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共 11565 条记录，以下是511-520 订阅

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Joint optimization Clearing of Electro-hydrogen Coupling System for New Energy Consumption 4

Joint Optimization Clearing of Electro-hydrogen Coupling Sys...

引用

4th International conference on intelligent Power and Systems, ICIPS 2024

作者： Yang, Fan Wu, Hao Peng, Chen Wang, Weijie Zhang, Yajian Shanghai University School of Mechatronic Engineering and Automation China Sichuan University of Science & Engineering Artificial Intelligence Key Laboratory of Sichuan Province Yibin China Xuchang Xuji Software Technology Co. Ltd Xuchang China

ISBN: (纸本)9798331532765

In this study, a joint optimization clearing model of electro-hydrogen coupling system considering the uncertain output of new energy in electricity spot market has been proposed. Firstly, Monte-Carlo sampling and scenario reduction technologies are used to address real-time market price uncertainty. Secondly, a Cournot equilibrium model is constructed to maximize the profit of each market participant. then, the nonlinear complementary method is used to solve the equilibrium results. Case studies have demonstrated that by optimizing the strategy of the electro-hydrogen coupling system and virtual bidding simultaneously. Meanwhile, the price gap between the day-ahead market and the real-time market can be effectively reduced, and the price of the electricity market can be more stable and reasonable. © 2024 IEEE.

关键词： Commerce

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Parameter identification method for doubly fed fan based on improved sparrow algorithm 4

Parameter identification method for doubly fed fan based on ...

引用

4th International conference on intelligent Power and Systems, ICIPS 2024

作者： Ye, Wei Wu, Wentao Cao, Xiaoqing Li, He Li, Zhi Chen, Di Hu, Yitong Qinghai Dehong Electric Power Technology Co. Ltd. Qinghai817000 China Wuhan Huayuan Electric Power Design Institute Co. Ltd. Wuhan430058 China China Three Gorges University College of Electrical Engineering and New Energy Yichang443002 China

ISBN: (纸本)9798331532765

With the expansion of wind power grid integration and the promotion of the "dual carbon"goal, the modeling and parameter identification of doubly fed wind turbine control systems become increasingly important. A control parameter identification method for doubly fed induction generators (DFIGs) based on improved sparrow search algorithm (ISSA) is proposed to address the problem of difficulty in accurately characterizing the output characteristics of DFIGs under random wind speeds. Firstly, a model of the DFIG was constructed and key control parameters were selected. Subsequently, based on the ISSA algorithm, significantly enhancing identification accuracy and convergence speed. the simulation results show that the ISSA algorithm performs better in DFIG control parameter identification compared to particle swarm optimization (PSO) and the original sparrow algorithm (SSA). © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Multi-objective power allocation strategy for electrolyzer array in photovoltaic hydrogen production system 4

Multi-objective power allocation strategy for electrolyzer a...

引用

4th International conference on intelligent Power and Systems, ICIPS 2024

作者： Li, Fei Sun, Hao Guo, Longchao Kang, Jiafei Lu, Zhihua Ning, Jing Hefei University of Technology National and Local Joint Engineering Laboratory for Renewable Energy Access to Grid Technology Hefei China

ISBN: (纸本)9798331532765

As global photovoltaic power generation capacity expands, solar energy plays an increasingly important role in hydrogen production. Hydrogen is a high-energy-density fuel that can be stored for long periods of time and transported over long distances, solving the intermittent problem of solar power generation. Based on existing literature, this paper uses the TOPSIS method to evaluate the hydrogen production and efficiency of electrolyzers under different input powers, and proposes a multi-objective power allocation optimization strategy, which integrates multiple objectives into one function through weighted assignment and optimizes it using a particle swarm algorithm in the case of photovoltaic power fluctuations. It is verified by actual data and simulations. the strategy significantly improves the TOPSIS score of the electrolyzer array, reduces the number of switching operations, and improves the energy efficiency and economic feasibility of the hydrogen production system. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Computer Vision for Detecting Attentional Behaviors 1

引用

4th International conference on optimization learning Algorithms and Applications

作者： Piza, Caio Bombacini, Marcos Roberto Lima, Jose Inst Politecn Braganca Campus Santa Apolonia P-5300253 Braganca Portugal Univ Tecnol Fed Parana Rua Cristo Rei 19 Toledo PR Brazil Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Braganca Portugal Inst Politecn Braganca Lab Sustentabilidade & Tecnol Regioes Montanha Su Braganca Portugal

ISBN: (数字)9783031774324

ISBN: (纸本)9783031774317;9783031774324

Nowadays, there is the paradox of technology: although smartphones have revolutionized our way of living, bringing convenience and connectivity, they have also introduced new challenges, notably distracted driving. this paper addresses the issue of visual distraction, one of the main contributors to traffic accidents, through the development of an innovative system that combines the application of convolutional neural networks and the functionality of mobile devices. the adopted methodology focused on the collection of a broad set of images to train an artificial intelligence model capable of classifying a qualitative variable with two distinct categories: attention and distraction of a driver. In particular, the study concentrated on creating a mobile application that uses a smartphone's camera to monitor the driver and issue auditory alerts if it detects prolonged distraction. the achieved results highlighted the efficacy of the model, especially after its optimization for the TensorFlow Lite format, suitable for implementation on mobile devices due to its efficiency in terms of speed and resource consumption.

