arXiv subjects

Jian Fang

Publications and source records attributed to Jian Fang.

At least 37 records · Page 2Linked to original sources

arXiv2021

Development of water extraction system for liquid scintillatorpurification of JUNO

The Jiangmen Underground Neutrino Observatory (JUNO) uses 20k tons of liquid scintillator (LS)to detect neutrinos. The content of radioactive substances in the liquid scintillator will affect theexperimental results. JUNO will use counter current water extraction to reduce the radioactive metalions inside the LS. In this article, The factors that affect the final water extraction like the partitioncoefficient and the factors that affect it, the optimal mass transfer droplet size, the flow rate ratio,theoretical stage and different working mode (LS as continuous phase or dispersed phase) have beenstudied. We built counter current extraction prototype in the laboratory and a pilot plant in Daya Bay.We not only study the factors above, but also get a lot of engineering experience that worth sharing.

physics.ins-det↗

arXiv2021

Hyperspectral and LiDAR data classification based on linear self-attention

An efficient linear self-attention fusion model is proposed in this paper for the task of hyperspectral image (HSI) and LiDAR data joint classification. The proposed method is comprised of a feature extraction module, an attention module, and a fusion module. The attention module is a plug-and-play linear self-attention module that can be extensively used in any model. The proposed model has achieved the overall accuracy of 95.40\% on the Houston dataset. The experimental results demonstrate the superiority of the proposed method over other state-of-the-art models.

cs.CV↗

arXiv2020

The replacement system of the JUNO liquid scintillator pilot experiment at Daya Bay

The Jiangmen Underground Neutrino Observatory (JUNO), a multi-purpose neutrino experiment, will use 20 kt liquid scintillator (LS). To achieve the physics goal of determining the neutrino mass ordering, 3$\%$ energy resolution at 1 MeV is required. This puts strict requirements on the LS light yield and the transparency. Four LS purification steps have been designed and mid-scale plants have been built at Daya Bay. To examine the performance of the purified LS and find the optimized LS composition, the purified LS was injected to the antineutrino detector 1 in the experimental hall 1 (EH1-AD1) of the Daya Bay neutrino experiment. To pump out the original gadolinium loaded LS and fill the new LS, a LS replacement system has been built in EH1 in 2017. By replacing the Gd-LS with purified water, then replacing the water with purified LS, the replacement system successfully achieved the designed goal. Subsequently, the fluorescence and the wavelength shifter were added to higher concentrations via the replacement system. The data taken at various LS compositions helped JUNO determine the final LS cocktail. Details of the design, the construction, and the operation of the replacement system are reported in this paper.

physics.ins-det↗

arXiv2020

An Iterative Machine-Learning Framework for RANS Turbulence Modeling

Machine-learning (ML) techniques provide a new and encouraging perspective for constructing turbulence models for Reynolds-averaged Navier--Stokes (RANS) simulations. In this study, an iterative ML-RANS computational framework is proposed that combines an ML algorithm with transport equations of a conventional turbulence model. This framework maintains a consistent procedure for obtaining the input features of an ML model in both the training and predicting stages, ensuring a built-in reproducibility. The effective form of the closure term is discussed to determine suitable target variables for the ML algorithm, and the multi-valued problem of existing constitutive theory is studied to establish a proper regression system for ML algorithms. The developed ML model is trained under a cross-case strategy with data from turbulent channel flows at three Reynolds numbers and \textit{a posteriori} simulations of channel flows show that the framework is able to predict both the mean flow field and turbulent variables accurately. Interpolation tests for the channel flow show the proposed framework can reliably predict flow features that lie between the minimum and maximum Reynolds numbers associated with the training data. A further test related to the flow over periodic hills also demonstrates a better result than a traditional turbulence model, indicating a promising predictive capability of the developed ML model for separated flow even though the model is only trained with planar channel flow data.

