• Proceedings of the Korean Vacuum Society Conference
• /
• 2010.02a
• /
• pp.327-327
• /
• 2010

디스플레이 및 반도체 산업이 발전함에 따라 회로의 선폭이 점차 줄어들고 있으며, 이에 따라서 대표적인 오염원이 되는 오염입자의 임계 직경(critical diameter) 또한 작아지고 있다. 현재 반도체 및 디스플레이 산업에서 사용되는 측정방법은 레이저를 이용하여 공정 후 표면에 남아 있는 오염입자를 측정하는 ex-situ 방법이 주를 이루고 있다. Ex-situ 방법을 이용한 오염입자의 제어는 웨이퍼 전체를 측정할 수 없을 뿐만 아니라 실시간 측정이 불가능하기 때문에 공정 모니터링 장비로 사용이 어려우며 오염입자와 공정 간의 상관관계 파악에도 많은 제약이 따르게 된다. 이에 따라 저압에서 in-situ 방법을 이용한 실시간 오염입자 측정 기술 개발이 요구되고 있다. 본 연구에서는 저압 환경에서 실시간으로 입자를 모니터링 할 수 있는 장비를 입자의 광산란 원리를 이용하여 개발하였다. 빛이 입자에 조사되면 크게 산란 및 흡수현상이 일어나게 되는데, 이 때 발생하는 산란광은 입자의 크기와 관계가 있으며 Mie 이론으로 널리 알려져 있다. 현재 이를 이용한 연구가 국내 및 국외에서 진행되고 있다. 수 백 nm 대의 입자를 측정하기 위해서는 빛의 강도가일정 수준 이상 되어야 하며, 이를 측정할 수 있는 수신부의 감도 또한 중요하다. 본 연구에서는 빛의 직경을 100 um 이하까지 집속할 수 있는 광학계를 상용 프로그램을 이용하여 설계하였으며, 강도가 약한 산란광 측정을 위하여 노이즈 제거 필터링 기술 등이 적용된 수신부 센서를 개발하여 전체 시스템에 적용하였다. 교정은 상압과 저압에서 수행 하였으며 약 5%의 측정효율로 최소 300 nm 이하의 입자까지 측정이 가능함을 확인 하였다. 또한, 타사의 실시간 입자 측정 센서와의 비교 실험을 통하여 성능평가를 수행하였다. 기존 광산란 방식 센서보다 높은 성능의 센서를 개발하기 위하여 추후 연구를 진행할 계획이며, 약 200 nm 이하의 입자까지 측정이 가능할 것으로 기대된다.

• Childhood Kidney Diseases
• /
• v.14 no.1
• /
• pp.1-9
• /
• 2010

Slowing the progression of chronic kidney disease is much more important in children and adolescents with a relatively longer remaining life span. A practical way to assess the rate of progression of chronic kidney disease is to measure the change of GFR estimated by formulae. To slow the progression, hypertension and proteinuria have to be controlled strictly, and hypoplastic anemia must be treated with erythropoietin. If not contraindicated, ACE inhibitor or angiotensin receptor blocker is recommended with monitoring of the side effects. Trials to slow the progression should be commenced as soon as the chronic kidney disease is confirmed and needs to be continued until renal transplantation as long as residual renal function remains. An online system, the Korean Pediatric Chronic Kidney Disease Registry (http://pedcrf.or.kr/), provides tools that are useful in evaluation and management of the children and adolescents with chronic kidney diseases.

• Journal of Intelligence and Information Systems
• /
• v.13 no.4
• /
• pp.35-44
• /
• 2007

This paper proposes a new method of weighted template matching for machine-printed numeral recognition. The proposed weighted template matching, which emphasizes the feature of a pattern using adaptive Hamming distance on local feature areas, improves the recognition rate while template matching processes an input image as one global feature. Template matching is vulnerable to random noises that generate ragged outlines of a pattern when it is binarized. This paper offers a method of chain code trimming in order to remove ragged outlines. The method corrects specific chain codes within the chain codes of the inner and the outer contour of a pattern. The experiment compares confusion matrices of both the template matching and the proposed weighted template matching with chain code trimming. The result shows that the proposed method improves fairly the recognition rate of the machine-printed numerals.

