• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.1-23
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• 2014

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2003.05a
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• pp.181-186
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• 2003

The satellite navigation system is widely used for identifying a user's position regardless of weather or geographic conditions and also make effect on new technology of marine LBS(Location Based Service), which has the technology of geographic information such as the ENC. Generally, there are conceivable systems of marine LBS such as ECDIS, or ECS that use the ENC itself with powerful processor in installed type on ships bridge. Since the ENC is relatively heavy structure with dummy format for data transfer between different systems, we should reduce the ENC to small and compact size in order to use it in mobile platform. In this paper, we assumed that the mobile system like PDA, or Webpad can be used for small capability of mobile platform. However, the ENC should be updated periodically by update profile data produced by HO. If we would reduce the ENC without a consideration of update, we could not get newly updated data furthermore. As summary, we studied considerations for ENC reduction with update capability. It will make the ENC be useful in many mobile platforms for various applications.

• Journal of Service Research and Studies
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• v.13 no.2
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• pp.181-203
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• 2023

YouTube exhibits a hybrid personality, incorporating traits of both over-the-top (OTT) and personal broadcasting platforms. However, limited research has investigated these hybrid characteristics, particularly in the context of paid YouTube channel memberships. Therefore, building upon consumption value theory and prior literature, this study examines the influence of consumption value factors associated with paid YouTube channel memberships on usage satisfaction and continuance intention. Specifically, the study identifies four perceived consumption value factors (functional, social, emotional, and epistemic values) within the paid YouTube channel membership context and assesses their impact on usage satisfaction and continuance intention. Additionally, the study explores the moderating role of conditional value (the experience of watching live streams on paid YouTube channels) in these relationships. Data was collected via an online survey from Korean adults who subscribed to multiple paid YouTube channel memberships, resulting in 274 responses. The proposed hypotheses were tested using structural equation modeling (SEM). The SEM results indicate that all four consumption value factors significantly influence usage satisfaction, with usage satisfaction in turn positively affecting continuance intention. Furthermore, the study reveals that conditional value moderates the relationships between functional/emotional values and usage satisfaction, as well as between usage satisfaction and continuance intention. This study is the first to focus on YouTube channel paid memberships, which encompass characteristics from both OTT and personal broadcasting platforms. It is anticipated that this research will offer insights to personal broadcasters and stakeholders regarding the motivational factors that impact user satisfaction and encourage subscriptions to channel memberships.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.107-107
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• 2017

Sweet corns are enjoyed worldwide as processed products and fresh ears. Types of sweet corn are based on the gene(s) involved. The oldest sweet corn type has a gene called "sugary (su)". Sugary-based sweet corn was typically named "sweet corn". With its relatively short shelf life and the discovery of a complementary gene, "sugary enhanced (se)", the sweet corn (su only) was rapidly replaced with another type of sweet corns, sugary enhanced sweet corn, which has recessive homozygous su/su, se/se genotype. With the incorporation of se/se genotype into existing su/su genotype, sugary enhanced sweet corn has better shelf life and increased sweetness while maintaining its creamy texture due to high level of water soluble polysaccharide, phytoglycogen. Super sweet corn as the name implies has higher level of sweetness and better shelf life than sugary enhanced sweet corn due to "shrunken2 (sh2)" gene although there's no creamy texture of su-based sweet corns. Distinction between sh2/sh2 and su/su genotypes in seeds is phenotypically possible. The Involvement of se/se genotype under su/su genotype, however, is visually impossible. The genotype sh2/sh2 is also phenotypically epistatic to su/su genotype when both genotypes are present in an individual, meaning the seed shape for double recessive sh2/sh2 su/su genotype is much the same as sh2/sh2 +/+ genotype. Hence, identifying the double and triple recessive homozygous genotypes from su, se and sh2 genes involves a testcross to single recessive genotype, chemical analysis or DNA-based marker development. For these reasons, sweetcorn breeders were hastened to put them together into one cultivar. This, however, appears to be no longer the case. Sweet corn companies began to sell their sweet corn hybrids with different combinations of abovementioned three genes under a few different trademarks or genetic codes, i.g. Sweet $Breed^{TM}$, Sweet $Gene^{TM}$, Synergistic corn, Augmented Supersweet corn. A total of 49 commercial sweet corn F1 hybrids with B73 as a check were genotyped using DNA-based markers. The genotype of field corn inbred B73 was +/+ +/+ +/+ for su, se and sh2 as expected. All twelve sugary enhanced sweet corn hybrids had the genotype of su/su se/se +/+. Of sixteen synergistic hybrids, thirteen cultivars had su/su se/se sh2/+ genotype while the genotype of two hybrids and the remaining one hybrid was su/su se/+ sh2/+, and su/su +/+ sh2/+, respectively. The synergistic hybrids all were recessive homozygous for su gene and heterozygous for sh2 gene. Among the fifteen augmented supersweet hybrids, only one hybrid was triple recessive homozygous (su/su se/se sh2/sh2). All the other hybrids had su/su se/+ sh2/sh2 for one hybrid, su/su +/+ sh2/sh2 for three hybrids, su/+ se/se sh2/sh2 for three hybrids, su/+ se/+ sh2/sh2 for four hybrids, and su/+ +/+ sh2/sh2 for three hybrids, respectively. What was believed to be a classic super sweet corn hybrids also had various genotypic combination. There were only two hybrids that turned out to be single recessive sh2 homozygous (+/+ +/+ sh2/sh2) while all the other five hybrids could be classified as one of augmented supersweet genotypes. Implication of the results for extension service and sweet corn breeding will be discussed.

