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IEEE International Conference on e-Business Engineering (ICEBE)

作者： Zhongneng Ma Zhuorui Qin Shuxia Shi Qingyao Wu South China University of Technology Guangzhou China School of Software Engineering South China University of Technology Key Laboratory of Big Data and Intelligent Robot Ministry of Education Guangzhou Henll Electronics Equipment Co. Ltd Guangzhou China Peng Cheng Laboratory

ISBN: (数字)9798350365856

ISBN: (纸本)9798350365863

Improving the risk prevention capability of grid operations is a crucial requirement for smart resilient grids, where efficient dispatch and scheduling of delivery capacity play a pivotal role in enhancing grid resilience. Therefore, it is necessary to develop targeted intelligent dispatch algorithms based on operational data. To address this issue, this paper proposes a framework with inherent constraints and utilizes reinforcement learning using recursive neural networks. A constraint module is designed to identify available vehicle combinations based on de-livery capacity, allocate goods considering vehicle number, load, and size constraints, and generate delivery routes sequentially. Additionally, a reinforcement learning model is introduced, using negative trip length as a reward signal, optimizing parameters of the recursive neural network through policy gradient methods to automatically improve strategies for selecting optimal deliv-ery routes with improved vehicle combinations. To validate the effectiveness of this algorithm, extensive experiments were conducted using both real-world application validation data and the TSPLIB public dataset. Within a limited computational budget, the proposed algorithm outperforms several mature methods in terms of effectiveness and real-time performance, achieving near-optimal results in Euclidean graphs with nearly 100 nodes. These results demonstrate that this approach effectively addresses the intelligent scheduling of delivery capacity in digital twin systems for power storage,

关键词： Processor scheduling Prevention and mitigation Neural networks Reinforcement learning Real-time systems Scheduling Digital twins Smart grids Resilience Multi-agent systems

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RESEARCH ON THE REWARDS AND PUNISHMENTS OF URBAN SEWAGE TREATMENT UNDER THE UNCERTAINTY THEORY

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2024年第3期86卷 111-124页

作者： Ruixi, Yuan Lin, Zhang Li, Cun-Lin Masron, Tajul Ariffin Yang, Bao-Jun School of management North Minzu University Yinchuan750021 China Universiti Sains Malaysia Pinang 13500 Malaysia School of Mathematics and Information Science Guangzhou University Guangzhou510006 China School of Management The Key Laboratory of Intelligent Information and Big Data Processing of NingXia North Minzu University Yinchuan750021 China School of Management Universiti Sains Malaysia Pinang 13500 Malaysia School of Business Northinzu University Yinchuan750021 China

This paper fills a gap in urban sewage treatment decision-making by exploring reward and punishment mechanisms within the uncertainty theory framework. Addressing challenges in characterizing sewage treatment capacity due to incomplete information, the study integrates incentive and punishment mechanisms in a decision-making model. Results indicate that government rewards encourage companies to treat more sewage, but a nuanced relationship exists between rewards, treatment capacity, and expected returns. The study emphasizes the need for a balanced approach, suggesting a focus on technology enhancement over sole reliance on rewards. Examining a double-headed sewage market, the research establishes an uncertain Cournot game model, providing insights into sewage treatment dynamics under asymmetric information. In conclusion, the paper contributes to practical reward and punishment strategies in sewage treatment policies, offering theoretical support for effective decision-making in urban sewage management within a concise framework.. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Sewage treatment

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AFM Probes Depth Estimation from Convolutional Neural Networks Based Defocus Depth Measurement

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INSTRUMENTS AND EXPERIMENTAL TECHNIQUES 2024年第5期67卷 1024-1032页

作者： Yuan, Shuai Wang, Zebin Yang, Yongliang Shenyang Jianzhu Univ Sch Comp Sci & Engn Shenyang 110168 Peoples R China Liaoning Prov Big Data Management & Anal Lab Urban Shenyang 110168 Peoples R China Natl Special Comp Engn Technol Res Ctr Shenyang Branch Shenyang 110168 Peoples R China Shenyang Jianzhu Univ Sch Elect & Control Engn Shenyang 110016 Peoples R China Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Chinese Acad Sci Inst Robot Shenyang 110169 Peoples R China Chinese Acad Sci Inst Intelligent Mfg Shenyang 110169 Peoples R China

