• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1397-1404
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• 2018

Due to the technical difficulties and high cost for obtaining 3-dimensional structure data, sequence-based approaches in proteins have not been widely acknowledged. A motif can be defined as any segments in protein or gene sequences. With this simplicity, motifs have been actively and widely used in various areas. However, the motif itself has not been studied comprehensively. The value of this study can be categorized in three fields in order to analyze the human proteins using artificial intelligence method: (1) Based on our best knowledge, this research is the first comprehensive motif analysis by analyzing motifs with all human proteins in Protein Data Bank (PDB) associated with the database of Enzyme Commission (EC) number and Structural Classification of Proteins (SCOP). (2) We deeply analyze the motif in three different categories: pattern, statistical, and functional analysis of clusters. (3) At the last and most importantly, we proposed random motif frequency(RMF) matric that can efficiently distinct the characteristics of proteins by identifying interface residues from non-interface residues and clustering protein functions based on big data while varying the size of random motif.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1163-1164
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• 2007

At present, one of the big characteristics in electric power market in korea is unique seller but in the near future competitions are expected in the market. so additional service for the electric power are expected. Also with development of IT technology, remote inspection for power usage are possible so as consumption pattern analysis. KORAIL is one of the biggest consumer in electric power market so it is necessary to investigate power consumption pattern. This paper presents electric power rate definition program based on billing system database and also basic power rate optimization method. Base on the substation annual power usage DB data, the characteristic of the substation power consumption are investigated and effective electrical billing system are compared each other. Through this program it is verified that we can save more then several hundred million won for a year.

• The Journal of Society for e-Business Studies
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• v.18 no.3
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• pp.211-222
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• 2013

Discovering zones which a1re sets of geographically adjacent regions are essential in sophisticated urban developments and people's movement improvements. While there are some studies that separately focus on movements between particular regions and zone discovery, they show limitations to understand people's movements from a wider viewpoint. Therefore, in this research, we propose a clustering based analysis method that aims at discovering movement patterns, which involves zones and their relations, based on a big data of smart card transaction systems. Moreover, the effectiveness of discovered movement patterns is quantitatively evaluated by using the proposed metrics. By using a real-world dataset obtained in Seoul metropolitan subway networks, we investigate and visualize hidden movement patterns in Seoul.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.254-265
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• 2021

Purpose: Inspection and diagnosis on the performance and safety through domestic port facilities have been conducted for over 20 years. However, the long-term development strategies and directions for facility renewal and performance improvement using the diagnosis history and results are not working in realistically. In particular, in the case of port structures with a long service life, there are many problems in terms of safety and functionality due to increasing of the large-sized ships, of port use frequency, and the effects of natural disasters due to climate change. Method: In this study, the maintenance history data of the gravity type quay in element level were collected, defined as big data, and a predictive approximation model was derived to estimate the pattern of deterioration and aging of the facility of project level based on the data. In particular, we compared and proposed models suitable for the use of big data by examining the validity of the state-based deterioration pattern and deterioration approximation model generated through machine learning algorithms of GP and SGP techniques. Result: As a result of reviewing the suitability of the proposed technique, it was considered that the RMSE and R² in GP technique were 0.9854 and 0.0721, and the SGP technique was 0.7246 and 0.2518. Conclusion: This research through machine learning techniques is expected to play an important role in decision-making on investment in port facilities in the future if port facility data collection is continuously performed in the future.

• Korean Journal of Food Science and Technology
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• v.51 no.2
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• pp.176-181
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• 2019

The aim of this study was to analyze dieting practices and tendencies in 2016 using big data. The keywords related to diet were collected from the portal site Naver and analyzed through simple frequency, N-gram, keyword network, and analysis of seasonality. The results showed that exercise had the highest frequency in simple frequency analysis. However, diet menu appeared most frequently in N-gram analysis. In addition, analysis of seasonality showed that the interest of subjects in diet increased steadily from February to July and peaked in October 2016. The monthly frequency of the keyword highfat diet was highest in October, because that showed the 'Low Carbohydrate High Fat' TV program. Although diet showed a certain pattern on a yearly basis, the emergence of new trendy diets in mass media also affects the pattern of diet. Therefore, it is considered that continuous monitoring and analysis of diet is needed rather than periodic monitoring.

• ETRI Journal
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• v.43 no.6
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• pp.1058-1080
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• 2021

Crime is not a completely random event but rather shows a pattern in space and time. Capturing the dynamic nature of crime patterns is a challenging task. Crime prediction models that rely only on neighborhood influence and demographic features might not be able to capture the dynamics of crime patterns, as demographic data collection does not occur frequently and is static. This work proposes a novel approach for crime count and hotspot prediction to capture the dynamic nature of crime patterns using taxi data along with historical crime and demographic data. The proposed approach predicts crime events in spatial units and classifies each of them into a hotspot category based on the number of crime events. Four models are proposed, which consider different covariates to select a set of independent variables. The experimental results show that the proposed combined subset model (CSM), in which static and dynamic aspects of crime are combined by employing the taxi dataset, is more accurate than the other models presented in this study.

