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Bin Zhou

Publications and source records attributed to Bin Zhou.

At least 127 records · Page 7Linked to original sources
arXiv2022

A novel time delay estimation algorithm of acoustic pyrometry for furnace

Acoustic pyrometry is a non-contact measurement technology for monitoring furnace combustion reaction, diagnosing energy loss due to incomplete combustion and ensuring safe production. The accuracy of time of flight (TOF) estimation of an acoustic pyrometry directly affects the authenticity of furnace temperature measurement. In this paper presented is a novel TOF (i.e. time delay) estimation algorithm based on digital lock-in filtering (DLF) algorithm. In this research, the time-frequency relationship between the first harmonic of the acoustic signal and the moment of characteristic frequency applied is established through the digital lock-in and low-pass filtering techniques. The accurate estimation of TOF is obtained by extracting and comparing the temporal relationship of the characteristic frequency occurrence between received and source acoustic signals. The computational error analysis indicates that the accuracy of the proposed algorithm is better than that of the classical generalized cross-correlation (GCC) algorithm, and the computational effort is significantly reduced to half of that the GCC can offer. It can be confirmed that with this method, the temperature measurement in furnaces can be improved in terms of computational effort and accuracy, which are vital parameters in furnace combustion control. It provides a new idea of time delay estimation with the utilization of acoustic pyrometry for furnace.

physics.ins-det↗
arXiv2022

Autofocus for Event Cameras

Focus control (FC) is crucial for cameras to capture sharp images in challenging real-world scenarios. The autofocus (AF) facilitates the FC by automatically adjusting the focus settings. However, due to the lack of effective AF methods for the recently introduced event cameras, their FC still relies on naive AF like manual focus adjustments, leading to poor adaptation in challenging real-world conditions. In particular, the inherent differences between event and frame data in terms of sensing modality, noise, temporal resolutions, etc., bring many challenges in designing an effective AF method for event cameras. To address these challenges, we develop a novel event-based autofocus framework consisting of an event-specific focus measure called event rate (ER) and a robust search strategy called event-based golden search (EGS). To verify the performance of our method, we have collected an event-based autofocus dataset (EAD) containing well-synchronized frames, events, and focal positions in a wide variety of challenging scenes with severe lighting and motion conditions. The experiments on this dataset and additional real-world scenarios demonstrated the superiority of our method over state-of-the-art approaches in terms of efficiency and accuracy.

cs.CV↗
arXiv2022

K-stability and polystable degenerations of polarized spherical varieties

In this paper, we study the K-stability of polarized spherical varieties. After reduction, it can be treated as a variational problem of the reduced functional of the Futaki invariant on the associated moment polytope. With the convexity constraint of the problem, the minimizers are shown to satisfy the homogeneous Monge-Ampère equation (HMA). When the spherical variety has rank two, a simpler characterization can be established through properties of the HMA. As an application, we determine the strict semistability and polystable degenerations for Fano spherical varieties of rank two.

math.DG↗
arXiv2022

Learning Fine-Grained Segmentation of 3D Shapes without Part Labels

Learning-based 3D shape segmentation is usually formulated as a semantic labeling problem, assuming that all parts of training shapes are annotated with a given set of tags. This assumption, however, is impractical for learning fine-grained segmentation. Although most off-the-shelf CAD models are, by construction, composed of fine-grained parts, they usually miss semantic tags and labeling those fine-grained parts is extremely tedious. We approach the problem with deep clustering, where the key idea is to learn part priors from a shape dataset with fine-grained segmentation but no part labels. Given point sampled 3D shapes, we model the clustering priors of points with a similarity matrix and achieve part segmentation through minimizing a novel low rank loss. To handle highly densely sampled point sets, we adopt a divide-and-conquer strategy. We partition the large point set into a number of blocks. Each block is segmented using a deep-clustering-based part prior network trained in a category-agnostic manner. We then train a graph convolution network to merge the segments of all blocks to form the final segmentation result. Our method is evaluated with a challenging benchmark of fine-grained segmentation, showing state-of-the-art performance.

cs.CV↗
arXiv2022

Learning Semantic Abstraction of Shape via 3D Region of Interest

In this paper, we focus on the two tasks of 3D shape abstraction and semantic analysis. This is in contrast to current methods, which focus solely on either 3D shape abstraction or semantic analysis. In addition, previous methods have had difficulty producing instance-level semantic results, which has limited their application. We present a novel method for the joint estimation of a 3D shape abstraction and semantic analysis. Our approach first generates a number of 3D semantic candidate regions for a 3D shape; we then employ these candidates to directly predict the semantic categories and refine the parameters of the candidate regions simultaneously using a deep convolutional neural network. Finally, we design an algorithm to fuse the predicted results and obtain the final semantic abstraction, which is shown to be an improvement over a standard non maximum suppression. Experimental results demonstrate that our approach can produce state-of-the-art results. Moreover, we also find that our results can be easily applied to instance-level semantic part segmentation and shape matching.

