• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.197-205
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• 2024

This paper presents a fully automated stand-alone irrigation system with GSM (Global System for Mobile Communication) module. Solar energy is utilized to power the system and it is aimed to conserve water by reducing water losses. The system is based on a DC water pump that draws energy from solar panels along with automated water flow control using a moisture sensor. It is also fitted with alert and protection system that consists of an ultrasonic sensor and GSM messages sender that transmits signals showing the levels of the water in the reservoir and the battery charge. The control system is designed to stop the water pump from pumping water either when the battery level drops to equal or less than 10% of its full charge, or when the water level becomes less than 10 cm high in the reservoir. The experimental results revealed that the system is appropriate to use in remote areas with water scarcity and away from the national grid.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.1059-1062
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• 2005

보안위협은 갈수록 심각해지고 다양한 정보보호시스템들을 통합하는 통합보안관리시스템에 관한 연구 개발도 활발히 진행 중이다. 이기종 정보보호시스템에서 발생하는 다량의 경보와 이벤트를 효과적으로 수집, 통합하고 상호연관 분석할 수 있는 방법이 절실하다. 현재 연구되고 있는 상호연관분석 방법들에 대해서 조사 분류하고 각 분류별로 장단점을 분석하여 이기종 통합보안관리에 적합한 상호연관분석 방법을 제안한다. 보안 경보 검증과정과 분산화된 경보처리방법으로 실시간 상호연관분석이 가능하도록 설계하였다.

• Journal of Trauma and Injury
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• v.32 no.3
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• pp.168-171
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• 2019

The treatment outcome remains poor of severe facial injuries because of the high risk of compromised airway or massive bleeding. We experienced two successful treatment cases of severe facial injury by the chainsaw. A 52-year-male had his face injured by the chainsaw during his work. He was transferred to the Level I trauma center using the Doctor-Helicopter. During his flight, bleeding control was tried and the information was given to the trauma surgeons before his arrival. His consciousness was alert and the vital signs were stable. The crushing wound, mandible open fracture, deep laceration of tongue, lip, neck and arterial bleeding were noted around his mandible. Nasotracheal intubation was performed under the bronchoscope-guided. Emergency operation (open reduction & internal fixation, primary repair with neurorrhaphy) was performed. At 30 hospital days, he was discharged with facial palsy on left mandibular area. A 30-year-male had his face injured by the chainsaw. He was transferred to our Level I trauma center from the local hospital. The deep-mutiple lacerations on right upper eyelid and forehead with the bony exposure were noted. The vital signs were stable and emergency operation was performed. He was discharged at 20 hospital days. Bone loss or tissue loss were not devastating than we expected even though the injury was occurred by the chainsaw. Aggressive treatment including airway manipulation or bleeding control and maximal opportunity of therapy are absolutely needed.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.517-517
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• 2023

현재 우리나라에서는 4대강 및 주요 호소 29지점을 대상으로 조류경보제가 시행되고 있으며 조류 경보 단계는 실시간 모니터링지점에서 측정되는 유해 조류의 셀농도를 기반으로 발령 단계가 결정된다. 상수원 구간은 관심, 경계, 조류 대발생, 해제 또는 미발생 총 4구간으로 구성되며, 친수 활동 구간의 경우 조류 대발생을 제외한 3구간으로 구성된다. 현재 시행되는 조류 경보제의 목적은 유해 조류 발생 시 사후 대응 방안 마련에 보다 초점이 맞춰져 있으며 특히, 모니터링 주기 확대 여부, 오염원 관리 방안 마련, 조류 제거 여부 등의 의사 결정 수단으로 사용되고 있다. 하지만 조류 경보 단계에 대한 사전 예측이 가능한 경우 유해 조류의 성장을 억제할 수 있으며 이를 통해 안전하고 깨끗한 수자원을 확보할 수 있다. 본 연구에서는 조류 경보 단계의 사전적 예측을 위해 국가 실시간 측정망에서 제공하는 전국 보 모니터링 종합 정보 자료, 기상측정망 자료, 실시간 보 현황 자료를 활용하여 예측 모델을 구축하였다. 또한, 단계 예측의 정확도를 개선하기 위해 변수 선택 기법을 활용하여 조류 경보 단계에 영향을 미치는 환경변수를 선정하였으며 자료의 불균형으로 인해 모델 학습 과정에서 발생하는 예측 오류를 최소화하기 위해 다양한 샘플링 기법을 적용하여 모델의 성능을 평가하였다. 변수 선택 및 샘플링 기법을 고려하지 않은 원자료를 사용하여 예측 모델을 구축한 결과 관심 단계(Level-1) 및 경보 단계(Level-2)에 대해 각각 50%, 62.5%의 예측 정확도를 보인 반면 비선형 변수 선택 기법 및 Synthetic Minority Over-sampling Technique-Edited Nearrest Neighbor(SMOTE-ENN) 샘플링 기법을 적용하여 구축한 모델에서는 Level-1은 85.7%, Level-2는 75.0%의 예측 정확도를 보였다.

