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3rd International Conference on Electronic Information engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Zhao, Jingsheng Ni, Qihui Yan, Shuai Zhu, Qiaoming School of Information and Control Engineering Qingdao University of Technology Qingdao Shandong266000 China School of Computer Science and Technology Suzhou University Suzhou Jiangsu215006 China

ISBN: (纸本)9781510680449

Traditional relation extraction methods are usually based on single text data, and other modality information such as image and video can improve the effect of text relation extraction. Aiming at the problem of heterogeneity between multi-modal data, a multi-modal relation extraction model MRECL based on contrastive learning is constructed. Firstly, the feature representation of image and text is obtained through ViT and BERT models;secondly, contrastive learning is introduced to align image-text features to reduce modality heterogeneity in advance;then image is used as prefix guidance information, and multi-modal interaction fusion is carried out with text information through attention mechanism;finally, the probability of all relation classification is calculated through Softmax layer and the prediction relation is output. Experimental results show that the precision, recall, and F1 values of MRECL on the MNRE dataset reach 83.23%, 82.19%, and 82.70%, respectively, achieving better performance than the current SOTA model. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image enhancement

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A Document Ensemble Clustering Approach via Dimensionality Reduction 43

A Document Ensemble Clustering Approach via Dimensionality R...

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43rd Chinese control Conference, CCC 2024

作者： Li, Xingzhi Wang, Zhiwei Lang, Qi Liu, Xiaodong School of Control Science and Engineering Dalian University of Technology Dalian116024 China School of Computer Science and Information Technology Northeast Normal University Changchun130117 China

ISBN: (纸本)9789887581581

The internet's rapid expansion has generated abundant text data, posing a formidable challenge in extracting key information, where document clustering demonstrates its powerful role. Previously, many people commonly used a single clustering method, which may not be suitable for certain datasets. Additionally, documents are often represented as high-dimensional vectors, leading to the curse of dimensionality and sparsity issues, and clustering algorithms' effectiveness in high-dimensional spaces is limited. Therefore, this paper proposes a document ensemble clustering method based on dimensionality reduction, which can overcome the limitations caused by using a single clustering method, making it more suitable for the majority of text data. Initially, dimensionality reduction techniques are applied to preprocess document embeddings generated. Subsequently, these embedding vectors are fed into an ensemble clustering module, where distances between samples are computed using multiple clustering methods. By adjusting weights, the distance matrices are subjected to weighted summation to obtain an appropriate distance matrix. Finally, the K-means method is applied to cluster division, yielding the most suitable clusters for extracting the most relevant keywords. The proposed method was evaluated empirically, achieving excellent results on the 20Newsgroups and Reuters-21578 datasets. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Dimensionality reduction

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Research on Grayscale Images Flame Detections Based on Colorazion Algorithm and YOLOv8

Research on Grayscale Images Flame Detections Based on Color...

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Liu, Peng Sun, Zhenfu Zhou, Biao Wang, Hongyu Qu, Haozhen Institute of Energy and Electrical Engineering Changchun University of Science and Technology Changchun China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798350378214

Flame detection algorithms are crucial for real-time fire monitoring using surveillance cameras. Current flame detection algorithms perform excellently on color cameras;however, many night vision cameras can only capture grayscale images, making flame detection less effective in such scenarios. Traditional flame detection algorithms rely heavily on color features, especially the red and yellow hues of flames. Conversely, grayscale images lack color information, making these algorithms ineffective. To address this limitation, we propose a novel flame detection algorithm that integrates the DeOldify model with the you only look once(YOLOv8) model. DeOldify can effectively add color to grayscale images. YOLOv8 is a powerful object detection model that excels in identifying various objects within an image. This algorithm leverages DeOldify's colorization capabilities and YOLOv8's powerful object detection abilities to enhance flame detection accuracy in grayscale images. This algorithm has been tested multiple times on public datasets, converting original images to grayscale and then colorizing them to construct the dataset. The results show that, compared to grayscale images, this algorithm improves the mean average precision (mAP@0.5) by 19.7% on different validation sets, by 4% on different training sets, and by 12.9% on grayscale images captured by night vision cameras. By comparing the training results of models such as YOLOv8n and YOLOv8s, the colorized images improve the model's performance. This demonstrates the high generalizability of this algorithm. © 2024 IEEE.

