Calvo model+New Keyesian model
Rane Model DC 24 动态控制器说明书
DC 24DYNAMIC CONTROLLERData Sheet-1General DescriptionThe Rane Model DC 24 Dynamic Controller is a two chan-nel Compressor, Limiter, Expander Gate system with very un-usual attributes. The DC 24 offers unprecedented control of its operating parameters as well as a built-in 24 dB/octave Linkwitz-Riley Crossover which gives it very impressive capabilities.Total freedom from control interaction highlight the DC 24 as well as the availability of separate Compressor and Limiter controls. The Compressor offers control over both Ratio and Threshold, while the Limiter allows setting a separate Threshold. In doing this, the DC 24 allows the operator to create a smooth transition between subtle compression over a wide dynamic range and peak-stop limiting at the sound system’s highest allow-able level. If that’s not enough, the DC 24 also offers indepen-dent Expander/Gate Ratio and Threshold controls. This third level of signal manipulation makes the DC 24 a most useful and revolutionary device.Attack and release times are automatic and program depen-dent. This simplifies use of the DC 24, as these subtle controls can confuse most users. History has proven that experienced compressor users rarely miss these controls after using a DC 24.The internal Crossover allows the DC 24 to operate as a two-way speaker dividing network along with all of the dy-namic characteristics of a fully featured Compressor Limiter. In addition to this application, the DC 24 supplies the necessary circuitry to allow the unit to divide a single channel of audio information in two separate frequency ranges and to then re-combine the program material into one Channel. Using the DC 24 in this way eliminates the pumping and breathing associated with compression and limiting when only one Channel is used to cover the entire audio spectrum.Refer to “The DC 24 User’s Guide,” on the Rane website, for an easy-to-understand guide of operation and applications.Features• Ratio & Threshold Controls for Compressor and Gate/Expander • Limiter Threshold Controls• Program Dependent Attack & Release• Linkwitz-Riley Crossover with 24 dB per Octave Slopes • Low-High Crossover Mode (1 In/2 Out)• Bandsplit Combine Mode (1 In/1 Out)• Stereo/Dual Modes (2 In/2 Out)• Side Chain Insert Jacks• Balanced XLR & ¼" TRS Connectors • -10 dBV / +4 dBu Gain Switch• UL/CSA Remote Power Supply (120 VAC)• CE (Low Voltage & EMC) Remote Power Supply (230 VAC)DATA SHEETDC 24DYNAMIC CONTROLLERData Sheet-2Parameter Specification Limit Units Conditions/CommentsCompressor..........Threshold Range -50 to +205dB ..........Ratio Range 1:1 to 10:110%Expander / Gate..........Threshold Range -50 to +105dB ..........Ratio Range 1:1 to 20:110%Limiter..........Threshold Range -20 to +202dBCrossover ..........Type Linkwitz-Riley 4th-order 24 dB per octave slopes..........Range 70 Hz to 7 kHz5%41-detent continuously variable pot Inputs: Type Active Balanced / Unbalanced ..........Connectors XLR & + ¼" TRS ..........Impedance20k 1%Ω..........Maximum Level +201dBuOutputs: Type Active Balanced ..........Connectors XLR & + ¼" TRS ..........Impedance1001%ΩEach output ..........Maximum Level +261dBu 2 kΩ or greater +201dBu 600 Ω or greaterOverall Gain Range -12 to +12±1dB Center detent unity gainRFI FiltersYes Passive Bypass SwitchYes LED Thresholds: Overload +221dBu Output or any internal level ..........Signal Present -403dBu Input LevelFrequency Response 20 Hz to 20 kHz +0/-.5dB THD+Noise0.05.01%+4 dBu, 1 kHzIM Distortion (SMPTE)0.1.01%60 Hz / 7 kHz, 4:1, +4 dBuSignal-to-Noise Ratio 1082dB Unity Gain re +20 dBu, 20 kHz BW 922dBUnity Gain re +4 dBu, 20 kHz BW Unit: Agency Listing ..........120 VAC model Class 2 Equipment National Electrical Code UL & CSA Exempt Class 2 equipment ..........230 VAC model CE-EMC EMC directive 89/336/EECCE-SafetyExempt per art. 1, LVD 73/23/EEC Power Supply: Agency Listing Rane model RS 1Class 2 Equipment ..........120 VAC model UL File no. E88261CSA File no. LR58948..........230 VAC modelCE-EMC EMC directive 89/336/EEC CE-Safety LV directive 73/23/EEC ..........100 VAC model Built to JISJapan onlyPower Supply Requirement 18 VAC w/center tap 0.1Vrms Maximum Current 600mA RMS current from remote supply Unit: Construction All Steel..........Size 1.75"H x 19"W x 5.3"D (1U)(4.4 cm x 48.3 cm x 13.5 cm) ..........Weight 5 lb(2.3 kg)Shipping: Size 4.5" x 20.3" x 13.75"(11.5 cm x 52 cm x 35 cm)..........Weight 9 lb(4.1 kg)Note 1: 0 dBu=0.775 VrmsNote 2: Unless otherwise stated, all measurements made with Thresholds set at maximum, Ratios set at minimum.DC 24DYNAMIC CONTROLLERData Sheet-3Block DiagramArchitectural SpecificationsThe dynamic processor shall be a two (2) channel unit, each channel of which provides independent control over its gating, compression and limiting functions. The gating function shall provide a means for setting the gate threshold as well as the ratio of the function thus providing a means for gentler slopes to oc-cur such as one would expect to find in an expander.The compressor shall also provide a means for setting thresh-old and ratio independently. The limiter shall also provide a means for setting its operational threshold, but shall differ from the other two functions in that limit ratio shall be a function of limit level.All attack and release characteristics provided by the dynam-ic controller shall be a function of the current program material, thus providing a high level of transparency to the listener.The dynamic processor shall provide an active crossover circuit for the purpose of using the unit to drive amplifiers con-nected to two-way loudspeaker systems as well as for dividing a single channel audio source into two frequency bands for ulti-mate recombination at the outupts of the device. The crossover shall be a fourth-order Linkwitz-Riley type configuration.Passive bypass switches shall be provided to ensure total bypass of the unit’s active circuitry in the event of power failure. The inputs and outputs shall be active balanced/unbalanced designs terminated with XLR & ¼" TRS connectors. The side-chain send and receive connectors shall be ¼" unbalanced types, wired tip=send, ring=return.RFI filters shall be provided at the processor’s inputs. LEDs shall be provided to indicate the presence of an input signal as well as high level overload conditions.The unit shall be exempt from agency safety requirements and powered from a UL listed / CSA certified remote power sup-ply (120 VAC), or CE approved (230 VAC) via a rear panel input modular plug.The unit shall be entirely constructed from cold-rolled steel, and mount into a standard EIA relay rack occupying 1 rack space.The unit shall be a Rane Corporation Model DC 24.DC 24DYNAMIC CONTROLLERData Sheet-4All features & specifications subject to change without notice.DOC 107501Rear PanelAvailable Accessories • SC 1.7 Security Cover©Rane Corporation 10802 47th Ave. W., Mukilteo WA 98275-5098 TEL 425-355-6000 FAX 425-347-7757 WEB Application InformationTraditionally, a product such as the DC 24 has been referred to as a “Compressor / Limiter” because the range of the Ratio control on the Compressor has been wide enough to accommo-date both gentle compression and harder limiting effects. Not, however, simultaneously. One had to make a choice between the two modes of operation. On some models a Gate has been pro-vided which may or may not be part of the Compressor function.In the DC 24, all three functions of each channel are inde-pendent. Gating may occur when low-level signals are present, compression may occur when the level increases, and “peak-stop” limiting is available for high-level signals. This provides a three slope capability which is rather unique in the audio industry.Additionally, the DC 24 can help out a great deal on the low end of the amplitude spectrum by serving as a noise gate simul-taneously. The Compressor may be used to “tighten” vocals and instrumentals while leaving the Limiter function available for use as a safety valve.To accomplish this feat, the DC 24 provides three separate “Side Chains” in each Channel, each having its own set of front panel controls. For the Gate / Expander function, input signal is converted from an audio format to a control signal and ap-plied to the threshold circuit. If the output of the controller is below the specified threshold, it is passed along to the Gate / Expander Ratio control. The Ratio control allows attenuation of the controller to inhibit the slope of the Expander. After this at-tenuation, the control signal is delivered to the Channel’s control summing amplifier where it will meet similar signals generated by the Compressor control system.The Compressor controller works remarkably similarly to the Gate, the exception being the polarity. While the Gate circuit reduces gain when input level decreases below Threshold, the Compressor decreases gain when input increases above Thresh-old. The Compressor also receives the output of the controller, applies it to its threshold determinator, and passes the signalto the ratio attenuator if threshold conditions are satisfied. The output of the Ratio control is applied to the summing amplifier referenced in the gate section.Side Chain inserts have been provided on the rear of the unit to allow the insertion of an equalizer into the control circuits of the Gate and the Compressor. This will allow the user to create a frequency-dependent threshold for the Gate and/or the Compressor. This feature is useful when attempting to control sibilance in vocals.The Limiter operates in an entirely different manner than the preceding sections. The control circuit for the Limiter monitors the output of the VCA, not the input of the unit. Anytime the output of the VCA exceeds the Threshold set on the front panel, Limiting begins to take place. The ratio of the Limiter is set automatically and is a function of the excess level the system is attempting to deliver above the preset Threshold. The attack and release time of the Limiter is a function of the speed at which the input signal is attempting to drive the output of the unit above the Threshold level.The Crossover function of the DC 24 is based on Rane’s time-proven 4th-order state-variable Linkwitz-Riley design. This yields a 24 dB per octave slope and an in-phase characteristic. Since the outputs are in phase with each other, they recombine properly when the channel summing mode is selected via the rear panel Separate/Combine switch.In its band-split mode, the DC 24 allows separate processing of low frequencies and high frequencies; a mode which makes its operation all the more transparent. When the Crossover is used in conjunction with a two-way loudspeaker system, adequate driver protection may be ensured while providing a very flexible means of program manipulation.For a better view of the various operational modes, refer to “The DC 24 Users Guide” RaneNote, from the Rane website.。
