• Journal of the Korean Society of Radiology
• /
• v.14 no.7
• /
• pp.849-856
• /
• 2020

In the gamma-ray energy spectrum study, nuclide analysis through energy analysis is very important. High-purity Ge detectors, which are commonly used for gamma-ray energy measurements, are commonly used because of their high energy resolution and relatively high detection efficiency. However, in order to maintain a high energy resolution, the semiconductor detector has a problem in that it is difficult to maintain the original performance if the noise generated from the surrounding environment is not effectively blocked, and the effect of the expensive device is not achieved. Therefore, in this study, ground loop isolator (NEXT-001HDGL) was used to remove the electrical noise generated from the detector. In order to test the effect of improving energy resolution, HPGe detection device newly installed in the proton accelerator KOMAC was used. In the case of gamma-ray energy 2614 keV, the energy resolution was improved from (0.16 ± 0.02) % to (0.11 ± 0.01) %, and in the case of gamma-ray energy 662 keV of 137Cs isotope, the energy resolution was improved from (0.72 ± 0.07) % to (0.27 ± 0.03) %. This result is considered to be very useful for the gamma ray spectrum study using the HPGe detection equipment of KOMAC(Korea Multi-Purpose Accelerator Complex).

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.23 no.1
• /
• pp.13-24
• /
• 2021

Tunnel boring machine (TBM) is widely used for tunnel excavation in hard rock and soft ground. In the perspective of TBM-based tunneling, one of the main challenges is to drive the machine optimally according to varying geological conditions, which could significantly lead to saving highly expensive costs by reducing the total operation time. Generally, drilling investigations are conducted to survey the geological ground before the TBM tunneling. However, it is difficult to provide the precise ground information over the whole tunnel path to operators because it acquires insufficient samples around the path sparsely and irregularly. To overcome this issue, in this study, we proposed a geological type classification system using the TBM operating data recorded in a 5 s sampling rate. We first categorized the various geological conditions (here, we limit to granite) as three geological types (i.e., rock, soil, and mixed type). Then, we applied the preprocessing methods including outlier rejection, normalization, and extracting input features, etc. We adopted a deep neural network (DNN), which has 6 hidden layers, to classify the geological types based on TBM operating data. We evaluated the classification system using the 10-fold cross-validation. Average classification accuracy presents the 75.4% (here, the total number of data were 388,639 samples). Our experimental results still need to improve accuracy but show that geology information classification technique based on TBM operating data could be utilized in the real environment to complement the sparse ground information.

• Journal of the Institute of Convergence Signal Processing
• /
• v.22 no.1
• /
• pp.14-19
• /
• 2021

This study has attempted to use ozone for the treatment of skin diseases as research results that ozonated olive oil has an excellent therapeutic effect on skin diseases are known. However, there is hardly any accurate data in Korea. Usually, animal tests related to cosmetics are not performed, but toxicity tests were conducted because they were absolutely necessary. In general, there are not many cases of measuring actual data through animal tests for the purpose of confirming the performance of cosmetics, but in the case of toxicity tests, it is recommended to accurately measure skin reactions, so this experiment was conducted. In this experiment, in order to evaluate the skin irritation of ozonated oil (high concentration) on the rabbit, the test substance was applied to the back of the rabbit for 24 hours, and then mortality, general symptoms and skin irritation were evaluated. Experimental Results As a result of evaluating the treatment site of the test substance after a certain period of time, no skin irritation was observed in all animals.

