下载文档原格式
llamaforsequenceclassification 分类-回复"llama for sequence classification" refers to the topic of using llamas for the task of sequence classification. In this article, we will explore the concept of sequence classification, discuss the potential benefits of using llamas for this purpose, and outline the steps involved in implementing such a system.Introduction to Sequence Classification:Sequence classification is a subfield of machine learning that deals with the categorization or labeling of sequences of data. It involves training models to recognize patterns or relationships within a sequence and using this knowledge to classify unknown sequences. This task finds applications in various fields, including natural language processing, speech recognition, bioinformatics, and financial analysis.The Potential Benefits of Using Llamas for Sequence Classification:Llamas are intelligent, perceptive, and trainable animals. They possess excellent visual recognition skills and are known for their ability to learn complex tasks. Leveraging these qualities, usingllamas for sequence classification can have several advantages over traditional methods:1. Visual Recognition Skills: Llamas have a keen sense of visual recognition, enabling them to distinguish patterns and sequences effectively. This ability can be harnessed for tasks such as recognizing patterns in DNA sequences or analyzing patterns in financial data.2. Adaptability: Llamas can be trained to perform a wide range of tasks, and sequence classification is no exception. Their adaptability allows them to learn and recognize complex patterns, making them suitable for various classification problems.3. Efficient Pre-processing: Pre-processing is an essential step in sequence classification tasks. Llamas can be trained to automatically filter or preprocess data, reducing manual efforts and making the overall classification process more efficient.4. Cost-Effective: Llamas are relatively affordable compared to complex computing infrastructure required for some sequence classification tasks. Their low maintenance costs, coupled with theirability to learn and classify sequences, make them a cost-effective alternative for certain applications.Steps Involved in Implementing Llama for Sequence Classification:Implementing a llama-based sequence classification system involves several steps. Let's explore each of these steps in detail:Step 1: Data Collection and Preparation:The first step is to gather relevant data for the sequence classification task. This could involve collecting DNA sequences, speech data, or any other form of sequences, depending on the application. Once the data is collected, it needs to be formatted and pre-processed, ensuring its suitability for llama-based classification.Step 2: Training Data Labeling:The collected data needs to be labeled according to the desired classification scheme. This step involves assigning labels or categories to each sequence, enabling the llama model to learn the patterns associated with each class.Step 3: Training the Llama Model:Using the labeled data, the llama model is trained to recognize patterns and classify sequences based on the provided labels. Various machine learning techniques, such as supervised learning or deep learning, can be employed during this step to optimize the model's performance.Step 4: Evaluation and Fine-tuning:After training, the llama model needs to be evaluated to assess its performance. Evaluation metrics like accuracy, precision, and recall can be used to measure the model's effectiveness. If necessary, the model can be fine-tuned by adjusting parameters or using advanced techniques to improve classification accuracy.Step 5: Testing and Deployment:Once the llama model is trained and evaluated, it can be tested using unseen or real-time data. This step ensures that the model generalizes well to new sequences that it has not encountered during training. Finally, the model can be deployed in the desired application, where it can classify sequences based on the learned patterns.Conclusion:Llamas have the potential to be valuable contributors to the field of sequence classification. Their visual recognition skills, adaptability, and cost-effectiveness make them a viable alternative for certain applications. By following the steps outlined above, we can harness the intelligence and capabilities of llamas to build robust and efficient sequence classification systems, opening up new possibilities for various industries.。
metagga泛函-概述说明以及解释1.引言1.1 概述Metagga泛函是一种新兴的数学方法，它在数据分析和机器学习领域有着广泛的应用。
通过结合元学习和元分析的技术，metagga泛函能够更好地挖掘数据中的隐藏信息，并为决策制定提供有力的支持。
本文将介绍metagga泛函的基本概念和原理，探讨其在不同领域中的应用场景，以及其相对于传统方法的优势所在。
通过深入探讨metagga 泛函的相关内容，我们希望读者能够更好地理解并应用这一新兴数学方法，从而为未来的数据分析和机器学习工作带来新的启发和突破。
1.2 文章结构文章结构部分将介绍整篇文章的组织和布局，帮助读者了解文章的结构和内容安排。
本文主要分为三个部分：引言、正文和结论。
引言部分包括概述、文章结构和目的。
在概述部分，我们将介绍metagga泛函的概念和背景，引导读者进入主题。
文章结构部分即当前所在部分，将解释整篇文章的组织和目的，帮助读者理清整个文章的脉络。
目的部分将介绍本文的写作目的和意义，为读者提供一个阅读的导引。
正文部分将详细介绍什么是metagga泛函、其应用领域和优势。
我们将深入探讨metagga泛函的定义、特点和相关概念，以及在实际应用中的具体场景和效果。
通过对其优势的分析，读者将更加深入地理解metagga泛函在实践中的价值和意义。
结论部分将总结metagga泛函的重要性和未来发展前景，并做出相应的展望和结论。
通过对整篇文章内容的回顾和总结，我们将强调metagga泛函在未来的发展趋势和潜力，为读者提供一个全面的认识和展望。
整篇文章的结构清晰明了，从引言到正文再到结论，依次展开，为读者提供了一个系统完整的解读metagga泛函的指南。
希望本文能够为读者提供有益的知识和见解，引发更广泛的讨论和研究。
1.3 目的：本文的目的是介绍metagga泛函的概念、应用领域和优势，以帮助读者对该概念有一个全面且深入的了解。
通过本文的阐述，读者可以了解到metagga泛函在不同领域的具体应用情况，以及其在解决问题中的优势和价值。
流式数字人源代码-概述说明以及解释1.引言1.1 概述在现代社会中，随着人工智能和数字化技术的不断发展，流式数字人作为一种全新的数字化人类形态，正逐渐引起人们的关注和兴趣。
流式数字人是指通过人工智能技术构建的具有高度交互性和个性化特征的数字化人类形象。
它可以通过语音、动作、表情等多种方式与人类进行交流和互动，具有极大的应用潜力。
流式数字人的出现不仅可以改变人机交互的方式，提升用户体验，还可以应用于教育、娱乐、客户服务等多个领域，为人们的生活带来便利和乐趣。
同时，流式数字人具有高度的定制化特性，可以根据用户需求和偏好进行个性化定制，提供更加个性化的服务和体验。
本文将从不同角度对流式数字人进行深入探讨，分析其优势和应用领域，展望其未来发展趋势，并总结流式数字人对社会的重要性。
通过对流式数字人的研究，可以更好地了解数字化技术在未来的发展方向，为推动人工智能技术的应用和发展提供参考和借鉴。
1.2 文章结构文章结构如下：文章结构部分主要介绍本篇文章的组织架构和内容安排。
首先将简要介绍引言部分，包括概述、文章结构、目的等内容。
接着将详细解释正文部分的内容，包括什么是流式数字人、流式数字人的应用领域、流式数字人的优势等方面。
最后，结论部分将总结流式数字人的重要性，展望其未来发展，并进行整体的结论。
文章结构清晰明了，有助于读者全面了解本文的主要内容和重点观点。
1.3 目的：本文旨在介绍流式数字人的概念、应用领域和优势，以便读者更全面地了解这一新兴技术。
通过深入探讨流式数字人的重要性和未来发展趋势，希望能够为读者带来新的思考和启发。
同时，通过对流式数字人的研究和探讨，也可以为相关领域的从业者提供参考和借鉴，促进该技术的进一步发展和应用。
最终目的是推动技术创新，推动数字人类的普及和发展，为未来建设数字化社会做出贡献。
2.正文2.1 什么是流式数字人流式数字人是一种基于人工智能技术和自然语言处理技术的虚拟助手，可以模拟人类的语言和交流方式，能够与用户进行智能对话和互动。
ocean modelling for beginners中cd-rom的代码-回复对于初学者来说，了解并掌握海洋模型软件中的代码编写是非常重要的一步。
本文将针对cdrom代码内容，一步一步回答初学者的问题，帮助他们逐渐掌握基本的海洋模型编程技巧。
首先，让我们先了解一下什么是ocean modelling（海洋模型）和cdrom （公共数据资源管理系统）。
海洋模型是通过数学方程对海洋进行仿真和模拟的过程，以研究海洋的特性和行为。
而cdrom是公共数据资源管理系统，用于存储和管理各种数据，包括地震数据、海洋观测数据等。
一、了解cdrom代码结构和基本知识1. 请问cdrom代码结构包括哪些部分？- cdrom代码主要由两个部分组成：前端代码和后端代码。
- 前端代码主要包括用户界面设计、交互逻辑等内容，负责用户与系统的交互。
- 例如，通过前端代码可以实现用户输入命令、展示模型结果等功能。
- 后端代码主要包括数据处理、模型计算等内容，负责具体的模型运行。
- 例如，后端代码可以读取海洋数据、处理数据、应用合适的数学方程等，从而模拟海洋的行为和特性。
2. cdrom代码的数据输入来源是什么？- cdrom代码的数据输入来源可以是多种数据，例如地理数据、气象数据、海洋数据等。
- 这些数据可以通过不同方法获取，如传感器观测、卫星遥感、浮标和船舶观测等。
二、cdrom代码编写1. 如何开始编写cdrom代码？- 在编写cdrom代码之前，需要先了解所用的编程语言以及对应的开发环境。
- 常见的编程语言包括Python、C++、Java等，选择适合自己的编程语言。
- 在选择编程语言后，可以安装相应的IDE（集成开发环境）工具，如PyCharm、Visual Studio等。
2. 如何读取和处理海洋数据？- 首先要确定需要处理的海洋数据的格式，如CSV、NetCDF等。
- 根据数据格式，选择合适的数据处理库，如Pandas、NetCDF4-Python等。
code, data, and materials availability模板1. 引言1.1 概述本文将探讨代码、数据以及材料的可用性，即它们在科研和学术领域中的存储、分享与共享方式，以及相关规范和标准。
随着科学研究的不断发展，完整且可复制的研究数据和实验材料变得越来越重要。
能够方便地访问和使用他人的代码、数据和材料对于科研共享和进一步的科学进展至关重要。
1.2 文章结构本文分为六个主要部分。
引言部分提供了文章的背景信息和概览。
其后是正文部分，其中包括对代码、数据和材料可用性的详细讨论。
在每个主题下，我们将探讨相关问题，并提出解决方案和建议。
最后，在结论部分对整篇文章进行总结并给出未来可能的改进方向。
1.3 目的本文的目标是认识到代码、数据和材料可用性在科研中的重要性，并介绍有效实施这些可用性原则所需采取的措施。
我们将突出强调如何使代码易于访问和使用，如何确保高质量且有适当元数据描述的数据可被共享和重复使用，以及如何保存并使材料相关信息公开透明。
以上是对“1. 引言”部分的详细内容介绍。
本部分旨在为读者提供文章的概述，并说明研究代码、数据和材料可用性的重要性及其背景。
2. 正文：在科学研究中，确保科研结果的可重复性和透明度至关重要。
为了实现这一目标，本文提出了一套完整的框架，即“code, data, and materials availability”，旨在促进科研中代码、数据和材料的共享与可用性。
本节将详细介绍这一框架的要点。
首先，为了确保代码的可用性，我们鼓励研究者将其代码存储在公共的存储平台或版本控制系统中，并分享对应的链接或访问方式。
此外，在共享代码时，要确保代码具有良好的可读性和可维护性。
