1. MATLAB/Simulink应用 ─ MATLAB/Simulink在工业界和学术界中的使用 ─ MATLAB/Simulink就业市场
张延亮 (math)
MATLAB中文论坛独立创始人，MathWorks机器人产品和市场部经理
2014年5月24日

2. 恭喜东南大学师生可以无限制安装使用MATLAB/Simulink…

3. 分享的内容 MATLAB中文论坛简介 演讲嘉宾介绍 MATLAB/Simulink应用

4. 你可能不知道的故事 ─ 2006年…

5. 你可能不知道的故事 ─ 2007年3月-11月…
6个月里有5万会员注册

6. 你可能不知道的故事 ─ 2007年至今…

7. 以往活动：

8. 到今天 80万+会员，每日新增~500会员 20万+问题，100万+有意义的回复 ~1万份程序资源 （20+Gb) 200+教学视频
~30本《有问必答》MATLAB/Simulink书籍

9. 杜昌文教授：使用MATLAB建立中国土壤DNA系统

10. 全国土壤数据 储存在云端 ISSAS工作人员 杜教授研究小组 免费使用 免费使用 授权使用 国内外研究机构 政府机关、政策制定者 商业用户
理化、光谱数据（库）的维护
核心算法的开发和维护
客户端的开发和维护
ISSAS工作人员
杜教授研究小组
免费使用
免费使用
授权使用
国内外研究机构
政府机关、政策制定者
商业用户

11. 孙忠萧：从新手到版主，再到主任工程师的7年转变

12. 刘鹏：一年解答5000个MATLAB专业问题

13. 徐平平教授：单片机专家
都是奶奶级单片机出身的人了，没有退休需要教学，很多优秀的学生编程能力很高，辅导有点力不从心了，发现了这个技术论坛，很学术，很踏实，我会常来的。 ...

14. 你一定在网上交流过的其他MathWorks职员陈炜
教育业务发展总监 杨兴
网名：柚籽
2007年加入论坛
董淑成
网名：老胡
2008年加入论坛
卓金武
书籍《MATLAB数学建模》作者 周翀
社区媒体经理

15. MATLAB/Simulink应用 ─ 近X个产品

17. 常见的数据分析和建模的工作流程 读取数据 分析和建模 分享 Automate 文件 数据库 数据源 报告 应用程序 应用整合 分析和可视化
STEVE: Advise keeping this short. Most people will have been to our seminars. Something like “ How do these customers use our tools to overcome their challenges ? O f course it varies between customers and among different segments as the presentations at this conference will illustrate, but we find that a few common threads connect most financial users. We can gather data from multiple sources, analyse that data, build models and build applications to make our modeling and analysis intuitive to ourselves and others, then share our output, models or software with others.”
So, how do these customers use our tools to overcome their challenges? Of course, it varies from customer to customer, but we find that a few common threads connect most of our financial users.
First, we need to collect data, potentially from many different data sources. The data can be stored in flat files (like Excel spreadsheets or CSV files), in databases (like Oracle or SQL Server), or in datafeeds from market providers like Bloomberg and Thomson Reuters. The first challenge is often to collect the data from these different sources into a common environment.
Once the data is in this common environment, we wish to analyze it. This analysis could involve statistical and other mathematical analysis, graphical visualizations, finance-specific modeling, and building applications that others could pick up and use.
Next, we want to share this analysis with coworkers, management, clients, or others. This sharing can take the form of automatically-generated reports, stand-alone applications like graphical user interfaces (or GUIs) or Excel add-ins, or it could be a stand-alone library of analysis functions that can be plugged into an existing production environment.
Finally, we would like to automate and accelerate this process so that it executes quickly and so that we can make improvements easily.
应用开发

