• Journal of the Korean Society for Nondestructive Testing
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• v.28 no.2
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• pp.184-190
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• 2008

Various ECT techniques have been applied basically to assess the integrity of steam generator tithing in nuclear power plant. Among these techniques, the bobbin probe technique is applied generally to examine the volumetric flaws such as a crack-like defect and wear which is generally occurred on steam generator tubing, and additionally MRPC probe is used to examine closely tile top of tubesheet and bending regions due to the high possibility of cracking. Dent and bulge also may be formed on tube during installation process and operation of steam generator, but the dent and bulge indications greater than specific size criteria are recorded on examination report because these indications are not considered as flaw. These indications can be easily detected with bobbin probe and approximately sized with profile bobbin probe, but the size and shape can not be accurately verified. Accordingly, in this study, the $8{\times}1$ multi-array EC probe was designed to increase the measurement accuracy of the sectional profiling EC testing of tube. As a result, we would like to propose the application of $8{\times}1$ multi-array EC probe for the measurement of size and shape of profile change on steam generator tube in OPR-1000 nuclear power plant.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.7
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• pp.922-930
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• 2020

Marine accidents involving fishing boats, caused by a loss of stability, have been increasing over the last decade. One of the main reasons for these accidents is a sudden wind attacks. In this regard, the wind loads acting on the ship hull need to be estimated accurately for safety assessments of the motion and maneuverability of the ship. Therefore, this study aims to develop a computational model for the inlet boundary condition and to numerically estimate the wind load acting on a fishing boat. In particular, wind loads acting on a fishing boat at the wind speed profile boundary condition were compared with the numerical results obtained under uniform wind speed. The wind loads were estimated at intervals of 15° over the range of 0° to 180°, and i.e., a total of 13 cases. Furthermore, a numerical mesh model was developed based on the results of the mesh dependency test. The numerical analysis was performed using the RANS-based commercial solver STAR-CCM+ (ver. 13.06) with the k-ω turbulent model in the steady state. The wind loads for surge, sway, and heave motions were reduced by 39.5 %, 41.6 %, and 46.1 % and roll, pitch, and yaw motions were 48.2 %, 50.6 %, and 36.5 %, respectively, as compared with the values under uniform wind speed. It was confirmed that the developed inlet boundary condition describing the wind speed gradient with respect to height features higher accuracy than the boundary condition of uniform wind speed. The insights obtained in this study can be useful for the development of a numerical computation method for ships.

• Therapeutic Science for Rehabilitation
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• v.13 no.2
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• pp.69-84
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• 2024

Objective : This study aimed to categorize the lifestyles of older adults into two types - healthy and unhealthy, and use machine learning to identify the personal values that influence these lifestyles. Methods : This cross-sectional study targeting middle-aged and older adults (55 years and above) living in local communities in South Korea. Data were collected from 300 participants through online surveys. Lifestyle types were dichotomized by the Yonsei Lifestyle Profile (YLP)-Active, Balanced, Connected, and Diverse (ABCD) responses using latent profile analysis. Personal value information was collected using YLP-Values (YLP-V) and analyzed using machine learning to identify the relative importance of personal values on lifestyle types. Results : The lifestyle of older adults was categorized into healthy (48.87%) and unhealthy (51.13%). These two types showed the most significant difference in social relationship characteristics. Among the machine learning models used in this study, the support vector machine showed the highest classification performance, achieving 96% accuracy and 95% area under the receiver operating characteristic (ROC) curve. The model indicated that individuals who prioritized a healthy diet, sought health information, and engaged in hobbies or cultural activities were more likely to have a healthy lifestyle. Conclusion : This study suggests the need to encourage the expansion of social networks among older adults. Furthermore, it highlights the necessity to comprehensively intervene in individuals' perceptions and values that primarily influence lifestyle adherence.

• Journal of Biosystems Engineering
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• v.26 no.6
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• pp.571-578
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• 2001

Production of green pepper has increased for ten years in Korea, as customer's preference of a pepper tuned to fiesta one. This study was conducted to develop an on-line fading algorithm of green pepper using machine vision and aimed to develop the automatic on-line grading and sorting system. The machine vision system was composed of a professive scan R7B CCD camera, a frame grabber and sets of 3-wave fluorescent lamps. The length and curvature, which were main quality factors of a green pepper were measured while removing the stem region. The first derivative of the thickness profile was used to remove the stem area of the segmented image of the pepper. A new boundary was generated after the stem was removed and a baseline of a pepper which was used for the curvature determination was also generated. The developed algorithm showed that the accuracy of the size measurement was 86.6% and the accuracy of the bent was 91.9%. Processing time spent far grading was around 0.17 sec per pepper.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.4 s.193
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• pp.115-122
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• 2007

Recently, remarkable progress has been made in both technology and production of optical elements including aspheric lens. Especially, requirements for machining glass materials have been increasing in terms of limitation on using environment, flexibility of material selection and surface accuracy. In the past, precision optical glass lenses were produced through multiple processes such as grinding and polishing, but mass production of aspheric lenses requiring high accuracy and having complex profile was rather difficult. In such a background, the high-precision optical GMP process was developed with an eye to mass production of precision optical glass parts by molding press. This GMP process can produce with precision and good repeatability special form lenses such as camera, video camera, aspheric lens for laser pickup, $f-\theta$ lens for laser printer and prism, and me glass parts including diffraction grating and V-grooved base. GMP process consist a succession of heating, forming, and cooling stage. In this study, as a fundamental study to develop molds for GMP used in fabrication of glass lens, we conducted a glass lens forming simulation. In prior to, to determine flow characteristics and coefficient of friction, a compression test and a compression farming simulation for PBK40, which is a material of glass lens, were conducted. Finally, using flow stress functions and coefficient of friction, a glass lens forming simulation was conducted.