QAbstractItemModel
QAbstractItemModel细节描述QAbstractItemModel类定义了M/V模式中能与其他组件(components)交互(interoperate)的数据模型(item model)所必须使⽤的标准接⼝(interfaces). 它不能够被直接实例化, 相反, 你应该继承(subclass)它, 创建⼀个新的模型.QAbstractItemModel是⼀个M/V类, 也是M/V框架下的⼀部分. 它可以被⽤作数据试图的底层数据模型.如果你需要⼀个⽤在QListView或QTableView等其他数据视图中的模型, 你应该考虑继承QAbstractListModel或QAbstractTableModel, ⽽不是这个类.底层数据模型作为⼀个表的层次结构暴露给视图和委托(The underlying data model is exposed to views and delegates as a hierarchy of tables). 如果你不使⽤层次结构, 那么这个模型就是⼀个简单的由⾏列组成的简单表格. 每个数据有⼀个独⼀⽆⼆的下标QModelIndex.每个可以通过模型获取的数据元素(item data)都有⼀个关联的模型下标. index()函数可以获取这个模型下标. 每个下标可能有⼀个sibling()下标; ⼦元素有⼀个parent()下标.每⼀个元素有⼀系列与之相关联的数据元素, 并且可以通过在模型的data函数中指定⼀个⾓⾊(role)来获取这些数据元素. itemData()函数可以获取同⼀时间下所有可⽤⾓⾊的数据.⽤Qt::ItemDataRole可以指定每个⾓⾊的数据. Data for individual roles are set individually with setData(), or they can be set for all roles with setItemData().通过flags()函数可以查看元素能否被选取, 拖拽或进⾏其他操作.If an item has child objects, hasChildren() returns true for the corresponding index.The model has a rowCount() and a columnCount() for each level of the hierarchy. Rows and columns can be inserted and removed with insertRows(), insertColumns(), removeRows(), and removeColumns().模型通过发送(emit)信号(singal)来表明改变.The items available through the model can be searched for particular data using the match() function.继承(Subclassing)继承QAbstractItemModel后, 你⾄少要实现index(), parent(), rowCount(), columnCount(), 和data()函数. 所有的只读(read-only)模型都会使⽤这些函数, 并且是可编辑模型(editable)的基础函数.你也可以重新实现hasChildren()函数,来为那些rowCount()函数实现成本很⾼的模型提供特定的⾏为. 这使得模型可以限制视图请求的数据量, 并且可以⽤作实现模型数据的惰性填充的⽅式(and can be used as a way to implement lazy population of model data).如果要⽣成可编辑模型, 你必须实现setData()函数, 并且重新实现flags()函数, 并确保返回中包含ItemIsEditable. You can also reimplement headerData() and setHeaderData() to control the way the headers for your model are presented.The dataChanged() and headerDataChanged() signals must be emitted explicitly when reimplementing the setData() and setHeaderData() functions, respectively.⾃定义模型必须创建其他组件可⽤的模型下标. 调⽤createIndex()函数, 并传⼊适当的row, column, 以及⼀个标识符(identifier), 标识符可以是指针或整型值. 每个元素的这些值的组合必须是独⼀⽆⼆的. ⾃定义模型通常在其他重新实现的函数中使⽤这些唯⼀标识符来检索项⽬数据并访问有关该项⽬的⽗项和⼦项的信息.没有必要⽀持Qt::ItemDataRole中定义的所有⾓⾊. 根据模型中所包含的数据类型的不同, 可能实现data()函数并返回⼀个更通⽤类型的有效信息会更有⽤. ⼤多数模型⾄少为Qt::DisplayRole提供项⽬数据的⽂本表⽰, 更好的模型也应为Qt::ToolTipRole和Qt::WhatsThisRole提供有效信息. 这些⾓⾊使得模型可以在标准Qt视图中使⽤. 对于那些⾼度特质化的数据, 仅为⽤户定义的⾓⾊提供数据才是合理的.模型的数据如果想要可以调整size, 就要实现insertRows(), removeRows(), insertColumns(), 和removeColumns(). 实现这些函数时, 最重要的是, 在事件发⽣前后, 通知所有连接的视图, 数据维度改变了.An insertRows() implementation must call beginInsertRows() before inserting new rows into the data structure, and endInsertRows() immediately afterwards.An insertColumns() implementation must call beginInsertColumns() before inserting new columns into the data structure, andendInsertColumns() immediately afterwards.A removeRows() implementation must call beginRemoveRows() before the rows are removed from the data structure, andendRemoveRows() immediately afterwards.A removeColumns() implementation must call beginRemoveColumns() before the columns are removed from the data structure, andendRemoveColumns() immediately afterwards.这些函数发出的专⽤信号使连接的组件有机会在任何数据变得不可⽤之前采取措. 使⽤这些开始和结束功能对插⼊和删除操作进⾏封装也使模型能够正确管理持久性模型索引. 如果要正确处理选择, 则必须确保调⽤这些函数. 如果您插⼊或删除带有⼦项的项⽬, 则⽆需为⼦项调⽤这些函数. 换句话说, ⽗项管理其⼦项.To create models that populate incrementally, you can reimplement fetchMore() and canFetchMore(). If the reimplementation of fetchMore() adds rows to the model, beginInsertRows() and endInsertRows() must be called.重要函数未完待续...。
fluent中专业词汇翻译
GridRead 读取文件:scheme 方案 journal 日志 profile 外形 Write 保存文件Import :进入另一个运算程序 Interpolate :窜改,插入 Hardcopy : 复制, Batch options 一组选项 Save layout 保存设计Check 检查Info 报告:size 尺寸 ;memory usage 内存使用情况;zones 区域 ;partitions 划分存储区 Polyhedral 多面体:Convert domain 变换范围 Convert skewed cells 变换倾斜的单元 Merge 合并 Separate 分割Fuse (Merge 的意思是将具有相同条件的边界合并成一个;Fuse 将两个网格完全贴合的边界融合成内部(interior)来处理,比如叶轮机中,计算多个叶片时,只需生成一个叶片通道网格,其他通过复制后,将重合的周期边界Fuse 掉就行了。
注意两个命令均为不可逆操作,在进行操作时注意保存case)Zone 区域: append case file 添加case 文档 Replace 取代;delete 删除;deactivate 使复位;Surface mesh 表面网孔Reordr 追加,添加:Domain 范围;zones 区域; Print bandwidth 打印 Scale 单位变换 Translate 转化Rotate 旋转 smooth/swap 光滑/交换Models 模型:solver 解算器Pressure based 基于压力density based 基于密度implicit 隐式,explicit 显示Space 空间:2D,axisymmetric(转动轴),axisymmetric swirl (漩涡转动轴);Time时间:steady 定常,unsteady 非定常Velocity formulation 制定速度:absolute绝对的;relative 相对的Gradient option 梯度选择:以单元作基础;以节点作基础;以单元作梯度的最小正方形。
predictmodel的例子(一)
predictmodel的例子(一)Predict Model 示例1. Linear Regression Model•简介:线性回归模型是一种用于建立连续型目标变量与自变量之间线性关系的预测模型。
通过最小化残差平方和,找到最佳拟合直线,使预测值与实际观测值的差距最小化。
•应用场景:适用于自变量与目标变量之间存在线性关系的问题,如销售额随广告投入的增加而增加的情况。
•优势:简单易懂,计算速度快。
•缺点:不能解决非线性问题。
2. Logistic Regression Model•简介:逻辑回归模型是一种用于建立二分类目标变量与自变量之间关系的预测模型。
通过利用S型函数将线性回归结果映射到概率预测结果,从而进行分类预测。
•应用场景:广泛应用于二分类问题,如判断邮件是否为垃圾邮件。
•优势:实现简单,预测结果可解释性强。
•缺点:不能解决多分类问题,对特征间存在高度相关性时容易产生过拟合。
3. Decision Tree Model•简介:决策树模型是一种通过对数据进行分割,构建树形结构来进行预测的模型。
通过将数据集分成多个子集,根据特征条件选择最佳分割点进行预测。
•应用场景:适用于离散型和连续型特征的分类和回归问题。
•优势:易于理解和解释,能够处理缺失值和异常值。
•缺点:容易过拟合,对数据的变化较敏感。
4. Random Forest Model•简介:随机森林模型是一种整合多个决策树模型的集成预测模型。
通过随机选择特征子集和数据子集,构建多棵决策树进行预测,并通过投票或平均预测结果得到最终结果。
•应用场景:适用于分类和回归问题,特别是特征较多的复杂问题。
•优势:准确性高,能够处理高维度数据,对特征选择不敏感。
•缺点:模型复杂度较高,训练时间较长。
5. Support Vector Machine Model•简介:支持向量机模型是一种用于分类和回归问题的监督学习模型。
通过将数据映射到高维空间,寻找超平面将不同类别的数据分开。
微芯片公司SyncServer S650高精确、安全和灵活的时间与频率标准说明书
SyncServer® S650Accurate, Secure and Flexible Time and Frequency StandardFeatures•<15 ns RMS to UTC (USNO) through GPS •<1x10–12 frequency accuracy •Modular timing architecture with unique and innovative FlexPort™ technology •Popular timing signal inputs/outputs standard in the base timing I/O module(IRIG B, 10 MHz, 1PPS)•Four standard GbE ports with NTP hardware time stamping, two additional10 GbE ports optional•Web-based management with high-security cipher suite•–20°C to 65°C operating temperature, shock and vibration qualified •Rubidium Atomic Clock or OCXO oscilla-tor upgrades•Dual power supply option •Additional timecode I/O including IRIG A/B/C37/E/G/NASA/2137/XR3/HaveQuick/PTTI available•T1/E1 Telecom I/O available •Superior 10 MHz low phase noise options•Galileo/GLONASS/BeiDou/SBAS/QZSS option•PTP multi-port/profile output option •PTP input option•S650i model with no GNSS •DISA/DoDIN approved product Applications•FlexPort timing technology efficiently and cost-effectively adds innovative“any signal, any connector” technology,eliminating the wasted space inherentwith legacy style fixed-signal modules/BNCs•Best-in-class low phase noise 10 MHz outputs for satellite ground stations and radar systems•Multiple GbE network ports for easy network configuration and adaptation •Reliable and rugged design for long product life and wide application scope •Many security-hardened, network basedfeatures for stringent IA requirementsS650 with Timing I/O Modules(Optional Configuration)Unparalleled FlexibilityThe modular SyncServer® S650 combinesthe best of time and frequency instrumen-tation with unique flexibility and powerfulnetwork/security-based features.The base Timing I/O module with eightBNC connectors comes standard with themost popular Timing I/O signals (IRIG B, 10MHz and 1 PPS). When more flexibility isrequired, the unique FlexPort technologyoption enables six of the BNCs to outputmany supported signals (time codes, sinewaves, programmable periods), all configu-rable in real time through the secure webinterface. This incredibly flexible BNC-by-BNC configuration makes efficient andcost-effective use of the 1U space available.Similar functionality is applied to the twoinput BNCs, as well. Unlike legacy moduleswith fixed count BNCs outputting fixedsignal types per module, FlexPort technol-ogy can allow up to 12 BNCs to output anycombination of supported signal types.The Timing I/O module is also available withT1/E1 Telecom I/O, HaveQuick/PTTI I/O andfiber input/output connectors.Superior Low Phase Noise (LPN)PerformanceFor applications requiring superior LPN 10MHz signals, two different LPN modules areavailable. Each module has eight extremelyisolated 10 MHz LPN outputs, with eachmodule offering excellent levels of LPN orultra LPN performance.Robust Timing and DesignThe 72-channel GNSS receiver coupledwith Microchip's patented active thermalcompensation technology provides excellentaccuracy of <15 ns RMS to UTC (USNO). Thisin addition to a durable hardware designsubjected to MIL-STD-810H testing, high-reli-ability components extending the operatingtemperature range to –20°C to 65°C, anddual power supply options. Further, upgrad-ing to a high-performance oscillator, such asa Rubidium atomic clock, keeps SyncServerS650 accurate for long periods in the eventof a GNSS service disruption.Secure NetworkingSecurity is an inherent part of SyncServerS650. In addition to many security featuresand protocols, services can be selectivelydisabled. The four standard GbE ports, andtwo optional 10 GbE ports, can accommo-date 10,000 NTP requests per second usinghardware time stamping and