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2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Orazovna, Yussupova Assel Neftissov, Alexandr Olegovna, Potapenko Alexandra Sansyzbayevna, Urazalimova Dariya Toraighyrov University Department Of Electrical Engineering And Automation Pavlodar Kazakhstan Research And Innovation Center 'Industry 4.0' Astana It University Nur-Sultan Kazakhstan Toraighyrov University Faculty Of Computer Science Pavlodar Kazakhstan

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

Modern diagnostic systems for asynchronous motors are widely used in the power industry. The most promising diagnostic systems for rotor eccentricity of induction motors are systems with capacitive measuring transducers in view of their simplicity, reliability and sufficient sensitivity. In the proposed work, the selection of the information sign of damage for the diagnosis system of rotor eccentricity with open and closed slots of an induction motor with a capacitive measuring transducer. © 2022 IEEE.

关键词： Induction motors

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MATLAB Implementation of Hp Finite Elements on Rectangles Using Hierarchical Basis Functions 1

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14th International Conference on Parallel Processing and Applied Mathematics, PPAM 2022

作者： Moskovka, Alexej Valdman, Jan Department of Mathematics Faculty of Applied Sciences University of West Bohemia Technická 8 Plzeň30100 Czech Republic Department of Computer Science Faculty of Science University of South Bohemia Branišovská 31 České Budějovice37005 Czech Republic The Czech Academy of Sciences Institute of Information Theory and Automation Pod vodárenskou věží 4 Prague 818208 Czech Republic

ISBN: (数字)9783031304453

ISBN: (纸本)9783031304446

A MATLAB implementation of hierarchical shape functions on 2D rectangles is explained and available for download. Global shape functions are ordered for a given polynomial degree according to the indices of the nodes, edges, or elements to which they belong. For a uniform p-refinement, the hierarchical structure enables an effective assembly of mass and stiffness matrices. A solution to a boundary value problem is approximated for various levels of uniform h and p refinements. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Boundary value problems

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Coreference disambiguation based on two-layer label dependence analysis 7

Coreference disambiguation based on two-layer label dependen...

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7th International Conference on Mechatronics and Intelligent Robotics, ICMIR 2023

作者： Xu, Hongfei Li, Yingna Faculty of Information Engineering and Automation Kunming University of Science and Technology 727 Jingming South Rd.Chenggong District Kunming650500 China Yunnan Key Laboratory of Computer Technology Applications 727 Jingming South Rd.Chenggong District Kunming650500 China

ISBN: (数字)9781510668089

ISBN: (纸本)9781510668072

A two-layer labeling-based dependency analysis model is proposed to introduce the location features of words for the coreference disambiguation of entities. Firstly, two layers of labels are used for labeling, the second layer labels the location information of words, the features of sentences are learned using a bidirectional long short-Term memory network, the dependency syntax analysis based on deep graph decoding obtains the dependency tree of sentences, and the two layers of labels are fused to improve the performance and accuracy of coreference disambiguation. Experiments are conducted on the text dataset of power security entity relationship extraction, and the results show that the two-layer labeled dependency analysis has an improved effect on co-finger disambiguation, which verifies the effectiveness of the model for cofinger disambiguation on the experimental dataset. © 2023 SPIE. All rights reserved.

关键词： Trees (mathematics)

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Automatic Detection of Tuberculosis with the Help of Image Processing and Machine Learning Classifier 7

Automatic Detection of Tuberculosis with the Help of Image P...

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7th International Conference on Computing in Engineering and Technology, ICCET 2022

作者： Sharma, Anshu Malhotra, Rahul Sharma, Anurag Department of Computer Science & Engineering CT University Ludhiana India Department of Electronics & Communication Engineering CT Group of Institutions Jalandhar India Faculty of Engineering Design and Automation GNA University Phagwara India

