• The Journal of the Acoustical Society of Korea
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• v.18 no.4
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• pp.47-52
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• 1999

In this paper, we proposed a new approach to weight each feature parameter by considering the dispersion of feature parameters and its degree of contribution to recognition rate. We determined the total distribution factor that is proportional to recognition rate of each feature parameter and the dispersion factor according to the dispersion of each feature parameter. Then. we determined state-dependent weighting using the total distribution factor and dispersion factor. To verify the validity of the proposed approach, recognition experiments were performed using the PLU(Phoneme-Like Unit)-based HMM. Experimental results showed the improvement of 7.7% at the recognition rate using the proposed method.

• Communications for Statistical Applications and Methods
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• v.29 no.4
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• pp.413-420
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• 2022

In this study we adapt discrete weibull regression model for clustered count data. Discrete weibull regression model has an attractive feature that it can handle both under and over dispersion data. We analyzed the eighth Korean National Health and Nutrition Examination Survey (KNHANES VIII) from 2019 to assess the factors influencing the 1 month outpatient stay in 17 different regions. We compared the results using clustered discrete Weibull regression model with those of Poisson, negative binomial, generalized Poisson and Conway-maxwell Poisson regression models, which are widely used in count data analyses. The results show that the clustered discrete Weibull regression model using random intercept model gives the best fit. Simulation study is also held to investigate the performance of the clustered discrete weibull model under various dispersion setting and zero inflated probabilities. In this paper it is shown that using a random effect with discrete Weibull regression can flexibly model count data with various dispersion without the risk of making wrong assumptions about the data dispersion.

• Genomics & Informatics
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• v.17 no.4
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• pp.39.1-39.20
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• 2019

Analyzing patterns in data points embedded in linear and non-linear feature spaces is considered as one of the common research problems among different research areas, for example: data mining, machine learning, pattern recognition, and multivariate analysis. In this paper, data points are heterogeneous sets of biosequences (composite data points). A composite data point is a set of ordinary data points (e.g., set of feature vectors). We theoretically extend the derivation of the largest generalized eigenvalue-based distance metric D_ij(γ₁) in any linear and non-linear feature spaces. We prove that D_ij(γ₁) is a metric under any linear and non-linear feature transformation function. We show the sufficiency and efficiency of using the decision rule $\bar{{\delta}}_{{\Xi}i}$(i.e., mean of D_ij(γ₁)) in classification of heterogeneous sets of biosequences compared with the decision rules min_𝚵iand median_𝚵i. We analyze the impact of linear and non-linear transformation functions on classifying/clustering collections of heterogeneous sets of biosequences. The impact of the length of a sequence in a heterogeneous sequence-set generated by simulation on the classification and clustering results in linear and non-linear feature spaces is empirically shown in this paper. We propose a new concept: the limiting dispersion map of the existing clusters in heterogeneous sets of biosequences embedded in linear and nonlinear feature spaces, which is based on the limiting distribution of nucleotide compositions estimated from real data sets. Finally, the empirical conclusions and the scientific evidences are deduced from the experiments to support the theoretical side stated in this paper.

• Smart Structures and Systems
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• v.16 no.1
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• pp.213-222
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• 2015

Early detection and precise location of leakage is of great importance for life-cycle maintenance and management of municipal pipeline system. In the past few years, acoustic emission (AE) techniques have demonstrated to be an excellent tool for on-line leakage detection. Regarding the multi-mode and frequency dispersion characteristics of AE signals propagating along a pipeline, the direct cross-correlation technique that assumes the constant AE propagation velocity does not perform well in practice for acoustic leak location. This paper presents an improved cross-correlation method based on wavelet transform, with due consideration of the frequency dispersion characteristics of AE wave and the contribution of different mode. Laboratory experiments conducted to simulate pipeline gas leakage and investigate the frequency spectrum signatures of AE leak signals. By comparing with the other methods for leak location identification, the feasibility and superiority of the proposed method are verified.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.50 no.3
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• pp.121-129
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• 2001

In this paper, a new efficient feature extraction method based on Daubechies discrete wavelet transform is presented. This paper especially deals with the assessment of process statistical parameter using the features extracted from the wavelet coefficients of measured acoustic emission signals. Since the parameter assessment using all wavelet coefficients will often turn out leads to inefficient or inaccurate results, we selected that level-3 stage of multi decomposition in discrete wavelet transform. We make use of the feature extraction parameter namely, maximum value of acoustic emission signal, average value, dispersion, skewness, kurtosis, etc. The effectiveness of this new method has been verified on ability a diagnosis transformer go through feature extraction in stage of aging(the early period, the middle period, the last period)

• Asian Journal of Atmospheric Environment
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• v.10 no.3
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• pp.125-136
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• 2016

