Article
Computer Science, Artificial Intelligence
Qi Wang, Wei Huang, Zhitong Xiong, Xuelong Li
Summary: This article proposes a multiscale representation method for scene classification in remote sensing images, which is achieved through a global-local two-stream architecture, and achieved good results.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Jin-Fan Hu, Ting-Zhu Huang, Liang-Jian Deng, Tai-Xiang Jiang, Gemine Vivone, Jocelyn Chanussot
Summary: The paper introduces a deep convolutional neural network architecture to fuse low-resolution HSI and high-resolution multispectral image for generating high-resolution HSI. By preserving spatial and spectral information using LR-HSI and HR-MSI, and utilizing attention and pixelShuffle modules for high-quality spatial details extraction, the proposed network achieves the best performance compared to recent HSI super-resolution approaches.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Runqi Chai, Antonios Tsourdos, Al Savvaris, Senchun Chai, Yuanqing Xia, C. L. Philip Chen
Summary: This article presents a deep neural network-based control scheme for predicting optimal motion commands for autonomous ground vehicles during the parking maneuver process. The proposed design includes a multilayer structure, where a trajectory optimization method is used to establish time-optimal parking trajectories and train multiple DNNs to learn the functional relationship between system state and control actions. The trained DNNs are then employed as motion controllers to generate real-time feedback actions. Numerical and experimental results validate the effectiveness and real-time applicability of the proposed control scheme.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Tieshan Li, Weiwei Bai, Qi Liu, Yue Long, C. L. Philip Chen
Summary: This article investigates the model-free fault-tolerant containment control problem for multiagent systems (MASs) with time-varying actuator faults. A distributed containment control method based on reinforcement learning (RL) is adopted to achieve the containment control objective without prior knowledge on the system dynamics. The article proposes an optimal regulation problem and employs the RL-based policy iteration method to deal with it, developing nominal and fault-tolerant controllers to compensate for actuator faults. Numerical simulations demonstrate the effectiveness and advantages of the proposed method.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Qinglin Yang, Yetong Zhao, Huawei Huang, Zehui Xiong, Jiawen Kang, Zibin Zheng
Summary: Metaverse, as the latest buzzword, seamlessly integrates the real world with the virtual world, promising to build an exciting digital world and transform the physical world. This survey explores the fusion of Blockchain and Artificial Intelligence (AI) in the Metaverse, emphasizing the collaboration between academia and industries for further research.
IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY
(2022)
Article
Computer Science, Hardware & Architecture
Jiankai Xue, Bo Shen
Summary: This paper presents a novel population-based technique called dung beetle optimizer (DBO) algorithm, inspired by dung beetle's behaviors. The DBO algorithm combines global exploration and local exploitation, resulting in fast convergence rate and satisfactory solution accuracy. Experimental results demonstrate that the DBO algorithm shows competitive performance and superiority over other popular optimization techniques.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Tong Yang, Ning Sun, Yongchun Fang
Summary: An adaptive full-state constraint controller is designed in this article to address the output and velocity constraints of a class of uncertain multi-input-multi-output underactuated systems. By constructing auxiliary functions and constraint terms, accurate positioning control of state variables such as displacements and angles is achieved, and the convergence and robustness are proven through rigorous stability analysis.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Faris A. Almalki, S. H. Alsamhi, Radhya Sahal, Jahan Hassan, Ammar Hawbani, N. S. Rajput, Abdu Saif, Jeff Morgan, John Breslin
Summary: The development of IoT and smart cities has brought changes and challenges. Green IoT can address environmental issues and make smart cities more sustainable and eco-friendly.
MOBILE NETWORKS & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Chao Deng, Xiao-Zheng Jin, Wei-Wei Che, Hai Wang
Summary: This article explores the distributed fault-tolerant resilient consensus problem for heterogeneous MASs under physical failures and network DoS attacks. A distributed resilient learning algorithm is proposed to learn the unknown dynamic model of the leader, and a new adaptive fault-tolerant resilient controller is designed to resist failures and attacks. The effectiveness of the proposed method is demonstrated through simulations.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Keke Huang, Shujie Wu, Fanbiao Li, Chunhua Yang, Weihua Gui
Summary: Hydraulic systems, as a typical complex nonlinear system, pose challenges in fault diagnosis; a deep learning model with multirate data samples is proposed, capable of automatically extracting features and suitable for industrial environments; experimental results demonstrate high diagnostic accuracy even in the case of imbalanced sample data.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Jiaming Pei, Kaiyang Zhong, Mian Ahmad Jan, Jinhai Li
Summary: In this study, a personalized federated anomaly detection framework for network traffic anomaly detection was proposed, and a new network traffic anomaly detection method based on the self-coding of long-and short-term memory networks was introduced. The proposed method was validated through testing on real network traffic.
