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Journal of Environmental Sciences 2025年第5期151卷 410-423页

作者： Se Hun Lee Kang Hyun Lee Sang-hwa Lee Soo Kweon Lee Ok Sung Jeon Young Pyo Jeon Dongpyo Hong Young Joon Yoo Sang Yoon Park Hah Young Yoo Advanced Institute of convergence Technology(Aict) Seoul National UniversitySuwon 16229Republic of Korea department of Biotechnology Sangmyung University20 Hongjimun 2-GilJongno-GuSeoul 03016Republic of Korea department of Bio-convergence Engineering Dongyang Mirae University445-8Gyeongin-roGuro-guSeoul 08221Republic of Korea Fermentation team Lotte R&D Center210 Magokjungang-RoGangseo-GuSeoul 07594Republic of Korea School of Electronic Engineering Kyonggi UniversityGyeonggi-do 16227Republic of Korea

Microalgae are one of the promising feedstocks for biorefinery,contributing significantly to net-zero emissions through carbon capture and ***,the disposal of microalgal byproducts from the manufacturing process causes additional environmental pollution,thus,a new application strategy is *** this study,the Tetraselmis suecica byproduct from the carotenoid extraction process was carbonized and converted into *** converted biochar was proved to be nitrogen-doped biochar(NDB),up to 4.69%,with a specific surface area of 206.59m^(2)/g andwas used as an electrode for a *** NDB electrode(NDB-E)in half-cell showed a maximum specific capacitance of 191 F/*** a full-cell test,the NDB-E exhibited a high energy density of 7.396 Wh/kg and a high-power density of 18,100 W/kg,and maintained specific capacity of 95.5%after charge and discharge of 10,000 *** conclusion,our study demonstrated that the carotenoid-extracted microalgal byproducts are a useful resource for the supercapacitor *** approach is the first to convert *** into active materials for supercapacitors.

关键词： Microalgae Tetraselmis suecica Carbonization N-doped biochar Supercapacitor

Advancing depth perception in spatial computing with binocular metalenses

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Opto-Electronic Advances 2025年第1期8卷 1-3页

作者： Junkyeong Park Gyeongtae Kim Junsuk Rho department of Mechanical Engineering Pohang University of Science and Technology(POSTECH)Pohang 37673Republic of Korea department of Chemical Engineering Pohang University of Science and Technology(POSTECH)Pohang 37673Republic of Korea department of Electrical Engineering Pohang University of Science and Technology(POSTECH)Pohang 37673Republic of Korea POSCO-POSTECH-RIST convergence Research Center for Flat Optics and Metaphotonics Pohang 37673Republic of Korea

Spatial computing and augmented reality are advancing rapidly,with the goal of seamlessly blending virtual and physical ***,traditional depth-sensing systems are bulky and energy-intensive,limiting their use in wearable *** overcome this,recent research by *** et *** a compact binocular metalens-based depth perception system that integrates efficient edge detection through an advanced neural *** system enables accurate,realtime depth mapping even in complex environments,enhancing potential applications in augmented reality,robotics,and autonomous systems.

关键词： metasurface metalens deep learning depth perception edge detection

Machine Learning-Based Detection and Selective Mitigation of Denial-of-Service Attacks in Wireless Sensor Networks

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Computers, Materials & Continua 2025年第2期82卷 2475-2494页

作者： Soyoung Joo So-Hyun Park Hye-Yeon Shim Ye-Sol Oh Il-Gu Lee department of Future convergence Technology Engineering Sungshin Women’s UniversitySeoul02844Republic of Korea

As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by monitoring packet log data. Based on the type of detected attack, it implements effective corresponding mitigation techniques to restore performance to nodes whose availability has been compromised. Experimental results reveal that the accuracy of the proposed model is 14% higher than that of a baseline anomaly detection model. Further, the appropriate mitigation techniques selected by the proposed system based on the attack type improve the average throughput by more than 440% compared to the case without a response.

关键词： Distributed coordinated function mechanism jamming attack machine learning-based attack detection selective attack mitigation model selective attack mitigation model selfish attack

Dragonfly Interaction Algorithm for Optimization of Queuing Delay in Industrial Wireless Networks

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Journal of Bionic Engineering 2024年第1期21卷 447-485页

作者： Sanjay Bhardwaj Da-Hye Kim Dong-Seong Kim department of IT convergence Engineering ICT Convergence Research CenterKumoh National Institute of TechnologyGumiSouth Korea