良好的注释规范、清晰简洁的命名以及适当的文档说明都是提高代码质量和易读性的重要因素。
其次，在数据方面，收集、处理和存储数据应遵循规范化和标准化的方法。
研究人员应该详细记录数据收集过程，并保留原始数据以备进一步分析验证之用。
2008-01-0085Model-Based Design for Hybrid Electric Vehicle SystemsSaurabh Mahapatra, Tom Egel, Raahul Hassan, Rohit Shenoy, Michael Carone Copyright © 2008 The MathWorks, Inc.ABSTRACTIn this paper, we show how Model-Based Design can be applied in the development of a hybrid electric vehicle system. The paper explains how Model-Based Design begins with defining the design requirements that can be traced throughout the development process. This leads to the development of component models of the physical system, such as the power distribution system and mechanical driveline. We also show the development of an energy management strategy for several modes of operation including the full electric, hybrid, and combustion engine modes. Finally, we show how an integrated environment facilitates the combination of various subsystems and enables engineers to verify that overall performance meets the desired requirements. 1. INTRODUCTIONIn recent years, research in hybrid electric vehicle (HEV) development has focused on various aspects of design, such as component architecture, engine efficiency, reduced fuel emissions, materials for lighter components, power electronics, efficient motors, and high-power density batteries. Increasing fuel economy and minimizing the harmful effects of the automobile on the environment have been the primary motivations driving innovation in these areas.Governmental regulation around the world has become more stringent, requiring lower emissions for automobiles (particularly U.S. EPA Tier 2 Bin 5, followed by Euro 5). Engineers now must create designs that meet those requirements without incurring significant increases in cost. According to the 2007 SAE’s DuPont Engineering survey, automotive engineers feel that cost reduction and fuel efficiency pressures dominate their work life [1] and will continue to play an important role in their future development work.In this paper, we explore key aspects of hybrid electric vehicle design and outline how Model-Based Design can offer an efficient solution to some of the key issues. Due to the limited scope of the paper, we do not expect to solve the problem in totality or offer an optimal design solution. Instead, we offer examples that will illustrateThe MathWorks, Inc.the potential benefits of using Model-Based Design in the engineering workflow. Traditionally, Model-Based Design has been used primarily for controller development.One of the goals of this paper is to show how Model-Based Design can be used throughout the entire system design process.In section 2, we offer a short primer on HEVs and the various aspects of the design. Section 3 is devoted to Model-Based Design and the applicability of the approach to HEV development. Sections 4, 5, and 6 will focus on examples of using Model-Based Design in a typical HEV design.2. HYBRID ELECTRIC VEHICLE DESIGNCONCEPTA block diagram of one possible hybrid electric vehicle architecture is shown in Figure1. The arrows represent possible power flows. Designs can also include a generator that is placed between the power splitter and the battery allowing excess energy to flow back into thebattery.Figure 1: The main components of a hybrid electric vehicle.Conceptually, the hybrid electric vehicle has characteristics of both the electric vehicle and the ICE (Internal Combustion Engine) vehicle. At low speeds, it operates as an electric vehicle with the battery supplying the drive power. At higher speeds, the engine and the battery work together to meet the drive power demand. The sharing and the distribution of power between thesetwo sources are key determinants of fuel efficiency. Note that there are many other possible designs given the many ways that power sources can work together to meet total demand.DESIGN CONSIDERATIONSThe key issues in HEV design [2] are typical of classical engineering problems that involve multilayer, multidomain complexity with tradeoffs. Here, we discuss briefly the key aspects of the component design: very similar to those of a traditional ICE. Engines used in an HEV are typically smaller than that of a conventional vehicle of the same size and the size selected will depend on the total power needs of the vehicle.design are capacity, discharge characteristics and safety. Traditionally, a higher capacity is associated with increase in size and weight. Discharge characteristics determine the dynamic response of electrical components to extract or supply energy to the battery. motors, AC induction motors, or Permanent Magnet Synchronous Motors (PMSM). Each motor has advantages and disadvantages that determine its suitability for a particular application. In this list, the PMSM has the highest power density and the DC motor has the lowest. [3].splitter that allows power flows from the two power sources to the driveshaft. The engine is typically connected to the sun gear while the motor is connected to the ring gear.aerodynamic drag interactions with weight and gradability factors accounted for in the equations.process of the hybrid powertrain is to study the maximum torque demand of the vehicle as a function of the vehicle speed. A typical graph is shown in Figure 2. Ratings of the motor and the engine are determined iteratively to satisfy performance criteria and constraints. The acceleration capabilities are determined by the peak power output of the motor while the engine delivers the power for cruising at rated velocity, assuming that the battery energy is limited. Power sources are coupled to supply power by the power-splitter, and the gear ratio of the power-splitter is determined in tandem. The next steps include developing efficient management strategies for these power sources to optimize fuel economy and designing the controllers. The final steps focus on optimizing the performance of this system under a variety of operating conditions.Figure 2: Maximum torque demand as a function of vehicle tire speed.3. MODEL-BASED DESIGN OF AN HEVMOTIVATIONIn this section, we outline some of the challenges associated with HEV design and explain the motivation for using Model-Based Design as a viable approach for solving this problem.of an HEV design problem is reflected in the large number of variables involved and the complex nonlinear relationships between them. Analytical solutions to this problem require advanced modeling capabilities and robust computational methods.set of requirements to meet the vehicle performance and functionality goals. Requirements refinement proceeds iteratively and depends on implementation costs and equipment availability.conceptualize the operation of the system’s various components and understand the complex interactions between them. This often requires experimentation with various system topologies. For example, studies may include comparing a series configuration with a parallel configuration. Because the goal is a better understanding of the overallsystem behavior, the models must include the appropriate level of detail. system level to a more detailed implementation, engineers elaborate the subsystem models to realize the complete detailed system model. This can be accomplished by replacing each initial model of a component with the detailed model and observing the effects on performance. Completing this process andrealizing a detailed model of the system requires robust algorithms for solving complex mathematics in a timely fashion.and mechanical components. Typically these components are designed by domain specialists. To speed development, these engineers need to effectively communicate and exchange design ideas with a minimum of ambiguity.typical HEV design is to increase the fuel efficiency of