• Journal of Korea Port Economic Association
• /
• v.28 no.2
• /
• pp.1-27
• /
• 2012

As more manufacturing and global businesses are being pulled into the port area, the idea of a Port Business Valley (PBV) is being pondered as it would create jobs and added value. The PBV would be centered around the harbor and be connected to the port, a logistics district, an industrial district, and the city. The resulting domestic and foreign investment in logistics, industry, business, tourism, living, etc. would vitalize the geographical characteristics of Incheon Port. It would also generate the largest amount of ripple effects between industries in the PBV. However, up until recently, the most frequently offered examples of planning that have helped logistics of a port to grow that have used a PBV have been those of Busan New Port and Gwangyang Port. However, this study is the result of the recent inception of the idea of creating a PBV centered around Incheon Port and the need for experts to develop a plan for such a PBV in Incheon by conducting a site specific study. The aim of this study is to set up the concept of PBV and establish PBV model of Incheon Port. In addition, this study identifies construct factors and their strategies for establish PBV of Incheon Port and then, shows the key factors and related-strategies on Fuzzy-AHP analysis from a survey of logistics experts with Incheon Port.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.6
• /
• pp.749-754
• /
• 2011

In this study, we propose the design of optimized pRBFNNs-based face recognition system using two-dimensional Image and ASM algorithm. usually the existing 2 dimensional face recognition methods have the effects of the scale change of the image, position variation or the backgrounds of an image. In this paper, the face region information obtained from the detected face region is used for the compensation of these defects. In this paper, we use a CCD camera to obtain a picture frame directly. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. AdaBoost algorithm is used for the detection of face image between face and non-face image area. We can butt up personal profile by extracting the both face contour and shape using ASM(Active Shape Model) and then reduce dimension of image data using PCA. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of RBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to real-time face image database and then demonstrated from viewpoint of the output performance and recognition rate.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.4
• /
• pp.685-693
• /
• 2017

This study aims to effectively respond to urban local rainstorms by classifying the risk against flood damage for each use district. The risk classification is based on sensitivity analysis of the socio-economic damage caused by local rainstorms in Changwon city, Korea by a Fuzzy model using data, such as the districts that provide institutional bases for land use, land prices, which estimate the property values, and floor area ratios, which measures the density and areas of flood damage. The analysis result indicated that flood damage in five districts of Changwon (Masan happo-gu, Masan Hoewon-gu, Sungsan-gu, Euichang-gu, and Jinhae-gu) is highest in the order of commercial areas, residential areas, industrial areas, and forests, which was attributed to high land price and floor area ratio of commercial areas. On the other hand, specific analysis in Masan Hoewon-gu and Sungsan-gu was different from the previous result, indicating that the risk against flood damage may vary according to the districts depending on their local conditions. The analysis from this study can be applied to future urban planning and be used as a guideline to estimate the potential flood damage. Overall, this study is meaningful in that it proposes an effective management of land use as a new resolution to mitigate of urban flood damage within a broader perspective of climate change and urbanization.

• Journal of Digital Convergence
• /
• v.17 no.4
• /
• pp.95-105
• /
• 2019

The failure of company management that does not overcome the recession of shipping economy has negative impact on front-end and back-end industries in relation to shipping industry overall. This study aims to derive a measure of government policy support for win-win of ship owners and shippers by performing a survey with experts in ship owners, shippers, and port-related institutions. This study employed a consistent fuzzy preference relation (CFPR) method to provide the priority of government policies. The study results showed that out of all 14 policies, the policy perceived most important was "expansion of participation in share of shipping company or ships of shipper (0.102)" followed by "strengthening of national shipper-centered service quality (0.101)", and "providing a long-term transportation contract model of container cargo (0.085)". To recover the Korean shipping industry via win-win of ship owners and shipper, the policy enforcement is important through correct government policy establishment and priority selection. In this regard, this study contributed to proposing policies and priority of the policies. For the future study, detailed analysis on comparison of perception difference among stakeholders in the shipping industry is needed.

