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مشخصات مقاله
انتشار مقاله سال 2018
تعداد صفحات مقاله انگلیسی 12 صفحه
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منتشر شده در نشریه الزویر
نوع مقاله ISI
عنوان انگلیسی مقاله Regionalization of water environmental carrying capacity for supporting the sustainable water resources management and development in China
ترجمه عنوان مقاله منطقه بندی ظرفیت حمل محیطی آب برای حمایت از مدیریت و توسعه منابع آب پایدار در چین
فرمت مقاله انگلیسی  PDF
رشته های مرتبط محیط زیست، مهندسی عمران
گرایش های مرتبط مدیریت منابع آب
مجله منابع، حفاظت و بازیافت – Resources Conservation & Recycling
دانشگاه School of Environment – Beijing Normal University – China
کلمات کلیدی ظرفیت زیست محیطی آب، تفاوت های فضایی، منطقه بندی خوشه ای k-means، توسعه پایدار، چین
کلمات کلیدی انگلیسی Water environmental carrying capacity, Spatial differences, k-means clustering zoning, Sustainable development, China
شناسه دیجیتال – doi https://doi.org/10.1016/j.resconrec.2018.03.030
کد محصول E8147
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بخشی از متن مقاله:
1. Introduction

Water environmental carrying capacity (WECC) refers to the primary ability of water bodies to supply resources to the socio-economic development and to remove the pollutants discharged by rural and urban areas and factories. It is an important indicator that reflects the regional sustainability and is closely related to the economy, population, technology and natural environment (Graymore et al., 2010; Liu and Borthwick, 2011). With a rapid economic growth and social development in China, the associated problems of water contamination and shortage of water resources have become serious bottlenecks which challenge and limit the sustainable development at the regional as well as at national levels (Chen et al., 2016a; Liu et al., 2017). The disordered spatial development and irrational processes in the urbanization and industrialization destroy the essential water environment thereby affecting the local people at this stage and potentially might affect their next generation, which further brings a huge threat to the sustainable development of these regions (Fan and Li, 2009). At present, an increasing number of researchers have realized that the scale and intensity of economic and social development cannot exceed certain carrying capacities of the water system in a specific region. At the same time, the potential damages to water system cannot threaten the survival and development of future generations (Gunderson, 2014; Hák et al., 2016; Tran, 2016). Thus, it is of great significance for local decision makers to be well aware of information related to carrying capacity of water environment within a specific region. China’s climate is mainly dominated by dry seasons and wet monsoons, which leads to prominent precipitation differences between the south and north provinces. Also, the eastern and coastal provinces are much more densely populated than the western and interior regions. The vast majority of population lives in major cities that are mainly located in the Yangtze River Delta, Pearl River Delta and North China Plain. The gap in GDP per capita between coastal and inland areas had increased from 200 Yuan in 1978 to 19,630 Yuan in 2008 (Fan et al., 2010). In addition, the eastern developed regions are affected with severe environmental problems of water due to overexploitation, for instance, the degradation of water quality in the regional environment. The western underdeveloped regions usually have vulnerable environment for water. Especially, the upstream areas of western rivers are the key conservation areas of water source and are the ecologically fragile districts in China (Fan and Li, 2009). Since the functions in regional economy and society and basic conditions for water environment are varied, and thus the corresponding strategies and development policies should be approached in a different way. The reasonable spatial arrangement targeting the development and conservation requires an urgent attention for the rapidly changing society in China (Liu et al., 2015). Zoning is an effective measure to divide an area into sub-areas based on similar characteristics in order to identify the differences between sub-areas and to implement the appropriate environmental management policies (Fadlelmawla et al., 2011; Oliveira et al., 2011; Shi and Zeng, 2014).

نوشته مقاله انگلیسی رایگان در مورد حمایت از مدیریت و توسعه منابع آب پایدار در چین – الزویر 2018 اولین بار در دانلود مقالات ISI. پدیدار شد.


