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Multi-Scale Deformable Transformers for Student Learning Behavior Detection in Smart Classroom

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arXiv 2024年

作者： Wang, Zhifeng Wang, Minghui Zeng, Chunyan Li, Longlong The Faculty of Artificial Intelligence in Education Central China Normal University Wuhan430079 China The CCNU Wollongong Joint Institute Central China Normal University Wuhan430079 China The Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of Technology Wuhan430068 China

The integration of Artificial intelligence (AI) into the modern educational system is rapidly evolving, particularly in monitoring student behavior in classrooms-a task traditionally dependent on manual observation. This conventional method is notably inefficient, prompting a shift toward more advanced solutions like computer vision. However, existing target detection models face significant challenges such as occlusion, blurring, and scale disparity, which are exacerbated by the dynamic and complex nature of classroom settings. Furthermore, these models must adeptly handle multiple target detection. To overcome these obstacles, we introduce the Student Learning Behavior Detection with Multi-Scale Deformable Transformers (SCB-DETR), an innovative approach that utilizes large convolutional kernels for upstream feature extraction, and multi-scale feature fusion. This technique significantly improves the detection capabilities for multi-scale and occluded targets, offering a robust solution for analyzing student behavior. SCB-DETR establishes an end-to-end framework that simplifies the detection process and consistently outperforms other deep learning methods. Employing our custom Student Classroom Behavior (SCBehavior) Dataset, SCB-DETR achieves a mean Average Precision (mAP) of 0.626, which is a 1.5% improvement over the baseline model's mAP and a 6% increase in AP50. These results demonstrate SCB-DETR's superior performance in handling the uneven distribution of student behaviors and ensuring precise detection in dynamic classroom environments. The source code of this study is publicly available at https://***/CCNUZFW/SCB-DETR. Copyright © 2024, The Authors. All rights reserved.

关键词： Distribution transformers

Train Safety control Based on Deep Deterministic Policy Gradient with control Barrier Function

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Train Safety Control Based on Deep Deterministic Policy Grad...

IEEE International Conference on Networking, Sensing and control

作者： Guoteng Wang Jidong Lv Hongjie Liu Ming Chai Tao Tang Wentao Zhang School of Automation and Intelligence Beijing Jiaotong University Beijing China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing China Beijing Zhengshi Environmental Engneering Company Beijing China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Train Relative Distance Braking Mode (RDBM) has great potential to increase line capacity by enabling shorter following distances based on train-to-train communication in-formation. However, it is difficult for traditional train control methods to ensure avoidance of potential collision risks in RDBM while dealing with the nonlinearity and uncertainty of train dynamics. In this paper, a novel train safety control method combining Deep Deterministic Policy Gradient (DDPG) and control Barrier Function(Cbf)is proposed. Firstly, the safety constraint set for adjacent trains, considering their braking capabilities, is analyzed, and the uncertainty of train dynamics is modeled using a Gaussian process. Secondly, DDPG is used to explore the optimal control strategy under the action of uncertain train dynamics, and CBF provides compensation control input to ensure safety during the learning process. Then, a DDPG-CBF algorithm is presented to achieve efficient safety controller design and implementation. Finally, sufficient numerical simulations demonstrate that, compared with DDPG algorithm alone, the DDPG-CBF algorithm can not only ensure the safe and stable operation of trains, but also learn train control strategies expe-ditiously and accurately under different operating environments, with an average efficiency increase of 38.94 %.

关键词： Rails Uncertainty Heuristic algorithms Process control Optimal control Numerical simulation Stability analysis Safety Sensors Numerical stability

Location IoU: A New Evaluation and Loss for Bounding Box Regression in Object Detection

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Location IoU: A New Evaluation and Loss for Bounding Box Reg...

International Joint Conference on Neural Networks (IJCNN)

作者： Lu Yang Kai Zhang Jiaqi Liu Chongke Bi Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China Autobrain (Tianjin) Technology LTD Tianjin China College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the field of object detection bounding box regression, IoU (Intersection over Union) is a commonly used evaluation metric for measuring the overlap between predicted and ground truth bounding boxes. One limitation of IoU, however, is that the gradient goes to zero when there is no intersection between bounding boxes. Recent evaluation metrics have been developed to overcome this problem by incorporating penalty terms and other techniques for improving IoU. Despite these advances, existing methods still suffer to some degree from the problems of small gradients and slow rate of convergence. In this paper, a new evaluation method named LIoU (Location IoU) is proposed to address these issues. It can maximize the shared area of the intersection when used as the loss function and incorporate a variable gradient parameter to fit to datasets of different sizes. The experimental results indicate that we achieve 0.27% and 3.48% accuracy improvements respectively in SSD and YOLOv5 network on the VOC dataset, and 2.23% in the YOLOv5 network on the COCO dataset. The results of this study highlight the effectiveness of LIoU in improving the performance of object detection models.

