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science and technology for Energy Transition (STET) 2024年第1期79卷

作者： Rui-Ming, Fang Liao, Qiangqiang Askar, Shavan Pallathadka, Harikumar Sapaev, I.B. Baldaniya, Lalji Chahar, Mamata Saini, Suman Kapila, Ish Jewahery, Hassan Farhan, Mohsen Aued Mohmmedi, M. Information Systems Engineering Department Erbil Technical Engineering College Erbil Polytechnic University Erbil Iraq Manipur International University Manipur Imphal India Department Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Institute National Research University Tashkent Uzbekistan Scientific Researcher University of Tashkent for Applied Sciences Str. Gavhar 1 Tashkent100149 Uzbekistan Scientific Researcher Western Caspian University Baku Azerbaijan Marwadi University Research Centre Department of Pharmacy Faculty of Health Science Marwadi University Gujarat Rajkot360003 India Deparment of Chemistry NIMS Institute of Engineering & Technology NIMS University Rajasthan Jaipur India Department of Applied Sciences Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab Jhanjeri140307 India Centre for Research Imapct & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Department of Computer Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Techniques Engineering College of Technical Engineering The Islamic University of Babylon Babylon Iraq Department of Management Al-Nisour University College Nisour Seq. Karkh Baghdad Iraq Department of Electrical Engineering Islamic Azad University Branch of Central Tehran Tehran Iran

This study proposes day-ahead power scheduling for electrical systems in off-grid mode, emphasizing consumer involvement. Bi-Demand Side Management (DSM) approaches like strategic conversion and demand shifting are proposed for consumer involvement. Multiple objectives are modelled to voltage profile improvement and reduce the operation energy cost. The non-dominated solutions of the voltage of buses and operation energy cost are generated by enhanced epsilon-constraint technique, simultaneously. The General Algebraic Modeling System (GAMS) software is proposed for solving optimization problems. A combination of decision-making methods like weight sum and fuzzy procedures are implemented for finding optimal solution non-dominated solutions. The proposed method's effectiveness is confirmed through numerical simulations carried out on several case studies that utilize the 33-bus electrical system. The findings illustrate the substantial effectiveness of demand-side participation in improving power dispatch and the optimal rate of multiple objectives. By using DSM, operation cost is reduced by 21.58% and the voltage index is improved by 13.36% than the lack of implementing DSM. © The Author(s), published by EDP sciences, 2024.

关键词： Demand response

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Preface

Preface

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作者： Hu, Zhengbing Dychka, Ivan Petoukhov, Sergey He, Matthew School of Computer Science Hubei University of Technology Wuhan China Mechanical Engineering Research Institute of the Russian Academy of Sciences Moscow Russia Faculty of Applied Mathematics National Technical University of Ukraine "Igor Sikorsky Kiev Polytechnic Institute" Kiev Ukraine Halmos College of Arts and Sciences Nova Southeastern University Ft. LauderdaleFL United States

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Analysis of Dense Descriptors in 3D Face Recognition

Analysis of Dense Descriptors in 3D Face Recognition

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11th IEEE International Conference on System engineering and technology, ICSET 2021

作者： Zebari, Dilovan Asaad Abrahim, Araz Rajab Ibrahim, Dheyaa Ahmed Othman, Gheyath M. Ahmed, Falah Y. H. Nawroz University Department of Computer Science Kurdistan Region Duhok42001 Iraq Duhok Polytechnic University Technical College of Informatics Department of Information Technology Iraq Information Technology Collage Imam Ja 'Afar Al-Sadiq University Communications Engineering Techniques Department Baghdad Iraq Management Science University Faculty of Information Sciences Engineering Selangor Shah Alam Malaysia

