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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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Short-Term Power Load Forecasting Using MOGOA and ConvBiLSTM During COVID-19 Pandemic

Short-Term Power Load Forecasting Using MOGOA and ConvBiLSTM...

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2023 International Conference on Power System Technology, PowerCon 2023

作者： Xu, Da Liu, Bowen Lam, Chi-Seng Huang, Zhangyou University of Macau State Key Laboratory of Analog and Mixed-Signal Vlsi Macao China Institute of Microelectronics University of Macau Macao China School of Automation China University of Geosciences Wuhan430074 China University of Macau Fst Department of Electrical and Computer Engineering Macao China

ISBN: (纸本)9798350300222

Due to the high restrictions of the global COVID-19 on society, economy and production, the electrical supply-demand balance is facing a great challenge. This paper proposes a short-term load forecasting (STLF) method based on multi-factor fusion during the COVID-19. After fully analyzing and screening the nonlinear coupling interactions among cross-domain weather and public health safety features, a convolutional bi-directional long short-term memory neural network (ConvBiLSTM) is developed based on the convolutional and bidirectional loop enhancements of long short-term memory (LSTM). To avoid early termination of training the global optimal solution, multi-objective grasshopper optimization algorithm (MOGOA) is adopted to select the inherent hyperparameters of the forecasting network. Attention mechanism is further introduced to enhance the learning ability of the key features in temporal data, improving the forecasting accuracy and robustness of ConvBiLSTM. Comparisons on cross-domain COVID-19 dataset over classical methods are performed to verify its effective and superior performances. © 2023 IEEE.

关键词： Coronavirus

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Design of new duty-cycle modulator structures for industrials applications, an alternative to pulse-width modulation

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Revue des Composites et des Materiaux Avances 2021年第2期23卷 103-111页

作者： Ngaleu, Gildas Martial Kom, Charles Hubert Yeremou, Aurélien Tamtsia Eke, Samuel Nanfak, Arnaud Laboratory of Energy Materials Modelling and Methods National Higher Polytechnic School of Douala University of Douala. PO Box 2701 Douala Cameroon Laboratory of Computer Engineering and Automation University of Douala PO Box 8698 Douala Cameroon

In this paper, two new duty-cycle modulator structures are proposed, as a good alternative to existing modulation techniques: the symmetrical linear duty-cycle modulator, and the general linear duty-cycle modulator. The proposed structures consist of two stages with operational amplifiers and theirs period and duty cycle depend on the control voltage. The first stage is a Schmitt trigger comparator which generates the modulated rectangular signal. The other, by integrating the difference between modulated and reference signals, generates a triangular signal is which is sent back to the input of the comparator. To evaluate the performance of the proposed structures, a detailed study of sinusoidal pulse width modulator, non-inverting duty-cycle modulator and they were made. The simulation results computed in MATLAB/Simulink software show that proposed modulator scan operate in low frequency range, maintaining a constant current ripple and an acceptable total harmonic distortion. Their duty-cycles are independent of the values of the passive components used. The experimental tests carried out on symmetrical linear duty-cycle modulator under various reference voltage confirms the robustness of the structure in relation to the values of the components. © 2021 Lavoisier. All rights reserved.

关键词： Comparators (optical)

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Review of Inference Time Prediction Approaches of DNN: Emphasis on Service robots with cloud-edge-device architecture

Review of Inference Time Prediction Approaches of DNN: Empha...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Xiang, Tian Meng, Qiwei Zhang, Ji Zhang, Beibei Song, Wei Xie, Anhuan Gu, Jason Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Research Center for Intelligent Robotics Hangzhou311100 China The Chinese University of Hong Kong T Stone Robotics Institute Department of Mechanical and Automation Engineering Hong Kong Research Institute of Intelligent Computing Zhejiang Laboratory Research Center for Intelligent Computing Software Hangzhou311100 China Dalhousie University Electrical and Computer Engineering Halifax Canada

ISBN: (纸本)9798350325706

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model's partitioning strategy. It also serves as an important metric influencing the model's suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future. © 2023 IEEE.

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A comparative study of machine learning models for predicting length of stay in hospitals

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Journal of Information Science and engineering 2021年第5期37卷 1025-1038页

作者： MEKHALDI, RACHDA NAILA CAULIER, PATRICE CHAABANE, SONDES CHRAIBI, ABDELAHAD PIECHOWIAK, SYLVAIN Laboratory of Industrial and Human Automation Control Mechanical Engineering and Computer Science Polytechnic University of Hauts-de-France Valenciennes CEDEX9 59313 France Alicante Company Seclin59113 France

There has been a growing interest in recent years in correctly predicting the Length of Stay (LoS) in a hospital setting. Estimating the LoS on patient' admission helps hospitals in planning, controlling costs and, providing better services. In this paper, we consider predicting the LoS as a regression problem for which we implement and compare different Machine Learning (ML) algorithms. Multiple Linear Regression (MLR), Support Vector Machines (SVM), Random Forests (RF), and Gradient Boosting model (GBM) are implemented using an open-source dataset. The methodological process involves a preprocessing step combining data transformation, data standardization, and categorical data encoding. Moreover, the Synthetic Minority Over Sampling Technique for Regression (SMOTER) is applied to handle unbalanced data. Then, ML algorithms are employed, with a hyperparameter tuning phase to obtain optimal coefficients. Finally, Mean Absolute Error (MAE), R-squared (R2), and Adjusted R-squared (Adjusted R2) metrics are selected to evaluate the model with parameters. © 2021 Institute of Information Science. All rights reserved.

