• Journal of Digital Convergence
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• v.10 no.9
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• pp.1-13
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• 2012

This study aims to analyze actual conditions of the present national defense information network operation, the structure and management of the system, communication lines, security equipments for the lines, the management of network and software, stored data and transferred data and even general vulnerable factors of our army tactical C4I system. Out of them, by carrying out an extensive analysis of the army tactical C4I system, likely to be the core of future information warfare, this study suggested plans adaptive to better information security, based on the vulnerable factors provided. Firstly, by suggesting various information security factor technologies, such as VPN (virtual private network), IPDS (intrusion prevention & detection system) and firewall system against virus and malicious software as well as security operation systems and validation programs, this study provided plans to improve the network, hardware (computer security), communication lines (communication security). Secondly, to prepare against hacking warfare which has been a social issue recently, this study suggested plans to establish countermeasures to increase the efficiency of the army tactical C4I system by investigating possible threats through an analysis of hacking techniques. Thirdly, to establish a more rational and efficient national defense information security system, this study provided a foundation by suggesting several priority factors, such as information security-related institutions and regulations and organization alignment and supplementation. On the basis of the results above, this study came to the following conclusion. To establish a successful information security system, it is essential to compose and operate an efficient 'Integrated Security System' that can detect and promptly cope with intrusion behaviors in real time through various different-type security systems and sustain the component information properly by analyzing intrusion-related information.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.1
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• pp.56-63
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• 2007

In this paper, an eye tracking method is presented using a neural network (NN) and mean-shift algorithm that can accurately detect and track user's eyes under the cluttered background. In the proposed method, to deal with the rigid head motion, the facial region is first obtained using skin-color model and con-nected-component analysis. Thereafter the eye regions are localized using neural network (NN)-based tex-ture classifier that discriminates the facial region into eye class and non-eye class, which enables our method to accurately detect users' eyes even if they put on glasses. Once the eye region is localized, they are continuously and correctly tracking by mean-shift algorithm. To assess the validity of the proposed method, it is applied to the interface system using eye movement and is tested with a group of 25 users through playing a 'aligns games.' The results show that the system process more than 30 frames/sec on PC for the $320{\times}240$ size input image and supply a user-friendly and convenient access to a computer in real-time operation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.8
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• pp.1620-1625
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• 2004

To advance fisheries, we set developmental directions of fishery information by grasping present situations and analyzing maritime & fisheries issues. We promote various policies through effective systematical information data bases, based on both control and utilization of oceanic resources. For these puposes, it is imperative that we set up fisheries communication networks. There are satellite assisted informational networks to assist fishing vessels with their marine based movements. However, there's no hope for poorly equipped fishermen to adopt this network because of extravagant network call charges. So we think that using existing SSB communication system is the best plan. We organize fishery communication network by HF SSB communication which doesn't have operational costs. We build wireless transmitting and receiving stations that are basic systems of informnation, and equip wireless data communication systems by the use of wireless communication network protocols in coastal stations. It is necessary that a fish boat has a terminal device for wireless data communication. In this research we can conclude that if we transmit the location of a fishing boat in-real time through GPS channels then we propose that some methods be formulated to able terminal devices on fishing boats to collect various types of information, such as meteorological and oceanic conditions.

• Journal of KIISE:Information Networking
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• v.28 no.3
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• pp.389-405
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• 2001

