• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.111-119
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• 2014

The cloud environment need resource management method that to enable the big data issue and data analysis technology. Existing resource management uses the limited calculation method, therefore concentrated the resource bias problem. To solve this problem, the resource management requires the learning-based scheduling using resource history information. In this paper, we proposes the ART (Adaptive Resonance Theory)-based adaptive resource management. Our proposed method assigns the job to the suitable method with the resource monitoring and history management in cloud computing environment. The proposed method utilizes the unsupervised learning method. Our goal is to improve the data processing and service stability with the adaptive resource management. The propose method allow the systematic management, and utilize the available resource efficiently.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.18 no.6
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• pp.85-95
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• 2015

Recent years, nationwide projects for ecological restoration are implemented with emerging issues on the stream ecosystem. In order to enhance effectiveness of the ecosystem restoration and reduce negative impact, the appraisal of effectiveness through the follow-up monitoring and the adaptive management process are executed in consecutive phase. In this study, planning phase, monitoring and adaptive management in Hakui stream which is part of An Yang stream restoration project is introduced as representative ongoing case of effective adaptive management. The aim of this study is to verify the adaptive management process and suggest direction of effective restoration. Restoration project of Hakui stream resulted in increasing number and diversity of species (vegetation, fish, bird, invertbrates, amphibian and reptilia) according to monitoring from 2004 to 2013, and enhancing natural river landscape by evaluation of river naturalness among 2001(before restoration), 2007 (after), 2015 (recent). However, excessive vegetation expansion or sediment deposition on channel over time caused unexpected results such as terrestrialization or degradation of habitats. Adaptive management action such as removing disturbance species (Humulus japonicus)(2007), coppicing willow (2007), release of march snail (2007), creation of wetland (2014) were implemented based on monitoring results. And then appraisal of management action was discussed.

• The Journal of the Korea Contents Association
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• v.4 no.1
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• pp.8-17
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• 2004

In this paper, we design an intelligent loaming management logics which provide personalized teaming considering adaptive learning content dement and content sequencing. We enhance the existing functional model including adaptive learning management functions. Also, we present a system architecture to implement the adaptive learning management system. We realize the adaptive teaming management system based on the SCORM run-time engine.

• IE interfaces
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• v.18 no.3
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• pp.343-349
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• 2005

This study presents the empirical results of comparing several demand forecasting methods for Supply Chain Management(SCM). Adaptive exponential smoothing using change detection statistics (Jun) is compared with Trigg and Leach's adaptive methods and SAS time series forecasting systems using weekly SCM demand data. The results show that Jun's method is superior to others in terms of one-step-ahead forecast error and eight-step-ahead forecast error. Based on the results, we conclude that the forecasting performance of SCM solution can be improved by the proposed adaptive forecasting method.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.2
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• pp.169-180
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• 2020

Globalization has led to a dramatic increase in intercultural service encounters between services providers and customers from diverse cultural backgrounds. This paper explores the causal relationship between service employees‟ cultural intelligence and adaptive sales behavior in intercultural service encounters, and the mediating effect of cognitive and emotional empathy on this relationship. A quantitative survey methodology was utilized to collect data on 341 salespeople at duty-free shops located on Jeju Island, Korea. Data analysis was conducted using SPSS 18 and Amos 18. The results show that cultural intelligence has a significant impact on cognitive empathy, emotional empathy, and adaptive sales behavior. Cognitive empathy has a positive impact on adaptive sales behavior, whereas the relationship between emotional empathy and adaptive sales behavior is not significant. Additionally, cognitive empathy mediates the relationship of cultural intelligence and adaptive sales behavior. This study has useful managerial implications for employee selection, training, and development in service firms engaged in intercultural service encounters. This study extends prior research on intercultural service encounters by exploring the direct impact of cultural intelligence on intercultural adaptation and the mediating effect of empathy, suggesting the presence of a cognitive mechanism that plays a key role in the impact of cultural intelligence on adaptive sales behavior.