ISBN: (纸本)9781665437660

In the past years, a revolution took place in the world of technology and developed rapidly in all areas, covering various aspects of life. One of the hottest topics that researchers work in is computer vision including artificial intelligence. As it has a great importance, it represents the basics for many applications that are currently used in various sectors. The technology of biometric recognition has progressively developed specially in security for identification purposes. Such technology is face recognition, which uses facial information of humans to recognize people. In this paper 3D (three-dimensional) face recognition approach is proposed by using dense descriptors Local Binary Pattern (LBP), Local Ternary Pattern (LTP), and Gabor with Support Vector Machine (SVM). LTP technique which is a variant and extension of LBP. LBP and LTP have been used for feature information extraction individuality and merging with Gabor from the 3D images, and then the SVM technique is employed to classify and recognize the faces according to extracted features. The database that depended in this work is Three-Dimensional Face Recognition Database (Texas 3DFRD). The accuracy obtained from the proposed model was about 94.9% after many attempts the better one is selected. © 2021 IEEE.

关键词： Support vector machines Three-dimensional displays Image resolution Databases Face recognition Merging Feature extraction

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An improved deep convolutionary neural network for bone marrow cancer detection using image processing

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Informatics in Medicine Unlocked 2023年 38卷

作者： Ramasamy, Manjula Devi Dhanaraj, Rajesh Kumar Pani, Subhendu Kumar Das, Rashmi Prava Movassagh, Ali Akbar Gheisari, Mehdi Liu, Yang Porkar, P. Banu, Sabitha Department of Computer Science and Engineering KPR Institute of Engineering and Technology India School of Computing Science and Engineering Galgotias University India Krupajal Engineering College Biju Patnaik University of Technology Odisha India Department of Computer Science and Engineering CV Raman Global University Bhubaneswar India Department of Biomedical Engineering School of Medicine Tehran University of medical sciences Tehran Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies China Department of Cognitive Computing Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Young researchers and elite club Islamic Azad University Parand Iran

Bone Marrow Cancer is a type of cancer that develops in the stem cells of the bone marrow that are responsible for blood formation. AML(Acute Myeloid Leukaemia) and MM(Multiple Myeloma) are both types of malignancy that can affect bone marrow. Conventionally, the process was carried out manually by a skilled professional in a considerable amount of time. Hence, in order to provide accurate treatment, a better diagnosis technique is required. In this regard, the suggested study has improved a unique technique, specifically classification and segmentation, allowing for the identification of more intricate disorders. The study's findings on the accuracy of cancer cell identification, as well as the decrease in the possibility of false alarm rates, continue to fall on the negative side of the outcome spectrum, according to the researchers. The hybrid approach has been proposed in this article that includes deep convolutional neural networks, hyperparameter sets utilising adaptive multi-objective CAT algorithms and other image processing techniques. To train the suggested model, it is critical to use previously processed cell images. The suggested model is then used to train an Optimized Convolutional Neural Network (OCNN), which is then used to determine the type of tumour identified in the bone area. The SN-AM datasets were thoroughly examined, and a number of presentation measures, such as accuracy (recall), F1-score, and specificity (specificity), were generated and analysed. When it came to predicting different types of cancer, researchers observed that they had an overall accuracy of 99.45 percent. In terms of recognizing the sorts of cancer cells, the proposed model surpassed all existing learning models. © 2023

关键词： Bone marrow CAT Algorithm Leukaemia Multiple myeloma Optimized convolutional neural network

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A fully-informed search scheme-based particle swarm optimisation for parameter estimation of proton exchange membrane fuel cell

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International Journal of Ambient Energy 2025年第1期46卷

作者： Jangir, Pradeep Arpita Premkumar, Manoharan Agrawal, Sunilkumar P. Pandya, Sundaram B. Parmar, Anil Abualigah, Laith University Centre for Research and Development Chandigarh University Mohali India Department of CSE Graphic Era Hill University Dehradun India Department of CSE Graphic Era Deemed To Be University Dehradun India Applied Science Research Center Applied Science Private University Amman Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electrical and Electronics Engineering Dayananda Sagar College of Engineering Bengaluru India Department of Electrical Engineering Government Engineering College Gandhinagar India Department of Electrical Engineering Shri K.J. Polytechnic Bharuch India Computer Science Department Al al-Bayt University Mafraq Jordan

