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6th International Conference on Mechatronics and Intelligent Robotics, ICMIR 2022

作者： Zhang, Hengqi Lin, Yong Qian, Qian Pan, Jiawen Feng, Yong Fu, Yunfa Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming Yunnan650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming University of Science and Technology Kunming Yunnan650500 China Library and Archives Yunnan Communications Vocational and Technical College Kunming Yunnan650500 China

ISBN: (纸本)9781510656635

An adaptive random reverse learning mayfly algorithm is proposed to overcome the shortcomings of mayfly optimization. Specifically, hyperbolic tangent function is used as adaptive curve to flexibly optimize the personal experience and social experience coefficients of the algorithm, so that the algorithm can better balance the global and local search capabilities through the evolutionary iterations. In addition, on the basis of elite mayfly in each generation, random reverse learning mechanism is used to expand population diversity and speed up algorithm convergency. The calculation results of several test functions show that the algorithm presented has good convergence accuracy and improved algorithm performance. © 2022 SPIE.

关键词： Hyperbolic functions

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Texture Analysis of Breast US Images Using Morphological Transforms, Hausdorff Dimension and Bagging Ensemble Method

Texture Analysis of Breast US Images Using Morphological Tra...

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Mediterranean Conference on Control and automation (MED)

作者： Gigi Tăbăcaru Simona Moldovanu Marian Barbu Department of Automatic Control and Electrical Engineering Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati Romania Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati The Modelling & Simulation Laboratory Dunarea de Jos University of Galati Galati Romania

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based on bagging. The proposed work uses three morphological transformations for image preprocessing: hit-and-miss transform (HMT), white (WHT), and black top-hat (BHT). The pattern texture of US breast images is described by extracting the HFD from the regions of interest (ROI) after the ultrasound (US) images have been preprocessed. The main objective of this study was achieved by comparatively analyzing the classification performance of features using the Random Forest (RF), Extra Trees (ET) classifier, and bagging ensemble method based on XGBoot classifier. In presented study, the XGBoost classifier and BHT image processing method give an accuracy of 89.8% in a binary classification, benign versus malignant breast cancer.

关键词： Ultrasonic imaging Transforms Feature extraction Time measurement Fractals Ensemble learning Image preprocessing

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Pancreatic Tumor Recognition from CT Images through Advanced Deep Learning Techniques

Pancreatic Tumor Recognition from CT Images through Advanced...

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IEEE International Conference on automation, Quality and Testing, Robotics, AQTR

作者： Delia Mitrea Raluca Brehar Razvan Itu Sergiu Nedevschi Mihai Socaciu Radu Badea Department of Computer Science Faculty of Automation and Computer Science Technical University of Cluj-Napoca Cluj-Napoca Romania Department of Medical Imaging I. Hatieganu University of Medicine and Pharmacy Cluj-Napoca Romania Octavian Fodor Regional Institute of Gastroenterology and Hepatology Cluj Napoca – Romania Cluj-Napoca Romania

ISBN: (数字)9798350361933

ISBN: (纸本)9798350361940

Cancer constitutes a major affection nowadays, leading to death in many situations. Pancreatic malignant tumors represent the fourth most frequent cause of cancer related death in United States and Europe. The most trustworthy cancer diagnosis method is the biopsy, but this technique raises several risks, the most relevant one being the danger of tumoral spread through the human body. Thus, in the context of our research, we employ computerized methods for achieving highly accurate abdominal tumor recognition and segmentation within medical images. In the current approach, we performed the automatic recognition of pancreatic malignant tumors within Computed Tomography (CT) images, with the aid of Convolutional Neural Networks (CNN), by employing last generation architectures, as well as their improved versions, respectively combinations of these structures at classifier and decision level. The classification performance was assessed through specific metrics, an accuracy above 98% being achieved.

关键词： Image recognition Accuracy Computed tomography Malignant tumors Neural networks Convolutional neural networks Robots

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Strong Stabilization of the Acrobot at the Down–Up Equilibrium Point ⋆

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IFAC-PapersOnLine 2023年第2期56卷 9167-9172页

作者： Ziyu Wang Xin Xin Yannian Liu School of Automation Southeast University and Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing 210096 China Faculty of Computer Science and Systems Engineering Okayama Prefectural University Okayama 719-1197 Japan

