Polysaccharides are widely distributed in natural sources from monocytic microorganisms to higher animals, and are found in a variety of biological activities in recent decades. Natural polysaccharides have the characteristics of large molecular weight, diverse composition, and complex structure, so their purification and structural analysis are difficult issues in research. Chromatography as a powerful separation technique, plays an irreplaceable role in the separation and structural analysis of natural polysaccharides, especially in the purification of polysaccharides, the separation of hydrolysates, and the analysis of monosaccharide composition. The separation mechanisms and application of different chromatographic methods in the studies of polysaccharides were summarized in this review. Moreover, the advantages and drawbacks of various chromatography methods were discussed as well.

Application of chromatography in purification and structural analysis of natural polysaccharides: A review.

Suitable posture configurations are urgently requested to ensure capabilities, such as maneuverability of robotic machining, especially for complex operation tasks with mobile robots. Traditional configuration optimization methods are mainly studied for unilateral performance constraint, which are not applicable to large complex components (LCCs). This work proposes a novel global configuration method for mobile robotic grinding of LCCs that solves the optimal redundant parameters while enhancing the configuration performance, collision avoidance, and reachability. First, a robot configuration performance index (RCPI) is proposed by integrating the joint performance, singularity constraint, and collision constraint indexes based on consistency mapping. Next, the configuration optimization model for individual trajectory points is constructed, consisting of the RCPI objective function with redundant parameters and constraints taking into account the collision and reachability. Subsequently, a global configuration optimization method is developed based on an improved particle swarm optimization with compression factor, where the comprehensive fitness function is designed with the consideration of global configurations and trajectory continuity. Finally, the effectiveness and superiority of the proposed method are verified by applying optimal configuration to the mobile robotic grinding of the high-speed train body.

Optimal Configuration for Mobile Robotic Grinding of Large Complex Components Based on Redundant Parameters

Urban digitalization, autonomous driving, and 3D map construction all benefit greatly from the ability of mobile LiDAR systems (MLS) to swiftly capture centimeter-level accuracy point cloud data of urban scenes. However, the positional accuracy of MLS point clouds is affected by factors such as GNSS signal loss and IMU inertial drift, which can lead to ghosting and layering of point clouds in repeated areas, reducing its usability. Point cloud registration which was developed to solve the MLS localization problem has been studied for a long time. Most current registration methods only consider the point clouds globally, so the registration of repeated areas has not received adequate attention; and the few methods that target repeated areas fail to balance registration accuracy and automation. To address the above-mentioned issues, we propose an automatic multi-constraint joint registration method for MLS point clouds in repeated areas. Firstly, based on the strict correspondence between trajectory and point clouds, this method identifies point cloud repeated areas by searching for repeated areas in the trajectory. Secondly, it adaptively segments the point clouds of repeated areas based on changes in trajectory angle and speed. Then, it uses a deep learning network to automatically extract the scene features of these segmented point clouds, which are essentially constrained by a variety of basic features such as points, lines, and surfaces. Finally, these multi-constrained scene features are used to perform joint registration of point clouds in repeated areas. We carried out repeated areas registration experiments in a campus scene using our self-developed MLS. The results show that the best registration combination can produce errors of 0.021 m and 0.033 m, respectively, compared with pre-registration errors of 0.094 m and 0.103 m. Our method specifically considers MLS point clouds in repeated areas and ensures satisfactory accuracy while achieving automatic point cloud registration. It is also expected that the proposed method can be extended to other LiDAR platforms that also have trajectories, such as Airborne Laser Scanning (ALS) and Simultaneous Localization and Mapping (SLAM), to realize high-precision automatic registration of point clouds in repeated areas, with little adaptation to the complex scenes. 

An automated multi-constraint joint registration method for mobile LiDAR point cloud in repeated areas

In this work we present a new construction of basis functions that generate the space of geometrically smooth splines on an unstructured quadrilateral mesh. The basis is represented in terms of biquintic Bézier polynomials on each quadrilateral face. The gluing along the face boundaries is achieved using quadratic gluing data functions, leading to globally G1–smooth spaces. We analyze the latter space and provide a combinatorial formula for its dimension as well as an explicit basis construction. Moreover, we assess the use of this basis in point cloud fitting problems. To apply G1 least squares fitting, a quadrilateral structure as well as parameters in each quadrilateral is required. Even though the general problem of segmenting and parametrizing point clouds is beyond the focus of the present work, we describe a procedure that produces such a structure as well as patch-local parameters. Our experiments demonstrate the accuracy and smoothness of the obtained reconstructed models in several challenging instances. 