the vehicle while maintaining performance demands. Intuitively, one can look at this problem as finding the optimal use of the power sources as a function of the vehicle internal states, inputs, and outputs satisfying various constraints. This translates to the requirement for switching between various operational “power modes” of the vehicle as a func tion of the states, inputs, and measured outputs [4]. In a true environment for Model-Based Design the power management algorithms co-exist with the physical system models.complexity of the various subsystems, HEV controller design is typically a complex task. A variety of control algorithms specific to each subsystem may be required. For example, the controller that manages the frequency of the input voltage to the synchronous motor will be different from the simple control used for torque control of the same motor. Typically, this will manifest itself as a multistage, multiloop control problem. Successful implementation of the controllers requires deployment of these algorithms on processors that are integrated while interfacing with the physical plant. testing ensures that it continues to meet requirements. Detection of errors early in the process helps reduce costs associated with faulty designs. As design errors trickle down the various workflow stages the costs associated with correcting them increase rapidly[5]. The ability to continually verify and validate that requirements are being satisfied isa key aspect of Model-Based Design.A software development environment for Model-Based Design must be able to address the aforementioned challenges. Additionally, a single integrated environment facilitates model sharing between team members. The ability to create models at various levels of abstraction is needed to optimize the simulation time. A mechanism for accelerating the simulation as the complexity increases will also be important. PROCESS OF MODEL-BASED DESIGNModel-Based Design can be thought of as a process of continually elaborating simulation models in order to verify the system performance. The overall goal is to ensure first pass success when building the physicalprototype. Figure 3 shows the key elements of Model-Based Design.The system model forms the “executable specification” that is used to communicate the desired system performance. This model is handed over to the various specialists who use simulation to design and further elaborate the subsystem models. These specialists refine the requirements further by adding details or modifying them. The detailed models are then integrated back into the system level realization piece by piece and verified through simulation. This goes on iteratively until a convergence to an optimal design that best meets the requirements results. During Model-Based Design, C-code generation becomes an essential tool for verifying the system model. The control algorithm model can be automatically converted to code and quickly deployed to the target processor for immediate testing. Code can also be generated for the physical system to accelerate the simulation and/or to test the controller with Hardwarein the Loop simulation.Figure 3: The key elements of Model-Based Design.4. SYSTEM LEVEL MODELING OF AN HEVIn the first stage of the HEV design, the system-level description of the system is realized. Experimentation enables the system designer to explore innovative solutions in the design space resulting in optimal architectures. Our approach has been inspired by an earlier SAE paper [6]. REQUIREMENTSIn the initial stages of the project, it is not uncommon for the specifications of subsystem components to shift. The requirements are in a preliminary form, and are based on previous designs, if available, or best engineering judgment. Requirements are refined when each of the component models is delivered to component designers for additional refinement. There are, however, certain requirements that the system architect understands fully, and can lock down. As the project moves from requirements gathering to specification, the concepts of the system architects can be included in the model. Collaboration between architects and designers leads to a much better and more complete specification. The system can be expressed as a series of separate models that are to be aggregated into an overall system model for testing. Breaking down the model into components facilitates component-based development and allows several teams to work on individual components in parallel. This kind of concurrent development was facilitated by the parallel configuration we chose for our example, in which the electrical and mechanical power sources supply power in parallel. The broad design goals were:Improve fuel efficiency to consume less than 6.5liters per 100 km (L/100 km) for the driver input profile shown in Figure 4.Cover a quarter mile in 25 seconds from rest. Attain a top speed of 193 kph.Figure 4: Driver input profile as outlined in the requirements document.These and other such requirements are typically captured in a requirements document that engineers can associate with the design models. This provides the ability to trace the requirements throughout the model, a key component of Model-Based Design. VEHICLE DYNAMICSModeling the vehicle dynamics can be a challenging task. When creating any simulation model it is important to consider only the effects that are needed to solve the problem at hand. Superfluous details in a model will only slow down the simulation while providing little or noadditional benefit. Because we are primarily interested in the drive cycle performance, we will limit our vehicle model to longitudinal dynamics only. For example, the vehicle was initially modeled as a simple inertial load on a rotating shaft connected to the drive train. ENGINEA complete engine model with a full combustion cycle is also too detailed for this application. Instead, we need a simpler model that provides the torque output for a given throttle command. Using Simulink® and SimDriveLine™, we modeled a 57kW engine with maximum power delivery at 523 radians per second, as shown in Figure5.Figure 5: Engine modeled using blocks from the SimDriveline™ library. SYNCHRONOUS MOTOR/GENERATORThe synchronous motor and generator present an interesting example of electromechanical system modeling. Standard techniques for modeling synchronous machines typically require complex analysis of equations involving electrical and mechanical domains. Because the input source to this machine drive is a DC battery and the output is AC, this would require the creation of complex machine drive and controller designs – often a significant challenge at this stage.An averaged model that mathematically relates the control voltage input with the output torque and resulting speed is a useful alternative. This simplification allows us to focus on the overall behavior of this subsystem without having to worry about the inner workings. Furthermore, we can eliminate the machine drive by simply feeding the DC voltage directly to this subsystem. With this averaged model, we only need a simple Proportional-Integral (PI) controller to ensure effective torque control. TheMotor/Generator subsystem design will be explored in more detail in the next section. POWER-SPLITTERThe power-splitter component is modeled as a simple planetary gear, as shown in Figure 6. With these building blocks, more complex gear topologies can easily be constructed and tested within the overall system model.Figure 6: Power-splitter modeled as a planetary gear with connections.POWER MANAGEMENTThe power management subsystem plays a critical role in fuel efficiency.The subsystem has three main components:• Mode logic that manages the various operatingmodes of the vehicle.