18. 常见的基于模型设计的工作流程 集成 实现
嵌入式软件
数字和混合信号
测试和验证
系统设计
系统级模型
算法和单元模型 研究 需求

19. Airbus 使用基于模型的设计为 A380 开发出 燃油管理系统
Airbus A380, the world’s largest commercial aircraft.
Airbus 使用基于模型的设计为 A380 开发出
燃油管理系统 挑战
为 Airbus A380 燃油管理系统开发控制器。
解决方案
使用 MATLAB、Simulink 和 Stateflow 进行基
于模型的设计，可以建模和仿真控制逻辑，沟通
和交流功能规范并加速控制器的开发周期。 结果
节省了几个月的开发时间
在开发中重复使用模型
无需增加员工就可以处理更大的复杂性
“Simulink 和 Stateflow 模型使我们可以提前验证需求并向我们的供应商传达功能规范，以便按照 ARP 4754 补充书面需求。我们可以重复使用这些模型来创建桌面仿真器，试运行我们 HIL 测试平台，在我们的虚拟集成平台上运行以及向客户展示系统功能。”
Christopher Slack Airbus
Primary Industry: Aerospace and Defense
Secondary Industries: N/A
Products Used: MATLAB, Simulink, Curve Fitting Toolbox, MATLAB Distributed Computing Server, Parallel Computing Toolbox, Signal Processing Toolbox, SimPowerSystems, Simulink Coder, Stateflow, System Identification Toolbox
Applications: Embedded systems, Control systems, Digital signal processing
Product Capabilities: Mathematical modeling, Algorithm development, Parallel computing, System design and simulation, Physical modeling, Verification, validation, and test
Country: United Kingdom
Airbus Develops Fuel Management System for the A380 Using Model-Based Design
The Airbus A380, the largest commercial aircraft currently in operation, has a range of more than 8,000 miles. To enable such long non-stop flights, the A380’s 11 fuel tanks have a capacity of 250 metric tons (320,000 liters).
The A380’s sophisticated fuel management system handles fueling and defueling operations on the ground, as well as fuel flow to engines and between tanks while airborne. The system can move fuel between tanks to optimize the aircraft’s center of gravity, reduce wing bending, and keep fuel within the acceptable temperature range.
Airbus engineers used Simulink® and Stateflow® to develop a model of the fuel management system that was reused throughout the project. “With Model-Based Design, the model we used to represent the functional specification enabled us to validate requirements months earlier than was previously possible,” says Christopher Slack, computational analysis expert in fuel systems at Airbus.
The A380’s fuel management system must be able to safely handle any failure in the system’s 21 pumps, 43 valves, and other mechanical components. In a complex system, it’s challenging for engineers at the requirements stage to predict any problem that can result from combinations of relatively minor failures.
The Challenge
The fuel system specification document for the A380’s predecessor, the A340, had over a thousand written requirements. “Text requirements can leave room for ambiguity and misinterpretation. When you have that many requirements, it’s difficult for anyone to understand all the possible interactions between them and identify, for example, that a requirement on page 20 conflicts with one on page 340,” says Slack.
“Model-Based Design gave us advanced visibility into the functional design of the system. We also completed requirements validation earlier than was previously possible and simulated multiple simultaneous component failures, so we know what will happen and have confidence that the control logic will manage it.”
- Christopher Slack, Airbus
The Solution
Airbus used Model-Based Design to model the A380’s fuel management system, validate requirements through simulation, and clearly communicate the functional specification.
Airbus engineers used Simulink and Stateflow to model the system’s control logic, which comprises 45 top-level charts, almost 6000 states, and more than 8700 transitions. This model defines modes of operation on the ground (including refuel, defuel, and ground transfer) and in flight (including normal engine feed, center of gravity control, load alleviation, and fuel jettison).
The functionality within each top-level mode is grouped into subcharts, enabling engineers to work independently on individual components in the hierarchy.
The team developed a parameterized plant model of the tanks, pumps, valves, and electrical components using Simulink. Engineers can set parameter values to configure this model to represent fuel systems for any Airbus aircraft.
After running closed-loop simulations of individual operational components in Simulink, the team integrated them into a complete model for system-level simulations.
Using Parallel Computing Toolbox™ and MATLAB Distributed Computing Server™, the team performed Monte Carlo simulations on a 50-worker computing cluster. Over a weekend, they can run 100,000 simulated flights under varied environmental conditions and aircraft operational scenarios.
The team created a desktop simulator by generating code from the plant and control logic models with Simulink Coder™. A MATLAB® based user interface enables suppliers, airline customers, maintenance engineers, and other Airbus teams to visualize how the fuel management system works and interacts with other aircraft systems.
The team also used the Simulink models to develop hardware-in-the-loop (HIL) tests and commission their HIL testing rig well before the real hardware was available.
After successful flight tests of the A380, the team used System Identification Toolbox™ to tune their plant model using measured flight test data. They used Signal Processing Toolbox™ to remove noise from the test data, and Curve Fitting Toolbox™ to evaluate differences between the measured data and predicted results and to predict system performance beyond the usual flight envelopes. While refining the plant model, they used SimPowerSystems™ to incorporate relays and other elements of the electrical power system.
Based on the success of implementing Model-Based Design for the A380, Airbus engineers are now using this approach to develop the Airbus A350XWB’s fuel management system, reducing development time of this aircraft by one year.
■ Months of development time eliminated. “On earlier projects, it took up to nine months to integrate our fuel system design with the simulated cockpit, or iron bird. Using Model-Based Design on the A380, it took less than a month,” says Slack. “Similarly, by reusing the model to commission the HIL rig, we saved three months of development and shortened the time from initial concept to first flight.”
The Results
Models reused throughout development. “The Simulink and Stateflow models enabled us to validate requirements early and communicate the functional specification to our suppliers, complementing the written requirements in conformance with ARP 4754,” says Slack. “These models were reused to create desktop simulators, commission our HIL test rig, run on our virtual integration bench, and demonstrate system functionality to customers.”
Additional complexity handled without staff increases. “The fuel system of the A380 is three to four times more complex than that of the A340,” notes Slack. “Model-Based Design enabled us to handle a substantially more complex project with the same size engineering team.”
Learn more about Airbus:
Link to user story

