检索结果-内蒙古大学图书馆

Parameter identification and SOC estimation of lithium-ion batteries based on AGCOA-ASRCKF

JOURNAL OF POWER ELECTRONICS 2023年第2期23卷 308-319页

作者： Chu, Yunkun Li, Junhong Gu, Juping Qiang, Yujian Nantong Univ Sch Elect Engn Nantong Peoples R China

The state of charge (SOC) is an important parameter in battery management systems (BMS), and its accuracy is very important. In this paper, a co-estimation with the adaptive global optimal guided coyote optimization algorithm and the adaptive square root cubature Kalman filter (AGCOA-ASRCKF) is used to perform the parameter identification and SOC estimation of a lithium-ion second-order RC model. The AGCOA effectively solves the problems where traditional heuristic algorithms tend to fall into local optimum and have a slow convergence speed. The AGCOA can accurately identify the parameters of the battery model. At the same time, when compared with the cubature Kalman filter, the ASRCKF introduces a square root filter and adds a residual sequence to adaptively update the covariance of the process noise and measurement noise, which improves the estimation accuracy of the SOC. The method proposed in this paper is verified by intermittent constant current test, dynamic stress test, and the federal urban driving schedule. Simulation results show that a high-precision battery model can be established by AGCOA-ASRCKF. In addition, the predicted value of the terminal voltage is basically consistent with the actual value. At the same time, the SOC estimation error can be controlled to within 1.5%, and the algorithm has good robustness and reliability in the presence of errors in the initial SOC.

关键词： Lithium-ion battery Second-order RC model State of charge coyote optimization algorithm Cubature Kalman filter

来源：评论

学校读者我要写书评

暂无评论

Heuristic optimization algorithm Based Watermarking on Content Authentication and Tampering Detection for English Text

引用

IEEE ACCESS 2023年 11卷 86104-86111页

作者： Al-Wesabi, Fahd N. Alrowais, Fadwa Mohamed, Heba G. Duhayyim, Mesfer Al Hilal, Anwer Mustafa Motwakel, Abdelwahed King Khalid Univ Coll Sci & Art Mahayil Dept Comp Sci Abha 62529 Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh 11671 Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Engn Dept Elect Engn Riyadh 11671 Saudi Arabia Prince Sattam bin Abdulaziz Univ Coll Sci & Humanities Aflaj Dept Comp Sci Al Kharj 16278 Saudi Arabia Prince Sattam bin Abdulaziz Univ Dept Comp & Self Dev Preparatory Year Deanship Al Kharj 16278 Saudi Arabia

Text information is a natural language dependent, which enhances reliability and security of text data exchanged through Internet networks and is becoming main concern for researchers. Content authentication, Tampering detection, and integrity verification of digital content are considered challenges in the information and communication exchange area through the Internet. Watermarking algorithms emerge as a potential method for achieving this with text. In simple words, text watermarking is embedding imperceptible text messages within the content that serve as unique signatures or identifiers. The text message has data regarding the content creator, copyright data, or other metadata utilized for authentication purposes. Therefore, this study presents a new coyote optimization algorithm with Watermarking-based Content Authentication and Tampering Detection (COAW-CATD) technique for English text. The presented COAW-CATD technique aims to secure the English text via content authentication and tampering recognition. To accomplish this, the presented COAW-CATD technique designs a zero-watermarking (ZWM) approach to produce watermarks depending on the textual content. The generated watermark can be extracted to assure the authentication of the text document. Moreover, the COA can be utilized to optimize the placement of the watermarks in the content to ensure that it is imperceptible and robust to tampering. The experimental outcomes of the COAW-CATD algorithm are tested utilizing a series of simulations and the comparative study reported its superior performance with improved tampering detection accuracy of 95.93%.

关键词： INDEX TERMS Watermarking algorithm content authentication tampering detection coyote optimization algorithm security

来源：评论

学校读者我要写书评

暂无评论

A hybrid deep kernel incremental extreme learning machine based on improved coyote and beetle swarm optimization methods

引用

COMPLEX & INTELLIGENT SYSTEMS 2021年第6期7卷 3015-3032页

作者： Wu, Di Li, Ting Wan, Qin Hunan Inst Engn Coll Elect & Informat Engn Xiangtan 411104 Peoples R China Hunan Inst Engn Hunan Prov Cooperat Innovat Ctr Wind Power Equipm Xiangtan 411104 Peoples R China

The iteration times and learning efficiency of kernel incremental extreme learning machines are always affected by the redundant nodes. A hybrid deep kernel incremental extreme learning machine (DKIELM) based on the improved coyote and beetle swarm optimization methods was proposed in this paper. A hybrid intelligent optimization algorithm based on the improved coyote optimization algorithm (ICOA) and improved beetle swarm optimization algorithm (IBSOA) was proposed to optimize the parameters and determine the number of effectively hidden layer neurons for the proposed DKIELM. A Gaussian global best-growing operator was adopted to replace the original growing operator in the intelligent optimization algorithm to improve COA searching efficiency and convergence. In the meantime, IBSOA was designed based on tent mapping inverse learning and dynamic mutation strategies to avoid falling into a local optimum. The experimental results demonstrated the feasibility and effectiveness of the proposed DKIELM with encouraging performances compared with other ELMs.

