• Journal of Korean Society of Forest Science
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• v.99 no.1
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• pp.9-15
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• 2010

This study was conducted to develop a stand growth model and a stand yield table for Eucalyptus pellita and Acacia mangium plantations in Kalimantan, Indonesia. To develop a stand growth model, Weibull robability density function, a diameter class model, was applied in this study. In the development of stand growth model by site index and stand age, a hierarchy is generally required - estimation, recovery and prediction of the diameter class model. A number of grow equations were also involved in each process to estimate diameter, height, basal area, minimum or maximum diameter. To examine whether the grow equations are adequate for Eucalyptus pellita or Acacia mangium plantations, a fitness index was analyzed for each equation. The results showed that fitness indices were ranged from 65 to 89% for Eucalyptus pellita plantations and from 72 to 95% for Acacia mangium plantations. As being highly adequate for the plantations, a stand yield table was developed based on the resulted growth model, and applied to estimate the stand growth with midium site index for 10-year period. The highest annual stand growth of Eucalyptus pellita plantations was estimated to be 21.25 $m^3$/ha, while that of Acacia mangium plantations was 27.5 $m^3$/ha. In terms of annual stand growth, Acacia mangium plantations appeared to be more beneficial than Eucalyptus pellita plantations. Also, to estimate commercial timber volume available from the plantations, an assumption that a log would be cut by 2.7 m in length and the rest of the log would be cut by 1.5m was involved. The commercial timber volume available from Eucalyptus pellita plantations was 68.0 $m^3$/ha, 33% from the total stand volume, 203.2 $m^3$/ha. Also 96.7 $m^3$/ha of commercial timbers were available from Acacia mangium plantations, which was 42% from the 232.9 $m^3$/ha in total. Presenting a good information about the stand growth in Eucalyptus pellita and Acacia mangium plantations, this study might be useful for whom proceeds or considers an abroad plantation for merchantable timber production or carbon credit in tropical regions.

• 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.

• Economic and Environmental Geology
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• v.45 no.2
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• pp.71-78
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• 2012

Illite-smectite mixed layers (I-S) occurring authigenically in diagenetic and hydrothermal environments reacts toward more illite-rich phases as temperature and potassium ion concentration increase. For that reason, I-S is often used as geothermometry and/or geochronometry at the field of hydrocarbons or ore minerals exploration. Generally, I-S shows X-ray powder diffraction (XRD) patterns of ultra-thin lamellar structures, which consist of restricted numbers of sillicate layers (normally, 5 ~ 15 layers) stacked in parallel to a-b planes. This ultra-thinness is known to decrease I-S expandability (%S) rather than theoretically expected one (short-stacking effect). We attempt here to quantify the short stacking effect of I-S using the difference of two types of expandability: one type is a maximum expandability ($%S_{Max}$) of infinite stacks of fundamental particles (physically inseparable smallest units), and the other type is an expandability of finite particle stacks normally measured using X-ray powder diffraction (XRD) ($%S_{XRD}$). Eleven I-S samples from the Geumseongsan volcanic complex, Uiseong, Gyeongbuk, have been analyzed for measuring $%S_{XRD}$ and average coherent scattering thickness (CST) after size separation under 1 ${\mu}m$. Average fundamental particle thickness ($N_f$) and $%S_{Max}$ have been determined from $%S_{XRD}$ and CST using inter-parameter relationships of I-S layer structures. The discrepancy between $%S_{Max}$ and $%S_{XRD}$ (${\Delta}%S$) suggests that the maximum short-stacking effect happens approximately at 20 $%S_{XRD}$, of which point represents I-S layer structures consisting of ca. average 3-layered fundamental particles ($N_f{\approx}3$). As a result of inferring the $%S_{XRD}$ range of each Reichweite using the $%S_{XRD}$ vs. $N_f$ diagram of Kang et al. (2002), we can confirms that the fundamental particle thickness is a determinant factor for I-S Reichweite, and also that the short-stacking effect shifts the $%S_{XRD}$ range of each Reichweite toward smaller $%S_{XRD}$ values than those that can be theoretically prospected using junction probability.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.129-141
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• 2023

