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A New Hybrid Model for Segmentation of the Skin Lesion Based on Residual Attention U-Net

Computers, Materials & Continua 2023年第6期75卷 5177-5192页

作者： Saleh Naif Almuayqil Reham Arnous Noha Sakr Magdy M.Fadel Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Communications and Electronics Delta Higher Institute for Engineering and Technology(DHIET)MansouraEgypt Computers Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt

Skin segmentation participates significantly in various biomedical applications,such as skin cancer identification and skin lesion *** paper presents a novel framework for segmenting the *** framework contains two main stages:The first stage is for removing different types of noises from the dermoscopic images,such as hair,speckle,and impulse noise,and the second stage is for segmentation of the dermoscopic images using an attention residual U-shaped Network(U-Net).The framework uses variational Autoencoders(VAEs)for removing the hair noises,the Generative Adversarial Denoising Network(DGAN-Net),the Denoising U-shaped U-Net(D-U-NET),and Batch Renormalization U-Net(Br-U-NET)for remov-ing the speckle noise,and the Laplacian Vector Median Filter(MLVMF)for removing the impulse *** the second main stage,the residual attention u-net was used for *** framework achieves(35.11,31.26,27.01,and 26.16),(36.34,33.23,31.32,and 28.65),and(36.33,32.21,28.54,and 27.11)for removing hair,speckle,and impulse noise,respectively,based on Peak Signal Noise Ratio(PSNR)at the level of(0.1,0.25,0.5,and 0.75)of *** framework also achieves an accuracy of nearly 94.26 in the dice score in the process of segmentation before removing noise and 95.22 after removing different types of *** experiments have shown the efficiency of the used model in removing noise according to the structural similarity index measure(SSIM)and PSNR and in the segmentation process as well.

关键词： Skin tumor speckle noise impulse noise hair noise deep learning segmentation

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation Systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Detection of Oscillations in Process control Loops From Visual Image Space Using Deep Convolutional Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 982-995页

作者： Tao Wang Qiming Chen Xun Lang Lei Xie Peng Li Hongye Su the Department of Electronic Engineering the School of InformationYunnan UniversityKunming 650091China DAMO Academy Alibaba GroupHangzhou 311100China the State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhou 310027China

Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant *** numerous automatic detection techniques have been proposed,most of them can only address part of the practical *** oscillation is heuristically defined as a visually apparent periodic ***,manual visual inspection is labor-intensive and prone to missed *** neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction *** this work,an exploration of the typical CNN models for visual oscillation detection is ***,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation *** feasibility and validity of this framework are verified utilizing extensive numerical and industrial *** with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and *** addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.

关键词： Convolutional neural networks(CNNs) deep learning image processing oscillation detection process industries

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Energy-Efficient Data Collection over Underwater MI-Assisted Acoustic Cooperative MIMO WSNs

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China Communications 2023年第11期20卷 96-110页

作者： Qingyan Ren Yanjing Sun Song Li Bin Wang Zhengda Yu School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Department of Information Technology and Electrical Engineering ETH ZurichZurich 8092Switzerland School of Communication and Information Engineering Xi’an University of Science and TechnologyXi’an 710054China Xuzhou Engineering Research Center of Intelligent Industry Safety and Emergency Collaboration Xuzhou 221000China

Underwater magnetic induction(MI)-assisted acoustic cooperative multiple-input-multipleoutput(MIMO) has been recently proposed as a promising technique for underwater wireless sensor networks(UWSNs).For the more,the energy utilization of energy-constrained sensor nodes is one of the key issues in UWSNs,and it relates to the network *** this paper,we present an energy-efficient data collection for underwater MI-assisted acoustic cooperative MIMO wireless sensor networks(WSNs),including the formation of cooperative MIMO and relay link ***,the cooperative MIMO is formed by considering its expected transmission range and the energy balance of nodes with ***,from the perspective of the node’s energy consumption,the expected cooperative MIMO size and the selection of master node(MN) are ***,to improve the coverage of the networks and prolong the network lifetime,relay links are established by relay selection algorithm that using matching ***,the simulation results show that the proposed data collection improves its efficiency,reduces the energy consumption of the master node,improves the networks’ coverage,and extends the network lifetime.

