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第42届中国控制会议

作者： Ao Liu Ding Wang Menghua Li Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing Laboratory of Smart Environmental Protection Beijing University of Technology

This paper investigates robust control problems of nonlinear continuous-time multiplayer systems with infinite horizon by utilizing adaptive dynamic programming algorithms. Combined with pre-training, an improved policy iteration(PI) algorithm is developed to solve robust control issues of multiplayer nonzero-sum(NZS) games with actuator uncertainties. Pre-training of initial weights is added to the PI algorithm to relax the requirement of the initial admissible control policy. It implies that the admissible control pair can be obtained by pre-training of initial weights given randomly. Then, critic neural networks(NNs) are utilized to approximate the optimal control pair by applying the PI algorithm. Robust controllers can be obtained by modifying the optimal control pair. The algorithm accomplishes robust stabilization of multiplayer NZS games with uncertainties. Besides,initial weights of NNs can be set arbitrarily. Finally, a simulation example is given to demonstrate the effectiveness of the developed algorithm.

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FLIP-80M: 80 Million Visual-Linguistic Pairs for Facial Language-Image Pre-Training 24

FLIP-80M: 80 Million Visual-Linguistic Pairs for Facial Lang...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Yudong Hou, Xianxu Dezhi, Zheng Shen, Linlin Zhao, Zhe School of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Tencent AI Lab Beijing China

ISBN: (纸本)9798400706868

While significant progress has been made in multi-modal learning driven by large-scale image-text datasets, there is still a noticeable gap in the availability of such datasets within the facial domain. To facilitate and advance the field of facial representation learning, we present FLIP-80M, a large-scale visual-linguistic dataset comprising over 80 million face images paired with text descriptions. FLIP-80M is constructed by leveraging the large openly available image-text-pair dataset LAION-5B and a mixed-method approach to filter face-related pairs from both visual and linguistic perspectives. Our curation process involves face detection, face caption classification, text de-noising, and synthesis-based image augmentation. As a result, FLIP-80M stands as the largest face-text dataset to date. To evaluate the potential of our dataset, we fine-tune the CLIP model using the proposed FLIP-80M, to create FLIP (Facial Language-Image Pretraining) and assess its representation capabilities across various downstream tasks. Our experiments demonstrate that our FLIP model achieves state-of-the-art results in a range of face analysis tasks, including face parsing, face alignment, and face attribute classification. The dataset and models are available at https://***/ydli-ai/FLIP. © 2024 ACM.

关键词： Face recognition

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Attention-enhanced BLSTM Network for Liquid Volume Estimation based on Tactile Sensing

Attention-enhanced BLSTM Network for Liquid Volume Estimatio...

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2023 IEEE International Conference on Real-Time computing and Robotics, RCAR 2023

作者： Su, Yuanzhe Wang, Jingnan Huang, Binhua Li, Xiaoyu Liu, Yiwen Liu, Chengliang Zhou, Zhenning Yi, Zhengkun Wu, Xinyu Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Siat Branch Shenzhen518055 China

ISBN: (纸本)9798350327182

The task of liquid volume estimation based on tactile sensing is challenging. Previous researches mainly focused on the classification of different contents in the container, rather than achieving accurate estimation of a certain property of the contents in container, such as volume or viscosity. In addition, few studies are conducted for the regression task with advanced tactile sensors, but the performance is unsatisfactory. We propose a new deep-learning algorithm for processing the tactile signals collected from the photoresistors in the task of liquid content estimation. The proposed algorithm doesn't need hand-crafted features. In addition, we use attention after BLSTM, so that more important features can be selected to achieve better performance for liquid volume estimation. We utilize a bi-directional Long Short-Term Memory (BLSTM) architecture, which allows for the incorporation of information from both preceding and subsequent time, thereby providing a more comprehensive and informative feature set for learning. Additionally, we employ an attention mechanism following the BLSTM, allowing for the selection of more critical features and improved performance in liquid volume estimation. To prove the effectiveness of our method, we conduct experiments utilizing tactile sensors on a physical robot, achieving superior performance in liquid volume estimation. © 2023 IEEE.