关键词： YOLO Measurement Location awareness Accuracy Shape Neural networks Benchmark testing

Preparation of TiO_x Films by Cathode Multi Arc Ion and Its Performance

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兰州交通大学学报 2007年第6期26卷 163-164页

作者： Fan Duojin Fan Duowang Wang Chenglong Liu Hongzhong Lanzhou Dachen Automatic Control System Co. Ltd Lanzhou 730070 China key Laboratory of Opto-Electronic Technology and Intelligence Control Ministry of Education Lanzhou Jiaotong University Lanzhou 730070China State Key Laboratory for Manufacturing Systems Engineering Xi＇an Jiaotong University Xi＇an 710049 China

Experimental Procedure Fig.1 shows the custom-made Cathode Multi Arc Ion equipment,which is made up of three pars:CCS (computer contral system,Fig.1′s left part),Vacuum Chamber (Fig.1′s middle part) and Air Pump par... 详细信息

关键词：钛氧化物薄膜阴极多相电极离子制备性能

PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators

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arXiv 2023年

作者： Cong, Runmin Yang, Wenyu Zhang, Wei Li, Chongyi Guo, Chun-Le Huang, Qingming Kwong, Sam Institute of Information Science Beijing Jiaotong University Beijing100044 China School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China College of Computer Science Nankai University Tianjin300350 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China Department of Computer Science City University of Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

Due to the light absorption and scattering induced by the water medium, underwater images usually suffer from some degradation problems, such as low contrast, color distortion, and blurring details, which aggravate the difficulty of downstream underwater understanding tasks. Therefore, how to obtain clear and visually pleasant images has become a common concern of people, and the task of underwater image enhancement (UIE) has also emerged as the times require. Among existing UIE methods, Generative Adversarial Networks (GANs) based methods perform well in visual aesthetics, while the physical model-based methods have better scene adaptability. Inheriting the advantages of the above two types of models, we propose a physical model-guided GAN model for UIE in this paper, referred to as PUGAN. The entire network is under the GAN architecture. On the one hand, we design a Parameters Estimation subnetwork (Par-subnet) to learn the parameters for physical model inversion, and use the generated color enhancement image as auxiliary information for the Two-Stream Interaction Enhancement subnetwork (TSIE-subnet). Meanwhile, we design a Degradation Quantization (DQ) module in TSIE-subnet to quantize scene degradation, thereby achieving reinforcing enhancement of key regions. On the other hand, we design the Dual-Discriminators for the style-content adversarial constraint, promoting the authenticity and visual aesthetics of the results. Extensive experiments on three benchmark datasets demonstrate that our PUGAN outperforms state-of-the-art methods in both qualitative and quantitative metrics. The code and results can be found from the link of https://***/proj_***. © 2023, CC BY-NC-SA.

关键词： Discriminators

Utility-Oriented Underwater Image Quality Assessment Based on Transfer Learning

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arXiv 2022年

作者： Chen, Weiling Lin, Rongfu Liao, Honggang Zhao, Tiesong Gu, Ke Le Callet, Patrick Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Peng Cheng Laboratory Shenzhen518000 China The Faculty of Information Technology Beijing University of Technology Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Équipe Image Perception et Interaction Laboratoire des Sciences du Numérique de Nantes Université de Nantes France

The widespread image applications have greatly promoted the vision-based tasks, in which the Image Quality Assessment (IQA) technique has become an increasingly significant issue. For user enjoyment in multimedia systems, the IQA exploits image fidelity and aesthetics to characterize user experience;while for other tasks such as popular object recognition, there exists a low correlation between utilities and perceptions. In such cases, the fidelity-based and aesthetics-based IQA methods cannot be directly applied. To address this issue, this paper proposes a utility-oriented IQA in object recognition. In particular, we initialize our research in the scenario of underwater fish detection, which is a critical task that has not yet been perfectly addressed. Based on this task, we build an Underwater Image Utility Database (UIUD) and a learning-based Underwater Image Utility Measure (UIUM). Inspired by the top-down design of fidelity-based IQA, we exploit the deep models of object recognition and transfer their features to our UIUM. Experiments validate that the proposed transfer-learning-based UIUM achieves promising performance in the recognition task. We envision our research provides insights to bridge the researches of IQA and computer vision. Copyright © 2022, The Authors. All rights reserved.

