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LQTTrack:Multi-Object Tracking by Focusing on Low-Quality Targets Association

Computers, Materials & Continua 2024年第10期81卷 1449-1470页

作者： Suya Li Ying Cao Hengyi Ren Dongsheng Zhu Xin Xie Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information EngineeringHenan UniversityKaifeng475001China College of Information Science and Technology&Artificial Intelligence Nanjing Forestry UniversityNanjing210037China School of Computer Nanjing University of Posts and TelecommunicationsNanjing210023China

Multi-object tracking(MOT)has seen rapid improvements in recent ***,frequent occlusion remains a significant challenge in MOT,as it can cause targets to become smaller or disappear entirely,resulting in lowquality targets,leading to trajectory interruptions and reduced tracking *** from some existing methods,which discarded the low-quality targets or ignored low-quality target ***,with a lowquality association strategy(LQA),is proposed to pay more attention to low-quality *** the association scheme of LQTTrack,firstly,multi-scale feature fusion of FPN(MSFF-FPN)is utilized to enrich the feature information and assist in subsequent data ***,the normalized Wasserstein distance(NWD)is integrated to replace the original Inter over Union(IoU),thus overcoming the limitations of the traditional IoUbased methods that are sensitive to low-quality targets with small sizes and enhancing the robustness of low-quality target ***,the third association stage is proposed to improve the matching between the current frame’s low-quality targets and previously interrupted trajectories from earlier frames to reduce the problem of track fragmentation or error tracking,thereby increasing the association success rate and improving overall multi-object tracking *** experimental results demonstrate the competitive performance of LQTTrack on benchmark datasets(MOT17,MOT20,and DanceTrack).

关键词： Low-quality targets association strategy feature fusion multi-object tracking tracking-by-detection

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Controlled-source electromagnetic noise attenuation via a deep convolutional neural network and high-quality sounding curve screening mechanism

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Geophysics 2025年第3期90卷 WA125-WA140页

作者： Liu, Yecheng Li, Diquan Li, Jin Zhang, Xian Central South University Monitoring Ministry of Education Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Changsha China Hunan Provincial Key Laboratory of Non-ferrous Resources and Geological Hazard Detection Changsha China Central South University School of Geoscience and Info-physics Changsha China Hunan Normal University College of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha China Hunan University of Finance and Economics School of Information Technology and Management Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology Changsha China

Strong noise is one of the biggest challenges in controlled-source electromagnetic (CSEM) exploration, which severely affects the quality of the recorded signal. We develop a novel and effective CSEM noise attenuation method that integrates a deep convolutional neural network (DCNN) with a high-quality sounding curve screening mechanism. This method first uses a DCNN to learn the large-scale noise characteristics of CSEM measured data and build a DCNN denoising model for attenuating the noise. Because some impulse noise remains after DCNN denoising, the high-quality sounding curve screening mechanism is innovatively applied to optimize the DCNN denoised data. The core of this method is the smoothness value (SV), which quantitatively evaluates the smoothness of the sounding curve (apparent resistivity curve). In effect, the mechanism segments the overall DCNN denoised data and calculates the corresponding sounding curve and its SV for each segment. The highest-quality sounding curve can be screened out by comparing the SVs of all curves as well as obtaining the highest-quality data segment present in the overall DCNN denoised data. Finally, our method is validated with synthetic and field data, demonstrating its feasibility and effectiveness in attenuating large-scale CSEM noise. The quality of CSEM data, which is heavily affected by noise, is significantly improved after processing by our method. © 2025 Society of Exploration Geophysicists. All rights reserved.

关键词： Deep neural networks

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Towards annotation-free evaluation of cross-lingual image captioning 2

Towards annotation-free evaluation of cross-lingual image ca...

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2nd ACM International Conference on Multimedia in Asia, MMAsia 2020

作者： Chen, Aozhu Huang, Xinyi Lin, Hailan Li, Xirong Moe Key Lab of Data Engineering and Knowledge Engineering Renmin University of China Beijing China

ISBN: (纸本)9781450383080

Cross-lingual image captioning, with its ability to caption an unlabeled image in a target language other than English, is an emerging topic in the multimedia field. In order to save the precious human resource from re-writing reference sentences per target language, in this paper we make a brave attempt towards annotation-free evaluation of cross-lingual image captioning. Depending on whether we assume the availability of English references, two scenarios are investigated. For the first scenario with the references available, we propose two metrics, i.e., WMDRel and CLinRel. WMDRel measures the semantic relevance between a model-generated caption and machine translation of an English reference using their Word Mover's Distance. By projecting both captions into a deep visual feature space, CLinRel is a visual-oriented cross-lingual relevance measure. As for the second scenario, which has zero reference and is thus more challenging, we propose CMedRel to compute a cross-media relevance between the generated caption and the image content, in the same visual feature space as used by CLinRel. We have conducted a number of experiments to evaluate the effectiveness of the three proposed metrics. The combination of WMDRel, CLinRel and CMedRel has a Spearman's rank correlation of 0.952 with the sum of BLEU-4, METEOR, ROUGE-L and CIDEr, four standard metrics computed using references in the target language. CMedRel alone has a Spearman's rank correlation of 0.786 with the standard metrics. The promising results show high potential of the new metrics for evaluation with no need of references in the target language. © 2021 ACM.

