• Journal of the Korean Institute of Rural Architecture
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• v.23 no.1
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• pp.1-8
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• 2021

In this study, a real-sized experimental equipment (pilot plant) was built at the site based on the preliminary research data to develop a movable separator for the mixed construction waste that can be implemented in agricultural areas to review its feasibility through the evaluation of its separation efficiency by waste types. The final construction of the movable separator and experimental results of the separation efficiency are summarized as follows. 1) The separation performance according to the blade type was the best for the combustible wastes either with 26 numbers of L-type blades and 32 numbers of pin type blades. As far as combination of blade types, when the L-type and pin-type were combined, the best separation efficiency was achieved. 2) The separation efficiency for waste wood by the conveyor type and angle of inclination (slope) of the trommel was the best when the conveyor had ribs of seagull shape with the angle of inclination 45°. 3) The separation efficiencies by process showed that 65.9% was separated as inorganic demolition wastes, 18.2% as waste woods, and 16.0% as combustible wastes at conveyor speed of 2-3 rpm, and the error rate was the least from the waste types generated in the dismantle site.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.81-85
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• 2022

Mist-CVD is known to have advantages of low cost and high productivity method since the precursor solution is misting with an ultrasonic generator and reacted on the substrate under vacuum-free conditions of atmospheric pressure. However, since the deposition distribution is not uniform, various efforts have been made to derive optimal conditions by changing the angle of the substrate and the position of the outlet to improve the result of the preceding study. Therefore, in this study, a deposition distribution uniformity model was derived through the shape and position of the substrate support and the conditions of inlet flow rate using the particle tracking method of computational fluid dynamics (CFD). The results of analysis were compared with the previous studies through experiment. It was confirmed that the rate of deposition area was improved from 38.7% to 100%, and the rate of deposition uniformity was 79.07% which was higher than the predicted result of simulation. Particle tracking method can reduce trial and error in experiments and can be considered as a reliable prediction method.

• Journal of the Microelectronics and Packaging Society
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• v.29 no.3
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• pp.43-48
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• 2022

In this study, simulation method was developed to improve the accuracy of the warpage simulation based on the equivalent anisotropic viscoelastic model. First, a package with copper traces and bumps was modeled to implement anisotropic viscoelastic behavior. Then, equivalent anisotropic viscoelastic properties and thermal expansion coefficient for the bump region were derived through the representative volume element model. A thermal cycle of 0 to 125 degrees was applied to the package based on the derived mechanical properties, and the warpage according to the thermal cycle was simulated. To verify the simulation results, the actual package was manufactured, and the warpage with respect to the thermal cycle was measured through shadow moiré interferometer. As a result, by applying the equivalent anisotropic viscoelastic model, it was possible to calculate the warpage of the package within 5 ㎛ error and predict the shape of the warpage.

• Composites Research
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• v.34 no.6
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• pp.426-433
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• 2021

The carbon fiber reinforced plastic manufacturing process has a problem in that a dimensional error occurs due to thermal deformation such as residual stress, spring-in, and warpage. The main causes of thermal deformation are various, including the shape of the product, the chemical shrinkage, thermal expansion of the resin, and the mold effect according to the material and surface condition of the mold. In this study, a viscoelastic model was applied to the plate model to predict the thermal deformation. The effects of chemical shrinkage and thermal expansion of the resin, which are the main causes of thermal deformation, were analyzed, and the analysis technique of the 3-D viscoelastic model with and without mold was also studied. Then, the L-shaped mold effect was analyzed using the verified 3D viscoelastic model analysis technique. The results show that different residual deformation occurs depending on the surface condition even when the same mold is used.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.135-142
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• 2020

In this paper, we describe the iris localization method in RGB images. Most of the iris localization methods are developed for infrared images, thus an iris localization method in RGB images is required for various applications. The proposed method consists of four stages: i) detection of the candidate irises using circular Hough transform (CHT) from an input image, ii) detection of a pupil center based on deep learning, iii) determine the iris using the pupil center, and iv) correction of the iris region. The candidate irises are detected in the order of the number of intersections of the center point candidates after generating the Hough space, and the iris in the candidates is determined based on the detected pupil center. Also, the error due to distortion of the iris shape is corrected by finding a new boundary point based on the detected iris center. In experiments, the proposed method has an improved accuracy about 27.4% compared to the CHT method.

