Multiagent Systems is the title of a collection of papers dedicated to surveying specific themes of Multiagent Systems (MAS) and Distributed Artificial Intelligence (DAI). All of them authored by leading researchers of this dynamic multidisciplinary field.

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Multiagent systems: a modern approach to distributed artificial intelligence

Various textbooks have been written on the epidemiology of plant diseases. Most of them present theory of epidemiology, thus giving a description of epidemiological principles. Some of these are of a general nature, whereas others concentrate on specialized subjects, but there are no books on experimental techniques in epidemiology. This omission has been recognized and rectified by Kranz and Rotem. As mentioned in their preface they intended, by editing and publishing the book, 'to help in the development of a methodology able to provide a choice of adequate methods widely acceptable amongst epidemiologists for varying applications and objectives'. For this purpose, they invited 27 distinguished plant pathologists, a number of them from Kranz's 'stable', to participate, and to provide information from their own rich experience. There are 20 chapters spread over four sections. Section I (General techniques), deals with techniques and approaches used in experiments in the field and under controlled conditions. Advantages and disadvantages of such experiments and the relations between them are explained. In Section II (.Measurements and their analysis) disease assessment, crop development and estimation of spore production, dispersal, survival and infectiousness are treated. The analysis of spatial patterns of soil-borne pathogens {s discussed in a separate chapter, for soil-borne diseases have to be approached differently. This section is concluded with two chapters on the monitoring and analysis of environmental factors. Section III (Special topics) is a collection of items, such as the analysis of the effects of control measures, fungicide resistance, virulence in pathogen populations, components in yield loss, development of forecasters, aphid-borne epidemiology, quantitative nematology, geophytopathology and long-distance dispersal. As the editors explain, these are 'not necessarily connected with each other, but all dealing with definite objectives in epidemiological experimentation'. The reasoning for the addition of these topics to the contents is clear. Most are worth treatment, and each has specialized techniques and approaches. The reasoning for collecting them all together under 'Special topics' is a little unsatisfactory. A rather simple rearrangement and naming of the subjects could separate them into a couple of better defined chapters, which would have made the contents more accessible to the eager user. Section IV (The synopsis of experimental restilts) gives the reader a look into the methods in comparative epidemiology, and into the development and use of simulation models for root and foliar diseases. The limitations, necessarily imposed upon the authors, to prevent the publication of an unwieldy and ever more expensive handbook lead to the question whether the editors 'bit more than they could chew'. Especially the chapters on modeling and simulation suffer under these limitations. Most authors have achieved a fine work, though readers, familiar with the authors' publications, can 'taste' a bit of subject.ivity in the choice and approach of techniques and methods by some of them. The book might be a useful addition to the library of all those involved in experimental work in epidemiology, but particularly those called 'novices in epidemiology' in the preface. The editors may have had undergraduates and young postgraduates in mind, but this work might be useful for other epidemiologists too. It is a pity that the high price will hinder young scientists from purchasing the book, particularly those in developing countries.

Book review: Experimental techniques in plant disease epidemiology, J. Kranz & J. Rotem (eds.). Springer-Verlag Berlin, 1989, 299 pp.

A large number of factors such as the increasingly stringent pollutant emission policies, fossil fuel scarcity and their price volatility have increased the interest towards the partial or total electrification of current vehicular technologies. These transition of the vehicle fleet into electric is being carried out progressively. In the last decades, several technological milestones have been achieved, which range from the development of basic components to the current integrated electric drives made of silicon (Si) based power modules. In this context, the automotive industry and political and social agents are forcing the current technology of electric drives to its limits. For example, the U.S Department of Energy’s goals for 2020 include the development of power converter technologies with power densities higher than 14.1 kW/kg and efficiencies greater than 98%. Additionally, target price of power converters has been set below $3.3/kW. Thus, these goals could be only achieved by using advanced semiconductor technologies. Wide-bandgap (WBG) semiconductors, and, most notably, silicon carbide (SiC) based power electronic devices, have been proposed as the most promising alternative to Si devices due to their superior material properties. As the power module is one of the most significant component of the traction power converter, this work focuses on an in-deep review of the state of the art concerning such element, identifying the electrical requirements for the modules and the power conversion topologies that will best suit future drives. Additionally, current WBG technology is reviewed and, after a market analysis, the most suitable power semiconductor devices are highlighted. Finally, this work focuses on practical design aspects of the module, such as the layout of the module and optimum WBG based die parallelization, placement and Direct Bonded Copper (DBC) routing.

Power module electronics in HEV/EV applications: New trends in wide-bandgap semiconductor technologies and design aspects

In recent decades, several factors such as environmental protection, fossil fuel scarcity, climate change and pollution have driven the research and development of a more clean and sustainable transport. In this context, several agencies and associations, such as the European Union H2020, the United States Council for Automotive Research (USCAR) and the United Nations Economic and Social Commission for Asia (UN ESCAP) have defined a set of quantitative and qualitative goals in terms of efficiency, reliability, power losses, power density and economical costs to be met by next generation hybrid and full electric vehicle (HEV/EV) drive systems. As a consequence, the automotive electric drives (which consists of the electric machine, power converter and their cooling systems) of future vehicles have to overcome a number of technological challenges in order to comply with the aforementioned technical objectives. In this context, this paper presents, for each component of the electric drive, a comprehensive review of the state of the art, current technologies, future trends and enabling technologies that will make possible next generation HEV/EVs.

