Advanced 3D ordered electrodes for PEMFC applications: From structural features and fabrication methods to the controllable design of catalyst layers

The catalyst layers(CLs) electrode is the key component of the membrane electrode assembly(MEA) in proton exchange membrane fuel cells(PEMFCs). Conventional electrodes for PEMFCs are composed of carbon-supported, ionomer, and Pt nanoparticles, all immersed together and sprayed with a micron-level thickness of CLs. They have a performance trade-off where increasing the Pt loading leads to higher performance of abundant triple-phase boundary areas but increases the electrode cost. Major challenges must be overcome before realizing its wide commercialization. Literature research revealed that it is impossible to achieve performance and durability targets with only high-performance catalysts, so the controllable design of CLs architecture in MEAs for PEMFCs must now be the top priority to meet industry goals. From this perspective, a 3D ordered electrode circumvents this issue with a support-free architecture and ultrathin thickness while reducing noble metal Pt loadings. Herein, we discuss the motivation in-depth and summarize the necessary CLs structural features for designing ultralow Pt loading electrodes. Critical issues that remain in progress for 3D ordered CLs must be studied and characterized. Furthermore, approaches for 3D ordered CLs architecture electrode development, involving material design, structure optimization, preparation technology, and characterization techniques, are summarized and are expected to be next-generation CLs for PEMFCs. Finally, the review concludes with perspectives on possible research directions of CL architecture to address the significant challenges in the future.

Mesh representation matters:investigating the influence of different mesh features on perceptual and spatial fidelity of deep 3D morphable models

Background Deep 3D morphable models(deep 3DMMs)play an essential role in computer vision.They are used in facial synthesis,compression,reconstruction and animation,avatar creation,virtual try-on,facial recognition systems and medical imaging.These applications require high spatial and perceptual quality of synthesised meshes.Despite their significance,these models have not been compared with different mesh representations and evaluated jointly with point-wise distance and perceptual metrics.Methods We compare the influence of different mesh representation features to various deep 3DMMs on spatial and perceptual fidelity of the reconstructed meshes.This paper proves the hypothesis that building deep 3DMMs from meshes represented with global representations leads to lower spatial reconstruction error measured with L_(1) and L_(2) norm metrics and underperforms on perceptual metrics.In contrast,using differential mesh representations which describe differential surface properties yields lower perceptual FMPD and DAME and higher spatial fidelity error.The influence of mesh feature normalisation and standardisation is also compared and analysed from perceptual and spatial fidelity perspectives.Results The results presented in this paper provide guidance in selecting mesh representations to build deep 3DMMs accordingly to spatial and perceptual quality objectives and propose combinations of mesh representations and deep 3DMMs which improve either perceptual or spatial fidelity of existing methods.

Building Facade Point Clouds Segmentation Based on Optimal Dual-Scale Feature Descriptors

To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-scale feature descriptors. First, we select the optimal dual-scale descriptors from a range of feature descriptors. Next, we segment the facade according to the threshold value of the chosen optimal dual-scale descriptors. Finally, we use RANSAC (Random Sample Consensus) to fit the segmented surface and optimize the fitting result. Experimental results show that, compared to commonly used facade segmentation algorithms, the proposed method yields more accurate segmentation results, providing a robust data foundation for subsequent 3D model reconstruction of buildings.

Face recognition using SIFT features under 3D meshes

Expression, occlusion, and pose variations are three main challenges for 3D face recognition. A novel method is presented to address 3D face recognition using scale-invariant feature transform(SIFT) features on 3D meshes. After preprocessing, shape index extrema on the 3D facial surface are selected as keypoints in the difference scale space and the unstable keypoints are removed after two screening steps. Then, a local coordinate system for each keypoint is established by principal component analysis(PCA).Next, two local geometric features are extracted around each keypoint through the local coordinate system. Additionally, the features are augmented by the symmetrization according to the approximate left-right symmetry in human face. The proposed method is evaluated on the Bosphorus, BU-3DFE, and Gavab databases, respectively. Good results are achieved on these three datasets. As a result, the proposed method proves robust to facial expression variations, partial external occlusions and large pose changes.

