• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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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.

• Korean Journal of Applied Biomechanics
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• v.20 no.3
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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.

• Phonetics and Speech Sciences
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• v.5 no.1
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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.

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.4
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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.

• Fashion & Textile Research Journal
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• v.22 no.5
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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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• v.19 no.2
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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.

• Journal of Biomedical Engineering Research
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• v.30 no.5
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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.

• The Korean Journal of Applied Statistics
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• v.34 no.1
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• pp.99-114
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• 2021

With the Fourth Industrial Revolution based on new technology, the semiconductor manufacturing industry researches various analysis methods such as detecting process abnormalities and predicting yield based on equipment sensor data generated in the manufacturing process. The semiconductor manufacturing process consists of hundreds of processes and thousands of measurement processes associated with them, each of which has properties that cannot be defined by chemical or physical equations. In the individual measurement process, the actual measurement ratio does not exceed 0.1% to 5% of the target product, and it cannot be kept constant for each measurement point. For this reason, efforts are being made to determine whether to manage by using equipment sensor data that can indirectly determine the normal state of each step of the process. In this study, the Functional Data Analysis (FDA) was proposed to define a process abnormality detection process based on equipment sensor data and compensate for the disadvantages of the currently applied statistics-based diagnosis method. Anomaly detection accuracy was compared using machine learning on actual field case data, and its effectiveness was verified.

• Journal of Korean Institute of Industrial Engineers
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• v.36 no.3
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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.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.27-37
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• 2020

In this paper, we propose the methodology based on data-driven approach using Natural Language Processing and Machine Learning for classifying requirements into functional requirements and non-functional requirements. Through the analysis of the results of the requirements classification, we have learned that the trained models derived from requirements classification with data-preprocessing and classification algorithm based on the characteristics and information of existing requirements that used term weights based on TF and IDF outperformed the results that used stemming and stop words to classify the requirements into functional and non-functional requirements. This observation also shows that the term weight calculated without removal of the stemming and stop words influenced the results positively. Furthermore, we investigate an optimized method for the study of classifying software requirements into functional and non-functional requirements.