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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2023

作者： Sun, Jintao Wen, Shiguang Wen, Yongzhao Liu, Jing Wu, Qi Han, Lu Wang, Fei College of Information Science and Engineering Northeastern University Liaoning Shenyang110167 China Northeastern University Faculty of Robot Science and Engineering Liaoning Shenyang110167 China LiaoNing Disabled Persons' Service Center Liaoning Shenyang China

ISBN: (纸本)9798350315196

Although great progress has been made in the research on rehabilitation exoskeletons based on steady-state visually evoked potential (SSVEP)-based brain-computer interface (BCI), some factors that limit the practical applications of rehabilitation exoskeletons based on BCIs still exist. On the one hand, traditional SSVEP-BCI uses the computer screen (CS) to generate visual stimuli, which leads to poor portability, on the other hand, most BCIs are in a synchronous mode, which prevents users from autonomously controlling the system. To overcome these two problems, augmented reality (AR) technology is introduced to generate the visual stimuli, and an asynchronous control method based on the blocking phenomenon of alpha waves is proposed in this study. The offline experiment is designed to determine the classification threshold of the idle state and control state and compare the different performances of SSVEP-BCI with and without an AR environment. Finally, an intelligent rehabilitation exoskeleton platform is used for online validation. Results of the online experiment indicate that all subjects could control the rehabilitation exoskeleton to complete the rehabilitation tasks, which indicates the applicability of the intelligent rehabilitation exoskeleton system and improves the potential for application of the rehabilitation exoskeleton based on BCI. © 2023 IEEE.

关键词： Brain computer interface

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Expansive Spatial-Temporal Kernel Convolution Network for Skeleton-Based Action Recognition

Expansive Spatial-Temporal Kernel Convolution Network for Sk...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Ziao Li Junyi Wang Quan Zhou Chengda Li Faculty of Robot Science and Engineering Northeastern University Shenyang China Faculty of Robot Science and Engineering Foshan Graduate School of Innovation Northeastern University China

In skeleton-based action recognition, graph convolutional networks (GCNs) have become the most common method due to their strong modeling ability on graph data and the utilization of prior knowledge on human skeleton. However, convolutional methods have been overlooked in this field despite their wide applications in visual tasks, mature libraries, and learning resources. In this paper, we propose an Expansive Spatial-Temporal Kernel Convolution Network (ESTCN) consisting of Expansive Spatial Kernel Convolution and Expansive Temporal Kernel Convolution for spatial feature extraction. Our ESTCN adopts large kernel convolution to achieve global receptive field and utilize prior knowledge on human joints. Extensive experiments on NTU-RGB+D dataset demonstrate that our method achieves comparable performance with GCN-based methods and outperforms other convolutional methods.

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Intelligent interaction system based on multimodal conformal triboelectric nanogenerator patch for disabled sports and life

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science China(Technological sciences) 2025年第2期68卷 290-305页

作者： Yuzhang WEN Mengqi ZHANG Zhenning XIE Zida AN Bing LIU Fengxin SUN Tianming ZHAO Zuojun YU Fei WANG Yupeng MAO Physical Education Department Northeastern UniversityShenyang 110819China Faculty of Robot Science and Engineering Northeastern UniversityShenyang 110819China China Ice Sports College Beijing Sport UniversityBeijing 100084China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Criminal Investigation Police University of China Shenyang 110035China

Intelligent interaction technology based on advanced sensing units has become a research *** this paper,an intelligent interaction system(IIS)based on a multimodal conformal triboelectric nanogenerator patch(MCTP)is reported for sports monitoring and life promotion of the *** IIS consists of a cloud-based smart sports recognition program and a smart life *** porous micro-reinforced structure of polyurethane(PU)Sponge&Ecoflex,and the surface modification of PTFE by coating it with MXene endows the MCTP with excellent electrical *** shows good angle and force sensitivities of 1.1 V/°and 1.61 V/N,respectively,and has an ultra-fast response time of 15 ms,meeting the needs of human motion *** MCTP can also capture the mechanical energy,producing a maximum output power of 139μ*** with the commercial inertial measuring unit(IMU),the MCTP shows better consistency in human joint angle sensing.A precise analysis of wheelchair curling sports monitoring points is achieved by combining the MCTP and the human anatomy *** cloud-based smart sports recognition program utilizes machine learning to achieve visual feedback on the three throwing technologies of draw,takeout and *** addition,the developed smart life application uses a highsensitivity MCTP-sensor array design to achieve a variety of smart home interactions through simple finger ***,the IIS integrates the three major functions of smart sports sensing,energy capture,and smart home control,providing important application prospects in the fields of smart sports and human-computer interaction for the disabled.

