• Journal of Ocean Engineering and Technology
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• v.18 no.6 s.61
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• pp.79-83
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• 2004

The curved blocks which compose the bow and stem of a ship contain many skewed joints that are inclined horizontally and vertically. Most of these joints have a large fitness error and are continuously changing their form and are not easily accessible. The welding position and parameter values should be appropriately set in correspondence to the shape and the inclination of the joints. The welding parameters such as current, voltage, travel speed, and melting rate, are related to each other and their values must be in a specific limited range for the sound welding. These correlations and the ranges are dependent up on the kind and size of wire, shielding gas, joint shape and fitness. To determine these relationships, extensive welding experiments were performed. The experimental data were processed using several information processing technologies. The regression method was used to determine the relationship between current voltage, and deposition rate. When a joint is inclined, the weld bead should be confined to a the limited size, inorder to avoid undercut as well as overlap due to flowing down of molten metal by gravity. The dependency of the limited weld size which is defined as the critical deposited area on various factors such as the horizontally and vertically inclined angle of the joint, skewed angle of the joint, up or down welding direction and weaving was investigated through a number of welding experiments. On the basis of this result, an ANN system was developed to estimate the critical deposited area. The ANN system consists of a 4 layer structure and uses an error back propagation learning algorithm. The estimated values of the ANN were validated using experimental values.

• Journal of Distribution Science
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• v.13 no.1
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• pp.7-18
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• 2015

Purpose - This study integrates a corporate competition system with a bird predation system to examine how organizational strategic adjustment capacity influences firm performance. By proving the prominent effects on performance, a financial vector is constructed to represent corporate strategic adjustment results, and an operation capacity vector is constructed, which can be categorized as a parameter for locating birds. All these works help us to propose a new method of investment, the portfolio decision model based on the strategic adjustment capacity. Research design, data, and methodology - Strategic adjustment capacity can be decomposed into three aspects: the organizational learning capacity from the top firms, the extent to which firms maintainor rely on the best operational capacity vector in history, and the ability to eliminate the disadvantages or retain the advantages of the operation capacity vector from the previous year. The method of solving cyclic equations is designed to evaluate strategic adjustment. Firms manufacturing specialized equipment are chosen to test the effects of the strategic adjustment capacity on three aspects of firm performance. Results - There is a positive correlation between the capacity to learn from the best firms and performance improvement. The relationship between the dependence or maintenance of a firm's advantages and performance improvement is a U-shape curve, and there is no significant effect of inertial control on performance improvement. Conclusions - A firm's competition system is a sophisticated adaptation, and competitive advantage and performance can be investigated based on the principles of competition in nature.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.3
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• pp.395-400
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• 2011

In this paper, we propose an study of the facial emotion recognition considering the dynamical variation of emotional state in facial image sequences. The proposed system consists of two main step: facial image based emotional feature extraction and emotional state classification/recognition. At first, we propose a method for extracting and analyzing the emotional feature region using a combination of Active Shape Model (ASM) and Facial Action Units (FAUs). And then, it is proposed that emotional state classification and recognition method based on Hidden Markov Model (HMM) type of dynamic Bayesian network. Also, we adopt a Harmony Search (HS) algorithm based heuristic optimization procedure in a parameter learning of HMM in order to classify the emotional state more accurately. By using all these methods, we construct the emotion recognition system based on variations of the dynamic facial image sequence and make an attempt at improvement of the recognition performance.

• Journal of Drive and Control
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• v.13 no.1
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• pp.1-9
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• 2016

Nowadays automatic transmission equipped vehicles prevail in construction and agricultural equipment due to their convenience in driving and operation. Though domestic vehicle manufacturers install imported electronic controlled transmissions at present, overseas products will be replaced by domestic ones in the near future owing to development efforts over the past 10 years. For passenger cars, there are many kinds of shift control algorithms that enhance the shift quality such as feedback and learning control. However, since shift control technologies for heavy duty vehicles are not highly developed, it is possible to improve the shift quality with an organized control method. A feedback control algorithm for neutral-into-gear shift, which is enabled during the inertia phase for the master clutch slip speed to track the slip speed reference, is proposed based on the power transmission structure of TH100. The performance of the feedback shift control is verified by a vehicle test which is implemented with firmware embedded TCU. As the master clutch engages along the predetermined speed trajectory, it can be concluded that the shift quality can be managed by a shift time control parameter. By extending the proposed feedback algorithm for neutral-into-gear shift to gear change and shuttle shift, it is expected that the quality of the shift can be improved.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.1-10
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• 2021

In the past, designers relied primarily on past experience and reference to industry standard thresholds to design spaces. Such design often results in spaces that do not meet the needs of users. The purpose of this paper is to investigate the process and way of generating design parameters by constructing a BP neural network algorithm for spatial design. From the perspective. This paper adopts an experimental research method to take a kindergarten with a large number of complex needs in space as the object of study, and through the BP neural network algorithm in machine learning, the correlation between environmental behavior parameters and spatial design parameters is imprinted. The way of generating spatial design parameters is studied. In the future, the corresponding spatial design parameters can be derived by replacing specific environmental behavior influence factors, which can be applied to a wider range of scenarios and improve the efficiency of designers.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.133-142
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• 2021