• Journal of Korean Society of Water and Wastewater
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• v.23 no.5
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• pp.583-598
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• 2009

From the engineering standpoint, this study puts a special emphasis on application of adaptive management. To do this, we analyze the recent issue about water scarcity of the Gwangdong dam. Using the system dynamics model, we defined the system including water balance in the dam, dam manager's operation rules, regional water supply and local water distribution, and customer damage. It was expected that the model is useful to explain the real case, and also water scarcity of Gwangdong led to total damage of about 2.56 billion won, mainly to customers in the Taebaeck city. Two adaptive management options (i.e., optimal allocation of limited water resources, and early control of dam storage) were applied to the model in order to examine whether adaptive management is effective to mitigate the damage, it is concluded that the case study could largely reduce or entirely avoid the damage with adaptive engineering options.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2396-2413
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• 2018

Trust management is one of the most challenging issues for the highly heterogeneous Internet of Things (IoT). In the context of the IoT, it is difficult to evaluate the node's trustworthiness in the same trust model when a node provides different services. Guaranteeing the availability of the trust management service is another significant challenge because of the dynamic nature of IoT environments. With these issues in mind, this paper propose a robust and adaptive trust management system for the IoT that is able to measure the trustworthiness of nodes based on feedbacks collected from participants in a specific context and ensure the availability of trust management services. The main contributions of our system are: 1) Proposing a partly decentralized trust management framework, which improves the resiliency of the trust mechanism; 2) Proposing an adaptive trust evaluation scheme and a three-dimensional context representation makes trust evaluation more accurate and specific; 3) Enhancing the adaptive trust evaluation scheme by incorporating a bad behavior factor in trust estimation, which efficiently distinguishes misleading feedbacks from On-Off attacks. Simulation results show the good performance of the proposed system and especially show effectiveness against On-Off attacks compared to other trust mechanisms.

• Journal of Distribution Science
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• v.18 no.11
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• pp.91-103
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• 2020

The recent Covid-19 outbreak has caused severe disruption of the global supply chain, which tests firms' ability to survive and build resilience. The concept of adaptive supply chain management (A-SCM) has never been tested against a severe supply chain disruption, such as a pandemic. Purpose: The aim of this study is to examine how firms in Indonesia develop resilience through the implementation of components of adaptive supply chain management, namely risk management, resource reconfiguration and supply chain flexibility, in order to survive severe supply chain disruption. Research design, data and methodology: A qualitative method and PLS-SEM were used to analyze 120 data collected from Indonesian manufacturing firms in various industries. Results: The findings show that risk management, resource reconfiguration, and supply chain flexibility are important components that make up A-SCM. However, only risk management contributes to help build firm resilience in the presence of severe supply chain disruption. Conclusions: The components of A-SCM have been empirically tested. The implication is that managers should carefully use RM to prepare firms for different scenarios to develop contingency strategies. This research contributes to the supply chain management body of knowledge in the context of pandemic-level disruption and broadens the dynamic capabilities perspective.

• Journal of Information Technology Applications and Management
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• v.17 no.1
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• pp.127-139
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• 2010

Customer behavior pattern discovery is the fundament for conducting customer oriented services and the services management. But, the composition, need, interest and experience of customers may be continuously changing, thereof lead to the difficulty in refining a stable description of their consistent behavior pattern. This paper presented a new method for the behavior pattern discovery from a changing collection of customers. It was originally inspired from the swarm intelligence of ant colony. By the adaptive clustering, some typical behavior patterns which reflect the characteristics of related customer clusters can extracted dynamically and adaptively.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.4
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• pp.1-18
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• 2007

In this paper, we develop an adaptive genetic algorithm (aGA). The aGA has an adaptive scheme which can automatically determine the use of local search technique and adaptively regulate the rates of crossover and mutation operations during its search process. For the adaptive scheme, the ratio of degree of dispersion resulting from the various fitness values of the populations at continuous two generations is considered. For the local search technique, an improved iterative hill climbing method is used and incorporated into genetic algorithm (GA) loop. In order to demonstrate the efficiency of the aGA, i) a canonical GA without any adaptive scheme and ii) several conventional aGAs with various adaptive schemes are also presented. These algorithms, including the aGA, are tested and analyzed each other using various test problems. Numerical results by various measures of performance show that the proposed aGA outperforms the conventional algorithms.