• Fashion & Textile Research Journal
• /
• v.7 no.5
• /
• pp.535-541
• /
• 2005

The health of feet is connected with individual's health and affects a man's activity. Shoes need to be designed to protect feet and to absorb the impact of land. In order to choose suitable shoes for feet, the foot size and shape must be considered, so it is essential to grasp the exact size and shape of the foot. This study aims to present fundamental data on shoes' easy order prototype development for choosing shoes of good wearing comfort, by classifying feet size and shape junior high school boys in the early adolescent period. The subject were 234 Korean junior high school boys age from 14 to 16 years old. The subjects were directly measured anthropometrically and indirectly analyzed photographically. 6 factors were extracted through factor analysis and those factors comprised 79.42% of total variance. The factors characterizing foot girth and width, foot length, foot height, foot shape around the fifth toes, angle of foot breadth and foot shape around the first toes. 3 clusters as their foot shape were categorized using 6 factor scores by cluster analysis. Type 1 was characterized by long large foot with deformed first toe. Type 2 had smaller in foot girth, width and length than other types and with deformed fifth toe. Type 3 had average size and high foot shape. Shoes prototype which is to be developed later on will be able to generate 2D flattening in the foot sole form. Therefore, it would be a great support in producing and choosing appropriate shoes if forms are classified by subdividing foot form classification and extract a factor which shows only the foot sole shape.

• Fashion & Textile Research Journal
• /
• v.17 no.2
• /
• pp.227-236
• /
• 2015

This study analyzed the lower body type of 30's Korean men to develop a slim-fit pants pattern. As the analysis data, direct measurement data of anthropometric measured value in the 6th Size Korea(KATS, 2010) was used as basic data with 481 men in 30's as analysis objects. The result are as follows. First, the result of analyzing factors for the lower body type classification indicated five factors. Second, the result of executing group analysis (with the independent variable of 5 clusters extracted from the factor analysis)classified the following three types. Type 1(36.8%) displayed a medium height of lower body type, small waist and hip, slim and fit body type with a slim shape between the knee and ankle. The shape between the waist and hip had characteristics of a slight curve and short length. Type 2(35.6%) displayed lowest height of a lower body type that was large and thick between the waist and the hip. The drop value of the waist and the hip was small; therefore, the body type was flat with a minimal curve. The underpart type (below the knee) was the thickest and the length was short. Type 3(27.7%) displayed the highest lower body type, a medium level waist size, flat and narrow waist and belly. This body type had a curve with big drop value of the waist and the hip, lower part from the hip to the ankle (including the knee) and a thick calf with along leg.

• Fashion & Textile Research Journal
• /
• v.9 no.3
• /
• pp.319-326
• /
• 2007

The health of feet is connected with individual's health and affects a man's activity. Shoes need to be designed to protect feet and to absorb the impact of land. In order to choose suitable shoes for feet, the foot size and shape must be considered, so it is essential to grasp the exact size and shape of the foot. This study aims to present fundamental data on shoes' easy order prototype development for choosing shoes of good wearing comfort, by classifying feet size and shape junior high school boys in the early adolescent period. The subject were 217 Korean junior high school girls in age from 14 to 16 years old. The subjects were directly measured anthropometrically and indirectly analyzed photographically. 7 factors were extracted through factor analysis and those factors comprised 78.59% of total variance. The factors were characterized foot length, foot girth and width, foot shape around the fifth toes, foot shape around the first toes, angle of foot breadth, foot height, and foot length of upper foot. 3 clusters as their foot shape were categorized using 7 factor scores by cluster analysis. Type 1 had smaller in foot girth, width and length than other types and with deformed fifth toe. Type 2 had average size and high foot shape. Type 3 was characterized by long large foot with deformed first toe. The results would be a great support in producing and choosing appropriate shoes if forms are classified by subdividing foot form classification and extract a factor which shows only the foot sole shape.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.6
• /
• pp.49-59
• /
• 2014

Recently, wireless sensor networks(WSNs) are widely used for intrusion detection and ecology, environment, atmosphere, industry, traffic, fire monitoring. In this paper, an energy efficient clustering algorithm is proposed. The proposed algorithm forms clusters uniformly by selecting cluster head that optimally located based on receiving power. Besides, proposed algorithm can induce uniform energy consumption regardless of location of nodes by multi-hop transmission and MST formation with limited maximum depth. Through the above, proposed algorithm elongates network life time, reduces energy consumption of nodes and induces fair energy consumption compared to conventional LEACH and HEED. The results of simulation show that the proposed clustering algorithm elongates network life time through fair energy consumption.

• Proceedings of the Korean Institute Of Construction Engineering and Management
• /
• 2007.11a
• /
• pp.892-898
• /
• 2007

This study aims to implement an efficient process management system for small and medium sized(local) construction companies and a performance management system for the Korean construction industry. The process management system by Lean Construction is Web 2.0 platform-based and creates clusters with numerous general contractors and sub-contractors, which will enable mutually organic process management. Plus, this system will enable them to compare project performance management by analyzing it during or after a project by collecting and accumulating lots of data occurring in pursuit of a project. These performance management cases will be of help in process planning during similar upcoming projects. This study is expected to somewhat reduce the burden of implementing a complicated process management protocol and system that Korean small and medium sized (local) construction companies experience with their web-based process management, and is supposed to realize accurate performance management with highly reliable data which are significantly accumulated within the database.

