• Journal of the military operations research society of Korea
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• v.28 no.1
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• pp.115-135
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• 2002

The limits of current DN(Defense networks), private and closed network, become to reality; for Example, high expense of construction and maintenance of networks, restriction of new subscribers on DN. Therefore, a network using web environment that reflect fast development of If and IS(Information Security) technology is demanded for MND. Meeting the requirement of reliable IS system and extension and improvement of DN using common network, we can reduce the expense to extend, maintain, repair DN, form the environment that makes military business cooperate better with civil company and government agency, advance implementing Defense computing and networking service for field small size units that was a exception of Defense digitalization. But it is essential to construct DN based on common network that there are security requisites; confidentiality, integrity, availability, efficiency, log, backup, restoration, that have to be realized at demanding level for IS. This thesis suggested four measurements; replacement DN with common network to resolve the requirements of building new network and improvement of performance for private DN, linkage with common network for new requirement, distribution of traffic using common network, configuration of DN using Internet and Proposed a refinement of IS management organization to treat security threat of common network flexibly, and LAN IS standard model of DN based on the web environment.

• The Transactions of the Korea Information Processing Society
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• v.4 no.1
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• pp.67-75
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• 1997

This paper presents a virtual-parallel multistage interconnection network (MIN) which provides multipath between processor and memory module. The proposed virtual-parallel MIN network which uses $m{\times}1$ mutiplexer at the input switching block, $1{\times}m$ demultiplexer at the output switching block and logN-1 switching stages has maximum $2{\times}m$ unique paths between processor and memory module. Because it has multi-redundance paths, a number of processors can connect a specific Also, this new virtual-parallel structured MIN network can reduce packet collision possibility at switching block and it has cost. It shown to improve a performance and to be a very simple structure in comparision with MBSF structured MIN.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.155-164
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• 2006

VoIP technology is Eight New Services among Ubiquitous-IT839 strategies. This paper tested wiretapping or VoIP service in connected a soft phone and LAN and WAN sections, Internet telephones and a device. IP PBX, a banner operator network to have been connected to VoIP Internet network. As a result of having experimented on wiretapping of VoIP networks, Vulnerability was found. and a wiretapping by attacks of a hacker was succeeded in a terminal and proxy and attachment points of a VoIP network like a hub to follow a CVE list. Currently applied a security plan of an each wiretapping section in viewpoints of 6 security function of Access Control. Confidentiality, Authentication. Availability, Integrity. Non-repudiation in VoIP networks named to 070. Prevented wiretapping of contents by the results, the AES encryption that executed wiretapping experiment about a packet after application of a security plan. Prevented wiretapping, and kept security and audit log. and were able to accomplish VoIP information protection to network monitoring and audit log by an access interception and qualification and message hash functions and use of an incoming refusal.

• The Journal of the Acoustical Society of Korea
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• v.40 no.2
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• pp.176-182
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• 2021

This paper evaluates and compares the performance of the Deep Nerual Network (DNN)-based speech enhancement models according to various loss functions. We used a complex network that can consider the phase information of speech as a baseline model. As the loss function, we consider two types of basic loss functions; the Mean Squared Error (MSE) and the Scale-Invariant Source-to-Noise Ratio (SI-SNR), and two types of perceptual-based loss functions, including the Perceptual Metric for Speech Quality Evaluation (PMSQE) and the Log Mel Spectra (LMS). The performance comparison was performed through objective evaluation and listening tests with outputs obtained using various combinations of the loss functions. Test results show that when a perceptual-based loss function was combined with MSE or SI-SNR, the overall performance is improved, and the perceptual-based loss functions, even exhibiting lower objective scores showed better performance in the listening test.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.378-381
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• 2011

The group management system with Wireless Sensor Network and android application is proposed in this paper. The proposed system was composed of personal devices with sensor nodes of WSN, manager device of android platform, and the web server. The sensor node used by each group member send a data packet to the manager device every 2 seconds. The leader device displays and transmits entire information to the web server. The web server represents these information through web page. Therefore, guardians can assure their group member's safety and security on the web page. The RSSI value of each sensor node converted by computed log-normal path loss model into distance value and displays on the manager device and the web page.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.1
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• pp.35-45
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• 2007

