检索结果-内蒙古大学图书馆

5th IEEE Global Power, Energy and Communication Conference (GPECOM)

作者： Kalyan, Ch. Naga Sai Rao, Kambhampati Venkata Govardhan Goud, B. Srikanth Bajaj, Mohit Jurado, Francisco Kamel, Salah Vasireddy Venkatadri Inst Technol EEE Dept Guntur 522508 Andhra Pradesh India St Martins Engn Coll EEE Dept Secunderabad 500100 Telangana India Anurag Univ EEE Dept Hyderabad 500088 India Graph Era Dept Elect Engn Dehra Dun 248002 India Univ Jaen Dept Elect Engn Jaen Spain Aswan Univ Fac Engn Dept Elect Engn Aswan Egypt

ISBN: (纸本)9798350301984

This work put forward the design of a sooty tern optimization algorithm (STOA) tuned cascaded proportional-integral and tilt-integral-derivative-filter (CPI-TIDF) for combined load frequency control and automatic voltage regulation (CLFC-AVR) of the interconnected power system (IPS). The CLFC-AVR analysis is initiated on laying 10% step load disturbance (10%SLD) on area-1 of the considered power system. The supremacy of designed controller performance is deliberated with other widely accepted controllers. Moreover, the impact of AVR and its necessity of considering LFC are demonstrated. Further to enhance the CLFC-AVR performance territorial control strategy of battery energy storage (BES) and Thyristor controlled phase shifter (TCPS) devices are enacted with the system. Simulation analysis revealed a considerable improvement in CLFC-AVR performance with the BES-TCPS mechanism.

关键词： CLFC-AVR CPI-TIDF controller Simulation analysis BES-TCPS mechanism sooty tern optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Research on Residual Power Reconfiguration of Hybrid Energy Storage System Based on Microgrid 4

Research on Residual Power Reconfiguration of Hybrid Energy ...

引用

4th IEEE Sustainable Power and Energy Conference (ISPEC)

作者： Liu Haitao Ma Bingtai Hao Sipeng Zhang Kuangyi Huang Cheng Lu Heng Nanjing Inst Technol Jiangsu Collaborat Innovat Ctr Smart Distribut Ne Nanjing Peoples R China Nanjing Inst Technol Nanjing Peoples R China

ISBN: (纸本)9781665485227

In the photovoltaic hybrid energy storage microgrid system, in order to reduce the unreasonable value of decomposition mode number (K) and secondary penalty factor (a) in VMD affect the accuracy of system reconstruction power. A new intelligent algorithm called sooty tern optimization algorithm(STOA) is proposed for the K and a optimization analysis. The parameters of VMD are optimized by STOA to obtain the [K, a] optimal combination quickly and stably, and then the result is applied in VMD to decompose the residual power of microgrid system. So as to improve the coincidence degree between the reconstructed power and the original residual power signal and can allocate the residual power to hybrid energy storage system reasonably, which will be beneficial to optimize the initial power allocation and capacity allocation of hybrid energy storage. This paper analyzes the algorithm and compares it with the results of particle swarm optimization and gray wolf algorithm to verify the effectiveness and superiority of the method.

关键词： Residual power Parameters optimization Reconstruction power sooty tern optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Osteosarcoma cancer detection using ghost-faster RCNN model from histopathological images

引用

Iran Journal of Computer Science 2024年第1期8卷 217-231页

作者： Stephe, S. Manjunatha, B. Revathi, V. Thirumalraj, Arunadevi Department of Electronics and Communication Engineering K. Ramakrishnan College of Engineering Tiruchirapalli India New Horizon College of Engineering Ring Road Bellandur Post Bengaluru India Department of Computer Science and Engineering K. Ramakrishnan College of Technology Tamil Nadu Trichy 621112 India

