• 산업경영시스템학회지
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• 제32권2호
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• pp.85-93
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• 2009

GOMS model is a cognitive modeling method of human performance based on Goal, Operators, Methods, Selection rules. GOMS model was originally designed for desktop environment so that it is difficult for GOMS model to be implemented into the mobile environment. In addition, GOMS model would be inaccurate because the original GOMS model was based on serial processing, excluding one of most important human information processing characteristics, parallel processing. Therefore this study was designed to propose a modified GOMS model including mobile computing and parallel processing. In order to encompass mobile environment, an operator of 'look for' was divided into 'visual move to' and 'recognize' whereas 'point to' and 'click' were combined into 'tab.' The results showed that newly introduced operators were necessary to estimate more accurate mobile computing behaviors. In conclusion, modified-GOMS model could predict human performance more accurately than the original GOMS model in the mobile computing environment.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.571-573
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• 1995

This paper presents a method for optimal dispatch which minimizes transmission losses and improves voltage profile of power systems using genetic algorithm based on the mechanism of natural genetics and natural selection. The constraints are VAR sources(transformer tap, generator voltage magnitude and shunt capacitor/reactor), load bus voltages and generator reactive power. Real variable-based genetic algorithms which can save coding times and maintain the accuracy are applied for optimal dispatch of reactive power. The genes of genetic algorithm consisted of integers for considering discrete VAR sources. A efficient operator for crossover is proposed to consider the effect of close genes. The algorithm proposed can apply to problems for large scale power systems with multi-variables and complex nonlinear functions efficiently. The proposed method is applied to IEEE 30 buses model system to show its effectiveness.

• Computers and Concrete
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• 제24권1호
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• pp.7-17
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• 2019

The complex phenomenon of the bond formation in geopolymer is not well understood and therefore, difficult to model. This paper present applied statistical models for evaluating the compressive strength of geopolymer. The applied statistical models studied are divided into three different categories - linear regression [least absolute shrinkage and selection operator (LASSO) and elastic net], tree regression [decision and bagging tree] and kernel methods (support vector regression (SVR), kernel ridge regression (KRR), Gaussian process regression (GPR), relevance vector machine (RVM)]. The performance of the methods is compared in terms of error indices, computational effort, convergence and residuals. Based on the present study, kernel based methods (GPR and KRR) are recommended for evaluating compressive strength of Geopolymer concrete.

• 전기학회논문지
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• 제68권1호
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• pp.145-152
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• 2019

A composite guidance scheme is proposed for air-to-ground anti-tank missiles launched from an airborne platform. Long-range anti-tank missiles usually use a fiber optic line (FOL) for the datalink between an operator and the missile to obtain real-time target information and to command the missile. Also, impact angle control is used to maximize the warhead effectiveness, but it should be carefully implemented due to interference between the launch platform and the FOL. Thus, the proposed guidance scheme takes into account both impact angle and FOL constraints. Under system lag and acceleration limits, a selection method of guidance gains and calculation logic of the maximum achievable impact angle are proposed for a guideline of practical implementation. The performance of the proposed guidance scheme is investigated by nonlinear simulations with various engagement conditions.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.157-157
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• 2018

A reliable and accurate downscaling model which can provide climate change information, obtained from global climate models (GCMs), at finer resolution has been always of great interest to researchers. In order to achieve this model, linear methods widely have been studied in the past decades. However, nonlinear methods also can be potentially beneficial to solve downscaling problem. Therefore, this study explored the applicability of some nonlinear machine learning techniques such as neural network (NN), extreme learning machine (ELM), and ELM autoencoder (ELM-AE) as well as a linear method, least absolute shrinkage and selection operator (LASSO), to build a reliable temperature downscaling model. ELM is an efficient learning algorithm for generalized single layer feed-forward neural networks (SLFNs). Its excellent training speed and good generalization capability make ELM an efficient solution for SLFNs compared to traditional time-consuming learning methods like back propagation (BP). However, due to its shallow architecture, ELM may not capture all of nonlinear relationships between input features. To address this issue, ELM-AE was tested in the current study for temperature downscaling.

• Communications for Statistical Applications and Methods
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• 제28권1호
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• pp.21-37
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• 2021

There is a direct connection between linear discriminant analysis (LDA) and linear regression since the direction vector of the LDA can be obtained by the least square estimation. The connection motivates the penalized LDA when the model is high-dimensional where the number of predictive variables is larger than the sample size. In this paper, we study the penalized LDA for a class of penalties, called the moderately clipped LASSO (MCL), which interpolates between the least absolute shrinkage and selection operator (LASSO) and minimax concave penalty. We prove that the MCL penalized LDA correctly identifies the sparsity of the Bayes direction vector with probability tending to one, which is supported by better finite sample performance than LASSO based on concrete numerical studies.

