• Smart Structures and Systems
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• 제31권4호
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• pp.421-436
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• 2023

This paper presents a vibration displacement measurement and damage identification method for a space truss structure from its vibration videos. Features from Accelerated Segment Test (FAST) algorithm is combined with adaptive threshold strategy to detect the feature points of high quality within the Region of Interest (ROI), around each node of the truss structure. Then these points are tracked by Kanade-Lucas-Tomasi (KLT) algorithm along the video frame sequences to obtain the vibration displacement time histories. For some cases with the image plane not parallel to the truss structural plane, the scale factors cannot be applied directly. Therefore, these videos are processed with homography transformation. After scale factor adaptation, tracking results are expressed in physical units and compared with ground truth data. The main operational frequencies and the corresponding mode shapes are identified by using Subspace Stochastic Identification (SSI) from the obtained vibration displacement responses and compared with ground truth data. Structural damages are quantified by elemental stiffness reductions. A Bayesian inference-based objective function is constructed based on natural frequencies to identify the damage by model updating. The Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) is applied to minimise the objective function by tuning the damage parameter of each element. The locations and severities of damage in each case are then identified. The accuracy and effectiveness are verified by comparison of the identified results with the ground truth data.

• Composites Research
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• 제36권3호
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• pp.217-223
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• 2023

본 연구에서는 폴리아크릴로니트릴계 탄소나노섬유 스펀지의 제조 및 오일 흡착 거동을 조사하였다. 제조된 탄소나노섬유 스펀지는 물과 기름의 혼합용액에서 우수한 선택적 오일 흡착 능력을 보였으며, 탄소나노섬유 스펀지의 재사용시 흡착 능력도 확인하였다. 또한, 흡착제 내부구조에 정렬된 채널을 형성함으로써, 모세관 현상에 의하여 빠른 오일 흡착 거동을 보이는 탄소나노섬유 스펀지를 제조할 수 있었다. 이후 사용 폐기된 탄소나노섬유 스펀지는 질소 분위기에서 800℃로 열처리하여, 4-아미노페놀의 전기화학적 검출을 위한 센서로의 가능성을 검토하였다.

• 센서학회지
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• 제32권5호
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• pp.290-294
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• 2023

Among various types of harmful gases, hydrogen sulfide is a strong toxic gas that is mainly generated during spillage and wastewater treatment at industrial sites. Hydrogen sulfide can irritate the conjunctiva even at low concentrations of less than 10 ppm, cause coughing, paralysis of smell and respiratory failure at a concentration of 100 ppm, and coma and permanent brain loss at concentrations above 1000 ppm. Therefore, rapid detection of hydrogen sulfide among harmful gases is extremely important for our safety, health, and comfortable living environment. Most hydrogen sulfide gas sensors that have been reported are electrical resistive metal oxide-based semiconductor gas sensors that are easy to manufacture and mass-produce and have the advantage of high sensitivity; however, they have low gas selectivity. In contrast, the electrochemical sensor measures the concentration of hydrogen sulfide using an electrochemical reaction between hydrogen sulfide, an electrode, and an electrolyte. Electrochemical sensors have various advantages, including sensitivity, selectivity, fast response time, and the ability to measure room temperature. However, most electrochemical hydrogen sulfide gas sensors depend on imports. Although domestic technologies and products exist, more research is required on their long-term stability and reliability. Therefore, this study includes the processes from electrode material synthesis to sensor fabrication and characteristic evaluation, and introduces the sensor structure design and material selection to improve the sensitivity and selectivity of the sensor. A sensor case was fabricated using a 3D printer, and an Ag reference electrode, and a Pt counter electrode were deposited and applied to a Polytetrafluoroethylene (PTFE) filter using PVD. The working electrode was also deposited on a PTFE filter using vacuum filtration, and an electrochemical hydrogen sulfide gas sensor capable of measuring concentrations as low as 0.6 ppm was developed.

