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CNN-Based RF Fingerprinting Method for Securing Passive Keyless Entry and Start System

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Computers, Materials & Continua 2023年第8期76卷 1891-1909页

作者： Hyeon Park SeoYeon Kim Seok Min Ko TaeGuen Kim Department of smart convergence Security Soonchunhyang UniversityAsanKorea Department of Information Security engineering Soonchunhyang UniversityAsanKorea

The rapid growth of modern vehicles with advanced technologies requires strong security to ensure customer *** key system that needs protection is the passive key entry system(PKES).To prevent attacks aimed at defeating the PKES,we propose a novel radio frequency(RF)fingerprinting *** method extracts the cepstral coefficient feature as a fingerprint of a radio frequency *** feature is then analyzed using a convolutional neural network(CNN)for device *** evaluation,we conducted experiments to determine the effectiveness of different cepstral coefficient features and the convolutional neural network-based *** experimental results revealed that the Gammatone Frequency Cepstral Coefficient(GFCC)was the most compelling feature compared to Mel-Frequency Cepstral Coefficient(MFCC),Inverse Mel-Frequency Cepstral Coefficient(IMFCC),Linear-Frequency Cepstral Coefficient(LFCC),and Bark-Frequency Cepstral Coefficient(BFCC).Additionally,we experimented with evaluating the effectiveness of our method in comparison to existing approaches that are similar to ours.

关键词： RF fingerprint cepstral coefficient convolutional neural network

Blue TiO2 polymorph: An efficient material for dye-sensitized solar cells fabricated using a low-temperature sintering process

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Progress in Natural Science:Materials International 2018年第5期28卷 548-553页

作者： Huy Hao Nguyen Gobinda Gyawali Tae Ho Kim Sami Bin Humam Soo Wohn Lee Department of Environmental and Bio-chemical engineering Sun Moon University division of Basic engineering Sun Moon University division of Mechanics and ICT convergence engineering Sun Moon University

Recently,surface-disordered or non-stoichiometric TiO2,such as blue TiO2（B-TiO2）,has received much attention owing to its unique properties,such as structural disorder near the surface and the existence of Ti3+ions and oxygen ***,surface-disordered TiO2has been applied for microwave absorption,photocatalysis,in photoelectrochemical sensors and rechargeable lithium-ion *** this work B-TiO2,a polymorph consisting of nanoparticles,nanotubes and nanosheets,was successfully synthesized and employed as a semiconductor layer in dye-sensitized solar cells（DSSCs）fabricated using a low-temperature heat treatment process（120°C）.Based on the analyses of the experimental results regarding the structure and those from the characterization of B-TiO2,and its application to DSSCs,it has been found that the B-TiO2material has an effect on electron-hole pair *** conversion efficiency of the B-TiO2DSSC（BTiO2-DSSC）was 6.18%,whereas that of the TiO2-P25 DSSC（P25-DSSC）was 3.61%,and that of the TiO2polymorph DSSC（PTiO2-DSSC）which is the precursor of B-TiO2was 4.51%.

关键词： Surface-disordered TiO2 Blue TiO2 Dye-sensitized solar cell(DSSC) Oxygen vacancy Efficiency

Interworking between Modbus and internet of things platform for industrial services

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Digital Communications and Networks 2024年第2期10卷 461-471页

作者： Sherzod Elamanov Hyeonseo Son Bob Flynn Seong Ki Yoo Naqqash Dilshad JaeSeung Song Department of convergence engineering for Intelligent Drone Sejong UniversitySouth Korea SyncTechno Inc South Korea Exacta Global smart Solutions USA Coventry University UK

In the era of rapid development of Internet of Things(IoT),numerous machine-to-machine technologies have been applied to the industrial *** to the divergence of IoT solutions,the industry is faced with a need to apply various technologies for automation and *** fact leads to a demand for an establishing interworking mechanism which would allow smooth interoperability between heterogeneous *** of the major protocols widely used today in industrial electronic devices is ***,data generated by Modbus devices cannot be understood by IoT applications using different protocols,so it should be applied in a couple with an IoT service layer ***2M,a global IoT standard,can play the role of interconnecting various protocols,as it provides flexible tools suitable for building an interworking framework for industrial ***,in this paper,we propose an interworking architecture between devices working on the Modbus protocol and an IoT platform implemented based on oneM2M *** the proposed architecture,we introduce the way to model Modbus data as oneM2M resources,rules to map them to each other,procedures required to establish interoperable communication,and optimization methods for this *** analyze our solution and provide an evaluation by implementing it based on a solar power management use *** results demonstrate that our model is feasible and can be applied to real case scenarios.

