• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.113-123
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• 2023

This paper introduces the Cherry system, a user-independent blockchain donation system. This is a procedure that is delivered to the beneficiary's bank account through a virtual account when a donor makes a donation, so there is no difference from the existing donation delivery method from the user's point of view However, within the blockchain, Cherry Points, a virtual currency based on the user ID, are issued and delivered to the beneficiary, while all transactions and the beneficiary's usage history are managed on the blockchain. By adopting this method, there was an improvement in blockchain performance, with transaction processing exceeding 1,000 TPS in typical transaction condition and service completion within 21.3 seconds. By applying the automatic influence control algorithm to this system, the influence according to stake, which is an individual donation, is greatly reduced to 0.3 after 2 months, thereby concentrating influence could be controlled automatically. In addition, it was designed to enable micro tracking by adding a tracking function by timestamp to the donation ledger for each individual ID, which greatly improved the transparency in the use of donations. From a service perspective, existing blockchain donation systems were handled as limited donation delivery methods. Since it is a direct service in a user-independent method, convenience has been greatly improved by delivering donations in various forms.

• Journal of Venture Innovation
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• v.6 no.1
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• pp.1-16
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• 2023

Due to the progress of the 4th industrial revolution and the COVID-19 pandemic, the subscription economy was rapidly expanding. In particular, the subscription economy was expected to expand further as the servicing of products(servitization) rapidly progresses. In this study, we tried to empirically analyze the factors that promote and hinder the spread of the subscription economy from the consumer's point of view. To this end, based on the Service Profit Chain (SPC) model, which identified mechanisms leading from quality to satisfaction, loyalty, and performance, a research model was established by combining the framework of the Value-based Adoption Model (VAM), which covers both benefit and sacrifice factors. Usefulness and convenience were derived as benefit factors, and perceived risks and perceived costs were derived as sacrifice factors. The effects of these factors on satisfaction and continuous use intention were analyzed. For empirical analysis, a survey was conducted targeting people who have experience in subscription economy, and 300 effective samples were analyzed. The analysis was performed as a structural equation model using AMOS 24. As a result of the empirical study, it was found that convenience had a significant positive (+) effect on satisfaction. Perceived risk and perceived cost were analyzed to have a negative (-) effect on satisfaction. On the other hand, usefulness was found to have no significant effect on satisfaction. The influences affecting satisfaction were in the order of perceived cost, convenience, and perceived risk. Satisfaction was found to have a significant positive (+) effect on continuous use intention. The results of this study were considered meaningful in that they broadened the horizons of research by combining existing validated models at the academic level and testing their validity, and found that perceived cost was still an important factor at the practical level.

• Composites Research
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• v.36 no.5
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• pp.289-296
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• 2023

In this study, we investigated the electromagnetic properties and microwave absorption characteristics of M-type hexagonal ferrites, which are known as millimeter-wave absorbing materials, according to their calcination temperature. The M-type ferrites synthesized using a molten salt-based sol-gel method exhibited a single-phase M-type crystal structure at calcination temperatures above 850℃. The synthesized particle size increased as well with the calcination temperature. Saturation magnetization increased gradually with increasing calcination temperature, but coercivity reached a maximum at 1050℃ and then rapidly decreased. After preparing a thermoplastic polyurethane (TPU) composite containing 70 wt% of M-type ferrites, we measured the complex permittivity and permeability in the Q-band (33-50 GHz) and V-band (50-75 GHz) frequency ranges, where ferromagnetic resonance occurred. Strong magnetic loss from ferromagnetic resonance occurred in the 50 GHz band for all composite samples. Based on the measured results, we calculated the reflection loss of the TPU/M-type ferrite composite. By calculating the reflection loss of the M-type ferrite composite, the M-type ferrite calcined at 1250℃ showed excellent electromagnetic wave absorption performance of more than -20 dB at 52 GHz with a thickness of about 0.5 mm.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.119-125
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• 2023

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.

• Tunnel and Underground Space
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• v.33 no.5
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• pp.348-372
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• 2023

In relation to the high-level radioactive waste disposal project in deep fractured rock aquifer environments, it is essential to evaluate hydrogeological characteristics for evaluating the suitability of the site and operational stability. Such subsurface hydrogeological data is obtained through in-situ tests using boreholes excavated at the target site. The accuracy and reliability of the investigation results are directly related to the selection of appropriate test methods, the performance of the investigation system, standardization of the investigation procedure. In this report, we introduce the detailed procedures for the representative test method, the constant pressure injection test (CPIT), which is used to determine the key hydrogeological parameters of the subsurface fractured rock aquifer, namely hydraulic conductivity and storativity. This report further refines the standard test method suggested by the KSRM in 2022 and includes practical field application case conducted in volcanic rock aquifers where this investigation procedure has been applied.

