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2024 China Automation Congress, CAC 2024

作者： Zhou, Aoran Qiu, Lipeng Wu, Yali Department of Information and Control Engineering Xi'an University of Technology Shaan'xi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China State Key Laboratory of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co Ltd Xi'an710016 China

ISBN: (纸本)9798350368604

In view of the problems of high energy consumption, long inventory time and low production efficiency in the production process of traditional aluminum extrusion machine, this paper establishes an efficient two-stage parallel scheduling optimization model based on the integration of flexible job shop scheduling and assembly sequence planning (IFJSS-ASP). On this basis, an evolutionary algorithm combining strong dominance relationship and simple grouping method (SDREA-SGM) is proposed. The algorithm utilizes a two-stage hybrid chromosome encoding and decoding method to establish a rational mapping from the decision space to the solution space. Furthermore, in order to prevent the model from easily falling into local optima, an adaptive intra-class and inter-class pairing selection strategy has been adopted by the algorithm. This enhances global search and improves local search ***, the feasibility and effectiveness of the algorithm are verified by solving problems of different scales. © 2024 IEEE.

关键词： Job shop scheduling

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A Spatio-Temporal Transformer for Enhancing the Coherence on 3D Human Motion Prediction 18th

A Spatio-Temporal Transformer for Enhancing the Coherence ...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Xie, Miner Wang, Lei Liu, Feng College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9789819610709

Human motion prediction is of great importance for artificial intelligence systems, particularly in fields like autonomous driving and human-computer interaction. Existing methods have achieved good results in simple motions with small changes but still struggle with coherence in predicting complex motions with large changes in a short time. Regarding the issue in current methods, this paper proposes a novel network composed of three modules: spatial encoders that captures correlations between joints, temporal encoders that models the movement patterns of joints across frames, and a multi-frame temporal encoder that learns high-level temporal correlations to enhance prediction coherence. In addition, we propose to use Weighted Mean Per-Joint Position Error (WMPJPE) as the loss function by considering that different joints of the human skeleton have different effects on the entire action. We evaluate our method on the Human3.6M, AMASS, and 3DPW datasets. The quantitative results indicate that our method notably outperforms other methods in the prediction range of 160ms to 560ms. The qualitative results show that our method can improve the coherence of prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Variational Clustering and Denoising of Spatial Transcriptomics

Variational Clustering and Denoising of Spatial Transcriptom...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Li, Cuiyuan Zhang, Fa Hu, Kai Cui, Xuefeng Shandong University School of Computer Science and Technology Qingdao China Beijing Institute of Technology School of Medical Technology Beijing China Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan China Xiangnan University Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Chenzhou China

ISBN: (纸本)9798350337488

Spatial transcriptomics data provides a unique opportunity to investigate both gene expression and spatial structure in tissues at the same time. However, incorporating spatial information to accurately identify spatial domains is difficult due to factors such as high-dimensionality, sparsity, noise, and dropout events. To address these issues, we introduce vGraphST, a novel graph-based deep learning approach tailored for spatial transcriptomics data. Our method combines auto-encoder and contrastive learning techniques to process high-dimensional data and generate meaningful low-dimensional embeddings. Additionally, we use continuous distributions instead of discrete values in both the latent space and the denoised gene expression space. Specifically, Gaussian distributions are used to model the latent space, while zero-inflated Poisson distributions are used to model the denoised gene expression space. Experimental results demonstrate the effectiveness of vGraphST in accurately representing and analyzing spatial transcriptomics data. When compared to other methods using the DLPFC dataset, vGraphST achieves an average Adjusted Rand Index (ARI) of 0.58, demonstrating its superiority in segmenting spatial domains and recognizing biologically relevant spatiotemporal patterns. © 2023 IEEE.

