• Journal of Navigation and Port Research
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• v.36 no.1
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• pp.51-58
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• 2012

In spite of today's rapid growth of world cruise industry, little academic attention has yet been given to the decision making problems for cruise operations. This research deals with strategic cruise itinerary planning that any cruise company should face. Increasing demands for international itineraries and redeployments of cruise ships are adding complexity to the itinerary planning. A slight modification of the conventional PERT/CPM network is adopted. to cope with this complexity systematically. By this, the concept of candidate itinerary network is suggested for each cruise ship. To integrate these candidate itinerary networks for each ship in a single framework, an integer programming model has been developed to find the optimal itinerary planning for any fleet of cruise ships. A numerical example, based on real cruise itinerary practices, is tested to validate and interpret the model.

• The KIPS Transactions:PartD
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• v.9D no.2
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• pp.215-226
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• 2002

Considering the rapid process of media's breakthrough and diverse patterns of consumptions's analysis, a uniform analysis might be much rooms to be desired for interpretation of new phenomena. In special, the products happening intensive sails on around an anniversary or fresh food have the restricted marketing hours. Moreover, traditional association rule discovery algorithms might not be appropriate for analysis of sales pattern given in a specific time because existing approaches require iterative scan operation to find association rule in large scale transaction databases. in this paper, we propose an incremental candidate set management model based on twin-hashing technique to find association rule in special sales pattern using database trigger and stored procedure. We also prove performance of the proposed model through implementation and experiment.

• Korean Journal of Construction Engineering and Management
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• v.10 no.6
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• pp.127-134
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• 2009

This study aims to provide an optimal model for the layout of multiple tower cranes and material stockyards which have multiple candidate positions. In a high-rise building construction, the positional allocation of tower cranes and material stockyard has an effect on the travel time of material hauling. In addition, in case of using multiple tower cranes, specific location of a tower crane allocated to each material determines the efficiency of the works. Current optimal model limited to the optimization of position of single tower crane and material stockyards. This study suggests optimal model both for the positions of multiple tower cranes and material stockyards. Layout of multiple tower cranes requires additional allocation of each crane to each material hauling and control on the minimum distance between tower cranes. This optimization model utilizes genetic algorithm to deal with complex interaction on the candidate positions of multiple tower cranes, material stockyards, and types of materials. In order to identify its utility, case study was performed.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.3
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• pp.348-355
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• 2021

Hantaan virus(HTNV) causes hemorrhagic fever with renal syndrome(HFRS) with a case fatality rate ranging from <1 to 15 % in human. Hantavax^Ⓡ is a vaccine against the Hantavirus, which has been conditionally approved by the Ministry of Food and Drug Safety(MFDS). However, only 50 % of volunteers had neutralizing antibodies 1 year following the boost. Effective antiviral treatments against HTNV infection are limited. Hantaviruses generally cause asymptomatic infection in adult mice. On the other hand, infection of suckling and newborn mice with hantaviruses causes lethal neurological diesease or persistant infection, which is different from the disease in humans. The development of vaccines and antiviral strategies for HTNV has been partly hampered by the lack of an efficient lethal mouse model to evaluate the efficacy of the candidate vaccines or antivirals. In this report, we established a lethal mouse model for HTNV, which may facilitate in vivo studies on the evaluation of candidate drugs against HTNV. The median lethal dose value of HTNV was calculated by probit analysis of deaths occurring within two weeks. Five groups of ten ICR mice were injected intracranially with serial 2-fold dilutions (from 50 to 3.125 PFU/head) of HTNV. Mice injected with HTNV began to die at 8 days post-infection. The lethal dose required to kill 50 % of the mice (LD₅₀) was calculated to be 2.365 PFU/head.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.2
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• pp.85-90
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• 2021

