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4th International Conference on information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Jiang, Chengjia Wang, Tao Li, Sien Wang, Jinyang Wang, Shirui Antoniou, Antonios Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Fujian Education Institutions Wuyi University The Key Laboratory of Cognitive Computing and Intelligent Information Processing Wuyishan China College of Computer and Data Science Fuzhou University Fuzhou China European University Cyprus Department of Computer Science and Engineering Nicosia Cyprus

ISBN: (纸本)9798350337181

We address the problem of learning new classes for semantic segmentation models from few examples, which is challenging because of the following two reasons. Firstly, it is difficult to learn from limited novel data to capture the underlying class distribution. Secondly, it is challenging to retain knowledge for existing classes and to avoid catastrophic forgetting. For learning from limited data, we propose a pseudo-labeling strategy to augment the few-shot training annotations in order to learn novel classes more effectively. Given only one or a few images labeled with the novel classes and a much larger set of unlabeled images, we transfer the knowledge from labeled images to unlabeled images with a coarse-to-fine pseudo-labeling approach in two steps. Specifically, we first match each labeled image to its nearest neighbors in the unlabeled image set at the scene level, in order to obtain images with a similar scene layout. This is followed by obtaining pseudo-labels within this neighborhood by applying classifiers learned on the few-shot annotations. In addition, we use knowledge distillation on both labeled and unlabeled data to retain knowledge on existing classes. We integrate the above steps into a single convolutional neural network with a unified learning objective. Extensive experiments on the Cityscapes and KITTI datasets validate the efficacy of the proposed approach in the self-driving domain. Code is available from https://***/ChasonJiang/FSCILSS. © 2023 IEEE.

关键词： Distillation

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Spatiotemporal evolution laws of sector-shaped dielectric-barrier-discharge plasma actuator

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Plasma Science and Technology 2024年第10期26卷 108-115页

作者： Borui ZHENG Jianbo ZHANG Shaojie QI Jianghua XU Yiche LI Yuanzhong JIN Dongliang BIAN School of Automation and Information Engineering Xi’an University of TechnologyXi’an 710048People’s Republic of China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an 710048People’s Republic of China School of Mechanical and Precision Instrument Engineering Xi’an University of TechnologyXi’an 710048People’s Republic of China Science and Technology on Plasma Dynamics Laboratory Air Force Engineering UniversityXi’an 710038People’s Republic of China

Dielectric barrier discharge(DBD)plasma actuators are widely used in active flow control due to their simple design and rapid ***,they need more effectiveness and discharge *** overcome these limitations,a sector-shaped dielectric barrier discharge(SS-DBD)plasma actuator with an adjustable jet angle was developed to enhance flow control *** flow field dynamics induced by the SS-DBD plasma actuator were quantitatively analyzed using particle image velocimetry(PIV).Experimental investigations showed that precise adjustments to the actuation voltage can modulate the maximum velocity of the induced ***,a quasi-linear relationship between the sector-shaped angles of the SS-DBD and the deflected jet angles was established,indicating that changes in the sector-shaped angles directly influence the direction of the deflected *** correlation enables precise control over jet angles,significantly enhancing flow control by adjusting the SS-DBD-PA's sector-shaped angle.

关键词： plasma flow control plasma actuator particle image velocimetry

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Semantic enhancement methods for image captioning

Semantic enhancement methods for image captioning

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2022 International Conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2022

作者： Cui, Luming Li, Lin School of Computer Science Qinghai Normal University Xining810016 China State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810008 China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province Qinghai Normal University Xining810008 China

ISBN: (纸本)9781510662988

Image captioning, a cross-modal study, aims to generating a description for a given image, which plays an important role in many fields like image retrieval and computer-assisted instruction. Currently, the challenge in image captioning is the limited quality of generated descriptions including insufficient utilization of image feature information and the limited language learning ability of the decoder. In this paper, we address the above problems by constructing a semantic enhancement module and a multi-round decoding mechanism to enhance the decoding ability of the model, which uses the Transformer model as the primary structure. To validate the efficacy of the model, we conducted intensive experiments on the MSCOCO2014 benchmark and evaluated its performance using five evaluation metrics. The experimental results show that the proposed method in this paper has improved to varying degrees on all five-evaluation metrics. © 2023 SPIE. All rights reserved.

