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A Deep GRU-Autoencoder for Dimentionality Reduction in Hand Skeleton Data Sequences classification

A Deep GRU-Autoencoder for Dimentionality Reduction in Hand ...

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Artificial Intelligence (AMCAI), IEEE Afro-Mediterranean Conference on

作者： Safa Ameur Mahjoub MohamedAli Anouar BEN K HALI FA Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse Tunisie Université de Jendouba Institut National des Technologies et des Sciences du Kef Le Kef Tunisie

Hand Gesture Recognition (HGR) has gained significant attention in various fields, including sign language interpretation and human-computer interaction. The classification of hand skeleton data sequences poses a challenge due to the high dimensionality of the input data. In this work, we propound a novel approach for dimensionality reduction in hand skeleton data sequences classification using a Deep Gated Reccurent Unit Autoencoder model (DGRU-AE). Our primary goal in this study is to efficiently decrease the dimensionality of hand gesture data, all the while retaining both the spatial and temporal information to ensure precise classification. We accomplish this by leveraging a DGRU-AE architecture, combining the strengths of recurrent neural networks and autoencoders. Our methodology involves preprocessing the hand skeleton data sequences, including normalization and feature extraction, through training the DGRUAE using suitable loss functions and optimization algorithms. Experimental evaluation on a benchmark datasets demonstrates the effectiveness of our suggested approach. The DGRU-AE achieves significant dimensionality reduction while maintaining high classification accuracy, outperforming existing methods. Our approach not only reduces computational complexity but also facilitates a more compact and meaningful representation of hand gestures. This work contributes to the advancement of HGE techniques by addressing the challenge of dimensionality reduction in classification tasks.

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Efficient Facial Expression Recognition Network Based on Spot-Adaptive Knowledge Distillation 26

Efficient Facial Expression Recognition Network Based on Spo...

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26th International Conference on Industrial technology, ICIT 2025

作者： Wen, Xin Liu, Zhen-Tao Chen, Dan School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9798331521950

Facial expression recognition plays a key role in promoting the development of comprehensive intelligence and building friendly human-computer interaction. Due to the interference of feature noise in expression data, the lightweight facial expression recognition model with fewer parameters is difficult to learn more expression features through simple training, which limits the improvement of its recognition performance. An efficient facial expression recognition network based on Spot-adaptive Knowledge Distillation is proposed in this paper. Inspired by VoVNetV2, the network designed in this paper is lightweightly improved using Depthwise Separable Convolution and the parameter-free SimAM attention mechanism, reducing the number of parameters to 0.21 M. To further improve the recognition accuracy of the model, Spot-adaptive Knowledge Distillation is employed to improve the characterization ability of the model. The recognition accuracies of the student network designed in this paper on the KDEF and RAF-DB datasets are 93.05% and 81.17% respectively after spot-adaptive distillation. © 2025 IEEE.

关键词： Facial Expression Recognition Knowledge Distillation Lightweight Neural Network

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Physical Key Point Detection Algorithm Based on Multi-scale Feature Fusion 12th

Physical Key Point Detection Algorithm Based on Multi-scale ...

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12th International Conference on Image and Graphics, ICIG 2023

作者： Wang, Xiao Liu, Pengyu Zhao, Peng Jiancuo, A. School of Information Science Beijing University of Technology Beijing100124 China Advanced Information Network Beijing Laboratory Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent Systems Beijing University of Technology Beijing100124 China China Institute of Sport Science Beijing100061 China Qinghai Red Cross Hospital Qinghai Xining810000 China

ISBN: (纸本)9783031463044

Physical posture is a reflection of the orderly arrangement of the body’s bones and the proper functioning of its muscle tissue, and it is also a guarantee of good health. Evaluating physical posture generally involves detecting key points on the human body, which is essentially a dense detection task in machine vision. This paper proposes a new key point detection method for evaluating physical posture, based on multi-scale feature fusion and self-attention mechanism. The self-attention mechanism is added in the algorithm to capture global feature dependencies, while the patching merging down-sampling structures help reduce information loss during the down-sampling process. Additionally, a de-convolution module is added in the prediction phase to generate higher quality feature maps and improve the spatial accuracy of the key points. The proposed algorithm achieves an average mAP of 85.5% on key point detection models for the front, side, and back of the body on a self-built dataset. The results demonstrate the algorithm’s good performance in the field of physical posture health evaluation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Feature extraction

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Semantic Segmentation of Indoor Point Clouds with the Channel Attention Mechanism

Semantic Segmentation of Indoor Point Clouds with the Channe...

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2022 Chinese Automation Congress, CAC 2022

作者： Liu, Wei Lu, Yisheng Zhang, Tao Chen, Jun School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geoexploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9781665465335

In recent years, the semantic segmentation of 3D point cloud has received increasing attention the field of computer vision, because 3D point cloud can better reflect our 3D space. Because of the unstructured and disordered characteristics of 3D point cloud data, semantic segmentation of point cloud is still a difficult task. Our network automatically learns the importance of feature channels by adding a channel attention module, which enables the network to obtain better training results. After the channel attention module is fused, the important channels in the features are enhanced, and the unimportant channels are suppressed, making the network training more efficient. In this paper, we propose an indoor point cloud semantic segmentation method combined with channel attention mechanism. The experimental results show that our method achieves better results than other methods. © 2022 IEEE.

