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14th International Conference on Information science and Technology, ICIST 2024

作者： Liu, Yabing Yu, Shanshan Guo, Wei Leung, Man-Fai School of Electronic and Information Engineering Southwest University Chongqing China South-Central Minzu University State Ethnic Affairs Commission Key Laboratory of Cyber-Physical Fusion Intelligent Computing Wuhan430074 China School of Computing and Information Science Anglia Ruskin University Faculty of Science and Engineering Cambridge United Kingdom

ISBN: (纸本)9798350353334

Fault detection is the process of detecting and diagnosing faults or abnormalities in a system by analyzing its operational data. However, with the complexity of modern industrial processes, some faults are difficult to be detected in a timely manner due to various factors such as noise and data nonlinearity. Therefore, data-driven Fault Detection (FD) has become a widely used method to detect abnormal events in functional modules. Non-negative Matrix Factorization (NMF), as an efficient dimensionality reduction technique, has not had potential applications in fault detection (FD) thoroughly explored. In order to improve the FD methods based on NMF, we have developed a new approach, named graph low-rank non-negative matrix Factorization with auto-encoders (GLNMFA). GLN-MFA integrates the Laplacian operator effectively identifies t he l ocal s tructure a mong d ata p oints, enhancing the performance of dimensionality reduction algorithms. It also introduces the nuclear norm to find a low-rank approximation to the original matrix, thereby constraining sparsity. Additionally, auto-encoders are incorporated to learn a low-dimensional representation of the data and extract key features, which are subsequently applied for fault detection purposes. We employ an optimization algorithm based on Alternating Direction Method of Multipliers (ADMM) to optimize this model. Two test statistics T2 (Hotelling's T-squared), SPE (Squared Prediction Error) are used to evaluate detection efficiency. K ernel Density Estimation (KDE) are used to estimate control limits for fault detection. The effectiveness of GLNMFA is validated on two benchmark datasets. © 2024 IEEE.

关键词： Non-negative matrix factorization

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DPT‐tracker:Dual pooling transformer for efficient visual tracking

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 948-959页

作者： Yang Fang Bailian Xie Uswah Khairuddin Zijian Min Bingbing Jiang Weisheng Li Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and TelecommunicationsChongqingChina Department of Mechanical Precision Engineering Malaysia‐Japan International Institute of TechnologyUniversity of Technology MalaysiaKuala LumpurMalaysia Department of Electrical and Computer Engineering Inha UniversityIncheonRepublic of Korea School of Information Science and Technology Hangzhou Normal UniversityHangzhouChina

Transformer tracking always takes paired template and search images as encoder input and conduct feature extraction and target‐search feature correlation by self and/or cross attention operations,thus the model complexity will grow quadratically with the number of input *** alleviate the burden of this tracking paradigm and facilitate practical deployment of Transformer‐based trackers,we propose a dual pooling transformer tracking framework,dubbed as DPT,which consists of three components:a simple yet efficient spatiotemporal attention model(SAM),a mutual correlation pooling Trans-former(MCPT)and a multiscale aggregation pooling Transformer(MAPT).SAM is designed to gracefully aggregates temporal dynamics and spatial appearance information of multi‐frame templates along space‐time *** aims to capture multi‐scale pooled and correlated contextual features,which is followed by MAPT that aggregates multi‐scale features into a unified feature representation for tracking *** tracker achieves AUC score of 69.5 on LaSOT and precision score of 82.8 on Track-ingNet while maintaining a shorter sequence length of attention tokens,fewer parameters and FLOPs compared to existing state‐of‐the‐art(SOTA)Transformer tracking *** experiments demonstrate that DPT tracker yields a strong real‐time tracking baseline with a good trade‐off between tracking performance and inference efficiency.

关键词： human‐computer interfacing image motion analysis pattern recognition signal processing tracking

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Enhanced Discriminant Analysis Based Fault Diagnosis Scheme for Marine Diesel Engines 11

Enhanced Discriminant Analysis Based Fault Diagnosis Scheme ...

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11th IEEE data Driven Control and Learning Systems Conference, DDCLS 2022

作者： Zhong, Kai Wang, Jiayi Pan, Donghui Institutes of Physical Science and Information Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Anhui Hefei230601 China School of Mathematical Science Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui Hefei230601 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Since the marine diesel engine is one of the most popular power equipments for modern shipping, accurate and timely diagnose the faults occurred in diesel engine is extraordinary important for long service life and high reliability. Note that Fisher discriminant analysis (FDA) has been widely applied in fault diagnosis field. However, it is quite susceptible to the small sample size (SSS) problem, which is very common in the running process of diesel engine. In response to the problem, this study pro-poses a new fault classification criterion based exponential discriminant analysis (EDA) scheme for diesel engine fault diagnosis, in which the exponential transformation is carried out first to enlarge the margin between different classes, thus the different fault classes are further separated and the diagnosis capability is enhanced. In addition, the theorems of matrix exponential guarantee that the matrix exp (Sw) is always full-rank, even though the Sw is singular. That means, the novel methodology is still appli-cable when confronts with the SSS problem. Finally, based on the more reasonable fault classification criterion, the simulation results on a real-world two-stroke six-cylinder low-speed marine diesel engine indicate that the EDA based diagnosis scheme achieves higher fault diagnosis accuracy with good robustness and practicability in real-world engineering applications. © 2022 IEEE.

