• Transactions of the Korean Society of Mechanical Engineers
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• v.9 no.4
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• pp.414-420
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• 1985

The melting phenomenal of the horizontal cylindrical ice-bar submerged in water are experimentally investigated for the temperature range from 2.5.deg. C to 15.deg. C. The shapes of the melting ice-bar are recorded by the Photo-elasticity Apparatus with modification of the test section. The shadowgraphs of the melting ice-bar show that water adjacent to the bar flows upward for the temperature range from 2.5.deg. C to 5.6.deg. C while above the temperature of 5.6.deg. C the flow is downward direction. The local and average Nusselt numbers become minimum at 5.6.deg. C which is considered as a critical temperature and the Nusselt numbers increase as temperature difference from the critical temperature increase.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.3
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• pp.289-296
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• 2016

This study presents the kinematics of an intrinsic continuum robot actuated by pneumatic artificial muscles. The single section of a developed continuum robot consisted of three muscles in parallel. The contraction of each muscle according to applied air pressure produced spatial motions of a distal plate with respect to a base plate. Based on the bending behaviors of artificial muscles, the orientation and position of the end-effector of a continuum robot were formulated using a transformation matrix. The orientation and position was also determined for a single section of the distal plate. A Jacobian matrix relating the contraction rate or the pressure rate of the muscles to the velocity vector of the end-effector was calculated considering the assembled position of actuators between neighboring sections of the robot. Experimental results showed that the motions of the intrinsic continuum robot were accurately estimated by the proposed kinematics.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.10
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• pp.1291-1296
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• 2013

Risk-based inspection (RBI) is a well-known method that is used to optimize inspection activities based on risk analysis in order to identify the high-risk components of major facilities such as power plants. RBI, when implemented and maintained properly, improves plant reliability and safety while reducing unplanned outages and repair costs. Risk is given by the product of the probability of failure (POF) and the consequence of failure (COF). A semi-quantitative method is generally used for risk assessment. Semi-quantitative risk assessment complements the low accuracy of qualitative risk assessment and the high expense and long calculation time of quantitative risk assessment. The first step of RBI is to identify important failure modes and causes in the equipment. Once these are defined, the POF and COF can be assessed for each failure. During POF and COF assessment, an effective inspection method and range can be easily found. In this paper, the calculation of the POF is improved for accurate risk assessment. A modified semi-quantitative risk assessment was carried out for boiler facilities of thermal power plants, and the next maintenance schedules for the equipment were decided.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.11
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• pp.1691-1696
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• 2010

The efforts of reflecting the system's uncertainties in design step have been made and robust optimization or reliabilitybased design optimization are examples of the most famous methodologies. The statistical moments of a performance function and the constraints corresponding to probability conditions are involved in the formulation of these methodologies. Therefore, it is essential to effectively and accurately calculate them. The sensitivities of these methodologies have to be determined when nonlinear programming is utilized during the optimization process. The sensitivity of statistical moments and probability constraints is expressed in the integral form and limited to the normal random variable; we aim to expand the sensitivity formulation to nonnormal variables. Additional functional calculation will not be required when statistical moments and failure or satisfaction probabilities are already obtained at a design point. On the other hand, the accuracy of the sensitivity results could be worse than that of the moments because the target function is expressed as a product of the performance function and the explicit functions derived from probability density functions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.269-274
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• 2016

This paper proposes the development of a low frequency electronic hammer drive system that is used to prevent scaling or clogging in the hopper process. The electro-mechanical hammering driving method involves the generation of vibration and impact energy. The operation principles of the electromagnetic hammer were considered by parallel/series spring coefficient analysis and the amount of kinetic energy generated was calculated from the product of the equivalent spring constant, which is coupled with the E core and the gap of between the E core and I core. In addition, the Pulsation Driving algorithm was applied to the proposed electromagnetic hammer to obtain the maximizing kinetic energy. This algorithm was then implemented by a logical AND operation process and micro-controller (atmega128) built in functions with a timer interrupt and PWM generation function. The driving circuit of the electromagnetic hammer was designed using the H-bridge type IGBT circuit. The experimental test was performed by usefulness of the developed electromagnetic hammer systems with the acceleration measurement method. The experimental result showed that the proposed system has good kinetic energy generation performance and can be applied to the hopper process.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.37-44
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• 2021

