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Article
Automation & Control Systems
Fanrong Qu et al.
Summary: This paper investigates distributed fusion over sensor networks with probabilistic constraints and stochastic perturbations. A new event-triggering mechanism called TETM is utilized to save bandwidth resources, and a time-varying distributed fusion filter is designed. Global fusion is obtained through recursive linear matrix inequality technique, and local filter parameters are computed by solving an optimization problem. A numerical simulation is used to illustrate the effectiveness of the proposed distributed fusion strategy.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2022)
Article
Automation & Control Systems
Wen Li et al.
Summary: This study addresses the consensus tracking problem for second-order multi-agent systems under channel fading by introducing an event-triggered sliding mode controller to reduce network burden. The statistical characteristic of channel fading is integrated into the measurement function to mitigate the impact on transmission among followers. Theoretical analysis confirms the reachability and stability of the multi-agent system, while also demonstrating the event triggering strategy's ability to eliminate Zeno behavior. A simulation example is provided for verification.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2022)
Article
Medicine, General & Internal
Happy Nkanta Monday et al.
Summary: Chest X-ray is a useful method for COVID-19 evaluation, and a computer-aided diagnosis approach using AI can reduce clinician burden. This study proposes a super-resolutionbased Siamese wavelet multi-resolution CNN called COVID-SRWCNN for COVID-19 classification. By reconstructing high-resolution images and learning high-level features, the proposed model achieves high accuracy and useful performance for COVID-19 screening.
Article
Automation & Control Systems
Guangyan Bao et al.
Summary: This paper surveys the cooperative control of heterogeneous multi-agent systems (HMASs) subject to specific constraints. HMASs are classified into weak and strong categories based on different cooperative behaviors, and control strategies are discussed for dealing with various constraints on agent dynamics and communication networks. The latest results on cooperative control under different constraints are summarized, and conclusions are drawn along with possible future research directions.
SYSTEMS SCIENCE & CONTROL ENGINEERING
(2022)
Article
Automation & Control Systems
Han Li et al.
Summary: This paper reviews the use of mathematical tools to enhance LFIA performance, and proposes a novel taxonomy. It also presents the outlook of developing POCT in conjunction with other state-of-the-art techniques, and highlights the importance of applying computational intelligence methods in boosting POCT development.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Han Li et al.
Summary: In the context of the global COVID-19 pandemic, a computer aided diagnosis model called Cov-Net is proposed for accurate recognition of COVID-19 from chest X-ray images. Experimental results demonstrate the high feasibility and accuracy of the model in identifying COVID-19. Compared to other algorithms, Cov-Net exhibits superior performance and competitiveness in this task.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Biology
Han Li et al.
Summary: In this paper, a feature learning enhanced convolutional neural network (FLE-CNN) is proposed for cancer detection from histopathology images. Experimental results demonstrate the merits of the proposed FLE-CNN in terms of feature extraction, achieving improved performance compared to other advanced deep learning models.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Engineering, Electrical & Electronic
Nianyin Zeng et al.
Summary: In this article, a novel enhanced multiscale feature fusion method called ABFPN is proposed to improve the detection performance of small objects. It is evaluated on benchmark datasets and applied to detect surface defects on printed circuit boards. Experimental results demonstrate its reliability and efficiency.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Computer Science, Artificial Intelligence
Waleed M. Bahgat et al.
Summary: Accurate and fast detection of COVID-19 patients is crucial, especially in countries with scarce testing kits. The study introduces an Optimized Transfer Learning-based Approach for Automatic Detection of COVID-19 (OTLD-COVID-19) that uses an optimization algorithm to improve CNN architectures' classification performance. Experimental results show that the DenseNet121 optimized architecture achieves the best performance across various evaluation metrics.
PEERJ COMPUTER SCIENCE
(2021)
Article
Automation & Control Systems
Jingyang Mao et al.
Summary: Recursive filtering for nonlinear systems is a core technology in modern industrial systems, facing challenges such as communication scheduling, limited bandwidth, and security vulnerability. It is of utmost significance in theory and great importance in applications to establish engineering-feasible recursive filtering algorithms for networked nonlinear systems. This paper provides an up-to-date survey of existing nonlinear filtering techniques and raises several challenging issues for further research and practical applications.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2021)
Article
Automation & Control Systems
Hang Geng et al.
Summary: Multi-sensor filtering fusion (MSFF) is a fascinating subject in the realm of networked filtering due to its advantage of effectively integrating sensor outputs from multiple sources. This paper aims to provide a bibliographical review on MSFF problems with censored measurements under a constrained network environment, including canonical approaches, mathematical models, handling strategies, communication constraints, latest progress, challenges, and future directions.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Yuqi Fan et al.
Summary: The rapid spread of COVID-19 poses a global threat to health and lives, highlighting the importance of early detection and isolation. This study proposes a multi-kernel-size spatial-channel attention method for detecting COVID-19 from chest X-ray images, which improves detection performance with an accuracy of 98.2%.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Samson Anosh P. Babu et al.
