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3rd International Conference on High Performance Computing and Communication engineering, HPCCE 2023

作者： Yang, Ming Sha, Zhiqiang Sun, Yanqing School of Information Engineering Harbin University Harbin150086 China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin University Harbin150086 China Daqing Software Technology Research Institute Co. Ltd. Daqing163711 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

The detection of oil well dynamic liquid level using acoustic methods requires digital signal processing techniques to reduce environmental noises. Most denoising techniques consist of filter-based approaches and spectral methods with time or frequency transformations. In this study, the Ensemble Empirical Mode Decomposition (EEMD) algorithm is implemented at the filtering step within the standard procedures for oil industrial use to detect dynamic liquid level. Five datasets from oil well production are tested using EEMD compared to standard filtering procedures in industrial use. The EEMD algorithm achieves results in good agreement with the reference results in general with worst case relative error reaching 0.51%. In particular, EEMD processed signal visibly displays more salient echo wave feature compared to reference for one case with unknown submerging noises or disturbances. Under such circumstances, the combination of a denoising filter and EEMD furthermore stabilizes the results, the maximum relative error falling to 0.39%. Importantly, the ensemble averaged Intrinsic Mode Functions (IMFs) of frequencies linked to reflected infra-sound could provide good knowledge helping locate the reflection of infra-sound wave. EEMD is a promising method for filtering signals of echo wave based dynamic liquid level detection. Further in-depth investigations are required to better interpret signals mixed with unknown noises or disturbances. © 2024 SPIE.

关键词： Empirical mode decomposition

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An Evaluation System for Large Language Models based on Open-Ended Questions 11

An Evaluation System for Large Language Models based on Open...

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11th IEEE International Conference on Cyber Security and Cloud Computing, CSCloud 2024

作者： Cao, Zhiyuan Ma, Zeyu Chen, Mingang Computer and Information Engineering College Shanghai Polytechnic University Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer software Technology Mathematical and Science College Shanghai Normal University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer software Technology Shanghai China

ISBN: (纸本)9798350376982

We designed a large language model evaluation system based on open-ended questions. The system accomplished multidimensional evaluation of LLMs using open-ended questions, and it presented evaluation results with evaluation reports. Currently, the evaluation of large-scale language models often exists with two prominent limitations: (1) The evaluation methods are often single-minded, resulting in less credible results. (2) Most evaluations are based on datasets with closed-ended questions, treating generative large language models as discriminative models, which fails to adequately reflect the high output flexibility characteristic of these models. For these two limitations, we proposed an evaluation system for LLMs based on open-ended questions. Our experiments on the adapted open-source datasets demonstrated the effectiveness of this system. © 2024 IEEE.

关键词： Natural language processing systems

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Blood Cell Image Classification Based on Improved EfficientNetB3 Model 4

Blood Cell Image Classification Based on Improved EfficientN...

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4th International Conference on Electronic Information engineering and Computer Science, EIECS 2024

作者： Feng, Haiwen Wu, Yuhan Software College of Shenyang University of Technology Shenyang Intelligent Key Laboratory of High-end Industrial Equipment Manufacturing Shenyang China Software College of Shenyang University of Technology Shenyang China

ISBN: (纸本)9798331531409

Accurate classification of blood cells is very important for clinical diagnosis, but traditional classification methods are not only time-consuming, inefficient, but also easy to make subjective judgments. With the development of technology, neural network has made great progress in the field of blood cell analysis, especially deep learning technology has shown broad application prospects in the field of medical images. By using the lightweight EfficientNetB3 model to classify four kinds of cells, CBAM attention mechanism is added to the network to improve efficiency. The experimental results show that this method has excellent recognition ability and is helpful for the diagnosis and treatment of blood diseases in the future. © 2024 IEEE.

关键词： Deep learning

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A novel thermally-stable single-phase phosphor Ca_(2)InTaO_(6):Dy^(3+)for NUV-excited white LEDs

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Journal of Rare Earths 2024年第10期42卷 1855-1864页

作者： Jian Hu Ruirui Cui Jun Zhang Peng Linghu Youzhen Shi Xiang Guo Key Laboratory of Micro-Nano-Electronics and Software Technology of Guizhou Province College of Big Data and Information EngineeringGuizhou UniversityGuiyang550025China College of Computer and Information Engineering Guizhou University of CommerceGuiyang550014China

