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Engineered in Japan presents a unique and comprehensive examination of technology management in the most successful Japanese companies: unique in that all chapters go beyond superficial descriptions of stylized practices to look in depth at particular issues, often contradicting or qualifying the conventional wisdom; comprehensive in that it covers the entire technology life cycle from basic R&D, to development engineering, to manufacturing processes, to learning from the Japanese. Each chapter is based on original research by noted scholars in the field, and identifies technology management practices that have become a major source of competitive advantage for highly successful Japanese companies. Engineered in Japan documents the best practices from such companies as Toyota, Hitachi, Toshiba, and Nippondenso, and discusses how these technology management practices can be usefully adopted in other cultural contexts. Going beyond past observations, the authors all delve below the surface of Japanese management approaches. They look more closely than has been done before at how particular methods are applied, and they identify some new practices that have not yet been highlighted in books on Japanese methods. Presenting recent data that contradict some conventional thinking about U.S.-Japanese differences, they look at old techniques from a new perspective. "U.S. managers can perhaps learn more from the process of creation in Japan and the organizational structures that support innovation," say the editors in their introduction, "than from the particular approaches, tools, and technologies created." A running theme throughout the book is that Japanese managers and engineers tend to think in terms of systems, focusing not just on the parts but on the connections between them. Engineered in Japan is must reading for technology managers and engineers, along with anyone interested in Japanese business, engineering, and management.
There is a growing social interest in developing vision-based vehicle guidance systems for improving traffic safety and efficiency and the environment. Ex amples of vision-based vehicle guidance systems include collision warning systems, steering control systems for tracking painted lane marks, and speed control systems for preventing rear-end collisions. Like other guidance systems for aircraft and trains, these systems are ex pected to increase traffic safety significantly. For example, safety improve ments of aircraft landing processes after the introduction of automatic guidance systems have been reported to be 100 times better than prior to installment. Although the safety of human lives is beyond price, the cost for automatic guidance could be compensated by decreased insurance costs. It is becoming more important to increase traffic safety by decreasing the human driver's load in our society, especially with an increasing population of senior people who continue to drive. The second potential social benefit is the improvement of traffic efficiency by decreasing the spacing between vehicles without sacrificing safety. It is reported, for example, that four times the efficiency is expected if the spacing between cars is controlled automatically at 90 cm with a speed of 100 kmjh compared to today's typical manual driving. Although there are a lot of tech nical, psychological, and social issues to be solved before realizing the high density jhigh-speed traffic systems described here, highly efficient highways are becoming more important because of increasing traffic congestion.
A collection of papers dealing with complete systems of intelligent robots, focusing on autonomy. The contributions cover intelligent perception, intelligent planning and control, and integrated systems.
This book constitutes the refereed proceedings of the 4th International Workshop on Ant Colony Optimization and Swarm Intelligence, ANTS 2004, held in Brussels, Belgium in September 2004. The 22 revised full papers, 19 revised short papers, and 9 poster abstracts presented were carefully reviewed and selected from 79 papers submitted. The papers are devoted to theoretical and foundational aspects of ant algorithms, ant colony optimization and swarm intelligence and deal with a broad variety of optimization applications in networking and operations research.
This book constitutes the thoroughly refereed post-proceedings of the Second International Conference on Logical Aspects of Computational Linguistics, LACL '97, held in Nancy, France in September 1997. The 10 revised full papers presented were carefully selected during two rounds of reviewing. Also included are two comprehensive invited papers. Among the topics covered are type theory, various types of grammars, linear logic, parsing, type-directed natural language processing, proof-theoretic aspects, concatenation logics, and mathematical languages.
Recent Advances in Robot Learning contains seven papers on robot learning written by leading researchers in the field. As the selection of papers illustrates, the field of robot learning is both active and diverse. A variety of machine learning methods, ranging from inductive logic programming to reinforcement learning, is being applied to many subproblems in robot perception and control, often with objectives as diverse as parameter calibration and concept formulation. While no unified robot learning framework has yet emerged to cover the variety of problems and approaches described in these papers and other publications, a clear set of shared issues underlies many robot learning problems. Machine learning, when applied to robotics, is situated: it is embedded into a real-world system that tightly integrates perception, decision making and execution. Since robot learning involves decision making, there is an inherent active learning issue. Robotic domains are usually complex, yet the expense of using actual robotic hardware often prohibits the collection of large amounts of training data. Most robotic systems are real-time systems. Decisions must be made within critical or practical time constraints. These characteristics present challenges and constraints to the learning system. Since these characteristics are shared by other important real-world application domains, robotics is a highly attractive area for research on machine learning. On the other hand, machine learning is also highly attractive to robotics. There is a great variety of open problems in robotics that defy a static, hand-coded solution. Recent Advances in Robot Learning is an edited volume of peer-reviewed original research comprising seven invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 23, Numbers 2 and 3).
One of the most important problems in the field of engineering and technology is the development of so-called intelligent systems, which can perform various intellectual tasks. This book is dedicated to the current progress of research in this vast field and specifically explores the topics of robotics, mechatronics and manufacturing systems.