关键词： triboelectric nanogenerator machine learning smart sports smart home human-computer interaction

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Divided Block Multiscale Convolutional Network for Micro-expression Recognition

Divided Block Multiscale Convolutional Network for Micro-exp...

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Cyber-Energy Systems and Intelligent Energy (ICCSIE), International Conference on

作者： Quan Zhou Shiyu Liu Yiheng Wang Junyi Wang Faculty of Robot Science and Engineering Northeastern University Shenyang China Faculty of Robot Science and Engineering and Foshan Graduate School of Innovation Northeastern University Shenyang China

Micro-expression (ME) is a subtle change in the face, which can be used to judge human subjective feelings. It has broad application prospects in medical diagnosis and business negotiation. However, due to the complexity of ME muscle movements and the lack of ME trainable data, the research of micro-expression recognition (MER) still faces a series of challenges. In this paper, we propose a new divided block multiscale convolution network (DBMNet), which could learn from four different optical flow (OF) feature images obtained between the onset and apex frames of ME samples. Through the proposed block-divided multiscale convolution module (BMCM), more detailed and useful multiscale advanced features of ME could be effectively extracted. In order to better address the problem of class imbalance on the ME dataset, this paper uses the weighted cross entropy (CE) loss function, which could obviously alleviate the impact of class imbalance. Finally, 5-class experiment is conducted on the composite dataset to show that the proposed method has superior performance and is comparable to those of the most advanced methods.

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Abnormal Object Detection System in Cloud Server Centers Based on Transformer 6

Abnormal Object Detection System in Cloud Server Centers Bas...

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6th International Conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Li, Ziheng Jiang, Yang Zhao, Bin Northeastern University Faculty of Robot Science and Engineering Shenyang110169 China School of Information Science & Engineering Northeastern University Shenyang110819 China Liaoning University of Technology College of Interdisciplinary Sciences Jinzhou121001 China Siasun Semiconductor Equipment Co. Ltd Shenyang110169 China

ISBN: (纸本)9798331541750

In order to solve the problem of foreign object detection in the cloud server center, this paper proposes an abnormal object detection system based on a transformer network, aiming to identify and warn abnormal objects in the computer room environment. Firstly, this paper constructs an anomaly object dataset (NEU AID) to contain information about the computer room environment, and it improves the diversity and generalization ability through data enhancement technology. Secondly, an improved Dual Flow Transformer (DFT-NET) is proposed, which can extract both global and local features to optimize the detection effect. This paper introduces transfer learning and uses the COCO data set to pre-train the network. Finally, through practical application verification, the proposed DFT-NET network model shows the high accuracy identification ability of unknown abnormal items when the inspection robot performs the abnormal items detection task in the computer room, proving the model's effectiveness and reliability in practical application scenarios. © 2024 IEEE.

关键词： Anomaly detection component Deep learning DFT-NET network Transfer learning

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A Research on Bank Card Number Recognition Based on ASFF+YOLOv7 and Multi-Scale Feature Line Fusion 13

A Research on Bank Card Number Recognition Based on ASFF+YOL...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2023

作者： Cheng, Jiajun Wu, Yaoyan Gao, Weitao Fu, Jun Northeastern University Faculty of Robot Science and Engineering Liaoning Shenyang China School of Electrical and Control Engineering State Key Laboratory of Dynamic Testing Technology North University of China Shanxi Taiyuan China

ISBN: (纸本)9798350315196

Electronic payments have become the primary mode of payment today, and bank card recognition is widely used in industries such as mobile, mobile banking, and third-party payments. To address issues such as low fault tolerance due to factors such as complex backgrounds, significant perspective distortion that is difficult to recover, and high illumination variation, we propose a Bank Card Number Recognition algorithm based on ASFF + YOLOv7 and Multi-Scale Feature Line Fusion. The algorithm comprises two parts: the front-end dataset generation stage introduces an efficient semi-automatic traditional bank card dataset generation algorithm, which reduces the manual annotation cost while ensuring relative accuracy and significantly improves work efficiency. In the back-end recognition stage, we have improved the traditional deep learning model for the long-tail digit string dataset, achieving a precision of 99.19% and a recall rate of 98.65%. © 2023 IEEE.

关键词： Fault tolerance

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Efficient Object Localization for Unseen Object 6D Pose Estimation

Efficient Object Localization for Unseen Object 6D Pose Esti...

