• Journal of Environmental Health Sciences
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• v.44 no.6
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• pp.563-571
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• 2018

Objectives: The purpose of this study was to investigate the environmental pollution caused by exposure to air pollution in Korea. Therefore, it is necessary to investigate environmental and health factors through big data. Methods: Among the environmental diseases, the data centered on "percentage per day in 2015 to 2018". Data of environmental diseases and concentrations of air pollution monitoring network were analyzed. Results: Lung cancer and bronchiolitis obliterans were correlated with 0.027 and 0.0158, respectively, in the contamination concentration of fine dust ($PM_{10}$). Ozone, COPD, allergic rhinitis, and bronchiolitis obliterans were correlated with 0.0022, 0.0028 and 0.0093, respectively. At the concentration of $SO_2$ and the diseases of asthma, atopic dermatitis, lung cancer and bronchiolitis obliterans were 0.0008, 0.0523, 0.0016 and 0.0126, respectively. Conclusions: We surveyed the trends of air pollution according to the characteristics of Seoul area in Korea and evaluated the perception of Korea and the world. As a result, respiratory lung disease is thought to be a major factor in exposure to environmental pollution.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.253-262
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• 2018

Objectives: We reviewed digital epidemiological studies to characterize how researchers are using digital data by topic domain, study purpose, data source, and analytic method. Methods: We reviewed research articles published within the last decade that used digital data to answer epidemiological research questions. Data were abstracted from these articles using a data collection tool that we developed. Finally, we summarized the characteristics of the digital epidemiological studies. Results: We identified six main topic domains: infectious diseases (58.7%), non-communicable diseases (29.4%), mental health and substance use (8.3%), general population behavior (4.6%), environmental, dietary, and lifestyle (4.6%), and vital status (0.9%). We identified four categories for the study purpose: description (22.9%), exploration (34.9%), explanation (27.5%), and prediction and control (14.7%). We identified eight categories for the data sources: web search query (52.3%), social media posts (31.2%), web portal posts (11.9%), webpage access logs (7.3%), images (7.3%), mobile phone network data (1.8%), global positioning system data (1.8%), and others (2.8%). Of these, 50.5% used correlation analyses, 41.3% regression analyses, 25.6% machine learning, and 19.3% descriptive analyses. Conclusions: Digital data collected for non-epidemiological purposes are being used to study health phenomena in a variety of topic domains. Digital epidemiology requires access to large datasets and advanced analytics. Ensuring open access is clearly at odds with the desire to have as little personal data as possible in these large datasets to protect privacy. Establishment of data cooperatives with restricted access may be a solution to this dilemma.

• Quality Improvement in Health Care
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• v.24 no.2
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• pp.41-61
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• 2018

Purpose: Falls are one of the most frequent health events in medical institutions, however, they can be predicted and prevented. The Quality Improvement Nurse Society clinical practice guideline Steering Committee developed the Clinical Practice Guideline for the assessment and prevention of falls in adult people. The purpose of this study was to assess the risk factors for falls in adults aged 19 years and older, to present an evidence for preventing falls, formulate a recommendations, and indicators for applying the recommendations. Methods: This clinical practice guideline was developed using a 23-step adaptation method according to the Handbook for clinical practice guideline developer (version 1.0) by National Evidence-based Healthcare Collaborating Agency. Evidence levels and recommendation ratings were established in accordance to SIGN 2011 (The Scottish Intercollegiate Guidelines Network). Results: The final 15 recommendations from four domains were derived from experts' advice; 1) assessment of risk factor for falls in adult 2) preventing falls and reducing the risks of falls or falls-related injury 3) management and reassessment after a person falls 4) leadership and culture. Conclusion: This clinical practice guideline can be used as a basis for evaluation and prevention of fall risk factors for adults, to formulate recommendations for fall risk assessment and fall prevention, and to present monitoring indicators for applying the recommendations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.711-732
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• 2019

Many unmanned aerial vehicle (UAV) applications have been employed for performing data collection in facilitating tasks such as surveillance and monitoring objectives in remote and dangerous environments. In light of the fact that most of the existing UAV relaying applications operate in conventional half-duplex (HD) mode, a full-duplex (FD) based UAV relay aided wireless network is investigated, in which the UAV relay helps forwarding information from the source (S) node to the destination (D). Since the activated UAV relays are always floating and flying in the air, its channel state information (CSI) as well as channel capacity is a time-variant parameter. Considering decode-and-forward (DF) relaying protocol in UAV relays, the cooperative relaying channel capacity is constrained by the relatively weaker one (i.e. in terms of signal-to-noise ratio (SNR) or signal-to-interference-plus-noise ratio (SINR)) between S-to-relay and relay-to-D links. The channel capacity can be optimized by adaptively optimizing the transmit power of S and/or UAV relay. Furthermore, a hybrid HD/FD mode is enabled in the proposed UAV relays for adaptively optimizing the channel utilization subject to the instantaneous CSI and/or remaining self-interference (SI) levels. Numerical results show that the channel capacity of the proposed UAV relay aided wireless networks can be maximized by adaptively responding to the influence of various real-time factors.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1147-1158
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• 2020

