arXiv subjects

Jianfeng Zhou

Publications and source records attributed to Jianfeng Zhou.

18 recordsLinked to original sources

arXiv2022

Reconstruction of Point Events in Liquid-Scintillator Detectors Subjected to Total Reflection

The outer water buffer is an economic option to shield the external radiative backgrounds for liquid-scintillator neutrino detectors. However, the consequential total reflection of scintillation light at the media boundary introduces extra complexity to the detector optics. This paper develops a precise detector-response model by investigating how total reflection complicates photon propagation and degrades reconstruction. We first parameterize the detector response by regression, providing an unbiased energy and vertex reconstruction in the total reflection region while keeping the number of parameters under control. From the experience of event degeneracy at the Jinping prototype, we then identify the root cause as the multimodality in the reconstruction likelihood function, determined by the refractive index of the buffer, detector scale and PMT coverage. To avoid multimodality, we propose a straightforward criterion based on the expected photo-electron-count ratios between neighboring PMTs. The criterion will be used to ensure success in future liquid-scintillator detectors by guaranteeing the effectiveness of event reconstruction.

physics.ins-det↗

arXiv2022

Correction Algorithm of Sampling Effect and Its Application

The sampling effect of the imaging acquisition device is long considered to be a modulation process of the input signal, introducing additional error into the signal acquisition process. This paper proposes a correction algorithm for the modulation process that solves the sampling effect with high accuracy. We examine the algorithm with perfect continuous Gaussian images and selected digitized images, which indicate an accuracy increase of 106 for Gaussian images, 102 at 15 times of Shannon interpolation for digitized images, and 105 at 101 times of Shannon interpolation for digitized images. The accuracy limit of the Gaussian image comes from the truncation error, while the accuracy limit of the digitized images comes from their finite resolution, which can be improved by increasing the time of Shannon interpolation.

astro-ph.IM↗

arXiv2022

Field Distortion Model Based on Fredholm Integral

Field distortion is widespread in imaging systems. If it cannot be measured and corrected well, it will affect the accuracy of photogrammetry. To this end, we proposed a general field distortion model based on Fredholm integration, which uses a reconstructed high-resolution reference point spread function (PSF) and two sets of 4-variable polynomials to describe an imaging system. The model includes the point-to-point positional distortion from the object space to the image space and the deformation of the PSF so that we can measure an actual field distortion with arbitrary accuracy. We also derived the formula required for correcting the sampling effect of the image sensor. Through numerical simulation, we verify the effectiveness of the model and reconstruction algorithm. This model will have potential application in high-precision image calibration, photogrammetry and astrometry.

eess.IV↗

arXiv2022

A Direct Slip Ratio Estimation Method based on an Intelligent Tire and Machine Learning

Accurate estimation of the tire slip ratio is critical for vehicle safety, as it is necessary for vehicle control purposes. In this paper, an intelligent tire system is presented to develop a novel slip ratio estimation model using machine learning algorithms. The accelerations, generated by a triaxial accelerometer installed onto the inner liner of the tire, are varied when the tire rotates to update the contact patch. Meanwhile, the slip ratio reference value can be measured by the MTS Flat-Trac tire test platform. Then, by analyzing the variation between the accelerations and slip ratio, highly useful features are discovered, which are especially promising for assessing vertical acceleration. For these features, machine learning (ML) algorithms are trained to build the slip ratio estimation model, in which the ML algorithms include artificial neural networks (ANNs), gradient boosting machines (GBMs), random forests (RFs), and support vector machines (SVMs). Finally, the estimated NRMS errors are evaluated using 10-fold cross-validation (CV). The proposed estimation model is able to estimate the slip ratio continuously and stably using only the acceleration from the intelligent tire system, and the estimated slip ratio range can reach 30%. The estimation results have high robustness to vehicle velocity and load, where the best NRMS errors can reach 4.88%. In summary, the present study with the fusion of an intelligent tire system and machine learning paves the way for the accurate estimation of the tire slip ratio under different driving conditions, which create new opportunities for autonomous vehicles, intelligent tires, and tire slip ratio estimation.

cs.LG↗

arXiv2020

Tire Force Estimation in Intelligent Tires Using Machine Learning

The concept of intelligent tires has drawn attention of researchers in the areas of autonomous driving, advanced vehicle control, and artificial intelligence. The focus of this paper is on intelligent tires and the application of machine learning techniques to tire force estimation. We present an intelligent tire system with a tri-axial acceleration sensor, which is installed onto the inner liner of the tire, and Neural Network techniques for real-time processing of the sensor data. The accelerometer is capable of measuring the acceleration in x,y, and z directions. When the accelerometer enters the tire contact patch, it starts generating signals until it fully leaves it. Simultaneously, by using MTS Flat-Trac test platform, tire actual forces are measured. Signals generated by the accelerometer and MTS Flat-Trac testing system are used for training three different machine learning techniques with the purpose of online prediction of tire forces. It is shown that the developed intelligent tire in conjunction with machine learning is effective in accurate prediction of tire forces under different driving conditions. The results presented in this work will open a new avenue of research in the area of intelligent tires, vehicle systems, and tire force estimation.