关键词： Decoding

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Vibration-Based Fault Diagnosis for Railway Point Machines Using VMD and Multiscale Fluctuation-Based Dispersion Entropy

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Chinese Journal of Electronics 2024年第3期33卷 803-813页

作者： Yongkui SUN Yuan CAO Peng LI Guo XIE Tao WEN Shuai SU National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University School of Electronic and Information Engineering Beijing Jiaotong University Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University

As one of the most important railway signaling equipment,railway point machines undertake the major task of ensuring train operation *** fault diagnosis for railway point machines becomes a hot *** the advantage of the anti-interference characteristics of vibration signals,this paper proposes an novel intelligent fault diagnosis method for railway point machines based on vibration signals.A feature extraction method combining variational mode decomposition(VMD) and multiscale fluctuation-based dispersion entropy is developed,which is verified a more effective tool for feature ***,a two-stage feature selection method based on Fisher discrimination and ReliefF is proposed,which is validated more powerful than single feature selection ***,support vector machine is utilized for fault *** comparisons show that the proposed method performs *** diagnosis accuracies of normal-reverse and reverse-normal switching processes reach 100% and 96.57% ***,it is a try to use new means for fault diagnosis on railway point machines,which can also provide references for similar fields.

关键词： Fault diagnosis Railway point machine Vibration signal Variational mode decomposition Two-stage feature selection

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A Convolutional Knowledge Tracing Method based on Graph Embedding 4

A Convolutional Knowledge Tracing Method based on Graph Embe...

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4th International Symposium on Computer Engineering and intelligent Communications, ISCEIC 2023

作者： Huang, Guimin Li, Ying Guo, Qingkai Deng, Nanxiao Lin, Jianxing Guilin University of Electronic Technology School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798350306323

With online education rapidly developing, it is a significant issue to evaluate students' grasp of knowledge more accurately. Knowledge tracing models are good at it, in which convolutional knowledge tracing (CKT) based on convolutional neural networks(CNN) gets better performance. However, we found that the CKT model ignores the relation between the knowledge points, which we believe has an important impact on the correctness of students' answers. Therefore, we put forward a graph embedding based convolutional knowledge tracing model (CKT-GE), which feeds the relation between the knowledge points with other inputs into model for improving the effectiveness of knowledge tracing. Our model outperforms existing models on two classic datasets, and improves the performance over the original CKT model, allowing for more accurate assessment of student performance. © 2023 IEEE.

关键词： Students

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STABILITY IN p-TH MOMENT FOR UNCERTAIN SINGULAR SYSTEMS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2023年第3期85卷 37-48页

作者： Jia, Zhifu Li, Cunlin School of Sciences and Arts Suqian University Suqian223800 China Ningxia Key Laboratory of intelligent information and Big Data Processing Governance and Social Management Research Center of Northwest Ethnic Regions North Minzu University Yinchuan China

The uncertain singular system is a type of singular system that is disrupted by the canonical Liu process and is described as a multidimensional uncertain differential equation. Although stability in mean, stability in measure, and almost sure stability have been investigated for uncertain singular systems, these three types of stability may not apply in all cases. This paper aims to introduce the concept of stability in p-th moment for uncertain singular systems as a supplementary type of stability. A stability theorem is also presented for uncertain singular systems that are stable in p-th moment. Furthermore, this paper discusses the relationships between stability in measure and stability in p-th moment, between stability in p1-th moment and stability in p2-th moment for uncertain singular systems. An example is provided to demonstrate the effectiveness of our results. © 2023, Politechnica university of Bucharest. All rights reserved.

关键词： Differential equations

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Orthogonal Universal Kernel Non-Negative Matrix Factorization with Total Variation Regularization

Orthogonal Universal Kernel Non-Negative Matrix Factorizatio...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Zhou, Rong Chen, Wen-Sheng Pan, Binbin College of Mathematics and Statistics Shenzhen University Shenzhen518060 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798350303780

Kernel-based non-negative matrix factorization (KNMF) is a promising data analysis method for nonlinear feature extraction. It has achieved better performance in pattern classification. However, KNMF algorithms suffer from problems including kernel function restriction, inaccurate pre-image learning, and sensitivity to noise. This paper proposes a novel framework of orthogonal universal KNMF with total variation regularization (OUKNMF-TV) to address these issues. The model contains three objective functions subject to TV and orthogonal constraints. The optimization problems of the OUKNMF-TV model are solved using the gradient descent method, and its updated rules are the same in the case of using different kernel functions. Convergence of the OUKNMF-TV algorithm is guaranteed using auxiliary function techniques. The experiments on non-noisy and contaminated datasets are implemented, and the results confirm the effectiveness and robustness of the proposed algorithm. © 2023 IEEE.