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

• Korean Journal of Construction Engineering and Management
• /
• v.17 no.3
• /
• pp.61-70
• /
• 2016

Construction enterprises have performed various projects such as buildings, infrastructure and plant projects in the international market. Among these, the plant project's amount of orders accounted for about 68.9% of all. However, because of the enterprises won a contract with a low-budget for plant project in the last 10 years, the profit has dropped dramatically. And it is forecasted that there are extreme competition for bid award of plant projects because of the current falling oil prices and raising interest rates. In this circumstance, the comprehending of enterprises strength and weakness must be a priority to get a sustainable competitive advantage. Therefore this research suggests the enterprises's capability assessment framework and it is in order to diagnose the korean construction enterprises capabilities. The framework is based on the VRIO model that is on the basis of resource based theory. First, the capability assessment indices and their importance and priority that based on the life-cycle of plant project is deducted by literature review and survey. Second, the 5 point likert score applied VRIO survey is conducted to diagnose the enterprises and quantified the survey result using the fuzzy theory. Lastly, the competitvie implication and capability assessment are deducted.

• Journal of Navigation and Port Research
• /
• v.31 no.3 s.119
• /
• pp.263-269
• /
• 2007

The outbreaks of red tide were sporadic in the South Sea until 1994, but have become frequent and widespread in whole coastal waters of the South Sea and East Sea since 1995 For monitoring of red tide, many kinds of techniques such as remote sensing, GIS and fuzzy model system have been developed and applied. The purpose of this paper is to develop red tide monitoring system for collection of red tide data and biological-oceanography parameters using wireless sensor network. The wireless sensor network has been noticed as a core technology in order to realize ubiquitous computing. In this paper, we design red tide database using wireless sensor network and suggest red tide monitoring software and web-service for user and biological-oceanographer.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.6
• /
• pp.155-164
• /
• 2004

Recently, we have remarkable developments in intelligent robot systems. The remarkable features of intelligent robot are that it can track user and is able to doface recognition, which is vital for many surveillance-based systems. The advantage of face recognition compared with other biometrics recognition is that coerciveness and contact that usually exist when we acquire characteristics do not exist in face recognition. However, the accuracy of face recognition is lower than other biometric recognition due to the decreasing in dimension from image acquisition step and various changes associated with face pose and background. There are many factors that deteriorate performance of face recognition such as thedistance from camera to the face, changes in lighting, pose change, and change of facial expression. In this paper, we implement a new sliding active camera system to prevent various pose variation that influence face recognition performance andacquired frontal face images using PCA and HMM method to improve the face recognition. This proposed face recognition algorithm can be used for intelligent surveillance system and mobile robot system.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.14 no.7
• /
• pp.801-806
• /
• 2004

Although the technology for emotion recognition is important one which demanded in various fields, it still remains as the unsolved problem. Especially, it needs to develop the algorithm based on human facial image. In this paper, we propose the facial image analysis algorithm for emotion recognition. The proposed algorithm is composed as the facial image extraction algorithm and the facial component extraction algorithm. In order to have robust performance under various illumination conditions, the fuzzy color filter is proposed in facial image extraction algorithm. In facial component extraction algorithm, the virtual face model is used to give information for high accuracy analysis. Finally, the simulations are given in order to check and evaluate the performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.9 no.6
• /
• pp.55-61
• /
• 2009

This paper proposes a position estimation of a mobile robot based on USN(Ubiquitous Sensor Network) and encoder, and development of tele-operation system using Internet. USN used in experiments is based on ZigBee protocol and has location estimation engine which uses RSSI signal to estimate distance between nodes. By distortion the estimated distance using RSSI is not correct, compensation method is needed. We obtained fuzzy model to calculate more accurate distance between nodes and use encoder which is built in robot to estimate accurate position of robot. Based on proposed position estimation method, tele-operation system was developed. We show by experiment that proposed method is more appropriate for estimation of position and remote navigation of mobile robot through Internet.