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Safe Q-Learning for data-Driven Nonlinear Optimal Control With Asymmetric State Constraints

IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2408-2422页

作者： Mingming Zhao Ding Wang Shijie Song Junfei Qiao the School of Information Science and Technology the Beijing Key Laboratory of Computational Intelligence and Intelligent Systemthe Beijing Laboratory of Smart Environmental Protectionand the Beijing Institute of Artificial IntelligenceBeijing University of Technology.Beijing 100124China IEEE the School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

This article develops a novel data-driven safe Q-learning method to design the safe optimal controller which can guarantee constrained states of nonlinear systems always stay in the safe region while providing an optimal ***,we design an augmented utility function consisting of an adjustable positive definite control obstacle function and a quadratic form of the next state to ensure the safety and ***,by exploiting a pre-designed admissible policy for initialization,an off-policy stabilizing value iteration Q-learning(SVIQL)algorithm is presented to seek the safe optimal policy by using offline data within the safe region rather than the mathematical ***,the monotonicity,safety,and optimality of the SVIQL algorithm are theoretically *** obtain the initial admissible policy for SVIQL,an offline VIQL algorithm with zero initialization is constructed and a new admissibility criterion is established for immature iterative ***,the critic and action networks with precise approximation ability are established to promote the operation of VIQL and SVIQL ***,three simulation experiments are conducted to demonstrate the virtue and superiority of the developed safe Q-learning method.

关键词： Adaptive critic control adaptive dynamic programming(ADP) control barrier functions(CBF) stabilizing value iteration Q-learning(SVIQL) state constraints

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Selective arguments representation with dual relation-aware network for video situation recognition

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Neural Computing and Applications 2024年第17期36卷 9945-9961页

作者： Liu, Wei He, Qing Wang, Chao Peng, Yan Xie, Shaorong School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Artificial Intelligence Laboratory Shanghai201114 China School of Future Technology Shanghai University Shanghai200444 China Institute of Artificial Intelligence Shanghai University Shanghai200444 China

Argument visual states are helpful for detecting structured components of events in videos, and existing methods tend to use object detectors to generate their candidates. However, directly leveraging object features captured by bounding boxes overlooks a deep understanding of object relations and differences between them and real arguments. In this work, we propose a novel framework to generate selective contextual representations of videos, thereby reducing the interference of useless or incorrect object features. Firstly, we construct grid-based object features as graphs based on the internal grid connection and then use graph convolutional network to execute feature aggregation. Secondly, a weighted geometric attention module is designed to obtain the contextual representation of objects, which explicitly combines visual similarity and geometric correlation with different importance proportions. Then, we propose a dual relation-aware selection module for further feature selection. Finally, we utilize labels as the ladder to bridge the gap between object features and semantic roles, while considering the proximity in the semantic space. Experimental results and extensive ablation studies on the VidSitu indicate that our method effectively obtains a deep understanding of events in videos and outperforms state-of-the-art models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Object detection

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Constrained Two-Line Center Problems 35

Constrained Two-Line Center Problems

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35th International Symposium on Algorithms and Computation, ISAAC 2024

作者： Ahn, Taehoon Bae, Sang Won Graduate School of Artificial Intelligence Pohang University of Science and Technology Korea Republic of Division of Artificial Intelligence and Computer Science Kyonggi University Suwon Korea Republic of

ISBN: (纸本)9783959773546

Given a set P of n points in the plane, the two-line center problem asks to find two lines that minimize the maximum distance from each point in P to its closer one of the two resulting lines. The currently best algorithm for the problem takes O(n2 log2 n) time by Jaromczyk and Kowaluk in 1995. In this paper, we present faster algorithms for three variants of the two-line center problem in which the orientations of the resulting lines are constrained. Specifically, our algorithms solve the problem in O(n log n) time when the orientations of both lines are fixed;in O(n log3 n) time when the orientation of one line is fixed;and in O(n2α(n) log n) time when the angle between the two lines is fixed, where α(n) denotes the inverse Ackermann function. © Taehoon Ahn and Sang Won Bae.

关键词： Constrained optimization

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data-driven discovery of linear dynamical systems from noisy data

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science China(Technological sciences) 2024年第1期67卷 121-129页

作者： WANG YaSen YUAN Ye FANG HuaZhen DING Han School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China Department of Mechanical Engineering University of KansasLawrence 66045USA

In modern science and engineering disciplines,data-driven discovery methods play a fundamental role in system modeling,as data serve as the external representations of the intrinsic mechanisms within ***,empirical data contaminated by process and measurement noise remain a significant obstacle for this type of *** this study,we have developed a data-driven method capable of directly uncovering linear dynamical systems from noisy *** method combines the Kalman smoothing and sparse Bayesian learning to decouple process and measurement noise under the expectation-maximization framework,presenting an analytical method for alternate state estimation and system ***,the discovered model explicitly characterizes the probability distribution of process and measurement noise,as they are essential for filtering,smoothing,and stochastic *** have successfully applied the proposed algorithm to several simulation *** results demonstrate its potential to enable linear dynamical system discovery in practical applications where noise-free data are intractable to capture.

