System Reconfiguration using Artificial Intelligence
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Recent papers in System Reconfiguration using Artificial Intelligence
The paper presents a methodology based on a Modified Binary Bat Algorithm (MBBA) and Improved Seed Population search that provides nearly optimal solutions to the power loss minimization problem, considering network reconfiguration and a... more
The paper presents a methodology based on a Modified Binary Bat Algorithm (MBBA) and Improved Seed Population search that provides nearly optimal solutions to the power loss minimization problem, considering network reconfiguration and a large number of switches. The existence of many switches leads to a very large number of combinations, making it hard for algorithms to find a good solution. The proposed method is based on eliminating non-feasible solutions and defining an initial matrix with improved seed population for searching the optimal solution. This seed is used for the random process of the algorithm to produce new solutions and is continually updated to obtain better results close to the optimal solutions found during the searching process of the metaheuristic algorithm. This algorithm was tested against the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the Seed Population search alone on the modified versions of the IEEE 13-node test and IEEE 123-node test feeders. From several runs, the proposed method reached the optimal solution more times than the other algorithms and the remainder achieved near-optimal solutions. With this result, the MBBA provides good options to improve the solutions in the network reconfiguration problem with a large number of switches.
IET indexes its books and journal in SCOPUS and IEEE Xplore. Computer Vision (CV) and Sensors play a decisive role in the operation of Unmanned Aerial Vehicle (UAV), but there exists a void when it comes to analysing the extent of their... more
IET indexes its books and journal in SCOPUS and IEEE Xplore.
Computer Vision (CV) and Sensors play a decisive role in the operation of Unmanned Aerial Vehicle (UAV), but there exists a void when it comes to analysing the extent of their impact on the entire UAV system. In general, the fact that a UAV is a Cyber-Physical System (CPS) is not taken into account. In this proposal, we propose to expand on earlier books covering the use of CV and sensing in UAVs. Among other things, an entirely autonomous UAV can help to (i) obtain information about the environment, (ii) work for an extended period of time without human interference, (iii) move either all or part of itself all over its operating location devoid of human help and (iv) stay away from dangerous situations for people and their possessions. A Vision System (VS) entails the way CV data will be utilized, the appropriate architecture for total avionics integration, the control interfaces, and the UAV operation. Since the VS core is its sensors and cameras, multi-sensor fusion, navigation, hazard detection, and ground correlation in real time are important operational aspects that can benefit from CV knowledge and technology. This book will aim to collect and shed some light on the existing information on CV software and hardware for UAVs as well as pinpoint aspects that need additional thinking. It will list standards and a set of prerequisites (or lack of them thereof) when it comes to CV deployment in UAVs. The issue of data fusion takes a centre place when the book explores ways to deal with sensor data and images as well as their integration and display. The best practices to fuse image and sensor information to enhance UAV performance by means of CV can greatly improve all aspects of the corresponding CPS. The CPS viewpoint can improve the way UAVs interact with the Internet of Things (IoT), use cloud computing, meet communications requirements, implement hardware/software paradigms necessary to handle video streaming, incorporate satellite data, and combine CV with Virtual/Augmented Realities.
VOLUME 1-CONTROL AND PERFORMANCE: This tome explores how sensors and computer vision technologies are used in unmanned aerial vehicles for the navigation, control, stability, reliability, guidance, fault detection, self-maintenance, strategic replanning and reconfiguration of the entire system. It helps analyse the manner UAVs interact with the Internet of Things (IoT), use cloud computing, meet communications requirements, implement hardware/software paradigms necessary to handle still imagery, video streaming, incorporate satellite data, and combine computer vision with virtual/augmented realities (VR/AR).NB: This is planned to be the companion volume of Estrela, Hemanth, Saotome (Eds) / Imaging and Sensing for Unmanned Aerial Vehicles: Volume 2-Deployment and Applications
Computer Vision (CV) and Sensors play a decisive role in the operation of Unmanned Aerial Vehicle (UAV), but there exists a void when it comes to analysing the extent of their impact on the entire UAV system. In general, the fact that a UAV is a Cyber-Physical System (CPS) is not taken into account. In this proposal, we propose to expand on earlier books covering the use of CV and sensing in UAVs. Among other things, an entirely autonomous UAV can help to (i) obtain information about the environment, (ii) work for an extended period of time without human interference, (iii) move either all or part of itself all over its operating location devoid of human help and (iv) stay away from dangerous situations for people and their possessions. A Vision System (VS) entails the way CV data will be utilized, the appropriate architecture for total avionics integration, the control interfaces, and the UAV operation. Since the VS core is its sensors and cameras, multi-sensor fusion, navigation, hazard detection, and ground correlation in real time are important operational aspects that can benefit from CV knowledge and technology. This book will aim to collect and shed some light on the existing information on CV software and hardware for UAVs as well as pinpoint aspects that need additional thinking. It will list standards and a set of prerequisites (or lack of them thereof) when it comes to CV deployment in UAVs. The issue of data fusion takes a centre place when the book explores ways to deal with sensor data and images as well as their integration and display. The best practices to fuse image and sensor information to enhance UAV performance by means of CV can greatly improve all aspects of the corresponding CPS. The CPS viewpoint can improve the way UAVs interact with the Internet of Things (IoT), use cloud computing, meet communications requirements, implement hardware/software paradigms necessary to handle video streaming, incorporate satellite data, and combine CV with Virtual/Augmented Realities.
VOLUME 1-CONTROL AND PERFORMANCE: This tome explores how sensors and computer vision technologies are used in unmanned aerial vehicles for the navigation, control, stability, reliability, guidance, fault detection, self-maintenance, strategic replanning and reconfiguration of the entire system. It helps analyse the manner UAVs interact with the Internet of Things (IoT), use cloud computing, meet communications requirements, implement hardware/software paradigms necessary to handle still imagery, video streaming, incorporate satellite data, and combine computer vision with virtual/augmented realities (VR/AR).NB: This is planned to be the companion volume of Estrela, Hemanth, Saotome (Eds) / Imaging and Sensing for Unmanned Aerial Vehicles: Volume 2-Deployment and Applications
Nature of Information retrieval system are complicated due to complex queries, complex documents interactions and matching systems that involved in information retrieval process. Use of Genetic Algorithm in optimization of queries... more
Nature of Information retrieval system are complicated due to complex queries, complex documents interactions and matching systems that involved in information retrieval process. Use of Genetic Algorithm in optimization of queries achieved through adaptive method. Genetic Algorithms adapted for Text-Retrieval-Conference. Genetic algorithms uses phenomena of parallel searching for retrieval of the information. This algorithm is also applicable to large size of documents where for genetic modification relevant documents are presented to the users. This paper will elaborate usage of GA for Query optimization for Information Retrieval (IR). The query used for IR have Boolean logical operators. Encoding chromosomes were completed in IR for GA (Genetic Algorithms) in which indexing of all Boolean logical operators and terms are represented in the form tree prefix. Single point crossovers are used for GA operators. Use of genetic algorithm in Information Retrieval guarantees that only the relevant document is accessed for the IR. Boolean operator Like OR, AND, XOR and not are used in this regard.
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