• Journal of Korea Port Economic Association
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• v.24 no.4
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• pp.363-384
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• 2008

This study aims at improving Korea's competitiveness in port logistics through marketing strategy with integrating the conceptual approach into the empirical one and combining both the oldest military treatise and the newest evaluating model in social science that was applied by the HFP(hierarchical fuzzy process) model enhanced by the KJ method. The empirical results of this study show Busan in the middle among subject ports. At present, Korea plays a reciprocal role in the port market in East Asia, but in the medium- and long-term, Korea's ports will vie together with most major ports in the East Asian region. A descriptive investigation shows that Korea's developing tasks in port logistics must be considered in the context of the direction for developing port policies, the necessity of expanding port facilities in the capital region, securing the sufficient traffic volume through the establishment of the hinterland linking system and its positive utilization, and reforming the direction for developing the global logistics through increased port competitiveness. In the short- and medium-term, Korea must use the opportunity factor of 'Growth and open door policy of China' as a geoeconomic advantage and to utilize Korea's ports as a gate to Chinese foreign trade. With the rise of China's economy, China also plays a significant role in both port and airport markets. Hence, the linking system between the two must be established to meet the expanding traffic volume, especially in the capital area. Moreover, it is necessary for Korea to secure port logistics through the establishment of the hinterland linking system and its positive utilization. The great accomplishment of this paper is to present strategies to increase Korea's port competitiveness in the rapidly changing environments of world logistics with the focus on both the oldest military strategic treatise and the newest empirical method in social science. In order to reinforce this study, it needs further compensative research because the evaluation structure could be subdivided with more extensive and precise criteria.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.1
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• pp.72-84
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• 2023

The Rural Revitalization Strategy (2018-2022), published by the Chinese State Council in 2018, represents a new period of rural development in China. Suburban areas are more convenient than other rural areas in integrated urban-rural development but are under greater pressure from construction and industrial pollution. As a rural area with a high proportion of rural areas, it would be valuable for Henan province to gain a comprehensive grasp of rural human settlementst while identifying problems and proposing solutions. The purpose of this study is to analyze the satisfaction of the evaluation items based on the usage status and life perception of the residents of Tai Nan village, a suburb-type rural village in Henan province. The study proposes improvement programs based on the evaluation results. As a result of the study, 24 evaluation items were derived and divided into five categories: "Living Service Facilities", "Housing Environment, "Road Environment", "Health & Ecology Environment", and "Social & Cultural Environment". The Fuzzy Comprehensive Evaluation Method was used to find the overall satisfaction level of the human living environment in Tai Nan village, which was "average", among which "Living Service Facilities" was the most important "Health & Ecology Environment" was the least satisfied. Based on these results, an improvement plan is proposed in three stages. First, the living service will be improved while strengthening the facility management of the hygiene and the ecological environment. Second, reasonable improvement of housing and the road environment will be applied. Third, programs will be introduced to cultivate residents' ability to build their own and improve the social and cultural environment. This study provides basic data for the future improvement of rural settlements in the suburban areas of Henan province and is of great significance in gradually improving the the residents' quality of life.

• Journal of the Korea Institute of Building Construction
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• v.13 no.6
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• pp.530-540
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• 2013

The business environment that affects the management performance can be characterized by each Strategic Business Unit (SBU) since construction companies win overseas contracts due to the fairly good construction situations while experience a decline in the local housing market. Environmental changes can alter the strategic importance of the SBU when measuring the management performance. However, large construction companies apply BSC (Balanced Score Card) for collective calculation to determine the management performance, making it difficult to reflect the strategic importance of SBU. This method may create a distorted image of management performance that fails to take environmental changes into consideration, and as such it needs to be improved. Yet, there are no studies on the weight of each SBU considering environmental changes. Thus, the current study intends to analyze the weight of SBU for company-wide measurement of the performance of large construction companies. In addition, a model for analysis of SBU importance is proposed to respond to the constantly changing environmental situations and to modify the weight. For analysis of SBU weight, a questionnaire was conducted with 23 experts and hands-on workers, and the questionnaire result was quantitatively analyzed by applying the FD-AHP method. It is expected that the result will enable a model to be proposed to calculate the weight per division in a manner that reflects environmental changes and minimizes strategic distortion when measuring the management performance of large construction companies.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.14
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• pp.5721-5727
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• 2014