关键词： Failure analysis

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A Weighted Ensemble Causal Discovery Method for Effective Connectivity Estimation 9

A Weighted Ensemble Causal Discovery Method for Effective Co...

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9th IEEE Smart World Congress, SWC 2023

作者： Zhang, Qiqi Zhang, Yingwei Ding, Yanhui Chen, Yiqiang Song, Shuchao Wu, Shuang Shandong Normal University School of Information Science and Engineering Jinan China Shandong Academy of Intelligent Computing Technology Jinan China Chinese Academy of Sciences Institute of Computing Technology Beijing China Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing China

ISBN: (纸本)9798350319804

Exploring and explaining the effective connectivity (EC) between brain regions can help us understand the mechanisms behind neurodegenerative diseases such as Alzheimer's disease, thus helping us to diagnose patients better. Causal discovery techniques have been widely used in this domain recently, but diverse methods may produce conflicting outcomes because of the differences in underlying principles and search methods. To solve this problem, we integrate the causal relationships generated by different algorithms to obtain high-confidence inferences, resulting in a more reliable weighted ensemble causal discovery (WECD) method with higher accuracy. First of all, WECD defines a distribution metric function to measure the weights of different individual methods. Then, WECD adaptively chooses the critical threshold to obtain the confident edges set based on the generated directed edge confident metric curve. Finally, the directed acyclic graph condition is used to refine the confident edges set and acquire the final causal graph. We validate the effectiveness of WECD with both the simulated dataset and real functional near-infrared spectroscopy (fNIRS) dataset collected on AD patients, MCI patients and healthy controls. By comparing with other existing methods, experimental results demonstrate the potential of WECD as an effective tool for estimating brain EC. © 2023 IEEE.

关键词： Alzheimer's disease causal discovery effective connectivity ensemble learning functional near-infrared spectroscopy

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

arXiv

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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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Mixture of experts for audio-visual learning 24

Mixture of experts for audio-visual learning

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Ying Cheng Yang Li Junjie He Rui Feng School of Computer Science Fudan University and Shanghai Key Laboratory of Intelligent Information Processing and Shanghai Collaborative Innovation Center of Intelligent Visual Computing School of Computer Science Fudan University and Shanghai Key Laboratory of Intelligent Information Processing

ISBN: (纸本)9798331314385

With the rapid development of multimedia technology, audio-visual learning has emerged as a promising research topic within the field of multimodal analysis. In this paper, we explore parameter-efficient transfer learning for audio-visual learning and propose the Audio-Visual Mixture of Experts (AVMoE) to inject adapters into pre-trained models flexibly. Specifically, we introduce unimodal and cross-modal adapters as multiple experts to specialize in intra-modal and intermodal information, respectively, and employ a lightweight router to dynamically allocate the weights of each expert according to the specific demands of each task. Extensive experiments demonstrate that our proposed approach AVMoE achieves superior performance across multiple audio-visual tasks, including AVE, AVVP, AVS, and AVQA. Furthermore, visual-only experimental results also indicate that our approach can tackle challenging scenes where modality information is missing. The source code is available at https://***/yingchengy/AVMOE.

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A Self-adaptive Two-Stage Local Expansion Algorithm for Community Detection on Complex Networks 7th

A Self-adaptive Two-Stage Local Expansion Algorithm for Com...

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7th International Conference on data Mining and Big data, DMBD 2022

作者： Shan, Hui Li, Bin Yang, Haipeng Zhang, Lei Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China

ISBN: (纸本)9789811989902

Community detection is of great importance to find hidden information in complex networks. For this problem, local expansion algorithms are becoming popular due to the low time complexity. However, most of them depend heavily on seed selection or require setting some thresholds in advance, leading to inaccurate partition. To this end, this paper proposes a self-adaptive two-stage local expansion algorithm (SALEA) for community detection. Specifically, we propose a self-adaptive strategy that can be used in SALEA for finding communities spontaneously. In the first stage, we apply the self-adaptive strategy for nodes to conduct local expansion and get coarse community structures. In the second stage, we apply the self-adaptive strategy for weak communities obtained in the first stage to refine the coarse community structures and get more accurate partitions. Finally, the experimental results on real and synthetic networks demonstrate that SALEA is superior over several state-of-the-arts. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Complex networks