关键词： Kernel incremental extreme learning machine coyote optimization algorithm Beetle swarm optimization algorithm Hybrid intelligence

来源：评论

学校读者我要写书评

暂无评论

A novel hybrid approach to flood susceptibility assessment based on machine learning and land use change. Case study: a river watershed in Vietnam

引用

HYDROLOGICAL SCIENCES JOURNAL 2022年第7期67卷 1065-1083页

作者： Nguyen, Huu Duy Bui, Quang-Thanh Nguyen, Quoc-Huy Nguyen, Tien Giang Pham, Le Tuan Nguyen, Xuan Linh Vu, Phuong Lan Nguyen, Thi Ha Thanh Nguyen, Anh Tuan Petrisor, Alexandru-Ionut Vietnam Natl Univ VNU Univ Sci Fac Geog Hanoi Vietnam Vietnam Natl Univ VNU Univ Sci Fac Geog Ctr Appl Res Remote Sensing & Gis Hanoi Vietnam Vietnam Natl Univ VNU Univ Sci Fac Hydrol Meteorol & Oceanog Hanoi Vietnam Vietnam Natl Univ Int Sch VNU IS Hanoi Vietnam Ion Mincu Univ Architecture & Urbanism Doctoral Sch Urban Planning Bucharest Romania

This study aims to develop a comprehensive approach including an analysis of the relationships between flood susceptibility and land-use change, based on the relevance vector machine (RVM) and coyote optimization algorithm (COA) models, applied to Gianh River watershed, Quang Binh province, Central Vietnam. Standard statistical indices, e.g. area under the curve (AUC), were used to assess the model performance. Comparative analyses emphasize that the COA successfully improves the performance of the RVM model (AUC = 0.99) and is also better than the reference models such as support vector machine (AUC = 0.98), gradient boosting machine (AUC = 0.97), random forest (AUC = 0.99), extra trees regressor (AUC = 0.98), and AdaBoost (AUC = 0.96). The improved model, when used in conjunction with land use maps, is able to show that urbanization has increased in flood-susceptible areas. The results highlight that urbanization has increased in the low and very low flood susceptibility areas by 110% between 2005 and 2020, while in the high and very high areas it has increased by 30 to 40%, despite urban and demographic growth.

关键词： flood relevance vector machine coyote optimization algorithm Gianh River

来源：评论

学校读者我要写书评

暂无评论

Energy Aware Reliable Sleep Wakeup based Clustering Scheme in WSN

引用

IETE JOURNAL OF RESEARCH 2024年第8期70卷 7069-7081页

作者： Chithra, T. V. Karthikeyan, K. Hussain, D. Mansoor Muthukumaran, N. Arunachala Coll Engn Women Dept Comp Sci & Engn Nagercoil 629203 Tamil Nadu India Ramco Inst Technol Dept Elect & Elect Engn Rajapalayam Tamil Nadu India VIT Univ Sch Comp Sci & Engn SCOPE Chennai Tamil Nadu India Sri Eshwar Coll Engn Ctr Computat Imaging & Machine Vis Dept Elect & Commun Engn Coimbatore 641202 Tamil Nadu India

Wireless sensor networks (WSNs) are composed of spatially dispersed nodes monitored by sensors that communicate their collected data to a sink node. The primary method used by the WSN to spread the message throughout the whole network is flooding. Retransmission and rebroadcasting of the same message during flooding are important for reliability and energy efficiency. In this paper a novel Clustering and Sleep Wakeup based energy efficient Flooding (CSW-E2F) technique is proposed which improves the network lifetime and reliability. Initially, the proposed technique will form the nodes as clusters using the Gaussian mixture model (GMM). After clustering, the sleeping nodes (SN) and waking nodes (WN) were selected, and the awake nodes will take part in the random cluster head (CH) election process which is done by using the coyote optimization algorithm. After CH selection, the stability of the link has been checked using various parameters which will then choose the best route for message transmission. The suggested CSW-E2F approach was assessed in terms of network lifetime, energy consumption, average delivery delay, network coverage rate, average flooding delay, and throughput. When compared to existing schemes like Evolution Multipath Energy-Efficient Routing (EMEER), Binary Particle Swarm Optimisation (BPSO), and Energy-Balanced Power-Efficient Gathering in Sensor Information Systems (EB-PEGASIS), the proposed CSW-E2F improved network throughput by 17.5%, 19%, and 27%, respectively. The suggested solution exceeds previous approaches in terms of latency and energy cost by about 50% and 60%, respectively, thereby prolonging the system lifetime.

