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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Min, Shiquan Zhang, Xiang Wang, Shunfang Yunnan University School of Information Science and Engineering Kunming650504 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming650504 China

ISBN: (纸本)9798350337488

In this paper, we propose a novel method, namely boundary feature pyramid network (BFP-Net), which can effectively segment targets with blurred boundaries. Specifically, we first propose a global feature fusion module (GFFM) at the top of BFP-Net to fuse feature maps within different scales. It can learn more global region localization of the target and help the learning of boundaries more effectively. Then, we propose a series of boundary enhancement modules (BEMs) at the decoder to effectively extract and integrate boundary information during the upsampling process, thereby enhancing the ability to capture fine details (such as the boundaries). Furthermore, we introduce a boundary-enhanced composite loss function to effectively segment both the regions and their boundaries within different scales. Finally, extensive experiments on two widely-used datasets demonstrate that BFP-Net is more effective in fusing contextual information and guiding feature map boundaries compared to previous competitive methods. © 2023 IEEE.

关键词： Boundary enhancement Feature pyramid Global feature fusion Medical images Segmentation

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Observer-Based Sliding Mode Frequency Control for Photovoltaic Energy Storage Interconnected Power System 41

Observer-Based Sliding Mode Frequency Control for Photovolta...

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第41届中国控制会议

作者： Zhipeng Wang Liping He Huawei Liu Jian Lv Jianlong Qiu School of Automation and Electrical Engineering Linyi University Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,the load frequency control(LFC) of the photovoltaic(PV) energy storage system is studied,and the uncertainty disturbance of the system and the communication delay between energy storage equipment and traditional units are *** paper introduces white noise into PV power generation to simulate a more realistic PV power generation *** of the disturbance,the state error of the system cannot be accurately estimated,so the state observer is introduced and the state equation of the system is *** further maintain the stability of the system,the controller is designed based on sliding ***,the feasibility of the control strategy is verified by simulation.

关键词： Sliding mode control Time delay Load frequency control

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Efficient Parameter Learning of Bayesian Network with Latent Variables from High-Dimensional Data 5

Efficient Parameter Learning of Bayesian Network with Latent...

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5th IEEE International Conference on Artificial Intelligence Circuits and systems, AICAS 2023

作者： Wu, Xinran Chen, Xiang Yue, Kun Yunnan University School of Information Science and Engineering Kunming650500 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming650500 China

ISBN: (纸本)9798350332674

Bayesian network with latent variables (BNLV) plays an important role in the representation of dependence relations and inference of uncertain knowledge with unobserved variables. The variables with large cardinalities in high-dimensional data make it challenging to efficiently learn the large-scaled probability parameters as the conditional probability distributions (CPDs) of BNLV. In this paper, we first propose the multinomial parameter network to parameterize the CPDs w.r.t. latent variables. Then, we extend the M-step of the classic EM algorithm and give the efficient algorithm for parameter learning of BNLV. Experimental results show that our proposed method outperforms some state-of-the-art competitors. © 2023 IEEE.

关键词： Bayesian networks

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Decoding Motion Trajectories in an Upper Limb BCI: Linear Regression vs Deep Learning 2

Decoding Motion Trajectories in an Upper Limb BCI: Linear Re...

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2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2023

作者： Shane, McNiall McCreadie, Karl Charles, Darryl Korik, Attila Coyle, Damien School of Computing Engineering and Intelligent Systems Ulster University Londonderry Derry United Kingdom The Bath Institute for the Augmented Human University of Bath Bath United Kingdom

ISBN: (纸本)9798350300802

Non-invasive electroencephalogram (EEG) based brain-computer interfaces (BCI) aim to achieve control using only brain signals. Motion trajectory prediction (MTP) is a method that can be used for translating imagined three-dimensional (3D) movement to virtual limb control. This process can be enhanced in an experimental setup using 3D embodied visual feedback along with more advanced decoding approaches. Our previous studies have used multilinear regression (mLR) as a method of decoding imagined limb movement, achieving a correlation but lack the capacity to identify non-linear relationship in the data and depend on optimised features through a grid search or other approaches. Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM)-based decoders have achieved higher correlation due to their ability to address the shortcoming listed above with extensive hyperparameter optimisation. This work presents an experimental setup for an online MTP BCI using 2D and 3D visual feedback and focuses on comparing both mLR and CNN LSTM decoding methods. Preliminary results in this pilot study demonstrated that CNN LSTM significantly improves decoding performance with an average improvement of r=0.4(p © 2023 IEEE.

