Article
Chemistry, Analytical
Yunyun Dong, Chenbin Liang, Changjun Zhao
Summary: This work proposed a method for image registration based on ProbNet (deep convolutional neural network) and RANSAC, achieving higher accuracy through probabilistic model estimation and the comprehensive application of deep learning. Qualitative and quantitative experiments validated the effectiveness and advantages of the proposed method in improving image registration accuracy.
Article
Instruments & Instrumentation
Lichun Mei, Caiyun Wang, Huaiye Wang, Yuanfu Zhao, Jun Zhang, Xiaoxia Zhao
Summary: This paper proposes a scheme called Hybrid Matching by Pixel Distribution Mapping (HMPDM) that combines traditional methods and neural network methods to achieve fast template matching. The scheme overcomes the challenge of nonlinear intensity differences between multi-modal images by extracting and mapping pixel distribution information. The experimental results demonstrate the real-time and accurate performance of the scheme.
INFRARED PHYSICS & TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Runzhong Wang, Junchi Yan, Xiaokang Yang
Summary: This study proposes an unsupervised framework for graph matching, which can match two or multiple graphs and handle graphs with a mixture of modes. The framework is trained by minimizing the discrepancy between a second-order classic solver and a first-order differentiable Sinkhorn net. Experimental results show that our method performs well in real-world applications such as natural image matching.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Zahra Hossein-Nejad, Mehdi Nasri
Summary: This paper proposes a new approach for object recognition in remote-sensing images, using Scale Invariant Feature Transform (SIFT) for matching the object in the template and test images. An adaptive Random sample consensus (RANSAC) algorithm is used to reduce false matches of SIFT, and the extended region-growing algorithm is used to extract the exact object boundary.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Chemistry, Analytical
Junchai Gao, Zhen Sun
Summary: The ASIFT algorithm is a feature extraction algorithm suitable for image feature matching using UAVs. It improves the matching efficiency by simulating image distortion, detecting feature points using the SIFT algorithm, generating binary feature descriptors combined with the BRISK descriptor, and matching using the Hamming distance. In addition, a false matching elimination algorithm based on RANSAC is proposed to improve the matching accuracy of UAV images.
Article
Engineering, Electrical & Electronic
Lichun Mei, Yuanfu Zhao, Huaiye Wang, Caiyun Wang, Jun Zhang, Xiaoxia Zhao
Summary: This article introduces an efficient and accurate method for template matching in multisource images by combining traditional algorithms and neural networks to reduce the use of system resources. The experimental results show that this method achieves good matching performance with limited hardware resources.
IET SIGNAL PROCESSING
(2023)
Article
Engineering, Multidisciplinary
Mohamed Ramadan, Mohamed El Tokhey, Ayman Ragab, Tamer Fath-Allah, Ahmed Ragheb
Summary: This research compared the performance of recent detection, description, and matching techniques, with ORB detector, ORB descriptor, and either BruteForce-Hamming or BruteForce-HammingLUT matchers favored for indoor environments.
AIN SHAMS ENGINEERING JOURNAL
(2021)
Article
Computer Science, Artificial Intelligence
Li Guo, Pengfei Shi, Long Chen, Chenglizhao Chen, Weiping Ding
Summary: In this paper, a novel affinity matrix is proposed to store and present the image spatial information as prior knowledge for membership regularized fuzzy clustering methods. Experimental results show that the proposed method outperforms state-of-the-art clustering methods in image segmentation.
INFORMATION FUSION
(2023)
Article
Computer Science, Information Systems
Xintai He, Qing Li, Runze Wang, Kun Chen
Summary: This study proposes a spatio-temporal feature trajectory clustering algorithm based on deep learning, which improves the clustering performance by combining image matching technology with trajectory temporal features.
Article
Computer Science, Artificial Intelligence
Shuaicheng Liu, Nianjin Ye, Chuan Wang, Jirong Zhang, Lanpeng Jia, Kunming Luo, Jue Wang, Jian Sun
Summary: This paper proposes an unsupervised deep homography method with a new architecture design. The method learns an outlier mask to select reliable regions for homography estimation and calculates loss based on learned deep features instead of comparing image content directly. Experimental results show that the proposed method outperforms state-of-the-art solutions, including deep and feature-based approaches.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Hardware & Architecture
Erik Cuevas, Jorge Galvez, Omar Avalos, Angel Chavarin
Summary: A new image segmentation method based on the mean shift algorithm is proposed in this paper, dividing the image into operative and inactive elements and processing them in two stages. Experimental results demonstrate that the proposed approach outperforms other current segmentation methods in terms of consistency, quality, velocity, and accuracy.
