• Physical Therapy Korea
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• v.26 no.2
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• pp.34-48
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• 2019

Background: Fall-related injuries in older adults are a major health problem, and the risks and mechanisms of these injuries should be affected by race, culture, living environment, and/or economic status. Objects: Research articles have been systematically reviewed to understand fall-related injuries in older adults in South Korea. Methods: 128 published research papers have been found through the Korea Citation Index and the Korean Studies Information Service System, and reviewed in various perspectives, including incidents, fall death rates, medical costs, causes, injury sites and types, locations where falls occurred, prevention strategies, scholarly fields interested in fall injuries, and the role of physical therapy. Results: Fall-related injuries were found to be more common in women than in men, and the number of incidents increased with age, with the highest rate found in individuals over 85 years old. Risk of fall injury was associated with education level, comorbidities, and fear of falling. Common places where falls occurred included the bathroom, living room, stairs, and hallway. Common types of injury included bruises, fractures, and sprains in the lower extremities. Intervention strategies included exercise programs, education, and protective clothing. Scholarly fields interested in fall-related injuries in older adults included medicine, nursing, physical therapy, occupational therapy, physical education, pharmacology, oriental medicine, biomedical engineering, design, clothing, and textiles. Physical therapy intervention using proprioceptive neuromuscular facilitation has been used to improve one's balance. Conclusion: Any movement during the activities of daily living can lead to a fall. Physical therapists are highly educated to analyze human movements and should be involved in more research and practices to solve fall-related injuries in older adults.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.553-560
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• 2020

This paper presents an algorithm for identifying and eliminating errors by seagrasses in coastal bathymetry surveying using drone and HD camera. Survey errors due to seagrasses were identified, segmentated and eliminated using a L∗a∗b color space model. Bathymetry survey using a drone and HD camera has many advantages over conventional survey methods such as ship-board acoustic sounder or manual level survey which are time consuming and expensive. However, errors caused by sea bed reflectance due to seagrasses habitat hamper the development of new surveying tool. Seagrasses are the flowering plants which start to grow in November and flourish to maximum density until April in Korea. We developed a new algorithm for identifying seagrasses habitat locations and eliminating errors due to seagrasses to get the accurate depth survey data. We tested our algorithm at Wolpo beach. Bathymetry survey data which were obtained using a drone with HD camera and calibrated to eliminate errors due to seagrasses, were compared with depth survey data obtained using ship-board multi-beam acoustic sounder. The abnormal bathymetry data which are defined as the excess of 1.5 times of a standard deviation of random errors, are composed of 8.6% of the test site of area of 200 m by 300 m. By applying the developed algorithm, 92% of abnnormal bathymetry data were successfully eliminated and 33% of RMS errors were reduced.

• Trans-
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• v.11
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• pp.119-142
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• 2021

Video was a cultural practice based on image. The audience longs to experience new things, not everyday things through by video images. There are many components of the image, but among them, color, a visual representation, plays a big role. Since the advent of color films, color has constantly evolved as an important component of visual art and has become an important role in innovative visual art design. According to film history data, filmmakers were interested in color since the film was created in 1895, but in the early stages of film development, film colors were only black and white. Because these two colors no longer satisfy viewers, more natural colors began to emerge from the film as it was colored. However, with the development of historical paintings, the lack of artistic creation and the public's level increased, making people more active in using colors because simple reproduction of natural colors alone does not satisfy people. The colors in the video are both techniques of expression and can be understood by mind and thought. It is also an indication that colors do not just exist, but they work strongly on human psychology. Now people are so motivated by repetitive and unimportant information that they find that the human intuitive system simplifies the information they receive unconsciously that they have certain customs and characteristics when they see things. Color is part of the film language, or color language can express the film's ideological themes or portray vivid characters in the film, and people are receiving more intuitive messages. This study analyzed the basic color components of bisexual lighting, namely, pink, blue, and purple, and analyzed how human psychology is affected through color, combining the scenes from the video. The purpose of this paper is to explore what color language bisexual lighting is expressed using color properties in images and how bisexual lighting interacts with human psychology through color.

