arXiv subjects

Yanxia Zhang

Publications and source records attributed to Yanxia Zhang.

53 records · Page 3Linked to original sources

arXiv2020

Stochastic dynamics of the triple-well potential systems driven by colored noise

A stochastic averaging technique based on energy-dependent frequency is extended to dynamical systems with triple-well potential driven by colored noise. The key procedure is the derivation of energy-dependent frequency according to the four different motion patterns in triple-well potential. Combined with the stochastic averaging of energy envelope, the analytical stationary probability density (SPD) of tri-stable systems can be obtained. Two cases of strongly nonlinear triple-well potential systems are presented to explore the effects of colored noise and validate the effectiveness of the proposed method. Results show that the proposed method is well verified by numerical simulations, and has significant advantages, such as high accuracy, small limitation and easy application in multi-stable systems, compared with the traditional stochastic averaging method. Colored noise plays a significant constructive role in modulating transition strength, stochastic fluctuation range and symmetry of triple-well potential. While, the additive and multiplicative colored noises display quite different effects on the features of coherence resonance (CR). Choosing a moderate additive noise intensity can induce CR, but the multiplicative colored noise cannot.

math.DS↗

arXiv2020

Enhanced energy harvesting using time-delayed feedback control from random rotational environment

Motivated by improving performance of a bi-stable vibration energy harvester (VEH) from the viewpoint of vibration control, the time-delayed feedback control of displacement and velocity are constructively proposed into an electromechanical coupled VEH mounted on a rotational automobile tire, which is subject to colored noise and the periodic excitation. Using the improved stochastic averaging procedure based on energy-dependent frequency, the expressions of stationary probability density (SPD) and signal-to-noise ratio (SNR) are obtained analytically. Then, the efficiency of time-delayed feedback control on the stationary response and stochastic resonance (SR) for the delay-controlled VEH is explored in detail theoretically. Results show that both noise-induced SR and delay-induced SR can occur. Time delay is able to not only enhance the SR behavior but also weaken it. Furthermore, a larger negative feedback gain of displacement and a larger positive feedback gain of velocity are more beneficial for VEH. Interesting finding is that the optimal combination of time delay in maximizing the harvested performance, such as the harvest power, the output RMS voltage and the power conversion efficiency, is almost perfectly consistent with that in maximizing SNR. Compared with the uncontrolled VEH, the delay-controlled VEH can achieve certain desirable optimization in harvesting energy by choosing the appropriate combination of time delays and feedback gains.

math.OC↗

arXiv2020

Most Probable Dynamics of Stochastic Dynamical Systems with Exponentially Light Jump Fluctuations

The emergence of the exit events from a bounded domain containing a stable fixed point induced by non-Gaussian Lévy fluctuations plays a pivotal role in practical physical systems. In the limit of weak noise, we develop a Hamiltonian formalism under the Lévy fluctuations with exponentially light jumps for one- and two-dimensional stochastic dynamical systems. This formalism is based on a recently proved large deviation principle for dynamical systems under non-Gaussian Lévy perturbations. We demonstrate how to compute the most probable exit path and the quasi-potential by several examples. Meanwhile, we explore the impacts of the jump measure on the quasi-potential quantitatively and on the most probable exit path qualitatively. Results show that the quasi-potential can be well estimated by an approximate analytical expression. Moreover, we discover that although the most probable exit paths are analogous to the Gaussian case for the isotropic noise, the anisotropic noise leads to significant changes of the structure of the exit paths. These findings shed light on the underlying qualitative mechanism and quantitative feature of the exit phenomenon induced by non-Gaussian noise.