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Chinese Automation Congress (CAC)

作者： Xinwei Lan Chengdong Wu Xiangyue Zhang Faculty of Robot Science and Engineering Northeastern University Shenyang China

Object localization is utilized as the first step in standard 6D object pose estimation methods to obtain the position information of the objects. However, these object localization methods cannot be directly applied to unseen objects, which is the focus of recent research on 6D object pose estimation. In this paper, an accurate and efficient localization method for unseen object is proposed, based on a template matching strategy. The Hybrid Channel-Spatial Attention Model (HCSAM) is designed to focus on the target object by enhancing the contextual differences between the target object and background. Additionally, The Multi-Scale Integration Transformer (MSIT) module is designed to eliminate noise interference and enhance semantic information in low-dimensional features by integrating multidimensional information. Our method outperforms existing approaches on the complicated occluded dataset LINEMOD, as well as on the challenging generalized pose estimation dataset GenMOP.

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HierNet: Hierarchical Transformer U-Shape Network for RGB-D Salient Object Detection

HierNet: Hierarchical Transformer U-Shape Network for RGB-D ...

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Chinese Control and Decision Conference, CCDC

作者： Pengfei Lv Xiaosheng Yu Junxiang Wang Chengdong Wu Faculty of Robot Science and Engineering Northeastern University Shenyang China

With the popularity of depth sensors, research on RGB-D salient object detection (SOD) is also thriving. However, given the limitations of the external environment and the sensor itself, depth information is often less credible. To meet this challenge, existing models often purify the depth information using complex convolution and pooling operations. This causes a large amount of useful information besides noise to be dropped as well, and multi-modality interaction chances between RGB and depth become less. Also, with the gradual loss of information, the hidden relationship of features between multi-level is thus ignored. To tackle the aforementioned problems, we propose a Hierarchical Transformer U-Shape Network (HierNet) that include three aspects: 1) With a simple structure, a depth calibration module provides faithful depth information with minimal loss of information, providing conditions for cross-modality cross-layer information interaction; 2) With multi-head attention, a set of global view-based transformer encoders are employed to find the potential coherence between RGB and depth modalities. With weight sharing, several transformer encoder sets comprise the hierarchical transformer embedding module to search long-range dependencies cross-level; 3) Considering the complementary features of U-shape network, we use dual-stream U-shape network as our backbone. Extensive fair experiments on four challenging datasets have demonstrated the outstanding performance of the proposed model compared to state-of-the-art models.

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Binary Classification is Enough: A Lightweight Strategy for Drug Screening with Small Datasets

Binary Classification is Enough: A Lightweight Strategy for ...

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Health Big Data and Intelligent Healthcare (ICHIH), International Conference on

作者： Liang Wu Xiaoguang Ma Faculty of Robot Science and Engineering Northeastern University Shenyang China

Drug screening is an extremely costly and time-consuming process, wherein only small datasets are available in practice. We presented a particular method to estimate values of inhibition constant(Ki) or half-maximal inhibition concentration(IC50) of unknown compounds through a lightweight mutual information and logistic regression(MI-LR) united model that only needed to be trained on a small dataset. Biologists could then use this model to determine whether the compounds were initially eligible for screening, increasing efficiency of their work. A data augmentation strategy was used to sort independent samples of training datasets and solved the problem of sample shortage caused by the lightweight model, and transform a prediction task into a simpler binary classification task. In addition, we proposed an effective constraint mechanism to deal with the case when the classification results were contrary to the facts. By accurately predicting the interval of its inhibitory effect, we can improve the efficiency and accuracy of drug screening. Numerous evaluations on the Ki and IC50 dataset demonstrated high reliability of the MI-LR united approach to sort compounds according to a selected set of molecular descriptors.

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BDNet: A Method-based Forward and Backward Convolutional Networks for Action Recognition in Videos

BDNet: A Method-based Forward and Backward Convolutional Net...

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Chinese Control and Decision Conference, CCDC

作者： Chuanjiang Leng Chengdong Wu Ange Chen Faculty of Robot Science and Engineering Northeastern University Shenyang China

Human action recognition analyses the behaviour in a scene according to the spatial-temporal features carried in a series of image sequences. The critical challenge is to extract informative spatial-temporal features in a limited-length video that frequently constrains the receptive field of the 3D Convolutional Neural Network(CNN). However, present methods mainly consider modeling the action’s spatial-temporal features along a single direction and ignore the information in the opposite. Moreover, the fixed-weight fusion of spatial and temporal features does not distinguish their importance for each action sequence. To attack the problems above, we propose a bi-directional network (BDNet) to combinate the features from both directions of action for recognizing action. Two CNNs are set up to extract spatial-temporal features along the forward and backward action, respectively. Then, a dynamic fusion strategy is adopted to measure the importance of spatial and temporal features for each action. We conducted many experiments on the commonly used action recognition dataset UCF101. Compared with other work, the proposed method achieves promising performance in accuracy and efficiency.

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