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Power, Energy and Electrical engineering (CPEEE), International Conference on

作者： Omar Mater F Alarbidi Hassan Hadi H Awaji Ahmad Awadh Althobiti Abdullah Ali Alhussainy Sultan Alghamdi M.N. Ajour Muhyaddin Rawa A.M. Abusorrah Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia Department of Electrical and Computer Engineering K. A. CARE Energy Research and Innovation Center King Abdulaziz University Jeddah Saudi Arabia Department of Electrical and Computer Engineering K. A. CARE Energy Research and Innovation Center Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia Department of Electrical and Computer Engineering Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah 21589 Saudi Arabia

A wind turbine in wind energy conversion systems (WECS) captures a portion of the wind energy. It converts it to mechanical energy through a dynamic interaction with the electrical generator, transforming this mechanical energy into electrical energy. Therefore, wind turbine dynamics are crucial when researching and developing the whole WECS. It is highly desirable to study the behavior of wind energy and monitor the impact on power quality and short circuit level. In this study, a wind turbine using a permanent magnet synchronous machine (PMSM) simulation system is designed, simulated, cases study is discussed. The PMSM is torque-controlled using the field orientation method. A three-phase IGBT inverter with closed loop current PI-controllers powers the PMSM. The study and control system are executed on MATLAB SIMULINK.

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Elderly Standing Imbalance Detection Using Noise-Resilient Robust Mean Estimator and Deep Learning 15

Elderly Standing Imbalance Detection Using Noise-Resilient R...

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15th International Conference on software, Knowledge, Information Management and Applications, SKIMA 2023

作者： Raj, Drishya Hu, Shanfeng Aslam, Nauman Chen, Xiaomin Rueangsirarak, Worasak Uttama, Surapong Nauman, Fatimah Northumbria University Department of Computer and Information Sciences United Kingdom Ctr. of Excellence in Comp. and Commun. Engineering for Capacity Building of Information Technology Mae Fah Luang University Chiang Rai Thailand Center of Excellence in Ai and Emerging Technologies School of Information Technology Mae Fah Luang University Chiang Rai Thailand Lancaster University Faculty of Medicine United Kingdom

ISBN: (纸本)9798350316551

Elderly standing imbalance is a critical public health concern, demanding robust and accurate detection techniques for improved safety and well-being. In this paper, we propose a novel method employing unsupervised learning and Denoising Autoencoder with Multi-Layer Perceptron networks, along with a custom adaptive Huber loss function and activation function, to classify standing states in elderly individuals. The existing Standing imbalance detection research includes difficulties such as addressing irregularities in pressure sensor data, largely stressing binary classification due to algorithmic efficiency considerations while dealing with heavy-tailed data. The approach utilizes open-source smart insole datasets, capturing left and right foot pressure data. The ensemble model DAE-MLP efficiently captures the temporal dynamics of the imbalance scores produced using the Noise-resilient robust mean estimator, enabling accurate and robust classification. This method adapts to varied degrees of data imbalance, resulting in more accurate learning. Through comprehensive evaluations, our method achieves an overall accuracy of 94 percentage on a test dataset with 53 instances. This approach serves as a proactive standing imbalance detection system for the elderly, enhancing safety and quality of life by identifying and addressing standing imbalance risks. Our research introduces an innovative solution, paving the way for advancements in elderly healthcare and safety, reducing the risk of falls and related injuries. © 2023 IEEE.

关键词： Statistical tests

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Time-Aware Missing Traffic Flow Prediction for Sensors with Privacy-Preservation 11th

Time-Aware Missing Traffic Flow Prediction for Sensors with ...

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11th International Conference on computer engineering and Networks, CENet2021

作者： Qi, Lianyong Wang, Fan Xu, Xiaolong Dou, Wanchun Zhang, Xuyun Khosravi, Mohammad R. Zhou, Xiaokang School of Computer Science Qufu Normal University Qufu China School of Computer and Software Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China Department of Computing Macquarie University SydneyNSW2109 Australia Department of Computer Engineering Persian Gulf University Bushehr Iran Department of Electrical and Electronic Engineering Shiraz University of Technology Shiraz Iran Faculty of Data Science Shiga University Hikone Japan

ISBN: (纸本)9789811665530

With the continuous development of IoT, a number of sensors establish on the roadside to monitor traffic conditions in real time. The continuously traffic data generated by these sensors makes traffic management feasible. However, loss of data may occur due to inevitable sensor failure, impeding traffic managers to understand traffic dynamics clearly. In this situation, it is becoming a necessity to predict missing traffic flow accurately for effective traffic management. Furthermore, the traffic sensor data are often distributed and stored by different agencies, which inhibits the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it has become a major obstacle to balance the tradeoff between data sharing and vehicle privacy. In light of these challenges, we propose a privacy-aware and accurate missing traffic flow prediction approach based on time-aware Locality-Sensitive Hashing technique. At last, we deploy a set of experiments based on a real traffic dataset. Experimental reports demonstrate the feasibility of our proposal in terms of traffic flow prediction accuracy and efficiency while guaranteeing sensor data privacy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data privacy

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Research on Moving Target Behavior Recognition Method Based on Deep Convolutional Neural Network 4th

Research on Moving Target Behavior Recognition Method Based ...