关键词： traffic safety convolutional neural networks mobile applications distraction detection

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intelligent Room Allocation using Smart IoT Devices 4

Intelligent Room Allocation using Smart IoT Devices

引用

4th International conference on Pervasive Computing and Social Networking (ICPCSN)

作者： Mahendran, S. Nallathambi, B. Mahalakshmi, T. Saravanan, K. Viswanth, M. KGiSL Inst Technol Coimbatore India KGiSL Inst Technol Dept ECE Coimbatore India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356;9798350386349

the "intelligent Room Allocation System Using ESP32 and IoT Devices" revolutionizes the traditional manual process of classroom allocation in educational institutions. this system leverages IoT technology, particularly the ESP32 microcontroller, to automate the allocation process, thereby reducing staff workload and optimizing room utilization. through a user-friendly mobile application, staff can effortlessly upload class schedules, which are stored securely in the cloud. the ESP32 retrieves this data and employs an intelligent algorithm to allocate classrooms based on availability, staff preferences, and specific requirements. Real-time occupancy data collected from PIR sensors installed in each classroom ensures efficient utilization of resources. the system facilitates seamless communication with staff and students through GSM modules, providing timely SMS notifications about allocated classrooms. Furthermore, in instances of conflicts or special requests, the system handles resolution autonomously or prompts administrative intervention when necessary. Overall, this system not only streamlines the classroom allocation process but also enhances user experience, fosters resource optimization, and contributes to the efficient management of educational facilities. With its innovative approach and technological sophistication, the intelligent Room Allocation System sets a new standard for smart campus management.

关键词： intelligent Room Allocation System ESP32 Microcontroller IoT Devices Classroom Management Time table Scheduling Occupancy Monitoring

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Research on Adverse Drug Reaction Prediction Model Combining Knowledge Graph Embedding and Deep learning 4

Research on Adverse Drug Reaction Prediction Model Combining...

引用

4th International conference on Machine learning and intelligent Systems Engineering (MLISE)

作者： Li, Yufeng Zhao, Wenchao Dang, Bo Yan, Xu Gao, Min Wang, Weimin Xiao, Mingxuan Univ Southampton Southampton Hants England Univ Sci & Technol China Hefei Anhui Peoples R China San Francisco Bay Univ Fremont Comp Sci Fremont CA USA Trine Univ Phoenix AZ USA Trine Univ Allen Pk MI USA Hong Kong Univ Sci & Technol Hong Kong Peoples R China SouthWest JiaoTong Univ Chengdu Sichuan Peoples R China

ISBN: (纸本)9798350375084;9798350375077

In clinical treatment, identifying potential adverse reactions of drugs can help assist doctors in making medication decisions. In response to the problems in previous studies that features are high-dimensional and sparse, independent prediction models need to be constructed for each adverse reaction of drugs, and the prediction accuracy is low, this paper develops an adverse drug reaction prediction model based on knowledge graph embedding and deep learning, which can predict experimental results. Unified prediction of adverse drug reactions covered. Knowledge graph embedding technology can fuse the associated information between drugs and alleviate the shortcomings of high-dimensional sparsity in feature matrices, and the efficient training capabilities of deep learning can improve the prediction accuracy of the model. this article builds an adverse drug reaction knowledge graph based on drug feature data;by analyzing the embedding effect of the knowledge graph under different embedding strategies, the best embedding strategy is selected to obtain sample vectors;and then a convolutional neural network model is constructed to predict adverse reactions. the results show that under the DistMult embedding model and 400-dimensional embedding strategy, the convolutional neural network model has the best prediction effect;the average accuracy, F-1 score, recall rate and area under the curve of repeated experiments are better than the methods reported in the literature. the obtained prediction model has good prediction accuracy and stability, and can provide an effective reference for later safe medication guidance.

关键词： Knowledge graph embedding deep learning convolutional neural network prediction model adverse drug reactions (ADRs)

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Energy-Efficient Clustering in Wireless Sensor Networks: A Multi-Objective Approach Using PSO and Fuzzy Logic 4

Energy-Efficient Clustering in Wireless Sensor Networks: A M...

