• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.7
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• pp.303-312
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• 2006

This paper proposes the architecture and protocol of a data communication network for the safety system in nuclear power plants. First, we establish four design criteria with respect to determinability, reliability, separation and isolation, and verification/validation. Next we construct the architecture of the safety network for the following systems: PPS (Plant Protection System), ESF-CCS (Engineered Safety Features-Component Control System) and CPCS (Core Protection Calculator System). The safety network consists of 12 sub-networks and takes the form of a hierarchical star. Among 163 communication nodes are about 1600 origin-destination (OD) pairs created on their traffic demands. The OD pairs are allowed to exchange data only during the pre-assigned time slots. Finally, the communication protocol is designed in consideration of design factors for the safety network. The design factors include a network topology of star, fiber-optic transmission media, synchronous data transfer mode, point-to-point link configuration, and a periodic transmission schedule etc. The resulting protocol is the modification of IEEE 802.15.4 (LR-WPAN) MAC combined with IEEE 802.3 (Fast Ethernet) PHY. The MAC layer of IEEE 802.15.4 is simplified by eliminating some unnecessary (unctions. Most importantly, the optional TDMA-like scheme called the guaranteed time slot (GTS) is changed to be mandatory to guarantee the periodic data transfer. The proposed protocol is formally specified using the SDL. By performing simulations and validations using Telelogic Tau SDL Suite, we find that the proposed safety protocol fits well with the characteristics and the requirements of the safety system in nuclear power plants.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.11
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• pp.963-970
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• 2016

This research deals with design and performance validation of eco-pedals that generate tactile pedal force to guide fuel saving driving behavior. For eco-pedal control logic, allowable fuel consumption at given driving speed is calculated based on pre-defined "allowable acceleration", and if the actual fuel consumption exceeds the allowable fuel consumption, then pedal force is activated. Pedal force should be recognizable to driver while not causing unpleasantness, and should not interfere with normal operation of pedal. Reaction forces that increase pedal stiffness abruptly, such as step and ramp shape, turn out to be not suitable due to pedal overshoot after release of reaction force. With this regards, vibration type reaction force is adopted, and its optimal frequency, magnitude and duration is determined through subjective evaluation with consideration to effect to fuel efficiency. Though highway and city driving test, it is demonstrated that fuel efficiency increase of 13% for highway and 15% for city is achieved.

• Journal of Satellite, Information and Communications
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• v.6 no.2
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• pp.1-9
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• 2011

The Communication Ocean and Meteorological Satellite(COMS), the first geostationary observation satellite, was successfully launched on June 27th in 2010. The raw data of Meteorological Imager(MI) and Geostationary Ocean Color Imager(GOCI), the main payloads of COMS, is delivered to end-users through the on-ground processing. The COMS Image Data Acquisition and Control System(IDACS) developed by Korea Aerospace Research Institute(KARI) in domestic technologies performs radiometric and geometric corrections to raw data and disseminates pre-processed image data and additional data to end-users through the satellite. Currently the IDACS is in the nominal operations phase after successful in-orbit testing and operates in National Meteorological Satellite Center, Korea Ocean Satellite Center, and Satellite Operations Center, During the in-orbit test period, validations on functionalities and performance IDACS were divided into 1) image data acquisition and transmission, 2) preprocessing of MI and GOCI raw data, and 3) end-user dissemination. This paper presents that IDACS' operational validation results performed during the in-orbit test period after COMS' launch.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.36 no.3
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• pp.113-120
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• 2020

The purpose of this study is to compare the models of Deep Learning-based Convolution Neural Network(CNN) for concrete crack detection. The comparison models are AlexNet, GoogLeNet, VGG16, VGG19, ResNet-18, ResNet-50, ResNet-101, and SqueezeNet which won ImageNet Large Scale Visual Recognition Challenge(ILSVRC). To train, validate and test these models, we constructed 3000 training data and 12000 validation data with 256×256 pixel resolution consisting of cracked and non-cracked images, and constructed 5 test data with 4160×3120 pixel resolution consisting of concrete images with crack. In order to increase the efficiency of the training, transfer learning was performed by taking the weight from the pre-trained network supported by MATLAB. From the trained network, the validation data is classified into crack image and non-crack image, yielding True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), and 6 performance indicators, False Negative Rate (FNR), False Positive Rate (FPR), Error Rate, Recall, Precision, Accuracy were calculated. The test image was scanned twice with a sliding window of 256×256 pixel resolution to classify the cracks, resulting in a crack map. From the comparison of the performance indicators and the crack map, it was concluded that VGG16 and VGG19 were the most suitable for detecting concrete cracks.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.21-28
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• 2021

This study proposes a deep learning-based flat foot classification methodology using transfer learning. We used a transfer learning with VGG16 pre-trained model and a data augmentation technique to generate a model with high predictive accuracy from a total of 176 image data consisting of 88 flat feet and 88 normal feet. To evaluate the performance of the proposed model, we performed an experiment comparing the prediction accuracy of the basic CNN-based model and the prediction model derived through the proposed methodology. In the case of the basic CNN model, the training accuracy was 77.27%, the validation accuracy was 61.36%, and the test accuracy was 59.09%. Meanwhile, in the case of our proposed model, the training accuracy was 94.32%, the validation accuracy was 86.36%, and the test accuracy was 84.09%, indicating that the accuracy of our model was significantly higher than that of the basic CNN model.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.175-181
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• 2022

COVID-19 is an acute respiratory syndrome that affects the host's breathing and respiratory system. The novel disease's first case was reported in 2019 and has created a state of emergency in the whole world and declared a global pandemic within months after the first case. The disease created elements of socioeconomic crisis globally. The emergency has made it imperative for professionals to take the necessary measures to make early diagnoses of the disease. The conventional diagnosis for COVID-19 is through Polymerase Chain Reaction (PCR) testing. However, in a lot of rural societies, these tests are not available or take a lot of time to provide results. Hence, we propose a COVID-19 classification system by means of machine learning and transfer learning models. The proposed approach identifies individuals with COVID-19 and distinguishes them from those who are healthy with the help of Deep Visual Embeddings (DVE). Five state-of-the-art models: VGG-19, ResNet50, Inceptionv3, MobileNetv3, and EfficientNetB7, were used in this study along with five different pooling schemes to perform deep feature extraction. In addition, the features are normalized using standard scaling, and 4-fold cross-validation is used to validate the performance over multiple versions of the validation data. The best results of 88.86% UAR, 88.27% Specificity, 89.44% Sensitivity, 88.62% Accuracy, 89.06% Precision, and 87.52% F1-score were obtained using ResNet-50 with Average Pooling and Logistic regression with class weight as the classifier.

• The korean journal of orthodontics
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• v.52 no.2
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• pp.112-122
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• 2022

Objective: This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs. Methods: The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt: pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two new orthodontists in order to compare their diagnosis to the artificial intelligence (AI) model's performance using weighted kappa and Cohen's kappa statistical analyses. Results: The model's validation and test accuracy for the six-class CVM diagnosis were 62.63% and 61.62%, respectively. Moreover, the model's validation and test accuracy for the three-class classification were 75.76% and 82.83%, respectively. Furthermore, substantial agreements were observed between the two orthodontists as well as one of them and the AI model. Conclusions: The newly developed AI model had reasonable accuracy in detecting the CVM stage and high reliability in detecting the pubertal stage. However, its accuracy was still less than that of human observers. With further improvements in data quality, this model should be able to provide practical assistance to practicing dentists in the future.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.239-249
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• 2020

Rice flour varieties have been developed to replace wheat, and consumption of rice flour has been encouraged. damage related to pre-harvest sprouting was occurring due to a weather disaster during the ripening period. Thus, it is necessary to develop pre-harvest sprouting rate prediction system to minimize damage for pre-harvest sprouting. Rice cultivation experiments from 20 17 to 20 19 were conducted with three rice flour varieties at six regions in Gangwon-do, Chungcheongbuk-do, and Gyeongsangbuk-do. Survey components were the heading date and pre-harvest sprouting at the harvest date. The weather data were collected daily mean temperature, relative humidity, and rainfall using Automated Synoptic Observing System (ASOS) with the same region name. Gradient Boosting Machine (GBM) which is a machine learning model, was used to predict the pre-harvest sprouting rate, and the training input variables were mean temperature, relative humidity, and total rainfall. Also, the experiment for the period from days after the heading date (DAH) to the subsequent period (DA2H) was conducted to establish the period related to pre-harvest sprouting. The data were divided into training-set and vali-set for calibration of period related to pre-harvest sprouting, and test-set for validation. The result for training-set and vali-set showed the highest score for a period of 22 DAH and 24 DA2H. The result for test-set tended to overpredict pre-harvest sprouting rate on a section smaller than 3.0 %. However, the result showed a high prediction performance (R2=0.76). Therefore, it is expected that the pre-harvest sprouting rate could be able to easily predict with weather components for a specific period using machine learning.

