• Journal of Integrative Natural Science
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• v.7 no.1
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• pp.62-66
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• 2014

This study aims to implement the emergency monitoring robot system which predicts the current state of the patients without visiting the medical institutions by measuring the basic health status of the user's blood pressure, heartbeat, and basic health status of body temperature in the disaster emergency situation based on the Smart Grid. By arranging a large number of sensor(blood pressure, heartbeat, body temperature sensor) and measuring the bio signs, so the attached wireless XBee sensor can be stored in DB of robot, and it aims to draw the current state of the patients by analysis of stored bio data. Among 300 data obtained from the sensor, 1st data to 100th data were used for learning, and from 101st data to 300th data were used for assessment. 12 results were different among the total 300 assessment data, so it shows about 96% accuracy.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.3
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• pp.87-94
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• 2008

In this study, deals with selection of monitoring objects to handle emergency cases of multi-modal transfer stations and information required for emergency surveillance, recognition, verification, propagation, processing and situation closing. Furthermore, this article suggests integrated management scheme for the above information and methods which offer appropriate information required for situation handling decisions at each stage of situation changes. The transfer station which consists of facilities, passengers, and transportations has limitations in required monitoring information. So, for the situation recognition and handling strategy, case-based reasoning of the expert system was used to apply experience, knowledge, and past cases of situation handling experts. The article also suggests methods to control facilities which are operated at transfer stations and these methods can minimize spatial confusions and damages at the emergency situation. The real time situation information will be shared by proper facility controls to support services from external institutions.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.3
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• pp.157-167
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• 2020

Fingerprinting method is useful when estimating the location of a requestor based on LTE signals in an urban area. To do this, it is necessary to acquire location-based signals everywhere in the service area for fingerprint DB generation in advance. However, there may be signal uncollected area within a wide service area, which may cause a problem that the positioning accuracy of the requestor is low. In order to solve this problem, in this paper, signal propagation modeling is performed based on the obtained measurements, and based on this model, the signal information in the non-acquisition region is estimated. To this end, techniques for modeling signal propagation according to a method using measurements are proposed. The performance of the proposed techniques is verified based on the measurements obtained on a test bed selected as Seocho-gu, Seoul. As a result, it can be seen that signal propagation modeling performed based on multidivision segmented measurements has the most performance improvement.

• Journal of the Society of Disaster Information
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• v.15 no.2
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• pp.282-291
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• 2019

Purpose: In recent, mobile technology, as an axis of the fourth industry paradigm, is evolving into our daily life, economic activities and disaster safety management. However, since the location information service is insufficient, it is difficult to response the emergency situation adequately in the golden time. The purpose of this study is to propose a method to fine precisely the location of people who are in need of an emergency in the event of accidents and disasters. Method: This study investigates and compares existing literature and safety apps for national index number NFC application development. In addition, the system structure and the design method through the element technology through analysis of necessary function of the demander were carried out. Results: The results of this study were developed as a design and system that can be implemented in both direction and function to inform the location for emergency situation or disaster reporting in mobile. Conclution: It is possible to provide the disaster safety location service which can be utilized by the citizens in case of crisis by unifying the address system and integrating the location information using NFC.

• Advances in Computational Design
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• v.4 no.2
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• pp.91-118
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• 2019

Every year, many people are severely injured or lose their lives in accidents such as fire, chemical spill, public pandemonium, school shooting, and workplace violence. Research indicates that the fate of people in an emergency situation involving one or more hazards depends not only on the design of the space (e.g., residential building, industrial facility, shopping mall, sports stadium, school, concert hall) in which the incident occurs, but also on a host of other factors including but not limited to (a) occupants' characteristics, (b) level of familiarity with and cognition of the surroundings, and (c) effectiveness of hazard intervention systems. In this paper, we present EVAQ, a simulation framework for modeling large crowd evacuation by taking into account occupants' behaviors and interactions during an emergency. In particular, human's personal (i.e., age, gender, disability) and interpersonal (i.e., group behavior and interactions) attributes are parameterized in a hazard-impacted environment. In addition, different hazard types (e.g., fire, lone wolf attacker) and propagation patterns, as well as intervention schemes (simulating building repellent systems, firefighters, law enforcement) are modeled. Next, the application of EVAQ to crowd egress planning in an airport terminal under human attack, and a shopping mall in fire emergency are presented and results are discussed. Finally, a validation test is performed using real world data from a past building fire incident to assess the reliability and integrity of EVAQ in comparison with existing evacuation modeling tools.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1130-1135
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• 2022

