• Journal of Advanced Marine Engineering and Technology
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• v.35 no.1
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• pp.53-59
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• 2011

The using of diesel engine will be increased in the world for fuel economy. But diesel engine emits harmful emissions such as much NOx, smoke etc. In this study, experiments were performed to investigate the spray characteristics of diesel spray in a common-rail system according to fuel temperature, injection pressure, injection period and fuel viscosity etc. using a high speed video camera. Diesel oil has different spray patten due to injection pressure and injection period in a common-rail system. A Filter pressure was influenced by fuel temperature which was turned to fuel viscosity related to a fluid flowing. The effect of the bio-diesel fuel mixing ratio on the spray and atomization characteristics was also investigated at various experimental conditions. It shows that the droplet atomization characteristics of bio-diesel fuel showed deteriorated results as the mixing ratio of biodiesel increased because of the high viscosity.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2107-2115
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• 2018

Over recent years, high-concentration of particulate matters (e.g., a.k.a. fine dust) in South Korea has increasingly evoked considerable concerns about public health. It is intractable to track and report $PM_{10}$ measurements to the public on a real-time basis. Even worse, such records merely amount to averaged particulate concentration at particular regions. Under this circumstance, people are prone to being at risk at rapidly dispersing air pollution. To address this challenge, we attempt to build a predictive model via deep learning to the concentration of particulates ($PM_{10}$). The proposed method learns a binary decision rule on the basis of video sequences to predict whether the level of particulates ($PM_{10}$) in real time is harmful (>$80{\mu}g/m^3$) or not. To our best knowledge, no vision-based $PM_{10}$ measurement method has been proposed in atmosphere research. In experimental studies, the proposed model is found to outperform other existing algorithms in virtue of convolutional deep learning networks. In this regard, we suppose this vision based-predictive model has lucrative potentials to handle with upcoming challenges related to particulate measurement.

• Korean Journal of Occupational Health Nursing
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• v.11 no.2
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• pp.121-131
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• 2002

Objective: The purpose of this study was to determine the physical load by identifying harmfully working postures and to develop recommendations for improving the existing situation with nurses in ICU, thereby to provide the basis for development of work-related musculoskeletal preventive program. Method: Various types of tasks were recorded with a video camera to chart and analyze different postures by OWAS(Ovako Working Posture Analysing System). Collected data showed that poor postures were adopted, not only for lifting or repositoning a patient, but also for other tasks. Data Analysis: The performed activities were then divided into Nursing Intervention Classification. Altogether 128 postures were selected for analysis. Then they were classified into different OAC (OWAS Action Categories). From all the observation, unhealthy postures, for which corrective measures had to be considered immediately (i.e., 75% classified as OACII+III+IV) were found. Collected data were analyzed in terms of percentage, 2-tail Mann-Whitney U test. Result: Poor postures mainly occur during 'positioning the patient' and 'airway suctioning' in NIC. No difference was found (p=0.060) between the percentage of harmful posture adopted during the patient handling tasks and non-patient handling tasks. Conclusion: This study shows, that in the nursing profession with ICU not only occur during patient handling, but also during other activities. The OWAS method was useful in determining the physical load by locating potential activities due to harmfully working postures, providing a detailed description with analysis, and suggesting successful means to reduce postural load.

• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.75-80
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• 2013

In this paper, the Ka-band maritime satellite communication systems for mobile terminals are proposed. The design includes the link budget analysis, determination of modulation and coding schemes and the overall structure of a transmitter. To avoid the harmful effects on the existing DVB-S2 services, the proposed maritime satellite system using the same spectrum with DVB-S2 at the same time employs the very wideband spreading transmission. Additionally, omni-directional low-gain antennas should be equipped in a mobile terminal to reduce the system cost. These two considerations limit the maximum transmission rate of the proposed system. Due to the limitations, the proposed system includes 36 dB or 39 dB spreading gain depending on the modulation scheme and a link-adaptive repetition method depending on the level of rain attenuation. To support short packets with minimal performance loss, the turbo code used in 3GPP instead of LDPC(low density parity check code) is adopted. By combining them, the overall structure of low-rate maritime satellite communication system is designed.

