• Journal of the Korean Society of Marine Environment & Safety
• /
• v.28 no.1
• /
• pp.83-90
• /
• 2022

About 90.5 % of barge-towing tugs weigh less than 200 gross tonnage and most are served by the master alone. They are also not subject to the regulations on the working hours and manning levels stipulated in the Seafarers' Act. Therefore, the master of barge-towing tugs cannot take sufficient rest during the navigational watch. Moreover, barge-towing tugs do not satisfy the human seaworthiness due to the inevitable performance of the navigational watch which must be alternately undertaken with an unqualified person, called the "Boatswain". Furthermore, there are many cases in which the master or owner of a tug fails to comply with the additionally required minimum manning levels stipulated in the Ship Of icers' Act when a tug tows a barge. This study reviews the following: (1) the regulations on the working hours and manning levels that are stipulated in the Seafarers' Act, (2) the regulations on the minimum manning levels for ship of icers of the tug's deck part that are stipulated in the Ship officers' Act, (3) marine accidents in the barge-towing tugs. As a result I suggested that one additional deck officer should be on board when a tug tows a barge through the revision of the minimum manning level for ship of icer on the deck part in order to prevent marine accidents of tugs effectively. Especially, the Act on the Punishment, etc. of the Serious Accident came into effect on January 27, 2022. If marine casualties occur continuously at sea due by the same cause, and the cause of such marine casualties would be turned out by the fatigue of the ship of icer caused by insufficient institutional arrangements, the administrator of competent Authorities of Maritime and Port could be punished, so it seems to prepare for it.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.21 no.2
• /
• pp.62-73
• /
• 2022

As the competition among the autonomous vehicle (AV, here after) developers are getting fierce, Korean government has been supporting developers by deregulating safety standards and providing financial subsidies. Recently, some OEMs announced their plans to market Lv3 and Lv4 automated driving systems. However, these market changes raised concern among public road management sectors for monitoring road conditions and alleviating hazardous conditions for AVs and human drivers. In this regards, the authors proposed a methodology for monitoring road infrastructure to identify hazardous factors for AVs and categorizing the hazards based on their level of impact. To evaluate the degrees of the harm on AVs, the authors suggested a methodology for managing road hazard factors based on vehicle performance features including vehicle body, sensors, and algorithms. Furthermore, they proposed a method providing AVs and road management authorities with potential risk information on road by delivering them on the monitoring map with node and link structure.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.26 no.2
• /
• pp.28-36
• /
• 2022

As port structures are exposed to various extreme external loads such as wind (typhoons), sea waves, or collision with ships; it is important to evaluate the structural safety periodically. To monitor the port structure, especially the rubber fender, a fender segmentation system using a vision sensor and deep learning method has been proposed in this study. For fender segmentation, a new deep learning network that improves the encoder-decoder framework with the receptive field block convolution module inspired by the eccentric function of the human visual system into the DenseNet format has been proposed. In order to train the network, various fender images such as BP, V, cell, cylindrical, and tire-types have been collected, and the images are augmented by applying four augmentation methods such as elastic distortion, horizontal flip, color jitter, and affine transforms. The proposed algorithm has been trained and verified with the collected various types of fender images, and the performance results showed that the system precisely segmented in real time with high IoU rate (84%) and F1 score (90%) in comparison with the conventional segmentation model, VGG16 with U-net. The trained network has been applied to the real images taken at one port in Republic of Korea, and found that the fenders are segmented with high accuracy even with a small dataset.

• Journal of Practical Agriculture & Fisheries Research
• /
• v.23 no.2
• /
• pp.99-111
• /
• 2021

The connected farm that agricultural land, agricultural machinery and farmer are connected with an IoT gateway is in the commercialization stage. That has increased productivity, efficiency and profitability by intimate information exchange among those. In order to develop the educational program of intelligent agricultural machinery and the agricultural machinery safety education performance indicator, this study analyzed patent trends of agricultural machine with unmanned technology used in agriculture and efficiency technology applied advanced technologies such as ICT, robots and artificial intelligence. We investigated and analyzed patent trends in agricultural machinery of Korea, the USA and Japan as well as the countries in Europe. The United States is an advanced country in the field of unmanned technology and efficiency technology used in agriculture. Agricultural automation technology in Korea is insufficient compared to developed countries, which means rapid technological development is needed. In the sub-fields of field automation technology, path generation and following technology and working machine control technology through environmental awareness have activated.

• Journal of Music and Human Behavior
• /
• v.19 no.1
• /
• pp.1-20
• /
• 2022

The purpose of this study was to evaluate how accurately children with autism spectrum disorder (ASD; n = 9) recognized four basic emotions (i.e., happiness, sadness, anger, and fear) following musical or visual cues. Their performance was compared to that of typically developing children (TD; n = 14). All of the participants were between the ages of 7 and 13 years. Four musical cues and four visual cues for each emotion were presented to evaluate the participants' ability to recognize the four basic emotions. The results indicated that there were significant differences between the two groups between the musical and visual cues. In particular, the ASD group demonstrated significantly less accurate recognition of the four emotions compared to the TD group. However, the emotion recognition of both groups was more accurate following the musical cues compared to the visual cues. Finally, for both groups, their greatest recognition accuracy was for happiness following the musical cues. In terms of the visual cues, the ASD group exhibited the greatest recognition accuracy for anger. This initial study support that musical cues can facilitate emotion recognition in children with ASD. Further research is needed to improve our understanding of the mechanisms involved in emotion recognition and the role of sensory cues play in emotion recognition for children with ASD.

