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4th International Conference on Consumer Electronics and computer Engineering, ICCECE 2024

作者： Liu, Lu Jiang, Min Xu, Lifa School of Computer Science and Technology Wuhan University of Science and Technology Hubei WuHan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China

ISBN: (纸本)9798350384062

Texture defect detection has wide applications in both textiles and industry, where traditional defect detection methods focus on costly manual labeling and defect samples are complex to collect in real-world industrial scenarios. Furthermore, labeling errors are common in datasets from industrial scenarios, and the problem still needs to be explored. Therefore, we propose a novel texture defect detection method. It has two stages;the defective samples are synthesized in the first stage. In the second stage, we represent normal samples using a self-supervised learning method. Then, we construct a single classifier based on this learned representation. Subsequently, the model is trained to distinguish between normal and defective samples within the embedding space. To increase the robustness of our method, we adopt a novel strategy to construct defective samples and compute the feature similarity through a similarity-weighted aggregation method to achieve defect detection, which is more robust to noise. Experimental results on the DAGM2007 dataset show that our method performs comparably to the leading texture defect detection methods without label noise. In contrast, in the presence of noise, our method can effectively minimize the impact of noise on model performance. © 2024 IEEE.

关键词： Defects

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Heterophilic Graph Representation Learning Based on Multi-Order Information Extraction and High and Low Pass Filters 10

Heterophilic Graph Representation Learning Based on Multi-Or...

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10th IEEE Smart World Congress, SWC 2024

作者： Wu, Ling Li, Xinyu Yang, Yingjie Guo, Kun Zhang, Qishan Fuzhou University College of Computer and Data Science Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing China De Montfort University School of Computer Science and Informatics Leicester United Kingdom Shanghai International Studies University College of Economics and Humanities Shanghai China

ISBN: (纸本)9798331520861

Real-world networks can be divided into homophilic and heterophilic graphs. Significantly, many nodes tend to be heterophilic in a homophilic graph. Graph representation learning in heterophilic graphs has attracted considerable significance in recent years as it breaks the homophily assumption and achieves higher-quality graph embeddings. However, existing heterophilic graph representation learning methods often mix attribute and topological information, neglecting the mutual exclusion between attribute information and topological information. Furthermore, algorithms in heterogeneous graphs usually rely on specific matrix structures to capture high-order information, which may restrict the applicability of these methods. To tackle these issues, this paper proposes a GNN-based strategy for Heterophilic graph Representation Learning based on Multi-order information extraction and High and Low pass filters (HRLMHL). First, we designed two relatively independent modules to learn attribute information and topological information, respectively, to address the issue of mismatch. Second, we built a multi-order information extraction module to extract multi-order similarity features, thereby achieving the learning of high-order graph information. Experiments on real datasets show that the node classification accuracy is superior to that of the state-of-the-art methods on most datasets. © 2024 IEEE.

关键词： Contrastive Learning

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Spatio-Temporal Context Modeling for Road Obstacle Detection 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wu, Xiuen Wang, Tao Liang, Lingyu Li, Zuoyong Ching, Fum Yew College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University Nanjing211189 China Guangzhou510320 China Penang11800 Malaysia

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Road obstacle detection is an important problem for vehicle driving safety. In this paper, we aim to obtain robust road obstacle detection based on spatio-temporal context modeling. Firstly, a data-driven spatial context model of the driving scene is constructed with the layouts of the training data. Then, obstacles in the input image are detected via the state-of-the-art object detection algorithms, and the results are combined with the generated scene layout. In addition, to further improve the performance and robustness, temporal information in the image sequence is taken into consideration, and the optical flow is obtained in the vicinity of the detected objects to track the obstacles across neighboring frames. Qualitative and quantitative experiments were conducted on the Small Obstacle Detection (SOD) dataset and the Lost and Found dataset. The results indicate that our method with spatio-temporal context modeling is superior to existing methods for road obstacle detection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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An Improved Capsule Network for Speech Emotion Recognition 1st

An Improved Capsule Network for Speech Emotion Recognition

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1st Southwest Data Science Conference, SDSC 2022

作者： Zhang, Huiyun Huang, Heming School of Computer Science Qinghai Normal University Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China

