• Journal of the Korean Society of Radiology
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• v.16 no.7
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• pp.995-1006
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• 2022

The purpose of this study was to investigate the current status of practical course operation in a non-face-to-face online environment, learning effects, and students' experiences and perceptions for radiology major students using a survey. The questionnaire consisted of a total of 34 items in 5 areas: general characteristics of subjects, current learning participation in non-face-to-face environments, learning satisfaction, learning outcomes, improvement and requirements. For the analysis of the questionnaire responses, frequency analysis was performed on the response frequency, ratio, and scale for each item. Based on the general characteristics of the survey respondents, cross-analysis was performed using the chi-square test for participation in non-face-to-face learning, learning performance, and learning satisfaction. implemented. Improvements and requirements were qualitatively analyzed for the repetition frequency of words with the same meaning. Through the results of analyzing the responses of a total of 397 questionnaires, the direction of design and development of practical classes in a non-face-to-face environment in the future and basic information and implications for efficient operation were confirmed. Based on this, it is necessary to continue to think and make efforts for the efficient operation of non-face-to-face practice classes in the post-corona era.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.2
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• pp.109-116
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• 2022

In this paper, we propose a band rejection filter (BRF) with a state variable filter (SVF) structure to effectively remove the influence of 60 Hz line frequency noise introduced into the sensor system. The conventional BRF of the SVF structure uses an additional operational amplifier (OPAMP) to add a low pass filter (LPF) output and a high pass filter (HPF) output or an input signal and a band pass filter. Therefore, the notch frequency and the notch depth that determine the signal attenuation of the BRF greatly depend on the tolerance of the resistors used to obtain the sum or difference of the signals. On the other hand, in the proposed BRF, since the BRF output is formed naturally within the SVF structure, there is no need for a combination between each port. The notch frequency of the proposed BRF is 59.99 Hz, and it can be confirmed that it is not affected at all by the tolerance of the resistor through the Monte Carlo simulation results. The notch depth also has an average of -42.54dB and a standard deviation of 0.63dB, confirming that normal operation as a BRF is possible. Also, with the proposed BRF, noise filtering was applied to the electrocardiogram (ECG) signal that interfered with 60 Hz noise, and it was confirmed that the 60 Hz noise was appropriately suppressed.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.6
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• pp.1054-1062
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• 2022

The population of Korea's solar salt-producing regions is rapidly aging, resulting in a decrease in the number of productive workers. In solar salt production, salt collection is the most labor-intensive operation because existing salt collection vehicles require human operators. Therefore, we intend to develop an unmanned solar salt collection vehicle to reduce manpower requirements. The unmanned solar salt collection vehicle is designed to identify the salt collection status and location in the salt plate via color detection, the color detection performance is a crucial consideration. Therefore, an image processing algorithm was developed to improve color detection performance. The algorithm generates an around-view image by using resizing, rotation, and perspective transformation of the input image, set the RoI to transform only the corresponding area to the HSV color model, and detects the color area through an AND operation. The detected color area was expanded and noise removed using morphological operations, and the area of the detection region was calculated using contour and image moment. The calculated area is compared with the set area to determine the location case of the collection vehicle within the salt plate. The performance was evaluated by comparing the calculated area of the final detected color to which the algorithm was applied and the area of the detected color in each step of the algorithm. It was confirmed that the color detection performance is improved by at least 25-99% for salt detection, at least 44-68% for red color, and an average of 7% for blue and an average of 15% for green. The proposed approach is well-suited to the operation of unmanned solar salt collection vehicles.

• Journal of the Korea Computer Graphics Society
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• v.28 no.2
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• pp.1-9
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• 2022

In this paper, we propose a vector-based augmentation technique that can generate data required for crack detection and a ConvNet(Convolutional Neural Network) technique that can learn it. Detecting cracks quickly and accurately is an important technology to prevent building collapse and fall accidents in advance. In order to solve this problem with artificial intelligence, it is essential to obtain a large amount of data, but it is difficult to obtain a large amount of crack data because the situation for obtaining an actual crack image is mostly dangerous. This problem of database construction can be alleviated with elastic distortion, which increases the amount of data by applying deformation to a specific artificial part. In this paper, the improved crack pattern results are modeled using ConvNet. Rather than elastic distortion, our method can obtain results similar to the actual crack pattern. By designing the crack data augmentation based on a vector, rather than the pixel unit used in general data augmentation, excellent results can be obtained in terms of the amount of crack change. As a result, in this paper, even though a small number of crack data were used as input, a crack database can be efficiently constructed by generating various crack directions and patterns.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.4
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• pp.221-232
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• 2023

This paper presents a novel convolutional neural network (CNN)-based approach for predicting bolt clamping force in the early bolt loosening state of bolted structures. The approach entails tightening eight bolts with different clamping forces and generating frequency responses, which are then used to create a similarity map. This map quantifies the magnitude and shape similarity between the frequency responses and the initial model in a fully fastened state. Krylov subspace-based model order reduction is employed to efficiently handle the large amount of frequency response data. The CNN model incorporates a regression output layer to predict the clamping forces of the bolts. Its performance is evaluated by training the network by using various amounts of training data and convolutional layers. The input data for the model are derived from the magnitude and shape similarity map obtained from the frequency responses. The results demonstrate the diagnostic potential and effectiveness of the proposed approach in detecting early bolt loosening. Accurate bolt clamping force predictions in the early loosening state can thus be achieved by utilizing the frequency response data and CNN model. The findings afford valuable insights into the application of CNNs for assessing the integrity of bolted structures.