关键词： Internet of things Interoperability Interworking Modbus oneM2M

Visualization for Explanation of Deep Learning-Based Defect Detection Model Using Class Activation Map

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Computers, Materials & Continua 2023年第6期75卷 4753-4766页

作者： Hyunkyu Shin Yonghan Ahn Mihwa Song Heungbae Gil Jungsik Choi Sanghyo Lee Center for AI Technology in Construction Hanyang University ERICAAnsan15588Korea School of Architecture and Architectural engineering Hanyang University ERICAAnsan15588Korea ICT convergence Research division Korea Expressway Corporation Research InstituteHwaseong18489Korea Department of Architecture College of EngineeringKangwon National UniversitySamcheok25913Korea division of smart convergence engineering Hanyang University ERICAAnsan15588Korea

Recently,convolutional neural network(CNN)-based visual inspec-tion has been developed to detect defects on building surfaces *** CNN model demonstrates remarkable accuracy in image data analysis;however,the predicted results have uncertainty in providing accurate informa-tion to users because of the“black box”problem in the deep learning ***,this study proposes a visual explanation method to overcome the uncertainty limitation of CNN-based defect *** visual repre-sentative gradient-weights class activation mapping(Grad-CAM)method is adopted to provide visually explainable information.A visualizing evaluation index is proposed to quantitatively analyze visual representations;this index reflects a rough estimate of the concordance rate between the visualized heat map and intended *** addition,an ablation study,adopting three-branch combinations with the VGG16,is implemented to identify perfor-mance variations by visualizing predicted *** reveal that the proposed model,combined with hybrid pooling,batch normalization,and multi-attention modules,achieves the best performance with an accuracy of 97.77%,corresponding to an improvement of 2.49%compared with the baseline ***,this study demonstrates that reliable results from an automatic defect classification model can be provided to an inspector through the visual representation of the predicted results using CNN models.

关键词： Defect detection visualization class activation map deep learning explanation visualizing evaluation index

Comparison of Missing Data Imputation Methods in Time Series Forecasting

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Computers, Materials & Continua 2022年第1期70卷 767-779页

作者： Hyun Ahn Kyunghee Sun Kwanghoon Pio Kim division of Software convergence Hanshin UniversityGyeonggi18101Korea Contents convergence Software Research Institute Kyonggi UniversityGyeonggi16227Korea division of AI Computer Science and engineering Kyonggi UniversityGyeonggi16227Korea

Time series forecasting has become an important aspect of data analysis and has many real-world ***,undesirable missing values are often encountered,which may adversely affect many forecasting *** this study,we evaluate and compare the effects of imputationmethods for estimating missing values in a time *** approach does not include a simulation to generate pseudo-missing data,but instead perform imputation on actual missing data and measure the performance of the forecasting model created *** an experiment,therefore,several time series forecasting models are trained using different training datasets prepared using each imputation ***,the performance of the imputation methods is evaluated by comparing the accuracy of the forecasting *** results obtained from a total of four experimental cases show that the k-nearest neighbor technique is the most effective in reconstructing missing data and contributes positively to time series forecasting compared with other imputation methods.

关键词： Missing data imputation method time series forecasting LSTM

Effect of ZnO Electrodeposited on Carbon Film and Decorated with Metal Nanoparticles for Solar Hydrogen Production

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Journal of Materials Science & Technology 2016年第10期32卷 1059-1065页

作者： Young Kwang Kim Hye-Jin Seo Soonhyun Kim Sung-Ho Hwang Hyunwoong Park Sang Kyoo Lim division of Nano and Energy convergence Research Daegu Gyeongbuk Institute of Science and Technology (DGIST) School of Energy engineering Kyungpook National University

In this study, we prepared horn-like ZnO structures on carbon films（ZnO/CF） by electrodeposition and decorated the ZnO horns with different metals（Ag, Au, and Pt） via photodeposition（M-ZnO/CF）. Using M-ZnO/CF as photocatalysts, we examined ways to enhance solar hydrogen production from various points of view, such as modifying the intrinsic physical properties and thermodynamics of the materials, and varying the chemical environment during M-ZnO/CF fabrication. In particular, we focused on the effects of the carbon film and metals in M-ZnO/CF hybrid photocatalysts on solar hydrogen production. The type of metal nanoparticles is an important factor in solar hydrogen production because the deposition rate and electrical conductivity of each metal affect the proton-water reduction ability.