关键词： Premixed flames

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TRGR: Transmissive RIS-aided Gait Recognition Through Walls

TRGR: Transmissive RIS-aided Gait Recognition Through Walls

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Huang, Yunlong Liu, Junshuo Zhang, Jianan Mi, Tiebin Shi, Xin Qiu, Robert Caiming Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China North China Electric Power University School of Control and Computer Engineering Beijing China

ISBN: (纸本)9798350351255

Gait recognition with radio frequency (RF) signals enables many potential applications requiring accurate identification. However, current systems require individuals to be within a line-of-sight (LOS) environment and struggle with low signal-to-noise ratio (SNR) when signals traverse concrete and thick walls. To address these challenges, we present TRGR, a novel transmissive reconfigurable intelligent surface (RIS)-aided gait recognition system. TRGR can recognize human identities through walls using only the magnitude measurements of channel state information (CSI) from a pair of transceivers. Specifically, by leveraging transmissive RIS alongside a configuration alternating optimization algorithm, TRGR enhances wall penetration and signal quality, enabling accurate gait recognition. Furthermore, a residual convolution network (RCNN) is proposed as the backbone network to learn robust human information. Experimental results confirm the efficacy of transmissive RIS, highlighting the significant potential of transmissive RIS in enhancing RF-based gait recognition systems. Extensive experiment results show that TRGR achieves an average accuracy of 97.88% in identifying persons when signals traverse concrete walls, demonstrating the effectiveness and robustness of TRGR. © 2024 IEEE.

关键词： Channel state information

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A WSN Node Fault Diagnosis Model Based on BRB with Self-Adaptive Quality Factor

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computers, Materials & Continua 2023年第4期75卷 1157-1177页

作者： Guo-Wen Sun Gang Xiang Wei He Kai Tang Zi-Yi Wang Hai-Long Zhu School of Computer Science and Information Engineering Harbin Normal UniversitySongbei DistrictHarbin150025China Beihang University School of Automation Science and Electrical Engineering Beijing100191China Beijing Aerospace Automatic Control Institute Beijing100074China Rocket Force University of Engineering Xi’anShaanxi Province710025China

Wireless sensor networks (WSNs) operate in complex and harshenvironments;thus, node faults are inevitable. Therefore, fault diagnosis ofthe WSNs node is essential. Affected by the harsh working environment ofWSNs and wireless data transmission, the data collected by WSNs containnoisy data, leading to unreliable data among the data features extracted duringfault diagnosis. To reduce the influence of unreliable data features on faultdiagnosis accuracy, this paper proposes a belief rule base (BRB) with a selfadaptivequality factor (BRB-SAQF) fault diagnosis model. First, the datafeatures required for WSN node fault diagnosis are extracted. Second, thequality factors of input attributes are introduced and calculated. Third, themodel inference process with an attribute quality factor is designed. Fourth,the projection covariance matrix adaptation evolution strategy (P-CMA-ES)algorithm is used to optimize the model’s initial parameters. Finally, the effectivenessof the proposed model is verified by comparing the commonly usedfault diagnosis methods for WSN nodes with the BRB method consideringstatic attribute reliability (BRB-Sr). The experimental results show that BRBSAQFcan reduce the influence of unreliable data features. The self-adaptivequality factor calculation method is more reasonable and accurate than thestatic attribute reliability method.

关键词： Self-adaptive quality factor belief rule base wireless sensor networks fault diagnosis

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Propofol-Induced Moderate-Deep Sedation Modulates Pediatric Neural Activity:A Functional Connectivity Study

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iRADIOLOGY 2025年第1期3卷 61-71页

作者： Qiang Zheng Yiyu Zhang Lin Zhang Jian Wang Jungang Liu School of Computer and Control Engineering Yantai UniversityYantaiChina Department of Radiology Xiamen Children's HospitalChildren's Hospital of Fudan University at XiamenXiamenChina