Atomic force microscopy (AFM) is a valuable method for measuring the surface properties of a sample, where the accurate estimation of the relative distance between the probe and the sample (depth) is a prerequisite for fast, accurate measurement. This paper uses geometric optical modeling to establish a function model between the blur amount in the AFM probe image and depth. This model estimates the depth by the lens via the blur information of strong edge images according to the characteristics of a small depth of field and multiple blurs in microscopic images. To estimate the probe position, the study embedded the convolutional block attention module attention mechanism model into the Resnet18 network, allowing the network to learn blur target classification under different depths. After being trained with a custom dataset, the neural network achieved 99.26% accuracy and the error of 1.25 mu m in the measurement range to +/- 100 mu m. In actual operational application, the neural network achieved an accuracy of 90.10% with an error of 1.35 mu m over a measurement range of +/- 100 mu m. Experimental results illustrate the effectiveness of the proposed method, and have guidance in implementing efficient, accurate, and fully automatic probe depth estimation.

关键词： Probes

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Deep Generative Modeling in Constraint Discovery and Anomaly Detection

SSRN

引用

SSRN 2023年

作者： Hu, Wentao Jiang, Dawei Wu, Sai Chen, Ke Chen, Gang Zhejiang Police College Hangzhou China The Key Laboratory of Big Data Intelligent Computing of Zhejiang Province Zhejiang University China

Domain experts in fraud detection and data Security often face challenges in automatically discovering constraints from relational databases, which is a critical process for identifying anomalous data. Its objective is to discover a set of inherent constraints underlying the database such that tuples violating them are considered anomalous. IceBerg, a novel system, is designed to simultaneously detect anomalous tuples and discover associated human-readable constraints, enabling both efficient anomaly detection and interpretable fraud. The backbone of IceBerg is a novel generative network, namely KD-VAE. By integrating Kernel Density estimation with Variational AutoEncoder, KD-VAE addresses the challenge of capturing complex data distributions, a critical aspect for effective anomaly detection. The KD-VAE, with its unique capability of combining kernel density estimation with variational autoencoders, provides superior performance in capturing the distributions of normal tuples, paving the way for effective anomaly detection. We can perform anomalous data detection by calculating the likelihood that the tuple fits the distributions of normal tuples and abnormality interpretation by comparing the detected anomalous tuples with their generated normal counterparts. We conducted empirical comparisons of IceBerg with several state-of-the-art outlier detection methods, evaluating performance metrics such as F1-score, across 13 real-world datasets. The results show that IceBerg outperforms its competitors in most cases. Its inherent capability to model complex, high-dimensional distributions makes it especially potent when handling datasets with high-dimensional features. © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Improve Cross-domain Mixed Sampling with Guidance Training for Adaptive Segmentation

arXiv

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arXiv 2024年

作者： Zhou, Wenlve Zhou, Zhiheng Wang, Tianlei Zeng, Delu School of Electronic and Information Engineering South China University of Technology Guangdong Guangzhou510641 China Key Laboratory of Big Data and Intelligent Robot Ministry of Education South China University of Technology Guangzhou510641 China The Department of Intelligent Manufacturing Wuyi University Jiangmen529020 China