• Journal of Animal Science and Technology
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• v.65 no.3
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• pp.519-534
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• 2023

Rumination in cattle is closely related to their health, which makes the automatic monitoring of rumination an important part of smart pasture operations. However, manual monitoring of cattle rumination is laborious and wearable sensors are often harmful to animals. Thus, we propose a computer vision-based method to automatically identify multi-object cattle rumination, and to calculate the rumination time and number of chews for each cow. The heads of the cattle in the video were initially tracked with a multi-object tracking algorithm, which combined the You Only Look Once (YOLO) algorithm with the kernelized correlation filter (KCF). Images of the head of each cow were saved at a fixed size, and numbered. Then, a rumination recognition algorithm was constructed with parameters obtained using the frame difference method, and rumination time and number of chews were calculated. The rumination recognition algorithm was used to analyze the head image of each cow to automatically detect multi-object cattle rumination. To verify the feasibility of this method, the algorithm was tested on multi-object cattle rumination videos, and the results were compared with the results produced by human observation. The experimental results showed that the average error in rumination time was 5.902% and the average error in the number of chews was 8.126%. The rumination identification and calculation of rumination information only need to be performed by computers automatically with no manual intervention. It could provide a new contactless rumination identification method for multi-cattle, which provided technical support for smart pasture.

• Research in Community and Public Health Nursing
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• v.12 no.1
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• pp.150-167
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• 2001

This cross sectional study was done to find out the situation of computer online pornography access by 4th to 6th grade elementary school students. The subjects of this study were three elementary schools in Andong, which were selected randomly and 120 students randomly selected from 4th to 6th grade for a total of 360 students. The questionnaire was composed with the help of a community health nursing professor and through the reference review. The collected data were analysed by a PC SPSS 10.0 according to school grade and sex, an 12-test was implemented to learn what percent of students have computers and software which prohibit the viewing of online pornography, access motivation and time, content viewed, attitude after accessing the contents, and opinions about computer online pornography. The results of this study were as follows. 1. A total of 86.1% of students had computers at home. Most students placed computers in their own room, 78.9% of students had computer online communication experience. Higher grade students tended to have more experience of communications on the internet (P<0.001). Most students chatted on the internet once or twice a week and higher grade students tended to communicate online more frequently. (P<0.001). Most students used computers more than one hour a day (P<0.05). They used computers according to the following priorities: game, studying, listening to music, communications, chatting. 2. Regarding the frequency of going computer online more than seven times per week, male students showed a higher frequency than female students (P<0.05). Male students used computers more than one hour a day and female students less than half an hour (P<0.001). Male students mostly used computers to play computer games. On the other hand. female students used the computer to study, listen to music, and chat (P<0.001). 3. Regarding software installation to prevent access of computer online pornography, a total of 19.4% of students responded they installed one. but 80.6% didn't have any, 20.3% of students had accessed pornography, 46.6% of students responded. they first get to know to the computer online pornography through friends. 4. The reasons for accessing computer online pornography were ranked as follows: curiosity, interest, to release stress, and sex drive (P<0.05). Obscene contents were found in computer games, pictures, and video. Among these, pictures were the most common. 5. Regarding students' attitude toward online pornography, most students responded that they took much pleasure in the contents (P<0.05). 6. Regarding the question 'Does online pornography degrade morals and cause a sex crime', most students responded as 'no'. They also responded positively to the suggestion that the contents would be effective in preventing sex crimes and to release stress. 74.7% of students responded that males' access of the contents as proper but females' access as immoral. Regarding social efforts to protect students from this harmful environment. most replied that those efforts were unnecessary (P<0.001).