关键词： Hospitals

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Dual High-Order Total Variation Model for Underwater Image Restoration

arXiv

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arXiv 2024年

作者： Li, Yuemei Hou, Guojia Zhuang, Peixian Pan, Zhenkuan The College of Computer Science and Technology Qingdao University Qingdao266071 China The Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education The School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China

Underwater images are typically characterized by color cast, haze, blurring, and uneven illumination due to the selective absorption and scattering when light propagates through the water, which limits their practical applications. Underwater image enhancement and restoration (UIER) is one crucial mode to improve the visual quality of underwater images. However, most existing UIER methods concentrate on enhancing contrast and dehazing, rarely pay attention to the local illumination differences within the image caused by illumination variations, thus introducing some undesirable artifacts and unnatural color. To address this issue, an effective variational framework is proposed based on an extended underwater image formation model (UIFM). Technically, dual high-order regularizations are successfully integrated into the variational model to acquire smoothed local ambient illuminance and structure-revealed reflectance in a unified manner. In our proposed framework, the weight factors-based color compensation is combined with the color balance to compensate for the attenuated color channels and remove the color cast. In particular, the local ambient illuminance with strong robustness is acquired by performing the local patch brightest pixel estimation and an improved gamma correction. Additionally, we design an iterative optimization algorithm relying on the alternating direction method of multipliers (ADMM) to accelerate the solution of the proposed variational model. Considerable experiments on three real-world underwater image datasets demonstrate that the proposed method outperforms several state-of-the-art methods with regard to visual quality and quantitative assessments. Moreover, the proposed method can also be extended to outdoor image dehazing, low-light image enhancement, and some high-level vision tasks. The code is available at https://***/Hou-Guojia/UDHTV. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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Medical Insurance Fraud Detection using Graph Neural Networks with Spatio-temporal Constraints

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第2期7卷 480-498页

作者： Chen, Jin-Po Lu, Ping Yang, Fan Chen, Ruicong Lin, Kaibiao School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Key Laboratory of Ecological Environment and Information Atlas Fujian Provincial University Putian351100 China Shenzhen Research Institute of Xiamen University Shenzhen China Dept.of Automation Xiamen University Fujian Xiamen China

With the rapid development of social insurance in China, medical insurance fraud is increasing, seriously threatening the stable operation of the medical insurance system. Traditional machine learning, anomaly detection, and other medical insurance fraud detection methods focus only on feature attributes, ignoring the rich behavioral attributes in the medical process. Considering the behavioral attributes, medical insurance fraud mainly comprises two domains: spatial and temporal, corresponding to gang fraud and individual anomaly, respectively. Therefore, this paper proposes a medical insurance fraud detection method utilizing a spatiotemporal constraint graph neural network (StGNN). Specifically, we first construct multiple medical insurance heterogeneous graphs based on the medical insurance dataset. Next, groups with the same behavioral trajectory are sampled using multi-semantic metapaths, while a two-layer attention mechanism assigns neighboring nodes and different behavioral trajectory weights. Then we use Bi-LSTM to detect individual anomalies during the patients’ treatment process. Finally, the proposed model is trained end-to-end, employing a cross-entropy loss function. Extensive experiments on two real datasets show that StGNN outperforms the existing methods on the anomaly detection task. © 2022, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Insurance

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Multimodal Cross-Lingual Features and Weight Fusion for Cross-Cultural Humor Detection 4

Multimodal Cross-Lingual Features and Weight Fusion for Cros...

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4th Multimodal Sentiment Analysis Challenge and Workshop, MuSe 2023, In conjunction with ACM Multimedia 2023

作者： Xie, Heng Cui, Jizhou Cao, Yuhang Chen, Junjie Tao, Jianhua Fan, Cunhang Liu, Xuefei Wen, Zhengqi Lu, Heng Yang, Yuguang Lv, Zhao Li, Yongwei Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China School of Information Science and Technology ShanghaiTech University Shanghai China Ximalaya Inc. Shanghai China College of Computer and Information Engineering Tianjin Normal University Tianjin China Department of Automation Tsinghua University Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400702709

Sentiment analysis plays a crucial role in interpreting human interactions across different modalities. This paper addresses the Cross-Cultural Multilingual Humour Detection Sub-Challenge in the MuSe 2023 Multi-modal Sentiment Analysis Challenge, which aims to identify humor instances in cross-cultural situations using multimodal data. In this paper, we explore the unique window length processing of audio data to more accurately and fully capture humor information. Furthermore, we employ a data augmentation technique for text to bridge the gap between the train set and the test set. This augmentation involves converting German and English samples in the database to each other, as well as translating both languages into Chinese. These techniques aim to enhance the model's generalization across different languages and alleviate the scarcity of humor samples. For multimodal fusion, we use the late fusion technique applied to different Gated Recurrent Unit (GRU) models with modality-specific weights obtained through gradient descent. Experimental results demonstrate the effectiveness of our approach, achieving an Area Under the Curve (AUC) score of 0.872 on the MuSe-Humor test set. This performance ranked second place in the sub-challenge. © 2023 ACM.

关键词： Sentiment analysis

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning 22

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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