eess.SY↗

arXiv2020

A Statistical Model for Imaging Systems

The behavior of photons is controlled by quantum mechanics, not as deterministic as classical optics shows. To this end, we defined a new statistic $Z$, which is equal to the variance minus the expectation or mean. Then, we established a statistical model for imaging systems and obtained three fundamental imaging formulas. Among them, the first formula is entirely consistent with the classic convolution equation. The second and third ones link the $Z$ quantities of the object and noise elegantly with the $Z$ image and covariance image, revealing new laws. Consequently, besides the flux density, the $Z$ quantity of an object is also imageable, which opens a new realm for imaging systems to explore the physical world.

eess.IV↗

arXiv2019

Global well-posedness of magnetohydrodynamic equations

We study the global well-posedness of magnetohydrodynamic (MHD) equations. The hydrodynamic system consists of the Navier-Stokes equations for the fluid velocity coupled with a reduced from of the Maxwell equations for the magnetic field. The fluid velocity is assumed to satisfy a no-slip boundary condition, while the magnetic field is subject to a time-dependent Dirichlet boundary condition. We first establish the global existence of weak and strong solutions to (1.1)-(1.4). Then we derive the existence of a uniform attractor for (1.1)-(1.4).

math.AP↗

arXiv2019

Regularity of weak solutions to a certain class of parabolic system

We study the regularity of weak solutions to a certain class of second order parabolic system under the only assumption of continuous coefficients. By using the $A-$caloric approximation argument, we claim that the weak solution $u$ to such system is locally Hölder continuous with any exponent $α\in(0,1)$ outside a singular set with zero parabolic measure. In particular, we prove that the regularity point in $Q_T$ is an open set with full measure, and we obtain a general criterion for a weak solution to be regular in the neighborhood of a given point. Finally, we deduce the fractional time and fractional space differentiability of $D u$, and at this stage, we obtain the Hausdorff dimension of singular set of $u$.

math.AP↗

arXiv2019

Well-posedness of compressible magneto-micropolar fluid equations

We are concerned with compressible magneto-micropolar fluid equations (1.1)-(1.2). The global existence and large time behaviour of solutions near a constant state to the magneto-micropolar-Navier-Stokes-Poisson (MMNSP) system is investigated in $\mathbb{R}^3$. By a refined energy method, the global existence is established under the assumption that the $H^3$ norm of the initial data is small, but the higher order derivatives can be large. If the initial data belongs to homogeneous Sobolev spaces or homogeneous Besov spaces, we prove the optimal time decay rates of the solution and its higher order spatial derivatives. Meanwhile, we also obtain the usual $L^p-L^2$ $(1\leq p\leq2)$ type of the decay rates without requiring that the $L^p$ norm of initial data is small.

math.AP↗

arXiv2019

Training Multi-Task Adversarial Network for Extracting Noise-Robust Speaker Embedding

Under noisy environments, to achieve the robust performance of speaker recognition is still a challenging task. Motivated by the promising performance of multi-task training in a variety of image processing tasks, we explore the potential of multi-task adversarial training for learning a noise-robust speaker embedding. In this paper we present a novel framework which consists of three components: an encoder that extracts noise-robust speaker embedding; a classifier that classifies the speakers; a discriminator that discriminates the noise type of the speaker embedding. Besides, we propose a training strategy using the training accuracy as an indicator to stabilize the multi-class adversarial optimization process. We conduct our experiments on the English and Mandarin corpus and the experimental results demonstrate that our proposed multi-task adversarial training method could greatly outperform the other methods without adversarial training in noisy environments. Furthermore, experiments indicate that our method is also able to improve the speaker verification performance the clean condition.

cs.SD↗

arXiv2018

On optimal boundary control of Ericksen-Leslie system in dimension two

In this paper, we consider the boundary value problem of a simplified Ericksen-Leslie system in dimension two with non-slip boundary condition for the velocity field $u$ and time-dependent boundary condition for the director field $d$ of unit length. For such a system, we first establish the existence of a global weak solution that is smooth away from finitely many singular times, then establish the existence of a unique global strong solution that is smooth for $t>0$ under the assumption that the image of boundary data is contained in the hemisphere $\mathbb S^2_+$. Finally, we apply these theorems to the problem of optimal boundary control of the simplified Ericksen-Leslie system and show both the existence and a necessary condition of an optimal boundary control.

