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5th International Conference on Inventive Research in computing applications, ICIRCA 2023

作者： George, Abin Mankar, Sham H. Suryanarayana, N.V.S. Gupta, Manish Sathish, R. Priya, A. Knowledge and Technology Management Division Council of Scientific & Industrial Research National Aerospace Laboratories Karnataka Bangaluru India Pimpri Chinchwad College of Engineering and Research Department of Mechanical Engineering Maharashtra Pune India Central Tribal University of Andhra Pradesh Administrative Officer Andhra Pradesh Vizianagaram India Lovely Professional University Division of Research and Development Punjab Phagwara India Shri Nehru Maha Vidayala College of Arts and Science Department of Computer Science Tamil Nadu Coimbatore India School of Architecture Koneru Lakshmaiah Educational Foundation Andhra Pradesh Gundur India

ISBN: (纸本)9798350321425

The solid waste management is the process of proper decomposition of waste materials within a period of time. This includes the collection of garbage's and then proceeded through certain measures for decomposition. There are various methods adopted in the garbage separation process. This includes the artificial intelligence techniques for the estimation and determination of the solid waste through automatic detection and separation of the garbage waste using control and sensing units. They are integrated with the internet of things to enable the two way communication system. This helps to visualize the functioning of the system adopting the digital platform. The proposed system is implemented through the smart dust bin held in every household that automatically senses the non-biodegradable and biodegradable waste materials. The classification of the waste materials are identified through the image processing techniques. © 2023 IEEE.

关键词： genetic algorithms

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FPGA Implementation of genetic Algorithm-Driven Commutation for H-Bridge Inverters

FPGA Implementation of Genetic Algorithm-Driven Commutation ...

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IEEE International Autumn Meeting on Power, Electronics and computing (ROPEC)

作者： Cristian Sámano Adolfo R. Lopez José M. Sosa Gerardo Vazquez-Guzman Susana Estefany De León Aldaco Jesus Aguayo-Alquicira Felipe J. Torres Electronics Division TecNM/ITS de Irapuato Irapuato Mexico Electronics Department TecNM/CENIDET Cuernavaca Mexico Mechanics Department DICIS Universidad de Guanajuato Salamanca Mexico

ISBN: (数字)9798331530525

ISBN: (纸本)9798331530532

This paper presents the implementations of a commutation sequence for a five and seven-level cascaded H-bridge multilevel inverter (CHBMLI) using a Field Programmable Gate Array (FPGA). The switching angles were calculated using a genetic Algorithm (GA) to minimize the Total Harmonic Distortion (THD) in the output. The versatility and efficiency of the CHBMLI inverters are highlighted due to their modular configuration, which allows for generating high-fidelity output voltages and minimizing harmonics in the output signal. The fundamental frequency switching modulation technique generates the required output voltages. The FPGA implementation, carried out in VHDL, achieved THD values of 15.4% and 10.4% for the 5-level and 7-level inverters respectively, closely matching the theoretical values. The capability of FPGAs to handle the generation of control sequences for each voltage level is demonstrated with a laboratory prototype. These results demonstrate the effectiveness of the proposed approach, offering a viable and efficient solution for applications in renewable energy systems and motor control. Future work could explore further optimizations and extensions of this method to even more complex inverter configurations.

关键词： VHDL Commutation Switches Multilevel inverters Inverters Voltage control Field programmable gate arrays Power harmonic filters Optimization genetic algorithms

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Evolutionary learning driven load forecasting and demand response management model for smart grid

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Applied Soft computing 2025年 177卷

作者： Kumar, Jatinder Rani, Pooja Saxena, Deepika Singh, Ashutosh Kumar Makkar, Aaisha Department of Computer Applications National Institute of Technology Kurukshetra India Division of Information Systems University of Aizu Japan Indian Institute of Information Technology Madhya Pradesh Bhopal India College of Science and Engineering University of Derby Derby United Kingdom University of Economics and Human Sciences in Warsaw Poland

The smart meter data generated from the customers’ usage help utility providers to manage and control the electricity distribution among the customers for reliable services in the smart grid. Smart meter data provide an opportunity for utility providers to address these challenges by implementing demand response management (DRM), which enables customers to reduce consumption and balance load across the grid. For the demand response management in the smart grid, a multi-objective problem (MOP) is formulated considering three objectives, i.e., power scarcity, wastage and load factor. These objectives are computed by the newly proposed load forecasting neural network that precisely predicts upcoming loads on the demand side. The weights of this neural network are optimized using a hybrid learning approach that integrates evolutionary algorithms with Adam optimization. Additionally, the Multi-Objective Programming problem is efficiently addressed through the Non-dominated Sorting genetic Algorithm (NSGA-III). Experimental results indicate that the proposed method surpasses state-of-the-art techniques in the terms of load forecasting. The proposed approach achieves a forecasting accuracy of up to 96.30% and a root mean squared error of 0.1367, based on the Open Energy Data Initiative (OEDI) dataset provided by the National Renewable Energy Laboratory of the United States. © 2025 Elsevier B.V.