 

مشخصات مقاله
انتشار مقاله سال 2018
تعداد صفحات مقاله انگلیسی 22 صفحه
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منتشر شده در نشریه الزویر
نوع مقاله ISI
عنوان انگلیسی مقاله Scaling relation of earthquake seismic data
ترجمه عنوان مقاله روابط مقیاس گذاری داده های لرزه ای زمین لرزه
فرمت مقاله انگلیسی  PDF
رشته های مرتبط مهندسی عمران
گرایش های مرتبط زلزله
مجله فیزیک آ – Physica A
دانشگاه Chengdu University of Technology – China
کلمات کلیدی مجموعه داده های لرزه ای؛ رابطه مقیاس گذاری؛ پیچیدگی فضایی و زمانی؛ شبکه زلزله وزنی
کلمات کلیدی انگلیسی seismic dataset; scaling relation; spatio-temporal complexity; weighted earthquake network
شناسه دیجیتال – doi https://doi.org/10.1016/j.physa.2017.11.126
کد محصول E8148
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بخشی از متن مقاله:
1. Introduction

Earthquake is one of the most important and common natural phenomenon that directly affects human life and property. Therefore, scientists have been studying the genesis of earthquakes, earthquake forecast, hazard evaluation and other dynamics of earthquakes for a long history [1, 2]. It is widely believed that seismicity can be characterized by extremely rich phenomenology, and then some known empirical laws are summarized, for example, the Omori law [3] for the temporal pattern of aftershocks showing slow relaxation and the Gutenberg-Richter law [4] for describing the frequency of tremors of a given magnitude. In addition, physicists take earthquake dynamics as a scale invariant process and study the correlation between different shocks. Some studies claim that the spatial positions of earthquake epicenters can form fractal sets [5, 6]. The fractal dimension D proposed by Aviles, for instance, is used to measure the irregularity of the fault trace in the selected band and may changes significantly from the short-length band to the long-length band [5]. Kagan analyzed the fractal distribution of epicenters and discovered that seismicity is controlled by scale-invariant statistical distributions [6]. Burridge and Knopoff proposed a model for describing the slow creeping of the continental plates along the fault lines as a stick-slip process [7]. Bak et al. believed that the earthquake phenomenon can be regarded as a Self-Organized Critical process for studying the evolution of earthquakes and the scaling relations [8]. Although the correlations among different shocks have been studied, the actual mechanism of the underlying dynamics of this complex phenomenon has not been possible yet [1]. In a real complex system, detailed information on properties of system elements may not always be available, especially the interaction or correlation among them [9]. In such a situation, the network description offers a useful tool to build the relationships of system elements and mine the interactions between them. Scientists widely believe that complex network theory plays an increasingly pivotal role in revealing the complicated dynamics of real systems [10-13]. The vertices and the links connecting them respectively represent elements and their interaction or correlation.

نوشته مقاله انگلیسی رایگان در مورد روابط مقیاس گذاری داده های لرزه ای زمین لرزه – الزویر 2018 اولین بار در دانلود مقالات ISI. پدیدار شد.


 

مشخصات مقاله
انتشار مقاله سال 2018
تعداد صفحات مقاله انگلیسی 10 صفحه
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منتشر شده در نشریه الزویر
نوع مقاله ISI
عنوان انگلیسی مقاله Characterization of gas hydrate morphology from seismic data in the northern South China Sea
ترجمه عنوان مقاله مشخصات مورفولوژی هیدرات گاز از داده های لرزه ای در دریای شمال چین
فرمت مقاله انگلیسی  PDF
رشته های مرتبط ژئوفیزیک
گرایش های مرتبط لرزه نگاری
مجله مرزهای علوم زمین – Geoscience Frontiers
دانشگاه  China University of Geosciences (Beijing) – China
کلمات کلیدی مورفولوژی هیدرات گاز، دریای شمال چین، مکانیزم از دست دادن Mesoscopic، ضریب بازتاب در مقایسه با فرکانس
کلمات کلیدی انگلیسی Gas hydrate morphology, Northern South China Sea, Mesoscopic-loss mechanism, Reflection coefficient versus frequency
شناسه دیجیتال – doi https://doi.org/10.1016/j.gsf.2018.03.016
کد محصول E8149
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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بخشی از متن مقاله:
1. Introduction