General and robust voxel feature learning with Transformer for 3D object detection

The self-attention networks and Transformer have dominated machine translation and natural language processing fields,and shown great potential in image vision tasks such as image classification and object detection.Inspired by the great progress of Transformer,we propose a novel general and robust voxel feature encoder for 3D object detection based on the traditional Transformer.We first investigate the permutation invariance of sequence data of the self-attention and apply it to point cloud processing.Then we construct a voxel feature layer based on the self-attention to adaptively learn local and robust context of a voxel according to the spatial relationship and context information exchanging between all points within the voxel.Lastly,we construct a general voxel feature learning framework with the voxel feature layer as the core for 3D object detection.The voxel feature with Transformer(VFT)can be plugged into any other voxel-based 3D object detection framework easily,and serves as the backbone for voxel feature extractor.Experiments results on the KITTI dataset demonstrate that our method achieves the state-of-the-art performance on 3D object detection.

A Novel Airborne 3D Laser Point Cloud Hole Repair Algorithm Considering Topographic Features

Hole repair processing is an important part of point cloud data processing in airborne 3-dimensional(3D)laser scanning technology.Due to the fragmentation and irregularity of the surface morphology,when applying the 3D laser scanning technology to mountain mapping,the conventional mathematical cloud-based point cloud hole repair method is not ideal in practical applications.In order to solve this problem,we propose to repair the valley and ridge line first,and then repair the point cloud hole.The main technical steps of the method include the following points:First,the valley and ridge feature lines are extracted by the GIS slope analysis method;Then,the valley and ridge line missing from the hole are repaired by the mathematical interpolation method,and the repaired results are edited and inserted to the original point cloud;Finally,the traditional repair method is used to repair the point cloud hole whose valley line and ridge line have been repaired.Three experiments were designed and implemented in the east bank of the Xiaobaini River to test the performance of the proposed method.The results showed that compared with the direct point cloud hole repair method in Geomagic Studio software,the average repair accuracy of the proposed method,in the 16 m buffer zone of valley line and ridge line,is increased from 56.31 cm to 31.49 cm.The repair performance is significantly improved.

Monocular 3D object detection with Pseudo-LiDAR confidence sampling and hierarchical geometric feature extraction in 6G network

The high bandwidth and low latency of 6G network technology enable the successful application of monocular 3D object detection on vehicle platforms.Monocular 3D-object-detection-based Pseudo-LiDAR is a low-cost,lowpower solution compared to LiDAR solutions in the field of autonomous driving.However,this technique has some problems,i.e.,(1)the poor quality of generated Pseudo-LiDAR point clouds resulting from the nonlinear error distribution of monocular depth estimation and(2)the weak representation capability of point cloud features due to the neglected global geometric structure features of point clouds existing in LiDAR-based 3D detection networks.Therefore,we proposed a Pseudo-LiDAR confidence sampling strategy and a hierarchical geometric feature extraction module for monocular 3D object detection.We first designed a point cloud confidence sampling strategy based on a 3D Gaussian distribution to assign small confidence to the points with great error in depth estimation and filter them out according to the confidence.Then,we present a hierarchical geometric feature extraction module by aggregating the local neighborhood features and a dual transformer to capture the global geometric features in the point cloud.Finally,our detection framework is based on Point-Voxel-RCNN(PV-RCNN)with high-quality Pseudo-LiDAR and enriched geometric features as input.From the experimental results,our method achieves satisfactory results in monocular 3D object detection.

Attention Guided Multi Scale Feature Fusion Network for Automatic Prostate Segmentation

The precise and automatic segmentation of prostate magnetic resonance imaging(MRI)images is vital for assisting doctors in diagnosing prostate diseases.In recent years,many advanced methods have been applied to prostate segmentation,but due to the variability caused by prostate diseases,automatic segmentation of the prostate presents significant challenges.In this paper,we propose an attention-guided multi-scale feature fusion network(AGMSF-Net)to segment prostate MRI images.We propose an attention mechanism for extracting multi-scale features,and introduce a 3D transformer module to enhance global feature representation by adding it during the transition phase from encoder to decoder.In the decoder stage,a feature fusion module is proposed to obtain global context information.We evaluate our model on MRI images of the prostate acquired from a local hospital.The relative volume difference(RVD)and dice similarity coefficient(DSC)between the results of automatic prostate segmentation and ground truth were 1.21%and 93.68%,respectively.To quantitatively evaluate prostate volume on MRI,which is of significant clinical significance,we propose a unique AGMSF-Net.The essential performance evaluation and validation experiments have demonstrated the effectiveness of our method in automatic prostate segmentation.

