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De-noising of images from salt and pepper noise using hybrid filter, fuzzy logic noise detector and genetic optimization algorithm (HFGOA)

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第5-6期79卷 4115-4131页

作者： Selvi, A. Senthil Kumar, K. Pradeep Mohan Dhanasekeran, S. Maheswari, P. Uma Ramesh, S. Pandi, S. Senthil SRM Inst Sci & Technol Dept CSE Chennai Tamil Nadu India Kalasalingam Univ Dept CSE Srivilliputhur Tamil Nadu India Anna Univ Dept MCA Reg Campus Madurai Madurai Tamil Nadu India Velagapudi Ramakrishna Siddhartha Engn Coll Dept Informat Technol Vijayawada Andhra Pradesh India Sethu Inst Technol Dept Informat Technol Pulloor Tamil Nadu India

The main objective of image de-noising is to remove the noise present in the noisy image. Like that, main objective of proposed methodology is to restore the impulse noised standard test image based on hybrid filter, fuzzy logic system and genetic algorithm. The proposed HFGOA method consists of three steps. In the first step noisy image is de-noised using mean filter and median filter, individually. In the second step the difference vector is calculated using two filters output then it is given as input to fuzzy logic system. Fuzzy rules were generated from the difference vector value using triangular membership function. In the third step using genetic optimization algorithm optimal rule will be selected. Fitness value (PSNR) calculated for each population. The new population was repeatedly created using genetic operator until getting best fitness value. The performance of the proposed method was measured using PSNR value. HFGOA method is tested over standard test image (lena image) for different percentage of salt and pepper noise. The Experimental results of HFGOA method is compared with results of different exiting filters. An experimental result shows the HFGOA method rectifies the drawbacks of exiting filters and increases the visual quality of the image by increasing the PSNR value.

关键词： Mean filter Median filter Fuzzy logic system genetic optimization algorithm

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Optimum design of vibration absorber for cracked beam under the effect of moving concentrated force utilizing genetic optimization algorithm

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第1期7卷 557-564页

作者： Du, Lihong Caofeidian Coll Technol Tangshan 063200 Hebei Peoples R China

This study presents a multi-objective optimization method for designing a vibration absorber to reduce the vibrations of a cracked Euler-Bernoulli beam with flexible support under moving forces. Using the assumption of an open crack, the crack is modeled as a decrease in cross-sectional flexibility. After adding an absorber to the beam, the effect of cracks with different intensities on its vibration behavior was examined. First, the dynamic response of the cracked beam has been determined under the influence of different speeds of the moving force. genetic algorithms have been used to optimize the parameters of the absorber and to examine the effect of mass and damping constant on its efficiency. Despite cracks increasing the dynamic deflection of a beam with an absorber, a cracked beam without an absorber still gains more dynamic deflection than one with an absorber. As a result, vibration absorbers that are designed for a healthy beam are still effective in reducing the dynamic deflection of the beam following the occurrence of cracks and changes in the structure's dynamics.

关键词： genetic optimization algorithm Vibration absorber Cracked beam Natural frequency Oscillations amplitude

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A Modified Model with genetic optimization algorithm for Land Evaluation

A Modified Model with Genetic Optimization Algorithm for Lan...

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International Conference on Artificial Intelligence and Computational Intelligence

作者： Miao Zuohua Liu Yanzhong Chen Yong Zeng Xiangyang Wuhan Univ Sci & Technol Sch Resource & Environm Engn Wuhan Peoples R China

ISBN: (纸本)9780769538167

Trained speed of model based on traditional BP neural network was slowly and produced emanative result. A novel land evaluation model based on neural network with genetic optimization algorithm was presented in this paper. The neural network of model is front-network which comprised with five layers architecture which composed of dynamic inference with fuzzy rules where the consequent sub-models are implemented by recurrent neural networks. The recurrent neural networks with internal feedback paths and dynamic neuron synapses. In order to optimized the parameter structure and link weight between layers, the author adopted genetic algorithm into model. Experiment results demonstrated that the novel model exhibit superior performance such as enhanced representation power, calculation speed and veracity of result than traditional BP neural network and the other land evaluation models.