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4th EAI International Conference on Advanced Hybrid Information Processing, ADHIP 2020

作者： Liu, Jian-fang Zheng, Hao Peng, He Department of Computer and Software Pingdingshan University Pingdingshan467000 China Center of Engineering Practice Training Tianjin Polytechnic University Tianjin300387 China

ISBN: (纸本)9783030678739

In order to solve the problem that the average recognition degree of moving target line is low by the traditional method of moving target behavior recognition. Therefore, a motion recognition method based on deep convolutional neural network is proposed. Construct a deep convolutional neural network target model, and use the model to design the basic unit of the network. The returned unit is calculated to the standard density map by the set unit, and the moving target position is determined by the local maximum method to realize the moving target behavior recognition. The experimental results show that The experimental results of the multi-parameter SICNN256 model are slightly better than other model structures. And the average recognition rate and the recognition rate of the moving target behavior recognition method based on deep convolutional neural network are higher than the traditional method, which proves its effectiveness. Since a single target is more frequent than multiple recognitions and there is no target similar recognition, similar target error detection cannot be excluded. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Convolution

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Comparative Analysis of Random Forest and Multilayer Perceptron Algorithms for Predicting Gross Domestic Product

Comparative Analysis of Random Forest and Multilayer Percept...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Ambrose A. Azeta Muyideen D. Adewale Patrick O. Omote Funmi O. Onaolapo Idowo A. Bakare Steven Tjiraso Department of Software Engineering Namibia University of Science and Technology Namibia. Africa Centre of Excellence on Technology Enhanced Learning National Open University of Nigeria Nigeria. Department of Computer and Mathematical Sciences Crawford University Ogun State Nigeria.

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

This study examines the efficacy of machine learning techniques, particularly Multilayer Perceptron (MLP), and Random Forest and in forecasting Nigeria's Gross Domestic Product (GDP). Traditional statistical methods frequently fail to capture intricate, nonlinear relationships, thus necessitating advanced techniques. The dataset employed in this research encompasses 22 instances from 2000 to 2021. It includes variables such as GDP, healthcare expenditure, population, and the index of economic freedom obtained from the World Bank as well as the Nigerian Perceptions Index for corruption. The data was prepared to undergo training and testing following the CRISP-DM methodology. The models were trained and evaluated using a five-member cross-validation method. Performance metrics indicated that MLP outperformed Random Forest, with MAE values of 16.8228 and 25.3074 and RMSE values of 23.2111 and 33.2027, respectively. These findings suggest that MLP offers more accurate GDP predictions for Nigeria, effectively managing complex data relationships. This study provides valuable insights for enhancing predictive accuracy and supporting more effective economic planning and decision-making.

关键词： Training Machine learning algorithms Accuracy Economic indicators Biological system modeling Multilayer perceptrons Predictive models Prediction algorithms Random forests Testing

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The Impact of Weather Conditions on the Harmonic Behaviour of Grid-Connected Photovoltaic Systems

The Impact of Weather Conditions on the Harmonic Behaviour o...

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Power, Energy and Electrical engineering (CPEEE), International Conference on

作者： Ahmad Awadh Althobiti Faisal Almalki Abdullah Ali Alhussainy Sultan Alghamdi Muhyaddin Rawa Yusuf A. Al-Turki Department of Electrical and Computer Engineering K. A. CARE Energy Research and Innovation Center King Abdulaziz University Jeddah Saudi Arabia Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia Department of Electrical and Computer Engineering Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia Department of Electrical and Computer Engineering K. A. CARE Energy Research and Innovation Center Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia

The utilization of solar photovoltaic systems has seen a marked uptick in recent years. Research efforts have been geared towards improving efficiency and reducing the cost of solar PV plants. In light of concerns over the potential consequences of high penetration of PV systems, governments have been implementing renewable energy programs. This study presents a time-domain model for analyzing harmonic effects in utility-scale PV plants under varying levels of irradiance and temperature conditions.

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A Power Quality Analysis Comparison between Single and Double PV stages of a Voltage Source Converter based on a d-q frame-tied grid

A Power Quality Analysis Comparison between Single and Doubl...