• An energy computation block that computes theenergy required to be delivered by the engine, the motor, or both in response to gas pedal input at any given speed.• An engine controller that ensures the engine is theprimary source of power and provides most of the torque. The motor and generator controllers provide torque and speed control.MODE LOGICFor efficient power management, an understanding of the economics of managing the power flow in the system is required. For example, during deceleration, the kinetic energy of the wheels can be partially converted to electrical energy and stored in the batteries. This implies that the system must be able to operate in different modes to allow the most efficient use of the power sources.We used the conceptual framework shown in Figure 7 to visualize the various power management modes.Algorithm design starts with a broad understanding of the various possible operating modes of the system. In our example, we identified four modes—low speed/start, acceleration, cruising, and braking modes. For each of these modes, we determined which of the power sources should be on and which should be off.The conceptual framework of the mode logic is easily implemented as statechart. Statecharts enable the algorithm designer to communicate the logic in an intuitive, readable form.Figure 7: Mode logic conceptualized for the hybrid vehicle.The Stateflow® chart shown in Figure 8 is a realization of the conceptual framework shown in Figure 7. While very similar to the conceptual framework, the Stateflow chart has two notable differences. The “acceleration” and “cruise” states have been grouped to form the “normal” superstate, and the “low speed/start” and “normal” states have been grouped together to form the “motion” superstate. This grou ping helps organize the mode logic into a hierarchical structure that is simpler to visualize and debug.Figure 8: Mode logic modeled with Stateflow®. SYSTEM REALIZATIONAfter the HEV components have been designed, they can be assembled to form the parallel hybrid system shown in Figure9.Figure 9: System-level model of the parallel HEV.This system model can then be simulated to determine if the vehicle meets the desired performance criteria over different drive cycles. As an example, for the input to the system shown in Figure 4, the corresponding speed and the liters per 100 km (L/100 km) outputs are shown in Figure 10. Once the baseline system performance has been evaluated using the system model, we begin the process of model elaboration. In this process, we add more details to the subsystems models to make them more closely represent the actual implementation. During this process, design alternatives can be explored and decisions made based on the analysis results. This is a highly iterative process that is accelerated using Model-Based Design.5. MODEL ELABORATIONIn the model elaboration stage, the subsystem components undergo elaboration in parallel with requirements refinement.A subsystem block is an executable specification because it can be used to verify that the detailed model meets the original set of requirements.As an example, we show how the generator machine drive undergoes requirements refinement and model elaboration. We assume that the engineer responsible for themachine drive design will carry out the model elaboration of the plant and the associated controller.REQUIREMENTS REFINEMENTThe machine drive is an aggregated model of the machine and the power electronics drive. In the system level modeling phase, the key specification is the torque-speed relationship and the power loss. This information was sufficient to define an abstract model to meet the high-level conceptual requirements.Figure 10: Output speed and L/100 km metric for the averaged model.As additional design details are specified, the model must become more detailed to satisfy the subsystem requirements. For example, the generator model will need parameters such as the machine circuit equivalent values for resistance and inductance. Engineers can use this specification as the starting point towards the construction of an electric machine customized for this HEV application.In the case of the generator drive, as the machine model is elaborated from an averaged model to a full three phase synchronous machine implementation, the controller must also be elaborated. PLANT ELABORATIONThe machine model for the synchronous generator is elaborated using SimPowerSystems™ blocks that represent detailed models of power system components and drives. For this model, the electrical and mechanical parts of the machine are each represented by a second-order state-space model. Additionally, the internal flux distribution can be either sinusoidal or trapezoidal. This level of modeling detail is needed to make design decisions as the elaboration process progresses.Figure 11: Detailed PMSM model parameters.The details of this model are captured in the model parameters shown in Figure 11, which specify the effects of internal electrical and magnetic structures.CONTROL ELABORATIONThe controller used in the averaged model of the AC machine drive is a simple PI controller. In model elaboration of the synchronous machine plant, a DC battery source supplies energy to the AC synchronous machine via an inverter circuit that converts DC to AC. These changes in plant model structure and detail require appropriate changes to the controller model to handle different control inputs and implement a new strategy. For example, the power flow to the synchronous machine is controlled by the switching control of the three phase inverter circuit. This added complexity was not present in the initial model of the machine drive because we focused on its behavior rather than its structure. We implemented a sophisticated control strategy, shown in Figure 12, that included cascaded speed and vector controllers [7]. The controllers were developed using Simulink® Control Design™ to satisfy stability and performance requirements.VERIFICATION AND VALIDATIONAt every step of the model elaboration process, the model is verified and validated. Figure 13 shows the averaged and detailed models as they are tested in parallel.Figure 12: Controller elaboration as we move from averaged (top) to detailed (below) model.The test case is a 110 radians per second step input to the machine. The response, shown in Figure 14, reveals comparable performance of both models. This serves as a visual validation that the detailed model is performing as desired. More elaborate testing schemes and formal methods can be devised with test case generation and error detection using assertion blocks from Simulink® Verification and Validation™ [8].Figure 13: Testing of the averaged and the detailed models for speed control with a 1000 rpm step input.SYSTEM INTEGRATIONAfter the component model elaboration and testing is complete, the subsystem containing the averaged model is replaced with the detailed model and the overall system is simulated again.Figure 14: Comparison between the averaged andthe detailed models of the machine drive. This integration will proceed, one component at a time, until the overall system level model contains all the detailed models for each component. This ensures each component is tested and verified in the system model. A single modeling environment for multidomain modeling facilitates the integration. In our example, we used Simulink for this purpose. In Figure 15, we compare the results of the averaged and the detailed models for the driver input profile shown in Figure 4. The detailed model shows deterioration in the speed and L/100 km performance metrics, which can be attributed to the additional detail incorporated into the model.4. CONTROLLER DEPLOYMENTThe electronic control unit (ECU) layout, deployment, and