Gas hydrates are formed due to the high hydraulic pressure present under the cold seabed over long periods of time. Gas hydrates are stable under the temperature and pressure conditions typical of water depths greater than 500 m in oceanic sediments along the continental margins. In the South China Sea, gas hydrates were found by the Guangzhou Marine Geologic Survey (GMGS) in 2007 (Zhang et al., 2007). Gas hydrates can be summarized as pore-filling or fracturefilling based on their morphologies in sediments. In the process of exploiting hydrates, the gas hydrates existence in sediments must first be known. The exploitation methods would be different according to the hydrate morphology. In 2013, GMMS once again conducted a drilling program that targeted the gas hydrate deposits in the northern South China Sea. A total of 23 holes were drilled at 13 sites (Zhang et al., 2014b). The logging data and core analysis indicated the occurrence of gas hydrates from approximately 5 m to 220 m below mudline (BML). Gas hydrates occur as solid nodules, disseminated within pore spaces of sediments and fracture fillings in veins (Yang et al., 2014; Zhang et al., 2014a, b). Nowadays, many researchers have studied the presence and amounts of gas hydrate. Still, we cannot identify the morphology of gas hydrate in sediments before we drill the site. There’s no such research to give a way to solve the problem. In this paper, we will discuss how to identify the gas hydrate morphology in sediments based on the frequency dependent reflection coefficient.

2. Mesoscopic-loss mechanism

Previous studies found that the major cause of attenuation occurs at three scales: macroscopic, mesoscopic, and microscopic. At macroscopic scale, the Biot theory (Biot, 1962) gives the attenuation mechanisms. The drawback of this is that the macroscopic-flow mechanism underestimates the velocity dispersion and attenuation in rocks. At the microscopic scale, the so-called squirt flow is incapable of describing the measured levels of dissipation at seismic frequencies. Pride et al. (2004) studied the mesoscopic loss mechanism and found that the mesoscopic model provides the proper attenuation to explain the field data, and therefore, we use the mesoscopic theory to study the reflection coefficient varying with frequency at BSR.

نوشته مقاله انگلیسی رایگان در مورد مشخصات مورفولوژی هیدرات گاز از داده های لرزه ای – الزویر 2018 اولین بار در دانلود مقالات ISI. پدیدار شد.


 

مشخصات مقاله
انتشار مقاله سال 2017
تعداد صفحات مقاله انگلیسی 23 صفحه
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منتشر شده در نشریه الزویر
نوع مقاله ISI
عنوان انگلیسی مقاله Matching pursuit parallel decomposition of seismic data
ترجمه عنوان مقاله تطبیق پیگیری داده های لرزه ای به صورت موازی
فرمت مقاله انگلیسی  PDF
رشته های مرتبط ژئوفیزیک
گرایش های مرتبط لرزه نگاری
مجله کامپیوترها و علوم زمین – Computers and Geosciences
دانشگاه China University of Geosciences (Beijing) – China
کلمات کلیدی پیگیری تطبیقی؛ سرعت محاسبه؛ الگوریتم موازی؛ داده های لرزه ای
کلمات کلیدی انگلیسی Matching pursuit; Computation speed; Parallel algorithm; Seismic data
شناسه دیجیتال – doi http://dx.doi.org/10.1016/j.cageo.2017.04.005
کد محصول E8150
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بخشی از متن مقاله:
1. Introduction

Matching pursuit is an algorithm for sparse representation of signals. It adaptively decomposes a signal into a series of time-frequency atoms that match the time-frequency characteristics of the signal (Mallat and Zhang, 1993). The matching pursuit decomposition of seismic signals has been widely used in seismic data processing and interpretation, such as migration (Wang and Pann, 1996; Li et al, 1998), filtering (Nguyen and Castagna, 2000), interpolation (Øzbek, 2009), inversion (Yang et al, 2011; Zhou et al, 2013), and spectral analysis for hydrocarbon recognition (Castagna et al, 2003; Wang, 2007) and channel detection (Liu and Marfurt, 2007). The Gabor wavelet is used as the time-frequency atoms in a conventional matching pursuit algorithm. Considering the characteristics of seismic signals, Liu et al (2004) applies the Ricker wavelet as the time-frequency atoms to decompose seismic data. Liu and Marfurt (2005), and Wang (2007) implement matching pursuit decomposition of seismic signals based on the Morlet wavelet. A conventional matching pursuit algorithm costs a huge amount of computation, because it searches the optimal time-frequency atoms from an abundant dictionary (Mallat and Zhang, 1993). Liu and Marfurt (2005) introduce complex-trace analysis into the algorithm to avoid the blind search of time-frequency atoms, greatly improving its computation speed. Based on this, Wang (2007) summarizes “three-stage procedure” for matching pursuit decomposition of seismic signals where the decomposition is more precise by executing local search around the complex-trace attributes. In order to improve the spatial continuity of the decomposition, Wang (2010) further proposes multichannel matching pursuit. Although matching pursuit decomposition of seismic signals has been greatly improved, the local search of time-frequency atoms and iterative implementation of the algorithm still cost lots of time. To further enhance its execution speed, in addition to the algorithm itself, with the help of high-performance computers to accelerate the decomposition could also be considered. Liu and Marfurt (2007) perform matching pursuit to seismic data by searching a suite of optimal time-frequency atoms at one iteration rather than one at a time as in a conventional algorithm. Because the search of optimal time-frequency atoms has the same implementation and is independent of each other, Liu and Marfurt (2007) inspire us to execute matching pursuit decomposition in parallel by parallel computer systems. Hence, in this paper, we design a parallel decomposition algorithm of matching pursuit to effectively improve the computation speed of the matching pursuit decomposition of seismic signals. It gives full play to the advantages of parallel computing and has good expandability. We believe that the realization of matching pursuit parallel decomposition for seismic data will benefit the application of matching pursuit in the processing and interpretation of large-scale 3D seismic data.