Crop models have been used to predict yield under diverse environmental and cultivation conditions, which can be used to support decisions on the management of forage crop. Cultivar parameters are one of required inputs to crop models in order to represent genetic properties for a given forage cultivar. The objectives of this study were to compare calibration and ensemble approaches in order to minimize the uncertainty of crop yield estimates using the SIMPLE crop model. Cultivar parameters were calibrated using Log-likelihood (LL) and Generic Composite Similarity Measure (GCSM) as an objective function for Metropolis-Hastings (MH) algorithm. In total, 20 sets of cultivar parameters were generated for each method. Two types of ensemble approach. First type of ensemble approach was the average of model outputs (E_em), using individual parameters. The second ensemble approach was model output (E_pm) of cultivar parameter obtained by averaging given 20 sets of parameters. Comparison was done for each cultivar and for each error calculation methods. 'Jowoo' and 'Yeongwoo', which are forage rice cultivars used in Korea, were subject to the parameter calibration. Yield data were obtained from experiment fields at Suwon, Jeonju, Naju and I ksan. Data for 2013, 2014 and 2016 were used for parameter calibration. For validation, yield data reported from 2016 to 2018 at Suwon was used. Initial calibration indicated that genetic coefficients obtained by LL were distributed in a narrower range than coefficients obtained by GCSM. A two-sample t-test was performed to compare between different methods of ensemble approaches and no significant difference was found between them. Uncertainty of GCSM can be neutralized by adjusting the acceptance probability. The other ensemble method (E_pm) indicates that the uncertainty can be reduced with less computation using ensemble approach.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.33-49
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• 2018

The impact of TV Drama success on TV Rating and the channel promotion effectiveness is very high. The cultural and business impact has been also demonstrated through the Korean Wave. Therefore, the early prediction of the blockbuster success of TV Drama is very important from the strategic perspective of the media industry. Previous studies have tried to predict the audience ratings and success of drama based on various methods. However, most of the studies have made simple predictions using intuitive methods such as the main actor and time zone. These studies have limitations in predicting. In this study, we propose a model for predicting the popularity of drama by analyzing the customer's viewing pattern based on various theories. This is not only a theoretical contribution but also has a contribution from the practical point of view that can be used in actual broadcasting companies. In this study, we collected data of 280 TV mini-series dramas, broadcasted over the terrestrial channels for 10 years from 2003 to 2012. From the data, we selected the most highly ranked and the least highly ranked 45 TV drama and analyzed the viewing patterns of them by 11-step. The various assumptions and conditions for modeling are based on existing studies, or by the opinions of actual broadcasters and by data mining techniques. Then, we developed a prediction model by measuring the viewing-time distance (difference) using Euclidean and Correlation method, which is termed in our study similarity (the sum of distance). Through the similarity measure, we predicted the success of dramas from the viewer's initial viewing-time pattern distribution using 1~5 episodes. In order to confirm that the model is shaken according to the measurement method, various distance measurement methods were applied and the model was checked for its dryness. And when the model was established, we could make a more predictive model using a grid search. Furthermore, we classified the viewers who had watched TV drama more than 70% of the total airtime as the "passionate viewer" when a new drama is broadcasted. Then we compared the drama's passionate viewer percentage the most highly ranked and the least highly ranked dramas. So that we can determine the possibility of blockbuster TV mini-series. We find that the initial viewing-time pattern is the key factor for the prediction of blockbuster dramas. From our model, block-buster dramas were correctly classified with the 75.47% accuracy with the initial viewing-time pattern analysis. This paper shows high prediction rate while suggesting audience rating method different from existing ones. Currently, broadcasters rely heavily on some famous actors called so-called star systems, so they are in more severe competition than ever due to rising production costs of broadcasting programs, long-term recession, aggressive investment in comprehensive programming channels and large corporations. Everyone is in a financially difficult situation. The basic revenue model of these broadcasters is advertising, and the execution of advertising is based on audience rating as a basic index. In the drama, there is uncertainty in the drama market that it is difficult to forecast the demand due to the nature of the commodity, while the drama market has a high financial contribution in the success of various contents of the broadcasting company. Therefore, to minimize the risk of failure. Thus, by analyzing the distribution of the first-time viewing time, it can be a practical help to establish a response strategy (organization/ marketing/story change, etc.) of the related company. Also, in this paper, we found that the behavior of the audience is crucial to the success of the program. In this paper, we define TV viewing as a measure of how enthusiastically watching TV is watched. We can predict the success of the program successfully by calculating the loyalty of the customer with the hot blood. This way of calculating loyalty can also be used to calculate loyalty to various platforms. It can also be used for marketing programs such as highlights, script previews, making movies, characters, games, and other marketing projects.