G1 spline functions for point cloud fitting

Automatic implant shape design for minimally invasive repair of pectus excavatum using deep learning and shape registration

Registration of partially overlapping, featureless three-dimensional (3D) point sets with noise is a difficult problem in the applications of large-scale metrology (LSM). Existing approaches use the sparse iterative closest points (SICP) method applying the l p norm to decrease the influence of outliers during registration. However, we reveal in this study that sparse point-to-point becomes easily trapped into local minima in featureless point clouds registration, and the error landscape of sparse point-to-plane is too shallow to restrain the sliding due to the lack of constraints in the large flat areas. Also, point clouds sampled from the large flat areas cause the low-rank matrix of linear equation in estimating the transformation matrix. Hence, we propose using the point-to-point l p distance constraints to restrain the sliding along large flat areas. We further define a weighted enhanced l p distance (WELD) error metric to slacken the constraints and escape from the local minima. Moreover, WELD can improve stability with the full-rank linear equation in estimating the transformation matrix. To verify the capability of escaping from the local minima and restraining the sliding, we choose SICP and two other algorithms to be compared with our method in simulated and actual point clouds. The comparisons show that our method successfully can escape from the local minima and restrain the sliding to handle outliers and noisy featureless point clouds effectively. The source code is available at https://github.com/Timbersaw-wangzw/WES-ICP. 

Robust point clouds registration with point-to-point <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si37.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> distance constraints in large-scale metr

Purification, characterisation and antioxidant properties of a novel polysaccharide from
 Physalis pubescens L
 . fruits

High Accuracy Hand-Eye Calibration Method Using Pose Graph Constraints and Sequential Quadratic Programming in Robotic Inspection

The generation of smoother and shorter spiral complete coverage paths in multi-connected domains is a crucial research topic in path planning for robotic cavity machining and other related fields. Traditional methods for spiral path planning in multi-connected domains typically incorporate a subregion division procedure that leads to excessive subregion bridging, requiring longer, more sharply turning, and unevenly spaced spirals to achieve complete coverage. To address this issue, this paper proposes a novel spiral complete coverage path planning method using conformal slit mapping. It takes advantage of the fact that conformal slit mapping can transform multi-connected domains into regular disks or annuluses without the need for subregion division. Firstly, a slit mapping calculation technique is proposed for segmented cubic spline boundaries with corners. Secondly, a spiral path spacing control method is developed based on the maximum inscribed circle radius between adjacent conformal slit mapping iso-parameters. Thirdly, the spiral coverage path is derived by offsetting iso-parameters. Numerical experiments indicate that our method shares a comparable order of magnitude in computation time with the traditional PDE-based spiral complete coverage path method, but it excels in optimizing total path length, smoothness, and spacing consistency. Finally, we performed experiments on cavity milling and dry runs to compare the new method with the traditional PDE-based method in terms of machining duration and steering impact, respectively. The comparison reveals that, with both algorithms achieving complete coverage, the new algorithm reduces machining time and steering impact by 12.34% and 22.78%, respectively, compared with the traditional PDE-based method.

Spiral Complete Coverage Path Planning Based on Conformal Slit Mapping in Multi-connected Domains

A Robust Simultaneous Calibration Method for Mobile Robotic Measurement System Based on Global-Photogrammetry-Tracking

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Ziwei Wang

Author Tools

Chat about Author

Papers

Robust point clouds registration with point-to-point <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si37.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> distance constraints in large-scale metr

Purification, characterisation and antioxidant properties of a novel polysaccharide from Physalis pubescens L . fruits

High Accuracy Hand-Eye Calibration Method Using Pose Graph Constraints and Sequential Quadratic Programming in Robotic Inspection

Spiral Complete Coverage Path Planning Based on Conformal Slit Mapping in Multi-connected Domains

A Robust Simultaneous Calibration Method for Mobile Robotic Measurement System Based on Global-Photogrammetry-Tracking