• Journal of Convergence for Information Technology
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• v.8 no.5
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• pp.107-113
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• 2018

With the rapid development of big data and Internet technologies among IT technologies, it is being changed into an environment where data stored in the cloud environment can be used wherever the Internet is connected, without storing important data in an external storage device such as USB. However, protection of users' privacy information is becoming increasingly important as the data being processed in the cloud environment is changed into an environment that can be easily handled. In this paper, we propose a user-reserving management model that can improve the user 's service quality without exposing the information used in the cloud environment to a third party. In the proposed model, user group is grouped into virtual environment so that third party can not handle user's privacy information among data processed in various cloud environments, and then identity property and access control policy are processed by block chain.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.3
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• pp.13-19
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• 2003

Mathematically simplified reaction scheme that simulates autoignitions of the end gases in spark ignition engines has been studied computationally. The five equation model is described, to predict the essential features of hydrocarbon oxidation. This scheme has been calibrated against autoignition delay times measured in rapid compression machines. The rate constants, activation temperatures, Ta, Arrhenius preexponential constants, A, and heats of reaction for stoichiometric n-heptane/air, iso-octane/air, and their mixtures have all been optimised. The optimisation has been guided by Morley's correlation of the ratio of chain branching to linear termination rates with octane number. Comparisons between computed and experimental autoignition delay times have validated the Present simplified reaction scheme and the influences of octane number upon autoignition delay times have been computationally investigated. It has been found that both cool flame and high temperature direct reactions can have an effect on autoignition delay times.

• Journal of Industrial Convergence
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• v.19 no.6
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• pp.1-10
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• 2021

This study sought ways to strengthen the competitiveness of local governments using public diplomacy, especially in Chungcheongbuk-do, at a time when it is urgent to revitalize the local economy by attracting investment from domestic and foreign companies due to changes in the global supply chain. The main issues that have recently emerged in the global business environment include strengthening the localization of the global value chain, diversifying reshoring and supply chains, and creating an efficient global value chain through digital transformation. Therefore, this study emphasized the necessity of a strategy for public diplomacy based on these changes, and derived the response conditions and detailed strategies of Chungcheongbuk-do through SWOT analysis. In addition, based on the ABCD model, we set up a Chungbuk-type public diplomacy strategy and suggested strategic implications for upgrading Chungbuk's competitiveness.

• Journal of Distribution Research
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• v.10 no.3
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• pp.1-14
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• 2005

It is important to control performance and a Sustainable Collaboration (SC) for the successful Supply Chain Management (SCM). This research developed a control model which analyzed SCM performances based on a Balanced Scorecard (ESC) and an SC using Support Vector Machine (SVM). 108 specialists of an SCM completed the questionnaires. We analyzed experimental data set using SVM. This research compared the forecasting accuracy of an SCMSC through four types of SVM kernels: (1) linear, (2) polynomial (3) Radial Basis Function (REF), and (4) sigmoid kernel (linear > RBF > Sigmoid > Polynomial). Then, this study compares the prediction performance of SVM linear kernel with Artificial Neural Network. (ANN). The research findings show that using SVM linear kernel to forecast an SCMSC is the most outstanding. Thus SVM linear kernel provides a promising alternative to an SC control level. A company which pursues an SCM can use the information of an SC in the SVM model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.4
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• pp.54-60
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• 2002

In the Supply chain, The ATP(Available to Promise) function doesn't only give customers to conformation of delivery. It can be used by the core function with ATP rule that can reconcile supplies and demands on the supply chain. Therefore We can be acquire the conformation about on the due date of supplier by using the ATP function of management about real and concurrent access on the supply chain, also decide the affect about product availability due to forecasting or customer's orders through the ATP. In this paper, It consolidates the necessity on a ATP and analyzes data which is concerned of ATP. tinder the these environments, defines the ATP nile that can improve the customer value and data flow related the eCRM and builds on a algorithm.

