• Animal Bioscience
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• v.36 no.4
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• pp.601-608
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• 2023

Objective: This study was conducted to investigate the effects of energy and protein levels in the diet of Hanwoo heifers on growth response and animal behavior. Methods: Forty heifers were randomly allocated into three experimental groups according to the target daily weight gain in 8 pens (T-0.2, 2 replications; T-0.4 and -0.6, 3 replications) based on similar body weight (BW) and age in months. The target average daily gain (ADG) was set at 0.2 (T-0.2), 0.4 (T-0.4), and 0.6 kg/d (T-0.6), and feed was based on National Institute of Animal Science (NIAS, 2017). In order to minimize hunger stress of T-0.2 and -0.4, the feeding ratio of rice straw was set to 55%, 50%, and 45% for T-0.2, -0.4 and T-0.6, respectively, so that the dry matter (DM) intake for all treatment groups was uniform but the energy and protein levels in the diet were adjusted differently. A total of 6 items (lying, standing, eating, rumination, walking and drinking) of animal behavior were analyzed. Results: During the whole period of the experiment, the ADG of the T-0.2, -0.4 and -0.6 treatments were 0.48, 0.56, and 0.65 kg/d (p<0.05), respectively, showing higher gain than the predicted value, especially for the low target ADG group. Based on these results, regression equations for the total digestible nutrient (TDN) and crude protein (CP) requirements were derived. No behavioral differences were found according to the energy and protein levels in the diet because the DM intake was kept constant by adjusting the roughage and concentration ratio. However, eating time was longer (p<0.05) at T-0.2 than T-0.6 during the whole day. Conclusion: Through this study, it was possible to derive regression equations for predicting TDN and CP requirements according to the target ADG and BW.

• Animal Bioscience
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• v.36 no.5
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• pp.810-817
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• 2023

Objective: The aim was to compare the behavioral response to full separation of cows and calves maintained together for 100 days or 24 h. Methods: Twelve Holstein-Friesian cow-calf pairs were enrolled into either treatment or industry groups (n = 6 cow-calf pairs/group). The treatment cows and calves were maintained on pasture together for 106±8.6 d and temporarily separated twice a day for milking. The Industry cows and their calves, were separated within 24 h postpartum. Triaxial accelerometer neck-mounted sensors were fitted to cows 3 weeks before separation to measure hourly rumination and activity. Before separation, cow and calf behavior was observed by scan sampling for 15 min. During the separation process, frequency of vocalizations and turn arounds were recorded. At separation, cows were moved to an observation pen where behavior was recorded for 3 d. A CCTV camera was used to record video footage of cows within the observation pens and behavior was documented from the videos in 15 min intervals across the 3 d. Results: Before separation, industry calves were more likely to be near their mother than Treatment calves. During the separation process, vocalization and turn around behavior was similar between groups. After full separation, treatment cows vocalized three times more than industry cows. However, the frequency of time spent close to barrier, standing, lying, walking, and eating were similar between industry and treatment cows. Treatment cows had greater rumination duration, and were more active, than industry cows. Conclusion: These findings suggest a similar behavioral response to full calf separation and greater occurrence of vocalizations, from cows maintained in a long-term, pasture-based, cow-calf rearing system when ompared to cows separated within 24 h. However, further work is required to assess the impact of full separation on calf behavior.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.35-40
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• 2021

People counting data is crucial for most business owners, since they can derive meaningful information about customers movement within their businesses. For example, owners of the supermarkets can increase or decrease the number of checkouts counters depending on number of occupants. Also, it has many applications in smart buildings, too. Where it can be used as a smart controller to control heating and cooling systems depending on a number of occupants in each room. There are advanced technologies like camera-based people counting system, which can give more accurate counting result. But they are expensive, hard to deploy and privacy invasive. In this paper, we propose a method and a hardware sensor for counting people passing through a passage or an entrance using IR Infrared sensors. Proposed sensor operates at low voltage, so low power consumption ensure long duration on batteries. Moreover, we propose a new method that distinguishes human body and other objects. Proposed method is inexpensive, easy to install and most importantly, it is real-time. The evaluation of our proposed method showed that when counting people passing one by one without overlapping, recall was 96% and when people carrying handbag like objects, the precision was 88%. Our proposed method outperforms IR Infrared based people counting systems in term of counting accuracy.

• Journal of Korean Medical Ki-Gong Academy
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• v.21 no.1
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• pp.1-12
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• 2022

Objective : The purpose of this study is to report the results of treating patients with acute low back pain syndrome who are difficult to walk with Whidam's Vibrator Pelvic Sugi Therapy. Methods : We used combinations of korean medicine such as herbal medicine, acupuncture, cupping therapy, and thermal therapy. At the same time, the patient was treated with Whidam's Vibrator Pelvic Sugi Therapy. The first period of hospitalization was 14 days and the second period was 54 days. At the second hospitalization, back pain recurred and was unable to walk. At the first hospitalization, L5-S1 HIVD and L4-5 Mild HIVD were diagnosed on lumbar MRI, and there was no abnormality in blood tests. Results : By relieving muscle tension and restoring the weakened muscle elasticity with herbal treatment and Whidam's Vibrator Pelvic Sugi Therapy, back pain decreased and the inability to walk gradually improved, making it possible to walk on its own. Conclusions : The results of standing and walking on one's own were obtained by relieving muscle tension and restoring weakened muscle elasticity in acute back pain syndrome with gait disturbance diagnosed with lumbar herniated intervertebral disc through korean medicine treatment and Whidam's Vibrator Pelvic Sugi Therapy. Whidam's Vibrator Pelvic Sugi Therapy is thought to help acute back pain by restoring muscle elasticity and strengthening muscle strength. In the future, it is expected that additional clinical studies will be conducted on various pain diseases with Whidam's Vibrator Pelvic Sugi Therapy.