This study aims to expand the effect of online education into the aspect of active exploration and sharing of class-related issues by learners. Based on theoretical discussions, Two types of motivation (personal and social) to explore issues, engagement, attitude toward issue content, and eWOM model were verified. As a result of the study, it was found that the impact of personal and social motivations that online education has on engagement on specific issues, and the positive(+) influence on attitudes toward issue content and word of mouth intentions on SNS, considering engagement as a parameter. In this study, the role of engagement in inducing the next learning by oneself was confirmed, and it can be seen that social and personal motives for issues and class content should be utilized to increase engagement.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.433-442
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• 2018

The reservoir area is defined as the area surrounded by the planned flood level of the dam or the land under the planned flood level of the dam. In this study, supervised classification based on RF (Random Forest), which is a representative machine learning technique, was performed to detect cropland in the reservoir area. In order to classify the cropland in the reservoir area efficiently, the GLCM (Gray Level Co-occurrence Matrix), which is a representative technique to quantify texture information, NDWI (Normalized Difference Water Index) and NDVI (Normalized Difference Vegetation Index) were utilized as additional features during classification process. In particular, we analyzed the effect of texture information according to window size for generating GLCM, and suggested a methodology for detecting croplands in the reservoir area. In the experimental result, the classification result showed that cropland in the reservoir area could be detected by the multispectral, NDVI, NDWI and GLCM images of UAV, efficiently. Especially, the window size of GLCM was an important parameter to increase the classification accuracy.

• Translational and Clinical Pharmacology
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• v.26 no.4
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• pp.166-171
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• 2018

Although there are many commercially available training software programs for pharmacokinetics, they lack flexibility and convenience. In this study, we develop simulation software to facilitate pharmacokinetics education. General formulas for time courses of drug concentrations after single and multiple dosing were used to build source code that allows users to simulate situations tailored to their learning objectives. A mathematical relationship for a 1-compartment model was implemented in the form of differential equations. The concept of population pharmacokinetics was also taken into consideration for further applications. The source code was written using R. For the convenience of users, two types of software were developed: a web-based simulator and a standalone-type application. The application was built in the JAVA language. We used the JAVA/R Interface library and the 'eval()' method from JAVA for the R/JAVA interface. The final product has an input window that includes fields for parameter values, dosing regimen, and population pharmacokinetics options. When a simulation is performed, the resulting drug concentration time course is shown in the output window. The simulation results are obtained within 1 minute even if the population pharmacokinetics option is selected and many parameters are considered, and the user can therefore quickly learn a variety of situations. Such software is an excellent candidate for development as an open tool intended for wide use in Korea. Pharmacokinetics experts will be able to use this tool to teach various audiences, including undergraduates.

• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.1-8
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• 2021

In this paper, we propose reinforced VGG style network structure for low performance embedded system to classify low resolution infrared image. The combination of reinforced VGG style network structure and global average pooling makes lower computational complexity and higher accuracy. The proposed method classify the synthesize image which have 9 class 3,723,328ea images made from OKTAL-SE tool. The reinforced VGG style network structure composed of 4 filters on input and 16 filters on output from max pooling layer shows about 34% lower computational complexity and about 2.4% higher accuracy then the first parameter minimized network structure made for embedded system composed of 8 filters on input and 8 filters on output from max pooling layer. Finally we get 96.1% accuracy model. Additionally we confirmed the about 31% lower inference lead time in ported C code.

• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.621-635
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• 2020

Water quality models are scientific tools that simulate and interpret the relationship between physical, chemical and biological reactions to external pollutant loads in water systems. They are actively used as a key technology in environmental water management. With recent advances in computational power, water quality modeling technology has evolved into a coupled three-dimensional modeling of hydrodynamics, water quality, and ecological inputs. However, there is uncertainty in the simulated results due to the increasing model complexity, knowledge gaps in simulating complex aquatic ecosystem, and the distrust of stakeholders due to nontransparent modeling processes. These issues have become difficult obstacles for the practical use of water quality models in the water management decision process. The objectives of this paper were to review the theoretical background, needs, and development status of water quality modeling technology. Additionally, we present the potential future directions of water quality modeling technology as a scientific tool for national environmental water management. The main development directions can be summarized as follows: quantification of parameter sensitivities and model uncertainty, acquisition and use of high frequency and high resolution data based on IoT sensor technology, conjunctive use of mechanistic models and data-driven models, and securing transparency in the water quality modeling process. These advances in the field of water quality modeling warrant joint research with modeling experts, statisticians, and ecologists, combined with active communication between policy makers and stakeholders.