ISBN: (纸本)9781839537042

Tuberculosis is the substantial irresistible disease present in human that principally influences lungs, this tuberculosis is one the main ten noticeable reasons for death for human. It has very typical symptoms so without testing it isn't sure that patient has infected with TB. In this paper, an automated framework dependent on image handling is proposed to help specialists and radiologists in deciphering Chest X-beams (CXR) for early identification of lung Tuberculosis (TB). CXR can show the abnormalities in lungs as well as TB which helps to precisely and rapidly distinguish TB on the grounds that early treatment will provide to patients and disease will stop spreading in body. The proposed system comprised of preprocessing, segmentation, features extraction, and classification. In this paper proposed a strategy of image segmentation and AI to conclusion and characterize the tuberculosis (TB). These tool and procedures are utilized to earlier uncover of tuberculosis (TB) illness. Additionally arrange the sort of TB. The framework recognizes the tuberculosis with more prominent precision. © 2022 The Institution of Engineering and Technology.

关键词： Diseases

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Sparse Attention-based Hierarchical Node Representation for Spatial Domain Identification

Sparse Attention-based Hierarchical Node Representation for ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wei Peng Zhihao Ping Wei Dai Xiaodong Fu Li Liu Lijun Liu Ning Yu Faculty of Information Engineering and Automation Kunming University of Science and Technology Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Department of Computing Sciences The College at Brockport State University of New York Brockport NY

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Using deep learning models on spatial transcriptomics data to identify the spatial domain is crucial for uncovering the spatial distribution of cells and gene expression patterns within tissues, essential for understanding complex biological processes and disease mechanisms. Existing methods for spatial domain partitioning often rely on predefined adjacency relationships at a single scale, overlooking the hierarchical structure and functional characteristics of biological tissues. In this paper, we propose SpaNFM, a novel method that leverages sparse attention-based hierarchical node representation and multi-view contrastive learning for spatial domain identification in spatial transcriptomics data. The SpaNFM first treats each spot as a node and constructs two views using different data augmentation techniques based on tissue image information, gene expression profiles, and spatial coordinates of cells. Subsequently, SpaNFM utilizes a sparse attention-based hierarchical node fusion module to generate coarse-grained node representations. This fine-to-coarse hierarchical structure integrates complementary information from multi-granularity node features and reduces model complexity due to the decreased node size. The model parameters are updated using gene expression reconstruction loss and contrastive loss on the coarse-grained node representations from the two views. Finally, the learned node features are subjected to downstream clustering using the Leiden algorithm. We tested SpaNFM on the human dorsolateral prefrontal cortex dataset. The results demonstrate that SpaNFM outperforms other state-of-the-art methods in most cases. The data and code are available at: https://***/weiba/SpaNFM

关键词： Biological system modeling Biological tissues Transcriptomics Contrastive learning Feature extraction Brain modeling Spatial databases Gene expression Resource management Image reconstruction

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Modeling Defect Formation Processes at Different Stages of Metallurgical Production 3

Modeling Defect Formation Processes at Different Stages of M...

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3rd International Conference on Control Systems, Mathematical Modeling, automation and Energy Efficiency, SUMMA 2021

作者： Galkin, Alexander Pimenov, Vladimer Lipetsk State Technical University Faculty of Automation and Computer Science Lipetsk Russia Novolipetsk Steel Corporation Lipetsk Russia

ISBN: (纸本)9781665439817

The paper proposes models for predicting the appearance of surface defects in flat-rolled steel production at ferrous metallurgy enterprises. A 'shell' type surface defect is considered. At the complete cycle metallurgical enterprises with flat-rolled steel production, the quality of the finished product is affected by the various stages of production. In particular, the appearance of the 'shell' type defect may influence the quality of the starting material, namely slabs. Therefore, one of the proposed models is a model of the cracks occurrence in continuous steel casting process. The model uses a criterion based on the time the ingot is in the curvilinear zone of the continuous steel casting facility (CSSF) with the crust temperature in the range of the embrittlement temperature of the corresponding steel grades. In the process of cold rolling, the 'shell' type defect is shaped. It can be completely rolled out or appear as a surface defect in cold rolling. Therefore, the next model considered in the paper is a model of defect shaping in the process of cold rolling. The stress-strain state in the defect zone is modeled, and the influence of initial geometrical characteristics and rolling modes on the degree of rolling-out were analyzed. © 2021 IEEE.

