检索结果-内蒙古大学图书馆

作者： Hengtian Zhao Junjia Du Yayue Xu Yujie Li Yingai Jin State Key Laboratory of Automotive Simulation and Control Jilin University College of Automotive Engineering Jilin University College of Computer Science and Technology Jilin University College of Journalism & Communication Jilin University

In order to help archaeologists better study ancient glass, uncover the mystery of the early Silk Road, and also better protect these cultural *** this paper, the properties and composition of ancient glass are *** types of glass samples were analyzed by PCA principal component analysis and weighted summation, and the type characteristic equation was *** calculating the candidate points iteratively in MATLAB, the optimal critical eigenvalue was set to 5, and the type judgment model was *** to get the type of the characteristic equation, the selection is related components as high potassium and the composition of class division, choose negative correlation components as lead, barium and the composition of class division, through the PCA principal component analysis of two types of glass data are reduced to 3d in the class we USES Kmeans clustering algorithm for two types of glass, For the sensitivity analysis of subclass partitioning, we used SOBOL global sensitivity analysis method, and obtained that principal component 1 would have the greatest impact on the model output.

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Amplitude-frequency-aware deep fusion network for optimal contact selection on STN-DBS electrodes

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Science China(Information Sciences) 2022年第4期65卷 48-63页

作者： Linxia XIAO Caizi LI Yanjiang WANG Weixin SI Hai LIN Doudou ZHANG Xiaodong CAI Pheng-Ann HENG College of Control Science and Engineering China University of Petroleum (East China) Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Department of Neurosurgery Shenzhen Second People's Hospital School of Medicine Shenzhen University Department of Computer Science and Engineering The Chinese University of Hong Kong

Parkinson's disease(PD) is treated effectively by deep brain stimulation(DBS) of the subthalamic nucleus(STN), using an electrode inserted into the head of a PD patient. The electrode has multiple electrical contacts along its length, so the best may be chosen for selectively stimulating the STN. Neurosurgeons usually determine the optimal stimulated contact via the clinical experience of the neurosurgeon and the motor improvement of PD patients. This is a time-consuming and labor-intensive trial-and-error process. The selection of optimal stimulated contact highly depends on the locations of sweet spots, which are manually identified by the characteristic features of microelectrode recordings(MERs). This paper presents an amplitude-frequency-aware deep fusion network for optimal contact selection on STN-DBS *** method first obtains the amplitude-frequency fusion features by combining the MERs time sequence features and the amplitude sequence features, and then uses the convolutional neural network(CNN) with convolutional block attention module(CBAM) to identify both the border of the STN and the sweet spots to implant the electrode. The optimal stimulated contact can be selected according to the distribution of the sweet spots. Experimental results indicate that, for successful surgeries, neurosurgeons and the proposed AI solution selected the same optimal contacts. Furthermore, the proposed method outperforms the state-of-the-art methods for STN and sweet spot identification. The proposed method shows great potential for optimal contact selection to improve the efficiency of STN-DBS surgery and reduce the dependence on clinicians' experience.

关键词： optimal contact selection sweet spots microelectrode recordings amplitude-frequency feature deep fusion network

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A Formation control for Multi-Agent Systems for Event-Trigger-Based Variable Gain controllers

A Formation Control for Multi-Agent Systems for Event-Trigge...

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Annual Conference of Industrial Electronics Society

作者： Daiki Asada Takuya Nakagawa Yoshikatsu Hoshi Hidetoshi Oya Shunya Nagai Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan The Department of Computer Science Tokyo City University Tokyo Japan Department of Electronics and Control Engineering National Institute of Technology (KOSEN) Niihama College Ehime Japan

This paper deals with a design problem for a new event-trigger-based variable gain controller which achieves consensus for multi-agent systems (MASs) with the leader-follower structure. The proposed variable gain controller consists of stabilizing state feedback laws, state feedback inputs for consensus, and compensation inputs with adjustable parameters, and is designed by taking relative positions into account explicitly. In this paper, we show that sufficient conditions for the existence of the proposed formation control system are given in terms of Linear Matrix Inequalities (LMIs).

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Industrial Semantic Communications for Tool Wear Monitoring in Industrial IoT Networks

Industrial Semantic Communications for Tool Wear Monitoring ...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Mingqiang Zhang Yajuan Yang Baokun Song Zexin Chen Xiaocong Ma Dongyang Li Na Li Feng Cao Yanping Zhang Haixia Zhang School of Cyber Science and Engineering Qufu Normal University Qufu China College of Oceanography and Space Informatics China University of Petroleum (East China) Huangdao China School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China School of Computer and Information Engineering Qilu Institute of Technology Jinan China School of Control Science and Engineering Shandong University Jinan China Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration Shandong University Jinan China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

In this paper, we propose an Industrial Semantic Communications (ISC) framework to ensure efficient and in-telligent communication in Industrial Internet-of-Things (IIoT) networks, and further apply the proposed framework to the task of tool wear monitoring. A semantic-aware tool wear monitoring network (TWM-Net) is designed to accurately identify the idling state from tool wear data, thus assisting the semantic data transmission process of the ISC framework. Then, a demo software system is implemented to verify the delay performance of the proposed ISC framework. Experimental results demonstrated that the proposed ISC framework can facilitate the semantic information exchange between edge industrial system and cloud platform together with the deep compressed sensing method, and significantly reduce the amount of data transmissions in IIoT networks. The source code and dataset is available through https://***/pnixsoft/TWM-Net.