• Korean Journal of Materials Research
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• v.32 no.8
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• pp.345-353
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• 2022

In this study, using deep learning, super-resolution images of transmission electron microscope (TEM) images were generated for nanomaterial analysis. 1169 paired images with 256 × 256 pixels (high resolution: HR) from TEM measurements and 32 × 32 pixels (low resolution: LR) produced using the python module openCV were trained with deep learning models. The TEM images were related to DyVO₄ nanomaterials synthesized by hydrothermal methods. Mean-absolute-error (MAE), peak-signal-to-noise-ratio (PSNR), and structural similarity (SSIM) were used as metrics to evaluate the performance of the models. First, a super-resolution image (SR) was obtained using the traditional interpolation method used in computer vision. In the SR image at low magnification, the shape of the nanomaterial improved. However, the SR images at medium and high magnification failed to show the characteristics of the lattice of the nanomaterials. Second, to obtain a SR image, the deep learning model includes a residual network which reduces the loss of spatial information in the convolutional process of obtaining a feature map. In the process of optimizing the deep learning model, it was confirmed that the performance of the model improved as the number of data increased. In addition, by optimizing the deep learning model using the loss function, including MAE and SSIM at the same time, improved results of the nanomaterial lattice in SR images were achieved at medium and high magnifications. The final proposed deep learning model used four residual blocks to obtain the characteristic map of the low-resolution image, and the super-resolution image was completed using Upsampling2D and the residual block three times.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1274-1280
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• 2022

The resistance performance evaluation of general ships using computational fluid dynamics requires a lot of time and cost, and various methods are being studied to reduce the time and cost. Existing methods using main particulars or cross sections of ships have limitations in estimating resistance performance that is greatly dependent on the shape of the ship. In this paper, we propose a deep neural network model that can quickly predict the resistance performance of the hull surface by inputting the geometric information of the hullform mesh. The proposed deep neural network model based on Perceiver IO can immediately predict resistance performance, unlike computational fluid dynamics techniques that require calculation in each time step. It shows the result of estimating the resistance performance with an average error of less than 1% in the data set for a 50 K tanker ship, a type of low-speed full ship.

• Journal of the Earthquake Engineering Society of Korea
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• v.27 no.2
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• pp.77-82
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• 2023

The structural analysis module is an essential part of any integrated structural system. Diverse integrated systems today require, from the analysis module, efficient real-time responses to real-time input such as earthquake signals, extreme weather-related forces, and man-made accidents. An integrated system may also be for the entire life span of a civil structure conceived during the initial conception, developed throughout various design stages, effectively used in construction, and utilized during usage and maintenance. All these integrated systems' essential part is the structural analysis module, which must be automated and computationally efficient so that responses may be almost immediate. The finite element method is often used for structural analysis, and for automation, many effective finite element meshes must be automatically generated for a given analysis. A computationally efficient finite element mesh generation scheme based on the r-h method of mesh refinement using strain deviations from the values at the Gauss points as error estimates from the previous mesh is described. Shape factors are used to sort out overly distorted elements. A standard cantilever beam analyzed by four-node plane stress elements is used as an example to show the effectiveness of the automated algorithm for a time-domain dynamic analysis. Although recent developments in computer hardware and software have made many new applications in integrated structural systems possible, structural analysis still needs to be executed efficiently in real-time. The algorithm applies to diverse integrated systems, including nonlinear analyses and general dynamic problems in earthquake engineering.

• Journal of IKEEE
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• v.26 no.1
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• pp.43-49
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• 2022

This paper compares the sensorless control performance according to the applied voltage waveform by injecting sinusoidal, triangular, and square waveform in the harmonic injection sensorless control method. By injecting various voltage shape waveform with a frequency of 1kHz, the error amount of the estimated angle for each waveform is compared and analyzed. For the experiment, the HILS(hardware in the loop simulation) system was used. The hardware is the control board, and the inverter and motor models implemented in Simulik are located in the real-time simulator. The control algorithm is implemented by the FPGA control board, which includes a PWM interrupt service routine with a frequency of 10 kHz, harmonic injection and position detection sensorless algorithm.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.958-963
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• 2023

Lately, there has been increasing research on the prediction of motion performance using artificial intelligence for the safe design and operation of ships. However, compared to conventional ships, research on small fishing boats is insufficient. In this paper, we propose a model that estimates the motion response essential for calculating the motion performance of small fishing boats using a deep neural network. Hydrodynamic analysis was conducted on 15 small fishing boats, and a database was established. Environmental conditions and main particulars were applied as input data, and the response amplitude operators were utilized as the output data. The motion response predicted by the trained deep neural network model showed similar trends to the hydrodynamic analysis results. The results showed that the high-frequency motion responses were predicted well with a low error. Based on this study, we plan to extend existing research by incorporating the hull shape characteristics of fishing boats into a deep neural network model.