Building Extraction from LiDAR Point Clouds Based on Revised RandLA-Net

3D building models is crucial for applications in smart cities. Automatic reconstruction of 3D buildings has been investigated based on various data sources. Point clouds from airborne LiDAR scanners can be used to extract buildings data due to its high accuracy and point density. In this paper, we present a methodology to segment buildings and corresponding rooftop structure from point clouds. First, RandLA-Net, which is an efficient and lightweight neural network for semantic segmentation of large-scale point clouds, is revised and adopted for building segmentation. By implementing local feature aggregation of each point, RandLA-Net can effectively preserve geometric details in point clouds. Besides 3D coordinates of point clouds, we incorporated point attributes including pulse intensity and return numbers into the network as additional features. Feature normalizations are applied to the input features. To achieve a better result of the local feature aggregation, hyperparameters of the network are fine-tuned according to the density of points and building size. Based on the classified building point clouds, DBSCAN clustering algorithm is implemented for segmenting individual buildings. Elevation histogram analysis is conducted to determine optimal threshold values for delineating candidate rooftop point clouds of individual buildings. For the buildings with multiple rooftops, multiple elevation threshold values are necessary to extract corresponding rooftops or walls. Then DBSCAN is employed again for segmentation of individual rooftops and denoising of point clouds of each building. Finally, Alpha-shape analysis is applied based on adaptive threshold values to build the envelope of each rooftop. Experiments show that our implementation of building segmentation using RandLA-net achieves higher mean IoU (Intersection over Union) and better classification performance in building segmentation. ISPRS benchmark data was used in our experiment and our methodology produce results with accuracy of 90.79%

Example-based image colorization using locality consistent sparse representation

—Image colorization aims to produce a natural looking color image from a given grayscale image, which remains a challenging problem. In this paper, we propose a novel examplebased image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target grayscale image by sparse pursuit. For efficiency and robustness, our method operates at the superpixel level. We extract low-level intensity features, mid-level texture features and high-level semantic features for each superpixel, which are then concatenated to form its descriptor. The collection of feature vectors for all the superpixels from the reference image composes the dictionary. We formulate colorization of target superpixels as a dictionary-based sparse reconstruction problem. Inspired by the observation that superpixels with similar spatial location and/or feature representation are likely to match spatially close regions from the reference image, we further introduce a locality promoting regularization term into the energy formulation which substantially improves the matching consistency and subsequent colorization results. Target superpixels are colorized based on the chrominance information from the dominant reference superpixels. Finally, to further improve coherence while preserving sharpness, we develop a new edge-preserving filter for chrominance channels with the guidance from the target grayscale image. To the best of our knowledge, this is the first work on sparse pursuit image colorization from single reference images. Experimental results demonstrate that our colorization method outperforms state-ofthe-art methods, both visually and quantitatively using a user stud

Adaptive neural network-based fault estimation for nonlinear systems with actuator faults in simultaneous multiplicative and additive forms

Non-linearities and actuator faults often exist in practical systems which may degrade system performance or even lead to catastrophic accidents. In this article, a fault-tolerant compensation control strategy is proposed for a class of non-linear systems with actuator faults in simultaneous multiplicative and additive forms. First, radial basis function neural network is employed to approximate the system non-linearity. The approximation is achieved by only one adaptive parameter, which simplifies the computation burden. Then, by means of the backstepping technique, an adaptive neural controller is developed to cope with the adverse effects brought by the system non-linearity and actuator faults in multiplicative and additive forms. Meanwhile, the proposed control design scheme can guarantee that the considered closed-loop system is stable. The novelty of the article lies in that the system non-linearity, the additive actuator faults, and the multiplicative actuator faults that often exist in practical engineering are catered for simultaneously. Furthermore, compared with some existing works, the approximation of the system non-linearity is achieved by only one adaptive parameter for the purpose of reducing the computation burden. Therefore, its applicability is more general. Finally, a numerical simulation and a comparative simulation are carried out to show the effectiveness of the developed controller

Feasibility Study on Soilless Cultivation of Organic Ginseng

Based on the present situation and problems concerning ginseng cultivation as well as soilless cultivation features, we analyze the growth indicators and input-output ratio of different ginseng cultivation patterns, and conform that the soilless cultivation technology for organic ginseng is feasible. And this technology provides theoretical basis and technological feasibility for the sustainable development of ginseng industry

First-Principles Study of the Effect of Titanium Doping on Carbon Monoxide Poisoning Properties of Zirconium-Cobalt Alloys

It is very important to study impurity gas poisoning in ZrCo alloy because it is associated directly with the performance of ZrCo alloy as a hydrogen storage material. In this work, the effects of atomic replacement on the mechanism and properties of CO impurity gas poisoning in doped (Ti) ZrCo hydrogen storage alloys were investigated using the first principles method, based on the pseudopotential plane wave method. The adsorption energy, lattice constant, density of states, and charge density difference of the compounds before and after doping were calculated. Then, surface adsorption models of the ZrCo and Zr0.8Ti0.2Co alloys were established with the assistance of a conventional model. The resulting adsorption energy values of the clean surface and the surface adsorption energy values in the presence of CO impurity gases manifested that the Ti element-doped Zr0.8Ti0.2Co alloy was more susceptible to CO gas poisoning compared to ZrCo, which was consistent with the existing experimental results. In addition, by analyzing the conventional model, the electrons from the doped atoms overlapped with the surrounding electrons of C atoms, the phenomenon of orbital hybridization occurred, and the interactions increased. Consequently, Ti doping was not conducive to ZrCo to improve the ability to resist CO poisoning. The research results of this paper have laid a good foundation for the study of the effect of Ti doping on the antitoxicity performance

Quaternization-spiro design of chlorine-resistant and high-permeance lithium separation membranes

Abstract Current polyamide lithium extraction nanofiltration membranes are susceptible to chlorine degradation and/or low permeance, two problems that are hard to reconcile. Here we simultaneously circumvented these problems by designing a quaternized-spiro piperazine monomer and translating its beneficial properties into large-area membranes (1 × 2 m2) via interfacial polymerization with trimesoyl chloride. The quaternary ammonium and spiral conformation of the monomer confer more positive charge and free volume to the membrane, leading to one of the highest permeance (~22 L m−2 h−1 bar−1) compared to the state-of-the-art Mg2+/Li+ nanofiltration membranes. Meanwhile, membrane structures are chlorine resistant as the amine–acyl bonding contains no sensitive N-H group. Thus the high performance of membrane is stable versus 400-h immersion in sodium hypochlorite, while control membranes degraded readily. Molecular simulations show that the high permeance and chlorine resistance, which were reproducible at the membrane module level, arise from the spiral conformation and secondary amine structures of the monomer