• Journal of the Korean Crystal Growth and Crystal Technology
• /
• v.31 no.1
• /
• pp.55-62
• /
• 2021

In order to improve fuel economy and reduce CO2, HEV adopts ISG system as a standard. This ISG system increased the electric load that the battery had to bear, and the number of starting increased rapidly. AGM Lead Acid batteries have been developed and used, but the charging time is about three times longer as the electrolyte amount control during formation must be maintained at a higher level compared to conventional lead-acid batteries. In this study, we tried to shorten the charging time by increasing the charging efficiency through the optimization of the formation pattern. In order to optimize the Formation Pattern, 10 charging steps and 6 discharging steps were applied to 16 multi steps, and the charging current for each step was controlled, and the test was conducted under 4 conditions (21 hr, 24 hr, 27 hr, 30 hr). As a result of simultaneous application of multi-step and discharge step, it was verified that minimizing the current loss and eliminating the sudden polarization during charging contributes to the improvement of charging efficiency. As a result, it showed excellent results in reducing the charging time by about 30 % with improved charging efficiency compared to the previous one.

• Korean Journal of Optics and Photonics
• /
• v.31 no.6
• /
• pp.281-289
• /
• 2020

This paper reports a slim mobile lens design using a hybrid refractive/diffractive optical element. Conventionally a wide field of view (FOV) camera-lens design adopts a retrofocus type having a negative (-) lens at the forefront, so that it improves in imaging performance over the wide FOV, but with the sacrifice of longer total track length (TTL). However, we chose a telephoto type as a baseline design layout having a positive (+) lens at the forefront, to achieving slimness, based on the specification analysis of 23 reported optical designs. Following preliminary optimization of a baseline design and aberration analysis based on Zernike-polynomial decomposition, we applied a hybrid refractive/diffractive element to effectively reduce the residual chromatic spherical aberration. The optimized optical design consists of 6 optical elements, including one hybrid element. It results in a very slim telephoto ratio of 1.7, having an f-number of 2.0, FOV of 90°, effective focal length of 2.23 mm, and TTL of 3.7 mm. Compared to a comparable conventional lens design with no hybrid elements, the hybrid design improved the value of the modulation transfer function (MTF) at a spatial frequency of 180 cycles/mm from 63% to 71-73% at zero field (0 F), and about 2-3% at 0.5, 0.7, and 0.9 fields. It was also found that a design with a hybrid lens with only two diffraction zones at the stop achieved the same performance improvement.

• Journal of Broadcast Engineering
• /
• v.26 no.1
• /
• pp.26-38
• /
• 2021

In this paper, DLKF (Deep Learning Keypoint Filtering), the deep learning-based keypoint filtering method for the rapidization of the image registration method for remote sensing images is proposed. The complexity of the conventional feature-based image registration method arises during the feature matching step. To reduce this complexity, this paper proposes to filter only the keypoints detected in the artificial structure among the keypoints detected in the keypoint detector by ensuring that the feature matching is matched with the keypoints detected in the artificial structure of the image. For reducing the number of keypoints points as preserving essential keypoints, we preserve keypoints adjacent to the boundaries of the artificial structure, and use reduced images, and crop image patches overlapping to eliminate noise from the patch boundary as a result of the image segmentation method. the proposed method improves the speed and accuracy of registration. To verify the performance of DLKF, the speed and accuracy of the conventional keypoints extraction method were compared using the remote sensing image of KOMPSAT-3 satellite. Based on the SIFT-based registration method, which is commonly used in households, the SURF-based registration method, which improved the speed of the SIFT method, improved the speed by 2.6 times while reducing the number of keypoints by about 18%, but the accuracy decreased from 3.42 to 5.43. Became. However, when the proposed method, DLKF, was used, the number of keypoints was reduced by about 82%, improving the speed by about 20.5 times, while reducing the accuracy to 4.51.

• Journal of Broadcast Engineering
• /
• v.27 no.2
• /
• pp.228-239
• /
• 2022

In this paper, we propose an unmanned vehicle scratch detection deep learning model for car sharing services. Conventional scratch detection models consist of two steps: 1) a deep learning module for scratch detection of images before and after rental, 2) a manual matching process for finding newly generated scratches. In order to build a fully automatic scratch detection model, we propose a one-step unmanned scratch detection deep learning model. The proposed model is implemented by applying transfer learning and fine-tuning to the deep learning model that detects changes in satellite images. In the proposed car sharing service, specular reflection greatly affects the scratch detection performance since the brightness of the gloss-treated automobile surface is anisotropic and a non-expert user takes a picture with a general camera. In order to reduce detection errors caused by specular reflected light, we propose a preprocessing process for removing specular reflection components. For data taken by mobile phone cameras, the proposed system can provide high matching performance subjectively and objectively. The scores for change detection metrics such as precision, recall, F1, and kappa are 67.90%, 74.56%, 71.08%, and 70.18%, respectively.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.5
• /
• pp.1-6
• /
• 2021