In these days, we have increasing demands on the real-time services, especially for the multimedia data transmission in both of wired and wireless environments and thus efficient and stable ways of transmitting realtime data are needs. Although RTP is widely used for internet-based realtime applications, it cannot avoid packet losses, due to the use of UDP stack and its underlying layers. In the case of mobile computing applications, the packet losses are more frequent and consecutive because of the limited bandwidth. In this paper, we first statistically analyze the characteristics of packet losses in the wired and wireless communications, based on Gilbert model, and a new packet recovery scheme for realtime data transmission is presented. To reflect the transmission characteristics of the present network environment, our scheme makes the sender to dynamically adjust the amount of redundant information, using the current packet loss characteristic parameters reported by the receiver. Additionally, we use relatively large and discontinuous offset values, which enables us to recover from both of the random and consecutive packet losses. Due to these characteristics, our scheme is suitable for the mobile computing environment where packet loss rates are relatively high and varies rapidly in a wide range. Since our scheme is based on the analytic model form statistics, it can also be used for other network environments. We have implemented the scheme with Mobile IP and RTP/RTCP protocols to experimentally verify its efficiency.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.9-16
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• 2016

Wireless sensor networks (WSN) are composed of sensor nodes and a base station. The sensor nodes deploy a non-accessible area, receive critical information, and transmit it to the base station. The information received is applied to real-time monitoring, distribution, medical service, etc.. Recently, the WSN was extended to mobile wireless sensor networks (MWSN). The MWSN has been applied to wild animal tracking, marine ecology, etc.. The important issues are mobility and energy consumption in MWSN. Because of the limited energy of the sensor nodes, the energy consumption for data transmission affects the lifetime of the network. Therefore, efficient data transmission from the sensor nodes to the base station is necessary for sensing data. This paper, proposes an energy consumption model using two-tier clustering in mobile sensor networks (TTCM). This method divides the entire network into two layers. The mobility problem was considered, whole energy consumption was decreased and clustering methods of recent researches were analyzed for the proposed energy consumption model. Through analysis and simulation, the proposed TTCM was found to be better than the previous clustering method in mobile sensor networks at point of the network energy efficiency.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.149-162
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• 2005

This paper suggest a way to detect a specific wanted figure in public places such as subway stations and banks by comparing color face images extracted from the real time CCTV with the face images of designated specific figures. Assuming that the characteristic of the surveillance camera allows the face information in screens to change arbitrarily and to contain information on numerous faces, the accurate detection of the face area was focused. To solve this problem, the normalization work using subsampling with $20{\times}20$ pixels on arbitrary face images, which is based on the Perceptron Neural Network model suggested by R. Rosenblatt, created the effect of recogning the whole face. The optimal linear filter and the histogram shaper technique were employed to minimize the outside interference such as lightings and light. The addition operation of the egg-shaped masks was added to the pre-treatment process to minimize unnecessary work. The images finished with the pre-treatment process were divided into three reception fields and the information on the specific location of eyes, nose, and mouths was determined through the neural network. Furthermore, the precision of results was improved by constructing the three single-set network system with different initial values in a row.

• Korean Journal of Ecology and Environment
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• v.36 no.1 s.102
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• pp.29-37
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• 2003

Benthic macro-invertebrates were seasonally collected in the Onchen Stream in Pusan, from July 2001 to March 2002. Generally 4 phylum 5 class 10 order 19 family 23 species were observed in the study sites. Ephemeroptera, Plecoptera and various species appeared in headwater stream while Oligochaeta and Chironomidae were dominated in downstream sites. Community abundance patterns, especially the dominant taxa, Oligochaeta and Chironomidae, appeared to be different depending upon the sampling months. Oligochaeta was usually observed in July, December and March while Chironomidae was appeared in September. The biological indices, TBI(Trent Biotic Index), BS (Biotic Score), BMWP (Biological Monitoring Working Party)were calculated with the appeared communities of the sampling sites through the survey months. TBI showed 1 to 8, BMWP was 1 to 93 and CBI appeared 9 to 387 in the different sites. The biological indices decreased from headstream to downstream sites, We implemented the unsupervised Kohonen network for patterning of community abundance of the sampling sites. The patterning map by the Kohonen network was well represented community abundance of the sampling sites. Also, we conducted RTRN (Real Time Recurrent Neural Network) for predicting of the biological indices in the different sites. The results appeared that the predicting values by RTRN were well matched field data (correlation coefficient of TBI, BMWP and CBI were 0.957, 0.979 and 0.967, respectively).