• Journal of Music and Human Behavior
• /
• v.19 no.1
• /
• pp.43-67
• /
• 2022

This is a case study of therapeutic song making activities aimed at improving the self-identity of adolescents in residential treatment facility. The participants were three male teenagers (16 to 18 years of age). The song making intervention was conducted individually with the participants once a week over 13 weeks, and each session lasted 60 minutes. The participants took the lead in making songs by discussing on the self-image and his/her role in the relationship and using musical elements to reflect his/her perception. For analysis, an evaluation method was used to analyze the pre- and post-test results for each sub-domain of the self-identity scale, and changes in the verbal and musical responses during each session. Two of the participants demonstrated higher post-test results compared to their pre-test performance, and their highest post-test scores were for the subdomains of intimacy and initiative respectively. In terms of verbal and musical responses per session, all three participants improved their subjectivity through the self-exploration process, which contributed to the establishment of a more positive self-image. This study suggests that facility youth engaging in making creative songs can positively change their perception of their present and future selves and have a positive effect on their sense of identity.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.40 no.3
• /
• pp.239-247
• /
• 2022

Cities are becoming more complex due to rapid industrialization and population growth in modern times. In particular, urban areas are rapidly changing due to housing site development, reconstruction, and demolition. Thus accurate road information is necessary for various purposes, such as High Definition Map for autonomous car driving. In the case of the Republic of Korea, accurate spatial information can be generated by making a map through the existing map production process. However, targeting a large area is limited due to time and money. Road, one of the map elements, is a hub and essential means of transportation that provides many different resources for human civilization. Therefore, it is essential to update road information accurately and quickly. This study uses Semantic Segmentation algorithms Such as LinkNet, D-LinkNet, and NL-LinkNet to extract roads from drone images and then apply hyperparameter optimization to models with the highest performance. As a result, the LinkNet model using pre-trained ResNet-34 as the encoder achieved 85.125 mIoU. Subsequent studies should focus on comparing the results of this study with those of studies using state-of-the-art object detection algorithms or semi-supervised learning-based Semantic Segmentation techniques. The results of this study can be applied to improve the speed of the existing map update process.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_2
• /
• pp.1633-1641
• /
• 2022

Urban population concentration and indiscriminate development are causing various environmental problems such as air pollution and heat island phenomena, and causing human resources to deteriorate the damage caused by natural disasters. Urban trees have been proposed as a solution to these urban problems, and actually play an important role, such as providing environmental improvement functions. Accordingly, quantitative measurement and analysis of individual trees in urban trees are required to understand the effect of trees on the urban environment. However, the complexity and diversity of urban trees have a problem of lowering the accuracy of single tree detection. Therefore, we conducted a study to effectively detect trees in Dongjak-gu using high-resolution aerial images that enable effective detection of tree objects and You Only Look Once Version 5 (YOLOv5), which showed excellent performance in object detection. Labeling guidelines for the construction of tree AI learning datasets were generated, and box annotation was performed on Dongjak-gu trees based on this. We tested various scale YOLOv5 models from the constructed dataset and adopted the optimal model to perform more efficient urban tree detection, resulting in significant results of mean Average Precision (mAP) 0.663.

• Journal of the Korean Geotechnical Society
• /
• v.37 no.11
• /
• pp.119-129
• /
• 2021

The process of constructing the DB in the current geotechnical information DB system needs a lot of human and time resource consumption. In addition, it causes accuracy problems frequently because the current input method is a person viewing the PDF and directly inputting the results. Therefore, this study proposes building an automatic digital DB using AI (artificial intelligence) of boring logs. In order to automatically construct DB for various boring log formats without exception, the boring log forms were classified using the deep learning model ResNet 34 for a total of 6 boring log forms. As a result, the overall accuracy was 99.7, and the ROC_AUC score was 1.0, which separated the boring log forms with very high performance. After that, the text in the PDF is automatically read using the robotic processing automation technique fine-tuned for each form. Furthermore, the general information, strata information, and standard penetration test information were extracted, separated, and saved in the same format provided by the geotechnical information DB system. Finally, the information in the boring log was automatically converted into a DB at a speed of 140 pages per second.

• Journal of Digital Convergence
• /
• v.20 no.2
• /
• pp.241-250
• /
• 2022

Due to the COVID-19 pandemic, with increased 'untact' services and with unstable household economy, the bike insurance fraud is expected to surge. Moreover, the fraud methodology gets complicated. However, the fraud detection model for bike insurance is absent. we deal with the issue of skewed class distribution and reflect the criterion of fraud detection expert. We utilize a balanced random-forest algorithm to develop an efficient bike insurance fraud detection model. As a result, while the predictive performance of balanced random-forest model is superior than it of non-balanced model. There is no significant difference between the variables used by the experts and the confirmatory models. The important variables to detect frauds are turned out to be age and gender of driver, correspondence between insured and driver, the amount of self-repairing claim, and the amount of bodily injury liability.