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arXiv 2023年

作者： Zhao, Tianci Bai, Xue Fang, Jianwu Xue, Jianru College of Transportation Engineering Chang’an University Xi’an China Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing China College of Artificial Intelligence Xi’an Jiaotong University Xi’an China

Driver attention prediction implies the intention understanding of where the driver intends to go and what object the driver concerned about, which commonly provides a driving task-guided traffic scene understanding. Some recent works explore driver attention prediction in critical or accident scenarios and find a positive role in helping accident prediction, while the promotion ability is constrained by the prediction accuracy of driver attention maps. In this work, we explore the network connection gating mechanism for driver attention prediction (Gate-DAP). Gate-DAP aims to learn the importance of different spatial, temporal, and modality information in driving scenarios with various road types, occasions, and light and weather conditions. The network connection gating in Gate-DAP consists of a spatial encoding network gating, long-short-term memory network gating, and information type gating modules. Each connection gating operation is plug-and-play and can be flexibly assembled, which makes the architecture of Gate-DAP transparent for evaluating different spatial, temporal, and information types for driver attention prediction. Evaluations on DADA-2000 and BDDA datasets verify the superiority of the proposed method with the comparison with state-of-the-art approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Gated Driver Attention Predictor

Gated Driver Attention Predictor

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International Conference on Intelligent Transportation

作者： Tianci Zhao Xue Bai Jianwu Fang Jianru Xue College of Transportation Engineering Chang'an University Xi'an China Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing China College of Artificial Intelligence Xi'an Jiaotong University Xi'an China

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Prediction of Hybrid Asynchronous Frequency Hopping Pattern based on Deep Learning in Wideband Spectrum

Prediction of Hybrid Asynchronous Frequency Hopping Pattern ...

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Chinese Automation Congress (CAC)

作者： Ya Wang Jiaqi Wang Shuai Yuan Naijin Liu Yuanming Lu Qian Xuesen Laboratory of Space Technology China Academy of Space Technology Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China Unit 63893 of PLA Luoyang China

In non-cooperative scenarios, wideband frequency hopping (FH) communication reconnaissance including FH signal detection, parameter estimation, and network sorting under single-channel reception is challenging. The FH pattern contains the most information about FH signals. So it is the core of FH parameter estimation. Based on the task analysis, this paper proposes a blind prediction framework combining deep learning (DL) and clustering methods to improve the accuracy and efficiency of FH pattern estimation for mixed signals in the wideband spectrum. The unique advantage of this framework is that it requires no prior signal information and anchors but exploits the inherent time-frequency (TF) properties of asynchronous FH signals. It has a strong generalization ability and can adapt to signals of any shape. Moreover, a new comprehensive evaluation metric, normalized root mean square error including missed detection (NRMSE-MD), suitable for sequence data prediction is proposed to evaluate the level of missed detection and false detection in signal monitoring. Experimental results demonstrate the superiority of the proposed framework and the effectiveness of the proposed evaluation metric.

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Liu, Yajun Ding, Yulian Li, Aimin Fei, Rong Guo, Xie Wu, Fangxiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China University of Saskatchewan Division of Biomedical Engineering Saskatoon Canada Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9781665468190

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs' functions. © 2022 IEEE.

关键词： Embeddings

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AGI in Metaverse for Smart Cities and Societies: A Cyber Physical Social Approach

AGI in Metaverse for Smart Cities and Societies: A Cyber Phy...

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control Conference (ANZCC), Australian and New Zealand

作者： Xiao Wang Jun Huang Yong-Lin Tian Fei-Yue Wang Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei China Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China Macao Institute of Systems Engineering Macau University of Science and Technology Macao China

