• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.04a
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• pp.426-426
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• 1999

;There are many sources of uncertainty in a typical production and inventory system. There is uncertainty as to how many items customers will demand during the next day, week, month, or year. There is uncertainty about delivery times of the product. Uncertainty exacts a toll from management in a variety of ways. A spurt in a demand or a delay in production may lead to stockouts, with the potential for lost revenue and customer dissatisfaction. Firms typically hold inventory to provide protection against uncertainty. A cushion of inventory on hand allows management to face unexpected demands or delays in delivery with a reduced chance of incurring a stockout. The proposed strategies are used for the design of a probabilistic inventory system. In the traditional approach to the design of an inventory system, the goal is to find the best setting of various inventory control policy parameters such as the re-order level, review period, order quantity, etc. which would minimize the total inventory cost. The goals of the analysis need to be defined, so that robustness becomes an important design criterion. Moreover, one has to conceptualize and identify appropriate noise variables. There are two main goals for the inventory policy design. One is to minimize the average inventory cost and the stockouts. The other is to the variability for the average inventory cost and the stockouts The total average inventory cost is the sum of three components: the ordering cost, the holding cost, and the shortage costs. The shortage costs include the cost of the lost sales, cost of loss of goodwill, cost of customer dissatisfaction, etc. The noise factors for this design problem are identified to be: the mean demand rate and the mean lead time. Both the demand and the lead time are assumed to be normal random variables. Thus robustness for this inventory system is interpreted as insensitivity of the average inventory cost and the stockout to uncontrollable fluctuations in the mean demand rate and mean lead time. To make this inventory system for robustness, the concept of utility theory will be used. Utility theory is an analytical method for making a decision concerning an action to take, given a set of multiple criteria upon which the decision is to be based. Utility theory is appropriate for design having different scale such as demand rate and lead time since utility theory represents different scale across decision making attributes with zero to one ranks, higher preference modeled with a higher rank. Using utility theory, three design strategies, such as distance strategy, response strategy, and priority-based strategy. for the robust inventory system will be developed.loped.

• Journal of the Korean Institute of Landscape Architecture
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• v.38 no.3
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• pp.115-125
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• 2010

A theme park is not just a recreational space for leisure activities, but also a place of storytelling as collected around abstract boundaries called themes. These stories are 'a space that tells the meaning' that the visitor is looking for and the Robot Land space offers robots, humans, and nature. This study is a description of the design strategy and content of the work which was elected as a subject of the subsequent rank negotiation of the Masan Robot Land design contest for the selection of a private contractor. The focus of the plan is, first, the organizational power of each space and the delivery power of a theme for the history of revisits, which might be considered depending on whether or not the theme park has been successful in the visitor's mind. Second, it is to actively use the potential of Masan, which is not only the key hub of the mechanical industry but also has beautiful coastal resources. First, they created a space that can flexibly react depending on the user's desire and the change of form, minimizing environmental damage by using a linear metabolism that can provide an amalgam of the elemental characteristics of robots, humans, and nature as motifs. They introduced a planting plan for the admissions square, an existing forest, slope, vacation spot, the inside of a complex, and Eco Island, etc. by utilizing symbolic meaning and adjusting to the spatial characteristics of each space. In addition, they sought a detailed space by setting up zones tailored to the use and character of the subject area, having exhibitions and education about robots, vacation facilities for lodgers, various recreational and commercial facilities, and space for utopian gardens as themes. They planned Masan Robot Land to be a true cultural space that creates mental richness on the basis of not only the economical effects but also local emotion.

• Korean Journal of Environment and Ecology
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• v.35 no.5
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• pp.503-524
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• 2021

A largemouth bass (Micropterus salmoides) is an ecosystem disturbance fish species at the highest rank in the aquatic ecosystem, causing a serious imbalance in freshwater ecosystems. Although various attempts have been made to eradicate and control largemouth bass, no effective measures were found. Therefore, it is necessary to find an approach to maximize the effective population reduction based on the unique characteristics of largemouth bass. This study used the transcriptome analysis to derive 182,887 unigene contigs and select 12 types of final target sequences for applying the CRISPR/Cas9 system in the genes of IZUMO1 and Zona pellucida sperm-binding protein, which are proteins involved in sperm-egg recognition. After synthesizing 12 types of sgRNA capable of recognizing each target sequence, 12 types of Cas9-sgRNA ribonucleoprotein (RNP) complexes to be used in subsequent studies were prepared. This study searched the protein-coding gene of sperm-egg through the Next Generation Sequencing (NGS) and edited genes through the CRISPR/Cas9 system to induce infertile individuals that produced reproductive cells but could not form fertilized eggs. Through such a series of processes, it successfully established a composition development process for largemouth bass. It is judged that this study contributed to securing the valuable basic data for follow-up studies to verify its effect for the management of ecological disturbances without affecting the habitat of other endemic species in the same water system with the largemouth bass.