math.AP↗

arXiv2018

Composing photomosaic images using clustering based evolutionary programming

Photomosaic images are a type of images consisting of various tiny images. A complete form can be seen clearly by viewing it from a long distance. Small tiny images which replace blocks of the original image can be seen clearly by viewing it from a short distance. In the past, many algorithms have been proposed trying to automatically compose photomosaic images. Most of these algorithms are designed with greedy algorithms to match the blocks with the tiny images. To obtain a better visual sense and satisfy some commercial requirements, a constraint that a tiny image should not be repeatedly used many times is usually added. With the constraint, the photomosaic problem becomes a combinatorial optimization problem. Evolutionary algorithms imitating the process of natural selection are popular and powerful in combinatorial optimization problems. However, little work has been done on applying evolutionary algorithms to photomosaic problem. In this paper, we present an algorithm called clustering based evolutionary programming to deal with the problem. We give prior knowledge to the optimization algorithm which makes the optimization process converges faster. In our experiment, the proposed algorithm is compared with the state of the art algorithms and software. The results indicate that our algorithm performs the best.

cs.CV↗

arXiv2015

Point source detection performance of Hard X-ray Modulation Telescope imaging observation

The Hard X-ray Modulation Telescope (HXMT) will perform an all-sky survey in hard X-ray band as well as deep imaging of a series of small sky regions. We expect various compact objects to be detected in these imaging observations. Point source detection performance of HXMT imaging observation depends not only on the instrument but also on its data analysis since images are reconstructed from HXMT observed data with numeric methods. Denoising technique plays an import part in HXMT imaging data analysis pipeline alongside with demodulation and source detection. In this paper we have implemented several methods for denoising HXMT data and evaluated the point source detection performances in terms of sensitivities and location accuracies. The results show that direct demodulation with 1-fold cross correlation should be the default reconstruction and regularization methods, although both sensitivity and location accuracy could be further imporved by selecting and tuning numerical methods in data analysis of HXMT imaging observations.

astro-ph.IM↗

arXiv2014

Transformed Auto-correlation

A transformed auto-correlation method is presented here, where a received signal is transformed based on a priori reflecting model, and then the transformed signal is cross-correlated to its original one. If the model is correct, after transformation, the reflected signal will be coherent to the transmitted signal, with zero delay. A map of transformed auto-correlation function with zero delay can be generated in a given parametric space. The significant peaks in the map may indicate the possible reflectors nearby the central transmitter. The true values of the parameters of reflectors can be estimated at the same time.

astro-ph.IM↗

arXiv2010

Calibrating the atomic balance by carbon nanoclusters

Carbon atoms are counted at near atomic-level precision using a scanning transmission electron microscope calibrated by carbon nanocluster mass standards. A linear calibration curve governs the working zone from a few carbon atoms up to 34,000 atoms. This linearity enables adequate averaging of the scattering cross sections, imparting the experiment with near atomic-level precision despite the use of a coarse mass reference. An example of this approach is provided for thin layers of stacked graphene sheets. Suspended sheets with a thickness below 100 nm are visualized, providing quantitative measurement in a regime inaccessible to optical and scanning probe methods.

cond-mat.mes-hall↗

arXiv2010

Two-step splitting the expandable graphite for few-layer graphene

Few-layer graphene sheets are prepared by splitting the expanded graphites using a high-power sonication. Atomic-level quantitative scanning transmission electron microscopy (Q-STEM) is employed to carry out the efficient layer statisticsm, enabling global optimization of the experimental conditions. A two-step splitting mechanism is thus revealed, in which the mean layer number was firstly reduced to less than 20 by heating to 1100°C and then tuned to the few-layer region by a 5-minute 104W/litre sonication. Raman spectroscopic analysis confirms the above mechanism and demonstrates that the sheets are largely free of defects and oxides.

cond-mat.mes-hall↗

arXiv2010

Films with the discrete nano-DLC-particles as the field emission cascade

The films with the discrete diamond-like-carbon nanoparticles were prepared by the deposition of the carbon nanoparticle beam. Their morphologies were imaged by Scanning Electron Microscopy (SEM) and Atomic Force Microscopy (AFM). The nanoparticles were found distributed on the silicon (100) substrate discretely. The semisphere shapes of the nanoparticles were demonstrated by the AFM line profile. EELS was measured and the sp3 ratio as high as 86% was found. The field-induced electron emission of the as-prepared cascade (nanoDLC/ Si) was tested and the current density of 1mA/cm2 was achieved at 10.2V/μm.

cond-mat.mes-hall↗

arXiv2006

Doppler Boosting and Deboosting Effects in the Forward Relativistic Jets of AGNs and GRBs

It is widely accepted that the Doppler deboosting effects exist in counter relativistic jets. However, people often neglect another important fact that both Doppler boosting and deboosting effects could happen in forward relativistic jets. Such effects might be used to explain some strange phenomena, such as the invisible gaps between the inner and outer jets of AGNs, and the rapid initial decays and re-brightening bumps in the light curves of GRBs.

astro-ph↗