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Particle swarm optimization algorithm based RBF neural network prediction model for the load of green data center

Particle swarm optimization algorithm based RBF neural netwo...

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3rd International conference on control Theory and Applications (ICoCTA 2023)

作者： F. Mei J. Jiang X. Liu State Grid Zhejiang Electric Hangzhou People's Republic of China

With the development of big data, data centers (DCs) are springing up like mushrooms, and their energy consumption problems are increasingly prominent. The load forecasting of DC is the prerequisite for its workload scheduling and energy saving optimization. Particle swarm optimization algorithm based radial basis function (PSORBF) neural network prediction model is proposed to forecast data center load more accurately. The experimentation of PSORBF is implemented on MATLAB R2021a through a comparative study against the RBF neural network, and the expeirmental results verified the significance of the proposed solution in improving prediction accuracy of data center load.

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Proceedings - 2022 3rd Asia conference on Computers and Communications, ACCC 2022

Proceedings - 2022 3rd Asia Conference on Computers and Comm...

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3rd Asia conference on Computers and Communications, ACCC 2022

ISBN: (纸本)9798350332957

The proceedings contain 24 papers. The topics discussed include: a quantitative evaluation method for cryptography applications in industrial control systems;mobile application SDK version detection and security alert based on multi-partition LSH;design of ai-based TCM prescription analysis applet;AASH: a lightweight and efficient static IoT malware detection technique at source code level;research on test task management scheduling of integrated test platform based on improved genetic algorithm;improved seir model based on recovery rate optimization to predict COVID-19;flight arrival delay time prediction based on machine learning;an intelligent security operation method for cloud-network integration services in multi-cloud scenes;research on a three-dimensional attention module;and research on a three-dimensional attention module.

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Research on Subway Harmonic Compensation control Method Based on SSA Optimized Adaline Algorithm 3

Research on Subway Harmonic Compensation Control Method Base...

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2023 3rd International conference on Power System and Energy Internet, PoSEI 2023

作者： Wang, Zongyan Mao, Zhongya Guo, Qiyi Han, Xinyu College of Electronics and Information Engineering Tongji University Shanghai201804 China

To avoid a large number of harmonics generated by the subway train in the process of operation on the power grid security, the theoretical analysis and simulation experiments on the harmonics of the subway traction power supply system are carried out. Aiming at the problem of severe distortion and fluctuation of network side current at the early stage of train startup, a harmonic analysis and compensation control method based on the Sparrow Search Algorithm (SSA) optimized Adaline Algorithm is proposed, which can be employed to carry out real-time adaptive detection and suppression of harmonics generated by the subway traction power supply system. This method can form a closed-loop training loop with the subway system, and the sparrow search algorithm is used to optimize the initial weight value and learning rate hyperparameters of the Adaline model. Simulation results show that the proposed compensating control can reduce harmonics from 15.12% to 2.89% and the current distortion rate by up to 80.8%. Four error evaluation indexes were used to evaluate the errors before and after the optimization. This method is highly adaptive and can be employed to analyze and compensate system harmonics efficiently. © Published under licence by IOP Publishing Ltd.

关键词： Harmonic analysis

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Proceedings of the 2023 International conference on Emerging Techniques in Computational Intelligence, ICETCI 2023

Proceedings of the 2023 International Conference on Emerging...

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3rd International conference on Emerging Techniques in Computational Intelligence, ICETCI 2023

ISBN: (纸本)9798350300604

The proceedings contain 52 papers. The topics discussed include: analysis of wireless sensor network security models: a salient approach for deeper inspection using deep neural networks;an improved hybrid model for target detection;gesture controlled virtual mouse and keyboard using OpenCV;NLP based topic modeling for healthcare: analyzing patient reviews to improve quality of care and access to services;performance optimization for transformer models on text classification tasks;a GIS-based framework applied to assess vulnerability and impact heatwave scenarios in urban systems;adaptive neural dynamic surface control for course tracking of ships with full state constraints;design of slotted triangular microstrip patch antenna with different feeding techniques;and support vector machines: spherical separability: limitations of learning.

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Third International conference on Image Processing and Intelligent control, IPIC 2023

Third International Conference on Image Processing and Intel...