关键词： BP neural network land evaluation model genetic optimization algorithm

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A Method for Structure Synthesis of Reconfigurable Mechanisms Based on genetic optimization algorithm

A Method for Structure Synthesis of Reconfigurable Mechanism...

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International Conference on Reconfigurable Mechanisms and Robots

作者： Ma, Lu-Zhong Liu, An-Xin Shen, Hui-Ping Yang, Ting-Li Jiangsu Univ Sch Mech Engn Zhenjign Jiangsu Prov Peoples R China PLA Univ Sci & Technol Nanjing Jiangsu Peoples R China Jiangsu Polytech Univ Changzhou Jiangsu Peoples R China Jinling Petrochem Corp Nanjing Jiangsu Peoples R China

ISBN: (纸本)9788889007372

Structure synthesis method for reconfigurable mechanism based on genetic optimization algorithm is proposed in this paper. Main characteristics of this method include: (1) POC matrix of the mechanism corresponding to different motion stage of the reconfigurable mechanism is used as the objective function. (2) Design variables are determined based on the influence of three basic topological structure elements over POC matrix. (3) According to the genetic optimization algorithm, more than one solution may usually be obtained. The optimum reconfigurable mechanism may be selected based on other engineering requirements. An example is given to illustrate the synthesis procedure.

关键词： Structure Synthesis Reconfigurable Mechanisms genetic optimization algorithm

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Joint Task Offloading Based on Distributed Deep Reinforcement Learning-Based genetic optimization algorithm for Internet of Vehicles

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JOURNAL OF GRID COMPUTING 2024年第1期22卷 34-34页

作者： Jin, Hulin Kim, Yong-Guk Jin, Zhiran Fan, Chunyang Xu, Yonglong Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Sejong Univ Dept Comp Engn Seoul 05006 South Korea Foothill Preparatory Sch Temple City CA 91780 USA Tencent Yantai Emerging Engn Res Inst Yantai 264006 Peoples R China

The growing number of individual vehicles and intelligent transportation systems have accelerated the development of Internet of Vehicles (IoV) technologies. The Internet of Vehicles (IoV) refers to a highly interactive network containing data regarding places, speeds, routes, and other aspects of vehicles. Task offloading was implemented to solve the issue that the current task scheduling models and tactics are primarily simplistic and do not consider the acceptable distribution of tasks, which results in a poor unloading completion rate. This work evaluates the Joint Task Offloading problem by Distributed Deep Reinforcement Learning (DDRL)-Based genetic optimization algorithm (GOA). A system's utility optimisation model is initially accomplished objectively using divisions between interaction and computation models. DDRL-GOA resolves the issue to produce the best task offloading method. The research increased job completion rates by modifying the complexity design and universal best-case scenario assurances using DDRL-GOA. Finally, empirical research is performed to validate the proposed technique in scenario development. We also construct joint task offloading, load distribution, and resource allocation to lower system costs as integer concerns. In addition to having a high convergence efficiency, the experimental results show that the proposed approach has a substantially lower system cost when compared to current methods.

关键词： IoV genetic optimization algorithm Task offloading Sustainable development Distributed deep reinforcement method System utility function maximisation

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Fuzzy Reliability Analysis Using genetic optimization algorithm Combined with Adaptive Descent Chaos Control

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ASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS PART A-CIVIL ENGINEERING 2020年第2期6卷

作者： Bagheri, Mansour Keshtegar, Behrooz Zhu, Shun-Peng Meng, Debiao Correia, J. A. F. O. De Jesus, A. M. P. Birjand Univ Technol Dept Civil Engn Birjand *** Iran Univ Zabol Dept Civil Engn Zabol 53898615 Iran Univ Elect Sci & Technol China Sch Mech & Elect Engn Ctr Syst Reliabil & Safety Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Peoples R China Univ Porto Fac Engn Inst Nacl Estadist & Geog P-4200465 Porto Portugal Univ Porto Fac Engn P-4200465 Porto Portugal