compensa-tion. NTP monitoring, charting and MRUlogging assist in managing the NTP clientactivity. For more secure NTP operations,enable the optional security-hardened NTPReflector™ with line speed, 100% hardware-based NTP packet processing.Leverage Built-In HardwareSyncServer S650 includes many built-inhardware features enabled throughsoftware license keys, such as the security-hardened NTP Reflector, Galileo/GLONASS/BeiDou/QZSS support, and multi-port/pro-file IEEE 1588 PTP output/input operations.SyncServer S650, the future of time andfrequency, today. Four GbE Ports for Performance, Flexibility and SecurityThe S650 has four dedicated and isolated GbE Ethernet ports, each equipped with NTP hardware time stamping. These are connected to a high-speed microprocessor with microsecond-accurate timestamps to assure high-bandwidth NTP performance. This exceeds the need of servicing 10,000 NTP requests per second with no degrada-tion in time stamp accuracy.Multiple ports provide the flexibility to adapt to different network topologies as networks grow and change. An S650 can be the single time source to synchronize clients on different subnets and physi -cal networks. There is only one time reference, but it can appear as though there are four clocks available because each port is independent.NTP can be served on all four ports (six if 10 GbE ports are added). The highly secure web-based management interface is only available on port 1, so that administrators may choose to keep that IP address private and secure. Unique access control lists per port can govern server response to client requests for time.Intuitive, Secure and Easy-to-Use Web InterfaceThe modern web interface is the primary control interface of the S650. Once the keypad and display bring the unit online, complete status and control functions are easily found on the left navigation menu. A REST API also included.Standard Management Access SecurityAll of the expected network management protocols are standard in the S650. These include mandatory password access, HTTPS/SSL only (using the high-encryption cipher suite), SSH, access control lists, ser-vice termination, SNMPv2/v3, and NTP MD5 authentication. All traffic to the S650 CPU is bandwidth-limited for protection against DoSattacks. The local keypad on the server can be password-protected to prevent tampering.Security-Hardening OptionThe SyncServer S650 can be further hardened from both an NTP perspective and an authentication perspective through the Security Protocol License option that includes the security-hardened NTP Reflector.Operational hardening through the 360,000 NTP packet per second NTP Reflector with 100% hardware-based NTP packet processing also works with a CPU-protecting firewall by bandwidth limiting all non-NTP traffic. The Reflector also monitors packet flow for DoS detection and reporting, yet remains impervious to the level of network traffic as it operates at line speed.Authentication hardening is available for NTP client/server authenti-cation through the NTP Autokey function or user access authentica-tion through TACACS+, RADIUS and LDAP. Third party CA-signed X.509 certificates are installable for further hardening of manage -ment access and secure syslog operations. For more information about the Security Protocol License option, see the SyncServerOptions datasheet.The four GbE ports provide network configura -tion flexibility and enhanced security. Multiple isolated and synchronized time servers can also be configured. Two 10 GbE SFP+ ports can be added for NTP/PTP operations as well.At-a-glance dashboard presentation combined with logical organization andintuitive controls that make configuring the S650 easy.An entire drop-down menu in the S650 dedicated to security-related protocols.Unprecedented NTP AccuracyThe Stratum 1 level S650 derives nanosecond-accurate time directly from the atomic clocks aboard the GPS satellites. By using an integrated, 72-channel GNSS receiver, every visible satellite can be tracked and used to maintain accurate and reliable time. Even in urban canyon environments where direct satellite visibility can be limited, manually inputting the position can be sufficient to acquire accurate time from as few as one intermittent satellite.Ultra-High Performance NTPThe S650 can effortlessly support hundreds of thousands of network clients while maintaining microsecond-caliber NTP time stamp accuracy. NTP request throughput rates can exceed 10,000 requests/ second while maintaining NTP time stamp accuracy. NTP monitoring, charting and MRU logging assist in managing the NTP client activity. If the Security Protocol License option is enabled, the NTP Reflector can process over 360,000 NTP requests per second with 15-nanosecond caliber time stamp accuracy with the added benefit of security-hardening the network port.Superior Low Phase Noise PerformanceThe S650 can be optimized to provide the best possible low phase noise 10 MHz signals. Two LPN modules are available to choose from depending on the phase noise sensitivity of the user application. Each module has eight extremely isolated 10 MHz LPN outputs with each module offering excellent levels of LPN and Ultra LPN perfor-mance from the close in 1 Hz out to 100 kHz.Multi-Port/Profile IEEE 1588 PTP Grandmaster Applications demanding very precise time accuracy can require the IEEE 1588 precise time protocol (PTP). The S650 PTP Output License enables multi-port/profile PTP grandmaster operations leveraging the built-in hardware time stamping on each LAN port of the S650. IEEE 1588 PTP Input LicensePTP input is useful for tunneling time to the S650 over the network. PTP input can be the primary time reference or used as a backup reference in the event of GPS signal loss. With GPS, the S650 can automatically calibrate and store observed network path delay asym-metries for PTP input use if the GPS signal is lost.Multi-GNSS Constellation Support for Enhanced ReliabilityTiming integrity, continuity and reliability can be improved withthe GNSS option that adds support for Galileo, GLONASS, BeiDou, QZSS and SBAS constellations in addition to the standard GPS constellation. With more satellites in view, timing performance can be improved in challenging environments, such as urban canyons. SyncServer S650s ship with GNSS hardware ready to be enabled with a software license. The S650 is also available without GNSS in theS650i model.More Timing I/O StandardThe base S650 can host two modules. The Timing I/O modules are equipped with eight connectors for timing signal input and output. The standard configuration offers a broad yet fixed selection of signal I/Os that include IRIG B, 10 MHz and 1PPS.FlexPort—The Ultimate in Timing FlexibilityOur unique FlexPort technology efficiently and cost-effectively adds innovative “any signal, any connector” capabilities, eliminating the wasted space inherent with legacy style fixed signal modules.The FlexPort option enables the six output connectors (J3-J8) to output many supported signals (time codes, sine waves, program-mable periods) all configurable in real time through the secure web interface. User-entered, nanosecond caliber phase offsets for each connector output accommodates variable cable lengths. The two input connectors (J1-J2) can support a wide variety of input signal types.This level of timing signal flexibility is unprecedented and can even eliminate the need for additional signal distribution chassis as there is no degradation in the precise quality of the coherent signals.Oscillator Upgrades Improve Holdover Accuracy and Save Valuable TimeThe standard S650 is equipped with a crystal oscillator that keeps the S650 accurate to nanoseconds when tracking GPS. However, if GPS connectivity is lost and the server is placed in holdover, the oscillator begins to drift, impacting timing accuracy. Upgrading the oscillator improves the holdover accuracy significantly. For example, consider the following drift rates for the standard oscillator compared to theOCXO and Rubidium upgrades.The value of the upgraded oscillator is that if the GPS signal is lost, the S650 can continue to provide accurate time and frequency. This provides personnel time to correct the problem with only gradualdegradation or disruption in time synchronization accuracy.SpecificationsGNSS Receiver/Antenna• 72 parallel channel GNSS receiver• GPS time traceable to UTC (USNO)• Static and dynamic operational modes• Acquisition time of 30 seconds (cold start)• Cable length up to 900 feet (275 m).• GNSS option adds Galileo/GLONASS/BeiDou/SBAS/QZSS support in addition to GPSTime Accuracy at 1 PPS Output• Standard: <15 ns RMS to UTC (USNO), typical• OCXO: <15 ns RMS to UTC (USNO)• Rubidium: <15 ns RMS to UTC (USNO)After one day locked to GPS; evaluated over normal environment (test range <±5 °F) defined in GR-2830.Oscillator Aging (Monthly)• Standard: ±1×10–7• OCXO: ±5×10–9• Rubidium: ±1×10–10After one month of continuous operation.Holdover Accuracy (One Day)• Standard: 400 µs• OCXO: 25 µs• Rubidium: <1 µsEvaluated over normal environment (test range <±5 °F) defined in GR-2830 after five days locked to GPS.Frequency Output Accuracy and Stability• <1x10–12 at 1 day, after locked to GPS for 1 dayStandard Network Protocols• NTP v3,4 (RFC 1305/5905/8633), SNTP(RFC4330)• NTP v3,4 Symmetric keys: SHA1/256/512 and MD5• SNMP v2c, v3• SNMP MIB II, Custom MIB, system status via SNMP• DHCP/DHCPv6• HTTPS/SSL* (TLS 1.2/1.3)• SMTP forwarding• SSHv2• Telnet• IPv4/IPv6• Syslog: 1 to 8 servers (RFC 3164/5424)• Key management protocols can be individually disabled• Port 1: Management and Time protocols• Port 2, 3 and 4 (optional 5 and 6): Time protocols only Optional Network Protocols NTP Server Performance• 10,000 NTP requests per second while maintaining accuracy associated with reference time source.