Article
Computer Science, Artificial Intelligence
Guohuai Lin, Hongyi Li, Choon Ki Ahn, Deyin Yao
Summary: This article focuses on the event-based finite-time neural attitude consensus control problem for the six-rotor unmanned aerial vehicle (UAV) systems with unknown disturbances. It addresses the issues of external disturbances and uncertain nonlinear dynamics using a disturbance observer and radial basis function neural networks (RBF NNs). The proposed finite-time command filtered (FTCF) backstepping method effectively manages the complexity explosion problem and an event-triggered mechanism is considered to alleviate the communication burden.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Information Systems
Xiao Wang, Deyu Bo, Chuan Shi, Shaohua Fan, Yanfang Ye, Philip S. Yu
Summary: This survey provides a comprehensive review of recent developments in heterogeneous graph embedding methods and techniques. It introduces the basic concepts of heterogeneous graphs and discusses the unique challenges they pose for embedding. The state-of-the-art methods are systematically surveyed and categorized based on the information they use to address these challenges. The paper also explores the real-world applicability of different embedding methods and presents successful systems. Open-source code, graph learning platforms, and benchmark datasets are summarized to facilitate future research and applications in this area.
IEEE TRANSACTIONS ON BIG DATA
(2023)
Review
Computer Science, Artificial Intelligence
Honggang Chen, Xiaohai He, Linbo Qing, Yuanyuan Wu, Chao Ren, Ray E. Sheriff, Ce Zhu
Summary: This article provides a comprehensive review of real-world single image super-resolution (RSISR), covering critical datasets, assessment metrics, and four major categories of RSISR methods. It compares representative RSISR methods on benchmark datasets in terms of reconstruction quality and computational efficiency, while also discussing challenges and promising research topics in RSISR.
INFORMATION FUSION
(2022)
Article
Computer Science, Information Systems
Nannan Xi, Juan Chen, Filipe Gama, Marc Riar, Juho Hamari
Summary: This study examines the impact of Augmented Reality (AR) and Virtual Reality (VR) on the difficulty of everyday tasks. Findings from a shopping-related task experiment show that AR significantly affects workload, particularly in terms of mental demand and effort, while VR has no significant effect on any workload dimensions. There is a significant interaction effect between AR and VR on physical demand, effort, and overall workload.
INFORMATION SYSTEMS FRONTIERS
(2023)
Article
Computer Science, Theory & Methods
Jianhui Mou, Peiyong Duan, Liang Gao, Xinhua Liu, Junqing Li
Summary: This paper introduces an energy-efficient distributed permutation flow-shop inverse scheduling problem and proposes an effective hybrid collaborative algorithm to meet dynamic market demand. By improving heuristic and random methods for population initialization, the algorithm's performance is enhanced. The algorithm achieves a balance between global exploration and local development capability through a double-population cooperative search link based on learning mechanism.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Zhiqin Zhu, Xianyu He, Guanqiu Qi, Yuanyuan Li, Baisen Cong, Yu Liu
Summary: In this paper, a brain tumor segmentation method based on the fusion of deep semantics and edge information in multimodal MRI is proposed. The method utilizes Swin Transformer for semantic feature extraction, introduces a shifted patch tokenization strategy, and designs an edge spatial attention block and a multi-feature inference block based on graph convolution for feature enhancement and fusion. The experimental results demonstrate that the proposed method outperforms other methods in brain tumor segmentation.
INFORMATION FUSION
(2023)
Article
Computer Science, Information Systems
Jiguo Li, Yichen Zhang, Jianting Ning, Xinyi Huang, Geong Sen Poh, Debang Wang
Summary: This article proposes a CP-ABE scheme for access control of IoT data on the cloud, providing fine-grained and flexible access control and addressing key abuse issues.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2022)
Article
Automation & Control Systems
Radu-Emil Precup, Radu-Codrut David, Raul-Cristian Roman, Alexandra-Iulia Szedlak-Stinean, Emil M. Petriu
Summary: This paper presents a novel application of the metaheuristic Slime Mould Algorithm (SMA) to the optimal tuning of interval type-2 fuzzy controllers. The newly developed version of the algorithm, SMAF1, shows superiority over other metaheuristic algorithms for the position control of nonlinear servo systems.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Liyong Fu, Zechao Li, Qiaolin Ye, Hang Yin, Qingwang Liu, Xiaobo Chen, Xijian Fan, Wankou Yang, Guowei Yang
Summary: In this article, a new robust discriminant subspace (RDS) learning method is presented for feature extraction. The method uses a different objective function formulation to ensure the subspace is both robust and discriminative. An efficient nongreedy iterative algorithm is proposed to solve the challenging optimization problem. The experimental results on image classification databases demonstrate the effectiveness of RDS.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Di Jin, Quan Z. Sheng, Philip S. Yu
Summary: This article discusses the applications of deep learning in community detection, providing a classification of different methods and models. It introduces popular datasets, evaluation metrics, and open-source implementations, and discusses the practical applications of community detection in various domains. The article concludes with suggestions for future research directions in this growing field of deep learning.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Review
Computer Science, Artificial Intelligence
Zaid Abdi Alkareem Alyasseri, Mohammed Azmi Al-Betar, Iyad Abu Doush, Mohammed A. Awadallah, Ammar Kamal Abasi, Sharif Naser Makhadmeh, Osama Ahmad Alomari, Karrar Hameed Abdulkareem, Afzan Adam, Robertas Damasevicius, Mazin Abed Mohammed, Raed Abu Zitar
Summary: COVID-19, caused by SARS-CoV-2, has become a pandemic infecting over 152 million people in more than 216 countries. This review paper summarizes over 200 studies from December 2019 to April 2021 on COVID-19 diagnosis using machine learning and deep learning techniques, highlighting SVM and CNN as widely used mechanisms for diagnosing and predicting outbreaks. Accuracy, sensitivity, and specificity are commonly used measurements in previous studies.
