• The Korean Journal of Emergency Medical Services
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• v.24 no.2
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• pp.27-38
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• 2020

Purpose: Stroke is a time-sensitive disease that could have reduced complications and mortality with timely diagnosis and treatment. This study aimed to analyze the causes of delay in detecting the clinical signs and symptoms of stroke. Methods: This retrospective observational study analyzed the emergency medical services reports of suspected stroke patients with positive predictive values on the Cincinnati Prehospital Stroke Scale. The study was conducted in Daejeon, Republic of Korea from January 1, 2016 through December 31, 2017. Results: Prolonged prehospital time was associated with high blood pressure, history of cerebrovascular disease, and incidences during daily activities, and sleep. High blood pressure and complications from a previous stroke strongly associated with the prolonged stroke-detection phase (p<.05). Total prehospital time was shortened when patients had evident stroke symptoms, such as decreased level of consciousness, dysarthria, and hemiplegia (p<.05). There was no significant difference in gender or age as a factor that delayed the total prehospital time of the suspected stroke patients. Conclusion: Many patients did not recognize the early clinical symptoms and signs of a stroke. Furthermore, risk factors, such as high blood pressure and history of stroke, prolonged the total prehospital time. Therefore, we need targeted interventions that educate about warning symptoms of stroke, along with emphasis on the importance of emergency calls to substantially reduce the prehospital delays.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.9
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• pp.841-849
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• 2008

Natural disaster such as inundation due to the typhoon induced storm-surge has inflicted severe losses on the coastal area. The problem of global warming and sea surface rising has issued and thus influences the increase of frequency and potential power of storm-surge. What we can do is to make intelligent effort to predict and prevent the losses through the early warning and prevention activity from the accurate prediction and forecasting about the time-varying storm-surge height and its arriving time resulted from the numerical simulation with sea observations. In this paper, we developed the web service based GIS-Enabled storm-surge visualization system to predict and prevent the storm-surge disasters. Moreover. for more accurate topography around coastal area and fine-grid storm-surge numerical model, we have accomplished GIS-based coastal mapping through LiDAR measurement.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.153-163
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• 2017

The objective of this study is to enhance the model's speed of estimating weather variables (e.g., minimum/maximum temperature, sunshine hour, PRISM (Parameter-elevation Regression on Independent Slopes Model) based precipitation), which are applied to the Agrometeorological Early Warning System (http://www.agmet.kr). The current process of weather estimation is operated on high-performance multi-core CPUs that have 8 physical cores and 16 logical threads. Nonetheless, the server is not even dedicated to the handling of a single county, indicating that very high overhead is involved in calculating the 10 counties of the Seomjin River Basin. In order to reduce such overhead, several cache and parallelization techniques were used to measure the performance and to check the applicability. Results are as follows: (1) for simple calculations such as Growing Degree Days accumulation, the time required for Input and Output (I/O) is significantly greater than that for calculation, suggesting the need of a technique which reduces disk I/O bottlenecks; (2) when there are many I/O, it is advantageous to distribute them on several servers. However, each server must have a cache for input data so that it does not compete for the same resource; and (3) GPU-based parallel processing method is most suitable for models such as PRISM with large computation loads.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.106-116
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• 2024

Purpose: This paper conducts IoT and CCTV-based safety monitoring to analyze accidents and potential risks occurring at construction sites, and detect and analyze risks such as falls and collisions or abnormalities and to establish a system for early warning using devices like a walkie-talkie and chatbot service. Method: A safety management service model is presented through smart construction technology case studies at the construction site and review a relevant literature analysis. Result: According to 'Construction Accident Statistics,' in 2021, there were 26,888 casualties in the construction industry, accounting for 26.3% of all reported accidents. Fatalities in construction-related accidents amounted to 417 individuals, representing 50.5% of all industrial accident-related deaths. This study suggests implementing AI chatbot services for construction site safety management utilizing IoT-based health monitoring technologies in smart construction practices. Construction sites where stakeholders such as workers participate were demonstrated by implementing an artificial intelligence chatbot system by selecting major risk areas within the workplace, such as scaffolding processes, openings, and access to hazardous machinery. Conclusion: The possibility of commercialization was confirmed by receiving more than 90 points in the satisfaction survey of participating workers regarding the empirical results of the artificial intelligence chatbot service at construction sites.

• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.4
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• pp.359-367
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• 2014

Drought index can be used to implement an early warning system for drought and to operate a drought monitoring service. In this study, an approach was examined to determine agricultural drought index (ADI) at high spatial resolution, e.g., 270 m. The value of ADI was calculated based on soil water balance between supply and demand of water. Water supply is calculated by the cumulative effective precipitation with the application of the weight to the precipitation from two months ago. Water demand is derived from the actual evapotranspiration, which was calculated applying a crop coefficient to the reference evapotranspiration. The amount of surface runoff on a given soil type was also used to calculate soil residual moisture. Presence of drought was determined based on the probability distribution in the given area. In order to assess the reliability of this index, the amount of residual moisture, which represents severity of drought, was compared with measurements of soil moisture at three experimental between July 2012 and December 2013. As a result, the ADI had greater correlation with measured soil moisture compared with the standardized precipitation index, which suggested that the ADI would be useful for drought warning services.

• Journal of Communications and Networks
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• v.16 no.4
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• pp.407-414
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• 2014

The Internet is a highly distributed and complex system consisting of billion devices and has become the field of various kinds of conflicts during the last two decades. As a matter of fact, various actors utilise the Internet for illicit purposes, such as for performing distributed denial of service attacks (DDoS) and for spreading various types of aggressive malware. Despite the fact that numerous services provide information regarding the threat level of the Internet, they are mostly based on information acquired by their sensors or on offline statistical sampling of various security applications (antivirus software, intrusion detection systems, etc.). This paper introduces proactive threat observatory system (PROTOS), an open-source early warning system that does not require a commercial license and is capable of estimating the threat level across the Internet. The proposed system utilises both a global and a local approach, and is thus able to determine whether a specific host is under an imminent threat, as well as to provide an estimation of the malicious activity across the Internet. Apart from these obvious advantages, PROTOS supports a large-scale installation and can be extended even further to improve the effectiveness by incorporating prediction and forecasting techniques.

• Steel and Composite Structures
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• v.43 no.2
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• pp.165-183
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• 2022

Most transportation departments have recognized and developed procedures to address the ever-increasing weights of trucks traveling on bridges in a service today. Transportation agencies also recognize the issues with overheight vehicles' collisions with bridges, but few stakeholders have definitive countermeasures. Bridges are becoming more vulnerable to collisions from overheight vehicles. The exact response under lateral impact force is difficult to predict. In this paper, nonlinear impact analysis shows that the degree of deformation recorded through the modeling of the unprotected vehicle-girder model provides realistic results compared to the observation from the US-61 bridge overheight vehicle impact. The predicted displacements are 0.229 m, 0.161 m, and 0.271 m in the girder bottom flange (lateral), bottom flange (vertical), and web (lateral) deformations, respectively, due to a truck traveling at 112.65 km/h. With such large deformations, the integrity of an impacted bridge becomes jeopardized, which in most cases requires closing the bridge for safety reasons and a need for rehabilitation. We proposed different sacrificial cushion systems to dissipate the energy of an overheight vehicle impact. The goal was to design and tune a suitable energy absorbing system that can protect the bridge and possibly reduce stresses in the overheight vehicle, minimizing the consequences of an impact. A material representing a Sorbothane high impact rubber was chosen and modeled in ANSYS. Out of three sacrificial schemes, a sandwich system is the best in protecting both the bridge and the overheight vehicle. The mitigation system reduced the lateral deflection in the bottom flange by 89%. The system decreased the stresses in the bridge girder and the top portion of the vehicle by 82% and 25%, respectively. The results reveal the capability of the proposed sacrificial system as an effective mitigation system.

