• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.302-304
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• 2011

Rule based machine learning techniques are very important in our real world now. We can list out some important application which we can apply rule based machine learning algorithm such as medical data mining, business transaction mining. The different between rules based machine learning and model based machine learning is that model based machine learning out put some models, which often are very difficult to understand by expert or human. But rule based techniques output are the rule sets which is in IF THEN format. For example IF blood pressure=90 and kidney problem=yes then take this drug. By this way, medical doctor can easy modify and update some usable rule. This is the scenario in medical decision support system. Currently, Rough set is one of the most famous theory which can be used for produce the rule. LEM2 is the algorithm use this theory and can produce the small set of rule on the database. In this paper, we present an improvement of LEM2 algorithm which incorporates the variable precision techniques.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.288-300
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• 2006

In this paper, we consider a channel ordering problem that seeks to minimize the total interference in mobile radio networks. If a base station receives connection request from a mobile user, one of the empty channels that are fixed to the base station is assigned to the mobile user. Among several channels available, we can choose one that seems to make least interference with other channels assigned to adjacent base stations. However, a pair of channels that are not separated enough do not generate interference if both of them are not simultaneously used by mobile users. That is, interference between channels may vary depending on the channel assignment sequence for each base station and on the distribution of mobile users. To find a channel assignment sequence that seems to generate minimum interference, we develop an optimization model considering various scenarios of mobile user distribution. Simulation results show that channel assignment sequence determined by the scenario based optimization model significantly reduces the interference provided that scenarios and interference costs are properly generated.

• Journal of Distribution Science
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• v.15 no.7
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• pp.25-33
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• 2017

Purpose - This study aims to explore how a combination of the advertisement presentation vividness and consumers' regulatory focus affects choice. In addition, it seeks to the understanding for the psychological process by using consumers' response with experimental designs. Research design, data, and methodology - This research conducted two experiments based on the scenario. Specifically, Experiment 1 used a 2 (vividness of advertisement presentation: picture vs. word) × 2 (regulatory focus: prevention focus vs. promotion focus) between-subjects design. Experiment 2 used a 2 (vividness of advertisement presentation: detailed description vs. less detailed description) × 2 (regulatory focus: prevention focus vs. promotion focus) between-subjects design. Results - Two studies showed that prevention-focused individuals, when presented with a vivid presentation, were more likely to choose the advertised option compared with advertisements presented less vividly appearance. In contrast, promotion-focused individuals showed no difference in choice shares regardless of advertisement presentation vividness. In addition, these effects were mediated by the imagery toward the advertised information. Conclusions - The current research found how consumers' inherent motivation affects the extent of imagery in a purchase decision and a new perspective to previous studies with regards to regulatory focus. Further, this research suggested new advertisement strategies to corporations.

• The Journal of Industrial Distribution & Business
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• v.9 no.5
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• pp.25-35
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• 2018

Purpose - The purpose of this paper is to partition a last-mile delivery network into zones and to determine locations of last mile delivery centers (LMDCs) in Bangkok, Thailand. Research design, data, and methodology - As online shopping has become popular, parcel companies need to improve their delivery services as fast as possible. A network partition has been applied to evaluate suitable service areas by using METIS algorithm to solve this scenario and a facility location problem is used to address LMDC in a partitioned area. Research design, data, and methodology - Clustering and mixed integer programming algorithms are applied to partition the network and to locate facilities in the network. Results - Network partition improves last mile delivery service. METIS algorithm divided the area into 25 partitions by minimizing the inter-network links. To serve short-haul deliveries, this paper located 96 LMDCs in compact partitioning to satisfy customer demands. Conclusions -The computational results from the case study showed that the proposed two-phase algorithm with network partitioning and facility location can efficiently design a last-mile delivery network. It improves parcel delivery services when sending parcels to customers and reduces the overall delivery time. It is expected that the proposed two-phase approach can help parcel delivery companies minimize investment while providing faster delivery services.

• Environmental Engineering Research
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• v.21 no.3
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• pp.219-225
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• 2016

Energy self-reliance is important for economic growth and development for any nation. An energy self-reliance technological analysis for Kampala the capital city of Uganda is presented. Three renewable energy sources: Municipal Solid Waste (MSW), solar and wind are assessed for the period of 2014 to 2030. Annual MSW generation will increase from $6.2{\times}10^5$ tons in 2014 to $8.5{\times}10^5$ and $1.14{\times}10^6$ tons by 2030 at 2% and 3.9% population growth respectively. MSW energy recovery yield varies from 136.7 GWh (2014, 65% collection) to 387.9 GWh (2030, 100% collection). MSW can at best contribute 2.1% and 1.6% to total Kampala energy demands for 2014 and 2030 respectively. Wind contribution is 5.6% and 2.3% in those respective years. To meet Kampala energy demands through solar, 26.6% of Kampala area and 2.4 times her size is required for panel installation in 2014 and 2030 respectively. This study concludes that improving renewable energy production may not necessarily translate into energy self-reliant Kampala City based on current and predicted conditions on a business as usual energy utilization situation. More studies should be done to integrate improvement in renewable energy production with improvement in efficiency in energy utilization.

