• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.31-37
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• 2021

As the size of buildings increases due to urbanization due to the development of industry, the need to purify the air and maintain a comfortable indoor environment is also increasing. With the development of monitoring technology for refrigeration systems, it has become possible to manage the amount of electricity consumed in buildings. In particular, refrigeration systems account for about 40% of power consumption in commercial buildings. Therefore, in order to develop the refrigeration system failure diagnosis algorithm in this study, the purpose of this study was to understand the structure of the refrigeration system, collect and analyze data generated during the operation of the refrigeration system, and quickly detect and classify failure situations with various types and severity . In particular, in order to improve the classification accuracy of failure types that are difficult to classify, a three-step diagnosis and classification algorithm was developed and proposed. A model based on SVM and LGBM was presented as a classification model suitable for each stage after a number of experiments and hyper-parameter optimization process. In this study, the characteristics affecting failure were preserved as much as possible, and all failure types, including refrigerant-related failures, which had been difficult in previous studies, were derived with excellent results.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.209-216
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• 2022

In this paper, we propose an effective software education program to promote employment of the disabled and verify the effectiveness of SW education through pilot operation. In this SW education program, we develop a SW curriculum consisting of the basic course, Unity programming course, and the advanced course, AR/VR digital content development course. The SW education achievement standard develops the basic and advanced course achievement standards in consideration of the level of the virtual reality content production job of the National Competency Standards(NCS) and the SW education achievement standards of youth with visual, hearing, and physical disabilities. SW education materials are developed on a project basis so that one AR/VR digital content can be implemented step by step according to the intellectual level of the disabled based on Unity. SW education pilot training is conducted as online education based on Blended Learning due to COVID-19. In order to derive the SW education effect and each learner's individual SW education academic achievement for the SW education pilot training, a survey is conducted on learners, and the results are analyzed. In the basic course, 77.3% of learners achieved academic achievement above excellent(80-90), and in the advanced course, 48.8% of learners achieved academic achievement above excellent(80-90). These results verify that the SW education program for the disabled developed in this paper is effective in activating employment for the disabled.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.33-43
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• 2022

In this paper, we propose an ensemble model facilitated by multi-channel palm images with attention U-Net models and pretrained convolutional neural networks (CNNs) for establishing a contactless palm-based user identification system using conventional inexpensive camera sensors. Attention U-Net models are used to extract the areas of interest including hands (i.e., with fingers), palms (i.e., without fingers) and palm lines, which are combined to generate three channels being ped into the ensemble classifier. Then, the proposed palm information-based user identification system predicts the class using the classifier ensemble with three outperforming pre-trained CNN models. The proposed model demonstrates that the proposed model could achieve the classification accuracy, precision, recall, F1-score of 98.60%, 98.61%, 98.61%, 98.61% respectively, which indicate that the proposed model is effective even though we are using very cheap and inexpensive image sensors. We believe that in this COVID-19 pandemic circumstances, the proposed palm-based contactless user identification system can be an alternative, with high safety and reliability, compared with currently overwhelming contact-based systems.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.237-250
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• 2022

In this study, we propose a method to estimate the biomass of invertebrate grazers from the videos with underwater drones by using a multi-object tracking model based on deep learning. In order to detect invertebrate grazers by classes, we used YOLOv5 (You Only Look Once version 5). For biomass estimation we used DeepSORT (Deep Simple Online and real-time tracking). The performance of each model was evaluated on a workstation with a GPU accelerator. YOLOv5 averaged 0.9 or more mean Average Precision (mAP), and we confirmed it shows about 59 fps at 4 k resolution when using YOLOv5s model and DeepSORT algorithm. Applying the proposed method in the field, there was a tendency to be overestimated by about 28%, but it was confirmed that the level of error was low compared to the biomass estimation using object detection model only. A follow-up study is needed to improve the accuracy for the cases where frame images go out of focus continuously or underwater drones turn rapidly. However,should these issues be improved, it can be utilized in the production of decision support data in the field of invertebrate grazers control and monitoring in the future.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1925-1934
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• 2022

Monitoring nuclear activity for inaccessible areas using remote sensing technology is essential for nuclear non-proliferation. In recent years, deep learning has been actively used to detect nuclear-activity-related small objects. However, high-resolution satellite imagery containing small objects can result in class imbalance. As a result, there is a performance degradation problem in detecting small objects. Therefore, this study aims to improve detection accuracy by analyzing the effect of the ratio of small objects related to nuclear activity in the input data for the performance of the deep learning model. To this end, six case datasets with different ratios of small object pixels were generated and a U-Net model was trained for each case. Following that, each trained model was evaluated quantitatively and qualitatively using a test dataset containing various types of small object classes. The results of this study confirm that when the ratio of object pixels in the input image is adjusted, small objects related to nuclear activity can be detected efficiently. This study suggests that the performance of deep learning can be improved by adjusting the object pixel ratio of input data in the training dataset.