In this study, the effects of the mesh barrier on the free dispersion of ammonia were numerically investigated under different atmospheric conditions. This study presents the detail and flow feature of the dispersion of ammonia through the mesh barrier on various free stream conditions to decline and limit the toxic danger of the ammonia. It is assumed that the dispersion of the ammonia occurred through the leakage in the pipeline. Parametric studies were conducted on the performance of the mesh barrier by using the Reynolds-averaged Navier-Stokes equations with realizable k-${\varepsilon}$ turbulence model. Numerical simulations of ammonia dispersion in the presence of mesh barrier revealed significant results in a fully turbulent free stream condition. The results clearly show that the flow behavior was found to be a direct result of mesh size and ammonia dispersion is highly influenced by these changes in flow patterns in downstream. In fact, the flow regime becomes laminar as flow passes through mesh barrier. According to the results, the mesh barrier decreased the maximum concentration of the ammonia gas and limited the risk zone (more than 500 ppm) lower than 2 m height. Furthermore, a significant reduction occurs in the slope of the upper boundary of $NH_3$ risk zone distribution at downstream when a mesh barrier is presented. Thus, this device highly restricts the leak distribution of ammonia in the industrial plan.

• Journal of Korean Society for Atmospheric Environment
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• v.23 no.4
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• pp.405-419
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• 2007

A series of tracer experiments for the evaluation of atmospheric dispersion was performed over the urban area of Seoul using two inert, non-deposition perfluorocarbon (PMCH and m-PDCH) gases during three years campaign on 2002, 2003 and 2005. 30 sampling sites for collecting these tracers were located along two arcs of 2.5 and 5 kilometers downwind from the release point. About ten measurements which each lasted for 2 hours or 4 hours were made over the two consecutive days during each campaign. CALPUFF and MM5 meteorological model were applied to evaluate the urban dispersion in detail. Size of Modeling domain was $27\;km{\times}23\;km$ and the fine nest in the modeling domain had a grid size of 0.5 km. The results showed that CALPUFF dispersion model had a tendency to estimate tracer concentrations about $2{\sim}5$ times less than those of ambient samples under many conditions. These consistent inaccuracy in urban dispersion was attributed to inherent inaccuracy and lack of details in terrain data at urban area.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.9-9
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• 2000

Accurate counting and packaging pills is one of the most fundamental works of the pharmaceutical industry. But it is so labor consuming and very hard to be automated. As the pharmaceutical industry is growing bigger, the need of counting and packaging automation is increasing to obtain effective mass production. Precise and quick sensing is required in the counting and processing of quickly dropping pills to improve the productivity. There are many trials for this automation and automatic machine. But the performance of the existing counting machine varies with the size, shape and the dispersion degree of pills In this research, authors design the counting and packing machine of medicinal pills that is more accurate and highly trustworthy After getting analog signal from optical sensor, pill passage is discriminated from chosen characteristic feature using microprocessor.

• Journal of Navigation and Port Research
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• v.28 no.3
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• pp.185-191
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• 2004

The mean and Clustering are important methods of data mining, which is now widely applied to various multi-attributes problem However, feature weighting and feature selection are important in those methods bemuse features may differ in importance and such differences need to be considered in data mining with various multiful-attributes problem. In addition, in the event of arithmetic mean, which is inadequate to figure out the most fitted result for structure of evaluation with attributes that there are weighted and ranked. Moreover, it is hard to catch hold of a specific character for assume the form of user's group. In this paper. we propose a dispersion mean algorithm for evaluation of similarity measure based on the geometrical figure. In addition, it is applied to mean classified by user's group. One of the key issues to be considered in evaluation of the similarity measure is how to achieve objectiveness that it is not change over an item ranking in evaluation process.

• Asian Journal of Atmospheric Environment
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• v.8 no.2
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• pp.81-88
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• 2014

Noise-barriers on both sides of the roadway (hereafter referred to as double noise-barriers), are a common feature along roads in Korea, and these are expected to have important effects on the near-road air pollution dispersion of vehicle emissions. This study evaluated the double noise-barrier impact on near-road air pollution dispersion, using a FLUENT computational fluid dynamics (CFD) model. The realizable k-${\varepsilon}$ model in FLUENT CFD code was used to simulate vehicle air pollutant dispersion, in around 11 cases of double noise-barriers. The simulated concentration profiles and surface concentrations under no barrier cases were compared with the experimental results. The results of the simulated flows show the following three regimes in this study: isolated roughness (H/W=0.05), wake interface (H/W=0.1), and skimming flow (H/W>0.15). The results also show that the normalized average concentrations at surface (z=1 m) between the barriers increase with increasing double noise-barrier height; however, normalized average concentrations at the top position between the barriers decrease with increasing barrier height. It was found that the double noise-barrier decreases normalized average concentrations of leeward positions, ranging from 0.8 (H/W=0.1, wake interface) to 0.1 (H/W=0.5, skimming flow) times lower than that of the no barrier case, at 10 x/h downwind position; and ranging from 1.0 (H/W=0.1) to 0.4 (H/W=0.5) times lower than that of the no barrier case, at 60 x/h downwind position.