关键词： Solar hydrogen production ZnO horn Carbon film Metal nanoparticle Electrodeposition

Finger vein recognition using weighted local binary pattern code based on a support vector machine

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Journal of Zhejiang University-Science C(Computers and Electronics) 2010年第7期11卷 514-524页

作者： Hyeon Chang LEE Byung Jun KANG Eui Chul LEE Kang Ryoung PARK division of Electronics and Electrical engineering Dongguk University Electronics and Telecommunications Research Institute division of Fusion and convergence of Mathematical Sciences the National Institute for Mathematical Sciences

Finger vein recognition is a biometric technique which identifies individuals using their unique finger vein patterns. It is reported to have a high accuracy and rapid processing speed. In addition, it is impossible to steal a vein pattern located inside the finger. We propose a new identification method of finger vascular patterns using a weighted local binary pattern (LBP) and support vector machine (SVM). This research is novel in the following three ways. First, holistic codes are extracted through the LBP method without using a vein detection procedure. This reduces the processing time and the complexities in detecting finger vein patterns. Second, we classify the local areas from which the LBP codes are extracted into three categories based on the SVM classifier: local areas that include a large amount (LA), a medium amount (MA), and a small amount (SA) of vein patterns. Third, different weights are assigned to the extracted LBP code according to the local area type (LA, MA, and SA) from which the LBP codes were extracted. The optimal weights are determined empirically in terms of the accuracy of the finger vein recognition. Experimental results show that our equal error rate (EER) is significantly lower compared to that without the proposed method or using a conventional method.

关键词： Finger vein recognition Support vector machine (SVM) Weight Local binary pattern (LBP)

Defect-Detection Model for Underground Parking Lots Using Image Object-Detection Method

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Computers, Materials & Continua 2021年第3期66卷 2493-2507页

作者： Hyun Kyu Shin Si Woon Lee Goo Pyo Hong Lee Sael Sang Hyo Lee Ha Young Kim Architectural engineering Hanyang UniversityERICAAnsan15588Korea Department of Artificial Intelligence Ajou UniversitySuwon16499Korea division of Architecture and Civil engineering Kangwon National UniversitySamcheok25913Korea division of smart convergence engineering Hanyang UniversityERICAAnsan15588Korea Graduate School of Information Yonsei UniversitySeoul03722Korea

The demand for defect diagnoses is gradually gaining ground owing to the growing necessity to implement safe inspection methods to ensure the durability and quality of ***,conventional manpower-based inspection methods not only incur considerable cost and time,but also cause frequent disputes regarding defects owing to poor ***,the demand for an effective and efficient defect-diagnosis model for concrete structures is imminent,as the reduction in maintenance costs is significant from a long-term ***,this paper proposes a deep learning-based image objectidentification method to detect the defects of paint peeling,leakage peeling,and leakage traces that mostly occur in underground parking lots made of concrete *** deep learning-based object-detection method can replace conventional visual inspection methods.A faster region-based convolutional neural network(R-CNN)model was used with a training dataset of 6,281 images that utilized a region proposal network to objectively localize the regions of interest and detect the surface *** defects were classified according to their type,and the learning of each exclusive model was ensured through test sets obtained from real underground parking *** a result,average precision scores of 37.76%,36.42%,and 61.29%were obtained for paint peeling,leakage peeling,and leakage trace defects,***,this study verified the performance of the faster RCNN-based defect-detection algorithm along with its applicability to underground parking lots.

关键词： Faster R-CNN deep learning defect detection concrete structures

Growth of BaTiO_3-Ag hybrid composite films at room temperature by aerosol deposition

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中国有色金属学会会刊：英文版 2012年第S3期22卷 735-739页

作者： Young Joon YOON Sung Hwan CHO Jihoon KIM Hyo Tae KIM Hong Koo BAIK Jong-Hee KIM Nano-convergence Intelligence Material Team Korea Institute of Ceramic Engineering and Technology division of Advanced Materials engineering Kongju National University Department of Materials Science and engineering Yonsei University

Barium titanate (BaTiO3) and silver (Ag) composite film with high dielectric constant was grown at room temperature by an aerosol deposition *** dielectric constant increases by 0.5 times after adding Ag to the BaTiO3 matrix,compared with pure *** high dielectric constant can be attributed to the percolation effect of Ag inclusions in the BaTiO3 *** Ag was present in the form of discrete layer in the BaTiO3 *** dielectric properties of BaTiO3 Ag were discussed in detail taking into account the changes in microstructures.

关键词： composites percolation aerosol deposition BaTiO3 Ag