A ADAS(Advanced Driver Assistance System) for the safe driving is an important area in autonumous car. Specially, a ADAS software using an image sensors attached in previous car is low in building cost, and utilizes for various purpose. A algorithm for detecting the break-lamp from the tail-lamp of preceding vehicle is proposed in this paper. This method can perceive the driving condition of preceding vehicle. Proposed method uses the YOLO techinicque that has a excellent performance in object tracing from real scene, and extracts the intensity variable region of break-lamp from HSV image of detected vehicle ROI(Region Of Interest). After detecting the candidate region of break-lamp, each isolated region is labeled. The break-lamp region is detected finally by using the proposed selective-attention model that percieves the shape-similarity of labeled candidate region. In order to evaluate the performance of the preceding vehicle break-lamp detection system implemented in this paper, we applied our system to the various driving images. As a results, implemented system showed successful results.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.5
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• pp.437-450
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• 2014

In this study, we developed a methodology to determine the reference parameter for an aircraft impact induced risk assessment of nuclear power plant (NPP) using finite element impact analysis of containment building. The target structure used to develop the method of reference parameter selection is one of the typical Korean PWR type containment buildings. We composed a three-dimensional finite element model of the containment building. The concrete damaged plasticity model was used for the concrete material model. The steels in the tendon, rebar, and liner were modeled using the piecewise-linear stress-strain curves. To evaluate the correlations between structural response and each candidate parameter, we developed Riera's aircraft impact force-time history function with respect to the variation of the loading parameters, i.e., impact velocity and mass of the remaining fuel. For each force-time history, the type of aircraft is assumed to be a Boeing 767 model. The variation ranges of the impact velocity and remaining fuel percentage are 50 to 200m/s, and 30 to 90%, respectively. Four parameters, i.e., kinetic energy, total impulse, maximum impulse, and maximum force are proposed for candidates of the reference parameter. The wellness of the correlation between the reference parameter and structural responses was formulated using the coefficient of determination ($R^2$). From the results, we found that the maximum force showed the highest $R^2$ value in most responses in the materials. The simplicity and intuitiveness of the maximum force parameter are also remarkable compared to the other candidate parameters. Therefore, it can be concluded that the maximum force is the most proper candidate for the reference parameter to assess the aircraft impact induced risk of NPPs.

• Journal of the Optical Society of Korea
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• v.18 no.4
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• pp.317-329
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• 2014

In vision measurement systems based on structured light, the key point of detection precision is to determine accurately the central position of the projected laser line in the image. The purpose of this research is to extract laser line centers based on a decision function generated to distinguish the real centers from candidate points with a high recognition rate. First, preprocessing of an image adopting a difference image method is conducted to realize image segmentation of the laser line. Second, the feature points in an integral pixel level are selected as the initiating light line centers by the eigenvalues of the Hessian matrix. Third, according to the light intensity distribution of a laser line obeying a Gaussian distribution in transverse section and a constant distribution in longitudinal section, a normalized model of Hessian matrix eigenvalues for the candidate centers of the laser line is presented to balance reasonably the two eigenvalues that indicate the variation tendencies of the second-order partial derivatives of the Gaussian function and constant function, respectively. The proposed model integrates a Gaussian recognition function and a sinusoidal recognition function. The Gaussian recognition function estimates the characteristic that one eigenvalue approaches zero, and enhances the sensitivity of the decision function to that characteristic, which corresponds to the longitudinal direction of the laser line. The sinusoidal recognition function evaluates the feature that the other eigenvalue is negative with a large absolute value, making the decision function more sensitive to that feature, which is related to the transverse direction of the laser line. In the proposed model the decision function is weighted for higher values to the real centers synthetically, considering the properties in the longitudinal and transverse directions of the laser line. Moreover, this method provides a decision value from 0 to 1 for arbitrary candidate centers, which yields a normalized measure for different laser lines in different images. The normalized results of pixels close to 1 are determined to be the real centers by progressive scanning of the image columns. Finally, the zero point of a second-order Taylor expansion in the eigenvector's direction is employed to refine further the extraction results of the central points at the subpixel level. The experimental results show that the method based on this normalization model accurately extracts the coordinates of laser line centers and obtains a higher recognition rate in two group experiments.