20. (SKYACTIV TECHNOLOGY)的发动机
马自达的创驰蓝天柴油发动机
马自达加快开发下一代应用创驰蓝天技术
(SKYACTIV TECHNOLOGY)的发动机 挑战
在符合各项严苛的国际排放标准的前提下优化创
驰蓝天发动机的效率
解决方案
使用 Simulink 和 Model-Based Calibration
Toolbox（基于模型的标定工具箱）来加速生成和
开发最佳标定设置、ECU 可嵌入式模型以及用于
HIL 仿真的发动机模型 结果
最大限度地减少发动机标定工作量
模型的复杂性减半
提高模型精确度
“最初，我们的嵌入式最大汽缸压力模
型有38个参数。通过使用Model-Based
Calibration Toolbox，我们将参数减少到 20 个，从而降低了 CPU 上的负载。同样，该工具箱还使得我们能够将排气
温度模型的参数从大约 40 个减少到 20
个，而且保持同等级别的精确性。”
Shingo Harada Mazda
Secondary Industries: NA
Primary Industry: Automotive
Product Capabilities: Mathematical modeling, Algorithm development, System design and simulation
Applications: Embedded systems, Control systems
Products Used: MATLAB, Simulink, Model-Based Calibration Toolbox
Country: Japan
Mazda Speeds Next-Generation Engine Development of SKYACTIV TECHNOLOGY
SKYACTIV TECHNOLOGY engine development is enabling Mazda to commercialize fuel-efficient diesel and gasoline engines that do not rely on downsizing and lean burn. SKYACTIV-G is the world’s first gasoline engine for mass production vehicles to achieve a compression ratio of 14.0:1, resulting in a 15% increase in efficiency and torque. Its diesel counterpart, SKYACTIV-D, has the world’s lowest diesel-engine compression ratio, enabling it to deliver 20% more fuel efficiency while meeting strict exhaust regulations—including Euro6 and automobile exhaust gas regulations in Japan—without using costly exhaust after-treatment that reduces nitrogen oxide (NOx) emission.
Mazda engineers relied on MATLAB®, Simulink®, and Model-Based Calibration Toolbox™ for engine controller design, verification, and calibration.
“SKYACTIV engines incorporate hardware advances that deliver more torque and improve fuel economy,” says Shingo Harada, assistant manager at Mazda. “Model-Based Calibration Toolbox helped us exploit these advances, extracting better fuel efficiency and lower exhaust emissions than would have possible with manual, spreadsheet-based calibration approaches.”
The Challenge
As Mazda engines have grown more complex, it has become increasingly difficult to find optimal calibration settings using traditional approaches. “Trial and error with a spreadsheet and a test cell required extensive lab time, making it difficult to meet delivery schedules,” says Harada. “More importantly, finding an optimal solution in a search space of five or more dimensions is difficult even for experienced calibration engineers, so we could never be certain that we had found the best possible settings.”
Mazda wanted to reduce the SKYACTIV-D’s compression ratio to minimize soot and NOx emissions. To achieve this and other design objectives, engineers required ECU-embeddable statistical models of maximum cylinder pressure and exhaust gas temperature. Initial versions of these models had 40 parameters each and were too complex to run on the ECU. Mazda needed to reduce model complexity without sacrificing accuracy.
“Model-Based Calibration Toolbox not only enabled us to identify optimal calibration settings for the SKYACTIV-D engine, it greatly reduced the engineering effort required. The models it generated accelerated control logic development, provided valuable insights, and made it easy to try new ideas.”
- Shingo Harada, Mazda
The Solution
Mazda used Simulink and Model-Based Calibration Toolbox to define test plans, develop statistical models, and generate optimal calibrations for the SKYACTIV-D engine. They used the same products to develop statistical models for the SKYACTIV-G and perform hardware-in-the-loop (HIL) simulation of engine control logic.
Mazda used Model-Based Calibration Toolbox to design an optimized test plan for the SKYACTIV-D engine based on design of experiments. The plan included only the test points required to characterize engine performance and emission responses, minimizing testing time.
After conducting tests on a test cell, the engineers used Model-Based Calibration Toolbox to import the measured data and develop statistical models of the engine responses.
Using the Calibration Generation (CAGE) tool in Model-Based Calibration Toolbox and a MATLAB based optimization interface developed in-house, the team generated optimal calibrations from the engine models.
To define a realistic operating region for simulation, optimization, and embedded model evaluation, they used Model-Based Calibration Toolbox to create a boundary model.
With Model-Based Calibration Toolbox, Mazda engineers generated embeddable models, including the maximum cylinder pressure model used on the production SKYACTIV-D ECU.
For the same ECU, they generated a total mass of injected fuel as a function of multiple operating-point variables. This model was used with an exhaust temperature model, also generated by Model-Based Calibration Toolbox, to improve the reliability and performance of the fuel mass model.
SKYACTIV-D engines meet stringent European and Japanese emission standards and are installed in production vehicles, including the Mazda CX-5.
Engineers working on the SKYACTIV-G engine developed a statistical engine fuel-consumption model using Model-Based Calibration Toolbox. They exported this model to Simulink for use in the development, debugging, and HIL simulation of the engine control logic. The model was reused in automatic transmission fuel consumption simulations, further reducing the model development effort.
■ Engine calibration workload minimized. “With traditional methods, getting data when calibrating a new engine required a large amount of testing,” says Harada. “With Model-Based Calibration Toolbox, we reused the existing data and simulated the responses, which enabled us to minimize both the workload to obtain test data and test cell usage.”
The Results
■ Model complexity cut in half. “Our initial embedded maximum cylinder pressure model had 38 parameters. With Model-Based Calibration Toolbox, we reduced that number to 20, which in turn reduced the load on the CPU,” notes Harada. “Similarly, the toolbox enabled us to reduce the number of parameters in our exhaust gas temperature model from about 40 to 20 while maintaining the same level of accuracy.”
■ Model accuracy improved. “Using a boundary model created with Model-Based Calibration Toolbox, we improved the accuracy of our smoke model and reduced its root-mean-square error (RMSE) by 80%,” says Harada.
To learn more about Mazda SKYACTIV TECHNOLOGY, visit
Link to user story