Background:Previous studies have demonstrated the underlying neurophysiologic mechanism during general anesthesia in ***,the mechanism of propofol-induced moderate-deep sedation(PMDS)in modulating pediatric neural activity remains unknown,which therefore was investigated in the present study based on functional magnetic resonance imaging(fMRI).Methods:A total of 41 children(5.10�1.14 years,male/female 21/20)with fMRI were employed to construct the functional connectivity network(FCN).The network communication,graph-theoretic properties,and network hub identification were statistically analyzed(t test and Bonferroni correction)between sedation(21 children)and awake(20 children)*** involved analyses were established on the whole-brain FCN and seven sub-networks,which included the default mode network(DMN),dorsal attentional network(DAN),salience network(SAN),auditory network(AUD),visual network(VIS),subcortical network(SUB),and other networks(Other).Results:Under PMDS,significant decreases in network communication were observed between SUB-VIS,SUB-DAN,and VIS-DAN,and between brain regions from the temporal lobe,limbic system,and subcortical ***,no significant decrease in thalamus-related communication was *** graph-theoretic properties were significantly decreased in the sedation group,and all graphical features of the DMN showed significant group *** superior parietal cortex with different neurological functions was identified as a network hub that was not greatly ***:Although the children had a depressed level of neural activity under PMDS,the crucial thalamus-related communication was maintained,and the network hub superior parietal cortex stayed active,which highlighted clinical prac-tices that the human body under PMDS is still perceptible to external stimuli and can be awakened by sound or touch.

关键词： functional connectivity network moderate-deep sedation neural activity pediatric propofol

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Compression Format and Systolic Array Structure Co-design for Accelerating Sparse Matrix Multiplication in DNNs 24th

Compression Format and Systolic Array Structure Co-design f...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Cao, Yongxiang Jiang, Jixiang Zhao, Guocheng Wang, Wei Jiang, Hongxu Song, Yanfei School of Computer Science and Engineering Beihang University Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9789819615445

The systolic array (SA) architecture is widely used in accelerator/AI chip design due to its excellent matrix multiplication acceleration effect. However, the mismatch between the traditional sparse matrix compression format and the SA architecture results in poor SA performance in computing sparse matrices. To solve the problem, we propose a method for co-designing the sparse compression format and SA architecture. This work proposes the Vector Group Compressed Coordinate (VGCC) data stream compression format and designs a sparse SA architecture based on hardware encoding and decoding. VGCC can handle data streams of any size without increasing the index bit width. The VGCC compression size is significantly smaller than general sparse compression formats such as COO/CSR. In a sparse matrix with a sparsity of 90%, it saves 67.13% of storage space compared to the state-of-the-art ECOO data stream compression format and achieves an average acceleration of 1.54 times. We also propose a VGCC-friendly SA architecture. Our SA architecture engine reduces logic consumption by 51.6% compared to Sparse TPU while achieving a 3.23x speedup. To mitigate the issue of high no-load rates in systolic arrays, we introduce a Data Transmission Priority (DTP) data matching algorithm. With sparsity levels ranging from 50% to 90%, the algorithm’s calculation speed is enhanced by 1.124x to 1.991x. We implemented a sparse DNN accelerator on an FPGA Zynq UltraScale+MPSoC ZCU102, utilizing the VGCC compression format proposed in this study for compressing input data. When compared to the S2 Engine, the accelerator achieved a 3.37x speed improvement in matrix multiplication with 75% sparsity, significantly enhancing the speed of sparse matrix multiplication. The accelerator achieved an average throughput of 1079.33GOP/s when inferencing DNNs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Systolic arrays

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BOAformer: Object Detection Network Based on Background-Object Attention Transformer 6

BOAformer: Object Detection Network Based on Background-Obje...

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6th International Conference on Communications, Information System and computer engineering, CISCE 2024

作者： Yang, Yong Liang, Chenbin Huang, Shuying Wang, Xiaozheng School of Computer Science and Technology Tiangong University Tianjin China School of Electronics and Information Engineering Tiangong University Tianjin China School of Software Tiangong University Tianjin China School of Control Science and Engineering Tiangong University Tianjin China

ISBN: (纸本)9798350353174

At present, deep learning has achieved great success in the field of object detection. To ensure that positive samples in the image are not missed, most deep-learning object detection methods set many prediction boxes and use the same prediction operation on them. Although these methods can obtain higher predictive values for high certainty objects, they may identify objects with lower certainty, such as small or fuzzy objects, as negative samples or miss them. To address this issue, this paper presents an object detection network based on background-object attention transformer (BOAformer), which improves the accuracy of object detection by establishing the relationship between background and object features. Firstly, the existing classification model is employed to obtain scores of object prediction boxes. On this basis, a scoring mechanism is designed to filter out high scoring object prediction boxes to reduce the computational complexity of the later network. Then, to improve the accuracy of object detection, BOAformer is constructed, which obtains auxiliary classification values of objects by learning the relationship between background and object features, and adds them to the initial detection results. Finally, many experiments are conducted to verify the effectiveness of BOAformer. The experimental results show that the designed network improves the accuracy of object detection and can detect small or lost objects in the background. © 2024 IEEE.