math.ST↗

arXiv2019

A New Strategy for Estimating Photometric Redshifts of Quasars

Based on the SDSS and SDSS-WISE quasar datasets, we put forward two schemes to estimate the photometric redshifts of quasars. Our schemes are based on the idea that the samples are firstly classified into subsamples by a classifier and then photometric redshift estimation of different subsamples is performed by a regressor. Random Forest is adopted as the core algorithm of the classifiers, while Random Forest and kNN are applied as the key algorithms of regressors. The samples are divided into two subsamples and four subsamples depending on the redshift distribution. The performance based on different samples, different algorithms and different schemes are compared. The experimental results indicate that the accuracy of photometric redshift estimation for the two schemes generally improve to some extent compared to the original scheme in terms of the percents in \frac{|Δz|}{1+z_{i}}<0.1 and \frac{|Δz|}{1+z_{i}}<0.2 and mean absolute error. Only given the running speed, kNN shows its superiority to Random Forest. The performance of Random Forest is a little better than or comparable to that of kNN with the two datasets. The accuracy based on the SDSS-WISE sample outperforms that based on the SDSS sample no matter by kNN or by Random Forest. More information from more bands is considered and helpful to improve the accuracy of photometric redshift estimation. Evidently it can be found that our strategy to estimate photometric redshift is applicable and may be applied to other datasets or other kinds of objects. Only talking about the percent in \frac{|Δz|}{1+z_{i}}<0.3, there is still large room for further improvement in the photometric redshift estimation.

astro-ph.IM↗

arXiv2019

Efficient Selection of Quasar Candidates Based on Optical and Infrared Photometric Data Using Machine Learning

We aim to select quasar candidates based on the two large survey databases, Pan-STARRS and AllWISE. Exploring the distribution of quasars and stars in the color spaces, we find that the combination of infrared and optical photometry is more conducive to select quasar candidates. Two new color criterions (yW1W2 and izW1W2) are constructed to distinguish quasars from stars efficiently. With izW1W2, 98.30% of star contamination is eliminated, while 99.50% of quasars are retained, at least to the magnitude limit of our training set of stars. Based on the optical and infrared color features, we put forward an efficient schema to select quasar candidates and high redshift quasar candidates, in which two machine learning algorithms (XGBoost and SVM) are implemented. The XGBoost and SVM classifiers have proven to be very effective with accuracy of 99.46% when 8Color as input pattern and default model parameters. Applying the two optimal classifiers to the unknown Pan-STARRS and AllWISE cross-matched data set, a total of 2,006,632 intersected sources are predicted to be quasar candidates given quasar probability larger than 0.5 (i.e. P_QSO>0.5). Among them, 1,201,211 have high probability (P_QSO>0.95). For these newly predicted quasar candidates, a regressor is constructed to estimate their redshifts. Finally 7,402 z>3.5 quasars are obtained. Given the magnitude limitation and site of the LAMOST telescope, part of these candidates will be used as the input catalogue of the LAMOST telescope for follow-up observation, and the rest may be observed by other telescopes.

astro-ph.IM↗

arXiv2018

Using Topic Models to Mine Everyday Object Usage Routines Through Connected IoT Sensors

With the tremendous progress in sensing and IoT infrastructure, it is foreseeable that IoT systems will soon be available for commercial markets, such as in people's homes. In this paper, we present a deployment study using sensors attached to household objects to capture the resourcefulness of three individuals. The concept of resourcefulness highlights the ability of humans to repurpose objects spontaneously for a different use case than was initially intended. It is a crucial element for human health and wellbeing, which is of great interest for various aspects of HCI and design research. Traditionally, resourcefulness is captured through ethnographic practice. Ethnography can only provide sparse and often short duration observations of human experience, often relying on participants being aware of and remembering behaviours or thoughts they need to report on. Our hypothesis is that resourcefulness can also be captured through continuously monitoring objects being used in everyday life. We developed a system that can record object movement continuously and deployed them in homes of three elderly people for over two weeks. We explored the use of probabilistic topic models to analyze the collected data and identify common patterns.

cs.HC↗

arXiv2018

Automated Spectral Classification of Galaxies using Machine Learning Approach on Alibaba Cloud AI platform (PAI)

Automated spectral classification is an active research area in astronomy at the age of data explosion. While new generation of sky survey telescopes (e.g. LAMOST and SDSS) produce huge amount of spectra, automated spectral classification is highly required to replace the current model fitting approach with human intervention. Galaxies, and especially active galactic nucleus (AGNs), are important targets of sky survey programs. Efficient and automated methods for galaxy spectra classification is the basis of systematic study on physical properties and evolution of galaxies. To address the problem, in this paper we carry out an experiment on Alibaba Cloud AI plaform (PAI) to explore automated galaxy spectral classification using machine learning approach. Supervised machine learning algorithms (Logistic Regression, Random Forest and Linear SVM) were performed on a dataset consist of ~ 10000 galaxy spectra of SDSS DR14, and the classification results of which are compared and discussed. These galaxy spectra each has a subclass tag (i.e. AGNs, Starburst, Starforming, and etc.) that we use as training labels.

