• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.333-343
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• 2007

The auxiliary renewal function has an important role in analyzng queues in which the either of the inter-arrival time and the service time of customers is not exponential. As like the renewal function, the auxiliary renewal function is hard to compute although it can be defined theoretically. In this paper, we suggest two approximations for auxiliary renewal function and compare the two with the true value of auxiliary renewal function which can be computed in some special cases.

• Korean Journal of Mathematics
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• v.22 no.3
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• pp.529-552
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• 2014

As we know, some indices and data are strong influence to the price movement of some assets now, but not to another assets and in future. Thus we define some asset models for several time intervals; intraday, weekly, monthly, and yearly asset models. We define these asset models by using Brownian motion with volatility and Poisson process, and several deterministic functions(index function, twitter data function and big-jump simple function etc). In our asset models, these deterministic functions are the positive or negative levels of auxiliary indices, of analyzed data, and for imminent and extreme state(for example, financial shock or the highest popularity in the market). These functions determined by indices, twitter data and shocking news are a kind of one of speciality of our asset models. For reasonableness of our asset models, we introduce several real data, figurers and tables, and simulations. Perhaps from our asset models, for short-term or long-term investment, we can classify and reference many kinds of usual auxiliary indices, information and data.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.685-696
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• 2014

In this paper, we develop Bayesian inference of the finite population mean with the assumption of posterior linearity rather than normality of the superpopulation in the presence of auxiliary information under the balanced loss function. We compare the performance of the optimal Bayes estimator under the balanced loss function with ones of the classical ratio estimator and the usual Bayes estimator in terms of the posterior expected losses, risks and Bayes risks.

• Korean Institute of Interior Design Journal
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• v.21 no.1
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• pp.170-176
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• 2012

Korean living culture raised the users' needs for sub-kitchen. In the traditional Korean house, there was large space related to kitchen area for preparing food and stock big and bulky housing stuffs. As apartment housing became dominant as Korean dwelling, sub-kitchen has been planed in the balcony that is not included in the sales area. In this study, the case of the apartment housing in Esiapolis, Daegu was analyzed. To plan the user-oriented sub-kitchen, the consumer research was carried out. Consumers needed a pantry, more storage near the kitchen, and wanted to place washing machine and washing stand in a sub-kitchen. Sub-kitchens were planed to meet those consumers' needs. Through this case study and former studies analysis, sub-kitchen's function unit was derived: wash, storage, auxiliary work. By combining each function unit, sub-kitchen was classified into 3 types, wash & auxiliary work, wash & storage, and wash & storage & auxiliary work. For each sub-kitchen type, components of function units, available layouts, and minimum size were recommended.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.25 no.3
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• pp.107-116
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• 2021

We introduce optimization algorithms using Bregman Divergence for solving non-negative matrix factorization (NMF) problems. Bregman divergence is known a generalization of some divergences such as Frobenius norm and KL divergence and etc. Some algorithms can be applicable to not only NMF with Frobenius norm but also NMF with more general Bregman divergence. Matrix Factorization is a popular non-convex optimization problem, for which alternating minimization schemes are mostly used. We develop the Bregman proximal gradient method applicable for all NMF formulated in any Bregman divergences. In the derivation of NMF algorithm for Bregman divergence, we need to use majorization/minimization(MM) for a proper auxiliary function. We present algorithmic aspects of NMF for Bregman divergence by using MM of auxiliary function.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.4
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• pp.192-197
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• 2005

This paper presents a development of the auxiliary power supply simulator for a electric rolling stock. An auxiliary power supplies are required for operating air conditioning units, ventilation fans, lighting and battery charging. Traditionally this function has been fulfilled by Motor-Alternator sets. In recent years, high performance of semiconductor and micro processor, availability and price have made three phase voltage source inverters as an attractive alternative to MA Sets. From the baseline model of the SIV(Static InVerter) for electric rolling stock, we designed the scale down model of the auxiliary power supply simulator consisting of an IGBT three phase voltage source inverter. The auxiliary power supply simulator can be used educatory purpose for training efficiently about operating principles of SIV.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2310-2332
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• 2020