Unsupervised Domain Adaptation (UDA) endeavors to adjust models trained on a source domain to perform well on a target domain without requiring additional annotations. In the context of domain adaptive semantic segmentation, which tackles UDA for dense prediction, the goal is to circumvent the need for costly pixel-level annotations. Typically, various prevailing methods baseline rely on constructing intermediate domains via cross-domain mixed sampling techniques to mitigate the performance decline caused by domain gaps. However, such approaches generate synthetic data that diverge from real-world distributions, potentially leading the model astray from the true target distribution. To address this challenge, we propose a novel auxiliary task called Guidance Training. This task facilitates the effective utilization of cross-domain mixed sampling techniques while mitigating distribution shifts from the real world. Specifically, Guidance Training guides the model to extract and reconstruct the target-domain feature distribution from mixed data, followed by decoding the reconstructed target-domain features to make pseudo-label predictions. Importantly, integrating Guidance Training incurs minimal training overhead and imposes no additional inference burden. We demonstrate the efficacy of our approach by integrating it with existing methods, consistently improving performance. The implementation will be available at https://***/Wenlve-Zhou/Guidance-Training. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Chronic Disease Detection Via Non-negative Latent Feature Analysis 18

Chronic Disease Detection Via Non-negative Latent Feature An...

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18th IEEE International Conference on Networking, Sensing and Control, ICNSC 2021

作者： Zhou, Leming Li, Qing Shang, Mingsheng Academy of Sciences College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chongqing China Academy of Sciences Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Chongqing China

ISBN: (纸本)9781665440486

Chronic diseases such as coronary heart disease (CHD) are of significant harm to human health. However, chronic disease detection commonly relies on many examinations to implement reliable diagnosis, resulting in a fatal delay in therapy. On the other hand, obtained examination data are often incomplete with numerous missing data, which further increases the difficulty in detection. To address this issue, this paper develops an inherently non-negative latent feature analysis-incorporating into a chronic disease detection (INC) model. The main idea are as follows: 1) adopting an inherently non-negative latent feature analysis model with a non-linear function to extract the non-negative latent features from incomplete examination data for accurate prediction of the unknown missing ones;2) detecting the chronic disease based on the high-dimensional and different types of inspection features via classifiers. Experiments on actual CHD datasets demonstrate that the proposed INC model outperforms state-of-the-art models in detection accuracy, which well fits the desire of preliminary screening on coronary heart disease (CHD). © 2021 IEEE.

关键词： Diagnosis

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Relep: Region and Edge Synergetic Level Set Model Based on Edge-Enhanced Entropy and Variable Exponential P-Laplace Equation

SSRN

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SSRN 2023年

作者： Wu, Jiangfeng Zhou, Zhiheng Zhou, Wenlue Tao, Xiyuan Deng, Ming School of Electronic and Information Engineering South China University of Technology Guangdong Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education South China University of Technology Guangzhou China

Image segmentation under conditions such as noise and intensity inhomogeneity poses a significant challenge for models. Hybrid level set approaches integrating area and edge information show promise in addressing these challenges. In this study, we propose a novel hybrid level set model, called region and edge synergetic level set model based on edge-enhanced entropy and variable exponential p-Laplace equation (RELEP), which is based on an edge-enhanced entropy and a variable exponential p-Laplace equation. Firstly, a new probability distribution is defined to enhance the edge extraction ability of image entropy in the presence of image intensity inhomogeneity and noise, termed as edge-enhanced entropy. The feature map obtained after edge-enhanced entropy processing is then used to co-fit the energy function with the original map to form the region term. Furthermore, the incorporation of edge-enhanced entropy into the p-Laplace equation leads to the formation of a variable exponential p-Laplace equation edge term. This integration facilitates the acquisition of precise and smooth boundaries, while simultaneously bolstering the robustness of the model. Finally, by incorporating the region term and edge term into the variational level set framework, the RELEP model is proposed. Experimental results on both synthetic and natural images demonstrate that the proposed RELEP model outperforms several representative and state-of-the-art models in terms of segmentation performance. © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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MULTI-GRANULARITY REPRESENTATION LEARNING FOR SKETCH-BASED DYNAMIC FACE IMAGE RETRIEVAL

arXiv

引用

arXiv 2023年

作者： Wang, Liang Dai, Dawei Fu, Shiyu Wang, Guoyin Key Laboratory of Big Data Intelligent Computing School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

