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Prediction of heat transfer performance of vacuum glass based on recurrent neural network 24

Prediction of heat transfer performance of vacuum glass base...

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2nd international conference on Artificial Intelligence, Systems and network Security, AISNS 2024

作者： Su, Jing Wang, Lei College of Information and Communication Engineering Hainan University State Key Lab of Marine Resource Utilisation in South China Sea Hainan University Hainan Haikou China

ISBN: (纸本)9798400711237

Vacuum glass, a thermal insulation material that is highly effective, has a diverse array of applications in the manufacturing of home appliances and the improvement of energy efficiency in buildings. Accurately determining the heat transfer coefficient of vacuum glass is essential for optimising its design and application. The heat transfer coefficient of vacuum glass cannot be rapidly and batch-detected using conventional methods. Deep learning models are capable of acquiring the law of heat transfer coefficient from historical data and providing a rapid and precise prediction of the heat transfer coefficient for new samples. In recent years, recurrent neural networks (RNN) and their variants have demonstrated exceptional performance in time-series prediction tasks, thereby offering novel solutions to the issue of swiftly determining the heat transfer coefficient of the vacuum glass. The objective of this investigation is to compare three recurrent neural network models—simple RNN, long short-term memory network (LSTM), and gated recurrent unit (GRU)—in order to ascertain the most appropriate model for predicting the heat transmission coefficients of vacuum glass. The experimental results show that the prediction accuracy of the GRU model on the test set is significantly better than that of the simple RNN and LSTM models, in addition, the GRU model maintains a low computational complexity. In the future, there will be additional research conducted on deep learning models that are similar to GRU in order to enhance the interpretability and reliability of the prediction and apply them to real-time monitoring systems. © 2024 Copyright held by the owner/author(s).

关键词： Domestic appliances

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Scene Graph and Dependency Grammar Enhanced Remote Sensing Change Caption network (SGD-RSCCN) 31

Scene Graph and Dependency Grammar Enhanced Remote Sensing C...

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31st international conference on Computational Linguistics, COLING 2025

作者： Sun, Qiaoli Wang, Yan Song, Xiaoyu College of Computer Science Inner Mongolia University Hohhot010021 China Portland State University Portland97207 United States

ISBN: (纸本)9798891761964

With the continuous advancement of remote sensing technology, it is easier to obtain high-resolution, multi-temporal and multi-spectral images. The images carry rich information of ground objects. However, how to effectively extract useful information from the complex image data and convert it into understandable semantic descriptions remains a challenge. To deal with the challenges, we propose a Scene Graph and Dependency Grammar Enhanced Remote Sensing Change Caption network (SGD-RSCCN) to improve the accuracy and naturalness of extracting and describing change information from remote sensing images. By combining advanced visual analysis technology and natural language processing technology, the network not only optimizes the problem of insufficient understanding of complex scenes, but also enhances the ability to capture dynamic changes, thereby generating more accurate and smooth natural language description. In addition, we also proposes the decoder based on prior knowledge, which further improves the readability and comprehensibility of the description. Extensive experiments on LEVIR-CC and Dubai-CC datasets verify the advantages of the proposed method in generating accurate and true descriptions. © 2025 Association for Computational Linguistics.

关键词： Semantics

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A personalized glucose prediction model based on deep Long Short-Term Memory networks 4

A personalized glucose prediction model based on deep Long S...

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4th international Computational Imaging conference, CITA 2024

作者： Zou, Yuanyuan Wang, Yixuan Yan, Jiasheng Dai, Bin Guo, Jinhong School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China School of Information and Communication Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)9781510688834

Diabetes mellitus has emerged as one of the most prevalent chronic diseases leading to death due to its high occurrence and rising incidence rates, resulting in numerous life-threatening complications. Consequently, the effective treatment and management of diabetes mellitus are of paramount importance. Blood glucose prediction technology plays a crucial role in identifying the risk of hypoglycemia or hyperglycemia in patients promptly, aiding physicians in adjusting treatment plans, and guiding patients in modifying their lifestyles to effectively reduce the incidence of complications. Additionally, blood glucose prediction can leverage machine learning algorithms to analyze historical data and offer personalized health recommendations to patients. Addressing the issues of low prediction accuracy, complex features, and poor generalization, this paper proposes a blood glucose prediction model based on a deep Long Short-Term Memory (LSTM) network. By incorporating steps such as feature selection, model optimization, data preprocessing, and hyperparameter tuning, a blood glucose prediction model using only historical blood glucose data as input was constructed. The algorithm's performance was evaluated on the Ohio T1DM dataset. The results demonstrated that the model exhibited strong accuracy in predicting both short-term and long-term blood glucose levels, with a root mean square error (RMSE) of 10.61 mg/dL at a prediction horizon (PH) of 15 minutes and 32.19 mg/dL at a PH of 60 minutes. Furthermore, Clark's Error Grid Analysis (CEGA) was employed to assess the model's clinical performance. At a PH of 15 minutes, over 99% of the data were within the zone of optimal clinical effect (zone A). At a PH of 60 minutes, over 95% of the data were in clinically acceptable zones A and B. © 2025 SPIE.

关键词： Prediction models

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Strategies to Minimize network Latency for Better Electromagnetic Compatibility 5th

Strategies to Minimize Network Latency for Better Electromag...

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5th international conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Sampath, A.K. Savita Karthikeyan, M.P. Ramola, Abhishek C. Selection Grade School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India

ISBN: (纸本)9789819780426

Latency in a community will profoundly impact the electromagnetic compatibility (EMC) of a machine, wherein latency can cause interference and reduce the performance of a machine. Several techniques can be employed. The strategies encompass growing network throughput, minimizing interrupt latency, decreasing latency from packet scheduling, and optimizing higher-layer protocols. Growing network throughput can be accomplished by using more bandwidth, better throughput links, or including redundant hyperlinks in the community route. Minimizing interrupt latency may be executed by using interrupt coalescence, optimizing tool drivers, and shifting devices to isolated hardware additives. Furthermore, network topology designs can be stepped forward to lessen the time spent processing packets. It could be executed by setting nodes closer to each other or by imparting routers with more excellent computing assets, including more processing cores. Similarly, information transmission can be optimized to lessen latency, for instance, by using strategies such as selective acknowledgment or direction exploration. Eventually, interference may be minimized through filters and antennas, allowing it to restrict the communication variety. In conjunction with others, these techniques can ensure that community latency is minimized so EMC is improved. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： conjunction encompass minimized

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Enhancing IoT network Defense: A Comparative Study of Machine Learning Algorithms for Attack Classification 33rd

Enhancing IoT Network Defense: A Comparative Study of Machin...

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33rd international conference on Software and data engineering

作者： McNair, Alkendria Precious-Esue, Divine Newson, Soundra Rahimi, Nick Univ Southern Mississippi Sch Comp Sci & Comp Engn Hattiesburg MS 39401 USA

ISBN: (纸本)9783031752001;9783031752018

As the Internet of Things (IoT) continues to expand rapidly, securing these interconnected devices and networks from cyber threats has become a critical challenge. This research investigates the application of machine learning techniques for accurately classifying IoT network traffic data to discriminate between benign activities and various types of cyber-attacks targeting IoT systems. We propose a program that employs multiple machine learning algorithms, including Decision Tree, Logistic Regression, Naive Bayes, and Random Forest, trained on a comprehensive IoT network traffic dataset the CICIoTdataset2023. Through extensive experiments, we evaluate the performance of these classification models in detecting different IoT attack categories such as web-based attacks, spoofing, denial-of-service, Mirai, reconnaissance, distributed denial-of-service, and brute force attacks. Our results demonstrate the efficacy of machine learning approaches, with the Random Forest algorithm emerging as the top performer, achieving an overall accuracy of 98.41%. We also address challenges like class imbalance through hybrid sampling techniques and implement strategies like regularization and hyperparameter tuning to mitigate overfitting and enhance model generalization. Additionally, we conduct a performance analysis of the classification models on different IoT attack categories to gain insights into their specific strengths and weaknesses. By leveraging machine learning for accurate IoT attack classification, this research contributes to developing robust security solutions that can proactively identify and mitigate cyber threats, enabling a more secure IoT ecosystem. The findings pave the way for safeguarding interconnected devices, protecting user privacy, and fostering confidence in the widespread adoption of IoT technologies.

关键词： Internet of Things (IoT) Machine Learning data Classification Decision Tree Logistic Regression Naive Bayes Overfitting Random Forest

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Location data Publishing Mechanism in Semi-Trusted Vehicle networks based on Differential Privacy

Location Data Publishing Mechanism in Semi-Trusted Vehicle N...

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2024 international conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Zhao, Zheng Zhang, Tianhao Mao, Qian Zhao, Qi Xie, Xin Dalian Maritime University Dalian China Liaoning University Shenyang China Northeastern University Shenyang China Hunan University of Information Technology Changsha China Hunan Province Higher Education Key Laboratory of Intelligent Sensing and Computing Changsha China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Vehicular ad-hoc networks (VANETs) are essential in mobile ad-hoc network applications, utilizing vehicle location data. However, safeguarding the privacy and security of this data is challenging, especially with untrusted roadside units. To address this, we propose a novel differential privacy-based location data publishing mechanism. It consists of three key components: dynamic group leader selection, data aggregation, and privacy-preserving data publishing. The group leader selection algorithm is based on directional thresholds, hop cycles, and lifespans, assuming semi-trusted vehicles. Additionally, the differential privacy-based algorithm uses map grid segmentation, privacy budget recovery, and a filtering mechanism to ensure location privacy. Comparative experiments show that our scheme outperforms existing models in total hops, group leader lifetime, and mean absolute error. © 2025 SPIE.

关键词： Differential privacy

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Exploring Digital Signal Processing for Electromagnetic Compatibility in network Applications 5th

Exploring Digital Signal Processing for Electromagnetic Comp...

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5th international conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Chauhan, Aakriti Kapse, Vinod Mansiram Karthikeyan, M.P. Pandeya, Megha Department of Agri-Business Management Vivekananda Global University Jaipur India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

This paper explores the software of virtual sign processing (DSP) strategies to characterize and examine the effects of electromagnetic compatibility (EMC) in network packages. The paper discusses the fundamentals of EMC and then presents information of the basics of DSP. FM modulation and demodulation based totally communication structures, a spurious unfastened dynamic range generator, and antenna radiation field’s measurement gadget are developed to look at the consequences of EMC. The performance of the advanced structures is compared towards the expected results in order to assess the effectiveness of the approach. The paper then discusses the demanding situations and opportunities associated with the use of DSP for EMC research. The results of the paintings are discussed and guidelines for future studies are diagnosed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Demodulation

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Efficient Facial Expression Recognition network Based on Spot-Adaptive Knowledge Distillation 26

Efficient Facial Expression Recognition Network Based on Spo...

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26th international conference on Industrial Technology, ICIT 2025

作者： Wen, Xin Liu, Zhen-Tao Chen, Dan School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9798331521950

Facial expression recognition plays a key role in promoting the development of comprehensive intelligence and building friendly human-computer interaction. Due to the interference of feature noise in expression data, the lightweight facial expression recognition model with fewer parameters is difficult to learn more expression features through simple training, which limits the improvement of its recognition performance. An efficient facial expression recognition network based on Spot-adaptive Knowledge Distillation is proposed in this paper. Inspired by VoVNetV2, the network designed in this paper is lightweightly improved using Depthwise Separable Convolution and the parameter-free SimAM attention mechanism, reducing the number of parameters to 0.21 M. To further improve the recognition accuracy of the model, Spot-adaptive Knowledge Distillation is employed to improve the characterization ability of the model. The recognition accuracies of the student network designed in this paper on the KDEF and RAF-DB datasets are 93.05% and 81.17% respectively after spot-adaptive distillation. © 2025 IEEE.

关键词： Facial Expression Recognition Knowledge Distillation Lightweight Neural network

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BEOUNet: Video Frame Interpolation via Bidirectional Encoding and Optimized U-Net 16

BEOUNet: Video Frame Interpolation via Bidirectional Encodin...

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2024 16th international conference on Graphics and Image Processing, ICGIP 2024

作者： Wang, Shaowen Yang, Xiaohui Si, Tongzhen Xu, Tao Feng, Zhiquan School of Information Science and Engineering University of Jinan Jinan250022 China Jinan Inspur Data Technology Co. Ltd China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Video Frame Interpolation (VFI) is a technique that enhances video quality by generating new frames between adjacent video frames. However, many existing VFI methods exhibit suboptimal performance when dealing with scenes involving complex backgrounds and significant motion. In this paper, we propose an innovative VFI approach named BEOUNet to address these challenges. The model adopts a bidirectional encoding structure to comprehensively capture motion and contextual information between video frames. Furthermore, we introduce depthwise over-parameterized recurrent residual convolutional units (DO-RRCUs) and convolutional block attention module (CBAM) to optimize the U-Net architecture, thereby enhancing performance while reducing computational complexity. Finally, a multi-scale frame synthesis network is employed to generate interpolated frames. We conduct benchmark tests on various datasets, and experimental results demonstrate its excellent performance in terms of efficiency and quality. © 2025 SPIE.

关键词： Encoding (symbols)

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Small-Scale Short-Term Spatio-Temporal Rainfall Prediction Based on EBK, GCN and Bi-LSTM Model 24

Small-Scale Short-Term Spatio-Temporal Rainfall Prediction B...

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8th international conference on Electronic Information Technology and Computer engineering, EITCE 2024

作者： Cai, Jiajing Ji, Xiang Zhou, Jinghe Peng, Ziyang College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

The field of meteorology and hydrology is significantly impacted by the spatial-temporal prediction of rainfall. Rainfall exhibits temporal correlation and periodicity in addition to significant spatial correlation. Currently, the primary objective of the majority of spatiotemporal rainfall prediction models is to extract the local temporal characteristics of rainfall events. We suggest an enhanced model for spatio-temporal rainfall prediction that is based on GCN and Bi-LSTM in order to overcome this obstacle. The model takes advantage of the EBK method in spatial interpolation to effectively generate the spatial distribution of small-scale short-term rainfall. The residual connection is implemented to derive spatial features on the basis of the GCN network, and the sequential operation of dilated convolution is converted to a parallel operation. Subsequently, the inflated sequence is fused into Bi-LSTM to extract short-term local features and macroscopic long-term characteristics of rainfall. To confirm the efficacy of the proposed model, the validation experiment employs historical rainfall data from the National Climatic data Center for testing. The experimental results demonstrate that ESFEM-DDTM outperforms traditional prediction models in the prediction of spatio-temporal rainfall. The model’s mean absolute error, mean absolute percentage error, and root mean square error are 2.6725, 123.153%, and 5.1447, respectively. © 2024 Copyright held by the owner/author(s).

关键词： Prediction models

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