关键词： Containers

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A New Image Quality Database for Multiple Industrial Processes

arXiv

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arXiv 2024年

作者： Ma, Xuanchao Jiang, Yanlin Liu, Hongyan Zhou, Chengxu Gu, Ke Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Laboratory of Smart Environmental Protection China Beijing Key Laboratory of Computational Intelligence and Intelligent System China Beijing Artificial Intelligence Institute China School of Electronic & Information Engineering Liaoning University of Technology Liaoning China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology China

Recent years have witnessed a broader range of applications of image processing technologies in multiple industrial processes, such as smoke detection, security monitoring, and workpiece inspection. Different kinds of distortion types and levels must be introduced into an image during the processes of acquisition, compression, transmission, storage, and display, which might heavily degrade the image quality and thus strongly reduce the final display effect and clarity. To verify the reliability of existing image quality assessment methods, we establish a new industrial process image database (IPID), which contains 3000 distorted images generated by applying different levels of distortion types to each of the 50 source images. We conduct the subjective test on the aforementioned 3000 images to collect their subjective quality ratings in a well-suited laboratory environment. Finally, we perform comparison experiments on IPID database to investigate the performance of some objective image quality assessment algorithms. The experimental results show that the state-of-the-art image quality assessment methods have difficulty in predicting the quality of images that contain multiple distortion types. Copyright © 2024, The Authors. All rights reserved.

关键词： Database systems

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Attention-Aware Based Differentiable Architecture Search Network for Waste Recognition

Attention-Aware Based Differentiable Architecture Search Net...

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IEEE International Conference on Cyber Technology in Automation, Control, and intelligent systems

作者： Qiyu Zhang Honggui Han Fangyu Li Yongping Du Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing Artificial Intelligence Institute Beijing University of Technology Beijing China

Manually designed deep neural networks have successfully forwarded waste recognition tasks in the resource recycling field. However, due to the diversity of waste samples, the feature extraction ability of inherently designed models fails to fully satisfy the requirements of real world applications. In this article, an attention-aware based differentiable architecture search (ADAS) network for waste recognition is proposed, which self-organizes to generate an optimal network structure according to diverse waste data. First, a structured search space with attention-aware modules is designed to enhance the diverse feature representations of waste data. Secondly, an efficient and differentiable structure search is achieved by continuously relaxing the representation of the network architecture and search space. Finally, the optimal architecture search process is evaluated by a bi-level optimization algorithm. Experimental results show that the proposed method achieves more satisfactory classification performances than the manually designed ResNet, DenseNet networks in the TrashNet dataset and the self-built household waste dataset.

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A Self-supervised Contrastive Learning Method for Grasp Outcomes Prediction

A Self-supervised Contrastive Learning Method for Grasp Outc...

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2023 IEEE International Conference on Real-Time computing and Robotics, RCAR 2023

作者： Liu, Chengliang Huang, Binhua Liu, Yiwen Su, Yuanzhe Mai, Ke Zhang, Yupo Yi, Zhengkun Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Siat Branch Shenzhen518055 China

ISBN: (纸本)9798350327182

In this paper, we probe the proficiency of contrastive learning techniques in forecast grasp outcomes, without supervision. Employing a dataset that's open to the public, we establish the effectiveness of contrastive learning techniques in accurately predicting grasp outcomes. More precisely, an impressive accuracy of 81.83% is achieved by the dynamic-dictionary-based method combined with the momentum updating technique, using data from just one tactile sensor, surpassing other unsupervised techniques. Our findings underscore the promise held by contrastive learning techniques in the domain of robotic grasping, while emphasizing the critical role played by precise grasp prediction in securing stable grasps. © 2023 IEEE.

关键词： Forecasting

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Joint Beamforming and Offloading Design for Integrated Sensing, Communication and Computation system

arXiv

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arXiv 2024年

作者： Liu, Peng Fei, Zesong Wang, Xinyi Zhou, Yiqing Zhang, Yan Liu, Fan The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The State Key Laboratory of Processors The Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China

Mobile edge computing (MEC) is powerful to alleviate the heavy computing tasks in integrated sensing and communication (ISAC) systems. In this paper, we investigate joint beamforming and offloading design in a three-tier integrated sensing, communication and computation (ISCC) framework comprising one cloud server, multiple mobile edge servers, and multiple terminals. While executing sensing tasks, the user terminals can optionally offload sensing data to either MEC server or cloud servers. To minimize the execution latency, we jointly optimize the transmit beamforming matrices and offloading decision variables under the constraint of sensing performance. An alternating optimization algorithm based on multidimensional fractional programming is proposed to tackle the non-convex problem. Simulation results demonstrates the superiority of the proposed mechanism in terms of convergence and task execution latency reduction, compared with the state-of-the-art two-tier ISCC framework. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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Fiber Bragg Grating Strain Sensing Sliding Mode Control based on Extended State Observer

Fiber Bragg Grating Strain Sensing Sliding Mode Control base...

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第35届中国过程控制会议

作者： Tongming Huo Xiaoli Li Lianqing Zhu Kang Wang Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing Engineering Research Center of Optoelectronic Information and Instruments Beijing Information Science & Technology University

Sliding mode control strategy based on extended observer is designed to address the issue of the inability to directly obtain strain, structural deformation and unknown disturbances in the health monitoring of structures such as aircraft using Fiber Bragg Grating strain sensing signals. The extended state observer is utilized to estimate both the system state and disturbances using strain signals. A nonlinear function is designed to enhance the convergence speed and tracking accuracy of the extended observer. This enables feedforward compensation for the sliding mode controller and reduces oscillations. By employing sat(·)function instead of the sign(·) function, it further suppresses sliding mode chattering while ensuring tracking of the state and convergence of errors. Simulation experiment demonstrate that the proposed control method effectively enhances the system performance.

关键词： Fiber Bragg Grating Strain Sensing Extended State Observer Sliding Mode Control Saturation Function

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Adaptive Enhancement Technology for Screen Content Photos 17th

Adaptive Enhancement Technology for Screen Content Photos

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17th International Forum on Digital TV and Wireless Multimedia Communication, IFTC 2020

作者： Zhou, Yazhen Kyefondeme, Dakurah Collins Zhou, Huayi Zhao, Min Guo, Nan Wu, Li Faculty of Information Technology Beijing University of Technology Beijing China Key Laboratory of Artificial Intelligence Ministry of Education Shanghai China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Artificial Intelligence Institute Beijing China

ISBN: (纸本)9789811611933

Recent years have witnessed the widespread applications of mobiles and other portable electronic devices, involving a variety of screen content photos. Different from natural scene photos, screen content photos are composed of more lines, fewer colors and unique text messages. Thus, it is difficult to realize the satisfactory enhancement effect of screen content photos using traditional image enhancement technology since they are designed for the natural scene. In this paper, we develop a novel enhancement model for screen content photos by considering text and picture separately. To be specific, we first use a fully convolutional network to divide a screen content photo into three independent parts: picture region, foreground text region, and background. Second, an optimal modification of histogram is used to automatically enhance the picture region’s contrast, and the guided image filter is used to enhance the foreground text region. Third, the enhanced picture region, the enhanced foreground text region, and background are fused to obtain the final enhanced image. Experimental results show that our model has produced less noise and derived outstanding enhancement effect than the popular enhancement techniques. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image segmentation

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Mitigate Target-level Insensitivity of Infrared Small Target Detection via Posterior Distribution Modeling

arXiv

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arXiv 2024年

作者： Li, Haoqing Yang, Jinfu Xu, Yifei Wang, Runshi Faculty of Information Beijing University of Technology Beijing100124 China Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing100124 China

Infrared Small Target Detection (IRSTD) aims to segment small targets from infrared clutter background. Existing methods mainly focus on discriminative approaches, i.e., a pixel-level front-background binary segmentation. Since infrared small targets are small and low signal-to-clutter ratio, empirical risk has few disturbances when a certain false alarm and missed detection exist, which seriously affect the further improvement of such methods. Motivated by the dense prediction generative methods, in this paper, we propose a diffusion model framework for Infrared Small Target Detection which compensates pixel-level discriminant with mask posterior distribution modeling. Furthermore, we design a Low-frequency Isolation in the wavelet domain to suppress the interference of intrinsic infrared noise on the diffusion noise estimation. This transition from the discriminative paradigm to generative one enables us to bypass the target-level insensitivity. Experiments show that the proposed method achieves competitive performance gains over state-of-the-art methods on NUAA-SIRST, IRSTD-1k, and NUDT-SIRST datasets. Code are available at https://***/LiHaoqing/IRSTD-Diff. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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