physics.flu-dyn↗

arXiv2020

Hierarchical Text Interaction for Rating Prediction

Traditional recommender systems encounter several challenges such as data sparsity and unexplained recommendation. To address these challenges, many works propose to exploit semantic information from review data. However, these methods have two major limitations in terms of the way to model textual features and capture textual interaction. For textual modeling, they simply concatenate all the reviews of a user/item into a single review. However, feature extraction at word/phrase level can violate the meaning of the original reviews. As for textual interaction, they defer the interactions to the prediction layer, making them fail to capture complex correlations between users and items. To address those limitations, we propose a novel Hierarchical Text Interaction model(HTI) for rating prediction. In HTI, we propose to model low-level word semantics and high-level review representations hierarchically. The hierarchy allows us to exploit textual features at different granularities. To further capture complex user-item interactions, we propose to exploit semantic correlations between each user-item pair at different hierarchies. At word level, we propose an attention mechanism specialized to each user-item pair, and capture the important words for representing each review. At review level, we mutually propagate textual features between the user and item, and capture the informative reviews. The aggregated review representations are integrated into a collaborative filtering framework for rating prediction. Experiments on five real-world datasets demonstrate that HTI outperforms state-of-the-art models by a large margin. Further case studies provide a deep insight into HTI's ability to capture semantic correlations at different levels of granularities for rating prediction.

cs.IR↗

arXiv2020

Propagation dynamics of a reaction-diffusion equation in a time-periodic shifting environment

This paper concerns the nonautonomous reaction-diffusion equation \[ u_t=u_{xx}+ug(t,x-ct,u), \quad t>0,x\in\mathbb{R}, \] where $c\in\mathbb{R}$ is the shifting speed, and the time periodic nonlinearity $ug(t,ξ,u)$ is asymptotically of KPP type as $ξ\to-\infty$ and is negative as $ξ\to+\infty$. Under a subhomogeneity condition, we show that there is $c^*>0$ such that a unique forced time periodic wave exists if and only $|c|< c^*$ and it attracts other solutions in a certain sense according to the tail behavior of initial values. In the case where $|c|\ge c^*$, the propagation dynamics resembles that of the limiting system as $ξ\to\pm \infty$, depending on the shifting direction.

math.AP↗

arXiv2020

On a population model in discrete periodic habitat. I. spreading speed and optimal dispersal strategy

Starting from an age-structured diffusive population growth law for single species in a discrete and periodic habitat, we formulate a stage structured population model with spatially periodic dispersal, mortality and recruitment. With a KPP type setting, after establishing the fundamental solution of a discretized heat equation with spatially periodic dispersal, we apply some recently developed dynamical system theories to obtain the existence of the spreading speed and its coincidence with the minimal speed of pulsating waves, as well as the variational characterization of the speed, by which we further analyze how the habitat periodicity influences the speed. In particular, there is a unique optimal dispersal strategy to maximize the speed.

math.AP↗

arXiv2019

An Initial Attempt of Converged Machine-Learning Assisted Turbulence Modeling in RANS Simulations with Eddy-Viscosity Hypothesis

This work presents a converged framework of Machine-Learning Assisted Turbulence Modeling (MLATM). Our objective is to develop a turbulence model directly learning from high fidelity data (DNS/LES) with eddy-viscosity hypothesis induced. First, the target machine-learning quantity is discussed in order to avoid the ill-conditioning problem of RANS equations. Then, the novel framework to build the turbulence model using the prior estimation of traditional models is demonstrated. A close-loop computational chain is designed to ensure the convergence of result. Besides, reasonable non-dimensional variables are selected to predict the target learning variables and make the solver re-converge to DNS mean flow field. The MLATM is tested in incompressible turbulent channel flows, and it proved that the result converges well to DNS training data for both mean velocity and turbulence viscosity profiles.

physics.flu-dyn↗

arXiv2018

Propagation dynamics of Fisher-KPP equation with time delay and free boundaries

Incorporating free boundary into time-delayed reaction-diffusion equations yields a compatible condition that guarantees the well-posedness of the initial value problem. With the KPP type nonlinearity we then establish a vanishing-spreading dichotomy result. Further, when the spreading happens, we show that the spreading speed and spreading profile are nonlinearly determined by a delay-induced nonlocal semi-wave problem. It turns out that time delay slows down the spreading speed.

math.AP↗

arXiv2018

Learning through deterministic assignment of hidden parameters

Supervised learning frequently boils down to determining hidden and bright parameters in a parameterized hypothesis space based on finite input-output samples. The hidden parameters determine the attributions of hidden predictors or the nonlinear mechanism of an estimator, while the bright parameters characterize how hidden predictors are linearly combined or the linear mechanism. In traditional learning paradigm, hidden and bright parameters are not distinguished and trained simultaneously in one learning process. Such an one-stage learning (OSL) brings a benefit of theoretical analysis but suffers from the high computational burden. To overcome this difficulty, a two-stage learning (TSL) scheme, featured by learning through deterministic assignment of hidden parameters (LtDaHP) was proposed, which suggests to deterministically generate the hidden parameters by using minimal Riesz energy points on a sphere and equally spaced points in an interval. We theoretically show that with such deterministic assignment of hidden parameters, LtDaHP with a neural network realization almost shares the same generalization performance with that of OSL. We also present a series of simulations and application examples to support the outperformance of LtDaHP

cs.LG↗

arXiv2017

seasonal influence on age-structured invasive species with yearly generation

How do seasonal successions influence the propagation dynamics of an age-structured invasive species? We investigate this problem by considering the scenario that the offsprings are reproduced in spring and then reach maturation in fall within the same year. For this purpose, a reaction-diffusion system is proposed, with yearly periodic time delay and spatially nonlocal response caused by the periodic developmental process. By appealing to the recently developed dynamical system theories, we obtain the invasion speed $c^*$ and its coincidence with the minimal speed of time periodic traveling waves. The characterizations of $c^*$ suggest that (i) time delay decreases the speed and its periodicity may further do so; (ii) the optimal time to slow down the invasion is the season without juveniles; (iii) the speed increases to infinity with the same order as the square root of the diffusion rate.

math.AP↗

arXiv2017

Notes on nonlocal dispersal equations in a periodic habitat

In this paper, we prove that the solution maps of a large class of nonlocal dispersal equations are $α$-contractions, where $α$ is the Kuratowski measure of noncompactness. Then we give some remarks on the spreading speeds and traveling waves for such evolution equations in a periodic habitat.

math.AP↗

arXiv2017

Fast Depth Imaging Denoising with the Temporal Correlation of Photons

This paper proposes a novel method to filter out the false alarm of LiDAR system by using the temporal correlation of target reflected photons. Because of the inevitable noise, which is due to background light and dark counts of the detector, the depth imaging of LiDAR system exists a large estimation error. Our method combines the Poisson statistical model with the different distribution feature of signal and noise in the time axis. Due to selecting a proper threshold, our method can effectively filter out the false alarm of system and use the ToFs of detected signal photons to rebuild the depth image of the scene. The experimental results reveal that by our method it can fast distinguish the distance between two close objects, which is confused due to the high background noise, and acquire the accurate depth image of the scene. Our method need not increase the complexity of the system and is useful in power-limited depth imaging.

physics.ins-det↗

arXiv2016

The laboratory measurement of radioactivity purification for Pb212 in liquid scintillator

The liquid scintillator (LS) has been widely utilized in the past, running and future neutrino experiments, and requirement to the LS radio-purity is higher and higher. The water extraction is a powerful method to remove soluble radioactive nuclei, and a mini-extraction station has been constructed. To evaluate the extraction efficiency and optimize the operation parameters, a setup to load radioactivity to LS and a laboratory scale setup to measure radioactivity which use Bi^{212}-Po^{212}-Pb^{208} cascade decay are developed. Experiences from laboratory study will be useful to large scale water extraction plants design and the optimization of working in future.