astro-ph.IM↗

arXiv2018

Imbalanced learning for RR Lyrae stars

We apply machine learning and Convex-Hull algorithms to separate RR Lyrae stars from other stars, like main sequence stars, white dwarf stars, carbon stars, CVs and carbon-lines stars, based on the Sloan Digital Sky Survey (SDSS) and Galaxy Evolution Explorer (GALEX). In the low-dimensional space, the Convex-Hull algorithm is applied to select RR Lyrae stars. Given different input patterns of (u-g, g-r), (g-r, r-i), (r-i, i-z), (u-g, g-r, r-i), (g-r, r-i, i-z), (u-g, g-r, i-z) and (u-g, r-i, i-z), different convex hulls can be built for RR Lyrae stars. Comparing the performance of different input patterns, u-g, g-r, i-z is the best input pattern. For this input pattern, the efficiency (the fraction of true RR Lyrae stars in the predicted RR Lyrae sample) is 4.2% with a completeness (the fraction of recovered RR Lyrae stars in the whole RR Lyrae sample) of 100%, increases to 9.9% with 97% completeness and to 16.1% with 53% completeness by removing some outliers. In the high-dimensional space, machine learning algorithms are used with input patterns (u-g, g-r, r-i, i-z), (u-g, g-r, r-i, i-z, r), (NUV-u, u-g, g-r, r-i, i-z) and (NUV-u, u-g, g-r, r-i, i-z, r). RR Lyrae stars, which belong to the class of interest in our paper, are rare compared to other stars. For the highly imbalanced data, cost-sensitive Support Vector Machine (SVM), cost-sensitive Random Forest and Fast Boxes are used. The results show that information from GALEX is helpful for identifying RR Lyrae stars and Fast Boxes are the best performers on the skewed data in our case.

astro-ph.IM↗

arXiv2016

Astronomical Data Fusion Tool Based on PostgreSQL

With the application of advanced astronomical technologies, equipments and methods all over the world, astronomy covers from radio, infrared, visible light, ultraviolet, X-ray and gamma ray band, and enters into the era of full wavelength astronomy. How to effectively integrate data from different ground- and space-based observation equipments, different observers, different bands, different observation time, requires the data fusion technology. In this paper we introduce the cross-match tool that is developed by the Python language and based on the PostgreSQL database and uses Q3C as the core index, facilitating the cross-match work of massive astronomical data. It provides four different cross-match functions, namely: I) cross-match of custom error range; II) cross-match of catalog error; III) cross-match based on the elliptic error range; IV) cross-match of the nearest algorithm. The cross-match result set provides good foundation for subsequent data mining and statistics based on multiwavelength data. The most advantage of this tool is a user-oriented tool applied locally by users. By means of this tool, users may easily create their own databases, manage their own data and cross-match databases according to their requirements. In addition, this tool is also able to transfer data from one database into another database. More importantly, the tool is easy to get started and used by astronomers without writing any code.

astro-ph.IM↗

arXiv2016

Photometric Redshift Estimation for Quasars by Integration of KNN and SVM

The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is still an unsolved problem for a long time and exists in the current photometric redshift estimation approaches (such as $k$-nearest-neighbor). In this paper, we propose a novel two-stage approach by integration of $k$-nearest-neighbor (KNN) and support vector machine (SVM) methods together. In the first stage, we apply KNN algorithm on photometric data and estimate their corresponding z$_{\rm phot}$. By analysis, we find two dense regions with catastrophic failure, one in the range of z$_{\rm phot}\in[0.3,1.2]$, the other in the range of z$_{\rm phot}\in [1.2,2.1]$. In the second stage, we map the photometric input pattern of points falling into the two ranges from original attribute space into a high dimensional feature space by Gaussian kernel function in SVM. In the high dimensional feature space, many outlier points resulting from catastrophic failure by simple Euclidean distance computation in KNN can be identified by a classification hyperplane of SVM and further be corrected. Experimental results based on the SDSS (the Sloan Digital Sky Survey) quasar data show that the two-stage fusion approach can significantly mitigate catastrophic failure and improve the estimation accuracy of photometric redshifts of quasars. The percents in different |$Δ$z| ranges and rms (root mean square) error by the integrated method are $83.47\%$, $89.83\%$, $90.90\%$ and 0.192, respectively, compared to the results by KNN ($71.96\%$, $83.78\%$, $89.73\%$ and 0.204).