In specific scenarios, face sketch can be used to identify a person. However, drawing a face sketch often requires exceptional skill and is time-consuming, limiting its widespread applications in actual scenarios. The new framework of sketch less face image retrieval (SLFIR)[1] attempts to overcome the barriers by providing a means for humans and machines to interact during the drawing process. Considering SLFIR problem, there is a large gap between a partial sketch with few strokes and any whole face photo, resulting in poor performance at the early stages. In this study, we propose a multigranularity (MG) representation learning (MGRL) method to address the SLFIR problem, in which we learn the representation of different granularity regions for a partial sketch, and then, by combining all MG regions of the sketches and images, the final distance was determined. In the experiments, our method outperformed state-of-the-art baselines in terms of early retrieval on two accessible datasets. Codes are available at https://***/ddw2AIGROUP2CQUPT/MGRL. Copyright © 2023, The Authors. All rights reserved.

关键词： Image retrieval

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Automatic mapping of gully from satellite images using asymmetric non-local LinkNet: A case study in Northeast China

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International Soil and Water Conservation Research 2024年第2期12卷 365-378页

作者： Panpan Zhu Hao Xu Ligang Zhou Peixin Yu Liqiang Zhang Suhong Liu College of Computer Sciences and Technology Chongqing University of Posts and TelecommunicationsChongqing Engineering Research Center for Spatial Big Data Intelligent TechnologyChongqing400065China College of Environment and Ecology Chongqing UniversityChongqing400044China Chongqing Municipal Ecological and Environmental Monitoring Center Chongqing401120China Beijing Key Laboratory of Environmental Remote Sensing and Digital Cities Faculty of Geographical ScienceBeijing Normal UniversityBeijing100875China China National Environmental Monitoring Centre Beijing100012China

Gully erosion can lead to the destruction of farmland and the reduction in crop *** mapping from remote sensing images is critical for quickly obtaining the distribution of gullies at regional scales and arranging corresponding prevention and control *** narrow and irregular shapes and similar colors to the surrounding farmland make mapping erosion gullies in sloping farmland from remote sensing images *** implement gully erosion mapping,we developed a small training samples-oriented lightweight deep leaning model,called asymmetric non-local LinkNet(ASNL-LinkNet).The ASNL-LinkNet integrates global context information through an asymmetric non-local operation and conducts multilayer feature fusion to improve the robustness of the extracted *** results show that the proposed ASNL-LinkNet achieves the best performance when compared with other deep learning *** quantitative evaluation results in the three test areas show that the F1-score of erosion gully recognition varies from 0.62 to *** study provides theoretical reference and practical guidance for monitoring erosion gullies on slope farmland in the black soil region of Northeast China.

关键词： Gully erosion mapping High-resolution satellite images Deep learning Black soil

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Attention-Based Residual Dense Shrinkage Network for ECG Denoising

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2809-2824页

作者： Dengyong Zhang Minzhi Yuan Feng Li Lebing Zhang Yanqiang Sun Yiming Ling Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer and Artificial Intelligence Huaihua UniversityHuaihua418000China School of Computer National University of Defense TechnologyChangsha410073China New Energy Centralized Control Center CHN Energy Hunan Power New Energy Co.Ltd.Changsha410000China

Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac ***,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affectsthe subsequent pathological ***,the effective removal of the noise from ECG signals has becomea top priority in cardiac diagnostic *** at the problem of incomplete signal shape retention andlow signal-to-noise ratio(SNR)after denoising,a novel ECG denoising network,named attention-based residualdense shrinkage network(ARDSN),is proposed in this ***,the shallow ECG characteristics are extractedby a shallow feature extraction network(SFEN).Then,the residual dense shrinkage attention block(RDSAB)isused for adaptive noise ***,feature fusion representation(FFR)is performed on the hierarchicalfeatures extracted by a series of RDSABs to reconstruct the de-noised ECG *** on the MIT-BIHarrhythmia database and MIT-BIH noise stress test database indicate that the proposed scheme can effectively resistthe interference of different sources of noise on the ECG signal.

关键词： Electrocardiogram signal denoising signal-to-noise ratio attention-based residual dense shrinkage network MIT-BIH

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