21. 使用基于模型设计来建造特斯拉电动跑车Roadster
The Tesla Roadster.
使用基于模型设计来建造特斯拉电动跑车Roadster 挑战
在有限资金的前提下，开发一款电动运动跑车
解决方案
使用MathWorks基于模型设计工具来对电动跑车建模、仿 真、评估和性能分析，并评估设计权衡 结果
得到矫正好的系统模型，提高仿真准确性
在不需要建造实际系统的情况下，在仿真里测试了数百 个动力总成的配置
仿真了多领域知识的整合，推动了电池技术的进步
“We couldn’t have built this car without MathWorks tools. It would have taken resources that our new automotive startup company simply did not have. We will continue to rely on MATLAB and Simulink to help us make informed design decisions for the next generation of Tesla vehicles.”
Dr. Chris Gadda, Dr. Andrew Simpson Tesla Motors
Primary Industry: Automotive
Secondary Industries: n/a
Product Capabilities: System Design and Simulation, Verification, Validation and Test
Applications: Control Design
Products Used: MATLAB, Simulink
Country: USA
Published in MATLAB Digest January 2009  
By Dr. Chris Gadda and Dr. Andrew Simpson, Tesla Motors
Using Model-Based Design to Build the Tesla Roadster
Large automakers invest billions of dollars in the design and development of a new vehicle. At Tesla, we developed the 2008 Tesla Roadster, the world’s first 100-percent electric production sports car, on a budget of just $145 million. Because our budget is tiny in comparison to that of traditional car companies, we were compelled to optimize engineering resources and make smart design decisions.
To help meet these objectives, we used MathWorks tools for Model-Based Design to model the entire vehicle and its major subsystems, run detailed simulations, analyze performance, and evaluate design trade-offs.
In a standard internal combustion engine, more horsepower means more fuel consumption, and two-thirds of the energy generated in the engine is dissipated as heat. As a result, designers are forced to sacrifice power to gain fuel efficiency.
Optimizing Power and Fuel Efficiency
With the Roadster, we did not need to make this trade-off. More than 85 percent of the energy in the batteries is used to propel the vehicle, and when we make the vehicle more powerful, we are also making it more efficient. Our design goals focused on making a car that was fast, safe, and energy-efficient. The Roadster can accelerate from 0 to 60 mph in under four seconds, but it is also environmentally friendly: it has a range of 244 miles per charge on EPA combined cycle, and recharge electricity consumption of only 28 kWh per 100 miles of driving.
Developing System Models
Tesla engineers began using MATLAB® about three years ago for a variety of tasks, including analyzing test data and developing early dynamic thermal models of the battery. Over time, we developed MATLAB models for each major system in the car, including the transmission, motor, power electronics, brakes, tires, and control systems. We also developed models for aerodynamic factors and for battery dynamics, cooling, and power loss in cables.
We combined these models into a full-system model of the entire car, which we used to simulate the overall vehicle performance, including its potential speed and range, how much heat would be generated from individual components, and how much energy was lost to tires, wind resistance, and other factors. By comparing simulated results against the measured results from road tests of prototype vehicles, we validated the model and modified it to improve its accuracy.
Documenting and Refining Models in Simulink
As the number of MATLAB models grew, it became more difficult for a single engineer to fully understand how all the components interacted with each other. After we adopted Model-Based Design with Simulink®, we were able to develop a top-level Simulink model of the vehicle that invoked the individual MATLAB subsystem models that we had already validated. This hierarchy helped us to visualize the vehicle-level structure of our simulations and provided live documentation of the model contents (Figure 1).
We have since replaced the MATLAB subsystem models with equivalent Simulink models. At the same time, we refined the simulation architecture so that we could more easily separate design from development. The top-level Simulink model references each component as an independent Simulink model file, enabling us to apply version control to each component. Engineers can then work on different components in parallel. For example, one engineer can edit the model of the battery while another works on the transmission.
