• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1199-1209
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• 2021

The detection of unexpected loose parts in the primary coolant system in a nuclear power plant remains an extremely important issue. It is essential to develop a methodology for the localization and mass estimation of loose parts owing to the high prediction error of conventional methods. An effective approach is presented for the localization and mass estimation of a loose part using machine-learning and deep-learning algorithms. First, a methodology was developed to estimate both the impact location and the mass of a loose part at the same times in a real structure in which geometric changes exist. Second, an impact database was constructed through a series of impact finite-element analyses (FEAs). Then, impact parameter prediction modes were generated for localization and mass estimation of a simulated metallic loose part using machine-learning algorithms (artificial neural network, Gaussian process, and support vector machine) and a deep-learning algorithm (convolutional neural network). The usefulness of the methodology was validated through blind tests, and the noise effect of the training data was also investigated. The high performance obtained in this study shows that the proposed methodology using an FEA-based database and deep learning is useful for localization and mass estimation of loose parts on site.

• Journal of information and communication convergence engineering
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• v.19 no.1
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• pp.48-53
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• 2021

Herein, we propose a document analysis system that analyzes papers or reports transformed into XML(Extensible Markup Language) format. It reads the document specified by the user, extracts keywords from the document, and compares the frequency of keywords to extract the top-three keywords. It maintains the order of the paragraphs containing the keywords and removes duplicated paragraphs. The frequency of the top-three keywords in the extracted paragraphs is re-verified, and the paragraphs are partitioned into 10 sections. Subsequently, the importance of the relevant areas is calculated and compared. By notifying the user of areas with the highest frequency and areas with higher importance than the average frequency, the user can read only the main content without reading all the contents. In addition, the number of paragraphs extracted through the deep learning model and the number of paragraphs in a section of high importance are predicted.

• Journal of Korea Water Resources Association
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• v.54 no.3
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• pp.157-166
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• 2021

Forecasting dam inflow based on high reliability is required for efficient dam operation. In this study, deep learning technique, which is one of the data-driven methods and has been used in many fields of research, was manipulated to predict the dam inflow. The Long Short-Term Memory deep learning with Sequence-to-Sequence model (LSTM-s2s), which provides high performance in predicting time-series data, was applied for forecasting inflow of Soyang River dam. Various statistical metrics or evaluation indicators, including correlation coefficient (CC), Nash-Sutcliffe efficiency coefficient (NSE), percent bias (PBIAS), and error in peak value (PE), were used to evaluate the predictive performance of the model. The result of this study presented that the LSTM-s2s model showed high accuracy in the prediction of dam inflow and also provided good performance for runoff event based runoff prediction. It was found that the deep learning based approach could be used for efficient dam operation for water resource management during wet and dry seasons.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.2
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• pp.433-438
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• 2019

This paper proposes a method of improving deep learning based numbers and characters recognition performance on parts of drawing through image preprocessing. The proposed character recognition system consists of image preprocessing and 7 layer deep learning model. Mathematical morphological filtering is used as preprocessing to remove the lines and shapes which causes false recognition of numbers and characters on parts drawing. Further.. Further, the used deep learning model is a 7 layer deep learning model instead of VGG-16 model. As a result of the proposed OCR method, the recognition rate of characters is 92.57% and the precision is 92.82%.

• Journal of Multimedia Information System
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• v.8 no.2
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• pp.79-84
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• 2021

Detecting brain tumors of different sizes is a challenging task. This study aimed to identify brain tumors using detection algorithms. Most studies in this area use segmentation; however, we utilized detection owing to its advantages. Data were obtained from 64 patients and 11,200 MR images. The deep learning model used was RetinaNet, which is based on ResNet152. The model learned three different types of pre-processing images: normal, general histogram equalization, and contrast-limited adaptive histogram equalization (CLAHE). The three types of images were compared to determine the pre-processing technique that exhibits the best performance in the deep learning algorithms. During pre-processing, we converted the MR images from DICOM to JPG format. Additionally, we regulated the window level and width. The model compared the pre-processed images to determine which images showed adequate performance; CLAHE showed the best performance, with a sensitivity of 81.79%. The RetinaNet model for detecting brain tumors through deep learning algorithms demonstrated satisfactory performance in finding lesions. In future, we plan to develop a new model for improving the detection performance using well-processed data. This study lays the groundwork for future detection technologies that can help doctors find lesions more easily in clinical tasks.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1233-1241
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• 2022

