• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.269-279
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• 2022

Purpose: The purpose of this study is to develop a deep learning-based personal protective equipment detection model for disaster prevention at construction sites, and to apply it to actual construction sites and to analyze the results. Method: In the method of conducting this study, the dataset on the real environment was constructed and the developed personal protective equipment(PPE) detection model was applied. The PPE detection model mainly consists of worker detection and PPE classification model.The worker detection model uses a deep learning-based algorithm to build a dataset obtained from the actual field to learn and detect workers, and the PPE classification model applies the PPE detection algorithm learned from the worker detection area extracted from the work detection model. For verification of the proposed model, experimental results were derived from data obtained from three construction sites. Results: The application of the PPE recognition model to construction site brings up the problems related to mis-recognition and non-recognition. Conclusions: The analysis outcomes were produced to apply the object recognition technology to a construction site, and the need for follow-up research was suggested through representative cases of worker recognition and non-recognition, and mis-recognition of personal protective equipment.

• Journal of the Korean Society of Radiology
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• v.16 no.2
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• pp.177-184
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• 2022

The purpose of this study is surveyed diagnostic radiography (DR) examination in veterinary hospital (VC) including non-ionization radiation such as ultra-sonography and magnetic resonance imaging. From June 1 to June 20. 2021, we surveyed the VC in 00 metropolitan city by using a structural questionnaire which are location of VC and X-ray unit et al.. Data are expressed as a mean with standard deviation for continuous variable or percent for categorical variable using SPSS ver. 26.0. As the first animal to be visited, dogs were the highest with 61.9%, followed by cats with 12.9%. In 87.1% fo cases, DR units were used, and 4 VCs did not. In 27 VCs using DR units, 48.1% separated examination room and control room, 19.8% examined in animal visited, protective clothing was in all VCs, 55.6% were measured radiation exposure dose, 92.6% was responded a necessary for examination education. From the above results, it will help to revise the guidelines for DR units and examination in korea VCs.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.171-182
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• 2022

The safe use of a structure requires it to be maintained in an undamaged state. Thus, a typical factor that determines the safety of a structure is a crack in it. In addition, cracks are caused by various reasons, damage the structure in various ways, and exist in different shapes. Making matters worse, if these cracks are unattended, the risk of structural failure increases and proceeds to a catastrophe. Hence, recently, methods of checking structural damage using deep learning and computer vision technology have been introduced. These methods usually have the premise that there should be a large amount of training image data. However, the amount of training image data is always insufficient. Particularly, this insufficiency negatively affects the performance of deep learning crack detection algorithms. Hence, in this study, a method of augmenting crack image data based on the image translation technique was developed. In particular, this method obtained the crack image data for training a deep learning neural network model by transforming a specific case of a asphalt crack image into a concrete crack image or vice versa . Eventually, this method expected that a robust crack detection algorithm could be developed by increasing the diversity of its training data.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.253-273
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• 2022

Automated vehicles rely on information collected through sensors to drive. Therefore, the uncertainty of the information collected from a sensor is an important to address. To this end, research is conducted in the field of road and traffic to solve the uncertainty of these sensors through infrastructure or facilities. Therefore, this study developed a traffic cone that can maintaing the gaze guidance function in the construction site by securing sufficient LiDAR detection performance even in rainy conditions and verified its improvement effect through demonstration. Two types of cones were manufactured, a cross-type and a flat-type, to increase the reflective performance compared to an existing cone. The demonstration confirms that the flat-type traffic cone has better detection performance than an existing cone, even in 50 mm/h rainfall, which affects a driver's field of vision. In addition, it was confirmed that the detection level on a clear day was maintained at the 20 mm/h rain for both cones. In the future, improvement measures should be developed so that the traffic cones, that can improve the safety of automated driving, can be applied.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.335-341
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• 2022

This article is devoted to the study of the process of formation of the digital generation in a distance learning environment. With the introduction of quarantine due to the spread of COVID-19, opponents of digital technologies were forced to turn to relevant resources, while supporters identified new opportunities for the development of didactics and education in general. The irreversibility of the former educational reality became apparent and only the scale of the vision of potential change by interested and disinterested groups differed. Using a comprehensive approach, the authors consider the issues related to the direct and indirect impact of distance learning on children and young people born after the beginning of the XXI century. The article reveals the prerequisites and implications of distance education for the interaction of participants in the educational process. IC technologies during the educational process in the primary grades, in addition to identifying the student's learning deficit, should provide the transmission of non-verbal signals, which are important for children of this age. At the same time in the secondary school IR-technologies are designed to replace frontal learning during the assimilation of knowledge and at the same time not to worsen the quality of the educational process. Formation of students in the HEI takes place in the political science format, constant discussion of problem situations, so the task of introducing IC technology in this process is the accurate transfer of the content of the discussions. Individualization and autonomization of the educational process, its dependence on the results of the choice of educational content, and the use of pedagogical management tools change the philosophy of education for children and youth. The authors conclude that the formation of a digital generation, characterized by an increased level of digital literacy of children and youth, the possession of a certain level of digital capacity requires the use of strategies aimed at optimizing the learning process in a digital educational environment.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.541-546
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• 2022

