• Journal of IKEEE
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• v.25 no.4
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• pp.782-785
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• 2021

In this paper, we propose a personalized cooling management system with thermal imaging camera. The proposed equipment uses a thermal imaging camera to control the amount of cold air and the system according to the difference between the user's skin temperature before and after the procedure. When the skin temperature is abnormally low, the cold air supply is cut off to prevent the possibility of a safety accident. It is economical by replacing the skin temperature sensor with a thermal imaging camera temperature measurement, and it can be visualized because the temperature can be checked with the thermal image. In addition, the proposed equipment improves the sensitivity of the sensor that measures the distance to the skin by calculating the focal length by using a dual laser pointer for the safety of a personalized cooling management system to which a thermal imaging camera is applied. In order to evaluate the performance of the proposed equipment, it was tested in an externally accredited testing institute. The first measured temperature range was -100℃~-160℃, indicating a wider temperature range than -150~-160℃(cryo generation/USA), which is the highest level currently used in the field. In addition, the error was measured to be ±3.2%~±3.5%, which showed better results than ±5%(CRYOTOP/China), which is the highest level currently used in the field. The second measured distance accuracy was measured as below ±4.0%, which was superior to ±5%(CRYOTOP/China), which is the highest level currently used in the field. Third, the nitrogen consumption was confirmed to be less than 0.15 L/min at the maximum, which was superior to the highest level of 6 L/min(POLAR BEAR/USA) currently used in the field. Therefore, it was determined that the performance of the personalized cooling management system applied with the thermal imaging camera proposed in this paper was excellent.

• Journal of Life Science
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• v.32 no.6
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• pp.438-446
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• 2022

Some flowers have a high sensual appeal owing to their unique shape, color, smell, and taste and have been used as functional food and oriental medicine. Recently, edible dry flowers (EDFs) have attracted social attention as noble sources of functional teas. In this study, for the risk assessment of EDFs, pathogenic bacteria, heavy metals, and pesticide residues were monitored in 23 types of commercial EDF. No Enterobacteria spp. and Listeria spp. were found in all EDF products. However, common aerobic bacteria (3.24~3.85 Log CFU/g) were found in EDF, namely, Pueraria lobata, Chamaemelum nobile, Acacia decurrens, Rhododendron mucronulatum Turcz, Oenothera lamarckiana, Brassica napus, and Prunus serrulata. Staphylococcus aureus was found in 11 and Salmonella sp. was found in 8 of the 23 EDFs. Considering the cold extraction of EDF for tea and beverages, the regulation of pathogenic bacteria in EDFs is necessary. No heavy metals such as Pb, Cd, Co, Cr, Cu, Ni, and As were found in all EDFs, except the dry flower of Hemerocallis fulva, which contained Pb at 0.08 ppm. Different pesticides and fungicides were found in EDFs, but their concentrations were very low (0.01~0.08 ppm) and below the maximal residue level. Only the dry flower of Chrysanthemum morifolium had a high content of chlorpyrifos (0.215 ppm), which is long-lasting pesticide. Our results suggest that the establishment of EDF regulations for pesticide residue, culture separation between edible and garden flowers, and guidelines for preventing pathogenic microbial contamination are necessary.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1407-1422
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• 2022

This study was conducted to classify tree species and assess the classification accuracy, using SE-Inception, a classification-based deep learning model. The input images of the dataset used Worldview-3 and GeoEye-1 images, and the size of the input images was divided into 10 × 10 m, 30 × 30 m, and 50 × 50 m to compare and evaluate the accuracy of classification of tree species. The label data was divided into five tree species (Pinus densiflora, Pinus koraiensis, Larix kaempferi, Abies holophylla Maxim. and Quercus) by visually interpreting the divided image, and then labeling was performed manually. The dataset constructed a total of 2,429 images, of which about 85% was used as learning data and about 15% as verification data. As a result of classification using the deep learning model, the overall accuracy of up to 78% was achieved when using the Worldview-3 image, the accuracy of up to 84% when using the GeoEye-1 image, and the classification accuracy was high performance. In particular, Quercus showed high accuracy of more than 85% in F₁ regardless of the input image size, but trees with similar spectral characteristics such as Pinus densiflora and Pinus koraiensis had many errors. Therefore, there may be limitations in extracting feature amount only with spectral information of satellite images, and classification accuracy may be improved by using images containing various pattern information such as vegetation index and Gray-Level Co-occurrence Matrix (GLCM).