implementation are challenging problems that require innovative thinking. Typically, this requires exploration of the design space to optimize various criteria.Once the design of the system controllers is complete, ECU layout strategy must be considered. In a typical vehicle, we would likely keep some of the controllers inside a centralized ECU, while distributing the others throughout the car.One potential layout would implement the controller for the synchronous motor on a dedicated floating point microcontroller situated closer to the machine, instead of incorporating the controller as part of the centralized ECU. Such a strategy would allow for faster response times from the motor controller for efficient control. If a mix of centralized and distributed controller architecture is under consideration, then the extra layer of complexity introduced by the communication networkshould be accounted for in the modeling.Figure 15: Speed and L/100 km metric comparisons for averaged and detailed models for the HEV. Cost and performance considerations will drive design decisions regarding the selection of floating point or fixed point implementation of each controller. For example, one may consider implementing the controller for the synchronous generator on a fixed-point processor to lower the cost of the overall architecture.6. SIMULATION PERFORMANCEThe final system-level model of the HEV will contain detailed lower-level models of the various components. As model complexity increases, it will take longer to simulate the model in the software environment. This behavior is expected because the model contains more variables, equations, and added components which incur an additional computational cost. Intuitively, this can be visualized as an inverse relationship between simulation performance and complexity of the model as shown in Figure 16.Running the simulations in a high-performance computing environment can offset the increase in simulation times that comes with increased complexity. . With the advent of faster, multicore processors, it is possible to run large simulations without having to investin supercomputer technology.Figure 16: Simulation performance deteriorates with increasing model complexity. We used Simulink simulation modes that employ code generation technology [9] to accelerate the simulation of our model. The improvements in the simulation performance are shown in Figure 17.Figure 17: Comparison of Simulink® simulation modes for the detailed HEV model.CONCLUSIONIn this paper, we first described a typical HEV design and gave an overview of the key challenges. We discussed how the multidomain complications arise from the complex interaction between various mechanical and electrical components—engine, battery, electric machines, controllers, and vehicle mechanics. This complexity, combined with the large number of subsystem parameters, makes HEV design a formidable engineering problem.We chose Model-Based Design as a viable approach for solving the problem because of its numerous advantages, including the use of a single environment for managing multidomain complexity, the facilitation of iterative modeling, and design elaboration. Continuous validation and verification of requirements throughout the design process reduced errors and development time.Our first step in the development process was the realization of a system-level model of the entire HEV. The subsystem components were averaged models, which underwent model elaboration with requirements refinement and modifications in parallel. We showed how statecharts can be used to visualize the operating modes of the vehicle. After each component model was elaborated, we integrated it into the system-level model, compared simulation results of the averaged and detailed models, and noted the effect of model elaboration on the outputs. When simulation times grew long as we moved towards a fully detailed model, we introduced techniques to alleviate this issue. ACKNOWLEDGMENTSThe authors would like to acknowledge the following fellow MathWorks staff who contributed towards the development of the HEV models used in this paper and the writing of this paper. In alphabetical order—Bill Chou, Craig Buhr, Jason Ghidella, Jeff Wendlandt, Jon Friedman, Rebecca Porter, Rick Hyde, and Steve Miller. REFERENCES1. L. Brooke, “Cost remains the boss”, AutomotiveEngineering International, April 2007, SAE International.2. Iqbal Husain, “Electric and Hybrid Vehicles—DesignFundamentals”, 1st E dition, © 2003 CRC Press.3. S J. Chapman, “Electric Machinery Fundamentals”,4th Edition, © 2004 McGraw-Hill Inc.4. Han, Zhang, Yuan, Zhu, Guangyu, Tian andQuanshi, Chen, “Optimal energy management strategy for hybrid electric vehicles”, SAE Paper 2004-01-0576. 5. P. F. Smith, S. Prabhu, and J. Friedman, “Best。
收稿日期:2002201207 作者简介:王凤娥(1972—),女,内蒙古自治区人,工程师,硕士,主要研究方向为信息调研。
Biography :WAN G Feng 2e (1972—),female ,engineer ,master.质子交换膜燃料电池的研究开发及应用新进展王凤娥(北京有色金属研究总院,北京100088)摘要:介绍了国内外研究质子交换膜燃料电池的整体现状及水平,从电催化剂、膜电极及其制备工艺、质子交换膜以及双极板等几个方面,综述了质子交换膜燃料电池在材料及部件方面取得的成绩及研究现状,概述了质子交换膜燃料电池目前在电动车、船舶、移动电源等方面的应用情况。
提出了我国质子交换膜燃料电池的发展方向。
关键词:质子交换膜燃料电池;电池材料;部件;研究开发中图分类号:TM 911.4 文献标识码:A 文章编号:10022087X (2002)0520383205State 2of 2arts of re search ,development and application ofproton exchange membrane fuel cellWAN G Feng 2e(General Research Instit ute f or Non 2f errous Metals ,Beiji ng 100088,Chi na )Abstract :Proton exchange membrane fuel cell (PEMFC )is the clean energy with high efficiency.It has wide ap 2plication prospect in stationary power supply ,portable power supply ,special power source for military ,and es 2pecially in electric vehicle (EV )as driving power.The comprehensive state 2of 2arts of research on PEMFC at home and abroad are introduced according to the R &D about it in recent years.The advances and status of re 2search on materials and assemblies for PEMFC are also reviewed from the point 2of 2view of electrocatalyst ,mem 2brane electrode and its fabricating technology ,proton exchange membrane ,current collector ,etc.Moreover ,the application status of PEMFC in EV ,ship and portable power supply is summarized ,and the developing trend of PEMFC in China is presented.K ey w ords :proton exchange membrane fuel cell (PEMFC );material of fuel cell ;assembly ;R &D 燃料电池是一种将氢燃料和氧化剂之间的化学能通过电极反应直接转化成电能的装置。
tfautomodelforsequenceclassification类-回复[tfautomodelforsequenceclassification类] 是一个基于TensorFlow 框架的自然语言处理模型类，用于序列分类任务。
它是TensorFlow 模型库中的一个关键类，提供了构建和训练序列分类模型的核心功能。
本文将通过一步一步的回答，介绍tfautomodelforsequenceclassification 类的重要特性和用法，以及如何使用它来构建和训练序列分类模型。
我们还将探讨tfautomodelforsequenceclassification 类背后的原理和关键概念，以帮助读者更好地理解该类的工作原理。
首先，让我们来了解一下什么是序列分类任务。
在自然语言处理领域，序列分类是指根据文本序列的内容对其进行分类。
例如，情感分析任务就是一种典型的序列分类任务，目标是根据输入的句子判断其情感倾向是积极的、消极的还是中性的。
tfautomodelforsequenceclassification 类就是为了解决这类序列分类任务而设计的。
tfautomodelforsequenceclassification 类的最大特点之一是其灵活性。
该类提供了多种模型架构的选择，可以根据任务的特点和需求选择合适的模型。
例如，我们可以选择使用BERT、Electra、GPT-2 等预训练模型来构建序列分类模型。
这些预训练模型能够通过大规模的数据进行预训练，具有优秀的语言表示能力。
tfautomodelforsequenceclassification 类还支持对预训练模型进行微调，以适应特定的序列分类任务。
在开始构建和训练序列分类模型之前，我们需要准备好训练数据。
tfautomodelforsequenceclassification 类要求输入数据以特定的格式提供。
通常情况下，我们将训练数据拆分为训练集和验证集，用于模型的训练和评估。