• 한국전자통신학회논문지
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• 제18권6호
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• pp.1307-1312
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• 2023

본 논문에서는 자율주행 인지 기술의 핵심 요소인 객체 인식과 거리 측정을 위해 서로 다른 초점거리를 가진 다시점 카메라와 라이다(LiDAR) 센서를 결합한 복합형 카메라 시스템을 제안한다. 제안한 복합형 카메라 시스템을 이용해 장면 안의 객체를 추출하고, 추출한 객체의 정확한 위치와 거리 정보를 생성한다. 빠른 계산 속도와 높은 정확도, 실시간 처리가 가능하다는 장점 때문에 자율주행 분야에서 많이 사용하고 있는 YOLO7 알고리즘을 이용해 장면 안의 객체를 추출한다. 그리고 객체의 위치와 거리 정보를 생성하기 위해 다시점 카메라를 이용해 깊이맵을 생성한다. 마지막으로 거리 정확도를 향상시키기 위해 라이다 센서에서 획득한 3차원 거리 정보와 생성한 깊이맵을 하나로 결합한다. 본 논문에서는 제안한 복합형 카메라 시스템을 기반으로 주행중인 주변 환경을 더욱 정확하게 인식함과 동시에 3차원 공간상의 정확한 위치와 거리 정보까지 생성할 수 있는 자율주행 차량 플랫폼을 제안하였으며, 이를 통해 자율주행 차량의 안전성과 효율성을 향상시킬 수 있을 것으로 기대한다.

• 한국표면공학회지
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• 제56권6호
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• pp.420-426
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• 2023

We prepared a highly sensitive hydrogen (H₂) sensor based on Indium oxides (In₂O₃) porous nanoparticles (NPs) loaded with Platinum (Pt) nanoparticle in the range of 1.6~5.7 at.%. In₂O₃ NPs were fabricated by microwave irradiation method, and decorations of Pt nanoparticles were performed by electroless plating on In₂O₃ NPs. Crystal structures, morphologies, and chemical information on Pt-loaded In₂O₃ NPs were characterized by grazing-incident X-ray diffraction, field-emission scanning electron microscopy, energy-dispersive X-ray spectroscopy, respectively. The effect of the Pt nanoparticles on the H₂-sensing performance of In₂O₃ NPs was investigated over a low concentration range of 5 ppm of H₂ at 150-300 ℃ working temperatures. The results showed that the H₂ response greatly increased with decreasing sensing temperature. The H₂ response of Pt loaded porous In₂O₃ NPs is higher than that of pristine In₂O₃ NPs. H₂ gas selectivity and high sensitivity was explained by the extension of the electron depletion layer and catalytic effect. Pt loaded porous In₂O₃ NPs sensor can be a robust manner for achieving enhanced gas selectivity and sensitivity for the detection of H₂.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권11호
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• pp.3099-3120
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• 2023

The present fast-moving era brings a serious stress issue that affects elders and youngsters. Everyone has undergone stress factors at least once in their lifetime. Stress is more among youngsters as they are new to the working environment. whereas the stress factors for elders affect the individual and overall performance in an organization. Electroencephalogram (EEG) based stress level classification is one of the widely used methodologies for stress detection. However, the signal processing methods evolved so far have limitations as most of the stress classification models compute the stress level in a predefined environment to detect individual stress factors. Specifically, machine learning based stress classification models requires additional algorithm for feature extraction which increases the computation cost. Also due to the limited feature learning characteristics of machine learning algorithms, the classification performance reduces and inaccurate sometimes. It is evident from numerous research works that deep learning models outperforms machine learning techniques. Thus, to classify all the emotions based on stress level in this research work a hybrid deep learning algorithm is presented. Compared to conventional deep learning models, hybrid models outperforms in feature handing. Better feature extraction and selection can be made through deep learning models. Adding machine learning classifiers in deep learning architecture will enhance the classification performances. Thus, a hybrid convolutional neural network model was presented which extracts the features using CNN and classifies them through machine learning support vector machine. Simulation analysis of benchmark datasets demonstrates the proposed model performances. Finally, existing methods are comparatively analyzed to demonstrate the better performance of the proposed model as a result of the proposed hybrid combination.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권2호
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• pp.456-477
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• 2024

