Novel effective X-path particle swarm optimization based deprived video data retrieval for smart city

2017 ◽  
Vol 22 (S6) ◽  
pp. 13085-13094 ◽  
Author(s):  
S. Thanga Ramya ◽  
Bhuvaneshwari Arunagiri ◽  
P. Rangarajan
Keyword(s):  
Particle Swarm Optimization ◽  
Smart City ◽  
Particle Swarm ◽  
Data Retrieval ◽  
Video Data ◽  
Swarm Optimization

Optimization of wireless network node deployment in smart city based on adaptive particle swarm optimization

2020 ◽  
Vol 39 (4) ◽  
pp. 4959-4969
Author(s):  
Weiqiang Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Wireless Network ◽  
Smart City ◽  
Smart Cities ◽  
Particle Swarm ◽  
Network Node ◽  
Swarm Optimization ◽  
Network Nodes ◽  
Node Deployment ◽  
Multi Objective

In smart city wireless network infrastructure, network node deployment directly affects network service quality. This problem can be attributed to deploying a suitable ordinary AP node as a wireless terminal access node on a given geometric plane, and deploying a special node as a gateway to aggregate. Traffic from ordinary nodes is to the wired network. In this paper, Pareto multi-objective optimization strategy is introduced into the wireless sensor network node security deployment, and an improved multi-objective particle swarm coverage algorithm based on secure connection is designed. Firstly, based on the mathematical model of Pareto multi-objective optimization, the multi-target node security deployment model is established, and the security connectivity and node network coverage are taken as the objective functions, and the problems of wireless sensor network security and network coverage quality are considered. The multi-objective particle swarm optimization algorithm is improved by adaptively adjusting the inertia weight and particle velocity update. At the same time, the elite archive strategy is used to dynamically maintain the optimal solution set. The high-frequency simulation software Matlab and simulation platform data interaction are used to realize the automatic modeling, simulation analysis, parameter prediction and iterative optimization of wireless network node deployment in smart city based on adaptive particle swarm optimization. Under the premise of meeting the performance requirements of wireless network nodes in smart cities, the experimental results show that although the proposed algorithm could not achieve the accuracy of using only particle swarm optimization algorithm to optimize the parameters of wireless network nodes in smart cities, the algorithm is completed. The antenna parameter optimization process takes less time and the optimization efficiency is higher.

An Innovative Information Retrieval Model Implementing Particle Swarm Optimization Technique

2020 ◽  
Vol 17 (12) ◽  
pp. 5613-5617
Author(s):  
S. Surya ◽  
P. Sumitra
Keyword(s):  
Particle Swarm Optimization ◽  
Information Retrieval ◽  
Fitness Function ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Data Retrieval ◽  
Swarm Optimization ◽  
User Query ◽  
Soft Computing Techniques ◽  
Particle Swarm Optimization Technique

The Internet has enormous information and it is growing rapidly. The vast amount of data creates challenges in relation to effective Information Retrieval (IR). The scope of the Information Retrieval System (IRS) is to provide the most relevant data for user query from large datasets. However the current IR system fails to provide the hidden and up to date data. This paper focused on soft computing techniques to overcome the above mentioned issues. Particle Swarm Optimization (PSO) is used to compute the fitness function to optimize the retrieval result. PSO has an efficient capability in global search and the implementation is easy to develop. The implementation result of the present study is feasible, that improves the retrieval effect and the accuracy of hidden data retrieval.

A Shot Boundary Detection Method Based on PSO-SVM

2011 ◽  
Vol 130-134 ◽  
pp. 3821-3825 ◽  
Author(s):  
Long Zhao ◽  
Xue Mei Sun ◽  
Ming Wei Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Video Retrieval ◽  
Particle Swarm ◽  
Boundary Detection ◽  
Detection Algorithm ◽  
Video Data ◽  
Shot Boundary Detection ◽  
Support Vector ◽  
Swarm Optimization ◽  
Shot Boundary