关键词： coyote optimization algorithm Flooding Gaussian mixture model Sleep wakeup

来源：评论

学校读者我要写书评

暂无评论

Parameter identification and SOC estimation of lithium-ion battery based on AGCOA optimization 34

Parameter identification and SOC estimation of lithium-ion b...

引用

34th Chinese Control and Decision Conference (CCDC)

作者： Chu, Yunkun Li, Junhong Li, Lei Qiang, Yujian Nantong Univ Sch Elect Engn Nantong 226019 Peoples R China

ISBN: (纸本)9781665478960

Considering the polarization effect of lithium-ion battery, this paper establishes a second-order RC model, and proposes an adaptive global optimal guided coyote optimal identification algorithm (AGCOA). It effectively solves the problems of traditional heuristic algorithms that are easy to fall into local optima and slow convergence. The battery testing equipment NEWARE BTS-4008 is used to charge and discharge the lithium-ion battery. The parameters identified by the AGCOA are used to predict the terminal voltage, and the extended Kalman filter algorithm (EKF) is used to estimate the battery SOC. The results show that the predicted voltage obtained by the identification is basically the same as the actual voltage, and the SOC estimation error is small, which verifies the efficiency and accuracy of the algorithm.

关键词： Lithium-ion battery second-order RC model coyote optimization algorithm extended Kalman filter

来源：评论

学校读者我要写书评

暂无评论

A Novel Visual-Textual Sentiment Analysis Framework for Social Media Data

引用

COGNITIVE COMPUTATION 2021年第6期13卷 1433-1450页

作者： Jindal, Kanika Aron, Rajni Lovely Profess Univ Jalandhar Punjab India SVKMs Narsee Monjee Inst Management Studies NMIMS Mumbai Maharashtra India

Background Sentiment analysis (SA) has turned out to be a new pattern in social networking, avidly helping people to realize views expressed in user-generated content and conventional platforms of social media. For performing numerous social media analytics tasks, SA of online user-produced content is vital. The performance of the sentiment classifiers utilizing a single modality, i.e., visual or textual, is still not matured because of the wide variety of data platforms. Methods In this paper, we propose a new framework called VIsual-TExtual SA (VITESA) that carries out visual analysis and textual analysis for polarity classification. In the VITESA framework, Brownian Movement-based Meerkat Clan algorithm-centered DenseNet (BMMCA-DenseNet) is proposed that integrates textual and visual information for robust SA. In the visual phase, the images that are in the Flickr dataset are taken as input, and the operations: (1) preprocessing (2) feature extraction, and (3) feature selection utilizing Improved coyote optimization algorithm (ICOA) are executed. In the textual phase, the user comments as of the Twitter dataset are taken as input, and the operations: (1) preprocessing (2) word embedding using adaptive Embedding for Language Models (ELMo), (3) emoticon and non-emoticon feature extraction, and SentiWordNet polarity assignment is carried out. The final stages of both phases are given as input to the proposed BMMCA-DenseNet classifier and intended to categorize the data into positive and negative polarity. The performance of BMMCA-DenseNet is compared with certain existing algorithms, and various performance metrics are evaluated. Results The proposed BMMCA-DenseNet classifier performs the polarity classification of the visual-textual data into two classes: positive or negative. The classifier categorizes the polarity of visual-textual data comprising 97% of accuracy, 94.44% of precision, 94.41% of recall, 94.41% of F-measure, 91.75% of Matthew's Correlation Coeffici

关键词： Sentiment analysis Multimodality analysis coyote optimization algorithm Visual-textual sentiment analysis DenseNet Convolutional neural network Meerkat Clan algorithm

来源：评论

学校读者我要写书评

暂无评论

An efficient fault tolerance scheme based enhanced firefly optimization for virtual machine placement in cloud computing

引用

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第7期35卷 e7610-e7610页

作者： Sheeba, Adlin Maheswari, B. Uma St Josephs Inst Technol Dept Comp Sci & Engn Chennai Tamil Nadu India Anna Univ St Josephs Coll Engn Comp Sci & Engn Chennai Tamil Nadu India

The virtual machine placement for the highly reliable cloud application is considered as one of the challenging and critical issues. To tackle such an issue, this article proposes the enhanced firefly algorithm based virtual machine placement model. But the migration time of the virtual machine placement is high and to reduce the migration time of the virtual machine placement, this article utilizes the K-means clustering algorithm. In addition, to obtain the optimal cluster for the virtual machine placement, the adaptive particle swarm optimization with the coyote optimization algorithm is employed. The experimental results are conducted for the proposed approach using various measures such as transmission overhead, total execution time, packet size, parallel applications numbers, and virtual machine numbers. The results demonstrate that the proposed method offers improved performance and an optimal virtual machine placement scheme with respect to the various constraint factors. The evaluation exposes that the proposed method offers less execution time when compared to other methods.