The robust result of analytical fuzzy reliability analysis (FRA) represents the main effort at evaluating the fuzzy reliability index. In this study, a bioloop-based hybrid method is proposed for structural FRA. The genetic operator as optimization solver combined with adaptive descent chaos control (ADCC) as a probabilistic solver called GA-ADCC is applied to evaluate the fuzzy reliability index. The ADCC-based reliability method is formulated based on a dynamical chaos control factor that is computed using an adaptive descent approach from the new and previous results. In GA-ADCC, an outer loop-based genetic optimizer constructs the membership reliability index using an alpha level set. To compute the membership functions of the reliability index, three structural problems are used to show the capability of the proposed method. Results demonstrate that the proposed GA-ADCC method can be used to evaluate reasonable uncertainty bounds in FRA, and it provides the accurate member shape functions for reliability index. (C) 2020 American Society of Civil Engineers.

关键词： Fuzzy reliability analysis (FRA) Adaptive descent chaos control genetic optimization algorithm Chaos control

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Double-layered beam shaping assembly design based on intelligent optimization method coupling neural network and genetic algorithm for BNCT

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2024年 1063卷

作者： Rong, Bo Song, Hongbing Li, Zhifeng Hu, Lei Wang, Jie Zheng, Qi Peng, Wentao Wang, Sheng Yang, Haoxian Xi An Jiao Tong Univ Sch Energy & Power Engn Dept Nucl Sci & Technol 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China XJTU Huzhou Neutron Sci Lab Sci Valley Medium Sized Bldg 1 Huzhou 313000 Zhejiang Peoples R China Wuhan Univ Wuhan 430060 Hubei Peoples R China Xi An Jiao Tong Univ State Key Lab Mech Behav Mat Xian 710049 Peoples R China Guangdong Prov Hosp Occupat Dis Prevent & Treatmen Guangzhou 510300 Peoples R China

The beam shaping assembly (BSA) plays a crucial role in the facility of accelerator-based boron neutron capture therapy (AB-BNCT). The quality of the neutron beam utilized for treatment is directly influenced by the design of the BSA. However, conventional step-by-step optimization approaches often encounter challenges in achieving the global optimal solution, as they tend to converge towards local optima. To address this issue, this study proposed an intelligent optimization method that combines neural network and genetic algorithm. The proposed method was applied to optimize a double-layered BSA based on the neutron source resulting from a lithium target bombarding by a 2.8 MeV, 20 mA proton beam. The obtained optimal BSA solution parameters well met the IAEA-TECDOC-1223 report, while also exhibiting significantly higher epithermal neutron flux compared to alternative BSA designs. The performance of the epithermal neutron beam was assessed by calculating the dose distribution and clinical parameters in the Snyder head phantom. The findings suggest that this beam exhibits promising therapeutic potential for treating tumors.

关键词： Beam shaping assembly AB-BNCT Neural network genetic optimization algorithm Monte Carlo simulation

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Convergence of Multi-Criteria optimization of a Building Energetic Resources by genetic algorithm 7

Convergence of Multi-Criteria Optimization of a Building Ene...

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7th International Conference on Smart Grid and Clean Energy Technologies (ICSGCE)

作者： Jean-Luc, Robyr Gonon, Frederick Favre, Ludovic Niederhauser, Elena-Lavinia Univ Appl Sci Western Switzerland HES SO Fribourg Energy Inst Fribourg Switzerland

