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2022 4th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2022

作者： Qian, Quan Zhu, Yuan School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

Medication design and repositioning are sped up by the prediction of drug-target interactions (DTIs). Two main kinds of prediction methods are commonly used, which are based on chemical structure feature extraction and deep learning methods. However, on the one hand, the DTI prediction approaches based on chemical structural feature extraction may not wholly explore the possible network characteristics in the data. On the other hand, many deep learning methods call for numerous layers of neural networks to be layered to learn higher-order feature interaction information. To sum up, the existing computation methods often have the limitations of gradient disappearance and overfitting. This study presents a novel method (JRD-NFM) by calculating Jaccard similarities, getting an eigenvector through Restarted random walk (RWR), and generating low-dimensional feature vectors by Disposition Component Analysis (DCA). Besides decoding the topological features and similarity information of target and drug node, it can also get the context information of a single network. Considering the advantages of Neural Factorization Machines (NFM) in extracting high-order nonlinear features and processing sparse data, this study use NFM to classifier the collection data to integrate drug and target biochemical structure information. The experimental results demonstrate that JRD-NFM can outperform widely used deep learning methods and conventional chemical structure approaches. It could provide fresh ideas for combining molecular structure and heterogeneous network data to predict DTIs. © Published under licence by IOP Publishing Ltd.

关键词： Feature extraction

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Correction of Images Projected on Non-White Surfaces Based on Deep Neural Network 14

Correction of Images Projected on Non-White Surfaces Based o...

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14th International Conference on Digital Image Processing, ICDIP 2022

作者： Wang, Zewei Zhang, Jingjing Du, Xingzhuo Cao, Sihua Wei, Wenxuan School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9781510657564

When projecting onto a non-white surface, the projected image is distorted or color mixing by complex luminance and chrominance information, which makes the projection result different from the visual perception of the human eye. The purpose of projection image correction is to remove these effects, and traditional solutions usually estimate parameters from the collected projection samples, compute an inverse model of the projection imaging process, and try to fit a correction function. In this paper, a deep neural network-based projection image correction network (PICN) is designed to implicitly learn complex correction functions. PICN consists of a U-shaped backbone network, a convolutional neural network that extracts projected surface features, and a perceptual loss network that optimizes the correction results. Such a structure can not only extract the deep features and surface interference features of the projected image, but also make the corrected projected image more in line with human visual perception. In addition, we built a projector-camera system under the condition of a fixed global illumination environment for verification experiment, and proved the effectiveness of the proposed method by calculating the evaluation metrics of projected images before and after correction. © 2022 SPIE.

关键词： Deep neural networks

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Full Attention Tracker: A Good Combination of Pixel-Level and Region-Level Cross-Correlation

Full Attention Tracker: A Good Combination of Pixel-Level an...

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Chinese control Conference (CCC)

作者： Yuxuan Wang Liping Yan Zihang Feng Yuanqing Xia Bo Xiao Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China

The tracker based on Siamese neural network is currently a technical method with high accuracy in the tracking field. With the introduction of transformer in the visual tracking field, the attention mechanism has gradually emerged in tracking tasks. However, due to the characteristics of attention operation, Transformer usually has slow convergence speed, and its pixel-level correlation discrimination in tracking is more likely to lead to overfitting, which is not conducive to long-term tracking. A brand new framework FAT was designed, which is the improvement of MixFormer. The operation for simultaneous feature extraction and target information integration in MixFormer is retained, and the Mixing block is introduced to suppress the background as much as possible before the information interaction. In addition, a new operation is designed: the result of region-level cross-correlation is used as a guidance to help the learning of pixel-level cross-correlation in attention, thereby accelerating the model convergence speed and enhancing the model generalization. Finally, a joint loss function is designed to further improve the accuracy of the model. Experiments show that the presented tracker achieves excellent performance on five benchmark datasets.

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Relative Roughness Measurement Based Real-Time Speed Planning for Autonomous Vehicles on Rugged Road

Relative Roughness Measurement Based Real-Time Speed Plannin...

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Liang Wang Tianwei Niu Shoukun Wang Shuai Wang Junzheng Wang Key Laboratory of Drive and Control of Servo Motion System School of Automation Beijing Institute of Technology Beijing China The State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology School of Automation Beijing Institute of Technology Beijing China

In order to guarantee autonomous vehicles' autonomy, mobility, and ride quality in rugged environments, a real-time speed planning method based on the time-frequency transformation of terrain characteristics is designed to achieve adaptive speed planning of autonomous vehicles in rough ground. On the one hand, the vertical profile of the lidar's point cloud data is converted from the time domain to the frequency domain in real time, and the integrated area of the sub-frequency range in the frequency domain is chosen as the relative roughness quantification value to realize the roughness quantification under various terrains. On the other hand, to model the relationship between vehicle speed and relative roughness, iterative search is utilized to create a speed and roughness model, and sliding windows are employed to update the roughness to achieve continuous mapping between speed and roughness. Ultimately, a number of tests were conducted on various rough roads using the oil exploration vehicle EV-56 as the study object. The experimental results show that the proposed method can identify the terrain roughness changes under complex terrain and change their speed within 0.2 m accuracy.

