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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Venkatesan, P. Gopinath, C. Prabhavathy, P. Department of Electrical and Electronics Engineering India Center for Integrated Smart Waste Management System Department of Electrical and Electronics Engineering Sriperumbudur India Anna University Department of Computer Science and Engineering College of Engineering Guindy Chennai India

ISBN: (纸本)9798331535476

Increasing energy demands and the need for efficient management necessitate innovative solutions. This study introduces an integrated Demand Side Management (DSM) framework tailored for residential buildings within smart grid systems. The proposed methodology employs Earth Worm Optimization Algorithm (EWOA) and Virulence Optimization Algorithm (VOA) to optimize scheduling for household appliance using integration of Renewable Energy Storage (RES) and Renewable Energy Resources (RERs). Real-Time Electricity Scheduling (RTES) is implemented using IoT technologies to enable adaptive energy management. This system demonstrates significant performance improvements, achieving a 76.19% reduction in the Peak-to-Average Ratio (PAR) using VOA and a 73.8% reduction with EWOA when RES is included. Comparative analyses highlight substantial cost reductions, enhanced grid stability, and improved energy efficiency. This work not only showcases the novelty of integrating advanced optimization algorithms with IoT but also provides a robust approach to achieve sustainable and cost-effective energy management. © 2024 IEEE.

关键词： Smart power grids

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Fine classification of rice paddy using multitemporal compact polarimetric SAR C band data based on machine learning methods

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Frontiers of Earth science 2024年第1期18卷 30-43页

作者： Xianyu GUO Junjun YIN Kun LI Jian YANG Huimin ZOU Fukun YANG School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China College of Global Change and Earth System Science Beijing Normal UniversityBeijing 100875China

Rice is an important food crop for human *** distinguishing different varieties and sowing methods of rice on a large scale can provide more accurate information for rice growth monitoring,yield estimation,and phenological monitoring,which has significance for the development of modern *** polarimetric(CP)synthetic aperture radar(SAR)provides multichannel information and shows great potential for rice monitoring and ***,the use of machine learning methods to build classification models is a controversial *** this paper,the advantages of CP SAR data,the powerful learning ability of machine learning,and the important factors of the rice growth cycle were taken into account to achieve high-precision and fine classification of rice ***,CP SAR data were simulated by using the seven temporal RADARSAT-2 C-band data ***,20-two CP SAR parameters were extracted from each of the seven temporal CP SAR data *** addition,we fully considered the change degree of CP SAR parameters on a time scale(ΔCP_(DoY)).Six machine learning methods were employed to carry out the fine classification of rice *** results show that the classification methods of machine learning based on multitemporal CP SAR data can obtain better results in the fine classification of rice paddies by considering the parameters ofΔCP_(DoY).The overall accuracy is greater than 95.05%,and the Kappa coefficient is greater than *** them,the random forest(RF)and support vector machine(SVM)achieve the best results,with an overall accuracy reaching 97.32%and 97.37%,respectively,and Kappa coefficient values reaching 0.965 and 0.966,*** the two types of rice paddies,the average accuracy of the transplant hybrid(T-H)rice paddy is greater than 90.64%,and the highest accuracy is 95.95%.The average accuracy of direct-sown japonica(D-J)rice paddy is greater than 92.57%,and the highest accuracy is 96.13%.

关键词： compact polarimetric(CP)SAR rice paddy machine learning fine classification multitemporal

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Fine-grained Energy-efficient Data Center Traffic Scheduling Mechanism Based on Hierarchical Control

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第3期8卷 742-757页

作者： Niu, Tianyu Li, Honghui Li, Weidong Por, Lip Yee College of Computer and Information Engineering Inner Mongolia Agricultural University Inner Mongolia Hohhot China Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia

With the continuous expansion of the data center, its energy consumption is also increasing. Aiming at the problem that the high redundancy of modern data center network causes low energy-consumption utilization, this paper proposes an energy-saving traffic scheduling algorithm (IMV-MLF) based on improved multi-layer-virtual-topology combined with maximum link-utilization path priority, which realizes more fine-grained control of each layer of virtual topology in data center network. Firstly, the relevant shap-ing linear programming mathematical model is established according to the optimization goal of energy conservation, which can minimize the energy consumption of the whole network as far as possible under the premise of meeting all network-flow requirements;Then, according to the optimization goal, the existing SDN based energy saving multi-layer-virtual-topology (EMV-SDN) method is improved. Whether the network devices in other layers are enabled is determined by the overall traffic level rather than the traffic level of a single link. On the basis of the opened level, the newly arrived network flows are preferentially allocated to the path with the highest link utilization, and overload link set is introduced, we can effectively use the available link routes with sufficient residual bandwidth to reduce congestion when the topology changes by marking the links whose link utilization reaches the critical value, and further count and close the links and switches which no network flows pass through. The experimental results show that the performance of IMV-MLF algorithm is better than multi-layer virtual topology energy saving (EMV-SDN) algorithm and the equivalent multipath (ECMP) algorithm in saving network energy. This result is helpful to provide another new idea for energy conservation of data center. © 2023, Journal of Network Intelligence.

关键词： Energy utilization

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Deep learning from three-dimensional Lithium-ion battery multiphysics model Part II:Convolutional neural network and long short-term memory integration

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Energy and AI 2024年第3期17卷 569-578页

作者： Yiheng Pang Anqi Dong Yun Wang Zhiqiang Niu Renewable Energy Resources Lab(RERL) Department of Mechanical and Aerospace EngineeringUniversity of CaliforniaIrvineCaliforniaUSA92697-3975 College of Engineering School of System Design and Intelligent ManufacturingSouthern University of Science and TechnologyChina Department of Aeronautical and Automotive Engineering Loughborough UniversityUnited KingdomLE113TU

Lithium-ion (Li-ion) batteries have emerged as a cornerstone of electric vehicles (EVs), enabling the road transportation towards net zero. The success of electric vehicles largely hinges on the battery performance and safety. It is challenging to test and predict battery performance and safety issues by conventional methods, which are usually time-consuming and expensive, involving significant human and measurement errors. To enable the quick estimation of battery performance and safety, we developed three data-driven machine learning (ML) models, namely a convolutional neural network (CNN), a long short-term memory (LSTM), and a CNN-LSTM to predict battery discharge curves and local maximum temperature (hot spot) under various operating conditions. The developed ML models mitigated data scarcity by employing a three-dimensional multi-physics Li-ion battery model to generate enormous and diverse high-quality data. It was found the CNN-LSTM model outperforms the others and achieved high accuracy of 98.68% to learn discharge curves and battery maximum temperature, owing to the integration of spatial and sequential feature extraction. The battery safety can be improved by comparing the predicted maximum battery temperature against safe temperature threshold. The proposed data development and data-driven ML models are of great potential to provide digital tools for engineering high-performance and safe EVs.

关键词： Li-ion batteries CNN LSTM Integration Performance Hot spot

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Optimizing flexural properties in PP/Sisal/PMA bio-composites by response surface methodology

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Interactions 2024年第1期245卷 1-14页

作者： Vadivel, M. Musrif, Pramod G. Satishkumar, P. Nasrulla, S. Mohamed Saminathan, Rajasekaran Vijayan, Vinesh Department of Civil Engineering Nehru Institute of Technology Coimbatore India Institute of Information Technology AISSMS Maharashtra Shivajinagar Pune India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Department of Mechanical Engineering PSNA College of Engineering & ampTechnology Tamilnadu Dindigul India Department of Mechanical Engineering College of Engineering and Computer Science Jazan University Jizan Saudi Arabia Rathinam Technical Campus Tamilnadu Coimbatore India

Applying the response surface methodology, this work aims to examine the flexural characteristics of bio composites composed of polypropylene (PP)/sisal fiber (Sisal)/polypropylene-grafted maleic anhydride (PMA). To predict the modulus behavior and flexural strength of the natural fiber bio-composite, mathematical models have been developed as a function of Sisal load, PMA, and fiber length compatibilizer content. These models are based on a three-factor, three-level Box-Behnken design (BBD), a subset of the response surface methodology (RSM). Sisal fiber (15–35 wt%), fiber length (4–12 mm), and PMA (2–6% wt%) were the three levels that were selected for the factors that were taken into consideration. The utilize of contour plots and response surface methodology allowed for the determination of optimal compositions with respect to improved flexural properties. According to the results produced from the design expert software, the flexural modulus (FM) is 1625.09 MPa and the ideal flexural strength (FS) is 46.3954 MPa. A high degree of agreement between the experimental data and the model’s predictions was indicated by the R2 values and normal probability maps. The specimens were then examined utilizing scanning electron microscopy (SEM) to determine their morphology. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Bending strength