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.1-9
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• 2022

Localization systems can be used with various circumstances like measuring population movement and rescue technology, even in security technology (like infiltration detection system). Vision sensors such as camera often used for localization is susceptible with light and temperature, and can cause invasion of privacy. In this paper, we used ultra-wideband radar technology (which is not limited by aforementioned problems) and machine learning techniques to measure the number and location of occupants in other indoor spaces behind the wall. We used four different algorithms and compared their results, including extremely randomized tree for four different situations; detect the number of occupants in a classroom, split the classroom into 28 locations and check the position of occupant, select one out of the 28 locations, divide it into 16 fine-grained locations, and check the position of occupant, and checking the positions of two occupants (existing in different locations). Overall, four algorithms showed good results and we verified that detecting the number and location of occupants are possible with high accuracy using machine learning. Also we have considered the possibility of service expansion using the oneM2M standard platform and expect to develop more service and products if this technology is used in various fields.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.71-86
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• 2023

The purpose of this exploratory study is to compare the differences in public perceptual characteristics of Korean and Chinese societies regarding the metaverse using big data analysis. Due to the environmental impact of the COVID-19 pandemic, technological progress, and the expansion of new consumer bases such as generation Z and Alpha, the world's interest in the metaverse is drawing attention, and related academic studies have been also in full swing from 2021. In particular, Korea and China have emerged as major leading countries in the metaverse industry. It is a timely research question to discover the difference in social awareness using big data accumulated in both countries at a time when the amount of mentions on the metaverse has skyrocketed. The analysis technique identifies the importance of key words by analyzing word frequency, N-gram, and TF-IDF of clean data through text mining analysis, and analyzes the density and centrality of semantic networks to determine the strength of connection between words and their semantic relevance. Python 3.9 Anaconda data science platform 3 and Textom 6 versions were used, and UCINET 6.759 analysis and visualization were performed for semantic network analysis and structural CONCOR analysis. As a result, four blocks, each of which are similar word groups, were driven. These blocks represent different perspectives that reflect the types of social perceptions of the metaverse in both countries. Studies on the metaverse are increasing, but studies on comparative research approaches between countries from a cross-cultural aspect have not yet been conducted. At this point, as a preceding study, this study will be able to provide theoretical grounds and meaningful insights to future studies.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.843-856
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• 2023

The global surge in maritime traffic has resulted in an increased number of ship collisions, leading to significant economic, environmental, physical, and human damage. The causes of these maritime accidents are multifaceted, often arising from a combination of crew judgment errors, negligence, complexity of navigation routes, weather conditions, and technical deficiencies in the vessels. Given the intricate nuances and contextual information inherent in each incident, a methodology capable of deeply understanding the semantics and context of sentences is imperative. Accordingly, this study utilized the SentenceBERT model to analyze maritime safety tribunal decisions over the last 20 years in the Busan Sea area, which encapsulated data on ship collision incidents. The analysis revealed important keywords potentially responsible for these incidents. Cluster analysis based on the frequency of specific keyword appearances was conducted and visualized. This information can serve as foundational data for the preemptive identification of accident causes and the development of strategies for collision prevention and response.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.2
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• pp.34-44
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• 2024

Recently, due to abnormal climate caused by climate change, natural disasters such as floods, landslides, and soil outflows are rapidly increasing. In Korea, more than 63% of the land is vulnerable to slope disasters due to the geographical characteristics of mountainous areas, and in particular, Quarry mines soil and rocks, so there is a high risk of landslides not only inside the workplace but also outside.Accordingly, this study built a DEM using UAV and aviation LiDAR for monitoring the quarry, conducted a time series change analysis, and proposed an optimal DEM construction method for monitoring the soil collection site. For DEM construction, UAV and LiDAR-based Point Cloud were built, and the ground was extracted using three algorithms: Aggressive Classification (AC), Conservative Classification (CC), and Standard Classification (SC). UAV and LiDAR-based DEM constructed according to the algorithm evaluated accuracy through comparison with digital map-based DEM.