关键词： 3D BCI Brain-Computer Interface CNN Kinematic Linear Regression LSTM Motion Trajectory Prediction Motor Imagery Upper Limb Virtual Environment Virtual Reality Visual Feedback

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Deep Learning-based Paddy Doctor for Sustainable Agriculture 7

Deep Learning-based Paddy Doctor for Sustainable Agriculture

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Garg, Vijay Agarwal, Stuti Sharma, Sahil International Institute of Information Technology Bangalore India Panjab University Chandigarh India Ulster University School of Computing Engineering And Intelligent Systems Londonderry United Kingdom

ISBN: (纸本)9798350371406

In promoting sustainable agriculture, the prompt and precise identification of plant diseases and pests becomes critical in mitigating economic setbacks via timely interventions. Utilizing EfficientNet, a deep learning-based convolutional neural network (CNN), we categorized images using open-source datasets on various rice strains. Our empirical findings confirm that our suggested model architecture attains a commendable accuracy of 91% with imbalanced classes. Discarding blurry images and employing data augmentation techniques enhanced the model's precision in rice disease categorization by an additional 8.4%. The corresponding code will be publicly accessible here. © 2023 IEEE.

关键词： Convolutional neural networks

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A Comparative Study of Hough Transform and PCA for Bolt Orientation Detection 22

A Comparative Study of Hough Transform and PCA for Bolt Orie...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Gambale, Antonio Coleman, Sonya Kerr, Dermot Vance, Philip Kerr, Emmett Fermuller, Cornelia Aloimonos, Yiannis School of Computing Engineering & Intelligent Systems Ulster University Londonderry United Kingdom Dept. of Electronic & Mechanical Engineering Atlantic Technological University Letterkenny Ireland Institute for Advanced Computer Studies University of Maryland United States

ISBN: (纸本)9798331527471

In the fields of manufacturing and robotics, accurately determining the orientation of manufacturing components, such as bolts, is a critical yet challenging problem due to the limitations of existing detection methods. This study introduces a novel methodology for addressing this issue, leveraging traditional computer vision techniques, by proposing a streamlined approach that exploits the inherent geometric properties of bolts for orientation detection. Two methods are presented to ascertain the initial axis angle of the bolt: the Progressive Probabilistic Hough Transform (PPHT) and Principal Component Analysis (PCA). These methods are used in conjunction with a novel tip direction detection approach. The results of the study demonstrate consistent accuracy in angle determination, with PPHT and PCA both achieving angular deviations below ±0.5◦ in the simple dataset, and PCA showing enhanced robustness in the dataset degraded by shadows, with a maximum error under ±1.5◦. This research not only reaffirms the viability of fundamental computer vision techniques in modern robotic applications but also sets a precedent for simple, generalisable, and reliable orientation detection solutions. These methods effectively bridge the gap between highly specialised machine learning systems, which often require tailored, complex models and extensive training data, and more universally applicable, straightforward approaches. © 2024 IEEE.

关键词： Bolts

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COVID-19 Lung CT Image Segmentation: A Comparison of Various U-Net Variants 2nd

COVID-19 Lung CT Image Segmentation: A Comparison of Various...

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2nd International Conference on intelligent Manufacturing and Robotics, ICIMR 2024

作者： Alebiosu, David Olayemi Folayan, Adeola Chen, Wei Tolulope, Abejide Ajibade, Samuel-Soma M. School of Engineering and Technology Department of Computing and Information Systems Sunway University Petaling Jaya Malaysia School of Medical and Health Science Monash University Petaling Jaya Malaysia School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China College of Science and Engineering University of Derby Derby United Kingdom Department of Computing and Information Systems Sunway University Petaling Jaya Malaysia Department of Computer Engineering Istanbul Ticaret Universitesi Istanbul Turkey

ISBN: (纸本)9789819639489

Lung segmentation has become a bedrock in the effective diagnosis, and classification of coronavirus (COVID-19) from radiological images such as computed tomography (CT) and X-ray images. Since the coronavirus (COVID-19) discovery, several methods have been employed to segment the COVID-19-infected areas from lung CT images. One of the most popular segmentation methods is the U-Net model. U-Net is a convolutional neural network used for medical image segmentation. U-Net and its variants have become a more reliable architecture used for medical image segmentation. U-Net models have produced outstanding results in segmenting diseases such as COVID-19 from lung CT images. The exceptional results produced by the U-Net model have inspired various researchers to explore the potential of U-Net for various segmentation tasks. This study compares the performances of recently used state-of-the-art U-Net models on lung CT images for tuberculosis segmentation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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MP-GAN: Cyber-Attack Detection and Localization for Cyber-Physical systems with Multi-Process Generative Adversarial Networks*

MP-GAN: Cyber-Attack Detection and Localization for Cyber-Ph...