**• Stratum 1 through GNSS: overall server time stamp accuracy of5 μs to UTC with 1-sigma variation of 20 μs (typical). All NTP timestamps are hardware-based or have real-time hardware compen-sation for internal asymmetric delays. The accuracy is inclusive of all NTP packet delays in and out of the server, as measured at the network interface. NTP serves the UTC timescale by definition,but the user can choose to serve GPS timescale instead. The user can also select the UTC leap second smearing/slewing behavior.The SyncServer easily supports millions of NTP clients.• NTP Activity Charting and MRU Logging: A rolling 24 hour chart displays overall NTPd requests/minute activity. An NTPd MostRecently Used (MRU) list provides details on the most recent 1024 NTP client IP addresses. Data is sortable and exportable. Selec-tion of an individual IP address charts the NTP request totals in 30 minute increments over the past 24 hours. These tools are useful to verify an NTP client is reaching the SyncServer and to identify NTP clients that may be requesting the time more frequently than desired.• NTP Reflector option: 360,000 NTP client mode three requests per second. NTP packets are timestamped 100% in hardware with prevailing clock accuracy. All non-NTP packets are provided to the CPU on a bandwidth-limited basis. The NTP Reflector is included as part of the Security Protocol License option.NTP Activity Chart• Autokey (RFC5906)• PTP• TACACS+• LDAPv3• RADIUS• X.509 certificates for HTTPSand secure syslogRolling 24-hour NTPd activity chart to accompany Most Recently Used (MRU) listwith individual NTPd client activity details and chart.*SSL_High_Encryption Cypher suite or the SSL_High_And_Medium_Encryption Cypher suite.**<5% NTPd packet drop at 10,000 NTPd requests per second. See SyncServer BlueSkyoption data sheet for performance specifications if BlueSky validator is enabled (optional)Mechanical/EnvironmentalShock and VibrationFront PanelRear PanelProduct IncludesS650SyncServer S650 (no option modules installed in base unit), locking power cord, rack mount ears and a two-year hardware warranty. Current manual and MIB are available online at . MIB and REST API can also be downloaded from the SyncServer.S650iSyncServer S650i (no GNSS receiver), one Timing I/O module, locking power cord, rack mount ears and a two-year hardware warranty. Current manual and MIB are available online at . MIB and REST API can also be downloaded from the SyncServer.S650 With Two Standard Timing I/O Modules (Optional Configuration)Ordering InformationCustom configure your build-to-order SyncServer S650 using theonline SyncServer Configurator tool at . Configura -tions can be submitted as requests for quotes.Note: The SyncServer S650 is TAA CompliantNote: The SyncServer S650 is on the DISA/DoDIN Approved Products ListThe Microchip name and logo, the Microchip logo and SyncServer are registered trademarks of Microchip Technology Incorporated in the U.S.A. and other countries. All other trademarks mentioned herein are property of their respective companies. © 2022, Microchip Technology Incorporated and its subsidiaries. All Rights Reserved. 4/22 900-00716-00 Rev N DS00002901FHardware OptionsTiming I/O Module(s)Equipped with eight connectors for timing signal input and output, the standard configuration offers a broad yet fixed selection of signal I/O, including IRIG B, 10 MHz and 1PPS. Five variations of the Timing I/O Module are available as listed below. See the SyncServer Options Datasheet (DS00002920) for more signal types.• Timing I/O Module• Timing I/O Module + Telecom I/O • Timing I/O Module + HaveQuick/PTTI • Timing I/O Module + Fiber Outputs • Timing I/O Module + Fiber Input10 MHz Standard Low Phase Noise ModuleEight isolated, phase-coherent 10 MHz LPN outputs, with excellent levels of LPN and exhibiting low spurious noise characteristics.10 MHz Ultra-Low Phase Noise ModuleSuperior levels of LPN provided on eight extremely isolated, phase-coherent 10 MHz LPN outputs with low spurious noise characteristics.10 GbE LAN PortsTwo additional 10 GbE SFP+ ports equipped with hardware time stamping that supports NTP, PTP and NTP Reflector operations.Rubidium Atomic Oscillator UpgradeImproves stability, accuracy, and holdover accuracy. Holdoveraccuracy is <1 μs for the first 24 hours and <3 μs after the first three days.OCXO Oscillator UpgradeImproves holdover accuracy to 25 μs for the first day.Dual AC Power SuppliesThe dual-corded, dual-AC power supply option provides load sharing and active power management system with hitless failover.Dual DC Power SuppliesThe dual-corded, dual-DC power supply option provides load sharing and active power management system with hitless failover.Antenna AccessoriesAntenna cables and accessories enable versatile solutions to meet most installation requirements.Note: For complete information, see the SyncServer Options Datasheet (DS00002920).Software OptionsSecurity Protocol License with Security-Hardened NTP ReflectorSecurity-hardened NTP Reflector and authentication hardening with NTP Autokey, TACACS+, RADIUS, LDAP and CA-signed X.509 certificates.PTP Output/Grandmaster(Simultaneous Multi-Port/Profile)Single license enables multi-port, multi-profile IEEE 1588 PTP Grand -master operations leveraging the built-in hardware time stamping in all SyncServers.PTP InputPTP as a timing input for tunneling time through PTP or as a backup time reference in the event of the loss of the GNSS signal.FlexPort Technology for Timing I/O ModulesEnables the output connectors to output many supported signals (time codes, sine waves, programmable rates) all configurable in real time. Input connectors can support a wide variety of input signal types.Multi-GNSS ConstellationTrack GPS/SBAS, Galileo, QZSS, GLONASS and/or BeiDou constel-lations for improved integrity and satellite visibility in an urban canyons.1PPS Time Interval/Event Time MeasurementsUse the S650 Timing I/O module to measure, store and statistically display in real time the difference of an external 1PPS relative to the S650. The Event Time capture feature time tags and stores external events.Time-Triggered Programmable PulseProvides a user defined, repetitive and precise time-of-day pulse(s) at specific times or provides periodic, time-based pulse outputs.BlueSky GPS Jamming and Spoofing Detection, Protection, AnalysisDetect GPS jamming and spoofing related anomalies in real-time to protect essential time and frequency outputs.Synchronization SoftwareComprehensive MS Windows-based network time synchronization software with monitoring and auditing functions.Information contained in this publication regarding device applications and the like is provided only for your convenience and may be superseded by updates. It is your responsibility to ensure that your application meets with your specifications. MICROCHIP MAKES NO REPRESENTATIONS OR WARRANTIES OF ANY KIND WHETHER EXPRESS OR IMPLIED, WRITTEN OR ORAL, STATUTORY OR OTHERWISE, RELATED TO THE INFORMATION, INCLUDING BUT NOT LIMITED TO ITS CONDITION, QUALITY, PERFORMANCE, MERCHANTABILITY OR FITNESS FOR PURPOSE. Microchip disclaims all liability arising from this information and its use. Use of Microchip devices in life support and/or safety applications is entirely at the buyer’s risk, and the buyer agrees to defend, indemnify and hold harmless Microchip from any and all damages, claims, suits, or expenses resulting from such use. No licenses are conveyed, implicitly or otherwise, under any Microchip intellectual property rights unless otherwise stated.。
lotka多种群竞合模型matlab代码
Lotka多种裙竞合模型是描述生态系统中多个物种相互作用的数学模型之一,它可以帮助我们理解不同物种在同一环境下的共生关系和竞争关系。
使用Matlab来实现Lotka多种裙竞合模型是一种常见的方法,下面我们将介绍如何使用Matlab代码来模拟Lotka多种裙竞合模型。
1. 概述Lotka多种裙竞合模型Lotka多种裙竞合模型是由意大利数学家Alfred J. Lotka在20世纪20年代提出的,它描述了生态系统中多个物种之间的相互作用。
该模型假设不同种裙之间存在竞争关系,即它们争夺同一资源,并且这种资源是有限的。
Lotka多种裙竞合模型可以用一组微分方程来描述,通过求解这组微分方程,我们可以得到不同种裙在时间上的演化规律。
2. Lotka多种裙竞合模型的基本形式假设存在n个种裙,用x1, x2, ..., xn来表示它们的种裙密度,Lotka多种裙竞合模型的基本形式可以用下面的方程组来表示:dx1/dt = x1*(a11 - b12*x2 - c13*x3 - ... - d1n*xn)dx2/dt = x2*(a22 - b21*x1 - c23*x3 - ... - d2n*xn)...dxn/dt = xn*(ann - b1n*x1 - b2n*x2 - ... - (n-1)n*x(n-1))其中本人i表示种裙i的自然增长率,bij表示种裙i和种裙j之间的竞争系数。
通过求解上述方程组,我们可以得到不同种裙在时间上的变化规律。
3. 使用Matlab实现Lotka多种裙竞合模型下面是使用Matlab来实现Lotka多种裙竞合模型的基本步骤:```matlab设定参数n = 3; 种裙数目a = [0.1, 0.2, 0.3]; 自然增长率b = [0.01, 0.02, 0.03; 0.02, 0.01, 0.04; 0.03, 0.02, 0.01]; 竞争系数定义微分方程function dxdt = lotkapetition(t, x)dxdt = zeros(n, 1);for i = 1:ndxdt(i) = x(i) * (a(i) - sum(b(i,:) * x));endend求解微分方程[t, x] = ode45(lotkapetition, [0, 10], ones(n, 1));可视化结果figure;plot(t, x);legend('种裙1', '种裙2', '种裙3');xlabel('时间');ylabel('种裙密度');```在上面的Matlab代码中,我们首先设定了模型的参数,然后定义了微分方程,并使用ode45函数来求解微分方程。
pyqt standarditemmodel 遍历
pyqt standarditemmodel 遍历在PyQt中,`QStandardItemModel`是一个通用的数据模型,可以用来存储和操作数据。
你可以使用`rowCount()`和`columnCount()`方法来获取行数和列数,然后使用`item()`方法来获取特定位置的项。
以下是一个遍历`QStandardItemModel`的简单示例:pythonfrom PyQt5.QtGui import QStandardItemModel, QStandardItemfrom PyQt5.QtWidgets import QApplication, QWidgetimport sysclass Example(QWidget):def __init__(self):super().__init__()self.model = QStandardItemModel()self.model.setHorizontalHeaderLabels(['Title', 'Summary'])for i in range(3): # 创建3行数据for j in range(2): # 每行有2列数据item = QStandardItem(f'Item {i}-{j}') # 创建QStandardItem对象self.model.setItem(i, j, item) # 将item添加到model的特定位置self.model.itemChanged.connect(self.on_item_changed) # 当item改变时触发on_item_changed方法self.setGeometry(300, 300, 300, 220)self.show()def on_item_changed(self, item):print(f'{item.text()} has been changed.')def main():app = QApplication(sys.argv)ex = Example()sys.exit(app.exec_())if __name__ == '__main__':main()在这个例子中,我们创建了一个`QStandardItemModel`,并向其添加了一些`QStandardItem`。
find_planar_calib_deformable_model 解析
find_planar_calib_deformable_model 解析摘要:1.全文概述2.什么是find_planar_calib_deformable_model3.find_planar_calib_deformable_model的原理与算法4.find_planar_calib_deformable_model的应用领域5.如何使用find_planar_calib_deformable_model6.总结与展望正文:【1.全文概述】本文将详细介绍find_planar_calib_deformable_model,包括其原理、算法、应用领域以及如何使用。
通过阅读本文,读者将对这一模型有更深入的理解,并能将其应用于实际问题中。