A Code Generation Metamodel for ULF-WareGenerating Code for SDL andInterfacing with the Runtime LibraryMichael Piefel and Toby NeumannInstitut f¨u r InformatikHumboldt-Universit¨a t zu BerlinUnter den Linden610099Berlin,Germany{piefel|tneumann}@informatik.hu-berlin.deAbstract.Models can be used in many stages of many different pro-cesses,but in software engineering,the ultimate purpose of modelling isoften code generation.While code can be generated from any model,wepropose to use an intermediate model that is tailored to code generationinstead.In order to be able to easily support different target languages,this model should be general enough;in order to support the whole pro-cess,the model has to contain behavioural as well as structural aspects.This paper introduces such a model and the ideas behind it.When the model is to cover several languages,differences occur also inthe available library functions.Furthermore,the input languages(e.g.SDL)may contain high-level concepts such as signal routing that are noteasily mapped into simple instructions.We propose a runtime library toaddress both challenges.1IntroductionModels,in general,do not relate to programs at all.However,in software en-gineering models do refer to systems and their components,which may be executable code.Ideally,model-driven development(such as embodied in the OMG’s Model Driven Architecture(MDA))eventually leads to code generation.Our research group has been involved in simulation and modelling for a long time.We developed SITE[1],a compiler and runtime environment for the Specification and Description Language of the ITU-T,SDL.This compiler uses conventional techniques of a hidden representation of the abstract syntax tree and code generation(to C++).Lately,we proposed an open framework for integrated tools for ITU-T lan-guages that is provisionarily called ULF-Ware[2].An overview of its architecture can be seen infig.1on the following page.Oversimplifying,ULF-Ware contains a model-based compiler for SDL.The input(in this case,a textual SDL specification)is parsed and a model in the SDL repository is generated from it that adheres to the SDL metamodel.The next step transforms this to a new model in the Java/C++repository adheringFig.1.ULF-Ware overviewto the Java/C++metamodel.Finally,code generators turn this model into C++ or Java.This paper shortly introduces the Java/C++metamodel,a metamodel that is applicable to both Java and C++and that comprises structural as well as behavioural aspects.The requirements for such a metamodel which is geared towards code generation are not obvious.Similar metamodels exist,each with strengths and weaknesses.At the end of the next section,we will give a short overview of them.Mostly,they are concerned with structural aspects only.There is,however,a need for a new metamodel to cover all aspects of a programming language.The second part of this paper is concerned with the runtime libraries.In SITE,we originally had only one output language(C++)and one ter, we added experimental support for Java[3].In general,for n input languages and m output languages,you need n·m libraries and just as many different com-pilers/code generators.With the CeeJay metamodel as a common intermediate, this number should ideally decrease to just n transformations and m libraries.Later,we added more C++libraries that supported different needs:one library for simulation(including model time,stopping of the simulation,inspec-tion of variables)and another for execution(only speed matters);also libraries that used different middle-ware platforms.These extra libraries did not require a new code generator,as they had the same interface as the old one.Generally, adding another library with the same interface does not require new transfor-mations.Therefore,this paper shows why a code generation metamodel and accom-panying libraries are useful and simplify the task of translating SDL.Section2will present general choices that had to be made in order to de-termine the shape of the metamodel in connection with the runtime library. Section3on page5presents the metamodel in detail.Finally,sec.4on page7describes runtime libraries as they are and how they will be interfacing with the CeeJay metamodel in order to simulate or run SDL systems.2Design considerations for CeeJayThe code-generation metamodel is conceived to be useful to generate C++as well as Java from it.Therefore we have named it CeeJay.2.1The meta-metamodelIt is advantageous to employ the same meta-metamodel for all steps–this is a novel point of view:A conventional compiler might use BNF for the parser,but builds its abstract syntax tree using other means such as Kimwitu++[4]or in an ad-hoc fashion.We have chosen to use MOF as the meta-metamodel.It is used in many OMG standards,most prominently as the meta-metamodel for the UML.MOF is closely tied to UML,in fact there is a number of packages called the UML Infrastructure that are shared between MOF and UML.MOF,however,is not than just a meta-metamodel,but it provides a meta-data management framework.There are a number of mappings from MOF to different platforms,such as a mapping to XML for model interchange and a mapping to Java which gives interfaces to create,navigate and modify models.Using MOF and an appropriate tool for it gives a standard way to access models.First,you define a metamodel based on the MOF meta-metamodel.The tool then generates interfaces for models of this metamodel and an implementa-tion to store the models.There are a number of tools,but the only one adhering to the new MOF2.0standard is“A MOF2.0for Java”[5].2.2Generic or specifcHigh-level models are quite different from programs in conventional program-ming languages.They abstract from most of the detail that a programming language exhibits.Once you want to generate real code,all this detail has to be filled in.This makes code generation from those models a difficult task.More-over,many decisions in this process are similar for different target languages, but it is hard to make use of these commonalities.Finally,the way back,i.e. from program text to high-level models,is very hard.Note that most tools that promise to do this(e.g.reverse engineering of Java to UML)only capture the structural aspects of the language(i.e.they only produce UML class diagrams).The reverse approach is to use models that are very low-level and close to a specific language.There have been a number of papers such as[6]implement-ing this.The metamodel obtained this way is close to the original BNF of the language,they are grammars in disguise.Models like this are difficult to obtain. They would be the result of a model transformation from a high-level model. Here,the intelligence would have to lie in the transformations.Thus the level of detail of the metamodel of a programming language de-termines whether there will be more work to do in the code generator or the model transformator.We have chosen a level of abstraction that allows true object-oriented models(as opposed to models closely relating to the syntax of a language)while still being close enough to programming to make code generation a straightforward process.We will add another criterion to the decision as to how close to the target language the model should be:Can we use one metamodel for many languages?2.3Commonalities of object-oriented programming languages Many languages share common concepts,such as the quite abstract concept of namespace.For programming languages,the similarities go even further.Many differences in those languages are purely syntactical or for simple static semantics,such as the declaration of variables before use.The most important differences are support for crash-avoidance and the extent of the available li-braries,neither of which affect the metamodel.Java and C++in particular are very similar to each other.Still,a complete metamodel would exhibit a number offine differences such as the(non-)existence of multiple inheritance.However,we want to use Java and C++as output lan-guages only.This allows us to build a metamodel that can represent only the intersection of features from Java and C++.Since Java and C++have so much in common,the combined metamodel is still expressive enough to allow arbitrarily complex models.In fact,other object-oriented languages share the same concepts in very similar ways.Some of the differences between languages are evened out because we want