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2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023

作者： Ohtomo, Kazuma Kitahara, Yukina Harakawa, Ryosuke Nakamura, Akihiro Shida, Yosuke Ogasawara, Wataru Iwahashi, Masahiro Nagaoka University of Technology Department of Information Science and Control Engineering Japan Nagaoka University of Technology Department of Electrical Electronics and Information Engineering Japan Nagaoka University of Technology Department of Science of Technology Innovation Japan Nagaoka University of Technology Department of Bioengineering Japan

ISBN: (纸本)9798350359855

Feature representation of oleaginous yeast images is essential for non-invasively observing their cultivation status. However, conventional methods are difficult to capture the visual characteristics of oleaginous yeasts at the extremely local spatial regions. To tackle this problem, this paper proposes micro-spatial attention with sparse constraints for self-supervised learning. Specifically, we newly design a micro-spatial attention module to focus on the extremely local spatial regions for feature representation. Unlike the existing spatial attention module, which requires the guide information (e.g., labels and textual queries) to focus on specific regions, our proposed module employs a sparse constraint. By combining the sparse constraint with self-supervised learning, we can automatically focus on essential regions, which appear in extremely local regions. This improves the feature representation power for oleaginous yeast images with micro-spatial contexts. Experiments on real Lipomyces starkeyi datasets, a specific type of oleaginous yeasts, confirm the superiority of our method. Moreover, our method realized clustering of oleaginous yeast images, enabling us to non-invasively observe the cultivation trends over time. © 2023 IEEE.

关键词： Image enhancement

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Uncertainty-aware Monocular 3D Object Detection for Safe Driving Assistance

Uncertainty-aware Monocular 3D Object Detection for Safe Dri...

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2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Ida, Masahiko Matsushima, Kousuke Advanced Engineering Course National Institute of Technology Kurume College Fukuoka Japan Department of Control and Information Systems Engineering National Institute of Technology Kurume College Fukuoka Japan

ISBN: (纸本)9798350389210

Safety is of utmost importance when utilizing mobile vehicles. The introduction of safe driver assistance and automated driving systems will not only solve this problem, but also reduce road accidents and make driving smoother. To achieve these goals, 3D object detection is an essential technology and various methods have been proposed. Among these, monocular 3D object detection is an important technique and it is very difficult to identify the dimensions, direction and position of an object from a monocular image. Previous methods have predicted the dimensions directly from monocular images, resulting in poor accuracy of the dimensions in the depth direction. However, this paper proposes an indirect method for estimating the dimensions in the depth direction by predicting the volume of 3D bounding box. Detection accuracy for distant and partially visible objects is improved by using a loss function that uses uncertainty to account for the variability in predictions that occurs during learning. The proposed method has received an excellent evaluation on KITTI dataset. © 2024 IEEE.

关键词： 3D modeling

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Multi-perspective regional continuity alignment network for hyperspectral and LiDAR image fusion and classification 43

Multi-perspective regional continuity alignment network for ...

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43rd Chinese control Conference, CCC 2024

作者： Yu, Wenbo Zhang, Miao Soochow University School of Electronics and Information Engineering Suzhou215021 China Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China

ISBN: (纸本)9789887581581

In these decades, remote sensing (RS) techniques have been widely used when facing diverse real-world tasks. These unique RS theories tend to observe on-ground objects and materials by several techniques from different perspectives. Generally, several works have verified that land cover classification tasks are always hampered when using one single RS modality owing to the lack of diverse information. In this paper, we propose a multi-perspective regional continuity aligning network (MPRCAnet) for hyperspectral and light detection and ranging (LiDAR) image fusion and classification. Our motivation is to capture and align the regional spatial-spectral continuities from different perspectives for better performance. Specifically, the whole network consists of three parallel streams, including 1) the HS spatial stream, 2) the HS spectral stream and 3) the LiDAR spatial stream. This network aims to discuss the potential of the continuous information extraction strategy in related tasks. Multiple experiments are conducted on two publicly available hyperspectral and LiDAR datasets to prove the effectiveness and feasibility of the proposed MP-RCAnet. Compared with several state-of-the-art techniques, MP-RCAnet is capable of generating fusion and classification results with higher evaluation accuracies. Its classification map has less scattered noise than other techniques. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Image fusion

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Intermittent fault diagnosis based on EMD decomposition and convolutional neural network 2

Intermittent fault diagnosis based on EMD decomposition and ...