【2.什么是find_planar_calib_deformable_model】Find_planar_calib_deformable_model是一种用于平面标定的变形模型。
它主要通过一系列的优化算法,对平面上的几何形状进行建模和拟合,以达到对平面参数进行准确估计的目的。
【3.find_planar_calib_deformable_model的原理与算法】Find_planar_calib_deformable_model基于线性代数的理论知识,采用最小二乘法等优化算法进行求解。
具体来说,该模型通过测量数据拟合平面,从而得到平面参数。
在这个过程中,模型会考虑到测量数据的误差,以及物体形状的变形因素,从而使拟合结果更为准确。
【4.find_planar_calib_deformable_model的应用领域】Find_planar_calib_deformable_model在许多领域都有广泛的应用,如计算机视觉、机器人导航、航空航天、工业测量等。
这些领域中的共同特点是,都需要对平面进行精确测量和建模,以便后续的数据处理和分析。
【5.如何使用find_planar_calib_deformable_model】要使用find_planar_calib_deformable_model,首先需要准备一组测量数据,这些数据可以是图像、点云或其他形式的观测数据。
model target generator 使用方法
model target generator 使用方法1.引言1.1 概述在计算机视觉领域中,模型的训练是一个重要的研究方向。
为了提高模型的准确性和泛化能力,研究人员提出了许多方法和技巧。
其中一种被广泛应用的方法是使用目标生成器(target generator)。
本文将详细介绍如何使用目标生成器来增强模型训练的效果。
目标生成器是一种用于生成训练数据中目标标签(target labels)的技术。
在计算机视觉任务中,目标标签是指对图像中的目标物体进行分类或定位的标记信息。
传统的目标生成方法依赖于人工标注的数据集,但这种方式不仅费时费力,而且很难满足大规模数据的需求。
为了解决这个问题,目标生成器使用了一种基于规则或模型的自动生成目标标签的方法。
通过对输入图像进行处理和分析,目标生成器可以快速生成准确的目标标签。
这种方法不仅可以提高训练数据的多样性和数量,还可以增加模型对于复杂场景的泛化能力。
因此,目标生成器在计算机视觉任务中得到了广泛的应用。
本文将首先介绍模型的基本概念和原理,然后详细讨论目标生成器的使用方法。
我们将介绍目标生成器的工作原理、常用的目标生成算法以及如何将目标生成器集成到模型训练中。
此外,我们还将讨论目标生成器在不同应用场景下的性能评估和使用建议。
通过阅读本文,读者将能够全面了解目标生成器的使用方法,并能够将其应用到自己的研究或实践中。
希望本文能够对读者在计算机视觉领域的研究和开发工作中起到一定的指导作用。
1.2 文章结构文章结构部分的内容应该包括以下内容:文章结构是指整篇文章的架构和组织方式。
一个清晰、有条理的文章结构有助于读者理解和掌握文章的主要内容。
本文将采用以下结构进行展开:1. 引言:介绍本文的背景和目的,概述文章的内容,并说明本文的结构。
2. 正文:2.1 模型介绍:详细介绍model target generator(以下简称MTG)的概念、原理和功能。
包括MTG是什么、它的作用是什么,以及它的工作原理等方面的内容。
basemodel 对象构造函数
标题:深度解析basemodel对象构造函数的重要性和应用目录:1. 引言2. basemodel对象构造函数的定义和作用3. basemodel对象构造函数的使用场景4. 深入解析basemodel对象构造函数的实现细节5. 结论与展望1. 引言在软件开发领域,对象构造函数是一个非常重要的概念。
它不仅决定了对象的创建方式和初始化过程,还影响了整个软件系统的质量和性能。
而在本文中,我们将聚焦于讨论basemodel对象构造函数的重要性和应用。
2. basemodel对象构造函数的定义和作用basemodel是一个基础的数据模型,它通常包含一些通用的属性和方法,如id、createTime、updateTime等。
而basemodel对象构造函数则负责初始化这些属性,以及为实例对象添加一些通用的方法。
通过合理设计和使用basemodel对象构造函数,我们可以避免重复的初始化工作,提高代码的复用性和可维护性。
3. basemodel对象构造函数的使用场景在实际的软件开发中,basemodel对象构造函数通常被用于创建基础模型,如用户模型、商品模型等。
通过调用basemodel对象构造函数,我们可以快速创建具有统一标准的实例对象,而无需重复编写初始化代码。
这对于大型软件项目来说尤为重要,可以大大提高开发效率和降低错误率。
4. 深入解析basemodel对象构造函数的实现细节在实际的开发过程中,我们可以通过JavaScript、Python等编程语言来实现basemodel对象构造函数。
具体实现上,我们可以通过封装一个构造函数,并在其中定义实例属性和方法,以实现对basemodel对象的初始化和扩展。
在构造函数内部,我们通常会使用this关键字来指代当前实例对象,以便初始化属性和添加方法。
```JavaScript中的basemodel对象构造函数可以如下所示:function basemodel(id, createTime, updateTime) {this.id = id;this.createTime = createTime;this.updateTime = updateTime;}basemodel.prototype = {// 添加通用方法method1: function() {// do something},method2: function() {// do something}};```5. 结论与展望通过本文对basemodel对象构造函数的深度解析,我们不仅理解了其重要性和应用场景,也掌握了其实现细节。
model value的使用
model value的使用"Model value" refers to a concept used in various fields, including finance, economics, and decision-making processes. It represents the perceived or calculated worth or benefit of a model. In this article, we will explore the different applications and implications of model value and its importance in decision-making processes.1. Introduction (150 words)Model value plays a crucial role in industries where decision-making is driven by data and complex calculations. Whether it is in finance, economics, or other fields, individuals and organizations heavily rely on models to make informed decisions. However, simply having a model is not sufficient; understanding its value is crucial. This article aims to explore how model value is measured, the different applications of model value, and the importance of considering it in decision-making processes.2. Defining Model Value (200 words)Model value refers to the worth or benefit derived from using a particular model. It can be seen as a measure of how well a model represents reality or how accurately it predicts outcomes. The process of determining model value involvesseveral factors, such as its accuracy, reliability, and applicability to the problem at hand. Model value can be subjective, depending on the specific requirements and context.3. Measuring Model Value (300 words)There are several ways to measure model value, depending on the field or application. In finance, for example, the value of a financial model may be assessed based on its ability to predict asset prices or portfolio returns accurately. This can be done using statistical measures such as mean squared error or correlation coefficients. In economics, model value may be measured by comparing model predictions to real-world data or by conducting sensitivity analyses to determine the model's robustness.4. Applications of Model Value (400 words)Model value finds applications in various industries. In the financial sector, accurate models can assist investors in making informed investment decisions. For example, a valuation model can help determine the fair value of a stock or company, guiding investors on whether to buy, hold, or sell. Model value is also crucial in risk management, where models are used to assess financial risks associated with investments or portfolios.In the field of economics, models are used to analyze theimpact of policy decisions or changes in market conditions. By understanding the model value, policymakers can gauge the potential consequences of their actions on various economic indicators. Additionally, models help forecast economic growth or inflation, aiding governments and organizations in planning and decision-making.In healthcare, models can assist in optimizing patient treatments and predicting disease outcomes. For example, a predictive model built on patient and disease data can help doctors determine the most effective treatment plan for individual patients, improving overall healthcare outcomes.5. Importance of Considering Model Value (400 words)Considering the value of a model is vital for effective decision-making. A model's predictions or recommendations carry weight and can have real-world consequences. A flawed or inaccurate model can lead to poor decisions, financial losses, or missed opportunities. Understanding the value of a model allows decision-makers to assess its reliability and make more informed choices.Furthermore, model value helps identify potential limitations or assumptions that need to be considered. Every model simplifies reality to a certain extent, and recognizingthe model's boundaries helps decision-makers avoid potential pitfalls. Without considering model value, decision-makers may rely on flawed assumptions or biased outputs, leading to incorrect conclusions.In conclusion, model value is a critical concept in various industries and decision-making processes. It represents the worth or benefit derived from using a particular model and helps decision-makers assess the reliability and limitations of a model's predictions. Accurate models with high value can guide sound financial investments, policy decisions, and healthcare treatments. Ignoring or undervaluing model value can have adverse effects on outcomes and lead to poor decision-making. Therefore, understanding, measuring, and considering model value is essential for making well-informed choices in a data-driven world.。
kaldi chain 模型训练例子
kaldi chain 模型训练例子
Kaldi是一款开源的语音识别工具包,而Chain模型是Kaldi中的一个声学
模型,用于语音识别。
在训练Chain模型时,通常需要使用MMI (Maximum Mutual Information)准则来优化声学模型参数。
以下是一个简单的Kaldi Chain模型训练例子:
1. 准备数据集:首先,你需要准备一个标注好的语音数据集,其中包含多个不同说话人的语音以及对应的转录文本。
数据集应该按照说话人、录制条件等因素进行划分,以便进行交叉验证。
2. 特征提取:使用Kaldi提供的特征提取工具,对语音数据进行特征提取。
常用的特征包括Mel频率倒谱系数(MFCC)和线性预测编码系数(LPCC)等。
3. 声学模型训练:使用Chain模型进行声学模型训练。
在训练过程中,可
以使用MMI准则来优化模型参数。
你可以通过调整不同的超参数、使用不
同的优化算法等方法来提高模型的性能。
4. 语言模型训练:为了提高语音识别的准确性,你还可以使用语料库中的文本数据训练语言模型。
语言模型可以用于平滑声学模型的输出,从而提高识别准确率。
5. 识别测试:使用训练好的声学模型和语言模型进行语音识别测试。
你可以使用不同的测试集来评估模型的性能,并比较不同模型之间的差异。
需要注意的是,以上只是一个简单的例子,实际训练过程中可能还需要进行更多的预处理、参数调整和优化等工作。
另外,为了获得更好的识别效果,你可能需要使用更复杂的声学模型和语言模型,以及更先进的训练算法和技术。
基于预测的邮轮定价策略研究策略
随着旅游行业的快速发展,邮轮旅游必将成为一种重要的旅游方式,然而如何合理定价油轮交通的费用吸引更多的旅游者,是邮轮公司厄待解决的问题。
本文分别讨论了以下问题:针对问题一,采用逐步分析法对每次航行各周预订舱位的人数进行预测。
首先利用已有的样本数据,通过BP神经网络和灰色预测GM(1,1)预测第5周到第0周的预定舱位的人数,并进行模型检验,得到拟合度数值分别为0.812和0.864。
根据预测结果,再利用曲线拟合的方法,发现七次拟合的效果最好,并对第2,4,6,8航次的二等舱进行拟合检验,得到的拟合度数值分别为:0.992,0.993,0.991,0.998。
针对问题二,采用自回归移动平均模型对每次航行各周预订舱位的价格进行预测。
得到检验模型为ARMA(6,3),拟合度为0.965。
预测结果均在价格范围内。
针对问题三,利用移动平均预测(MA)、简单指数平滑预测(ES)、性回归预测(LR)、加法增量预测(CP)、乘法增量预测(MP)和灰色动态预测法(GP)分别预测头等舱,二等舱,三等舱每航次每周意愿预订人数,预测结果大致符合实际情况。
针对问题四,本文从邮轮公司和旅客之间的非对抗性的合作博弈出发,建立需求模型和动态定价模型,同时对定价决策进行调整,利用非线性规划得到第八次航行的预期售票收益为123万的最优价格策略。
针对问题五,当乘客有升舱行为发生时,将会产生三个方面的费用变动,则本文从高等舱的服务费用增加,升舱所补的差价,低等舱的服务费用三个方面出发,建立线性规划模型,逐步调整三种费用的合理分配,从而使油轮公司将制定合适的差价。
关键字:BP神经网络、GM(1,1)、时间序列、博弈论、线性规划12近年来乘坐邮轮旅游的人越来越多,邮轮公司的发展也非常迅速。
如何通过合理的定价吸引更多的旅游者,从而为邮轮公司创造更多的收益,这也是众多邮轮公司需要探讨和解决的问题。
邮轮采用提前预订的方式进行售票,邮轮出发前0周至14周为有效预定周期,邮轮公司为了获得每次航行的预期售票收益,希望通过历史数据预测每次航行0周至14周的预定舱位人数、预订舱位的价格,为保证价格的平稳性,需要限定同一航次相邻两周之间价格浮动比,意愿预定人数转化为实际预定人数与定价方案密切相关。
软件工程专业英语
