• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.611-618
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• 2023

The purpose of the refueling protocol and the contents of SAE J2601, which is used as the basis for hydrogen vehicles refueling around the world, were investigated, and research contents related to domestic protocols were also investigated. In addition, the components of the hydrogen refueling performance evaluation device developed in Korea and the method for evaluating the performance and safety of hydrogen refueling stations were reviewed. And, the result were analyzed by applying it to the hydrogen refueling stations currently operating in Korea. In addition, an economic feasibility analysis was conducted using data collected from domestic hydrogen refueling stations. In order to secure the safety and economy of a hydrogen refueling station, the protocol must be satisfied, and in order to satisfy the protocol, it is necessary to evaluate whether the refueling temperature, refueling pressure, and refueling flow are controlled within a safe range.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.3
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• pp.218-227
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• 2016

Korean government plans to launch a lunar orbiter and a lander to the Moon by 2020. Before launch these two proves, an experimental lunar orbiter will be launched by 2018 to obtain key space technologies for the lunar exploration. Several payloads equipped in experimental lunar orbiter will monitor the surface of the Moon and will gather science data. Lunar orbiter sends telemetry and receives tele-command from ground using S-band while science data is sent to ground stations using X-band when the visibility is available. Korean deep space network will be mainly used for S and X-band communication with lunar orbiter. Deep Space Network or Universal Space Network can also be used for the S-band during trans-lunar phase when korean deep space network is not available and will be used for the S-band in normal mission orbit as a backup. This paper analyzes a visibility condition based on the combination of various ground antennas and its mask angles according to mission scenario to predict the number of contacts per day and to build an operational scenario for the lunar orbiter.

• Database Research
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• v.34 no.3
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• pp.148-158
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• 2018

The optical character recognition (OCR) is a technique to extract and recognize texts from images. It is an important preprocessing step in data analysis since most actual text information is embedded in images. Many OCR engines have high recognition accuracy for images where texts are clearly separable from background, such as white background and black lettering. However, they have low recognition accuracy for images where texts are not easily separable from complex background. To improve this low accuracy problem with complex images, it is necessary to transform the input image to make texts more noticeable. In this paper, we propose a method to segment an input image into text lines to enable OCR engines to recognize each line more efficiently, and to determine the final output by comparing the recognition rates of CLAHE module and Two-step module which distinguish texts from background regions based on image processing techniques. Through thorough experiments comparing with well-known OCR engines, Tesseract and Abbyy, we show that our proposed method have the best recognition accuracy with complex background images.

• Korean Journal of Heritage: History & Science
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• v.56 no.4
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• pp.216-228
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• 2023

The purpose of this study is to apply the handheld Raman spectrometer to identify the coloring materials used in a large Buddhist painting (of Maitreya Buddha) at Janggoksa Temple through cross-validation with HH-XRF. An in situ investigation was performed together with use of a digital microscope and HH-XRF analysis to verify the properties of pigments used in the gwaebul ("large Buddhist painting") via a non-destructive method. However, the identification of coloring materials composed of light elements and mixed or overlaid pigments is difficult using only non-destructive analysis data. Unlike in situ investigation, laboratory analysis often required samples yet the sampling is restricted to a small quantity due to the cultural heritage characteristic. Thus, it is necessary to develop a non-destructive in situ method to supplement the HH-XRF data. The large Buddhist painting at Janggoksa Temple was painted mainly using white, red, yellow, green, and blue colors. The Raman spectroscopy provides molecular information, while XRF spectroscopy provides information about elemental composition of the pigments. Analysis results identified various coloring materials: inorganic pigment, such as lead white, minium, cinnabar, and orpiment, as well as organic pigment such as gamboge and indigo. Therefore, it is possible to obtain more information for the identification of pigments; organic pigment and mixed or overlaid pigments, while at the same time minimizing the collection sample and simplifying the analysis procedure compared to previously used methods. The results of this study will be used as basic data for the analysis of painting cultural heritage through a non-destructive in situ method in the future.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.479-487
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• 2021

In this paper, we propose an Sound Event Detection (SED) model using self-training based on a noisy student model. The proposed SED model consists of two stages. In the first stage, a mean-teacher model based on an Residual Convolutional Recurrent Neural Network (RCRNN) is constructed to provide target labels regarding weakly labeled or unlabeled data. In the second stage, a self-training-based noisy student model is constructed by applying different noise types. That is, feature noises, such as time-frequency shift, mixup, SpecAugment, and dropout-based model noise are used here. In addition, a semi-supervised loss function is applied to train the noisy student model, which acts as label noise injection. The performance of the proposed SED model is evaluated on the validation set of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Challenge Task 4. The experiments show that the single model and ensemble model of the proposed SED based on the noisy student model improve F1-score by 4.6 % and 3.4 % compared to the top-ranked model in DCASE 2020 challenge Task 4, respectively.

