Article
Computer Science, Artificial Intelligence
Taotao Cai, Jianxin Li, Ajmal S. Mian, Ronghua li, Timos Sellis, Jeffrey Xu Yu
Summary: In this study, we address the issue of scheduling online campaigns or advertisements on social network platforms by proposing a novel holistic influence diffusion model that considers both cyber and physical user interactions. We also develop algorithms and solutions to solve the problem of holistic influence maximization. Experimental results demonstrate the efficiency and effectiveness of the proposed model and algorithms.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Jianzhong He, Shiliang Zhang, Ming Yang, Yanhu Shan, Tiejun Huang
Summary: In this paper, a bi-directional cascade network (BDCN) architecture is proposed for edge detection at different scales. The network is supervised at specific scales and utilizes a scale enhancement module (SEM) to generate multi-scale features. The proposed method encourages the learning of multi-scale representations and achieves improved performance in edge detection and other vision tasks.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Chemistry, Analytical
Qianyi Shangguan, Zihao Chen, Hua Yang, Shubo Cheng, Wenxing Yang, Zao Yi, Xianwen Wu, Shifa Wang, Yougen Yi, Pinghui Wu
Summary: The paper proposes an ultra-narrow band graphene refractive index sensor with high absorption efficiency, adjustability, and sensitivity, which can be applied to photon detection in the terahertz band and biochemical sensing.
Article
Computer Science, Interdisciplinary Applications
Richard J. Chen, Ming Y. Lu, Jingwen Wang, Drew F. K. Williamson, Scott J. Rodig, Neal Lindeman, Faisal Mahmood
Summary: This study proposes an interpretable strategy for multimodal fusion of histology image and genomic features for survival outcome prediction. The results on glioma and clear cell renal cell carcinoma datasets demonstrate that this approach improves the prognostic determinations.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2022)
Article
Automation & Control Systems
Chao Deng, Changyun Wen, Jiangshuai Huang, Xian-Ming Zhang, Ying Zou
Summary: This article addresses the distributed tracking problem for uncertain nonlinear multiagent systems under event-triggered communication. A new methodology is proposed to achieve stability and asymptotic tracking through grouping and designing event-triggered observers. A simulation example demonstrates the effectiveness of the proposed method.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Computer Science, Artificial Intelligence
Chongyi Li, Chunle Guo, Chen Change Loy
Summary: This paper introduces a novel method called Zero-Reference Deep Curve Estimation (Zero-DCE), which utilizes a deep network for pixel-wise and high-order curve estimation without the need for reference images. The method shows strong generalization and efficiency, achieving image enhancement through simple and intuitive nonlinear curve mapping. Additionally, a lightweight version called Zero-DCE++ is presented with fast inference speed and maintained enhancement performance.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Chemistry, Analytical
Upesh Nepal, Hossein Eslamiat
Summary: In-flight system failure is a safety concern for unmanned aerial vehicles (UAVs) in urban environments. This paper investigates the feasibility of using object detection methods to find safe landing spots for UAVs suffering from in-flight failures. Different versions of the YOLO objection detection method are compared, and the YOLOv5l algorithm is found to outperform YOLOv4 and YOLOv3 in terms of detection accuracy.
Article
Engineering, Civil
Szilard Aradi
Summary: Academic research in the field of autonomous vehicles has gained popularity in recent years, covering various topics such as sensor technologies, communication, safety, decision making, and control. Artificial Intelligence and Machine Learning methods have become integral parts of this research. Motion planning, with a focus on strategic decision-making, trajectory planning, and control, has also been studied. This article specifically explores Deep Reinforcement Learning (DRL) as a field within Machine Learning. The paper provides insights into hierarchical motion planning and the basics of DRL, including environment modeling, state representation, perception models, reward mechanisms, and neural network implementation. It also discusses vehicle models, simulation possibilities, and computational requirements. The paper surveys state-of-the-art solutions, categorized by different tasks and levels of autonomous driving, such as car-following, lane-keeping, trajectory following, merging, and driving in dense traffic. Lastly, it raises open questions and future challenges.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xiang Zhang, Hai Wang, Vladimir Stojanovic, Peng Cheng, Shuping He, Xiaoli Luan, Fei Liu
Summary: Based on the interval type-2 fuzzy approach, this article investigates the fault detection filter design problem for a class of nonhomogeneous higher level Markov jump systems with uncertain transition probabilities. The proposed asynchronous IT2F filter, utilizing hidden Markov model and Gaussian transition probability density function, can effectively detect faults without error alarms, as verified by the simulation study on a quarter-car suspension system.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Dingwen Zhang, Junwei Han, Gong Cheng, Ming-Hsuan Yang
Summary: Weakly supervised object localization and detection is an emerging and challenging problem in the computer vision community. This article provides a comprehensive survey of classic models, approaches using off-the-shelf deep networks, purely deep learning-based approaches, as well as publicly available datasets and evaluation metrics. The article also discusses the key challenges, development history, advantages/disadvantages of different methods, relationships between methods, applications, and potential future directions in this research field.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Automation & Control Systems