• Journal of The Korean Astronomical Society
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• v.56 no.2
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• pp.213-224
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• 2023

Type II solar radio bursts show frequency drifts from high to low over time. They have been known as a signature of coronal shock associated with Coronal Mass Ejections (CMEs) and/or flares, which cause an abrupt change in the space environment near the Earth (space weather). Therefore, early detection of type II bursts is important for forecasting of space weather. In this study, we develop a deep-learning (DL) model for the automatic detection of type II bursts. For this purpose, we adopted a 1-D Convolution Neutral Network (CNN) as it is well-suited for processing spatiotemporal information within the applied data set. We utilized a total of 286 radio burst spectrum images obtained by Hiraiso Radio Spectrograph (HiRAS) from 1991 and 2012, along with 231 spectrum images without the bursts from 2009 to 2015, to recognizes type II bursts. The burst types were labeled manually according to their spectra features in an answer table. Subsequently, we applied the 1-D CNN technique to the spectrum images using two filter windows with different size along time axis. To develop the DL model, we randomly selected 412 spectrum images (80%) for training and validation. The train history shows that both train and validation losses drop rapidly, while train and validation accuracies increased within approximately 100 epoches. For evaluation of the model's performance, we used 105 test images (20%) and employed a contingence table. It is found that false alarm ratio (FAR) and critical success index (CSI) were 0.14 and 0.83, respectively. Furthermore, we confirmed above result by adopting five-fold cross-validation method, in which we re-sampled five groups randomly. The estimated mean FAR and CSI of the five groups were 0.05 and 0.87, respectively. For experimental purposes, we applied our proposed model to 85 HiRAS type II radio bursts listed in the NGDC catalogue from 2009 to 2016 and 184 quiet (no bursts) spectrum images before and after the type II bursts. As a result, our model successfully detected 79 events (93%) of type II events. This results demonstrates, for the first time, that the 1-D CNN algorithm is useful for detecting type II bursts.

• Journal of the Korea Society for Simulation
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• v.32 no.4
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• pp.77-84
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• 2023

In the agricultural sector, power sources are being developed that use alternative energy sources such as electric tractors and hydrogen tractors, away from internal combustion engine tractors. As parts such as engines and transmissions used in conventional internal combustion engine tractors are replaced with motors and batteries, the center of gravity changes, and thus the risk of rollover should be considered. The purpose of this study is to analyze the overturn angle of the main parts of the electric tractor through dynamic simulation to minimize the overturn accident and to derive the optimal arrangement of parts to improve stability. A total of nine dynamics simulations were conducted by designing three components of the PTO motor, drive motor and the battery pack, and three factors of the arrangement method. As a result of the experiment, it was confirmed that Type3 Level3, in which the drive motor and the PTO motor are located at the front and rear of the tractor, and two battery packs are located in the middle of the tractor, has a high rollover angle. As a result of this study, the stability increased as the center of gravity was placed backward and located below. Future research needs to be done to find the optimal location of parts considering their performance and placement efficiency.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.519-533
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• 2023

In this study, the linear cutting tests of pick cutters were carried out on a granitic rock with the average compressive strength over 100 MPa. From the tests, the correlation between the cutter acting force and the temperature measured by infrared thermal imaging camera during rock cutting was analyzed. In every experimental condition, the maximum temperature was measured at the rock surface where the chipping occurred, and the temperature generated in the rock was closely correlated with the cutter acting force. On the other hand, the temperature of a pick cutter increased up to only 36℃ above the ambient temperature, and the correlation with the cutter force was not obvious. This can be attributed to the short cutting distance under laboratory conditions and the high thermal conductivity of the tungsten carbide inserts. However, the relatively high temperature of the tungsten carbide inserts was found to be maintained. Therefore, it is recommended that a reinforcement between the insert and the head of a pick cutter or the quality improvement of silvering brazing in the production of a cutter is necessary to maintain the high cutting performance of a pick cutter.

• Journal of Digital Policy
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• v.2 no.4
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• pp.1-10
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• 2023

This study conducted preliminary study to identify effective ways to use ChatGPT in traffic policing by analyzing ChatGPT's responses to the driver's license test and the road traffic accident appraiser test. I collected ChatGPT responses for the driver's license test item pool and the road traffic accident appraiser test using the OpenAI API with Python code for 30 iterative experiments, and analyzed the percentage of correct answers by test, year, section, and consistency. First, the average correct answer rate for the driver's license test and the for road traffic accident appraisers test was 44.60% and 35.45%, respectively, which was lower than the pass criteria, and the correct answer rate after 2022 was lower than the average correct answer rate. Second, the percentage of correct answers by section ranged from 29.69% to 56.80%, showing a significant difference. Third, it consistently produced the same response more than 95% of the time when the answer was correct. To effectively utilize ChatGPT, it is necessary to have user expertise, evaluation data and analysis methods, design a quality traffic law corpus and periodic learning.