• Science of Emotion and Sensibility
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• v.27 no.1
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• pp.3-12
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• 2024

This study examined affective representation by analyzing physiological responses measured using wearable devices and affective ratings in response to emotional videos. To achieve this aim, a published dataset was reanalyzed using multidimensional scaling to demonstrate affective representation in two dimensions. Cross-participant classification was also conducted to identify the consistency of emotional responses across participants. The accuracy and misclassification in each emotional condition were described by exploring the confusion matrix derived from the classification analysis. Multidimensional scaling revealed that the represented objects, namely, emotional videos, were positioned along the rated valence and arousal vectors, supporting the core affect theory (Russell, 1980). Vector fittings of physiological responses also showed the associations between heart rate acceleration and low arousal, increased heart rate variability and negative and high arousal, and increased electrodermal activity and negative and low arousal. Using the data of behavioral and physiological responses across participants, the classification results revealed that emotional videos were more accurately classified than the chance level of classification. The confusion matrix showed that awe, enthusiasm, and liking, which were categorized as positive, low arousal emotions in this study, were less accurately classified than the other emotions and were misclassified for each other. Through multivariate analyses, this study confirms the core affect theory using physiological responses measured through wearable devices and affective ratings in response to emotional videos.

• Journal of Music and Human Behavior
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• v.21 no.2
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• pp.107-134
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• 2024

This study aims to classify the semantic words commonly used to evaluate sound quality and to analyze their differences in reflecting the level of musical stimuli. Participants were thirty-one music majors in their 20s and 30s, with an average of 9.4 years of professional training. Each participant listened to nine pieces of music with variations in texture and instrument type and evaluated them using 18 pairs of semantic words describing sound quality. A factor analysis was conducted to group words influenced by the same latent factor, and a multivariate ANOVA determined the differences in ratings based on texture and instrument type. Radar charts were also drawn based on the identified sets of semantic words. The results showed that four factors were identified, and the word pairs 'soft-hard,' 'dull-sharp,' 'muddy-clean' and 'low-high' showed significant differences based on the level of musical stimuli. The radar charts effectively distinguished the sound quality evaluations for each music. These results indicate that developing Korean semantic words for sound quality evaluation requires a structure different from the previous categories used in Western countries and that linguistic and cultural factors are crucial. This study will provide foundational data for developing a verbal sound quality evaluation framework suited to the Korean context, while reflecting acoustic attributes in music listening.

• Journal of the Korean Society of Food Science and Nutrition
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• v.44 no.2
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• pp.216-225
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• 2015

The purpose of this study was to evaluate night time eating habits, dietary habits, and nutrient intake in university students according to residence type. A survey was conducted by administering questionnaires to 664 students. Questionnaire interview and 24-h dietary recall were conducted. Subjects were divided into three groups according to residence type: dormitory boarding (DB group, N=313), self-boarding (SB group, N=246), and living with parents (LWP group, N=105). Average ages in the DB, SB, and LWP groups were 21.3, 22.2, and 22.1 years, respectively. There were no significant differences in body mass index between the three groups. In total, 77.3% of students regularly ate night time snacks. The proportion of students who reported night time eating was 84.0% in the DB group, 73.6% in the SB group, and 65.7% in the LWP group (P<0.001). In terms of food types consumed during night time eating, the DB group showed a significantly higher rate of consumption of fried chicken and flour-based foods than the SB and LWP groups, whereas the SB group showed a significantly higher rate of consumption of alcohol beverages than the DB and LWP groups. Energy, carbohydrates, protein, fat, vitamins, and mineral intakes were significantly higher in the DB group than in the SB and LWP groups. In addition, intake of cholesterol per 1,000 kcal was significantly higher in the DB group than in the SB and LWP groups. Thus, SB and DB students seemed to have more night time eating problems than LWP students. Accordingly, nutritional education is needed to support the development of healthier eating habits, in particular, night time eating habits, among students living in dormitories and in self-boarding situations.