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Robust Model-Predictive Control for Inductively Coupled Plasma Generation With a Semiphysical Simulation

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2021年第4期68卷 3380-3389页

作者： Wang, Junjie Liu, Yanming Liu, Xiaotao Li, Xiaoping Zhang, Haojie Wang, Di Xidian Univ Sch Aerosp Sci & Technol Plasma Electromagnet Phys Lab Extreme Environm Key LabMinist Educ China Xian 710071 Peoples R China China Informat Technol Design & Consulting Inst C Beijing 100048 Peoples R China

In this article, a novel robust tuning approach based on model-predictive control (MPC) for an inductively coupled plasma (ICP) generation process is presented. Several key aspects related to this methodology are the modeling of the ICP generation process, the robust joint tuning (RJT) algorithm, and the semiphysical simulation of the MPC algorithm. The ICP generation process model is built by a system identification method based on steady and dynamic responses, followed by a general model-predictive controller. Then, an automatic RJT algorithm is developed for the model-predictive controller to quickly obtain the optimal weighting parameters. The proposed RJT algorithm can minimize the ICP generation control system's maximum total variations in the output with a specific dynamic performance requirement and model uncertainty, providing a tradeoff between the dynamic performance and robustness. Finally, a semiphysical simulation system based on dSPACE and a field-programmable gate array is designed to illustrate the effectiveness of the model-predictive controller for the ICP generation control process and the RJT algorithm. Experimental results indicate the validity and good performance of the proposed model-predictive controller and the RJT algorithm.

关键词： iterative closest point algorithm Plasmas Argon Process control Mathematical model Robustness Generators Inductively coupled plasma (ICP) model-predictive control (MPC) model uncertainty robust tuning semiphysical simulation

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A Novel Genetic Scan-Matching-Based Registration algorithm for Supporting Moving Objects Tracking Effectively and Efficiently

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IEEE ACCESS 2021年 9卷 91741-91753页

作者： Lenac, Kristijan Cuzzocrea, Alfredo Mumolo, Enzo Univ Rijeka Fac Engn Rijeka 51000 Croatia Univ Rijeka Ctr Artificial Intelligence & Cybersecur Rijeka 51000 Croatia Univ Calabria IDEA Lab I-87036 Arcavacata Di Rende Italy LORIA F-54506 Nancy France Univ Trieste Dept Engn & Architecture I-34127 Trieste Italy

In this paper we describe a scan-matching based registration algorithm for tracking moving objects which falls in the emerging area that predicates the integration between robotics and big data applications. The scan matching approaches track paths of a mobile object by comparing maps of the environment seen by the object during its movement. algorithms described in this paper are hybrid, i.e. they compare maps by using first a genetic pre-alignment based on a novel metrics, and then performing a finer alignment using a deterministic approach. This kind of hybridization is, indeed, not new. However, the novel metrics used in this paper leads to important new properties, namely to correct arbitrary rotational errors and to cover larger search spaces. The proposed algorithm is experimentally compared to other approaches, and better performance in terms of accuracy and robustness are reported. Finally, our algorithm is also very fast thanks to the genetic pre-alignment task and the novel metrics we propose.

关键词： Genetics Measurement Estimation Object tracking Licenses iterative closest point algorithm Robustness Moving objects scan-matching algorithms intelligent systems genetic optimization

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A Modified ICP algorithm Based on FAST and Optical Flow for 3D Registration 17

A Modified ICP Algorithm Based on FAST and Optical Flow for ...

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17th Conference on Computer Science and Intelligence Systems (FedCSIS)

作者： Koniarski, Konrad Myslinski, Andrzej Polish Acad Sci Syst Res Inst Ul Newelska 6 PL-01447 Warsaw Poland

ISBN: (数字)9788396242396

ISBN: (纸本)9788396242396

This paper presents a modified iterative closest point (ICP) algorithm based on a suitable selection of initial points and local optical flow to speed up registration of static scenes with high accuracy. The biggest disadvantages of using standard ICP algorithm are appropriate initialization and effective matching point step in each iteration. In the proposed modification we deal with these problems and optimize this method for Augmented Reality application. As this application uses RGB-D images sequence the changes between consecutive key-frames are small. Therefore only small subset of the source image key-points is selected using scale-space pyramid and FAST approaches. It leads to the significant reduction of the number of the processed image points. Since the point matching technique using local optical flow is applied, in each optimization step of ICP the costly point matching procedure can be abandoned. The proposed approach has been validated by the numerical examples.