The development of artificial general intelligence (AGI) and metaverse technologies hold great potential to enhance the effectiveness of smart cities and societies. we present a cyberphysical-social approach to integrating AGI into metaverse-based smart cities and societies, with a specific focus on parallel intelligence. The proposed approach considers the interplay between the physical environment, digital systems, social dynamics, and emerging forms of parallel intelligence, as well as swarm intelligence and collective intelligence. By leveraging these forms of parallel intelligence, the integration of AGI can create more responsive and proactive infrastructure, personalized and efficient services, and foster greater community engagement. The metaverse acts as a platform for simulating real-world scenarios and testing and refining AGI models before deployment in the physical world, while also facilitating the development and coordination of parallel intelligence. However, ethical considerations must be taken into account to ensure responsible deployment and avoid exacerbating existing societal inequalities or infringing upon individual privacy rights. This report provides a comprehensive framework for the integration of AGI and parallel intelligence into metaverse-based digital and smart organizations, while emphasizing the importance of human ethics, social responsibility, and ecological sustainability.

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Seamless Detection: Unifying Salient Object Detection and Camouflaged Object Detection

arXiv

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arXiv 2024年

作者： Liu, Yi Li, Chengxin Dong, Xiaohui Li, Lei Zhang, Dingwen Xu, Shoukun Han, Jungong School of Computer Science and Artificial Intelligence Changzhou University Changzhou213159 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China School of Automation Northwestern Polytechnical University Xi’an710129 China Department of Automation Tsinghua University Beijing100084 China

Achieving joint learning of Salient Object Detection (SOD) and Camouflaged Object Detection (COD) is extremely challenging due to their distinct object characteristics, i.e., saliency and camouflage. The only preliminary research treats them as two contradictory tasks, training models on large-scale labeled data alternately for each task and assessing them independently. However, such task-specific mechanisms fail to meet real-world demands for addressing unknown tasks effectively. To address this issue, in this paper, we pioneer a task-agnostic framework to unify SOD and COD. To this end, inspired by the agreeable nature of binary segmentation for SOD and COD, we propose a Contrastive Distillation Paradigm (CDP) to distil the foreground from the background, facilitating the identification of salient and camouflaged objects amidst their surroundings. To probe into the contribution of our CDP, we design a simple yet effective contextual decoder involving the interval-layer and global context, which achieves an inference speed of 67 fps. Besides the supervised setting, our CDP can be seamlessly integrated into unsupervised settings, eliminating the reliance on extensive human annotations. Experiments on public SOD and COD datasets demonstrate the superiority of our proposed framework in both supervised and unsupervised settings, compared with existing state-of-the-art approaches. Code is available on https://***/liuyi1989/Seamless-Detection. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Impact Time and Angle control Guidance Based on Cubic Bézier Curves: Theory and Experiment

SSRN

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SSRN 2024年

作者： Li, Heng Lv, Xiaokang Yu, Jianglong Zhang, Kun Wang, Yanyan Dong, Xiwang Institute of Artificial Intelligence Beihang University Beijing100191 China School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Institute of Unmanned System Beihang University Beijing100191 China School of Astronautics Beihang University Beihang University Beijing100191 China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing100074 China

A trajectory-shaping guidance strategy with constraints on impact time, impact angle, and maximum lateral acceleration is proposed based on a cubic Bézier curve fitting method. Firstly, an optimal guidance model is formulated, in which the Bézier curves are employed to fit the guidance trajectory, ensuring compliance with the required constraints. The manipulation transforms the optimal guidance problem into an optimal Bézier guidance trajectory design problem and a trajectory tracking problem. Then, by parameterizing the Bézier curves and analyzing the trajectory constraints in detail, the optimal Bézier guidance trajectory design is transformed into optimizing the control parameters of the Bézier curves. Moreover, a multi-layer zeroth-order Adam trajectory optimization algorithm based on general and fine tuning is introduced to determine the control parameters without relying on gradient information. Subsequently, a hyperbolic tangent vector field path tracking guidance algorithm is proposed to follow the designed trajectory and fulfill the guidance missions. Finally, numerical simulations and equivalent physical experiments verify the effectiveness of the proposed guidance strategy. © 2024, The Authors. All rights reserved.