关键词： Vibrations Source coding Software systems Multitasking Delays Data communication Monitoring Information exchange Industrial Internet of Things Testing

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Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baseline 39

Zero-Shot Learning in Industrial Scenarios: New Large-Scale ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Zekai Chen, Qinghui Xiong, Maomao Ding, Shijiao Su, Zhanzhi Yao, Xinjie Sun, Yiming Bai, Cong Zhang, Jinglin School of Control Science and Engineering Shandong University Jinan China National Supercomputing Center in Jinan Qilu University of Technology Jinan China College of Intelligence and Computing Tianjin University Tianjin China School of Automation Southeast University Nanjing China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

ISBN: (纸本)157735897X

Large Visual Language Models (LVLMs) have achieved remarkable success in vision tasks. However, the significant differences between industrial and natural scenes make applying LVLMs challenging. Existing LVLMs rely on user-provided prompts to segment objects. This often leads to suboptimal performance due to the inclusion of irrelevant pixels. In addition, the scarcity of data also makes the application of LVLMs in industrial scenarios remain unexplored. To fill this gap, this paper proposes an open industrial dataset and a Refined Text-Visual Prompt (RTVP) for zero-shot industrial defect detection. First, this paper constructs the Multi-Modal Industrial Open Dataset (MMIO) containing 80K+ samples. MMIO contains diverse industrial categories, including 6 super categories and 18 subcategories. MMIO is the first large-scale multi-scenes pre-training dataset for industrial zero-shot learning, and provides valuable training data for open models in future industrial scenarios. Based on MMIO, this paper provides a RTVP specifically for industrial zero-shot tasks. RTVP has two significant advantages: First, this paper designs an expert-guided large model domain adaptation mechanism and designs an industrial zero-shot method based on Mobile-SAM, which enhances the generalization ability of large models in industrial scenarios. Second, RTVP automatically generates visual prompts directly from images and considers text-visual prompt interactions ignored by previous LVLM, improving visual and textual content understanding. RTVP achieves SOTA with 42.2% and 24.7% AP in zero-shot and closed scenes of MMIO. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Zero-shot learning

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Kernel-based Class-specific Broad Learning System for software defect prediction

Kernel-based Class-specific Broad Learning System for softwa...

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2021 International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2021

作者： Chen, Wuxing Yang, Kaixiang Shi, Yifan Feng, Qiying Zhang, Chengxi Yu, Zhiwen Guangdong University of Technology School of Computer Science and Technology Guangzhou China Zhejiang University College of Control Science and Engineering Hangzhou China Huaqiao University School of Engineering Institute Quanzhou China South China University of Technology Computer Science and Engineering Guangzhou China Harbin Institute of Technology Shenzhen Electronic Information Engineering Shenzhen China

ISBN: (纸本)9781665402453

With the continuous expansion of the software industry, the problem of software defects is receiving more and more attention. There has been a series of machine learning methods applied to the field of software defect prediction (SDP) as a way to ensure the stability of software. However, SDP suffers from the imbalance problem. To solve this problem, we first propose a class-specific broad learning system (CSBLS), which assigns a specific penalty factor to each class in accordance with the data distribution. Then we design a class-specific kernel-based broad learning system (CSKBLS), which adopts kernel mapping instead of random projection. This additive kernel scheme takes into account both outliers and noise in the data set. Extensive experiments on the real-world NASA datasets show that CSKBLS outperforms the comparison methods on the tasks of software defect prediction. © 2021 IEEE.

关键词： Learning systems

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Sensing metric-based hybrid underlay/overlay transmission framework for CR-IoT networks for smart city applications

Sensing metric-based hybrid underlay/overlay transmission fr...

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7th IET Smart Cities Symposium (SCS 2023)

作者： Indu Bala Maad M. Mijwil Mostafa Abotaleb El-Sayed M. El-kenawy Abdelhameed Ibrahim School of Electrical and Electronics Engineering Lovely Professional University Punjab India Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

With the increasing number of IoT devices, there is a growing need for bandwidth to support their communication. Unfortunately, there is a shortage of available bandwidth due to preallocated bands for various services. To address this issue, Cognitive Internet of Things (CR-IoT) enables devices to optimize their efficiency and enhance their communication capabilities by intelligently accessing available bandwidth. This is achieved through the use of soft sensing metrics, where devices continuously monitor the RF environment and transmit data opportunistically in overlay mode if a free channel is detected, or in underlay mode if not. In this paper, a soft sensing metric based hybrid transmission framework is proposed for CR-IoT devices to meet the data rate requirement for the smart city applications. The efficacy of this approach is demonstrated through simulation results.