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Impulsive control of Heterogeneous Multi-agent systems with Saturation Constraint and Time Delay

Impulsive Control of Heterogeneous Multi-agent Systems with ...

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Chinese automation Congress (CAC)

作者： Ranran Jiao Bo Li Xiangyong Chen Xiaowei Jiang Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China School of Finance Anhui University of Finance and Economics Bengbu China Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Automation and Electrical Engineering Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper addresses the problem of leader-following consensus in heterogeneous multi-agent systems (HMAS) with input saturation and communication delay. Given the practical differences between leader and follower dimensions, we design virtual leaders to address this problem by allowing actual leaders and followers to communicate with virtual leaders separately, thus enabling indirect communication between actual leaders and followers. Additionally, the control protocol proposed in this paper takes into account the effects of input saturation and time delay that exist in real communication. At last, a simulation example is presented to demonstrate the effectiveness of the designed control protocol.

关键词： Energy consumption Protocols automation Event detection Delay effects control systems Delays Multi-agent systems

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Real-time In-situ Defect Detection in 3D Printing of Continuous Fiber-Reinforced Composites 4

Real-time In-situ Defect Detection in 3D Printing of Continu...

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Yue, Zhijian Shen, Zhen Dong, Xiangyang Fang, Qihang Wang, Weixing Dong, Xisong Xiong, Gang Wang, Fei-Yue University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Beijing100190 China Qingdao Academy of Intelligent Industries Intelligent Manufacturing Center Qingdao266109 China Chinese Academy of Sciences Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan523808 China Chinese Academy of Sciences State Key Laboratory for Management and Control for Complex Systems Institute of Automation Beijing100190 China

ISBN: (纸本)9798350349252

With the development of continuous fiber-reinforced composites (CFRCs) 3D printing technology, timely, efficient and accurate detection of fiber path defects is essential for ensuring product quality and performance. However, challenges still need to be addressed in this field, such as inadequate training data coverage and the complexity of fiber path structures. These factors hinder the capability of traditional methods to accurately assess the status of printed products. Therefore, developing accurate and robust fiber path defect detection techniques is crucial for CFRCs 3D printing technology. In this paper, we propose a precise detection method for fiber path defects in CFRCs 3D printing using the YOLOv7 object detection model combined with a Squeeze-and-Excitation (SE) attention mechanism. By introducing the SE attention mechanism, we enhance the perception capability of the YOLOv7 model for crucial features, thereby improving the accuracy of fiber path defect detection. The experimental results show that the YOLOv7 model, when augmented with an attention mechanism, achieves an average accuracy of 93.9% in identifying fiber path defects during the printing process. Compared to models without this attention mechanism, the results show that there is 10.7% increase, with a notably enhanced learning capacity during the training phase, which substantiates the efficacy and practicality of the proposed approach. © 2024 IEEE.

关键词： Fiber reinforced plastics

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Nonfragile Impulsive State Estimation for complex Networks With Markovian Switching Topologies Subject to Limited Bit Rate Constraints

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IEEE Transactions on Neural Networks and Learning systems 2024年第6期36卷 10450-10463页

作者： Yuru Guo Zidong Wang Jun-Yi Li Yong Xu Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control School of Automation Guangdong University of Technology Guangzhou China Department of Computer Science Brunel University London Uxbridge U.K. Pazhou Laboratory Guangzhou China

In this article, we consider the impulsive estimation problem for a specific category of discrete-time complex networks (CNs) characterized by Markovian switching topologies. The measurement outputs of the underlying CNs, transmitted to the observer over wireless networks, are subject to bit rate constraints. To effectively reduce the estimation error and enhance estimation performance, a mode-dependent impulsive observer is proposed that employs the impulse mechanism. The application of stochastic analysis techniques leads to the derivation of a sufficient condition for ensuring the mean-square boundedness of the estimation error dynamics. The upper bound of the error is then analyzed by iteratively exploring the Lyapunov relation at both impulsive and non-impulsive instants. Moreover, an optimization algorithm is presented for handling the bit rate allocation, which is coupled with the design of desired observer gains using the linear matrix inequality (LMI) approach. Within this theoretical framework, the relationship between the mean-square estimation performance and the bit rate allocation protocol is further elucidated. Finally, a simulation example is provided to demonstrate the validity and effectiveness of the proposed impulsive estimation approach.