In Seoul, about 75,000 CCTVs are installed in 25 district offices. Each ward office has built a control center for CCTV control and is performing 24-hour CCTV video control for the safety of citizens. Seoul Metropolitan Government is building a smart city integrated platform that is safe for citizens by providing CCTV images of the ward office to enable rapid response to emergency/emergency situations by signing an MOU with related organizations. In this paper, when an incident occurs at the Seoul Metropolitan Government Office, the escape route is predicted by discriminating people and vehicles using the AI DNN-based Template Matching technology, MLP algorithm and CNN-based YOLO SPP DNN model for CCTV images. In addition, it is designed to automatically disseminate image information and situation information to adjacent ward offices when vehicles and people escape from the competent ward office. The escape route prediction and tracking system using artificial intelligence can expand the smart city integrated platform nationwide.

• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4408-4425
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• 2023

Alarm flood due to abnormality propagation is the most difficult alarm overloading problem in nuclear power plants (NPPs). Root-cause analysis is suggested to help operators in understand emergency events and plant status. Multilevel Flow Modeling (MFM) has been extensively applied in alarm management by virtue of the capability of explaining causal dependencies among alarms. However, there has never been a technique that can identify the actual root cause for complex alarm situations. This paper presents an automated root-cause analysis system based on MFM. The causal reasoning algorithm is first applied to identify several possible root causes that can lead to massive alarms. A novel root-cause ranking algorithm can subsequently be used to isolate the most likely faults from the other root-cause candidates. The proposed method is validated on a pressurized water reactor (PWR) simulator at HAMMLAB. The results show that the actual root cause is accurately identified for every tested operating scenario. The automation of root-cause identification and ranking affords the opportunity of real-time alarm analysis. It is believed that the study can further improve the situation awareness of operators in the alarm flooding situation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.393-401
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• 2021

This study derives essential information elements that should be shared in the situation board by utilizing the concept of common operating picture (COP). The COP's concept and actual overseas cases were confirmed, and COP information elements that should be considered for disaster situations were redefined. The COP disaster response information elements were derived by analyzing the standard manual for disaster response and daily situation reports issued in Korea. The information elements were divided into four stages (①Report reception and recognition stages, ②Situation propagation and reporting stages, ③Emergency equipment operation stages, ④Recovery and recovery stages), centered on storm and flood damage. Further analysis of the detailed information elements was conducted to derive the information elements that must be shared in the context board. The information is shared along with spatial and geographical characteristics due to the characteristics of the COP, providing complex information to decisionmakers and officials, enabling diverse access to disaster situations. Furthermore, it is expected that disaster response will be more efficient by sharing the information in common.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.5
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• pp.957-962
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• 2016

Current terrestrial Digital Multimedia Broadcasting (T-DMB) is conducting the emergency alert broadcast, or can view a variety of broadcasting. However, propagation shadow area is a situation where the service is limited due to limitations of facilities investment. In addition, there is the problem of T-DMB broadcasting is for viewing only a restricted area and a mobile device because the mobile is also T-DMB viewing device impossible. In this paper, it receives a T-DMB broadcasting as a way to solve the problems of the T-DMB system, which was studied the re transmission to the mobile device. Accordingly, by receiving the broadcast may be watched in the mobile device the T-DMB reception impossible. Also provides a one-way/two-way authentication mechanism using a conditional access function, and the system was configured so that the user can watch only the registered broadcasting.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3275-3285
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• 2021

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies.