• The KIPS Transactions:PartC
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• v.10C no.6
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• pp.727-738
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• 2003

Much research efforts are being exerted for the study of intrusion detection system(IDS). However little work has been for the communication medels and performance eveluation of the IDS. Here we present a communication framework for doing hybrid intrusion detection in which agents are used for local intrusion detections with a centralized data anaysis componenta for a global intrusion detection at multiple domains environment. We also assume the combination of host-based and network-based intrusion detection systems in the oberall framework. From the local domain, a set of information such as alert, and / or log data are reported to the upper level. At the root of the hierarchy, there is a global manager where data coalescing is performed. The global manager delivers a security policy to its lower levels as the result of aggregation and correlation of intrusion detection alerts. In this paper, we model the communication mechanisms for the hybrid IDS and develop a simular using OPNET modeller for the performance evaluation of transmission capabillities for the delivery of data and policy. We present and compare simulation results based on several scenarios focuding on communication delay.

• Journal of Korean Society on Water Environment
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• v.39 no.1
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• pp.9-19
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• 2023

Drought is a natural disaster that can have serious social impacts. Drought's impact ranges from water supply for humans to ecosystems, but the impact of drought on river water quality requires careful investigation. In general, drought occurs meteorologically and is classified as agricultural drought, hydrological drought, and environmental drought. In this study, the BOD environmental drought is defined using the bivariate copula joint probability distribution model between the meteorological drought index and the river BOD, and based on this, the environmental drought condition index (EDCI-BOD) was proposed. The results of examining the proposed index using past precipitation and BOD observation data showed that EDCI-BOD expressed environmental drought well in terms of river BOD water quality. In addition, by classifying the calculated EDCI-BOD into four levels, namely, 'attention', 'caution', 'alert', and 'seriousness', a practical monitoring stage for environmental drought of BOD was constructed. We further estimated the sensitivity of the stream BOD to meteorological drought, and through this, we could identify the stream section in which the stream BOD responded relatively more sensitively to the occurrence of meteorological drought. The results of this study are expected to provide information necessary for river BOD management in the event of meteorological droughts.

• The Journal of Information Technology
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• v.1 no.2
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• pp.143-162
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• 1998

As a method of providing lifelong educational opportunities to working professionals, some legal provisions allowing for the establishment and management of an entrusted education system linked with college and corporation was duly approved in 1994. According to the recent trend in the military organization, to provide higher educational opportunities to noncommissioned officers and warrant officers with the purpose of inspiring their spirit and improving military capabilities has been much increased. Professional soldiers such as noncommissioned officers and warrant officers have a lot of problems to continue their attendance at school because of regular military maneuvers, constant alert status and long-term outdoor military training. Even more serious problem is that the current curriculums and some departments in college are not concurrent with the present job specifications and the military specialties of the individual soldier as well. The purpose of this paper is, therefore, to introduce a newly revised educational model that provides more viable academic courses and curriculums to military professionals along with a proposal for the establishment and management of long distance educational system within barracks, utilizing the latest information and telecommunication technology.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.2
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• pp.69-79
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• 2017

In this study, we developed real-time urban stream discharge forecasting model using short-term rainfall forecasts data simulated by a regional climate model (RCM). The National Centers for Environmental Prediction (NCEP) Climate Forecasting System (CFS) data was used as a boundary condition for the RCM, namely the Global/Regional Integrated Model System(GRIMs)-Regional Model Program (RMP). In addition, we make ensemble (ESB) forecast with different lead time from 1-day to 3-day and its accuracy was validated through temporal correlation coefficient (TCC). The simulated rainfall is compared to observed data, which are automatic weather stations (AWS) data and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA 3B43; 3 hourly rainfall with $0.25^{\circ}{\times}0.25^{\circ}$ resolution) data over midland of Korea in July 26-29, 2011. Moreover, we evaluated urban rainfall-runoff relationship using Storm Water Management Model (SWMM). Several statistical measures (e.g., percent error of peak, precent error of volume, and time of peak) are used to validate the rainfall-runoff model's performance. The correlation coefficient (CC) and the Nash-Sutcliffe efficiency (NSE) are evaluated. The result shows that the high correlation was lead time (LT) 33-hour, LT 27-hour, and ESB forecasts, and the NSE shows positive values in LT 33-hour, and ESB forecasts. Through this study, it can be expected to utilizing the real-time urban flood alert using short-term weather forecast.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.11
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• pp.82-88
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• 2013

It is important to give lightning warning prior to a cloud-to-ground (CG) discharge within an Area of Concern (AOC) because most of lightning damage and victim are usually occurred by the first lightning in the AOC. The aim of this study is to find the optimal operation conditions of the automated lightning warning systems in order to make the best use of the available data. In this paper, the test-operated results of the automated lightning alert and risk management system (ALARM) based on detections of CG discharge and eletrostatic field and optimized at probability of lightning have been described. It was possible to obtain the following warning performance parameters: probability of detection (POD), false alarm ratio (FAR), probability of lightning (POL) and failure-to-warn rate (FTW). The data obtained from trial operation for 5months were not sufficient but the first analysis of domestic lightning warning was carried out. We have observed that the evaluated statistical results through trial operation depend on the various factors such as analysis methods and criteria, topographical conditions, etc. Also we suggest some methods for improvement of POL and POD including the finding of the optimal electric field threshold level to be used, based on the high values of FAR and FTW found in this work.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.163-165
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• 2006