• 국제학술발표논문집
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• The 2th International Conference on Construction Engineering and Project Management
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• pp.739-748
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• 2007

The objective of this research study is to develop the permutation-based genetic algorithm for solving the resource-constrained project scheduling problem in construction engineering by incorporating elitism into genetic algorithm. A key aspect of the algorithm was the development of the elitist roulette selection operator to preserve the best individual solution for the next generation so the improved solution can be obtained. Another notable characteristic is the application of the parallel schedule generation scheme to generate a feasible solution to the problem. Case studies with a standard test problem were presented to demonstrate the performance and accuracy of the algorithm. The computational results indicate that the proposed algorithm produces reasonably good solutions for the resource-constrained project scheduling problem.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제15권2호
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• pp.102-110
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• 2015

The main source of noise in computed tomography (CT) images is a quantum noise, which results from statistical fluctuations of X-ray quanta reaching the detector. This paper proposes a neural network (NN) based hybrid filter for removing quantum noise. The proposed filter consists of bilateral filters (BFs), a single or multiple neural edge enhancer(s) (NEE), and a neural filter (NF) to combine them. The BFs take into account the difference in value from the neighbors, to preserve edges while smoothing. The NEE is used to clearly enhance the desired edges from noisy images. The NF acts like a fusion operator, and attempts to construct an enhanced output image. Several measurements are used to evaluate the image quality, like the root mean square error (RMSE), the improvement in signal to noise ratio (ISNR), the standard deviation ratio (MSR), and the contrast to noise ratio (CNR). Also, the modulation transfer function (MTF) is used as a means of determining how well the edge structure is preserved. In terms of all those measurements and means, the proposed filter shows better performance than the guided filter, and the nonlocal means (NLM) filter. In addition, there is no severe restriction to select the number of inputs for the fusion operator differently from the neuro-fuzzy system. Therefore, without concerning too much about the filter selection for fusion, one could apply the proposed hybrid filter to various images with different modalities, once the corresponding noise characteristics are explored.

• 의료ㆍ복지 건축 : 한국의료복지건축학회 논문집
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• 제24권2호
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• pp.65-77
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• 2018

Purpose: By identifying the contents of the "Empty Home Utilization & Community Revitalization Model Project" which is a pilot project of empty homes back into use in Kyoto City and analyzing the characteristics of the case housing, the purpose of this study is to find out implications and suggest improvements for the domestic empty home utilization project. Methods: The scope of the survey covers seven project sites in the period from 2014 to 2016. The research method is to derive the physical and operational characteristics of the project, through the literature reviews related to bringing empty homes back into use in Kyoto City and the field survey including the space measurement and the operator interview. Results: First, in order to succeed in bringing empty homes back into use for community revitalization, the selection process of the project and the role of the public in and after supporting the project are important. Second, the important features that are required as a physical characteristic of the project are an advantage in location and an interactive space that is available at all time like a community cafe at the entrance. Third, as an operational characteristic of the empty home utilization project, it is advantageous for the local residents to participate as a business actor or an operator, and it is the continuous use of residents and outsiders by implementing an operating program that is suitable for the characteristics of the local community. Implications: The physical and operational activation factors to bring empty homes back into use need to be included as the project screening standards. And it is necessary for the public to develop the Intermediate Support Organization and to participate in linking with residents in order to carry out the empty homes utilization project effectively.

• Journal of Biosystems Engineering
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• 제40권1호
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• pp.28-34
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• 2015

Purpose: This study aimed to install an RTK-GPS (Real Time Kinematic-Global Positioning System) and IMU (Inertial Measurement Unit) on a tractor used in a farm to measure positions, pasture topography, posture angles, and vibration accelerations, translate the information into maps using the GIS, analyze the characteristics of grass harvesting work, and establish new technologies and construction standards for pasture infrastructure improvement based on the analyzed data. Method: Tractor's roll, pitch, and yaw angles and vibration accelerations along the three axes during grass harvesting were measured and a GIS map prepared from the data. A VRS/RTK-GPS (MS750, Trimble, USA) tractor position measuring system and an IMU (JCS-7401A, JAE, JAPAN) tractor vibration acceleration measuring systems were mounted on top of a tractor and below the operator's seat to obtain acceleration in the direction of progression, transverse acceleration, and vertical acceleration at 10Hz. In addition, information on regions with bad workability was obtained from an operator performing grass harvesting and compared with information on changes in tractor posture angles and vibration acceleration. Results: Roll and pitch angles based on the y-axis, the direction of forward movements of tractor coordinate systems, changed by at least $9-13^{\circ}$ and $8-11^{\circ}$ respectively, leading to changes in working postures in the central and northern parts of the pasture that were designated as regions with bad workability during grass harvesting. These changes were larger than those in other regions. The synthesized vectors of the vibration accelerations along the y-axis, the x-axis (transverse direction), and the z-axis (vertical direction) were higher in the central and northwestern parts of the pasture at 3.0-4.5 m/s2 compared with other regions. Conclusions: The GIS map developed using information on posture angles and vibration accelerations by position in the pasture is considered sufficiently utilizable as data for selection of construction locations for pasture infrastructure improvement.