cs.CV↗
arXiv2022

Mixed QCD-EW corrections for Higgs leptonic decay via $HW^+W^-$ vertex

We consider the two-loop corrections to the $HW^+W^-$ vertex at order $αα_s$. We construct a canonical basis for the two-loop integrals using the Baikov representation and the intersection theory. By solving the $ε$-form differential equations, we obtain fully analytic expressions for the master integrals in terms of multiple polylogarithms, which allow fast and accurate numeric evaluation for arbitrary configurations of external momenta. We apply our analytic results to the decay process $H \to ν_e e W$, and study both the integrated and differential decay rates. Our results can also be applied to the Higgs production process via $W$ boson fusion.

hep-ph↗
arXiv2021

Higher-order topological Anderson insulators in quasicrystals

The disorder effects on higher-order topological phases in periodic systems have attracted much attention. However, in aperiodic systems, such as quasicrystalline systems, the interplay between disorder and higher-order topology is still unclear. In this paper, we investigate the effects of disorder on two types of second-order topological insulators, including a quasicrystalline quadrupole insulator and a modified quantum spin Hall insulator, in a two-dimensional Amman-Beenker tiling quasicrystalline lattice. We demonstrate that the higher-order topological insulators are robust against weak disorder in both models. More striking, the disorder-induced higher-order topological insulators called higher-order topological Anderson insulators are found at a certain region of disorder strength in both models. Our paper extends the study of the interplay between disorder and higher-order topology to quasicrystalline systems.

cond-mat.mes-hall↗
arXiv2021

FastGPL: a C++ library for fast evaluation of generalized polylogarithms

We present FastGPL, a C++ library for the fast evaluation of generalized polylogarithms which appear in many multi-loop Feynman integrals. We implement the iterative algorithm proposed by Vollinga and Weinzierl in a two-step approach, i.e., we generate concise expressions using an external program and hard-code them into the numeric library. This allows efficient and accurate numeric evaluations of generalized polylogarithms suitable for Monte Carlo integration and event generation. Floating-point arithmetics are carefully taken care of to avoid loss of accuracy. As an application and demonstration, we calculate the two-loop corrections for Higgs boson production in the vector boson fusion channel at electron-positron colliders. FastGPL is expected to be useful for event generators at the next-to-next-to-leading order accuracy.

hep-ph↗
arXiv2021

Super-accuracy calculation for the width of a Voigt profile

A simple approximation scheme to describe the width of the Voigt profile as a function of the relative contributions of Gaussian and Lorentzian broadening is presented. The proposed approximation scheme is highly accurate and provides accuracy better than 10^-17 for arbitrary aLaG ratios. In particular, the accuracy reaches an astonishing 10^-34 (quadruple precision) in the domain aLaG less than 0.2371 and aLaG greater than 33.8786.

physics.atom-ph↗
arXiv2021

Nature of $X(6900)$ and its production mechanism at LHCb

We show that the Pomeron exchanges play a unique role in vector charmonium scatterings. Such a mechanism can provide a natural explanation for the nontrivial structures in the di-$J/ψ$ spectrum observed by the LHCb Collaboration. The narrow structure $X(6900)$, as a dynamically generated resonance pole, can arise from the Pomeron exchanges and coupled-channel effects between the $J/ψ$-$J/ψ$, $J/ψ$-$ψ(2S)$ scatterings. A pole structure near the di-$J/ψ$ threshold is also found. Meanwhile, we predict that $X(6900)$ can produce significant threshold enhancement in the $J/ψ$-$ψ(2S)$ energy spectrum which can be searched for at LHCb.

hep-ph↗
arXiv2021

An accurate algorithm for calibration-free wavelength modulation spectroscopy based on even-order harmonics

This paper proposes an accurate algorithm to implement calibration-free wavelength modulation spectroscopy based on even-order harmonics. The proposed algorithm, analytically deduced from a much more accurate Voigt function model, enabling not only speedy measurement down to milliseconds, but also a general suitability for various degrees of line-shape broadening. The proposed method is validated by condition-controlled experiment, indicating calculation of gas temperature with the relative error less than 2.4% in the experiments.

physics.ins-det↗
arXiv2021

Mobile Optical Communications Using Second Harmonic of Intra-Cavity Laser

Optical wireless communication (OWC) meets the demands of the future six-generation mobile network (6G) as it operates at several hundreds of Terahertz and has the potential to enable data rate in the order of Tbps. However, most beam steering OWC technologies require high-accuracy positioning and high-speed control. Resonant beam communication (RBCom), as one kind of non-positioning OWC technologies, has been proposed for high-rate mobile communications. The mobility of RBCom relies on its self-alignment characteristic where no positioning is required. In a previous study, an external-cavity second-harmonic-generation (SHG) RBCom system has been proposed for eliminating the echo interference inside the resonator. However, its energy conversion efficiency and complexity are of concern. In this paper, we propose an intra-cavity SHG RBCom system to simplify the system design and improve the energy conversion efficiency. We elaborate the system structure and establish an analytical model. Numerical results show that the energy consumption of the proposed intra-cavity design is reduced to reach the same level of channel capacity at the receiver compared with the external-cavity one.

eess.SP↗
arXiv2021

Disorder-induced chiral and helical Majorana edge modes in a two-dimensional Ammann-Beenker quasicrystal

Recent research on disorder effects in topological phases in quasicrystalline systems has received much attention. In this work, by numerically computing the (spin) Bott index and the thermal conductance, we reveal the effects of disorder on a class D chiral topological superconductor and a class DIII time-reversal-invariant topological superconductor in a two-dimensional Ammann-Beenker tiling quasicrystalline lattice. We demonstrate that both the topologically protected chiral and helical Majorana edge modes are robust against weak disorder in the quasicrystalline lattice. More fascinating is the discovery of disorder-induced topologically nontrivial phases exhibiting chiral and helical Majorana edge modes in class D and DIII topological superconductor systems, respectively. Our findings open the door for the research on disorder-induced Majorana edge modes in quasicrystalline systems.

cond-mat.mes-hall↗
arXiv2021

Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing

Recent years pretrained language models (PLMs) hit a success on several downstream tasks, showing their power on modeling language. To better understand and leverage what PLMs have learned, several techniques have emerged to explore syntactic structures entailed by PLMs. However, few efforts have been made to explore grounding capabilities of PLMs, which are also essential. In this paper, we highlight the ability of PLMs to discover which token should be grounded to which concept, if combined with our proposed erasing-then-awakening approach. Empirical studies on four datasets demonstrate that our approach can awaken latent grounding which is understandable to human experts, even if it is not exposed to such labels during training. More importantly, our approach shows great potential to benefit downstream semantic parsing models. Taking text-to-SQL as a case study, we successfully couple our approach with two off-the-shelf parsers, obtaining an absolute improvement of up to 9.8%.

cs.CL↗
arXiv2021

Indoor Scene Generation from a Collection of Semantic-Segmented Depth Images

We present a method for creating 3D indoor scenes with a generative model learned from a collection of semantic-segmented depth images captured from different unknown scenes. Given a room with a specified size, our method automatically generates 3D objects in a room from a randomly sampled latent code. Different from existing methods that represent an indoor scene with the type, location, and other properties of objects in the room and learn the scene layout from a collection of complete 3D indoor scenes, our method models each indoor scene as a 3D semantic scene volume and learns a volumetric generative adversarial network (GAN) from a collection of 2.5D partial observations of 3D scenes. To this end, we apply a differentiable projection layer to project the generated 3D semantic scene volumes into semantic-segmented depth images and design a new multiple-view discriminator for learning the complete 3D scene volume from 2.5D semantic-segmented depth images. Compared to existing methods, our method not only efficiently reduces the workload of modeling and acquiring 3D scenes for training, but also produces better object shapes and their detailed layouts in the scene. We evaluate our method with different indoor scene datasets and demonstrate the advantages of our method. We also extend our method for generating 3D indoor scenes from semantic-segmented depth images inferred from RGB images of real scenes.

cs.CV↗
arXiv2021

A Cloud-Edge-Terminal Collaborative System for Temperature Measurement in COVID-19 Prevention

To prevent the spread of coronavirus disease 2019 (COVID-19), preliminary temperature measurement and mask detection in public areas are conducted. However, the existing temperature measurement methods face the problems of safety and deployment. In this paper, to realize safe and accurate temperature measurement even when a person's face is partially obscured, we propose a cloud-edge-terminal collaborative system with a lightweight infrared temperature measurement model. A binocular camera with an RGB lens and a thermal lens is utilized to simultaneously capture image pairs. Then, a mobile detection model based on a multi-task cascaded convolutional network (MTCNN) is proposed to realize face alignment and mask detection on the RGB images. For accurate temperature measurement, we transform the facial landmarks on the RGB images to the thermal images by an affine transformation and select a more accurate temperature measurement area on the forehead. The collected information is uploaded to the cloud in real time for COVID-19 prevention. Experiments show that the detection model is only 6.1M and the average detection speed is 257ms. At a distance of 1m, the error of indoor temperature measurement is about 3%. That is, the proposed system can realize real-time temperature measurement in public areas.

cs.CV↗
arXiv2021

Calibration-free wavelength modulation spectroscopy based on even-order harmonics

This paper proposes a novel and rapid calibration-free wavelength modulation spectroscopy algorithm based on even-order harmonics. The proposed algorithm, analytically deduced from Voigt line-shape function, only involves simple algebraic operations to describe the actual gas absorption spectra, thus eliminating the time-consuming simulations and line-shape fitting procedures adopted in traditional algorithms. Instead of acquiring the entirely scanned absorption line-shape, the proposed technique only requires extraction of the peak values of the harmonics. This characteristic significantly benefits gas diagnosis at elevated pressure and/or temperature, in which the entirely scanned absorption is very difficult to be obtained due to the broadened line-shapes. The proposed algorithm is validated by both numerical simulation and condition-controlled experiment, indicating millisecond-level calculation of gas parameters with the relative error less than 4% in the experiments.

physics.optics↗