Article
Engineering, Civil
Hongtian Chen, Bin Jiang, Steven X. Ding, Biao Huang
Summary: This paper provides a systematic review and categorization of data-driven FDD methods for traction systems in high-speed trains. It analyzes the challenges in implementing FDD and proposes several promising solutions.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Syed Sahil Abbas Zaidi, Mohammad Samar Ansari, Asra Aslam, Nadia Kanwal, Mamoona Asghar, Brian Lee
Summary: This article introduces the task of object detection and explores recent developments in deep learning-based object detectors. The article also provides a concise overview of benchmark datasets, evaluation metrics, and prominent backbone architectures used in detection, as well as lightweight classification models used on edge devices. Lastly, the article compares the performances of these architectures on multiple metrics.
DIGITAL SIGNAL PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Jing Wang, Chengyu Yang, Jianwei Xia, Zheng-Guang Wu, Hao Shen
Summary: This article investigates the sliding mode control issue for a class of discrete-time Takagi-Sugeno fuzzy networked singularly perturbed systems via an observer-based technique. A logarithmic quantizer and a weighted try-once-discard protocol are synthesized to relieve the communication burden and improve the network bandwidth utilization. A novel fuzzy sliding surface is established based on fuzzy observer states, and a sliding mode control law is synthesized to guarantee the reachability of the prescribed sliding surface. Sufficient conditions for the asymptotic stability of the sliding mode dynamics and the error system with expected H-infinity performance are developed using convex optimization theory and Lyapunov approach.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Review
Engineering, Civil
Sajjad Mozaffari, Omar Y. Al-Jarrah, Mehrdad Dianati, Paul Jennings, Alexandros Mouzakitis
Summary: This article provides a comprehensive review of deep learning-based approaches for vehicle behavior prediction. It discusses the challenges and issues in behavior prediction and categorizes and reviews the most recent solutions based on input representation, output type, and prediction method. The article also evaluates the performance of several solutions and outlines potential future research directions.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Information Systems
Olaide Nathaniel Oyelade, Absalom El-Shamir Ezugwu, Tehnan I. A. Mohamed, Laith Abualigah
Summary: This study proposes a novel bio-inspired and population-based optimization algorithm named Ebola Optimization Search Algorithm (EOSA) based on the propagation mechanism of the Ebola virus disease. The algorithm outperforms popular metaheuristic algorithms such as Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), and Artificial Bee Colony Algorithm (ABC) in terms of scalability, convergence, and sensitivity analyses. The algorithm is also successfully applied to the problem of selecting the best combination of convolutional neural network (CNN) hyperparameters in the image classification of digital mammography.
Article
Computer Science, Artificial Intelligence
Gao Huang, Zhuang Liu, Geoff Pleiss, Laurens van der Maaten, Kilian Q. Weinberger
Summary: Recent work has shown that adding shorter connections in convolutional networks can make the network deeper, more accurate, and more efficient in training. This paper introduces Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward manner. DenseNets alleviate the vanishing-gradient problem, encourage feature reuse, and improve parameter efficiency, leading to significant improvements in object recognition tasks.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Daniel Bolya, Chong Zhou, Fanyi Xiao, Yong Jae Lee
Summary: This paper presents a simple fully-convolutional model for real-time instance segmentation. By breaking instance segmentation into two parallel subtasks and linearly combining prototypes with mask coefficients, the model achieves competitive results with significantly faster speed. The authors also propose a faster replacement for standard non-maximum suppression and apply deformable convolutions to improve performance and efficiency.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Wenqi Wang, Run Wang, Lina Wang, Zhibo Wang, Aoshuang Ye
Summary: Deep neural networks have achieved remarkable success in various tasks, but they are vulnerable to adversarial examples in both image and text domains. Adversarial examples in the text domain can evade DNN-based text analyzers and pose threats to the spread of disinformation. This paper comprehensively surveys the existing studies on adversarial techniques for generating adversarial texts and the corresponding defense methods, aiming to inspire future research in developing robust DNN-based text analyzers against known and unknown adversarial techniques.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Tianfei Zhou, Fatih Porikli, David J. Crandall, Luc Van Gool, Wenguan Wang
Summary: Video segmentation is crucial in various practical applications such as enhancing visual effects in movies, understanding scenes in autonomous driving, and creating virtual background in video conferencing. Deep learning-based approaches have shown promising performance in video segmentation. This survey comprehensively reviews two main research lines - generic object segmentation and video semantic segmentation - by introducing their task settings, background concepts, need, development history, and challenges. Representative literature and datasets are also discussed, and the reviewed methods are benchmarked on well-known datasets. Open issues and opportunities for further research are identified, and a public website is provided to track developments in this field: https://github.com/tfzhou/VS-Survey.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Engineering, Aerospace
Zhi Lin, Hehao Niu, Kang An, Yong Wang, Gan Zheng, Symeon Chatzinotas, Yihua Hu