This study is to analyze the effect of rapid rise in the rescue activity of suffering on the increase of reactive oxygen species. There is no study that tested the change rate of reactive oxygen species according to the rapid rise in 119 rescue workers, so we want to check the symptoms that appear in rescue workers' bodies. There were 5 subjects, and B, C, and E showed similar values before and after diving: 0.41µmol/L, 0.11µmol/L, and 0.87µmol/L, respectively. However, in subject D, the level of active oxygen rise before and after diving was significantly higher at 1.41µmol/L, which is believed to be due to increased anxiety caused by poor underwater visibility and increased fatigue during rapid ascent after underwater rescue activities. Subject A showed a significantly low increase in active oxygen before and after diving at 0.07µmol/L. The reason seems to be that A is 54 years old and has the most diving experience among the test subjects, and it seems that it is the result of receiving less stress from the poor watch due to the abundant experience of rescue activities as a 119 rescue worker and the skillful underwater activities. Fatigue and anxiety were both high at 4. It is thought that the psychological tension during underwater activities increased fatigue, and the turbidity of the underwater vision raised anxiet.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.303-308
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• 2023

The purpose of this study is to provide a practical guide for establishing BSC that can be practically applied by SMEs. To this end, a case study was conducted to establish a performance evaluation system through a field-required Balanced Scorecard (BSC) for company J, a tent pole manufacturer, and to provide a management strategy system map. As a survey method, the requirements of the ordering organization were organized through a comparison of the BSC-related proposal requests in the first stage. The BSC establishment method was organized through the arrangement of the second stage result report. The 3rd stage BSC derived KPI indicators for SMEs for each of the 4 perspectives. A corporate vision was derived through a 4-step SWOT analysis. A strategy map was developed through 5-step field-required KPI, weight setting, and BSC. The 6-step final strategy system was also drawn up. As a result of the study, the four perspectives of the BSC were reconstructed by department. That is, the financial (financial) perspective is from the executives' perspective, the customer's perspective is from the sales department's perspective, the internal process perspective is from the design department/production quality department's perspective, and the learning/innovation perspective is from the management department's perspective. In addition, a total of 11 CSFs and a total of 49 KPIs of J company were derived. The limitation of the study is that the final strategy system through the company's BSC has only been carried out, and it needs to be linked with the company's compensation system in the future.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.27-33
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• 2023

Recently, water shortages have occurred due to climate change, and the need for water management of agricultural water has increased due to the occurrence of algal blooms in reservoirs. Existing algae prevention is operated by putting many people on site and misses the optimal spraying time due to movement through boats. In order to solve this problem, it is necessary to block contamination in advance and move within time to uniformly spray complex microorganisms uniformly. Control drones are used for pesticide spraying and can be applied to algae prevention work by utilizing control drones. In this paper, basic research for the establishment of a marine control system was conducted for application to the reservoir environment, and as one of the results, the characteristics of a drone nozzle, a core technology that can be used for control drones, were calculated. In particular, it was found that the existing agricultural control drones had a disadvantage that the concentration was non-uniform within the suggested spraying interval, and to compensate for this, nozzle positioning and nozzle spraying uniformity were calculated. Based on the experimental results, we develop a core algorithm for establishing an algal bloom monitoring system in the reservoir environment and propose a precision control technology that can be used for marine control work in the future.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.113-119
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• 2023

Most infrastructure structures were completed during periods of economic growth. The number of infrastructure structures reaching their lifespan is increasing, and the proportion of old structures is gradually increasing. The functions and performance of these structures at the time of design may deteriorate and may even lead to safety accidents. To prevent this repercussion, accurate inspection and appropriate repair are requisite. To this end, demand is increasing for computer vision and deep learning technology to accurately detect even minute cracks. However, deep learning algorithms require a large number of training data. In particular, label images indicating the location of cracks in the image are required. To secure a large number of those label images, a lot of labor and time are consumed. To reduce these costs as well as increase detection accuracy, this study proposed a learning structure based on mean teacher method. This learning structure was trained on a dataset of 900 labeled image dataset and 3000 unlabeled image dataset. The crack detection network model was evaluated on over 300 labeled image dataset, and the detection accuracy recorded a mean intersection over union of 89.23% and an F1 score of 89.12%. Through this experiment, it was confirmed that detection performance was improved compared to supervised learning. It is expected that this proposed method will be used in the future to reduce the cost required to secure label images.

• 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.