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2024 China Automation Congress, CAC 2024

作者： Jiao, Ranran Li, Bo Chen, Xiangyong Jiang, Xiaowei School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Finance Anhui University of Finance and Economics Bengbu China Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China

ISBN: (纸本)9798350368604

This paper addresses the problem of leader-following consensus in heterogeneous multi-agent systems (HMAS) with input saturation and communication delay. Given the practical differences between leader and follower dimensions, we design virtual leaders to address this problem by allowing actual leaders and followers to communicate with virtual leaders separately, thus enabling indirect communication between actual leaders and followers. Additionally, the control protocol proposed in this paper takes into account the effects of input saturation and time delay that exist in real communication. At last, a simulation example is presented to demonstrate the effectiveness of the designed control protocol. © 2024 IEEE.

关键词： Multi agent systems

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Live Demonstration: Smart Watchdog Mechanism for Real-time Fault Detection in RISC-V

Live Demonstration: Smart Watchdog Mechanism for Real-time F...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： David Simpson Jim Harkin Malachy McElholm Liam McDaid School of Computing Engineering and Intelligent Systems Ulster University Derry Northern Ireland UK

ISBN: (数字)9798350356830

ISBN: (纸本)9798350356847

This live demonstration relates to our paper titled "Smart Watchdog Mechanism for Fault Detection in RISC-V" also published at ISCAS 2025. Spiking neural networks (SNNs) can realise low power and area implementations compared to traditional neural network models. We present an interactive demonstration of a SNN-based smart watchdog circuit capable of real-time monitoring and detection of errors during program execution in a modern RISC-V processor. The smart watchdogs performance is demonstrated by monitoring the RISC-V core running a control task resembling a safety-critical application, where demo-attendees can inject faults into the program counter register and witness the detection of errors and how control flow errors impacts the motor operation. The demo permits various fault injection patterns, i.e. bit flips and stuck at zero/one faults.

关键词： Circuits and systems Fault detection Neural networks Spiking neural networks Electrical fault detection Motors Real-time systems Registers Integrated circuit modeling Monitoring

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Multiscale Bilateral Attention Fusion Network for Pansharpening

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5828-5843页

作者： Guo, Zhongyuan Li, Jiawei Lei, Jia Liu, Jinyuan Zhou, Shihua Wang, Bin Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand University of Ulster Intelligent Systems Research Center LondonderryBT48 7JL United Kingdom Bulgarian Academy of Sciences IICT Sofia1000 Bulgaria

High-resolution multispectral (HRMS) images combine spatial and spectral information originating from panchromatic (PAN) and reduced-resolution multispectral (LRMS) images. Pansharpening performs well and is widely used to obtain HRMS images. However, most pansharpening approaches determine the ratio of PAN and LRMS images through direct interpolation, which may introduce artifacts and distort the color of the fused results. To address this issue, an unsupervised progressive pansharpening framework, MSBANet, is proposed, which adopts a multistage fusion strategy. Each stage contains an attention interactive extraction module (AIEM) and a multiscale bilateral fusion module (MBFM). The AIEM extracts spatial and spectral features from input images and captures the correlations between features. The MBFM can efficiently integrate information from the AIEM and improve MSBANet context awareness. We design a hybrid loss function that enhances the ability of the fusion network to store spectral and texture details. In qualitative and quantitative experimental studies on four datasets, MSBANet outperformed state-of-the-art pansharpening techniques. The code will be released. © 2024 IEEE.

关键词： Data mining

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Feature-based Approaches to Multi-View Image Stitching

Feature-based Approaches to Multi-View Image Stitching

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Computational Intelligence in Image, Signal Processing and Synthetic Media Companion (CISM Companion), IEEE Symposium on

作者： Samuel Venezia Sonya Coleman Dermot Kerr John Fegan School of Computing Engineering and Intelligent Systems Ulster University Londonderry Derry Northern Ireland

ISBN: (数字)9798331508517

ISBN: (纸本)9798331508524

Image stitching, the technique of merging multiple images with overlapping regions, has various applications in real-life scenarios such as security systems, autonomous vehicles, and sports analytics. Several image stitching methodologies have been developed, resulting in a wide range of traditional feature-based and deep learning approaches. In this paper, we utilise the Adobe Panoramic Dataset to assess eight approaches for stitching four overlapping images. We develop six novel feature-based stitching approaches, including AKAZE-SIFT-BF KNN-RANSAC. As an example approach, KAZE-SIFT-BF KNN-RANSAC utilises Accelerated KAZE (AKAZE) for feature detection, Scale Invariant Feature Transformation (SIFT) to generate feature descriptors, Brute Force with k-Nearest Neighbours and a ratio test for keypoint matching, and Random Sample Consensus (RANSAC) for matched keypoint outlier removal. Evaluation is performed using the CombSIQE metric and qualitative comparison to evaluate the quality of the resulting stitched images. Performance evaluation shows that our proposed approaches produce comparable quality stitched images to the optimised SOTA SuperPoint and SuperGlue approach in outdoor scenes, while remaining consistent in indoor scenes.

关键词： Performance evaluation Object detection Nearest neighbor methods Signal processing Feature extraction Real-time systems Security Image stitching Optimization Sports

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BFP-Net: Boundary Feature Pyramid for Medical Image Segmentation

BFP-Net: Boundary Feature Pyramid for Medical Image Segmenta...

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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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