• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.7
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• pp.680-695
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• 1991

In this paper, the conditions on the number of required copies of a self-routing network with and without extra stages in back-to-back manner are presented respectively for a nonblocking $Multi-Log_2N$ multiconnection network. Actually the obained results hold regardless of connection patterns, i.e., whether a network deploys on-to-one connections or multiconnections. Thus open problems on the nonblocking condition for a multi $Multi-Log_2N$ multiconnection network are solved. Interestingly some of the given formulas comprise the Benes network and the Canto network as a special case repectively. A novel switching system architecture deploying a distributed calls-distribution algorithm is provided to design a nonblocking $Multi-Log_2N$ photonic switching network using a directional coupler. And a directional couplex based call holding demultiplexer is introduced to hold a call until blocking disappears in a switching network and let it enter to a network, provided that the number of switching networks is less than that of required switching networks for a nonblocking $Multi-Log_2N$ network.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.727-731
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• 2010

Recently, mobile life-log data are collected by mobile devices and used to recode one's life. In order to help a user search data, a mobile life-log semantic network is introduced for storing logs and retrieving associative information. However, associative search systems on common semantic networks in previous studies provide for a user with only found data as text to users. This paper proposes an associative search system for mobile life-log semantic network that supports selection and keyword associative search of which a process and result are a visualized graph representing associative data and their relationships when a user inputs a keyword for search. In addition, by using semantic abstraction, the system improves user's understanding of search result and simplifies the resulting graph. The system's usability was tested by an experiment comparing the system and a text-based search system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.414-430
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• 2016

Verifier-local revocation (VLR) seems to be the most flexible revocation approaches for any group signature scheme, because it just only requires the verifiers to possess some up-to-date revocation information, but not the signers. Langlois et al. (PKC 2014) proposed the first VLR group signature based on lattice assumptions in the random oracle model. Their scheme has at least Õ(n²) ⋅ log N bit group public key and Õ(n) ⋅ log N bit signature, respectively. Here, n is the security parameter and N is the maximum number of group members. In this paper, we present a simpler lattice-based VLR group signature, which is more efficient by a O(log N) factor in both the group public key and the signature size. The security of our VLR group signature can be reduced to the hardness of learning with errors (LWE) and small integer solution (SIS) in the random oracle model.

• Journal of Digital Contents Society
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• v.12 no.1
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• pp.133-139
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• 2011

Structured P2P (peer-to-peer) networks have received much attention in research communities and the industry. The data stored in structured P2P networks can be located in a log-scale time without using central severs. The link-structure of structured P2P networks should be maintained for keeping log-scale search performance of it. When nodes join or leave structured P2P networks frequently, some links become unavailable and search performance is degraded by these links. To sustain search performance of structured P2P networks, periodic link recovery scheme is generally used. However, when the link recovery period is short or long compared with node join and leave rates, it is possible that sufficient number of links are not restored or excessive messages are used after the link-structure is restored. We propose the adaptive link recovery determination algorithm to maintain the link-structure of structured P2P networks when the rates of node joining and leaving are changed dynamically. The simulation results show that the proposed algorithm can maintain similar QoS under various node leaving rates.

• The Journal of the Acoustical Society of Korea
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• v.39 no.3
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• pp.143-149
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• 2020

This paper presents the effect of the feature extraction methods used in the audio preprocessing on the classification performance of the Convolutional Neural Networks (CNN). We extract mel spectrogram, log mel spectrogram, Mel Frequency Cepstral Coefficient (MFCC), and delta MFCC from the UrbanSound8K dataset, which is widely used in environmental sound classification studies. Then we scale the data to 3 distributions. Using the data, we test four CNNs, VGG16, and MobileNetV2 networks for performance assessment according to the audio features and scaling. The highest recognition rate is achieved when using the unscaled log mel spectrum as the audio features. Although this result is not appropriate for all audio recognition problems but is useful for classifying the environmental sounds included in the Urbansound8K.

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.67-73
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• 2008

This paper describes a system for location tracking wireless sensor nodes in an indoor environment. The sensor reading used for the location estimation is the received signal strength indication (RSSI) as given by an RF interface. By tagging users with a mobile node and deploying a number of reference nodes at fixed position in the room, the received signal strength indicator can be used to determine the position of tagged users. The system combines Euclidean distance technique with signal strength obtained by measurement driven log-normal path loss model of 2.4 GHz wireless channel. The experimental results demonstrated the ability of this system to estimate the location with a error less than 1.3m.

