Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Anomaly detection in unknown environments using wireless sensor networks

This dissertation addresses the problem of distributed anomaly detection in Wireless Sensor Networks (WSN). A challenge of designing such systems is that the sensor nodes are battery powered, often have different capabilities and generally operate in dynamic environments. Programming such sensor nodes at a large scale can be a tedious job if the system is not carefully designed. Data modeling in distributed systems is important for determining the normal operation mode of the system. Being able to model the expected sensor signatures for typical operations greatly simplifies the human designer’s job by enabling the system to autonomously characterize the expected sensor data streams. This, in turn, allows the system to perform autonomous anomaly detection to recognize when unexpected sensor signals are detected. This type of distributed sensor modeling can be used in a wide variety of sensor networks, such as detecting the presence of intruders, detecting sensor failures, and so forth. The advantage of this approach is that the human designer does not have to characterize the anomalous signatures in advance. The contributions of this approach include: (1) providing a way for a WSN to autonomously model sensor data with no prior knowledge of the environment; (2) enabling a distributed system to detect anomalies in both sensor signals and temporal events online; (3) providing a way to automatically extract semantic labels from temporal sequences; (4) providing a way for WSNs to save communication power by transmitting compressed temporal sequences; (5) enabling the system to detect time-related anomalies without prior knowledge of abnormal events; and, (6) providing a novel missing data estimation method that utilizes temporal and spatial information to replace missing values. The algorithms have been designed, developed, evaluated, and validated experimentally in synthesized data, and in real-world sensor network applications

IGMN: An incremental connectionist approach for concept formation, reinforcement learning and robotics

This paper demonstrates the use of a new connectionist approach, called IGMN (standing for Incremental Gaussian Mixture Network) in some state-of-the-art research problems such as incremental concept formation, reinforcement learning and robotic mapping. IGMN is inspired on recent theories about the brain, especially the Memory-Prediction Framework and the Constructivist Artificial Intelligence, which endows it with some special features that are not present in most neural network models such as MLP, RBF and GRNN. Moreover, IGMN is based on strong statistical principles (Gaussian mixture models) and asymptotically converges to the optimal regression surface as more training data arrive. Through several experiments using the proposed model it is also demonstrated that IGMN learns incrementally from data flows (each data can be immediately used and discarded), it is not sensible to initialization conditions, does not require fine-tuning its configuration parameters and has a good computational performance, thus allowing its use in real time control applications. Therefore, IGMN is a very useful machine learning tool for concept formation and robotic tasks.Key words: Artificial neural networks, Bayesian methods, concept formation, incremental learning, Gaussianmixture models, autonomous robots, reinforcement learning

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 2

Papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake, held 1-3 Jun. 1992 at the Lyndon B. Johnson Space Center in Houston, Texas are included. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Lecture Notes in Electrical Engineering vol. 365

This book includes the original, peer-reviewed research papers from the 2nd International Conference on Electrical Systems, Technology and Information (ICESTI 2015), held during 9–12 September 2015, at Patra Jasa Resort & Villas Bali, Indonesia. The primary objective of this book is to provide references for dissemination and discussion of the topics that have been presented in the conference. This volume is unique in that it includes work related to Electrical Engineering, Technology and Information towards their sustainable development. Engineers, researchers as well as lecturers from universities and professionals in industry and government will gain valuable insights into interdisciplinary solutions in the field of Electrical Systems, Technology and Information, and its applications. The topics of ICESTI 2015 provide a forum for accessing the most up-to-date and authoritative knowledge and the best practices in the field of Electrical Engineering, Technology and Information towards their sustainable development. The editors selected high quality papers from the conference that passed through a minimum of three reviewers, with an acceptance rate of 50.6 %. In the conference there were three invited papers from keynote speakers, whose papers are also included in this book, entitled: “Computational Intelligence based Regulation of the DC bus in the On-Grid Photovoltaic System”, “Virtual Prototyping of a Compliant Spindle for Robotic Deburring” and “A Concept of Multi Rough Sets Defined on Multi-Contextual Information Systems”. The conference also classified the technology innovation topics into five parts: “Technology Innovation in Robotics, Image Recognition and Computational Intelligence Applications”, “Technology Innovation in Electrical Engineering, Electric Vehicle and Energy Management”, “Technology Innovation in Electronic, Manufacturing, Instrumentation and Material Engineering”, “Technology Innovation in Internet of Things and Its Applications” and “Technology Innovation in Information, Modeling and Mobile Applications”