• Title/Summary/Keyword: 확대 채널

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Estimation of Precipitable Water from the GMS-5 Split Window Data (GMS-5 Split Window 자료를 이용한 가강수량 산출)

  • 손승희;정효상;김금란;이정환
    • Korean Journal of Remote Sensing
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    • v.14 no.1
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    • pp.53-68
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    • 1998
  • Observation of hydrometeors' behavior in the atmosphere is important to understand weather and climate. By conventional observations, we can get the distribution of water vapor at limited number of points on the earth. In this study, the precipitable water has been estimated from the split window channel data on GMS-5 based upon the technique developed by Chesters et al.(1983). To retrieve the precipitable water, water vapor absorption parameter depending on filter function of sensor has been derived using the regression analysis between the split window channel data and the radiosonde data observed at Osan, Pohang, Kwangiu and Cheju staions for 4 months. The air temperature of 700 hPa from the Global Spectral Model of Korea Meteorological Administration (GSM/KMA) has been used as mean air temperature for single layer radiation model. The retrieved precipitable water for the period from August 1996 through December 1996 are compared to radiosonde data. It is shown that the root mean square differences between radiosonde observations and the GMS-5 retrievals range from 0.65 g/$cm^2$ to 1.09 g/$cm^2$ with correlation coefficient of 0.46 on hourly basis. The monthly distribution of precipitable water from GMS-5 shows almost good representation in large scale. Precipitable water is produced 4 times a day at Korea Meteorological Administration in the form of grid point data with 0.5 degree lat./lon. resolution. The data can be used in the objective analysis for numerical weather prediction and to increase the accuracy of humidity analysis especially under clear sky condition. And also, the data is a useful complement to existing data set for climatological research. But it is necessary to get higher correlation between radiosonde observations and the GMS-5 retrievals for operational applications.

Popularization of Marathon through Social Network Big Data Analysis : Focusing on JTBC Marathon (소셜 네트워크 빅데이터 분석을 통한 마라톤 대중화 : JTBC 마라톤대회를 중심으로)

  • Lee, Ji-Su;Kim, Chi-Young
    • Journal of Korea Entertainment Industry Association
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    • v.14 no.3
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    • pp.27-40
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    • 2020
  • The marathon has long been established as a representative lifestyle for all ages. With the recent expansion of the Work and Life Balance trend across the society, marathon with a relatively low barrier to entry is gaining popularity among young people in their 20s and 30s. By analyzing the issues and related words of the marathon event, we will analyze the spottainment elements of the marathon event that is popular among young people through keywords, and suggest a development plan for the differentiated event. In order to analyze keywords and related words, blogs, cafes and news provided by Naver and Daum were selected as analysis channels, and 'JTBC Marathon' and 'Culture' were extracted as key words for data search. The data analysis period was limited to a three-month period from August 13, 2019 to November 13, 2019, when the application for participation in the 2019 JTBC Marathon was started. For data collection and analysis, frequency and matrix data were extracted through social matrix program Textom. In addition, the degree of the relationship was quantified by analyzing the connection structure and the centrality of the degree of connection between the words. Although the marathon is a personal movement, young people share a common denominator of "running" and form a new cultural group called "running crew" with other young people. Through this, it was found that a marathon competition culture was formed as a festival venue where people could train together, participate together, and escape from the image of a marathon run alone and fight with themselves.

A Case Study on the Development of Environment Friendly Citrus Farming in Jeju - Focusing on Graduate Farms of Korea National College of Agriculture and Fisheries (제주 친환경 감귤 농업 발전을 위한 사례연구 - 한농대 졸업생 농가를 중심으로 -)

