• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.11
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• pp.2049-2058
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• 1992

In this work, a 6 degree of freedom lumped mass model is constructed for an OHC-type cam valve train analysis, and the model is verified experimentally. Using the verified model, an optimum cam profile is designed to minimize the maximum contact force between cam and follower under the constraints such as cam lift and cam event angle. The designed cam was carefully machined and tested experimentally. As operating the designed cam shaft on the test rig, the valve motion was precisely measured with laser displacement meter and the contact force was indirectly monitored by measuring strain at a certain point of the finger follower. Judging from the model simulation and experiment results, the maximum contact force can be reduced as much as more than 16.7 percent under maintaining the original valve flow area by adopting the optimum cam profile.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.5
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• pp.400-407
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• 2011

The suggested method of previous Son's study dichotomized subjective response data to modeling noise exposure-response. The method used maximum liklihood estimation instead of least square estimation and the noise exposure-response curve of the study was logistic regression analysis result. The method was originated to modeling community response rate such as %HA or %A. It can be useful when the subjective response was investigated based on predicted noise level. It is difficult to measure the single source emitting noise such as railway because various traffic noise sources combined in our life. The suggested method was adopted to model in this study and railway noise-exposure response curves were modeled because the noise level of this area was predicted data. The data of this study was used by previous Ko's paper but he dealt the area as combined noise area and divided the data by dominant noise source. But this study used all data of this area because the annoyance response to railway noise was higher than other noise according to the result of correlation analysis. The trend of the %HA and %A prediction model to train noise of this study is almost same as the model based on measured noise of previous Lim's study although the investigated areas and methods were different.

• Journal of computational fluids engineering
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• v.22 no.1
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• pp.103-109
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• 2017

The aerodynamic noise reduction of railway traction motor is required to satisfy new enhanced Korean noise regulations for a train. This paper is the study result on a noise reduction of a railway traction motor using Lattice Boltzmann Method(LBM). To verify the reliability of numerical analysis, the noise performance of the base model evaluated using LBM, and calculated result was compared with the experimental data. In addition, main noise sources were selected to design parameters through analyzing the flow field of the base model. Based on the noise sources analysis result, a design improvement model of traction motor for this study was derived to reduce the noise. The performance of a design improvement model was evaluated by applying a validated numerical scheme. As a result, it was confirmed that the noise was reduced due to the suppression of the internal turbulent flow components.

• Asian Journal of Atmospheric Environment
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• v.8 no.4
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• pp.184-191
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• 2014

Air quality in a subway tunnel has been crucial in most of the subway environments where IAQ could be affected by many factors such as the number of passengers, the amount and types of ventilation, train operation factors and other facilities. A modeling approach has been introduced to manage the general IAQ in a subway station. Field surveys and $CO_2$ measurements were initially conducted to analyze and understand the relationship between indoor and outdoor air quality while considering internal pollution sources, such as passengers and subway trains, etc. The measurement data were then employed for the model development with other statistical information. For the model development, the algorithm of simple continuity was set up and applied to model the subway IAQ concerned, while considering the major air transport through staircases and tunnels. Monitored $CO_2$ concentration on the concourse and platform were correlated with modeling results where the correlation values for the concourse and platform were $R^2=0.96$ and $R^2=0.75$, respectively. It implies that the box modeling approach introduced in this study would be beneficial to predict and control the indoor air quality in subway environments.

• Journal of Fashion Business
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• v.1 no.1
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• pp.84-98
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• 1997

This study is to develop a fashion drawing education program which is based on the theory of 'Split-brain' by Roger W. Sperry and 'Drawing on the Right Side of the Brain' by Betty Edwards. Students in Fashion Design start their training by developing a foundation in drawing and studing the tools, materials and methods of the Industry. Ideas are then developed on paper, later translated into three-dimensional shapes and finally into finished garments. Fashion drawing and design techniques train the hand and eye to all the nuances of fashion design and illustration. Fashion drawing course deals with the sketching of fashion models for the purpose of understanding the model figure, basic anatomy, movement and figure attitudes. Having mastered the basic skills, students take advanced drawing course which is developing awareness of design, needs, of fashion market' using various media for the purpose of developing a designer's sketch, with emphasis on the drawing and designs. Featured aspects of this study include the following; 1. Drawing the negative space; basic visual concepts 2. Contour drawing; constructs, visual measurement, movement 3. Model drawing; the classical method, proportion, symmetry. The primary aim of this study is to develop a sensitive, animated line based on observed form. It is important to let the students Imagine that they are actually touching the model, for in this way they can benefit from simulating the child's learning process. Instead of actually touching the model they are using their eyes as an extension of their sense of touch.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.64-64
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• 2011