astro-ph.IM↗

arXiv2014

ASERA: A Spectrum Eye Recognition Assistant for Quasar Spectra

Spectral type recognition is an important and fundamental step of large sky survey projects in the data reduction for further scientific research, like parameter measurement and statistic work. It tends out to be a huge job to manually inspect the low quality spectra produced from massive spectroscopic survey, where the automatic pipeline may not provide confident type classification results. In order to improve the efficiency and effectiveness of spectral classification, we develop a semi-automated toolkit named ASERA, A Spectrum Eye Recognition Assistant. The main purpose of ASERA is to help the user in quasar spectral recognition and redshift measurement. Furthermore it can also be used to recognize various types of spectra of stars, galaxies and AGNs (Active Galactic Nucleus). It is an interactive software allowing the user to visualize observed spectra, superimpose template spectra from the Sloan Digital Sky Survey (SDSS), and interactively access related spectral line information. It is an efficient and user-friendly toolkit for accurate classification of spectra observed by LAMOST (the Large Sky Area Multi-object Fiber Spectroscopic Telescope). The toolkit is available in two modes: a Java standalone application and a Java applet. ASERA has a few functions, such as wavelength and flux scale setting, zoom in and out, redshift estimation, spectral line identification, which helps user to improve the spectral classification accuracy especially for low quality spectra and reduce the labor of eyeball check. The function and performance of this tool is displayed through the recognition of several quasar spectra and a late type stellar spectrum from the LAMOST Pilot survey. Its future expansion capabilities are discussed.

astro-ph.IM↗

arXiv2012

SDSS quasars in the WISE preliminary data release and quasar candidate selection with optical/infrared colors

We present a catalog of 37,842 quasars in the SDSS Data Release 7, which have counterparts within 6" in the WISE Preliminary Data Release. The overall WISE detection rate of the SDSS quasars is 86.7%, and it decreases to less than 50.0% when the quasar magnitude is fainter than $i=20.5$. We derive the median color-redshift relations based on this SDSS-WISE quasar sample and apply them to estimate the photometric redshifts of the SDSS-WISE quasars. We find that by adding the WISE W1- and W2-band data to the SDSS photometry we can increase the photometric redshift reliability, defined as the percentage of sources with the photometric and spectroscopic redshift difference less than 0.2, from 70.3% to 77.2%. We also obtain the samples of WISE-detected normal and late-type stars with SDSS spectroscopy, and present a criterion in the $z-W1$ versus $g-z$ color-color diagram, $z-W1>0.66(g-z)+2.01$, to separate quasars from stars. With this criterion we can recover 98.6% of 3089 radio-detected SDSS-WISE quasars with redshifts less than four and overcome the difficulty in selecting quasars with redshifts between 2.2 and 3 from SDSS photometric data alone. We also suggest another criterion involving the WISE color only, $W1-W2>0.57$, to efficiently separate quasars with redshifts less than 3.2 from stars. In addition, we compile a catalog of 5614 SDSS quasars detected by both WISE and UKIDSS surveys and present their color-redshift relations in the optical and infrared bands. By using the SDSS $ugriz$, UKIDSS YJHK and WISE W1- and W2-band photometric data, we can efficiently select quasar candidates and increase the photometric redshift reliability up to 87.0%. We discuss the implications of our results on the future quasar surveys. An updated SDSS-WISE quasar catalog consisting of 101,853 quasars with the recently released WISE all-sky data is also provided.

astro-ph.CO↗

arXiv2012

Selecting Quasar Candidates by a SVM Classification System

We develop and demonstrate a classification system constituted by several Support Vector Machines (SVM) classifiers, which can be applied to select quasar candidates from large sky survey projects, such as SDSS, UKIDSS, GALEX. How to construct this SVM classification system is presented in detail. When the SVM classification system works on the test set to predict quasar candidates, it acquires the efficiency of 93.21% and the completeness of 97.49%. In order to further prove the reliability and feasibility of this system, two chunks are randomly chosen to compare its performance with that of the XDQSO method used for SDSS-III's BOSS. The experimental results show that the high faction of overlap exists between the quasar candidates selected by this system and those extracted by the XDQSO technique in the dereddened i-band magnitude range between 17.75 and 22.45, especially in the interval of dereddened i-band magnitude < 20.0. In the two test areas, 57.38% and 87.15% of the quasar candidates predicted by the system are also targeted by the XDQSO method. Similarly, the prediction of subcategories of quasars according to redshift achieves a high level of overlap with these two approaches. Depending on the effectiveness of this system, the SVM classification system can be used to create the input catalog of quasars for the GuoShouJing Telescope (LAMOST) or other spectroscopic sky survey projects. In order to get higher confidence of quasar candidates, cross-result from the candidates selected by this SVM system with that by XDQSO method is applicable.

astro-ph.IM↗

arXiv2008

Morphology Classification and Photometric Redshift Measurement of Galaxies

Based on the Sloan Digital Sky Survey Data Release 5 Galaxy Sample, we explore photometric morphology classification and redshift estimation of galaxies using photometric data and known spectroscopic redshifts. An unsupervised method, k-means algorithm, is used to separate the whole galaxy sample into early- and late-type galaxies. Then we investigate the photometric redshift measurement with different input patterns by means of artificial neural networks (ANNs) for the total sample and the two subsamples. The experimental result indicates that ANNs show better performance when the more parameters are applied in the training set, and the mixed accuracy $σ_{mix}=\sqrt{σ_{early}^2+{σ_{late}}^2}$ of photometric redshift estimation for the two subsets is superior to $σ_{z}$ for the overall sample alone. For the optimal result, the rms deviation of photometric redshifts for the mixed sample amounts to 0.0192, that for the overall sample is 0.0196, meanwhile, that for early- and late-type galaxies adds up to 0.0164 and 0.0217, respectively.

astro-ph↗

arXiv2008

The Application of kd-tree in Astronomy

The basic idea of the kd-tree algorithm is to recursively partition a point set P by hyperplanes, and to store the obtained partitioning in a binary tree. Due to its immense popularity, many applications in astronomy have been implemented. The algorithm can been used to solve a near neighbor problem for cross-identification of huge catalogs and realize the classification of astronomical objects. Since kd-tree can speed up query and partition spaces, some approaches based on it have been applied for photometric redshift measurement. We give the case studies of kd-tree in astronomy to show its importance and performance.

astro-ph↗

arXiv2007

Multi-parameter estimating photometric redshifts with artificial neural networks

We calculate photometric redshifts from the Sloan Digital Sky Survey Data Release 2 Galaxy Sample using artificial neural networks (ANNs). Different input patterns based on various parameters (e.g. magnitude, color index, flux information) are explored and their performances for redshift prediction are compared. For ANN technique, any parameter may be easily incorporated as input, but our results indicate that using dereddening magnitude produces photometric redshift accuracies often better than the Petrosian magnitude or model magnitude. Similarly, the model magnitude is also superior to Petrosian magnitude. In addition, ANNs also show better performance when the more effective parameters increase in the training set. Finally, the method is tested on a sample of 79, 346 galaxies from the SDSS DR2. When using 19 parameters based on the dereddening magnitude, the rms error in redshift estimation is sigma(z)=0.020184. The ANN is highly competitive tool when compared with traditional template-fitting methods where a large and representative training set is available.

astro-ph↗

arXiv2004

Automated Clustering Algorithms for Classification of Astronomical Objects

Data mining is an important and challenging problem for the efficient analysis of large astronomical databases and will become even more important with the development of the Global Virtual Observatory. In this study, learning vector quantization (LVQ), single-layer perceptron (SLP) and support vector machines (SVM) were put forward for multi-wavelength data classification. A feature selection technique was used to evaluate the significance of the considered features to the results of classification. From the results, we conclude that in the situation of less features, LVQ and SLP show better performance. In contrast, SVM shows better performance when considering more features. The focus of the automatic classification is on the development of efficient feature-based classifier. The classifiers trained by these methods can be used for preselecting AGN candidates.

astro-ph↗