to use the models only for code generation.In Python,for instance,variables do not have a declared type.While generating code from a model containing type information,it is easy to just suppress generation of type names.2.4The Role of the Runtime EnvironmentThe runtime environment serves two purposes:It hides differences between target languages and facilitates code generation for complex concepts.While the target languages that we consider are semantically similar and exhibit mostly syntactical differences which are easily covered by the code gen-erator,they have vastly differing standard libraries.The problem is already ob-vious in a simple Hello-World program.While C++uses printf or cout,Java instead has System.out.println.How will this be represented in the model in a uniform fashion?One way is to have special metamodel-elements for print-ing text,and similarly for all the other library calls that differ;this also means changes to the metamodel if we want to include another call.The other way is to use a common runtime library that offers a uniform interface to the model and encapsulates differing functionality;clearly,this approach is superior.In theory,code generation can generate code down to the most basic level. This would have the advantage of a minimal runtime library;this library wouldbe specific to the target language,but unconnected to the source language. But,as outlined in sec.2.2on page3,it is preferable not to burden the code generator with too much detail.Instead,the runtime library contains support for the advanced concepts of the source language.While this results in different libraries for different source languages,it greatly simplifies the code generator. In the past,this approach was proven to be very successful in SITE.The concepts of the library will be elaborated in sec.4on page7.2.5Related WorkThere are a number of metamodels for different languages around.However, the public availability of these metamodels is limited.Further,the focus of the metamodels can be quite different,as mentioned above.There is a project called jnome[7].The metamodel developed therein is tai-lored to generating documentation for Javafiles.It lacks support for the imple-mentation of methods and is as such not suitable for complete code generation.Both the Netbeans IDE and Eclipse seem to use a Java metamodel internally. Both IDEs bring their own repository functionality:Netbeans MDR,a MOF 1.4repository,and Eclipse EMF,a repository and metamodel that is similar to MOF.Both metamodels are not MOF2.0metamodels and do not cover dynamic aspects.The Object Management Group has a specification containing a Java meta-model[8].which seems to have been abandoned in an early stage of development.Numerous other works are concerned with automatically generating meta-models from grammars.The results are usually close to the grammar and,nat-urally,specific to the language they are based on.They are not suitable for a more general approach.A metamodel that captures some common features of Java and Smalltalk is presented in[9].This metamodel is concerned with common refactoring opera-tions in those two languages.3The CeeJay MetamodelThis is a condensed overview of the metamodel.For a more elaborate description, please refer to[10].3.1Basic building blocksThe basis for the CeeJay metamodel is a number of classes that are not shown in a diagram here.They have self-explanatory names such as NamedElement and are modeled very closely to the elements of the same name as in the UML Infrastructure.In fact,these classes can be viewed as a stripped-down version of the Infras-tructure.It remains to be seen whether it is advantageous to have them here, which will aid in understanding the concepts because it is simple,or whether we should rather use the Infrastructure itself.3.2PackagesAn important structural concept of Java is the package.In C++,there is the concept namespace.The two concepts are equivalent.The differences in usage are of no concern to the metamodel of the languages.While generating code, care is taken that every generated C++namespace has an associated headerfile declaring everything,such that the inclusion of it is equivalent to the import of a package in Java;in Java,files are distributed into directories of the package’s name.Consequently,a single metaclass Package(a specialization of Namespace) suffices for both Java packages and C++namespaces.They will be mapped accordingly,but see also3.5on the next page for an exception.3.3ClassJava does not allow multiple inheritance,so CeeJay can only support single inheritance for CeeJayClass.Java does,however,have interfaces.Those are GeneralizableElement s,and a CeeJayClass or an Interface can implement or enhance any number of Interface s.While C++does not have interfaces,they can be represented by abstract 3.4integers and booleans are used.The third primitive type here is void,which is used in functions(see3.5on the next page)as the return value.There is a constraint on the primitive types that is not expressed formally: There may be only one instance of the metamodel element PrimitiveType for each PrimitiveTypeKind,and there always has to be exactly one.This means that there will have to be a number of model elements in CeeJay models that are always supposed to be there,just as the package Predefined in SDL.The second variety of types is CeeJayClass itself.In both Java and C++, each class is also a type.Collections are mentioned explicitly in this metamodel.In order to use the built-in collections of C++or Java although they have different names(and different semantics and usage),there has to be a specific representation to unify them.3.5Functions and variablesAs can be seen infig.2on the facing page,both variables and functions are model elements that have a name and a type.Additionally,functions have parameters (ie.they inherit from ParameterizedElement and a body.Variables can be marked as being constant.This,however,necessitates initial values,and those can imply an order for the variables,which is not desired. Function bodies It is not yet completely clear how to represent the body–is it a nested namespace containing,among other things,the local variables,or is it only an ordered sequence of statements?The latter approach has the advantage of being simple,but it is also very close to an abstract syntax tree and as such not in the spirit of modelling.For instance,declarations of variables would just occur in the statement sequence,the variables would not truly live within the funtion’s namespace.The choice of the representation of the body is heavily influenced by the way the runtime library is built.While the languages that we want to generate code for are similar,their system libraries are very different.Parts of these differences will be covered by the metamodel,others by the supporting runtime library(cf. sec.4.2).The requirement that each function need a type leads to the need for a type void,which has to be explicitely disallowed for variables.The respective constraint is not shown here.The alternative solution,one that was also chosen in the UML metamodel,is to have the type being optional on a TypedElement, which,unfortunately,is not intuitive at all.4The Runtime Library4.1Related WorkAutomated code generation has become an accepted method in the design of new software systems(protocols,IT switches,control systems).There is a wide rangeof code generators available which produce code of a particular programming language from a particular specification or description language like SDL.Most of them use some kind of runtime environment for the generated code for reasons of abstraction,efficiency and adaptability.The environments could be realized quite differently.Some SDL code generators use a code library(like Tele-logic