ISBN: (纸本)9783031233869

Speech Emotion Recognition (SER) is an important research content in the field of speech recognition, and it can make Human-computer Interaction (HCI) more natural and harmonious. At present, the research of SER faces such problems as data scarcity and overfitting. To tackle these problems, an Improve Capsule Network (I-CapsNet) using Data Augmentation (DA) technique is proposed for efficient SER task. Firstly, the model I-CapsNet is used to reduce the number of parameters and retain more texture and background information. It can also retain the angle and discriminative information of the entity, thereby to relieve the overfiting. Secondly, noise is used to augment the baseline datasets (EMODB, CASIA, and SAVEE) under five signal-to-Noise Ratios (SNR), to solve the problem of data scarcity. Finally, an evaluation index EAI is defined to comprehensively analyze the performance of the model under different data division namely EIRD, EDRD, EICV, and EDCV. Compared to the previous research results, the model I-CapsNet has competitive performance on the corresponding augmented datasets. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Speech recognition

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On some properties of the bibasic Humbert hypergeometric functions Ξ_(1) and Ξ_(2)

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Applied Mathematics(A Journal of Chinese Universities) 2023年第4期38卷 614-630页

作者： CAI Qing-bo Ghazi S.Khammash Shimaa I.Moustafa Ayman Shehata Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information ProcessingSchool of Mathematics and Computer ScienceQuanzhou Normal UniversityQuanzhou 362000China Department of Mathematics Al-Aqsa UniversityGazaGaza StripPalestine Department of Mathematics Faculty of ScienceAssiut UniversityAssiut 71516Egypt Department of Mathematics Unaizah College of Science and ArtsUnaizah 56264Qassim UniversityBuraydah 52571QassimSaudi Arabia

The main object of this paper is to deduce the bibasic Humbert functions Ξ_(1) and Ξ_(2)Some interesting results and elementary summations technique that was successfully employed,q-recursion,q-derivatives relations,the q-differential recursion relations,the q-integral representations for Ξ_(1) and Ξ_(2)are *** summation formula derives a list of p-analogues of transformation formulas for bibasic Humbert functions that have been studied,also some hypergeometric functions properties of some new interesting special cases have been given.

关键词： q-calculus bibasic Humbert hypergeometric functions q-derivative

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Activation Template Matching Loss for Explainable Face Recognition 17

Activation Template Matching Loss for Explainable Face Recog...

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17th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2023

作者： Lin, Huawei Liu, Haozhe Li, Qiufu Shen, Linlin Computer Vision Institute School of Computer Science and Softwre Enginnering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (纸本)9798350345445

Can we construct an explainable face recognition network able to learn a facial part-based feature like eyes, nose, mouth and so forth, without any manual annotation or additionalsion datasets? In this paper, we propose a generic Explainable Channel Loss (ECLoss) to construct an explainable face recognition network. The explainable network trained with ECLoss can easily learn the facial part-based representation on the target convolutional layer, where an individual channel can detect a certain face part. Our experiments on dozens of datasets show that ECLoss achieves superior explainability metrics, and at the same time improves the performance of face verification without face alignment. In addition, our visualization results also illustrate the effectiveness of the proposed ECLoss. © 2023 IEEE.

关键词： Face recognition

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Fully Hyperbolic Rotation for Knowledge Graph Embedding 27

Fully Hyperbolic Rotation for Knowledge Graph Embedding

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Liang, Qiuyu Wang, Weihua Bao, Feilong Gao, Guanglai College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (纸本)9781643685489

Hyperbolic rotation is commonly used to effectively model knowledge graphs and their inherent ***, existing hyperbolic rotation models rely on logarithmic and exponential mappings for feature *** models only project data features into hyperbolic space for rotation, limiting their ability to fully exploit the hyperbolic *** address this problem, we propose a novel fully hyperbolic model designed for knowledge graph *** of feature mappings, we define the model directly in hyperbolic space with the Lorentz *** model considers each relation in knowledge graphs as a Lorentz rotation from the head entity to the tail *** adopt the Lorentzian version distance as the scoring function for measuring the plausibility of *** results on standard knowledge graph completion benchmarks demonstrated that our model achieves competitive results with fewer *** addition, our model get the state-ofthe-art performance on datasets of CoDEx-s and CoDEx-m, which are more diverse and challenging than *** code is available at https://***/llqy123/FHRE. © 2024 The Authors.