关键词： cooperative MIMO cooperative MIMO size data collection heterogeneous UWSNs relay link establishment

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The Large Language Models on Biomedical Data Analysis: A Survey

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4486-4497页

作者： Lan, Wei Tang, Zhentao Liu, Mingyang Chen, Qingfeng Peng, Wei Chen, Yi-Ping Phoebe Pan, Yi Guangxi University School of Computer Electronic and Information Nanning530004 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China La Trobe University Department of Computer Science and Information Technology MelbourneVIC3086 Australia Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

With the rapid development of Large Language Model (LLM) technology, it has become an indispensable force in biomedical data analysis research. However, biomedical researchers currently have limited knowledge about LLM. Therefore, there is an urgent need for a summary of LLM applications in biomedical data analysis. Herein, we propose this review by summarizing the latest research work on LLM in biomedicine. In this review, LLM techniques are first outlined. We then discuss biomedical datasets and frameworks for biomedical data analysis, followed by a detailed analysis of LLM applications in genomics, proteomics, transcriptomics, radiomics, single-cell analysis, medical texts and drug discovery. Finally, the challenges of LLM in biomedical data analysis are discussed. In summary, this review is intended for researchers interested in LLM technology and aims to help them understand and apply LLM in biomedical data analysis research. © 2013 IEEE.

关键词： Data consistency

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Network information Big Data Analysis Platform Based on Deep Learning technology 11th

Network Information Big Data Analysis Platform Based on Deep...

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11th International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Yan, Kai Gao, Hao Department of Information and Control Engineering Shenyang Institute of Science and Technology Liaoning Shenyang110000 China

With the rapid development of Internet technology, the amount of information and data is constantly increasing, leading to higher and higher demands for network big data analysis platforms. This paper primarily delves into the application of deep learning technology in digital text classification. In this paper, a deep learning-based algorithm is introduced in detail. It outlines the method of extracting specific numerical eigenvectors from large-scale non-negative eigenattribute samples and converting them into machine-readable code. ased on the aforementioned methodology, an analysis platform is designed, and its basic performance is simulated and tested. The test results indicate that the platform's ability to process abnormal data in Round A is 80, Round B is 90, Round C is 70, Round D is 85, and Round E is 75. The exception handling capacity of each category is represented visually through a bar chart, where the height of the bar signifies the size of the exception handling capacity for that specific category. © 2024 The Authors. Published by Elsevier B.V.

关键词： Adversarial machine learning

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An Adaptive control Method Based on Radial Basis Function Neural Network for Variable Stiffness Actuator 25

An Adaptive Control Method Based on Radial Basis Function Ne...

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25th IEEE International Conference on Industrial technology, ICIT 2024

作者： Wu, Xiujun Wang, Chen Xu, Changxian Liu, Keping Wang, Gang Sun, Zhongbo Changchun University Of Technology Department Of Control Science And Engineering Changchun China School Of Electrical And Information Engineering Jilin Engineering Normal University Department Of Control Science And Engineering Changchun China

ISBN: (纸本)9798350340266

Rehabilitation robots based on variable stiffness elastic actuators exhibit strong human-robot interaction char-acteristics due to the specific features of the actuators, including flexible drive and variable stiffness output capabilities. However, to ensure the robot can achieve rehabilitation actions smoothly, safely, and precisely, accurate modeling and control of the variable stiffness elastic actuators becomes a burning question. In order to address this challenge, a dynamic model is established considering the actuator's own gravity factor and the influence of disturbances, and a nominal dynamic model is designed. Accurate tracking of output location and stiffness is reached by designing a radial basis function (RBF) network to approximate the actual dynamic model. Through simulation experiments, it is verified that the block approximation RBF network adaptive controller designed in this article has lower actuator tracking error and higher control accuracy than the proportional integral derivative (PID) controller. © 2024 IEEE.

