• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1542-1550
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• 2019

Legacy studies for classifying arrhythmia have been studied to improve the accuracy of classification, Neural Network, Fuzzy, etc. Deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose optimal parameter extraction method based on a deep learning. For this purpose, R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 97.84% in PVC classification.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2008.11a
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• pp.248-251
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• 2008

일반적으로 phthalate esters의 전처리방법에는 액액추출법(liquid-liquid extraction), 고상추출법(Solid-phase Extraction, SPE), 고상미세추출법(Solid-phase micro extraction, SPME) 등의 분석방법이 있다. 그 중에서 본 연구에서는 SPE를 이용하여 간편하고 정확성이 높으며, 적은 양의 유기용제를 사용하여 전처리함으로써 2차적인 환경오염을 줄일 수 있다는 점에서 다른 전처리 방법보다 유용하다고 할 수 있다. 검출감도 측면에서 phthalate esters의 경우는 구조적으로 안정적인 편이나 페놀류의 경우 hydroxy group 때문에 GC/MS 분석시 유도체화 과정이 필수적인데 이번 연구에서 사용한 BSTFA/TMCS는 비교적 쉽고 빠르게 유도체 화할 수 있다는 이점이 있다. 더 많은 연구가 필요하겠지만 앞서서의 결과에서도 알 수 있듯이 전반적으로 검출한계가 0.05$\sim$0.5 $\mu$g/L 정도로 높고, 4-nonyl phenol을 제외하고 80% 이상의 양호한 회수율을 나타낸 것으로 미루어 보아 phthalate esters, phenols의 효율적인 동시분석이 가능함을 알 수 있다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.53-56
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• 2021

국내 산업화가 들어선 후 산업화 당시 지었던 낙후된 건물의 증가에 따라 구조물의 손상 조사 및 검사 방법의 수요가 늘어나고 있다. 일반적으로 구조물의 손상은 전문 검사원이 현장에서 직접 측량도구와 시각적인 방식으로 검사한다. 그러나 전문 검사원들이 직접 조사하는 수고에 비해 균열을 검사하는 방식 자체가 단순하고, 일반 사람이 검사하기에는 객관성이 떨어지는 한계가 있어 균열을 자동적으로 검출함으로써 객관성과 편의성을 보장할 기술이 필요하다. 본 연구에서는 이미지 기반으로 다양한 환경에서의 외벽 균열을 검출할 수 있는 딥러닝 모델 개발을 소개한다. 균열 검출을 위해 다양한 외벽 균열 관련 데이터셋을 확보 및 구축하고 각 데이터셋의 검출 정보를 보완할 반자동(semi-auto) 라벨링 작업을 수행하였다. 두 번째로 기존 높은 검출 성능을 보였던 모델들을 선정 및 비교하여 YOLO v5 모델을 최종적으로 선정하였고, 도메인이 각각 다른 데이터셋에 대한 교차 학습을 통해 각 데이터셋의 mAP의 편차가 31%에서 11%로 좁히는 작업을 수행하였다. 이를 통해 실제 상황에서의 균열 영상에서 균열을 검출할 수 있는 측량 시스템을 개발함으로써 실질적인 검사의 도구로 활용될 수 있길 기대한다.

• Journal of the Korean Society of Food Science and Nutrition
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• v.39 no.10
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• pp.1522-1527
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• 2010

This study was conducted to find out the minimal time needed for detection of Salmonella spp. which exist at very low concentration in foods by using real-time PCR. The sal-F and sal-R sequences were used as primers and sal-P was used as a probe. The detection limit of Salmonella spp. was $3.77{\times}10^2\;cfu/mL$ in buffered peptone water (BPW). Microbial growth was monitored after artificially inoculated Salmonella spp. into BPW. The obtained growth curve was well fitted with the equation, y＝$0.0127x^2$+0.5927x-0.4317 ($R^2$=0.99), if assuming that 1 cell exists in 25 g sample (0.04 cfu/mL). The microbial concentration will be reduced to 10 fold by adding BPW during sample treatment, so actual initial concentration at the starting point of enrichment is 0.004 cfu/mL. At this condition, real-time PCR detection would be possible only when microbial concentration increase occurs to exceed the detection limit (377 cfu/mL). The time needed for microbial increase was calculated from the growth curve equation as 7 hours and 20 minutes. Therefore the total time required for detection was less than 10 hours including the PCR operating time.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1044-1050
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• 2021

After the occurrence of several passenger ship accidents in Korea, various systems are being developed for passenger ship safety management. A total of 162 passenger ships operate along the coast of Korea, of which 105 (65 %) are car-ferries with open vehicle decks. The car-ferry has a navigation pattern that passes through 2 to 4 islands. Safety inspections at the departure point(home port) are carried out by the crew, the operation supervisor of the operation management office, and the maritime safety supervisor. In some cases, self-inspections are carried out for safety inspections at layovers. As with any system, there are institutional and practical limitations. To this end, this study was conducted to suggest a method of detecting a vehicle using image processing and linking it to the calculations for ship stability. For vehicle detection, a method using a difference image and one using machine learning were used. However, a limitation was observed in these methods that the vehicle could not be identified due to strong background lighting from the pier and the ship in the cases where the camera was backlit such as during sunset or at night. It appears necessary to secure sufficient image data and upgrade the program for stable image processing.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.86-97
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• 2011