• Korean Journal of Environmental Agriculture
• /
• v.40 no.3
• /
• pp.186-193
• /
• 2021

BACKGROUND: Before generating transgenic plant using the CRISPR-Cas9 system, the efficiency test of sgRNAs is recommended to reduce the time and effort for plant transformation and regeneration process. The efficiency of the sgRNA can be measured through the transient expression of sgRNA and Cas9 gene in tomato cotyledon; however, we found that the calculated efficiency showed a large variation. It is necessary to increase the precision of the experiment to obtain reliable sgRNA efficiency data from transient expression. METHODS AND RESULTS: The cotyledon of 11^th, 15^th, 19^th, and 23^rd-day-old tomato (Solanum lycopersicum cv. Micro-Tom) were used for expressing CRISPR-Cas9 transiently. The agrobacterium harboring sgRNA for targeting ALS2 gene of tomato was injected through the stomata of leaf adaxial side and the genomic DNA was extracted in 5 days after injection. The target gene edition was identified by amplifying DNA fragment of target region and analyzing with Illumina sequencing method. The target gene editing efficiency was calculated by counting base deletion and insertion events from total target sequence read. CONCLUSION: The CRISPR-Cas9 editing efficiency varied with tomato cotyledon age. The highest efficiency was observed at the 19-day-old cotyledons. Both the median and mean were the highest at this stage and the sample variability was also minimized. We found that the transgene of CRISPR-Cas9 system was strongly correlated with plant leaf development and suggested the optimum cotyledon leaf age for Agrobacterium-mediated transfection in tomato.

• Journal of the Korean Society of Radiology
• /
• v.13 no.5
• /
• pp.819-824
• /
• 2019

In this study, we proposed a method for identifying isotopes generated from high-energy proton $^{nat}Pb$(p,xn) nuclear reactions through the difference of gamma rays generated through nuclear reactions using different proton energies. The experiment was performed by using a high energy proton generated from a 100 MeV proton linear accelerator of the Korea Atomic Energy Research Institute. Gamma rays generated by various nuclides generated through proton nuclear reactions were measured using a gamma-ray spectroscopy system composed of HPGe detectors. Gamma-ray standard sources were used for accurate energy calibration and efficiency measurements of HPGe gamma-ray detectors. For the proposed method, 100 and 60 MeV proton energy beams were used for the same natural lead samples. This method was found to be very effective in identifying nuclides produced by comparing gamma rays generated from the same sample with each other. The results of this study are expected to be very effective in obtaining other proton nuclear reaction results in the future.

• Journal of the Institute of Convergence Signal Processing
• /
• v.21 no.2
• /
• pp.73-78
• /
• 2020

Musculoskeletal disease is often caused by sitting down for long period's time or by bad posture habits. In order to prevent musculoskeletal disease in daily life, it is the most important to correct the bad sitting posture to the right one through real-time monitoring. In this study, to detect the sitting information of user's without any constraints, we propose posture measurement system based on multi-channel pressure sensor and CNN model for classifying sitting posture types. The proposed CNN model can analyze 5 types of sitting postures based on sitting posture information. For the performance assessment of posture classification CNN model through field test, the accuracy, recall, precision, and F1 of the classification results were checked with 10 subjects. As the experiment results, 99.84% of accuracy, 99.6% of recall, 99.6% of precision, and 99.6% of F1 were verified.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.5
• /
• pp.177-183
• /
• 2022

Recently, interesting of home training is getting bigger due to COVID-19. Accordingly, research on applying HAR(human activity recognition) technology to home training has been conducted. However, existing paper of HAR proposed static activity instead of dynamic activity. In this paper, the deep learning model where dynamic exercise posture can be analyzed and the accuracy of the user's exercise posture can be shown is proposed. Fitness images of AI-hub are analyzed by blaze pose. The experiment is compared with three types of deep learning model: RNN(recurrent neural network), LSTM(long short-term memory), CNN(convolution neural network). In simulation results, it was shown that the f1-score of RNN, LSTM and CNN is 0.49, 0.87 and 0.98, respectively. It was confirmed that CNN is more suitable for human activity recognition than other models from simulation results. More exercise postures can be analyzed using a variety learning data.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.11
• /
• pp.1637-1643
• /
• 2022

