Identification Of Distribution Research Articles

Identification of land-use characteristics using bicycle sharing data: A deep learning approach

Extensive research has shown that urban land-use characteristics, including resident, work, consumption, transit, etc., are significantly interrelated with travel behaviors and travel demands. Many research efforts have been made to evaluate the impact of land use planning or policies on travel behavior, however, few studies are able to quantitatively measure the land-use characteristics based on the data of travel behaviors or travel demand. In this paper, a new hybrid model that combines time series feature extraction and deep neural network is proposed to identify regional land use characteristics and quantify land use intensity using ridership data of bicycle sharing. This method consists of four main parts: (i) A set of land-use characteristic labels are evaluated based on planning and Geographic Information System (GIS) data. (ii) An ensemble clustering method is used to determine the segmentation points of ridership time series. (iii) The statistical characteristics of the segmented time series are extracted and used as input to the neural network. (iv) A deep neural network is established and trained based on the processed ridership features and land-use labels. In terms of data collection, ridership data of the bicycle-sharing parking spots and land-use planning data are obtained from bicycle-sharing system and planning department in San Francisco Bay Area, California U.S.A., respectively. The test results show that this approach has high accuracy for identifying land-use characteristics based on several standard evaluation measures and that the identification distribution can be well explained. The extension results further prove that the model can be applied to effectively analyze the main land-use characteristics of the region although the identification results may become unstable after 3–4 months.

Hypothesis

ABSTRACT This paper presents an argument for the pursuit of a multidisciplinary approach to the study of sexual orientation and gender identification distribution, including, but not limited to, bisexuality. Proceeding from a hypothesis that much of the homosexual and bisexual population is latent, that is, hidden both from individuals and society, an experimental approach is proposed that may take that latency into account and provide a truly representative picture of sexual orientation and gender identification. It measures both psychological and physiological responses to questions about the subject's tolerance toward gay men and women, bisexual persons and transgender persons. Should this proposal have merit, it is expected that significant physiological responses will occur when latent homosexual or bisexual persons are questioned about their tolerance and feelings toward LGBT persons, thus revealing an otherwise hidden part of the distribution of sexual orientation.

331 直交関数系を用いたはりのパラメータ同定 : 同定精度の向上(信号・データ処理, OS-18 計測・評価・診断)

In this paper, we proposed new parameter identification technique using orthogonal functions to identify the density and the modulus of elasticity of each segment of the beam. Using orthogonal functions, we could consider the parameter distribution in identification and identified the distribution of the density and the modulus of elasticity with a few parameters. We applied the Legendre series and the Haar series for the uniform, the combined and the cracked beam. We checked the applicability of our technique for the parameter identification by the simulation and found the considerable reduction of the calculation time and good regularization ability.

Changes in the Determinants of Class Identification in Japan

:Along with the high growth of the Japanese economy, there was a gradual shift in class identification from “the lower (class)” to “the middle (class).” About 75 percent of Japanese people identified themselves as middle class in 1975. However, because the distribution of identification has not changed dramatically since 1975, it should be reexamined.In this article, the same linear causal model is applied to the male samples of the Social Stratification and Social Mobility (SSM) surveys of 1975, 1985, 1995 in order to examine the latent changes of the determinants of class identification in the course of these twenty years. The same model is used to analyze the female samples of the SSM surveys of 1985 and 1995. The results of the analysis confirm the appearance of tendencies similar to those in the male samples.All of these results suggest the following conclusions: (1) As M. Naoi (1979) and others have insisted, socioeconomic status had little effect on class identification in the 1970s; (2) In the 1980s, income and life satisJaction were the most significant determinants of class identification, whereas occupation and education did not have significant effects. This can be called the period of economic aspect and subjective evaluation; (3) In the 1990s, education, occupation, income, and life satisfaction have had significant effects on class identification. Consequently, R2, which was only 9.3 percent in 1975, rose to 18.4 percent in 1995. This marks the advent of the period of multiple determinants of class identification.

COUNTER-CURRENT DISTRIBUTION IN IDENTIFICATION OF TOCOPHEROL COMPOUNDS IN FECES

COUNTER-CURRENT DISTRIBUTION IN IDENTIFICATION OF TOCOPHEROL COMPOUNDS IN FECES