• Korean Journal of Radiology
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• v.24 no.6
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• pp.541-552
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• 2023

Objective: Detection of pneumoperitoneum using abdominal radiography, particularly in the supine position, is often challenging. This study aimed to develop and externally validate a deep learning model for the detection of pneumoperitoneum using supine and erect abdominal radiography. Materials and Methods: A model that can utilize "pneumoperitoneum" and "non-pneumoperitoneum" classes was developed through knowledge distillation. To train the proposed model with limited training data and weak labels, it was trained using a recently proposed semi-supervised learning method called distillation for self-supervised and self-train learning (DISTL), which leverages the Vision Transformer. The proposed model was first pre-trained with chest radiographs to utilize common knowledge between modalities, fine-tuned, and self-trained on labeled and unlabeled abdominal radiographs. The proposed model was trained using data from supine and erect abdominal radiographs. In total, 191212 chest radiographs (CheXpert data) were used for pre-training, and 5518 labeled and 16671 unlabeled abdominal radiographs were used for fine-tuning and self-supervised learning, respectively. The proposed model was internally validated on 389 abdominal radiographs and externally validated on 475 and 798 abdominal radiographs from the two institutions. We evaluated the performance in diagnosing pneumoperitoneum using the area under the receiver operating characteristic curve (AUC) and compared it with that of radiologists. Results: In the internal validation, the proposed model had an AUC, sensitivity, and specificity of 0.881, 85.4%, and 73.3% and 0.968, 91.1, and 95.0 for supine and erect positions, respectively. In the external validation at the two institutions, the AUCs were 0.835 and 0.852 for the supine position and 0.909 and 0.944 for the erect position. In the reader study, the readers' performances improved with the assistance of the proposed model. Conclusion: The proposed model trained with the DISTL method can accurately detect pneumoperitoneum on abdominal radiography in both the supine and erect positions.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.28 no.2
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• pp.363-385
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• 1998

In orthodontics and orthognathic surgery. cephalogram has been routine practice in diagnosis and treatment evaluation of craniofacial deformity. But its inherent distortion of actual length and angles during projecting three dimensional object to two dimensional plane might cause errors in quantitative analysis of shape and size. Therefore, it is desirable that three dimensional object is diagnosed and evaluated three dimensionally and three dimensional CT image is best for three dimensional analysis. Development of clinic necessitates evaluation of result of treatment and comparison before and after surgery. It is desirable that patient that was diagnosed and planned by three dimensional computed tomography before surgery is evaluated by three dimensional computed tomography after surgery. too. But Because there is no standardized normal values in three dimension now and three dimensional Computed Tomography needs expensive equipments and because of its expenses and amount of exposure to radiation. limitations still remain to be solved in its application to routine practice. If postoperative three dimensional image is constructed by pre and postoperative lateral and postero-anterior cephalograms and preoperative three dimensional computed tomogram. pre and postoperative image will be compared and evaluated three dimensionally without three dimensional computed tomography after surgery and that will contribute to standardize normal values in three dimension. This study introduced new method that computer-simulated three dimensional image was constructed by preoperative three dimensional computed tomogram and pre and postoperative lateral and postero-anterior cephalograms. and for validation of new method. in four cases of dry skull that position of mandible was displaced and four patients of orthognathic surgery. computer-simulated three dimensional image and actual postoperative three dimensional image were compared. The results were as follows. 1. In four cases of dry skull that position of mandible was displaced. range of displacement between computer-simulated three dimensional images and actual postoperative three dimensional images in co-ordinates values was from -1.8 mm to 1.8 mm and 94％ in displacement of all co-ordinates values was from -1.0 mm to 1.0 mm and no significant difference between computer-simulated three dimensional images and actual postoperative three dimensional images was noticed(p>0.05). 2. In four cases of orthognathic surgery patients, range of displacement between computer­simulated three dimensional images and actual postoperative three dimensional images in coordinates values was from -6.7 mm to 7.7 mm and 90％ in displacement of all co-ordinates values was from -4.0 to 4.0 mm and no significant difference between computer-simulated three dimensional images and actual postoperative three dimensional images was noticed(p>0.05). Conclusively. computer-simulated three dimensional image was constructed by preoperative three dimensional computed tomogram and pre and postoperative lateral and postero-anterior cephalograms. Therefore. potentiality that can construct postoperative three dimensional image without three dimensional computed tomography after surgery was presented.