Summary: This article focuses on joint beamforming design and optimization for RIS-aided hybrid satellite-terrestrial relay networks. By smartly coordinating the passive elements' phase shifts at the RIS, the desired satellite signals at the blocked users can be strengthened. An alternating optimization scheme is proposed to minimize the transmit power of both the satellite and base station while guaranteeing the rate requirements of users.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Hao Shen, Xiaohui Hu, Jing Wang, Jinde Cao, Wenhua Qian
Summary: This work explores the $H_{infinity }$ synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain uncertainties. A novel double-layer switching regulation containing Markov chain and persistent dwell-time switching regulation (PDTSR) is proposed to design a mode-dependent synchronization controller for the network. New sufficient conditions are established to ensure the mean-square exponential stability of the synchronization error systems with the specified level of the $H_{infinity }$ performance.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yingnan Pan, Yanmin Wu, Hak-Keung Lam
Summary: This article studies the design of resilient event-triggered security controllers for nonlinear networked control systems subjected to nonperiodic denial of service attacks. By transforming the state error caused by packet loss into an uncertain variable, a novel RET strategy is proposed to transmit necessary packets and reduce performance loss under nonperiodic DoS attacks. A new security controller is also designed to simplify the network control structure. Simulation results demonstrate the advantages of the proposed approach.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Review
Computer Science, Artificial Intelligence
Lin Wang, Kuk-Jin Yoon
Summary: This paper discusses the recent progress of knowledge distillation (KD) and student-teacher (S-T) learning, providing a comprehensive survey of KD methods and commonly used S-T frameworks for vision tasks. The study summarizes the working principles and effectiveness of KD, and analyzes the research status of KD in vision applications. Finally, it explores the potential developments and future directions of KD and S-T learning.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Kai Zhang, Qi Liu, Hao Qian, Biao Xiang, Qing Cui, Jun Zhou, Enhong Chen
Summary: This paper proposes a novel model called EATN for accurately classifying sentiment polarities towards aspects in multiple domains in sentiment analysis tasks. The model incorporates a Domain Adaptation Module (DAM) to learn common features and uses multiple-kernel selection method to reduce feature discrepancy among domains. Additionally, EATN includes an aspect-oriented multi-head attention mechanism to capture the direct associations between aspects and contextual sentiment words. Extensive experiments on six public datasets demonstrate the effectiveness and universality of the proposed method compared to current state-of-the-art methods.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Engineering, Civil
Ammar Haydari, Yasin Yilmaz
Summary: Latest technological improvements have enhanced the quality of transportation. The emergence of new data-driven approaches has opened up new research directions for control-based systems in various domains, including transportation, robotics, IoT, and power systems. This paper presents a survey of traffic control applications based on deep reinforcement learning (RL). It extensively discusses different problem formulations, RL parameters, and simulation environments for traffic signal control (TSC) applications. The survey also covers autonomous driving applications studied with deep RL models, categorizing them based on application types, control models, and algorithms studied. The paper concludes with a discussion on challenges and open questions in deep RL-based transportation applications.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yujun Shen, Ceyuan Yang, Xiaoou Tang, Bolei Zhou
Summary: This paper proposes the InterFaceGAN framework to interpret and study the disentangled face representation learned by GAN models, enabling more precise control of facial attribute manipulation through subspace projection. Experimental results suggest that learning to synthesize faces brings a disentangled and controllable face representation.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Keyan Ding, Kede Ma, Shiqi Wang, Eero P. Simoncelli
Summary: This paper presents a full-reference image quality model with explicit tolerance to texture resampling. By using a convolutional neural network, the authors construct an injective and differentiable function to transform images. The proposed method combines texture similarity and structure similarity to match human ratings of image quality and achieves competitive performance on texture classification and retrieval tasks.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Engineering, Civil
Seyed Mojtaba Marvasti-Zadeh, Li Cheng, Hossein Ghanei-Yakhdan, Shohreh Kasaei
Summary: This survey systematically investigates current deep learning-based visual tracking methods, benchmark datasets, and evaluation metrics, while extensively evaluating leading visual tracking methods.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Kai Zhang, Yawei Li, Wangmeng Zuo, Lei Zhang, Luc Van Gool, Radu Timofte
Summary: Recent works have shown that using a denoiser as the image prior can improve the performance of plug-and-play image restoration methods. However, existing methods are limited by the lack of suitable denoiser priors. In this study, we propose a deep denoiser prior that significantly outperforms other state-of-the-art model-based and learning-based methods for various image restoration tasks.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Geochemistry & Geophysics
Le Sun, Guangrui Zhao, Yuhui Zheng, Zebin Wu
Summary: In this article, the spectral-spatial feature tokenization transformer (SSFTT) method is proposed to capture spectral-spatial and high-level semantic features. Experimental analysis confirms that this method outperforms other deep learning methods in terms of computation time and classification performance.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
