• Journal of Korea Society of Industrial Information Systems
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• v.13 no.3
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• pp.23-30
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• 2008

A data hiding method based on least significant bit (LSB) substitution and multi-pixel differencing (MPD) is presented on the proposed method to improve the capacity of the hidden secret data and to provide an imperceptible visual quality. First, a sum of different values for four-pixel sub-block is calculated. The low value of the sum can be located on a smooth block and the high value is located on an edged block. The secret data are hidden into the cover image by LSB method in the smooth block, while MPD method in the edged block. The experimental results show that the proposed method has a higher capacity and maintains a good visual quality.

• The Journal of Korean Society for Radiation Therapy
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• v.13 no.1
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• pp.68-74
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• 2001

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.8
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• pp.24-34
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• 1992

A process control language is developed using function block configuration, to simplify software development for large scale process control systems, and to implement advanced control algorithms with ease. A function block parser and controller is implemented to be suitable for multi-loop control systems having hierachical structure. On-line change of controller parameter is possible, and inclusion of user defined function block is also possible. By adding plant model block, control performance can be checked in advance. Function blocks of the Smith Predicotor, auto-tuners are implemented to demonstrate usefulness of function block configuration.

• Korean Journal of Computational Design and Engineering
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• v.15 no.1
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• pp.1-10
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• 2010

Recently, a shipyard is making every effort to efficiently manage equipments of resources such as a gantry crane, transporter, and so on. So far block lifting scheduling of a gantry crane has been manually performed by a manager of the shipyard, and thus it took much time to get scheduling results and moreover the quality of them was not optimal. To improve this, a block lifting scheduling system of the gantry crane using optimization techniques was developed in this study. First, a block lifting scheduling problem was mathematically formulated as a multi-objective optimization problem, considering the minimization of travel distance at an idle state and wire replacement during block lifting. Then, to solve the problem, a meta-heuristic optimization algorithm based on the genetic algorithm was proposed. To evaluate the efficiency and applicability of the developed system, it was applied to an actual block lifting scheduling problem of the shipyard. The result shows that blocks can be efficiently lifted by the gantry crane using the developed system, compared to manual scheduling by a manager.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.2
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• pp.33-42
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• 1997

In this paper, a video coding method in wavelet transformed multi-resolution image using variable block sized motion estimation and multi-codebook is proposed. In the propoed method, the accuracy of motion estimation is increased by using variable block matching algorithm based on edge type of blocks which estimation is increased by using variable block matching algoritm based on edge type of blocks which is classified accoridng to the magnitude of wavelet coefficients in vertical subband and horizontal subband of the highest layer. Also, we increased the flexibility of bit allocation and decreased vector quantization error for motion compensated error transmission by using importance of each subband. Some experimental results confirm that he proposed mothod has fine reconstructed images without blocking effect at low bit rate, and especially reconstructs edges well to which human eyes are sensitive.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.647-648
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• 2008

Traditional FTL and flash file systems based of NAND flash memory may not be adaptively applied to new fusion flash memory which combines the advantages of NAND and NOR flash memory. In this paper, we propose a FTL performance measurement tool using Multi Block Erase function of fusion flash memory. The performance measurement tool shows that multi block erase function can be effectively utilized in performance enhancement of garbage collection for fusion flash memory.

• Journal of computational fluids engineering
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• v.21 no.1
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• pp.36-42
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• 2016

One of the obstacles on the grid generation for complex geometries with multi-block structured grids is the domain decomposition. In this paper, the domain decomposition for two-dimensional flow is studied using the flow characteristics. The potential flow equation with the source distribution on the panel surface is solved to extract the information of the flow. The current approach is applied to a two-dimensional cylinder and Bi-NACA0012 problems. The generated grids are applied to generic flow solvers and reasonable results are obtained. It can be concluded that the current methods is useful in the domain decomposition for the multi-block structured grid.

• Journal of the Society of Naval Architects of Korea
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• v.41 no.1
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• pp.15-22
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• 2004

Twin-skeg type stern shapes are recently adopted for very large commercial ships. However it is difficult to apply a CFD system to a hull form having twin-skeg, since grid topology around a twin-skeg type stern is more complicated than that of a conventional single-screw ship, or of an open-shaft type twin-screw ship with center-skeg. In the present study a surface mesh generator and a multi-block field grid generation program have been developed for twin-skeg type stern. Furthermore, multi-block flow solvers are utilized for potential and viscous flow analysis around a twin-skeg type stern The present computational system is applied to a 15,000TEU container ship with twin-skeg to prove the applicability. Wave profiles and wake distribution are calculated using the developed flow analysis tools and the results are compared with towing tank measurements.

• Industrial Engineering and Management Systems
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• v.11 no.4
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• pp.331-338
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• 2012

This research proposes a Pareto-based multi-objective optimization approach to class-based storage warehouse design, considering a two-block warehouse that operates under the class-based storage policy in a low-level, picker-to-part and narrow aisle warehousing system. A mathematical model is formulated to determine the number of aisles, the length of aisle and the partial length of each pick aisle to allocate to each product class that minimizes the travel distance and maximizes the usable storage space. A solution approach based on multiple objective particle swarm optimization is proposed to find the Pareto front of the problems. Numerical examples are given to show how to apply the proposed algorithm. The results from the examples show that the proposed algorithm can provide design alternatives to conflicting warehouse design decisions.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.3
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• pp.140-151
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• 2020

It is important to identify the location of ship hull blocks with exact block identification number when scheduling the shipbuilding process. The wrong information on the location and identification number of some hull block can cause low productivity by spending time to find where the exact hull block is. In order to solve this problem, it is necessary to equip the system to track the location of the blocks and to identify the identification numbers of the blocks automatically. There were a lot of researches of location tracking system for the hull blocks on the stockyard. However there has been no research to identify the hull blocks on the stockyard. This study compares the performance of 5 Convolutional Neural Network (CNN) models with multi-view image set on the classification of the hull blocks to identify the blocks on the stockyard. The CNN models are open algorithms of ImageNet Large-Scale Visual Recognition Competition (ILSVRC). Four scaled hull block models are used to acquire the images of ship hull blocks. Learning and transfer learning of the CNN models with original training data and augmented data of the original training data were done. 20 tests and predictions in consideration of five CNN models and four cases of training conditions are performed. In order to compare the classification performance of the CNN models, accuracy and average F1-Score from confusion matrix are adopted as the performance measures. As a result of the comparison, Resnet-152v2 model shows the highest accuracy and average F1-Score with full block prediction image set and with cropped block prediction image set.