• Journal of the HCI Society of Korea
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• v.5 no.2
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• pp.7-14
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• 2010

Urban Data collected through Sensor Network is becoming crucial to understand and analyse a city. Thus, the Ubiquitous Sensor Network builds the foundation of the u-City development. This research aims to develop an energy monitoring application with an intuitive visualization environment which integrates energy usage information on top of urban geospatial information. Such a system will be able to facilitate effective energy supply plan at the early stages of urban planning, and eventually to encourage citizens to conserve energy by giving them real time monitoring information in an easy to understand visual environment. The system provides multiple layers of energy-related information coupled with the geospatial information layer in order to accommodate multiple viewpoints. On the other hand, the system provides logical Level of Detail control based on urban spatial information hierarchy. We defined the system concept and functions, and designed the data structure and the methods of information visualization. This paper presents the visualization methods, data structure, interactions scenarios which combines spacial information, E-GIS data and the energy related sensor data. Furthermore this research tries to introduce the concept of Social Sensor Networking to enhance the monitoring quality.

• Journal of Broadcast Engineering
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• v.25 no.4
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• pp.545-559
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• 2020

In this paper, we propose a content creation framework that enables users without programming experience to easily create interactive media content that responds to user gestures. In the proposed framework, users define the gestures they use and the media effects that respond to them by numbers, and link them in a text-based configuration file. In the proposed framework, the interactive media content that responds to the user's gesture is linked with the dynamic projection mapping module to track the user's location and project the media effects onto the user. To reduce the processing speed and memory burden of the gesture recognition, the user's movement is expressed as a gray scale motion history image. We designed a convolutional neural network model for gesture recognition using motion history images as input data. The number of network layers and hyperparameters of the convolutional neural network model were determined through experiments that recognize five gestures, and applied to the proposed framework. In the gesture recognition experiment, we obtained a recognition accuracy of 97.96% and a processing speed of 12.04 FPS. In the experiment connected with the three media effects, we confirmed that the intended media effect was appropriately displayed in real-time according to the user's gesture.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2534-2546
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• 2021

Nuclear emergency preparedness and response is an essential part to ensure the safety of nuclear power plant (NPP). Key support technologies of nuclear emergency decision-making usually consist of accident diagnosis, source term estimation, accident consequence assessment, and protective action recommendation. Source term estimation is almost the most difficult part among them. For example, bad communication, incomplete information, as well as complicated accident scenario make it hard to determine the reactor status and estimate the source term timely in the Fukushima accident. Subsequently, it leads to the hard decision on how to take appropriate emergency response actions. Hence, this paper aims to develop a method for rapid source term estimation to support nuclear emergency decision making in pressurized water reactor NPP. The method aims to make our knowledge on NPP provide better support nuclear emergency. Firstly, this paper studies how to build a Bayesian network model for the NPP based on professional knowledge and engineering knowledge. This paper presents a method transforming the PRA model (event trees and fault trees) into a corresponding Bayesian network model. To solve the problem that some physical phenomena which are modeled as pivotal events in level 2 PRA, cannot find sensors associated directly with their occurrence, a weighted assignment approach based on expert assessment is proposed in this paper. Secondly, the monitoring data of NPP are provided to the Bayesian network model, the real-time status of pivotal events and initiating events can be determined based on the junction tree algorithm. Thirdly, since PRA knowledge can link the accident sequences to the possible release categories, the proposed method is capable to find the most likely release category for the candidate accidents scenarios, namely the source term. The probabilities of possible accident sequences and the source term are calculated. Finally, the prototype software is checked against several sets of accident scenario data which are generated by the simulator of AP1000-NPP, including large loss of coolant accident, loss of main feedwater, main steam line break, and steam generator tube rupture. The results show that the proposed method for rapid source term estimation under nuclear emergency decision making is promising.