In recent years, the demand for image-processing technology in digital marketing has increased due to the expansion and diversification of the digital market, such as video, security, and machine intelligence. Noise-processing is essential for image-correction and reconstruction, especially in the case of sensitive noises, such as in CT, MRI, X-ray, and scanners. The two main salt and pepper noises have been actively studied, but the details and edges are still unsatisfactory and tend to blur when there is a lot of noise. Therefore, this paper proposes an algorithm that applies a weight-based Euclidean distance equation to the partial mask and uses only the non-noisy pixels that are the most similar to the original as effective pixels. The proposed algorithm determines the type of filter based on the state of the internal pixels of the designed partial mask and the degree of mask deterioration, which results in superior noise cancellation even in highly damaged environments.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.86-86
• /
• 2021

온천천은 부산광역시 금정구, 동래구, 연제구를 흐르는 도심 하천으로 부산 시민들의 도심 속 산책길, 자전거 길 등으로 활용되는 도시하천이다. 그러나 온천천 양안의 동래 곡저 평야가 시가지화 되고 온천천 발원지인 금정산 주변에서 무허가 상수도를 사용하고 각종 쓰레기와 하수의 유입으로 인해 하천 전체가 하수관으로 변해왔다. 이에 따라 부산광역시는 온천천 정비 계획을 시행하여 하천 정비와 함께 자동측정망을 설치하여 하천의 DO (dissolved oxygen), 탁도, TDS농도 등 자료를 수집하고 있다. 그러나 자동측정망으로 쌓여가는 데이터를 활용하여 DO농도 예측은 거의 이뤄지지 않고 있다. DO는 하천의 수질 오염 정도를 판단하는 수질인자로 역사적으로 하천 연구의 주요 연구 대상이 되어 왔다. 본 연구에서는 일 자료 뿐만 아니라 시 자료를 기반으로 RNN-LSTM 알고리즘을 활용한 DO예측을 시도하였다. RNN-LSTM은 시계열 학습에 뛰어난 알고리즘으로 인공신경망의 발전된 형태인 순환신경망이다. 연구에 앞서 부산광역시 보건환경정보 공개시스템으로부터 받은 자료 중에서 교정, 보수 중, 비사용, 장비전원단절 등으로 인해 누락데이터를 2014년 1월 1일부터 2018년 12월 31일의 데이터 전수조사 후 이상데이터를 확인하여 선형 보간하여 데이터를 사용하였다. 연구에서는 Google에서 개발한 딥러닝 오픈소스 라이브러리인 텐서플로우를 활용하여 부산광역시 금정구 부곡동에 위치한 부곡교 관측소의 DO농도를 시간 또는 일 예측을 하였다. 일 예측 학습에는 2014년~ 2018년의 기상자료(기온, 상대습도, 풍속, 강수량), DO농도 자료를 사용하였고, 시 예측 학습에는 연속된 자료가 가장 많은 2015년 3월 ~ 12월까지의 데이터를 활용하여 연구를 진행하였다. 모형의 검증을 위해 결정계수(R square)를 이용하여 통계분석을 실시하였다.

• The Journal of the Acoustical Society of Korea
• /
• v.28 no.1
• /
• pp.32-43
• /
• 2009

In this paper, we improve the performance of cardiac disorder classification by continuous heart sound signals using automatic segmentation and extreme learning machine (ELM). The accuracy of the conventional cardiac disorder classification systems degrades because murmurs and click sounds contained in the abnormal heart sound signals cause incorrect or missing starting points of the first (S1) and the second heart pulses (S2) in the automatic segmentation stage, In order to reduce the performance degradation due to segmentation errors, we find the positions of the S1 and S2 pulses, modify them using the time difference of S1 or S2, and extract a single period of heart sound signals. We then obtain a feature vector consisting of the mel-scaled filter bank energy coefficients and the envelope of uniform-sized sub-segments from the single-period heart sound signals. To classify the heart disorders, we use ELM with a single hidden layer. In cardiac disorder classification experiments with 9 cardiac disorder categories, the proposed method shows the classification accuracy of 81.6% and achieves the highest classification accuracy among ELM, multi-layer perceptron (MLP), support vector machine (SVM), and hidden Markov model (HMM).