With information technology's rapid development, the Internet faces serious security problems. Studies have shown that malware has become a primary means of attacking the Internet. Therefore, adversarial samples have become a vital breakthrough point for studying malware. By studying adversarial samples, we can gain insights into the behavior and characteristics of malware, evaluate the performance of existing detectors in the face of deceptive samples, and help to discover vulnerabilities and improve detection methods for better performance. However, existing adversarial sample generation methods still need help regarding escape effectiveness and mobility. For instance, researchers have attempted to incorporate perturbation methods like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and others into adversarial samples to obfuscate detectors. However, these methods are only effective in specific environments and yield limited evasion effectiveness. To solve the above problems, this paper proposes a malware adversarial sample generation method (PixGAN) based on the pixel attention mechanism, which aims to improve adversarial samples' escape effect and mobility. The method transforms malware into grey-scale images and introduces the pixel attention mechanism in the Deep Convolution Generative Adversarial Networks (DCGAN) model to weigh the critical pixels in the grey-scale map, which improves the modeling ability of the generator and discriminator, thus enhancing the escape effect and mobility of the adversarial samples. The escape rate (ASR) is used as an evaluation index of the quality of the adversarial samples. The experimental results show that the adversarial samples generated by PixGAN achieve escape rates of 97%, 94%, 35%, 39%, and 43% on the Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Convolutional Neural Network and Recurrent Neural Network (CNN_RNN), and Convolutional Neural Network and Long Short Term Memory (CNN_LSTM) algorithmic detectors, respectively.

• Geomechanics and Engineering
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• 제36권3호
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• pp.205-215
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• 2024

Manually mapping fractures in construction stone mines is challenging, time-consuming, and hazardous. In this method, there is no physical access to all points. In contrast, digital image processing offers a safe, cost-effective, and fast alternative, with the capability to map all joints. In this study, two methods of detecting the trace of discontinuities using image processing in construction stone mines are presented. To achieve this, we employ two modified Hough transform algorithms and the degree of neighborhood technique. Initially, we introduced a method for selecting the best edge detector and smoothing algorithms. Subsequently, the Canny detector and median smoother were identified as the most efficient tools. To trace discontinuities using the mentioned methods, common preprocessing steps were initially applied to the image. Following this, each of the two algorithms followed a distinct approach. The Hough transform algorithm was first applied to the image, and the traces were represented through line drawings. Subsequently, the Hough transform results were refined using fuzzy clustering and reduced clustering algorithms, along with a novel algorithm known as the farthest points' algorithm. Additionally, we developed another algorithm, the degree of neighborhood, tailored for detecting discontinuity traces in construction stones. After completing the common preprocessing steps, the thinning operation was performed on the target image, and the degree of neighborhood for lineament pixels was determined. Subsequently, short lines were removed, and the discontinuities were determined based on the degree of neighborhood. In the final step, we connected lines that were previously separated using the method to be described. The comparison of results demonstrates that image processing is a suitable tool for identifying rock mass discontinuity traces. Finally, a comparison of two images from different construction stone mines presented at the end of this study reveals that in images with fewer traces of discontinuities and a softer texture, both algorithms effectively detect the discontinuity traces.

• Clinical and Experimental Pediatrics
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• 제52권6호
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• pp.661-666
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• 2009

