• Journal of Power Electronics
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• v.19 no.2
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• pp.529-539
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• 2019

The modular multilevel converter (MMC) has become a promising topology for high-voltage direct current (HVDC) transmission systems. To control a MMC system properly, the ac-side current, circulating current and submodule (SM) capacitor voltage are taken into consideration. This paper proposes a low-computation indirect model predictive control (IMPC) strategy that takes advantages of the conventional MPC and has no weighting factors. The cost function and duty cycle are introduced to minimize the tracking error of the ac-side current and to eliminate the circulating current. An optimized merge sort (OMS) algorithm is applied to keep the SM capacitor voltages balanced. The proposed IMPC strategy effectively reduces the controller complexity and computational burden. In this paper, a discrete-time mathematical model of a MMC system is developed and the duty ratio of switching state is designed. In addition, a simulation of an eleven-level MMC system based on MATLAB/Simulink and a five-level experimental setup are built to evaluate the feasibility and performance of the proposed low-computation IMPC strategy.

• Journal of Korea Port Economic Association
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• v.35 no.3
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• pp.125-150
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• 2019

The Hunchun area is the only development and opening line in Jilin Province and the Changchun-Jilin-Tumen Developmental Plan area, and as a hub area for regional logistics, promoting the logistics competitiveness of the Hunchun area is an important factor in promoting economic development in the Northeast region. The purpose of this study is to derive the factors for activating logistics competitiveness in Hunchun area by applying SWOT analysis and to present them to policy-makers by drawing priority of factors for promoting logistics competitiveness in Hunchun area through AHP survey of Chinese and Korean logistics experts. According to the analysis, the weighting was high in order of opportunity factors and strength factors, and the priority was high in order of factors such as promotion and expansion of One Belt, One Road policies, active support through national policies, construction of international logistics center cities, construction of logistics centers, and supply of bulk cargo. Finally, from a comprehensive perspective, this study presented policy implications such as SO Strategy (Strength-Occupancy Strategy) and ST Strategy (Strength-War Strategy) focusing on the strengths of the Hunchun Region for the strategy of strengthening the logistics competitiveness of the Hunchun area.

• The KIPS Transactions:PartB
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• v.15B no.5
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• pp.457-464
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• 2008

In the real-world operational environment, most of text classification systems have the problems of insufficient training documents and no prior knowledge of feature space. In this regard, $Na{\ddot{i}ve$ Bayes is known to be an appropriate algorithm of operational text classification since the classification model can be evolved easily by incrementally updating its pre-learned classification model and feature space. This paper proposes the improving technique of $Na{\ddot{i}ve$ Bayes classifier through feature weighting strategy. The basic idea is that parameter estimation of $Na{\ddot{i}ve$ Bayes considers the degree of feature importance as well as feature distribution. We can develop a more accurate classification model by incorporating feature weights into Naive Bayes learning algorithm, not performing a learning process with a reduced feature set. In addition, we have extended a conventional feature update algorithm for incremental feature weighting in a dynamic operational environment. To evaluate the proposed method, we perform the experiments using the various document collections, and show that the traditional $Na{\ddot{i}ve$ Bayes classifier can be significantly improved by the proposed technique.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.6
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• pp.943-950
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• 2002

Real time strategy game is a computer game genre of Playing with human or computer opponents in real time It differs from turn-type computer games in the game process method. Turn type games, such as chess, allow only one Player to move at a time. Real time strategy games allow two or more Players to move simultaneously. Therefore, in real time strategy computer games, the game components' movement plans must be calculated very quickly in order to not disturb other processes such as gathering resources, building structures, and combat activities. There are many approaches, which can reduce the amount of memory required for calculating path, search space, and reactive time of components. (or units). However, existing path finding algorithms tend to concentrate on achieving optimal Paths that are not as important or crucial in real time strategy game. This Paper introduces Dynamic Heuristic Af(DHA*) algorithm which is capable of reducing search space and reactive time of game units and compares with A* algorithm using static heuristic weighting.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.9-14
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• 2002