COMPUTERS & ELECTRICAL ENGINEERING
(2021)
Article
Environmental Sciences
Jairo Acuna Paz Y. Mino, Nicolas Duport, Benoit Beckers
Summary: The proposed method offers a more accurate assessment of urban surface temperatures by rectifying thermograms, especially when applied at human height.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Chemistry, Analytical
Yong Li, Chenguang Liu, Xiaoyu You, Jian Liu
Summary: In this paper, a white Gaussian noise estimation algorithm based on pixel-level low-rank, low-texture subblocks and principal component analysis is proposed. The algorithm utilizes adaptive clustering and eigenvalue selection methods to improve the accuracy and robustness of noise level estimation.
Article
Optics
Kecheng Yang, Long Yu, Min Xia, Tao Xu, Wei Li
Summary: A new algorithm, CCNL-RANSAC, is proposed in this article to detect curved line objects, improve cable localization success rate, and reduce execution time, achieving significant results in underwater cable detection experiments.
OPTICS AND LASERS IN ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Yan Liu, Jiawei Tian, Rongrong Hu, Bo Yang, Shan Liu, Lirong Yin, Wenfeng Zheng
Summary: In this paper, an improved feature-point pair purification algorithm based on SIFT and RANSAC is proposed to address the issue of limited imaging range in endoscopy. Experimental results validate the effectiveness of the proposed algorithm.
FRONTIERS IN NEUROROBOTICS
(2022)
Article
Engineering, Electrical & Electronic
Rongling Yu, Ping He, Heng Li, Jiannong Cao, Feiqi Deng
Summary: This article investigates the consensus problem of linear multi-agent systems (MASs) with unknown external disturbances under intermittent communication. Firstly, the distributed extended observer is utilized to observe the relative output information and unknown disturbance. Then, a distributed active disturbance rejection intermittent consensus protocol is proposed using the observer information. Finally, a simulation example is provided to demonstrate the effectiveness of the consensus protocol.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2023)
Review
Ergonomics
Maxwell Fordjour Antwi-Afari, Heng Li, Alan Hoi Shou Chan, JoonOh Seo, Shahnawaz Anwer, Hao-Yang Mi, Zezhou Wu, Arnold Yu Lok Wong
Summary: This review systematically analyzed and visualized research on work-related musculoskeletal disorders (WMSDs) among construction workers published between 2000 and 2021. The results identified influential authors and research topics, highlighting the significance of MSDs, ergonomics, and construction in this field. Contributions to WMSD research primarily came from the United States, Hong Kong, and Canada. The review also summarized mainstream research topics, identified research gaps, and proposed future directions.
JOURNAL OF SAFETY RESEARCH
(2023)
Article
Engineering, Electrical & Electronic
Zengcheng Sun, Ping He, Heng Li, Jiannong Cao, Feiqi Deng
Summary: The group consensus of second-order sample multi-agent systems is investigated using a distributed event-triggered mechanism. A consensus protocol is proposed, which is updated only when the event-triggered condition is met and the update only depends on the data collected at the moment of triggering. The sufficient condition for group consensus is obtained, and a differential equation is constructed to avoid the Zeno phenomenon and obtain a minimum positive and lower bound for any two trigger time intervals. The effectiveness is verified through a simulation example.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2023)
Article
Automation & Control Systems
Shijia Fan, Ping He, Peng Shi
Summary: This paper investigates the leader-following bipartite consensus of multi-agent systems (MASs) with matched uncertainty using the fully distributed asynchronous edge-based event-triggered mechanism. Event-triggered mechanisms are constructed for each edge and the leader to decrease the consumption of system resources. Sufficient conditions for the bipartite consensus of MASs are obtained using Lyapunov stability theory, and the feasibility of the proposed event-triggered mechanisms is further verified by excluding the Zeno phenomenon. The effectiveness of the control protocol is illustrated through simulation.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Health Care Sciences & Services
Tiffany H. T. Wong, Kaden S. K. Lee, Sharon M. C. Lo, Mandy M. P. Kan, Crystal Kwan, Emmanuelle Opsommer, Shahnawaz Anwer, Heng Li, Arnold Y. L. Wong, Veronika Schoeb
Summary: This study aimed to investigate the experiences, challenges, concerns, and coping strategies of older women with chronic low back pain (CLBP) in Hong Kong. The findings revealed that CLBP has negative impacts on older women's daily life, both physically and psychologically. They adopted various pain management strategies, with some influenced by the Chinese culture. Family support, as well as social activities and support from elderly community centers, were found to be important factors for their management of CLBP.
Article
Health Care Sciences & Services
Melissa Leung, Mandy M. P. Kan, Hugo M. H. Cheng, Diana E. De Carvalho, Shahnawaz Anwer, Heng Li, Arnold Y. L. Wong
Summary: The use of laptops leads to poor working postures and neck pain among university students. Postural braces have the potential to improve upper back/neck posture and may serve as an ergonomic aid for this population.
