• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.7
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• pp.846-853
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• 2018

In this paper, an improved model predictive control (MPC) method is proposed, which reduces the amount of calculations caused by the increased number of candidate voltage vectors with the increased voltage level in multi-level inverters. When the conventional MPC method is used for multi-level inverters, all candidate voltage vectors are considered to predict the next-step current value. However, in the case that the sampling time is short, increased voltage level makes it difficult to consider the all candidate voltage vectors. In this paper, the improved MPC method which can get a fast transient response is proposed with a small amount of the computation by adding new candidate voltage vectors that are set to find the optimal vector. As a result, the proposed method shows faster transient response than the method that considers the adjacent vectors and reduces the computational burden compared to the method that considers the whole voltage vector. the performance of the proposed method is verified through simulations and experiments.

• Industrial Engineering and Management Systems
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• v.14 no.1
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• pp.1-10
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• 2015

In this paper, we consider multi-item inventory management. When managing a multi-item inventory, we coordinate replenishment orders of items supplied by the same supplier. The associated problem is called the joint replenishment problem (JRP). One often-used approach to the JRP is to apply a can-order policy. Under a can-order policy, some items are re-ordered when their inventory level drops to or below their re-order level, and any other item with an inventory level at or below its can-order level can be included in this order. In the present paper, we propose a method for finding the optimal parameter of a can-order policy, the can-order level, for each item in a lost-sales model. The main objectives in our model are minimizing the number of ordering, inventory, and shortage (i.e., lost-sales) respectively, compared with the conventional JRP, in which the objective is to minimize total cost. In order to solve this multi-objective optimization problem, we apply a genetic algorithm. In a numerical experiment using actual shipment data, we simulate the proposed model and compare the results with those of other methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.11a
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• pp.147-148
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• 2015

This paper proposes a tile level rate control for High Efficiency Video Coding (HEVC). The proposed tile level rate control is designed by considering the multi-core platform of tile in HEVC. The proposed tile level rate control allocates the number of bits for each tile based on the predetermined weight generated from the current picture level rate control. According to the experimental results, the proposed tile level rate control for HEVC on multi-core platform loses negligibly the bitrate accuracy about 0.07% on average over the reference software HM-14.0.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.3
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• pp.90-96
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• 2002

RBAC(Role-Based Access Control) is an access control method based on the user's role and it provides more flexibility on the various computer and network security fields. But, RBAC models consider only users for roles or permissions, so for the purpose of exact access control within real application systems, it is necessary to consider additional subjects and objects. In this paper, we propose an Extended RBAC model, $ERBAC_3$, for access control of multi-level security system by adding users, subjects, objects and roles level to RBAC, which enables multi-level security control. 

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.176-190
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• 2018

This paper presents a novel convolutional neural network based multi-feature fusion learning method for non-rigid 3D model retrieval, which can investigate the useful discriminative information of the heat kernel signature (HKS) descriptor and the wave kernel signature (WKS) descriptor. At first, we compute the 2D shape distributions of the two kinds of descriptors to represent the 3D model and use them as the input to the networks. Then we construct two convolutional neural networks for the HKS distribution and the WKS distribution separately, and use the multi-feature fusion layer to connect them. The fusion layer not only can exploit more discriminative characteristics of the two descriptors, but also can complement the correlated information between the two kinds of descriptors. Furthermore, to further improve the performance of the description ability, the cross-connected layer is built to combine the low-level features with high-level features. Extensive experiments have validated the effectiveness of the designed multi-feature fusion learning method.

• Nuclear Engineering and Technology
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• v.50 no.8
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• pp.1217-1233
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• 2018

Following a surge of interest in multi-unit risk in the last few years, many recent studies have suggested methods for multi-unit probabilistic safety assessment (MUPSA) and addressed several related aspects. Most of the existing studies though focused on two-unit nuclear power plant (NPP) sites or used rather simplified probabilistic safety assessment (PSA) models to demonstrate the proposed approaches. When considering an NPP site with three or more units, some approaches are inapplicable or yield very conservative results. Since the number of such sites is increasing, there is a strong need to develop and validate practical approaches to the related MUPSA. This article provides several detailed approaches that are applicable to multi-unit Level 1 PSA for sites with up to six or more reactor units. To validate the approaches, a multi-unit Level 1 PSA model is developed and the site core damage frequency is estimated for each of four representative multi-unit initiators, as well as for the case of a simultaneous occurrence of independent single-unit initiators in multiple units. For this purpose, an NPP site with six identical OPR-1000 units is considered, with full-scale Level 1 PSA models for a specific OPR-1000 plant used as the base single-unit models.

