• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.180-182
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• 2021

This paper proposes a method for estimating the location of a user using a beacon to provide a service in an indoor environment. To estimate the location using the beacon, a Gaussian filter was applied to the RSSI value of the beacon, and the distance conversion function was obtained through the filtered RSSI value to estimate the tag location by trilateration. Then, in the indoor space where the beacons are installed, the location estimation accuracy of 8 places where 3 beacons are at a certain distance was confirmed. As a result, it was possible to confirm the position estimation accuracy of ±0.097 standard deviation and 0.242 distance error.

• Journal of The Korean Astronomical Society
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• v.44 no.2
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• pp.39-48
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• 2011

UBV I CCD photometry of NGC 2353 is performed as a part of the "Sejong Open cluster Survey" (SOS). Using photometric membership criteria we select probable members of the cluster. We derive the reddening and distance to the cluster, i.e., E(B - V ) = 0.10 ${\pm}$ 0.02 mag and 1.17 ${\pm}$ 0.04 kpc, respectively. We find that the projected distribution of the probable members on the sky is elliptical in shape rather than circular. The age of the cluster is estimated to be log(age)=8.1 ${\pm}$ 0.1 in years, older than what was found in previous studies. The minimum value of binary fraction is estimated to be about 48 ${\pm}$ 5 percent from a Gaussian function fit to the distribution of the distance moduli of the photometric members. Finally, we also obtain the luminosity function and the initial mass function (IMF) of the probable cluster members. The slope of the IMF is ${\Gamma}=-1.3{\pm}0.2$.

• Journal of the Optical Society of Korea
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• v.14 no.3
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• pp.228-234
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• 2010

This paper presents the joint characteristic function of the first- and second-order polarization-modedispersion (PMD) vectors in installed optical fibers that are almost linearly birefringent. The joint characteristic function is a Fourier transform of the joint probability density function of these PMD vectors. We regard the random fiber birefringence components as white Gaussian processes and use a Fokker-Planck method. In the limit of a large transmission distance, our joint characteristic function agrees with the previous joint characteristic function obtained for highly birefringent fibers. However, their differences can be noticeable for practical transmission distances.

• Journal of Powder Materials
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• v.10 no.3
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• pp.201-208
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• 2003

Distributions of diamond particles protruding on the surface of worn diamond segments in circular saw has been investigated. Scanning electron microscope was used to examine the worn ,surface and radial saw blade wear and grinding ratio was measured. The number of protruded diamond particle was approximately 50% of the total number of particles, and that was independent of diamond particle concentration and table speed. It was also noted that the inter-particle distance did not follow a symmetric function like Gaussian distribution function, instead it fitted well with a probability density function based on gamma function. The distribution of inter-particle spacing, therefore, was analyzed using a gamma function model.

• Journal of Environmental Impact Assessment
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• v.10 no.2
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• pp.123-133
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• 2001

Gaussian dispersion model is the most widely used tool for the ground level air pollution simulation. Though in spite of the convenience there are important problems on the Pasquill- Gifford' stability classification scheme which was used to define the turbulent state of the atmosphere or to describe the dispersion capabilities of the atmosphere which was each covers a broad range of stability conditions, and that they were very site specific, and the vertical dispersion calculation formula on the case of the unstable atmospheric condition. This paper was carried out to revise the Gaussian dispension model for the purposed of increase the modeling performance and propose the revised model, which was composed of the turbulent characteristics in the unstable atmospheric conditions. The proposed models in this study were composed of the profile method, Monin-Obukhove length, the probability density function model and the lateral dispersion function which was composed of the turbulent parameters, $u_*$(friction velocity), $w_*$(convective velocity scale), $T_L$(lagrangian time scale) for the model specific. There were very good performance results compare with the tracer experiment result on the case of the short distance (<1415m) from the source, but increase the simulation error(%) to stand off the source in the all models. In conclusion, the revised Gaussian dispersion model using the turbulent characteristics may be a good contribution for the development of the air pollution simulation model.

