• Journal of Korean Institute of Industrial Engineers
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• v.7 no.1
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• pp.21-31
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• 1981

This paper develops a short term forecasting model for household electric power consumption in Seoul, which can be used for the effective planning and control of utility management. The model developed is based on exponentially weighted moving average model and incorporates monthly average temperature as an exogeneous factor so as to enhance its forecasting accuracy. The model is empirically compared with the Winters' three parameter model which is widely used in practice and the Box-Jenkins model known to be one of the most accurate short term forecasting techniques. The result indicates that the developed hybrid exponential model is better in terms of accuracy measured by average forecast error, mean squared error, and autocorrelated error.

• Journal of applied mathematics & informatics
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• v.22 no.1_2
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• pp.307-316
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• 2006

Incomplete LU factorization preconditioning techniques often have difficulty on indefinite sparse matrices. We present hybrid reordering strategies to deal with such matrices, which include new diagonal reorderings that are in conjunction with a symmetric nondecreasing degree algorithm. We first use the diagonal reorderings to efficiently search for entries of single element rows and columns and/or the maximum absolute value to be placed on the diagonal for computing a nonsymmetric permutation. To augment the effectiveness of the diagonal reorderings, a nondecreasing degree algorithm is applied to reduce the amount of fill-in during the ILU factorization. With the reordered matrices, we achieve a noticeable improvement in enhancing the stability of incomplete LU factorizations. Consequently, we reduce the convergence cost of the preconditioned Krylov subspace methods on solving the reordered indefinite matrices.

• Management Science and Financial Engineering
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• v.11 no.3
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• pp.19-45
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• 2005

Collaborative filtering is one of the most common methods that e-commerce sites and Internet information services use to personalize recommendations. Collaborative filtering has the advantage of being able to use even sparse evaluation data to predict preference scores for new products. To date, however, no in-depth investigation has been conducted on how the data sparsity effect in customers' evaluation data affects collaborative filtering-based recommendation performance. In this study, we analyzed the sparsity effect and used a hybrid method based on customers' evaluations and purchases collected from an online bookstore. Results indicated that recommendation performance decreased monotonically as sparsity increased, and that performance was more sensitive to sparsity in evaluation data rather than in purchase data. Results also indicated that the hybrid use of two different types of data (customers' evaluations and purchases) helped to improve the recommendation performance when evaluation data were highly sparse.

• Journal of Korean Society for Atmospheric Environment
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• v.29 no.4
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• pp.486-503
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• 2013

This paper has comprehensively reviewed fine dust control technology from emission sources. Owing to the stringent national regulation, domestic industries have made consistent efforts to develop the high efficiency facilities since 1960s. In these days, harmful particulate pollutants including dioxins and PAHs as well as $PM_{2.5}$ are also of critical interests in government and civic groups. In addition, simultaneous treatment of gas and particles is being widely studied. It is believed that hybrid facilities which integrate a few advanced equipment may meet the atmospheric guidelines.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.291-304
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• 2002

This article is concerned with an effective algorithm for the identification of multiple outliers in linear regression. It proposes a hybrid algorithm which employs the least median of squares estimator, instead of the least squares estimator, to construct an Initial clean subset in the stepwise forward search scheme. The performance of the proposed algorithm is evaluated and compared with the existing competitor via an extensive Monte Carlo simulation. The algorithm appears to be superior to the competitor for the most of scenarios explored in the simulation study. Particularly it copes with the masking problem quite well. In addition, the orthogonal decomposition and Its updating techniques are considered to improve the computational efficiency and numerical stability of the algorithm.

• KIEAE Journal
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• v.8 no.1
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• pp.93-98
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• 2008

As high rise buildings and large span spatial structures are constructed, new composite members and construction techniques are continuously developed. Wide flange steel beam can be easily constructed but the fire proofing protection is necessary and the cost is high. Nowadays environmental pollution of structures is becoming a big issue. The material of fire proofing protection is not allowed to use for structural members in several countries because it cab be a cause of environment pollution. Composite beam is a new hybrid beam system which is not needed a fire proofing protection process. Composite beam has better construction capacity than that of RC system and has more economic advantages than that of wide flange steel beam. In this paper, structural design guide lines of composite beam were provided to apply design and construction.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.11 no.4
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• pp.667-673
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• 2000

This paper presents the modeling method of nonlinear circuits with lumped elements by using a hybrid Haar -wavelet MRTD/FDTD techniques. To analyze nonlinear circuits with lumped elements, the Haar-wavelet MRTD scheme is applied to the entire structure of interest and the conventional FDTD scheme is locally used to describe the characteristics of the lumped elements. To validate the scheme, microstrip structure with lumped elements and a single diode mixer are simulated.

• Korean Journal of Plant Resources
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• v.19 no.3
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• pp.401-404
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• 2006

In order to select the seeding machine for mechanizing cultivation of rapeseed in southern areas of Korea, three different seeding machines, ridge rotary, power tiller ridge rotary, tractor ridge rotary were used for sowing one of the high yielding rapeseed cv. Hybrid with five different seeding methods. Seeding of ridge rotary was reduced the seeding effort with 45% and yield components such as plant height, ear length, number of branches and pods, pod length and seed setting rate were higher. The seeding of ridge rotary also was showed highest seed yield. On the basis of time requirement for seeding, vegetative and yield parameters ridge rotary seeding machine was a suitable seeding machine for rapeseed cultivation at the southern area of Korea.

• KIEE International Transaction on Systems and Control
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• v.2D no.2
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• pp.53-58
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• 2002

In this paper, various aspects in magnetoencephalography (MEG) source localization are studied. To minimize the errors in experimental data, an approximation technique using a polynomial function is proposed. The simulation shows that the proposed technique yields more accurate results. To improve the convergence characteristics in the optimization algorithm, a hybrid algorithm of evolution strategy and sensitivity analysis is applied to the neuromagnetic inverse problem. The effectiveness of the hybrid algorithm is verified by comparison with conventional algorithms. In addition, an artificial neural network (ANN) is applied to find an initial source location quickly and accurately. The simulation indicates that the proposed technique yields more accurate results effectively.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1297-1300
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• 1993

Machine learning in an uncertain or unknown environment is of vital interest to those working with intelligent systems. The ability to garner new information, process it, and increase the understanding/ capability of the machine is crucial to the performance of autonomous systems. The field of artificial intelligence provides two major approaches to the problem of knowledge engineering-expert systems and neural networks. Harnessing the power of these two techniques in a hybrid, cooperating system holds great promise.