The work focuses on the utilization of the conventional solid-state sintering procedure to synthesize white phosphors Ca_(2)InTaO_(6):xDy^(3+)(0.02≤x≤0.12).Utilizing X-ray diffraction,the phase structure of samples was examined,and the crystal structure was refined using the Rietveld method.A scanning electron microscope was used to analyze the microstructure of ***-principles calculations confirm that the indirect bandgap of Ca_(2)InTaO_(6)is 3.786 eV,The luminous properties and energy transfer mechanism of Ca_(2)InTaO_(6):xDy^(3+)were studied using photoluminescence ***^(4)F_(9/2)→^(6)H_(13/2)transition of Dy^(3+)ions is responsible for the greatest emission peak,which was measured at 575 *** to research,the lifespan falls as the concentration of Dy^(3+)doping amount rises because of frequent interaction and ene rgy transfer between Dy^(3+)*** correlated color temperature of the WLEDs packaged with Ca_(2)InTaO_(6):0.08Dy^(3+)is 4677 K and CIE 1931 chromaticity coordinates are(0.3578,0.3831).Meantime,the phosphor also shows outstanding te mperature stability property,which maintains 83.8%of its initial emission intensity at 450 K(activation energy of 0.1467 eV).The W-LEDs retain their performance for 100 min when powered at 3.4 V voltage and 600 mA current,demonstrating the packed W-LEDs'sustaine d operation at high temperatures.

关键词： Ca_(2)InTaO_(6):Dy^(3+)phosphors Thermal stability Photoluminescence W-LEDs Thermal imaging Rare earths

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Effect of boundary conditions on shakedown analysis of heterogeneous materials

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Applied Mathematics and Mechanics(English Edition) 2024年第1期45卷 39-68页

作者： Xiuchen GONG Yinghao NIE Gengdong CHENG Kai LI State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentInternational Research Center for Computational MechanicsDepartment of Engineering MechanicsDalian University of TechnologyDalian 116023Liaoning ProvinceChina

The determination of the ultimate load-bearing capacity of structures made of elastoplastic heterogeneous materials under varying loads is of great importance for engineering analysis and design. Therefore, it is necessary to accurately predict the shakedown domains of these materials. The static shakedown theorem, also known as Melan's theorem, is a fundamental method used to predict the shakedown domains of structures and materials. Within this method, a key aspect lies in the construction and application of an appropriate self-equilibrium stress field(SSF). In the structural shakedown analysis, the SSF is typically constructed by governing equations that satisfy no external force(NEF) boundary conditions. However, we discover that directly applying these governing equations is not suitable for the shakedown analysis of heterogeneous materials. Researchers must consider the requirements imposed by the Hill-Mandel condition for boundary conditions and the physical significance of representative volume elements(RVEs). This paper addresses this issue and demonstrates that the sizes of SSFs vary under different boundary conditions, such as uniform displacement boundary conditions(DBCs), uniform traction boundary conditions(TBCs), and periodic boundary conditions(PBCs). As a result, significant discrepancies arise in the predicted shakedown domain sizes of heterogeneous materials. Built on the demonstrated relationship between SSFs under different boundary conditions, this study explores the conservative relationships among different shakedown domains, and provides proof of the relationship between the elastic limit(EL) factors and the shakedown loading factors under the loading domain of two load vertices. By utilizing numerical examples, we highlight the conservatism present in certain results reported in the existing literature. Among the investigated boundary conditions, the obtained shakedown domain is the most conservative under ***, utilizing PBCs to

关键词： heterogeneous material self-equilibrium stress field(SSF) shakedown analysis effect of boundary conditions

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Research on the prediction of stock prices in the food and media sectors based on the random forest algorithm 5

Research on the prediction of stock prices in the food and m...

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5th International Conference on Telecommunications, Optics, and Computer Science, TOCS 2024

作者： Liang, Yuhao Tian, Liwei Sun, Yu Xie, Guoli Yang, Lei Guangdong University of Science and Technology Dongguan China Dongguan Key Laboratory of Kunpeng Computing Dongguan China Dongguan Key Laboratory of Industrial Software for Intelligent Manufacturing Dongguan China

ISBN: (纸本)9781510691667

This article employs the Random Forest model to conduct predictive research on the food and media industries in the Chinese stock market. The data originates from the historical trading data of the Shanghai Stock Exchange Index from January to June 2022, selecting the stock data of three representative companies from each of the two industries. By analyzing data such as the opening date and price, the Random Forest algorithm is used to predict the closing price, with training and testing sets established and a specific splitting stopping criterion adopted. The study aims to verify the general applicability and accuracy of the Random Forest model in predicting the A-share market, and to explore the differences in the prediction results between the food and media industries. The experimental results show that the prediction bias for both industries is small, with no significant difference in the prediction results, and the model fitting effect is good. © 2025 SPIE.

