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13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Tang, Xianpiao Hu, Pengyun Yu, Dewei Xia, Daoxun School of Big Data and Computer Science Guizhou Normal University Guiyang China Guizhou Normal University Guizhou Key Laboratory of Information and Computing Science Guiyang China Guizhou Normal University Engineering Laboratory for Applied Technology of Big Data in Education Guiyang China

ISBN: (纸本)9798350319156

Facial expression recognition is the ability to interpret human feelings from facial features. Teachers can use it to visualize the state of emotion and estimate students' learning effects to take corresponding intervention strategies to improve teaching. The current technology has shortcomings such as network performance degradation and feature information loss, which cannot meet the needs of sentiment analysis of student feedback. Therefore, RFMNet, a novel network model based on deep learning theory, is proposed in this paper. Featuring higher extraction ability, it is conducive to accurately analysing students' expressions. First of all, this model introduces a relation-aware global attention (RGA) module to better integrate the more discriminative expression features in the image and achieve more refined sentiment analysis. Second, the Mish activation function and the focal loss function are employed. The Mish activation function is introduced to avoid disappearing feature information due to neuron deactivation when the ReLU gradient is 0. The model achieves an average recognition accuracy of 89.62%, as confirmed through a sequence of trials conducted on the public dataset FERPlus. The model helps achieve intelligent processing of teaching information and obtaining real-time feedback by detecting, extracting, and analysing the students' facial expressions. © 2023 IEEE.

关键词： Students

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Feature Enhancement for Multi-modal Entity Alignment Transformer

Feature Enhancement for Multi-modal Entity Alignment Transfo...

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IEEE International Conference on big knowledge (ICBK)

作者： Zewen Deng Zan Zhang Bingbing Dong Xiulin Zheng Xindong Wu Key Laboratory of Knowledge Engineering With Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9798331508821

ISBN: (纸本)9798331508838

Multi-modal entity alignment (MMEA) aims to identify equivalent entities across different multi-modal knowledge graphs. In these graphs, entities are enriched with information from various modalities, such as text, images, and numerical data, making the alignment task both challenging and crucial for improving knowledge graph quality. Current MMEA algorithms typically encode entity information separately for each modality using corresponding encoders, and then integrate these representations through various modal fusion strategies. However, these methods often fail to fully exploit the multi-modal information of entities. To address this issue, we propose a feature-enhanced multi-modal entity alignment transformer (FEMEAT). FEMEAT enhances entity attribute information by incorporating modal distribution data, which captures the inherent distribution of different modalities for each entity. This inclusion allows the model to have a richer understanding of entity characteristics across modalities. Additionally, FEMEAT utilizes an Optical Character Recognition (OCR) model to extract and incorporate textual information from images. By integrating this text extracted from images, the model can better utilize the visual modality, enhancing its ability to understand and process multi-modal information. Furthermore, FEMEAT employs a multi-head cross-modal attention (MHCA) mechanism for modal fusion to achieve comprehensive multi-modal entity representation. This mechanism enables the model to attend to different modalities simultaneously and learn a detailed representation of entities by considering the interactions between modalities. The multi-head cross-modal attention mechanism facilitates a nuanced understanding and integration of multi-modal data. Experimental results demonstrate that our model achieves state-of-the-art (SOTA) performance across various training scenarios. The code and datasets used in this study can be accessed at https://***/zewenD/FEMEAT.

关键词： Training Visualization Attention mechanisms Optical character recognition Noise knowledge graphs Transformers Robustness Numerical models data mining

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Research on optimal deep learning-based formation flight positioning of UAV

Research on optimal deep learning-based formation flight pos...

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作者： Lu, Jing Li, Yongyi Faculty of Science Beibu Gulf University Qinzhou China Qinzhou Key Laboratory of Big Data Resource Utilization College of Electronic and Information Engineering Beibu Gulf University Qinzhou China

The article examines the positional relationships and transmission/reception signal direction information within unmanned aerial vehicle (UAV) cluster formations. Considering the distinctive characteristics of various formation shapes (such as circle and cone), a study is conducted on the polar and Cartesian coordinate systems in neural network models to address position deviation issues arising in UAV formation flights. The passive receiving signal UAV positioning model is established by applying the triangular cosine theorem. Additionally, the position relationship model of the UAV is formulated using optimization theory. The article introduces a deep learning-based deviated UAV adjustment algorithm designed to automatically adjust UAV positions. This is achieved by establishing a triangular relationship between the passive signal receiving UAV and other UAVs. The implementation involves C# programming and MATLAB visualization. The proposed method not only resolves UAV positioning and position adjustment challenges but also optimizes the adjustment strategy. This optimization leads to maximum savings in time and distance costs associated with sending and receiving signals among UAVs, thereby enhancing the overall performance of UAVs during flights. © 2024 John Wiley & Sons Ltd.

关键词： Unmanned aerial vehicles (UAV)

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Measurement of Absorption Spectroscopy Based on a Dispersion-Managed Dual-Comb Laser

Measurement of Absorption Spectroscopy Based on a Dispersion...