In this paper, the strong stabilization of the Acrobot at the Down-Up equilibrium point is addressed, with the first link being downward and the second link being upright. By converting the strong stabilization of the Acrobot at the Down–Up equilibrium point equivalently to the existence and design of a stable stabilizing controller for a fourth-order single-input single-output linear plant with adjustable zeros, a pair of poles on the imaginary axis, and a pair of real poles located symmetrically with respect to the origin, this paper has three main contributions. Firstly, the existence of a stable stabilizing controller for any Acrobot which is linearly controllable at the Down–Up equilibrium point is proved by showing the range of the adjustable zero via adjusting a parameter of the output signal. Secondly, a necessary and sufficient condition on the mechanical parameters of the Acrobot is provided to guarantee the existence of a second-order stable stabilizing controller for the Acrobot around its Down– Up equilibrium point. Thirdly, a direct method is presented to design a second-order stable controller, whose transfer function is preset with three parameters. By utilizing the Liénard– Chipart criterion for a fourth-order polynomial, the necessary and sufficient conditions on these parameters for achieving the strong stabilization are obtained, which are expressed in a cascade form for obtaining these parameters conveniently. A numerical example is presented to validate the effectiveness of the proposed method.

关键词： Robust control strong stabilization underactuated mechanical systems reduced-order controller linear systems

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A Parallel and Asynchronous Blob Shape Descriptor Implemented in CMOS Technology

A Parallel and Asynchronous Blob Shape Descriptor Implemente...

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International Conference on Microelectronics, MIEL

作者： P. Bogacki M. Długosz T. Talaśka R. Długosz Aptiv Services Poland Kraków Poland Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications AGH University of Science and Technology Kraków Poland Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan University of Technology Poznan Poland Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz University of Science and Technology Bydgoszcz Poland

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in Advanced Driver Assistance Systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock.

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Cross-Guided Attention and Combined Loss for Optimizing Gastrointestinal Visual Question Answering

Cross-Guided Attention and Combined Loss for Optimizing Gast...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qian Yang Lijun Liu Xiaobing Yang Li Liu Wei Peng Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming P. R. China Yunnan Key Laboratory of Computer Technology Application Kunming China P. R. China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Medical Visual Question Answering (Med-VQA) aims at answering clinical questions related to medical imaging, providing important reference for medical imaging diagnosis. Gastrointestinal visual question answering can deeply understand and analyze gastrointestinal medical images, provides supporting information to answer questions about the diagnostic process, and improves diagnostic accuracy and efficiency. However, most existing Med-VQA can only handle simple and general questions, struggling to tackle visual question answering related to complex diseases. Therefore, this paper proposes a Med-VQA model called CACL, which uses cross-guidance and improved combined loss. The model focuses on gastrointestinal image analysis and achieves efficient and accurate feature extraction for gastrointestinal images. A cross-guided attention module is also designed to enhance the model’s reasoning ability when dealing with complex cross-modal tasks. In addition, a multi-task composite loss function is designed to balance the loss of segmentation task and classification task, and improve the overall performance. Experimental results show that the proposed method can effectively improve the accuracy of gastrointestinal visual question answering.

关键词： Visualization Image segmentation Accuracy Image analysis Feature extraction Multitasking Gastrointestinal tract Question answering (information retrieval) Medical diagnostic imaging Diseases

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Tackling Noisy Labels with Network Parameter Additive Decomposition

arXiv

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arXiv 2024年

作者： Wang, Jingyi Xia, Xiaobo Lan, Long Wu, Xinghao Yu, Jun Yang, Wenjing Han, Bo Liu, Tongliang The Department of Intelligent Data Science College of Computer Science and Technology National University of Defense Technology Changsha410073 China The Trustworthy Machine Learning Lab School of Computer Science Faculty of Engineering University of Sydney DarlingtonNSW2008 Australia The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China The Department of Automation University of Science and Technology of China Hefei230026 China The Department of Computer Science Hong Kong Baptist University Hong Kong

Given data with noisy labels, over-parameterized deep networks suffer overfitting mislabeled data, resulting in poor generalization. The memorization effect of deep networks shows that although the networks have the ability to memorize all noisy data, they would first memorize clean training data, and then gradually memorize mislabeled training data. A simple and effective method that exploits the memorization effect to combat noisy labels is early stopping. However, early stopping cannot distinguish the memorization of clean data and mislabeled data, resulting in the network still inevitably overfitting mislabeled data in the early training stage. In this paper, to decouple the memorization of clean data and mislabeled data, and further reduce the side effect of mislabeled data, we perform additive decomposition on network parameters. Namely, all parameters are additively decomposed into two groups, i.e. , parameters w are decomposed as w = σ + γ. Afterward, the parameters σ are considered to memorize clean data, while the parameters γ are considered to memorize mislabeled data. Benefiting from the memorization effect, the updates of the parameters σ are encouraged to fully memorize clean data in early training, and then discouraged with the increase of training epochs to reduce interference of mislabeled data. The updates of the parameters γ are the opposite. In testing, only the parameters σ are employed to enhance generalization. Extensive experiments on both simulated and real-world benchmarks confirm the superior performance of our method. The implementation is available at: https://***/wangjingyi9924/TNLPAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Design of A Third-order Inline Waveguide Bandpass Filter With Cross Coupling and Spurious Mode Suppression

Design of A Third-order Inline Waveguide Bandpass Filter Wit...