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Adaptive solar power generation forecasting using enhanced neural network with weather modulation

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Journal of Intelligent and Fuzzy systems 2024年第4期46卷 10955-10968页

作者： Sujeeth, T. Ramesh, C. Palwe, Sushila Ramu, Gandikota Basha, Shaik Johny Upadhyay, Deepak Chanthirasekaran, K. Sivasankari, K. Rajaram, A. Department of CSE Siddartha Educational Academy Group of Institutions Andhra Pradesh Tirupati India Department of Mechanical Engineering M. Kumarasamy College of Engineering Karur India Department of Computer Engineering and Technology MIT-WPU Maharashtra India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad India Andhra Pradesh Mylavaram India Department of Computer Science and Engineering Graphic Era hill univeristy Dehradun India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India Department of Electronics and Communication Engineering Akshaya College of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam India

Solar power generation forecasting plays a vital role in optimizing grid management and stability, particularly in renewable energy-integrated power systems. This research paper presents a comprehensive study on solar power generation forecasting, evaluating traditional and advanced machine learning methods, including ARIMA, Exponential Smoothing, Support Vector Regression, Random Forest, Gradient Boosting, and Physics-based Models. Moreover, we propose an innovative Enhanced Artificial Neural Network (ANN) model, which incorporates Weather Modulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed model is evaluated using real-world solar power generation data, and the results demonstrate its superior performance compared to traditional methods and other machine learning approaches. The Enhanced ANN model achieves an impressive Root Mean Square Error (RMSE) of 0.116 and a Mean Absolute Percentage Error (MAPE) of 36.26%. The integration of Weather Modulation allows the model to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the model to capture short-term trends, reducing forecasting errors arising from abrupt weather changes. The proposed Enhanced ANN model showcases its potential as a promising tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices. © 2024 – IOS Press.

关键词： Solar power generation

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Enhancing petroleum logistics with innovative decision support mechanism for fuel tanker selection

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Environmental science and Pollution Research 2025年第17期32卷 10910-10928页

作者： Murat, Mirac Seyhan, Mehmet Erdogan, Melike Ayyildiz, Ertugrul Department of Industrial Engineering Karadeniz Technical University Trabzon61080 Turkey Department of Mechanical Engineering Karadeniz Technical University Trabzon61080 Turkey Department of Computer Engineering Duzce University Duzce81620 Turkey College of Science and Engineering Hamad bin Khalifa University Doha Qatar

Transportation of petroleum-based products is a complex and hazardous logistics process. Selecting the appropriate transportation vehicles for this process is crucial for ensuring safety and efficiency. Therefore, prioritizing selection criteria and choosing the most suitable fuel tanker is essential in reducing potential risks and improving overall fleet operations. Given the many types of tankers available for petroleum product transportation, decision-makers should employ a multi-criteria decision-making (MCDM) approach to identify the best option for their specific needs. This study introduces a novel MCDM approach that combines the Weighted Aggregated Sum Product Assessment (WASPAS) and the Criteria Importance through Intercriteria Correlation (CRITIC) in a Fermatean fuzzy (FF) environment, addressing five main criteria and evaluating five fuel tanker alternatives. Using this approach, the research identified and prioritized key criteria for selecting an appropriate tanker model by providing insights into 24 sub-criteria. The results reveal that tanker price emerges as the most important sub-criterion, leading us to determine the M3 tanker model as the most suitable fuel tanker after carefully evaluating all 24 different sub-criteria. The findings can significantly assist petroleum firms in making informed decisions when choosing fuel tankers by considering various technical, operational, comfort, environmental, and economic factors. The results can also be used to inform policy on fuel distribution. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Fleet operations

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Cloud-edge-based We-Market:Autonomous Bidding and Peer-to-peer Energy Sharing Among Prosumers

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Journal of Modern Power systems and Clean Energy 2023年第4期11卷 1282-1293页

作者： Fangyuan Si Yinghua Han Qinqin Xu Jinkuan Wang Qiang Zhao College of Information Science and Engineering Northeastern UniversityShenyangChinaand F.Si is also with the State Key Laboratory of Power System and Generation EquipmentDepartment of Electrical EngineeringTsinghua UniversityBeijingChina School of Computer and Communication Engineering Northeastern University at QinhuangdaoQinhuangdaoChina School of Control Engineering Northeastern University at QinhuangdaoQinhuangdaoChina

