检索结果-内蒙古大学图书馆

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

A genetic-based clustering algorithm for efficient resource allocating of IoT applications in layered fog heterogeneous platforms

引用

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第2期27卷 1313-1331页

作者： Abedpour, Kimia Shirvani, Mirsaeid Hosseini Abedpour, Elmira Tabarestan Chalus Inst Dept Comp Engn Chalus Iran Islamic Azad Univ Dept Comp Engn Sari Branch Sari Iran Univ Turin Interuniv Dept Terr Sci Project & Polit Polytech Turin Italy

Fog Computing paradigm that provisions low-latency computing services at the edge network, is a bonanza for supply chain computing resources in Internet of Things (IoT) applications. In different scenarios such as smart homes/healthcare systems, multiple IoT applications are distributed simultaneously in cloud and fog nodes to provide different IoT-based services. In addition, each program requires resources and has its desired quality of service (QoS) which should be met. One of the key challenges in fog computing environment is how to efficiently allocate resources to IoT applications because inefficient resource allocation leads to burdening providers high costs and it lowers down the delivered QoS to users. Since the majority of IoT applications are time-sensitive, the low delay and near physically allocated resources improve the amount of delivered QoS. Therefore, the resource clustering algorithms with the lowest distance error rate and the lowest delay as a consequence are favorable. The aim is to reduce clustering errors and improve the overall performance of the system. This paper formulates resource allocation to IoT applications in heterogeneous 4-layered fog platforms to an optimization problem. To solve this problem, a fusion approach incorporating a genetic algorithm (GA) and the k-means clustering approach is presented. Firstly, it utilizes the k-means approach and Jaccard measurement to cluster fog nodes with a minimum clustering rate. Then, the resources of fog clusters are allocated to IoT devices with the minimum error rate by incorporating GA algorithm. This selection of processing nodes in a fog layer helps to minimize latency and allows applications to access resources simultaneously. The simulation results in extensive scenarios prove the superiority of the proposed algorithm against other successful meta-heuristic approaches in terms of the objective function and lowest error/delay rate.

关键词： Fog computing Internet of Things k-means clustering method Jaccard measurement genetic optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

DC-FIPD: Fraudulent IP Identification Method Based on Homology Detection

引用

Computers, Materials & Continua 2024年第11期81卷 3301-3323页

作者： Yuanyuan Ma Ang Chen Cunzhi Hou Ruixia Jin Jinghui Zhang Ruixiang Li College of Computer and Information Engineering Henan Normal UniversityXinxiang453007China Intelligent Medical Engineering SanQuan Medical CollegeXinxiang453003China Information Engineering University Information Engineering UniversityZhengzhou450001China Key Laboratory of Cyberspace Situation Awareness of Henan Province Zhengzhou450001China

Currently,telecom fraud is expanding from the traditional telephone network to the Internet,and identifying fraudulent IPs is of great significance for reducing Internet telecom fraud and protecting consumer ***,existing telecom fraud identification methods based on blacklists,reputation,content and behavioral characteristics have good identification performance in the telephone network,but it is difficult to apply to the Internet where IP(Internet Protocol)addresses change *** address this issue,we propose a fraudulent IP identification method based on homology detection and DBSCAN(Density-Based Spatial Clustering of Applications with Noise)clustering(DC-FIPD).First,we analyze the aggregation of fraudulent IP geographies and the homology of IP ***,the collected fraudulent IPs are clustered geographically to obtain the regional distribution of fraudulent ***,we constructed the fraudulent IP feature set,used the genetic optimization algorithm to determine the weights of the fraudulent IP features,and designed the calculation method of the IP risk value to give the risk value threshold of the fraudulent ***,the risk value of the target IP is calculated and the IP is identified based on the risk value *** results on a real-world telecom fraud detection dataset show that the DC-FIPD method achieves an average identification accuracy of 86.64%for fraudulent ***,the method records a precision of 86.08%,a recall of 45.24%,and an F1-score of 59.31%,offering a comprehensive evaluation of its performance in fraud *** results highlight the DC-FIPD method’s effectiveness in addressing the challenges of fraudulent IP identification.

关键词： Fraudulent IP identification homology detection clustering genetic optimization algorithm telecom fraud identification

来源：评论

学校读者我要写书评

暂无评论

Enhancing breast cancer diagnosis accuracy through genetic algorithm-optimized multilayer perceptron

引用

MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第4期7卷 4433-4449页

作者： Talebzadeh, Hossein Talebzadeh, Mohammad Satarpour, Maryam Jalali, Fereshtehsadat Farhadi, Bahar Vahdatpour, Mohammad Saleh Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran Iran Texas A&M Univ Dept Civil & Environm Engn College Stn TX USA Univ Pittsburgh Swanson Sch Engn Dept Bioengn Pittsburgh PA USA Shahid Beheshti Univ Med Sci Fac Med Dept Obstet & Gynecol Tehran Iran Islamic Azad Univ Fac Med Mashhad Branch Mashhad Iran Georgia State Univ Dept Comp Sci Atlanta GA USA

This research paper investigates the application of a genetic algorithm (GA) to optimize the performance of a Multilayer Perceptron (MLP) model for breast cancer diagnosis. Various configurations of hidden layers in the MLP are explored, and their corresponding accuracies range from 0.92 to 0.972. To ensure robust evaluation, k-fold cross-validation is employed, alongside comprehensive preprocessing of the dataset, including normalization, scaling, and encoding. These methodologies contribute to the model's consistent performance and its enhanced ability to generalize. However, the incorporation of a genetic algorithm significantly improves the accuracy range, resulting in values between about 0.97 and 0.99 across different generations. The genetic algorithm optimizes the MLP model by evolving a population of potential solutions (individuals) over multiple generations. Each individual represents a specific set of MLP parameters, such as the number of hidden layers, neurons per layer, and learning rate. The fitness of each individual is evaluated based on the MLP model's accuracy on the breast cancer dataset. The fittest individuals are selected for reproduction, and genetic operators like crossover and mutation are applied to generate new offspring. This iterative process of selection, crossover, and mutation gradually enhances the MLP model's performance. The primary objective of this research is to improve breast cancer diagnosis accuracy by leveraging the strengths of MLP and the optimization capabilities of the genetic algorithm. The results demonstrate that the combined approach effectively enhances the accuracy of breast cancer prediction compared to using MLP alone.

关键词： Breast cancer detection Artificial neural networks genetic optimization algorithm GA-MLP

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：