• The Journal of Bigdata
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• v.8 no.1
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• pp.99-109
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• 2023

Recently, not only in Korea but also around the world, we have been experiencing constant disasters such as typhoons, wildfires, and heavy rains. The property damage caused by typhoons and heavy rain in South Korea alone has exceeded 1 trillion won. These disasters have resulted in significant loss of life and property damage, and the recovery process will also take a considerable amount of time. In addition, the government's contingency funds are insufficient for the current situation. To prevent and effectively respond to these issues, it is necessary to collect and analyze accurate data in real-time. However, delays and data loss can occur depending on the environment where the sensors are located, the status of the communication network, and the receiving servers. In this paper, we propose a two-stage hybrid situation analysis and prediction algorithm that can accurately analyze even in such communication network conditions. In the first step, data on river and stream levels are collected, filtered, and refined from diverse sensors of different types and stored in a bigdata. An AI rule-based inference algorithm is applied to analyze the crisis alert levels. If the rainfall exceeds a certain threshold, but it remains below the desired level of interest, the second step of deep learning image analysis is performed to determine the final crisis alert level.

• The Journal of Bigdata
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• v.5 no.1
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• pp.125-138
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• 2020

After the spread of COVID-19 in 2020, the country's Crisis Alert Level went up to the highest level, Level 4. Respond of COVID-19 pandemic, Governments, and cities secured each province's duty for the citizens. The government provided health assistance first and stepped forward to support the necessary resources for the citizens. Busan City proposed policy response to prepare and implement the Corona support for each county as well. The high occupant rate of self-business owners lost basic incomes, and the effect varies on industries. In our paper, to avoid any crisis in such an epidemic, we propose a clustering analysis for the guidance of policy support for Busan City. By analyzing patterns and clustering on districts and Sectors, we would like to provide reference materials for determining the direction of support and guiding preemptive response in the event of a similar epidemic.

• Tuberculosis and Respiratory Diseases
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• v.79 no.2
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• pp.74-84
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• 2016

Nontuberculous mycobacteria (NTM) are emerging pathogens that affect both immunocompromised and immunocompetent patients. The incidence and prevalence of NTM lung disease are increasing worldwide and rapidly becoming a major public health problem. For the diagnosis of NTM lung disease, patients suspected to have NTM lung disease are required to meet all clinical and microbiologic criteria. The development of molecular methods allows the characterization of new species and NTM identification at a subspecies level. Even after the identification of NTM species from respiratory specimens, clinicians should consider the clinical significance of such findings. Besides the limited options, treatment is lengthy and varies by species, and therefore a challenge. Treatment may be complicated by potential toxicity with discouraging outcomes. The decision to start treatment for NTM lung disease is not easy and requires careful individualized analysis of risks and benefits. Clinicians should be alert to those unique aspects of NTM lung disease concerning diagnosis with advanced molecular methods and treatment with limited options. Current recommendations and recent advances for diagnosis and treatment of NTM lung disease are summarized in this article.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.4
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• pp.16-24
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• 2012

This paper proposes a method utilizing Differential Global Position System (DGPS) with Real-Time Kinematic (RTK) and pre-built Geo-graphic Information System (GIS) to detect lane departure of a vehicle. The position of a vehicle measured by DGPS with RTK has 18 cm-level accuracy. The preconditioned GIS data giving accurate position information of the traffic lanes is used to set up coordinate system and to enable fast calculation of the relative position of the vehicle within the traffic lanes. This relative position can be used for safe driving by preventing the vehicle from departing lane carelessly. The proposed system can be a key component in functions such as vehicle guidance, driver alert and assistance, and the smart highway that eventually enables autonomous driving supporting system. Experimental results show the ability of the system to meet the accuracy and robustness to detect lane departure of a vehicle at high speed.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.3
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• pp.134-141
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• 2017

The purpose of this study was to analyze physiological changes via a cold air reaction experiment to generate basic data that are useful for the development of an automobile active air conditioning system to prevent drowsiness. The $CO_2$ concentration causing drowsiness in vehicle operation was kept below a certain level. Air was blown to the driver's face by using an indoor air cooling apparatus. Sleepiness and the arousal state of the driver in cold wind were measured by physiological signals. It was evident in the EEG that alpha waves decreased and beta waves increased, caused by cold air stimulation. The ${\alpha}/{\beta}$ ratio was reduced by about 52.9% and an alert state confirmed. In the electrocardiogram analysis, the efficiency of cold air stimulation was confirmed by the mean heart rate interval change. The R-R interval had a delay time of about one minute compared to the EEG response. The findings confirmed an arousal effect from sleepiness due to cold air stimulation.

• Journal of Biomedical Engineering Research
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• v.18 no.3
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• pp.315-323
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• 1997

Driving is a complex vigilance task that includes improper lookout, excessive speed and inattention. The primary objective of this research is to detect driver drowsiness so that the driver can be alerted to an impending traffic accident in performance. We developed the automatic detection and warning system during drowsy driving. A drowsiness detection system must be able to monitor driver status and detect the detrimental changes of a driver performance. Eyeblink has been found to be a reliable factor of drowsiness detection in earlier studies. As an additional parameter, we also considered the yawning which often occurs in a low vigilance state and predicts the drowsy state. We used a computer vision method to extract the eyeblink and yawning in the face image sequences. When the drowsy state was detected, the driver was refreshed by alarming device and menthol scent generator after deciding the warning level by fuzzy logic. For the evaluation of our system, we measured the physiological parameters such as EOG and EEG. The results indicated that it is possible to detect and alert the driver drowsiness temporarily or continuously by using our system.