软件工程英语文档:Docume nts软件工具:Softwa re Tools工具箱:Tool Box集成工具:Integr atedTool软件工程环境:Softwa re Engine ering Enviro nment传统:Conven tiona l经典:Classi cal解空间:Soluti on Domain问题空间:Proble m Domain清晰第一,效率第二Cl arity the first, Effici encythe next.设计先于编码Desig n before coding使程序的结构适合于问题的结构Ma ke the progra m fit the proble m 开发伴随复用,开发为了复用Develo pment with reuse, Develo pment for reuse.靠度量来管理:Manage mentby Measur ement 软件度量学:Softwa re Metric s 软件经济学:Softwa re Econom ics 软件计划WH Y软件分析WHAT软件实现HO W软件生存周期过程的开发标准Standa rd for Develo pingSoftwa re Life CycleProces s软件开发模型:Softwa re Develo pment Model编码员:Coder瀑布模型:Waterf all Model快速原型模型:RapidProtot ype Model增量模型:Increm ental Model线性思维:Linear Thinki ng演化模型:Evolut ionar y Model螺旋模型:Spiral Model对象:Object类:Class继承:Inheri tance聚集:Aggreg ation消息:Messag e面向对象=对象Obje ct+分类Clas sific ation+继承Inhe ritan ce+消息通信Co mmuni catio n with Messag es构件集成模型:Compon ent Integr ation Model转换模型:Transf ormat ional Model净室软件工程:Cleanr oom Softwa re Engine ering净室模型:Cleanr oom 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ng公共偶合:Common Coupli ng 内容偶合: Conten t Coupli ng由底向上设计:Bottom-Up Design自顶向下设计:Top-Down Design 正式复审:Formal Review非正式复审:Inform al Review走查,排练:Walk-Throug h会审:Inspec tion映射:Mappin g传入路径:Affere nt path传出路径:Effere nt path变换中心:Transf orm Center接受路径:Recept ion path动作路径:Action path事务中心:Transa ction Center分支分解:Factor ing of Brandc hes瓮形:oval-shaped一个模块的控制域:Scopeof Contro l一个模块的作用域:Scopeof Effect结构化程序设计:Struct uredProgra mming通心面程序:Bowl of Spaghe tti 流程图:Flow Diagra m编码:Coding方框图:BlockDiagra mPDL (Pidgin):Progra m Design Langua ge伪代码:Pseudo CodeJSD:Jackso n System Develo pment 对象建模技术:Object Modeli ng Techni que基础设施:Infras truct ure控制线程:Thread of Contro l保护者对象:Guardi an Object协议:protoc olUML:Unifie d Modeli ng Langua ge OMG:Object Manage mentGroup统一方法:Unifie d Method关联:Associ ation泛化:Genera lizat ion依赖:Depend ency结点:Node接口:Interf ace包:Packag e注释:Note特化:Specia lizat ion元元模型:Meta-Meta Model用户模型:User Model静态图:Static Diagra m动态图:Dynami c Diagra m用例视图:Use Case View逻辑视图:Logica l View并发视图:Concur rentView构件视图:Compon ent View实现模型视图:Implem entat ion ModelView部署视图:Deploy mentView航向:Naviga bilit y重数:Multip licit y共享聚集:Shared Aggreg ation组合:Compos ition泛化:Genera lizat ion简单消息:Simple Messag e同步消息:Synchr onous Messag e 异步消息:Asynch ronou s Messag e事件说明:Event_Signa ture守卫条件:Guard_Condi tion动作表达式:Action_Expr essio n 发送子句:Send_C lause时序图:Sequen ce Diagra m协作图:Collab orati on Diagra m 前缀:Predec essor循环子句:Iterat ion-Clause活动图:Activi ty Diagra m构件图:Compon ent Diagra m配置图:Deploy mentDiagra m建模过程指导(RUP):Ration al Unifie d Proces s可执行代码:Execut albeCodes实现:Implem entat ion编码风格:Coding Style标准:Classi cal控制流的直线性:Linear ity of Contro l Flow 程序风格设计要素:先求正确后求快Make it rightbefore you make it faster.先求清楚后求快Make it clearbefore you make it faster.求快不忘保持程序正确Keep it rightwhen you make it faster.保持程序简单以求快Keep it simple to make it faster.书写清楚,不要为“效率”牺牲清楚Writeclearl y-don't sacrif ice clarit y for "effici ency"文档化:Code Docume ntati on内部文档编制:Intern al Docume ntati on序言:Prolog ue用户友善:User Friend ly纠错:Debugg ing测试用例:Test Case穷举测试:Exhaus tiveTestin g选择测试:Select ive Testin g静态分析:Static Analys is黑盒测试:BlackBox Testin g白盒测试:WhiteBox Testin g等价分类:Equiva lence Partio ning边界值分析法:Bounda ry ValueAnalys is所谓猜错:ErrorGuessi ng因果图:Cause-Effect Graph逻辑覆盖测试法:LogicCovera ge Testin g试凑:Trialand Error回溯:Back Tracki ng病因排除法:CauseElimin ation 测试纠错:Debugg ing by Testin g 蛮力纠错技术:Debugg ing by BruteForce回归测试:Regres sionTestin g单元测试:Unit Testin g综合测试:Integr ation Testin g确认测试: V alida tionTestin g系统测试:System Testin g模块测试:Module Testin g 高级测试:Higher orderTestin g 不可达的:Unreac hable办公桌检查:Desk Check走查:Walk-Throug h代码会审:Code Inspec tion测试驱动模块:Test Driver测试桩模块:Test Stub群:Cluste r混合方式测试:Sandwi ch Testin g 渐增式测试:Increm ental Testin g非渐增式:Non-Increm ental配置复审:Config urati on Review 测试终止标准:Test Comple tionCriter ia基于线程的测试:Thread-BasedTestin g基于使用:Use-Based基于构件的软件开发:Compon ent BasedSoftwa re Develo pment ,CBSD领域工程:Domain Engine ering需求规约:Requir ement s Specif icati on变体:Varian t组件对象模型,COM:Compon et Object Model对象链接与嵌入:Object Linkin g and Embedd ing公共对象请求代理体系结构,CORBA:Common Object Reques t Broker Archit ectur e枚举分类:Enumer aterClassi ficat ion呈面分类:Facete d Classi ficat ion 属性-值分类:Attrib ute-ValueClassi ficat ion应用系统工程,ASE:Applic ation System Engine ering完善性维护:Perfec tiveMainte nance适应性维护:Adapti ve Mainte nance纠错性维护:Correc tiveMainte nance预防性维护:Preven tiveMainte nance结构化的翻新:Struct uredRetrof it可维护性:Mainta inabi lity可理解性:Unders tanda bilit y可修改性:Modifi abili ty可测试性:Testab ility调用图:Call Graph交差引用表:Cross-Refere nce Direct ory数据封装技术:Data Encaps ulati on维护申请单M RF:Mainte nance Reques t Form软件问题报告单SPR:Softwa re Proble m Report软件修改报告单SCR:Softwa re Change Report修改控制组C CB:Change Contro l Board软件配置:Softwa re Config urati on 版本控制库:Versio n Contro l Librar y活动比:Activi ty Ratio工作量调节因子EAF:Effort Adjust mentFactor软件再工程:Softwa re Reengi neeri ng逆向工程:Revers e Engine ering 重构:Restru cture演化性:Evolva bilit y问题定义:Proble m Defini tion系统目标与范围的说明:Statem ent of Scopeand Object ives可行性研究:Feasib ility Study系统流程图:System Flowch art 成本-效益分析:Cost-Benifi t Analys is风险识别:Risk Identi ficat ion风险预测:Risk Projec tion风险估计:Risk Estima tion风险评价:Risk Assess ment估算模型:Estima tionModel资源模型:Resour ce Model构造性成本模型:Constr uctiv e cost Model组织:Organi c半独立:Semide tache d嵌入:Embede d算法模型:Algori thmic Model分类活动结构图WBS:Work Breakd own Struct ure人员-时间权衡定律People-Time Trade-Off Law无我小组:Egoles s T eam主程序员小组:Chief-Progra mmerTeam PERT:Progra m Evalua tionand Review Techni que关键路径:Critic al Path知识产权:Intell ectua l Proper ty 靠质量来管理:Manage mentby Measur ement质量保证:Qualit y Assura nce质量认证: Qualit y Certif icati on质量检验:Qualit y Inspec tion全面质量管理TQC:TotalQualit y Contro l质量体系:Qualit y System计划-实施-检查-措施 Plan-Do-Check-Action合格论证:Confor mityCertif icati on可靠性:Reliab ility效率:Effici ency运行工程:HumanEngine ering正确性:Correc tness使用性:Usabil ity完整性:Integr ity可理解性:Unders tanda bilit y可测试性:Testab ility可修改性:Modifi abili ty可移植性:Portab ility可维护性:Mainta inabi lity可适应性:Flexib ility可重用性:Reusab ility 交互操作性:Intero perab ility验证与确认:Verifi catio n and Valida tion,V&V基线:Baseli nes平均故障时间:Mean Time To Failur e ,MTTF错误传入:ErrorSeedin g冗余:Redund ancy容错:FaultTolera nce公理化归纳断言法:Axio-MaticInduct ive Assert ion循环不变式:Loop Invari ant能力成熟度模型:Capabi lityMaturi ty Model关键过程域:Key Proces s Area ,KPA关键实践:Key Practi ce初始级:Initia l可重复级:Repeat able已定义级:Define d已管理级:Manage d优化级:Optimi zing主任评估师:Lead Assess or极值程序设计:Extrem e Progra mming自适应软件开发:Adapti ve Softwa re Develo pment轻载:Lightweight重载:HeavyWeight返工:Rework进度:Schedu le时间:Durati on成本:Cost代码行LOC:Linesof Code面向功能:Functi on-Orient ed面向规模: Size-Orient ed功能点:Functi on Points权系数:Weight ing Coeffi cient用户输入:User Input用户输出: User Output用户查询: User Inquir ty主文件处理:Master File外部界面:Extern al Interf ace TCF:Techni cal Comple xityFactor技术复杂性因子测度:Measur ement最终用户:End-User;计算机辅助软件工程CA SE:Comput er AidedSoftwa re Engine ering拉出:pull-out下拉: pull-down一致性:Unific ation自动化:Automa tion过程模型:Proces s Model软件开发环境SDE:Softwa re Develo pment Enviro nment软件设计支持环境PSE:Progra mming Suppor t Enviro nment集成化项目支持IPSE:Integr atedProjec t Suppor t Enviro nment集成化框架:Integr ation Framew ork质量从头抓起:Qualit y from Beginn ing 缺陷:Defect变更请求:Change Reques t功能扩充:Enhanc ement Reques t。
DYNAMIXEL MX-64T一体化机械臂智能取向控制器说明书
DYNAMIXEL MX-64T**Cautions- MX-64T supports TTL communication.- Recommended voltage of MX-64 is different with that of former RX-64.(Operating Voltage : 10~14.8V (Recommended Voltage 12V)** DESCRIPTION•DYNAMIXEL is a robot exclusive smart actuator with fully integrated DC Motor + Reduction Gearhead + Controller + Driver + Network in one DC servo module.•MX series is a new concept of DYNAMIXEL with advanced functions like precise control, PID control, 360 degree of position control and high speed communication.** CHARACTERISTIC•Advanced durability, degree of precision, and wider control zone were achieved thanks to newly applied CONTACTLESS ABSOLUTE ENCODER•360¡Æ POSITION CONTROL without dead zone•4,096 PRECISE RESOLUTION by 0.088¡Æ•SPEED CONTROL at ENDLESS TURN MODE•Reliability and accuracy were advanced in the position control through PID CONTROL •High baud rate up to 3Mbps•TTL LEVEL COMMUNICATION•Torque control via current sensing**The assembly structure of the MX-64 and RX-64 are the same but there some modifications to the case.**** INCLUDESDescription Qty DYNAMIXEL MX-64T 1 HORN HN05-N101 (MX Exclusive) 1 WASHER Thrust Washer 1 CABLE 3P Cable 200mm 1Wrench Bolt M2.5*4 16pcs BOLT/NUTWrench Bolt M3*8 1pcsNut M2.5 18pcs** H/W SPECSProduct Name MX-64TWeight 126gDimension 40.2mm x 61.1mm x 41mmGear Ratio 200 : 1Operation Voltage (V) 10 12 14.8 Stall Torque (N.m) 5.567.3 Stall Current (A) 3.9 4.1 5.2 No Load Speed (RPM)586378Motor Maxon MotorMinimum ControlAngleAbout 0.088¡Æ x 4,096Operating Range Actuator Mode : 360¡Æ Wheel Mode : Endless turnOperating Voltage10~14.8V (Recommended voltage : 12V) Operating Temperature -5¡ÆC ~ 80¡ÆCCommand Signal Digital PacketProtocol Half duplex Asynchronous Serial Communication (8bit,1stop, No Parity)Link (physical)TTL Multi Drop(daisy chain type connector)ID 254 ID (0~253) Baud Rate8000bps ~ 3MbpsFeedback Functions Position, Temperature, Load, Input Voltage, Current, etc.Material Case : Engineering Plastic Gear : Full MetalPosition Sensor Contactless absolute encoderDefault ID #1 – 57600bps** After purchase, please change ID and baud rate according to your use.** COMPATIBLE PRODUCTS- Controller : CM-5, CM-510, CM-530, CM-2+, CM-700- Interface(I/F) : USB2Dynamixel (TTL)- NOTICE : Not compatible with the RX-64 horn. (HN05-N101 Set / T101 Set)** CONTROLLING ENVIRONMENT- Software for Dynamixel control : ROBOPLUS - Download- C/C++, C#, Labview, MATLAB, Visual Basic et. : Library – Download**Click here to download 2D and 3D drawings**Click here to go to e-Manual.。
keras中model类的定义
keras中model类的定义Keras是一种高级神经网络API,用于构建和训练深度学习模型。
它是基于Python编程语言的开源库,可以轻松地进行快速原型设计和实验。
在Keras中,模型类是一种重要的组件,用于定义和管理深度学习模型的架构。
模型类是Keras库中的一个核心概念,它提供了一种简单而灵活的方法来定义神经网络的结构。
通过模型类,用户可以以层的形式组织神经网络,并指定层与层之间的连接方式。
在Keras中,模型类的定义通常包括两个主要步骤:模型的初始化和模型的构建。
我们需要初始化一个模型类的实例。
这可以通过调用`Sequential`类来实现,`Sequential`类是Keras中的一个模型容器,它可以按照顺序将各个层组合在一起。
例如,我们可以使用以下代码初始化一个模型类的实例:```pythonfrom keras.models import Sequentialmodel = Sequential()```接下来,我们可以通过调用模型实例的方法来构建神经网络的结构。
Keras提供了多种不同类型的层,如全连接层、卷积层、池化层等。
用户可以根据任务的需求选择合适的层类型,并将其添加到模型中。
例如,我们可以使用以下代码添加一个全连接层到模型中:```pythonfrom yers import Densemodel.add(Dense(units=64, activation='relu', input_dim=100)) ```在上述代码中,我们添加了一个具有64个神经元和ReLU激活函数的全连接层。
`units`参数指定了神经元的数量,`activation`参数指定了激活函数的类型,`input_dim`参数指定了输入层的维度。
除了添加层之外,我们还可以通过调用模型实例的方法来配置模型的优化器、损失函数和评估指标等。
例如,我们可以使用以下代码来配置模型的优化器和损失函数:```pythonpile(optimizer='rmsprop',loss='categorical_crossentropy', metrics=['accuracy'])```在上述代码中,我们使用了RMSprop优化器和交叉熵损失函数。
flask model 定义
flask model 定义在Flask 中,通常使用Flask-RESTful 或类似的扩展来定义模型(Model)。
Flask-RESTful 是Flask 的一个扩展,用于构建RESTful API,它通常与SQLAlchemy 或其他ORM(对象关系映射)库一起使用来定义数据模型。
以下是使用Flask-RESTful 和SQLAlchemy 定义模型的简单示例:首先,确保你已经安装了Flask、Flask-RESTful 和SQLAlchemy。