关键词： Food industry

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Outlier item detection in bundle recommendation via the attention mechanism

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High-Confidence Computing 2024年第3期4卷 51-57页

作者： Yuan Liang School of Information Engineering Suqian UniversitySuqian 223800China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China

Bundle recommendation offers users more holistic insights by recommending multiple compatible items at ***,the intricate correlations between items,varied user preferences,and the pronounced data sparsity in combinations present significant challenges for bundle recommendation ***,current bundle recommendation methods fail to identify mismatched items within a given set,a process termed as‘‘outlier item detection’’.These outlier items are those with the weakest correlations within a *** them can aid users in refining their item *** the correlation among items can predict the detection of such outliers,the adaptability of combinations might not be adequately responsive to shifts in individual items during the learning *** limitation can hinder the algorithm’s *** tackle these challenges,we introduce an encoder–decoder architecture tailored for outlier item *** encoder learns potential item correlations through a self-attention ***,the decoder garners efficient inference frameworks by directly assessing item *** have validated the efficacy and efficiency of our proposed algorithm using real-world datasets.

关键词： Bundle recommendation Outlier item detection Attention mechanism Decoder encoder

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Student academic performance prediction via hypergraph and TabNet

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Journal of Big Data 2025年第1期12卷 1-24页

作者： Yang, Yisheng Zhao, Shiyu An, Sufang Li, Xiaoyong Zhang, Yong Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing University of Technology Beijing China Academic Affairs Office Beijing Open University Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China

Student academic performance prediction plays a very important role in student management. With the development of deep learning, there has been some related research work on predicting student academic performance. Although many factors influence students’ academic performance, students at the same university tend to have similar intellectual levels and learning environments. Therefore, learning behaviors can significantly impact their academic performance. Thus, modeling students’ behavior can enable the prediction of their academic performance. However, existing methods mostly focus on modeling individual student behaviors, neglecting the complex associations among students hidden within campus behavioral data. In reality, the associations between students often involve higher-order, multi-to-multi relationships, rather than simple, pairwise connections. At the same time, most data-driven deep learning models are not interpretable. But in fact, the analysis of behaviors that affect student academic performance is more useful in many cases. To address these issues, this paper proposes a student academic performance prediction model that combines hypergraphs and TabNet. This method first processes and extracts usable behavior features from collected multi-source campus behavior data;secondly, it utilizes K-Nearest Neighbors (KNN) to construct a hypergraph to describe the higher-order associations among students;then, it uses hypergraph convolution to aggregate neighborhood features to learn sample embedding representations;finally, the academic performance of students are predicted by the TabNet model. Experimental results on real student behavior datasets indicate that the precision, recall, and F1 score of the method proposed in this paper are improved compared to baseline methods. Additionally, using hypergraphs and TabNet can help to explain the relationship between student behavior and performance at the feature level. © The Author(s) 2025.

关键词： Students

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Adaptive fusion of structure and attribute guided polarized communities search

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Frontiers of Computer Science 2024年第1期18卷 255-257页

作者： Fanyi YANG Huifang MA Wentao WANG Zhixin LI Liang CHANG College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China Key Lab of Education Blockchain and Intelligent Technology(Ministry of Education) Guangxi Normal UniversityGuilin 541004China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China

1 Introduction and main contributions The exploration of polarized communities,which consist of two antagonistic subgraphs and include a set of query nodes,is a crucial task in community search on signed *** existing methods either predominantly rely on topological structure while disregarding node attributes or tend to prioritize the global identification of all polarized ***,they fail to consider two crucial ***,integrating node attributes with network structure can enhance the search quality for polarized communities in attributed signed networks by leveraging complementary ***,global criteria-based polarized community detection aims to identify all polarized communities,neglecting personalized analyses centered around individual users.

关键词： polarized structure integrating

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Analysis of china photovoltaic market development trend 4

Analysis of china photovoltaic market development trend

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4th International Conference on Laser, Optics, and Optoelectronic Technology, LOPET 2024

作者： Yuan, Quanhong Dongguan Key Laboratory of Industrial Software for Intelligent Manufacturing Depart. Mechanical and Electrical Engineering Guangdong University of Science and Technology Guangdong Dongguan523083 China

ISBN: (纸本)9781510681903

In recent years, China is the world's largest PV producer, the most complete PV industry chain, the largest PV market and the largest PV power generation capacity. Under the dual-carbon target, the future development trend of China's PV market is very important. Based on the sample data of China's annual cumulative PV installations from 2001 to 2022, its regression equations were obtained by MATLAB software using cubic polynomials, exponential curves, Peel's growth curves, linderuo's growth curves, quadratic exponential smoothing, and BP neural network training models, respectively. After comprehensive analysis, the cubic polynomial curve and exponential curve fitting are better in terms of continuity, growth, reliability and rapidity, but the exponential method accelerates the error with the increase of extrapolation year, so it is recommended to use the cubic polynomial curve. Finally, the cubic polynomial is used to forecast the annual cumulative installed capacity of PV (including distributed and centralized) in China from 2023 to 2030. Comparing with the results predicted by the China Photovoltaic Industry Association (CPIA), the prediction results of this paper are on the small side, which is mainly due to the neglect of the effects of favorable policies, technological breakthroughs, and increasing domestic and foreign market demand. © 2024 SPIE.

关键词： MATLAB

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