Absalom E. Ezugwu, Abiodun M. Ikotun, Olaide O. Oyelade, Laith Abualigah, Jeffery O. Agushaka, Christopher I. Eke, Andronicus A. Akinyelu
Summary: Clustering is an essential tool in data mining, and there is a need for improved, flexible, and efficient clustering techniques. This study presents a comprehensive review of traditional and state-of-the-art clustering techniques, demonstrating the importance of clustering in various disciplines and fields such as big data, artificial intelligence, and robotics.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Zhuotao Tian, Hengshuang Zhao, Michelle Shu, Zhicheng Yang, Ruiyu Li, Jiaya Jia
Summary: This article proposes a Prior Guided Feature Enrichment Network (PFENet) to address the challenges of reduced generalization ability on unseen classes and spatial inconsistency between query and support targets in few-shot segmentation. PFENet includes a training-free prior mask generation method and a Feature Enrichment Module (FEM). Experimental results demonstrate that PFENet significantly improves the baseline method and achieves outstanding performance even without labeled support samples.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Engineering, Civil
Yongze Song, Peng Wu, Qindong Li, Yuchen Liu, Lalinda Karunaratne
Summary: Strategic maintenance is crucial for sustainable road infrastructure development. Accurate estimation of road maintenance effects can support the assessment of maintenance strategies and reasonable allocation of budgets and resources. The study developed a dynamic trade-off model (DTOM) to quantify the impacts of different factors, and found that 12 years of maintenance activities at the network level have effectively reduced roughness deterioration and improved road performance.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Zhi Tian, Chunhua Shen, Hao Chen, Tong He
Summary: FCOS is a fully convolutional one-stage object detector that is anchor box free and achieves higher detection accuracy through post-processing non-maximum suppression.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Engineering, Civil
Zhihan Lv, Yuxi Li, Hailin Feng, Haibin Lv
Summary: The study aims to enhance the security performance of digital twins in the Cooperative Intelligent Transportation System in a deep learning environment. By combining Convolutional Neural Network with Support Vector Regression, a model is constructed and analyzed through simulation experiments. Results show that the proposed algorithm has significant advantages in security performance and data transmission speed.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Khaled B. Letaief, Yuanming Shi, Jianmin Lu, Jianhua Lu
Summary: Researchers proposed the vision of an edge AI system that integrates wireless communication strategies and decentralized machine learning models to enhance the efficiency, effectiveness, privacy, and security of 6G networks. Additionally, standardization, software and hardware platforms, and application scenarios are discussed to facilitate the industrialization and commercialization of edge AI systems.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Hongyi Li, Ying Wu, Mou Chen, Renquan Lu
Summary: This article proposes a fault-tolerant adaptive multigradient recursive reinforcement learning event-triggered tracking control scheme for strict-feedback discrete-time multiagent systems. The scheme improves system stability and tracking accuracy by introducing a new event-triggered control strategy, adaptive compensation technique, and multigradient recursive RL algorithm.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xiaohan Wang, Linchao Zhu, Yu Wu, Yi Yang
Summary: In this paper, a framework called SAOA is proposed to tackle egocentric action recognition by suppressing background distractors and enhancing action-relevant interactions. The framework introduces two extra sources of information, spatial location and discriminative features of candidate objects, to enable concentration on the occurring interactions. It includes an object-centric feature alignment method and a symbiotic attention mechanism to provide meticulous reasoning between the actor and the environment, achieving state-of-the-art performance on the largest egocentric video dataset.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Geochemistry & Geophysics
Fulin Luo, Zehua Zou, Jiamin Liu, Zhiping Lin
Summary: The research proposes a multistructure unified discriminative embedding (MUDE) method to extract the low-dimensional features of hyperspectral image (HSI), by considering the neighborhood, tangential, and statistical properties of each sample in HSI for achieving the complementarity of different characteristics. Experimental results demonstrate that the proposed method can improve the classification performance of HSI.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Engineering, Civil
Jinchao Chen, Chenglie Du, Ying Zhang, Pengcheng Han, Wei Wei
Summary: Unmanned aerial vehicles (UAVs) are widely utilized in civilian and military applications for their high autonomy and strong adaptability. This paper addresses the coverage path planning problem of autonomous heterogeneous UAVs on a bounded number of regions by proposing an exact formulation based on mixed integer linear programming and a clustering-based algorithm inspired from density-based clustering methods to achieve optimal flight paths and efficient coverage tasks. Experiments demonstrating the efficiency and effectiveness of the proposed approach with randomly generated regions are conducted.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