• The Transactions of the Korea Information Processing Society
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• v.6 no.9
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• pp.2442-2450
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• 1999

The audit logging system is to write the details of systems use and access on networks. These details are used for trailing the route, when illegal access or using system resource is occurred on networks. The logging system therefore, might be the first target of intruder. We developed the logging system which writes the information of logging and command execution on UNIX system. And we prepared the self-protecting functions of blocking intruder's attack on the logging system. They are protecting the logging process and the log file. To protect the logging process, we made it keep changing the process ID to avoid the intruder's attack. To protect the log file, we use hard link and mandatory file locking, so it can make it impossible to delete or change log file.

• Bulletin of the Korean Chemical Society
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• v.26 no.1
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• pp.139-145
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• 2005

Artificial neural networks (ANNs), for a first time, were successfully developed for the modeling and prediction of solvent effects on rate constant of [2+2] cycloaddition reaction of diethyl azodicarboxylate with ethyl vinyl ether in various solvents with diverse chemical structures using quantitative structure-activity relationship. The most positive charge of hydrogen atom (q$^+$), dipole moment ($\mu$), the Hildebrand solubility parameter (${\delta}_H^2$) and total charges in molecule (q$_t$) are inputs and output of ANN is log k$_2$ . For evaluation of the predictive power of the generated ANN, the optimized network with 68 various solvents as training set was used to predict log k$_2$ of the reaction in 16 solvents in the prediction set. The results obtained using ANN was compared with the experimental values as well as with those obtained using multi-parameter linear regression (MLR) model and showed superiority of the ANN model over the regression model. Mean square error (MSE) of 0.0806 for the prediction set by MLR model should be compared with the value of 0.0275 for ANN model. These improvements are due to the fact that the reaction rate constant shows non-linear correlations with the descriptors.

• Journal of the Korea Society of Computer and Information
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• v.13 no.5
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• pp.179-186
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• 2008

In proportion as the growth of the wireless sensor network applications, we need for more accuracy wireless channel information. In the case of indoor or outdoor wireless sensor networks, multipath propagation causes severe problems in terms of fading. Therefore, a path-loss model for multipath environment is required to optimize communication systems. This paper deals with log-normal path loss modeling of the indoor 2.4 GHz channel. We measured variation of the received signal strength between the sender and receiver of which separation was increased from 1 to 30m. The path-loss exponent and the standard deviation of wireless channel were determined by fitting of the measured data. By using the PRR(Packet Reception Rate) of this model. Wireless sensor channel is defined CR(Connect Region), DR(Disconnected Region). In order to verify the characteristics of wireless channel, we performed simulations and experiments. We demonstrated that connection ranges are 24m in indoor, and 14m in outdoor.

• Communications for Statistical Applications and Methods
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• v.30 no.1
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• pp.95-107
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• 2023

Purpose: The statistical analysis of point processes on linear networks is a recent area of research that studies processes of events happening randomly in space (or space-time) but with locations limited to reside on a linear network. For example, traffic accidents happen at random places that are limited to lying on a network of streets. This paper applies techniques developed for point processes on linear networks and the tools available in the R-package spatstat to estimate the intensity of traffic accidents in Leon County, Florida. Methods: The intensity of accidents on the linear network of streets is estimated using log-linear Poisson models which incorporate cubic basis spline (B-spline) terms which are functions of the x and y coordinates. The splines used equally-spaced knots. Ten different models are fit to the data using a variety of covariates. The models are compared with each other using an analysis of deviance for nested models. Results: We found all covariates contributed significantly to the model. AIC and BIC were used to select 9 as the number of knots. Additionally, covariates have different effects such as increasing the speed limit would decrease traffic accident intensity by 0.9794 but increasing the number of lanes would result in an increase in the intensity of traffic accidents by 1.086. Conclusion: Our analysis shows that if other conditions are held fixed, the number of accidents actually decreases on roads with higher speed limits. The software we currently use allows our models to contain only spatial covariates and does not permit the use of temporal or space-time covariates. We would like to extend our models to include such covariates which would allow us to include weather conditions or the presence of special events (football games or concerts) as covariates.