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17th Chinese Intelligent systems Conference, CISC 2021

作者： Qiu, Yang Ai, Zhijie Lin, Ye Xu, Zhezhuang Liu, Xinxiang College of Electrical Engineering and Automation Fuzhou University Fuzhou350000 China Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9789811663192

Wooden boards are widely used as raw materials for furniture. Detecting defects on the wooden is significant to improve the quality of products. In this paper, we propose an improved YOLOv4-tiny target detection algorithm with feature extraction and utilization tailored for wood defect recognition. The proposed algorithm replace the residual network of the original algorithm with the Res2Net network to enhance the feature extraction ability, and increase the different scales to improve the prediction accuracy. A testbed is deployed to collect the images of wooden boards, and the performance of the proposed algorithm is evaluated on the testbed. Compared with the original YOLOv4-tiny algorithm, the mean average precision for detecting the three types of defects deadknot, insectpest and crack was 80.06% which was an increase of 5%. Compared with other methods of wood defect detection, the proposed algorithm is more accurate and faster. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Testbeds

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Semi-supervised Learning with GAN for Semantic Segmentation of Wood Defect Image

Semi-supervised Learning with GAN for Semantic Segmentation ...

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Chinese Automation Congress (CAC)

作者： Kunxin Zheng Song Zheng Zhezhuang Xu Ye Lin Yuhang Zhu Education Department of Fujian Province College of Electrical Engineering and Automation Fuzhou University and Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Automatic defect detection on wood surface is essential for ensuring product quality. Semantic segmentation methods have shown outstanding performance in wood defect detection. However, it is costly to acquire correct pixel-level labels of wood surface defects due to the complicated characteristics. To mitigate the cost of acquiring high-quality datasets, this study proposes a semi-supervised defect detection approach based on generative adversarial networks, which contains a generator and a discriminator. The generator is utilized to generate pixel-level segmentation results of wood defect images. To improve the segmentation performance, the discriminator learns against the generator by judging the goodness of the prediction results and generates supervised signals for the prediction results of unlabeled images. With this learning model, the proposed method can enhance segmentation performance using unlabeled images and alleviate the burden of data annotation. We validate the effectiveness of the proposed approach on a collected dataset of wood defects. Experimental results demonstrate the remarkable performance of our method even when utilizing only a small amount of annotated data.

关键词： Semantic segmentation Semisupervised learning Generative adversarial networks Generators Robustness Product design Data models Quality assessment Data mining Defect detection

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Proximity Estimation with Position Adjustment for Autonomous Industrial Inspection

Proximity Estimation with Position Adjustment for Autonomous...

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Artificial Intelligence of Things and systems (AIoTSys), International Conference on

作者： Ziyi Zhou Ping Huang Song Zheng Zhezhuang Xu Zixiong Zhuang Education Department of Fujian Province College of Electrical Engineering and Automation Fuzhou University and Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China

ISBN: (数字)9798331507886

ISBN: (纸本)9798331507893

Due to the similarity in appearance and dense deployment of devices in industrial environments, relying solely on machine vision makes it challenging for inspection robots to accurately identify similar devices. Although wireless signals from industrial internet of things (IoT) can serve as identification features, signal fluctuations impact the accuracy and efficiency of recognition. To address this issue, this paper proposes a proximity estimation algorithm with position adjustment for autonomous industrial inspection. The algorithm considers the spatial relationships between nodes and the topological relationship between the signal strength and variations among the nodes. By analyzing the topological relationship between the signal strength and variations among the nodes, the robot autonomously adjusts its position and selects the proximal node based on the spatial topology relationship between them. We have built an inspection platform using quadruped robots to evaluate the effectiveness of the experiments. The experimental results demonstrate that the algorithm further improves the efficiency of identifying proximal devices while ensuring the estimation accuracy of the algorithm.

关键词： Wireless communication Accuracy Fluctuations Service robots Estimation Inspection Topology Object recognition Quadrupedal robots Industrial Internet of Things

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Wood Surface Discoloration Defect Detection Based on Improved HRNet

Wood Surface Discoloration Defect Detection Based on Improve...