你可以使用以下命令安装它们:pip install Flask Flask-RESTful Flask-SQLAlchemy然后,可以创建一个Flask 应用,并使用SQLAlchemy 定义一个简单的模型:from flask import Flaskfrom flask_restful import Api, Resourcefrom flask_sqlalchemy import SQLAlchemyapp = Flask(__name__)app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///example.db' api = Api(app)db = SQLAlchemy(app)class UserModel(db.Model):id = db.Column(db.Integer, primary_key=True)username = db.Column(db.String(80), unique=True,nullable=False)email = db.Column(db.String(120), unique=True,nullable=False)db.create_all()class UserResource(Resource):def get(self, user_id):user = UserModel.query.get(user_id)if user:return {'id': user.id, 'username': ername, 'email': user.email}else:return {'message': 'User not found'}, 404api.add_resource(UserResource, '/user/<int:user_id>')if __name__ == '__main__':app.run(debug=True)在这个示例中,我们定义了一个UserModel类,它继承自db.Model,其中包含了用户的id、username 和email。
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Econ5140Macroeconomic AnalysisFall2014Jenny XuHKUSTCalvo Model and New Keyesian Framework for Monetary Policy Analysis1Introduction•In the early1970s,1980s,and early1990s, the standard model used for most mone-tary policy analysis combined the assump-tion of nominal rigidity with a simple struc-ture linking the quantity of money to ag-gregate spending.•This linkage was usually directly through a quantity theory equation in which nominal demand was equal to the nominal money supply,often with a random disturbance included,or through a traditional textbook IS-LM model.•While the theoretical foundations of these models were weak,the approach proved remarkably useful in addressing a wide range of monetary policy topics.2•More recently,attention has been placed on ensuring that the model structure is consistent with the underlying behavior of optimizing economic agents.•The standard approach tody builds a dy-namic,stochastic,general equilibrium frame-work based on optimizing behavior,com-bined with some form of nominal wage and /or price rigidity.•Early examples of models with these prop-erties include those of Yun(1996),Good-friend and King(1997),Rotemberg and Woodford(1995,1997),and McCallum and Nelson(1999).•This lecture shows how a basic MIU model, combined with the assumption of monop-olistically competitive goods markets and price stickiness,can form the basic for a simple linear macroeconomic model that is useful for policy analysis.3Calvo’s model•An alternative model of staggered price adjustment is due to Calvo(1983).He assumed thatfirms adjustment their price infrequently and that opportunities to ad-just arrived as an exogenous Poisson pro-cess.•Each period,there is a constant probabil-ity1−ωthatfirm can adjust its price;the expected time between price adjustment .is11−ω•Because these adjustment opportunities oc-cur randomly,the interval between price changes for an individualfirm is a random variables.4•Following Rotemberg(1987),suppose the representativefirm i set its price to min-imize a quadratic loss function that de-pends on the difference between thefirm’s actual price in period t,p it,and its optimal price,p∗t.This latter price might denote the profit maximization price forfirm i in the absence of any restrictions or costs as-sociated with price adjustment.•If thefirm can adjust at time t,it will set its price to minimize1 2E t∞∑j=0βj(p t+j−p∗t+j)2(1)subject to the assumed process for determining when thefirm will next be able to adjust.•If only the terms in(1)involving the price set at time t are written out,they are1 2E t∞∑j=0(ωβ)j(p it−p∗t+j)2(2)whereωj is the probability thatfirm has not ad-justed after j periods so that the price set at t still holds in t+j.5•Thefirst order condition for the optimal choice of p it requires thatp it∞∑j=0ωjβj−∞∑j=0ωjβj E t p∗t+j=0(3)•Rearranging,and letting x t denote the op-timal price set by allfirms adjusting their price,x t=(1−ωβ)∞∑j=0ωjβj E t p∗t+j(4)The price set by thefirm at time t is a weighted average of current and expected future value of the target price p∗.•Equation(4)can be rewritten asx t=(1−ωβ)p∗t+ωβE t x t+1(5) If the price target p∗depends on the ag-gregate price level and output.6•We can replace p∗t with p t+γy t+ϵt,where ϵt is a random disturbance to capture other determinants of p∗.In general,thefirm’s optimal price will be shown to be a func-tion of its marginal cost,which,in turn, can be related to a measure of output.•With a large number offirms,a fraction 1−ωwill actually adjust their prices each period,and the aggregate price level can be expressed as p t=(1−ω)x t+ωp t−1.•We then have the following two equations to describe the evolution of x t and p t:x t=(1−ωβ)(p t+γy t+ϵt)+ωβE t x t+1(6)p t=(1−ω)x t+ωp t−1(7)•The aggregate inflation can be derived asπt=βE tπt+1+(1−ω)(1−ωβ)ω(γy t+ϵt)(8)7Discussion and Comparison•This expression is quite similar to the one we derived from Taylor’s model.Currentinflation depends on expected inflation andthe current output.•One difference is that the coefficient in front of E tπt+1is nowβ,but not1.Thisis because in??,we did not discount thereal wage.•Another difference,noted by Kiley(2002), is that the Tylor-type staggered adjust-ment model display less persistence thanthe Calvo-type model when both are cali-brated to the same frequency of price changes.•In Taylor(1979.1980),after two periods, all wages are adjusted after two periods.Or,no wages arefixed for more than twoperiods.8•In Calvo(1983),if we assumeω+12,then the expected time between price changes is two periods.So on average,prices are adjustment every two periods.•However,many prices remainfixed for more than two periods.For exam,w3=0.125 of all prices remainfixed for at least three periods.In Calvo,there is a tail of distri-bution of prices which have remainedfixed for many periods.•One attractive aspect of Calvo’s model is that it show how the coefficient on output in the inflation equation depends on the frequency with which prices are adjusted.•A rise inω,which means that the average time between price changes for an individ-ualfirm increase.Output movement havea smaller impact on current inflation,hold-ing expected future inflation constant.9A New Keyesian Model for MonetaryPolicy Analysis•The model is consistent with general equi-librium model in which all agents face well-defined decision problems and behave op-timally.•Three key modifications will be made here.–First,endogenous variations of the cap-ital stock are ignored.This follows Mc-Callum and Nelson(1999),who arguethat little is lost for the purpose of short-run business cycle analysis by assumingan exogenous process for the capitalstock.At least for the US,there is little relationshipbetween the capital stock and output dynamicsat business cycle frequencies.Cogley and Nason(1995)show that the re-sponse of investment and the capital stock toproductivity shocks contributes little to dynam-ics in RBC models.10•–The second key modification is to in-corporate differentiated goods whose in-dividual prices are set by monopolis-tically competitivefirms facing Calvo-type price stickiness.–Third,monetary policy is represented by a rule for setting the nominal rateof interest.The nominal quantity ofmoney is then endogenously determinedto achieve the desired nominal interestrate.•The modification yield a framework,often referred to as new Keynesian.The result-ing version of the MIU model can be linked directly to the more traditional aggregate supply-demand(AS-IS-LM)model.The new Keynesian model will be then used in later lectures to explore a variety of mon-etary policy issues.11The basic model•The model consists of households that sup-ply labor,purchase goods for consump-tion,and hold money and bonds andfirms that hire labor and produce and sell differ-entiated products in monopolistically com-petitive goods markets.•Eachfirm sets the price of the good it pro-duces,but not allfirms reset their price in each period.Households andfirm be-have optimally;Households maximize the expected present value of utility,andfirms maximize profits.•There is also a central bank that controls the nominal rate of interest.The central bank,in contrast to households andfirms, is not assumed to behave optimally.12Households•The preference of the representative house-hold are defined over a composite con-sumption good C t,real money balance M tP t ,and leisure1−N t,where N t is the time de-voted to market employment.Households maximize the expected present discounted value of utility:E t∞∑t=0βt[C1−σt+l1−σ+γ1−b(M t+iP t+i)1−b−χN1+ηt+i1+η](9)•There is a continuum of suchfirms of measure1,andfirm j produces good c j.The composite consumption good that en-ters the household’s utility function is de-fined asC t=[∫1cθ−1θjtdj]θθ−1,θ>1(10)The parameterθgoverns the price elastic-ity of demand for the individual goods.13•The household’s decision problem can be dealt with in two stages.–First,regardless of the level of C t the household decide on,it will always beoptimal to purchase combination of in-dividual goods that minimizes the costof achieving this level of the compositegood.–Second,given the cost of achieving any given level of C t,the household choosesC t,N t,and M t optimally.•Dealingfirst with the problem of minimiz-ing the cost of buying C t,the household’s decision problem is tomin c jt ∫1p jt c jt dj(11)subject to[∫1cθ−1θjtdj]θθ−1≥C t(12)where p jt is the price of good j.14•Lettingψt be Lagrangian multiplier on the constraint,thefirst order condition for good j isp jt−ψt[∫1cθ−1θjtdj]1θ−1c−1θjt=0(13)Rearranging,c jt=(p jtψt )−θC t,From the def-inition of the composite level of consump-tion,we can solve forψt and c jt.–The lagrangian multiplier is the appro-priately aggregated price index for con-sumption.ψt=[∫1p jt1−θdj]11−θ≡P t(14)–The demand for good j can then be written asc jt=(p jtP t)−θC t(15)The price elasticity of demand for good j is equal toθ.Asθ−→∞,the indi-vidual goods become closer and closer substitutes,and as a consequence,in-dividualfirms have less market power.15•Given the definition of the aggregate price index P t,the budget constraint of the house-hold is,in real terms,C t+M tP t+B tP t=W tP tN t+M t−1P t+(1+i t−1)B t−1P t+Πt(16)where M t(B t)is the household’s nomi-nal holding of money(one-period bonds).Bonds pay a nominal rate of interest i.Real profits received fromfirms are equal toΠt.