The Economics of Electronics Industry: Competitive Dynamics and Industrial Organization

This entry highlights fundamental changes in the electronics industry that have transformed its competitive dynamics and industrial organization: a high and growing knowledge intensity; the rapid pace of change in technologies and markets; and extensive globalization. That explosive mixture of forces has created two inter-related puzzles. The first puzzle is that a high degree of globalization may well go hand in hand with high and increasing concentration. This runs counter to the dominant view, based on the assumption of neo-classical trade theory, that globalization will increase competition and hence will act as a powerful equalizer both among nations and among firms. Multinational corporations, after all, may not be such effective "spoilers of concentration", as claimed by Richard Caves (1982). The second related puzzle is that this industry fails to act like a stable global oligopoly, even when concentration is extremely high: a market positions are highly volatile, new entry is possible, and not even market leaders can count on a guaranteed survival.

Automated experience-based learning for plug and produce assembly systems

YesThis paper presents a self-learning technique for adapting modular automated assembly systems. The technique consists of automatically analysing sensor data and acquiring experience on the changes made on an assembly system to cope with new production requirements or to recover from disruptions. Experience is generalised into operational knowledge that is used to aid engineers in future adaptations by guiding them throughout the process. At each step, applicable changes are presented and ranked based on: (1) similarity between the current context and those in the experience base; (2) estimate of the impact on system performance. The experience model and the self-learning technique reflect the modular structure of the assembly machine and are particularly suitable for plug and produce systems, which are designed to offer high levels of self-organisation and adaptability. Adaptations can be performed and evaluated at different levels: from the smallest pluggable unit to the whole assembly system. Knowledge on individual modules can be reused when modules are plugged into other systems. An experimental evaluation has been conducted on an industrial case study and the results show that, with experience-based learning, adaptations of plug and produce systems can be performed in a shorter time.European Union [grant number 314762]

A k-nearest neighbour technique for experience-based adaptation of assembly stations

YesWe present a technique for automatically acquiring operational knowledge on how to adapt assembly systems to new production demands or recover from disruptions. Dealing with changes and disruptions affecting an assembly station is a complex process which requires deep knowledge of the assembly process, the product being assembled and the adopted technologies. Shop-floor operators typically perform a series of adjustments by trial and error until the expected results in terms of performance and quality are achieved. With the proposed approach, such adjustments are captured and their effect on the station is measured. Adaptation knowledge is then derived by generalising from individual cases using a variant of the k-nearest neighbour algorithm. The operator is informed about potential adaptations whenever the station enters a state similar to one contained in the experience base, that is, a state on which adaptation information has been captured. A case study is presented, showing how the technique enables to reduce adaptation times. The general system architecture in which the technique has been implemented is described, including the role of the different software components and their interactions

Learning and reuse of engineering ramp-up strategies for modular assembly systems

YesWe present a decision-support framework for speeding up the ramp-up of modular assembly systems by learning from past experience. Bringing an assembly system to the expected level of productivity requires engineers performing mechanical adjustments and changes to the assembly process to improve the performance. This activity is time-consuming, knowledge-intensive and highly dependent on the skills of the engineers. Learning the ramp-up process has shown to be effective for making progress faster. Our approach consists of automatically capturing information about the changes made by an operator dealing with disturbances, relating them to the modular structure of the machine and evaluating the resulting system state by analysing sensor data. The feedback thus obtained on applied adaptations is used to derive recommendations in similar contexts. Recommendations are generated with a variant of the k-nearest neighbour algorithm through searching in a multidimensional space containing previous system states. Applications of the framework include knowledge transfer among operators and machines with overlapping structure and functionality. The application of our method in a case study is discussed.Funded by the European Commission as part of the 7th Framework Program under the Grant agreement CP-FP 229208-2, FRAME project

A symbiotic human–machine learning approach for production ramp-up

Constantly shorter product lifecycles and the high number of product variants necessitate frequent production system reconfigurations and changeovers. Shortening ramp-up and changeover times is essential to achieve the agility required to respond to these challenges. This work investigates a symbiotic human–machine environment, which combines a formal framework for capturing structured ramp-up experiences from expert production engineers with a reinforcement learning method to formulate effective ramp-up policies. Such learned policies have been shown to reduce unnecessary iterations in human decision-making processes by suggesting the most appropriate actions for different ramp-up states. One of the key challenges for machine learning based methods, particularly for episodic problems with complex state-spaces, such as ramp-up, is the exploration strategy that can maximize the information gain while minimizing the number of exploration steps required to find good policies. This paper proposes an exploration strategy for reinforcement learning, guided by a human expert. The proposed approach combines human intelligence with machine’s capability for processing data quickly, accurately, and reliably. The efficiency of the proposed human exploration guided machine learning strategy is assessed by comparing it with three machine-based exploration strategies. To test and compare the four strategies, a ramp-up emulator was built, based on system experimentation and user experience. The results of the experiments show that human-guided exploration can achieve close to optimal behavior, with far less data than what is needed for traditional machine-based strategies

Launch Vibration Attenuation For In-Space Assembly Cargo

This thesis investigates the implementation of a passive isolator with a pressurized air cushion for spacecraft payloads in mission architectures implementing in-space assembly technologies. A pressurized air bed capable of briefly surviving the space environment for cargo delivery was prototyped and experimentally evaluated for launch vehicle vibration dynamics resulting in a 72%, 93%, and 88% reduction in experienced GRMS loads for the X-Axis, Y-Axis, and Z-Axis, respectively. A preliminary Total Mass Loss evaluation of the Low-Density Polyethylene Film utilized for the air bed resulted in a mass loss of 0.7%, indicating that commercial off-the-shelf films might require minimal modification for flight readiness. An analytical model of a planar rectangular payload experiencing free vibrations with a Winkler foundation is generated and compared to the experimental results, showing a potential way for characterizing and designing such a foundation to reduce experienced vibrations. These preliminary results show a potential path for a non-cost-prohibitive method for space payloads to reduce loads experienced during launch as inspired by the successful hosted payloads program aboard the International Space Station

Accelerated neuromorphic cybernetics

Accelerated mixed-signal neuromorphic hardware refers to electronic systems that emulate electrophysiological aspects of biological nervous systems in analog voltages and currents in an accelerated manner. While the functional spectrum of these systems already includes many observed neuronal capabilities, such as learning or classification, some areas remain largely unexplored. In particular, this concerns cybernetic scenarios in which nervous systems engage in closed interaction with their bodies and environments. Since the control of behavior and movement in animals is both the purpose and the cause of the development of nervous systems, such processes are, however, of essential importance in nature. Besides the design of neuromorphic circuit- and system components, the main focus of this work is therefore the construction and analysis of accelerated neuromorphic agents that are integrated into cybernetic chains of action. These agents are, on the one hand, an accelerated mechanical robot, on the other hand, an accelerated virtual insect. In both cases, the sensory organs and actuators of their artificial bodies are derived from the neurophysiology of the biological prototypes and are reproduced as faithfully as possible. In addition, each of the two biomimetic organisms is subjected to evolutionary optimization, which illustrates the advantages of accelerated neuromorphic nervous systems through significant time savings

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 355)

This bibliography lists 147 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during October, 1991. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance