• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2000년도 International Symposium on Bioinformatics
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• pp.45-52
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• 2000

Genomic approach produces massive amount of data within a short time period, New high-throughput automatic sequencers can generate over a million nucleotide sequence information overnight. A typical DNA chip experiment produces tens of thousands expression information, not to mention the tens of megabyte image files, These data must be handled automatically by computer and stored in electronic database, Thus there is a need for systematic approach of data collection, processing, and analysis. DNA sequence information is translated into amino acid sequence and is analyzed for key motif related to its biological and/or biochemical function. Functional genomics will play a significant role in identifying novel drug targets and diagnostic markers for serious diseases. As an enabling technology for functional genomics, bioinformatics is in great need worldwide, In Korea, a new functional genomics project has been recently launched and it focuses on identi☞ing genes associated with cancers prevalent in Korea, namely gastric and hepatic cancers, This involves gene discovery by high throughput sequencing of cancer cDNA libraries, gene expression profiling by DNA microarray and proteomics, and SNP profiling in Korea patient population, Our bioinformatics team will support all these activities by collecting, processing and analyzing these data.

• 한국운동역학회지
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• 제20권3호
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• pp.327-336
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• 2010

The purpose of this study was to analyze muscle activities on functional insole with diet effect. Originally, ten healthy female subjects with an average age of 23.2 year(S.D=1.1), weight of 49.7 kg(S.D=4.9), height of 163.2 cm(S.D=3.5) and a shoe size of 237.5 cm(S.D=4.9) were participated in this experiment. Ten healthy females walked on a treadmill(speed=about 4.2 km/h) wearing two different insole types. Muscle activities data was collected using the EMG operating system. The surface EMG signal for tibialis anterior(TA), gastrocnemius(GA), vatus lateralis(VL) and biceps femoris(BF) were acquired at the RMS(10 Hz, 350 Hz) using Noraxon Telemyo DTS system(Noraxon inc, USA). This study processed the data using the Windows SPSS ver.17.0 to get an independent t-test, with the setting, p<.05. Analysis of muscle activity were measured and calculated during walking. The results are as follow: Functional insole wearing were increased muscle activities significantly from Tibialis anterior(TA) during total gait cycle. Normal distribution was demonstrated in total step of stances period. One foot standing position showed decreased muscle activity. Two foot standing position was demonstrated with gastrocnemius and biceps femoris. As a result of the analysis, Functional insole will inerease the diet effect in the use of four muscle groups.

• 말소리와 음성과학
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• 제5권1호
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• pp.63-69
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• 2013

This study attempted to investigate the characteristics of Phonation Threshold Pressure and Phonation Threshold Airflow of Patients who have Functional voice disorder. 50 subjects participated in study (32 subjects were patients who had functional voice disorders and 20 subjects were normal adults). The PAS (Phonatory aerodynamic system, model 6600, KAY electronics, Inc.) was used to measure the data and to do the analysis. Data from the Phonation Threshold Pressure was measured using voicing efficiency of the PAS protocol. Data from the Phonation Threshold Airflow was measured using Maximum Sustained Phonation of the PAS protocol. Those were used because of the ease of phonation. The results of this study showed that the differences in Phonation Threshold Pressure and Phonation Threshold Airflow between patients who had functional voice disorder and normal adults could be significant index. Patients who had functional voice disorder showed more higher figures than normal adults. These results suggest that Phonation Threshold Pressure and Phonation Threshold Airflow are very useful in diagnosing the voice disorder. The measured data also provided useful information for diagnosing patients with vocal fold diseases.

• 한국의류학회지
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• 제44권4호
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• pp.764-775
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• 2020

Lifespans are increasing and many consumers are interested in health issues in these busy modern days, developing functional clothing that can be worn everyday is one of the competitive solutions in the oversaturated clothing market. When developing a new item with a fresh idea, it is important to look into prior art beforehand to avoid unnecessary intellectual property right-related disputes. This study investigates Korean domestic patents and utility models about functional clothing in terms of human body performance and health promotion in order to suggest essential data to relevant developers. We selected 324 patents and utility models and made an analysis according to the year, functions, applied technologies, frequency of claims, target wearers and item types. We found problems in current functional clothing patent application trends and suggested new aspects when developing innovative functional clothing items. Data was limited to Korean domestic patents; however, this study is still meaningful giving references to technology roadmaps and encouraging new intellectual property development.