Objectives: To evaluate the performance of clustering methods used in the prognostic assessment of categorical clinical data for hepatocellular carcinoma (HCC) patients in China, and establish a predictable prognostic nomogram for clinical decisions. Materials and Methods: A total of 332 newly diagnosed HCC patients treated with hepatic resection during 2006-2009 were enrolled. Patients were regularly followed up at outpatient clinics. Clustering methods including the Average linkage, k-modes, fuzzy k-modes, PAM, CLARA, protocluster, and ROCK were compared by Monte Carlo simulation, and the optimal method was applied to investigate the clustering pattern of the indices including platelet count, platelet/lymphocyte ratio (PLR) and serum aspartate aminotransferase activity/platelet count ratio index (APRI). Then the clustering variable, age group, tumor size, number of tumor and vascular invasion were studied in a multivariable Cox regression model. A prognostic nomogram was constructed for clinical decisions. Results: The ROCK was best in both the overlapping and non-overlapping cases performed to assess the prognostic value of platelet-based indices. Patients with categorical platelet-based indices significantly split across two clusters, and those with high values, had a high risk of HCC recurrence (hazard ratio [HR] 1.42, 95% CI 1.09-1.86; p<0.01). Tumor size, number of tumor and blood vessel invasion were also associated with high risk of HCC recurrence (all p< 0.01). The nomogram well predicted HCC patient survival at 3 and 5 years. Conclusions: A cluster of platelet-based indices combined with other clinical covariates could be used for prognosis evaluation in HCC.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.825-832
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• 2014

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. Even if some methods have the advantage in low complexity, but they generally suffer form low sensitivity. Also, it is difficult to detect PVC accurately because of the various QRS pattern by person's individual difference. Therefore it is necessary to design an efficient algorithm that classifies PVC based on QRS pattern in realtime and decreases computational cost by extracting minimal feature. In this paper, we propose PVC classification based on QRS pattern using QS interval and R wave amplitude. For this purpose, we detected R wave, RR interval, QRS pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of R wave detection, PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 PVC. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 93.72% in PVC classification.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.140-145
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• 2010

In this work, to theoretically analyze the nonlinear charging characteristic, a Radial Basis Function Neural Network (RBFNN) is adopted. Based on the RBFNN, an charging characteristic tendency of a Linear Type Magnetic Flux Pump (LTMFP) is analyzed. In the paper, we developed the LTMFP that generates stable and controllable charging current and also experimentally investigated its charging characteristic in the cryogenic system. From these experimental results, the charging current of the LTMFP was also found to be frequency dependent with nonlinear quality due to the nonlinear magnetic behaviour of superconducting Nb foil. On the whole, in the case of essentially cryogenic experiment, since cooling costs loomed large in the cryogenic environment, it is difficult to carry out various experiments. Consequentially, in this paper, we estimated the nonlinear characteristic of charging current as well as realized the intelligent model via the design of RBFNN based on the experimental data. In this paper, we view RBF neural networks as predominantly data driven constructs whose processing is based upon an effective usage of experimental data through a prudent process of Fuzzy C-Means clustering method. Also, the receptive fields of the proposed RBF neural network are formed by the FCM clustering.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.8
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• pp.1947-1954
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• 2013

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. But it is difficult to detect the P and T wave signal because of person's individual difference. Therefore it is necessary to design efficient algorithm that classifies different arrhythmia in realtime and decreases computational cost by extrating minimal feature. In this paper, we propose arrhythmia detection based on binary coding using QRS feature varibility. For this purpose, we detected R wave, RR interval, QRS width from noise-free ECG signal through the preprocessing method. Also, we classified arrhythmia in realtime by converting threshold variability of feature to binary code. PVC, PAC, Normal, BBB, Paced beat classification is evaluated by using 39 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 97.18%, 94.14%, 99.83%, 92.77%, 97.48% in PVC, PAC, Normal, BBB, Paced beat classification.

• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.75-87
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• 2001

Inter-port competition is fiercer than in the past because of technological evolution in transport systems : the increasing side of containerships implies only a few calls in three or four ports at each end of the trade and the rest of the traffic being served by smaller feederships. It is therefore essential for big ports to be selected as one of these calls by the main shipowners, consortia and alliances to avoid rmarginalisation. In order to compete effectively, many ports have been obliged to modernise and extend considerably its existing ports or to build new port facilities. With the advent of major environmental legislation around the world, however, amenities such as fish and wildlife, clean air and water, access to the waterfront, and view protection took on greater importance. Ports are now being forced to incorporate environmental considerations into their planning and management functions in order to avoid additional costs or timing delays. The aim of this paper is to analyse the port value by which port comparison(or selection) will be made with HFP(Hierarchical Fuzzy Process) method. This was done by extracting and grouping the evaluation factors of port value by port experts : facility and location factor, logistics service factor environment and amenity factor, city and economic factor, and human and system factor. For empirical test of this method, 6 major ports in Northeast Asia were chosen and analysed. The order of importance for five evaluation factors were 1) facility and location factor 2) logistics service factor 3) human and system factor, 4) city and economic factor, and 5) environment and amenity factor. This means that geographical location and logistics services are still being considered as the most important factor to call the port by port users. even though environment and amenity factor shows relatively low figure. Among 6 major ports, Port of Kobe was ranked the first position in a comprehensive evaluation, while Ports of Busan and Kwangyang were 4th and 5th respectively. This implies that Port of Busan should make much efforts to enhance the existing facilities as well as management system.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.3
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• pp.322-331
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• 2003

As the complexity of a 3D game is increased by various factors of the game scenario, it has a problem for controlling the interrelation of the game objects. Therefore, a game system has a necessity of the coordination of the responses of the game objects. Also, it is necessary to control the behaviors of animations of the game objects in terms of the game scenario. To produce realistic game simulations, a system has to include a structure for designing the interactions among the game objects. This paper presents a method that designs the dynamic control mechanism for the interaction of the game objects in the game scenario. For the method, we suggest a game agent system as a framework that is based on intelligent agents who can make decisions using specific rules. Game agent systems are used in order to manage environment data, to simulate the game objects, to control interactions among game objects, and to support visual authoring interface that ran define a various interrelations of the game objects. These techniques can process the autonomy level of the game objects and the associated collision avoidance method, etc. Also, it is possible to make the coherent decision-making ability of the game objects about a change of the scene. In this paper, the rule-based behavior control was designed to guide the simulation of the game objects. The rules are pre-defined by the user using visual interface for designing their interaction. The Agent State Decision Network, which is composed of the visual elements, is able to pass the information and infers the current state of the game objects. All of such methods can monitor and check a variation of motion state between game objects in real time. Finally, we present a validation of the control method together with a simple case-study example. In this paper, we design and implement the supervised classification systems for high resolution satellite images. The systems support various interfaces and statistical data of training samples so that we can select the most effective training data. In addition, the efficient extension of new classification algorithms and satellite image formats are applied easily through the modularized systems. The classifiers are considered the characteristics of spectral bands from the selected training data. They provide various supervised classification algorithms which include Parallelepiped, Minimum distance, Mahalanobis distance, Maximum likelihood and Fuzzy theory. We used IKONOS images for the input and verified the systems for the classification of high resolution satellite images.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.