Shot boundary detection (SBD) is the first step which segments video data into elementary shots for content-based video retrieval. In this paper, a shot boundary detection algorithm based on support vector machine (SVM) and particle swarm optimization (PSO) is proposed. First of all, the extracted features of pixel domain and compressed domain are combined to form a multi-dimension feature vector by using the scheme of sliding window. Next, particle swarm optimization with global search capacity is adopted to seek the approximately optimal parameters of radial basis function of SVM. Finally the model trained by the parameters obtained is applied to judge and categorize the frames into cut transitions, gradual transitions and non-transitions. The experimental results on the TREC video set 2001 demonstrate our algorithm is efficient and robust, and it solves the difficulty in parameter selection of SVM well.

scholarly journals Total Optimization of Energy Networks in a Smart City by Multi-Population Global-Best Modified Brain Storm Optimization with Migration

Algorithms ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 15 ◽  
Author(s):  
Mayuko Sato ◽  
Yoshikazu Fukuyama ◽  
Tatsuya Iizaka ◽  
Tetsuro Matsui
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Computation ◽  
Smart City ◽  
Co2 Emission ◽  
Particle Swarm ◽  
Population Models ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Energy Networks ◽  
Brain Storm Optimization

This paper proposes total optimization of energy networks in a smart city by multi-population global-best modified brain storm optimization (MP-GMBSO). Efficient utilization of energy is necessary for reduction of CO2 emission, and smart city demonstration projects have been conducted around the world in order to reduce total energies and the amount of CO2 emission. The problem can be formulated as a mixed integer nonlinear programming (MINLP) problem and various evolutionary computation techniques such as particle swarm optimization (PSO), differential evolution (DE), Differential Evolutionary Particle Swarm Optimization (DEEPSO), Brain Storm Optimization (BSO), Modified BSO (MBSO), Global-best BSO (BSO), and Global-best Modified Brain Storm Optimization (GMBSO) have been applied to the problem. However, there is still room for improving solution quality. Multi-population based evolutionary computation methods have been verified to improve solution quality and the approach has a possibility for improving solution quality. The proposed MS-GMBSO utilizes only migration for multi-population models instead of abest, which is the best individual among all of sub-populations so far, and both migration and abest. Various multi-population models, migration topologies, migration policies, and the number of sub-populations are also investigated. It is verified that the proposed MP-GMBSO based method with ring topology, the W-B policy, and 320 individuals is the most effective among all of multi-population parameters.

scholarly journals Intelligent Power Grid Video Surveillance Technology Based on Efficient Compression Algorithm Using Robust Particle Swarm Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hongyang He ◽  
Yue Gao ◽  
Yong Zheng ◽  
Yining Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Monitoring System ◽  
Video Surveillance ◽  
Power Grid ◽  
Particle Swarm ◽  
Video Data ◽  
Surveillance Video ◽  
Swarm Optimization ◽  
Suggested Approach ◽  
Grid Monitoring

Companies that produce energy transmit it to any or all households via a power grid, which is a regulated power transmission hub that acts as a middleman. When a power grid fails, the whole area it serves is blacked out. To ensure smooth and effective functioning, a power grid monitoring system is required. Computer vision is among the most commonly utilized and active research applications in the world of video surveillance. Though a lot has been accomplished in the field of power grid surveillance, a more effective compression method is still required for large quantities of grid surveillance video data to be archived compactly and sent efficiently. Video compression has become increasingly essential with the advent of contemporary video processing algorithms. An algorithm’s efficacy in a power grid monitoring system depends on the rate at which video data is sent. A novel compression technique for video inputs from power grid monitoring equipment is described in this study. Due to a lack of redundancy in visual input, traditional techniques are unable to fulfill the current demand standards for modern technology. As a result, the volume of data that needs to be saved and handled in live time grows. Encoding frames and decreasing duplication in surveillance video using texture information similarity, the proposed technique overcomes the aforementioned problems by Robust Particle Swarm Optimization (RPSO) based run-length coding approach. Our solution surpasses other current and relevant existing algorithms based on experimental findings and assessments of different surveillance video sequences utilizing varied parameters. A massive collection of surveillance films was compressed at a 50% higher rate using the suggested approach than with existing methods.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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