• Applied Chemistry for Engineering
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• v.22 no.3
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• pp.301-307
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• 2011

Optimal culture conditions of Spirulina platensis NIES 39 have been established using different types of light sources. Several types of photobioreactors were designed and the increase of biomass, the amount of $CO_2$, fixation and the production of chlorophyll content were studied. The result revealed that the input conditions of a 10 min period per 4 h at the condition of 5% $CO_2$ and 0.1 vvm, were excellent in the growth. The growth showing the maximum biomass accumulation is limited to 1.411 g/L when using the fluorescent bulb and the low powered surface mount device (SMD) type LEDs which were equipped-inside in the photobioreactor. However, the biomass exceeded up to 1.758 g/L level when a high powered red LED (color temperature : 12000 K) photobioreactor system was used. The $CO_2$ fixation speed and rate were increased. Although the total production of chlorophyll content undergoes a proportional increase in the biomass, the net content per dry cell weight (DCW) showed the higher production with a blue LED (color temperature : 7500 K) light than that of any other wavelengths. The carbon dioxide loss was marked as 0.15% of the inlet gas (5% $CO_2/Air$, v/v) at the maximum biomass culture condition.

• Journal of Animal Science and Technology
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• v.63 no.2
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• pp.367-379
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• 2021

The objectives of this study were to evaluate convolutional neural network models and computer vision techniques for the classification of swine posture with high accuracy and to use the derived result in the investigation of the effect of dietary fiber level on the behavioral characteristics of the pregnant sow under low and high ambient temperatures during the last stage of gestation. A total of 27 crossbred sows (Yorkshire × Landrace; average body weight, 192.2 ± 4.8 kg) were assigned to three treatments in a randomized complete block design during the last stage of gestation (days 90 to 114). The sows in group 1 were fed a 3% fiber diet under neutral ambient temperature; the sows in group 2 were fed a diet with 3% fiber under high ambient temperature (HT); the sows in group 3 were fed a 6% fiber diet under HT. Eight popular deep learning-based feature extraction frameworks (DenseNet121, DenseNet201, InceptionResNetV2, InceptionV3, MobileNet, VGG16, VGG19, and Xception) used for automatic swine posture classification were selected and compared using the swine posture image dataset that was constructed under real swine farm conditions. The neural network models showed excellent performance on previously unseen data (ability to generalize). The DenseNet121 feature extractor achieved the best performance with 99.83% accuracy, and both DenseNet201 and MobileNet showed an accuracy of 99.77% for the classification of the image dataset. The behavior of sows classified by the DenseNet121 feature extractor showed that the HT in our study reduced (p < 0.05) the standing behavior of sows and also has a tendency to increase (p = 0.082) lying behavior. High dietary fiber treatment tended to increase (p = 0.064) lying and decrease (p < 0.05) the standing behavior of sows, but there was no change in sitting under HT conditions.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.62-73
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• 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 Korea Foundry Society
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• v.41 no.6
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• pp.550-557
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• 2021

With the advent of the fourth industrial revolution, the interest in the internet of things (IoT) in manufacturing is growing, even at foundries. There are several types of process data that can be automatically collected at a foundry, but considerable amounts of process data are still managed based on handwriting for reasons such as the limited functions of outdated production facilities and process design based on operator know-how. In particular, despite recognizing the importance of converting process data into big data, many companies have difficulty adopting these steps willingly due to the burden of system construction costs. In this study, the field applicability of IoT-based devices was examined by manufacturing devices and applying them directly to the site of a centrifugal foundry. For the centrifugal casting process, the temperature and humidity of the working site, the molten metal temperature, and mold rotation speed were selected as process parameters to be collected. The sensors were selected in consideration of the detailed product specifications and cost required for each process parameter, and the circuit was configured using a NodeMCU board capable of wireless communication for IoT-based devices. After designing the circuit, PCB boards were prepared for each parameter, and each device was installed on site considering the working environment. After the on-site installation process, it was confirmed that the level of satisfaction with the safety of the workers and the efficiency of process management increased. Also, it is expected that it will be possible to link process data and quality data in the future, if process parameters are continuously collected. The IoT-based device designed in this study has adequate reliability at a low cast, meaning that the application of this technique can be considered as a cornerstone of data collecting at foundries.