• Journal of Trauma and Injury
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• v.37 no.2
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• pp.97-105
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• 2024

Purpose: Massive transfusion protocols (MTPs) implementation improves clinical outcomes of the patient's resuscitation with hemorrhagic trauma. Various predictive scoring system have been used and studied worldwide to improve clinical decision. However, such research has not yet been studied in Korea. This systematic review aimed to assess the predictors of MTPs activation in patients with trauma in Korea. Methods: The PubMed, Embase, Cochrane Library, Research Information Sharing Service databases, KoreaMed, and KMbase were searched from November 2022. All studies conducted in Korea that utilized predictors of MTPs activation in adult patients with trauma were included. Results: Ten articles were eligible for analysis, and the predictors were assessed. Clinical assessments such as systolic and diastolic blood pressure, shock index (SI), prehospital modified SI, modified early warning system (MEWS) and reverse SI multiplied by the Glasgow Coma Scale (rSIG) were used. Laboratory values such as lactate level, fibrinogen degradation product/fibrinogen ratio, and rotational thromboelastometry (ROTEM) were used. Imaging examinations such as pelvic bleeding score were used as predictors of MTPs activation. Conclusions: Our systematic review identified predictors of MTPs activation in patients with trauma in Korea; predictions were performed using tools that requires clinical assessments, laboratory values or imaging examinations only. Among them, ROTEM, rSIG, MEWS, SI, and lactate level showed good effects for predictions of MTPs activation. The application of predictors for MTP's activation should be individualized based on hospital resource and skill set, also should be performed as a clinical decision supporting tools.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.146-157
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• 2019

One of the most distinctive features of the South Korean rural environment is that the variation of weather or climate is large even within a small area due to complex terrains. The Geospatial Schemes based on Topo-Climatology (GSTP) was developed to simulate such variations effectively. In the present study, we reviewed the progress of the geospatial schemes for production of farm-scale agricultural weather data. Efforts have been made to improve the GSTP since 2000s. The schemes were used to provide climate information based on the current normal year and future climate scenarios at a landscape scale. The digital climate maps for the normal year include the maps of the monthly minimum temperature, maximum temperature, precipitation, and solar radiation in the past 30 years at 30 m or 270 m spatial resolution. Based on these digital climate maps, future climate change scenario maps were also produced at the high spatial resolution. These maps have been used for climate change impact assessment at the field scale by reprocessing them and transforming them into various forms. In the 2010s, the GSTP model was used to produce information for farm-specific weather conditions and weather forecast data on a landscape scale. The microclimate models of which the GSTP model consists have been improved to provide detailed weather condition data based on daily weather observation data in recent development. Using such daily data, the Early warning service for agrometeorological hazard has been developed to provide weather forecasts in real-time by processing a digital forecast and mid-term weather forecast data (KMA) at 30 m spatial resolution. Currently, daily minimum temperature, maximum temperature, precipitation, solar radiation quantity, and the duration of sunshine are forecasted as detailed weather conditions and forecast information. Moreover, based on farm-specific past-current-future weather information, growth information for various crops and agrometeorological disaster forecasts have been produced.

• Journal of Digital Convergence
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• v.16 no.9
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• pp.147-157
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• 2018

Since Google launched a prediction service for influenza-like illness(ILI), studies on ILI prediction based on web search data have proliferated worldwide. In this regard, this study aims to build short-term predictive models for ILI in Korea using ILI and web search data and measure the performance of the said models. In these proposed ILI predictive models specific to Korea, ILI surveillance data of Korea CDC and Korean web search data of Google and Naver were used along with the ARIMA model. Model 1 used only ILI data. Models 2 and 3 added Google and Naver search data to the data of Model 1, respectively. Model 4 included a common query used in Models 2 and 3 in addition to the data used in Model 1. In the training period, the goodness of fit of all predictive models was higher than 95% ($R^2$). In predictive periods 1 and 2, Model 1 yielded the best predictions (99.98% and 96.94%, respectively). Models 3(a), 4(b), and 4(c) achieved stable predictability higher than 90% in all predictive periods, but their performances were not better than that of Model 1. The proposed models that yielded accurate and stable predictions can be applied to early warning systems for the influenza pandemic in Korea, with supplementary studies on improving their performance.