In this study, we employ a recent variant of Particle Swarm Optimisation specifically for single objective numerical optimisation (PSO-sono) to pinpoint the optimal parameters of proton exchange membrane fuel cells (PEMFC) across certain operational scenarios. This research focuses on optimising seven parameters–(Formula presented.) and B–to replicate polarisation curves that align closely with those provided in manufacturers’ datasheets. The PSO-sono algorithm incorporates hybrid paradigms and adaptive parameter schemes, enhancing the traditional PSO algorithm's performance. The efficacy of PSO-sono was evaluated using six commercial PEMFC stacks: BCS 500-W PEM, BCS 250-W PEM, Nedstack PS6 PEM, 500W SR-12PEM, H-12 PEM and HORIZON 500W PEM, under varied operational conditions. The objective function for optimisation was the sum of squared errors (SSE) between the parameter-derived results and the actual experimental data from these fuel cell stacks. To establish the robustness of PSO-sono, its outcomes were benchmarked against results from other PSO variants, including PSO, CLPSO, DNLPSO, SLPSO, EPSO, PSO-DE, HFPSO, MPSO and HPSO. The I/V and P/V curves derived from PSO-sono showed a precise alignment with the datasheet specifications for all PEMFC models tested. Conclusively, the PEMFC model utilising the PSO-sono approach outperformed all other algorithms in terms of accuracy and convergence speed. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Particle swarm optimization (PSO)

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Supervised optimal control in complex continuous systems with trajectory imitation and reinforcement learning

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Scientific Reports 2025年第1期15卷 1-13页

作者： Liu, Yingjun Liu, Fuchun Huang, Renwei School of Computer Science and Technology Guangdong University of Technology Guangzhou 510006 China College of Electronic and Information Engineering Guangzhou City Polytechnic Guangzhou 511370 China

Supervisory control theory (SCT) is widely used as safeguard mechanism with control of discrete event systems (DESs). In complex continuous systems, in order to avoid system’s behavior violating specifications, the supervised control problem of these systems is quite different. Continuous state and action spaces of high dimension make languages of automaton no longer suitable for describing the information of specifications which remains challenging on control of real physical systems. Reinforcement learning (RL) automatically learns complex decisions through trial and error, but it requires the design of precise reward functions combined with domain knowledge. For complex scenarios where the reward function cannot be achieved or is only with sparse rewards, we proposed a novel supervised optimal control framework based on trajectory imitation (TI) and reinforcement learning (RL) in this paper. Firstly, behavior cloning (BC) is adopted to pre-train the policy model based on a small number of human demonstrations. Secondly, a generative adversarial imitation learning (GAIL) method is carried out to obtain the implicit characteristics of demonstration data. Furthermore, after the primary and implicit features are extracted by the above steps, a Demo-based RL algorithm is designed by adding the demonstration data to the RL replay buffer with augmented loss function to enhance the system performance to its maximum potential. Finally, the proposed method is validated through multiple simulation experiments on object relocation and tool using task of dexterous multifingered hands. In handling the more complex tool using task, the proposed approach achieves a 19.7% decrease in convergence time as opposed to the latest method. And the proposed method for the two tasks results in policies that display natural movements and shows higher robustness compared with the baseline model. © The Author(s) 2025.