• Economic and Environmental Geology
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• v.51 no.1
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• pp.67-76
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• 2018

This study reviews three eco-friendly energy towns with hybrid thermal energy supply systems and borehole thermal energy storage (BTES) in Canada and Denmark. The district heating and cooling systems were designed by using multi-source energy for the higher efficiency and reliability as well as environment. ADEU (Alexandra District Energy Utility) located at the developing area in the city of Richmond, Canada was designed to supply district energy with the installation of 726 borehole heat exchangers (BHEs) and a backup boiler using natural gas. DLSC (Drake Landing Solar Community) located in the town of Okotoks, Canada is a district system to store solar thermal energy underground during the summer season by seasonal BTES with 144 BHEs. Brædstrup Solpark district heating system located in Denmark has been conducted energy supply from multiple energy sources of solar thermal, heat pump, boiler plants and seasonal BTES with 48 BHEs. These systems are designed based on social and economic benefits as well as nature-friendly living space according to the city based energy perspective. Each system has the energy center which distribute the stored thermal energy to each house for heating during the winter season. The BHE depth and ground thermal storage volume are designed by the heating and cooling load as well as the condition of ground water flow and thermophysical properties of the ground. These systems have been proved the reliance and economic benefits by providing consistent energy supply with competitive energy price for many years. In addition, the several expansions of the service area in ADEU and Brædstrup Solpark have been processed based on energy supply master plan. In order to implement this kind of project in our country, the regulation and policy support of government or related federal organization are required. As well as the government have to make a energy management agency associated with long-term supply energy plan.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.145-165
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• 2023

Recommendation system plays a significant role on relieving difficulties of selecting information among rapidly increasing amount of information caused by the development of the Internet and on efficiently displaying information that fits individual personal interest. In particular, without the help of recommendation system, E-commerce and OTT companies cannot overcome the long-tail phenomenon, a phenomenon in which only popular products are consumed, as the number of products and contents are rapidly increasing. Therefore, the research on recommendation systems is being actively conducted to overcome the phenomenon and to provide information or contents that are aligned with users' individual interests, in order to induce customers to consume various products or contents. Usually, collaborative filtering which utilizes users' historical behavioral data shows better performance than contents-based filtering which utilizes users' preferred contents. However, collaborative filtering can suffer from cold-start problem which occurs when there is lack of users' historical behavioral data. In this paper, hybrid music recommendation system, which can solve cold-start problem, is proposed based on the playlist data of Melon music streaming service that is given by Kakao Arena for music playlist continuation competition. The goal of this research is to use music tracks, that are included in the playlists, and metadata of music tracks and playlists in order to predict other music tracks when the half or whole of the tracks are masked. Therefore, two different recommendation procedures were conducted depending on the two different situations. When music tracks are included in the playlist, LightFM is used in order to utilize the music track list of the playlists and metadata of each music tracks. Then, the result of Item2Vec model, which uses vector embeddings of music tracks, tags and titles for recommendation, is combined with the result of LightFM model to create final recommendation list. When there are no music tracks available in the playlists but only playlists' tags and titles are available, recommendation was made by finding similar playlists based on playlists vectors which was made by the aggregation of FastText pre-trained embedding vectors of tags and titles of each playlists. As a result, not only cold-start problem can be resolved, but also achieved better performance than ALS, BPR and Item2Vec by using the metadata of both music tracks and playlists. In addition, it was found that the LightFM model, which uses only artist information as an item feature, shows the best performance compared to other LightFM models which use other item features of music tracks.