关键词： Demand side management

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An optimization algorithm with novel flexible grid: applications to parameter decision in LS-SVM

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Journal of computing Science and Engineering 2015年第2期9卷 39-50页

作者： Gao, Weishang Shao, Cheng Gao, Qin Information and Engineering College Dalian University Dalian China Institute of Advanced Control Dalian University of Technology Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China

genetic algorithm (GA) and particle swarm optimization (PSO) are two excellent approaches to multimodal optimization problems. However, slow convergence or premature convergence readily occurs because of inappropriate and inflexible evolution. In this paper, a novel optimization algorithm with a flexible grid optimization (FGO) is suggested to provide adaptive trade-off between exploration and exploitation according to the specific objective function. Meanwhile, a uniform agents array with adaptive scale is distributed on the gird to speed up the calculation. In addition, a dominance centroid and a fitness center are proposed to efficiently determine the potential guides when the population size varies dynamically. Two types of subregion division strategies are designed to enhance evolutionary diversity and convergence, respectively. By examining the performance on four benchmark functions, FGO is found to be competitive with or even superior to several other popular algorithms in terms of both effectiveness and efficiency, tending to reach the global optimum earlier. Moreover, FGO is evaluated by applying it to a parameter decision in a least squares support vector machine (LS-SVM) to verify its practical competence. © 2015.

关键词： Swarm intelligence

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ANFIS Data Driven Modeling and Real-Time Fuzzy Logic controller Test for a Two Tanks Hydraulic System

ANFIS Data Driven Modeling and Real-Time Fuzzy Logic Control...

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IEEE International Conference on Evolving and Adaptive Intelligent Systems

作者： L. A. Torres-Salomao J. Anzurez-Marin J. M. Orozco-Sixtos S. Ramirez-Zavala Automatic Control and Systems Engineering Department The University of Sheffield Division de Estudios de Postgrado Facultad de Ingenieria Electrica UMSNH

ISBN: (纸本)9781467366991

This paper presents a non-linear, data driven Adaptive Network based Fuzzy Inference System (ANFIS) modeling of a Two Tanks Hydraulic System (TTHS). The paper also addresses the design of a Type 1 Fuzzy Logic controller optimized with genetic algorithms (GA). The controller was designed and tested in simulation with the obtained ANFIS model and validated in real-time with the actual TTHS. Obtained model shows an accurate and adequate description of the real system, useful for many applications that require a non-linear functioning representation of the TTHS. The designed controller also demonstrates excellent performance by being able to follow diverse shaped references. This work successfully demonstrates the utility of soft-computing techniques in their application to real world industrial complex systems.

关键词： Modeling control Neural networks Fuzzy inference system Heuristics Real-time Complex systems Non-linear fuzzy inference system Complex systems Heuristics Neural network Modeling fuzzy logic controller data driven Hydraulic system Real time Standing Orders Tanks Tanks

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Development of a genetic algorithm toolkit in LabVIEW 3

Development of a genetic algorithm toolkit in LabVIEW

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3rd International Conference on Soft computing for Problem Solving, SocProS 2013

作者： Kumar, Vineet Rana, K.P.S. Kumar, Amit Sharma, Richa Mishra, Puneet Nair, Sreejith S. Division of Instrumentation and Control Engineering Netaji Subhas Institute of Technology Sector-3 Dwarka New Delhi110078 India Bhabha Atomic Research Centre Trombay Mumbai400085 India

ISBN: (纸本)9788132217701

It is a well known fact that LabVIEW is one of the finest tools for measurement and control applications. Requirement of intelligent controller tuning methods like genetic Algorithm (GA) has been felt at times in the LabVIEW environment as there is no standard LabVIEW GA toolkit supplied with the package. In this paper, a GA Toolkit developed in LabVIEW environment, has been presented. The developed toolkit is used for optimizing the gains of the PID (Proportional plus Integral plus Derivative) controller for the given performance indices of a closed loop system. For the purpose of tuning, the algorithm mimics the biological evolution and is used to find the suitable values of PID gains in order to improve the response of the given system. An integrated performance index comprising of rise time, settling time, overshoot, integral absolute error (IAE), integral square error (ISE), integral time weighted absolute error (ITAE) or a combination of these forms the objective function for the optimization. In this toolkit four selection methods, three crossover methods and three mutation methods have been incorporated. To test the developed toolkit a simulation example is also performed and results have been presented. © Springer India 2014.

关键词： genetic algorithms

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genetic programs constructed from layered logic gates in single cells

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NATURE 2012年第7423期491卷 249-253页