نوشته مقاله انگلیسی رایگان در مورد تطبیق پیگیری داده های لرزه ای به صورت موازی – الزویر 2017 اولین بار در دانلود مقالات ISI. پدیدار شد.


 

مشخصات مقاله
انتشار مقاله سال 2018
تعداد صفحات مقاله انگلیسی 9 صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
منتشر شده در نشریه الزویر
نوع مقاله ISI
عنوان انگلیسی مقاله Bootstrapping for multivariate linear regression models
ترجمه عنوان مقاله خودگردان سازی برای مدل های رگرسیون خطی چند متغیره
فرمت مقاله انگلیسی  PDF
رشته های مرتبط آمار
گرایش های مرتبط آمار ریاضی
مجله نامه های آمار و احتمال – Statistics and Probability Letters
دانشگاه Department of Biostatistics – Yale School of Public Health – USA
کلمات کلیدی بوت استرپ چند متغیره، مدل رگرسیون خطی چند متغیره، بوت استرپ باقی مانده
کلمات کلیدی انگلیسی Multivariate bootstrap, Multivariate linear regression model, Residual bootstrap
شناسه دیجیتال – doi https://doi.org/10.1016/j.spl.2017.11.001
کد محصول E8085
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بخشی از متن مقاله:
1. Introduction

The linear regression model is an important and useful tool in many statistical analyses for studying the relationship among variables. Regression analysis is primarily used for predicting values of the response variable at interesting values of the predictor variables, discovering the predictors that are associated with the response variable, and estimating how changes in the predictor variables affects the response variable (Weisberg, 2005). The standard linear regression methodology assumes that the response variable is a scalar. However, it may be the case that one is interested in investigating multiple response variables simultaneously. One could perform a regression analysis on each response separately in this setting. Such an analysis would fail to detect associations between responses. Regression settings where associations of multiple responses are of interest require a multivariate linear regression model for analysis. Bootstrapping techniques are well understood for the linear regression model with a univariate response (Bickel and Freedman, 1981; Freedman, 1981). In particular, theoretical justification for the residual bootstrap as a way to estimate the variability of the ordinary least squares (OLS) estimator of the regression coefficient vector in this model has been developed (Freedman, 1981). Theoretical extensions of residual bootstrap techniques appropriate for the multivariate linear regression model have not been formally introduced. The existence of such an extension is stated without proof and rather implicitly in subsequent works (Freedman and Peters, 1984; Diaconis and Efron, 1983). In this article we show that the bootstrap procedures in Freedman (1981) provide consistent estimates of the variability of the OLS estimator of the regression coefficient matrix in the multivariate linear regression model. Our proof technique follows similar logic as Freedman (1981). The generality of the bootstrap theory developed in Bickel and Freedman (1981) provide the tools required for our extension to the multivariate linear regression model.

نوشته مقاله انگلیسی رایگان در مورد خودگردان سازی برای مدل های رگرسیون خطی – الزویر 2018 اولین بار در دانلود مقالات ISI. پدیدار شد.