TAU[11]),some use preconfigured code fragments(like SINTEF ProgGen [12]),and others use runtime environments in a even broader ually each code generator is quitefixed regarding its runtime environment,target language, target platform and the configuration of the resulting executable system.SITE[1],the SDL code generation environment once developed in our re-search group,behaves similarly.Its generated C++code depends on a runtime library.But various efforts have been undertaken to provide moreflexibility.It is possible to exchange the library when conforming to its interface,and there is a mechanism to change the code generator output without changing the generator, and additional information could be embedded directly into the SDL specifica-tion.We perceive the desire to be moreflexible,to reuse the existing generator for a broader spectrum of application.The runtime library presented in[13]is meant as a contribution to that issue.That library allows the generated system to be adapted to different communication means to the environment,that is protocols and encodings.But restrictions remain.If we want to switch to a different target program-ming language,even if close the the one used,a completely review of the code generator is needed,possibly even a new code generator.Inevitably,we need a new runtime library,too.That is,for every desired combination of source and target language we need one code generator.If we could achieve a separation of syntactical issues from abstract concepts of the target language it would be suf-ficient to have one abstract transformation per source language and one concrete code generation per target language.4.2SDL runtime libraryElements of the library One aim of our code generation is simple and read-able code;another is a straightforward code generator.Both aims imply that the runtime library must give extensive support for the concepts of the source lan-guage.As has been previously shown in[1,3]this can be achieved by structural equivalence of the source and the target specifications.The result can be seen infig.3on the next page.The system type stype is transformed into a C++class of the same name that inherits from sdlsystem,a class defined in the library.Likewise,btype is a block type in SDL and inherits from sdlblock in C++.The gate and the channel are simply variables of the corresponding C++types.To this end,the runtime provides classes for SDL concepts that the concrete classes of the specification inherit.Other elements of the library not shown here are functions to route signals.signal alive;class alive:sdlsignal;system type stype;class stype:sdlsystem{ gate whorl out with alive;sdlgate whorl(0,alive);channel pingpipe from b via bwhorl sdlchannel pingpipe(b,whorl, to env via whorl with alive;env,whorl,alive);block type btype;class btype:sdlblock{ ......endblock type;};block b:btype;btype b;endsystem type;};system s;stype s;Fig.3.SDL and corresponding C++(simplified)Interfacing models and the library In order to use the library effectively, the library’s interface has to be specified.Often,this happens in plain English text.However,to access the library in the model,we need a formal definition of the interface in a package that is part of the model,just as a set of headerfiles for a C library become part of the program at compile time.In a similar fashion as the package Predefined in SDL,this package is considered to be in every CeeJay model.This package,however,does not contain any implementations, and no code will be generated for it;it is considered to exist in the target as well.This is necessary to use references in MOF(for specialization or function calls),as those are not by name.Furthermore,the interface must be consistent for all the runtime libraries (for different requirements and target languages).In the past,this was often not enforced,and differences between the libraries became apparent only when problems occured.To define the interface,we have yet to decide upon a standardized method such as IDL or eODL.Note that the interface is not limited to function calls, but also includes classes that are used as types and for inheritance.5ConclusionThe last step in a complete model driven software engineering process is the generation of implementation artifacts,usually in the form of source code.While code can be generated directly from high-level models such as UML,this puts too much work on the code generator.Instead,a stepwise refinement of the model into a model geared towards code generation is preferable.To this end we have prepared a metamodel that is reasonably close to the target languages Java and C++,while still being general enough to not only cover these two languages,but other object-oriented languages as well.This is different from existing metamodels that have been published(mainly for Java),which are close to the grammar of Java.Thus,they can often hardly be called metamodels,as they are no more than a MOF representation of the abstract grammar.The CeeJay metamodel will be used in a framework where C++is generated from SDL specifications.The aim of this open framework is to extend it for otheroutput languages,such as Java or Python,and other input languages,such as UML or domain specific languages.In order to execute SDL models,infrastructure is needed that will be in-cluded in the form of runtime libraries.These facilitate the code generation by covering the remaining differences between our target languages and by pro-viding solutions for complex concepts of the source language(SDL).Still more importantly,an infrastructure with a well-defined interface allows to change the runtime library to meet specific new needs.The code-generation metamodel CeeJay and its accompanying libraries will allow us to generate code for and execute or simulate arbitrary SDL systems. References1.Schr¨o der,R.,B¨o hme,H.,von L¨o wis,M.:SDL Integrated Tool Environment(1997–2003).URL rmatik.hu-berlin.de/SITE/.Last checked:Febru-ary27,20062.Fischer,J.,Kunert,A.,Piefel,M.,Scheidgen,M.:ULF-Ware–an open frameworkfor integrated tools for ITU-T languages.In:Prinz et al.[14],13.Neumann,T.:Abbildung von SDL-2000nach Java.Dissertation,Humboldt-Universit¨a t zu Berlin(2000)4.Neumann,T.,Piefel,M.:Kimwitu++–A Term Processor(2002).URL http://rmatik.hu-berlin.de/~tneumann/st checked:February 27,20065.Scheidgen,M.:A MOF2.0for Java.URL rmatik.hu-berlin.de/sam/meta-tools/st checked:February8,20066.Alanen,M.,Porres,I.:A relation between context-free grammars and meta objectfacility metamodels.Tucs technical report no606,Turku Centre for Computer Science(2003)7.Dockx,J.,Mertens,K.,Smeets,N.,Steegmans,E.:jnome:A Java meta model indetail.Report cw323,Department of Computer Science KULeuven(2001)8.OMG:Metamodel and UML Profile for Java and EJB Specification.Object Man-agement Group(2004).formal/04-02-029.Tichelaar,S.,Ducasse,S.,Demeyer,S.,Nierstrasz,O.:A meta-model for language-independent refactoring.In:Proceedings ISPSE2000.IEEE(2000),157–167 10.Piefel,M.:A common metamodel for code generation.In:J.Aguilar(ed.),Pro-ceedings of the3rd International Conference on Cybernetics and Information Tech-nologies,Systems and Applications.I I I S,Orlando,USA(2006)11.Telelogic:Communications software specification and software development Tele-logic TAU SDL Suite(2006).URL /corp/products/ tau/sdl/st checked:February28,200612.Floch,J.:ProgGen:SDL transformation Tool.SINTEF Telecom and Informatics(1998).URL http://www.sintef.no/units/informatics/products/proggen/.Last checked:February28,200613.Fischer,J.,Neumann,T.,Olsen,A.:SDL code generation for open systems.In:Prinz et al.[14],31314.Prinz,A.,Reed,R.,Reed,J.(eds.):SDL2005:Model Driven:12th InternationalSDL Forum,volume3530/2005of Lecture Notes in Computer Science.Springer-Verlag GmbH(2005).ISBN3-540-26612-7.ISSN0302-9743。