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3rd International conference on Image Processing and Intelligent control, IPIC 2023

ISBN: (纸本)9781510668140

The proceedings contain 54 papers. The topics discussed include: image recognition and position technology based on super-pixel fuzzy C-means clustering in industrial assembly systems;a method of extracting outer eye corners of terra cotta warriors based on point cloud data;research on UI design and optimization of digital media with artificial intelligence;video description method with fusion of instance-aware temporal features;a multi-modal information fusion-based method for repairing cracks in train hooks;research on optical detection technology for underwater archaeology;discharging state recognition method of intelligent ring network cabinet based on audio signal spectrum analysis;numerical analysis and calculation of urban landscape spatial pattern;and sparse signal recovery for multispectral demosaicking using pseudo-panchromatic image.

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Fuel Economy optimization for Different Drive Cycles in Hybrid Electric Vehicle 3

Fuel Economy Optimization for Different Drive Cycles in Hybr...

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3rd International conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Yadav, Kanchan Nigam, Nishesh Shah, Sanjeev Kumar Akhayyat, Ahmed Pareek, Shashank Chohan, Jasgurpreet Singh GLA University Department of Electrical Engineering Mathura India IES College of Technology Bhopal IES University Dept. of Computer Science and Engg. Bhopal India Uttaranchal Institute of Technology Uttaranchal University Electronics & Communication Engineering Department Dehradun248007 India College of Technical Engineering The Islamic University Najaf Iraq Chandigarh University Dept. of Computer Science and Engg. Mohali140413 India University Centre for Research and Development Chandigarh University Mohali140413 India

ISBN: (纸本)9798350303360

The important control parameters for the hybrid electric vehicle are the lower and upper limit of state of charge (SOC). This SOC of the vehicle directly affects the economy and the emission rates. In this work, the parallel HEV is modelled by using ADVISOR (Advanced Vehicle Simulator). Different SOC limits are taken for testing the emission performance and fuel economy for the same designed driving cycles. With the simulation results we will be able to specify best upper and lower limits of SOC such that vehicle will achieve optimum fuel economy and emission performance. For control and energy management, the most important factor which needs to be taken into concern is the 'State of Charge (SOC)' value for the battery. Practically 20 % to 80% is the range which is usually set for the state of charge constraint The simulation is performed by taking repetitive velocity profiles (drive cycles) of two different curves i.e., UDDS (Urban Dynamometer Driving Schedule) and ECE. The SOC and emission curves are observed for these different drive cycles and the results having emission rates for HC, CO and NOx (in g/miles) are tabulated. This study's results shed light on why and how SOC caps affect HEVs' gas mileage and pollution ratings. The vehicle's total efficiency and environmental effect can be improved by identifying the ideal SOC range for various driving cycles. These findings can be used to inform the evolution of control and energy management systems for HEVs, leading to a more environmentally friendly and efficient transportation network in the future. © 2023 IEEE.

关键词： Fuel economy

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On the optimization of New Energy Automobile Power System 3

On the Optimization of New Energy Automobile Power System

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3rd International conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC)

作者： Yang, Fan Guangdong Univ Sci & Technol Dongguan 523083 Peoples R China

ISBN: (纸本)9781665454551

In order to optimize the power control system of new energy vehicles,based on the design parameters of new energy vehicles, the simulation analysis model is established. In view of the design of switched reluctance drive motor and the selection of regenerative battery, this paper puts forward the problem, and studies the characteristics of battery capacity in influencing new energy *** according to the actual state of the new energy vehicle cycle control system operation, the new energy vehicle battery parameters optimization scheme is proposed to improve the efficiency of the battery, and by introducing the quality ratio coefficient of the battery's charged state, the design of charging monomer specification capacity and combined capacity is comprehensively *** at simultaneously improving the power and economic performance of new energy vehicles, optimize the setting of vehicle driveline ratio from the overall aspect, and then carry out simulation analysis on this *** is of great significance for improving the dynamic performance of new energy vehicles and increasing the operating mileage of new energy vehicles.

关键词： Power system Battery efficiency New energy vehicle Powertrain optimization technology

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Sequential disaster recovery strategy for distribution network considering maintenance crew scheduling 3

Sequential disaster recovery strategy for distribution netwo...