关键词： Knowledge graph

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Research on CMUTs Design with Different Cell Arrangements

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Recent Patents on Engineering 2024年第1期18卷 110-118页

作者： Cao, Xuewen Yu, Yuanyu Liu, Xin Ma, Jingyi Zhang, Hua Wang, Lijia Intelligent Manufacturing Institute Hebei Vocational University of Industry and Technology Shi Jiazhuang050091 China School of Artificial Intelligence Neijiang Normal University Neijiang641100 China State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau Macau 999078 China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau 999078 China

Background: Capacitive micromachined ultrasonic transducers (CMUTs) is a promising component of mechanical-electrical-acoustical conversion, which shows valuable applications in non-distructive testing and obstacle detection fields. In order to study the influence of dimension parameters on the directivity of CMUTs, the three-dimensional multi-physics coupling model is proposed for CMUTs in this paper. Methods: The model combines multiple physical fields through finite element method (FEM), which is more efficient and accurate. The influence of dimension parameters on the transducer is studied from two aspects: the pitch between adjacent cells and cell number, respectively. Results: When increasing the pitch, the directivity becomes better if the pitch is less than half of the wavelength, the grating lobe and side lobe are aroused. However, the bandwidth at-3dB is reduced by up to 71.1% at the same time. When increasing the cell number, the better the direcitvity, while the change of the cell number has little effect on the bandwidth of the array. However, 5.4% frequency shift was caused due to the effective mass change of the transducer. Conclusion: In this study, a 3D model based on FEM is proposed for the influence of dimension parameter on CMUTs. The simulation results indicate that the directivity can be enhanced by chang-ing the dimension. At the same time, it also introduces other performance issues, such as bandwidth decrease and frequency shift through this approach. Therefore, the simulation proposed the guidance of CMUTs performance optimization and patented technology application work in the future. © 2024, Bentham Science Publishers. All rights reserved.

关键词： Finite element method

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Cascade Temporal Convolutional Network for Multitask Learning 6

Cascade Temporal Convolutional Network for Multitask Learnin...

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6th International Conference on Artificial Intelligence and Big Data, ICAIBD 2023

作者： Zhang, Huiyun Huang, Heming Han, Henry Baylor University School of Engineering and Computer Science Department of Computer Science United States Qinghai Normal University School of Computer Science Xining China Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application School of Computer Science Xining China

ISBN: (纸本)9781665491259

Multi-task learning (MTL) is a powerful technique in machine learning that enables the simultaneous training of multiple tasks. In this paper, we propose a novel MTL approach for the recognition of emotions, speakers, and gender in speech signals. Our approach presents a novel deep learning model called the Cascade Temporal Convolutional Network (CTCN to handle MTL efficiently. CTCN is designed to effectively improve the parallel processing ability of the model in multitasking learning. It provides an effective solution for improving the performance of deep learning in multitasking scenarios. CTCN is particularly adept at processing sequences of data with varying lengths and extracting features that are relevant to multiple tasks. Our experimental results demonstrate that the proposed CTCN approach for MTL achieves significantly superior performance on all tasks on benchmark datasets compared to other deep learning models. This study will inspire more future work in the MTL field. © 2023 IEEE.

关键词： Speech recognition

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Path Planning based on Reinforcement Learning with Improved APF model for Synergistic Multi-UAVs 26

Path Planning based on Reinforcement Learning with Improved ...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Ding, Qun Xu, Xiaolong Gui, Wenming Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing210023 China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Jinling Institute of Technology School of Computer Science Nanjing211169 China

ISBN: (纸本)9798350331684

As the emerging technology of Unmanned Aerial Vehicles (UAVs) becomes mature, UAVs are widely used in environmental monitoring, communication and other fields. In view of this, this paper analyzed the task of synergistic multi-UAVs exploration of unknown environments, and proposed a path planning method for them based on reinforcement learning. Firstly, the path planning task of the UAVs was divided into two parts: the path travel strategy module and the information exploration strategy module. Models of the two modules were based on the Deep Deterministic Policy Gradient algorithm (DDPG), and an improved Artificial Potential Field (APF) force traction mechanism was introduced in the path travel strategy module. Its aim was to assist in guiding the generation of UAV flight path trajectories. Also it could enhance the learning capability of the model. The path travel strategy module would generate the complete flight path of the whole cluster in a distributed manner. A series of temporary target points provided by the information exploration strategy module helped. In maps with 21.5%, 25.3% and 29.6% of obstacles, multi-UAVs could achieve 84.2%, 76.7% and 69.9% of environmental exploration by the designed method. Compared with the APF method, the A star method and the Breath First Search (BFS) method, the proposed method is not only able to plan feasible paths in a more complex map model, but also the curvature of the planned paths is smoother, thus achieving the goal of reducing the energy cost of UAVs. © 2023 IEEE.

关键词： Reinforcement learning

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