• 한국식품위생안전성학회지
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• 제36권4호
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• pp.342-346
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• 2021

최근 원액상태로 장시간 보관이 용이하고 특유의 향을 유지할 수 있는 콜드브루커피가 남녀노소에게 크게 인기를 얻음에 따라 커피판매업체의 규모와 수가 증가하고 있으며, 명절 선물용으로도 각광을 받고 있다. 콜드브루커피는 차가운 물로 장시간 추출한 커피이므로 세균에 노출될 가능성이 크다. 따라서 본 연구는 시중판매되고 있는 콜드브루커피의 안전성을 조사하여 소비자에게 올바른 정보와 건강한 먹거리를 제공하고자 하였다. 총 75건의 콜드브루커피를 대상으로 식품공전 액상커피의 규격기준(세균수, 대장균군)과 식중독균 9종 및 카페인 함량검사를 실시하였다. 조사한 결과 온라인에서 구매한 9개 제품의 세균수가 규격기준을 크게 초과하여 검출되었으며, 대장균군 및 식중독균 9종은 검출되지 않았다. 또한 조사한 콜드브루 제품의 평균 카페인 함량은 1.6 mg/mL(240 mL 제품의 경우 카페인 384 mg 함유)이며, 카페인 과다 섭취 시 불면증, 신경과민 등 부정적인 작용들이 존재하므로 성인 기준으로 카페인 최대 일일섭취권고량 400 mg/day을 초과하지 않도록 주의가 필요한 것으로 나타났다.

• Smart Structures and Systems
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• 제29권4호
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

• 한국생산제조학회지
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• 제21권4호
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• pp.601-607
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• 2012

The magnetic abrasive polishing (MAP), for online monitoring with AE sensor attachment, was performed in this study. To predict the surface roughness after the magnetic abrasive polishing of NAK80, the signal data acquired from the AE sensor were analyzed. A dimensionless coefficient, which consisted of average of AErms and standard deviation of AE signal, was defined as a characteristic of the MAP and a prediction model was obtained using least square method. A neural network, which had multiple input parameters from AE signals and polishing conditions, was applied for predicting the surface roughness. As a result of this study, it was seen that there was very close correlation between the AE signal and the surface roughness in the MAP. And then on-line prediction of the surface roughness after the MAP of the NAK80 was possible by the developed prediction model.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 A
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• pp.177-179
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• 2000

In this paper, we developed the online Monitoring & Diagnostic(MD) system of distribution transformers. It consists of the Data Acquisition(DA) module and the DataBase(DB) module. The acquired data from the distribution transformers is stored to the DB in an on-line. The DB is planned and made to the RDBMS (Relative DataBase Management System) to manage the data efficiently. The developed MD system estimates the loss of life from the DB. Thus, it could be managed the career and the functional lifetime of the transformer more efficiently than existing systems. In the case study, the oil temperature rising experiment is performed by using the back loading method in the laboratory level. From the results, the proposed MB system can be a practical applications for the distribution transformers.

• 중환자간호학회지
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• 제13권1호
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• pp.63-75
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• 2020

Purpose : The purpose of this study was to review mobile health (mHealth) interventions based on studies from online databases for community-dwelling patients with heart diseases. Methods : Six databases (RISS, KISS, PubMed, CINAHL, EMBASE, and SCOPUS) were searched to select studies conducted from January 1 to September 30, 2010. After this, quality appraisals were carried out using the Scottish Intercollegiate Guidelines Network checklist and a total of 11 studies were selected. Results : The selected 11 studies included 7 randomized controlled studies, 1 quasi-experimental study and 3 pilot studies. The main components of mHealth interventions included symptom monitoring at home, provisions for individualized messages for health management using text messaging, telephone or smart phone applications, and running websites for symptom monitoring or health education. Intervention periods varied from 6 weeks to 12 months. The findings of the studies suggested that the mHealth interventions were effective in improving self-management of heart diseases, quality of life, and decreasing symptoms. Conclusions : The results of the review suggested that mHealth interventions had positive effects on community-dwelling patients with heart diseases. More mHealth intervention studies need to be conducted in Korea to aid community-dwelling patients with heart diseases.

• Asian-Australasian Journal of Animal Sciences
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• 제28권4호
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• pp.592-598
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• 2015

Stress adversely affects the wellbeing of commercial chickens, and comes with an economic cost to the industry that cannot be ignored. In this paper, we first develop an inexpensive and non-invasive, automatic online-monitoring prototype that uses sound data to notify producers of a stressful situation in a commercial poultry facility. The proposed system is structured hierarchically with three binary-classifier support vector machines. First, it selects an optimal acoustic feature subset from the sound emitted by the laying hens. The detection and classification module detects the stress from changes in the sound and classifies it into subsidiary sound types, such as physical stress from changes in temperature, and mental stress from fear. Finally, an experimental evaluation was performed using real sound data from an audio-surveillance system. The accuracy in detecting stress approached 96.2%, and the classification model was validated, confirming that the average classification accuracy was 96.7%, and that its recall and precision measures were satisfactory.