关键词： Deep reinforcement learning Imitation learning Optimal control

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Traffic engineering in Dynamic Hybrid Segment Routing Networks

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computers, Materials & Continua 2021年第7期68卷 655-670页

作者： Yingya Guo Kai Huang Cheng Hu Jiangyuan Yao Siyu Zhou College of Mathematics and Computer Science Fuzhou UniversityFuzhou350000China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350000China Key Laboratory of Spatial Data Mining&Information Sharing Ministry of EducationFuzhou350003China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou510006China School of Computer Science and Cyberspace Security Hainan UniversityHaiKou570228China Tandon School of Engineering New York UniversityNew York10012USA Department of Computing Hong Kong Polytechnic UniversityHong Hom999077Hong Kong

The emergence of Segment Routing(SR)provides a novel routing paradigm that uses a routing technique called source packet *** SR architecture,the paths that the packets choose to route on are indicated at the ingress *** with shortest-path-based routing in traditional distributed routing protocols,SR can realize a flexible routing by implementing an arbitrary flow splitting at the ingress *** the advantages of SR,it may be difficult to update the existing IP network to a full SR deployed network,for economical and technical *** partial of the traditional IP network to the SR network,thus forming a hybrid SR network,is a preferable *** the traffic is dynamically changing in a daily time,in this paper,we propose a Weight Adjustment algorithm WASAR to optimize routing in a dynamic hybrid SR *** algorithm can be divided into three steps:firstly,representative Traffic Matrices(TMs)and the expected TM are obtained from the historical TMs through ultrascalable spectral clustering ***,given the network topology,the initial network weight setting and the expected TM,we can realize the link weight optimization and SR node deployment optimization through a Deep Reinforcement Learning(DRL)***,we optimize the flow splitting ratios of SR nodes in a centralized online manner under dynamic traffic demands,in order to improve the network *** the evaluation,we exploit historical TMs to test the performance of the obtained routing configuration in *** extensive experimental results validate that our proposed WASAR algorithm has superior performance in reducing Maximum Link Utilization(MLU)under the dynamic traffic.

关键词： Traffic engineering routing optimization segment routing deep reinforcement learning ultra-scalable spectral clustering

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Sparse Representation of Rational Transfer Function by l1 minimization

Sparse Representation of Rational Transfer Function by l1 mi...

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International Conference on Information science and Control engineering (ICISCE)

作者： Li Shuang Computer Engineering Technical College (Artificial Intelligence College) Guangdong Polytechnic of Science and Technology (GDPST) Zhuhai China

In this paper, we present a new method for sparse representations of rational transfer functions in H 2 (D) space by using Iteratively Reweighted Least Squares (IRLS) and shifted Cauchy kernels. IRLS is an l 1 minimization algorithm. This problem originates from identification of transfer functions of FIR models. We also give three examples to illustrate the idea.

关键词： Information science Control engineering Finite impulse response filters Transfer functions Minimization Kernel

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Deep learning for macular fovea detection based on ultra-widefield fundus images 2

Deep learning for macular fovea detection based on ultra-wid...

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2nd International Conference on Electrical, Electronics, and Information engineering, EEIE 2023

作者： Wang, Mini Han Huang, Lina Hou, Guanghui Yang, Jie Xing, Lumin Yuan, Qiting Chong, Kelvin Kam-Lung Lin, Zhiyuan Zeng, Peijin Fang, Xiaoxiao Yao, Xiaoping Li, Qingqian Liu, Jiang Lin, Chen Department of Ophthalmology & Visual Sciences Faculty of Medicine Chinese University of Hong Kong Hong Kong999077 Hong Kong The faculty of Data Science City University of Macau 999078 China Zhuhai People's Hospital Zhuhai Hospital Affiliated with Jinan University Zhuhai519000 China The Department of AI and big data application Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai519000 China Perspective Technology Group Zhuhai519000 China Jinan University Affiliated Shenzhen Eye Hospital Shenzhen518000 China Aier Eye Hospital of Zhuhai Zhuhai519000 China College of Artificial Intelligence Chongqing Industry & Trade Polytechnic Chongqing408000 China The First Affiliated Hospital Shandong First Medical University Shandong Provincial Qianfoshan Hospital Jinan250014 China Business School Hong Kong University of Science and Technology Hong Kong999077 Hong Kong Company Ltd. Guangdong Shenzhen518000 China School of Computer Beijing Institute of Technology Zhuhai Zhuhai519000 China Union Hospital of Fujian Medical University Fuzhou353000 China Department of Ophthalmology People's Hospital of Yantian District Shenzhen518000 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518000 China Department of Ophthalmology Shenzhen People's Hospital Shenzhen518000 China