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Chinese Automation Congress (CAC)

作者： Haoran Zhang Zhezhuang Xu Yuhang Zhu Ye Lin Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing College of Electrical Engineering and Automation Fuzhou University Fuzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Discoloration defect detection is a significant task in the wood industry for ensuring the appearance and quality of wooden products. However, discoloration defect detection still faces some challenges due to the low contrast, blurred boundary, and slender shape of discoloration defects. To overcome these challenges, we propose a multi-branch feature encoding HRNet(MFE-HRNet) for wood surface discoloration defect detection. The network maintains high-resolution features and builds a parallel multi-resolution architecture to prevent the dilution of low-contrast discoloration defect features. Through repeated cross-fusion of multi-scale features, the network enhances the global representation of blurry features. In order to effectively preserve the detail features of the defect edge, a multi-branch feature encoding module (MFEM) is proposed as the encoding layer of the network. In the MFEM, multiple branches concurrently extract features, generating feature maps with complementary characteristics, thereby enhancing the ability of network to recognize fine details of defects. Experimental results verify the effectiveness of our method in detecting discoloration defects on wood surface.

关键词： Accuracy Automation Shape Image edge detection Wood industry Feature extraction Encoding Character recognition Faces Defect detection

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Analyzing the Correlation between Student Learning Behaviors and Academic Performance in Blended Learning Environments

Analyzing the Correlation between Student Learning Behaviors...

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Chinese Automation Congress (CAC)

作者： Ying Zhang Kang Chen Zhezhuang Xu Ye Lin Qingbo Zeng Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing Education College of Electrical Engineering and Automation Fuzhou University Fuzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Blended learning integrates online and face-to-face (FTF) teaching methods to offer students in higher education a flexible and customized educational experience. Despite its significant advantages in promoting academic achievement and increasing student participation, blended learning presents challenges for educators due to difficulties in collecting and analyzing learning behavior data. Existing research mainly focuses on single learning modes, and data collection methods primarily rely on interviews and questionnaires, which can be subjective. To address these challenges, this study developed a mobile software called EasyClass, integrated with an online Small Private Online Course(SPOC) platform, to automatically record student behaviors in a blended learning environment. Obtaining data from an undergraduate blended learning course, statistical methods are used to analyze the correlation between student behaviors and final grades, as well as to identify factors affecting grades. The results of multivariate linear regression models confirmed the feasibility and effectiveness of blended instruction. This study provides a new approach to identifying and disseminating effective data collection strategies in blended learning, enabling educators and educational institutions to better understand and assess student behaviors, and to further explore their impact on final academic achievement.

关键词： Correlation Automation Statistical analysis Education Linear regression Process control Data collection Software Hybrid learning Interviews

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CMAC-Based Sliding-Mode control for Large-Scale Fuzzy Descriptor systems

CMAC-Based Sliding-Mode Control for Large-Scale Fuzzy Descri...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Zhong, Zhixiong Shao, Zhenghua Huan, Zhijie Wang, Xingyi Minjiang University Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150080 China School of Electrical Engineering and Automation Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781665481090

This paper addresses the problem of sliding-mode control for large-scale fuzzy descriptor systems subject to unknown uncertainties. First, a CMAC neural network is used to approximate the unknown uncertainties, and the CMAC-based sliding-mode controller is proposed. Then, a compensation technique is employed to eliminate the influence on the approximation error stemmed from CMAC model. We use the gradient-descent method to online tune the parameters of CMAC and the Lyapunov function is introduced to ensure the stability of the closed-loop control system and the CMAC's convergence. Finally, the effectiveness and correctness of the obtained theories are verified by the tracking control of multi-autonomous underwater vehicles (AUVs). © 2022 IEEE.

关键词： Autonomous underwater vehicles

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Dielectric Resonator Antenna Array With Extended Beam-Scanning Range Using Dielectric Superstrate

Dielectric Resonator Antenna Array With Extended Beam-Scanni...

