point,the process for building Multisyn voices“A”,“B”and“C”described in the remainder of this sec-tion was repeated separately for the relevant utterance subset for each voice.We used HTK tools in a scripted process to perform forced alignment using frames of12MFCCs plus log energy(utter-ance based energy normalisation switched off)computed with a10msec window and2msec frame shift.The process be-gan with single mixture monophone models with three emitting states,trained from a“ﬂat start”.Initial labelling used a single phone sequence predicted by the Festival Multisyn front end. However,as the process progressed with further iterations of reestimation,realignment,mixing up,adding a short pause tee model,and so on,we switched to using a phone lattice for align-ment described in Section3.Once labelling was completed,we used it to perform a waveform power factor normalisation of all waveforms in the database.This process looks at the energy in the vowels of each utterance to compute a single factor to scale its waveform.The power normalised waveforms were then used throughout the remainder of the voice building process,which began with repeating the whole labelling process.Once the labelling had been completed,it was used to build utterance structures1,which are used as part of the internal rep-resentation within aﬁnal Multisyn voice.At this stage,the text prompts were run through a simple pitch accent prediction model(see Section4),and this information stored in the utter-ance structures.Additional information was also added to the utterance structures at this stage;for example,phones with a duration more than2standard deviations from the mean were ﬂagged.Such information could be used later at unit selection time in the target cost function.In addition to labelling and linguistic information stored in utteranceﬁles,Multisyn requires join cost coefﬁcients and RELP synthesis parameters.To create the synthesis parameters, weﬁrst performed pitchmarking using a custom script which makes use of Entropic’s epochs,get resid,get f0 and refcof programs.We then used the sig2fv and sigfilter programs from the Edinburgh Speech Tools for lpc analysis and residual signal generation respectively.The 1a data structure deﬁned in the Edinburgh Speech Tools libraryMultisyn join cost uses three equally weighted components: spectral,f0and log energy.The spectral and log energy join cost coefﬁcients were taken from the MFCCﬁles calculated by HTK’s HCopy used for labelling.The f0contours were pro-vided by the ESPS program get f0.All three of these feature streams were globally normalised and saved in the appropriate voice data structure.During unit selection,Multisyn does not use any acoustic prosodic targets in terms of pitch or duration.Instead,the target cost is a weighted normalised sum of a series of components which consider the following:lexical stress,syllable position, word position,phrase position,part of speech,left and right phonetic context,“bad duration”and“bad f0”.As mentioned above,“bad duration”is aﬂag which is set on a phone within a voice database utterance during voice building and suggests a segment should not be used.Similarly,the“bad f0”target cost component looks at a candidate unit’s f0at concatenation points,considering voicing status rather than a speciﬁc target f0 value.We have also used an additional target cost component for the presence or absence of a pitch accent on a vowel.This is described further in Section4.Finally,we stress that during concatenation of the best can-didate unit sequence,Multisyn does not currently employ any signal processing apart from a simple overlap-add windowing at unit boundaries.No prosodic modiﬁcation of candidate units is attempted and no spectral,amplitude or f0interpolation is performed across concatenation boundaries.3.Finite state phonetic lattice labelling For all three voices for this Blizzard Challenge we employed a forced alignment system we have been developing which makes use of aﬁnite state representation of the predicted phonetic real-isation of the recorded prompts.The advantage of theﬁnite state phonetic representation is that it makes it possible to elegantly encode and process a wide variety pronunciation variation dur-ing labelling of speech data.In the following two sections we ﬁrst give a general introduction to how our phonetic lattice la-belling works,and then give some more speciﬁc details of how the system was applied to building voices for this Blizzard Chal-lenge.3.1.General implementationIf we consider how forced alignment is standardly performed using HTK,for example,the user is required to provide,among other things,a pronunciation lexicon and word level transcrip-tion.The pronunciation lexicon contains a mapping between a given word and a corresponding sequence of phone model labels.During forced alignment,the HTK recognition engine loads the word level transcription and expands this into a recog-nition network,or“lattice”,of phone models using the pronun-ciation dictionary.This lattice is then used to align against the sequence of acoustic parameter vectors.The predominant way to include pronunciation variation within this system is to use multiple entries in the lexicon for the same word.This approach generally suits speech recognition,but in the case of labelling for building a unit selection voice,we could perhaps proﬁt from moreﬂpleteﬂexibility is achieved if we compose the phone lattice directly and pass that to the recognition engine.To build the phone lattice for a given prompt sentence,we ﬁrst lookup each word in the lexicon and convert the phone string to a simpleﬁnite state structure.When a word is not found in the lexicon,we use the CART letter-to-sound rules the ﬁnal festival voice would use to generate a phone string.Where multiple pronunciations for a word are found,we can combine these into a singleﬁnite state representation using the union op-eration.Theﬁnite state machines for the separate words are then concatenated in sequence to give a single representation of the sentence.The topﬁnite state acceptor(FSA)in Figure 1gives a simpliﬁed example of the result of this process for a phrase fragment“...wider economic...”.At this stage,there is little advantage over the standard HTK method,which would internally arrive at the same result.How-ever,once we have a predicted phonetic realisation for a record-ing prompt in aﬁnite state form,it is then straightforward to process this representation further in an elegant and robust way. This is useful to help perform simple tasks,such as splitting stops and affricates into separate symbols for their stop and release parts during forced alignment(done to identify a suit-able concatenation point).More signiﬁcantly,though,we can also robustly apply more complex context dependent postlex-ical rules,for example optional“r”epenthesis intervocalically across word boundaries for certain British English accents.This is indicated in the bottom FSA of Figure1.This may be conveniently achieved by writing rules in the form of context dependent regular expressions.It is then possi-ble to automatically compile these rules into an equivalentﬁnite state transducer which can operate on the input lattice which resulted from lexical lookup(e.g.top FSA in Figure1).Sev-eral variations of compilation methods have been previously described to convert a system of handwritten context dependent mapping rules into an equivalent FST machine to perform the transduction,e.g.[6,7,8].Note that the use of context depen-dent modiﬁcations is moreﬂexible and powerful than the stan-dard HTK methods.For example,a standard way to implement optional“r”epenthesis pronunciation variation using a pronun-ciation lexicon alone would be to include multiple entries for “wider”,one of which contains the additional“r”.However,this introduces a number of problems.The most signiﬁcant problem is the absence of any mechanism to disallow“r”epenthesis in environments where a vowel does not follow.The phonetic lattice alignment code has been implemented as a set of python modules which underlyingly use and extend the MIT Finite State Transducer Toolkit[9].We use CSTR’s Unisyn lexicon[5]to build voices and within the running syn-thesis system.For forced alignment,we use scripts which un-derlying make use of the HTK speech recognition library[10]. Finally,we are planning to make this labelling system publicly available once it reaches a more mature state of development.3.2.Application to EM001voiceSpeaker EM001exhibits a rather careful and deliberate ap-proach to pronunciation during the recordings and uses a rel-atively slow rate of speech.This in fact tends to limit the ap-plicability and usefulness of postlexical rules for the Blizzard Challenge voices somewhat.Postlexical rules are more use-fully applied to the processes of moreﬂuent and rapid connected speech.Thus,in building the three voices for the2007Bliz-zard Challenge,the sole postlexical rule we used was a“tap”rule.Under this rule,alveolar stops in an intervocalic cross word environment could undergo optional transformation to a tap.Speciﬁcally,the left phonetic context for this rule com-prised the set of vowels together with/r,l,n/(central and lateral approximants and alveolar nasal stop),while the right context contained just the set of vowels.4.Pitch accent predictionIn this year’s system,we have experimented with a simple pitch accent target cost function component.To use pitch accent pre-diction in the voices built for the Blizzard Challenge required three changes.First,we ran a pitch accent predictor on the textFigure1:Toy exampleﬁnite state phonetic lattices for the phrase fragment“wider economic”:a)after lexical lookup,the lattice encodes multiple pronunciation variants for“economic”b)after additional“r”insertion postlexical rule,the input lattice(top)is modiﬁed to allow optional insertion of“r”(instead of short pause“sp”).prompts andﬂagged words with a predicted accent as such in the voice data structures.Next,at synthesis time,our front end linguistic processor was modiﬁed to run the accent predictor on the input sentence to be synthesised,and words with a predicted accent were similarlyﬂagged.Finally,an additional target cost component compared the values of the pitch accentﬂag for the word associated with each target vowel and returned a suitable cost depending on whether they match or not.The method for pitch accent prediction we used here is very simple.It is centred on a look-up table of probabilities that a word will be accented,or“accent ratios”,along the lines of the approach described in[11].The accent predictor simply looks up a word in this list.If the word is found and its probability for being accented is less than the threshold of0.28,it is not accented.Otherwise it will receive an accent.These accent ratios are based on the BU Radio Corpus and six Switchboard dialogues.The list contains157words with an accent ratio of less than0.282.The pitch accent target cost component has recently been evaluated in a large scale listening test and was found to be beneﬁcial[12].5.Voice“C”and text selection Entrants to the2007Blizzard Challenge were encouraged to enter a third voice with a voice database size equal to that of the ARCTIC subset,but with a freely selected subset of utterances. The purpose of this voice is to probe the performance of each team’s text selection process,as well as to provide some insight into the suitability of the ARCTIC data set itself.5.1.Text selection processOrdinarily,when designing a prompt set for recording a unit selection voice database,we would seek to avoid longer sen-tences.They are generally harder to read,which means they are more taxing on the speaker and are more likely to slow down the recording process.In this case,however,since the sentences had been recorded already,we decided to relax this constraint.In a simple greedy text selection process,sentences were chosen in an iterative way.First,the diphones present in the EM001text prompts were subcategorised to include certain contextual features.The features we included were lexical stress,pitch accent and proximity to word boundary.Syllable boundary information was not used in the speciﬁcation of di-phone subtypes.Next,sentences were ranked according to the number of context dependent diphones contained.The top ranking sen-tence was selected,then the ranking of the remaining sentences was recomputed to reﬂect the diphones now present in the sub-set of selected sentences.Sentences were selected one at a time in this way until the total time of the selected subset reached the 2using the accent ratio table in this way is essentially equivalent to using an(incomplete)list of English function words.count of diphone type in full EM001 setcountofcountsofmissingdiphonetypesFigure2:Histogram of counts of unique context dependent di-phone types present in the full EM001set which are missing from the selected subset used to build for voice“C”.prescribed threshold.This resulted in a subset comprising431 utterances,with a total duration of2908.75seconds.Our deﬁnition of context dependent diphones implied a to-tal of6,199distinct diphones with context in the entire EM001 corpus.Our selected subset for voice“C”contained4,660of these,which meant1,539were missing.Figure2shows a his-togram of the missing diphone types in terms of their counts in the full EM001data set.We see that the large majority of the missing diphone types only occur1–5times in the full EM001 dataset.For example,773of the diphone types which are miss-ing from the selected subset only occur once in the full EM001 set,while only one diphone type which is missing occurred as many as26times in the full data set.5.2.Evaluation problemsAlthough it is certainly interesting to compare different text se-lection algorithms against the ARCTIC sentence set,we suggest the way it has been performed this year could potentially con-fuse this comparison.Theﬁrst issue to which we would like to draw attention concerns the consistency of the recorded speech material throughout the database.The second issue concerns the question of how far the full EM001data set satisﬁes the se-lection criteria used by arbitrary text selection algorithms.5.2.1.Consistency of recorded utterancesFigures3–5show plots of MFCC parameter means from the EM001database taken in alphabeticalﬁle ordering.To produceEMOO1 File (alphabetical sorting)m e a n f o r 9t h M F C C c h a n n e lFigure 3:Mean value for 9th MFCC channel for each ﬁle of the EM001voice database.EMOO1 File (alphabetical sorting)m e a n f o r 7t h M F C C c h a n n e lFigure 4:Mean value for 7th MFCC channel for each ﬁle of the EM001voice database.EMOO1 File (alphabetical sorting)m e a n f o r 11t h M F C C c h a n n e lFigure 5:Mean value for 11th MFCC channel for each ﬁle of the EM001voice database.these plots we have taken all ﬁles in the EM001data set in al-phabetical ordering (along the x-axis)and calculated the mean MFCC parameters 3for each ﬁle.In calculating these means,we have omitted the silence at the beginning and end of ﬁles us-ing the labelling provided by the force alignment we conducted during voice building.A single selected dimension of this mean vector is then plotted in each of the Figures 3–5.From these ﬁgures,we notice that there seem to be three distinct sections of the database,which correspond to the “ARC-TIC”,“BTEC”and “NEWS”ﬁle labels as indicated in the plots.Within each of these blocks,the MFCC mean varies randomly,but apparently uniformly so.Between these three sections,however,we observe marked differences.For example,com-pare the distributions of per-ﬁle means of the 9th (Fig.3)and 7th (Fig.4)MFCC parameters within the “NEWS”section with those from the other two sections of the database.We naturally expect the MFCC means to vary “randomly”from ﬁle to ﬁle according to the phonetic content of the utter-ance contained.However,an obvious trend such as that exhib-ited in these plots suggests the inﬂuence of something more than phonetic variation alone.Speciﬁcally,we suspect this situation has arisen due to the signiﬁcant difﬁculty of ensuring consis-tency throughout the many days necessary to record a speech corpus of this size.We have observed similar effects of incon-sistency within other databases,both those we have recorded at CSTR,as well as other commercially recorded databases.Recording a speech corpus over time allows the introduction of variability,with potential sources ranging from the acous-tic recording environment (e.g.microphone placement relative to speaker)to the quality of the speaker’s own voice,which of course can vary over a very short space of time [13].In addi-tion,even the genre and nature of the prompts themselves can inﬂuence a speaker’s reading style and voice characteristics.Note that although we do not see any trends within each of the three sections of the EM001data set,and that they appear relatively homogeneous,this does not imply that these subsec-tions are free of the same variability and inconsistency.These plots have been produced by taking the ﬁles in alphabetical,and hence numerical,order.But it is not necessarily the case that the ﬁles were recorded in this order.In fact,it is likely the ﬁle order-ing within the subsections has been randomised which has the effect of disguising inconsistency within the three sections.The inconsistency between the sections is evident purely because the genre identity tag has maintained three distinct groups.Therefore,despite the probable randomisation of ﬁle order within sections,we infer from the patterns evident in Figures 3–5that the speech data corresponding to the ARCTIC prompt set was recorded all together,and constitutes a reasonably con-sistent “block”of data.Meanwhile,the rest of the data seems to have been recorded at different times.This introduces in-consistency throughout the database,which a selection algo-rithm based entirely upon text features will not take account of.This means that unless it is explicitly and effectively dealt with by the synthesis system which uses the voice data,both at voice building time (ing cepstral mean normalisation dur-ing forced alignment)and at synthesis time,voice “C”stands a high chance of being disadvantaged by selecting data indis-criminately from inconsistent subsections of the database.The forced alignment labelling may suffer because of the increased variance of the speech data.Unit selection may suffer because the spectral component of the join cost may result in a nonuni-form probability of making joins across sections of the database,compared with the those joins within a single section.This has the effect of “partitioning”the voice database.3extractedusing HTK’s HCopy as part of our force alignment pro-cessing,and also subsequently used in the Multisyn join costThe Multisyn voice building process currently takes ac-count of amplitude inconsistency,and attempts waveform power normalisation on a per-utterance basis.However,other sources of inconsistency,most notably spectral inconsistency are not currently addressed.This means that Multisyn voice “C”is potentially affected by database inconsistency,which in-troduces uncertainty and confusion in any comparison between voices“B”and“C”.Within the subset of431sentences we se-lected to build voice“C”,261came from the“NEWS”section, 169came from the“BTEC”section,and the remaining36came from the“ARCTIC”section.This issue of inconsistency can potentially affect the com-parison between the“C”voices from different entrants.For example,according to our automatic phonetic transcriptions of the EM001sentence set,the minimum number of phones con-tained in a single sentence within the“NEWS”section is52. Meanwhile,the“BTEC”section contains1,374sentences with less than52phones.Although we have not done so here,it is not unreasonable for a text selection strategy to favour short sentences,in which case a large majority may be selected from the“BTEC”section.This would result in avoiding the large discontinuity we observe in Figures3and4and could poten-tially confer an advantage which is in fact unrelated to the text selection algorithm per se.The problem has the potential,however,to introduce most confusion into the comparison between entrants’voices“B”and “C”,as there is most likely to be a bias in favour of the ARCTIC subset,which seems to have been recorded as a single block. We suggest there are at least two ways of avoiding this bias in future challenges.One way would be to provide a database without the inconsistency we observe here,for example through post-processing.This is likely to be rather difﬁcult to realise, and our own previous attempts have failed toﬁnd a satisfactory solution,although[14]reported some success.A second,sim-pler way would be to record the set of ARCTIC sentences ran-domly throughout the recording of a future Blizzard Challenge corpus.5.2.2.Selection criteria coverageThe second problem inherent in attempting to compare text se-lection processes in this way arises from differing selection cri-teria.It is usual to choose text selection criteria(i.e.which di-phone context features to consider)which complement the syn-thesis system’s target cost function.Hence the criteria may vary between systems.The set of ARCTIC sentences was selected from a very large amount of text,and so the possibility for the algorithm to reach its optimal subset in terms of the selection criteria it used is maximised.In contrast,the text selection required for voice “C”was performed on a far smaller set of sentences.Although, admittedly,it is likely to be phonetically much richer than if the same number of sentences had been selected randomly from a large corpus,it is possible that the initial set of sentences does not contain a sufﬁcient variety of material to satisfy the selec-tion criteria of arbitrary text selection systems.This again may tend to accord an inherent advantage to voice“B”.6.ConclusionWe have introduced two new features of the Multisyn unit selec-tion system.We have also raised issues for discussion concern-ing the comparison of voices built with differing subsets of the provided data.Finally,we note that,as in previous years,par-ticipating in this Blizzard Challenge has proved both interesting and useful.7.AcknowledgmentsKorin Richmond is currently supported by EPSRC grant EP/E027741/1.Many thanks to Lee Hetherington for making the MITFST toolkit available under a BSD-style license,and for other technical guidance.Thanks to A.Nenkova for process-ing the Blizzard text prompts for pitch accent prediction.8.References[1]R.A.J.Clark,K.Richmond,and S.King,“Multisyn:Open-domain unit selection for the Festival speech syn-thesis system,”Speech Communication,vol.49,no.4,pp.317–330,2007.[2]R.Clark,K.Richmond,V.Strom,and S.King,“Multisyn voice for the Blizzard Challenge2006,”in Proc.Blizzard Challenge Workshop(Inter-speech Satellite),Pittsburgh,USA,Sept.2006, (/blizzard/blizzard2006.html).[3]R.A.Clark,K.Richmond,and S.King,“Multisyn voicesfrom ARCTIC data for the Blizzard challenge,”in Proc.Interspeech2005,Sept.2005.[4]J.Kominek and A.Black,“The CMU ARCTIC speechdatabases,”in5th ISCA Speech Synthesis Workshop,Pitts-burgh,PA,2004,pp.223–224.[5]S.Fitt and S.Isard,“Synthesis of regional English usinga keyword lexicon,”in Proc.Eurospeech’99,vol.2,Bu-dapest,1999,pp.823–826.[6]M.Mohri and R.Sproat,“An efﬁcient compiler forweighted rewrite rules,”in Proc.34th annual meeting of Association for Computational Linguistics,1996,pp.231–238.[7]R.Kaplan and M.Kay,“Regular models of phonologicalrule systems,”Computational Linguistics,vol.20,no.3, pp.331–378,Sep1994.[8]L.Karttunen,“The replace operator,”in Proc.33th an-nual meeting of Association for Computational Linguis-tics,1995,pp.16–23.[9]L.Hetherington,“The MITﬁnite-state transducer toolkitfor speech and language processing,”in Proc.ICSLP, 2004.[10]S.Young,G.Evermann,D.Kershaw,G.Moore,J.Odell,D.Ollason,D.Povey,V.Valtchev,and P.Woodland,TheHTK Book(for HTK version3.2),Cambridge University Engineering Department,2002.[11]J.Brenier,A.Nenkova,A.Kothari,L.Whitton,D.Beaver,and D.Jurafsky,“The(non)utility of linguistic features for predicting prominence on spontaneous speech,”in IEEE/ACL2006Workshop on Spoken Language Technol-ogy,2006.[12]V.Strom,A.Nenkova,R.Clark,Y.Vazquez-Alvarez,J.Brenier,S.King,and D.Jurafsky,“Modelling promi-nence and emphasis improves unit-selection synthesis,”in Proc.Interspeech,Antwerp,2007.[13]H.Kawai and M.Tsuzaki,“Study on time-dependentvoice quality variation in a large-scale single speaker speech corpus used for speech synthesis,”in Proc.IEEE Workshop on Speech Synthesis,2002,pp.15–18. [14]Y.Stylianou,“Assessment and correction of voice qualityvariabilities in large speech databases for concatentative speech synthesis,”in Proc.ICASSP-99,Phoenix,Arizona, Mar.1999,pp.377–380.。