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IEEE International Wireless Symposium (IWS)

作者： Wangtao Ye Zai-Cheng Guo Lei Zhu Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing School of Electrical and Automation Engineering Nanjing Normal University Nanjing Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China

ISBN: (数字)9798350389999

ISBN: (纸本)9798350390001

A third-order inline waveguide bandpass filter (BPF) with cross coupling and spurious mode suppression is designed in this paper. Based on the conventional cascaded-coupled filter, the cross coupling is realized by reducing the height of the second resonating cavity and introducing a stacked cavity for cross coupling, which generates a transmission zero (TZ) on the right side of passband. In order to eliminate the spurious modes caused by the cross-coupled cavity, a short-circuited post is introduced to make TE101 and TE102 mode of the cavity introduced for cross coupling self-suppressed. Therefore, the proposed inline waveguide BPF is with one TZ on the right side of passband while preserving almost the same bandwidth of stopband when compared to the conventional cascaded-coupled filter. The design process is illustrated in detail and the measured results verify the feasibility of the proposed inline waveguide BPF.

关键词： Band-pass filters Couplings Wireless communication Bandwidth Filtering theory Passband

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Network Traffic Prediction with Decomposition and Multi-Scale Autocorrelation in Large-Scale Cloud Data Centers

Network Traffic Prediction with Decomposition and Multi-Scal...

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IEEE International Conference on Networking, Sensing and Control

作者： Meijia Wang Haitao Yuan Zhenwei Kuang Hanbo Ma Jing Bi Jia Zhang MengChu Zhou School of Automation Science and Electrical Engineering Beihang University Beijing China Faculty of Information Technology Beijing University of Technology Beijing China Dept. of Computer Science Southern Methodist University Dallas TX USA Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

As the Internet and big data technologies advance, a tremendous amount of data is generated daily. Efficient network operations are essential for handling such data. Accurately predicting future network traffic in real-time enables prompt response from cloud infrastructure and efficient traffic scheduling and allocation, ultimately reducing costs, preventing economic losses, and optimizing the performance of downstream facilities. However, predicting network traffic in large-scale data centers is challenging due to the multidimensional, nonlinear, and high-volatility nature of the time series. Traditional prediction methods, e.g., regression algorithms, struggle to capture non-linear features effectively. Many deep learning models face issues such as gradient explosion or vanishing during their training. Current commonly used prediction methods fail to fully uncover vital information about the frequency domain features in the time series. To do so, this work proposes a novel network traffic prediction model that combines a Savitzky Golay filter, sequence decomposition, multi-scale attention, a temporal convolutional network, an autocorrelation mechanism, and bidirectional long short-term memory. It can accurately predict future network traffic by adaptively extracting important features without requiring excessive feature engineering of the original data. It performs end-to-end sequence prediction, feature selection, and automatic learning of data features and temporal dependencies to achieve accurate time series prediction and avoid redundant data processing. Experiments involving ablation studies and comparisons with advanced prediction models are performed with the Google cluster trace. Experimental results show that the proposed model improves the prediction accuracy by at least 38.52% over the state-of-the-art models.

关键词： Data centers Biological system modeling Time series analysis Telecommunication traffic Predictive models Feature extraction Prediction algorithms Autocorrelation Data mining Long short term memory

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Exploiting hybrid features extracted from color spaces of dermoscopic images

Exploiting hybrid features extracted from color spaces of de...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Simona Moldovanu Mihaela Miron Anisia Culea-Florescu Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galați Romania Department of Electronics and Telecommunications Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galați Romania

In the context of detecting dangerous illnesses of the patient affected by skin lesions, this study is focused on the problem of improving the classification of melanoma vs. normal nevus considering neural network and new proposed features based on high and low pixels intensity, and densities of the pattern detected with hit-or-miss (HoM) morphological transform. A large set of features from color spaces such as RGB (red, green, and blue), HSV (hue, saturation, and value) and L*a*b* (lightness variable L*, chromaticity indices a* and b*) were initially extracted and grouped in two parts: the first group includes first and second order features, and the second group contains hybrid features (a mix of first order, second order features and proposed features). The dimensionality of the features from these two groups is considerably reduced with principal component analysis (PCA) statistical method and fed three different types of neural network (NN): Radial Basis Function Neural Network (RBFNN), Cascade-Forward Neural Network (CFNN) and Pattern Recognition Neural Network (PRNN). From this corroboration of hybrid features, color space and NN, the L*a*b* color space with CFNN provided accuracy (ACC) of 0.897, F1-score of 0.91 and area under the ROC curve (AUC) of 0.896, respectively.

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