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12th symposium on neural network applications in electrical engineering, NEUREL 2014

ISBN: (纸本)9781479958887

The proceedings contain 43 papers. The topics discussed include: on projection matrices and dictionaries in ECG compressive sensing - a comparative study;possibilities for attractive telemedical system implementation;pulse rate assessment: Eulerian video magnification vs. electrocardiography recordings;implementation of a feed-forward artificial neural network in VHDL on FPGA;neural network model for efficient localization of a number of mutually arbitrary positioned stochastic EM sources in far-field;extraction of Pospieszalski's noise model parameters of microwave FETs based on ANNs;assessment of blast induced ground vibrations by artificial neural network;customer classification and load profiling using data from smart meters;fuzzy approach for evaluating risk of service interruption used as criteria in electricity distribution network planning;classifications of motor imagery tasks using k-nearest neighbors;and estimating profitability using a neural classification tool.

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Application of Intelligent Sensors in the Collection of electrical engineering Automation Equipment

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第9期49卷 135-146页

作者： Wang, Lei School of Electrical and Information Engineering Heilongjiang University of Technology Heilongjiang JiXi158100 China

This research presents a fuzzy PID controller enhanced by an RBF neural network, utilizing MATLAB for simulation and testing to explore the integration of intelligent sensors in electrical engineering automation systems. The study initially focuses on the specific characteristics and accuracy requirements of electrical engineering and automation control, using a permanent magnet brushless DC motor as a case study for intelligent technology applications. The fuzzy neural network PID algorithm is applied to analyze the control performance of the motor circuit breaker's closing speed. Simulation results indicate that the fuzzy neural network PID algorithm reduces the motor's closing tracking speed error rate by 0.33 m/s compared to traditional PID algorithms, demonstrating its superior control effect. Subsequently, an experimental platform was established with a permanent magnet brushless DC motor, and the fuzzy neural network PID control algorithm was implemented. Experimental results show that the algorithm maintains the closing tracking speed error rate at a low level of 0.22 m/s. These outcomes highlight the potential of integrating fuzzy neural network-based controllers in enhancing the efficiency and precision of automation systems. The findings confirm that intelligent fuzzy neural network control algorithms significantly improve the accuracy and reliability of automation control in electrical engineering, paving the way for more advanced applications. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Brushless DC motors

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A Survey on neural network Hardware Accelerators

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 3801-3822页

作者： Mohaidat, Tamador Khalil, Kasem University of Mississippi Electrical and Computer Engineering Department OxfordMS38677 United States Assiut University Electrical Engineering Department Asyut71515 Egypt

Artificial intelligence (AI) hardware accelerator is an emerging research for several applications and domains. The hardware accelerator's direction is to provide high computational speed with retaining low-cost and high learning performance. The main challenge is to design complex machine learning models on hardware with high performance. This article presents a thorough investigation into machine learning accelerators and associated challenges. It describes a hardware implementation of different structures such as convolutional neural network (CNN), recurrent neural network (RNN), and artificial neural network (ANN). The challenges such as speed, area, resource consumption, and throughput are discussed. It also presents a comparison between the existing hardware design. Last, the article describes the evaluation parameters for a machine learning accelerator in terms of learning and testing performance and hardware design. © 2020 IEEE.

关键词： Recurrent neural networks

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Optimizing quadratic programming problems with intuitionistic fuzzy parameters using a neural network

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engineering applications OF ARTIFICIAL INTELLIGENCE 2025年 151卷

作者： Rani, Pooja Mahajan, Sumati Punjab Engn Coll Dept Math Chandigarh 160012 India

The integration of intuitionistic fuzzy theory in optimization problems has significantly enhanced the ability to handle complex, uncertain and imprecise scenarios. Despite these advancements, existing models have not adequately addressed the specific challenges of intuitionistic fuzzy quadratic programming problems (IFQPPs). This study fills this gap by proposing a novel recurrent neural network for IFQPPs. First of all, IFQPP is transformed into a multi-objective optimization problem using alpha, /3-cuts. This technique allows to explore a wide range of possible solutions using various combinations of alpha, /3-cuts. Next, the multi-objective problem is converted into a weighted problem coupled with duality theory to remodel into a single-layer recurrent neural network. To validate the proposed approach, theorems as well as lemmas have been constructed and proved at appropriate places. The proposed neural network model is illustrated using numerical examples to explain the methodology. Later, it is applied to a small-scale electrical power grid to demonstrate the practical utility and impact of the proposed approach.