关键词： Continuous casting

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A novel fault-tolerant scheduling approach for collaborative workflows in an edge-IoT environment

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Digital Communications and Networks 2022年第6期8卷 911-922页

作者： Tingyan Long Yong Ma Lei Wu Yunni Xia Ning Jiang Jianqi Li Xiaodong Fu Xiangmi You Bo Zhang College of Computer Science Chongqing UniversityChongqing400030China School of Computer and Information Engineering Jiangxi Normal UniversityJiangxi330022China School of Mathematical Sciences University of Electronic Science and Technology of ChinaChengdu611731China Mashang Consumer Finance Co. Ltd.(MSCF)Chongqing401129China Global Energy Interconnection Research Institute Co.Ltd. Beijing102209China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyYunnan650500China CISDI Research&Development CO. LTDChongqing401122China

As a newly emerging computing paradigm, edge computing shows great capability in supporting and boosting 5G and Internet-of-Things (IoT) oriented applications, e.g., scientific workflows with low-latency, elastic, and on-demand provisioning of computational resources. However, the geographically distributed IoT resources are usually interconnected with each other through unreliable communications and ever-changing contexts, which brings in strong heterogeneity, potential vulnerability, and instability of computing infrastructures at different levels. It thus remains a challenge to enforce high fault-tolerance of edge-IoT scientific computing task flows, especially when the supporting computing infrastructures are deployed in a collaborative, distributed, and dynamic environment that is prone to faults and failures. This work proposes a novel fault-tolerant scheduling approach for edge-IoT collaborative workflows. The proposed approach first conducts a dependency-based task allocation analysis, then leverages a Primary-Backup (PB) strategy for tolerating task failures that occur at edge nodes, and finally designs a deep Q-learning algorithm for identifying the near-optimal workflow task scheduling scheme. We conduct extensive simulative case studies on multiple randomly-generated workflow and real-world edge-IoT server position datasets. Results clearly suggest that our proposed method outperforms the state-of-the-art competitors in terms of task completion ratio, server active time, and resource utilization.

关键词： Edge computing Fault tolerance DQN algorithm Primary-backup model

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A Dynamic Interval Multi-Objective Evolutionary Algorithm Based on Multi-Task Learning and Inverse Mapping

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IEEE Transactions on Evolutionary Computation 2025年

作者： Wang, Feimeng Sun, Jing Gan, Xingjia Gong, Dunwei Wang, Gaige Guo, Yongde Qingdao University of Science and Technology College of Automation and Electronic Engineering Qingdao Shandong266061 China Qingdao University of Science and Technology School of Data Science Qingdao Shandong266061 China Central South University School of Automation Changsha Hunan410083 China Ocean University of China Department of Computer Science and Technology Qingdao Shandong266071 China City University of Macau Faculty of Data Science Macau999078 China

Dynamic interval multi-objective optimization problems, such as those encountered in wireless sensor network scheduling and portfolio selection, are increasingly prevalent. However, they present significant challenges due to the inherent uncertainty and variability of their parameters. This paper proposes a dynamic interval multi-objective evolutionary algorithm in terms of multi-task learning and inverse mapping. The algorithm employs an interval-based MOEA/D as its foundational framework. Upon encountering environmental changes, prediction models for the midpoints and widths of intervals are developed using multi-task learning combined with a self-evolving fuzzy system. These models generate a predicted Pareto front (PF) in the objective space. Subsequently, inverse mappings of the objective functions at the new time are established to identify solutions that correspond to the predicted PF in the decision space. Finally, these solutions are expanded to form an initial population at the new time. Testing on eighteen benchmark optimization problems and an uncertain scheduling of underwater wireless sensor networks, the proposed algorithm demonstrates both competitiveness and superiority when compared to five state-of-the-art algorithms. © 1997-2012 IEEE.