关键词： Multimedia systems

On-Chip Vectorial Structured Light Manipulation via Inverse Design

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arXiv 2024年

作者： Lin, Xiaobin Wei, Maoliang Lei, Kunhao Wang, Zijia Wang, Chi Ma, Hui Ye, Yuting Zhan, Qiwei Li, Da Dai, Shixun Zhang, Baile Hu, Xiaoyong Li, Lan Li, Erping Lin, Hongtao State Key Laboratory of Brain-Machine Intelligence Key Laboratory of Micro-Nano Electronics and Smart System of Zhejiang Province College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province School of Engineering Westlake University Hangzhou310024 China Laboratory of Infrared Materials and Devices Ningbo University Ningbo315211 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore State Key Laboratory for Mesoscopic Physics Frontiers Science Center for Nano-optoelectronics School of Physics Peking University Beijing100871 China MOE Frontier Science Center for Brain Science & Brain-Machine Integration Zhejiang University Hangzhou310027 China

On-chip structured light, with potentially infinite complexity1-31-3, has emerged as a linchpin in the realm of integrated photonics. However, the realization of arbitrarily tailoring a multitude of light field dimensions in complex media remains a challenge1, 4-6. Through associating physical light fields and mathematical function spaces by introducing a mapping operator, we proposed a data-driven inverse design method to precisely manipulate between any two structured light fields in the on-chip high-dimensional Hilbert space. To illustrate, light field conversion in on-chip topological photonics was achieved. High-performance topological coupling devices with minimal insertion loss and customizable topological routing devices were designed and realized. Our method provides a new paradigm to enable precise manipulation over the on-chip vectorial structured light and paves the way for the realization of complex photonic functions. © 2024, CC BY.

关键词： Topology

Aadaptive genetic algorithm based on real coded and its application

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Beijing Gongye Daxue Xuebao / Journal of Beijing University of Technology 2007年第2期33卷 144-149页

作者： Gao, Xue-Jin Wang, Pu Sun, Chong-Zheng Yi, Jian-Qiang Zhang, Ya-Ting Zhang, Hui-Qing College of Electronic Information and Control Engineering Beijing University of Technology Beijing 100022 China Key Laboratory of Complex System and Intelligence Science Chinese Acad. of Sci. Beijing 100033 China

To solve the problem of the premature convergence and the slow convergence of Genetic Algorithms, an adaptive Genetic Algorithms based on real coded (RAGA) was proposed to improve the coding modes, genetic operators, etc. Optimization simulation results based on typical complex functions show that the optimal convergence rate and global hitting probability of the proposed algorithm are much better than those of simple Genetic algorithms.

关键词： Genetic algorithms

controllable Electrical Characteristic of Ti3c2t2/Mos2 (T=Oh, F, O) Interfaces for High Performance Electronics: Insights from First-Principles

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SSRN

SSRN 2023年

作者： Zhang, Qiang Liu, Yan Zhang, Riguang Lv, Bohan Hao, Runfang Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Electronic Information and Optical Engineering Taiyuan University of Technology Shanxi Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System of The Ministry of Education Taiyuan University of Technology Shanxi Taiyuan030024 China School of Software Taiyuan University of Technology Shanxi Taiyuan030024 China State Key Laboratory of Clean and Efficient Coal Utilization China Laboratory of Aerospace Servo Actuation and Transmission Beijing Institute of Precision Mechatronics and Controls Beijing100000 China

Two-dimensional (2D) semiconductors have shown competitive potential for applications into beyond-silicon electronics. However, the controllable electrical characteristic of metal-semiconductor interfaces is still a challenge for high performance electronics due to Fermi level pinning. To explore Ti3C2T2/MoS2 (T= OH, F, O) for high performance electronics, the vertical electric field and interlayer distance are adopted here to regulate the electrical characteristic of interfaces based on the first-principles calculations. Though this, the electrical characteristic of Ti3C2O2/MoS2 and Ti3C2F2/MoS2 are more sensitive to electric field or interlayer distance than that of Ti3C2(OH)2/MoS2. Especially, the negative electric field with larger than -0.2 V/Å makes Ti3C2O2/MoS2 interface change from p-type Schottky to n-type Schottky contact. Furthermore, the Ti3C2O2/MoS2 interface can not only switch from p-type Schottky to n-type Schottky contact, but also from p-type Schottky to n-type Ohmic contact by varying interlayer distance. Moreover, the tunneling barriers of all interfaces are more sensitive to interlayer distance than to electric field. This work provides vital insights for high-performance electronics of Ti3C2T2/MoS2 (T=OH, F, O) in experiments. © 2023, The Authors. All rights reserved.

关键词： Electric fields