We present a new quasi-stellar object (QSO) selection algorithm using a Support Vector Machine, a supervised classification method, on a set of extracted time series features including period, amplitude, color, and autocorrelation value. We train a model that separates QSOs from variable stars, non-variable stars, and microlensing events using 58 known QSOs, 1629 variable stars, and 4288 non-variables in the MAssive Compact Halo Object (MACHO) database as a training set. To estimate the efficiency and the accuracy of the model, we perform a cross-validation test using the training set. The test shows that the model correctly identifies ~80% of known QSOs with a 25% false-positive rate. The majority of the false positives are Be stars. We applied the trained model to the MACHO Large Magellanic Cloud (LMC) data set, which consists of 40 million lightcurves, and found 1620 QSO candidates. During the selection, none of the 33,242 known MACHO variables were misclassified as QSO candidates. In order to estimate the true false-positive rate, we crossmatched the candidates with astronomical catalogs including the Spitzer Surveying the Agents of a Galaxy's Evolution (SAGE) LMC catalog and a few X-ray catalogs. The results further suggest that the majority of the candidates, more than 70%, are QSOs.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2678-2685
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• 2020

The pipe bends and elbows in nuclear power plants (NPPs) are vulnerable to degradation mechanisms and can cause wall-thinning defects. As it is difficult to detect both the defects generated inside the wall-thinned pipes and the preliminary signs, the wall-thinning defects should be accurately estimated to maintain the integrity of NPPs. This paper proposes a deep fuzzy neural network (DFNN) method and estimates the collapse moment of wall-thinned pipe bends and elbows. The proposed model has a simplified structure in which the fuzzy neural network module is repeatedly connected, and it is optimized using the least squares method and genetic algorithm. Numerical data obtained through simulations on the pipe bends and elbows with extrados, intrados, and crown defects were applied to the DFNN model to estimate the collapse moment. The acquired databases were divided into training, optimization, and test datasets and used to train and verify the estimation model. Consequently, the relative root mean square (RMS) errors of the estimated collapse moment at all the defect locations were within 0.25% for the test data. Such a low RMS error indicates that the DFNN model is accurate in estimating the collapse moment for wall-thinned pipe bends and elbows.

• Journal of the Ergonomics Society of Korea
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• v.22 no.1
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• pp.81-94
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• 2003

The British railway safety research group has developed a risk assessment model for the railway infrastructure and major railway accidents. The major hazardous factors of the railway infrastructure were identified and classified in the model. The frequency rates of critical top events were predicted by the fault tree analysis method using failure data of the railway system components and ratings of railway maintenance experts, The consequences of critical top events were predicted by the event tree analysis method. They classified the Joss of accident due to railway system into personal. commercial and environmental damages. They also classified 110 hazardous event due to railway system into three categories. train accident. movement accident and non-movement accident. The risk assessment model of the British railway system has been designed to take full account of both the high frequency low consequence type events (events occurring routinely for which there is significant quantity of recorded data) and the low frequency high consequence events (events occurring rarely for which there is little recorded data). The results for each hazardous event were presented in terms of the frequency of occurrence (number of events/year) and the risk (number of equivalent fatalities per year).

• International Journal of Railway
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• v.9 no.1
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• pp.1-9
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• 2016

In order to achieve a comprehensive utilization of railway networks, it is necessary to accurately assess the timetable indicators that effect the train operation. This paper describes the parameter calibration for two timetable indicators: scheduled running time and scheduled dwell time. For the scheduled running time, an existing model is employed and the single timetable parameter (percentage of minimum running time) in that model is optimized. For the scheduled dwell time, two intrinsic characteristics: the significance of stations and the average headway at each station are proposed firstly to form a new model, and the corresponding timetable parameters (the weight of the significance and the weight of the average headway) are calibrated subsequently. The Floyd Algorithm is used to obtain the connectivity among stations, which represents the significance of the stations. A case study is conducted in a light rail transportation system with 17 underground stations. The results of this research show that the optimal value of the scheduled running time parameter can be automatically determined, and the proposed model for the scheduled dwell time works well with a high coefficient of determination and low relative root mean square error through the leave-one-out validation.

• Journal of Broadcast Engineering
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• v.23 no.2
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• pp.186-196
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• 2018

In this paper, we propose an on-line tracking method using convolutional neural network (CNN) for tracking object. It is well known that a large number of training samples are needed to train the model offline. To solve this problem, we use an untrained model and update the model by collecting training samples online directly from the test sequences. While conventional methods have been used to learn models by training samples offline, we demonstrate that a small group of samples are sufficient for online object tracking. In addition, we define a loss function containing color information, and prevent the model from being trained by wrong training samples. Experiments validate that tracking performance is equivalent to four comparative methods or outperforms them.