• Journal of Fashion Business
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• v.10 no.4
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• pp.95-113
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• 2006

This study was conducted comparative analysis of juvenile brassiere and adult brassiere to identify the problems of brassieres on the market. The raw data for this study was processed by SPSS 10.1 version(statistical software) and the results of this study can be summarized as follows. 1) The result of comparative analysis of juvenile brassiere and adult brassiere pattern is that each pattern showed no difference. 2) The results of the comparative analysis of juvenile and adult brassiere pattern in wing's length and angle is that even though there are some difference between juvenile and adult in side line inclination of brassiere. there is no setting difference but size of that brassiere. 3) The result of a comparative analysis pattern and cup size measurement of juvenile brassiere and adult brassiere is that even though the adult cup girth and angle is bigger than the juvenile because adults have more protrusive and bigger volume and well developed breast, some case rather the adult's cup angle is smaller than the juvenile as brand. And as we grow up to be a woman, difference of lower breast girth and breast girth are become big while difference of upper breast girth and breast girth are become small for that reason upper cup must be lower and lower cup must be higher but there is almost no difference between adult and juvenile. 4) The result of a comparative analysis of juvenile brassiere and adult brassiere bust point of the pattern is that adult brassiere's distance between bust points 6.12cm, and juvenile's 5.6cm, there are only 0.52cm difference between two products and just 0.4cm, size grading. These results are explained that even though when grow up to be a woman the distance between two bust points become to be long and breast toward out side but brassiere size don't vary with body characteristic.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.2
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• pp.58-71
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• 2021

The number of elderly people worldwide is rapidly increasing and the mobility handicapped suffering from inconvenient public transportation service is also increasing. In Korea and abroad, various policies are being implemented to provide high-quality transportation services for the mobility handicapped, and budget support and investment related to mobility facilities are being expanded. The mobility handicapped spends more time for transit transfer than normal users and their satisfaction with transit service is also lower. There exist transfer inconvenience points of the mobility handicapped due to various factors such as long transfer distances, absence of transportation facilities like elevators, escalators, etc. The purpose of this study is to find transfer inconvenience points for convenient transit transfer of the mobility handicapped using Smart card Big data. This study process traffic card transaction data and construct transfer travel data by user groups using smart card big data and analysis of the transfer characteristics for each user group ; normal, children, elderly, etc. Finally, find transfer inconveniences points by comparing transfer patterns between normal users and the mobility handicapped. This study is significant in that it can find transfer inconvenience points for convenient transit transfer of the mobility handicapped using Smart card Big data. In addition, it can be applicated of Smart card Big data for developing public transportation polices in the future. It is expected that the result of this study be used to improve the accessibility of transit transportation for mobility handicapped.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.157-163
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• 2013

Data mining is a useful technology that can support new discoveries based on the pattern analysis and a variety of linkages between data, and currently is utilized in various fields such as finance, marketing, medical. In this paper, we propose an effective utilization method of data based on analysis of spatial data mining. We make use of basic data of foreigners living in Seoul. However, the data has some features distinguished from other areas of data, classification as sensitive information and legal problem such as personal information protection. So, we use the basic statistical data that does not contain personal information. The main features and contributions of the proposed method are as follows. First, we can use Big Data as information through a variety of ways and can classify and cluster Big Data through refinement. Second. we can use these kinds of information for decision-making of future and new patterns. In the performance evaluation, we will use visual approach through graph of themes. The results of performance evaluation show that the analysis using data mining technology can support new discoveries of patterns and results.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.53-62
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• 2020

Product recommendation services that have been researched recently are only recommended through the customer's product purchase history. In this paper, we propose the digital signage service through customers' behavior pattern analysis that is recommending through not only purchase history, but also behavior pattern that customers take when choosing products. This service analyzes customer behavior patterns and extracts interests about products that are of practical interest. The service is learning extracted interest rate and customers' purchase history through the Wide & Deep model. Based on this learning method, the sparse vector of other products is predicted through the MF(Matrix Factorization). After derive the ranking of predicted product interest rate, this service uses the indoor signage that can interact with customers to expose the suitable advertisements. Through this proposed service, not only online, but also in an offline environment, it would be possible to grasp customers' interest information. Also, it will create a satisfactory purchasing environment by providing suitable advertisements to customers, not advertisements that advertisers randomly expose.