 

مشخصات مقاله
انتشار مقاله سال 2018
تعداد صفحات مقاله انگلیسی 25 صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
منتشر شده در نشریه الزویر
نوع مقاله ISI
عنوان انگلیسی مقاله An RKHS model for variable selection in functional linear regression
ترجمه عنوان مقاله مدل RKHS برای انتخاب متغیر در رگرسیون خطی کارکردی
فرمت مقاله انگلیسی  PDF
رشته های مرتبط آمار
گرایش های مرتبط آمار ریاضی
مجله مجله تحلیل چندمتغیره – Journal of Multivariate Analysis
دانشگاه Departamento de Matem´aticas – Universidad Aut´onoma de Madrid – Spain
کلمات کلیدی انتخاب ویژگی، رگرسیون خطی تابعی، نقاط ضربه، انتخاب متغیر
کلمات کلیدی انگلیسی feature selection, functional linear regression, impact points, variable selection
شناسه دیجیتال – doi https://doi.org/10.1016/j.jmva.2018.04.008
کد محصول E8086
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1. Introduction:

statement of the problem and motivation The problem under study: variable selection in functional regression The study of regression models is clearly among the leading topics in statistics. In particular, these models play a central role in the theory of statistics with functional data, often called Functional Data Analysis (FDA); see [7, 15, 16] for an overview on FDA. Throughout this paper, we will consider “functional data” consisting of independent X1 = X1(t), . . . , Xn = Xn(t) observations (trajectories) drawn from a second-order (L 2 ) stochastic process X = X(t), t ∈ [0, 1], with continuous trajectories and continuous mean and covariance functions, denoted by m = m(t) and K(s, t), respectively. All the involved random variables are supposed to be defined in a common probability space (Ω, A, Pr). We are interested on functional regression models with scalar response, of type Yi = g(Xi) + εi , where g is a real function defined on a suitable space X where the trajectories of our process are supposed to live. The random variables εi are independent errors (and also independent from the Xi) with mean zero and common variance σ 2 . More specifically, we are concerned with variable selection issues; see, [4, Sec. 1], [11] for additional information and references. Basically, a variable selection functional method is an automatic procedure that takes a function {x(t), t ∈ [0, 1]} to a finite-dimensional vector (x(t1), . . . , x(tp)). The overall idea for variable selection is to choose the variables x(ti) (or, equivalently, the “impact points” t1, . . . , tp ∈ [0, 1]; see [22]), in an “optimal way” so that the original functional problem (regression, classification, clustering,…) is replaced with the corresponding multivariate version, based on the selected variables. In the regression setting, this would amount to replace the functional model Yi = g(Xi) + εi with a finite dimensional version of type Yi = φ{X(t1), . . . , X(tp)} + ei . Nevertheless, note that still the problem is of a functional nature, since the methods to select the ti are generally based upon the full data trajectories.

نوشته مقاله انگلیسی رایگان در مورد مدل RKHS برای انتخاب متغیر در رگرسیون خطی – الزویر 2018 اولین بار در دانلود مقالات ISI. پدیدار شد.


 

مشخصات مقاله
انتشار مقاله سال 2018
تعداد صفحات مقاله انگلیسی 46 صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
منتشر شده در نشریه الزویر
نوع مقاله ISI
عنوان انگلیسی مقاله Consistent estimation of linear regression models using matched data
ترجمه عنوان مقاله برآورد مداوم مدل های رگرسیون خطی با استفاده از داده های همسان
فرمت مقاله انگلیسی  PDF
رشته های مرتبط آمار
گرایش های مرتبط آمار ریاضی
مجله مجله اقتصاد سنجی – Journal of Econometrics
دانشگاه Faculty of Economics – Setsunan University – Japan
کلمات کلیدی تصحیح تقاطع؛ استنتاج غیر مستقیم؛ رگرسیون خطی؛ برآورد تطبیقی؛ تصحیح خطای اندازه گیری
کلمات کلیدی انگلیسی Bias correction; indirect inference; linear regression; matching estimation; measurement error bias
شناسه دیجیتال – doi https://doi.org/10.1016/j.jeconom.2017.07.006
کد محصول E8087
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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بخشی از متن مقاله:
1 Introduction