The purpose of this study was to measure the engine torque and rotational speed of a 67 kW class agricultural tractor according to tire type during plow tillage and to analyze the gear strength of the driving shift for the tractor. A field test was performed under the condition with a single tire (Test A) and dual tires (Test B) to increase the ground width of the rear tires. A load monitoring system was developed, and the engine torque and rotational speed were measured using controller area network (CAN) communication. The engine torque and rotational speed during plow tillage were calculated as the equivalent torque and speed using Palmgren Miner's rule. As a result, the equivalent torque and speed in Test A and Test B were 181.0 Nm and 1,913 rpm and 206.1 Nm and 2,130 rpm, respectively. As the ground width of the rear tire was increased, the bending stress in Test B was about 9.9 to 10.5% higher than that of the Test A, and the contact stress was about 4.6 to 4.9% higher than that of the Test A. Under all conditions, the safety factor for the bending and contact stress was 1 or more. Thus, the driving shift gears for the dual tire type are considered safe.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1135-1145
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• 2020

The purpose of this study was to compare and analyze the emissions of SOx and NH3 for a 78 kW class agricultural tractor during agricultural operations. A real-time monitoring system was constructed for measuring the load data. The field test was conducted during plow and rotary tillage. The working conditions were selected with the transmission gears in M3 Low and M2 High for the plow tillage and L3 High and L3 Low for the rotary tillage. The engine torque and fuel consumption were measured using controller area network (CAN) communication, and the emissions of SOx and NH3 were calculated based on the fuel consumption. As a result of the field tests, the engine torque was higher for the plow tillage than for the rotary tillage. As the gear stage was increased, the engine torque became higher. The emissions of SOx and NH3 were higher for the plow tillage than for the rotary tillage because the fuel consumption increased. Moreover, the emissions of SOx and NH3 tended to be more distributed for the rotary tillage than for the plow tillage. To develop an emission factor for agricultural machinery, it is important to measure reliable emission data during agricultural operations. In a future study, we will collect various emission data using a portable emission measurement system during agricultural operations.

• Journal of IKEEE
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• v.25 no.1
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• pp.95-100
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• 2021

If we know the location of the hull block and the welding feeder in the shipyard, we can easily obtain the location information of the worker. That data is very useful for implementing a workplace safety monitoring system. However, it is difficult to apply a fixed communication network to the workplace due to the specificity of the hull structure and welding process. In this study, inductive power line communication, which can replace dedicated communication line, was reviewed. A ferrite core was used as an inductive coupler to be installed on the power cable of the welding machine, and a nano-crystalline core was applied as a coupler to be fastened to the support rod of the metal block. In order to visualize the operating principle of the proposed couplers, 3D modeling and finite element analysis were performed with the COMSOL AC/DC module. In the communication performance test using an aluminum profile, when the communication channel was formed by the contact of the welding electrode, the bandwidth was kept above 6 Mbps.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.4
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• pp.47-60
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• 2011

With the increase in the use of mobile banking service, mobile banking has become an attractive target to attackers. Even though many security measures are applied to the current mobile banking service, some threats such as physical theft or penetration to a mobile device from remote side are still remained as unsolved. With aiming to fill this void, we propose a novel approach to prevent e-financial incidents by analyzing mobile device user's input patterns. This approach helps us to distinguish between original user's usage and attacker's usage through analyzing personal input patterns such as input time-interval, finger pressure level on the touch screen. Our proposed method shows high accuracy, and is effective to prevent the e-finance incidents proactively.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.6
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• pp.145-152
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• 2009

SCADA(Supervisory Control And Data Acquisition) systems are widely used for control and monitoring of critical infrastructures including electricity, gas and transportation. Any compromise in the security of SCADA systems could result in massive chaos and disaster at a national level if a malicious attacker takes the control of the system. Therefore, sound countermeasures must be provided when the SCADA systems are being developed as well as when they are being operated. Unlike general information processing system, SCADA systems have different service responses, communication protocols and network architectures and therefore a different approach should be applied to each SCADA systems that takes into consideration of each system's security characteristics and architectures. In addition, legal basis should be established to ensure the nationwide management of the systems security. This paper examines the vulnerabilites of SCADA systems and proposes action plans to protect the systems against cyber attacks.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1209-1216
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• 2020

In this study, we propose a method for detecting coastal surface wastes using an UAV(Unmanned Aerial Vehicle) remote sensing method and an object detection algorithm based on deep learning. An object detection algorithm based on deep neural networks was proposed to detect coastal debris in aerial images. A deep neural network model was trained with image datasets of three classes: PET, Styrofoam, and plastics. And the detection accuracy of each class was compared with Darknet-53. Through this, it was possible to monitor the wastes landing on the shore by type through unmanned aerial vehicles. In the future, if the method proposed in this study is applied, a complete enumeration of the whole beach will be possible. It is believed that it can contribute to increase the efficiency of the marine environment monitoring field.