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Hybrid Optimization Algorithm for Handwritten Document Enhancement

Computers, Materials & Continua 2024年第3期78卷 3763-3786页

作者： Shu-Chuan Chu Xiaomeng Yang Li Zhang Václav Snášel Jeng-Shyang Pan College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China Faculty of Electrical Engineering and Computer Science VŠB-TU OstravaOstrava70080Czech Republic Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

关键词： Metaheuristic algorithm gannet optimization algorithm hybrid algorithm handwritten document enhancement

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Internet information Intelligent Recommendation System Under Deep Learning and Big Data

Internet Information Intelligent Recommendation System Under...

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

The explosive growth of information on the internet poses great challenges to users. In response to the above issues, this article studies the construction of an intelligent recommendation system for internet information using a combination of deep learning and big data methods. This article adopts deep learning methods to model user behavior data and combines big data analysis to provide personalized recommendation services for users. This article compared the information recommendation accuracy of two other intelligent recommendation systems through experiments. The system designed in this article achieved an accuracy of 92.5% in this regard, far superior to the other two. Experimental results have shown that the system designed in this article not only improves recommendation accuracy, but also enhances user satisfaction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data accuracy

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SEMANTIC PRESERVATION AND STYLISTIC DECOUPLING IN TEXTUAL STYLE MIGRATION

SEMANTIC PRESERVATION AND STYLISTIC DECOUPLING IN TEXTUAL ST...

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2024 International Conference on Electrical engineering and Intelligent control, EEIC 2024

作者： Chen, Hengzhen Department of Information and Control Engineering Shenyang Institute of Science and Technology Shenyang110167 China

ISBN: (纸本)9781837242672

With the continuous progress of text style transfer technology, its importance in natural language processing is becoming more and more prominent, especially in machine translation, literary creation and other fields with a wide range of applications. This study builds on the work of Tikhonov et al. and provides insights into the problems with their proposed method for assessing textual style migration. Through experiments, this study validates Tikhonov's model architecture and further explores the applicability and limitations of the assessment criteria. The study uses a model that combines an autoencoder (AE) architecture with a Gated Recurrent Unit Recurrent Neural Network (GRU RNN) generator and optimizes the performance by introducing a dedicated discriminator and a shifted autoencoder (SAE). The experimental results demonstrate significant improvements in semantic retention and style migration accuracy thanks to the refined model. However, this study also found that Bilingual Evaluation Understudy (BLEU) scores have limitations in assessing style migration tasks and may not fully reflect the semantic accuracy and fluency of the migrated text. Therefore, this study proposes a reflection on the existing evaluation metrics and suggests exploring a comprehensive evaluation system that is more applicable to style migration tasks. In addition, this study discusses the model's ability to generalize over different styles and domains of data, highlighting the need for further validation and optimization. This study provides valuable insights into the field of textual style migration by validating state-of-the-art models while providing insights into their limitations. © The Institution of engineering & technology 2024.

关键词： Recurrent neural networks

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A Voltage Sag Source Localization Method Combining VMD and XGBoost 9

A Voltage Sag Source Localization Method Combining VMD and X...

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9th Asia Conference on Power and Electrical engineering, ACPEE 2024

作者： Jinli, Fan Yaping, Deng Hao, Jia Xiangqian, Tong Xi'An University of Technology Department of Power Grid Information and Control Engineering Shaanxi China

ISBN: (纸本)9798350309638

With the widespread access of various new energy power generation equipment and power electronic interface equipment, voltage sag seriously affects the normal work of all kinds of equipment in the distribution network, and causes huge economic losses to the power network. The accurate positioning of voltage sag sources has an important impact on the stable, reliable, and economical operation of the power system. In terms of voltage sag source localization, in order to overcome the defects of using signal processing methods or neural network methods alone, we combine VMD and XGBoost fusion method to determine the specific line number where voltage sag occurs. The powerful of this method was tested through IEEE 33 model simulation, and compared with GRU, LSTM, LSTM+GRU and Lightgbm networks, indicating that this method has high testing accuracy and profound guiding significance for future power system adjustments. © 2024 IEEE.

关键词： Signal processing

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LoRaWAN Sensors Integration for Manufacturing Applications via Edge Device Model with OPC UA 28

LoRaWAN Sensors Integration for Manufacturing Applications v...

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28th International Conference on Applied Electronics, AE 2023

作者： Peniak, Peter Holečko, Peter Bubeniková, Emilia Kanáliková, Alžbeta University of Žilina Žilina Faculty of Electrical Engineering Engineering and Information Technology Department of Control and Information Systems Slovakia University of Žilina Faculty of Electrical Engineering and Information Technology Department of Control and Information Systems Žilina Slovakia

ISBN: (纸本)9798350335538

The article deals with the integration of Lo(ng) Ra(nge) Wide Area Network (LoRaWAN) sensors with generic manufacturing applications and Industrial Internet of Things (IIoT) use-cases. The focus is on creating a suitable Edge device model that enables the integration of specific Long Range (LoRa) sensors together with the other generic Internet of Things (IoT/IIoT). The model is based on the Open Platform Communications Unified Architecture (OPC UA) standard, which is the most suitable integration platform for various control systems and manufacturing applications. The model was implemented in Node-RED software and subsequently validated to confirm the required integration of LoRaWAN sensors within OPC UA models. In contrast to other approaches based on integration gateway or LoRaWAN/OPC UA protocol convertors, the proposed model utilizes extended capabilities of Edge computing. It enables Edge processing and data transformation of LoRaWAN sensors data transferred by Message Queuing Telemetry Transport (MQTT) protocol from the network server, based on requirements of manufacturing applications for cloud solutions. © 2023 IEEE.

关键词： Wide area networks

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

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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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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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