Mercury, once released, is not destroyed but accumulates and circulates in the natural environment, causing serious harm to ecosystems and human health. In the United States, sulfur-impregnated activated carbon is being considered for the removal of vapor mercury from the flue gas of coal-fired power plants, which accounts for about 32 % of the anthropogenic emissions of mercury. In this study, a high-efficiency porous mercury adsorption material was developed to reduce the mercury vapor in the exhaust gas of coal combustion facilities, and the mercury adsorption characteristics of the material were investigated. As a result of the investigation of the vapor mercury adsorption capacity at 30℃, the silica nanotube MCM-41 was only about 35 % compared to the activated carbon Darco FGD commercially used for mercury adsorption, but it increased to 133 % when impregnated with 1.5 % sulfur. In addition, the furnace fly ash recovered from the waste copper regeneration process showed an efficiency of 523 %. Furthermore, the adsorption capacity was investigated at temperatures of 30 ℃, 80 ℃, and 120 ℃, and the best adsorption performance was found to be 80 ℃. MCM-41 is a silica nanotube that can be reused many times due to its rigid structure and has additional advantages, including no possibility of fire due to the formation of hot spots, which is a concern when using activated carbon.

• KNOM Review
• /
• v.23 no.2
• /
• pp.18-28
• /
• 2020

With the development of the virtual community, the benefits that IT technology provides to people in fields such as healthcare, industry, communication, and culture are increasing, and the quality of life is also improving. Accordingly, there are various malicious attacks targeting the developed network environment. Firewalls and intrusion detection systems exist to detect these attacks in advance, but there is a limit to detecting malicious attacks that are evolving day by day. In order to solve this problem, intrusion detection research using machine learning is being actively conducted, but false positives and false negatives are occurring due to imbalance of the learning dataset. In this paper, a Random Oversampling method is used to solve the unbalance problem of the UNSW-NB15 dataset used for network intrusion detection. And through experiments, we compared and analyzed the accuracy, precision, recall, F1-score, training and prediction time, and hardware resource consumption of the models. Based on this study using the Random Oversampling method, we develop a more efficient network intrusion detection model study using other methods and high-performance models that can solve the unbalanced data problem.

• The Journal of the Convergence on Culture Technology
• /
• v.7 no.4
• /
• pp.869-878
• /
• 2021

This study is a study on 11-story apartments that increase the event of fires in old apartments where building-related laws and regulations are not retroactively applied. As a result of analyzing the risk of installing fire doors in Improvement Scenario 2-4, assuming that fire doors are installed as basic scenario 1 in the existing situation where fire doors are not installed at the entrance of direct stairs. In basic scenario 1, the visible distance to the entrance of the direct staircase due to the spread of smoke was 260 seconds. Improvement scenarios 3 to 4 with fire doors installed open 300 seconds after the fire was recognized, and when the fire doors were installed at the entrance of the direct stairs, the visibility to the entrance of the statistics team was less than 600 seconds. In this case, the visibility was 600 seconds at the time of installation of the fire door, and scenarios 3 to 4 increased 56.6% compared to scenario 1, lowering the risk of evacuation by more than 50%. In order to eliminate the risk of non-installation of direct statistical groups that increase the risk of smoke spread, building-related laws such as the Fire Fighting Act shall be retroactively applied when installing a direct stairway entrance or balcony folding evacuation system. The improvement caused by the installation of fire doors has numerically proven the necessity of fire doors during evacuation, and the importance of maintaining fire doors can be grasped.