Self-Organized Specialization and Controlled Emergence in Organic Computing Systems

In this chapter we studied a first approach to generate suitable rule sets for solving classification problems on systems of autonomous, memory constrained components. It was shown that a multi agent system that uses interacting Pittsburgh-style classifier systems can evolve appropiate rule sets. The system evolves specialists for parts of the classification problem and cooperation between them. In this way the components overcome their restricted memory size and are able to solve the entire problem. It was shown that the communication topology between the components strongly influences the average number of components that a request has to pass until it is classified. It was also shown that the introduction of communication costs into the fitness function leads to a more even distribution of knowledge between the components and reduces the communication overhead without influencing the classification performance very much. If the system is used to generate rule sets to solve classification tasks on real hardware systems, communication cost in the training phase can thus lead to a better knowledge distribution and small communication cost. That is, in this way the system will be more robust against the loss of single components and longer reliable in case of limited energy resources

An enhanced ant colony system algorithm for dynamic fault tolerance in grid computing

Fault tolerance in grid computing allows the system to continue operate despite occurrence of failure. Most fault tolerance algorithms focus on fault handling techniques such as task reprocessing, checkpointing, task replication, penalty, and task migration. Ant colony system (ACS), a variant of ant colony optimization (ACO), is one of the promising algorithms for fault tolerance due to its ability to adapt to both static and dynamic combinatorial optimization problems. However, ACS algorithm does not consider the resource fitness during task scheduling which leads to poor load balancing and lower execution success rate. This research proposes dynamic ACS fault tolerance with suspension (DAFTS) in grid computing that focuses on providing effective fault tolerance techniques to improve the execution success rate and load balancing. The proposed algorithm consists of dynamic evaporation rate, resource fitness-based scheduling process, enhanced pheromone update with trust factor and suspension, and checkpoint-based task reprocessing. The research framework consists of four phases which are identifying fault tolerance techniques, enhancing resource assignment and job scheduling, improving fault tolerance algorithm and, evaluating the performance of the proposed algorithm. The proposed algorithm was developed in a simulated grid environment called GridSim and evaluated against other fault tolerance algorithms such as trust-based ACO, fault tolerance ACO, ACO without fault tolerance and ACO with fault tolerance in terms of total execution time, average latency, average makespan, throughput, execution success rate and load balancing. Experimental results showed that the proposed algorithm achieved the best performance in most aspects, and second best in terms of load balancing. The DAFTS achieved the smallest increase on execution time, average makespan and average latency by 7%, 11% and 5% respectively, and smallest decrease on throughput and execution success rate by 6.49% and 9% respectively as the failure rate increases. The DAFTS also achieved the smallest increment on execution time, average makespan and average latency by 5.8, 8.5 and 8.7 times respectively, and highest increase on throughput and highest execution success rate by 72.9% and 93.7% respectively as the number of jobs increases. The proposed algorithm can effectively overcome load balancing problems and increase execution success rates in distributed systems that are prone to faults

Intelligent Agents and Their Potential for Future Design and Synthesis Environment

This document contains the proceedings of the Workshop on Intelligent Agents and Their Potential for Future Design and Synthesis Environment, held at NASA Langley Research Center, Hampton, VA, September 16-17, 1998. The workshop was jointly sponsored by the University of Virginia's Center for Advanced Computational Technology and NASA. Workshop attendees came from NASA, industry and universities. The objectives of the workshop were to assess the status of intelligent agents technology and to identify the potential of software agents for use in future design and synthesis environment. The presentations covered the current status of agent technology and several applications of intelligent software agents. Certain materials and products are identified in this publication in order to specify adequately the materials and products that were investigated in the research effort. In no case does such identification imply recommendation or endorsement of products by NASA, nor does it imply that the materials and products are the only ones or the best ones available for this purpose. In many cases equivalent materials and products are available and would probably produce equivalent results

An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots

The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so

Medical Informatics and Data Analysis

During recent years, the use of advanced data analysis methods has increased in clinical and epidemiological research. This book emphasizes the practical aspects of new data analysis methods, and provides insight into new challenges in biostatistics, epidemiology, health sciences, dentistry, and clinical medicine. This book provides a readable text, giving advice on the reporting of new data analytical methods and data presentation. The book consists of 13 articles. Each article is self-contained and may be read independently according to the needs of the reader. The book is essential reading for postgraduate students as well as researchers from medicine and other sciences where statistical data analysis plays a central role

Cyber-Physical Embedded Systems with Transient Supervisory Command and Control: A Framework for Validating Safety Response in Automated Collision Avoidance Systems

The ability to design and engineer complex and dynamical Cyber-Physical Systems (CPS) requires a systematic view that requires a definition of level of automation intent for the system. Since CPS covers a diverse range of systemized implementations of smart and intelligent technologies networked within a system of systems (SoS), the terms “smart” and “intelligent” is frequently used in describing systems that perform complex operations with a reduced need of a human-agent. The difference between this research and most papers in publication on CPS is that most other research focuses on the performance of the CPS rather than on the correctness of its design. However, by using both human and machine agency at different levels of automation, or autonomy, the levels of automation have profound implications and affects to the reliability and safety of the CPS. The human-agent and the machine-agent are in a tidal lock of decision-making using both feedforward and feedback information flows in similar processes, where a transient shift within the level of automation when the CPS is operating can have undesired consequences. As CPS systems become more common, and higher levels of autonomy are embedded within them, the relationship between human-agent and machine-agent also becomes more complex, and the testing methodologies for verification and validation of performance and correctness also become more complex and less clear. A framework then is developed to help the practitioner to understand the difficulties and pitfalls of CPS designs and provides guidance to test engineering design of soft computational systems using combinations of modeling, simulation, and prototyping

JSC research and technology

The primary roles and missions of JSC incorporate all aspects of human presence in space. Therefore, the Center is involved in the development of technology that will allow humans to stay longer in Earth orbit, allow safe flight in space, and provide capabilities to explore the Moon and Mars. The Center's technology emphasis areas include human spacecraft development, human support systems and infrastructure, and human spacecraft operations. Safety and reliability are critical requirements for the technologies that JSC pursues for long-duration use in space. One of the objectives of technology development at the Center is to give employees the opportunity to enhance their technological expertise and project management skills by defining, designing, and developing projects that are vital to the Center's strategy for the future. This report is intended to communicate within and outside the Agency our research and technology (R&T) accomplishments, as well as inform Headquarters program managers and their constituents of the significant accomplishments that have promise for future Agency programs. While not inclusive of all R&T efforts, the report presents a comprehensive summary of JSC projects in which substantial progress was made in the 1992 fiscal year. At the beginning of each project description, names of the Principal Investigator (PI) and the Technical Monitor (TM) are given, followed by their JSC mail codes or their company or university affiliations. The funding sources and technology focal points are identified in the index

Power System Simulation, Control and Optimization

This Special Issue “Power System Simulation, Control and Optimization” offers valuable insights into the most recent research developments in these topics. The analysis, operation, and control of power systems are increasingly complex tasks that require advanced simulation models to analyze and control the effects of transformations concerning electricity grids today: Massive integration of renewable energies, progressive implementation of electric vehicles, development of intelligent networks, and progressive evolution of the applications of artificial intelligence