VEGF-C siRNA载体构建及其对转染食管癌细胞中VEGF-C表达的影响

３郑州大学基础医学院）
［摘要】目的
观察小分子干扰ＲＡｓＮ对人食管癌细胞Ｅ９ｏＮ（ｉＡ）ＲＣ７６中ｍ管内皮生长因子ｃＶＧ－表（ＥＦｃ）
达的抑制作用。方法根据ｓＮｉＡ设计原则，Ｒ针对人ＶＧ — ＤＡ序列设计并合成编码ｓＮＥＦｃｃＮｉＡ的寡核苷酸序列，Ｒ
并将其克隆人ｓＮｉＡ表达载体，Ｒ构建重组质粒。将重组质粒转染入Ｅ９Ｏｃ７６细胞株，通过免疫组织化学、ＴＰＲ和Ｒ．Ｃ
原位杂交法检测转染细胞中ＶＧ．白和ｍＲＡ的表达。结果ＥＦＣ蛋Ｎ正常未转染（对照组）和无关序列转染（无关序列组）Ｃ７６中，Ｅ９Ｏ均有大量ＶＧ．ＥＦｃ蛋白阳性棕色颗粒、ＲＡ阳性条带和紫蓝色阳性颗粒，在ｓＮｍＮ而ｉＡ转染ＲＥ９０（ｉＮ３），ｃ７６８Ａ．组中仅有少量弱表达颗粒和较弱的阳性条带，Ｒ与对照组和无关序列组相比，Ｐ均＜０Ｏ。结论．１成功构建了ＶＧ — ｉＮＥＦｃｓＡ载体。此载体能有效抑制人食管癌细胞Ｅ９０Ｒｃ７６中ＶＧ－ＥＦＣ的表达，其可能成为抗食管癌淋巴管生成的一种新方法。
ｔ，ｓｍ—ｕｎｉｔｅＲ —Ｃｒｎｓｕｈｂｉａｏｅｒｏｅｔｅｓｂｅｔｎｆｃｏ，ｒｓｅｔｅｙｒｅｉａｔａｖＴＰＲａｄｉｉｙｒｔｎｂｆｅｒｒｈｔｌｒｓｔｎｅｐｃｖＩ．ＲｅＩｔｙｑｔｉＩｔ出ｚｉ０ａａｅｉｉｓＩＩｓ

双柏彝族学汉语普通话语音比较研究

续表标准普通话国际音标彝族学生汉语普通话声母声母小学六年级（Ａ）初三（Ｂ）高三（Ｃ）ＣＣＣｅｚｈｔ５ｔｓ（２）虹ｔｓ伍（３）ｃｈｔ５‘ｔｓ‘“）姆‘ｔｓ‘乜‘（５）ｓｈ｝§ｓ（６）ｓ（７）§ｓ（８）乙ｚ（９）ｚｏｍｚ（１１）ｚ∞ｚ０９ｚ００悔乜ｂ但Ｃ信‘忸‘信‘乜‘ＳＳｙｙＹｊａ９Ｙｊ００Ｙｊ０７）Ｕｖ０∞从上表可以看出，彝族学生汉语普通话与标准汉语普通话的声母基本一致，但在平翘舌上与标准汉语普通话的声母有很大不同，具体表现为：ｌ、声母ｈ（１）Ａ、Ｂ、Ｃ三人都把［ＸＵ“］（互）读为［ｆｖ“］三个发音合作人都把［ＸＵ“）（互）读为［ｆｖ“），在彝语中Ｘ和ｆ并没有相混现象，可能是受到当地汉语方言的影响，因为在当地汉语方言中往往把［ＸＵ］发为Ｉｆｖ］，如“虎”读为［ｆｖ”］，“户”读为［ｆｖ“］等等，同时可以这样认为：当地教师可能也没有注意到［ＸＵ］和［ｆｖ］的区别，对这一现象也没有进行过充分的讲解，所以从小学到高中三人一直保持稳定状态，完全没有意识到这两个音的差别。

２、声母ｔ｝（２）Ａ、Ｂ把所有声母为［ｔ５］的读为［ｔｓ］，如把［伍Ａ５‘］（炸）读为［赶Ａ５１］（见图ｌ－－Ａ），［ｔ翮１（知）读为［蜘”］，［ｔ§ａｕ５５］（周）读为［ｂｏｕ５ｒ］等等。

（３）Ｃ把［ｔ［Ａ“］（炸）读为［龇５１］（见图１－Ｃ），读［ｔ５ａ“］（遮）、［ｔ［ＱＵ２”］（找）、［ｔ§ａ矿］（周）、［ｔ§ａｎ５１］（战）、［ｔ§ｕｏｓ５］（桌）、［ｔ§ｕａｉ”］（拽）、［ｔ［ｕｅｉ“］（追）为［ｔｓ３舾］、［ｔｓ（１ｕ２１］、［ｔｓａｕ５５］［ｔｓ酽１］、［ｔｓｕ０５５］、［ｔｓｕａｉ”］、［ｔｓｕｅｌ５５］。

图ｌ—Ａ图ｌ—Ｃ汉语普通话里有ｚｈ、ｃｈ、ｓｈ、ｒ和ｚ、ｃ、ｓ两组声母，前一组是所谓的翘舌尖音，即把舌尖卷起顶住硬腭而发的音，后２组是所谓平舌尖音，即舌头放平用舌尖顶住下齿背发的音ｏ。

CFA FRM公式

定量分析类财务：资金的时间价值。

投资：是对未来事件进行评估。

储蓄：是延迟的消费。

也即用现在的消费换取将来的消费。

一、单一现金流的计算：(1)(1)ppn p n p FV FV PV i PV i =+⇒=+ s p i i m=p n m n =⨯利率（i ），收益率（r 或y ），增长率（g ），FV 未来现金值，PV 当前现金值，票面利率（s i ）又称年度回报率（A P R i ）：sA P R pi i m i ==实际年利率（EAR ）(1)1EARp mi i =+-二、连续复利求FV/PV ： ininF V F V P V eP V e=⇒=三、连续复利求有效年利率： 1iEAR e =- [1]i A P R L n E A R==+四、年金——相等的连续现金流终身年金的现值：1P PP M T P V I =普通年金的FV ： (1)1[]pn pp pi F V P M T i +-=五、年百分率： s p i A P R m i ==⨯有效年利率：(1)1mp EAR i =+-六、持有期收益率： 1V H P R V =-期末期初七、货币加权收益率=内部收益率(IRR)八、时间加权收益率=几何平均数九、银行贴现基准：十、实际年收益率：十一、货币市场收益率： =绝对频数相对频数样本总数十二、算术平均数：总体：-⎛⎫= ⎪⎝⎭0B D F P 360r Ft=+-365/tEAY (1HPY )1⎛⎫= ⎪⎝⎭M M 360r H P Y t 12NX XXNμ++=1nii X Xn==∑样本：几何平均数：加权平均数：投资组合的平均年回报率：十三、方差和标准差总体：样本：变异系数：或夏普比率：或十四、概率P(X)：事件X 发生的可能性特点：其中Xi 为一组互斥集体无遗漏事件概率分布的数字特征：期望/预期：方差、标准差——风险衡量()112NNX X X μ=1G r =-112233123..................N NNw X w X w X w X w w w w μ+++=+++11t t t V R V -=-221()Ni x i x x u Nσ=-=∑x σ=221()1ni i x x X S n =-=-∑x S =x xσμ=xS X =p Fp R R S -=p FpR R σ-=-=夏普比率P FXR R σ=X XσC V μ0P(X )1≤≤1()1Ni i P X ==∑()1()Ni i i EX X P X ==∑[]()()()2222221()()xNii i EXE X EX EXEX XP X σ==-=-⎡⎤⎣⎦=∑其中xσ=(){}1[[]][[]][,][][()]Nii i X YXE X Y E Y C o v X Y NEXEX Y E Y σ=--===--∑总体协方差：=XY XY X YCOV r σσ协方差和联合概率=X Y X Y X YC O V r σσ相关系数：投资组合的预期回报和方差=++=++22222P 1122121,222221122121,212σw σw σ2w w C O V w σwσ2w w r σσZ 值分布：Roy 安全第一条件——最佳投资是安全第一比率SFR 最大的组合总体均值的置信区间称为显著程度称为显著水平可以以总体平均值的样本估计值为中心构建置信区间α是显著性水平，等于1 -用%表示的置信水平。

