You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors

In this paper, we propose a novel local descriptor-based framework, called You Only Hypothesize Once (YOHO), for the registration of two unaligned point clouds. In contrast to most existing local descriptors which rely on a fragile local reference frame to gain rotation invariance, the proposed descriptor achieves the rotation invariance by recent technologies of group equivariant feature learning, which brings more robustness to point density and noise. Meanwhile, the descriptor in YOHO also has a rotation equivariant part, which enables us to estimate the registration from just one correspondence hypothesis. Such property reduces the searching space for feasible transformations, thus greatly improves both the accuracy and the efficiency of YOHO. Extensive experiments show that YOHO achieves superior performances with much fewer needed RANSAC iterations on four widely-used datasets, the 3DMatch/3DLoMatch datasets, the ETH dataset and the WHU-TLS dataset. More details are shown in our project page: https://hpwang-whu.github.io/YOHO/.Comment: Accepted by ACM Multimedia(MM) 2022, Project page: https://hpwang-whu.github.io/YOHO

Threshold for the Outbreak of Cascading Failures in Degree-degree Uncorrelated Networks

In complex networks, the failure of one or very few nodes may cause cascading failures. When this dynamical process stops in steady state, the size of the giant component formed by remaining un-failed nodes can be used to measure the severity of cascading failures, which is critically important for estimating the robustness of networks. In this paper, we provide a cascade of overload failure model with local load sharing mechanism, and then explore the threshold of node capacity when the large-scale cascading failures happen and un-failed nodes in steady state cannot connect to each other to form a large connected sub-network. We get the theoretical derivation of this threshold in degree-degree uncorrelated networks, and validate the effectiveness of this method in simulation. This threshold provide us a guidance to improve the network robustness under the premise of limited capacity resource when creating a network and assigning load. Therefore, this threshold is useful and important to analyze the robustness of networks.Comment: 11 pages, 4 figure

Energetic macroscopic representation control method for a hybrid-source energy system including wind, hydrogen, and fuel cell

This paper proposes a new control method for a hybrid energy system. A wind turbine, a hydrogen energy storage system, and a proton exchange membrane fuel cell are utilized in the system to balance the load and supply. The system is modeled in MATLAB/Simulink and is controlled by an improved energetic macroscopic representation (EMR) method in order to match the load profile with wind power. The simulation and test results have proved that (1) the proposed system is effective to meet the varying load demand with fluctuating wind power inputs, (2) the hybrid energy storage system can improve the stability and fault-ride-through performance of the system, and (3) the dynamic response of the proposed system is satisfactory to operate with wind turbines, energy storage, and fuel cells under EMR control

Winding Clearness for Differentiable Point Cloud Optimization

We propose to explore the properties of raw point clouds through the \emph{winding clearness}, a concept we first introduce for assessing the clarity of the interior/exterior relationships represented by the winding number field of the point cloud. In geometric modeling, the winding number is a powerful tool for distinguishing the interior and exterior of a given surface $\partial \Omega$, and it has been previously used for point normal orientation and surface reconstruction. In this work, we introduce a novel approach to assess and optimize the quality of point clouds based on the winding clearness. We observe that point clouds with reduced noise tend to exhibit improved winding clearness. Accordingly, we propose an objective function that quantifies the error in winding clearness, solely utilizing the positions of the point clouds. Moreover, we demonstrate that the winding clearness error is differentiable and can serve as a loss function in optimization-based and learning-based point cloud processing. In the optimization-based method, the loss function is directly back-propagated to update the point positions, resulting in an overall improvement of the point cloud. In the learning-based method, we incorporate the winding clearness as a geometric constraint in the diffusion-based 3D generative model. Experimental results demonstrate the effectiveness of optimizing the winding clearness in enhancing the quality of the point clouds. Our method exhibits superior performance in handling noisy point clouds with thin structures, highlighting the benefits of the global perspective enabled by the winding number

Neural-IMLS: Self-supervised Implicit Moving Least-Squares Network for Surface Reconstruction

Surface reconstruction is very challenging when the input point clouds, particularly real scans, are noisy and lack normals. Observing that the Multilayer Perceptron (MLP) and the implicit moving least-square function (IMLS) provide a dual representation of the underlying surface, we introduce Neural-IMLS, a novel approach that directly learns the noise-resistant signed distance function (SDF) from unoriented raw point clouds in a self-supervised fashion. We use the IMLS to regularize the distance values reported by the MLP while using the MLP to regularize the normals of the data points for running the IMLS. We also prove that at the convergence, our neural network, benefiting from the mutual learning mechanism between the MLP and the IMLS, produces a faithful SDF whose zero-level set approximates the underlying surface. We conducted extensive experiments on various benchmarks, including synthetic scans and real scans. The experimental results show that {\em Neural-IMLS} can reconstruct faithful shapes on various benchmarks with noise and missing parts. The source code can be found at~\url{https://github.com/bearprin/Neural-IMLS}