In industrial wireless networks,data transmitted from source to destination are highly *** often leads to the queuing of the data,and poor management of the queued data results in excessive delays,increased energy consumption,and packet ***,a nature-inspired-based Dragonfly Interaction Optimization Algorithm(DMOA)is proposed for optimization of the queue delay in industrial wireless *** term“interaction”herein used is the characterization of the“flying movement”of the dragonfly towards damselflies(female dragonflies)for *** a result,interaction is represented as the flow of transmitted data packets,or traffic,from the source to the base *** includes each and every feature of dragonfly movement as well as awareness of the rival dragonflies,predators,and damselflies for the desired optimization of the queue *** features are juxtaposed as noise and interference,which are further used in the calculation of industrial wireless metrics:latency,error rate(reliability),throughput,energy efficiency,and fairness for the optimization of the queue *** analysis,convergence analysis,the Wilcoxon test,the Friedman test,and the classical as well as the 2014 IEEE Congress of Evolutionary Computation(CEC)on the benchmark functions are also used for the evaluation of DMOA in terms of its robustness and *** results demonstrate the robustness of the proposed algorithm for both classical and benchmarking functions of the IEEE CEC ***,the accuracy and efficacy of DMOA were demonstrated by means of the convergence rate,Wilcoxon testing,and ***,fairness using Jain’s index in queue delay optimization in terms of throughput and latency,along with computational complexity,is also evaluated and compared with other *** results show that DMOA exceeds other bio-inspired optimization algorithms in terms of fairness in queue delay management and average packet *** proposed a

关键词： Dragonfly Damselfly Interaction Queuing delay Optimization Industrial wireless networks

Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1729-1755页

作者： Awais Khan Chomyong Kim Jung-Yeon Kim Ahsan Aziz Yunyoung Nam department of ict convergence Soonchunhyang UniversityAsan31538Korea ict convergence Research Center Soonchunhyang UniversityAsan31538Korea Emotional and Intelligent Child Care convergence Research Center Soonchunhyang UniversityAsan31538Korea

Sleep posture surveillance is crucial for patient comfort,yet current systems face difficulties in providing compre-hensive studies due to the obstruction caused by *** posture assessment remains challenging because of the complex nature of the human body and variations in sleep ***,this study introduces an innovative method utilizing RGB and thermal cameras for comprehensive posture classification,thereby enhancing the analysis of body position and *** method begins by capturing a dataset of sleep postures in the form of videos using RGB and thermal cameras,which depict six commonly adopted postures:supine,left log,right log,prone head,prone left,and prone *** study involves 10 participants under two conditions:with and without ***,the database is normalized into a video *** subsequent step entails training a fine-tuned,pretrained Visual Geometry Group(VGG16)and ResNet50 *** the third phase,the extracted features are utilized for *** fourth step of the proposed approach employs a serial fusion technique based on the normal distribution to merge the vectors derived from both the RGB and thermal ***,the fused vectors are passed to machine learning classifiers for final *** dataset,which includes human sleep postures used in this study’s experiments,achieved a 96.7%accuracy rate using the Quadratic Support Vector Machine(QSVM)without the ***,the Linear SVM,when utilized with a blanket,attained an accuracy of 96%.When normal distribution serial fusion was applied to the blanket features,it resulted in a remarkable average accuracy of 99%.

关键词： Human sleep posture VGG16 deep learning ResNet50 fusion machine learning

A Study on Edge Segmentation of Different Types of Datasets with Multiple Algorithms

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Journal of Computer and Communications 2025年第1期13卷 125-135页

作者： Faruque Hossain Mozumder Md. Sahidul Islam Md. Omar Faruq Masum Miah Md. Abdul Mannan ict Division Jamuna Bank PLC. Dhaka Bangladesh department of Physics Govt. H. S. S College Magura Bangladesh department of Mathematics Uttara University Dhaka Bangadesh

In this paper, we study edge detection or segmentation, which is recognized as a rudiment innovation as it can evaluate sharpness and analyze object boundaries. That’s the reason it has been an influential figure in the image-processing era. Because of this, it has a significant influence in the age of image processing. On the other hand, edge detection is the process of dividing an image into discontinuous regions. It specifies the intensity shift connected to the image’s edge. There are several methods for detecting edges. Four edge identification methods on satellite images and satellite images affected by Gaussian noise were examined. Known edge detection technologies such as Canny, Prewitt, Scharr, and Robert operators are included in this study. Additionally, the key feature of an image for evaluating its quality is the Image Quality Assessment (IQA) measure. We primarily take into account SSIM, MSE, PSNR, and RMSE when assessing image quality. Experimental validation has been obtained for the application of the Canny and Prewitt algorithms to the satellite dataset. However, when the Gaussian Noise effect is added to the same dataset, clever edge detection performs better.

关键词： SSIM MSE PSNR RMSE

Evaluation of Pencil Lead Based Electrodes for Electrocardiogram Monitoring in Hot Spring

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Computers, Materials & Continua 2021年第2期66卷 1411-1425页

作者： Ratha Yeu Namhui Ra Seong-A Lee Yunyoung Nam department of ict convergence Rehabilitation Engineering Soonchunhyang UniversityAsan31538Korea department of ict convergence Soonchunhyang UniversityAsan31538Korea department of Occupational Therapy Soonchunhyang UniversityAsan31538Korea department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Electrocardiogram(ECG)electrodes are conductive pads applied to the skin to measure cardiac ***/AgCl electrodes are the commercial product which widely used to obtain *** monitoring the ECG in a hot spring,Ag/AgCl electrodes must be waterproofed;however,this is time-consuming,and the adhesive may tear the skin on *** solving the problem,we developed the carbon pencil lead(CPL)electrodes for use in hot *** CPL and Ag/AgCl electrodes were connected to ECG100C’s *** Performance was evaluated in three conditions as following:hot spring water with and without bubble,and in cold *** each environment,the procedure was followed by three different protocols that are recording from the dry condition,hot spring water immersion with and without movement,post hot spring water *** dry and wet conditions,both electrodes can obtain the waveform of the ECG signal in which all PQRST waves were ***,the signal quality of both types of electrodes was different in water immersion with and without *** overall morphology obtained by Ag/AgCl electrodes was unstable higher than that of CPL electrodes in immersion without movement *** CPL electrodes provided better ECG waveform quality compared to Ag/AgCl electrodes in which the ECG signal had high waveforms distortion in water immersion with movement condition.

