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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Jian, Caiqing Qin, Yongbin Wang, Lihui Ye, Chen Cheng, Xinyu Guizhou University Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China

ISBN: (纸本)9798350387384

Gland instance segmentation is an essential but challenging task in the diagnosis of adenocarcinoma. The existing models usually achieve gland instance segmentation through multi-task learning and boundary loss constraint. However, how to deal with the problems of gland adhesion and inaccurate boundary in segmenting the complex samples remains a challenge. In this work, we propose a novel geometric feature named displacement field (DF), which can represent the geometric information such as shape, size, and orientation of the glands. Based on the explicit constraint of DF, a displacement-field assisted graph energy transmitter (DFGET) framework is proposed for solving the above problems. Using such graph framework, the gland semantic segmentation map and the displacement field of the graph nodes are estimated with two graph network branches. With the constraint of DF, a graph cluster module is presented to improve the intra-class feature consistency and inter-class feature discrepancy, as well as to separate the adherent glands. Extensive comparison and ablation experiments on the GlaS dataset demonstrate the effectiveness of the displacement field and the superiority of DFGET. Compared to the best comparative model, DFGET increases the object-Dice and object-F1 score by 2.27% and 3.11% respectively, while decreases the object-HD by 15.58, achieving state-of-the-art performance, which indicates that DFGET can serve as an auxiliary tool for the diagnosis of adenocarcinoma. © 2024 IEEE.

关键词： Semantic Segmentation

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Predictive control for greenhouse temperature and humidity and energy optimization by improved NMPC objective function algorithm

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International Journal of Agricultural and Biological Engineering 2024年第5期17卷 128-136页

作者： Lina Wang Ying Zhang Mengjie Xu Qiuhui Liu Binrui Wang Institute of Mechanical and Electrical Engineering China Jiliang UniversityHangzhou 310018China Zhejiang Province Key Laboratory of On-line Testing Equipment Calibration Technology Research China Jiliang UniversityHangzhou 310018China Zhejiang Provincial Key Laboratory of Intelligent Manufacturing Quality Big Data Traceability and Application China Jiliang UniversityHangzhou 310018China

Persistent low temperatures in autumn and winter have a huge impact on crops,and greenhouses rely on solar radiation and heating equipment to meet the required indoor *** the energy cost of frequent operation of the actuators is exceptionally *** relationship between greenhouse environmental control accuracy and energy consumption is one of the key issues faced in greenhouse *** this study,a non-linear model predictive control method with an improved objective function was *** improved objective function used tolerance intervals and boundary constraints to optimize the objective *** nonlinear model predictive control(NMPC)controller design was based on the wavelet neural network(WNN)data-driven model and applied the interior point method to solve the optimal solution of the objective function control,thus balancing the contradiction between energy consumption and control *** simulation results showed that the improved NMPC method reduced energy consumption by 21.02%and 9.54%compared with the model predictive control and regular NMPC,which proved the method achieved good results in a low-temperature *** research can provide an important reference for the field as it offers a more efficient approach to managing greenhouse climates,potentially leading to substantial energy savings and enhanced sustainability in agricultural practices.

关键词： greenhouse environmental control greenhouse energy optimization nonlinear model predictive control objective function improvement

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A deformation measurement method based on surface texture information of rocks and its application

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International Journal of Mining science and technology 2023年第9期33卷 1117-1130页

作者： Yanbo Zhang Xin Han Peng Liang Xulong Yao Qun Li Guangyuan Yu Qi Wang College of Mining Engineering North China University of Science and TechnologyTangshan 063210China Green Intelligent Mining Technology Innovation Center of Hebei Province Tangshan 063210China Key Laboratory for Geo-mechanics and Deep Underground Engineering China University of Mining and Technology-BeijingBeijing 100083China

Users of the digital image correlation method are faced with the problem of poor operability,low repeatability,and lack of standardized specifications for spraying *** solve the problem,the research proposed a rock deformation measurement method that obviates the need to spray speckles.A local binary model was established by using the local binary pattern(LBP)operator based on deep texture features on rock *** resulting LBP digital speckle pattern can substitute artificial speckle patterns and demonstrates high quality and strong *** on the LBP digital speckle pattern,the target tracking algorithm was employed to achieve non-contact measurement of the dynamic displacements of *** feasibility and effectiveness of the algorithm in practical application were verified by conducting shear tests on granite and *** results show that the deformation characteristics in the displacement nephograms are in line with the measured data pertaining to rock fracturing and conform to the basic characteristics of the shear failure of *** deformation measurement method based on surface texture information can realize non-contact displacement measurement of rocks under conditions without speckles:this obviates the influence of the quality of sprayed speckles on the accuracy of the measurement of deformation.

关键词： Deformation measurement Texture information Digital speckle Local binary model Target tracking algorithm

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A Lightweight Garbage Classification Algorithm for Scenic Spots 25th

A Lightweight Garbage Classification Algorithm for Scenic Sp...