  • Kang, S.K.;Kim, J.S.
    • Journal of Practical Agriculture & Fisheries Research
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    • v.16 no.1
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    • pp.37-53
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    • 2014
  • The purpose of this research is to find what difficulties the agricultural successors, the Korea National College of Agriculture and Fisheries (KNCAF) graduates, face with in implementing eco-friendly agriculture in Jeju, and what solutions they can be provided with. This research, a case study on the basis of open-ended survey questions, has 6 cases out of 8 graduates who have or had implemented eco-friendly citrus farming. In Jeju, 24 graduates have involved in citrus farming. According to the case study, only one case was environment-friendly farming method at the pesticide-free level, and the others at organic farming level. All the cases have tried to alter main crops or to diversify management for coping with global climate change and market-opening. On analyzing operating cost to gain product of merchantable quality, it revealed that the environment-friendly farming method needs much more managing efforts than the conventional farming does. But to the contrary, the materials cost in the environment-friendly farming method was lower than in the conventional farming method. In the total production and the price, the environment-friendly farming was 20~50% lower and 10~50% higher than the conventional farming, respectively. Difficulties which the graduates confronted with in implementing the environment-friendly agriculture are as below. Firstly, many of the difficulties have resulted from lack of the environment-friendly farming techniques, and the high cost of farm scale improvement due to high price of land and topographical features of Jeju. Secondly, the agricultural successors, the KNCAF graduates, have trouble in obtaining approval of their parents to changeover from the conventional farming to the environment-friendly farming. Lastly, there is no advisory organizations and experts for environment-friendly farming in the given area. For shift to the environment-friendly farming, followings are needed. Agricultural Technology & Extension center, with cooperation of leading farms in environment-friendly farming, should have a key role in offering education and consults on the environment-friendly farming techniques. Also, this organization should inform rapidly the research results to the farmers, and their feed-back should be involved in the next research. Therefore, it is suggested that the forum called 'Environment-friendly Organic Farming Forum in Jeju' tentatively is organized.

The Direction of Development of Leisure and Tourism Contents in Connection with Osaek District (강원도 오색지구 레저·관광 콘텐츠 개발 방향)

  • Lee, Gye-Young;Kim, Tae-Dong
    • Journal of Korea Entertainment Industry Association
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    • v.13 no.7
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    • pp.307-319
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    • 2019
  • This study aims to provide the basic materials for the development of leisure and tourism contents in connection with Osaek cableway for the revitalization of Osaek District. For such a purpose, the following policy directions were presented through the analysis of the present situation and conditions of Osaek District, the direction of development of leisure and tourism contents of Osaek District, etc. The first is increasing the participation of local residents and reinforcing their capabilities. The suggested promotion plans are ① establishing organizational system and strengthening support, ② reinforcing the capabilities of local residents and ③ constructing networks with external human resources. The second is setting the guidelines for contents development. It was proposed to prepare contents for leisure experience using the natural environment of Osaek District in response to the trend of increase of people who enjoy "contents using culture and arts" and leisure. The third is typological approach to contents. It was proposed to develop cultural contents with the theme of Osaek such as "Osaek Light Festival", "Osaek Concert", "Osaek Photo Exhibition" and "Osaek Good Men and Women Contest" for the promotion of the brand of the place name of Osaek and the creation of the "Picture Book Village" for the compilation of the history and culture of Osaek District with pictures. The fourth is securing marketing channels. For this, it was proposed to produce the website of Yangyang County or a website tentatively named as "Osaek-ri with Beautiful Osaek" and introduce an integrated travel product (transportation + lodging + foods + experience (hot spring, mineral water therapy, leisure experience, etc.) + purchasing local specialty products, etc.) composed of the leisure and tourism contents, transportation, lodging, foods, etc. of Osaek District through travel agencies. The final policy direction presented was phased implementation of the development and operation of the contents. Proposed policies include support of a consulting project to upgrade the organization of local residents; implementation of "Tourism Dure (Cooperative)" project for the solution of the problem of tourism in Osaek District by the residents themselves together using the space of culture and arts made by remodeling idle public and private facilities after benchmarking exemplary places; system improvement for the introduction of leisure and tourism contents appropriate for local conditions; and the establishment of a master plan for the introduction of various leisure and tourism contents in Osaek District.

SKU recommender system for retail stores that carry identical brands using collaborative filtering and hybrid filtering (협업 필터링 및 하이브리드 필터링을 이용한 동종 브랜드 판매 매장간(間) 취급 SKU 추천 시스템)

  • Joe, Denis Yongmin;Nam, Kihwan
    • Journal of Intelligence and Information Systems
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    • v.23 no.4
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    • pp.77-110
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    • 2017
  • Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

Self-optimizing feature selection algorithm for enhancing campaign effectiveness (캠페인 효과 제고를 위한 자기 최적화 변수 선택 알고리즘)

  • Seo, Jeoung-soo;Ahn, Hyunchul
    • Journal of Intelligence and Information Systems
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    • v.26 no.4
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    • pp.173-198
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    • 2020
  • For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.