Summary: During the COVID-19 pandemic, doctors in radiology achieved good performance in COVID-19 chest X-ray classification using an improved deep learning technique and ResNet-50 model, outperforming several existing methods.
APPLIED INTELLIGENCE
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Daphna Keidar et al.
Summary: During the COVID-19 outbreak, chest X-ray imaging has been important in diagnosing and monitoring patients. A deep learning model was proposed for COVID-19 detection from CXRs, along with a tool for retrieving similar patients. The model achieved high accuracy, specificity, and sensitivity on a test dataset.
EUROPEAN RADIOLOGY
(2021)
Article
Automation & Control Systems
Shanjiang Tang et al.
Summary: Efficient screening of COVID-19 cases is crucial to prevent the rapid spread of the disease, and the EDL-COVID model, combining deep learning and ensemble learning, shows promising results in COVID-19 case detection with a higher accuracy compared to the COVID-Net model.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Information Systems
Sarra Guefrechi et al.
Summary: This study designed a deep learning system for extracting features and detecting COVID-19 from chest X-ray images, and fine-tuned three powerful neural networks on an enhanced dataset through transfer learning, achieving efficient and accurate COVID-19 detection methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Xiaohang Fu et al.
Summary: Multimodal PET-CT combines PET's sensitivity for tumor detection with CT's anatomical information for cancer assessment. Existing automated segmentation methods are often ineffective, leading to manual segmentation by imaging experts which is labor-intensive and error-prone.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2021)
Article
Automation & Control Systems
Zehao Li et al.
Summary: This paper addresses the distributed filtering problem for a certain class of delayed nonlinear systems with random sensor saturation under a dynamic event-triggered mechanism. It utilizes Bernoulli-distributed random variables and saturation functions to model the phenomenon of random sensor saturation. The aim is to design a sub-optimal filter to minimize the covariance of the filtering error by appropriately calculating the filter gain.
SYSTEMS SCIENCE & CONTROL ENGINEERING
(2021)
Article
Automation & Control Systems
Peng Lu et al.
Summary: A new approach that combines convolutional neural network with augmented dataset is proposed in this paper to address the human face recognition issue on a small original dataset, achieving higher accuracy in face recognition by effectively extracting features from augmented face image dataset. This novel approach's effectiveness and superiority are confirmed through experiments and comparisons with commonly used face recognition methods.
SYSTEMS SCIENCE & CONTROL ENGINEERING
(2021)
Article
Automation & Control Systems
Jie Xu et al.
Summary: This paper focuses on the fault estimation problem for a class of nonlinear systems with sensor gain degradation and stochastic protocol based on strong tracking filtering. The method constructs an augmented system and introduces a fading factor into the filter structure to address sensor gain degradation and data conflicts in multi-signal transmission. Simulation results demonstrate the effectiveness and applicability of the proposed approach.
SYSTEMS SCIENCE & CONTROL ENGINEERING
(2021)
Article
Automation & Control Systems
Baoye Song et al.
Summary: This paper introduces a new workspace model to describe the environment of coal mines and develops an improved ant colony optimization algorithm for path planning of coal mine robots. Simulation experiments confirm the effectiveness and superiority of the new approach.
SYSTEMS SCIENCE & CONTROL ENGINEERING
(2021)
Article
Automation & Control Systems
Munif Alotaibi et al.
Summary: COVID-19 is a virus that causes various symptoms and spreads easily. Early detection is crucial, but current diagnostic tests have limitations. Using deep learning algorithms for accurate diagnosis is a pressing need.
INTELLIGENT AUTOMATION AND SOFT COMPUTING
(2021)
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Computer Science, Artificial Intelligence
Yanfei Hong et al.
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Mathematics, Interdisciplinary Applications
Harsh Panwar et al.
CHAOS SOLITONS & FRACTALS
(2020)
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Radiology, Nuclear Medicine & Medical Imaging
Geoffrey D. Rubin et al.
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Biology
Tanvir Mahmud et al.
COMPUTERS IN BIOLOGY AND MEDICINE
(2020)
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Biology
Tulin Ozturk et al.
COMPUTERS IN BIOLOGY AND MEDICINE
(2020)
Proceedings Paper
Biochemical Research Methods
Lulu Wang et al.
2020 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE
(2020)
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Multidisciplinary Sciences
Linda Wang et al.
SCIENTIFIC REPORTS
(2020)
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Computer Science, Artificial Intelligence
Goncalo Marques et al.
APPLIED SOFT COMPUTING
(2020)
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Computer Science, Information Systems
Muhammad E. H. Chowdhury et al.
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Mathematical & Computational Biology
Shaoguo Cui et al.
JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS
(2019)
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Computer Science, Artificial Intelligence
Stefan Elfwing et al.
Proceedings Paper
Computer Science, Artificial Intelligence
Gao Huang et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
(2017)