关键词： clustering contrastive learning denoising spatial transcriptomics variational auto-encoder

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Boosting Meaningful Dependency Mining with Clustering and Covariance Analysis 40

Boosting Meaningful Dependency Mining with Clustering and Co...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Wang, Xi Jin, Ruochun Huang, Wanrong Tang, Yuhua Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Computer Science and Technology Department of Intelligent Data Science National University of Defense Technology Changsha China

ISBN: (纸本)9798350317152

Functional dependencies (FDs) form a valuable ingredient for various data management tasks. However, existing methods can hardly discover practical and interpretable FDs, especially in large noisy real-life datasets. This paper studies the problem of discovering meaningful functional dependencies (FDms) that utilize support and error parameters to capture interesting dependencies in such datasets and proposes an efficient discovery algorithm called FDM?. In order to scale with large datasets, FDM ? employs an efficient sampling method with accuracy guarantees to capture the differences between tuple pairs and to quantify the connection between support/error of dependencies on samples and those on the entire dataset. Moreover, it adopts a clustering-based correlated attributes extraction to divide the exponentially large search space into multiple small sub-spaces and proposes an easy-first traversal strategy with covariance-based guidance that quickly detects candidate dependencies and validates them. Additionally, we prove a covariance lower bound as an additional pruning criterion to reduce the search space. Extensive experiments on real-life and synthetic datasets demonstrate that FDM ? is 14 times faster than existing discovery algorithms on average, up to 31 times, and scales to larger datasets with the least memory cost. © 2024 IEEE.

关键词： Large datasets

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Structure-constrain Source Free Domain Adaptation with Regularized Knowledge Distillation of Source Model 13

Structure-constrain Source Free Domain Adaptation with Regul...

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13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Yang, Dan Peng, Xin Su, Cheng Tan, Dayu Chen, Chaoyang Zhong, Weimin Ministry of Education East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai200237 China Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing Anhui Hefei230601 China School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9798350361674

Transfer learning aims to leverage the knowledge of the source domain to help the learning of unlabeled target domain models. However, all conventional transfer learning methods assume that samples from source and target domains are always freely accessible. However, this assumption is rarely plausible in real-world due to data privacy. To combat this challenge, we introduce Structure-constrain Source Free Domain Adaptation with regularized Knowledge Distillation of source model (SSFDA-KD). Our method is based on the observations that a deterministic classification result can be obtained when the target data distribution aligns with the source data distribution, and a well-trained model not only yields a deterministic classification for a sample but also has clear category boundaries, which implies samples that are close together belong to the same category. From those, we first generate the source model with labeled source data, which is regarded as a well-trained model. Then, we identify target prototypes with lower self-entropy and assign the pseudo-labels to other samples according to the neighborhood structure. Finally, we refine the target model by minimizing prediction loss, constraining internal structure via triplet loss, and reducing knowledge distillation loss. Experiments of fault diagnosis based on WWTPs are conducted to validate its effectiveness. © 2024 IEEE.

关键词： Domain Knowledge

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Learning to Localize Cross-Anatomy Landmarks in X-Ray Images with a Universal Model

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Biomedical Engineering Frontiers 2022年第1期3卷 298-308页

作者： Heqin Zhu Qingsong Yao Li Xiao S.Kevin Zhou Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China Center for Medical Imaging RoboticsAnalytic Computing&Learning(MIRACLE)School of Biomedical Engineering&Suzhou Institute for Advanced ResearchUniversity of Science and Technology of ChinaSuzhou 215123China

Objective and Impact *** this work,we develop a universal anatomical landmark detection model which learns once from multiple datasets corresponding to different anatomical *** with the conventional model trained on a single dataset,this universal model not only is more light weighted and easier to train but also improves the accuracy of the anatomical landmark *** accurate and automatic localization of anatomical landmarks plays an essential role in medical image ***,recent deep learning-based methods only utilize limited data from a single *** is promising and desirable to build a model learned from different regions which harnesses the power of big *** model consists of a local network and a global network,which capture local features and global features,*** local network is a fully convolutional network built up with depth-wise separable convolutions,and the global network uses dilated convolution to enlarge the receptive field to model global *** evaluate our model on four 2D X-ray image datasets totaling 1710 images and 72 landmarks in four anatomical *** experimental results show that our model improves the detection accuracy compared to the state-of-the-art *** model makes the first attempt to train a single network on multiple datasets for landmark *** results qualitatively and quantitatively show that our proposed model performs better than other models trained on multiple datasets and even better than models trained on a single dataset separately.