Suppose that we are interested in estimating a linear regression model Y = β0 + X 0 1β1 + X 0 2β2 + Z 0 γ + u := W0 θ + u, E (u| W) = 0, (1) using a random sample, where X1 ∈ R d1 , X2 ∈ R d2 and Z ∈ R d3 . The reason for distinguishing between the regressors X1, X2 and Z will become clear shortly. In addition, while d1 = 0 is allowed, d2, d3 > 0 must be the case in our setup. When W = (1, X0 1 , X0 2 , Z0 ) 0 ∈ R d+1, where d := d1 + d2 + d3, is exogenous and a single random sample of (Y, X1, X2, Z) can be obtained, the ordinary least squares (OLS) estimator of θ = (β0, β0 1 , β0 2 , γ0 ) 0 is consistent. In reality, however, we often face the problem that (Y, X1, X2, Z) cannot be taken from a single data source. It is not uncommon that economists who use survey data for empirical analysis must collect all necessary variables from more than one source. Examples include Lusardi (1996), Bj¨orklund and J¨antti (1997), Currie and Yelowitz (2000), Dee and Evans (2003), Borjas (2004), Bover (2005), Fujii (2008), Bostic et al. (2009), and Murtazashvili et al. (2015), to name a few. Ridder and Moffitt (2007) provide an excellent survey. This is the setting in which we are interested. Specifically, suppose that instead of observing a complete data set (Y, X1, X2, Z), we have the following two overlapping subsets of data, (Y, X1, Z) and (X2, Z), i.e., some of the regressors are not available in the initial data set, where the initial data set is the one containing observations on the dependent variable along with a few other regressors. In such a setting, it is natural to construct a matched data set via exploiting the proximity of the common regressor(s) Z across the two samples. This is often called “probabilistic record linkage”. Here are two examples of the setting.

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مشخصات مقاله
انتشار مقاله سال 2018
تعداد صفحات مقاله انگلیسی 9 صفحه
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نوع مقاله ISI
عنوان انگلیسی مقاله Automated face recognition of rhesus macaques
ترجمه عنوان مقاله تشخیص چهره خودکار میمون رزوس
فرمت مقاله انگلیسی  PDF
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مجله مجله روش های علوم اعصاب – Journal of Neuroscience Methods
دانشگاه Institute of Neuroscience – Newcastle University – UK
کلمات کلیدی میمون، شناسایی چهره، تشخیص چهره، دید کامپیوتری
کلمات کلیدی انگلیسی Monkey, Face detection, Face recognition, Computer vision
شناسه دیجیتال – doi https://doi.org/10.1016/j.jneumeth.2017.07.020
کد محصول E8088
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بخشی از متن مقاله:
1. Introduction

Automated methods for monitoring behavior of laboratory animals such as rodents and zebrafish are becoming widespread (Bains et al., 2016; Nema et al., 2016; Steele et al., 2007). They allow the monitoring of behavior effects in response to experi mental manipulations such as drugs, lesions, genetic modifications and disease. Concurrently similar automated behavior systems are being developed for monitoring health and welfare in a range of species including farm and laboratory animals (Roughan et al., 2009; Rushen et al., 2012). Rhesus macaques are one of the most common non-human primate species used in biomedical research including neuroscience research but to date the use of automated systems with non-human primates has been limited. A major challenge in measuring the behavior of any grouphoused animal is to reliably identify an individual animal. With macaques this is not an issue if the animals are singly-housed but welfare concerns are driving a move towards pair- and grouphousing of non-human primates in many countries. One solution is to add a tracking device to each animal; for example these could be colored jackets (Rose et al., 2012) or collars (Ballesta et al., 2014) in combination with video monitoring or electronic devices such as RFID tags (Maddali et al., 2013). These require regular handling of the animals and it is not currently known how the use of jackets and collars affects the behavior of the animals (personal observations with rhesus macaques suggest that the use of jackets can drastically reduce social behaviors such as grooming). Another solution is to use biometric identification based on the distinguishing visual characteristics of that species (e.g. coat pattern; Kühl and Burghardt, 2016). This has the advantage of being non-invasive. Rhesus macaques, in common with many primate species, do not have obvious individually identifiable features but the macaques themselves are capable of recognizing conspecifics by their faces (Parr et al., 2000). Face recognition technology has already been used in several non-human primate species including guenons (Allen and Higham, 2015), chimpanzees (Freytag et al., 2016) and gorillas (Loos, 2012) but not rhesus macaques. Freytag et al. (2016) achieved success rates of over 90% with images of captive chimpanzees. Face recognition technology was originally developed for use with humans and is becoming commonplace in daily life. Uses include automatic passport gates at airports, tagging of faces in photos on Facebook and use of facial image to unlock smart phones. Many of the early techniques focused on either reducing the dimensionality of the facial image or on extracting a particular feature from the image and then on classifying this output. Some of these methods for face recognition include EigenFaces (based on principal component analysis) and FisherFaces (based on linear discriminant analysis; Belhumeur et al., 1997). Some of the main challenges facing any face recognition system are coping with changes in light intensity and pose. A method based on local binary patterns (Ahonen et al., 2006) has been shown to be relatively robust to changes in light intensity. Most recently deep learning techniques have been applied to face recognition with a high level of success (Freytag et al., 2016 for chimpanzees; Parkhi et al., 2015 for humans).