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2023 International Conference on Artificial Intelligence of Things and systems, AIoTSys 2023

作者： Zhou, Yikui Wang, Jie Tang, Junnan Gou, Chao Jiang, Zigui Li, Dan Ng, See-Kiong Sun-Yat Sen University School of Software Engineering Zhuhai China Sun-Yat Sen University School of Intelligent Systems Engineering Shenzhen China National University of Singapore Institute of Data Science School of Computing Singapore

ISBN: (纸本)9798350312270

Cyber-Physical System (CPS) integrates sensing, computation, cybernetics, and networking to control a hybrid physical system consisting of different functional subsystems, making the production process more intelligent and controllable. However, cyber-attacks during its operation will lead to abnormal system behaviors or even system breakdowns. In recent years, data-driven anomaly detection methods have been adopted to judge whether the CPS system is under cyber-attacks based on rich sensor measurements to avoid further economic losses or safety issues. However, the multi-process essence of CPS has not been adequately addressed in existing works to locate the processes or points being attacked for in-time actions. In this work, we proposed a Multi-Process Generative Adversarial Network (MP-GAN) framework to detect anomalous CPS statuses and locate cyber-attacks. Specifically, the Long-Short-Term-Memory Recurrent Neural Networks (LSTM-RNN) was adopted as the base model to capture the temporal correlation of time series distributions, and the data was transferred between latent space and data space in a bi-directional manner, which regulated the generator to generate more realistic samples and thus better grasping the underline principles of the system. Moreover, parallel generators were employed to capture system performances at different physical processes, thus localizing the attacked CPS processes. Experiments on three CPS datasets, two collected from the Secure Water Treatment (SWaT) system and one collected from the water Distribution (WADI) system, showed that the proposed MP-GAN framework effectively reported anomalies caused by various cyber-attacks inserted in these complex multiprocess CPSs, which outperforms the state-of-the-art methods and can also locate most of the detected irregularities at the corresponding stages where the attacks were inserted. © 2023 IEEE.

关键词： Cyber Physical System

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A survey on deep learning for polyp segmentation: techniques, challenges and future trends

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Visual Intelligence 2025年第1期3卷 1-20页

作者： Mei, Jiaxin Zhou, Tao Huang, Kaiwen Zhang, Yizhe Zhou, Yi Wu, Ye Fu, Huazhu PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Southeast University Nanjing China Institute of High Performance Computing A*STAR Singapore Singapore

Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist clinicians in accurately locating and segmenting polyp regions. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had problems capturing global context and lacked robustness to complex scenarios. With the advent of deep learning, more and more medical image segmentation algorithms based on deep learning networks have emerged, making significant progress in the field. This paper provides a comprehensive review of polyp segmentation algorithms. We first review some traditional algorithms based on manually extracted features and deep segmentation algorithms, and then describe benchmark datasets related to the topic. Specifically, we carry out a comprehensive evaluation of recent deep learning models and results based on polyp size, taking into account the focus of research topics and differences in network structures. Finally, we discuss the challenges of polyp segmentation and future trends in the field. © The Author(s) 2025.

关键词： Comprehensive evaluation Deep learning Medical imaging Polyp segmentation

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Comparative Study of Parameter Selection for Enhanced Edge Inference for a Multi-Output Regression model for Head Pose Estimation

Comparative Study of Parameter Selection for Enhanced Edge I...

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2022 IEEE Region 10 International Conference, TENCON 2022

作者： Lindamulage, Asiri Kodagoda, Nuwan Reyal, Shyam Samarasinghe, Pradeepa Yogarajah, Pratheepan Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka School of Computing Engineering and Intelligent Systems Ulster University United Kingdom

Magnitude-based pruning is a technique used to optimise deep learning models for edge inference. We have achieved over 75% model size reduction with a higher accuracy than the original multi-output regression model fo... 详细信息

ISBN: (数字)9781665450959

ISBN: (纸本)9781665450959

关键词： Deep learning

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