Subclassifying cancer tumours based on molecular characteristics holds promise for improving treatment response rates among patients. However, conventional machine learning approaches often fall short of effectively subclassifying osteosarcomas (OS) due to the intricate cellular origins involved. In this study, we address this challenge by leveraging an unequally distributed dataset comprising eosin-stained histopathological images with osteosarcoma haematoxylin. We investigate how this dataset's distribution impacts the performance and reliability of models and studies utilizing it, recognizing the complexity it introduces to osteosarcoma classification efforts. To tackle this issue, we propose a novel approach integrating GhostNet with an upgraded version of ResNet for osteosarcoma classification. Additionally, we employ an augmented faster region convolutional neural network (FRCNN) for feature extraction, enhancing the system's ability to discern subtle histopathological patterns indicative of osteosarcoma. Furthermore, we introduce the sooty tern optimization algorithm (STOA) to fine-tune the parameters of the deep learning model, thereby boosting classification accuracy. The practical implications of our study extend to telemedicine, mobile healthcare systems, and medical professionals' support tools, where our proposed system offers significant benefits. By leveraging advanced deep learning techniques, our system enables remote diagnosis and consultation, facilitates real-time analysis of histopathological images on mobile devices, and serves as a valuable decision-support tool for medical professionals involved in osteosarcoma diagnosis and treatment planning. In our evaluation, we find that our proposed system outperforms existing models, achieving an impressive accuracy rate of 98%. This performance enhancement, compared to results reported in four previous publications, underscores the efficacy of our approach in accurately identifying osteosarcoma from h

关键词： Eosin-stained image Faster region convolutional neural network Osteosarcoma cancer sooty tern optimization algorithm Traditional machine learning

来源：评论

学校读者我要写书评

暂无评论

Study onWear Fault Diagnosis of Planetary Gearbox Based on STOA-VMD Combined with 1.5-Dimensional Envelope Spectrum 1

引用

International Conference of The Efficiency and Performance Engineering Network (TEPEN)

作者： Zhang, Jiashuai Jiang, Zhanglei Wu, Guoxin Bi, Haocheng Informat Sci & Technol Univ Key Lab Modern Measurement & Control Technol Minist Educ Beijing 100192 Peoples R China

ISBN: (数字)9783031261930

ISBN: (纸本)9783031261954;9783031261930;9783031261923

Aiming at the problems of obvious nonlinear characteristics and difficult identification of fault characteristics of planetary gearbox vibration signals, a fault diagnosis method based on sooty tern optimization algorithm (STOA) optimized Variational Modal Decomposition (VMD) and 1.5-dimensional envelope spectrum is proposed. Firstly, the STOA is used to optimize the parameters of variational modal decomposition;Secondly, the signal is decomposed by variational mode method to obtain multiple eigenmode components;Then, based on the correlation kurtosis, the vibration signal is reconstructed with corresponding coefficients of eigenmode components;Finally, the reconstructed signal is analyzed using 1.5-dimensional envelope spectrum. The effectiveness of STOA-VMD is verified by simulation signals. The planetary gearbox test-bed is built, the whole life cycle data of the tooth surface wear fault is collected, and the wear fault is diagnosed using STOA-VMDcombinedwith 1.5-dimensional envelope spectrum, and compared with other methods. The results show that the proposed method is effective and accurate for fault feature extraction.

关键词： Planetary gearbox Vibrational modal decomposition sooty tern optimization algorithm Fault diagnosis

来源：评论

学校读者我要写书评

暂无评论

Multi-Objective Seagull optimization algorithm with Deep Learning-Enabled Vulnerability Detection for Secure Cloud Environments