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Graph Attention Network for Word Embeddings 7th

Graph Attention Network for Word Embeddings

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Long, Yunfei Xu, Huosheng Qi, Pengyuan Zhang, Liguo Li, Jun College of Computer Science and Technology Harbin Engineering University Harbin150001 China Wuhan Digital Engineering Institute Wuhan430000 China China Industrial Control Systems Cyber Emergency Research Team Beijing100000 China

ISBN: (纸本)9783030786113

The word embeddings approaches have attracted extensive attention and widely used in many natural language processing (NLP) tasks. Relatedness between words can be reflected in vector space by word embeddings. However, the current word embeddings approaches commonly do not explore the context-specific information of word deeply in the overall corpus. In this paper, we propose to use graph attention network for word embeddings. We build a large single word graph for a corpus based on word order, then learn a word embeddings graph attention network (WEGAT) for the corpus. Our WEGAT is initialized with one-hot representation for word. We propose to use masked language model (MLM) as supervised task. In addition, through the text classification experiment, it is showed that accuracy of the word embeddings represented by WEGAT is higher than the current method for the same classification method. © 2021, Springer Nature Switzerland AG.

关键词： Embeddings

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Structural Regularization Based Discriminative Multi-View Unsupervised Feature Selection

SSRN

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SSRN 2023年

作者： Zhou, Shixuan Song, Peng Yu, Yanwei Zheng, Wenming School of Computer and Control Engineering Yantai University Yantai264005 China College of Computer Science and Technology Ocean University of China Qingdao266400 China Key Laboratory of Child Development and Learning Science Ministry of Education Southeast University Nanjing210096 China

Multi-view unsupervised feature selection (MUFS) has recently aroused considerable attention, which can select the compact representative feature subset from original multi-view data. Despite the promising preliminary performance, most previous MUFS methods fail to explore the discriminative ability of multi-view data. In addition, they usually utilize spectral analysis to maintain the geometrical structure, which will inevitably increase the difficulty of parameter selection. To address these issues, we present a novel MUFS method, named structural regularization based discriminative multi-view unsupervised feature selection (SDFS). Specifically, we calculate the similarity matrix of sample space from different views and automatically weight each view-specific graph to learn a consensus similarity graph, in which these two types of graphs can promote each other. Further, we treat the learned latent representation as the cluster indicator, and employ a graph regularization without introducing additional parameters to maintain the geometrical structure of data. Besides, a simple yet efficient iterative updating algorithm with theoretical convergence property is developed. Extensive experiments on several benchmark datasets verify that the designed model is superior to several state-of-the-art MUFS models. © 2023, The Authors. All rights reserved.

关键词： Spectrum analysis

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Sde-Det: A Precision Network for Shatian Pomelo Detection in Complex Orchard Environments

SSRN

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SSRN 2025年

作者： Hu, Yihao Wang, Pan Bai, Xiaodong Cai, Shijie Wang, Hang Liu, Huazhong Yang, Aiping Li, Xiangxiang Ding, Meiping Liu, Hongyan Yao, Jianguo School of Computer Science and Technology Hainan University Hainan Haikou570228 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China Agricultural Meteorological Center Jiangxi Meteorological Bureau Jiangxi Nanchang330045 China Hainan University Hainan Haikou570228 China School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Jiangsu Nanjing210003 China

Pomelo detection is an essential process for their localization, automated robotic harvesting, and maturity analysis. However, detecting Shatian pomelo in complex orchard environments poses significant challenges, including multi-scale issues, obstructions from trunks and leaves, small object detection, etc. To address these issues, this study constructs a custom dataset STP-AgriData and proposes the SDE-DET model for Shatian pomelo detection. SDE-DET first utilizes the Star Block to effectively acquire high-dimensional information without increasing the computational overhead. Furthermore, the presented model adopts Deformable Attention in its backbone, to enhance its ability to detect pomelos under occluded conditions. Finally, multiple EMA mechanisms are integrated into our model to reduce the computational overhead and extract deep visual representations, thereby improving the capacity for small object detection. In the experiment, we compared SDE-DET with the Yolo series and other mainstream detection models in Shatian pomelo detection. The presented SDE-DET model achieved scores of 0.883, 0.771, 0.838, 0.497, and 0.823 in Precision, Recall, mAP@0.5, mAP@0.5:0.95 and F1-score, respectively. SDE-DET has achieved state-of-the-art performance on the STP-AgriData dataset. Experiments indicate that the SDE-DET provides a reliable method for Shatian pomelo detection, laying the foundation for the further development of automatic harvest robots. © 2025, The Authors. All rights reserved.

关键词： Orchards

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