Using Simulink, we have architected the vehicle model so that it is flexible at the component level, which enables us to support multiple component models at different levels of detail. Careful partitioning of the model across component-level boundaries enabled us to use less detailed component models to speed up simulations of the entire vehicle. For example, we have detailed Simulink models of the Roadster’s power electronics and motor. We run simulations to characterize the performance of these two components using time steps of just 50 microseconds.
Simulating the Full Vehicle
We incorporate the results from the detailed simulation into a lookup table, which we can plug in as a higher-level model for longer vehicle-level simulations. An engineer designing an inverter can run a detailed dynamic simulation in the complete vehicle while another engineer uses a less detailed model of the inverter to predict the vehicle’s maximum range. This approach helps us acquire the information we need to make design decisions much faster.
We use our parametric vehicle model to simulate vehicles that are in production, vehicles about to go into production, and even future designs. We do this by capturing all the characteristics of the car in a standardized inputs template that we modify from simulation to simulation. This approach is particularly efficient for component sizing. For example, to simulate different transmission configurations, instead of substituting transmission blocks or changing the Simulink model we simply define the necessary parameters as input vectors. We then use a MATLAB script to invoke multiple simulations that sweep through the design options.
Recently, Tesla began an initiative to improve the Roadster’s total range per change. We had some ideas on how to improve the system but lacked real evidence that our ideas would work. Because we had a well-calibrated Simulink model of the vehicle and we trusted the simulation results it produced, we could quantify the effect of design changes with actual data.
Enhancing the Powertrain
During road tests on Roadster prototypes, we collected enough real data to refine and validate our models. Using the validated model of the original Roadster, we mapped out the entire design space for the Roadster 1.5 powertrain. We had a large matrix of different powertrain configurations that included various motor sizes, transmission configurations, battery chemistries, and inverter sizes. We used MATLAB scripts to sweep through hundreds of combinations in multiple rounds of simulation that became progressively more detailed.
This effort would have taken years and been prohibitively expensive without modeling and simulation. Each physical prototype takes six months or a year to produce. We could not afford to iterate through hundreds of gear ratios in hardware. The enhanced powertrain is now working as designed in prototype vehicles.
With Simulink we can tackle problems in domains that would typically require specialized—and more expensive—analysis tools. For example, many of our initial battery models were empirical, with an ideal voltage source and a fixed impedance. We now use much more sophisticated first-principal models, and as a result, have gained invaluable insights into the battery as an electrochemical device. We used Simulink to build advanced equivalent circuit models that can predict performance at different states of charge, discharge rates, temperatures, and levels of aging.
Spanning Multiple Disciplines
We used a similar approach to perform safety-critical simulations to predict cooling performance within the battery and ensure that the battery packs would not overheat. To capture all the multidomain physical, chemical, and heat-transfer effects would typically require a finite-element analysis tool and significant effort. With MathWorks tools we performed analyses and gained insights that lead to dramatic advances in battery technology. The Roadster has more than twice the energy storage density of any other production electric vehicle.
As we generate more and more road-test data, we are once again using MATLAB to process, visualize, and incorporate analysis results into ever more accurate models of the Roadster. We couldn’t have built this car without MathWorks tools. It would have taken resources that our new automotive startup company simply did not have. We will continue to rely on MATLAB and Simulink to help us make informed design decisions for the next generation of Tesla vehicles.
Future Roadsters
Link to article