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IEEE PES Conference on Innovative Smart Grid Technologies - Middle East (ISGT Middle East)

作者： Faisal Almalki Abdulellah Alsulami Abdullah Ali Alhusssainy Sultan Alghamdi A. H. Milyani Muhyaddin Rawa Y. A. Al-Turki Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia Department of Electrical and Computer Engineering K. A. CARE Energy Research and Innovation Center King Abdulaziz University Jeddah Saudi Arabia Department of Electrical and Computer Engineering Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia Department of Electrical and Computer Engineering K. A. CARE Energy Research and Innovation Center Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz University Jeddah Saudi Arabia

Due to increasing the demand for using photovoltaic power system that uses sustainable and renewable energy sources to meet power demand, many studies were investigated to improve the photovoltaic power system operation. In this paper, the power system quality will be discussed to show the impact of power quality on the electrical grid by analyzing the nominal current and voltage output from the inverter at different weather conditions and different switching frequencies. Power quality is considered one of the major issues that need to be addressed. The low quality ofpower operation may cause disturbance and economic losses. The realtime simulation will be done by using MATLAB/Simulink.

关键词： Photovoltaic systems Switching frequency System performance Power quality Inverters Temperature control Smart grids

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Realtime Middleware-based Distributed Micro-Smart Electricity Meters

Realtime Middleware-based Distributed Micro-Smart Electricit...

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International Smart Grid Workshop and Certificate Program (ISGWCP)

作者： Basem Almadani Abdullahi Sani Shuaibu Sami Ul Haq Farouq Aliyu Computer Engineering Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Center of Excellence in Development of Nonprofit Organizations King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

ISBN: (数字)9798350361612

ISBN: (纸本)9798350361629

Research shows that consumers want to know their appliances’ energy consumption. Also, providing consumers with more information about their energy use and giving them more control over it can lead to choices that reduce energy consumption. This research proposes a sensor network of Smart Electricity Meters (SEMs) that system uses a Data Distribution Service (DDS) publish/subscribe middleware (DPSM) for real-time fine-grain sensing of electric loads. The SEMs are connected to household appliances to measure their power consumption and send it to a server for storage and data analysis. Our results indicate an average throughput of 302 bytes/second and an average latency of 25.44 ms. The proposed solution demonstrates promise for real-time monitoring of household appliances’ power consumption in Smart Grid (SG) environments.

关键词： Meters Energy consumption Power demand Power measurement Electricity Throughput Real-time systems

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Enhancing Intrusion Detection Systems Using RNN, LSTM, and Hybrid RNN-LSTM Models

Enhancing Intrusion Detection Systems Using RNN, LSTM, and H...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Sahar Ebadinezhad Nooshin Nooraei Nia Nasratullah Shirzad Nwabueze Kenneth Osemeha Department of Computer Information Systems Computer Information Systems Research and Technology Center (CISRTC) Near East University Nicosia Turkey Artificial Intelligence Near East University Nicosia North Cyprus Software Engineering Department Near East University Nicosia North Cyprus

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Due to The lack of comparison studies and practical applications of RNN, LSTM, and hybrid RNN-LSTM models for intrusion detection systems, especially when managing class imbalances in complex network datasets, represents a gap in the literature. This study investigates the application of deep learning techniques to improve the detection capabilities of IDS. For this study, we applied and assessed Recurrent Neural Network, Long Short-Term Memory, and Hybrid RNN-LSTM models on a UNSW-NB15 dataset, and to redress class imbalance we trained and validated our models using synthetic minority over-sampling. The RNN-LSTM model was shown to perform the best with a 9 4. 0 0 % accuracy in comparison to other models. These experimental results signify the possibility of enhancing IDS performance and a dependable method of detecting various network intrusions with the use of hybrid models comprised of RNN & LSTM.

关键词： Deep learning Recurrent neural networks Accuracy Computational modeling Intrusion detection computer architecture Network security Hyperparameter optimization Network intrusion Long short term memory

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Topology entropy: Enhancing graph partitioning for TAD identification and single-cell clustering

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Computational and Structural Biotechnology Journal 2025年 27卷 1864-1886页

作者： Liang, Qiushi Zhao, Shengjie Chen, Lingxi Li, Shuai Cheng School of Computer Science and Technology Tongji University Shanghai201804 China Department of Computer Science City University of Hong Kong 999077 Hong Kong Department of Biomedical Sciences City University of Hong Kong 999077 Hong Kong Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education China

Entropy quantifies the limits of information compression and provides a theoretical foundation for exploring complex structures in large-scale graphs. However, effective metrics are needed to capture the intricate structural details in biological graphs. In this paper, we introduce the topology entropy encoding tree to quantify the complexity of biological graphs and show that minimizing the associated entropy is equivalent to optimal graph partitioning. We develop two methods, TEC-O and TEC-U, for partitioning ordered and unordered biological graphs. TEC-O is applied to identify Topologically Associated Domains (TADs) in Hi-C contact maps, while TEC-U is used for cell clustering in single-cell sequencing data. Results from simulated datasets demonstrate that topology entropy is robust to noise and effectively captures structural information, outperforming existing methods. Experiments on Hi-C data from five cell lines and ten single-cell sequencing datasets show that TEC-O and TEC-U achieve the highest accuracy in TAD detection and cell clustering, respectively, providing biologically meaningful insights. © 2025 The Authors

关键词： Cell culture

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