•In the second stage of the household’s de-cision problem,consumption,labor supply, money,and bond holding are chosen to maximize expected utility subject to the budget constraint.•This leads to the following conditions,which, in addition to the budget constraint,must hold in equilibrium:16•C−σt =β(1+i t)E tP tP t+1C−σt+1(17)γ(M tP t)−bC−σt=i t1+i t(18)χNηtC−σt=W tP t(19)These conditions represent–The Euler condition for the optimal in-tertermporal allocation of consumption,–The intratemporal optimality condition setting the MRS between money and consumption equal to the opportunity cost of holding money,–and the intratemporal optimality con-dition setting the MRS between leisure and consumption equal to the real wage.17Firms•Firms maximize profits,subject to three constraints.Thefirst is the production summarizing the avail-able technology.For simplicity,we have ignored capital,so output is a function solely of labor sup-ply input N jt and an aggregate productivity distur-bance Z t:c jt=Z t N jt,E(Z t)=1(20)where constant return to scale has been assumed.The second constraint on thefirm is the demand curve each faces.This is given by(15).The third constraint is that each period somefirms are not able to adjust their price.•The specific model of price stickiness we will use in due to Calvo(1983).Each period,thefirms that adjust their price are randomly selected,and a fraction1−ωof allfirms adjust while the remainingωfraction do not adjust.The parameterωis a measure of the degree of nominal rigidity;18•Before analyzing thefirm’s pricing decision,con-sider its cost minimization problem,which involves minimizing W t N jt subject to producing c jt=Z t N jt.This problem can be written,in real terms,asmin N j (W tP t)N jt+φt(c jt−Z t N jt)(21)whereφt is equal to thefirm’s real marginal cost. Thefirst order condition impliesφt=W tP t Z t(22)•Thefirm’s pricing decision problem then involves picking p jt to maximizeE t∞∑i=0ωi△i,t+i[(p jtP t+i)C jt+i−φt+i C jt+i](23)where the discount factor△i,t+i is given byβi(C t+iC t)−σ. Using the demand curve(15)to eliminate c jt,this objective function can be written asE t∞∑i=0ωi△i,t+i[(p jtP t+i)1−θ−φt+i(p jtP t+i)−θ]C t+i(24)while individualfirms produce differentiated prod-ucts,they all have the same production technology and face demand curves with constant and equal demand elasticities.19•All firms adjusting in period t face the same prob-lem,so all adjusting firms will set the same price.Let P ∗t be the optimal price chosen by all firms ad-justing at time t .The first order condition for the optimal choice of P ∗t isP ∗t P t =θθ−1E t ∑∞i =0ωi βi C 1−σt +i φt +i (P t +i P t )θE t ∑∞i =0ωi βi C 1−σt +i (P t +i P t)θ−1(25)•Consider the case in which all firms are able to adjust their prices every period (ω=0),it reduce toP ∗t P t =θθ−1φt =µφt (26)Each firm sets price P ∗t equal to a markup µ>1over its nominal marginal cost P t φt .Because price exceeds marginal cost,output will be inefficiently low.•When prices are flexible,all firms charge the same price.In this case,p ∗t =P t and φt =1µt .Using the definition of real marginal cost,this meansW t P t =Z t µ(27)in a flexible price equilibrium.20•However,the real wage must also equal the marginal rate of substitution between leisure and consump-tion to be consistent with household optimization.This conditions implies thatW t P t =Z tµ=χNηtC−σt(28)•Letˆx t denote the percentage deviation of a vari-able X t around its steady state and let the super-script f denote theflexible price equilibrium.Then approximating(28)around the steady state yields ηˆn f t+σˆc f t=ˆz t.From the production function,ˆy f t =ˆn ft+ˆz t,and because output is equal to con-sumption in equilibrium,ˆy ft =ˆc ft.•Combining these conditions,theflexible-price equi-librium outputˆy ftcan be expressed asˆy f t =1+ησ+ηˆz t(29)When prices are sticky,output can differ from the flexible price equilibrium level.•The average price in period t satisfiesP1−θt =(1−ω)(P∗t)1−θ+ωP1−θt−1(30)21•Equation(25)and(30)can be approximated arounda zero average inflation,steady-state equilibriumto obtain an expression for aggregate inflation of the formπt=βE tπt+1+˜κˆφt(31)where˜κ=(1−ω)(1−βω)ωis an increasing function ofthe fraction offirms able to adjust each period andˆφt is the real marginal cost,expressed as a percentage deviation around its steady state value.•Equation(31)is often referred to as the new Key-nesian Philips curve.Unlike more traditional Philips curve equations,the new Keynesian Philips curve implies that real marginal cost is the correct driving variables for the inflation process.It also implies that the inflation process is forward-looking,with current inflation a function of ex-pected future inflation.•Solving(31),πt=˜κ∞∑i=0βi E tˆφt+i(32)which shows that inflation is a function of the present discounted value of current and future real marginal costs.This derivation reveals how˜κ,the impact of real marginal cost on inflation,depends on the structural parametersβandω.22•We can show that inflation is also related to the an output gap measurement.We rewriteˆφt=γ(ˆy t−ˆy f t)(33) whereγ=σ+η.This implies that the inflation adjustment equation becomesπt=βE tπt+1+κx t(34)and x t= whereκ=γ˜κ=γ(1−ω)(1−βω)ωˆy t−ˆy f t is the gap between actual output andflexible price equilibrium output.•Equation(34)relates output,in the form of the deviation around the level of output that would occur in the absence of nomi-nal price rigidities,to inflation.It forms one of the two key components of an optimizing model that can be used for monetary analysis.The other component is a linearized version of the household’s Euler condition.23Equilibrium•We now have all the components of a simple general equilibrium model that isconsistent with optimizing behavior on thepart of households andfirms.Because consumption is equal to outputin this model,(17),(25),and(30)provideequilibrium conditions that determines out-put,the price set byfirms adjusting theirprice,and the aggregate price level oncethe behavior of the nominal rate of inter-est rate is specified.With the nominal interest rate treated asthe monetary policy instrument,(18)sim-ply determines the nominal quantity of money in equilibrium.•Equation(17)can be approximated around the zero-inflation steady state asˆy t=E tˆy t+1−1σ(ˆi t−E tπt+1)(35)24•Expressing this in terms of the output gap x t=ˆy t−ˆy f t,x t=E t x t+1−1σ(ˆi t−E tπt+1)+µt(36)whereµt=E tˆy ft+1−ˆy f t depends only onthe exogenous productivity disturbance.Combining(36)and(34)gives a simple two-equation,forward-looking,rational-expectations model for inflation and the output gap measure x t.For convenience,(34)is repeated here:πt=βE tπt+1+κx t(37)•This two equation model represents the equilibrium conditions for a well-specified general equilibrium model.Equation(36) and(35)represents the demand side of the economy(an expectational,forward-looking IS curve),while the new Keyne-sian Philips curve(37)corresponds to the supply side.25•The model can be closed by assuming that the central bank implements monetary pol-icy through control of the nominal interest rate.–The linearized version of(18)can then be used tofind the equilibrium nominal money supply.–Alternatively,if the central bank implements money policy by setting a path for the nominalsupply of money,(36)and(37)together withthe linearized version of(18),determines x t,πt,andˆi t.•If a policy rule for the nominal interest rule is added to the model,this must be done with care to ensure that the policy rule does not render the system unstable or introduce multiple equilibria.For example, suppose monetary policy is represented by the following rule forˆi t,ˆi t=ργˆi t−1+v t(38) With this policy rule,there exist multiple bounded equilibria and the equilibrium is locally indeterminate.Stationary sunspot equilibria are possible.26•Bullard and Mitra(2002)show that a unique stationary equilibrium exists as long asδ>1.Settingδ>1is referred to as the Tay-lor Principle because John Taylor was the first to stress the importance of interest rate rules that called for responding more than one for one to changes in inflation.•Suppose that,instead of reacting solely to inflation,the central bank respond to both inflation and the output gap according toˆi t=δππt+δx x t+v t(39)This type of policy rule is called a Taylor rule(Tay-lor1993),and variants of it have been shown to provide a reasonable empirical description of the policy behavior of many central banks(Clarida, Gali,and Gertler2000).With this policy rule,stability now depends on both the policy parametersδπandδx.27The monetary transmission mechanism•The model consisting of(36)and(37)as-sumes that the impact of monetary policy on output and inflation operates through the real rate of interest.•As long as the central bank is able to affect the real interest rate through its control of nominal interest rate,monetary policy can affect real output.•The basic interest rate transmission mech-anism for monetary policy could be ex-tended to include effects on investment spending if capital were reintroduced into the model(Christiano,Eichenbaum,and Evans2001).Increases the real interest rate would reduce the demand for capital and lead to a fall in investment spending.28•In addition to these interest rate channels, monetary policy is often thought to af-fect the economy either indirectly through credit or directly through the quantity of money.–Since measure of money and bank credit move together.This is called the creditchannel of money transmission process.–It is sometimes argued that changes in the money supply have direct effectson aggregate demand that are indepen-dent of the interest rate channels thatoperates on consumption.Real money holding represent part ofhousehold wealth;an increase in realbalance should induce an increase inconsumption spending through a wealtheffect.This channel is often called thePigou effect.29。