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25th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2024

作者： Zhang, Yu Yu, Fu Xing College of Artificial Intelligence North China University of Science and Technology Tangshan063210 China Key Laboratory of Industrial Intelligent Perception in Hebei Province North China University of Science and Technology Tangshan063210 China

ISBN: (纸本)9789819642069

A multi-scale lightweight garbage classification target detection algorithm is proposed in view of the fact that the garbage in scenic spots is concentrated and the target scales are different, the cost of manual picking is high, the efficiency is low, and the traditional detection algorithm is difficult to effectively identify the garbage types. Firstly, C2f-MS module is applied to the backbone network to reduce the number of model parameters and enhance its ability to handle complex multi-scale scenes;Secondly, the neck network is redesigned, and a multi-scale feature fusion network, MergenNet, is proposed to enhance the extraction ability and speed of targets with different scales, and improve the extraction ability of small target features;Finally, the improved QFL Shape IOU is used as the loss function of the model to improve the positioning ability of objects in overlapping environments;The experimental results show that in the domestic waste classification detection dataset, the parameters of the improved algorithm model are reduced by 11%, FLOPs are reduced by 4%, and Map is increased by 3.3% compared with YOLOv8n. Through the scenic spot robot capture experiment, the improved algorithm has good use value in processing scenic spot garbage tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lightweight Multi-scale Waste Classification Detection

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Long-Term and Short-Term Perception in Knowledge Tracing 1

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11th CCF big data Conference, bigdata 2023

作者： Chen, Zihang Zhu, Mengxiao Wang, Fei Shen, Shuanghong Huang, Zhenya Liu, Qi Anhui Province Key Laboratory of Science Education and Communication Institute of Advanced Technology and School of Humanities and Social Sciences University of Science and Technology of China Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science and School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9789819989799

ISBN: (纸本)9789819989782

Knowledge Tracing (KT) is a fundamental task in contemporary intelligent educational systems, which tracks the knowledge states of the learners based on their response sequences. KT is crucial for the effectiveness of computer-assisted intelligent educational systems, such as intelligent tutoring systems and educational resource recommendation systems. In recent years, KT models benefited from the deep learning approaches and improved dramatically compared with the traditional probabilistic approaches. However, deep learning based KT models also have significant limitations. For example, Recurrent Neural Network (RNN)-based KT models can not capture dependencies in long-term sequences effectively, and attention mechanism-based KT models rely on the positional encoding to perceive sequential information, which may disrupt the semantics of the original embeddings. Moreover, the data sparsity question remains a big challenge in existing KT models. This study is based on the observation that KT shows a stronger sequential dependence in the long term than in the short term. In this paper, we propose a novel KT model called "Long-term and Short-term perception in knowledge tracing (LSKT)", which uses multilayer perceptrons and attention mechanism to capture the long-term and short-term dependencies, and employs 2PL-IRT based embedding to alleviate the data sparsity question. Extensive experiments on multiple datasets demonstrate the effectiveness of our proposed LSKT model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Deep Learning Educational data Mining intelligent Education Knowledge Tracing

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Task Separation and Knowledge Sharing for Class Incremental Learning 5

Task Separation and Knowledge Sharing for Class Incremental ...

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5th International Conference on intelligent Computing and Human-Computer Interaction, ICHCI 2024

作者： Zhang, Jiali Qiao, Xiaoyan School of Computer Science and Technology Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China School of Mathematics and Information Science Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China

ISBN: (纸本)9798350368284

Methods based on dynamically expanding architectures can effectively mitigate catastrophic forgetting in class incremental learning (CIL), but they often overlook information sharing and integration between subnetworks, leading to performance degradation and increased parameter count and memory consumption as the network expands. To address these complex issues, we introduce a new method called Task Separation and Knowledge Sharing (TSKS), which transfers knowledge at both the logical and feature levels through Hybrid Knowledge Distillation (HKD). In logical distillation, we combine Global Knowledge Distillation (GKD) and Task Knowledge Distillation (TKD) to establish connections between tasks. To prevent the model from favoring new classes, we design an Attention- Based Feature Fusion (ABFF) mechanism to capture important features. Additionally, we introduce a Soft-Margin Auxiliary Loss (SMAL) to help the model learn new concepts. Extensive experiments show that TSKS outperforms existing models on CIFAR100 and ImageNet100, regardless of whether memory samples are used. Notably, TSKS demonstrates superior performance in memory-free settings, indicating a reduced reliance on rehearsal memory. © 2024 IEEE.