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Computer aided diagnosis of diabetic retinopathy based on multi-view joint learning

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Computers in Biology and Medicine 2024年 174卷 108428-108428页

作者： Xu, Xuebin Liu, Dehua Huang, Guohua Wang, Muyu Lei, Meng Jia, Yang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China Weinan Central Hospital Shaanxi Xi'an714099 China

Diabetic retinopathy (DR) is a kind of ocular complication of diabetes, and its degree grade is an essential basis for early diagnosis of patients. Manual diagnosis is a long and expensive process with a specific risk of misdiagnosis. Computer-aided diagnosis can provide more accurate and practical treatment recommendations. In this paper, we propose a multi-view joint learning DR diagnostic model called RT2Net, which integrates the global features of fundus images and the local detailed features of vascular images to reduce the limitations of single fundus image learning. Firstly, the original image is preprocessed using operations such as contrast-limited adaptive histogram equalization, and the vascular structure of the extracted DR image is segmented. Then, the vascular image and fundus image are input into two branch networks of RT2Net for feature extraction, respectively, and the feature fusion module adaptively fuses the feature vectors’ output from the branch networks. Finally, the optimized classification model is used to identify the five categories of DR. This paper conducts extensive experiments on the public datasets EyePACS and APTOS 2019 to demonstrate the method's effectiveness. The accuracy of RT2Net on the two datasets reaches 88.2% and 85.4%, and the area under the receiver operating characteristic curve (AUC) is 0.98 and 0.96, respectively. The excellent classification ability of RT2Net for DR can significantly help patients detect and treat lesions early and provide doctors with a more reliable diagnosis basis, which has significant clinical value for diagnosing DR. © 2024 The Authors

关键词： Computer aided diagnosis

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Multi-attribute wearable pressure sensor based on multilayered modulation with high constant sensitivity over a wide range

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Nano Research 2023年第5期16卷 7583-7592页

作者： Ning Li Song Gao Yang Li Jianwen Liu Wenhao Song Guozhen Shen School of Information Science and Engineering University of JinanJinan 250022China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of JinanJinan 250022China School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China School of Aeronautical and Astronautical Engineering Purdue University West LafayetteIN 47907USA

Flexible pressure sensors capable of monitoring diverse physiological signals and body movements have garnered tremendous attention in wearable electronic ***,high constant sensitivity over a wide pressure range combined with breathability,biocompatibility,biodegradability is pivotal for manufacturing of reliable pressure sensors in practical sensing *** this work,inspired by the multilayered structure of skin epidermis,we propose and demonstrate a multi-attribute wearable piezoresistive pressure sensor consisting of multilayered gradient conductive poly(ε-caprolactone)nanofiber membranes *** response to externally applied pressure,a layer-by-layer current path is activated inside the multilayered membranes composites,leading to the most salient sensing performance of high constant sensitivity of 33.955 kPa^(−1) within the pressure range of 0–80 *** proposed pressure sensor also exhibits a fast response–relaxation time,a low detection limit,excellent stability,which can be successfully used to measure human physiological ***,an integrated sensor array system that can locate objects’positions is constructed and applied to simulate sitting posture *** results indicate that the proposed pressure sensor holds great potential in health monitoring and wearable electronic devices.

关键词： wearable pressure sensors multilayer structure high constant sensitivity multi-attribute physiological signals monitoring

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keyword mask pre-training model for the review of standard declaration 4

Keyword mask pre-training model for the review of standard d...

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4th International Conference on Computer Engineering and Application, ICCEA 2023

作者： Feng, Chuang Che, Chao Zhang, Qiang Ministry of Education Dalian University Key Laboratory of Advanced Design and Intelligent Computing Dalian116622 China School of Computer Science and Technology Dalian University of Technology Dalian116081 China

ISBN: (纸本)9798350347548

The review of standard declaration of import and export commodities refers to the evaluation of whether the declaration information of the commodities filled in by enterprises conforms to the customs filling standards according to the standardized declaration catalogue The manual review method cannot meet the actual work needs. Therefore, using text classification algorithm to conduct this task is necessary. The classification method based on the pre-training model would rely on the keywords. However, for the review of standard declaration task, excessive reliance on keywords will reduce the classification effect because of ignoring the overall information of the text. keyword mask pre-training model is proposed. In the fine-tuning stage, the model designs a keyword masking strategy and a keyword blinding strategy. The keyword masking strategy regularizes the BERT model with a mask mechanism, that is, the keywords are reconstructed with other text information except keywords, so that the model focuses on non-keywords information;keyword blinding strategy makes the model unable to distinguish the categories of text information without contextual text information, which can eliminate the influence of keywords on model decision-making. The experimental results on the real customs declaration data set showing that the method in this thesis exceeds the baseline method in terms of precision and true negative rate. © 2023 IEEE.

关键词： Classification (of information)

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