• 한국의류산업학회지
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• 제22권5호
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• pp.551-560
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• 2020

Due to the nature of fashion design that responds quickly and sensitively to changes, accurate forecasting for upcoming fashion trends is an important factor in the performance of fashion product planning. This study analyzed the major phenomena of fashion trends by introducing text mining and a big data analysis method. The research questions were as follows. What is the key term of the 2010SS~2019FW fashion trend? What are the terms that are highly relevant to the key trend term by year? Which terms relevant to the key trend term has shown high frequency in news articles during the same period? Data were collected through the 2010SS~2019FW Pre-Trend data from the leading trend information company in Korea and 45,038 articles searched by "fashion+material" from the News Big Data System. Frequency, correlation coefficient, coefficient of variation and mapping were performed using R-3.5.1. Results showed that the fashion trend information were reflected in the consumer market. The term with the highest frequency in 2010SS~2019FW fashion trend information was material. In trend information, the terms most relevant to material were comfort, compact, look, casual, blend, functional, cotton, processing, metal and functional by year. In the news article, functional, comfort, sports, leather, casual, eco-friendly, classic, padding, culture, and high-quality showed the high frequency. Functional was the only fashion material term derived every year for 10 years. This study helps expand the scope and methods of fashion design research as well as improves the information analysis and forecasting capabilities of the fashion industry.

• Environmental Engineering Research
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• 제19권2호
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• pp.157-163
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• 2014

The adsorption of Cr(VI) from aqueous solutions to activated carbon fiber (ACF) was investigated using both batch and flow-through column experiments. The batch experiments (adsorbent dose, 10 g/L; initial Cr(VI) concentration, 5-500 mg/L) showed that the maximum adsorption capacity of Cr(VI) to ACF was determined to 20.54 mg/g. The adsorption of Cr(VI) to ACF was sensitive to solution pH, decreasing from 9.09 to 0.66 mg/g with increasing pH from 2.6 to 9.9; the adsorption capacity was the highest at the highly acidic solution pHs. Kinetic model analysis showed that the Elovich model was the most suitable for describing the kinetic data among three (pseudo-first-order, pseudo-second-order, and Elovich) models. From the nonlinear regression analysis, the Elovich model parameter values were determined to be ${\alpha}$ = 162.65 mg/g/h and ${\beta}$ = 2.10 g/mg. Equilibrium isotherm model analysis demonstrated that among three (Langmuir, Freundlich, Redlich-Peterson) models, both Freundlich and Redlich-Peterson models were suitable for describing the equilibrium data. In the model analysis, the Redlich-Peterson model fit was superimposed on the Freundlich fit. The Freundlich model parameter values were determined to be $K_F$ = 0.52 L/g and 1/n = 0.56. The flow-through column experiments showed that the adsorption capacities of ACF in the given experimental conditions (column length, 10 cm; inner diameter, 1.5 cm; flow rate, 0.5 and 1.0 mL/min; influent Cr(VI) concentration, 10 mg/L) were in the range of 2.35-4.20 mg/g. This study demonstrated that activated carbon fiber was effective for the removal of Cr(VI) from aqueous solutions.

• 대한의용생체공학회:의공학회지
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• 제30권5호
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• pp.373-380
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• 2009

In general, Independent component analysis (ICA) is a statistical blind source separation technique, used either in spatial or temporal domain. The spatial or temporal ICAs are designed to extract maximally independent sources in respective domains. The underlying sources for spatiotemporal data (sequence of images) can not always be guaranteed to be independent, therefore spatial ICA extracts the maximally independent spatial sources, deteriorating the temporal sources and vice versa. For such data types, spatiotemporal ICA tries to create a balance by simultaneous optimization in both the domains. However, the spatiotemporal ICA suffers the problem of source ambiguity. Recently, constrained ICA (c-ICA) has been proposed which incorporates a priori information to extract the desired source. In this study, we have extended the c-ICA for better analysis of spatiotemporal data. The proposed algorithm, i.e., constrained spatiotemporal ICA (constrained st-ICA), tries to find the desired independent sources in spatial and temporal domains with no source ambiguity. The performance of the proposed algorithm is tested against the conventional spatial and temporal ICAs using simulated data. Furthermore, its performance for the real spatiotemporal data, functional magnetic resonance images (fMRI), is compared with the SPM (conventional fMRI data analysis tool). The functional maps obtained with the proposed algorithm reveal more activity as compared to SPM.