关键词： Adversarial machine learning

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Modulating perovskite crystallization and band alignment using coplanar molecules for high-performance indoor photovoltaics

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Journal of Energy Chemistry 2024年第11期98卷 383-390页

作者： Qu Yang Shuhan Fan Haozhe Zhang Zhenhuang Su Xingyu Gao Hui Shen Mingkui Wang Xiu Gong College of Physics &Guizhou Province Key Laboratory for Photoelectrics Technology and ApplicationGuizhou UniversityGuiyang 550025GuizhouChina State Key Laboratory of Public Big Data Guizhou UniversityGuiyang 550025GuizhouChina Shanghai Synchrotron Radiation Facility(SSRF) Shanghai Advanced Research InstituteChinese Academy of SciencesShanghai 201204China Wuhan National Laboratory for Optoelectronics School of Optical and Electronic InformationHuazhong University of Science and TechnologyWuhan 430074HubeiChina

The proper bandgap and exceptional photostability enable CsPbI_(3) as a potential candidate for indoor photovoltaics(IPVs),but indoor power conversion efficiency(PCE) is impeded by serious nonradiative recombination stemming from challenges in incomplete DMAPbI_(3) conversion and lattice structure ***,the coplanar symmetric structu re of hexyl sulfide(HS) is employed to functionalize the CsPbI_(3) layer for fabricating highly efficient *** hydrogen bond between HS and DMAI promotes the conversion of DMAPbI_(3) to CsPbI_(3),while the copianar symmetric structure enhances crystalline ***,surface sulfidation during HS-induced growth results in the in situ formation of PbS,spontaneously creating a CsPbI_(3) N-P homojunction to enhance band alignment and carrier *** a result,the CsPbI_(3)&HS devices achieve an impressive indoor PCE of 39.90%(P_(in):334.6 μW cm^(-2),P_(out):133.5 μW cm^(-2)) under LED@2968 K,1062 lux,and maintain over 90% initial PCE for 800 h at ^(3)0% air ambient humidity.

关键词： Perovskite indoor photovoltaics CsPbI_(3) Coplanar symmetric structure molecules Crystallization kinetics Hydrogen bond N-P homojunction

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Multi-layer network embedding on scc-based network with motif

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Digital Communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

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Research on Metaverse Multi-person Linkage Using Mobile Edge Computing Based on Extended Reality Under the Immersive Experience of Zhijiang Peace Culture Memorial Hall 16th

Research on Metaverse Multi-person Linkage Using Mobile Edge...

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16th EAI International Conference on Wireless Internet, WiCON 2023

作者： Liu, Yiwen Fu, Jinrong Yan, Haobo Gao, Yanxia Peng, Ling Qu, Taiguo School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China

ISBN: (纸本)9783031580529

This study aims to deeply explore the impact of mobile edge computing based on extended reality technology on the multi-person linkage of the Zhijiang Peace Culture Memorial Hall under the immersive experience. In the part of research background and significance, it discusses the rise of the application of XR technology in the field of culture and education, emphasizing the importance of education on the history of revolution and its value in the social sense. Then, it expounds the core topic of this research aimed at integrating XR technology with education on the history of revolution, and presenting visitors with more realistic and immersive historical scenes by creating an immersive experience of multi-person linkage. The use of these technologies enables visitors to transcend time and space barriers, directly participate in historical events, and deeply appreciate the intrinsic value of red culture. In order to achieve multi-person linkage, this study adopts mobile edge computing technology to ensure that multiple visitors can realize real-time interaction in the virtual scene and jointly build a collective experience of the Metaverse. This research has achieved positive results in the fields of XR technology application, education on the history of revolution, and mobile edge computing. It provides a useful reference for the modernization and upgrading of the Zhijiang Peace Culture Memorial Hall, and also provides a new paradigm for the cultural promotion of multi-person linkage experience in Metaverse. © ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Mobile edge computing

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MAQMC:Multi-Agent Deep Q-Network for Multi-Zone Residential HVAC Control

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Computer Modeling in Engineering & sciences 2023年第9期136卷 2759-2785页

作者： Zhengkai Ding Qiming Fu Jianping Chen You Lu Hongjie Wu Nengwei Fang Bin Xing School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou215009China Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency Suzhou University of Science and TechnologySuzhou215009China School of Architecture and Urban Planning Suzhou University of Science and TechnologySuzhou215009China Chongqing Industrial Big Data Innovation Center Co. Ltd.Chongqing400707China

The optimization of multi-zone residential heating,ventilation,and air conditioning(HVAC)control is not an easy task due to its complex dynamic thermal model and the uncertainty of occupant-driven cooling *** reinforcement learning(DRL)methods have recently been proposed to address the HVAC control ***,the application of single-agent DRL formulti-zone residential HVAC controlmay lead to non-convergence or slow *** this paper,we propose MAQMC(Multi-Agent deep Q-network for multi-zone residential HVAC Control)to address this challenge with the goal of minimizing energy consumption while maintaining occupants’thermal *** is divided into MAQMC2(MAQMC with two agents:one agent controls the temperature of each zone,and the other agent controls the humidity of each zone)and MAQMC3(MAQMC with three agents:three agents control the temperature and humidity of three zones,respectively).The experimental results showthatMAQMC3 can reduce energy consumption by 6.27%andMAQMC2 by 3.73%compared with the fixed point;compared with the rule-based,MAQMC3 andMAQMC2 respectively can reduce 61.89%and 59.07%comfort *** addition,experiments with different regional weather data demonstrate that the well-trained MAQMC RL agents have the robustness and adaptability to unknown environments.

关键词： Deep reinforcement learning multi-zone residential HVAC multi-agent energy conservation comfort

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