• 한국IT서비스학회:학술대회논문집
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• 2009.11a
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• pp.9-19
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• 2009

SCM은 시장의 변화를 신속하고 파악하고 IT 기술을 활용해 정보를 공유함으로써 변화에 보다 적극적으로 대처해 전체 Supply Chain의 이익을 높이고자 하는 전략적 사고라고 할 수 있다. SCM에서 파트너 선정은 장기적이고 전략적인 관점에서 이루어져야 하는 지식 집약적인 업무 Process이다. 본 연구는 SCM에서 파트너 선정의 절차를 Task Modeling을 통해 재사용 가능한 Knowledge-base를 개발하는 것이다. 이를 위해, 첫 번째로 전문가의 문제 해결 과정을 분석해 문제 해결 과정을 대상으로 한 Problem-Solving Ontology(Task Ontology)를 도출하고, 두 번째로 문제 해결 과정에 필요한 Domain Knowledge를 추출해 파트너 선정 문제 해결에 필요한 Domain Ontology를 개발한다. 끝으로 Problem-Solving Ontology와 Domain Ontology를 Protege를 통해 구현하고자 한다.

• Journal of the Korea Society for Simulation
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• v.15 no.2
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• pp.13-21
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• 2006

Due to the exhaustion of resources and increasing concerns about the environment, reverse logistics has become a critical issue in recent years. We discuss the conventional logistics operations and the reverse logistics operations in the automotive industry. The traditional supply chain (TSC) and the reverse supply chain (RSC) models are developed to compare performance between two systems. In addition, results of the simulation models are discussed in respect to total costs, inventory levels and stock-outs to analyze the effects of introducing of reverse logistics system.

• Journal of Information Technology and Architecture
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• v.9 no.2
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• pp.177-186
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• 2012

Navy establishes the Naval Task Forces (TF) for many kinds of maritime operations. Then the TF in the maritime environment performs simultaneous component operations such as ASUW (Anti-Surface Warfare), ASW (Anti-Submarine Warfare), AAW (Anti-Aircraft Warfare), and assault operations. The TF consists of many tactical systems for the completion of missions C4I, VOIP (Voice Over Internet Protocol), DMHS (Digital Massage Handling System), and TDLs (Tactical Data Links) such as LINK-11, 16, ISDL (Inter Site Data Link). When the TF executes naval operations to complete a mission, we are interested in the kill chain for the maritime operations in the TF. The kill chain is a standard procedure for the naval operations to crush enemy defenses. Although each ship has a procedure about a manual for 'how to fight', it leave something to be desired for the TF detailed kill chain currently. Therefore, in this paper, we propose the naval TF's kill chain to perform the naval operations. Then, the operational effectiveness of the TF in the kill chain environment is determined through operation scenarios of TDL system implementation. It is to see the operational information sharing effect to a data link model based on MND-AF OV 6c (statement of tracking operational status) in the maritime operations applied to TDL and is to identify improvements in information dissemination process. We made the kill chain of maritime TF for the effective naval operations.

• Electronics and Telecommunications Trends
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• v.38 no.6
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• pp.1-11
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• 2023

Large language models seem promising for handling reasoning problems, but their underlying solving mechanisms remain unclear. Large language models will establish a new paradigm in artificial intelligence and the society as a whole. However, a major challenge of large language models is the massive resources required for training and operation. To address this issue, researchers are actively exploring compact large language models that retain the capabilities of large language models while notably reducing the model size. These research efforts are mainly focused on improving pretraining, instruction tuning, and alignment. On the other hand, chain-of-thought prompting is a technique aimed at enhancing the reasoning ability of large language models. It provides an answer through a series of intermediate reasoning steps when given a problem. By guiding the model through a multistep problem-solving process, chain-of-thought prompting may improve the model reasoning skills. Mathematical reasoning, which is a fundamental aspect of human intelligence, has played a crucial role in advancing large language models toward human-level performance. As a result, mathematical reasoning is being widely explored in the context of large language models. This type of research extends to various domains such as geometry problem solving, tabular mathematical reasoning, visual question answering, and other areas.

• Journal of Convergence for Information Technology
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• v.8 no.3
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• pp.79-84
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• 2018

Cloud services are readily available through a variety of media, attracting a lot of attention from users. However, there are various security damages that abuse the privacy of users who use cloud services, so there is not enough technology to prevent them. In this paper, we propose a protection model to safeguard user's privacy in a cloud environment so as not to illegally exploit user's privacy. The proposed model randomly manages the user's signature to strengthen the role of the middle manager and the cloud server. In the proposed model, the user's privacy information is provided illegally by the cloud server to the user through the security function and the user signature. Also, the signature of the user can be safely used by bundling the random number of the multiplication group and the one-way hash function into the hash chain to protect the user's privacy. As a result of the performance evaluation, the proposed model achieved an average improvement of data processing time of 24.5% compared to the existing model and the efficiency of the proposed model was improved by 13.7% than the existing model because the user's privacy information was group managed.