关键词： Semantic Segmentation

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Preliminary Research on Super-Regenerative Receiver for Intracardiac Communication

Preliminary Research on Super-Regenerative Receiver for Intr...

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2023 Cross Strait Radio Science and Wireless technology Conference, CSRSWTC 2023

作者： Weng, Xueyu Li, Dongming Zhao, Wenxiang Pun, Sio Hang Vai, Mang I. Gao, Yueming College of Advanced Manufacturing Fuzhou University China Fuzhou University The International Joint Laboratory on Health Intelligent Monitoring Systems China University of Macau State Key Laboratory of Analog and Mixed-Signal VLSI China University of Macau Faculty of Science and Technology China College of Physics and Information Engineering Fuzhou University China

ISBN: (纸本)9798350358971

Existing commercial pacemakers only provide single-chamber pacing and cannot achieve multi-chamber sequential pacing. Conduction Intracardiac Communication(CIC) has the characteristics of low power consumption and high security, which is very suitable for communication between multi-chamber leadless pacemakers. Because the pacemaker needs to be implanted into the heart, which limits its volume, power consumption, and other related parameters, and the super-regenerative circuit has its low power consumption and simple circuit structure, the super-regenerative receiver scheme is a good choice for intracardial communication where power consumption is the main problem. In this paper, a prototype super-regenerative receiver suitable for intracardiac communication is proposed. The differential instrument amplifier with high input impedance is used to filter out the external electromagnetic interference, reduce the input equivalent noise, and improve the signal-to-noise ratio. A super-regenerative oscillator and a filter amplifier circuit are designed to enhance the effectiveness and reliability of the communication. The experimental results show that the bit error rate of the circuit is lower than 1 × 10-5 for the received signal of -92 dBm. © 2023 IEEE.

关键词： CIC Leadless pacemaker super-regenerative

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Predicting microbe-disease associations via attention guided graph convolutional networks 4

Predicting microbe-disease associations via attention guided...

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4th International Conference on Biomedicine and Bioinformatics Engineering, ICBBE 2024

作者： Gong, Shiyun Zhu, Yuan School of Automation China University of Geosciences Lumo Road Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Lumo Road Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Lumo Road Hongshan District Hubei Wuhan430074 China School of Mathematics and Physics China University of Geosciences Lumo Road Wuhan430074 China

ISBN: (纸本)9781510682443

It is important to predict microbe-disease associations, as it helps to understand the cause of diseases episodes, the prevention of diseases, among other roles. Traditionally, the study of microbe-disease association is mainly based on laboratory purified culture for research and observation, which is highly scientific but has some limitations, such as some microbes needing to be cultured in a laboratory environment and costs heavily. This study proposes an attention guided graph convolutional network d approach (AGGCN) to predict possible potential associations between microbes and diseases. In the first place, AGGCN constructs four different similarity networks of microbes and diseases, which are combined with association networks to form a microbe-disease heterogeneous network. Subsequently, it is used as an input to the graph convolutional neural network to obtain each node embedding of this heterogeneous network. In this study, a three-layer network is used, so three different types of node embeddings can be obtained, and these three different types of node embedding representations are integrated via the multichannel graph attention mechanism to obtain the final node embeddings. Finally, the node embeddings obtained above are used for association prediction of microbes-diseases in heterogeneous networks. Experimentally verified, the AUC of AGGCN is 0.8972, and the AUPR is 0.5387. Comparative experimental results indicated that AGGCN shows better than the other five mainstream methods. It provides a new insight to explore network potential feature representation and predict associations between microbe and disease. © 2024 SPIE.

关键词： Graph neural networks

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LSTM-based system for multiple obstacle detection using ultra-wide band radar 13

LSTM-based system for multiple obstacle detection using ultr...

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13th International Conference on Agents and Artificial Intelligence, ICAART 2021

作者： Mimouna, Amira Khalifa, Anouar Ben Alouani, Ihsen Taleb-Ahmed, Abdelmalik Rivenq, Atika Amara, Najoua Essoukri Ben IEMN-DOAE UMR CNRS 8520 Polytechnic University Hauts-de-France Valenciennes59300 France Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS - Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia

ISBN: (纸本)9789897584848

Autonomous vehicles present a promising opportunity in the future of transportation systems by providing road safety. As significant progress has been made in the automatic environment perception, the detection of road obstacles remains a major challenge. Thus, to achieve reliable obstacle detection, several sensors have been employed. For short ranges, the Ultra-Wide Band (UWB) radar is utilized in order to detect objects in the near field. However, the main challenge appears in distinguishing the real target's signature from noise in the received UWB signals. In this paper, we propose a novel framework that exploits Recurrent Neural Networks (RNNs) with UWB signals for multiple road obstacle detection. Features are extracted from the time-frequency domain using the discrete wavelet transform and are forwarded to the Long short-term memory (LSTM) network. We evaluate our approach on the OLIMP dataset which includes various driving situations with complex environment and targets from several classes. The obtained results show that the LSTM-based system outperforms the other implemented related techniques in terms of obstacle detection. © 2021 by SCITEPRESS - Science and technology Publications, Lda.

