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Deep learning based water leakage detection for shield tunnel lining

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Frontiers of Structural and Civil Engineering 2024年第6期18卷 887-898页

作者： Shichang LIU Xu XU gwanggil jeon Junxin CHEN Ben-Guo HE College of Computer Science Sichuan UniversityChengdu 610065China School of Computer Science and Engineering Northeastern UniversityShenyang 110004China Department of Embedded Systems Engineering Incheon National UniversityIncheon 22012Korea School of Software Dalian University of TechnologyDalian 116621China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern UniversityShenyang 110819China

Shield tunnel lining is prone to water leakage,which may further bring about corrosion and structural damage to the walls,potentially leading to dangerous *** avoid tedious and inefficient manual inspection,many projects use artificial intelligence(Al)to detect cracks and water leakage.A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced in this *** proposal includes a ConvNeXt-S backbone,deconvolutional-feature pyramid network(D-FPN),spatial attention module(SPAM).and a detection *** can extract representative features of leaking areas to aid inspection *** further improve the model's robustness,we innovatively use an inversed low-light enhancement method to convert normally illuminated images to low light ones and introduce them into the training *** experiments are performed,achieving the average precision(AP)score of 56.8%,which outperforms previous work by a margin of 5.7%.Visualization illustrations also support our method's practical effectiveness.

关键词： water leakage detection deep learning deconvolutional-feature pyramid spatial attention

Abnormal Traffic Detection for Internet of Things Based on an Improved Residual Network

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Computers, Materials & Continua 2024年第6期79卷 4433-4448页

作者： Tingting Su Jia Wang Wei Hu Gaoqiang Dong jeon gwanggil School of Computer Science and Technology Xinjiang UniversityUrumqi830046China School of Cyber Science and Engineering Jinling Institute of Technology CityNanjing210000China College of Information Technology Incheon National UniversityIncheon22012Korea

Along with the progression of Internet of Things(IoT)technology,network terminals are becoming continuously more *** has been widely applied in various scenarios,including urban infrastructure,transportation,industry,personal life,and other socio-economic *** introduction of deep learning has brought new security challenges,like an increment in abnormal traffic,which threatens network *** feature extraction leads to less accurate classification *** abnormal traffic detection,the data of network traffic is high-dimensional and *** data not only increases the computational burden of model training but also makes information extraction more *** address these issues,this paper proposes an MD-MRD-ResNeXt model for abnormal network traffic *** fully utilize the multi-scale information in network traffic,a Multi-scale Dilated feature extraction(MD)block is *** module can effectively understand and process information at various scales and uses dilated convolution technology to significantly broaden the model’s receptive *** proposed Max-feature-map Residual with Dual-channel pooling(MRD)block integrates the maximum feature map with the residual *** module ensures the model focuses on key information,thereby optimizing computational efficiency and reducing unnecessary information *** results show that compared to the latest methods,the proposed abnormal traffic detection model improves accuracy by about 2%.

关键词： Abnormal network traffic deep learning residual network multi-scale feature extraction max-feature-map

Fuzz-classification(p,l)-Angel:An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches

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Digital Communications and Networks 2023年第5期9卷 1131-1140页

作者： Tehsin Kanwal Hasina Attaullah Adeel Anjum Abid Khan gwanggil jeon Department of Computer Science COMSATS University IslamabadPakistan Department of Information Technology Quaid-e-Azam University IslamabadPakistan College of Science and Engineering School of Computing and MathsUniversity of DerbyDE221GBUK Department of Embedded Systems Engineering Incheon National UniversitySouth Korea

The inability of traditional privacy-preserving models to protect multiple datasets based on sensitive attributes has prompted researchers to propose models such as SLOMS,SLAMSA,(p,k)-Angelization,and(p,l)-Angelization,but these were found to be insufficient in terms of robust privacy and performance.(p,l)-Angelization was successful against different privacy disclosures,but it was not *** the best of our knowledge,no robust privacy model based on fuzzy logic has been proposed to protect the privacy of sensitive attributes with multiple *** this paper,we suggest an improved version of(p,l)-Angelization based on a hybrid AI approach and privacy-preserving approach like ***-classification(p,l)-Angel uses artificial intelligence based fuzzy logic for classification,a high-dimensional segmentation technique for segmenting quasi-identifiers and multiple sensitive *** demonstrate the feasibility of the proposed solution by modelling and analyzing privacy violations using High-Level Petri *** results of the experiment demonstrate that the proposed approach produces better results in terms of efficiency and utility.