关键词： Prediction models

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SCPSN: Spectral Clustering-based Pyramid Super-resolution Network for Hyperspectral Images 24

SCPSN: Spectral Clustering-based Pyramid Super-resolution Ne...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yang, Yong Zhao, Aoqi Huang, Shuying Wang, Xiaozheng Fan, Yajing School of Computer Science and Technology Tiangong University Tianjin China School of Software Tiangong University Tianjin China School of Control Science and Engineering Tiangong University Tianjin China School of Electronics and Information Engineering Tiangong University Tianjin China

ISBN: (纸本)9798400706868

Single hyperspectral image super-resolution aims to reconstruct a high-resolution hyperspectral image (HRHSI) from an observed low resolution hyperspectral image (LRHSI). Most current methods combine CNN and Transformer structures to directly extract features of all channels in LRHSI for image reconstruction, but they do not consider the interference of redundant information in adjacent bands, resulting in spectral and spatial distortions in the reconstruction results and an increase in model computational complexity. To address this issue, this paper proposes a spectral clustering-based pyramid super-resolution network (SCPSN) to progressively reconstruct HRHSI at different scales. In each image reconstruction layer, a clustering super-resolution block (CSRB) consisting of spectral clustering block (SCB), patch non local attention block (PNAB), and dynamic fusion block (DFB) is designed to achieve the reconstruction of detail features. Specifically, for the high correlation between adjacent spectral bands in LRHSI, a SCB is first constructed to achieve clustering of spectral channels and filtering of hyperchannels. This can reduce the interference of redundant spectral information and the computational complexity of the model. Then, by utilizing the non-local similarity of features within the channel, a patch non-local attention block (PNAB) is constructed to enhance the features of hyperchannels. Next, a dynamic fusion block (DFB) is designed to reconstruct the features of all channels in LRHSI by establishing correlations between enhanced hyperchannels and other channels. Finally, the reconstructed channels are upsampled and added to the corresponding channels to obtain the reconstructed HRHSI. Extensive experiments validate that the performance of SCPSN is superior to that of some other state-of-the-art (SOTA) HSSR methods in terms of visual effects and quantitative metrics. In addition, our model does not require training on large-scale datasets compared to oth

关键词： Image reconstruction

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Probabilistic Tube-based control Synthesis of Stochastic Multi-Agent Systems under Signal Temporal Logic 63

Probabilistic Tube-based Control Synthesis of Stochastic Mul...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Vlahakis, Eleftherios E. Lindemann, Lars Sopasakis, Pantelis Dimarogonas, Dimos V. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden University of Southern California Viterbi School of Engineering Department of Computer Science Los AngelesCA90089 United States Queen's University School of Electronics Electrical Engineering and Computer Science Northern Ireland Belfast United Kingdom

ISBN: (纸本)9798350316339

We consider the control design of stochastic discrete-time linear multi-agent systems (MASs) under a global signal temporal logic (STL) specification to be satisfied at a predefined probability. By decomposing the dynamics into deterministic and error components, we construct a probabilistic reachable tube (PRT) as the Cartesian product of reachable sets of the individual error systems driven by disturbances lying in confidence regions (CRs) with a fixed probability. By bounding the PRT probability with the specification probability, we tighten all state constraints induced by the STL specification by solving tractable optimization problems over segments of the PRT, and relax the underlying stochastic problem with a deterministic one. This approach reduces conservatism compared to tightening guided by the STL structure. Additionally, we propose a recursively feasible algorithm to attack the resulting problem by decomposing it into agent-level subproblems, which are solved iteratively according to a scheduling policy. We demonstrate our method on a ten-agent system, where existing approaches are impractical. © 2024 IEEE.

关键词： Stochastic systems

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