-软开关技术(soft technique)

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8.3.1 零电压开通脉冲宽度调制（ZVS PW 变换器工作原理（续4）
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第8章
谐振开关型变换器 --软开关技术（soft-switch）
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现代电力电子的发展------高频化

VEGF—C和VEGFR3在细支气管肺泡癌组织中的表达及意义

Ｅｉｓｎ法检测ＶＧ．ｌｉｖｏＥＦＣ和ＶＧＲＥＦ３表达，并用Ｄ－０标记淋巴管，２４计算微淋巴管密度（Ｖ；析各指标间的关系ＬＤ）分
及与肿瘤临床病理参数的关系。结果ＢＣ组ＶＧ — ＶＧＲＡＥＦＣ、ＥＦ３阳性率及ＬＤ均显著高于对照组（ＶＰ均＜．１；００）ＶＧ — ＢＣ组非黏液型阳性率显高著于黏液型（００）伴淋巴结转移组阳性率明显高于无转移组（ＥＦＣ在ＡＰ＜．１、Ｐ＜
中图分类：０２２６２１）７０２－３１０－Ｘ（０２２－０３０６
ＥｘｒｓｉｎａｄｓｇｉｃｎｅｏＧＣｎＧＲｎｂＯｃｉａｖ０ａａｃｎｍａｐｅｓｏｎｉｎｆａｃｆＶＥＦ－ａｄＶＥＦ３ｉｒｎｈＯＩｅＩｒｃｒｉｏｉ
ｐａｃｃｏｅｓｌｅｓｙＬＤ）ｅａｏｓｉｅｅｎｅｃｄｘａｄｔｉｒｌｉｓｉｓｉｌｉｐｔｏ百ａａｔｓｈｔｍｉｖｓｅｄｎｉ（Ｖ．Ｒｌｉｈｐｂｔｅａｈｉｅｎｅｒｅａｏｈｐｔｃｉｃａｌｃｐｒｅｉｒｔｔｎｗｎｈｔｎｗｈｎ０ｈｏｍｅｒ
ａｄＶＥＲｅｅｄｔｃｅｙｕｉｇｉｎＧＦ３ｗｒｅｅｔｄｂｓｍｍｕｏｉｔｃｅｃ１Ｌｍｐａｉｅｓｌｅｅｉｅｔｉｄｂ－０ｔａｕａｅｌｍ— ｎｎｈｓｏｈｍｉａ．ｙｈｔｖｓｅｓｗｒｄｎｉｅｙＤ２４ｏｃｃｌｔｙｃｆ

(精品)Fluent-UDF宏大全全

5.2 单元格宏流体变量宏名称（参数）参数类型返回值C_T(c,t) cell t c, Thread *t 温度C_T_G(c,t) cell t c, Thread *t 温度梯度矢量C_T_G(c,t)[i] cell t c, Thread *t, int i 温度梯度矢量的分量C_T_RG(c,t) cell t c, Thread *t 改造后的温度梯度矢量C_T_RG(c,t)[i] cell t c, Thread *t, int i 改造后的温度梯度矢量的分量C_T_M1(c,t) cell t c, Thread *t 温度的前一次步长C_T_M2(c,t) cell t c, Thread *t 温度的前二次步长C_P(c,t) cell t c, Thread *t 压力C_DP(c,t) cell t c, Thread *t 压力梯度矢量C_DP(c,t)[i] cell t c, Thread *t, int i 压力梯度矢量的分量C_U(c,t) cell t c, Thread *t u 方向的速度C _V(c,t) cell t c, Thread *t v方向的速度C_W(c,t) cell t c, Thread *t w方向的速度C_H(c,t) cell t c, Thread *t 焓C_YI(c,t,i) cell t c, Thread *t, int i 物质质量分数C_K(c,t) cell t c, Thread *t 湍流运动能C_D(c,t) cell t c, Thread *t 湍流运动能的分散速率C_O(c,t) cell t c, Thread *t 确定的分散速率读写导数的宏名称（参数）参数类型返回值C DUDX(c,t) cell t c, Thread *t velocity derivativeC DUDY(c,t) cell t c, Thread *t velocity derivativeC DUDZ(c,t) cell t c, Thread *t velocity derivativeC DVDX(c,t) cell t c, Thread *t velocity derivativeC DVDY(c,t) cell t c, Thread *t velocity derivativeC DVDZ(c,t) cell t c, Thread *t velocity derivativeC DWDX(c,t) cell t c, Thread *t velocity derivativeC DWDY(c,t) cell t c, Thread *t velocity derivativeC DWDZ(c,t) cell t c, Thread *t velocity derivative存取材料性质的宏名称（参数）参数类型返回值C_FMEAN(c,t) cell t c, Thread *t 第一次混合分数的平均值C_FMEAN2(c,t) cell t c, Thread *t 第一次混合分数的平均值C_FVAR(c,t) cell t c, Thread *t 第一次混合分数变量C_FVAR2(c,t) cell t c, Thread *t 第二次混合分数变量C_PREMIXC(c,t) cell t c, Thread *t 反应过程变量C_LAM FLAME SPEED(c,t) cell t c, Thread *t 层流焰速度C_CRITICAL STRAIN cell t c, Thread *t 临界应变速度RATE(c,t)C_ POLLUT(c,t,i) cell t c, Thread *t, int i 第i个污染物质的质量分数C_R(c,t) cell t c, Thread *t 密度C_MU L(c,t) cell t c, Thread *t 层流速度C_MU T(c,t) cell t c, Thread *t 湍流速度C_MU EFF(c,t) cell t c, Thread *t 有效粘度C_K_L(c,t) cell t c, Thread *t 热传导系数C_K_T(c,t) cell t c, Thread *t 湍流热传导系数C_K_ EFF(c,t) cell t c, Thread *t 有效热传导系数C_CP(c,t) cell t c, Thread *t 确定的热量C_RGAS(c,t) cell t c, Thread *t 气体常数层流物质的扩散率C_DIFF L(c,t,i,j) cell t c, Thread *t, int i,int jC_DIFF EFF(c,t,i) cell t c, Thread *t, int i 物质的有效扩散率C_ABS COEFF(c,t) cell t c, Thread *t 吸附系数C_SCAT COEFF(c,t) cell t c, Thread *t 扩散系数C_NUT(c,t) cell t c, Thread *t 湍流速度forSpalart-Allmaras为单元格读写用户定义的标量和存储器的宏名称（参数）参数类型返回值C _UDSI(c,t,i) cell t c, Thread *t, int i 用户定义的标量（单元格）C_UDSI M(c,t,i) cell t c, Thread *t, int i 前一次步长下用户定义的标量（单元格）C_UDSI_DIFF(c,t,i) cell t c, Thread *t, int i 用户定义的标量的分散率（单元格）C_UDMI(c,t,i) cell t c, Thread *t, int i 用户定义的存储器（单元格）给雷诺兹压力模型读写变量的宏名字（参数）参数类型返回值C RUU(c,t) cell t c, Thread *t uu 雷诺兹压力C RVV(c,t) cell t c, Thread *t vv 雷诺兹压力C RWW(c,t) cell t c, Thread *t ww 雷诺兹压力C RUV(c,t) cell t c, Thread *t uv雷诺兹压力sC RVW(c,t) cell t c, Thread *t vw 雷诺兹压力C RUW(c,t) cell t c, Thread *t uw 雷诺兹压力5.3表面宏mem.h中的流体变量读写的宏名称（参数）参数类型返回值F_R(f,t) face t f, Thread *t, 密度F_P(f,t) face t f, Thread *t, 压力F_U(f,t) face t f, Thread *t, u方向的速度F_V(f,t) face t f, Thread *t, v 方向的速度F_W(f,t) face t f, Thread *t, w方向的速度F_T(f,t) face t f, Thread *t, 温度F_H(f,t) face t f, Thread *t, 焓F_K(f t) face t f, Thread *t, 湍流运动能F_D(f,t) face t f, Thread *t, 湍流运动能的分散速率F_YI(f,t,i) face t f, Thread *t, int i 物质的质量分数F_FLUX(f,t) face t f, Thread *t 通过边界表面的质量流速用于给表面读写用户定义的标量和存储器的宏名称（参数）参数类型返回值F_UDSI(f,t,i) face t f, Thread *t, int i 用户确定的标量（表面）F_UDMI(f,t,i) face t f, Thread *t, int i 用户定义的存储器（表面）混合面变量宏其余的表面变量宏在表5.3.3中列出名称（参数）参数类型返回值F_C0(f,t) face t f, Thread *tF_C0_THREAD(f,t) face t f, Thread *tF_C1(f,t) face t f, Thread *tF_C1_ THREAD(f,t) face t f, Thread *t5.4几何宏5.4.1节点和面的数量在表5.4.1中列出的宏C_NNODES和C_NFACES返回相应的节点和面的整数值。