SGT-Net: A Transformer-Based Stratified Graph Convolutional Network for 3D Point Cloud Semantic Segmentation

In recent years,semantic segmentation on 3D point cloud data has attracted much attention.Unlike 2D images where pixels distribute regularly in the image domain,3D point clouds in non-Euclidean space are irregular and inherently sparse.Therefore,it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud space.Most current methods either focus on local feature aggregation or long-range context dependency,but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation tasks.In this paper,we propose a Transformer-based stratified graph convolutional network(SGT-Net),which enlarges the effective receptive field and builds direct long-range dependency.Specifically,we first propose a novel dense-sparse sampling strategy that provides dense local vertices and sparse long-distance vertices for subsequent graph convolutional network(GCN).Secondly,we propose a multi-key self-attention mechanism based on the Transformer to further weight augmentation for crucial neighboring relationships and enlarge the effective receptive field.In addition,to further improve the efficiency of the network,we propose a similarity measurement module to determine whether the neighborhood near the center point is effective.We demonstrate the validity and superiority of our method on the S3DIS and ShapeNet datasets.Through ablation experiments and segmentation visualization,we verify that the SGT model can improve the performance of the point cloud semantic segmentation.

Three-Dimensional Model Retrieval Using Dynamic Multi-Descriptor Fusion

In this paper, we propose a dynamic multi-descriptor fusion （DMDF） approach to improving the retrieval accuracy of 3-dimensional （3D） model retrieval systems. First, an independent retrieval list is generated by using each individual descriptor. Second, we propose an automatic relevant/irrelevant models selection （ARMS） approach to selecting the relevant and irrelevant 3D models automatically without any user interaction. A weighted distance, in which the weight associated with each individual descriptor is learnt by using the selected relevant and irrelevant models, is used to measure the similarity between two 3D models. Furthermore, a descriptor-dependent adaptive query point movement （AQPM） approach is employed to update every feature vector. This set of new feature vectors is used to index 3D models in the next search process. Four 3D model databases are used to compare the retrieval accuracy of our proposed DMDF approach with several descriptors as well as some well-known information fusion methods. Experimental results have shown that our proposed DMDF approach provides a promising retrieval result and always yields the best retrieval accuracy.

Experimental Evaluation of the Performance of Local Shape Descriptors for the Classification of 3D Data in Precision Farming

Object classification in high-density 3D point clouds with applications in precision farming is a very challenging area due to high intra-class variances and high degrees of occlusions and overlaps due to self-similarities and densely packed plant organs, especially in ripe growing stages. Due to these application specific challenges, this contribution gives an experimental evaluation of the performance of local shape descriptors (namely Point-Feature Histogram (PFH), Fast-Point-Feature Histogram (FPFH), Signature of Histograms of Orientations (SHOT), Rotational Projection Statistics (RoPS) and Spin Images) in the classification of 3D points into different types of plant organs. We achieve very good results on four representative scans of a leave, a grape bunch, a grape branch and a flower of between 94 and 99% accuracy in the case of supervised classification with an SVM and between 88 and 96% accuracy using a k-means clustering approach. Additionally, different distance measures and the influence of the number of cluster centres are examined.

(1,3)-β-D-葡聚糖在不同证据级别的侵袭性肺曲霉病中的表现

目的分析(1,3)-β-D-葡聚糖实验(G实验)在不同证据级别的侵袭性肺曲霉病中的表现。方法选取2014年1月-2017年12月在广东省人民医院诊断为侵袭性肺曲霉病的患者病例信息,收集患者临床信息:人口学资料、肺穿刺组织病理结果、呼吸道标本(痰、纤支镜冲洗液、肺泡灌洗液)培养结果、G实验结果。根据病理和培养的结果分为3组:病理+培养双阳性组、单培养阳性组、单病理阳性组。结果最后纳入分析的病例76例,男性46人,女性30人,平均年龄59±21岁。分组结果为:单病理阳性组39例(41.5%),病理+培养双阳性组10例(10.6%),单培养阳性组45例(47.9%)。3组的G实验阳性率有统计学差异(χ^2=9.34,P=0.036),进行两两比较发现,a调整为0.0125后,双阳性组的G实验阳性率显著高于单病理阳性组(χ^2=9.06,P1=0.004),G实验的中位值也是双阳性组最高(249.30pg/ml)。呼吸道标本培养最常见的是烟曲霉36例(占61.02%),其次是黄曲霉12例(占20.34%),但不同曲霉感染其G实验结果无统计学差异(H=4.021,P=0.403)。结论病理阳性的侵袭性肺曲霉患者其G实验的结果不一定高,最好结合培养结果进行诊断。