• Journal of Korean Institute of Industrial Engineers
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• v.1 no.2
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• pp.27-38
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• 1975

This paper explores a quantitative decision-making system for planning production, inventories and work-force in a multi-item production system. The Multi-item Parametric Decision Rule (MPDR) model, which assumes the existence of two types of linear feed-back rules, one for work-force level and one for production rates, is basically an extension of the existing method of Parametric Production Planning (PPP) proposed by C.H. Jones. The MPDR model, however, explicitly considers the effect of manufacturing progress and other factors such as employee turn-over, difference in work-days between month etc., and it also provides decision rules for production rates of individual items. First, the cost relations of the production system are estimated in terms of mathematical functions, and then decision rules for work-force level and production rates of individual items are establised based upon the estimated objective cost function. Finally, a direct search technique is used to find a set of parameters which minimizes the total cost of the objective function over a specified planning horizon, given estimates of future demands and initial values of inventories and work-force level. As a case problem, a hypothetical decision rule is developed for a particular firm (truck assembly factory).

• Journal of the Korean Society for Precision Engineering
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• v.28 no.4
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• pp.482-489
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• 2011

In practical design process, designer needs to find an optimal solution by using full factorial discrete combination, rather than by using optimization algorithm considering continuous design variables. So, ANOVA(Analysis of Variance) based on an orthogonal array, i.e. Taguchi method, has been widely used in most parts of industry area. However, the Taguchi method is limited for the shape optimization by using CAE, because the multi-level and multi-objective optimization can't be carried out simultaneously. In this study, a combined method was proposed taking into account of multi-level computational orthogonal array and TOPSIS(Technique for Order preference by Similarity to Ideal Solution), which is known as a classical method of multiple attribute decision making and enables to solve various decision making or selection problems in an aspect of multi-objective optimization. The proposed method was applied to a case study of the multi-level shape optimization of lower arm used to automobile parts, and the design space was explored via an efficient application of the related CAE tools. The multi-level shape optimization was performed sequentially by applying both of the neural network model generated from seven-level four-factor computational orthogonal array and the TOPSIS. The weight and maximum stress of the lower arm, as the objective functions for the multi-level shape optimization, showed an improvement of 0.07% and 17.89%, respectively. In addition, the number of CAE carried out for the shape optimization was only 55 times in comparison to full factorial method necessary to 2,401 times.

• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.699-707
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• 2015

Although digit character recognition has got a significant improvement in recent years, it is still challenging to achieve satisfied result if the data contains an amount of distracting factors. This paper proposes a novel digit character recognition approach using a multi-layer hierarchical model, Hybrid Restricted Boltzmann Machines (HRBMs), which allows the learning architecture to be robust to background distracting factors. The insight behind the proposed model is that useful high-level features appear more frequently than distracting factors during learning, thus the high-level features can be decompose into hybrid hierarchical structures by using only small label information. In order to extract robust and compact features, a stochastic 0-1 layer is employed, which enables the model's hidden nodes to independently capture the useful character features during training. Experiments on the variations of Mixed National Institute of Standards and Technology (MNIST) dataset show that improvements of the multi-layer hierarchical model can be achieved by the proposed method. Finally, the paper shows the proposed technique which is used in a real-world application, where it is able to identify digit characters under various complex background images.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.9C
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• pp.880-886
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• 2009

Recently, the memory is used in many electronic devices, thus, the many researchers make a study of the memory. To increase a storage capacity per memory block, the researchers study for reducing the fabrication process of memory and multi-level cell memory which is storing more than 2-bits in a cell. However, the multi-level cell memory has low bit-error rates by various noises. In this paper, we study the noise of multi-level cell memory, and we propose the channel model of multi-level cell memory.