• Journal of Welding and Joining
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• v.27 no.6
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• pp.68-73
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• 2009

Recently, as titanium and titanium alloys are being increasingly used in wide areas, there are on-going researches to obtain high quality weld zone. In particular, growing interest is being drawn to laser welding, which involves low heat input and large aspect ratio in various welding processes and can facilitate shield in atmospheric condition compared with electron beam welding. The first report covered the analysis of embrittlement by the bead color of weld zone through quantitative analysis of oxygen and nitrogen and measurement of hardness as basic experiment to apply laser welding to titanium. Results indicated that the element that affect embrittlement the most was nitrogen, and as embrittlement and oxygenation go on, bead color changed to silver, gold, brown, blue and gray. This study performed butt welding of pure titanium and STS304 by using 1kW CW Nd:YAG laser, and to find out basic physical properties, evaluated welding performance by laser output, welding speed, root gap and misalignment etc, and examined mechanical properties through tensile stress and Erichsen test. The reason particles of pure titanium welded metal and HAZ are greater than STS304 is because they are pure metal and do not include many impure elements that work as nuclei in case of resolidification, thus becoming coarse columnar crystals eventually. In addition, the reason STS304 requires more energy during welding than pure titanium is because the particle size of base metal is smaller.

• Journal of Drive and Control
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• v.12 no.3
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• pp.18-24
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• 2015

The plug-in hybrid electric vehicle has a high fuel economy and can be driven long distances. Its different modes include the electric vehicle, hybrid electric vehicle, and only engine operating mode. A power management strategy is important to determine which mode should be selected. The strategy makes the vehicle more efficient using appropriate power sources for driving. However, the strategy usually needs a driving speed profile which is future driving cycle. If the profile is known, the strategy easily determines which mode is driven efficiently. However, it is difficult to estimate the speed profile for a real system. To address this problem, this paper proposes a new power distribution strategy using a neural network. The average speed and driving range are used as input parameters to train the neural network system. The strategy determines a limit for the use of the battery and the desired power is distributed between the engine and the motor simultaneously. Its fuel economy can increase by improving the basic strategy.

• Journal of the Korea Society for Simulation
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• v.26 no.3
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• pp.55-63
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• 2017

In R&D of artillery system, error budget method is used to predict artillery firing error without field firing test. The error budget method for artillery has been consistently developed but apply for practical R&D of the weapon system has been avoided because of lacks of error budget source information. The error budget source is composed of every detailed error components which affect total distance and deflection error of artillery, and most of them are difficult to be calculated or measured. Also with the inaccuracy of source information, simulated error result dose not reflect real firing error. To resolve that problem, an optimization algorithm is adopted to figure out error budget sources from existing filed firing test. The method of finding input parameter estimation which is commonly used in aerodynamics was applied. As an optimization algorithm, CMA-ES is used and presented in the paper. The error budget sources which are figured out by the presented method can be applied to compute ROC of new weapon systems and may contribute to an improvement of accuracy in artillery.

• Journal of IKEEE
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• v.25 no.3
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• pp.540-543
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• 2021

In this paper, we present an experimental comparative study of recurrent neural network based techniques for trajectory prediction of flight vehicles. We defined and investigated various relationships between input and output under the same experimental setup. In particular, we proposed a relationship based on the relative positions of flight vehicles. Furthermore, we conducted an ablation study on the network architectures and hyperparameters. We believe that this comprehensive comparative study serves as a reference point and guide for developers in choosing an appropriate recurrent neural network based techniques for building (flight) vehicle trajectory prediction systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.3
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• pp.247-253
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• 2019

In this paper, we present a new method for real-time tracking of human walking around a laser sensor system. The method converts range data with $r-{\theta}$ coordinates to a 2D image with x-y coordinates. Then human tracking is performed using human's features, i.e. appearances of human walking pattern, and the input range data. The laser sensor based human tracking method has the advantage of simplicity over conventional methods which extract human face in the vision data. In our method, the problem of estimating 2D positions and orientations of two walking human's ankle level is formulated based on a moving trajectory algorithm. In addition, the proposed tracking system employs a HMM to robustly track human in case of occlusions. Experimental results using a real system demonstrate usefulness of the proposed method.

• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.321-329
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• 2022

This study analyzes how companies of prospector type with inherent risks from new products and R&D costs affect audit hours, and further analyzes how they affect rank-specific audit hours. Samples were empirically analyzed using samples from 2018 to 2019 for KOSPI-listed and KOSDAQ-listed companies. As a result of the analysis, first, it was found that auditors were aware of the inherent risks of companies of prospector type and were striving to improve audit quality. Second, it was found that the corresponding degree of risk differs depending on the position and role in the audit team, so higher efforts were made in core positions with high risk levels. The results of this study are meaningful in verifying how the type of Business Strategies affects the audit efforts and resource input of auditors who are external parties, not internal factors such as financial reporting quality or tax avoidance. It also has important implications that a company's Business Strategies can be an significant factor to consider in preparing policies and systems for improving audit quality.

• Journal of Biomedical Engineering Research
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• v.42 no.5
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• pp.241-249
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• 2021

Recently, artificial intelligence for diagnosis system of obstetric diseases have been actively studied. Artificial intelligence diagnostic assist systems, which support medical diagnosis benefits of efficiency and accuracy, may experience problems of poor learning accuracy and reliability when inappropriate images are the model's input data. For this reason, before learning, We proposed an algorithm to exclude unread cervical imaging. 2,000 images of read cervical imaging and 257 images of unread cervical imaging were used for this study. Experiments were conducted based on the statistical method Radiomics to extract feature values of the entire images for classification of unread images from the entire images and to obtain a range of read threshold values. The degree to which brightness, blur, and cervical regions were photographed adequately in the image was determined as classification indicators. We compared the classification performance by learning read cervical imaging classified by the algorithm proposed in this paper and unread cervical imaging for deep learning classification model. We evaluate the classification accuracy for unread Cervical imaging of the algorithm by comparing the performance. Images for the algorithm showed higher accuracy of 91.6% on average. It is expected that the algorithm proposed in this paper will improve reliability by effectively excluding unread cervical imaging and ultimately reducing errors in artificial intelligence diagnosis.

• Composites Research
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• v.21 no.1
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• pp.22-29
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• 2008

This paper presents the evaluation of crashworthiness and rollover characteristics of low-floor bus vehicles made of aluminum honeycomb sandwich composites with glass-fabric epoxy laminate facesheets. Crashworthiness and rollover analysis of low-floor bus was carried out using explicit finite element analysis code LS-DYNA3D with the lapse of time. Material testing was conducted to determine the input parameters for the composite laminate facesheet model, and the effective equivalent damage model for the orthotropic honeycomb core material. The crash conditions of low-floor bus were frontal accident with speed of 60km/h. Rollover analysis were conducted according to the safety rules of European standard (ECE-R66). The results showed that the survival space for driver and passengers was secured against frontal crashworthiness and rollover of low-floor bus. Also, The modified Chang-Chang failure criterion is recommended to predict the failure mode of composite structures for crashworthiness and rollover analysis.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.43-52
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• 2023

Rubber produced by rubber companies is subjected to quality suitability inspection through rheometer test, followed by secondary processing for automobile parts. However, rheometer test is being conducted by humans and has the disadvantage of being very dependent on experts. In order to solve this problem, this paper proposes a deep learning-based rheometer quality inspection system. The proposed system combines LSTM(Long Short-Term Memory) and CNN(Convolutional Neural Network) to take advantage of temporal and spatial characteristics from the rheometer. Next, combination materials of each rubber was used as an auxiliary input to enable quality conformity inspection of various rubber products in one model. The proposed method examined its performance with 30,000 validation datasets. As a result, an F1-score of 0.9940 was achieved on average, and its excellence was proved.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.272-281
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• 2016

The effective exploitation of resources is critical for startup firms with insufficient resources to compete with well-resourced large firms. The direction and consistency of the resource input should be retained to utilize resources effectively, which will lead to a necessity for a fit between the strength in resources and the strategy of a firm. This study suggests hypotheses and verifies them empirically based on the logic that the attribute of resources in which a firm with a core competence decides the type of strategy and the formulated strategy presents the direction of the resource input, which enables the effective utilization of resources and facilitates high performance. According to the statistical results, the R&D capability affects the innovative differentiation strategy, marketing capability affects marketing differentiation strategy, and financial and production capability affects low cost strategy, in which the efficacy of the strategy depends on the attribute of resources. In addition, the R&D capability and marketing capability adversely affect the low cost strategy and the production capability negatively affects the innovative differentiation strategy, which implies that the exclusive choice of a strategy by the strength in resources results in improved performance. These results show that the fit between the resource and strategy is an essential cause of high performance in venture businesses.