作者： Moon, Tae Seok Lou, Chunbo Tamsir, Alvin Stanton, Brynne C. Voigt, Christopher A. MIT Dept Biol Engn Synthet Biol Ctr Cambridge MA 02139 USA Univ Calif San Francisco Tetrad Grad Program San Francisco CA 94158 USA

genetic programs function to integrate environmental sensors, implement signal processing algorithms and control expression dynamics(1). These programs consist of integrated genetic circuits that individually implement operations ranging from digital logic to dynamic circuits(2-6), and they have been used in various cellular engineering applications, including the implementation of process control in metabolic networks and the coordination of spatial differentiation in artificial tissues. A key limitation is that the circuits are based on biochemical interactions occurring in the confined volume of the cell, so the size of programs has been limited to a few circuits(1,7). Here we apply part mining and directed evolution to build a set of transcriptional AND gates in Escherichia coli. Each AND gate integrates two promoter inputs and controls one promoter output. This allows the gates to be layered by having the output promoter of an upstream circuit serve as the input promoter for a downstream circuit. Each gate consists of a transcription factor that requires a second chaperone protein to activate the output promoter. Multiple activator-chaperone pairs are identified from type III secretion pathways in different strains of bacteria. Directed evolution is applied to increase the dynamic range and orthogonality of the circuits. These gates are connected in different permutations to form programs, the largest of which is a 4-input AND gate that consists of 3 circuits that integrate 4 inducible systems, thus requiring 11 regulatory proteins. Measuring the performance of individual gates is sufficient to capture the behaviour of the complete program. Errors in the output due to delays (faults), a common problem for layered circuits, are not observed. This work demonstrates the successful layering of orthogonal logic gates, a design strategy that could enable the construction of large, integrated circuits in single cells.

关键词： Applied physics Bacteria genetics

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Plato: a genetic algorithm approach to run-time reconfiguration in autonomic computing systems

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CLUSTER computing-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND applications 2011年第3期14卷 229-244页

作者： Ramirez, Andres J. Knoester, David B. Cheng, Betty H. C. McKinley, Philip K. Michigan State Univ E Lansing MI 48823 USA

Increasingly, applications need to be able to self-reconfigure in response to changing requirements and environmental conditions. Autonomic computing has been proposed as a means for automating software maintenance tasks. As the complexity of adaptive and autonomic systems grows, designing and managing the set of reconfiguration rules becomes increasingly challenging and may produce inconsistencies. This paper proposes an approach to leverage genetic algorithms in the decision-making process of an autonomic system. This approach enables a system to dynamically evolve target reconfigurations at run time that balance tradeoffs between functional and non-functional requirements in response to changing requirements and environmental conditions. A key feature of this approach is incorporating system and environmental monitoring information into the genetic algorithm such that specific changes in the environment automatically drive the evolutionary process towards new viable solutions. We have applied this genetic-algorithm based approach to the dynamic reconfiguration of a collection of remote data mirrors, demonstrating an effective decision-making method for diffusing data and minimizing operational costs while maximizing data reliability and network performance, even in the presence of link failures.

关键词： Autonomic computing Evolutionary algorithm genetic algorithm Intelligent control Distributed systems

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Optimizing continued fraction expansion based iir realization of fractional order differ-integrators with genetic algorithm

Optimizing continued fraction expansion based iir realizatio...

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International Conference on Process Automation, control and computing

作者： Das, Saptarshi Majumder, Basudev Pakhira, Anindya Pan, Indranil Das, Shantanu Gupta, Amitava School of Nuclear Studies and Applications Salt-Lake Campus Jadavpur University Kolkata-700098 India Department of Instrumentation and Electronics Engineering Salt-Lake Campus Jadavpur University Kolkata-700098 India Department of Power Engineering Salt-Lake Campus Jadavpur University Kolkata-700098 India Bhabha Atomic Research Centre Reactor Control Division Mumbai-400085 India

ISBN: (纸本)9781612847641

Rational approximation of fractional order (FO) differ-integrators via Continued Fraction Expansion (CFE) is a well known technique. In this paper, the nominal structures of various generating functions are optimized using genetic Algorithm (GA) to minimize the deviation in magnitude and phase response between the original FO element and the rationalized discrete time filter in Infinite Impulse Response (IIR) structure. The optimized filter based realizations show better approximation of the FO elements in comparison with the existing methods and is demonstrated by the frequency response of the IIR filters. © 2011 IEEE.

关键词： genetic algorithms

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genetic Algorithm Based Improved Sub-Optimal Model Reduction in Nyquist Plane for Optimal Tuning Rule Extraction of PID and PIlambdaDi controllers via genetic Programming

Genetic Algorithm Based Improved Sub-Optimal Model Reduction...

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International Conference on Process Automation, control and computing (PACC)

作者： Saptarshi Das Indranil Pan Shantanu Das Amitava Gupta School of Nuclear Studies and Applications (SNSA) Jadavpur University Kolkata India Department of Power Engineering Jadavpur University Kolkata India Reactor Control Division Bhabha Atomic Research Centre Mumbai India

genetic Algorithm (GA) has been used in this paper for a new Nyquist based sub-optimal model reduction and optimal time domain tuning of PID and fractional order (FO) PI λ D μ controllers. Comparative studies show that the new model reduction technique outperforms the conventional H 2 -norm based reduced order modeling techniques. Optimum tuning rule has been developed next with a test-bench of higher order processes via genetic Programming (GP) with minimum value of weighted integral error index and control signal. From the Pareto optimal front which is a trade-off between the complexity of the formulae and control performance, an efficient set of tuning rules has been generated for time domain optimal PID and PI λ D μ controllers.

关键词： Tuning Reduced order systems Process control genetic algorithms genetic programming Accuracy Optimization

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