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2nd IEEE International Conference on Electrical, Automation and Computer engineering, ICEACE 2024

作者： Peng, Yu Liu, Yuhang Lin, Ruishi Xiang, Gang Wang, Shenhang Yang, Zhiming Beijing Aerospace Automatic Control Institute Department of System Engineering Beijing China Harbin Institute of Technology School of Electronics and Information Engineering Harbin China

ISBN: (纸本)9798350368208

The paper introduces an intermittent fault diagnosis method for analog circuits using EMD and CNN. The proposed method obtains the circuit output characteristics under different intermittent fault type using SPICE simulation. Then, EMD is used to extract the features and finally, the CNN model for fault detection and localization is built. The effectiveness of the method is validated through experiments in a three-phase SVPWM circuit. © 2024 IEEE.

关键词： Convolutional neural networks

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A Dimensional Difference-Based Population Size Adjustment Framework for Gannet Optimization Algorithm 16th

A Dimensional Difference-Based Population Size Adjustment Fr...

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Pan, Jeng-Shyang Han, Kunpeng Chu, Shu-Chuan Li, Zhi Zhang, Li College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China School of Artificial Intelligence Nanjing University of Information Science and Engineering Nanjing210044 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan School of Information and Control Engineering Qingdao University of Technology Qingdao266590 China

ISBN: (纸本)9789819615346

In this paper, an improved Gannet optimization algorithm (DDPSA-GOA) is presented. The Gannet optimization algorithm (GOA) was formed by mathematically studying the various foraging behaviors of Gannet. GOA has had better results in the past. A framework DDPSA to improve GOA is cited in this paper. DDPSA aims to adjust population size and replace stagnant individuals with different strategies by determining the stage of the population. The framework divides the population into two stages, premature integration and evolutionary stagnation, which are two population states with stagnant individual replacement mechanisms. Optimal elimination replacement or elite replacement is adopted for different stages. The GOA algorithm in the DDPSA framework was tested against the initial GOA and other comparative algorithms in the CEC 2013 test set. Experimental results show that DDPSA-GOA outperforms other algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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Day-Ahead Economic Dispatch of Community Integrated Energy System Considering Building Virtual Energy Storage 42

Day-Ahead Economic Dispatch of Community Integrated Energy S...

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42nd Chinese control Conference, CCC 2023

作者： Liu, Xinghua Zu, Longyu Li, Xiang School of Electrical Engineering Xi'an University of Technology Department of Power Grid Information and Control Engineering Xi'an710054 China

ISBN: (纸本)9789887581543

This paper adopts the scheduling strategy of model predictive control for the regionally integrated energy system with virtual energy storage and hydrogen production. First of all, based on the detailed modeling of the equipment, the mathematical model of the virtual energy storage system is established by using the heat storage characteristics of the building envelope. Then, the model predictive control technology is used to scroll and optimize each control time domain to realize the accurate adjustment of the operation scheme of the regionally integrated energy system. Finally, the effectiveness of the strategy is verified by three cases. The simulated results show that the proposed strategy can reduce the operating cost in the stage of optimal economic dispatching, on the premise of ensuring the comfort of the internal temperature of the building. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Model predictive control

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Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

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Computers, Materials & Continua 2023年第1期74卷 801-815页

作者： Yongjie Yang Shanshan Tu Raja Hashim Ali Hisham Alasmary Muhammad Waqas Muhammad Nouman Amjad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Information Security and Cybersecurity Unit King Khalid UniversityAbhaSaudi Arabia Computer Engineering Department College of Information TechnologyUniversity of Bahrain32038Bahrain School of Engineering Edith Cowan UniversityJoondalup PerthWA6027Australia School of Engineering University of Management and TechnologyLahorePakistan

With the recent developments in the Internet of Things(IoT),the amount of data collected has expanded tremendously,resulting in a higher demand for data storage,computational capacity,and real-time processing *** computing has traditionally played an important role in establishing ***,fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility,location awareness,heterogeneity,scalability,low latency,and geographic ***,IoT networks are vulnerable to unwanted assaults because of their open and shared *** a result,various fog computing-based security models that protect IoT networks have been developed.A distributed architecture based on an intrusion detection system(IDS)ensures that a dynamic,scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is *** this study,we examined the time-related aspects of network traffic *** presented an intrusion detection model based on a twolayered bidirectional long short-term memory(Bi-LSTM)with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark *** showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy,precision,recall and F1 score.

关键词： Fog computing intrusion detection bi-LSTM attention mechanism

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Factor analysis thermography for defect detection of panel paintings

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Quantitative InfraRed Thermography Journal 2023年第1期20卷 25-37页

作者： Liu, Kaixin Huang, Kai-Lun Sfarra, Stefano Yang, Jianguo Yi, Liu Yuan, Yao Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China Department of Chemical Engineering National Tsing Hua University Hsinchu Taiwan Department of Industrial and Information Engineering and Economics University of L’Aquila L’Aquila Italy