Article
Engineering, Environmental
Ziwei Qin, Yi Yang, Qingli Tian, Hao-Yang Mi, Heng Li, Runhao Guo, Ying Wang, Chuntai Liu, Changyu Shen
Summary: Developing protective materials with intelligent and stimuli-responsive properties is essential for safety purposes. However, combining contradictory properties into a single polymer is challenging. In this study, a strain-hardening supramolecular polyurethane nanocomposite (SPN) with high mechanical strength and unique impact protection properties is synthesized by incorporating quadruple hydrogen bonds (H-bonds), disulfide bonds, and modified silica nanoparticles into polyurethane elastomer. This SPN material exhibits high energy absorption efficiency and impact protection properties through the dynamic break and re-form characteristic of quadruple H-bonds and the stress dissipation behavior of nanoparticles.
CHEMICAL ENGINEERING JOURNAL
(2023)
Article
Engineering, Manufacturing
Ziran Du, Cheng Zhou, Hao-Yang Mi, Heng Li, Ziwei Qin, Ruyan Xu, Yaming Wang, Chuntai Liu, Changyu Shen
Summary: High-performance electromagnetic interference (EMI) shielding materials with low reflection are urgently needed to address secondary electromagnetic pollution. This study proposes ecoflex-encased gradient MXene-decorated melamine foams (eGMMFs) to achieve both ultra-low reflection and efficient shielding. By designing a gradual increased gradient MXene conductive network and a reflector of reduced graphene oxide, the eGMMF integrates multiple layers for improved impedance matching, loss, and reflection, resulting in an average shielding efficiency of 78.8 dB and a reflection coefficient (R) of 0.022 in the X-band. A record-low R value of 3.84 x 10(-5) at 11.52 GHz is achieved, surpassing previously reported MXene-based shields. Furthermore, the developed eGMMF exhibits remarkable piezoresistive sensing and anti-impacting performance. This work provides new insights and guidance for the development of multifunctional shielding and absorbing materials.
COMPOSITES PART A-APPLIED SCIENCE AND MANUFACTURING
(2023)
Article
Automation & Control Systems
Xin Fang, Heng Li, Haitao Wu, Lang Fan, Ting Kong, Yue Wu
Summary: This paper introduces a method based on 360 degrees panoramic images and deep learning for fast end-to-end interior progress evaluation. The proposed method takes only one or two 360 degrees panoramic images as input, estimates key corners, generates and registers room layouts, and semantically segments sparse point cloud. The experimental results show that the proposed method achieves comparable performance against those on public data sets.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Automation & Control Systems
Xin Fang, Heng Li, Sherong Zhang, Jikang Zhang, Chao Wang, Xiaohua Wang, Ziao Ma, He Jia
Summary: This study provides an innovative automated underwater inspection analysis scheme using ROV and computer vision techniques to inspect and evaluate the invasion of L. fortunei in water conveyance structures. The scheme improves underwater image quality and accurately detects L. fortunei through image enhancement and segmentation methods.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Green & Sustainable Science & Technology
Pan Zhang, Haitao Wu, Heng Li, Botao Zhong, Ivan W. H. Fung, Yiu Yin Raymond Lee
Summary: Efficient information sharing is crucial for the success of Modular integrated Construction (MiC) projects. Blockchain provides powerful solutions to address information barriers and foster trust among MiC stakeholders. However, the construction industry lacks empirical evidence and is cautious about adopting this disruptive technology. This study uses game theory to examine stakeholders' decision-making processes and identifies key factors influencing their decisions, such as diffusion rate value thresholds, benefits of blockchain adoption, government subsidies, and relevant costs. The study also suggests pilot projects and subsidy or incentive policies to enhance stakeholders' perceived values and promote blockchain adoption in MiC projects.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Construction & Building Technology
Shahnawaz Anwer, Heng Li, Waleed Umer, Maxwell Fordjour Antwi-Afari, Imran Mehmood, Yantao Yu, Carl Haas, Arnold Yu Lok Wong
Summary: This study analyzed heart rate variability (HRV) using nonlinear methods to determine the level of physical fatigue in construction workers. It showed that both linear and nonlinear HRV analyses can be used to detect and classify physical fatigue in construction workers.
JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT
(2023)
Article
Construction & Building Technology
Jie Ma, Heng Li, Xinge Yu, Xin Fang, Bo Fang, Zeyu Zhao, Xingcan Huang, Shahnawaz Anwer, Xuejiao Xing
Summary: This study proposes a novel approach to monitor construction rebar benders' fatigue levels by measuring chemical biomarkers using sweat sensors. The study selected sodium, lactate, glucose, and sweat rate as detectable biomarkers, which can indicate hydration status, energy consumption, and electrolyte balance. A fatigue model was constructed using supervised machine learning approaches based on the results. The study demonstrates the potential of sweat-based biomarkers as a noninvasive and accessible fatigue monitoring alternative.
JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT
(2023)
Article
Engineering, Industrial
Tai Wai Kwok, SiWei Chang, Heng Li
Summary: This study investigates client satisfaction with UCWS products in Hong Kong and identifies reduction in construction time and reduction in construction waste as the most important factors influencing client satisfaction.
ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT
(2023)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