关键词： Contrastive Learning

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DFGCN: Decoupled dual-flow dynamic graph convolutional network for multivariate time series forecasting

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Knowledge-Based Systems 2025年 323卷

作者： Ye, Junjie Li, Jinhong Su, Rui Yang, Sen Huang, Yaqun Zhao, Chunna School of Information Science & Engineering Yunnan University Kunming Yunnan650504 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming Yunnan650500 China Department of Computer Science and Technology West Yunnan University of Applied Sciences Dali Yunnan671000 China

Multivariate time series forecasting (MTSF) is of critical importance in various domains, including climate prediction, financial analysis, and energy management. Multivariate time series data typically exhibit both intra-series and inter-series dependencies, leading to intricate and intertwined relationships. Recently, numerous studies have focused on capturing both types of correlations. Most existing methods adopt a single-flow structure, attempting to capture features from both the time and variable dimensions simultaneously. However, due to the heterogeneous dependencies between the time dimension and variable dimension, single-flow structures are prone to interference in feature learning, making it difficult to effectively model complex dependencies. To address this issue, we propose a decoupled Dual-Flow dynamic Graph Convolutional Network (DFGCN) for MTSF, which separately models the intra-series and inter-series dependencies through decoupled feature learning. First, a dual-flow embedding layer is employed to separately represent the features of the time and variable dimensions. Next, we propose a dual-flow dynamic sparse GCN incorporating Pearson correlation, which robustly learns both intra-series and inter-series dynamic dependencies, while effectively reducing the computational cost of the GCN. Then, we introduce a dual-flow global attention layer, which separately learns the global information of inter-series and intra-series dependencies. By combining dynamic sparse GCN and the global attention mechanism, DFGCN is able to effectively model both local and global features in multivariate time series data. Experimental results on multiple real-world datasets show that DFGCN outperforms strong baseline models, with improvements ranging from 2.40% to 19.84%. Ablation experiments further validate the model's robustness and the effectiveness of its components. Code is available at this repository: https://***/junjieyePhD/DFGCN. © 2025 Elsevier B.V.

关键词： Cost benefit analysis

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Efficient Visual Recognition:A Survey on Recent Advances and Brain-inspired Methodologies

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Machine Intelligence Research 2022年第5期19卷 366-411页

作者： Yang Wu Ding-Heng Wang Xiao-Tong Lu Fan Yang Man Yao Wei-Sheng Dong Jian-Bo Shi Guo-Qi Li Applied Research Center Laboratory Tencent Platform and Content Group Shenzhen 518057China School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi′an Jiaotong UniversityXian710049China School of Artificial Intelligence Xidian UniversityXi'an 710071China Division of Information Science Nara Institute of Science and TechnologyNara 6300192Japan Peng Cheng Laboratory Shenzhen 518000China Department of Computer and Information Science University of PennsylvaniaPhiladelphia PA19104-6389USA Institute of Automation Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100190China

Visual recognition is currently one of the most important and active research areas in computer vision,pattern recognition,and even the general field of artificial *** has great fundamental importance and strong industrial needs,particularly the modern deep neural networks(DNNs)and some brain-inspired methodologies,have largely boosted the recognition performance on many concrete tasks,with the help of large amounts of training data and new powerful computation *** recognition accuracy is usually the first concern for new progresses,efficiency is actually rather important and sometimes critical for both academic research and industrial ***,insightful views on the opportunities and challenges of efficiency are also highly required for the entire *** general surveys on the efficiency issue have been done from various perspectives,as far as we are aware,scarcely any of them focused on visual recognition systematically,and thus it is unclear which progresses are applicable to it and what else should be *** this survey,we present the review of recent advances with our suggestions on the new possible directions towards improving the efficiency of DNN-related and brain-inspired visual recognition approaches,including efficient network compression and dynamic brain-inspired *** investigate not only from the model but also from the data point of view(which is not the case in existing surveys)and focus on four typical data types(images,video,points,and events).This survey attempts to provide a systematic summary via a comprehensive survey that can serve as a valuable reference and inspire both researchers and practitioners working on visual recognition problems.

关键词： Visual recognition deep neural networks(DNNS) brain-inspired methodologies network compression dynamic inference survey

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