基于特征点的3D人脸姿态跟踪

针对视频序列中的人脸跟踪问题,提出一种单摄像头的人脸3D姿态跟踪方法。利用SIFT特征点匹配算法来得到可靠的帧间特征匹配。将前帧与所选的关键帧特征匹配信息及融入到对当前的姿态估计中,利用SIFT特征点匹配算法来得到可靠的帧间特征匹配。最后通过利用RANSAC随机选取特征点对,并用POSIT和最小化误差组合的3D投影方法以迭代的方式得到精确的当前帧人脸姿态估计。通过多组实验数据对比,表明了该算法在严重遮挡、头部摆动幅度较大、匹配点较少的复杂情况干扰下仍具有鲁棒性,并且解决了3D人脸跟踪的漂移问题,实现对目标人脸的稳定跟踪,对比以往2D跟踪算法在复杂环境下具有明显的改善。

Support Vector Machine active learning for 3D model retrieval

In this paper, we present a novel Support Vector Machine active learning algorithm for effective 3D model retrieval using the concept of relevance feedback. The proposed method learns from the most informative objects which are marked by the user, and then creates a boundary separating the relevant models from irrelevant ones. What it needs is only a small number of 3D models labelled by the user. It can grasp the user＇s semantic knowledge rapidly and accurately. Experimental results showed that the proposed algorithm significantly improves the retrieval effectiveness. Compared with four state-of-the-art query refinement schemes for 3D model retrieval, it provides superior retrieval performance after no more than two rounds of relevance feedback.

A Novel Human Action Recognition Algorithm Based on Decision Level Multi-Feature Fusion

In order to take advantage of the logical structure of video sequences and improve the recognition accuracy of the human action, a novel hybrid human action detection method based on three descriptors and decision level fusion is proposed. Firstly, the minimal 3D space region of human action region is detected by combining frame difference method and Vi BE algorithm, and the three-dimensional histogram of oriented gradient(HOG3D) is extracted. At the same time, the characteristics of global descriptors based on frequency domain filtering(FDF) and the local descriptors based on spatial-temporal interest points(STIP) are extracted. Principal component analysis(PCA) is implemented to reduce the dimension of the gradient histogram and the global descriptor, and bag of words(BoW) model is applied to describe the local descriptors based on STIP. Finally, a linear support vector machine(SVM) is used to create a new decision level fusion classifier. Some experiments are done to verify the performance of the multi-features, and the results show that they have good representation ability and generalization ability. Otherwise, the proposed scheme obtains very competitive results on the well-known datasets in terms of mean average precision.

High Efficient Methods of Content-based 3D Model Retrieval

Content-based 3D model retrieval is of great help to facilitate the reuse of existing designs and to inspire designers during conceptual design. However, there is still a gap to apply it in industry due to the low time efficiency. This paper presents two new methods with high efficiency to build a Content-based 3D model retrieval system. First, an improvement is made on the ＂Shape Distribution （D2）＂ algorithm, and a new algorithm named ＂Quick D2＂ is proposed. Four sample 3D mechanical models are used in an experiment to compare the time cost of the two algorithms. The result indicates that the time cost of Quick D2 is much lower than that of D2, while the descriptors extracted by the two algorithms are almost the same. Second, an expandable 3D model repository index method with high performance, namely, RBK index, is presented. On the basis of RBK index, the search space is pruned effectively during the search process, leading to a speed up of the whole system. The factors that influence the values of the key parameters of RBK index are discussed and an experimental method to find the optimal values of the key parameters is given. Finally, ＂3D Searcher＂, a content-based 3D model retrieval system is developed. By using the methods proposed, the time cost for the system to respond one query online is reduced by 75% on average. The system has been implemented in a manufacturing enterprise, and practical query examples during a case of the automobile rear axle design are also shown. The research method presented shows a new research perspective and can effectively improve the content-based 3D model retrieval efficiency.