关键词： Bit rate Observers Switches Topology Resource management Quantization (signal) Estimation error

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Dynamic Event-Triggered Leader-Following Consensus of Multiagent systems under DoS Attacks

Dynamic Event-Triggered Leader-Following Consensus of Multia...

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第43届中国控制会议

作者： Ji Ma Xiangyong Chen Yue Wu Ji Sun Jianhua Liu The Key Laboratory of Complex Systems and Intelligent Computing in Universities of Shandong School of Automation and Electrical Engineering Linyi University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In this paper, the leader-following consensus of multi-agent systems(MASs) under denial-of-service(DoS) attacks over undirected networks is studies. First, introduce an estimator under DoS attacks to design a dynamic event-triggered communication protocol, which avoids continuous communication between agents. Then, with the presence of the estimator between the triggering instants can reduce the state shift. In contrast to the conventional method, the existence of dynamic threshold need fewer triggering times. Under this control strategy, the systems only need the states in triggering instants according to estimated states. Finally, verify the effectiveness of the proposed approach by a simulation example.

关键词： Multi-agent systems Event-triggered protocol Leader-following consensus DoS attacks

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PiAGI: The Concept, Mechanism and Framework 4

PiAGI: The Concept, Mechanism and Framework

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Li, Qiang Ju, Zhuoya Fan, Lili Liu, Xin Liu, Wenhua Wang, Feiyue Scientific and Technological Innovation Center Beijing China Beijing Institute of Technology The School of Automation Beijing China China University of Petroleum School of Big Data & Cyber Security Qingdao China Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China

ISBN: (纸本)9798350349252

As social production and living spaces gradually expand from the physical and information domains to the cognitive and social domains, this article addresses the new changes brought about by these expansions. Starting from the new impact of era of intelligent connectivity on social cognitive management, it analyzes the urgent need to enhance social cognitive management capabilities centered on cognitive decision-making, identifying the core key as Adversarial Generated Intelligence (AGI). It delves into the conceptual characteristics, operating mechanisms, and winning strategies of AGI, identifies the new challenges it faces, and proposes a Parallel Intelligence empowering AGI (PiAGI) system architecture through the empowerment of parallel intelligence. This provides theoretical support for improving social cognitive management capabilities centered on cognitive decision-making. © 2024 IEEE.

关键词： Adversarial machine learning

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DriveRP: RAG and Prompt Engineering Embodied Parallel Driving in Cyber-Physical-Social Spaces 4

DriveRP: RAG and Prompt Engineering Embodied Parallel Drivin...

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Huang, Jun Ma, Hao Zhang, Tengchao Lin, Fei Ma, Siji Wang, Xiao Wang, Fei-Yue Macau University of Science and Technology Faculty of Innovation Engineering China Anhui University School of Artificial Intelligence Hefei China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing China

ISBN: (纸本)9798350349252

In recent years, numerous technological advancements in Artificial Generative Intelligences (AGIs) have demonstrated significant potential to transform the intelligence acquisition mechanisms in connected autonomous vehicles (CAVs). Integrating technologies like ChatGPT into CAVs can enhance human-machine interactions. However, the emergence of such new traffic entities may introduce unforeseen hallucinations and complex risks that surpass our current understanding. To address these challenges, Retrieval-Augmented Generation (RAG) and prompt engineering technologies are being explored to enhance the reliability and safety of autonomous driving systems. RAG retrieves relevant contextual information, such as driving experiences and real-time road network status, from external databases to ensure that foundation models have access to accurate and timely data for informed decision-making. Prompt engineering optimizes the performance of large language models in autonomous driving systems by designing and refining prompts that guide the models' responses, thereby improving their relevance and accuracy in various driving scenarios. Together, these technologies enhance the robustness and trustworthiness of autonomous driving systems. This paper proposes DriveRP, a framework that integrates RAG and prompt engineering within the Descriptive-Predictive-Prescriptive Intelligence framework of Parallel Driving theory. DriveRP aims to enhance the safety and interpretability of autonomous vehicle trajectory planning, decision-making, and motion control, ultimately achieving the "6S"goals. Grounded in Digital Twins and Metaverse-embodied parallel driving theory, DriveRP provides the infrastructure and foundational intelligence for parallel driving with Multi-modal Large Lange Models(MLLMs). Additionally, the paper discusses future trends and potential research directions, focusing on the "6S"goals of parallel driving: Smart, Safe, Secure, Sensitive, Sustainable, and Serviceable. © 2

关键词： intelligent systems

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