李氏朝鲜相臣名录

李氏朝鲜相臣名录太祖朝（王朝）姓名官职君号谥号姓名官职君号谥号裴克廉领议政星山府院君贞节郑道传领议政奉化府院君文献赵浚领议政平壤府院君文忠金士衡左议政上洛府院君翼元定宗朝（王朝）沉德符左议政青城府院君定安李和领议政义安大君襄宪太宗朝（王朝）李舒领议政安平府院君文简闵霁左议政骊兴府院君文度河仑领议政晋山府院君文忠成石璘领议政昌宁府院君文景李居易领议政西原府院君文度李茂右议政丹山府院君翼平赵英武右议政汉山府院君忠武权仲和领议政醴丰府院君文节李稷领议政星山府院君文景南在领议政宜宁府院君忠景柳亮右议政文城府院君忠景柳廷显领议政贞肃朴訔左议政锦川府院君平度韩尙敬领议政西原府院君文简沉温领议政青川府院君安孝世宗朝（王朝）李原左议政铁城府院君襄宪郑擢右议政清城府院君翼景柳宽左议政文贞赵涓右议政汉平府院君良敬黄喜领议政南原府院君益成孟思诚左议政文贞权轸左议政文景崔润德左议政贞烈卢闭右议政恭肃许稠左议政文敬申槩左议政文僖李贵龄左议政康胡河演领议政文孝皇甫仁领议政忠贞南智左议政宜城府院君忠简文宗朝（王朝）金宗瑞左议政忠翼端宗朝（王朝）郑苯右议政忠庄郑麟趾领议政河东府院君文成韩确左议政西原府院君襄节世祖朝（王朝）李思哲左议政甄城府院君文安姜孟卿领议政晋山府院君文景权擥左议政吉昌府院君翼平具致宽领议政绫城府院君忠烈黄守身右议政南原府院君烈成朴元亨领议政延城府院君文宪洪达孙左议政南阳府院君安武李浚领议政龟城君忠武金绸左议政上洛府院君文靖郑昌孙领议政蓬原府院君忠贞申叔舟领议政高灵府院君文忠韩明浍领议政上党府院君忠成李仁孙右议政忠僖沈浍领议政靑松府院君恭肃曺锡文领议政昌宁府院君恭简崔恒领议政宁城府院君文靖康纯领议政信川府院君庄愍金礩左议政上洛府院君文靖洪允成领议政仁山府院君威平睿宗朝（王朝）尹子云领议政茂松府院君文宪尹士昐右议政夷靖金国光左议政光山府院君定靖成宗朝（王朝）韩伯伦右议政淸州府院君襄惠尹士昕右议政坡川府院君襄平洪应左议政益城府院君忠贞卢思愼领议政宣城府院君文匡尹壕右议政铃原府院君平靖成奉祖右议政昌城府院君襄靖尹弼商领议政坡平府院君例克培领议政广陵府院君翼平许琮右议政政安府院君忠贞愼承善领议政居昌府院君章成燕山君朝（王朝）郑佸左议政恭肃成俊领议政明肃许琛右议政文贞慎守勤左议政益昌府院君信度鱼世谦左议政咸从府院君文贞李克均左议政广南府院君朴崇质左议政恭顺金寿童领议政永嘉府院君文敬韩致亨领议政淸城府院君质景柳洵领议政文城府院君文僖姜龟孙右议政晋原府院君肃宪中宗朝（王朝）朴元宗领议政平城府院君武烈宋轶领议政砺原府院君肃靖申用漑左议政文景金诠领议政忠贞沉贞领议政花川府院君文靖韩效元领议政章成尹殷辅领议政靖成金克成领议政光城府院君忠贞柳顺汀领议政靑川府院君文成郑光弼领议政文翼安瑭左议政贞愍李惟清左议政韩原府院君恭胡李荇左议政文献金谨思领议政柳溥左议政文成尹仁镜领议政坡城府院君孝成成希颜领议政昌山府院君忠定金应箕左议政文戴南衮领议政文敬权钧右议政永昌府院君忠成张顺孙领议政文肃金安老领议政洪彦弼领议政益城府院君文僖仁宗朝（王朝）柳灌左议政忠肃成世昌左议政文庄明宗朝（王朝）李芑领议政丰城府院君文敬沉连源领议政靑川府院君忠惠尹元衡领议政瑞原府院君沉通源左议政郑顺朋右议政温阳府院君尚震领议政感恩府院君成安安玹左议政文僖李蓂左议政贞简黄宪左议政尹溉左议政铃平府院君李浚庆领议政忠正权辙领议政康定宣祖朝（王朝）闵箕右议政文景洪暹领议政景宪吴谦右议政贞简郑大年右议政忠贞李铎领议政定肃朴淳领议政文忠卢守慎领议政文简姜士尚右议政贞靖金贵荣左议政上洛府院君郑芝衍右议政郑惟吉左议政柳琠领议政始宁府院君文贞李山海领议政鹅城府院君文忠郑彦信右议政郑澈左议政寅城府院君文清沉守庆右议政柳成龙领议政豊原府院君文忠李阳元领议政汉山府院君文宪崔兴源领议政宁平府院君忠贞尹斗寿领议政海原府院君文靖俞泓左议政杞城府院君忠穆郑琢左议政西原府院君贞简金应南左议政原城府院君忠靖李元翼领议政完平府院君文忠李德馨领议政文翼李恒福领议政鳌城府院君文忠李宪国左议政完城府院君金命元左议政庆林府院君忠翼尹承勋领议政文肃柳永庆领议政全阳府院君奇自献领议政德平府院君沉喜寿左议政文贞许顼左议政阳陵府院君贞穆韩应寅右议政清平府院君忠靖光海君朝（王朝）郑仁弘领议政瑞宁府院君郑昌衍左议政蓬莱府院君闵梦龙左议政韩孝纯左议政庄献朴承宗领议政密阳府院君肃愍朴弘耇左议政赵挺右议政汉川府院君仁祖朝（王朝）尹昉领议政海昌府院君文翼申钦领议政文贞吴允谦领议政忠简金瑬领议政升平府院君文忠李廷龟左议政文忠金尚容右议政文忠洪瑞凤领议政益宁府院君文靖李弘胄领议政忠贞李圣求领议政贞肃崔鸣吉领议政完城府院君文忠张维右议政新丰府院君文忠申景禛领议政平城府院君忠翼沉悦领议政忠靖姜硕期右议政文贞沉器远左议政靑原府院君金自点领议政洛兴府院君李敬舆领议政文贞徐景雨右议政李景奭领议政文忠金尚宪左议政文正南以雄左议政春城府院君文贞李行远右议政孝贞郑太和领议政忠翼孝宗朝（王朝）赵翼左议政文孝金堉领议政淸风府院君文贞李时白领议政延阳府院君忠翼韩兴一右议政靖温具仁垕左议政绫川府院君忠武沉之源领议政元斗杓左议政原平府院君忠翼李厚源右议政完南府院君忠贞显宗朝（王朝）郑维城右议政忠贞洪命夏领议政文简许积领议政郑致和左议政宋时烈左议政文正洪重普右议政忠翼金寿恒领议政文忠李庆亿左议政文翼金寿兴领议政文翼郑知和左议政李浣右议政贞翼肃宗朝（王朝）权大运领议政许穆右议政文正闵熙左议政文忠吴始寿右议政闵鼎重左议政骊阳府院君文忠李尚真右议政忠贞金锡胄右议政淸城府院君文忠南九万领议政文忠郑载嵩右议政李端夏左议政文忠赵师锡左议政东平府院君忠宪李翱右议政吕圣斋领议政靖惠睦来善左议政金德远右议政闵黯左议政朴世采左议政文纯尹趾完右议政忠正柳尚运领议政忠简申翼相右议政贞简尹趾善左议政忠正徐文重领议政恭肃崔锡鼎领议政文贞李世白左议政忠正闵镇长右议政文孝申琓领议政平川府院君文庄李畬领议政文敬金构右议政忠宪李濡领议政惠定徐宗泰领议政文孝金昌集领议政忠献李颐命左议政忠文尹拯右议政文成赵相愚右议政孝宪金宇杭右议政忠靖权尚夏左议政文纯赵泰采右议政忠翼李健命左议政忠愍景宗朝（王朝）赵泰耇右议政崔锡恒左议政文正李光佐领议政崔奎瑞领议政忠贞英祖朝（王朝）柳凤辉左议政赵泰亿左议政文忠郑澔领议政文敬闵镇远左议政文忠李观命左议政文靖洪致中领议政忠简赵道彬右议政靖僖李宜显领议政文简沉寿贤领议政吴命恒右议政海恩府院君忠孝李台佐左议政忠定李集左议政忠宪赵文命左议政豊陵府院君文忠徐命均左议政文翼金兴庆领议政靖献金在鲁领议政忠靖宋寅明左议政忠宪俞拓基领议政文翼赵显命领议政丰陵府院君忠孝郑锡五左议政贞简闵应洙右议政文宪金若鲁左议政忠正郑羽良右议政文忠李宗城领议政文忠李天辅领议政文简金尚鲁领议政翼献赵载浩右议政申晚领议政孝正李厚左议政闵百祥右议政正献洪凤汉领议政翼靖郑翚良左议政文宪尹东度领议政靖文金相福领议政文献金致仁领议政宪肃徐志修领议政文清金阳泽领议政文简韩翼謩领议政文肃金尚喆领议政忠翼李昌谊左议政翼献申晦领议政李溵领议政李思观左议政孝靖元仁孙右议政文敬洪麟汉右议政正祖朝（王朝）郑存谦领议政文安徐命善领议政忠文李徽之右议政文宪郑弘淳右议政忠宪洪乐纯左议政文宪洪乐性领议政孝安李福源左议政文贞金熤领议政文贞赵璥右议政忠定李在协左议政仁陵府院君俞彦镐右议政忠文李性源左议政文肃蔡济恭领议政文肃金钟秀左议政文忠朴宗岳右议政忠献金履素左议政翼宪金熹右议政孝简李秉模领议政文翼尹蓍东右议政文翼沉焕之领议政文忠李时秀领议政忠正纯祖朝（王朝）徐龙辅领议政翼献金观柱右议政文翼徐迈修领议政翼宪李敬一左议政孝定金载瓒领议政文忠金思穆左议政敬献韩用龟领议政翼贞金达淳右议政翼宪南公辙领议政文献林汉浩右议政贞简李相璜领议政文翼李书九右议政文简沉象奎领议政文肃李存秀左议政文翼金履乔右议政文贞郑晚锡右议政肃献洪奭周左议政文简朴宗熏左议政文贞宪宗朝（王朝）李止渊右议政文翼赵寅永领议政文忠金弘根左议政文翼郑元容领议政文忠权敦仁领议政文献金道喜左议政孝宪朴晦寿右议政肃宪哲宗朝（王朝）金兴根领议政忠文朴永元左议政文翼李宪球右议政忠简金左根领议政忠翼赵斗淳领议政文献高宗朝（王朝）李景在右议政文简李裕元领议政忠文任百经右议政文贞金炳学领议政文献柳厚祚右议政文宪洪淳穆领议政文翼韩启源右议政姜氵老右议政翼宪朴珪寿右议政文翼金炳国领议政忠文李最应领议政兴寅君文忠闵奎镐右议政忠献徐堂辅领议政文简申应朝右议政文敬宋近洙右议政文献金炳德左议政文献沉舜泽领议政文忠金炳始领议政忠文金弘集总理大臣忠献金有渊右议政贞翼赵秉世右议政忠正郑范朝左议政文献尹容善议政文忠李根命议政闵泳奎议政赵秉镐议政文献纯宗朝（王朝）李完用总理大臣。