Article
Computer Science, Artificial Intelligence
Weiwei Bai, Tieshan Li, Yue Long, C. L. Philip Chen
Summary: This article investigates the tracking control problem of event-triggered multigradient recursive reinforcement learning for nonlinear multiagent systems. It focuses on the distributed reinforcement learning approach, using a critic neural network to estimate the long-term strategic utility function and an actor neural network to approximate uncertain dynamics. The multigradient recursive strategy is used to learn the weight vector in the neural network, eliminating local optimal problems and reducing dependence on initial values. Furthermore, reinforcement learning and event-triggered mechanism improve energy conservation of multiagent systems.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Senrong You, Baiying Lei, Shuqiang Wang, Charles K. Chui, Albert C. Cheung, Yong Liu, Min Gan, Guocheng Wu, Yanyan Shen
Summary: Magnetic resonance (MR) imaging is crucial in clinical and brain exploration, yet it is challenging to acquire high-resolution MR images due to hardware limitations, scanning time, and cost. In this article, the authors propose FP-GANs, a model that uses divide-and-conquer approach to generate super-resolution MR images. The model separates and processes the low-frequency and high-frequency components of MR images and achieves better structure recovery and classification performance.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Valerian Rey, Pedro Miguel Sanchez Sanchez, Alberto Huertas Celdran, Gerome Bovet
Summary: With the increasing number of IoT devices and the growing importance of data privacy and security, researching the application and security issues of federated learning in IoT malware detection becomes crucial. This study explores the use of federated learning to detect malware while preserving data privacy and finds that it has the capability to detect malware, but further efforts are needed to enhance its robustness.
Article
Computer Science, Artificial Intelligence
Jinjin Xu, Wenli Du, Yaochu Jin, Wangli He, Ran Cheng
Summary: Federated learning is a privacy-preserving and secure machine learning approach. This paper proposes a federated trained ternary quantization algorithm and a ternary federated averaging protocol to reduce communication costs and improve performance.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Nikhil Kumar Tomar, Debesh Jha, Michael A. Riegler, Havard D. Johansen, Dag Johansen, Jens Rittscher, Pal Halvorsen, Sharib Ali
Summary: This paper introduces a novel architecture called feedback attention network (FANet) that leverages the information of each training epoch to prune the prediction maps of the subsequent epochs and rectify the predictions iteratively during the test time. Experimental results demonstrate that FANet provides significant improvement on segmentation metrics tested on various biomedical imaging datasets.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, Andrew Lim
Summary: This article introduces a deep reinforcement learning framework to learn improvement heuristics for routing problems, using a self-attention-based deep architecture as the policy network. The method has been applied to the traveling salesman problem and the capacitated vehicle routing problem, outperforming existing DL methods with strong generalization capabilities.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Di Wu, Mingsheng Shang, Xin Luo, Zidong Wang
Summary: This article introduces a novel LF model that combines the characteristics of L1 and L2 norms to improve accuracy and stability in handling HiDS data with outliers. Experimental results demonstrate that the model outperforms state-of-the-art models in predicting missing data, indicating its potential for real-world HiDS data applications.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Laith Abualigah, Muhammad Alkhrabsheh
Summary: This paper proposes an efficient optimization method for task scheduling based on a hybrid multi-verse optimizer with a genetic algorithm called MVO-GA, aiming to enhance the performance of tasks transfer via the cloud network based on cloud resources' workload, achieving successful optimization in a large number of tasks.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Yongliang Yang, Bahare Kiumarsi, Hamidreza Modares, Chengzhong Xu
Summary: This article presents a model-free lambda-policy iteration algorithm for the discrete-time linear quadratic regulation problem. It introduces the weighted Bellman operator and the composite Bellman operator to solve the algebraic Riccati equation. Compared to the PI algorithm, the lambda-PI algorithm does not require an initial policy and has a faster convergence rate. The model-free extension of the lambda-PI algorithm is developed using the off-policy reinforcement learning technique.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