• The Transactions of the Korea Information Processing Society
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• v.13 no.9
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• pp.444-452
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• 2024

This study proposes a deep learning architecture optimized for fire detection derived through Layer Importance Evaluation. In order to solve the problem of unnecessary complexity and operation of the existing Convolutional Neural Network (CNN)-based fire detection system, the operation of the inner layer of the model based on the weight and activation values was analyzed through the Layer Importance Evaluation technique, the layer with a high contribution to fire detection was identified, and the model was reconstructed only with the identified layer, and the performance indicators were compared and analyzed with the existing model. After learning the fire data using four transfer learning models: Xception, VGG19, ResNet, and EfficientNetB5, the Layer Importance Evaluation technique was applied to analyze the weight and activation value of each layer, and then a new model was constructed by selecting the top rank layers with the highest contribution. As a result of the study, it was confirmed that the implemented architecture maintains the same performance with parameters that are about 80% lighter than the existing model, and can contribute to increasing the efficiency of fire monitoring equipment by outputting the same performance in accuracy, loss, and confusion matrix indicators compared to conventional complex transfer learning models while having a learning speed of about 3 to 5 times faster.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.34 no.1
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• pp.40-52
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• 2016

Buyongjeong, a pavilion in the Rear Garden of Changdeok Palace, was appointed as Treasure No. 1763 on March 2, 2012, by the South Korea government since it shows significant symmetry and proportion on its unique planar shape, spatial configuration, building decoration, and so forth. However, the designation of Treasure selection was mainly evaluated by concrete science, in that the selection has not clearly articulated how and why Buoungjeong was constructed as a present unique form. Therefore, this study aims to clarify the identity of Buyongjeong at the time of construction by considering its historical, ideological, philosophical background and building intention. Summary are as follows: First, Construction backgrounds and characters of Buyongjeong: Right after the enthronement, King Jeongjo had founded Kyujanggak(奎章閣), and sponsored civil ministers who were elected by the national examination, as a part of political reform. In addition, he established his own political system by respecting "Kaksin(閣臣)", Kyujanggak's officials as much as "Kain(家人)", internal family members. King Jeongjo's aggressive political reform finally enabled King's lieges to visit King's Rear Garden. In the reign of King Jeongjo's 16th year(1792), Naekaksangjohoe(內閣賞釣會) based on "Kaksin" was officially launched and the Rear Garden visitation became a regular meeting. The Rear Garden visitation consisted of "Sanghwajoeoyeon(賞花釣魚宴)" - enjoying flowers and fishing, and activities of "Nanjeongsugye". Afterward, it eventually became a huge national event since high rank government officials participated the event. King Jeongjo shared the cultural activities with government officials together to Buyongjeong as a place to fulfill his royal politics. Second, The geographical location and spatial characteristics of Buyongjeong: On the enthronement of King Jeongjo(1776), he renovated Taeksujae. Above all, aligning and linking Gaeyuwa - Taeksujae - a cicular island - Eosumun - Kyujangkak along with the construction axis is an evidence for King Jeongjo to determine how the current Kyujangkak zone was prepared and designed to fulfill King Jeonjo's political ideals. In 17th year(1793) of the reign of King Jeongjo, Taeksujae, originally a square shaped pavilion, was modified and expanded with ranks to provide a place to get along with the King and officials. The northern part of Buyongjeong, placed on pond, was designed for the King's place and constructed one rank higher than others. Discernment on windows and doors were made with "Ajasal" - a special pattern for the King. The western and eastern parts were for government officials. The center part was prepared for a place where government officials were granted an audience with the King, who was located in the nortern part of Buyongjeong. Government officials from the western and eastern parts of Buyongjeong, could enter the central part of the Buyongjeong from the southern part by detouring the corner of Buyongjeong. After all, Buyongjeong is a specially designed garden building, which was constructed to be a royal palace utilizing its minimal space. Third, Cultural Values of Buyongjeong: The Buyongjeong area exhibits a trait that it had been continuously developed and it had reflected complex King's private garden cultures from King Sejo, Injo, Hyunjong, Sukjong, Jeongjo and so forth. In particular, King Jeongjo had succeded physical, social and imaginary environments established by former kings and invited their government officials for his royal politics. As a central place for his royal politics, King Jeongjo completed Buyongjeong. Therefore, the value of Buyongjeong, as a garden building reflecting permanency of the Joseon Dynasty, can be highly evaluated. In addition, as it reflects Confucianism in the pavilion - represented by distinguishing hierarchical ranks, it is a unique example to exhibit its distinctiveness in a royal garden.