Next generation electric drives for HEV/EV propulsion systems: Technology, trends and challenges

Gallium Nitride High Electron Mobility Transistors (GaN HEMTs) enable higher efficiency, higher power density, and smaller passive components resulting in lighter, smaller and more efficient electrical systems as opposed to conventional Silicon (Si) based devices. This paper investigates the detailed benefits of using GaN devices in transportation electrification applications. The material properties of GaN including the applications of GaN HEMTs at different switch ratings are presented. The challenges currently facing the transportation industry are introduced and possible solutions are presented. A detailed review of the use of GaN in the Electric Vehicle (EV) powertrain is discussed. The implementation of GaN devices in aircraft, ships, rail vehicles and heavy-duty vehicles is briefly covered. Future trends of GaN devices in terms of cost, voltage level, gate driver design, thermal management and packaging are investigated.

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Current Status and Future Trends of GaN HEMTs in Electrified Transportation

Abstract One of the crucial challenges of solving many-objective optimization problems is uniformly well covering of the Pareto-front (PF). However, many the state-of-the-art optimization algorithms are capable of approximating the shape of many-objective PF by generating a limited number of non-dominated solutions. The exponential increase of the population size is an inefficient strategy that increases the computational complexity of the algorithm dramatically—especially when solving many-objective problems. In this paper, we introduce a machine learning-based framework to cover sparse PF surface which is initially generated by many-objective optimization algorithms; either by classical or meta-heuristic methods. The proposed method, called many-objective reverse mapping (MORM), is based on constructing a learning model on the initial PF set as the training data to reversely map the objective values to corresponding decision variables. Using the trained model, a set of candidate solutions can be generated by a variety of inexpensive generative techniques such as Opposition-based Learning and Latin Hypercube Sampling in both objective and decision spaces. Iteratively generated non-dominated candidate solutions cover the initial PF efficiently with no further need to utilize any optimization algorithm. We validate the proposed framework using a set of well-known many-objective optimization benchmarks and two well-known real-world problems. The coverage of PF is illustrated and numerically compared with the state-of-the-art many-objective algorithms. The statistical tests conducted on comparison measures such as HV, IGD, and the contribution ratio on the built PF reveal that the proposed collaborative framework surpasses the competitors on most of the problems. In addition, MORM covers the PF effectively compared to other methods even with the aid of large population size. 

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Machine learning-based framework to cover optimal Pareto-front in many-objective optimization

This paper proposes a discrete-time model-reference sliding mode control of three-phase four-leg voltage source inverters for stand-alone distributed generation systems. Three-phase four-leg voltage source inverters (VSIs) are used in a broad area. A discrete-time model-reference sliding mode controller, having superior advantages such as insensitivity to external disturbances, system parameter variations, and modeling errors, is applied to control the output voltage of three-phase four-leg VSIs. As a new study, the discrete-time model-reference sliding mode control with a supplemented output feedback integral controller is proposed so that the controlled output voltages of the VSI tracks the input reference signal. The controller is designed using the augmented discrete-time error dynamics presented in this paper and is applied to control the output voltage control of a three-phase four-leg based uninterruptable power supply. It is shown that the output voltage of the VSI controlled by the proposed method is accurately regulated for various load conditions such as linear balanced/unbalanced, nonlinear balanced/unbalanced, and instantaneous load changes. The robustness of the proposed method against variations by 20{\%}--50{\%} in R, L, and C filter parameters and 5 different cases are showed. \vs{1mm}

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Model-reference sliding mode control of a three-phase four-leg voltage source inverter for stand-alone distributed generation systems

This study investigates the usage of Gallium Nitride (GaN) and Silicon Carbide (SiC), in DC/DC converters; as an alternative to the conventional Si Converters exploiting their high switching capabilities with low losses. This work focuses mainly on the converter used in Electric vehicles for the purposes of charging the battery. A complete model has been developed and a comparison between using SiC and GaN has been performed to determine the viability of using each one in terms of temperature and converter losses. A case study of Nissan Leaf EV is being adopted as a proof of concept and a prototype for it was built in the lab. The experimental results regarding each component and their relationship with the simulation results. It was obvious that the simulation results match perfectly the experimental ones on a system-level basis.

Performance of Wide Band Gap Devices in Electric Vehicles Converters: A Case Study Evaluation

Diabetes is one of the most serious diseases which is becoming increasingly common in recent years. Diabetes can be treated and its consequences are prevented or delayed if predicted timely. This paper investigates an evolutionary computation approach for diabetes prediction. By utilizing the multi-objective Genetic Programming Symbolic Regression, the prediction accuracy level of 79.17% is achieved. Two utilized objectives are namely prediction accuracy and complexity level of the created model (i.e., formula). Moreover, a majority-voting scheme is proposed and compared with other conventional classification algorithms. A widely studied dataset for diabetes prediction, the Pima Indian Diabetes dataset shared in University of California Irvine dataset repository, has been selected for conducting our experimental studies. The work presented here has profound implications for future applications of diabetes prediction and may one help to solve the problem of diabetes by their timely prediction.

Diabetes Mellitus Prediction Using Multi-objective Genetic Programming and Majority Voting

Turkish Research and Development (R&D) system contains a lot of distributed processing entities. Even though many processes aim to produce Science and Technology (S&T), there may occur some dissipation in limited resources. A structural framework is purposed to prevent this situation. Ministry of Science and Technology (MOST) is suggested as a conceptual ministry for Turkish R&D system. Finally a Multi Agent Based Architecture is built and integrated to MOST to increase the efficiency, effectiveness and productivity.

Zekiye Erdem

Papers

Machine learning-based framework to cover optimal Pareto-front in many-objective optimization

Model-reference sliding mode control of a three-phase four-leg voltage source inverter for stand-alone distributed generation systems

Performance of Wide Band Gap Devices in Electric Vehicles Converters: A Case Study Evaluation

Diabetes Mellitus Prediction Using Multi-objective Genetic Programming and Majority Voting

A conceptual framework for effective management of Turkish R&D processes as a Multi Agent system approach