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Jing, Xiuhan Zhang, Chenghong Zhang, He Lyu, Wenhao Fu, Bo Xu, Lijun School of Instrumentation and Optoelectronic Engineering Beihang University Key Laboratory of Precision Opto-Mechatronics Technology of Education Ministry Beijing China School of Engineering Medicine Beihang University Key Laboratory of Big Data-Based Precision Medicine Ministry of Industry and Information Technology Beijing China

ISBN: (纸本)9798350378214

Dual-comb spectroscopy is a competitive means of laser absorption spectroscopy owing to broad spectral range and high spectral resolution. Single-cavity dual-comb systems based on mode-locked lasers hold promising potential for further simplifying dual-comb spectroscopy. Two combs possess natural coherence because of common mode noise suppression. Herein, we proposed an all-fiber dispersion-managed dual-comb laser to broaden the spectra of dual combs. The output laser with a 3-dB bandwidth as broad as 9 nm at 1.5 μm was experimentally obtained. Dual combs were realized by the walk-off of orthogonal polarization modes and directly employed to absorption spec-troscopy due to their overlapped spectra. In order to improve the signal-to-noise ratio of dual-comb measurement, interferogram averaging was conducted, where the signal-to-noise ratio was generally proportional to the square root of averaging times. Besides, phase and frequency jitter was corrected by cross-correlation method. High resolution absorption lines of hydrogen cyanide from P18 to P26 were measured. Positions of measured absorption lines were in line with ones in HITRAN database, with an average error of approximate 1 pm. To the best of our knowledge, the 9-line result was the broadest bandwidth of polarization-multiplexed single-cavity dual combs without additional spectral spreading operation. The results demonstrate that the compact dual-comb spectrometer can be considered a potential candidate for quantitative measurements in practical applications. © 2024 IEEE.

关键词： Spectral resolution

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Research on Electricity Consumption Model of Library Building Based on data Mining

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Energy engineering 2022年第6期119卷 2407-2429页

作者： Jiaming Dou Hongyan Ma Rong Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing102616China Institute of Distributed Energy Storage Safety Big Data Beijing University of Civil Engineering and ArchitectureBeijing102616China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing102616China

With the exponential development of Chinese population,the massive energy consumption of buildings has recently become an interest *** much research has been conducted on residential buildings,heating ventilation and air conditioning(HVAC),little research has been conducted on the relationship between student’s behavior,campus buildings,and their *** classical seasonal decomposition,hierarchical clustering,and apriori algorithm,this paper aims to provide an empirical model for consumption data in campus *** meter data from a library in Beijing,China,is adopted in this *** electricity consumption patterns are investigated on an hourly/daily/monthly *** to the monthly analysis,electricity consumption peaks each year around June and December due to teaching programs,social exams,and outdoor *** data analysis revealed a relatively stable consumption *** shows three different types of daily load *** data analysis demonstrated a high relationship between HVAC consumption and building total consumption,with a lift value of ***,links between temperature and subsystems were also *** a case study of library,this study provides a unique insight into campus electricity *** results could help to develop operational strategies for campus facilities.

关键词： Electricity consumption data mining load profile campus building

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knowledge expression,numerical modeling and optimization application of ethylene thermal cracking:From the perspective of intelligent manufacturing

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Chinese Journal of Chemical engineering 2021年第10期34卷 1-17页

作者： Kexin Bi Shuyuan Zhang Chen Zhang Haoran Li Xinye Huang Haoyu Liu Tong Qiu Beijing Key Laboratory of Industrial Big Data System and Application Department of Chemical EngineeringTsinghua UniversityBeijing 100084China

Applications of process systems engineering(PSE)in plants and enterprises are boosting industrial reform from automation to digitization and *** ethylene thermal cracking,knowledge expression,numerical modeling and intelligent optimization are key steps for intelligent *** paper provides an overview of progress and contributions to the PSE-aided production of thermal cracking;introduces the frameworks,methods and algorithms that have been proposed over the past10 years and discusses the advantages,limitations and applications in industrial *** entire set of molecular-level modeling approaches from feedstocks to products,including feedstock molecular reconstruction,reaction-network auto-generation and cracking unit simulation are *** control and optimization methods are exhibited,including at the operational,cycle,plant and enterprise *** software packages are ***,an outlook in terms of future directions is presented.

关键词： Ethylene thermal cracking PSE Intelligent manufacturing Molecularization and digitization Modeling and optimization

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Low-cost irrigation system based on multi-tenant and WSN technologies(SCA) 36

Low-cost irrigation system based on multi-tenant and WSN tec...