Active infrared thermography is an important non-destructive testing method used for revealing defect structures in materials. In many applications, thermographic data processing is necessary to extract defect features from a large number of thermal images. This work proposes to use a factor analysis thermography (FAT) method that automatically extracts defect features from thermograms via exploratory factor analysis, in tandem with a fuzzy c-means (FCM) clustering algorithm to segment the defects and background. By means of factor rotation, factor analysis minimises the complexity of factor loadings and makes the results more interpretable. Consequently, the defect information is extracted while large signal-to-noise ratios are obtained. Employing the FCM image segmentation algorithm on factor loading images reduces the interference of background on human visual detection. Additionally, the parameter selection is emphasised and addressed. Experiments on a panel painting illustrate that the proposed method promotes the accuracy and efficiency of thermographic detection of defects, compared with the popular principal component thermography (PCT) method. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Nondestructive examination

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A Real-Time Semantic Segmentation Method Based on Transformer for Autonomous Driving

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Computers, Materials & Continua 2024年第12期81卷 4419-4433页

作者： Weiyu Hao Jingyi Wang Huimin Lu Donald Bren School of Information and Computer Sciences University of CaliforniaIrvineCA 92612USA Department of Control Engineering Kyushu Institute of TechnologyKitakyushu804-8550Japan School of Automation Southeast UniversityNanjing210096China

While traditional Convolutional Neural Network(CNN)-based semantic segmentation methods have proven effective,they often encounter significant computational challenges due to the requirement for dense pixel-level predictions,which complicates real-time *** address this,we introduce an advanced real-time semantic segmentation strategy specifically designed for autonomous driving,utilizing the capabilities of Visual *** leveraging the self-attention mechanism inherent in Visual Transformers,our method enhances global contextual awareness,refining the representation of each pixel in relation to the overall *** enhancement is critical for quickly and accurately interpreting the complex elements within driving sce-narios—a fundamental need for autonomous *** experiments conducted on the DriveSeg autonomous driving dataset indicate that our model surpasses traditional segmentation methods,achieving a significant 4.5%improvement in Mean Intersection over Union(mIoU)while maintaining real-time *** paper not only underscores the potential for optimized semantic segmentation but also establishes a promising direction for real-time processing in autonomous navigation *** work will focus on integrating this technique with other perception modules in autonomous driving to further improve the robustness and efficiency of self-driving perception frameworks,thereby opening new pathways for research and practical applications in scenarios requiring rapid and precise decision-making *** experimentation and adaptation of this model could lead to broader implications for the fields of machine learning and computer vision,particularly in enhancing the interaction between automated systems and their dynamic environments.

关键词： Visual transformer semantic segmentation autonomous driving

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MM-UKAN++: A Novel Kolmogorov–Arnold Network-Based U-Shaped Network for Ultrasound Image Segmentation

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IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency control 2025年第4期72卷 498-514页

作者： Zhang, Boheng Huang, Haorui Shen, Yi Sun, Mingjian Harbin Institute of Technology Department of Control Science and Engineering Heilongjiang Harbin150001 China Pohang University of Science and Technology Department of Convergence IT Engineering Pohang37673 Korea Republic of Harbin Institute of Technology Suzhou Research Institute Jiangsu Suzhou215000 China Harbin Institute of Technology Department of Information Science and Engineering Shandong Weihai264200 China Harbin Institute of Technology Shandong Weihai264200 China Shandong Laboratory of Advanced Biomaterials and Medical Devices Shandong Weihai264209 China

Ultrasound (US) imaging is an important and commonly used medical imaging modality. Accurate and fast automatic segmentation of regions of interest (ROIs) in US images is essential for enhancing the efficiency of clinical and robot-assisted diagnosis. However, US images suffer from low contrast, fuzzy boundaries, and significant scale variations in ROIs. Existing convolutional neural network (CNN)-based and transformer-based methods struggle with model efficiency and explainability. To address these challenges, we introduce MM-UKAN++, a novel U-shaped network based on Kolmogorov-Arnold networks (KANs). MM-UKAN++ leverages multilevel KAN layers as the encoder and decoder within the U-network architecture and incorporates an innovative multidimensional attention mechanism to refine skip connections by weighting features from frequency-channel and spatial perspectives. In addition, the network effectively integrates multiscale information, fusing outputs from different scale decoders to generate precise segmentation predictions. MM-UKAN++ achieves higher segmentation accuracy with lower computational cost and outperforms other mainstream methods on several open-source datasets for US image segmentation tasks, including achieving 69.42% IoU, 81.30% Dice, and 3.31 mm HD in the BUSI dataset with 3.17 G floating point of operations (FLOPs) and 9.90 M parameters. The excellent performance on our automatic carotid artery US scanning and diagnostic system further proves the speed and accuracy of MM-UKAN++. Besides, the good performance in other medical image segmentation tasks reveals the promising applications of MM-UKAN++. The code is available on GitHub. © 1986-2012 IEEE.

关键词： Image segmentation

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