Summed volume region selection based three-dimensional automatic target recognition for airborne LIDAR

Airborne LIDAR can flexibly obtain point cloud data with three-dimensional structural information,which can improve its effectiveness of automatic target recognition in the complex environment.Compared with 2D information,3D information performs better in separating objects and background.However,an aircraft platform can have a negative influence on LIDAR obtained data because of various flight attitudes,flight heights and atmospheric disturbances.A structure of global feature based 3D automatic target recognition method for airborne LIDAR is proposed,which is composed of offline phase and online phase.The performance of four global feature descriptors is compared.Considering the summed volume region(SVR) discrepancy in real objects,SVR selection is added into the pre-processing operations to eliminate mismatching clusters compared with the interested target.Highly reliable simulated data are obtained under various sensor’s altitudes,detection distances and atmospheric disturbances.The final experiments results show that the added step increases the recognition rate by above 2.4% and decreases the execution time by about 33%.

Fast and Stable Surface Feature Simulation for Particle-Based Fluids

In order to efficiently and realistically capture microscopic features of fluid surface,a fast and stable surface feature simulation approach for particle-based fluids is presented in this paper.This method employs a steady tension and adhesion model to construct surface features with the consideration of the adsorption effect of fluid to solid.Molecular cohesion and surface area minimization are appended for surface tension,and adhesion is added to better show the microscopic characteristics of fluid surface.Besides,the model is integrated to an implicit incompressible smoothed particle hydrodynamics(SPH)method to improve the efficiency and stability of simulation.The experimental results demonstrate that the method can better simulates surface features in a variety of scenarios stably and efficiently.

A fast, accurate and dense feature matching algorithm for aerial images

Three-dimensional(3D)reconstruction based on aerial images has broad prospects,and feature matching is an important step of it.However,for high-resolution aerial images,there are usually problems such as long time,mismatching and sparse feature pairs using traditional algorithms.Therefore,an algorithm is proposed to realize fast,accurate and dense feature matching.The algorithm consists of four steps.Firstly,we achieve a balance between the feature matching time and the number of matching pairs by appropriately reducing the image resolution.Secondly,to realize further screening of the mismatches,a feature screening algorithm based on similarity judgment or local optimization is proposed.Thirdly,to make the algorithm more widely applicable,we combine the results of different algorithms to get dense results.Finally,all matching feature pairs in the low-resolution images are restored to the original images.Comparisons between the original algorithms and our algorithm show that the proposed algorithm can effectively reduce the matching time,screen out the mismatches,and improve the number of matches.

Surface characteristics analysis of fractures induced by supercritical CO_(2)and water through three-dimensional scanning and scanning electron micrography

Morphology of hydraulic fracture surface has significant effects on oil and gas flow,proppant migration and fracture closure,which plays an important role in oil and gas fracturing stimulation.In this paper,we analyzed the fracture surface characteristics induced by supercritical carbon dioxide(SC-CO_(2))and water in open-hole and perforation completion conditions under triaxial stresses.A simple calculation method was proposed to quantitatively analyze the fracture surface area and roughness in macro-level based on three-dimensional(3D)scanning data.In micro-level,scanning electron micrograph(SEM)was used to analyze the features of fracture surface.The results showed that the surface area of the induced fracture increases with perforation angle for both SC-CO_(2)and water fracturing,and the surface area of SC-CO_(2)-induced fracture is 6.49%e58.57%larger than that of water-induced fracture.The fractal dimension and surface roughness of water-induced fractures increase with the increase in perforation angle,while those of SC-CO_(2)-induced fractures decrease with the increasing perforation angle.A considerable number of microcracks and particle peeling pits can be observed on SC-CO_(2)-induced fracture surface while there are more flat particle surfaces in water-induced fracture surface through SEM images,indicating that fractures tend to propagate along the boundary of the particle for SC-CO_(2)fracturing while water-induced fractures prefer to cut through particles.These findings are of great significance for analyzing fracture mechanism and evaluating fracturing stimulation performance.