• International Commerce and Information Review
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• v.13 no.3
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• pp.169-201
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• 2011

There was a new wave in grocery business with development of information and technology, thus a movement from traditional stores to online stores, In order to expand the scale of traditional supermarket and to satisfy the customers' needs, they provide offline and online services simultaneously. This paper is based on the previous studies which had been researched in developed countries from late 1990s to early 2000s and the purpose of this study is to introduce the idea and operation system of e-Grocery business. Moreover, we suggest the alternatives on delivery service methods in order to satisfy the customers' needs through analyzing the current condition of e-Grocers in Korea. According to the result of this study, Korean e-Grocers offer only attended home delivery services. In our opinion, Korean supermarkets have to take hybrid model which Tesco.com is using. There are some alternatives to increase the profits of Korean e-Grocers and to provide better services to their customers as follows: As an alternatives for delivery services, picking service is the easiest and cheapest way to apply for supermarkets. This is very useful for working women and also it is possible to order by smartphone recently. They can order the goods to the closest local supermarkets from working place, and then they pick them up on the way home from working off. In order to improve the quality of delivery services, to use the reception box will be the way to provide better services to the customers. The reception box is a way to protect the quality of goods such as fresh-cut product, which require the freshness through the temperature adjustment, and also to keep the safety of ordered goods through locking system Through this system, supermarkets are able to use attended or unattended services under the customers' satisfaction. However, using the reception box is expensive, so shared reception box will be an alternative. As an alternative for development of e-Grocery business, the advertisement for e-Grocery business has to be supported in order to attract potential customers in e-Grocery business. Furthermore, the main concerns of e-Grocery business such as the sanitation and safety of goods, and convenience must be guaranteed in order to keep the loyal customers and to attract new customers.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.73-92
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• 2014

An Adaptive Clustering-based Collaborative Filtering Technique was proposed to solve the fundamental problems of collaborative filtering, such as cold-start problems, scalability problems and data sparsity problems. Previous collaborative filtering techniques were carried out according to the recommendations based on the predicted preference of the user to a particular item using a similar item subset and a similar user subset composed based on the preference of users to items. For this reason, if the density of the user preference matrix is low, the reliability of the recommendation system will decrease rapidly. Therefore, the difficulty of creating a similar item subset and similar user subset will be increased. In addition, as the scale of service increases, the time needed to create a similar item subset and similar user subset increases geometrically, and the response time of the recommendation system is then increased. To solve these problems, this paper suggests a collaborative filtering technique that adapts a condition actively to the model and adopts the concepts of a context-based filtering technique. This technique consists of four major methodologies. First, items are made, the users are clustered according their feature vectors, and an inter-cluster preference between each item cluster and user cluster is then assumed. According to this method, the run-time for creating a similar item subset or user subset can be economized, the reliability of a recommendation system can be made higher than that using only the user preference information for creating a similar item subset or similar user subset, and the cold start problem can be partially solved. Second, recommendations are made using the prior composed item and user clusters and inter-cluster preference between each item cluster and user cluster. In this phase, a list of items is made for users by examining the item clusters in the order of the size of the inter-cluster preference of the user cluster, in which the user belongs, and selecting and ranking the items according to the predicted or recorded user preference information. Using this method, the creation of a recommendation model phase bears the highest load of the recommendation system, and it minimizes the load of the recommendation system in run-time. Therefore, the scalability problem and large scale recommendation system can be performed with collaborative filtering, which is highly reliable. Third, the missing user preference information is predicted using the item and user clusters. Using this method, the problem caused by the low density of the user preference matrix can be mitigated. Existing studies on this used an item-based prediction or user-based prediction. In this paper, Hao Ji's idea, which uses both an item-based prediction and user-based prediction, was improved. The reliability of the recommendation service can be improved by combining the predictive values of both techniques by applying the condition of the recommendation model. By predicting the user preference based on the item or user clusters, the time required to predict the user preference can be reduced, and missing user preference in run-time can be predicted. Fourth, the item and user feature vector can be made to learn the following input of the user feedback. This phase applied normalized user feedback to the item and user feature vector. This method can mitigate the problems caused by the use of the concepts of context-based filtering, such as the item and user feature vector based on the user profile and item properties. The problems with using the item and user feature vector are due to the limitation of quantifying the qualitative features of the items and users. Therefore, the elements of the user and item feature vectors are made to match one to one, and if user feedback to a particular item is obtained, it will be applied to the feature vector using the opposite one. Verification of this method was accomplished by comparing the performance with existing hybrid filtering techniques. Two methods were used for verification: MAE(Mean Absolute Error) and response time. Using MAE, this technique was confirmed to improve the reliability of the recommendation system. Using the response time, this technique was found to be suitable for a large scaled recommendation system. This paper suggested an Adaptive Clustering-based Collaborative Filtering Technique with high reliability and low time complexity, but it had some limitations. This technique focused on reducing the time complexity. Hence, an improvement in reliability was not expected. The next topic will be to improve this technique by rule-based filtering.