The log data collected from mobile devices contains diverse meaningful and practical personal information. However, this information is usually ignored because of its limitation of memory capacity, computation power and analysis. We propose a novel method that detects landmarks of meaningful information for users by analyzing the log data in distributed modules to overcome the problems of mobile environment. The proposed method adopts Bayesian probabilistic approach to enhance the inference accuracy under the uncertain environments. The new cooperative modularization technique divides Bayesian network into modules to compute efficiently with limited resources. Experiments with artificial data and real data indicate that the result with artificial data is amount to about 84% precision rate and about 76% recall rate, and that including partial matching with real data is about 89% hitting rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.5
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• pp.1105-1110
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• 2011

It is possible to synchronize the spatio-temporal data with a server using a wireless network. These data has the benefit to be up-to-date and able to service to the user efficiently. However, the mobile devices or the wireless network are very vulnerable to various hard or soft failures. If an failure occurs during a synchronization process, the spatio-temporal data which is synchronizing could be inconsistent state. In this paper, we propose the recovery scheme to solve the problem caused by the errors which occur during the synchronization on mobile environments. The proposed scheme writes the log records for the synchronization process into the both local storages of the server and the mobile client. When the errors arise, the data which are synchronizing are recovered using the written log records. Also, we design and implement the proposed recovery scheme on the synchronization system.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.2
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• pp.54.2-54.2
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• 2016

We present a machine learning approach to estimating stellar atmospheric parameters, effective temperature (Teff), surface gravity (log g), and metallicity ([Fe/H]) for stars observed during the course of the Sloan Digital Sky Survey (SDSS). For training a neural network, we randomly sampled the SDSS data with stellar parameters available from SEGUE Stellar Parameter Pipeline (SSPP) to cover the parameter space as wide as possible. We selected stars that are not included in the training sample as validation sample to determine the accuracy and precision of each parameter. We also divided the training and validation samples into four groups that cover signal-to-noise ratio (S/N) of 10-20, 20-30, 30-50, and over 50 to assess the effect of S/N on the parameter estimation. We find from the comparison of the network-driven parameters with the SSPP ones the range of the uncertainties of 73~123 K in Teff, 0.18~0.42 dex in log g, and 0.12~0.25 dex in [Fe/H], respectively, depending on the S/N range adopted. We conclude that these precisions are high enough to study the chemical and kinematic properties of the Galactic disk and halo stars, and we will attempt to apply this technique to Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), which plans to obtain about 8 million stellar spectra, in order to estimate stellar parameters.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.675-679
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• 2001

In this paper, we present a new competitive learning algorithm called Dynamic Competitive Learning (DCL). DCL is a supervised learning method that dynamically generates output neurons and initializes automatically the weight vectors from training patterns. It introduces a new parameter called LOG (Limit of Grade) to decide whether an output neuron is created or not. If the class of at least one among the LOG number of nearest output neurons is the same as the class of the present training pattern, then DCL adjusts the weight vector associated with the output neuron to learn the pattern. If the classes of all the nearest output neurons are different from the class of the training pattern, a new output neuron is created and the given training pattern is used to initialize the weight vector of the created neuron. The proposed method is significantly different from the previous competitive learning algorithms in the point that the selected neuron for learning is not limited only to the winner and the output neurons are dynamically generated during the learning process. In addition, the proposed algorithm has a small number of parameters, which are easy to be determined and applied to real-world problems. Experimental results for pattern recognition of remote sensing data and handwritten numeral data indicate the superiority of DCL in comparison to the conventional competitive learning methods.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.4
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• pp.83-91
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• 2018

Land reclamation, coastal construction, coastline extension and port construction, all of which involve dredging, are increasingly required to meet the growing economic and societal demands in the coastal zone. During the land reclamation, a portion of landfills are lost from the desired location due to a variety of causes, and therefore prediction of sediment transport is very important for economical and efficient land reclamation management. In this study, laboratory disposal tests were performed using an open channel, and suspended sediment transport was analyzed according to flow velocity and grain size. The relationships between the average and standard deviation of the deposition distance and the flow velocity were almost linear, and the relationships between the average and standard deviation of deposition distance and the grain size were found to have high non-linearity in the form of power law. The deposition distribution of sediments was demonstrated to have log-normal distributions regardless of the flow velocity. Based on the experimental results, modeling of suspended sediment transport was performed using deep neural network, one of deep learning techniques, and the deposition distribution was reproduced through log-normal distribution.