关键词： Generalization Fuzzy-logic MSA Privacy disclosures Membership function (p,l)-Angelization QT HLPN

Towards Collaborative Robotics in Top View Surveillance:A Framework for Multiple Object Tracking by Detection Using Deep Learning

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IEEE/CAA Journal of Automatica Sinica 2021年第7期8卷 1253-1270页

作者： Imran Ahmed Sadia Din gwanggil jeon Francesco Piccialli Giancarlo Fortino the Center of excellence in Information Technology Institute of Management SciencesPeshawar 25000Pakistan the Department of Information and Communication Engineering Yeungnam UniversitySouth Korea the School of Electronic Engineering Xidian UniversityXi’an 710071China the Department of Embedded Systems Engineering Incheon National UniversityIncheon 22012Korea the Department of Mathematics and Applications“R.Caccioppoli” University of Naples Federico IINapoli 80138Italy the Department of Informatics ModelingElectronics and SystemsUniversity of CalabriaRendeCS 87036Italy

Collaborative Robotics is one of the high-interest research topics in the area of academia and *** has been progressively utilized in numerous applications,particularly in intelligent surveillance *** allows the deployment of smart cameras or optical sensors with computer vision techniques,which may serve in several object detection and tracking *** tasks have been considered challenging and high-level perceptual problems,frequently dominated by relative information about the environment,where main concerns such as occlusion,illumination,background,object deformation,and object class variations are *** order to show the importance of top view surveillance,a collaborative robotics framework has been *** can assist in the detection and tracking of multiple objects in top view *** framework consists of a smart robotic camera embedded with the visual processing *** existing pre-trained deep learning models named SSD and YOLO has been adopted for object detection and *** detection models are further combined with different tracking algorithms,including GOTURN,MEDIANFLOW,TLD,KCF,MIL,and *** algorithms,along with detection models,help to track and predict the trajectories of detected *** pre-trained models are employed;therefore,the generalization performance is also investigated through testing the models on various sequences of top view data *** detection models achieved maximum True Detection Rate 93%to 90%with a maximum 0.6%False Detection *** tracking results of different algorithms are nearly identical,with tracking accuracy ranging from 90%to 94%.Furthermore,a discussion has been carried out on output results along with future guidelines.

关键词： Collaborative robotics deep learning object detection and tracking top view video surveillance

Integrating digital twins and deep learning for medical image analysis in the era of COVID-19

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Virtual Reality & Intelligent Hardware 2022年第4期4卷 292-305页

作者： Imran AHMED Misbah AHMAD gwanggil jeon School of Computing and Information Science Faculty of Science and EngineeringAnglia Ruskin UniversityEast RoadCambridgeEnglandUK Center of Excellence in IT Institute of Management SciencesHayatabadPeshawarPakistan Department of Embedded Systems Engineering Incheon National UniversityIncheonKorea

Background Digital twins are virtual representations of devices and processes that capture the physical properties of the environment and operational algorithms/techniques in the context of medical devices and *** twins may allow healthcare organizations to determine methods of improving medical processes,enhancing patient experience,lowering operating expenses,and extending the value of *** the present COVID-19 pandemic,various medical devices,such as X-rays and CT scan machines and processes,are constantly being used to collect and analyze medical *** collecting and processing an extensive volume of data in the form of images,machines and processes sometimes suffer from system failures,creating critical issues for hospitals and *** To address this,we introduce a digital-twin-based smart healthcare system in-tegrated with medical devices to collect information regarding the current health condition,configuration,and maintenance history of the device/machine/***,medical images,that is,X-rays,are analyzed by using a deep-learning model to detect the infection of *** designed system is based on the cascade recurrent convolution neural network(RCNN)*** this architecture,the detector stages are deeper and more sequentially selective against small and close false *** architecture is a multi-stage extension of the RCNN model and sequentially trained using the output of one stage for training the *** each stage,the bounding boxes are adjusted to locate a suitable value of the nearest false positives during the training of the different *** this manner,the arrangement of detectors is adjusted to increase the intersection over union,overcoming the problem of *** train the model by using X-ray images as the model was previously trained on another *** The developed system achieves good accuracy during the detection phase of *** experimental o

关键词： Digital twins Deep learning Healthcare COVID-19 Chest X-rays Artificial intelligence

Blockchain-based DDoS attack mitigation protocol for device-to-device interaction in smart home

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Digital Communications and Networks 2023年第2期9卷 383-392页

作者： Bello Musa Yakubu Majid Iqbal Khan Abid Khan Farhana Jabeen gwanggil jeon Department of Mathematics and Computer Science Chulalongkorn UniversityBangkok10330Thailand Department of Computer Science COMSATS University Islamabad(CUI)Islamabad45550Pakistan College of Science and Engineering University of DerbyDerbyDE221GBUnited Kingdom Department of Embedded Systems Engineering Incheon National UniversityIncheon22012South Korea