关键词： cloud computing coyote optimization algorithm enhanced firefly algorithm fault tolerance K-means algorithm particle swarm optimization virtual machine placement

来源：评论

学校读者我要写书评

暂无评论

Automatic Video Traffic Surveillance System with Number Plate Character Recognition Using Hybrid optimization-Based YOLOv3 and Improved CNN

引用

INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2023年第0期

作者： Bhosale, Manoj Krishna Patil, Shubhangi B. Patil, Babasaheb B. Shivaji Univ Kolhapur Dept Technol Kolhapur Maharashtra India Shivaji Univ Kolhapur Dr JJ Magdum Coll Engn Elect Dept Kolhapur Maharashtra India PVPIT Budhgaon CSE Dept Budhgaon Maharashtra India

Recently, the increased count of surveillance cameras has manipulated the demand criteria for a higher effective video coding process. Moreover, the ultra-modern video coding standards have appreciably enhanced the efficiency of video coding, which has been developed for gathering common videos over surveillance videos. Various vehicle recognition techniques have provided a challenging and promising role in computer vision applications and intelligent transport systems. In this case, most of the conventional techniques have recognized the vehicles along with bounding box depiction and thus failed to provide the proper locations of the vehicles. Moreover, the position details have been vigorous in terms of various real-time applications trajectory of vehicle's motion on the road as well as movement estimation. Numerous advancements have been offered throughout the years in the traffic surveillance area through the random propagation of intelligent traffic video surveillance techniques. The ultimate goal of this model is to design and enhance intelligent traffic video surveillance techniques by utilizing the developed deep learning techniques. This model has the ability to handle video traffic surveillance by measuring the speed of vehicles and recognizing their number plates. The initial process is considered the data collection, in which the traffic video data is gathered. Furthermore, the vehicle detection is performed by the Optimized YOLOv3 deep learning classifier, in which the parameter optimization is performed by using the newly recommended Modified coyote Spider Monkey optimization (MCSMO), which is the combination of coyote optimization algorithm (COA) and Spider Monkey optimization (SMO). Furthermore, the speed of the vehicles has been measured from each frame. For high-speed vehicles, the same Optimized YOLOv3 is used for detecting the number plates. Once the number plates are detected, plate character recognition is performed by the Improved Convolutiona

关键词： Traffic surveillance system modified coyote spider monkey optimization improved convolutional neural network YOLOv3 coyote optimization algorithm spider monkey optimization

来源：评论

学校读者我要写书评

暂无评论

Stochastic optimisation algorithm for optimisation of controller parameters for control of dissolved oxygen in wastewater treatment plant

引用

JOURNAL OF WATER PROCESS ENGINEERING 2023年 51卷

作者： Piotrowski, Robert Wonia, Michal Wonia, Adam Gdansk Univ Technol Fac Elect & Control Engn EkoTech Ctr G Narutowicza 11 12 PL-80233 Gdansk Poland

Wastewater treatment plants (WWTPs) are very important facilities for mankind. They enable the removal and neutralisation of man-made pollutants. Therefore, it is important for wastewater treatment plants to operate as efficiently as possible so that the level of pollutants in the treated wastewater meets specific requirements. This paper concerns the design of a hierarchical nonlinear adaptive control system for dissolved oxygen (DO) concentration in wastewater for a biological Sequential Batch Reactor (SBR). The parameters of the control system used are optimised to ensure the best possible control quality and low energy consumption at the same time. Based on data collected from a case study WWTP, an Activated Sludge Model 2d (ASM2d) of the biological processes and a model of the aeration system are applied. The coyote optimization algorithm (COA) is used to optimize the adaptive controller parameters. A Proportional-Integral-Derivative (PID) control system is also developed to compare the control results. The results obtained from simulation studies for both control systems are presented. As a result of optimised parameters, higher wastewater treatment efficiency and reduced electricity consumption are achieved.

关键词： Wastewater treatment plant SBR Dissolved oxygen Adaptive control coyote optimization algorithm

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：