22. 三星（英国）利用 Simulink 开发出 4G 无线系统
伦敦三星电子研究院. 挑战
开发下一代移动通信技术和推进数字通信研究
解决方案
为快速设计他们的通信系统，同时提升协作和重
复使用性，用 MathWorks 工具进行了标准化 处理 结果
将上手时间缩到最短
超预期完成任务
建立了协作开发平台
“由于 ATG 的所有研究人员都使用 MATLAB 和 Simulink，因此很容易共享工作和维护应用。另外，Simulink 还协助我们与欧盟项目中的其他三星办公室及合作伙伴进行交流，因为我们可以在
Simulink 下整合他们的成果。”
Dr. Thierry Lestable Samsung UK
Primary Industry: Communications
Product Capabilities: Data analysis, Algorithm Development, System design and simulation
Application Areas: Digital signal processing
Secondary Industries: n/a
Country: United Kingdom
Products Used: MATLAB, Simulink, Communications System Toolbox, DSP System Toolbox
The Advanced Technology Group (ATG) in the Samsung Electronics Research Institute (SERI) in London is at the forefront of the effort to unify baseband algorithm development and advance physical layer research for 4G wireless technologies.
Samsung UK Develops 4G Wireless Systems with Simulink
As part of this effort, SERI is involved with industry-wide research projects in the European Union (EU) Framework Programme to develop a new wireless system concept that will improve peak data rate, latency, spectrum efficiency, and coverage while reducing cost.
SERI has standardized on Simulink® to unify baseband algorithm development and physical layer research and is working within a consortium to establish an integrated software library for wireless applications.
“Simulink provides an easy-to-use environment that enables efficient development, debugging, and testing for wireless communication technology,” explains Dr. Thierry Lestable, senior researcher at SERI. “It also provides a comprehensive set of blocksets and promotes improved quality and increased code reuse between designs.”
SERI needed a development platform that enhanced and streamlined the group’s data analysis, algorithm development, and simulation capabilities, along with facilitating cross-organizational collaboration. They also needed to compare and integrate their physical layer transmission chains
Challenge .
SERI faced a tight schedule imposed by the consortium for producing simulation results for proposed technologies.
“To meet strict project deadlines, we need to maintain intellectual property developed by multiple sources within our team,” Lestable explains. “Additionally, on our EU projects, we need a common software platform and library to simplify interfacing with and integrating contributions from other consortium members.”
"Simulink enables us to easily share proposals and knowledge with other design centers. Simulink also allows us to focus on algorithm design and perform state-of-art mathematical analysis, assessment, simulation, and optimization.“
Dr. Thierry Lestable, Samsung UK
SERI standardized on MathWorks tools to develop their next-generation wireless technologies. They’re also migrating all their existing algorithms to Simulink to facilitate their reuse in future development initiatives.
Solution
With limited experience with Simulink, SERI worked with MathWorks Consulting to accelerate this transition.
The ATG uses Simulink to develop algorithms and to create models of 4G communications systems. They use Communications System Toolbox™ and DSP System Toolbox™ to speed their model development.
The team also uses Simulink to integrate C and C++ code into their simulations. By integrating custom code, they can include algorithms from other consortium partners and their own proprietary optimized implementations of algorithms once the generic models are complete. Their own algorithms will ultimately become part of SERI’s patent portfolio.
SERI uses MATLAB® for statistical analysis and graphical plotting to better visualize and understand system behavior and experimental data. They generate sets of BER curves for their Simulink models with MATLAB scripts to understand the effect of variables and to quantify the merits of competing technologies. For multiple-carrier code division multiple access (MC-CDMA), this enables them to assess the robustness of various detection algorithms to multiple access interference.
During the calibration phase of the project, in which all partners contributing to physical layer research compare their results using a set of predetermined scenarios, SERI relies on Simulink and Communications System Toolbox.
The team also uses DSP System Toolbox to develop advanced encoding algorithms, such as low-density parity check (LDPC) coding.
The ATG researchers use Simulink to develop new interference-cancellation algorithms using soft information obtained from several coding schemes and LDPC. After obtaining reference data from single- and multi-user detection scenarios, the researchers apply advanced channel coding to implement the interference-cancelling loop.
The ATG is approaching completion of the entire MC-CDMA assessment at the physical level.
■ Ramp-up time minimized. “Within one week of using Simulink for the first time, we finished the first complete transmission chain simulation. Within three weeks, we completed the full multi-carrier transmission chains including MC-CDMA and COFDM,” says Lestable.
Results
■ Aggressive deadlines met. “Using Simulink, we have consistently completed development milestones and deadlines ahead of target—significantly ahead in our MC-CDMA development,” explains Lestable.
■ Platform for collaborative development established. “Because all the researchers within ATG use MATLAB and Simulink, it’s easy to share work and maintain applications,” explains Lestable. “Also, Simulink helps us interface with other Samsung offices and partners on EU projects because we can integrate their contributions under Simulink.”
Learn more about Samsung UK:
Link to user story