关键词： Carbon pencil lea ECG electrodes ECG monitoring hot spring water cold water hot spring water bubble

Data Augmentation Using Contour Image for Convolutional Neural Network

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Computers, Materials & Continua 2023年第6期75卷 4669-4680页

作者： Seung-Yeon Hwang Jeong-Joon Kim department of Computer Engineering Anyang UniversityAnyang-si14058Korea department of ict convergence Engineering Anyang UniversityAnyang-si14058Korea

With the development of artificial intelligence-related technologies such as deep learning,various organizations,including the government,are making various efforts to generate and manage big data for use in artificial ***,it is difficult to acquire big data due to various social problems and restrictions such as personal information *** are many problems in introducing technology in fields that do not have enough training data necessary to apply deep learning ***,this study proposes a mixed contour data augmentation technique,which is a data augmentation technique using contour images,to solve a problem caused by a lack of ***,a famous convolutional neural network(CNN)architecture,and CIFAR-10,a benchmark data set,are used for experimental performance evaluation to prove the superiority of the proposed *** to prove that high performance improvement can be achieved even with a small training dataset,the ratio of the training dataset was divided into 70%,50%,and 30%for comparative *** a result of applying the mixed contour data augmentation technique,it was possible to achieve a classification accuracy improvement of up to 4.64%and high accuracy even with a small amount of data *** addition,it is expected that the mixed contour data augmentation technique can be applied in various fields by proving the excellence of the proposed data augmentation technique using benchmark datasets.

关键词： Data augmentation image classification deep learning convolutional neural network mixed contour image benchmark dataset

A Deep Learning Hierarchical Ensemble for Remote Sensing Image Classification

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Computers, Materials & Continua 2022年第8期72卷 2649-2663页

作者： Seung-Yeon Hwang Jeong-Joon Kim department of Computer Engineering Anyang UniversityAnyang-si14028Korea department of ict convergence Engineering Anyang UniversityAnyang-si14028Korea

Artificial intelligence,which has recently emerged with the rapid development of information technology,is drawing attention as a tool for solving various problems demanded by society and *** particular,convolutional neural networks(CNNs),a type of deep learning technology,are highlighted in computer vision fields,such as image classification and recognition and object *** these CNN models requires a large amount of data,and a lack of data can lead to performance degradation problems due to *** CNN architecture development and optimization studies become active,ensemble techniques have emerged to perform image classification by combining features extracted from multiple CNN *** this study,data augmentation and contour image extraction were performed to overcome the data shortage *** addition,we propose a hierarchical ensemble technique to achieve high image classification accuracy,even if trained from a small amount of ***,we trained the UCMerced land use dataset and the contour images for each image on pretrained VGGNet,GoogLeNet,ResNet,DenseNet,and *** then apply a hierarchical ensemble technique to the number of cases in which each model can be *** experiments were performed in cases where the proportion of training datasets was 30%,50%,and 70%,resulting in a performance improvement of up to 4.68%compared to the average accuracy of the entire model.

关键词： Image classification deep learning CNNs hierarchical ensemble UC-Merced land use dataset contour image

A Universal Activation Function for Deep Learning

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Computers, Materials & Continua 2023年第5期75卷 3553-3569页

作者： Seung-Yeon Hwang Jeong-Joon Kim department of Computer Engineering Anyang UniversityAnyang-si14028Korea department of ict convergence Engineering Anyang UniversityAnyang-si14028Korea

Recently,deep learning has achieved remarkable results in fields that require human cognitive ability,learning ability,and reasoning *** functions are very important because they provide the ability of artificial neural networks to learn complex patterns through *** activation functions are being studied to solve problems such as vanishing gradients and dying nodes that may occur in the deep learning ***,it takes a lot of time and effort for researchers to use the existing activation function in their ***,in this paper,we propose a universal activation function(UA)so that researchers can easily create and apply various activation functions and improve the performance of neural *** can generate new types of activation functions as well as functions like traditional activation functions by properly adjusting three *** famous Convolutional Neural Network(CNN)and benchmark datasetwere used to evaluate the experimental performance of the UA proposed in this *** compared the performance of the artificial neural network to which the traditional activation function is applied and the artificial neural network to which theUA is *** addition,we evaluated the performance of the new activation function generated by adjusting the hyperparameters of *** experimental performance evaluation results showed that the classification performance of CNNs improved by up to 5%through the UA,although most of them showed similar performance to the traditional activation function.

关键词： Deep learning activation function convolutional neural network benchmark datasets universal activation function