关键词： Semantics

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Robust Federated Semi-Supervised Learning for Medical Image Classification via Pseudo-Label Filtering

Robust Federated Semi-Supervised Learning for Medical Image ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Ziwei Guo, Shuyu Zhu, Mingzhu Bai, Tian Jilin University College of Software Changchun China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China Jilin University College of Computer Science and Technology Changchun China

ISBN: (纸本)9798350386226

Federated learning (FL) enables collaborative model training across multiple medical institutions to ensure data security. However, due to the variations in medical imaging equipment and regions at different medical institutions, FL methods usually suffer from insufficient data annotations and irrelevant noise within private datasets. To address these issues, a robust federated semi-supervised learning method via pseudo-label filtering (PFRFed) is introduced to utilize unlabeled data while mitigating the impact of noise data. Compared with existing federated semi-supervised learning methods, we propose a pseudo-label filtering mechanism with double dynamic thresholds, which allows the model to adopt more unlabeled data by adjusting the confidence and entropy thresholds at each stage of model training. Moreover, to reduce the degradation caused by noise data in private datasets from different clients, a noise-tolerant loss function and a grouping aggregation method based on the local model similarity are employed. The comparative experiments demonstrate the effectiveness of PFRFed, which has achieved the best classification accuracy of 95.20% and 88.72% on two public medical datasets. Also, PFRFed exhibits heightened resilience to variations in noisy data ratio and labeled data ratio, reaffirming its versatility and robustness. © 2024 IEEE.

关键词： Federated learning

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Mechanical properties of thermoelectric generators

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Journal of Materials Science & Technology 2023年第17期148卷 64-74页

作者： Xin Bao Shuaihang Hou Zhixin Wu Xiaodong Wang Li Yin Yijie Liu Huolun He Sichen Duan Baolin Wang Jun Mao Feng Cao Qian Zhang School of Materials Science and Engineering and Institute of Materials Genome&Big DataHarbin Institute of TechnologyShenzhen518055China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin150001China School of Science Harbin Institute of TechnologyShenzhen518055China

To satisfy the requirements of practical applications,thermoelectric generators should be highly efficient and mechanically ***,progress in designing high-performance thermoelectric generators has been ***,the mechanical properties of thermoelectric generators are still *** this review,studies on the mechanical properties of thermoelectric generators are *** me-chanical properties of bulk thermoelectric generators will be first *** this section,the mechan-ical properties of thermoelectric materials and the strategies for improving their mechanical properties are *** the device’s failure usually occurs at the interface between the thermoelectric ma-terials and electrode,the joint strength of electrodes and thermoelectric materials will be *** that,the mechanical properties of the inorganic thin-film thermoelectric devices will be *** the figure of merit for the flexibility of thermoelectric materials depends on the film thickness,elastic modulus,and yield strength,the synthesis methods of thin-film thermoelectric materials will be ***,this review will be concluded with a discussion on flexible organic thermoelectric de-vices and flexible devices using bulk legs.

关键词： Thermoelectric generator Mechanical properties Interface Joint strength

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Optimization of flink distributed system with FPGA-based architecture 4

Optimization of flink distributed system with FPGA-based arc...

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4th International Conference on Internet of Things and Smart City, IoTSC 2024

作者： Zhang, Juan You, Jia Hao, Yang Li, Xisheng Liu, Yin University of Science and Technology Beijing Beijing China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9781510681767

With the continuous increase in the volume of Internet of Things (IoT) data, the limited number of servers deployed at the edge poses a pressing challenge for efficiently processing large-scale data streams. This study aims to explore the application of FPGA hardware acceleration technology to enhance the performance of the Apache Flink framework to meet the growing demands of users. The paper advocates the utilization of FPGA as a hardware accelerator for enhancing the performance of the Flink framework. This is achieved by harnessing the power of PCIe technology and integrating seamlessly with the OpenCL standard, which is tailored for heterogeneous systems. The JVM-FPGA communication mechanism is optimized using a data transmission pipeline mechanism. Through experiments and evaluations of two typical computationally intensive tasks, matrix multiplication and vector addition, it is demonstrated that the performance of the new framework is reduced in terms of latency, throughput is increased by 2.77 times, and CPU utilization is significantly enhanced. This study provides an innovative solution for efficiently processing large-scale data streams in edge environments, successfully demonstrating that hardware acceleration can significantly improve the performance of the Apache Flink framework under resource constraints in edge environments, better meeting the continuously growing demands of IoT data. © 2024 SPIE.