Smart home devices are vulnerable to a variety of *** matter gets more complicated when a number of devices collaborate to launch a colluding attack(e.g.,Distributed-Denial-of-Service(DDoS))in a network(e.g.,Smart home).To handle these attacks,most studies have hitherto proposed authentication protocols that cannot necessarily be implemented in devices,especially during Device-to-Device(D2D)*** into the potential of Ethereum blockchain and smart contracts,this work proposes a lightweight authentication mechanism that enables safe D2D interactions in a smart *** Ethereum blockchain enables the implementation of a decentralized prototype as well as a peer-to-peer distributed ledger *** work also uses a single server queuing system model and the authentication mechanism to curtail DDoS attacks by controlling the number of service requests in the *** simulation was conducted twenty times,each with varying number of devices chosen at random(ranging from 1 to 30).Each requester device sends an arbitrary request with a unique resource requirement at a *** is done to measure the system's consistency across a variety of device *** experimental results show that the proposed protocol not only prevents colluding attacks,but also outperforms the benchmark protocols in terms of computational cost,message processing,and response times.

关键词： DDoS attack Device-to-device Smart homes Blockchain Authentication Ethereum smartcontract

Energy Theft Detection in Smart Grids:Taxonomy,Comparative Analysis,Challenges,and Future Research Directions

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 578-600页

作者： Mohsin Ahmed Abid Khan Mansoor Ahmed Mouzna Tahir gwanggil jeon Giancarlo Fortino Francesco Piccialli the Department of Computer Science COMSATS University Islamabad the Department of Computer Science School of ComputingEngineering and Digital TechnologiesTeesside University the Innovative Value Institute Maynooth University the Department of Computer Science Bahria University the School of Electronic Engineering Xidian University the Department of Embedded Systems Engineering Incheon National University IEEE the Department of Informatics ModelingElectronics and SystemsUniversity of Calabria the Department of Mathematics and Applications“R.Caccioppoli” University of Naples FedericoⅡ

Electricity theft is one of the major issues in developing countries which is affecting their economy *** with the introduction of emerging technologies,this issue became more *** many new energy theft detection(ETD)techniques have been proposed by utilising different data mining(DM)techniques,state&network(S&N)based techniques,and game theory(GT)***,a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and *** levels of taxonomy are presented to classify state-of-the-art ETD *** types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from *** challenges of different ETD techniques and their mitigation are suggested for future *** is observed that the literature on ETD lacks knowledge management techniques that can be more effective,not only for ETD but also for theft *** can help in the prevention of energy theft,in the future,as well as for ETD.

关键词： Challenges comparative analysis energy theft detection future research directions smart grid taxonomy

Multi-layer composite autoencoders for semi-supervised change detection in heterogeneous remote sensing images

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Science China(Information Sciences) 2023年第4期66卷 124-125页

作者： Jiao SHI Tiancheng WU Hanwen YU A.K.QIN gwanggil jeon Yu LEI School of Electronics and Information Northwestern Polytechnical University School of Resources and Environment University of Electronic Science and Technology of China Department of Computer Science and Software Engineering Swinburne University of Technology Department of Embedded Systems Engineering Incheon National University

With the increasing complexity of application scenarios, the fusion of different remote sensing data types has gradually become a trend, which can greatly improve the utilization of massive remote sensing *** the problem of change detection for heterogeneous remote images can be much more complicated than the traditional change detection for homologous remote sensing images,

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基于主特征提取的Retinex多谱段图像增强

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物理学报 2016年第16期65卷 57-72页

作者：李红吴炜杨晓敏严斌宇刘凯 gwanggil jeon 四川大学电子信息学院成都610065 四川大学电气信息学院成都610065 韩国仁川大学信息技术学院

根据Retinex视觉模型中照射分量和反射分量的统计特性,融合多尺度主特征提取法、平台直方图算法、非局部均值滤波及局部细节增强算法可对多谱段图像进行有效增强.首先利用多尺度主特征提取法估计照射分量,对照射分量进行平台直方图操作... 详细信息

根据Retinex视觉模型中照射分量和反射分量的统计特性,融合多尺度主特征提取法、平台直方图算法、非局部均值滤波及局部细节增强算法可对多谱段图像进行有效增强.首先利用多尺度主特征提取法估计照射分量,对照射分量进行平台直方图操作,增强全局对比度及图像主结构边缘细节;然后将原图与照射分量相除获取反射分量,对反射分量进行非局部均值滤波抑制噪声,再进行基于局部方差的局部细节增强;最后将增强后的照射分量与反射分量相乘,即为增强图像.从主观和客观两方面,对X光图像、紫外图像、可见光图像、低照度可见光图像和红外图像实验结果的分析表明,本文算法能够有效地抑制图像噪声、增强图像对比度及细节、改善图像视觉效果,是一种通用有效的多谱段图像增强算法.

关键词：多谱段图像增强主特征提取