关键词： system identification sparse Bayesian learning Kalman smoothing process and measurement noise

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A Deep Phishing Email Detection Model Combing Multi-Features Based on Word Frequency and Contextual Semantics 4

A Deep Phishing Email Detection Model Combing Multi-Features...

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4th International Conference on Communication technology and Information technology, ICCTIT 2024

作者： Liu, Yuntian Zhu, Erzhou Ma, Zhujuan Anhui University School of Computer Science and Technology Hefei230601 China Anhui Xinhua University School of Big Data and Artificial Intelligence Hefei230088 China

ISBN: (纸本)9798331528973

Due to the ability to automatically extract phishing features without relying on expert knowledge, deep learning methods have been widely applied in the research of phishing email classification and detection. However, the unique structure of emails and the complexity of publishing strategies mean that current deep learning-based phishing email detection studies still require further performance enhancement. To effectively detect phishing emails, this paper proposes TFW-CNBL (Term Frequency and Word2Vec-CNN and BiLSTM), a deep detection model for phishing emails based on the fusion of term frequency and contextual semantic multi-features. The TFW-CNBL model generates a hybrid feature vector that comprehensively understands the characteristics of phishing emails by integrating term frequency and contextual information. The model combines a convolutional neural network (CNN) with an attention mechanism and a bidirectional long short-term memory network (BiLSTM), creating an underlying architecture capable of rapid text classification. The paper utilized a publicly available dataset from Kaggle containing 18,650 samples to train and test the TFW-CNBL model. Experimental results indicate that the TFW-CNBL model can effectively detect phishing emails, achieving an average accuracy, precision, recall, and F1 score of 0.984, 0.986, 0.989, and 0.987, respectively. © 2024 IEEE.

关键词： Phishing

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Optimization of semi-supervised generative adversarial network models:a survey

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International Journal of Intelligent Computing and Cybernetics 2024年第4期17卷 705-736页

作者： Yongqing Ma Yifeng Zheng Wenjie Zhang Baoya Wei Ziqiong Lin Weiqiang Liu Zhehan Li School of Computer Science Minnan Normal UniversityZhangzhouChina and Key Laboratory of Data Science and Intelligence ApplicationFujian Province UniversityZhangzhouChina

Purpose-With the development of intelligent technology,deep learning has made significant progress and has been widely used in various *** learning is data-driven,and its training process requires a large amount of data to improve model ***,labeled data is expensive and not readily ***/methodology/approach-To address the above problem,researchers have integrated semisupervised and deep learning,using a limited number of labeled data and many unlabeled data to train *** this paper,Generative Adversarial Networks(GANs)are analyzed as an entry ***,we discuss the current research on GANs in image super-resolution applications,including supervised,unsupervised,and semi-supervised learning ***,based on semi-supervised learning,different optimization methods are introduced as an example of image ***,experimental comparisons and analyses of existing semi-supervised optimization methods based on GANs will be ***-Following the analysis of the selected studies,we summarize the problems that existed during the research process and propose future research ***/value-This paper reviews and analyzes research on generative adversarial networks for image super-resolution and classification from various learning *** comparative analysis of experimental results on current semi-supervised GAN optimizations is performed to provide a reference for further research.

关键词： Deep learning Generative adversarial network Semi-supervised learning Model optimization Image classification

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LQTTrack:Multi-Object Tracking by Focusing on Low-Quality Targets Association

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computers, Materials & Continua 2024年第10期81卷 1449-1470页

作者： Suya Li Ying Cao Hengyi Ren Dongsheng Zhu Xin Xie Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information EngineeringHenan UniversityKaifeng475001China College of Information Science and Technology&Artificial Intelligence Nanjing Forestry UniversityNanjing210037China School of Computer Nanjing University of Posts and TelecommunicationsNanjing210023China

Multi-object tracking(MOT)has seen rapid improvements in recent ***,frequent occlusion remains a significant challenge in MOT,as it can cause targets to become smaller or disappear entirely,resulting in lowquality targets,leading to trajectory interruptions and reduced tracking *** from some existing methods,which discarded the low-quality targets or ignored low-quality target ***,with a lowquality association strategy(LQA),is proposed to pay more attention to low-quality *** the association scheme of LQTTrack,firstly,multi-scale feature fusion of FPN(MSFF-FPN)is utilized to enrich the feature information and assist in subsequent data ***,the normalized Wasserstein distance(NWD)is integrated to replace the original Inter over Union(IoU),thus overcoming the limitations of the traditional IoUbased methods that are sensitive to low-quality targets with small sizes and enhancing the robustness of low-quality target ***,the third association stage is proposed to improve the matching between the current frame’s low-quality targets and previously interrupted trajectories from earlier frames to reduce the problem of track fragmentation or error tracking,thereby increasing the association success rate and improving overall multi-object tracking *** experimental results demonstrate the competitive performance of LQTTrack on benchmark datasets(MOT17,MOT20,and DanceTrack).