• Transactions of the Korean Society of Mechanical Engineers
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• v.7 no.4
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• pp.451-460
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• 1983

Computational four-equation turbulence model is developed and is applied to predict twodimensional unsteady thermal surface discharge into a reservoir. Turbulent stresses and heat fluxes in the momentum and energy equations are determined from transport equations for the turbulent kinetic energy (R), isotropic rate of kinetic energy dissipation (.epsilon.), mean square temperature variance (theta. over bar $^{2}$), and rate of destruction of the temperature variance (.epsilon. $_{\theta}$). Computational results by four-equation model are favorably compared with those obtained by an extended two-equation model. Added advantage of the four-equation model is that it yields quantitative information about the ratio between the velocity time scale and the thermal time scale and more detailed information about turbulent structure. Predicted time scale ratio is within experimental observations by others. Although the mean velocity and temperature fields are similarly predicted by both models, it is found that the four-equation model is preferably candidate for prediction of highly buoyant turbulent flows.

• KDI Journal of Economic Policy
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• v.31 no.1
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• pp.27-69
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• 2009

This paper studies the logic of fiscal constraints and fiscal autonomy in a political agency model with both moral hazard and adverse selection. The electoral process not only disciplines incumbents who may act against the public interest but also opts in politicians who are most likely to act along voters' interests. We characterize perfect Bayesian equilibria under shared tax system and fiscal autonomy with fiscal constraints for local public good provision. It is shown that the local voters' expected welfare under fiscal autonomy is higher than under shared tax system if the same fiscal constraints are applied. In order to examine the effects of party's candidate selection processes on the behavior of local politician and national politician, we extend the model to an environment where local politician can compete for the candidacy of national assembly with incumbent national politician. If local politician wins majority of votes against incumbent national politician, then he can move on to serve as a national politician. Otherwise, his political career will end as a local politician. It is the gist of this primary system portrayed by this setup that local politician and national politician compete to garner more votes. Therefore, primary system as a candidate selection mechanism enhances local residents' welfare compared to top-down candidate selection processes.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.1-8
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• 2013

Data mining techniques are used to find important and meaningful information from huge databases, and pattern mining is one of the significant data mining techniques. Pattern mining is a method of discovering useful patterns from the huge databases. Frequent pattern mining which is one of the pattern mining extracts patterns having higher frequencies than a minimum support threshold from databases, and the patterns are called frequent patterns. Traditional frequent pattern mining is based on a single minimum support threshold for the whole database to perform mining frequent patterns. This single support model implicitly supposes that all of the items in the database have the same nature. In real world applications, however, each item in databases can have relative characteristics, and thus an appropriate pattern mining technique which reflects the characteristics is required. In the framework of frequent pattern mining, where the natures of items are not considered, it needs to set the single minimum support threshold to a too low value for mining patterns containing rare items. It leads to too many patterns including meaningless items though. In contrast, we cannot mine any pattern if a too high threshold is used. This dilemma is called the rare item problem. To solve this problem, the initial researches proposed approximate approaches which split data into several groups according to item frequencies or group related rare items. However, these methods cannot find all of the frequent patterns including rare frequent patterns due to being based on approximate techniques. Hence, pattern mining model with multiple minimum supports is proposed in order to solve the rare item problem. In the model, each item has a corresponding minimum support threshold, called MIS (Minimum Item Support), and it is calculated based on item frequencies in databases. The multiple minimum supports model finds all of the rare frequent patterns without generating meaningless patterns and losing significant patterns by applying the MIS. Meanwhile, candidate patterns are extracted during a process of mining frequent patterns, and the only single minimum support is compared with frequencies of the candidate patterns in the single minimum support model. Therefore, the characteristics of items consist of the candidate patterns are not reflected. In addition, the rare item problem occurs in the model. In order to address this issue in the multiple minimum supports model, the minimum MIS value among all of the values of items in a candidate pattern is used as a minimum support threshold with respect to the candidate pattern for considering its characteristics. For efficiently mining frequent patterns including rare frequent patterns by adopting the above concept, tree based algorithms of the multiple minimum supports model sort items in a tree according to MIS descending order in contrast to those of the single minimum support model, where the items are ordered in frequency descending order. In this paper, we study the characteristics of the frequent pattern mining based on multiple minimum supports and conduct performance evaluation with a general frequent pattern mining algorithm in terms of runtime, memory usage, and scalability. Experimental results show that the multiple minimum supports based algorithm outperforms the single minimum support based one and demands more memory usage for MIS information. Moreover, the compared algorithms have a good scalability in the results.