• Convergence Security Journal
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• v.13 no.4
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• pp.11-17
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• 2013

All u-Health system has security vulnerabilities. This vulnerability locally(local) or network(network) is on the potential risk. Smart environment of health information technology, Ad-hoc networking, wireless communication environments, u-health are major factor to increase the security vulnerability. u-health care information systems user terminal domain interval, interval public network infrastructure, networking section, the intranet are divided into sections. Health information systems by separating domain specific reason to assess vulnerability vulnerability countermeasure for each domain are different. u-Healthcare System 5 layers of security risk assessment system for domain-specific security vulnerability diagnosis system designed to take the security measures are needed. If you use this proposed model that has been conducted so far vaguely USN-based health information network security vulnerabilities diagnostic measures can be done more systematically provide a model.

• Journal of Biomedical Engineering Research
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• v.9 no.1
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• pp.17-24
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• 1988

This paper describes a neural network modelling of a vertebrate retina using a discrete-time and discrete-space approach based on neuro-anatomical data, and the computer simulations of the model which approximate the frog/amphibian negro-physiological data. It then compares them and describes how such a model can be beneficially used for confirming the hypothesis of a given neural system and further predict yet unknown experimental data.

• Water for future
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• v.29 no.4
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• pp.109-118
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• 1996

The neural network is a mathematical model of theorized brain activity which attempts to exploit the parallel local processing and distributed storage properties. The neural metwork is a good model to be applied for the classification problem, large combinatorial optimization and nonlinear mapping. A multi-layer neural network is constructed to predict rainfall. The network learns continuourvalued input and output data. Application of neural network to 1-hour real data in Seoul metropolitan area and the Soyang River basin shows slightly good predictions. Therefore, when good data is available, the neural network is expected to predict the complicated rainfall successfully.

• Journal of Urban Science
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• v.12 no.1
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• pp.21-34
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• 2023

Based on the theory of network governance, this study aims to establish a n'work governance model and present strategic measures for the use of return donor zones. In a network society, it is difficult for the government to handle complex problems alone, so close cooperation between related agencies has become important. After the return of the USFK base, most of the policies established by the government have not been carried out smoothly, so it is necessary to introduce cooperative network governance.Accordingly, technical statistics were analyzed for camp market business institutions to examine the centrality and structure of network relations (information sharing relationship, idea exchange relationship, civil complaint sharing relationship). As a result of the analysis, the Camp Market Division of Incheon City had the most central role and great influence in the camp market, which was maintained even when the time changed. Currently, it is in the form of a vertical network centered on local governments, but in the future, a horizontal network is suitable to strengthen cooperative relations and must be systematically managed around managers.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.142-145
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• 2001

I did research or design technology of(integration) network of PSTN and PSDN in this paper. Network structure model in association with soft switching and access gateway can create various services based on web and live time and service fee at the rate of bandwidth which can't be provided by existing network based on SONET. Also, interns of capability(function) of each area and construction of local network subscribers, it has great recovery capability by separating each network as local areas. It indicates that it can additionally maximize the application of its own communication network and drastic reduction of communication cost because it is alternated to exclusive usage cost.

• ETRI Journal
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• v.25 no.1
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• pp.34-40
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• 2003

This paper considers a TMN-based management system for the management of public ATM switching networks using a four-level hierarchical structure consisting of one network management system, several element management systems, and several agent-ATM switch pairs. Using Jackson's queuing model, we analyze the effects of one TMN command on the performance of the component ATM switch in processing local calls. The TMN command considered is the permanent virtual call connection. We analyze four performance measures of ATM switches- utilization, mean queue length and mean waiting time for the processor directly interfacing with the subscriber lines and trunks, and the call setup delay of the ATM switch- and compare the results with those from Jackson's queuing model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.907-923
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• 2020

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

• Journal of the Korea Society of Computer and Information
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• v.24 no.1
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• pp.137-143
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• 2019

As the performance of computers increases, the use of deep learning, which has faced technical limitations in the past, is becoming more diverse. In many fields, deep learning has contributed to the creation of added value and used on the bases of more data as the application become more divers. The process for obtaining a better performance model will require a longer time than before, and therefore it will be necessary to find an optimal model that shows the best performance more quickly. In the artificial neural network modeling a tuning process that changes various elements of the neural network model is used to improve the model performance. Except Gride Search and Manual Search, which are widely used as tuning methods, most methodologies have been developed focusing on heuristic algorithms. The heuristic algorithm can get the results in a short time, but the results are likely to be the local optimal solution. Obtaining a global optimal solution eliminates the possibility of a local optimal solution. Although the Brute Force Method is commonly used to find the global optimal solution, it is not applicable because of an infinite number of hyper parameter combinations. In this paper, we use a statistical technique to reduce the number of possible cases, so that we can find the global optimal solution.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.1
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• pp.37-44
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• 2019

With the advance of digital IT technology, the performance of the printing and scanning devices is improved and their price becomes cheaper. As a result, the public can easily access these devices for crimes such as forgery of official and private documents. Therefore, if we can identify which printing device is used to print the documents, it would help to narrow the investigation and identify suspects. In this paper, we propose a deep learning model for printer identification. A convolutional neural network model based on local features which is widely used for identification in recent is presented. Then, another model including a step to calculate global features and hence improving the convergence speed and accuracy is presented. Using 8 printer models, the performance of the presented models was compared with previous feature-based identification methods. Experimental results show that the presented model using local feature and global feature achieved 97.23% and 99.98% accuracy respectively, which is much better than other previous methods in accuracy.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.3
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• pp.263-267
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• 2007

This study proposes a new methodology to estimate the traffic capacity of a personal rapid transit(PRT) system. The proposed method comprises three steps. The first step models the guideway network(GN) of PRT as a digraph, where its node and link represent a station and a one-way guideway link between two stations, respectively. Given local vehicle control strategies, the second step formulates the local traffic capacities through the nodes and links of the GN model. The third step estimates the worst-case local traffic demands based on a shortest-path routing algorithm and an empty vehicle allocation algorithm. By comparing the traffic estimates to the local traffic capacities, we can determine the feasibility of the given GN in traffic capacity.