关键词： data streams

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Semantic-Reconstructed Graph Transformer Network for Event Detection

Semantic-Reconstructed Graph Transformer Network for Event D...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhuochun Miao Xingrui Zhuo Gongqing Wu Chenyang Bu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology Anhui China

Event detection (ED) is a key subtask of information extraction to extract key events, such as stock rise and fall and social public opinion, from news or social media. Although current GCN-based event detection methods achieve remarkable success via building graphs with dependency trees, they typically suffer from two challenges: 1) They use sequence models to learn contextual information of sentences, ignoring the longterm dependencies problem of sequence models might learn ineffective information and make it propagate in GCN layers. 2) Most methods do not exploit global dependency label information and grammatical structure information that convey rich linguistic knowledge directly, and only consider local dependency label information. To cope with these challenges, we propose a novel event detection model via semantic-reconstructed graph transformer networks (SRGTNED), which incorporates semantic reconstruction and path information collection methods. Using the semantic reconstruction method, we assign a pruned sequence to each word based on the path information to capture contextual information consistent with sentence semantics. Moreover, to better utilize global dependency label information and grammatical structure information, a Graph Transformer Network (GTN)-based heterogeneous graph embedding framework is introduced to automatically learn path information between important words by converting sentences as heterogeneous graphs. We conduct experiments on the ACE2005 dataset and the Commodity News dataset, and the experimental results demonstrate that our method significantly outperforms 11 state-of-the-art baselines in terms of the F1-score.

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Carbon-neutral pathway to mitigating transport-power grid cross-sector effects

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The Innovation 2024年第3期5卷 27-29页

作者： Jing Ma Huiwen Kong Jianxiao Wang Haiwang Zhong Bo Li Jie Song Daniel M.Kammen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical and Electronic EngineeringNorth China Electric Power UniversityBeijing 102206China National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijing 100871China Peking University Ordos Research Institute of Energy Ordos 017000China State Key Laboratory of Power Systems and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijing 100084China School of Electrical Engineering Guangxi UniversityNanning 530004China Department of Industrial Engineering and Management College of EngineeringPeking UniversityBeijing 100871China Energy and Resources Group and Goldman School of Public PolicyUniversity of CaliforniaBerkeleyBerkeleyCA 94720USA

Dear Editor,The growing demand for transportation energy has brought increasing challenges to reducing greenhouse gas ***,many countries or regions have proposed solutions to achieve carbon-neutral transportation such as the rapid expansion of the global electric vehicle(EV)***,these benefits are not *** the goal of decarbonization in electricity is achieved,the“pseudo net zero emissions”effect of the transportation sector will inevitably be accompanied by a quiet shifting of carbon responsibility.1 This shift is undoubtedly fatal for both net zero emissions at the national level and carbon responsibility at the industry level;however,the corresponding effects have not yet been clarified.

关键词： neutral doubt shifting

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Fast Multivariate Time Series Anomaly Detection Based on Matrix Completion

Fast Multivariate Time Series Anomaly Detection Based on Mat...

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2023 International Conference on Frontiers of Robotics and Software engineering, FRSE 2023

作者： He, Shiming Yi, Tongzhijian Yang, Bo Guo, Chenxi Changsha University of Science & Technology School of Computer and Communication Engineering The Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha China

ISBN: (纸本)9798350301113

Increasingly complex systems contain large numbers of devices that generate great number of multivariate time series that are monitored and recorded. For anomaly detection of these complex time series, deep learning technology is often used to build a learning model for anomaly detection. The method based on deep learning usually takes a long time to train data in practice, and the online service system is deployed or changed very frequently, which makes the online service system need to frequently spend a lot of time updating the anomaly detection model. To overcome this challenge, this paper proposes a fast multivariate time series anomaly detection (FMAD) approach based on matrix completion. After sampling the primitive low-rank matrix, FMAD uses matrix completion to reconstruct the original low-rank matrix to detect anomalies in multivariate time series. We conducted related experimental analysis and discussion. The experimental results on three real data sets show that FMAD has improved the problem of too long model training time and improved the detection efficiency. © 2023 IEEE.

关键词： Anomaly detection

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LogADCH: Hyperspheric-Based Log Message Anomaly Detection And Classification

LogADCH: Hyperspheric-Based Log Message Anomaly Detection An...

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2023 International Conference on Frontiers of Robotics and Software engineering, FRSE 2023

作者： He, Shiming Xiao, Jinpan Chen, Bowen Lei, Ying Changsha University of Science & Technology School of Computer and Communication Engineering The Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha China

ISBN: (纸本)9798350301113

Web services have been integrated with all walks of life in society. Abnormalities in the network and services seriously affect user experience and company revenue. The system log records various information of the system, and the information-rich log messages usually show different anomalies. It is particularly important to accurately and timely identify the anomalies of the log messages and classify the anomalies. In addition, most of the current existing work only detects anomalies and does not classify them, and a few existing works require anomaly labels to classify anomalies. Based on these potential challenges, we propose an unsupervised hypersphere-based log message anomaly detection and classification method, LogADCH. We use the hypersphere loss function to let the BERT model learn compact representations of log messages and perform anomaly detection. Finally, K-Means clustering is performed on the semantic feature vectors detected as anomalies to form clusters of different types of anomalies. Compared with other works, LogADCH further classifies the abnormal types after detecting the abnormal logs, helping the operation and maintenance personnel to quickly locate the fault. We compare our method with other methods on two public datasets, and the experimental results show that LogADCH has superior performance in anomaly detection as well as anomaly classification. © 2023 IEEE.

关键词： Classification (of information)

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