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2024 3rd International conference on New Energy, Energy Storage and Power Engineering, NESP 2024

作者： Qi, Mutao Zhang, Feng Zhang, Gang Electrical Engineering Shan Dong University Shan Dong Province Jinan250100 China Electrical Engineering Harbin Engineering University Shan Dong Province Yantai264000 China

To efficiently restore electricity customers from a large-scale blackout, this paper proposes a novel mixed-integer linear programming (MILP) model for the optimal disaster recovery of power distribution systems. The physical maintenance crew (PMC) repairs damaged components and fosters synergy between the PMC scheduling model and the load restoration scheme. This collaboration generates optimal control sequences for scheduling maintenance routes and managing switches. Furthermore, there is a mismatch between the time scales of maintenance personnel scheduling and equipment repair, which can span from minutes to hours, and the much shorter time scales involved in load restoration and switching actions. To reconcile this mismatch, a "maintenance event triggering"mechanism is suggested. This mechanism aims to achieve harmony between the longer and shorter time frames, ensuring seamless integration and optimal performance. Finally, this paper verified the practical effect of this co-optimization model on a modified IEEE33 busbar test system. © Published under licence by IOP Publishing Ltd.

关键词： Restoration

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Data Sharing Based on Neural network Realizes Transparent Component Cost Allocation in Collaborative Light Industry 3rd

Data Sharing Based on Neural Network Realizes Transparent Co...

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3rd World conference on Intelligent and 3D Technologies, WCI3DT 2024

作者： Wang, Hao Chen, Lei Zhu, Jing Wu, Yanfeng Nanning University Guangxi Nanning530200 China Guangxi Quality Technology Engineering School Guangxi530200 China Big Data Academy Zhongke Henan Zhengzhou450000 China SPD Bank Nanning Branch Shanghai530022 China

ISBN: (纸本)9789819791231

With the continuous development of global economic integration, light industry, as an important part of manufacturing industry, faces the challenge of cost control and resource optimization. In light industry, transparent component cost allocation is a key issue, which involves the collaboration between multiple links and multiple participants. To solve this problem, a data sharing method based on neural network is proposed in this paper. First, we introduce the background and related research of transparent component cost allocation in light industry. We then use convolutional neural networks (CNNS) and data distribution Estimation (DDE) to train and model the data. By learning patterns and trends in the data, we are able to predict future supply needs and optimize transparent cost and cost allocation for data sharing. In addition, we also improved a data volume model (DVM) for data sharing to achieve synergistic light industry effects and improvement space through CNN and DDE. Next, through the learning and training of the historical data, the Volume Warp Model (VWM) model can accurately predict the cost of transparent components. In order to verify the effectiveness of this method, we conducted a set of experiments and carried out experimental analysis. Finally, the experimental results show that the data sharing method based on neural network can accurately predict the cost of transparent components and achieve fair distribution among participants, and the model has higher accuracy and efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data consistency

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Accuracy optimization in Speech Pathology Diagnosis with Data Preprocessing Techniques 1

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3rd International conference on optimization, Learning Algorithms and Applications (OL2A)

作者： Teixeira Fernandes, Joana Filipa Freitas, Diamantino Rui Teixeira, Joao Paulo Inst Politecn Braganca IPB Res Ctr Digitalizat & Intelligent Robot CeDRI P-5300 Braganca Portugal Univ Porto FEUP Fac Engn P-4200465 Porto Portugal Inst Politecn Braganca Associate Lab Sustainabil & Technol SusTEC Campus Santa Apolonia P-5300253 Braganca Portugal

ISBN: (数字)9783031530258

ISBN: (纸本)9783031530241;9783031530258

Using acoustic analysis to classify and identify speech disorders non-invasively can reduce waiting times for patients and specialists while also increasing the accuracy of diagnoses. In order to identify models to use in a vocal disease diagnosis system, we want to know which models have higher success rates in distinguishing between healthy and pathological sounds. For this purpose, 708 diseased people spread throughout 19 pathologies, and 194 control people were used. There are nine sound files per subject, three vowels in three tones, for each subject. From each sound file, 13 parameters were extracted. For the classification of healthy/pathological individuals, a variety of classifiers based on Machine Learning models were used, including decision trees, discriminant analyses, logistic regression classifiers, naive Bayes classifiers, support vector machines, classifiers of closely related variables, ensemble classifiers and artificial neural network classifiers. For each patient, 118 parameters were used initially. The first analysis aimed to find the best classifier, thus obtaining an accuracy of 81.3% for the Ensemble Sub-space Discriminant classifier. The second and third analyses aimed to improve ground accuracy using preprocessing methodologies. Therefore, in the second analysis, the PCA technique was used, with an accuracy of 80.2%. The third analysis combined several outlier treatment models with several data normalization models and, in general, accuracy improved, obtaining the best accuracy (82.9%) with the combination of the Greebs model for outliers treatment and the range model for the normalization of data procedure.

关键词： Outliers Normalization Speech Pathologies Speech Features Machine Learning Vocal Acoustic Analysis

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