• Journal of ILASS-Korea
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• v.25 no.1
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• pp.8-14
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• 2020

Recently, 1-D model-based engine development using virtual engine system is getting more attention than experimental-based engine development due to the advantages in time and cost. Injection rate profile is the one of the main parameters that determine the start and end of combustion. Therefore, it is essential to set up a sophisticated model to accurately predict the injection rate as starting point of virtual engine system. In this research, procedure of 1-D model setup based on AMESim is introduced to predict the dynamic behavior and injection rate of diesel injector. As a first step, detailed 3D cross-sectional drawing of the injector was achieved, which can be done with help of precision measurement system. Then an approximate AMESim model was provided based on the 3D drawing, which is composed of three part such as solenoid part, control chamber part and needle and nozzle orifice part. However, validation results in terms of total injection quantity showed some errors over the acceptable level. Therefore, experimental work including needle movement visualization, solenoid part analysis and flow characteristics of injector part was performed together to provide more accuracy of 1-D model. Finally, 1-D model with the accuracy of less than 10% of error compared with experimental result in terms of injection quantity and injection rate shape under normal temperature and single injection condition was established. Further work considering fuel temperature and multiple injection will be performed.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.1
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• pp.107-115
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• 2002

We propose a new hybrid value predictor which achieves high performance by combining several predictors. Because the proposed hybrid value predictor can update the prediction table speculatively, it efficiently reduces the number of mispredicted instructions due to stale data. Also, the proposed predictor can enhance the prediction accuracy and efficiently decrease the hardware cost of predictor, because it allocates instructions into the best-suited predictor during instruction fetch stage by using the information of static classification which is obtained from the profile-based compiler implementation. For the 16-issue superscalar processors, simulation results based on the SimpleScalar/PISA tool set show that we achieve the average prediction rates of 73% by using speculative update and the average prediction rates of 88% by adding static classification for the SPECint95 benchmark programs.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.9
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• pp.167-173
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• 2004

The heterodyne He-Ne laser interferometer is the most widely used sensing unit to measure the position error. It measures the positioning error from the displacement of a moving reflector in terms of the wave length. But, the wave length is affected by the variation of atmospheric temperature. Temperature variation of 1$^\circ C$ results in the measuring error of 1ppm. In this paper, for measuring more accurately the position error of the ultra precision stage, the refractive index compensation method is introduced. The wave length of the laser interferometer is compensated using the simultaneously measured room temperature variations in the method. In order to investigate the limit of compensation, the stationary test against two fixed reflectors mounted on the zerodur$\circledR$ plate is performed firstly. From the experiment, it is confirmed that the measuring error of the laser interferometer can be improved from 0.34${\mu}m$ to 0.11${\mu}m$ by the application of the method. Secondly, for the verification of the compensating effect, it is applied to estimate the positioning accuracy of an ultra precision aerostatic stage. Two times of the refractive index compensation are performed to acquire the positioning error of the stage from the initially measured data, that is, to the initially measured positioning error and to the measured positioning error profile after the NC compensation. Although the positioning error of an aerostatic stage cannot be clarified perfectly, it is known that by the compensation method, the measuring error by the laser interferometer can be improved to within 0.1${\mu}m$.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1271-1278
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• 2020

Deep learning-based profiling side-channel analysis is a powerful analysis method that utilizes the neural network to profile the relationship between the side-channel information and the intermediate value. Since the neural network interprets each point of the signal in a different dimension, jitter makes it much hard that the neural network with dimension-wise weights learns the relationship. This paper shows that replacing the fully-connected layer of the traditional CNN (Convolutional Neural Network) with global average pooling (GAP) allows us to design the inherently robust neural network inherently for jitter. We experimented with the ChipWhisperer-Lite board to demonstrate the proposed method: as a result, the validation accuracy of the CNN with a fully-connected layer was only up to 1.4%; contrastively, the validation accuracy of the CNN with GAP was very high at up to 41.7%.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.2
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• pp.135-145
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• 2023

Developing high-fidelity Computational Fluid Dynamics (CFD) simulation methods used to evaluate the airwake characteristics along a flight deck of a large ship, the various kind of data such as actual ship measurement and wind tunnel results are required to verify the accuracy of CFD simulation. Inflow velocity profile at the bow, local unsteady flow field data around the flight deck, and highly reliable wind tunnel data which were measured after reviewing Atmospheric Boundary Layer (ABL) simulation and Reynolds Number effects were also used to determine the key parameters such as turbulence model, time resolution and accuracy, grid resolution and type, inflow condition, domain size, simulation length, and so on in STAR CCM⁺. Velocity ratio and turbulent intensity difference between Full-scale CFD and actual ship measurement at the measurement points show less than 2% and 1.7% respectively. And differences in velocity ratio and turbulence intensity between wind tunnel test and small-scale CFD are both less than 2.2%. Based upon this fact, the selected parameters in CFD simulation are highly reliable for a specific wind condition.