The subway passengers are usually alert to the current location of the train in order not to miss the destination or transfer stations. The Global Positioning System (GPS), although expensive, can give an alarm if properly programmed, but cannot receive the satellite signals, underground. Therefore, a novel approach to context-aware location-based subway information system is motivated. The passengers, who are equipped with mobile devices such as laptop, PDA, and mobile phone as clients of the Personal Area Network (PAN), request the Bluetooth connection to the server which is installed in each car of the train. While the sorrel broadcasts the location-based information including the previous station, the current velocity of the train, the distance and time to the next station, the clients provide additional services based on the recognized context of the information, and what the passengers individually want. The services are spontaneous and autonomous rather than passive. The services include not only the information on the nearby stations, exit numbers, connection buses but also the location-based alarms which can be set specific to various personal requests, and sounded by the location of the train rather than time. Whereas the arrival time may not be accurate due to the delays of the train, the location can be accurately traced and broadcast by the server. Also, the clients do not need any expensive systems like GFS. Towards validating the proposed approach, we implemented a Bluetooth PAN including a PC server, two PDA clients and a laptop client. We modeled a train on the Incheon Subway Line #1 and a train on the Seoul-to-Incheon Line on the server, simulated the virtual trains together with the real clients. and verified that all the services were successfully provided.

• KIISE Transactions on Computing Practices
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• v.23 no.11
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• pp.641-649
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• 2017

As Internet of Things devices become pervasive, services improve to better assess the context and to alert other users to deal with emergencies. Such services use Internet of Things devices to detect the context around the user and promptly notify public institutions, hospitals or other parental users in emergencies. Most of these systems analyze an event when the value of the device is unchanged for a period of time or if it detects an abnormal value. However, just monitoring sensor values makes it difficult to accurately understand the context surrounding a user. Also if the device is inactive, it can not identify the context or provide services again. However, understanding the user requirements, services provided through other devices, information sent to other users lets, appropriate actions be taken. This paper, proposes a device search method and system based on a context-aware model that includes user demands. The proposed system analyzes the user's context and demands by using data collected from the internet of things devices. If user devices are inactive, they can recognize other devices by searching for other devices and providing services to users again. Through the proposed method, the user-centric services are provided. This method also analyzes and responds to requirements in various emergencies.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.157-161
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• 2009

In recent years, there is a remarkable progress in ICTs (Information and Communication Technologies), and then many attempts to apply ICTs to other industries are being made. In the field of disaster managements, ICTs such as RFID (Radio Frequency IDentification) and USN (Ubiquitous Sensor Network) are used to provide safe environments. Actually, various types of early warning systems using USN are now widely used to monitor natural disasters such as floods, landslides and earthquakes, and also to detect human-caused disasters such as fires, explosions and collapses. These early warning systems issue alarms rapidly when a disaster is detected or an event exceeds prescribed thresholds, and furthermore deliver alarm messages to disaster managers and citizens. In general, these systems consist of a number of various sensors and measure real-time stream data, which requires an efficient and rapid data processing technique. In this study, an event-driven architecture (EDA) is presented to collect event effectively and to provide an alert rapidly. A publish/subscribe event processing method to process simple event is introduced. Additionally, a complex event processing (CEP) technique is introduced to process complex data from various sensors and to provide prompt and reasonable decision supports when many disasters happen simultaneously. A basic concept of CEP technique is presented and the advantages of the technique in disaster management are also discussed. Then, how the main processing methods of CEP such as aggregation, correlation, and filtering can be applied to disaster management is considered. Finally, an example of flood forecasting and early alarm system in which CEP is incorporated is presented It is found that the CEP based on the EDA will provide an efficient early warning method when disaster happens.

• Journal of the Korean Society for Precision Engineering
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• v.34 no.1
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• pp.41-46
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• 2017

Magnetically levitated (Maglev) vehicles maintain a constant air gap between guideway and car bogie, and thereby achieves non-contact riding. Since the straightness and the flatness of the guideway directly affect the stability of levitation as well as the ride comfort, it is necessary to monitor the status of the guideway and to alert the train operators to any abnormal conditions. In order to develop a signal processing algorithm that extracts guideway irregularities from sensor data, virtual testing using a simulation model would be convenient for analyzing the exact effects of any input as long as the model describes the actual system accurately. Simulation model can also be used as an estimation model. In this paper, we develop a state-space dynamic model of a maglev vehicle system, running on the guideway that contains jumps. This model contains not only the dynamics of the vehicle, but also the descriptions of the power amplifier, the anti-aliasing filter and the sampling delay. A test rig is built for the validation of the model. The test rig consists of a small-scale maglev vehicle, tracks with artificial jumps, and various sensors measuring displacements, accelerations, and coil currents. The experimental data matches well with those from the simulation model, indicating the validity of the model.

• Journal of Advanced Navigation Technology
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• v.22 no.5
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• pp.472-477
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• 2018

In this paper, we develop interactive content services for preventing depression of users through an intelligent Internet of Things(IoT) mirror system. For interactive content services, an IoT mirror device measures attention and meditation data from an EEG headset device and also measures facial expression data such as "sad", "angery", "disgust", "neutral", " happy", and "surprise" classified by a multi-layer perceptron algorithm through an webcam. Then, it sends the measured data to an oneM2M-compliant IoT server. Based on the collected data in the IoT server, a machine learning model is built to classify three levels of depression (RED, YELLOW, and GREEN) given by a proposed merge labeling method. It was verified that the k-nearest neighbor (k-NN) model could achieve about 93% of accuracy by experimental results. In addition, according to the classified level, a social network service agent sent a corresponding alert message to the family, friends and social workers. Thus, we were able to provide an interactive content service between users and caregivers.