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36th International Conference on Software engineering and knowledge engineering, SEKE 2024

作者： Ma, Jinwei Li, Meian Chao, Lumeng Computer and Information Engineering College Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry Inner Mongolia Agriculture University Hohhot China

ISBN: (纸本)1891706594

The increase in irrigated crop areas lead to the requirements for freshwater will in fact rise significantly. Considering this kind of need, this study aims to develop a low cost irrigation system that provide the monitoring and control of irrigated agriculture using Cloud Computing, Internet of Things(IoT) to improve the efficiency of water use in irrigated agriculture and reduce the cost of the solution. In this paper, an irrigation system driven by Internet of Things and cloud based web application is developed. The web application uses a multi-tenant web framework (MWF) that can provide cost-effective services for scenarios where a large number of small tenants, only some of which are active at the same time. The system uses wireless sensor network(WSN) communication technology to integrate various sensors, solenoid valves and edge devices scattered throughout the crop area for monitoring and control. The solution significantly improves water use efficiency while reducing costs, making it attractive to users. © 2024 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Solenoid valves

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High Dynamic Collaborative Team Query via Multi-Fuzzy-Constrained Graph Pattern Matching

High Dynamic Collaborative Team Query via Multi-Fuzzy-Constr...

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IEEE International Conference on Cloud Computing and big data Analysis (ICCCBDA)

作者： Tao Hu Zan Zhang Chenyang Bu Lei Li Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9798350373554

ISBN: (纸本)9798350373561

Graph pattern matching is a technique widely used in various fields such as protein structure analysis, social group querying, and expert localization. This technique involves finding matching subgraphs in large social networks that align with the patterns specified in the pattern graph. In this paper, we focus on a specific sub-problem in social group querying, known as the cooperative team query, which arises from practical applications, where the nodes in the pattern graph and the data graph represent team member entities, while the edges represent their social relationships. We note that the requirements of many teams in the real world are dynamic, necessitating iterative computation for graph pattern matching using traditional methods. To address this challenge in highly dynamic systems, we propose a graph pattern matching method based on core pattern graph matching cache. This approach involves extracting the core pattern graph, and comprising core team members based on the characteristics of cooperative teams. The core graph-based matching cache enables the second half of the algorithm to operate on an order-of-magnitude smaller graph, significantly improving efficiency. Additionally, the multi-threaded approach fully leverages hardware resources, synchronizing multiple matching result of the core pattern graph to reduce matching time. Experimental results on three real social network datasets demonstrate that our proposed algorithm, Core Pattern Graph Matching Cache-based Multi-threaded Exploration (CCMTE), significantly outperforms existing methods in terms of efficiency.

关键词： Proteins Location awareness Social networking (online) Social groups Heuristic algorithms knowledge graphs Hardware

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Numerical study on the optimal power distribution of server racks in a data center

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Building Simulation 2023年第6期16卷 983-995页

作者： MengXuan Song Kai Chen School of Energy and Materials Shanghai Key Laboratory of Engineering Materials Application and EvaluationShanghai Polytechnic UniversityShanghai201209China Shanghai Thermophysical Properties Big Data Professional Technical Service Platform Shanghai Engineering Research Center of Advanced Thermal Functional MaterialsShanghai201209China Key Laboratory of Enhanced Heat Transfer and Energy Conservation of the Ministry of Education School of Chemistry and Chemical EngineeringSouth China University of TechnologyGuangzhou510640China

data centers,as the infrastructure of all information services,cost tremendous amount of *** the hot spot temperature in the data center room is benefit to prevent overheating of devices,and to increase cooling system *** this paper,we study the problem of optimal power distribution among racks for minimal hot spot *** temperature rise matrix(TRM)model is used for the purpose of fast estimation of the thermal *** accuracy of the model is evaluated by conducting numerical simulations of computational fluid dynamics(CFD).Using the TRM model,optimal distributing of heating power is converted into a linear programming problem,which can be solved by highly efficient algorithms,such as ***,with realistic constraints including rack idle power and power upper limit,an iteration method is proposed to calculate the optimal power distribution along with the optimal on/off states of the *** solutions are discussed and validated by comparing with CFD *** show that the TRM model is acceptable in evaluating temperature rises in the forced-convection-dominated scenarios,and the proposed method is able to obtain optimal power distributions under various levels of total power demand.

关键词： data center numerical simulation temperature rising matrix power distribution optimization

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MECNet: Multi-Scale Exposure-Consistency Learning via Fourier Transform for Exposure Correction

MECNet: Multi-Scale Exposure-Consistency Learning via Fourie...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Qiao, Gaofei Zhang, Zhibin He, Liqiang Inner Mongolia University Key Laboratory of Wireless Networks and Mobile Computing Engineering Research Center of Ecological Big Data Ministry of Education Hohhot010021 China Geomechanica Inc Canada

ISBN: (纸本)9781665410205

In the real world, due to various challenging lighting conditions such as low light, underexposure, and overexposure, captured images often exhibit undesirable appearances. Given that images with different exposure levels require different correction processes, a single neural network struggles to produce satisfactory results. We propose a coarse-to-fine exposure correction model for learning exposure consistency representation to address underexposure and overexposure issues. Building upon the bilateral activation mechanism, we introduce the Fourier transform to capture global information and fuse it with locally extracted information through convolution to achieve superior feature representation. Additionally, we employ Laplacian pyramids to decompose the source image into different spatial frequency bands, then the image details are enhanced by denoising high-frequency layers. Experimental results on the MSEC and SICE datasets demonstrate the superiority of our proposed method over current state-of-the-art approaches. Our code will be made available on GitHub. © 2024 IEEE.

关键词： Image enhancement

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