Earticle

일상생활 속에서 다양한 밀도와 크기를 가진 사물들의 개수를 추정하는 것은 인간이 가진 중요한 능력이다. 그러나 기존의 개수 추정 연구들 대부분은 자극의 크기 변산을 주요 변인으로 고려하지 않아 다양한 환경 속에서 개수를 추정할 수 있는 인간의 능력을 한정적으로밖에 보여주지 못했다(Burr & Ross, 2008a; Durgin, 1995). 본 논문에서는 개수 추정에 중요한 두 변인인 밀도와 크기 변산이 개수 추정에 미치는 영향을 연구하였다. 연구 결과, 밀도와 크기 변산은 상호 작용 없이 개수 추정에 독립적인 영향을 미쳤다. 구체적으로, 밀도와 크기의 다양성이 커질수록 개수는 과소 추정되었다. 또한 개수 추정이 밀도를 통해 간접적으로 추정된다는 기존 연구 결과와는 달리, 본 연구에서 개수는 밀도에 상관없이 직접적으로 추정되는 것으로 나타났다. 밀도를 통하지 않고 직접적인 개수 추정이 가능했던 것은 자극의 크기가 다양하여 자극들이 개별적으로 잘 구분되었기 때문인 것으로 보인다.

In our daily life, it is important to estimate the approximate number of objects containing various sizes and appearing in different densities. However, previous studies mostly used dot stimuli with uniform sizes (Burr & Ross, 2008a; Durgin, 1995). In this study, we investigated how density and size variance influenced numerosity estimation. We found that numerosity was underestimated when density and size variance increased. There was no significant interaction between the density and the size variance. Also, we observed that numerosity was directly estimated by the number mechanism itself across all the range of density, different from the results of previous research. We suggest that this different result is due to various sizes used in this study, which helps the visual system to individuate each object to count.

Density-based spatial clustering of applications with noise (DBSCAN) is a base algorithm for density based clustering. It can find out the clusters of different shapes and sizes from a large amount of data, which is containing noise and outliers. However, it fails to handle the local density variation that exists within the cluster. Thus, a good clustering method should allow a significant density variation within the cluster because, if we go for homogeneous clustering, a large number of smaller unimportant clusters may be generated. In this paper an enhancement of DBSCAN algorithm is proposed, which detects the clusters of different shapes, sizes that differ in local density. We introduce new algorithm Dynamic Method DBSCAN (DMDBSCAN). It selects several values of the radius of a number of objects (Eps) for different densities according to a k-dist plot. For each value of Eps, DBSCAN algorithm is adopted in order to make sure that all the clusters with respect to the corresponding density are clustered. For the next process, the points that have been clustered are ignored, which avoids marking both denser areas and sparser ones as one cluster. Experimental results are obtained from artificial data sets and UCI real data sets. The final results show that our algorithm get a good results with respect to the original DBSCAN and DVBSCAN algorithms.

Building on some early works, Lewbel (2000) proposed estimators for binary and ordered discrete response models with endogenous regressors. These estimators have been extended for panel data and for truncated and censored models by later papers. The estimators are particularly innovative in that the latent linear regression functions are pulled out of the nonlinear limited dependent variable models, which are then treated as if they were the usual linear models. But understanding the estimators and their applications have been “hampered” by less-than-ideal expositions and assumptions. For this problem, this short note reviews the estimators and makes the following three points. First, the derivation and proper insight of the asymptotic variances are provided. Second, the inefficiency of the ordered discrete response version is pointed out and corrected. Third, assumptions in the panel data extension by Honore and Lewbel (2002) are relaxed.

In this study we reported the Smoke Density values of interior materials of railroad passenger car and investigated the specific smoke density(Ds) by NBS smoke chamber to the variance of some test conditions. First we compared the result of Ds from ISO 5659-2 with that from ASTM E 662 for same material. Secondary studied the Ds value to the variance of specimen shape and thickness and to the variance of other conditions.

In this study we reported the Smoke Density test method of interior panel of railroad passenger car and investigated the specific smoke density(Ds) by NBS smoke chamber to the variance of some test conditions. First we compared the result of Ds from ISO 5659-2 with that from ASTM E662 for same phenol FRP Secondary studied the Ds value to the variance of resin type and to the variance of specimen shape.