With the extensive penetration of distributed renewable energy and self-interested prosumers,the emerging power market tends to enable user autonomy by bottom-up control and distributed *** paper is devoted to solving the specific problems of distributed energy management and autonomous bidding and peer-to-peer(P2P)energy sharing among prosumers.A novel cloud-edge-based We-Market is presented,where the prosumers,as edge nodes with independent control,balance the electricity cost and thermal comfort by formulating a dynamic household energy management system(HEMS).Meanwhile,the autonomous bidding is initiated by prosumers via the modified Stone-Geary utility *** the cloud center,a distributed convergence bidding(CB)algorithm based on consistency criterion is developed,which promotes faster and fairer bidding through the interactive iteration with the edge ***,the proposed scheme is built on top of the commercial cloud platform with sufficiently secure and scalable computing *** results show the effectiveness and practicability of the proposed We-Market,which achieves 15%cost reduction with shorter running *** analysis indicates better scalability,which is more suitable for largerscale We-Market implementation.

关键词： We-Market bidding energy sharing prosumer peer-to-peer cloud-edge

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Enhancing mechanical performance of kenaf fiber reinforced polymer composites: influence of fiber characteristics and processing techniques

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Interactions 2024年第1期245卷 1-20页

作者： Chanderasekaran, J. H. Rakini Naidu, B. Vishnu Vardhana Palanisamy, Satishkumar Vallinayagam, Sugumari Saminathan, Rajasekaran Arignar Anna Government Arts College Tamilnadu Musiri India Department of Mechanical Engineering Sree Vidyanikethan Engineering College Mohan Babu University Andhra Pradesh Tirupati India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Department of Biotechnology Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Tamilnadu Chennai India Department of Mechanical Engineering College of Engineering and Computer Science Jazan University Jizan Saudi Arabia

The inherent fiber strength of kenaf is decent because of the high cellulose content and low micro fibrillar angle of the fiber. During the hand layup process of creating the epoxy matrix composite, it will be used as reinforcement in the proposed project. Composite testing has looked at how different fiber types, orientations, and content affect mechanical properties. Different types of fibers (short length and long length fibers), orientations (0°, 90°, and 60°), and fiber contents (6, 12, and 18) were altered to prepare the samples. At 18 wt% fiber content, the mechanical characterization results show that the short fiber composite achieves its maximum tensile strength of 24.90 MPa and its ultimate fiber strength of 45.94 MPa. Nevertheless, the highest tensile and fracture strengths of long fiber reinforced composites containing 5% fiber are 38.96 MPa and 59.43 MPa, respectively. The composite showed an impact strength of 25.30 J m− 2 and a highest flexural strength of 56.32 MPa with fibres orientated at 0°. A maximum water absorption value of 1.25% was recorded for long fiber reinforced composites and 1.74% for short fiber reinforced composites. At last, the morphological analysis was performed to identify the most common causes of composite failure: debonding, splitting, and fiber pull-out. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Kenaf fibers

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Condition Monitoring of Brake Disc Images using Convolutional Neural Network 10

Condition Monitoring of Brake Disc Images using Convolutiona...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Bharadwaj, P.S.S. Jakkula, Tarun Sandhya, B. Madhavi, M. Engineering College Department of Computer Science and Engineering Hyderabad India Engineering College Department of Mechanical Engineering Hyderabad India

ISBN: (纸本)9789380544472

Predictive health maintenance in automobiles is soon a necessity fueled by the evolution of autonomous vehicles. Data driven approaches are popularly being used for vehicle maintenance to discover anomalies and predict malfunctions. Health monitoring systems in an automobile can be employed for brakes, suspension elements, tyres, and components of an engine. Brake system is one of the critical components of an automobile which must be supervised regularly to avoid serious consequences during driving. Brake disc is one of the contributing factors to complaints about faulty brake systems. Brake disc are damaged due to cracks formed either as a result of thermal stresses which arise because of sudden braking or due to high braking cycles. An intelligent system which can monitor the condition of brake system using CNN with the help of images of brake disc is discussed. A dataset of disc images is prepared from the videos of brake disc captured at varying accelerations in a test reg in addition to static images captured with varying scale, rotation and background. Performance of pre-trained CNN models is tested on the dataset of disc images in order to identify a healthy or a unhealthy disc. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Statistical tests

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