Automatic classification of brain MRI images play an important role in early diagnosis of brain tumors. In this work, we present a deep learning-based brain tumor classification model in MRI images using ensemble of deep features. In our proposed framework, three different deep features from brain MR image are extracted using three different pre-trained models. After that, the extracted deep features are fed to the classification module. In the classification module, the three different deep features are first fed into the fully-connected layers individually to reduce the dimension of the features. After that, the output features from the fully-connected layers are concatenated and fed into the fully-connected layer to predict the final output. To evaluate our proposed model, we use openly accessible brain MRI dataset from web. Experimental results show that our proposed model outperforms other machine learning-based models.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.5
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• pp.273-278
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• 2019

In this study, a new simple method for the estimation of hyperelastic material properties by indentation tests is proposed. Among hyperelastic material models, the Yeoh model with three material properties ($C_{10}$, $C_{20}$, $C_{30}$) is adopted to describe the strain energy density in terms of strain invariants. Finite element simulations of the spherical indentation of hyperelastic materials of the Yeoh model with different material properties are performed to establish a database of indentation force-displacement curves. The indentation force-displacement curves are fitted by cubic polynomials, which are approximated as a product of third-order polynomials of ($C_{10}$, $C_{20}$, $C_{30}$). A regression analysis is conducted to determine the coefficients of the equations for the indentation force-displacement curve approximations. A regression equation is used to estimate the hyperelastic material properties. The present method is verified by comparing the estimated material properties with true values.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.177-188
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• 2022

Recently, the application of distributed acoustic sensors (DAS), which can replace geophones and seismometers, has significantly increased along with interest in micro-seismic monitoring technique, which is one of the CO₂ storage monitoring techniques. A significant amount of temporally and spatially continuous data is recorded in a DAS monitoring system, thereby necessitating fast and accurate data processing techniques. Because event detection and seismic phase picking are the most basic data processing techniques, they should be performed on all data. In this study, a machine learning-based P, S wave phase picking algorithm was developed to compensate for the limitations of conventional phase picking algorithms, and it was modified using a transfer learning technique for the application of DAS data consisting of a single component with a low signal-to-noise ratio. Our model was constructed by modifying the convolution-based EQTransformer, which performs well in phase picking, to the ResUNet structure. Not only the global earthquake dataset, STEAD but also the augmented dataset was used as training datasets to enhance the prediction performance on the unseen characteristics of the target dataset. The performance of the developed algorithm was verified using K-net and KiK-net data with characteristics different from the training data. Additionally, after modifying the trained model to suit DAS data using the transfer learning technique, the performance was verified by applying it to the DAS field data measured in the Pohang Janggi basin.

• Korean Journal of Construction Engineering and Management
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• v.21 no.5
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• pp.11-19
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• 2020

A daily progress monitoring and further schedule management of a construction project have a significant impact on the construction manager's decision making in schedule change and controlling field operation. However, a current site monitoring method highly relies on the manually recorded daily-log book by the person in charge of the work. For this reason, it is difficult to take a detached view and sometimes human error such as omission of contents may occur. In order to resolve these problems, previous researches have developed automated site monitoring method with the object recognition-based visualization or BIM data creation. Despite of the research results along with the related technology development, there are limitations in application targeting the practical construction projects due to the constraints in the experimental methods that assume the fixed equipment at a specific location. To overcome these limitations, some smart devices carried by the field workers can be employed as a medium for data creation. Specifically, the extracted information from the site picture by object recognition technology of CNN model, and positional information by GIPS are applied to update 4D BIM data. A standard CNN model is developed and BIM data modification experiments are conducted with the collected data to validate the research suggestion. Based on the experimental results, it is confirmed that the methods and performance are applicable to the construction site management and further it is expected to contribute speedy and precise data creation with the application of automated progress monitoring methods.

• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.274-280
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• 2024

Machine learning techniques utilizing neural networks have been employed in various fields such as disease gene discovery and diagnosis, drug development, and prediction of drug-induced liver injury. Disease features can be investigated by molecular information of DNA. In this study, we developed a neural network to predict the length of DNA and the number of DNA species in mixture solution which are representative molecular information of DNA. In order to address the time-consuming limitations of gel electrophoresis as conventional analysis, we analyzed the dynamic data of a microfluidic concentrating device. The dynamic data were reconstructed into a spatiotemporal map, which reduced the computational cost required for training and prediction. We employed a convolutional neural network to enhance the accuracy to analyze the spatiotemporal map. As a result, we successfully performed single DNA length prediction as single-variable regression, simultaneous prediction of multiple DNA lengths as multivariable regression, and prediction of the number of DNA species in mixture as binary classification. Additionally, based on the composition of training data, we proposed a solution to resolve the problem of prediction bias. By utilizing this study, it would be effectively performed that medical diagnosis using optical measurement such as liquid biopsy of cell-free DNA, cancer diagnosis, etc.