• Journal of Radiation Protection and Research
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• v.28 no.4
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• pp.311-319
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• 2003

We developed a grid-based Gaussian plume model to evaluate tracer release data measured at Young Gwang nuclear site in 1996. Downwind distance was divided into every 10m from 0.1km to 20km, and crosswind distance was divided into every 10m centering released point from -5km to 5km. We determined dispersion factors, ${\sigma}_y\;and\;{\sigma}_z$ using Pasquill-Gifford method computed by atmospheric stability. Forecasting ability of the grid-based Gaussian plume model was better at the 3km away from the source than 8km. We confirmed that dispersion band must be modified if receptor is far away from the source, otherwise P-G method is not appropriate to compute diffusion distance and diffusion strength in case of growing distance. So, we developed an empirical equation using linear programming. An objective function was designed to minimize sum of the absolute value between observed and computed values. As a result of application of the modified dispersion equation, prediction ability was improved rather than P-G method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.2
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• pp.268-275
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• 2008

In this paper, we introduce a new method for detecting and estimating faults on a power line using the time-frequency domain reflectometry system. The system rests upon time-frequency signal analysis and uses a chirp signal which is multiplied by Gaussian envelope. The chirp signal is used as a reference signal, and we can get the reflected signal from a fault on a wire. To detect and estimate faults, we analyze the reflected signal by Wigner time-frequency distribution function and normalized time-frequency cross correlation function. In this paper we design an optimal reference signal for power line and implement a system for estimating fault distance on a power line with the TFDR implemented by PXI equipments. This approach is verified by some experiments with HIV 2.25mm power lines.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.9C
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• pp.853-858
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• 2006

TMHMM(Tied Mixture Hidden Markov Model) is an important approach to reduce the number of free parameters in speech recognition. However, this model suffers from a degradation in recognition accuracy due to its GPDF (Gaussian Probability Density Function) clustering error. This paper proposes a clustering algorithm, called HCNN(Homogeneous Centroid Neural network), to cluster acoustic feature vectors in TMHMM. Moreover, the HCNN uses the heterogeneous distance measure to allocate more code vectors in the heterogeneous areas where probability densities of different states overlap each other. When applied to Korean digit isolated word recognition, the HCNN reduces the error rate by 9.39% over CNN clustering, and 14.63% over the traditional K-means clustering.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.7C
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• pp.931-936
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• 2004

GBFCM(DM), Gradient-based Fuzzy c-means with Divergence Measure, for efficient clustering of GPDF(Gaussian Probability Density Function) in MPEG VBR video data modeling is proposed in this paper. The proposed GBFCM(DM) is based on GBFCM( Gradient-based Fuzzy c-means) with the Divergence for its distance measure. In this paper, sets of real-time MPEG VBR Video traffic data are considered. Each of 12 frames MPEG VBR Video data are first transformed to 12-dimensional data for modeling and the transformed 12-dimensional data are Pass through the proposed GBFCM(DM) for classification. The GBFCM(DM) is compared with conventional FCM and GBFCM algorithms. The results show that the GBFCM(DM) gives 5∼15% improvement in False Alarm Rate over conventional algorithms such as FCM and GBFCM.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9C
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• pp.861-866
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• 2007

A clustering algorithm for Gaussian Probability Distribution Function (GPDF) data called Centroid Neural Network with a Bhattacharyya Kernel (BK-CNN) is proposed in this paper. The proposed BK-CNN is based on the unsupervised competitive Centroid Neural Network (CNN) and employs a kernel method for data projection. The kernel method adopted in the proposed BK-CNN is used to project data from the low dimensional input feature space into higher dimensional feature space so as the nonlinear problems associated with input space can be solved linearly in the feature space. In order to cluster the GPDF data, the Bhattacharyya kernel is used to measure the distance between two probability distributions for data projection. With the incorporation of the kernel method, the proposed BK-CNN is capable of dealing with nonlinear separation boundaries and can successfully allocate more code vector in the region that GPDF data are densely distributed. When applied to GPDF data in an image classification probleml, the experiment results show that the proposed BK-CNN algorithm gives 1.7%-4.3% improvements in average classification accuracy over other conventional algorithm such as k-means, Self-Organizing Map (SOM) and CNN algorithms with a Bhattacharyya distance, classed as Bk-Means, B-SOM, B-CNN algorithms.