引用

SENSORS 2023年第23期23卷 9383页

作者： Aljebreen, Mohammed Alohali, Manal Abdullah Mahgoub, Hany Aljameel, Sumayh S. Alsumayt, Albandari Sayed, Ahmed King Saud Univ Community Coll Dept Comp Sci POB 28095 Riyadh 11437 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Khalid Univ Coll Sci & Art Mahayil Dept Comp Sci Abha 61413 Saudi Arabia Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol SAUDI ARAMCO Cybersecur Chair Dept Comp Sci POB 1982 Dammam 31441 Saudi Arabia Imam Abdulrahman Bin Faisal Univ Appl Coll Dept Comp Sci POB 1982 Dammam 31441 Saudi Arabia Future Univ Egypt Res Ctr New Cairo 11835 Egypt

Cloud computing (CC) is an internet-enabled environment that provides computing services such as networking, databases, and servers to clients and organizations in a cost-effective manner. Despite the benefits rendered by CC, its security remains a prominent concern to overcome. An intrusion detection system (IDS) is generally used to detect both normal and anomalous behavior in networks. The design of IDS using a machine learning (ML) technique comprises a series of methods that can learn patterns from data and forecast the outcomes consequently. In this background, the current study designs a novel multi-objective seagull optimization algorithm with a deep learning-enabled vulnerability detection (MOSOA-DLVD) technique to secure the cloud platform. The MOSOA-DLVD technique uses the feature selection (FS) method and hyperparameter tuning strategy to identify the presence of vulnerabilities or attacks in the cloud infrastructure. Primarily, the FS method is implemented using the MOSOA technique. Furthermore, the MOSOA-DLVD technique uses a deep belief network (DBN) method for intrusion detection and its classification. In order to improve the detection outcomes of the DBN algorithm, the sooty tern optimization algorithm (STOA) is applied for the hyperparameter tuning process. The performance of the proposed MOSOA-DLVD system was validated with extensive simulations upon a benchmark IDS dataset. The improved intrusion detection results of the MOSOA-DLVD approach with a maximum accuracy of 99.34% establish the proficiency of the model compared with recent methods.

关键词： cloud computing deep learning intrusion detection system sooty tern optimization algorithm seagull optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Financial Time Series Forecasting: A Combinatorial Forecasting Model Based on STOA Optimizing VMD

引用

INternATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2022年第8期31卷 2250042-2250042页

作者： Yao, Tianxiang Liu, Xichun Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Forecast & Evaluat Meteoro Nanjing 210044 Peoples R China

The complexity and frequent fluctuations of economic data pose a significant challenge to forecasting studies. In order to predict financial data more accurately, we build a fusion model for predicting financial data based on the idea of decomposition-recombination by combining the sooty tern optimization algorithm (STOA), Variational Mode Decomposition (VMD), Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN). Firstly, after finding the optimal parameters of the VMD by the STOA, adjust the parameters of the VMD and decompose the financial data, remove the residual from the series, and constitute the remaining necessary information within the financial data into new series modeled by applying the SVM. Finally, calculate the error between the predicted and actual values. Notably, the residual is treated specially in the modeling. A Double-layer BPNN model is used to establish a mechanism to increase the sensitivity of the model fluctuations. The influence factor series and the residual are introduced as input variables to the BPNN to establish a mapping relationship between them and the error series. The results show that the model improves data utilization through five experiments, solves the problem of the VMD insensitivity to fluctuations, and improves the prediction accuracy of financial time series effectively.

关键词： Financial data combined prediction model sooty tern optimization algorithm support vector machines

来源：评论

学校读者我要写书评

暂无评论

A Novel Fault Classification Method for Process Industries Incorporating Multi-sphere Support Tensor Data Description and Hybrid Synchronization optimization

引用

IFAC-PapersOnLine 2023年第2期56卷 8073-8078页

作者： Chuanfang Zhang Kaixiang Peng Jie Dong Liang Ma Xueyi Zhang Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing 100083 PR China

Process monitoring is important for ensuring the safe running of process industries. From the perspective of pattern recognition, fault detection can be regarded as a binary classification problem, while fault classification can be regarded as a multi-class classification issue. As the extension of one-class classifier, multi-sphere support vector data description (MSVDD) are oriented to vector data and cannot deal with tensor data directly. Moreover, MSVDD cannot deal with feature selection and parameter optimization synchronously. In order to deal with above problems, a faut classification method based on multi class support tensor data description (MSTDD) is proposed in this paper. Based on sooty tern optimization algorithm (STOA) and beetle antennae search (BAS), a hybrid synchronization optimization (HSO) strategy is used to select features and optimize parameters at the same time. Application to the Tennessee Eastman process (TEP) verifies the classification performance of HSO-MSTDD comparing to MSTDD.