Boolean retrieval is simple and elegant. However, since there is no provision for term weighting, no ranking of the answer set is generated. As a result, the size of the output might be too large or too small. Relevance feedback is the most popular query reformulation strategy. in a relevance feedback cycle, the user is presented with a list of the retrieved documents and, after examining them, marks those which are relevant. In practice, only the top 10(or 20) ranked documents need to be examined. The main idea consists of selecting important terms, or expressions, attached to the documents that have been identified as relevant by the user, and of enhancing the importance of these terms in a new query formulation. The expected effect is that the new query will be moved towards the relevant documents and away from the non-relevant ones. Local analysis techniques are interesting because they take advantage of the local context provided with the query. In this regard, they seem more appropriate than global analysis techniques. In a local strategy, the documents retrieved for a given query q are examined at query time to determine terms for query expansion. This is similar to a relevance feedback cycle but might be done without assistance from the user.

• Korean Management Science Review
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• v.10 no.1
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• pp.81-105
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• 1993

The objective of the paper is to develop models for determining the aggregate budget size in long-range R&D planning of KT(Korea Telecom.) and for allocating it by strategically adopted technologies for KT's TOP(Telecommunication-Oriented Paradise) Strategy. In the model of R&D budget size determination, the linear regression analysis is applied. In allocating the R&D expenditure, criteria weighting and technological importance ranking are determined by means of the Analytic Hierarchy Process(AHP) as a decision aid, along with hierarchical representation and pairwse comparisons. R&D budget analysis provides to basic data for the mid-and long-range R&D planning. The model then needs to be adjusted as the TOP project plan becomes specific. Resource allocation model for R&D based on AHP can be used to identify the importance of the technologies for TOP according to short-, mid-, and long-term perspectives without further modification. It is expected that the R&D budget analysis model works as the basis for planning R&D investment strategies and that the resource allocation model for R&D contributes to the effective use of the limited resource.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.5
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• pp.149-161
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• 1994

The Max-Min CRI method , a traditional inference method , has three problems: subjective formulation of membership functions, error-prone weighting strategy, and inefficient compositional rule of inference. Because of these problems, there is an insurmountable error region between desired output and inferred output. To overcome these problems, we propose an Intelligent Neuro-Fuzzy System (INFS) based on fuzzy thoery and self-organizing functions of neural networks. INFS makes use of neural networks(Error Back Propagation) to solve the first problem, and NCRI(New Max-Min CRI) method for the second. With a proposed similarity measure, NCRI method is an improved method compared to the traditional Max-Min CRI method. For the last problem, we propose a new defuzzification method which combines only the appropriate rules produced by the rule selection level. Applying INFS to a D.C. series motor, we can conclude that the error region is reduced and NCRI method performs better than Max-Min CRI method.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.633-635
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• 1999

We design to the temperature control system based on Receding horizon control(RHC) with a terminal output weighting for stochastic state model. This system has a large time delay, a nonlinear temperature characteristics, a perturbation, a disturbance, etc. In this paper, we show that RHC can easily be applied to the system to track the desired temperature, since it takes the receding horizon strategy for both controller and filter.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.424-427
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• 1995

In this paper, a control law based on the receding horizon concept which robustly stabilizes time-varying discrete linear systems, is proposed. A dynamic game problem minimizing the worst case performance, is adopted as an optimization problem which should be resolved at every current time. The objective of the proposed control law is to guarantee the closed loop stability and the infinite horizon $H^{\infty}$ norm bound. It is shown that the objective can be achieved by selecting the proper terminal weighting matrices which satisfy the inequality conditions proposed in this paper. An example is included to illustrate the results..

• Journal of Information Technology Services
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• v.9 no.4
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• pp.109-121
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• 2010

Most organizations adopt their own performance measurement systems. Those organizations select performance measures to meet their goals. Organizations can give only limited description of what performance measures are. Kaplan and Norton suggest that the Balanced Scorecard (BSC) to complement the conventional performance measures. The BSC can provide management system with a comprehensive strategic vision and integrates non-financial measures with financial measures. The BSC is widely used for measuring corporate performance. This paper investigates how the BSC-based performance measures can be applied to Learning City. The Learning City's performance measures and strategy map on the basis of the BSC are suggested in this research. This paper adopt the AR(assurance region)-DEA model which could limit the range of weight on performance measures to prevent each viewpoint of BSC from having unlimited elasticity. The proposed model is based on CCR model including a property of unit invariance to use the data without normalization process.