带通滤波器BPF知识讲解

0
( 0 ) 相频响应 f arctg0 3
2. RC串并联选频网络的选频特性
FV
1
32 ( 0 )2 0
( 0 )
f arctg0 3
0 .1
当 0R 1C 或ff02π1 RC
幅频响应有最大值
0.1
1 FVmax 3
相频响应 f 0
FV 0 .4 1
3 0 .3
0 .2
0 .1 0 1 (a ) f 90 60
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带通滤波器BPF
复习
1. 滤波电路的功能
使指定频段的信号顺利通过，其它频率的信号被衰减。
2. 滤波电路的种类
低通滤波器（LPF）
通带放大倍数
理想幅频特性无过渡带
通带截止频率
下降速率
用幅频特性描述滤波特性，要研究 Au p 、Au ( fP、下降速率)。
复习
3、理想滤波器的幅频特性
高通滤波器（HPБайду номын сангаас）
AVFV 1 稳幅
讨论
不符合相位条件不符合幅值条件
1)是否可用共射放大电路？ 2)是否可用共集放大电路？ 3)是否可用共基放大电路？ 4)是否可用两级共射放大电路？
输入电阻小、输出电阻大，影响f0
可引入电压串联负反馈，使电压放大倍数大于3，且Ri大、 Ro小，对f0影响小
应为RC 串并联网路配一个电压放大倍数略大于3、输入电阻趋于无穷大、输出电阻趋于0的放大电路。
Z2 R C
Vi Vf
=
R1
热敏电阻的作用
－
Vo
Io
Rf 功耗 Rf 温度 Rf 阻值

信号产生电流

F
V
2 2 2 (1 R C ) j3 RC
( FF V 9 . 2 . 2 ) 2 2 2 V (1 R C1 ) 3 3 j( RC ) j RC RC

j RC
1 1
0

0
)
RC串并联选频网络的幅频响应及相频响应
建立振荡：使电路自激，而产生持续的振荡，由直流电变为交流电信号。
对于RC 振荡电路，直流电源就是能源。自激的因素：电路中存在噪声，频谱分布很广，其中包括 0 1 ( RC ) 这一成分。微弱的信号经过放大，通过正反馈的选频网络，使输出幅度愈来愈大，最后受电路中非线性元件的限制，使振幅自动稳定下来。开始时稳定平衡时
1X
A
a
2
Xf F xf
或设得
AF 1
( 9 . 1 . 1)
(b) 正弦波振荡电路的方框图
A Aa , F Ff
A F AF ( a f ) 1
| A F | AF 1 a f 2 n , n 0 ,1, 2 ,
负反馈放大电路的自激条件：都是要求环路增益等于1. 但振荡、自激（正、负反馈）的相位条件不一致.
★振荡条件ch901
振荡频率 f 0 ：一个正弦振荡电路只在一个频率下
满足相位平衡条件，该频率是 f 0 。
f 0 是由相位平衡条件决定的。
.
.
AV
.
＋
.
Vo
输出阻抗低。
F V ( Z 1、 Z 2）正反馈网络. :
Z2
R