关键词： Fault classification multi-sphere support tensor data description sooty tern optimization algorithm beetle antennae search hot strip mill process

来源：评论

学校读者我要写书评

暂无评论

Enhanced botnet detection in IoT networks using zebra optimization and dual-channel GAN classification

引用

SCIENTIFIC REPORTS 2024年第1期14卷 1-19页

作者： Shareef, S. K. Khaja Chaitanya, R. Krishna Chennupalli, Srinivasulu Chokkakula, Devi Kiran, K. V. D. Pamula, Udayaraju Vatambeti, Ramesh Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Informat Technol Hyderabad India SRKR Engn Coll Dept ECE Bhimavaram 534204 India Inst Aeronaut Engn Dept CSE Hyderabad India Nadimpalli Satyanarayana Raju Inst Technol Dept Comp Sci & Engn Visakhapatnam 531173 India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram India SRM Univ AP Dept Comp Sci & Engn Amaravati 522502 Andhra Pradesh India VIT AP Univ Sch Comp Sci & Engn Vijayawada 522237 India

The Internet of Things (IoT) permeates various sectors, including healthcare, smart cities, and agriculture, alongside critical infrastructure management. However, its susceptibility to malware due to limited processing power and security protocols poses significant challenges. Traditional antimalware solutions fall short in combating evolving threats. To address this, the research work developed a feature selection-based classification model. At first stage, a preprocessing stage enhances dataset quality through data smoothing and consistency improvement. Feature selection via the Zebra optimization algorithm (ZOA) reduces dimensionality, while a classification phase integrates the Graph Attention Network (GAN), specifically the Dual-channel GAN (DGAN). DGAN incorporates Node Attention Networks and Semantic Attention Networks to capture intricate IoT device interactions and detect anomalous behaviors like botnet activity. The model's accuracy is further boosted by leveraging both structural and semantic data with the sooty tern optimization algorithm (STOA) for hyperparameter tuning. The proposed STOA-DGAN model achieves an impressive 99.87% accuracy in botnet activity classification, showcasing robustness and reliability compared to existing approaches.

关键词： Internet of things Zebra optimization algorithm Graph attention network sooty tern optimization algorithm Node attention networks

来源：评论

学校读者我要写书评

暂无评论

Single-phase-to-ground Fault Line Selection Method Based on STOA-SVM

Single-phase-to-ground Fault Line Selection Method Based on ...

引用

作者： Yicen Liu Xiaojiang Liu Songhai Fan Xiaomin Ma Sijing Deng State Grid Sichuan Electric Power Research Institute Automation and Information Engineering Sichuan University of Science & Engineering

In view of the complex characteristics of nonlinearity and non-stableness of the zero-order current of each line after the single-phase ground fault of the distribution network,a distribution network fault selection method based on sooty tern optimization algorithm(STOA) and the combination of support vector machine is *** first,the zero-sequence current before and after fault is obtained,then five kinds of IMFs including different components are obtained by ensemble empirical mode decomposition,and the energy entropy of the fault transient zero-sequence current is obtained by Hilbert transform,the results of training and testing are obtained by inputting the feature *** simulation results show that the accuracy of the proposed line selection model is 97.5%.

关键词： sooty tern optimization algorithm Support Vector Machine Single phase to ground fault Ensemble Empirical Mode Decomposition

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：