• Horticultural Science & Technology
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• v.33 no.6
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• pp.941-946
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• 2015

A new Doritaenopsis cultivar 'Hwasu 5205' was bred by Kyungpook National University, Korea, which produces young plants through tissue culture techniques. The new cultivar 'Hwasu 5205', showing the phenotype of vivid red and large flower type characteristics, was derived from crossing between Phalaenopsis Happy Valentine and Doritaenopsis Happy Rose. An elite individual, number '02-05-205' later named as 'Hwasu 5205', was selected among about 300 individual progenies after more than 2 years of intensive selection covering vegetative and flowering distinctiveness. In year 2004-2005, 1st and 2nd characteristic analyses were carried out through performance and uniformity tests. 'Hwasu 5205' produces vivid red (RHS #PN78B) flowers of i ncurved type with large size, of 9.2 and 12.0 cm in flower height and width, respectively. Leaves of 'Hwasu 5205' grow horizontally and are about 24.3cm in length and 8.5cm in width, respectively. This cultivar possesses no genetic variation. It can be propagated rapidly in vitro and is easy to grow due to its vigorous growth habit. 'Hwasu 5205' was registered (Reg. #: 2915) to Korea Seed & Variety Service (KSVS) on 1st December, 2009 and the PBR(plant breeder's right)is currently controlled by Sangmiwon Orchid Company, Korea.

• Korean Journal of Environmental Biology
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• v.24 no.4
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• pp.359-367
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• 2006

The vascular plants of this site were identified as 641 taxa through field investigation; 101 families, 340 genera, 547 species, 80 varieties, 12 forms, 1 subspecies and 1 hybrid. Based on the Rare plants of the Forest Service and Korea Forest Research Institute, 9 taxa were listed except implanted species; Loranthus tanakae, Aristolochia manshuriensis, Aristolochia contorta, Viola albida, Cypripedium macranthum, Schpolia japonioa, Acanthopanax chiisanensis, Rhododendron micranthum. Based on the Korean endemic plants, 18 taxa were listed; Salix purpurea var, japonica, Asarum sieboldii var. mandshuricum for. misnadrum, Persicaria lapathifolia for. alba, Pseudostellaria sylvatica, Aconitum pseudolaeve var. erectum, Corydalis maculata, Corydazis albipetala, Corydalis grandicazyx, Cardamine amaraeiormis, Chrysosplenium barbatum, Filipendula glaberrima, Lespedeza x tomentella, Acanthopanax chiisanensis, Melampyrum setaceum var. nakaianum, Weigela subsessilis, Adenophora triphylla var. hirsute, Cirsium setidens, Saussurea pseudogracilis. Specific plant species by floral region were total 81 taxa (12.6%); Prunus yedoensis in class V, 13 taxa (Thuja orientalis, Cimicifuga heracleiiolia, Sedum middendorffianutn, Rhododendron micranthum, etc.) in class IV, 17 taxa (Equisetum palustre, Aceriphyllum rossii, Angelica gigas, Cirsium setidens, etc.) in class III, 15 taxa (Heloniopsis orientalis, Lychnis cognata, Saxifraga oblongifolia, Viola orientalis, etc.) in class II, 35 taxa (Hosta capitata, Cimicifuga simplex, Chrysosplenium flagelliferum, Campanula punctata, etc.) in class I. So, the naturalized plants were listed 53 taxa and the naturalization index was 8.2%, urbanization index was 20.7%.