• Journal of Korean Society of Forest Science
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• v.98 no.4
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• pp.496-503
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• 2009

Reclamation area is characterized by high soil salinity and high ground water table that are not favorable environment for tree growth. However, the increasing demand to convert the reclamation area from rice cultivation fields to industrial or residential complex, or parks accompanies the idea of introduction of trees in the area. This study aimed to suggest better performed tree species for the tree planting in the Seosan reclamation area, Chungchungnam-do. Seedlings of 8 tree species (Pinus densiflora, Pinus thunbergii, Metasequoia glyptostroboides, Chionanthus retusus, Cornus kousa, Prunus sargentii, Quercus acutissima, and Zelkova serrata) were planted in 4 types of 10 m ${\times}$ 10 m experimental plots. The survival rate and the height growth of seedlings were measured from April 2006 to November 2008 on an annual basis. The experimental plots were constructed using 2 different soil material (dredged sand and dredged sand + forest soil), and 2 soil covering depth (1.5 m and 2.0 m). The tree species showed different survival rates for 3 years since planting (F = 9.632, P < 0.001). C. kousa, Q. acutissima, and P. sargentii showed high mortality rate while P. thunbergii, M. glyptostroboides and Z. serrata showed lower mortality rates. The seedling height growth for 3 years was also significantly different among species (F=4.749, P=0.002). Most of seedlings showed lower height growth in the second year, and the growth began to recover in the third year after transplanting. The survival rate and the growth of the seedlings were better in higher soil covering depth and forest soil material plots regardless of species. The combination of rank orders in survival rate and relative height growth indicates that P. thunbergii, M. glyptostroboides and Z. serrata would perform better than other species used in the experiment, while C. retusus, C. kousa and P. sargenti may not adapt well to this area.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.117-137
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• 2009

Recently, intelligent traffic information systems have enabled people to forecast traffic conditions before hitting the road. These convenient systems operate on the basis of data reflecting current road and traffic conditions as well as distance-based data between locations. Thanks to the rapid development of ubiquitous computing, tremendous context data have become readily available making vehicle route planning easier than ever. Previous research in relation to optimization of vehicle route planning merely focused on finding the optimal distance between locations. Contexts reflecting the road and traffic conditions were then not seriously treated as a way to resolve the optimal routing problems based on distance-based route planning, because this kind of information does not have much significant impact on traffic routing until a a complex traffic situation arises. Further, it was also not easy to take into full account the traffic contexts for resolving optimal routing problems because predicting the dynamic traffic situations was regarded a daunting task. However, with rapid increase in traffic complexity the importance of developing contexts reflecting data related to moving costs has emerged. Hence, this research proposes a framework designed to resolve an optimal route planning problem by taking full account of additional moving cost such as road traffic cost and weather cost, among others. Recent technological development particularly in the ubiquitous computing environment has facilitated the collection of such data. This framework is based on the contexts of time, traffic, and environment, which addresses the following issues. First, we clarify and classify the diverse contexts that affect a vehicle's velocity and estimates the optimization of moving cost based on dynamic programming that accounts for the context cost according to the variance of contexts. Second, the velocity reduction rate is applied to find the optimal route (shortest path) using the context data on the current traffic condition. The velocity reduction rate infers to the degree of possible velocity including moving vehicles' considerable road and traffic contexts, indicating the statistical or experimental data. Knowledge generated in this papercan be referenced by several organizations which deal with road and traffic data. Third, in experimentation, we evaluate the effectiveness of the proposed context-based optimal route (shortest path) between locations by comparing it to the previously used distance-based shortest path. A vehicles' optimal route might change due to its diverse velocity caused by unexpected but potential dynamic situations depending on the road condition. This study includes such context variables as 'road congestion', 'work', 'accident', and 'weather' which can alter the traffic condition. The contexts can affect moving vehicle's velocity on the road. Since these context variables except for 'weather' are related to road conditions, relevant data were provided by the Korea Expressway Corporation. The 'weather'-related data were attained from the Korea Meteorological Administration. The aware contexts are classified contexts causing reduction of vehicles' velocity which determines the velocity reduction rate. To find the optimal route (shortest path), we introduced the velocity reduction rate in the context for calculating a vehicle's velocity reflecting composite contexts when one event synchronizes with another. We then proposed a context-based optimal route (shortest path) algorithm based on the dynamic programming. The algorithm is composed of three steps. In the first initialization step, departure and destination locations are given, and the path step is initialized as 0. In the second step, moving costs including composite contexts into account between locations on path are estimated using the velocity reduction rate by context as increasing path steps. In the third step, the optimal route (shortest path) is retrieved through back-tracking. In the provided research model, we designed a framework to account for context awareness, moving cost estimation (taking both composite and single contexts into account), and optimal route (shortest path) algorithm (based on dynamic programming). Through illustrative experimentation using the Wilcoxon signed rank test, we proved that context-based route planning is much more effective than distance-based route planning., In addition, we found that the optimal solution (shortest paths) through the distance-based route planning might not be optimized in real situation because road condition is very dynamic and unpredictable while affecting most vehicles' moving costs. For further study, while more information is needed for a more accurate estimation of moving vehicles' costs, this study still stands viable in the applications to reduce moving costs by effective route planning. For instance, it could be applied to deliverers' decision making to enhance their decision satisfaction when they meet unpredictable dynamic situations in moving vehicles on the road. Overall, we conclude that taking into account the contexts as a part of costs is a meaningful and sensible approach to in resolving the optimal route problem.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.