关键词： Roads and streets

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Disturbance Rejection for Pedaling Rehabilitation Robot Based on Integration of Equivalent-Input-Disturbance and Repetitive Control Methods 32

Disturbance Rejection for Pedaling Rehabilitation Robot Base...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Zhou, Yujian She, Jinhua Wang, Feng Iwasaki, Makoto School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Tokyo University of Technology School of Engineering Tokyo Hachioji192-0982 Japan Nagoya Institute of Technology Department of Electrical and Mechanical Engineering Nagoya466-8555 Japan

ISBN: (纸本)9798350399714

This study integrated an improved equivalent-input-disturbance (EID) and a repetitive control methods to ensure reference tracking and enhance disturbance-rejection performance for a pedaling rehabilitation robot. A repetitive controller ensures steady-state tracking of a periodic reference input. An EID estimator with a state observer estimates and compensates for the effect of disturbances generated by patients with lower-limb impairment. The stability condition of the closed-loop system is analyzed based on the Lyapunov stability theory. Simulation results show the effectiveness of this method. © 2023 IEEE.

关键词： Disturbance rejection

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Automatic Depression Detection Network Based on Facial Images and Facial Landmarks Feature Fusion

Automatic Depression Detection Network Based on Facial Image...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Min Hu Lingxiang Xu Lei Liu Xiaohua Wang Hongbo Li Jiaoyun Yang Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China National Smart Eldercare International Science and Technology Cooperation Base Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Artificial intelligence methods offer objectivity and convenience in automatic depression detection, however, current research often neglects the critical role of facial landmarks. This oversight results in insufficient spatial structure information and a lack of detailed local representation, which fails to capture the nuanced semantic information crucial for identifying depression-related clues. To address these issues, we introduce a novel dual-branch network model comprising the Landmark-Image-Landmark Net (LIL Net) and the Global Context Vision Transformer Net (GCVit Net). Through a dual-stream, multiscale, and cross-fusion strategy, LIL Net is designed to extract original facial image features alongside landmark features, prioritizing the detailed semantic information of potential depression clues. LIL Net employs an innovative LIL Attention approach to jointly learn multiscale features from facial landmarks and images, thereby enhancing the model’s ability to capture fine-grained depression-related cues. Furthermore, the Multi-scale Feature Fusion (MSFF) module fuses the obtained multiscale features, augmenting the semantic expression of potential depression clues within facial landmarks via attention mechanisms. Meanwhile, the GCVit Net branch network supplements global information by extracting global facial features. Finally, the features from both branches are concatenated to enhance the accuracy of depression degree predictions. Experimental results demonstrate that our model has superior performance in detecting depression compared to existing methods. We release our code at https://***/xlx777/LIL-Net.

关键词： Accuracy Shape Fuses Semantics Depression Feature extraction Transformers Robustness Data mining Facial features

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Identification and Prediction of Microvascular Invasion in Hepatocellular Carcinoma Based on Multiple Sequence Fusion Images

Identification and Prediction of Microvascular Invasion in H...

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Chinese Control Conference (CCC)

作者： Mingguang Lu Yuan Zhu School of Automation China University of Geosciences Wuhan Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Hepatocellular carcinoma (HCC) is one of the most difficult diseases to be treated fundamentally in the medical field in today’s world, and the recurrence rate of HCC has been increasing year by year in recent years. The study on the identification and prediction of microvascular invasion phenomenon (MVI) in hepatocellular carcinoma is beneficial to the postoperative prevention of hepatocellular carcinoma patients, which can greatly improve the survival probability of patients after surgery. In this study, a new MVI prediction method (MSF-GWO-GPR) was proposed, which combines the multi-sequence image features of imaging histology, sifts out the unimportant features through the T-test method and the dimensionality reduction of Lasso regression, and establishes the multi-sequence Gaussian process regression (GPR) prediction model for data fusion, and combined with the Gray Wolf Optimization (GWO) algorithm to design a suitable optimization objective function to optimize the hyper-parameters in the GPR, in order to get better prediction results. Compared with the existing methods, the data from different sequences of MRI images are selected for fusion in this study, which makes the feature data richer and more complete, and the model applicability is better; moreover, by optimizing the hyperparameters in the model through the GWO, the model can be made more accurate, reduce the risk of misidentified prediction, and improve the accuracy rate. The experimental results show that MSF-GWO-GPR outperforms widely used machine learning methods in terms of accuracy, F1 score, precision, recall and AUC metrics. This may provide a new idea for the identification and prediction of MVI in hepatocellular carcinoma.

关键词： Liver cancer Accuracy Magnetic resonance imaging Prevention and mitigation Surgery Predictive models Feature extraction

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