This paper presents a novel algorithm to detect more accurate road lane with image sensor-based color classification and directional edge clustering. With treatment of road region and lane as a recognizable color object, the classification of color cues is processed by an iterative optimization of statistical parameters to each color object. These clustered color objects are taken into considerations as initial kernel information for color object detection and recognition. In order to improve the limitation of object classification using the color cues, the directional edge cures within the estimated region of interest in the lane boundary (ROI-LB) are clustered and combined. The results of color classification and directional edge clustering are optimally integrated to obtain the best detection of road lane. The characteristic of the proposed system is to obtain robust result to all real road environments because of using non-parametric approach based only on information of color and edge clustering without a particular mathematical road and lane model. The experimental results to the various real road environments and imaging conditions are presented to evaluate the effectiveness of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.852-859
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• 2009

Normally, to extract the defect in TFT-LCD inspection system, the image is obtained by using line scan camera or area scan camera which is achieved by CCD or CMOS sensor. Because of the limited dynamic range of CCD or CMOS sensor as well as the effect of the illumination, these images are frequently degraded and the important features are hard to decern by a human viewer. In order to overcome this problem, the feature vectors in the image are obtained by using the average intensity difference between defect and background based on the weber's law and the standard deviation of the background region. The defect detection method uses non-linear SVM (Supports Vector Machine) method using the extracted feature vectors. The experiment results show that the proposed method yields better performance of defect classification methods over conveniently method.

• Analytical Science and Technology
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• v.19 no.5
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• pp.441-448
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• 2006

11-nor-9-carboxy-${\Delta}^9$-tetrahydrocannabinol (THCCOOH) is the major metabolite of tetrahydrocannabinol (THC) which is the primary psychoactive component of marijuana. It is also the target analyte for the discrimination marijuana use. A method using solid-phase extraction (SPE) and gas chromatography/mass spectrometry (GC/MS) was developed for the determination of THCCOOH in human urine. Urine samples (3 mL) were extracted by SPE column with a cation exchange cartridge after basic hydrolysis. The eluents were then evaporated, derivatized, and injected into the GC/MS. The limits of detection (LOD) and quantitation (LOQ) were 0.4 and 1.2 ng/mL, respectively. The response was linear with a correlation coefficient of 0.999 within the concentration range of 1.2 (LLE 1.3)~50.0 ng/mL. The precision and accuracy were stable within 1.20% and the recovery was 83.6~90.7%. The recovery of SPE method was lower than that of liquid-liquid extraction (LLE), but there were no apparent differences in LOD, LOQ, precision and accuracy between the two methods. While SPE method is used as a very effective and rapid procedure for sample pretreatment, and clean extracts, LLE method was not suitable for the extraction procedure of THCCOOH in urine. The applicability of the method was proven by analyzing a urine samples from a marijuana abusers.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.5
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• pp.465-470
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• 2010

This paper proposes an adaptive video watermarking algorithm according to bit detection rate of watermark in MPEG2 system. The watermark strength is adaptively applied as BER(bit error rate) of watermark extracted from decoded frame for motion compensation. Watermark insertion uses a frequency spread spectrum method. A realtime watermark extraction is done directly in the DCT domain during MPEG decoding. The experimental simulations show that PSNR(peak signal to noise ratio) results 31.5dB for a fixed watermark strength and 33.dB for an adaptive watermark strength. Also average BER is 0.126 and less than 0.2 avaliable value.

• Journal of Korean Society of Environmental Engineers
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• v.31 no.1
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• pp.64-69
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• 2009

Musty and earth odors caused by geosmin and 2-MIB are the major complaints from the drinking water consumers. Therefore, early detection of geosmin and 2-MIB is the key to prevent off-flavor occurrence. In this study, a rapid method using stir bar sorptive extraction (SBSE) in combination with the GC/MS was investigated to analyze geosmin and 2-MIB in water. The SBSE method, a solventless extraction technique, was optimized and then applied to the analysis of geosmin and 2-MIB in water. The SBSE technique was found to be a simple and fast procedure that allows many samples to be extracted simultaneously using very small volume (10~20 mL). In addition, the SBSE method offered high recovery and good linear regression coefficient for the geosmin and 2-MIB. The good repeatability of this method can be deduced from the low RSD (7.2~7.6%) at 10 ng/L for geosmin and 2-MIB. The limit of detection was determined 1~2 ng/L and the limit of quantitation was 3~6 ng/L. Above all, the SBSE method proved to be a very practical technique for the analysis of geosmin and 2-MIB in water.