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مشخصات مقاله
انتشار مقاله سال 2018
تعداد صفحات مقاله انگلیسی 32 صفحه
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نوع مقاله ISI
عنوان انگلیسی مقاله Face recognition based on recurrent regression neural network
ترجمه عنوان مقاله تشخیص چهره بر اساس رگرسیون مجدد شبکه عصبی
فرمت مقاله انگلیسی  PDF
رشته های مرتبط مهندسی کامپیوتر
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مجله محاسبات عصبی – Neurocomputing
دانشگاه Key Laboratory of Child Development and Learning Science of Ministry of Education – Southeast University – China
کلمات کلیدی رگرسیون مجدد شبکه عصبی (RRNN)، تشخیص چهره، یادگیری عمیق
کلمات کلیدی انگلیسی Recurrent regression neural network (RRNN), face recognition, deep learning
شناسه دیجیتال – doi https://doi.org/10.1016/j.neucom.2018.02.037
کد محصول E8089
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1. Introduction

Face recognition is a classic topic in past decades and now still attracts much attention in the field of computer vision and pattern recognition. Face recognition has a great potential in multimedia applications, e.g. video surveillance, personal identification, digital entertainment and so on [1, 2, 3, 4, 5]. With the rapid development of electric equipment techniques, more and more face images can be easily captured in the wild, especially videos from cameras of surveillance or cell phones. Therefore, video or image set based face recognition becomes more important in most of real-world applications and also becomes a popular topic in face analysis more recently. As face images captured from the unconstrained conditions are usually with complex appearance variations in poses, expressions, illuminations, etc., the existing face recognition algorithms still suffer from a severe challenge in fulfilling real applications to large-scale data scenes, although the current deep learning techniques have made a great progress on the unconstrained small face dataset, e.g., the recent success of deep learning methods on Labeled Faces in the Wild (LFW) [6]. In the task of face recognition, however, we cannot bypass this question of pose variations, which has been extensively studied and explored in past decades, and has not been well-solved yet. The involved methods may be divided into 20 3D [7, 8, 9] and 2D methods [10, 11, 12, 13, 14, 15, 16]. Since pose variations are basically caused by 3D rigid motions of face, 3D methods are more intuitive for pose generation. But 3D methods usually need some 3D data or recovery of 3D model from 2D data which is not a trivial thing. Moreover, the inverse transform from 3D model to 2D space is sensitive to facial appearance varia tions. In contrast to 3D model, due to decreasing one degree of freedom, 2D methods usually attempt to learn some transforms across poses, including linear models [17] or non-linear models [10, 18]. Because of its simplicity, 2D model has been widely used to deal with cross-pose face recognition with a comparable performance with 3D model. However, in many real scenes of face image sets, e.g., face video sequences, the changes of poses may be regarded as a nearly continuous stream of motions, while the existing methods usually neglect or do not make full use of this prior. Moreover, the pose variation is not the only factor between different images even for the same subject, which involves other complex factors.

نوشته مقاله انگلیسی رایگان در مورد تشخیص چهره بر اساس رگرسیون مجدد شبکه عصبی – الزویر 2018 اولین بار در دانلود مقالات ISI. پدیدار شد.