关键词： Trajectories

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Learning Pre-Grasp Manipulation of Flat Objects in Cluttered Environments using Sliding Primitives

Learning Pre-Grasp Manipulation of Flat Objects in Cluttered...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jiaxi Wu Haoran Wu Shanlin Zhong Quqin Sun Yinlin Li State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China Department of Automation University of Science and Technology of China Heifei China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Science Beijing China Science and Technology on Thermal Energy and Power Laboratory Wuhan China

Flat objects with negligible thicknesses like books and disks are challenging to be grasped by the robot because of the width limit of the robot's gripper, especially when they are in cluttered environments. Pre-grasp manipulation is conducive to rearranging objects on the table and moving the flat objects to the table edge, making them graspable. In this paper, we formulate this task as Parameterized Action Markov Decision Process, and a novel method based on deep reinforcement learning is proposed to address this problem by introducing sliding primitives as actions. A weight-sharing policy network is utilized to predict the sliding primitive's parameters for each object, and a Q-network is adopted to select the acted object among all the candidates on the table. Meanwhile, via integrating a curriculum learning scheme, our method can be scaled to cluttered environments with more objects. In both simulation and real-world experiments, our method surpasses the existing methods and achieves pre-grasp manipulation with higher task success rates and fewer action steps. Without fine-tuning, it can be generalized to novel shapes and household objects with more than 85% success rates in the real world. Videos and supplementary materials are available at https://***/view/pre-grasp-sliding.

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Event Trigger Mechanism-Based Robust control for Cyber-Physical systems under DoS Attack

Event Trigger Mechanism-Based Robust Control for Cyber-Physi...

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Chinese Automation Congress (CAC)

作者： Peng Tian Huiling Xu Jiajia Zhao Lewei Dong School of Mathematics and Statistics Nanjing University of Science and Technology Nanjing P-R-China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing P.R.China

In this paper, the event trigger mechanism-based robust control problem is investigated for cyber-physical systems under denial of service attacks and disturbances. The effects of DoS attacks are regarded as a type of data packet loss. An event trigger mechanism is introduced into the control scheme design to effectively compensate the packet loss data. The robust attenuation technology is composited into control scheme to reduce the impact of disterbances. Then, by modeling the cyber-physical systems under denial of service(DoS) attack as a class of bounded sequential switching system, and constructing the corresponding Lyapunov function, the stability of the system is verified and analyzed. Finally, simulation experiments are given to verify the correctness of the above theoretical results.

关键词： Robust control Automation Switching systems Packet loss Switches Cyber-physical systems control systems

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Adaptive terminal sliding mode control for flexible air-breathing hypersonic vehicle

Adaptive terminal sliding mode control for flexible air-brea...

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AIAA science and technology Forum and Exposition, AIAA SciTech Forum 2021

作者： Qin, Decong Chen, Haibing Lin, Wei Tang, Peng Ma, Tielin Xu, Cheng School of Transportation Science and Engineering Beihang University Beijing China Aircraft/Engine Integrated System Safety Beijing Key Laboratory Beijing China Beijing System Design Institute of the Electro-mechanic Engineering Beijing China Institute of Unmanned System Beihang University Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781624106095

Flexible air-breathing hypersonic vehicles show significant and complex aerodynamic, scramjet propulsion system, flight dynamic and elastic coupling characteristics, which usually are highly nonlinear. These result in huge uncertainties, parameter perturbation and internal disturbance, which can no longer be ignored, and therefore, extreme difficulties for flight control design. A unique parameter adaptive non-singular terminal sliding mode method is proposed for longitudinal control law design of a flexible coupling air-breathing hypersonic vehicle. This method uses adaptive reaching law gain instead of additional adaptive compensation term to handle the uncertainty to improve robustness. The stability of the close loop system is proved via a Lyapunov way. The longitudinal tracking control law for velocity and angle of attack is designed based on a rigid dynamic model of a flexible air-breathing hypersonic vehicle. A strong coupling model of the same vehicle, considering aerodynamic-scramjet engine-flight dynamic-elastic couplings, is established as the verification platform of control law designed. Simulation results obtained based on the coupling model demonstrate that the control law designed has good performance and acceptable robustness. © 2021, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Hypersonic vehicles

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