THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Pattern recognition

Parallel control and management have been proposed as a new mechanism for conducting operations of complex systems, especially those that involved complexity issues of both engineering and social dimensions, such as transportation systems. This paper presents an overview of the background, concepts, basic methods, major issues, and current applications of Parallel transportation Management Systems (PtMS). In essence, parallel control and management is a data-driven approach for modeling, analysis, and decision-making that considers both the engineering and social complexity in its processes. The developments and applications described here clearly indicate that PtMS is effective for use in networked complex traffic systems and is closely related to emerging technologies in cloud computing, social computing, and cyberphysical-social systems. A description of PtMS system architectures, processes, and components, including OTSt, Dyna CAS, aDAPTS, iTOP, and TransWorld is presented and discussed. Finally, the experiments and examples of real-world applications are illustrated and analyzed.

Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications

The agent computing paradigm is rapidly emerging as one of the powerful technologies for the development of large-scale distributed systems to deal with the uncertainty in a dynamic environment. The domain of traffic and transportation systems is well suited for an agent-based approach because transportation systems are usually geographically distributed in dynamic changing environments. Our literature survey shows that the techniques and methods resulting from the field of agent and multiagent systems have been applied to many aspects of traffic and transportation systems, including modeling and simulation, dynamic routing and congestion management, and intelligent traffic control. This paper examines an agent-based approach and its applications in different modes of transportation, including roadway, railway, and air transportation. This paper also addresses some critical issues in developing agent-based traffic control and management systems, such as interoperability, flexibility, and extendibility. Finally, several future research directions toward the successful deployment of agent technology in traffic and transportation systems are discussed.

A Review of the Applications of Agent Technology in Traffic and Transportation Systems

Knocking combustion in spark-ignition engines

The World Wide Web has provided an opportunity for design and analysis of control systems through the Internet. An increasing number of Web-based software packages have been developed to enhance the teaching and design of control systems. A Web-based interactive control design and analysis system (WCDAS) based on Ch (which is a C/C++ interpreter) and Ch control systems toolkit is developed. Most functions in the system support both continuous-time and discrete-time linear time-invariant systems modeled in state space, transfer functions, or zero-pole-gain representation. Users can select a design and analysis method and specify system model type, system type and system parameters in the Web browser. These data are transferred to the server for numerical computation, and the simulation results are sent back to the client through the common gateway interface (CGI) using the Ch interpretative environment. Because both CGI scripts and simulation programs are written in Ch, data exchange between client and server is easily achieved. The system is available for use through the Web without any software installation, system configuration, or programming. This Web-based system is ideal for teaching as well as for solving practical problems in control systems design and analysis. The software packages Ch, CCST and WCDAS are available for downloading on the Web. The design, implementation and salient features of WCDAS are described in this article.

Web based control system design and analysis

The continued development of connected and automated vehicle technologies presents the opportunity to utilize these technologies for vehicle energy management. Leveraging this connectivity among vehicles and infrastructure allows a powertrain controller to be predictive and forward-looking. This paper presents a real-time predictive powertrain control strategy for a Plug-in Hybrid Electric Vehicle (PHEV) in a connected vehicle environment. This work focuses on the optimal energy management of a multi-mode PHEV based on predicted future velocity, power demand, and road conditions. The powertrain control system in the vehicle utilizes vehicle connectivity to a cloud-based server in order to obtain future driving conditions. For predictive powertrain control, a Nonlinear Model Predictive Controller (NMPC) is developed to make torque-split decisions within each operating mode of the vehicle. The torque-split among two electric machines and one combustion engine is determined such that fuel consumption is minimized while battery SOC and vehicle velocity targets are met. The controller has been extensively tested in simulation across multiple real-world driving cycles where energy savings in the range of 1 to 4% have been demonstrated. The developed controller has also been deployed and tested in real-time on a test vehicle equipped with a rapid prototyping embedded controller. Real-time in-vehicle testing confirmed the energy savings observed in simulation and demonstrated the ability of the developed controller to be effective in a real-time environment.

Real-Time Model Predictive Powertrain Control for a Connected Plug-In Hybrid Electric Vehicle

Modern manufacturing systems are increasingly becoming highly dynamic due to the integration with advanced information technology in response to rapid changes in products and market conditions. A more flexible platform is critically needed for developing a new generation of manufacturing systems in order to address the challenges of uncertainty and flexibility requirements. This paper presents a mobile agent-based framework that supports dynamic deployment of control algorithms and tasks in automation systems. The framework is based on a mobile agent system called Mobile-C. It uses Ch, an embeddable interpretive C/C++ environment for mobile agent execution. Since Ch has been ported to most existing computing platforms, the framework can control automation systems that work in different operating systems. This mobile agent-based framework has been applied to the control of an automation work cell. Using an automaton package in Ch as a middleware, automation tasks can be described as high-level control programs and are portable to heterogeneous mechatronic devices that comprise the automation cell. The validation of the dynamic deployment of different tasks has been conducted in an experimental automation work cell that consists of a Puma 560, an IBM 7575, and a conveyor system. The results show that the mobile agent approach can effectively deploy and execute new control algorithms and tasks as mobile agents on any subsystem in a network.

A Mobile Agent-Based Framework for Flexible Automation Systems

Vehicle electrification presents a great opportunity to reduce transportation greenhouse gas emissions. The greater use of plug-in electric vehicles (PEVs), however, puts stress on local distribution networks. This paper presents an optimal PEV charging control method integrated with utility demand response (DR) signals to mitigate the impact of PEV charging to several aspects of a grid, including load surge, distribution accumulative voltage deviation, and transformer aging. To build a realistic PEV charging load model, the results of National Household Travel Survey (NHTS) have been analyzed and a stochastic PEV charging model has been defined based on survey results. The residential distribution grid contains 120 houses and is modeled in GridLAB-D. Co-simulation is performed using Matlab and GridLAB-D to enable the optimal control algorithm in Matlab to control PEV charging loads in the residential grid modeled in GridLAB-D. Simulation results demonstrate the effectiveness of the proposed optimal charging control method in mitigating the negative impacts of PEV charging on the residential grid.

/pdf/mitigation-of-the-impact-of-high-plug-in-electric-vehicle-e9h24dnub5.pdf

Bo Chen

Papers

Web based control system design and analysis

Real-Time Model Predictive Powertrain Control for a Connected Plug-In Hybrid Electric Vehicle

A Mobile Agent-Based Framework for Flexible Automation Systems

Mitigation of the impact of high plug-in electric vehicle penetration on residential distribution grid using smart charging strategies

Distributed control for large-scale plug-in electric vehicle charging with a consensus algorithm