• Journal of Convergence Korean Medicine
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• v.1 no.1
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• pp.35-44
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• 2021

Objectives: The purpose of this study is to investigate the effect of ultrasound therapy on left knee pain and instability diagnosed with Baker's cyst in a 25-year-old male patient. Methods: Patient was treated with Radio frequency ablation treatment on left knee from Naeseulan (EX-LE4) to Wijoong (BL40). The effect of radio frequency ablation was assessed by Visual Analog Scale (VAS), Western Ontario and McMaster Universities (WOMAC) and Euro Qol-5 Dimension Index (EQ-5D Index). The treatment was executed one time a day everyday form May 28th to June 1st of 2020. Patient was observed from May 26th to June 1st. Results: After five sessions of radio frequency ablation, VAS of left posterior knee pain was improved while no improvement was discovered at left medial knee. In Korean WOMAC scale, only subscale of weight bearing and walking around plain were improved. EQ-5D did not show any improvement. Conclusion: The results suggest that radio frequency ablation treatment can be a valuable option in treating knee pain and daily living function. Further study seems to be needed based on long-term intervention.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.1-16
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• 2022

Recently, e-scooters have been attracting attention as eco-friendly modes of transportation in cities due to an increasing interest in the environment. Accordingly, various studies on usage behavior are being conducted, but studies that reflect individual travel attitudes are insufficient. Therefore, this study surveyed commuters in Seoul and analyzed respondents' traveling attitudes through factor analysis. It also built a binary logistic regression model for the intention to use shared e-scooters to determine how individual travel behaviors are affected. In particular, the model results showed that age, the main mode of transportation (car), walking time to the bus stop, and four travel attitude variables (disutility of travel, preference to self-drive, internet/smartphone friendliness, and willingness to pay extra money for services) significantly affected the intention to use shared e-scooters. This study is expected to be used as basic data, with aspect to travel behavior, for the efficient operation and use of shared e-scooters in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2D
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• pp.147-154
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• 2008

A variety of activity happens around the sidewalk in the city. Particularly, a large variety of activity happens in shopping area, but it causes an obstruction of economical revitalization since the pedestrians require time and cost to find what they want. So, this study will develop the path searching method to minimize the economical loss of shoppers by providing the significant path and supporting the walking movement. Firstly, consider existing network expression techniques and approach three points which are physical and environmental factor, the recognition of the pedestrians' space when changing the direction, and the recognition of restriction of vision and accessibility. Try to design the network DB and simulate the algorithm. As a result, it is now possible to do the path searching that considers variety of recognition factors and show the method how to make the path-searching algorithm for pedestrian.

• Korean Journal of Applied Biomechanics
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• v.33 no.2
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• pp.62-72
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• 2023

Objective: Although balance training has been used as an effective ankle injury rehabilitation program to restore neuromuscular deficits in patients with chronic ankle instability, it is not effectively used in terms of motor learning. Attentional focusing can be an effective method for improving ankle kinematics to prevent recurrent ankle injuries. This study aimed to 1) evaluate the effects of attentional focus, including internal and external focus, and 2) determine a more effective focusing method for patients with chronic ankle instability to learn balance tasks. Method: Twenty-four patients with chronic ankle instability were randomly assigned to three groups (external focus, internal focus, and no feedback) and underwent four weeks of progressive balance training. The three-dimensional ankle kinematics of each patient were measured before and after training as the main outcomes. Ensemble curve analysis, discrete point analysis, and post hoc pairwise comparisons were performed to identify interactions between groups and time. Results: The results showed that (1) the external focus group was more dorsiflexed and everted than the internal focus group; (2) the external focus group was more dorsiflexed than the no feedback group; and (3) the no feedback group was more dorsiflexed than the internal focus group. Conclusion: Because dorsiflexion and eversion are ankle motions that oppose the mechanism of lateral ankle sprain, using the external focus method during balance training may be more effective in modifying these motions, thereby reducing the risk of ankle sprain.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.199-208
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• 2023

The emergence of COVID-19 virus has shaken almost every aspect of human life including but not limited to social, financial, and economic changes. One of the most significant impacts was obviously healthcare. Now though the pandemic has been over, its aftereffects are still there. Among them, a prominent one is people lifestyle. Work from home, enhanced screen time, limited mobility and walking habits, junk food, lack of sleep etc. are several factors that have still been affecting human health. Consequently, diseases like diabetes, high blood pressure, anxiety etc. have been emerging at a speed never witnessed before and it mainly includes the people at young age. The situation demands an early prediction, detection, and warning system to alert the people at risk. AI and Machine learning has been investigated tremendously for solving the problems in almost every aspect of human life, especially healthcare and results are promising. This study focuses on reviewing the machine learning based approaches conducted in detection and prediction of diabetes especially during and post pandemic era. That will help find a research gap and significance of the study especially for the researchers and scholars in the same field.

• Journal of the Society of Disaster Information
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• v.20 no.2
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• pp.315-328
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• 2024

Purpose: The purpose of this paper is to present a service construction plan using multiple complex sensor information to detect abnormal situations in urban life safety that are difficult to identify on CCTV. Method: This study selected service scenarios based on actual testbed data and analyzed service importance for local government control center operators, which are main users. Result: Service scenarios were selected as detection of day and night dynamic object, Detection of sudden temperature changes, and Detection of time-series temperature changes. As a result of AHP analysis, walking and mobility collision risk situation services and fire foreshadowing detection services leading to immediate major disasters were highly evaluated. Conclusion: This study is significant in proposing a plan to build an anomaly detection service that can be used in local governments based on real data. This study is significant in proposing a plan to build an anomaly detection service that can be used by local governments based on testbed data.