• Journal of Bio-Environment Control
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• v.28 no.1
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• pp.16-27
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• 2019

Using virtual reality technology, users can learn and experience many interactions in virtual space like the actual physical space. This study was conducted to develop air flow simulator that allows farmers and consultants to consult air flow through VR devices by creating a greenhouse or pigpen model. It can help educate farmers about the importance of ventilation effects for agricultural facilities. We proposed CFD visualization system by building a virtual reality environment and constructing database of CFD and structure of agricultural facilities. After consultants can set up situations according to environmental conditions, the users experience the visualized air flow of agricultural facility according to the ventilation effects. Also it can provide a quantified environmental distribution in the agricultural facility. Currently, the CFD data in agricultural facilities are established during winter and summer. In order to experience various environmental conditions in the developed system, The experts need to run CFD data under various environmental conditions and register them in the system requirements.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.4
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• pp.139-147
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• 2021

In this study, waves were defined using the water surface elevation data observed from the HeMOSU-1 and 2 marine meteorological observation towers installed on the west coast of Korea, and correlation analysis was performed between wave parameters. The wave height and wave period were determined using the wave-train analysis method and the wave spectrum analysis method, and the relationship between the wave parameters was calculated and compared with the previous study. In the relation between representative wave heights, most of the correlation coefficients between waves showed a difference of less than 0.1% in error rate compared to the previous study, and the maximum wave height showed a difference of up to 29%. In addition, as a result of the correlation analysis between the wave periods, the peak period was estimated to be abnormally large at rates of 2.5% and 1.3% in HeMOSU-1&2, respectively, due to the effect of the bimodal spectrum that occurs when the spectral energy density is small.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.991-1005
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• 2022

Satellite imaging is an effective supplementary data source for detecting and verifying nuclear activity. It is also highly beneficial in regions with limited access and information, such as nuclear installations. Time series analysis, in particular, can identify the process of preparing for the conduction of a nuclear experiment, such as relocating equipment or changing facilities. Differences in the semantic segmentation findings of time series photos were employed in this work to detect changes in meaningful items connected to nuclear activity. Building, road, and small object datasets made of KOMPSAT 3/3A photos given by AIHub were used to train deep learning models such as U-Net, PSPNet, and Attention U-Net. To pick relevant models for targets, many model parameters were adjusted. The final change detection was carried out by including object information into the first change detection, which was obtained as the difference in semantic segmentation findings. The experiment findings demonstrated that the suggested approach could effectively identify altered pixels. Although the suggested approach is dependent on the accuracy of semantic segmentation findings, it is envisaged that as the dataset for the region of interest grows in the future, so will the relevant scope of the proposed method.

• Journal of Korean Society of Disaster and Security
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• v.14 no.4
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• pp.17-27
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• 2021

Streamflow prediction is a very vital disaster mitigation approach for effective flood management and water resources planning. Lately, torrential rainfall caused by climate change has been reported to have increased globally, thereby causing enormous infrastructural loss, properties and lives. This study evaluates the contribution of rainfall to streamflow prediction in normal and peak rainfall scenarios, typical of the recent flood at Piney Resort in Vernon, Hickman County, Tennessee, United States. Daily streamflow, water level, and rainfall data for 20 years (2000-2019) from two USGS gage stations (03602500 upstream and 03599500 downstream) of the Piney River watershed were obtained, preprocesssed and fitted with Long short term memory (LSTM) model. Tensorflow and Keras machine learning frameworks were used with Python to predict streamflow values with a sequence size of 14 days, to determine whether the model could have predicted the flooding event in August 21, 2021. Model skill analysis showed that LSTM model with full data (water level, streamflow and rainfall) performed better than the Naive Model except some rainfall models, indicating that only rainfall is insufficient for streamflow prediction. The final LSTM model recorded optimal NSE and RMSE values of 0.68 and 13.84 m³/s and predicted peak flow with the lowest prediction error of 11.6%, indicating that the final model could have predicted the flood on August 24, 2021 given a peak rainfall scenario. Adequate knowledge of rainfall patterns will guide hydrologists and disaster prevention managers in designing efficient early warning systems and policies aimed at mitigating flood risks.

• The Journal of the Acoustical Society of Korea
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• v.42 no.5
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• pp.460-468
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• 2023

Developmental Dysplasia of the Hip (DDH) is a pathological condition commonly occurring during the growth phase of infants. It acts as one of the factors that can disrupt an infant's growth and trigger potential complications. Therefore, it is critically important to detect and treat this condition early. The traditional diagnostic methods for DDH involve palpation techniques and diagnosis methods based on the detection of keypoints in the hip joint using X-ray or ultrasound imaging. However, there exist limitations in objectivity and productivity during keypoint detection in the hip joint. This study proposes a deep learning model-based keypoint detection method using X-ray and ultrasound imaging and analyzes the performance of keypoint detection using various deep learning models. Additionally, the study introduces and evaluates various data augmentation techniques to compensate the lack of medical data. This research demonstrated the highest keypoint detection performance when applying the residual network 152 (ResNet152) model with simple & complex augmentation techniques, with average Object Keypoint Similarity (OKS) of approximately 95.33 % and 81.21 % in X-ray and ultrasound images, respectively. These results demonstrate that the application of deep learning models to ultrasound and X-ray images to detect the keypoints in the hip joint could enhance the objectivity and productivity in DDH diagnosis.