ZFX Easy Vision产品说明书

Easy Vision – Touch,Connect & Go•Easy vision – intuitive teach & go user interfaces•Live – built-in LCD touch monitors for setup and immediate feedback •Communication – centralized setup & inspection via Ethernet•Versatile – app. 20 tools, 32 inspections per image•Simplicity – auto-adjustment functions for easy image setupOrdering InformationControllersCamerasAppearancePower supplyCircuit type ModelDC21.6 to 26.4VNPNZFX-C10PNP ZFX-C15AppearanceTypeSetting distanceSensing areaModelRemarks Camera with lighting Monochrome type34mm to 49mm5mm x 4.9mm to9mm x 8.9mm (variable)ZFX-SR10Cable length: 2m38mm to 194mm 10mm x 9.8mm to50mm x 49mm (variable)ZFX-SR50Color type34mm to 49mm 5mm x 4.9mm to9mm x 8.9mm (variable)ZFX-SC1034mm to 187mm 10mm x 9.8mm to50mm x 49mm (variable)ZFX-SC50ZFX-SC50W(IP67)67mm to 142mm 50mm x 49mm to90mm x 89mm (variable)ZFX-SC90ZFX-SC90W(IP67)115mm to 227mm 90mm x 89mm to150mm x 148mm (variable)ZFX-SC150ZFX-SC150W(IP67)Camera onlyMonochrome typeThe CCTV lens is selected according to the range of detection and the installation distance.ZFX-S –Color typeZFX-SC(ZFX-SC50)CablesAccessoriesSpecificationsControllerTypeCable lengthModel Camera Cable *1*1.It is necessary for ZFX-S and ZFX-SC. ZFX-SR_/SC_ is a cable drawing out type, it doesn't use it.Normal type 3m, 8mZFX-VSRobot cable type 3m ZFX-VSR Camera Extension CableNormal type 3m ZFX-XC3A *2*2.Up to two camera extension cables can be connected to the camera cable as long as the total cable length between the controller and the camera does not exceed 19 m.8m ZFX-XC8A *2Robot cable type 3mZFX-XC3AR *2Parallel I/O Cable 2m, 5m ZFX-VP RS-232C Cable 2m ZFX-XPT2A RS-422 Cable 2m ZFX-XPT2B Monitor Cable 2m, 5mFZ-VMTypeModelConsole ZFX-KP (2m / 5m)LCD MonitorFZ-M08Panel Mount Adapters ZFX-XPM Optional Lighting *1*1.It is possible to ZFX-SC50 and ZFX-SC90 use it.bar lighting ZFV-LTL01bar double-lightingZFV-LTL02bar low-angle lightingZFV-LTL04light source for through beamZFV-LTF01CCTV Lenses/Extension Tubes 3Z4S-LE series External Lighting 3Z4S-LT series Strobe Controller *2*2.It is possible to ZFX-S and ZFX-SC use it. It uses it so that the controller may control an external lighting.Manufactured byMORITEX Corporation 3Z4S-LTMLEK-C100E1TSXItemZFX-C10ZFX-C15Number of connected cameras 1Connectable camera ZFX-SR_/SC_/S/SCProcessing resolution When ZFX-SR_/SC_ is connected:464 (H) x464 (V)When ZFX-S/SC is connected:608 (H) x464 (V)DisplayLCD monitor 3.5" TFT color LCD (320 x 240 pixels)Indicator"Measuring" indicator (color: green): RUN Trigger indicator (color: blue): ENABLEJudgment indicator (color: orange): OUTPUT Error indicator (color: red): ERRORExter-nal I/FParallel InterfaceInput 12 points (RESET, DSA, DI0 to 8, TRIG)Output 22 points (OR, ERROR, RUN, ENABLE, GATE, STGOUT0, DO0 to 15)Circuit type NPN PNP Serial InterfaceUSB2.0 1 port, FULL SPEED, MINI-B connectorRS-232C 1 port, max. 115200 bps (cannot be used simultaneously with RS-422 interface)RS-422 1 port, max. 115200 bps (cannot be used simultaneously with RS-232C interface)Networkcommunications Ethernet1 port, 100BASE-TX/10BASE-TMonitor output Analog RGB output, 1 ch (resolution VGA: 640 x 480)Memory card I/FSD card slot 1 chOperation I/F Touch panel, key operation, console connection Main functions Number of registered banks 32 banksNumber of setup items32 items/1 bankMeasurement items Shape inspection Pattern search, sensitive search Size inspectionAreaEdge inspectionPosition, width, count Brightness/color inspection Brightness, HUE Application-based inspectionDefectsPostion correction1 model search,2 model search, position, area Support Image memory function Max. 100 imagesMenu language Japanese/English (can be switched)Ratings Power supply voltage21.6 to 26.4 VDC (including ripple)Current consumption 1.5 A max.Insulation resistance Across all lead wires and controller case: 20 M (by 250 V megger)Dielectric strengthAcross all lead wires and controller case, 1000 VAC, 50/60 Hz, 1 minCameraOperation environment robustnessAmbient temperature range Operating: 0 to + 50°C, Storage: -15 to +60°C (with no icing or condensation)Ambient humidity range Operating and storage: 35% to 85% (with no condensation)Ambient atmosphere No corrosive gases allowed Degree of protection IP20 (IEC60529)Vibration resistance (durability)Vibration frequency: 10 to 150 Hz Single-amplitude: 0.35 mm Acceleration: 50 m/s 2 10 times for 8 minutesShock resistance (destructive)150 m/s 2 3 times each in 6 directions (up/down, left/right, forward/backward)Material Case: Polycarbonate (PC), Plate face: PMMA WeightApprox. 620 gAccessoriesTouch pen (ZFX-TP), Exhaust unit (ZFX-EU), Terminal block adapter (ZFX-XTB), Ferrite core (2 p'ces), Instruction SheetItemZFX-C10ZFX-C15Operation environment robustness Ambient temperature rangeOperating: 0 to + 40°C, Storage: -20 to +65°C (with no icing or condensation)Operating: 0 to + 50°C, Storage: -20 to +65°C(with no icing or condensa-tion)Ambient hu-midity range Operating and storage: 35% to 85% (with no condensation)Ambient atmosphere No corrosive gases allowed Degree of protection IP65 (IEC60529)ZFX-SC___: IP65 (IEC60529), ZFX-SC___W: IP67 (IEC60529)IP20 (IEC60529)Dielectric strength 1000 VAC 50 Hz/60 Hz 1 min500VAC 50 Hz/60Hz 1 minVibration resistance (durability)10 to 150 Hz Single-amplitude 0.35 mm 10 times for 8 min each in X, Y, and Z directionsShock resistance (destructive)150 m/s 2 3 times each in 6 directions (up/down, left/right, forward/backward)Connection methodCable built-in type (cable length: 2 m)Connector connection type (camera cableZFX-VS/VSR required)Material Case: ABS, mounting fixture: PBTCase: Aluminum die-cast, Cover: Zinc-platedcopper plate 0.5 mm thick, Camera mounting base: ABSWeightApprox. 200 g (including mounting fixture and cable)Approx. 270 g (including mounting fixture and cable)Approx. 300 g (including mounting fixture and cable)Approx. 600 g (including mounting fixture and cable)Approx. 80 g Accessoriesmounting fixture (ZFV-XMF) 1 p'ce,Ferrite core 2 p'ce,Instruction Sheetmounting fixture(ZFV-XMF) 1 p'ce,Ferrite core 2 p'ces,Instruction Sheetmounting fixture(ZFV-XMF2) 1 p'ce,Ferrite core 2 p'ces,Warning label 1,Instruction Sheetmounting fixture(ZFV-XMF2) 1 p'ce,Ferrite core 2 p'ces,Warning label 1,Instruction SheetFerrite core 2 p'ces,Instruction SheetInstruction Sheet *1.Applicable standards IEC60825-1:1993 +A1:1997 +A2:2001, EN60825-1:1994 +A2:2001Item ZFX-SR10ZFX-SR50ZFX-SC10ZFX-SC50/SC50W ZFX-SC90/SC90W ZFX-SC150/SC150WZFX-S(monochrome type)ZFX-SC(color type)CCTV LensesThe CCTV Lenses and Extension Tubes described in this page are not yet released.Optical GraphIf using the ZFX-S/SC Camera (Camera only), refer to the optical graph below and select the lens and Extension Tubes. The lens to be selected will depend on the size of the measurement object and the camera distance.CCTV LensesCCTV LensModel 3Z4S-LE ML-06143Z4S-LE ML-08133Z4S-LE ML-12143Z4S-LE ML-16143Z4S-LE ML-25143Z4S-LE ML-35193Z4S-LE ML-50183Z4S-LE ML-75273Z4S-LE ML-10035AppearanceFocal length 6 mm 8 mm 12 mm 16 mm 25 mm 35 mm 50 mm 75 mm 100 mm Brightness F1.4F1.3F1.4F1.4F1.4F1.9F1.8F2.7F3.5Filter sizeM27 P05M25.5 P0.5M27 P0.5M27 P0.5M27 P0.5M27 P0.5M30 P0.5M30 P0.5M30 P0.530 dia.3034.530 dia.34.530 dia.24.530 dia.24.530 dia.2930 dia.3732 dia.42.532 dia.43.932 dia.External Dimensions(Unit: mm)ControllersZFX-C10/C15Optional Lighting220001002020140165.4Four, M4(through-hole)165.4Four, M4(through-hole)1037070109(light-emitting surface)CamerasLCD MonitorConsoleFZ-M08ZFX-KP52.52.50.81014052.525.531(15.5)(14.5)112920.54813518.6Heat-resistant PVC shielded cable 4.8 mm dia. standard length 2 m,5mIn the interest of product improvement, specifications are subject to change without notice. Cat. No. E381-E1-01-XOMRON EUROPE B.V.Wegalaan 67-69,NL-2132 JD, Hoofddorp,The NetherlandsPhone:+31 23 568 13 00Fax:+31 23 568 13 88。

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Schneider Electric XPSAF5130 产品数据手册说明书

i s c l a i me r : T h i s d o c u m e n t a t i o n i s n o t i n t e n d e d a s a s u b s t i t u t ef o r a n d i s n o t t o b e u s e d f o r d e t e r m i n i ng s u i t a b i l i t y o r r e l i a b i l i t y o f th e s e p r o d u c t s f o r s p e ci f i c u s e r a p p l i c a t i o n sProduct data sheetCharacteristicsXPSAF5130module XPSAF - Emergency stop - 24 V AC DCProduct availability : Stock - Normally stocked in distribution facilityPrice* : 300.00 USDMainRange of productPreventa Safety automation Product or component type Preventa safety module Safety module name XPSAFSafety module application For emergency stop and switch monitoring Function of moduleMonitoring of a movable guardEmergency stop monitoring 1-channel wiring Emergency stop monitoring 2-channel wiringSafety levelCan reach PL e/category 4 conforming to EN/ISO 13849-1Can reach SILCL 3 conforming to EN/IEC 62061Safety reliability dataDC > 99 % conforming to EN/ISO 13849-1MTTFd = 243 years conforming to EN/ISO 13849-1PFHd = 4.62E-9 1/h conforming to EN/IEC 62061Type of startConfigurableConnections - terminalsCaptive screw clamp terminals, clamping capacity: 2 x 0.5...2 x 1.5 mm² flexible cable with cable end,with double bezelCaptive screw clamp terminals, clamping capacity: 1 x 0.14...1 x 2.5 mm² flexible cable with cable end, with double bezelCaptive screw clamp terminals, clamping capacity: 1 x 0.14...1 x 2.5 mm² solid cable with cable end,with double bezelCaptive screw clamp terminals, clamping capacity: 1 x 0.25...1 x 1.5 mm² flexible cable with cable end, with double bezelCaptive screw clamp terminals, clamping capacity: 1 x 0.25...1 x 2.5 mm² flexible cable with cable end, with double bezelCaptive screw clamp terminals, clamping capacity: 2 x 0.14...2 x 0.75 mm² flexible cable with cable end, with double bezelCaptive screw clamp terminals, clamping capacity: 2 x 0.14...2 x 0.75 mm² solid cable with cable end,with double bezelCaptive screw clamp terminals, clamping capacity: 2 x 0.25...2 x 1 mm² flexible cable with cable end,with double bezelOutput typeRelay instantaneous opening 3 NO, volt-free Number of additional circuits 0[Us] rated supply voltage24 V AC (- 15...10 %)24 V DC (- 15...10 %)ComplementarySynchronisation time between inputs UnlimitedSupply frequency50/60 HzPower consumption in VA<= 5 VA ACInput protection type Internal, electronicControl circuit voltage24 VLine resistance90 OhmBreaking capacity C300: 180 VA, AC-15 (holding) relay outputC300: 1800 VA, AC-15 (inrush) relay outputBreaking capacity 1.5 A at 24 V (DC-13) time constant: 50 ms for relay outputOutput thermal current 6 A per relay relay output18 A[Ith] conventional free air thermalcurrentAssociated fuse rating 4 A fuse type gG or gL for relay output conforming to EN/IEC 60947-5-1, DIN VDE 0660 part 2006 A fuse type fast blow for relay output conforming to EN/IEC 60947-5-1, DIN VDE 0660 part 200 Minimum output current10 mA relay outputMinimum output voltage17 V relay outputResponse time on input open<= 40 ms[Ui] rated insulation voltage300 V (degree of pollution: 2) conforming to DIN VDE 0110 part 1300 V (degree of pollution: 2) conforming to IEC 60947-5-1[Uimp] rated impulse withstand voltage 4 kV overvoltage category III conforming to IEC 60947-5-14 kV overvoltage category III conforming to DIN VDE 0110 part 1Local signalling 3 LEDsCurrent consumption30 mA at 24 V AC (on power supply)Mounting support35 mm symmetrical DIN railProduct weight0.55 lb(US) (0.25 kg)EnvironmentStandards EN 1088/ISO 14119EN 60204-1EN/IEC 60947-5-1EN/ISO 13850Product certifications CSATÜVULIP degree of protection IP20 (terminals) conforming to EN/IEC 60529IP40 (enclosure) conforming to EN/IEC 60529Ambient air temperature for operation-13...140 °F (-25...60 °C)Ambient air temperature for storage-40...185 °F (-40...85 °C)Ordering and shipping detailsCategory22477 - SAFETY MODULES (PREVENTA)Discount Schedule SAF2GTIN00785901445463Nbr. of units in pkg.1Package weight(Lbs)0.55000000000000004Returnability YCountry of origin IDContractual warrantyWarranty period18 months。