• Tribology and Lubricants
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• 제37권2호
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• pp.41-47
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• 2021

An experimental device was developed for analysis of hydraulic oil characteristics with dielectric constant sensors. Online analysis is the most effective method of the three methods used for analyzing lubricant oils. This is because it can monitor the machine condition effectively using oil sensors in real time without requiring excellent analysis skill and eliminates human errors. Determining the oil quality usually requires complex laboratory equipment for measuring factors such as density, viscosity, base number, acid number, water content, additive, and wear debris. However, the electric constant is another indicator of oil quality that can be measured on-site. The electric constant is the ratio of the capacitance of a capacitor using that material as a dielectric, compared with a similar capacitor that has a vacuum as its dielectric. The electric constant affects the factors such as the base oil, additive, temperature, electric field frequency, water content, and contaminants. In this study, the tendency of the electric constant is investigated with a variation of temperature, water content, and dust weight. The experimental device can control working temperature and mix the contaminants with oil. A machine condition monitoring program developed to analyze hydraulic oil is described. This program provides graph and digital values with variation of time. Moreover, it includes an alarm system for when the oil condition is bad.

• Healthcare Informatics Research
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• 제24권4호
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• pp.387-393
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• 2018

Objectives: This paper describes an experience of implementing seamless service trials online and offline by adopting Internet of Things (IoT) technology based on near-field communication (NFC) tags and Bluetooth low-energy (BLE) beacons. The services were provided for both patients and health professionals. Methods: The pilot services were implemented to enhance healthcare service quality, improve patient safety, and provide an effective business process to health professionals in a tertiary hospital in Seoul, Korea. The services to enhance healthcare service quality include healing tours, cancer information/education, psychological assessments, indoor navigation, and exercise volume checking. The services to improve patient safety are monitoring of high-risk inpatients and delivery of real-time health information in emergency situations. In addition, the services to provide an effective business process to health professionals include surveys and web services for patient management. Results: Considering the sustainability of the pilot services, we decided to pause navigation and patient monitoring services until the interference problem could be completely resolved because beacon signal interference significantly influences the quality of services. On the other hand, we had to continue to provide new wearable beacons to high-risk patients because of hygiene issues, so the cost increased over time and was much higher than expected. Conclusions: To make the smart connected hospital services sustainable, technical feasibility (e.g., beacon signal interference), economic feasibility (e.g., continuous provision of new necklace beacons), and organizational commitment and support (e.g., renewal of new alternative medical devices and infrastructure) are required.

• International Journal of Knowledge Content Development & Technology
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• 제11권2호
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• pp.77-105
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• 2021

In order to establish a digital-based use environment for the provision of new information services suitable for the 5G era, it is necessary to discuss the direction of service provision by the National Library of Children and Young Adults in the 5G era. Based on utilization services in other fields, library services in the 5G era, including the development and provision of employee education and training services, ultra-high-definition and 360-degree realistic contents and education on library use, provision of multi-dimensional realistic media streaming broadcasting services, provision of telepresence education programs, activation of virtual communities, implementation of hologram performance halls/exhibit centers, and provision of unmanned book delivery services, environment monitoring, safety monitoring, and customized services, were proposed. In addition, based on 5G service, 5G technology, and library application direction, advancing into a producing and supporting base for ultra-realistic and immersive contents in the 5G era, strengthening online and mobile services in the non-contact era, and establishing a smart library environment were proposed as the service provision direction for the National Library of Children and Young Adults in the 5G era.

• 대한원격탐사학회지
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• 제38권1호
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• pp.1-20
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• 2022

As unmanned aerial vehicle (UAV) technology grew in popularity over the years, it was introduced for air quality monitoring. This can easily be used to estimate the sidewalk emission concentration by calculating road traffic emission factors of different vehicle types. These calculations require a simulation of the spread of pollutants from one or more sources given for estimation. For this purpose, a Gaussian plume dispersion model was developed based on the US EPA Motor Vehicle Emissions Simulator (MOVES), which provides an accurate estimate of fuel consumption and pollutant emissions from vehicles under a wide range of user-defined conditions. This paper describes a methodology for estimating emission concentration on the sidewalk emitted by different types of vehicles. This line source considers vehicle parameters, wind speed and direction, and pollutant concentration using a UAV equipped with a monocular camera. All were sampled over an hourly interval. In this article, the YOLOv5 deep learning model is developed, vehicle tracking is used through Deep SORT (Simple Online and Realtime Tracking), vehicle localization using a homography transformation matrix to locate each vehicle and calculate the parameters of speed and acceleration, and ultimately a Gaussian plume dispersion model was developed to estimate the CO, NOx concentrations at a sidewalk point. The results demonstrate that these estimated pollutants values are good to give a fast and reasonable indication for any near road receptor point using a cheap UAV without installing air monitoring stations along the road.