Multisensor Demining Robot

The paper describes an advanced multisensor demining robot. The robot transport system is based on a simple structure using pneumatic drive elements. The robot has robust design and can carry demining equipment up to 100 kg over rough terrains. Due to the adaptive possibilities of pedipulators to obstacles, the robot can adjust the working position of the demining sensors while searching for mines. The detection block consists of a metal detector, an infrared detector, and a chemical explosive sensor. The robot is controlled by means of an on-board processor and by an operator remote station in an interactive mode. Experimental results of the transport, control, and detection systems of the robot are presented.Michael Yu. Rachkov is Professor of Automation at the Moscow State Industrial University. He graduated in Automatic Control Systems from Moscow Higher Technical School, 1979. He held academic posts at the Institute for Problems in Mechanics, Russian Academy of Sciences. In 1986 he completed his PhD in industrial robotics and received his DSc in mobile robotics in 1997. Professor Rachkov has been leading in several international projects like EUREKA and REMAPHOS. He has published over 170 papers and several books in the field of automation, robotics and optimal control. He is a member of Russian Cosmonautics Academy and International Informatization Academy.Lino Marques is a research engineer at the Institute of Systems and Robotics of the University of Coimbra. He received the Engineering and MsC. degrees in Electrical Engineering from the Faculty of Science and Technology of this University in 1992 and 1997 respectively. He is currently working toward the Ph.D. degree and teaching in the Electrical and Computer Engineering Department. His current research interests include sensors, mechatronics, mobile robotics and industrial automation.Anábal T. De Almeida graduated in Electrical Engineering, University of Porto, 1972, and received a Ph.D. in Electrical Engineering, from Imperial College, University of London, 1977. Currently he is a Professor in the Department of Electrical Engineering, University of Coimbra, and he is the Director of the Institute of Systems and Robotics since 1993. Professor De Almeida is a consultant of the European Commission Framework Programmes. He is the co-author of five books and more than one hundred papers in international journals, meetings and conferences. He has coordinated several European and national research projects.     

MAOC降凝剂的研究

含蜡原油和柴油低温流动性是迫切需要解决的问题．ＭＡＯＣ降凝剂对大庆原油和鞍山２０重柴油的纯降凝度是１０℃和１３℃．结合热处理加入０．５％～１．０％（Ｗ）ＭＡＯＣ剂，在一定条件下可使大庆原油疑点降低２０～２２℃．同时对ＭＡＯＣ剂的使用条件、影响因素进行了考查．对合成ＭＡＯＣ剂的原料、中间产品和产品进行了分析．推算出ＭＡＯＣ剂的主要结构．     

Drift velocity and diffusivity of hot carriers in germanium: Model calculations

A simple analytical model with two adjustable parameters is employed in describing the drift velocity and longitudinal diffusion coefficient of charge carriers (electrons and holes) in purified germanium, over a wide range of electric field (10-10⁴ V/cm). The effects of the lattice temperature for the range 130–300 K are also considered, and a comparison with experimental data is made. The model may be used for the simulation of the behaviour of germanium devices.     

Water–formaldehyde–hydrogen sulfide system. II. Formation and growth of thiomethylenic chains.

A linear polymer with a high melting point is obtained from the water–formaldehyde–hydrogen sulfide system in the presence of sulfuric acid. This polymer is substantially formed by a polythiomethylene chain with a few oxymethylene units. Its formation involves a topochemical reaction of the mercaptomethanol present in solution on the first separation solid of the system. On heating, the polymer loses oxymethylene units; in the same way, the first solid product separated from the system loses formaldehyde and undergoes a morphological and chemical transformation to polythiomethylene.     

Synthesis of Fractional-order PI Controllers and Fractional-order Filters for Industrial Electrical Drives

This paper introduces an electrical drives control architecture combining a fractional-order controller and a setpoint pre-filter. The former is based on a fractional-order proportional-integral (PI) unit, with a non-integer order integral action, while the latter can be of integer or non-integer type. To satisfy robustness and dynamic performance specifications, the feedback controller is designed by a loop-shaping technique in the frequency domain. In particular, optimality of the feedback system is pursued to achieve input-output tracking. The setpoint pre-filter is designed by a dynamic inversion technique minimizing the difference between the ideal synthesized command signal (i.e., a smooth monotonic response) and the prefilter step response. Experimental tests validate the methodology and compare the performance of the proposed architecture with well-established control schemes that employ the classical PIbased symmetrical optimum method with a smoothing pre-filter.      

Experimental studies on chemical concentration map building by a multi-robot system using bio-inspired algorithms

In this article we describe implementations of various bio-inspired algorithms for obtaining the chemical gas concentration map of an environment filled with a contaminant. The experiments are performed using Khepera III and miniQ miniature mobile robots equipped with chemical gas sensors in an environment with ethanol gas. We implement and investigate the performance of decentralized and asynchronous particle swarm optimization (DAPSO), bacterial foraging optimization (BFO), and ant colony optimization (ACO) algorithms. Moreover, we implement sweeping (sequential search algorithm) as a base case for comparison with the implemented algorithms. During the experiments at each step the robots send their sensor readings and position data to a remote computer where the data is combined, filtered, and interpolated to form the chemical concentration map of the environment. The robots also exchange this information among each other and cooperate in the DAPSO and ACO algorithms. The performance of the implemented algorithms is compared in terms of the quality of the maps obtained and success of locating the target gas sources.     

Assessing complexity in cellular automata using information theory

ABSTRACT

We discuss two ways in which information theory can be used to assess complexity in a system of interacting agents. In the first part, we adopt a global viewpoint and propose a characterization of complexity based on successive maximum entropy estimations of the probability density describing the system, thereby quantifying the respective role played by low and high orders of interaction. In the second part we reconsider the question from a local perspective, focussing on the statistical dependencies between neighbouring agents. These tools are tried on simple cellular automata in order to put them in perspective with other notions of complexity usually employed for such systems. We show that these approaches are hardly comparable, despite some overlap in simple cases. However this allows to interpret complexity in terms of interactions at work in a system (instead of making reference to any particular realization of this dynamics), and to shed some light on the role of initial conditions in complex systems.

Clustering of the 88 non-equivalent Elementary Cellular Automata according to their position in the space of information processing features. Rules are coloured according to their Wolfram class. ECA in class I are shown in black, class II in red, chaotic automata (class III) in green and automata displaying complex behaviour (class IV) in blue. In spite of some important important differences, information features and Wolfram class are seen to overlap to a certain extent.