引用

4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Sabaresan, V. thanigai Selvan, M. Rajkumar, S. St.Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529635

Energy efficiency is one of the biggest challenges in WSNs;typically, these networks are plagued with several problems such as uneven distribution of energy amongst nodes, hotspots, and clustering with big disparities. this work tries to some extent solve some of these problems through a prototype: Efficient Wireless Sensor Network with Multi-Objective Clustering, or EEMOC, which is a new framework that exploits the power of Particle Swarm optimization to realize optimal cluster head selection. In addition, it uses the FIS in the dynamic selection of a cluster radius to maximize its effectiveness in networking. EEMOC exploits the fuzzy logic benefits in governing uncertainty in data and optimizes the sizing of clusters, thereby controlling hotspots and optimizing the energy distribution scheme. Simulation results show EEMOC to be far superior to traditional clustering algorithms and may significantly extend the network lifetime with the utmost saving in energy under various operating conditions. the technique which integrates PSO with FIS provides a robust platform for improving WSN performance. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Adversarial Transfer learning Architecture for Sleep Apnea Detection and Classification 4

Adversarial Transfer Learning Architecture for Sleep Apnea D...

引用

4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Angeljulie, J. Pradish Pranam, S. Yosuva, B. Shuaib Khan, S. Coimbatore India Kahe Dept of Ai&ds India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

Sleep Apnea is a common sleep disorder which causes repeated interruption in their breathing during sleep. Sleep apnea detection and classification is a vital task for identification of Sleep quality that are prone to hypopnea and Sleep apnea events which further lead to changes in the immune system especially on the various memory and metabolism properties. Sleep Apnea can be diagnosed using invasive or non-invasive approaches. In Particular, single channel ECG signals has been used in the large studies as it is processed by dividing the signal into 30 second segments containing complex time characteristics and frequency characteristics. However, it makes it difficult for manual techniques to discriminate between the different types of sleep apnea. In order to address those challenges, automated sleep apnea detection and classification model using deep learning mechanism has to be proposed. In this article, two adversarial transfer learning has been framed as prediction function to detect and classifies the type of Sleep apnea with AHI (Apnea-Hypopnea Index). Initially ECG derived respiration signal considered as EDR is extracted from PSG dataset through signal processing methods. In this work, Gabor filtering is used for noise reduction and Pan-Tompkins technique is used to determine R peaks in the QRS region of the noise eliminate signal. Next, minute by minute processing of RR interval is used to the extract crucial features for sleep apnea detection. Extracted feature is processed using the particle swarm optimization to select the optimal features. Finally adversarial neural network composed of multiple layers is used for detection of the sleep apnea on processing the source apnea features and target sleep apnea features in form map. Feature map with AHI is used to classify the sleep apnea into low, mild and severe API in SoftMax layer. We used approximately 100 patches of signals from different patients extracted from a SC4001E0-PSG dataset and 80% of dataset

关键词： Sleep research

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Passive Beamforming based on Cosine Similarity for Space Shift Keying with Reconfigurable intelligent Surfaces 4

Passive Beamforming based on Cosine Similarity for Space Shi...

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4th International conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Wu, Yun Zhang, Xiao Bai, Enjian Hai, Han Donghua University School of Information Science and Technology Shanghai China

ISBN: (纸本)9798350394375

Reconfigurable intelligent surface (RIS)-assisted transmission and space shift keying (SSK) appear as promising candidates for future wireless communication systems. In this paper, a low-complexity cosine similarity-based method for phase optimization in RIS-assisted SSK systems with passive beamforming is proposed. Specifically, based on cosine similarity, we optimize the phase shift of reflected elements by maximizing the Euclidean distance (ED) between the different emission vectors corresponding to the ideal received signal, which shows a lower complexity compared with the sub-exhaustive search algorithm. Moreover, the proposed method avoids the poor bit error rate (BER) performance of the method based on maximizing the instantaneous receive signal-to-noise ratio (SNR). Our simulation results demonstrate the superiority of the proposed solutions over the relevant benchmarks. © 2024 IEEE.

关键词： Bit error rate

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Research on V2V-based Cooperative Collision Avoidance System for Autonomous Driving 24

Research on V2V-based Cooperative Collision Avoidance System...

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4th International conference on Internet of things and Machine learning

作者： Zhang, Yongjie GuangXi Vocat Normal Univ Nanning Peoples R China Guangxi Vocat Normal Univ Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Applicat Technol Intelligent Connected Ve Nanning Peoples R China

ISBN: (纸本)9798400710353

In this paper, we design an autonomous driving collision avoidance system based on V2V networked communication collaboration. the system uses Node-RED to deploy MQTT server at the edge, and uses publish/subscribe mode to cooperate with multi-vehicle positioning information, motion state information, and driving trajectory information to realize networked collaboration. this paper proposes a collision avoidance architecture and method with versatility and safety guarantee by studying the self-driving collision model and obstacle avoidance model, solving the problems of insufficient accuracy, poor versatility and poor real-time collision avoidance prediction of the traditional model, and providing solutions and technical references for the product development of small and medium-sized low and medium speed self-driving cars.

关键词： V2V MQTT Autonomous Driving Collision model Anti-collision model

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