• 응용통계연구
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• 제34권1호
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• pp.99-114
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• 2021

반도체 제조 산업에서는 Big Data에 기초한 Smart Factory 도입과 적용이 가시화되면서 생산 공정의 각 단계에서 수집 가능한 다양한 센서(sensor) 데이터를 활용하여 공정 이상 탐지 및 최종 수율 예측 등에 다양한 분석 방법을 시도하고 있다. 현재 반도체 공정은 원료인 잉곳(ingot)에서 패키징(packaging) 작업 이전의 웨이퍼(wafer) 생산까지 500 600개 이상의 세부 공정과 이와 연계된 수천 개의 계측 공정으로 구성된다. 개별 계측 공정 내의 실제 계측 비율은 대상 제품 대비 0.1%에서 최대 5%를 넘지 못하고 계측 시점별로 일정하게 유지할 수 없다. 이러한 이유로 공정 각 단계의 정상 상태를 간접적으로 판단할 수 있는 장비 센서(sensor) 데이터를 활용하여 관리 여부를 판단하고자 하는 노력이 계속되고 있다. 본 연구에서는 장비 센서 데이터 기반의 공정 이상 탐지 프로세스를 정의하고 현재 적용 되고 있는 기술 통계량 기반 진단 방법의 단점을 보완하기 위해 FDA(Functional Data Analysis)방법을 활용하였다. 실제 현장 사례 데이터에 머신러닝을 이용하여 이상 탐지 정확도 비교를 통해 효과성을 검증하였다.

• 대한산업공학회지
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• 제36권3호
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• pp.154-163
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• 2010

Process monitoring of output variables affecting final performance have been mainly executed in semiconductor manufacturing process. However, even earlier detection of causes of output variation cannot completely prevent yield loss because a number of wafers after detecting them must be re-processed or cast away. Semiconductor manufacturers have put more attention toward monitoring process inputs to prevent yield loss by early detecting change-point of the process. In the paper, we propose the method to efficiently monitor functional input variables in multi-phase semiconductor manufacturing process. Measured input variables in the multi-phase process tend to be of functional structured form. After data pre-processing for these functional input data, change-point analysis is practiced to the pre-processed data set. If process variation occurs, key variables affecting process variation are selected using contribution plot for monitoring efficiency. To evaluate the propriety of proposed monitoring method, we used real data set in semiconductor manufacturing process. The experiment shows that the proposed method has better performance than previous output monitoring method in terms of fault detection and process monitoring.

• 한국컴퓨터정보학회논문지
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• 제25권7호
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• pp.27-37
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• 2020

본 연구의 목적은 다양한 도메인에 대한 소프트웨어 요구사항 명세서로부터 수집된 요구사항을 데이터로 활용하여 데이터 중심적 접근법(Data-driven Approach)의 연구를 통해 요구사항을 분류한다. 이 과정에서 기존 요구사항의 특징과 정보를 바탕으로 다양한 자연어처리를 이용한 데이터 전처리와 기계학습 모델을 통해 요구사항을 기능적 요구사항과 비기능적 요구사항으로 분류하고 각 조합의 결과를 제시한다. 그 결과로, 요구사항을 분류하는 과정에서, 자연어처리를 이용한 데이터 전처리에서는 어간 추출과 불용어제거와 같은 토큰의 개수와 종류를 감소하여 데이터의 희소성을 좀 더 밀집형태로 변형하는 데이터 전처리보다는 단어 빈도수와 역문서 빈도수를 기반으로 단어의 가중치를 계산하는 데이터 전처리가 다른 전처리보다 좋은 결과를 도출할 수 있었다. 이를 통해, 모든 단어를 고려하여 가중치 값은 기계학습에서 긍정적인 요인을 볼 수 있고 오히려 문장에서 의미 없는 단어를 제거하는 불용어 제거는 부정적인 요소로 확인할 수 있었다.