 

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انتشار مقاله سال 2018
تعداد صفحات مقاله انگلیسی 15 صفحه
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منتشر شده در نشریه الزویر
نوع مقاله ISI
عنوان انگلیسی مقاله Learning context and the other-race effect: Strategies for improving face recognition
ترجمه عنوان مقاله زمینه یادگیری و تاثیر نسل دیگر: استراتژی برای بهبود تشخیص چهره
فرمت مقاله انگلیسی  PDF
رشته های مرتبط مهندسی کامپیوتر
گرایش های مرتبط هوش مصنوعی
مجله تحقیق بصری – Vision Research
دانشگاه The University of Texas at Dallas – USA
کلمات کلیدی شناسایی چهره، تاثیر نسل دیگر، آموزش، زمینه یادگیری
کلمات کلیدی انگلیسی Face recognition, Other-race effect, Training, Learning context
شناسه دیجیتال – doi https://doi.org/10.1016/j.visres.2018.03.003
کد محصول E8090
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بخشی از متن مقاله:
1. Introduction

The other-race effect (ORE) is the tendency for people to recognize faces of their “own” race more accurately than faces of “other” races (cf., Meissner & Brigham, 2001). Often characterized as an inability to discriminate or individualize other-race faces, the ORE suggests that there are clear differences in how own- and other-race faces are processed. The ORE has been studied extensively in face perception. Its effects have been examined across a variety of racial and ethnic groups, using multiple paradigms, (Meissner & Brigham, 2001), and more recently in face recognition algorithms (Phillips, Jiang, Narvekar, Ayyad, & O’Toole, 2011). Additionally, the ORE has been found in infants as young as three months (Sangrigoli & de Schonen, 2004), in children (Anzures et al., 2014; Pezdek, Blandon-Gitlin, & Moore, 2003; Tham, Bremner, & Hay, 2017), and in non-typically developing populations, such as individuals with schizophrenia (Pinkham et al., 2008) and autism spectrum disorder (Wilson, Palermo, Burton, & Brock, 2011; Yi et al., 2016). In recent research, there has been new emphasis on ways to improve face recognition (Heron-Delaney et al., 2011; Hugenberg, Miller, & Claypool, 2007; Ritchie & Burton, 2016; Rodríguez, Bortfeld, & Gutiérrez-Osuna, 2008; Tanaka & Pierce, 2009; White, Kemp, Jenkins, & Burton, 2014; Xiao et al., 2015). Several of these studies have explored ways to reduce the ORE. For example, participant awareness of the ORE (Hugenberg et al., 2007) and viewing caricatured images of faces (Rodríguez et al., 2008) reduce the ORE. Furthermore, developmental studies show that perceptual training in infants can prevent an ORE from emerging (Heron-Delaney et al., 2011). The effects of perceptual training on learning other-race faces have been studied also in children (Xiao et al., 2015) and adults (Tanaka & Pierce, 2009). For example, Xiao et al. (2015) examined the influence of individuation and categorization training of other-race faces for preschool children. Recognition performance was measured in the context of implicit racial bias. When trained to individuate African American faces, Chinese preschoolers demonstrated a reduced implicit bias for other-race faces (they were less likely to categorize an angry racially ambiguous face as African American). These results suggest that individuation training reduces implicit bias, which is a potential step towards reducing the ORE. Tanaka and Pierce (2009) investigated the effect of individuation and categorization training of Hispanic and African American faces on Caucasian adults. Participants in the individuation condition showed marginally better recognition than those in the categorization condition. These results suggest that individuation training may reduce the ORE. In addition to the focus on individuation training, repetitive and variable-image learning have been tested as factors that may improve recognition. Repeated exposure to a single image has been shown to increase recognition accuracy (cf., for a meta-analysis of these effects, Shapiro & Penrod, 1986). Recently, the importance of within-person variability has been considered as a factor in face recognition (Dowsett, Sandford, & Burton, 2016; Jenkins, White, Van Montfort, & Burton, 2011; Murphy, Ipser, Gaigg, & Cook, 2015). The human ability to “see” multiple, variable images of the same person as a single identity has been referred to in the literature as the ability to “tell people together” (Andrews, Jenkins, Cursiter, & Burton, 2015; Jenkins et al., 2011). This contrasts to the oft-cited human ability to “tell faces apart”, which is long thought to be the core of human face expertise for faces. It is generally understood now that the former is a characteristic of familiar face perception, whereas the latter applies to both familiar and unfamiliar face processing.

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