• International journal of advanced smart convergence
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• v.11 no.1
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• pp.19-27
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• 2022

Across the world, 'housing' comprises a significant portion of wealth and assets. For this reason, fluctuations in real estate prices are highly sensitive issues to individual households. In Korea, housing prices have steadily increased over the years, and thus many Koreans view the real estate market as an effective channel for their investments. However, if one purchases a real estate property for the purpose of investing, then there are several risks involved when prices begin to fluctuate. The purpose of this study is to design a real estate price 'return rate' prediction model to help mitigate the risks involved with real estate investments and promote reasonable real estate purchases. Various approaches are explored to develop a model capable of predicting real estate prices based on an understanding of the immovability of the real estate market. This study employs the LSTM method, which is based on artificial intelligence and deep learning, to predict real estate prices and validate the model. LSTM networks are based on recurrent neural networks (RNN) but add cell states (which act as a type of conveyer belt) to the hidden states. LSTM networks are able to obtain cell states and hidden states in a recursive manner. Data on the actual trading prices of apartments in autonomous districts between January 2006 and December 2019 are collected from the Actual Trading Price Disclosure System of the Ministry of Land, Infrastructure and Transport (MOLIT). Additionally, basic data on apartments and commercial buildings are collected from the Public Data Portal and Seoul Metropolitan Government's data portal. The collected actual trading price data are scaled to monthly average trading amounts, and each data entry is pre-processed according to address to produce 168 data entries. An LSTM model for return rate prediction is prepared based on a time series dataset where the training period is set as April 2015~August 2017 (29 months), the validation period is set as September 2017~September 2018 (13 months), and the test period is set as December 2018~December 2019 (13 months). The results of the return rate prediction study are as follows. First, the model achieved a prediction similarity level of almost 76%. After collecting time series data and preparing the final prediction model, it was confirmed that 76% of models could be achieved. All in all, the results demonstrate the reliability of the LSTM-based model for return rate prediction.

• The Journal of Advanced Prosthodontics
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• v.15 no.3
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• pp.155-170
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• 2023

PURPOSE. This study aims to clinically compare the fitness and trueness of zirconia crowns fabricated by different combinations of open CAD-CAM systems. MATERIALS AND METHODS. Total of 40 patients were enrolled in this study, and 9 different zirconia crowns were prepared per patient. Each crown was made through the cross-application of 3 different design software (EZIS VR, 3Shape Dental System, Exocad) with 3 different processing devices (Aegis HM, Trione Z, Motion 2). The marginal gap, absolute marginal discrepancy, internal gap(axial, line angle, occlusal) by a silicone replica technique were measured to compare the fit of the crown. The scanned inner and outer surfaces of the crowns were compared to CAD data using 3D metrology software to evaluate trueness. RESULTS. There were significant differences in the marginal gap, absolute marginal discrepancy, axial and line angle internal gap among the groups (P < .05) in the comparison of fit. There was no statistically significant difference among the groups in terms of occlusal internal gap. The trueness ranged from 36.19 to 43.78 ㎛ but there was no statistically significant difference within the groups (P > .05). CONCLUSION. All 9 groups showed clinically acceptable level of marginal gaps ranging from 74.26 to 112.20 ㎛ in terms of fit comparison. In the comparison of trueness, no significant difference within each group was spotted. Within the limitation of this study, open CAD-CAM systems used in this study can be assembled properly to fabricate zirconia crown.

• Composites Research
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• v.36 no.6
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• pp.435-440
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• 2023

This study aimed to enhance the thermal conductivity of B₄C/Al composite materials, commonly used in transport/storage containers for spent nuclear fuel, by incorporating both boron carbide (B₄C) and cubic boron nitride(cBN) as reinforcing agents in an aluminum (Al) matrix. The composite materials were successfully manufactured through a stir casting process and practical neutron-absorbing materials were obtained by rolling the fabricated composite ingot. The evaluation of the thermal conductivity of the fabricated composites was carried out because thermal conductivity is critical for neutron absorbing materials. The thermal conductivity measurement results indicated an approximately 3% increase in thermal conductivity under the same volume fraction when compared to composite materials using only B₄C particles. Through neutron absorption cross-sectional area calculations, it was confirmed that the neutron absorption capability decreased to a negligible level. Based on the findings of this study, new design approaches for neutron absorption materials are proposed, contributing to the development of high-performance transport/storage containers.