ISBN: (纸本)9781538667965

Better energy management systems for buildings could play a significant role in achieving nowadays greenhouse gas emission reduction targets. In this context, a regulation algorithm to manage the interaction between local renewable energy production, local energy storage devices and an external power source (power grid) was developed. The innovative aspect of this project compared to existing solution is the simultaneous optimization following three criteria: the external energy consumption, the cost and ecological impacts. The new optimization algorithm is based on the genetic algorithm method due to the large solutions space and the non-linearity of the optimization function. This method is coupled to a physical model of the building under study (a typical dwelling house) and its energetic network (production and storage). In addition, weather forecast data as well as data on the user habits are integrated. This paper shows the results of the optimization algorithm applied to a set of realistic values. The genetic algorithm is compared to a pure random optimization approach and their optimization efficiencies are analyzed. Finally, the best strategy obtained by the genetic algorithm for a realistic computation time of several minutes is presented and investigated in detailed. This results shows that the genetic algorithm can perform a 48 hours simulation with no outcome costs, a global production of 4.3 kWh of energy and a greenhouse gas production of -1.4 kg of CO2 equivalent, whereas the consumption of the building costs +1.3 CHF, consumes 7.0 kWh of energy and generates +1.3 kg of CO2 equivalent.

关键词： building energy management thermal modelling genetic optimization algorithm renewable energy greenhouse gases self-consumption self-production

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Research on speed optimization of fixed route ship with low data dependence

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OCEAN ENGINEERING 2025年 328卷

作者： Hu, Chaodong Wang, Yu Han, Xu Liu, Hui Sun, Qun Zhou, Bo Dalian Univ Technol Sch Naval Architecture & Ocean Engn State Key Lab Struct Anal Optimizat & CAE Software Dalian 116024 Peoples R China COSCO Shipping Heavy Ind Co Ltd Dalian 116113 Peoples R China Marine Design & Res Inst China Shanghai 200011 Peoples R China

A low data demand long-term fuel consumption prediction model suitable for variable pitch propeller ships has been established for the first time, and a segmented route speed optimization method has been provided. The event triggered Informer (ET-Informer) algorithm has the ability to predict long-term high-precision sequences and capture key data to reduce data redundancy and improve computational efficiency. The event triggering mechanism allows for data loss at a certain stage, improving the algorithm's fault tolerance and reducing communication requirements. An ordered sample clustering algorithm based on the weighted event-triggered mechanism is introduced, enabling the adjustment of clustering weights according to demand. The threshold for dynamically adjusting similarity measures triggered by events can solve the problem of uneven clustering distribution. This study reduces data dependency and improves the fault tolerance and accuracy of speed optimization through two aspects: fuel consumption model prediction and route segment speed optimization. The proposed algorithm was validated using real-world data from 24 passenger ferry voyages on major international routes in 2021, achieving a fuel consumption prediction accuracy of 98.4 % and a 4.4 % reduction in fuel consumption, while maintaining travel time. The results confirm the effectiveness, robustness, and practical applicability of the fixed-route speed optimization algorithm.

关键词： Speed optimization Weighted event triggering mechanism Informer time series prediction Ordered sample clustering segmentation algorithm genetic optimization algorithm

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Multi-objective optimization of supply air inlet structure for impinging jet ventilation system based on radial basis function neural network

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CASE STUDIES IN THERMAL ENGINEERING 2025年 65卷

作者： Wang, Chen Hu, Ke Liu, Yin Zhongyuan Univ Technol Sch Smarts Energy & Environm Zhengzhou 451191 Peoples R China

A multi-objective optimization of the supply air inlet structure for Impinging Jet Ventilation (IJV) was conducted based on the Radial Basis Function Neural Network (RBFNN) and using a genetic optimization algorithm. The Predicted Mean Vote at the occupant's ankle level (PMV0.1) and the Energy Utilization Coefficient (Et) exhibited significant variability across different inlet structures, thus they were selected as optimization objectives. The predicted results showed substantial consistency with numerical simulations. Within the selected parameter range, the optimal PMV0.1 value was -0.17, and the optimal Etvalue was 3.57. Furthermore, by adjusting the weights of different optimization objectives, suitable structural parameters can be determined. It was also concluded that, for the given indoor ventilation conditions, the length of the supply air inlet structure should be shorter than its width to better enhance the PMV0.1 value in the areas surrounding occupants.

关键词： Radial basis function neural network genetic optimization algorithm Impinging jet ventilation Supply air inlet structure Indoor ventilation

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