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2023 IEEE Conference on Antenna Measurements and Applications, CAMA 2023

作者： Su, Zhi-Li Xiao, Fei Li, MingChao Lu, Kai Leung, Kwok Wa City University of Hong Kong State Key Laboratory of Terahertz and Millimeter Waves Department of Electrical Engineering Hong Kong CityU Shenzhen Research Institute Information and Communication Technology Centre Shenzhen China Limited Guangzhou China Sun Yat-sen University School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou China

ISBN: (纸本)9798350323047

An E-plane cylindrical dielectric resonator antenna (DRA) array with a dielectric superstrate is investigated for wide-angle scanning applications. Each slot-coupled DRA element operates at its fundamental HEM11δ mode. The dielectric superstrate is loaded above the DRA array. By adjusting its dimension and position properly, the beamwidth of the center active element pattern is significantly broadened. Meanwhile, the mutual coupling effect among various elements is effectively reduced. Both the wide-beam element pattern and reduced mutual coupling level contribute to the improvement of the array beam-steering capability. Finally, our proposed E-plane DRA array has a wide range of ±80° with a scan loss of 3 dB. © 2023 IEEE.

关键词： Antenna arrays

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Data-driven modeling of heat pumps and thermal storage units for MPC

Data-driven modeling of heat pumps and thermal storage units...

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2023 International Conference on the Built Environment in Transition, CISBAT 2023

作者： Brandes, M. Cai, H. Vivian, J. Croci, L. Heer, P. Smith, R. Urban Energy Systems Laboratory Empa Dübendorf Switzerland Dept. of Industrial Engineering University of Padova Padua Italy Dept. of Power System Development Ricerca Sistema Energetico SpA Milan Italy Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zurich Switzerland

Heat pumps can play a crucial role in the European energy strategy 2050, which aims to achieve net-zero greenhouse gas emissions. When coupled with thermal energy storage and integrated with advanced control strategies, heat pump operation can be optimized to reduce carbon footprint and respond to the needs of system operators. However, to scale in a multitude of buildings, the transferability of the modeling into heterogeneous systems is crucial. In this paper, two different interpretable linear models, a hybrid (grey-box) and a fully data-driven (black-box) model are investigated. Specifically, two regression-based identification methods (SINDYc and DMDc) are used for dynamic models and the LASSO regression is used for static models. The transferability of the approach is evaluated using two real-world facilities with heterogeneous sizing and configuration. The results show a similar simulation performance for both cases with a maximum normalized RMSE of 0.41 and 0.60, respectively. This confirms the transferability of the approach that is necessary for large-scale implementation. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Carbon footprint

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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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Computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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Symbiotic control of Uncertain Dynamical systems: Harnessing Synergy Between Fixed-Gain control and Adaptive Learning Architectures

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IEEE Transactions on Automatic control 2025年

作者： Yucelen, Tansel Sarsilmaz, Selahattin Burak Yildirim, Emre University of South Florida Department of Mechanical Engineering Laboratory for Autonomy Control Information and Systems TampaFL33620 United States Utah State University Department of Electrical and Computer Engineering LoganUT84322 United States

Both fixed-gain control and adaptive learning architectures aim to mitigate the effects of uncertainties. In particular, fixed-gain control offers more predictable closed-loop system behavior but requires the knowledge of uncertainty bounds. In contrast, while adaptive learning does not necessarily require such knowledge, it often results in less predictable closed-loop system behavior compared to fixed-gain control. To this end, this paper presents a novel symbiotic control framework that offers the strengths of fixed-gain control and adaptive learning architectures. Specifically, this framework synergistically integrates these architectures to mitigate the effects of uncertainties in a more predictable manner as compared to adaptive learning alone and it does not require any knowledge on such uncertainties. Both parametric and nonparametric uncertainties are considered, where we utilize neural networks to approximate the unknown uncertainty basis for the latter case. Counterintuitively, the proposed framework has the ability to achieve a desired level of closed-loop system behavior even with an insufficient number of neurons (e.g., when the neural network approximation error is large) or in the face of injudiciously selected adaptive learning parameters (e.g., high leakage term parameters). © 1963-2012 IEEE.

关键词： Closed loop control systems

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