physics.ins-det↗

arXiv2015

Neutrino Physics with JUNO

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purpose underground liquid scintillator detector, was proposed with the determination of the neutrino mass hierarchy as a primary physics goal. It is also capable of observing neutrinos from terrestrial and extra-terrestrial sources, including supernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos, atmospheric neutrinos, solar neutrinos, as well as exotic searches such as nucleon decays, dark matter, sterile neutrinos, etc. We present the physics motivations and the anticipated performance of the JUNO detector for various proposed measurements. By detecting reactor antineutrinos from two power plants at 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4 sigma significance with six years of running. The measurement of antineutrino spectrum will also lead to the precise determination of three out of the six oscillation parameters to an accuracy of better than 1\%. Neutrino burst from a typical core-collapse supernova at 10 kpc would lead to ~5000 inverse-beta-decay events and ~2000 all-flavor neutrino-proton elastic scattering events in JUNO. Detection of DSNB would provide valuable information on the cosmic star-formation rate and the average core-collapsed neutrino energy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400 events per year, significantly improving the statistics of existing geoneutrino samples. The JUNO detector is sensitive to several exotic searches, e.g. proton decay via the $p\to K^++\barν$ decay channel. The JUNO detector will provide a unique facility to address many outstanding crucial questions in particle and astrophysics. It holds the great potential for further advancing our quest to understanding the fundamental properties of neutrinos, one of the building blocks of our Universe.

physics.ins-det↗

arXiv2015

Preliminary study of light yield dependence on LAB liquid scintillator composition

Liquid scintillator (LS) will be adopted as the detector material in JUNO (Jiangmen Underground Neutrino Observatory). The energy resolution requirement of JUNO is 3%, which has never previously been reached. To achieve this energy resolution, the light yield of liquid scintillator is an important factor. PPO (the fluor) and bis-MSB (the wavelength shifter) are the two main materials dissolved in LAB. To study the influence of these two materials on the transmission of scintillation photons in LS, 25 and 12 cm-long quartz vessels were used in a light yield experiment. LS samples with different concentration of PPO and bis-MSB were tested. At these lengths, the light yield growth is not obvious when the concentration of PPO is higher than 4 g/L. The influence from bis-MSB becomes insignificant when its concentration is higher than 8 mg/L. This result could provide some useful suggestions for the JUNO LS.

physics.ins-det↗

arXiv2015

Traveling waves and spreading speeds for time-space periodic monotone systems

The theory of traveling waves and spreading speeds is developed for time-space periodic monotone semiflows with monostable structure. By using traveling waves of the associated Poincaré maps in a strong sense, we establish the existence of time-space periodic traveling waves and spreading speeds. We then apply these abstract results to a two species competition reaction-advection-diffusion model. It turns out that the minimal wave speed exists and coincides with the single spreading speed for such a system no matter whether the spreading speed is linearly determinate. We also obtain a set of sufficient conditions for the spreading speed to be linearly determinate.

math.AP↗

arXiv2014

Simulation of background reduction and Compton depression in low-background HPGe spectrometer at a surface laboratory

High-purity germanium detectors are well suited to analysis the radioactivity of samples. In order to reduce the environmental background, low-activity lead and oxygen free copper are installed outside of the probe to shield gammas, outmost is a plastic scintillator to veto the cosmic rays, and an anti-Compton detector can improve the Peak-to-Compton ratio. Using the GEANT4 tools and taking into account a detailed description of the detector, we optimize the sizes of the detectors to reach the design indexes. A group of experimental data from a HPGe spectrometer in using were used to compare with the simulation. As to new HPGe Detector simulation, considering the different thickness of BGO crystals and anti-coincidence efficiency, the simulation results show that the optimal thickness is 5.5cm, and the Peak-to-Compton ratio of 40K is raised to 1000 when the anti-coincidence efficiency is 0.85. As the background simulation, 15 cm oxygen-free copper plus 10 cm lead can reduce the environmental gamma rays to 0.0024 cps/100 cm3 Ge (50keV~2.8MeV), which is about 10-5 of environmental background.

physics.ins-det↗