New Adaptive Control Strategy for a Wind Turbine Permanent Magnet Synchronous Generator (PMSG)

Wind energy conversion systems have become a key technology to harvest wind energy worldwide. In permanent magnet synchronous generator-based wind turbine systems, the rotor position is needed for variable speed control and it uses an encoder or a speed sensor. However, these sensors lead to some obstacles, such as additional weight and cost, increased noise, complexity and reliability issues. For these reasons, the development of new sensorless control methods has become critically important for wind turbine generators. This paper aims to develop a new sensorless and adaptive control method for a surface-mounted permanent magnet synchronous generator. The proposed method includes a new model reference adaptive system, which is used to estimate the rotor position and speed as an observer. Adaptive control is implemented in the pulse-width modulated current source converter. In the conventional model reference adaptive system, the proportional-integral controller is used in the adaptation mechanism. Moreover, the proportional-integral controller is generally tuned by the trial and error method, which is tedious and inaccurate. In contrast, the proposed method is based on model predictive control which eliminates the use of speed and position sensors and also improves the performance of model reference adaptive control systems. In this paper, the proposed predictive controller is modelled in MATLAB/SIMULINK and validated experimentally on a 6-kW wind turbine generator. Test results prove the effectiveness of the control strategy in terms of energy efficiency and dynamical adaptation to the wind turbine operational conditions. The experimental results also show that the control method has good dynamic response to parameter variations and external disturbances. Therefore, the developed technique will help increase the uptake of permanent magnet synchronous generators and model predictive control methods in the wind power industry

An Accurate Virtual Signal Injection Control of MTPA for IPMSM with Fast Dynamic Response

A maximum torque per ampere (MTPA) control based on virtual signal injection for interior permanent magnet synchronous motor (IPMSM) with fast dynamic response is proposed in this paper. A small square wave signal is mathematically injected into current angle for accurately tracking MTPA points. The extracted derivative of elctromagnetic torque is utilized to compensate the initially set current angle to the real MTPA operation current angle. Due to the absence of bandpass and lowpass filters which are essential in the sinusoidal injected signal scheme, this method shows good dynamic response. By incorporating a modified equation for the torque after signal injection, the steady-state accuracy is also enhanced. The d- and q-axes current references are obtained through the current vector magnitude and optimal current angle instead of using the torque equation with nominal motor parameters, which guarantees the accuracy of the output torque. The proposed scheme is parameter independent and no real signal is injected to the current or voltage command. Thus, the problems of high-frequency signal injection method are avoided. A prototype is set up and experiments are carried out to verify effectiveness and robustness of the proposed control scheme

Establishment and Application of Recombinase-Mediated Strand Displacement Isothermal Amplification Assay for Rapid Detection of Horse-Derived Components in Meat Products

Objective: A recombinase-mediated strand displacement isothermal amplification assay for rapid detection of horse-derived components in meat and meat products was established. Methods: A series of specific primers and Exo probes were designed using the horse-derived ATpase 6 gene as the target gene. The primers were screened and the reaction parameters were optimized. The specificity, sensitivity and stability of the assay were evaluated, and the detection limit, applicability and accuracy were analyzed by detecting simulated samples with different mixing ratios and different processing technologies, and commercial samples. Results: The assay was characterized by rapid response, high sensitivity and specificity. The reaction was completed within 16 minutes under a constant temperature of 39 ℃. The system had good specificity to 23 non-target sources. The detection sensitivity of the target DNA was 1.8 copies/μL. The detection limit was 0.01% for raw meat (mass fraction), and 0.1% (mass fraction) for processed meat products. For 90 commercial samples, the results of this method were consistent with those of the standard method. Conclusion: The recombinase-mediated isothermal amplification assay can be used for the detection of horse-derived components in meat and meat products

Delayed impact of natural climate solutions

Acknowledgement: This work was supported by the National Basic Research Program of China (2016YFA0602701), the National Natural Science Foundation of China (41975113; 91937302), and the Guangdong Provincial Department of Science and Technology (2019ZT08G090). We appreciate the support from the China Association for Science and Technology Working Group for UN Environment Consultation. The authors declare no conflict of interests.Peer reviewedPostprin

Construction and Characterization of a Chimeric Virus (BIV/HIV-1) Carrying the Bovine Immunodeficiency Virus \u3ci\u3egag\u3c/i\u3e-\u3ci\u3epol\u3c/i\u3e Gene: Research Letters

HIV-1HXB2 5′LTR region, most of BIVR29 gag-pol segment and HIV-1HXB2 pol IN-3′LTR region were respectively amplified. A chimeric clone, designated as pHBIV3753, was constructed by cloning three fragments sequentially into pUC18. MT4 cells were transfected with pHBIV3753. The replication and expressions of the chimeric virus (HBIV3753) were monitored by RT activity and IFA. The results firstly demonstrated that it is possible to generate a new type of the BIV/HIV-1 chimeric virus containing BIV gag-pol gene