In recent years, deep learning techniques have achieved tremendous successes in natural language processing, speech recognition and image processing. Collaborative filtering(CF) recommendation is one of widely used methods and has significant effects in implementing the new recommendation function, but it also has limitations in dealing with the problem of poor scalability, cold start and data sparsity, etc. Combining the traditional recommendation algorithm with the deep learning model has brought great opportunity for the construction of a new recommender system. In this paper, we propose a novel collaborative recommendation model based on auxiliary stacked denoising autoencoder(ASDAE), the model learns effective the preferences of users from auxiliary information. Firstly, we integrate auxiliary information with rating information. Then, we design a stacked denoising autoencoder based collaborative recommendation model to learn the preferences of users from auxiliary information and rating information. Finally, we conduct comprehensive experiments on three real datasets to compare our proposed model with state-of-the-art methods. Experimental results demonstrate that our proposed model is superior to other recommendation methods.

• Journal of Power Electronics
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• v.19 no.2
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• pp.380-393
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• 2019

Traditional boost converters have difficulty realizing high efficiency and high voltage gain conversion due to 1) extremely large duty cycles, 2) high voltage and current stresses on devices, and 3) low conversion efficiency. Therefore, a function decoupling high voltage gain DC/DC converter composed of a DC transformer (DCX) and an auxiliary converter is proposed. The role of DCX is to realize fixed gain conversion with high efficiency, whereas the role of the auxiliary converter is to regulate the output voltage. In this study, different forms of combined high voltage gain converters are compared and analyzed, and a structure is selected for the function decoupling high voltage gain converter. Then, topologies and control strategies for the DCX and auxiliary converter are discussed. On the basis of the discussion, an optimal design method for circuit parameters is proposed, and design procedures for the DCX are described in detail. Finally, a 400 W experimental prototype based on the proposed optimal design method is built to verify the accuracy of the theoretical analysis. The measured maximum conversion efficiency at rated power is 95.56%.

• Korean Journal of Computational Design and Engineering
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• v.3 no.1
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• pp.57-67
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• 1998

Genetic programming (GP) is an extension of a genetic algoriths paradigm, deals with tree structures representing computer programs as individuals. In recent, there have been many research activities on applications of GP to various engineering problems including system identification, data mining, function approximation, and so forth. However, standard GP suffers from the lack of the estimation techniques for numerical parameters of the GP tree that is an essential element in treating various engineering applications involving real-valued function approximations. Unlike the other research activities, where nonlinear optimization methods are employed, I adopt the use of a weighted linear associative memory for estimation of these parameters under GP algorithm. This approach can significantly reduce computational cost while the reasonable accurate value for parameters can be obtained. Due to the fact that the GP algorithm is likely to fall into a local minimum, the GP algorithm often fails to generate the tree with the desired accuracy. This motivates to devise a group of additive genetic programming trees (GAGPT) which consists of a primary tree and a set of auxiliary trees. The output of the GAGPT is the summation of outputs of the primary tree and all auxiliary trees. The addition of auxiliary trees makes it possible to improve both the teaming and generalization capability of the GAGPT, since the auxiliary tree evolves toward refining the quality of the GAGPT by optimizing its fitness function. The effectiveness of this approach is verified by applying the GAGPT to the estimation of the principal dimensions of bulk cargo ships and engine torque of the passenger car.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.6
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• pp.1223-1229
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• 2009

The performance of Radial Basis Function Neural Networks depends on setting up the Radial Basis Functions over the input space which are the important design procedure of Radial Basis Function Neural Networks. The existing method to initialize the location of the radial basis functions over the input space is to use the conditional fuzzy C-means clustering. However, the researchers which are interested in the conditional fuzzy C-means clustering cannot get as good modeling performance as they expect because the conditional fuzzy C-means clustering cannot project the information which is extracted over the output space into the input space. To compensate the above mentioned drawback of the conditional fuzzy C-means clustering, we apply a fuzzy K-nearest neighbors approach to project the auxiliary information defined over the output space into the input space without lose of the information.