23. 隆德大学使用人工神经网络来匹配心脏移植捐赠者与受助人
Plots showing actual and predicted survival, best and worst donor-recipient match, best and worst simulated match (left); and survival rate by duration of ischemia and donor age (right).
隆德大学使用人工神经网络来匹配心脏移植捐赠者与受助人 挑战
寻找心脏移植捐赠者与受助人的最佳匹配
解决方案
使用MathWorks工具开发一个预测的人工神经网络模型， 并在一个56处理器集群上模拟成千上万个风险组合 结果
未来的五年生存率提高了10％
神经网络训练时间节省了2/3
仿真时间从数周减少到几天
“I spend a lot of time in the clinic, and don’t have the time or the technical expertise to learn, configure, and maintain software. MATLAB makes it easy for physicians like me to get work done and produce meaningful results.”
Dr. Johan Nilsson Skåne University Hospital Lund University
Primary Industry: Biotech and Pharmaceutical
Secondary Industries: Academia
Product Capabilities: Data Analysis, Mathematical Modeling, Parallel Computing
Applications: Computational Biology
Products Used: MATLAB, MATLAB Distributed Computing Server, Neural Network Toolbox, Parallel Computing Toolbox
Country: Sweden
Lund University Develops an Artificial Neural Network for Matching Heart Transplant Donors with Recipients
A heart transplant recipient’s survival depends on dozens of variables, including the weight, gender, age, and blood type of both donor and recipient, and the ischemic time— or the time during a transplant when there is no blood flow to the organ.
To better understand transplant risk factors and improve patient outcomes, researchers at Lund University and Skåne University Hospital in Sweden use artificial neural networks (ANNs) to explore the complex nonlinear relationships among multiple variables. The ANN models are trained using donor and recipient data obtained from two global databases: the International Society for Heart and Lung Transplantation (ISHLT) registry and the Nordic Thoracic Transplantation Database (NTTD). The Lund researchers accelerated the training and simulation of their ANNs by using MATLAB, Neural Network Toolbox, and MathWorks parallel computing products.
“Many of the techniques we use are computer-intensive and time-consuming,” says Dr. Johan Nilsson, Associate Professor in the Division of Cardiothoracic Surgery at Lund University. “We used Parallel Computing Toolbox with MATLAB Distributed Computing Server to distribute the work on a 56-processor cluster. This enabled us to rapidly identify an optimal neural network configuration using MATLAB and Neural Network Toolbox, train the network using data from the transplantation databases, and then run simulations to analyze risk factors and survival rates.”
Understanding how various risk factors affect survival rates involved hundreds of thousands of computationally and data-intensive operations—for example, the team had to test hundreds of ANN configurations to identify the best one. An analysis of six variables requires the simulation of 30,000 different combinations. Simulating all these combinations for 50,000 patients took weeks using an open-source software package.
The Challenge
Nilsson and his colleagues encountered reliability problems with the software they were using, as well. “The software was unstable, which led to crashes during long, multiday simulations,” Nilsson explains. “In addition, some of the results it produced were not quite right. When we publish our findings, we need to be very sure we can trust the results.”
“I spend a lot of time in the clinic, and don’t have the time or the technical expertise to learn, configure, and maintain software. MATLAB makes it easy for physicians like me to get work done and produce meaningful results.”
- Dr. Johan Nilsson, Skåne University Hospital, Lund University
The Solution
To address the speed and reliability challenges, Lund University researchers developed their initial ANN model using MATLAB and Neural Network Toolbox. To find the optimal network configuration, they wrote MATLAB scripts that varied the number of hidden nodes used in the network for a range of weight decay (or regularization) values.
The team used Parallel Computing Toolbox and MATLAB Distributed Computing Server to accelerate the simulation of more than 200 ANN configurations. They then evaluated the results to find the best-performing configuration.
After training the ANN using donor and recipient information from the databases, they verified the model’s accuracy by simulating outcomes for 10,000 patients that had been omitted from the training set. They then compared the results against actual survival rates.
In the next phase, the team conducted thousands of simulations in parallel to rank the 57 risk factors considered in the study for predicting long-term survival.
Using results from Monte Carlo simulations on the computer cluster and simulated annealing techniques, the researchers identified the best and worst possible donors for any particular recipient.
As a final step, the team developed an automated process that ranks the recipient waiting list to identify the best candidates for a prospective donor.
In the next major phase of the project, Lund University researchers are using the ANN to investigate the use of Human Leukocyte Antigen (HLA) genetic profiles to match donors with recipients.
The Results
■ Prospective five-year survival rate raised by up to 10%. “In a simulated randomized trial, the preliminary results show that the ANN model we developed using MATLAB and Neural Network Toolbox would transplant approximately 20% more patients than would have been considered using traditional selection criteria,” says Nilsson. “The prospective five-year survival rate for the ANN-selected patients was 5–10% higher than those matched with the criteria physicians use today.”
■ Network training time reduced by more than two-thirds. “Using Neural Network Toolbox and MATLAB, it took us 5 to 10 minutes to train our ANNs,” says Nilsson. “Training took 30 to 60 minutes using open-source software. That is a big difference, because we were training and evaluating hundreds of network configurations.”
■ Simulation time cut from weeks to days. “When we switched to MATLAB and MathWorks parallel computing technologies, we completed experiments that regularly took 3 to 4 weeks in about 5 days,” says Nilsson. “More importantly, the simulations were completed reliably, with no crashes.”
Learn More About Lund University:
This project is a collaboration with:
• The Department of Clinical Sciences Lund at Lund University and Skåne University Hospital
• The Department of Astronomy and Theoretical Physics at Lund University
• The Competence Center of Clinical Research at Skåne University Hospital
Link to user story