关键词： Low-quality targets association strategy feature fusion multi-object tracking tracking-by-detection

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Don’t Ignore the Drive of Curiosity: Rethinking Subtleties Between Universality of Commonsense Knowledge and Excellence of Large Language Models

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SN computer science 2024年第6期5卷 798页

作者： Wang, Chao Chen, Tao Liu, Jingping School of Future Technology Shanghai University Shanghai China Institute of Artificial Intelligence Shanghai University Shanghai China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China School of Information Science and Engineering East China University of Science and Technology Shanghai China

Commonsense reasoning is one of the abilities necessary for artificial intelligence to be as intelligent as humans. However, how to make AI understand commonsense has been a problem that has plagued artificial intelligence for more than 60 years. Existing efforts focus more on the means of knowledge acquisition and strive to enrich the capacity of commonsense knowledge (CSK) bases and dimensions of CSK through advanced methods. Unfortunately, this exuberance has obscured a general consideration of CSK, such as how to follow human habits to obtain the most representative knowledge we need to understand the world. In this paper, this representative knowledge is referred to as core CSK. The influence of core CSK is extensive, and it constitutes almost the fundamental element of human life and the most fundamental cognition of the world. Harnessing human curiosity to find solutions to the above problems is an effective and straightforward route. Specifically, we focus on a special corpus to mine core CSK, namely, why-questions. For example, we can harvest “the sky is blue” from “why is the sky blue?”. To this end, we propose a novel method to extract CSK from why-questions, which mainly consist of two modules. The first is a question classification module used to determine whether a question contains CSK. In this module, we propose a classifier based on a one-sided bootstrapping method and design several informative features for the classifier. The second is a crowdsourcing module used to improve the quality of the extracted commonsense. We conduct extensive experiments, and the experimental results show that our method effectively mines CSK from question corpora. Furthermore, statistical analysis demonstrates the feasibility of this curiosity-driven approach, implying that we provide a basic idea for collecting core CSK. Remarkably, today’s outstanding large language models do not have such simple knowledge summarization capabilities, demonstrating the barrier between

关键词： Classification Core Commonsense knowledge Crowdsourcing Curiosity Psychologically

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Heterogeneous information alignment and re-ranking for cross-modal pedestrian re-identification

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Multimedia Tools and Applications 2024年 1-24页

作者： Zhao, Tiezhu Liang, Xiaolun He, Kejing Yang, Qiuhong Ren, Ziliang School of Computer Science and Technology Dongguan University of Technology Guangdong Dongguan523808 China School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou510641 China School of Artificial Intelligence and mathematical Dongguan City University Guangdong Dongguan523419 China

Cross-modal pedestrian re-identification, encompassing visible and infrared images, presents a significant challenge owing to inherent disparities in imaging principles, resulting in substantial cross-modal discrepancies. Consequently, matching and extracting heterogeneous information becomes exceedingly arduous. To address these issues, this research introduces a novel cross-modal pedestrian re-identification methodology, leveraging a heterogeneous information alignment algorithm and a reranking strategy. Specifically, a novel algorithm for aligning heterogeneous local information is proposed, which dynamically aligns the diverse local details of a given pedestrian by identifying the shortest path within the distance matrix of the pedestrian’s heterogeneous local information. Furthermore, to tackle the predicament of extracting heterogeneous pedestrian information, an expanded k-nearest neighbor reranking strategy is introduced, which integrates the heterogeneous information of the same pedestrian by dynamically developing the k-nearest neighbor heterogeneous information derived from the query image. Extensive experimental evaluations substantiate the effectiveness of the proposed approach across diverse scenarios. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Nearest neighbor search

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Dynamic Priority-based Ordered Transmission for Mixed Flows in data Center Networks 22

Dynamic Priority-based Ordered Transmission for Mixed Flows ...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zou, Shaojun Jiang, Yi Yang, Laurence T. Qu, Jiacheng Hainan University School of Computer Science and Technology Haikou China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou China

ISBN: (纸本)9798331509712

Increasing diversity of applications and services are being migrated to modern data center networks (DCNs), and these applications and services are typically generating various combinations of long and short flows with or without deadlines. However, most existing flow scheduling solutions for DCNs either adopt single-queue strategy (e.g., D2TCP) that inevitably results in non-urgent flows blocking urgent flows or multi-queue mechanism (e.g., Aemon) that is at the cost of packet reordering. In this paper, we present DPOT, a Dynamic Priority-based Ordered Transmission mechanism that is aimed at minimizing the flow completion time and deadline missing rate. To avoid the urgent flows being blocked by the non-urgent flows, the DPOT switch adopts different priority queues to buffer packets for different types of flows. What's more, when the switch detects that a data flow promotes the priority of its packets, it utilizes a disorder-free flow scheduling mechanism based on dynamic prioritization to make sure that packets of the same flow arrive at the receiver without reordering. Through a series of experimental tests, we demonstrate that DPOT can decrease the deadline miss rate by up to 95% while reducing flow completion time by up to 45% in comparison to the state-of-the-art flow scheduling approaches. © 2024 IEEE.

关键词： Queueing theory

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