• 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.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.5
• /
• pp.1-6
• /
• 2021

Mercury, once released, is not destroyed but accumulates and circulates in the natural environment, causing serious harm to ecosystems and human health. In the United States, sulfur-impregnated activated carbon is being considered for the removal of vapor mercury from the flue gas of coal-fired power plants, which accounts for about 32 % of the anthropogenic emissions of mercury. In this study, a high-efficiency porous mercury adsorption material was developed to reduce the mercury vapor in the exhaust gas of coal combustion facilities, and the mercury adsorption characteristics of the material were investigated. As a result of the investigation of the vapor mercury adsorption capacity at 30℃, the silica nanotube MCM-41 was only about 35 % compared to the activated carbon Darco FGD commercially used for mercury adsorption, but it increased to 133 % when impregnated with 1.5 % sulfur. In addition, the furnace fly ash recovered from the waste copper regeneration process showed an efficiency of 523 %. Furthermore, the adsorption capacity was investigated at temperatures of 30 ℃, 80 ℃, and 120 ℃, and the best adsorption performance was found to be 80 ℃. MCM-41 is a silica nanotube that can be reused many times due to its rigid structure and has additional advantages, including no possibility of fire due to the formation of hot spots, which is a concern when using activated carbon.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
• /
• v.28 no.1
• /
• pp.15-21
• /
• 2022

The purpose of our study was to investigate the migration level of lead (Pb), cadmium (Cd), and barium (Ba) from glassware into a food simulant and to evaluate the exposure of each element. The test articles were glassware, including tableware, pots, and other containers. Pb, Cd, and Ba were analysed by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). The analytical performance of the method was validated in terms of its linearity, limit of detection (LOD), limit of quantification (LOQ), recovery, precision, and uncertainty. The monitoring was performed for 110 samples such as glass cups, containers, pots, and bottles. a food simulant. Migration test was conducted at 25? for 24 hours in a dark place using 4% acetic acid as a food simulant. Based on the data; exposure assessment was carried out to compare the estimated daily intake (EDI) to the human safety criteria. The risk levels of Pb and Ba determined in this study were approximately 1.9% and 0.3% of the provisional tolerable weekly intake (PTWI) and tolerable daily intake (TDI) value, respectively, thereby indicating a low exposure to the population.

• Journal of the Korea Computer Graphics Society
• /
• v.28 no.3
• /
• pp.55-65
• /
• 2022

Recently, deep learning-based automated systems for identifying and detecting landmarks have been proposed. In order to train such a deep learning-based model without overfitting, a large amount of image and labeling data is required. Conventionally, an experienced reader manually identifies and labels landmarks in a patient's image. However, such measurement is not only expensive, but also has poor reproducibility, so the need for an automated labeling method has been raised. In addition, in the X-ray image, since various human tissues on the path through which the photons pass are displayed, it is difficult to identify the landmark compared to a general natural image or a 3D image modality image. In this study, we propose a geometric data augmentation technique that enables the generation of a large amount of labeling data in X-ray images. In addition, the optimal attention mechanism for landmark detection was presented through the implementation and application of various attention techniques to improve the detection performance of 16 major landmarks in the skull. Finally, among the major cranial landmarks, markers that ensure stable detection are derived, and these markers are expected to have high clinical application potential.

• Journal of Internet Computing and Services
• /
• v.23 no.2
• /
• pp.29-35
• /
• 2022

UV rays have beneficial or harmful effects on the human body depending on the degree of exposure. An accurate UV information is required for proper exposure to UV rays per individual. The UV rays' information is provided by the Korea Meteorological Administration as one component of daily weather information in Korea. However, it does not provide an accurate UVI at the user's location based on the region's Ultraviolet index. Some operate measuring instrument to obtain an accurate UVI, but it would be costly and inconvenient. Studies which assumed the UVI through environmental factors such as solar radiation and amount of cloud have been introduced, but those studies also could not provide service to individual. Therefore, this paper proposes a deep learning model to calculate UVI using solar object information and sunlight characteristics to provide an accurate UVI at individual location. After selecting the factors, which were considered as highly correlated with UVI such as location and size and illuminance of sun and which were obtained through the analysis of sky images and solar characteristics data, a data set for DNN model was constructed. A DNN model that calculates the UVI was finally realized by entering the solar object information and sunlight characteristics extracted through Mask R-CNN. In consideration of the domestic UVI recommendation standards, it was possible to accurately calculate UVI within the range of MAE 0.26 compared to the standard equipment in the performance evaluation for days with UVI above and below 8.