• Applied Chemistry for Engineering
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• v.34 no.4
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• pp.404-411
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• 2023

For the direct reforming of biogas, a three-phase gliding arc plasma reformer was designed to expand the plasma discharge region, and the operation conditions of the plasma reformer, such as the S/C ratio, the gas flow rate, and the plasma input power, were optimized. The H₂ production efficiency is increased at a lower specific plasma input energy density, but byproducts such as C_XH_Y and carbon soot are generated along with the increase in H₂ production efficiency. The formation of byproducts is decreased at higher specific plasma input energy densities and S/C ratios. The optimized operation conditions are 5.5 ~ 6.0 kJ/L for the specific plasma input energy density and 3 for the S/C ratio, considering the conversion efficiency, H₂ production, and byproduct formation. It is expected that the H₂ production efficiency will improve with the decrease in fuel consumption in biogas burners because the heat generated from plasma discharge heats up the feed gas to over 500 ℃.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.103-112
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• 2009

In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be calibrated. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. In this paper, principal component-based Auto-Associative support vector regression (PCSVR) was proposed for the sensor signal validation of the NPP. It utilizes the attractive merits of principal component analysis (PCA) for extracting predominant feature vectors and AASVR because it easily represents complicated processes that are difficult to model with analytical and mechanistic models. With the use of real plant startup data from the Kori Nuclear Power Plant Unit 3, SVR hyperparameters were optimized by the response surface methodology (RSM). Moreover the statistical techniques are integrated with PCSVR for the failure detection. The residuals between the estimated signals and the measured signals are tested by the Shewhart Control Chart, Exponentially Weighted Moving Average (EWMA), Cumulative Sum (CUSUM) and generalized likelihood ratio test (GLRT) to detect whether the sensors are failed or not. This study shows the GLRT can be a candidate for the detection of sensor drift.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.195-204
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• 2023

In this study, prompt engineering that improves prompts was explored to improve the performance of teaching and learning materials recommendations using generative artificial intelligence such as GPT and Stable Diffusion. Picture materials were used as the types of teaching and learning materials. To explore the impact of the prompt composition, a Zero-Shot prompt, a prompt containing learning target grade information, a prompt containing learning goals, and a prompt containing both learning target grades and learning goals were designed to collect responses. The collected responses were embedded using Sentence Transformers, dimensionalized to t-SNE, and visualized, and then the relationship between prompts and responses was explored. In addition, each response was clustered using the k-means clustering algorithm, then the adjacent value of the widest cluster was selected as a representative value, imaged using Stable Diffusion, and evaluated by 30 elementary school teachers according to the criteria for evaluating teaching and learning materials. Thirty teachers judged that three of the four picture materials recommended were of educational value, and two of them could be used for actual classes. The prompt that recommended the most valuable picture material appeared as a prompt containing both the target grade and the learning goal.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.703-709
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• 2023

It is most desirable to train with a real crane in an environment similar to that of a port for crane operation training in charge of loading and unloading in a port, but it has time and space limitations and cost problems. In order to overcome these limitations, VR(Virtual Reality) based crane training programs and related devices are receiving a lot of attention. In this paper, we designed and implemented a VR-based harbor crane simulator operating on an HMD. The simulator developed in this paper consists of a crane simulator program that operates on the HMD, an IoT driving terminal that processes trainees' crane operation input, and a training server that stores trainees' training information. The simulator program provides VR-based crane training scenarios implemented with Unity3D, and the IoT driving terminal developed based on Arduino is composed of two controllers and transmits the user's driving operation to the HMD. In particular, the crane simulator in this paper uses a training server to create a database of environment setting values for each educator, progress and training time, and information on driving warning situations. Through the use of such a server, trainees can use the simulator in a more convenient environment and can expect improved educational effects by providing training information.

• Journal of the Korean Geotechnical Society
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• v.26 no.8
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• pp.77-88
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• 2010

In this study, 1 g shaking table group pile tests were performed for various conditions of subgrade and pile spacing. The pile spacing was changed from three to seven times of pile diameters. It could be confirmed that the dynamic p-y curves for the group pile observed as the results of a series of shaking table tests show difference according to the pile spacing, the pile location within the pile group, the relative density of subgrade and the excess pore pressure during earthquake. The dynamic p-multipliers were calculated by comparing the dynamic p-y backbone curves of a single pile suggested by Yang (2009) and dynamic p-y curves for the group pile. Dynamic p-multiplier values overall increase as the relative density of subgrade and amplitude of input acceleration increase. The dynamic group pile effect was neglected, if the pile spacing was seven times as large as pile diameters. It was found that the exisiting p-multiplier values suggested by various researchers for the static and dynamic loading, and the values recommended by globally used specifications show difference with the test results by up to 0.7 (approximately 70%). Therefore, the dynamic p-multipliers were newly suggested according to the pile spacing and the relative density of subgrade using the test results.