• The Journal of Korean Society for Radiation Therapy
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• v.29 no.2
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• pp.27-32
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• 2017

Objectives: Oncological hyperthermia is a treatment to selectively kill cancer cells by directly applying heat to cancer cells or indirectly demage cancer cells. One of the most side effects of treatment is burn that can appear on the skin. In areas with irregularities such as the umbilicus, the patient feels a sense of hot and treatment may be discontinued. Therefore, in order to eliminate the irregularities of these areas, compensators are manufactured and measured to decrease in temperature. Materials and Methods: The temperature of the four sites (umbilicus, near the umbilicus, 5 cm below the umbilicus, back) was measured five times around the umbilicus in patients who were treated at oncological hyperthermia treatment device(EHY-2000, Oncotherm Kft, Hungary). The temperature sensor (TM-100, Oncotherm Kft, Hungary) was attached to four sites and the changes were observed at 5, 15, 25, 35, and 50 minutes after treatment. Compensators of three materials were used(Vaseline, Bolus, Dental resin). The data measured five times were compared for each compensator. Results: The temperature change when the compensator was not used increase from 34.65 degrees to 42.9 degrees on average. The near umbilicus was changed from 32.20 degrees to 37.00 degrees, and the 5 cm below the umbilicus was changed from 31.90 to 34.41 degrees. When the compensator material was inserted into the umbilicus, the temperature change was measured as 5.42 degrees for bolus, 6.55 degrees for vaseline, and 6.83 degrees for resin. Conclusion: Using the compensator in the region where the irregularities such as the umbilicus, the heat sensation could be reduced. the use of a resin that can be customized not only lowers the temperature but also significantly reduces the feeling of the patient. It will be possible to reduce the heat sensation in the treatment and to treat it in a more comfortable condition.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.25-44
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• 2013

Accessing movie contents has become easier and increased with the advent of smart TV, IPTV and web services that are able to be used to search and watch movies. In this situation, there are increasing search for preference movie contents of users. However, since the amount of provided movie contents is too large, the user needs more effort and time for searching the movie contents. Hence, there are a lot of researches for recommendations of personalized item through analysis and clustering of the user preferences and user profiles. In this study, we propose recommendation system which uses ontology based knowledge base. Our ontology can represent not only relations between metadata of movies but also relations between metadata and profile of user. The relation of each metadata can show similarity between movies. In order to build, the knowledge base our ontology model is considered two aspects which are the movie metadata model and the user model. On the part of build the movie metadata model based on ontology, we decide main metadata that are genre, actor/actress, keywords and synopsis. Those affect that users choose the interested movie. And there are demographic information of user and relation between user and movie metadata in user model. In our model, movie ontology model consists of seven concepts (Movie, Genre, Keywords, Synopsis Keywords, Character, and Person), eight attributes (title, rating, limit, description, character name, character description, person job, person name) and ten relations between concepts. For our knowledge base, we input individual data of 14,374 movies for each concept in contents ontology model. This movie metadata knowledge base is used to search the movie that is related to interesting metadata of user. And it can search the similar movie through relations between concepts. We also propose the architecture for movie recommendation. The proposed architecture consists of four components. The first component search candidate movies based the demographic information of the user. In this component, we decide the group of users according to demographic information to recommend the movie for each group and define the rule to decide the group of users. We generate the query that be used to search the candidate movie for recommendation in this component. The second component search candidate movies based user preference. When users choose the movie, users consider metadata such as genre, actor/actress, synopsis, keywords. Users input their preference and then in this component, system search the movie based on users preferences. The proposed system can search the similar movie through relation between concepts, unlike existing movie recommendation systems. Each metadata of recommended candidate movies have weight that will be used for deciding recommendation order. The third component the merges results of first component and second component. In this step, we calculate the weight of movies using the weight value of metadata for each movie. Then we sort movies order by the weight value. The fourth component analyzes result of third component, and then it decides level of the contribution of metadata. And we apply contribution weight to metadata. Finally, we use the result of this step as recommendation for users. We test the usability of the proposed scheme by using web application. We implement that web application for experimental process by using JSP, Java Script and prot$\acute{e}$g$\acute{e}$ API. In our experiment, we collect results of 20 men and woman, ranging in age from 20 to 29. And we use 7,418 movies with rating that is not fewer than 7.0. In order to experiment, we provide Top-5, Top-10 and Top-20 recommended movies to user, and then users choose interested movies. The result of experiment is that average number of to choose interested movie are 2.1 in Top-5, 3.35 in Top-10, 6.35 in Top-20. It is better than results that are yielded by for each metadata.