• Korean Journal of School Psychology
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• v.17 no.2
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• pp.223-241
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• 2020

The purpose of this study was to explore the latent profiles of social achievement goals and to investigate the differences in peer status (perceived popularity, social preference) and aggression (overt, relational, cyber) among those profile groups. Social achievement goals and cyber aggression data was acquired through self-reporting, and perceived popularity, social preference, and overt and relational aggression were assessed through peer nomination. Applying the latent profile analysis(LPA) to 1,239 elementary school students, three distinct groups of social achievement goals were identified: a development-oriented achievement goal group, an average social goal group, and a overall-high social achievement goal group. Using logistic regression analysis, the relationships between the latent group, peer status, and aggression were examined. The result indicated that the higher the social preference, the lower the probability of belonging to the 'overall-high social achievement goal group'. And the higher the cyber aggression, the lower the probability of belonging to the 'development-oriented achievement goal group'. In addition, the higher the relational aggression of the second time, the higher the probability of belonging to the 'overall-high social achievement goal group' as compared to the 'average social goal group'.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.117-126
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• 2022

Currently, the number of bridge structures in Korea is continuously increasing and enlarged, and the number of old bridges that have been in service for more than 30 years is also steadily increasing. Bridge aging is being treated as a serious social problem not only in Korea but also around the world, and the existing manpower-centered inspection method is revealing its limitations. Recently, various bridge damage detection studies using deep learning-based image processing algorithms have been conducted, but due to the limitations of the bridge damage data set, most of the bridge damage detection studies are mainly limited to one type of crack, which is also based on a close set classification model. As a detection method, when applied to an actual bridge image, a serious misrecognition problem may occur due to input images of an unknown class such as a background or other objects. In this study, five types of bridge damage including crack were defined and a data set was built, trained as a deep learning model, and an open set recognition-based bridge multiple damage recognition model applied with OpenMax algorithm was constructed. And after performing classification and recognition performance evaluation on the open set including untrained images, the results were analyzed.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.5
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• pp.308-314
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• 2022

This study deals with the technology Convergence Analysis by IPC Code-Based Social Network Analysis of Healthcare Patents filed in Korea. The relationship between core technologies is visualized using Social Network Analysis. At the subclass level of healthcare patents, 1,155 cases (49.4%) of patents with complex IPC codes were investigated, and as a result of Social Network Analysis on them, the IPC codes with the highest Degree Centrality were A61B, G16H, and G06Q, in that order. The IPC codes with the highest Betweenness Centrality are in the order of A61B, G16H, and G06Q. In addition, it was confirmed that healthcare patents consist of two large technology clusters. Cluster-1 corresponds to related business models centered on A61B, G16H and G06Q, and Cluster-2 is consisting of H04L, H04W and H04B. The technology convergence core pairs of the healthcare patent is [G16H-A61B] and [G16H-G06Q] in Cluster-1, and [H04L-H04W] in Cluster-2. The results of this study can contribute to the development of core technologies for healthcare patents.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.2
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• pp.363-368
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• 2021

Ship engine rooms have improved automation systems owing to the advancement of technology. However, there are many variables at sea, such as wind, waves, vibration, and equipment aging, which cause loosening, cutting, and leakage, which are not measured by automated systems. There are cases in which only one engineer is available for patrolling. This entails many risk factors in the engine room, where rotating equipment is operating at high temperature and high pressure. When the engineer patrols, he uses his five senses, with particular high dependence on vision. We hereby present a preliminary study to implement an engine-room patrol robot that detects and informs the machine room while a robot patrols the engine room. Images of ship engine-room equipment were classified using a convolutional neural network (CNN). After constructing the image dataset of the ship engine room, the network was trained with a pre-trained CNN model. Classification performance of the trained model showed high reproducibility. Images were visualized with a class activation map. Although it cannot be generalized because the amount of data was limited, it is thought that if the data of each ship were learned through transfer learning, a model suitable for the characteristics of each ship could be constructed with little time and cost expenditure.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.1
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• pp.1-18
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• 2023

Similar software is often developed with the Clone-And-Own (CAO) approach that copies and modifies existing artifacts. The CAO approach is considered as a bad practice because it makes maintenance difficult as the number of cloned products increases. Software product line engineering is a methodology that can solve the issue of the CAO approach by developing a product family through systematic reuse. Migrating product families that have been developed with the CAO approach to the product line engineering begins with finding, integrating, and building them as reusable assets. However, cloning occurs at various levels from directories to code lines, and their structures can be changed. This makes it difficult to build product line code base simply by finding clones. Successful migration thus requires unifying the source code's file path, class name, and method signature. This paper proposes a clustering method that identifies a set of similar codes scattered across product variants and some of their method full paths are different, so path unification is necessary. In order to show the effectiveness of the proposed method, we conducted an experiment using the Apo Games product line, which has evolved with the CAO approach. As a result, the average precision of clustering performed without preprocessing was 0.91 and the number of identified common clusters was 0, whereas our method showed 0.98 and 15 respectively.