24. 罗斯胡尔曼理工学院学生使用Simulink和SimDriveline设计混合动力汽车动力总成系统
Rose-Hulman team for Challenge X. 挑战
设计了混合动力汽车的动力总成，并获得现实世界的工程经验
解决方案
使用MathWorks基于模型设计工具来建模和仿真汽车的电子、机械和控制系统 结果
动力总成系统开发时间降低80%
学生获得了现实世界的工程经验
继续把焦点放在工程上
“It was a difficult process to develop a simple model. With Simulink and SimDriveline, we built that same model in 20 minutes.”
Zac Chambers
Rose-Hulman Institute of Technology
Secondary Industries: Automotive
Primary Industry: Education
Application Areas: Model-Based Design, Simulation
Products Used: MATLAB, Simulink, MATLAB Coder, SimDriveline, Simulink Coder, Stateflow
Country: USA
Rose-Hulman Institute of Technology Students Design Hybrid Vehicle Powertrain with Simulink and SimDriveline
Often, undergraduate engineering students spend their college years immersed in math and theory, with little opportunity to apply their knowledge and gain practical, real-world experience. That is not the case for students at the Rose-Hulman Institute of Technology participating in the Challenge X competition.
As part of the competition, Rose-Hulman engineering students will spend as many as three years using MathWorks tools for Model-Based Design to develop and build a hybrid vehicle powertrain. By applying the same tools as many of their industry counterparts on a real-world project, the students are acquiring engineering experience not easily found in a classroom.
"With MathWorks tools, our students can focus on design and engineering tradeoffs, instead of spending all their time on linear algebra and differential equations," says Zac Chambers, faculty coadviser of Rose-Hulman's Challenge X team. "Students on our Challenge X team will gain years of vehicle development experience using MathWorks tools."
The Challenge
Sponsored by General Motors and the U.S. Department of Energy, the Challenge X competition presents students with many challenges found in the automotive industry. The students are designing a vehicle that will reduce energy consumption and emissions production while maintaining acceptable levels of consumer satisfaction, performance, utility, and safety.
In addition to the technical challenge, the competition has a broader objective. “Our goal is to provide our undergraduate students with up to three years of experience in the design of fuel-efficient vehicles,” says Marc Herniter, associate professor at Rose-Hulman. “This technology-–and these students-–will likely drive the market for cars, trucks, and offroad equipment for the next twenty years.”
"For our initial model, we began by writing differential equations and incorporating the kinematic constraints. It was a very difficult six-week process to develop a very simple model. With Simulink and SimDriveline, we built that same model in 20 minutes.“
- Zac Chambers, Rose-Hulman Institute of Technology
The Solution
In the first year of the competition, Rose-Hulman engineering students used MathWorks tools for Model-Based Design to model and simulate electrical, mechanical, and control systems of the hybrid vehicle.
After choosing a power-split architecture, the team began using Simulink, SimDriveline, and Stateflow to develop models of their supervisory control system and a plant that included models of the engine, motors, batteries, brakes, and powertrain.
"By modeling the plant and the controller in the same environment, we quickly fixed an unstable mode in our vehicle controller by adding a second feedback loop," says Herniter. "I don’t know how we would have done this without Simulink and SimDriveline."
Students used Simulink and SimDriveline to create the initial model, which included a Planetary Gear Set (PGS) and a constant torque for the engine and both motors. The model helped them understand how the PGS interacts with the three power sources in the vehicle and the remainder of the driveline. Students added detail to each component to model nonlinearities and actual components.
"Simulink and SimDriveline helped us to teach a design philosophy, develop an understanding of the vehicle’s physics, design the vehicle, and choose components," explains Herniter. "With this approach, we gained confidence in our model and chose and verified the operation of the components based on vendor-supplied data."
Using Stateflow, they designed the entire supervisory controller for the vehicle, which incorporates vehicle speed, battery state of charge, and driver torque request. By analyzing results from Stateflow simulations, students simplified their design by eliminating unnecessary states as well as hazards in the control logic that could damage vehicle components.
"In the competition, we were among the first to get our vehicle to move because we used Stateflow to put together a control strategy," explains Chambers. "With Stateflow, we spent our time improving the logic, not trying to code it."
The team used MATLAB to postprocess simulation results and to automate extensive sets of overnight simulation runs.
The students then used Simulink Coder™ to automatically generate DLLs for the supervisory controller and for the plant model, including the vehicle and powertrain. The DLLs were downloaded to hardware for real-time testing.
In the next two years, the students will continue to calibrate and optimize their design as they conduct hardware-in-the-loop testing of all the components, assemble the powertrain, and install the system into a vehicle for onroad testing.
The Results
■ Powertrain development time cut by 80%. "Without SimDriveline, we would have spent four or five times the amount of effort and time on developing our powertrain. SimDriveline enables us to fully understand how the power is split from the engine and the motors to get to the rear wheels and how all the components interact in the power flow," notes Herniter.
■ Practical experience acquired. "We have a large group of students graduating from Rose-Hulman with significant experience in vehicle modeling using MathWorks tools," says Chambers. "Two of our recent graduates now work at General Electric and Caterpillar, and they are both using Simulink in their jobs."
■ Focus on engineering maintained. "We have courses where students develop differential equations to describe systems, but there is a point where it is just too ugly," explains Herniter. "SimDriveline allows us to concentrate on the motors, the engine, the control systems, and the engineering."
Learn more about Rose-Hulman Institute of Technology:
Link to user story

25. MATLAB/Simulink就业市场
数据来源：51job.com

26. 今天其他行程： 这是我演讲的最后一个幻灯片！ 陈炜：东南大学师生安装使用MATLAB/Simulink
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这是我演讲的最后一个幻灯片！