ISBN: (纸本)9781510672840

Macula fovea detection is a crucial molecular biological prerequisite for screening and diagnosing macular diseases. Without early detection and proper treatment, any abnormality involving the macula may lead to blindness. However, with the ophthalmologist shortage and time-consuming artificial evaluation, neither the accuracy nor effectiveness of the diagnosis process could be guaranteed. In this project, we proposed a light-weighted deep learning model based on ultra-widefield fundus (UWF) images for macula fovea detection tasks. This study collected 2300 ultra-widefield fundus images from Shenzhen Aier Eye Hospital in China. A light-weighted method based on a U-shape network (Unet) and Fully Convolution Network (FCN) approach is implemented on 1800 (before amplifying process) training fundus images, 400 (before amplifying process) validation images, and 100 test images. Three professional ophthalmologists were invited to mark the fovea. A method from the anatomy perspective is investigated. This approach is derived from the spatial relationship between the macula fovea and optic disc center in UWF. A set of parameters of this method is set based on the experience of ophthalmologists and verified to be effective. The ultra-widefield swept-source optical coherence tomography (UWF-OCT) approach is the grounded method. Through a comparison of proposed methods, we conclude that the proposed light-weighted Unet method outperformed other methods on macula fovea detection tasks. © 2024 SPIE.

关键词： Convolution

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Critical analysis: Bat algorithm based investigation and application on several domains

arXiv

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arXiv 2021年

作者： Umar, Shahla U. Rashid, Tarik A. Technical College of Informatics Sulaimani Polytechnic University Sulaimani KRG Iraq Network Department College of Computer Science and Information Technology Kirkuk University Kirkuk Iraq Computer Science and Engineering School of Science and Engineering University of Kurdistan Hewler Erbil- KRG Iraq

In recent years several swarm optimization algorithms, such as Bat Algorithm (BA) have emerged, which was proposed by Xin-She Yang in 2010. The idea of the algorithm was taken from the echolocation ability of bats. Purpose- The purpose of this study is to provide the reader with a full study of the Bat Algorithm, including its limitations, the fields that the algorithm has been applied, versatile optimization problems in different domains, and all the studies that assess its performance against other meta-heuristic algorithms. Design/Methodology/Approach- Bat Algorithm is given in-depth in terms of backgrounds, characteristics, limitations, it has also displayed the algorithms that hybridized with BA (K-Medoids, Back-propagation neural network, Harmony Search Algorithm, Differential Evaluation Strategies, Enhanced Particle Swarm Optimization, and Cuckoo Search Algorithm) and their theoretical results, as well as to the modifications that have been performed of the algorithm (Modified Bat Algorithm (MBA), Enhanced Bat Algorithm (EBA), Bat Algorithm with Mutation (BAM), Uninhabited Combat Aerial Vehicle-Bat algorithm with Mutation (UCAV-BAM), Nonlinear Optimization). It also provides a summary review that focuses on improved and new Bat Algorithm (Directed Artificial Bat Algorithm (DABA), Complex-valued Bat Algorithm (CBA), Principal Component Analyses- Bat Algorithm (PCA-BA), multiple strategies coupling Bat Algorithm (MixBAT), Directional Bat Algorithm (dBA)). Findings- Shed light on the advantages and disadvantages of this algorithm through all the researches that dealt with the algorithm in addition to the fields and applications it has addressed in the hope that it will help scientists understand and develop it. Originality/value- As far as the research community knowledge, there is no comprehensive survey study conducted on this algorithm coverıng all its aspects. © 2021, CC BY-NC-ND.

关键词： Swarm intelligence

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