关键词： Face recognition

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ADHD diagnosis guided by functional brain networks combined with domain knowledge

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Computers in Biology and Medicine 2024年 177卷 108611-108611页

作者： Cao, Chunhong Fu, Huawei Li, Gai Wang, Mengyang Gao, Xieping MOE Key Laboratory of Intelligent Computing and Information Processing Xiangtan University Xiangtan411100 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China

Utilizing functional magnetic resonance imaging (fMRI) to model functional brain networks (FBNs) is increasingly prominent in attention-deficit/hyperactivity disorder (ADHD) research, revealing neural impact and mechanisms through the exploration of activated brain regions. However, current FBNs-based methods face two major challenges. The primary challenge stems from the limitations of existing modeling methods in accurately capturing both regional correlations and long-distance dependencies (LDDs) within the dynamic brain, thereby affecting the diagnostic accuracy of FBNs as biomarkers. Additionally, limited sample size and class imbalance also pose a challenge to the learning performance of the model. To address the issues, we propose an automated diagnostic framework, which integrates modeling, multimodal fusion, and classification into a unified process. It aims to extract representative FBNs and efficiently incorporate domain knowledge to guide ADHD classification. Our work mainly includes three-fold: 1) A multi-head attention-based region-enhancement module (MAREM) is designed to simultaneously capture regional correlations and LDDs across the entire sequence of brain activity, which facilitates the construction of representative FBNs. 2) The multimodal supplementary learning module (MSLM) is proposed to integrate domain knowledge from phenotype data with FBNs from neuroimaging data, achieving information complementarity and alleviating the problems of insufficient medical data and unbalanced sample categories. 3) An ADHD automatic diagnosis framework guided by FBNs and domain knowledge (ADF-FAD) is proposed to help doctors make more accurate decisions, which is applied to the ADHD-200 dataset to confirm its effectiveness. The results indicate that the FBNs extracted by MAREM perform well in modeling and classification. After with MSLM, the model achieves accuracy of 92.4%, 74.4%, and 80% at NYU, PU, and KKI, respectively, demonstrating its ability to effecti

关键词： Domain Knowledge

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FASTC: A Fast Attentional Framework for Semantic Traversability Classification Using Point Cloud

arXiv

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arXiv 2024年

作者： Chen, Yirui Wei, Pengjin Liu, Zhenhuan Wang, Bingchao Yang, Jie Liu, Wei Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education China

Producing traversability maps and understanding the surroundings are crucial prerequisites for autonomous navigation. In this paper, we address the problem of traversability assessment using point clouds. We propose a novel pillar feature extraction module that utilizes PointNet to capture features from point clouds organized in vertical volume and a 2D encoder-decoder structure to conduct traversability classification instead of the widely used 3D convolutions. This results in less computational cost while even better performance is achieved at the same time. We then propose a new spatio-temporal attention module to fuse multi-frame information, which can properly handle the varying density problem of LIDAR point clouds, and this makes our module able to assess distant areas more accurately. Comprehensive experimental results on augmented Semantic KITTI and RELLIS-3D datasets show that our method is able to achieve superior performance over existing approaches both quantitatively and quantitatively. Our code is publicly available at https://***/chenyirui/FASTC. © 2024, CC BY.

关键词： Semantics

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Towards Efficient and Secure Verifiable Aggregation for Federated Learning 9

Towards Efficient and Secure Verifiable Aggregation for Fede...

引用

9th International Conference on Computer and Communications, ICCC 2023

作者： Wang, Chang-Ji Jiang, Yu-Ning Gan, Qing-Qing Liu, Ning Yin, Yu-Chen Guangdong University of Foreign Studies School of Information Science and Technology Guangzhou Key Laboratory of Intelligent Processing of Non-Common Languages Guangzhou China

ISBN: (纸本)9798350317251

Federated learning is an advanced technique that outperforms traditional centralized approaches in machine learning. It allows participants to keep their data locally and obtain a global model through aggregation. However, this technique introduces parameter exchange processes, increasing privacy risks. In this study, we investigate two privacy concerns: protecting users' data during training and verifying the accuracy of aggregation results from the server. To address these issues, we propose a verifiable Federated Learning aggregation scheme based on the RSA accumulator. We employ double-masking techniques for secure federated learning and to accommodate disconnected users. Using the RSA accumulator and homomorphic hash function, our scheme offers efficient correctness verification with minimal storage requirements and constant verification time, even with more users. Through experiments and analysis, we demonstrate that our scheme reduces computation and communication costs while ensuring lightweight verifiability. © 2023 IEEE.

关键词： Digital storage

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