Article
Computer Science, Artificial Intelligence
Danfeng Hong, Lianru Gao, Jing Yao, Naoto Yokoya, Jocelyn Chanussot, Uta Heiden, Bing Zhang
Summary: This paper proposes a deep learning approach called endmember-guided unmixing network (EGU-Net) to improve the efficiency and accuracy of hyperspectral unmixing. By utilizing a two-stream Siamese deep network and adding spectrally meaningful constraints, EGU-Net is able to extract endmembers better and achieve more accurate and interpretable unmixing solutions.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Shuangming Yang, Jiang Wang, Xinyu Hao, Huiyan Li, Xile Wei, Bin Deng, Kenneth A. Loparo
Summary: The article introduces a biologically-inspired cognitive supercomputing system, BiCoSS, which integrates multiple GRs of SNNs to create a hybrid neuromorphic platform with efficient and scalable architecture and low power consumption. The system has successfully replicated various biological cognitive activities, demonstrating its high performance and potential applications.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yuxiang Zhang, Wei Li, Mengmeng Zhang, Ying Qu, Ran Tao, Hairong Qi
Summary: This article introduces a Topological structure and Semantic information Transfer network (TSTnet) for the problem of cross-scene hyperspectral image classification. The method uses a graph structure to characterize topological relationships and utilizes graph convolutional networks (GCN) to analyze the data. Experimental results demonstrate that TSTnet outperforms other domain adaptation methods.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yue Wu, Jiaheng Li, Yongzhe Yuan, A. K. Qin, Qi-Guang Miao, Mao-Guo Gong
Summary: An unsupervised change detection method is proposed using a convolutional autoencoder and a commonality autoencoder to extract common features in heterogeneous images, distinguishing changed and unchanged regions. Experimental results demonstrate the promising performance of this method compared to existing approaches.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Information Systems
Jiguo Li, Hao Yan, Yichen Zhang
Summary: To increase the availability and durability of outsourced data, many customers store multiple copies on multiple cloud servers. Existing PDP protocols mainly focus on single-copy storage and rely on PKI technique, which has security vulnerabilities and high communication/computational costs. In this paper, we propose a novel identity-based PDP scheme for multi-copy on multiple cloud storage servers, achieving both security and efficiency.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Hongjing Liang, Zhixu Du, Tingwen Huang, Yingnan Pan
Summary: This article investigates the problem of adaptive performance guaranteed tracking control for multiagent systems (MASs) with power integrators and measurement sensitivity. A new control approach is proposed to guarantee the convergence of the relative position error between neighboring agents within a preassigned finite time. By utilizing the Nussbaum gain technique and neural networks, a novel control scheme is developed to solve the unknown measurement sensitivity on the sensor, relaxing the restrictive condition. Based on the Lyapunov functional method, it is proven that the relative position error can converge into the prescribed boundary.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Qingxin Meng, Xuzhi Lai, Ze Yan, Chun-Yi Su, Min Wu
Summary: This article discusses an uncertain two-link rigid-flexible manipulator with vibration amplitude constraint, and aims to achieve its position control through motion planning and adaptive tracking approach. The motion trajectories planning for the manipulator's two links can guarantee reaching desired angles and suppress vibration, while the adaptive tracking controller enables the two links to track the planned trajectories under various uncertainties. Simulation results confirm the effectiveness of the proposed control strategy and the superior performance of motion planning and tracking controller.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Shuangming Yang, Jiang Wang, Nan Zhang, Bin Deng, Yanwei Pang, Mostafa Rahimi Azghadi
Summary: This article introduces a large-scale cerebellar network model and a cerebellum-inspired neuromorphic architecture, demonstrating improved biomimicry. Experimental results show that real-time operation can be achieved, with system throughput up to 4.70 times larger than previous works.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yongming Li, Jiaxin Zhang, Wei Liu, Shaocheng Tong
Summary: This work investigates an adaptive neural network optimized output-feedback control problem for a class of stochastic nonlinear systems with unknown nonlinear dynamics, input saturation, and state constraints. It proposes an optimized control strategy based on the backstepping technique and actor-critic architecture to prevent system violations of state constraints and ensure bounded signals in the closed-loop system.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