목 적 : 최근 호흡기 감염의 원인에 대한 검사 방법의 발달로 중복검출에 대한 높은 비율이 보고되고 있다. 이에 소아에서의 급성 호흡기 감염의 원인으로 중복검출에 대한 임상 양상과 그 특징에 대해 알아보고자 하였다. 방 법 : 2003년 9월부터 2005년 6월까지 단국대학교병원 소아과에 급성 호흡기 증상으로 입원한 환아들을 대상으로 의무기록지를 후향적으로 조사하였다. 비인두 흡인물의 호흡기 바이러스(AdV, RSV, PIV 1, 2, 3, IFA, IFB)의 항원과 배양 검사, 혈청의 MP 항체 측정(1:640 이상 혹은 4배 이상의 항체 상승시 양성으로 함), 비인두 흡인물의 MP PCR, CT 항원의 PCR 검사, enterovirus PCR 및 배양검사, 객담의 항산균 도말 염색 및 배양 검사를 받은 환아들 중 2가지 이상에서 양성으로 나온 환아들의 임상 양상에 대해서 조사하였다. 결 과 : 1) 호흡기 감염의 원인에 대한 검사에서 2가지 이상의 양성을 보인 환아는 총 28명이었으며 남아가 17명이었고, 평균 나이는 2년 3개월(19일-13세), 6개월 미만이 11명이었다. 2) 중복검출의 원인 중 RSV가 14례, PIV 3 10례, AdV 10례, MP 8례, PIV 2가 7례, CT, PIV 3가 각각 3례였다. 중복검출되는 원인으로 RSV＋PIV 2 6례, AdV＋MP 4례, AdV＋PIV 3, RSV＋MP, PIV 1＋PIV 3가 각각 3례였다. 3) 임상 진단으로 폐렴 19례, 세기관지염 16례, 후두염이 3례였으며, 4) MP, 호흡기 바이러스의 각각의 유행 시기에 이들에 의한 중복검출이 있었다. 결 론 : 소아의 급성 호흡기 감염의 원인에 대한 중복검출은 영아에서 흔하며, 원인으로 RSV, PIV, AdV, MP가 많았다. MP, 호흡기 바이러스의 각각의 유행 시기에 이들에 의한 중복 검출률의 빈도가 증가함을 볼 수 있었다. 호흡기 감염의 원인에 대한 검사 방법의 발달로 중복 검출률이 높아지고 있어 이의 임상적 의미에 대한 연구가 더 필요하다고 사료된다.

• Spatial Information Research
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• 제20권5호
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• pp.99-109
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• 2012

최근 소셜 미디어의 발달과 스마트폰의 확산으로 SNS(Social Network Service)가 활성화가 되면서 데이터양이 폭발적으로 증가하였다. 이에 맞춰 빅데이터 개념이 새롭게 대두되었으며, 빅데이터를 활용하기 위한 많은 방안이 연구되고 있다. 여러 기업이 보유한 빅데이터의 가치창출을 극대화하기 위해 기존 데이터와의 융합이 필요하며, 물리적, 논리적 저장구조가 다른 이기종 데이터 소스를 통합하고 관리하기 위한 시스템이 필요하다. 빅데이터를 처리하기 위한 시스템인 맵리듀스는 분산처리를 활용하여 빠른게 데이터를 처리한다는 이점이 있으나 모든 키워드에 대해 시스템을 구축하여 저장 및 검색 등의 과정을 거치므로 실시간 처리에 어려움이 따른다. 또한, 이기종 데이터를 처리하는 구조가 없어 복합 이벤트를 처리하는데 추가 비용이 발생할 수 있다. 이를 해결하는 방안으로 기존에 연구된 복합 이벤트 처리 시스템을 활용하여 실시간 복합 이벤트 탐지를 위한 기법을 제안하고자 한다. 복합 이벤트 처리 시스템은 서로 다른 이기종 데이터 소스로부터 각각의 데이터들을 통합하고 이벤트들의 조합이 가능하며 스트림 데이터를 즉시 처리할 수 있어 실시간 처리에 유용하다. 그러나 SNS, 인터넷 기사 등 텍스트 기반의 비정형 데이터를 텍스트형으로 관리하고 있어 빅데이터에 대한 질의가 요청될 때마다 문자열 비교를 해야 하므로 성능저하가 발생할 여지가 있다. 따라서 복합 이벤트 처리 시스템에서 비정형 데이터를 관리하고 질의처리가 가능하도록 문자열의 논리적 스키마를 부여하고 데이터 통합 기능을 제안한다. 그리고 키워드 셋을 이용한 필터링 기능으로 문자열의 키워드를 정수형으로 변환함으로써 반복적인 비교 연산을 줄인다. 또한, 복합 이벤트 처리 시스템을 활용하면 인 메모리(In-memory)에서 실시간 스트림 데이터를 처리함으로써 디스크에 저장하고 불러들이는 시간을 줄여 성능 향상을 가져온다.