• Journal of Korea Game Society
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• v.19 no.6
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• pp.61-70
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• 2019

In this paper, we apply reinforcement learning in the field of management simulation game to check whether game agents achieve autonomously given goal. In this system, we apply PPO (Proximal Policy Optimization) algorithm in the Unity Machine Learning (ML) Agent environment and the game agent is designed to automatically find a way to play. Five game scenario simulation experiments were conducted to verify their usefulness. As a result, it was confirmed that the game agent achieves the goal through learning despite the change of environment variables in the game.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.4 s.181
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• pp.99-108
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• 2006

Since mold industries are regarded as belonging to three types of bad business, capable young people are reluctant to work in this field. The industries are hard to employ skilled workers who have much experience and knowledge On the mold manufacturing. Thus, effective CAM systems are required for unskilled workers to create process plans and NC data for the manufacturing, and process plans play important roles in the downstream manufacturing processes, such as NC machining, polishing, and final assembly. This study proposes a decision support system that facilitates unskilled workers to easily select high quality NC-data, as well as to increase productivity. The proposed system is assumed to follow a CAM operation scenario that consists of next three steps: 1) identifying several process plans and enumerating feasible unit machining operations (UMOs) from material and part surface information, 2) creating all feasible NC-data based on UMOs using a commercial CAM system, 3) selecting the best NC data among the feasible NC data using four screening criteria, such as machining accuracy, machining allowance, cutting load, and processing time. A case study on the machining of a camera core mold is provided to demonstrate the proposed system.

• Journal of Distribution Science
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• v.17 no.8
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• pp.5-14
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• 2019

Purpose - This study aims to proposes a conceptual framework to segment multi-national products based on a Chinese consumer's perception of multi-national products, to find the role of consumer ethnocentrism (CET) in country of origin (COO) effects for Chinese, and to figure out how different dimension of CET Effects on purchase intention developed market and home country. Research design, data and methodology - This study selected a 2×2×2 factorial design for the hypothesis test based on the product category × combination of manufactured type × Ethnocentrism level. This study distinguishes products between luxury (Burberry) and non-luxury (Nike) products and choose combination of manufactured type (Spain vs India/ Spain vs China) in order to perform comparative studies. A total of 223 Chinese participated in the experiment. After being exposed to each scenario, participants were asked to respond to questions about brand preference and purchase intention Results - Regarding to luxury made in developed country, it is worth that exposing COO information to low level of ethnocentrism consumers. Regarding to non-luxury product made in emerging country, it makes it worse when COO information to high level of ethnocentrism consumers. Lastly, regarding to non-luxury product, patriotic consumers prefer to purchase product made in home country.

• Knowledge Management Research
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• v.20 no.4
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• pp.75-97
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• 2019

In the rapidly changing business environment, knowledge has been recognized as a core asset for sustaining an organization's competitive advantage. In addition, knowledge sharing is one of the key elements of knowledge management, emphasizing external knowledge sharing beyond initial internal knowledge sharing. However, while knowledge management research emphasizes knowledge sharing, which is a positive aspect, research on preventing knowledge leakage that can have negative consequences is relatively lacking. Companies have tried to minimize the negative effects of knowledge management but many knowledge leakage accidents are still occurring. Therefore, this study aims to examine the effects of external factors based on deterrence theory and internal factors based on self-determination theory on knowledge leakage intention focusing on tacit knowledge. The results of the empirical analysis of 100 data sets collected through a scenario-based survey show that certainty of sanctions, social disapproval, and competence are found to have a significant effect on reducing tacit knowledge leakage intention. Furthermore, informal sanctions have a greater impact on tacit knowledge leakage intention than formal sanctions and external factors have a greater effect on tacit knowledge leakage intention than internal factors.

• Asian Journal of Business Environment
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• v.7 no.4
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• pp.35-41
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• 2017

Purpose - This paper aims to examine several time series models to predict sales of department stores and discount store markets in South Korea, while other previous trial has performed sales of convenience stores and supermarkets. In addition, optimal predicted values on the underlying model can be got and be applied to distribution industry. Research design, data, and methodology - Two retailing types, under investigation, are homogeneous and comparable in size based on 86 realizations sampled from January 2010 to February in 2017. To accomplish the purpose of this research, both ARIMA model and exponential smoothing methods are, simultaneously, utilized. Furthermore, model-fit measures may be exploited as important tools of the optimal model-building. Results - By applying Holt-Winters' additive seasonality method to sales of two large-scale retailing types, persisting increasing trend and fluctuation around the constant level with seasonal pattern, respectively, will be predicted from May in 2017 to February in 2018. Conclusions - Considering 2017-2018 forecasts for sales of two large-scale retailing types, it is important to predict future sales magnitude and to produce the useful information for reforming financial conditions and related policies, so that the impacts of any marketing or management scheme can be compared against the do-nothing scenario.