Tumors of the brain are the deadliest, with a life expectancy of only a few years for those with the most advanced forms. Diagnosing a brain tumor is critical to developing a treatment plan to help patients with the disease live longer. A misdiagnosis of brain tumors will lead to incorrect medical treatment, decreasing a patient's chance of survival. Radiologists classify brain tumors via biopsy, which takes a long time. As a result, the doctor will need an automatic classification system to identify brain tumors. Image classification is one application of the deep learning method in computer vision. One of the deep learning's most powerful algorithms is the convolutional neural network (CNN). This paper will introduce a novel deep learning structure and image gradient to classify brain tumors. Meningioma, glioma, and pituitary tumors are the three most popular forms of brain cancer represented in the Figshare dataset, which contains 3,064 T1-weighted brain images from 233 patients. According to the numerical results, our method is more accurate than other approaches.

• Journal of information and communication convergence engineering
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• v.19 no.2
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• pp.73-78
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• 2021

As interest in beauty has increased, various studies have been conducted, and related companies have considered the anthropometric data handled between humans and interfaces as an important factor. However, owing to the nature of 3D human body scanners used to extract anthropometric data, it is difficult to accurately analyze a user's body shape until a service is provided because the user only scans and extracts data. To solve this problem, the body shape of several users was analyzed, and the collected anthropometric data were obtained using a 3D human body scanner. After processing the extracted data and the anthropometric data, a custom deep learning model was designed, the designed model was learned, and the user's body shape information was predicted to provide a service suitable for the body shape. Through this approach, it is expected that the user's body shape information can be predicted using a 3D human body scanner, based upon which a beauty service can be provide.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.326-328
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• 2021

The use of shared electric kickboards that can move quickly within a short distance at a relatively low price is increasing significantly. In this paper, we propose a system for recognizing incorrect parking of an abandoned shared kickboard by applying deep learning-based object recognition technology. In this paper, a model similar to CNN was created separately considering the characteristics of the experimental data, and it was shown that a recognition rate of 60% was obtained through the experiment.

• Journal of Internet Computing and Services
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• v.18 no.5
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• pp.69-77
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• 2017

In this paper, a disaster response scheme based on deep learning and augmented reality technology is proposed and a network resource reservation scheme is presented accordingly. The features of deep learning, augmented reality technology and its relevance to the disaster areas are explained. Deep learning technology can be used to accurately recognize disaster situations and to implement related disaster information as augmented reality, and to enhance disaster response capabilities by providing disaster response On-site disaster response agent, ICS (Incident Command System) and MCS (Multi-agency Coordination Systems). In the case of various disasters, the fire situation is focused on and it is proposed that a plan to strengthen disaster response capability effectively by providing fire situation recognition based on deep learning and augmented reality information. Finally, a scheme to secure network resources to utilize the disaster response method of this paper is proposed.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.2
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• pp.115-122
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• 2017

This paper proposes a deep learning algorithm based sign detection and recognition system for the blind. The proposed system is composed of sign detection stage and sign recognition stage. In the sign detection stage, aggregated channel features are extracted and AdaBoost classifier is applied to detect regions of interest of the sign. In the sign recognition stage, convolutional neural network is applied to recognize the regions of interest of the sign. In this paper, the AdaBoost classifier is designed to decrease the number of undetected signs, and deep learning algorithm is used to increase recognition accuracy and which leads to removing false positives which occur in the sign detection stage. Based on our experiments, proposed method efficiently decreases the number of false positives compared with other methods.