关键词： Radial basis function networks

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Intelligent Proximate Analysis of Coal Based on Near-Infrared Spectroscopy and Multioutput Deep Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1398-1410页

作者： Zou, Liang Qiao, Jiahui Yu, Xinhui Chen, Xun Lei, Meng School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China

Proximate analysis of coal indicates the moisture, ash, volatile content, and calorific value, which has been widely utilized as the basis for coal characterization. It involves heating the coal under various conditions until a constant weight is obtained. Although it is a relatively simple process that does not require expensive analytical equipment, determining these characteristics is time consuming. An alternative way for proximate analysis is spectral analysis in combination with various machine learning methods. However, most previous works analyze individual characteristics and fail to explore the relationship among them. In this study, we propose a method for proximate analysis based on near-infrared spectroscopy and a multioutput attention Unet (MOA-Unet), which can predict multiple characteristics simultaneously. First, an attention-based Unet is designed as the shared feature extraction subnetwork, including an encoder, a decoder, convolutional block attention modules, and multiscale feature fusion modules, which can improve the representation power of the U-shape network through aggregating features of shallower layers and concatenating features of deeper layers. Second, four individual subnetworks with fully connected layers, designed for four outputs, are utilized for regressing those four characteristics. We employ the gradient normalization algorithm to alleviate the gradient magnitude masking effect caused by training imbalance among different tasks. The proposedMOA-Unet is compared with classical chemometric methods on 670 coal samples from on-site *** experimental results demonstrate that the proposedmodel achieves state-of-the-art performance with correlation coefficients of 0.9015, 0.9538, 0.8986, and 0.8884, corresponding to moisture, ash, volatile content, and calorific value, respectively. Impact Statement-The proximate analysis of coal has been widely utilized as the basis for determining the rank of coal which is in connection with coa

关键词： Feature extraction

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A Finite-time Distributed Cooperative control Approach for Microgrids

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CSEE Journal of Power and Energy Systems 2022年第4期8卷 1194-1206页

作者： Jiancheng Zhang Xinsheng Wang Lingyu Ma Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150006China Department of Information Engineering Shandong Management UniversityJinan 250100China

Reactive power sharing cannot be achieved using many existing microgrid(MG)control methods,but the convergence speed of these methods is *** solve these problems,a finite-time distributed control approach is proposed in this paper,which is based on the hierarchical control *** hierarchical control structure consists of a dual loop control,a droop control used as a primary control and a secondary ***,the secondary controller is modeled,and the MG system composed of distributed generators(DGs)is considered as a multi-agent *** secondary controller consists of a frequency regulator,voltage regulator and power ***,the adaptive virtual impedance module is established,using the output of the reactive power regulator as its ***,a dual loop controller is combined with a primary controller and secondary controller to generate a pulse width modulation(PWM)signal to control the power and voltage of the *** order to reduce the fluctuation of the MG,a damping module is introduced when the structure of the system ***,the stability of the proposed control strategy is proved by the related theorems.A simulation system is established in the Matlab environment,and the simulation results show that the proposed method is effective.

关键词： Consensus droop control finite-time microgrid power sharing virtual impedance

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Bipolar interval-valued fuzzy set in graph and hypergraph settings

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Journal of Intelligent and Fuzzy Systems 2023年第2期44卷 1755-1767页

作者： Gong, Shu Hua, Gang School of Information and Control Engineering China University of Mining and Technology Xuzhou China Department of Computer Science Guangdong University Science and Technology Dongguan China

Graphs and hypergraphs are popular models for data structured representation. For example, traffic data, weather data, and animal skeleton data are all described by graph structures. Interval-valued fuzzy sets change the membership function of general fuzzy sets from single value functions to interval-valued functions, and thus describe the fuzzy attributes of things in terms of fuzzy intervals, which is more in line with the characteristics of fuzzy objectives. This paper aims to define the bipolar interval-valued fuzzy hypergraph to reveal the inner relationship of fuzzy data, and give some characterizations of it. The characteristics of bipolar interval-valued intuitionistic fuzzy hypergraph and bipolar interval-valued Pythagorean fuzzy hypergraph are studied. In addition, we discuss the characteristics of the bipolar interval-valued fuzzy threshold graph. Finally, some instances are presented as the applications of bipolar interval-valued fuzzy hypergraphs. © 2023 - IOS Press. All rights reserved.

关键词： Membership functions

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