关键词： convolution utilize separable

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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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Dual-Band HRRP Fusion Recognition via Wavelet Decomposition Embedded Autoencoder 2

Dual-Band HRRP Fusion Recognition via Wavelet Decomposition ...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Wang, Weijia Qi, Zhuchang Wang, Lue Yang, Wei Zhang, Liang Wang, Yanhua Xi'an Institute of Modern Control Technology Xi'an710065 China Beijing Institute of Technology Zhengzhou Academy of Intelligent Technology Zhengzhou450000 China Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (纸本)9798331515669

High-resolution range profile (HRRP) is a crucial technique for radar target recognition. Due to the coupling of azimuthal scattering information and aspect sensitivity, fine target recognition using HRRP remains challenging. One way to mitigate HRRP aspect sensitivity is through enhancing the dimensionality of radar information acquisition. Therefore, this paper proposes a Dual-Band HRRP Fusion Recognition Model via Wavelet Decomposition Embedded Autoencoder (DF-WD-AENet). The method we proposed mainly comprises two modules. Firstly, the autoencoder model based on wavelet decomposition (WD-AEM) achieves constraint on feature information, making it better suited for dual-band fusion. Next is the dual-band information fusion module (DFM), in which we achieved the interaction and fusion of feature information through convolutional block attention module (CBAM) and omni-dimensional dynamic convolution (ODConv), and used a fusion loss function to train the model. Experiment shows that the recognition rate of our method is 7.73% higher than that of single-band, proving the effectiveness of the proposed method. © 2024 IEEE.

关键词： Wavelet decomposition

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Personality recognition based on ECG in response to VR emotional stimuli 23

Personality recognition based on ECG in response to VR emoti...

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3rd International Conference on Bioinformatics and intelligent computing, BIC 2023

作者： Lan, Ping Xie, Jialan Hu, Zhaonian Liu, Guangyuan College of Electronic and Information Engineering Southwest University Chongqing400715 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing400715 China Institute of Affective Computing and Information Processing Southwest University Chongqing400715 China Key Laboratory of Cognition and Personality Ministry of Education Faculty of Psychology Southwest University Chongqing400715 China

ISBN: (纸本)9798400700200

This paper explores the feasibility and effect of using ECG signals in response to virtual reality(VR) emotional stimuli for personality recognition. Personality represents an individual''s behavior pattern, so it has extensive research significance to identify personality. In the field of personality recognition, there have been researches on the use of physiological signals to recognize personality recently, but there have been no studies on personality recognition in VR scenarios. VR technology has been applied in many fields, and in the field of emotion recognition, the advantage of VR video as emotional arousal material has been proved. We designed the emotional stimulation experiment in VR scene to collect ECG signals, and constructed the personality recognition model. The results showed that the F1 scores of neuroticism, extraversion, openness, agreeableness and conscientiousness using only ECG were 0.79, 0.80, 0.81, 0.73 and 0.81, respectively, which were much higher than the random level. And we found that in VR scenes, negative emotional stimulation can achieve better personality recognition effect. Our results confirm the feasibility of personality recognition using ECG signals in response to VR emotional stimuli. © 2023 ACM.

关键词： Electrocardiograms

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Digital Human Techniques for Education Reform 24

Digital Human Techniques for Education Reform

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7th International Conference on Educational technology Management, ICETM 2024

作者： Guo, Peirong Zhang, Qi Tian, Chunwei Xue, Wanli Feng, Xiaocheng School of Software Northwestern Polytechnical University Shaanxi Xi’an China School of Economics and Management Harbin Institute of Technology at Weihai Shandong Weihai China Shenzhen Research Institute of Northwestern Polytechnical University Northwestern Polytechnical University Guangdong Shenzhen China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology Heilongjiang Harbin China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400717468

The rapid evolution of artificial intelligence, big data, and generative AI models has ushered in significant transformations across various sectors, including education. Digital Human Technique, an innovative technology grounded in advanced computer science and artificial intelligence, is reshaping educational paradigms by enabling virtual humans to simulate human behavior, express emotions, and interact with users. This paper explores the application of Digital Human Technique in education reform, focusing on creating immersive, intelligent classroom experiences that foster meaningful interactions between teachers and students. We define Digital Human Technique and delve into its key technical components such as character modeling and rendering, natural language processing, computer vision, and augmented reality technologies. Our methodology involves analyzing the role of educational digital humans created through these technologies, assessing their impact on educational processes, and examining various application scenarios in educational reform. Results indicate that Digital Human Technique significantly enhances the learning experience by enabling personalized teaching, increasing engagement, and fostering emotional connections. Educational digital humans serve as virtual teachers, interactive learning aids, and facilitators of emotional interaction, effectively addressing the challenges of traditional educational methods. They also promote a deeper understanding of complex concepts through simulated environments and interactive digital content. © 2024 Copyright held by the owner/author(s).

关键词： Digital elevation model

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