• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.39 no.4
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• pp.99-110
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• 2021

This study intends to examine the characteristics of the construction method of the urban water system in the historical cities of Korea, focusing on Gyeongju, Gyeongju, Goryeo, Hanyang, and Suwon Hwaseong, which were created as new towns in the late Joseon Dynasty. It examines the meaning of waterways in connection with the urban skeletal structure, focusing on the location of cities, roads, and arrangement of urban facilities, and analyzes the compositional form of the water system. We tried to derive the relationship with the structure. In particular, it can be seen that water and natural water systems act as key factors in determining the location of a city, and have a close relationship with the urban structure, urban development process, and urban structure. In addition to the symbolic meaning of water in harmony with the geographical concept, realistic demands in terms of water level and water retention are an important background. In order to respond to various demands related to water space, various planning and technical elements for managing water space were introduced in the process of city formation and development. The planning elements of the urban water system in the process of urban formation and development are summarized as follows through the analysis of the research site. First, in the process of building the urban water system in Gyeongju, Goryeo, Goryeo, Hanyang, and Hwaseong, Suwon, which were selected as the research destinations, the water system in consideration of drainage and disaster is common, but the water system construction method and usability are common. shows the difference. Second, water and natural water systems act as symbolic elements to secure the legitimacy of the city location, and as a key factor in determining the location of the city in harmony with the geographical concept and determining the left direction of the city. Third, the natural water system prior to the formation of a city works as a basis for determining the compositional form of the urban water system constructed in the process of urban formation and development in harmony with the topographical conditions. Fourth, the urban water system built on the basis of natural water systems is constructed by linking natural waterways and planned artificial waterways. Fifth, the urban water system is being built in a planned manner in consideration of the utility in connection with the urban structure, such as securing of urban land, arrangement of urban facilities and areas, composition of functional areas, and land division, in addition to the perspective of drainage system and flood control in consideration of disasters.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.11 no.4
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• pp.158-164
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• 2006

Yongdam Lake is the fifth largest artificial lake in Korea newly formed by the first impounding the Yongdam Multi-purpose Dam on December, 2002. Yongdam Lake, with her total water storage of 820 million M/T, is located at the roof-top region of the streams flowing into the just-constructed new Saemankeum Lake. Seasonal succession of phytoplakton in Yongdam Lake might affect cyanobacterial blooms in Saemankeum Lake by inoculating seasonal dominants. During 2002-2003 when the first impounding after the construction of Yongdam Multi-purpose Dam was still undergoing, summer cyanobacterial blooms by Anabaena, Microcystis, and Aphanizomenon were observed. Among these three, filamentous Anabaena is well known to have its species with $N_2-fixing$ ability and special cells such as heterocysts and akinetes as well as the vegetative cells. We established a clonal culture of Anabaena spiroides v. crasse (KNU-YD0310) from the live water samples collected at the bloom site of Yongdam Lake. The N- and P-nutrient requirement of the KNU-YD0310 was explored by the experimental cultivation of the laboratory strain. Ratio of heterocysts to vegetative cells increased as N-deficiency extended with its maximum at $N_2-fixing$ condition. The strain KNU-YD0310 exhibited considerable growth under N-limiting conditions while its growth was proportional to the initial phosphate-P concentration under P-deficient conditions. Under P-limiting conditions akinete density increased, which could be interpreted as an adaptation strategy to survive severe environment by transforming into resting stage. The above eco-physiological characteristics of Anabaena spiroides v. crassa might be useful as an ecological criterion in controlling cyanobacterial blooms at Shaemankeum Lake in near future.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.93-112
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• 2015

Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.