关键词： neural network Intuitionistic fuzzy parameters Quadratic programming problems a,/3-cut

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applications of integrated response surface methodology statistic techniques and artificial neural network-based machine learning to optimize residual chlorine production and energy consumption

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WATER AND ENVIRONMENT JOURNAL 2024年第3期38卷 373-384页

作者： Yimam, Solomon Ali Kang, Joon Wun Kassahun, Shimelis Kebede Addis Ababa Univ Addis Ababa Inst Technol Sch Chem & Bio Engn Addis Ababa Ethiopia

A multifactor interaction study was performed using the combined response surface methodology and an artificial neural network on the operational parameters and their influence on residual chlorine production. The operating variables, sodium chloride concentration, electrical potential, electrolysis time, and electrode gap, were evaluated over the response, residual chlorine and energy consumption. The results indicated that the optimum value for residual chlorine was 2450 mg/L achieved at an electrical potential of 8.8 V for 25 min in the presence of 25 g/L of sodium chloride and an electrode distance of 1 cm, and the optimum corresponding energy consumption was measured at 21.76 kWh/L. The study reveals that electric potential, sodium chloride concentration, and electrolysis time positively influence residual chlorine production. ANN models showed superior prediction ability compared with RSM models. This suggests electrolysis can be used for active chlorine production from saline solutions, potentially for industrial applications and water disinfection. The electrode gap was shown to have little effect on the formation of residual chlorine. The electrolysis time and electric potential have a direct impact on energy consumption. Artificial neural network models demonstrated superior capability for process prediction. A maximum of 21.756 kWh/L of energy can be utilized for producing residual chlorine.

关键词： ANN electro-oxidation energy consumption optimization residual chlorine RSM

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An improved pulse coupled neural networks model for semantic IoT

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Digital Communications and networks 2024年第3期10卷 557-567页

作者： Rong Ma Zhen Zhang Yide Ma Xiping Hu Edith C.H.Ngai Victor C.M.Leung School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China School of Medical Technology Beijing Institute of TechnologyBeijing100081China Department of Electrical and Electronic Engineer The University of Hong KongHong Kong 999077China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Electrical and Computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data ***,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication ***,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic *** the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to *** contrast,the Pulse Coupled neural network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image ***,the parameters of PCNN are determined by trial and error,which limits its *** overcome this limitation,an Improved Pulse Coupled neural networks(IPCNN)model is proposed in this *** IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of *** segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation *** IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

关键词： Internet of things(IoT) Semantic information Real-time application Improved pulse coupled neural network Image segmentation

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Operation optimization for a CHP system using an integrated approach of ANN and simulation database

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APPLIED THERMAL engineering 2025年 266卷

作者： Cao, Yue Hu, Hui Chen, Ranjing Si, Fengqi Southeast Univ Sch Energy & Environm Key Lab Energy Thermal Convers & Control Minist Educ Nanjing 210096 Peoples R China

Combined heat and power (CHP) systems are thermodynamically and economically viable options for satisfying the rapid increase in the heating demands of society. To improve the flexibility and reduce heat consumption of CHP systems is significant to broaden their applications. In this paper, an integrated approach using an artificial neural network and a simulation database is proposed to achieve operating optimization for a CHP system with high back-pressure and steam extraction structures. Simulations were conducted to search for key operational variables under part-load conditions. The results showed that heat and electrical load dispatch has the potential to enhance the performance of the CHP system using the chaos swarm optimization algorithm. The relative error of the optimal total heat consumption was less than 0.2 % indicating that the wild point, which elucidates the artificial neural network model is reliable for predicting the performance of the CHP system. Moreover, both the high back-pressure and extraction heating units have the potential to respond to high heat and electrical loads with a satisfactory total heat consumption of 2886.70 MW. Generally, the CHP system exhibited better part-load performance when employing the integrated approach of an artificial neural network and a simulation database for operation optimization.

关键词： Combined heat and power system Part-load performance Operation optimization Artificial neural network Simulation database

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Accurate Weight Prediction of a New Aircraft's electrical System Based on neural network

Accurate Weight Prediction of a New Aircraft's Electrical Sy...