• Journal of the Korean association of regional geographers
• /
• v.22 no.2
• /
• pp.397-410
• /
• 2016

Although industrial complexes have played as an engine of the Korean economy for the last 40 years, the majority of industrial complexes shows limitations to the continuous growth such as a lack of innovation capabilities and social capital, conceived as a key to transforming into clusters of innovation. To overcome those problems, the Korean government embarked on the cluster policy from the mid 2000's, focusing on promoting the endogenous development capabilities of individual industrial complexes. Drawing upon the in-depth case study of the Gumi IT cluster, one of the representative large-scale industrial complexes in Korea, the authors conclude that the cluster policy has contributed to making the Gumi IT cluster enhance the capabilities of endogenous development through the facilitation of self-organizing learning communities within the cluster.

• KIPS Transactions on Computer and Communication Systems
• /
• v.3 no.8
• /
• pp.251-260
• /
• 2014

3D point data is utilized in various industry domains for its high accuracy to the surface information of an object. It is substantially utilized in geography for terrain scanning and analysis. Generally, 3D point data need to be changed by Gridding which produces a regularly spaced array of z values from irregularly spaced xyz data. But it requires long processing time and high resource cost to interpolate grid coordination. Kriging interpolation in Gridding has attracted because Kriging interpolation has more accuracy than other methods. However it haven't been used frequently since a processing is complex and slow. In this paper, we presented a parallel Gridding algorithm which contains Kriging and an application of grid data structure to fit MapReduce paradigm to this algorithm. Experiment was conducted for 1.6 and 4.3 billions of points from Airborne LiDAR files using our proposed MapReduce structure and the results show that the total execution time is decreased more than three times to the convention sequential program on three heterogenous clusters.

• Smart Media Journal
• /
• v.10 no.3
• /
• pp.23-30
• /
• 2021

Although the manufacturing time series data clustering technique is an important grouping solution in the field of detecting and improving manufacturing large data-based equipment and process defects, it has a disadvantage of low accuracy when applying the existing static data target clustering technique to time series data. In this paper, an evolutionary computation-based time series cluster analysis approach is presented to improve the coherence of existing clustering techniques. To this end, first, the image shape resulting from the manufacturing process is converted into one-dimensional time series data using linear scanning, and the optimal sub-clusters for hierarchical cluster analysis and split cluster analysis are derived based on the Pearson distance metric as the target of the transformation data. Finally, by using a genetic algorithm, an optimal cluster combination with minimal similarity is derived for the two cluster analysis results. And the performance superiority of the proposed clustering is verified by comparing the performance with the existing clustering technique for the actual manufacturing process image.

• The Journal of Information Systems
• /
• v.30 no.2
• /
• pp.147-163
• /
• 2021

Purpose The study aims to predict real estate prices by utilizing regional characteristics. Since real estate has the characteristic of immobility, the characteristics of a region have a great influence on the price of real estate. In addition, real estate prices are closely related to economic development and are a major concern for policy makers and investors. Accurate house price forecasting is necessary to prepare for the impact of house price fluctuations. To improve the performance of our predictive models, we applied LSTM, a widely used deep learning technique for predicting time series data. Design/methodology/approach This study used time series data on real estate prices provided by the Ministry of Land, Infrastructure and Transport. For time series data preprocessing, HP filters were applied to decompose trends and SOM was used to cluster regions with similar price directions. To build a real estate price prediction model, SVR and LSTM were applied, and the prices of regions classified into similar clusters by SOM were used as input variables. Findings The clustering results showed that the region of the same cluster was geographically close, and it was possible to confirm the characteristics of being classified as the same cluster even if there was a price level and a similar industry group. As a result of predicting real estate prices in 1, 2, and 3 months, LSTM showed better predictive performance than SVR, and LSTM showed better predictive performance in long-term forecasting 3 months later than in 1-month short-term forecasting.

• Fashion & Textile Research Journal
• /
• v.23 no.6
• /
• pp.799-809
• /
• 2021

This study aimed to grasp the perceptions and trends in fashion platforms and fashion smart factories using big data analysis. As a research method, big data analysis, fashion platform, and smart factory were identified through literature and prior studies, and text mining analysis and network analysis were performed after collecting text from the web environment between April 2019 and April 2021. After data purification with Textom, the words of fashion platform (1,0591 pieces) and fashion smart factory (9750 pieces) were used for analysis. Key words were derived, the frequency of appearance was calculated, and the results were visualized in word cloud and N-gram. The top 70 words by frequency of appearance were used to generate a matrix, structural equivalence analysis was performed, and the results were displayed using network visualization and dendrograms. The collected data revealed that smart factory had high social issues, but consumer interest and academic research were insufficient, and the amount and frequency of related words on the fashion platform were both high. As a result of structural equalization analysis, it was found that fashion platforms with strong connectivity between clusters are creating new competitiveness with service platforms that add sharing, manufacturing, and curation functions, and fashion smart factories can expect future value to grow together, according to digital technology innovation and platforms. This study can serve as a foundation for future research topics related to fashion platforms and smart factories.