欧姆龙 ZFV-C 智能传感器说明书

Cat.No.Z240-CN5-03ZFV-CCat.No.Z240-CN5-03智能传感器用户手册带有超高速彩色C C D 相机ZFV-C 智能传感器带有超高速彩色CCD 相机用户手册编号：Z240-CN5-03200801SXX介绍本手册提供了使用传感器所需的相关功能、性能和操作方式。

使用 ZFV-C 智能传感器时，请务必遵循以下说明：·ZFV-C 智能传感器必须由懂得电子工程方面知识的人员方可操作。

·为确保正确使用，请通读本手册以加深您对产品的理解。

·请将本手册妥善保存在安全的地方以备日后参考。

介绍第1章第2章第3 章第4章第5 章第6章第7 章操作手册智能传感器带有超高速彩色 CCD 相机ZFV-C介绍第 1 章第 2 章第 3 章第 4 章第 5 章第 6 章第 7 章应用考虑（请阅读）特点关于安装和连接使用功能和操作检查条件设定设定附加功能附录应用和设定2介绍ZFV-C 操作手册介绍阅读并理解本文档在使用本产品之前请阅读并理解本文档。

如果有任何疑问或意见，请咨询欧姆龙业务代表。

保修欧姆龙的唯一保修范围为自购买日期起一年（或其他指定时间）内产品材料和工艺方面的不良。

对于将本产品用于特殊用途的无侵害性、适销性或适当性，欧姆龙不作任何明示或默示之保证或担保。

任何购买者或用户认可其本身已确定本产品符合其使用需求。

欧姆龙不承担其他任何明示或默示之责任。

责任限制对于由本产品引起的任何特殊损害、间接损害以及结果性损害，利润损失或商业损失，无论该主张是基于合同、保修卡、疏忽或是严格责任，欧姆龙公司概不承担任何责任。

如有任何赔偿，则欧姆龙的赔偿金额不超过单个产品的售价。

除非经欧姆龙分析确认本产品被正确处理、存放、安装和保养，且未经污染、滥用、错误使用、不恰当地改装或维修，否则欧姆龙概不承担任何关于本产品的保修、维修或其他主张。

3ZFV-C 操作手册介绍介绍适宜用途本文档中所述产品无法保证安全。

序半群的Fuzzy反C-内理想及完全Fuzzy内理想

，
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， ≤
ห้องสมุดไป่ตู้
理想方面做了大量的工作［３讨论了各种不同类型１］＿，的理想，而从不同角度研究了序半群．９５年，进１６
Ｚｄｈ出了Ｆｚｙ集的概念．ａｅ［提ｕｚ随后，Ｋｕｏｉ，Ｎ．ｒｋ［］
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摘要：入了序半群ＡＦｚｙ反ｃ内理想的概念，助于强均Ｆｚｙ子集和Ｆｚｙ集的截集给出了Ｆｚｙ反Ｃ内引：ｕｚ一借ｕｚｕｚ一ｕｚ一理想的一些刻画．一步给出了完全Ｆｚｙ内理想的概念，论了它在正则序半群中的一些特殊性质．为应用，进ｕｚ讨作这些结论在一般半群（含序）不中都成立．
符号．（称，・ ≤ ）为序半群，果（，如ｓ，・是半群，）

6-1 导体的分类

A 2、的测定: l A l 取决于电导池的几何特征，称为电导池常数（或电极常数）。

R1 R R R2 R3
1 G R
电导池常数直接测定有困难，往往用间接法测。即：用此电解池测已知电导率溶液（常用KCl）的电导，来求电导池常数。
例[6 1]25C时，一电导池放入C1： 1mol dm3 KCl测R1 56.81 同一电导池，放入C2： 0.1mol dm3未知溶液，R2 25.82
结束
§6-1
1. 第一类导体
导体的分类
一、按导电机制不同导体可以分为两类
又称电子导体，如金属、石墨等。 A.自由电子作定向移动而导电
B.导电过程中导体本身不发生变化 C.温度升高，电阻也升高 D.导电总量全部由电子承担
⒉ 第二类导体
又称离子导体，如电解质溶液、熔融电解质等。
A.正、负离子作反向移动而导电 B.导电过程中有化学反应发生 C.温度升高，电阻下降 D.导电总量分别由正、负离子分担
电解池(electrolytic cell)
电极①：
① ②
与外电源负极相接Байду номын сангаас是负极。发生还原反应，是阴极。 Cu2++2e-→Cu(S)
电极②：
与外电源正极相接，是正极。发生氧化反应，是阳极。 Cu(S)→ Cu2++2e-
二、法拉第定律
• 电流产生的化学作用与电量之间的关系： • （1）电解时，电极上起反应物质的量与通过溶液的电量成正比。（法拉第第一定律） • （2）以相同电量通过含有不同电解质溶液的电解槽时，在每个电极上发生反应的各物质，其得（失）电子的物质的量相等。 Q zF
• • Q—1mol物质发生电化学反应所需电量（库仑） z—1mol物质发生电化学反应所需电子的量 Fe3 3e Fe 需3mol电子。 F—法拉第常数（96500库仑/mol） 1法拉第—1mol电子带电量但实际电极上可能发生副反应，所以消耗的电量往往大于理论计算量，二者之比称为电流效率。

蒸汽指标系数.doc

蒸汽等级1kg蒸汽能量折算值折合标准煤MPa MJ kg10.00 3.852/ 0.131433.50 / 3.684/ 0.125701.00 / 3.182/ 0.108570.30 / 2.763/ 0.09427＜0.30 / 2.303 0.07858_______________________________________________标准煤折算值:29.308MJ/kg计算公式如下：折合标准煤(kg)＝1kg蒸汽能量折算值(MJ)/标准煤折算值(29.308MJ/kg各类能源参考折标系数表能源名称计量单位当量参考折标系数（吨标煤）等价参考折标系数（吨标煤）原煤吨0.7143 0.7143洗精煤吨0.9 0.9其它洗煤吨0.2-0.7 0.2-0.7煤制品吨0.5-0.7型煤吨0.5-0.7水煤浆吨0.714煤粉吨0.7143 0.7143焦炭吨0.9714 1.143 其他焦化产品吨 1.1-1.5焦炉煤气万立方米 5.714-6.143 7.23高炉煤气万立方米 1.286其他煤气万立方米 1.7-12.1天然气万立方米11-13.3 11-13.3原油吨 1.4286 1.4286汽油吨 1.4714 1.6186煤油吨 1.4714 1.6186柴油吨 1.4571 1.7286燃料油吨 1.4286 1.551液化石油气吨 1.7143 1.886炼厂干气吨 1.5714其他石油制品吨1-1.4热力百万千焦0.0341电力万千瓦时 1.229 3.6煤矸石吨0.1786 0.1786自来水万立方米 2.571 饱和蒸汽：压力1—2.5千克/平方厘米，温度127℃以下，每千克蒸汽的热焓按620千卡计算，1吨蒸汽折0.0886吨标煤；压力3—7千克/平方厘米，温度135—165℃，每千克蒸汽的热焓按630千卡计算，1吨蒸汽折0.09吨标煤压力8千克/平方厘米，温度170℃以下，每千克蒸汽的热焓按640千卡计算，1吨蒸汽折0.0914吨标煤过热蒸汽（压力150千克/平方厘米）：200℃以下，每千克蒸汽的热焓按650千卡计算，1吨蒸汽折0.0929吨标煤220—260℃，每千克蒸汽的热焓按680千卡计算，1吨蒸汽折0.0971吨标煤280—320℃，每千克蒸汽的热焓按700千卡计算，1吨蒸汽折0.1吨标煤350—500℃，每千克蒸汽的热焓按750千卡计算，1吨蒸汽折0.1071吨标煤1其计算方法是根据锅炉出口蒸汽和热水的温度压力在焓熵图(表)内查得每千克的热焓减去给水(或回水)热焓，乘上锅炉实际产出的蒸汽或热水数量(流量表读出)计算。