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Asia-Pacific International symposium on Aerospace Technology (APISAT)

作者： Dang, Juhong Gao, Yining First Aircraft Inst 1 Renmin Rd Xian 710089 Peoples R China

ISBN: (纸本)9789819740000;9789819739981;9789819739974

Weight prediction is essentially important in the overall design of an aircraft. A conservative weight prediction will result in uncompetitive performance of the aircraft, while an optimistic one will result in the compromise of weight limit and aircraft performance. According to the composition and characteristics of aircraft electrical system and the experience of other aircraft design, a three-step weight prediction method of the electrical system is proposed. The weight prediction of electrical system is divided into three parts: the weight prediction of newly developed airborne equipment, the weight optimization and prediction of cables, the weight prediction of equipment and cable installations. Firstly, the weight of the newly developed airborne equipment is predicted by introducing two neural network structures: generalized regression neural network (GRNN) and fitnet neural network, then the neural networks are optimized to reduce the prediction deviation. For the best case, the weighted deviation reaches 3.40%. Secondly, the electric cable weight is accurately predicted through the optimization of cable weight by using three-dimensional (3D) numerical model and empirical method. Thirdly, the auxiliary weight of the installation of cables and airborne equipment include mounting brackets, protective covers, grounding, etc. are accurately predicted by using empirical data from weight database. The results show that the weight prediction process basically realizes accurate prediction of the total weight of the electrical system, and therefore improves the reliability of aircraft technical scheme and the accuracy of aircraft performance calculation.

关键词： neural network electrical System Weight Prediction Airborne Equipment Aircraft Design

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Wavelet and VMD enhanced traffic forecasting and scheduling method for edge cloud networks

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COMPUTERS & electrical engineering 2025年 121卷

作者： Liu, Siyuan He, Qian Chen, Yiting Zhang, Fan Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin Peoples R China Guilin Univ Elect Technol Sch Informat & Commun Guilin Peoples R China

With the proliferation of Intelligent applications, more and more organizations migrate their applications from cloud to edge cloud network to reduce the latency of applications and alleviate the workload of cloud. When the network congestion occurs, network monitoring and detection system may disable, and edge cloud network will be vulnerable to be attacked. Traffic forecasting can identify the traffic patterns in advance, and dynamically allocate network resources to decrease latency of applications and avoid security risks caused by network congestion. Therefore, we propose a network scheduling framework WVNF (Wavelet VMD Based network Flow Management) based on traffic prediction, which utilizes neural networks to forecast network traffic and deploys route strategies to optimize network scheduling. Specifically, in order to accurately forecast network traffic, we propose a neural network model, named TSWNet (Traffic Sequence Wavelet network). TSWNet uses VMD (variational mode decomposition) to decompose time series and extract signal structure information on different time scales, and adopts wavelet transformation to extract the local and global features of the traffic sequence in the time and frequency domain. In addition, we model this traffic scheduling problem and propose a route strategy, which utilizes the result of TSWNet to find the best path. In extensive tests, TSWNet significantly outperformed existing models, reducing MSE and MAE by up to 48.8% and 27.8% respectively, demonstrating its effective traffic prediction and network scheduling capabilities.

关键词： TSWNet Traffic forecasting Wavelet transform Edge cloud network Routing policy

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Mapping network-Coordinated Stacked Gated Recurrent Units for Turbulence Prediction

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1331-1341页

作者： Zhiming Zhang Shangce Gao MengChu Zhou Mengtao Yan Shuyang Cao Faculty of Engineering University of ToyamaToyama 930-8555Japan Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA Gaoling School of Artificial Intelligence Renmin University of ChinaBeijing 100872China College of Civil Engineering Tongji UniversityShanghai 200092China IEEE

Accurately predicting fluid forces acting on the sur-face of a structure is crucial in engineering ***,this task becomes particularly challenging in turbulent flow,due to the complex and irregular changes in the flow *** this study,we propose a novel deep learning method,named mapping net-work-coordinated stacked gated recurrent units(MSU),for pre-dicting pressure on a circular cylinder from velocity ***-cally,our coordinated learning strategy is designed to extract the most critical velocity point for prediction,a process that has not been explored *** our experiments,MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the *** method significantly reduces the workload of data measure-ment in practical engineering *** experimental results demonstrate that MSU predictions are highly similar to the real turbulent data in both spatio-temporal and individual ***,the comparison results show that MSU predicts more precise results,even outperforming models that use all velocity field *** with state-of-the-art methods,MSU has an average improvement of more than 45%in various indicators such as root mean square error(RMSE).Through comprehensive and authoritative physical verification,we estab-lished that MSU’s prediction results closely align with pressure field data obtained in real turbulence *** confirmation underscores the considerable potential of MSU for practical applications in real engineering *** code is available at https://***/zhangzm0128/MSU.

关键词： Convolutional neural network deep learning recurrent neural network turbulence prediction wind load predic-tion.

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