关键词： point cloud compression Image registration Three-dimensional displays Simultaneous localization and mapping iterative closest point algorithm Image sequences iterative methods

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Non-Penetration iterative closest points for Single-View Multi-Object 6D Pose Estimation 39

Non-Penetration Iterative Closest Points for Single-View Mul...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zhang, Mengchao Hauser, Kris Univ Illinois Dept Mech Sci & Engn Urbana IL 61801 USA Univ Illinois Dept Comp Sci Urbana IL USA

ISBN: (纸本)9781728196817

This paper presents a novel iterative closest points (ICP) variant, non-penetration iterative closest points (NPICP), which prevents interpenetration in 6DOF pose optimization and/or joint optimization of multiple object poses. This capability is particularly advantageous in cluttered scenarios, where there are many interactions between objects that constrain the space of valid poses. We use a semi-infinite programming approach to handle non-penetration constraints between complex, non-convex 3D geometries. NPICP is applied to a common use case for ICP as a post-processing method to improve the pose estimation accuracy of a rough guess. The results show that NPICP outperforms ICP, assists in outlier detection, and also outperforms the best result on the IC-BIN dataset in the Benchmark for 6D Object Pose Estimation.

关键词： Geometry Three-dimensional displays Automation iterative closest point algorithm Pose estimation Programming Benchmark testing

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Langmuir Probe Diagnostics of Inductively Coupled Plasma Generated Using Flat Spiral Antenna

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IEEE TRANSACTIONS ON PLASMA SCIENCE 2021年第2期49卷 615-623页

作者： Karar, Partha Kumar, Gourav Kar, Rajib Kewlani, Hitesh M. Patil, D. S. Dusane, Rajiv O. Indian Inst Technol Dept Met Engn & Mat Sci Semicond Thin Film & Plasma Proc Lab Bombay 400076 Maharashtra India Bhabha Atom Res Ctr Laser & Plasma Surface Proc Sect Mumbai 400085 Maharashtra India Bhabha Atom Res Ctr Accelerator Dev Div Mumbai 400085 Maharashtra India Homi Bhabha Natl Inst Mumbai 400094 Maharashtra India

Langmuir probe (LP) diagnostics is performed on an inductively coupled plasma (ICP) generated using a flat spiral antenna. The current-voltage ( I-V) characteristics are measured using a radio frequency (RF) compensated LP to mitigate the effect of RF distortion in I-V measurements. All-important plasma parameters such as electron density (ne), electron temperature (Te), plasma potential ( Vp), and electron energy distribution function (EEDF) are measured in a cylindrical-shaped ICP. The plasma is generated by feeding RF (13.56 MHz) current in the planer multiple spiral antennas. It is seen that the plasma is uniform over an area with 200 mm diameter, with an electron number density in the range from similar to 1011 to 1012 cm-3. Typically for plasma processing of materials, the required density fall in that region. Variation of plasma parameters is investigated as a function of applied RF power and operating pressure and the observed results are discussed.

关键词： Plasmas Radio frequency Probes iterative closest point algorithm Electric potential Capacitors Spirals Electron energy probability function (EEPF) inductively coupled plasma (ICP) system plasma diagnostics radio frequency (RF) compensated Langmuir probe (CLP)

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Human-to-Robot Manipulability Domain Adaptation with Parallel Transport and Manifold-Aware ICP

Human-to-Robot Manipulability Domain Adaptation with Paralle...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Reithmeir, Anna Figueredo, Luis Haddadin, Sami TUM MIRMI D-80637 Munich Germany

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Manipulability ellipsoids efficiently capture the human pose and reveal information about the task at hand. Their use in task-dependent robot teaching - particularly their transfer from a teacher to a learner - can advance emulation of human-like motion. Although in recent literature focus is shifted towards manipulability transfer between two robots, the adaptation to the capabilities of the other kinematic system is to date not addressed and research in transfer from human to robot is still in its infancy. This work presents a novel manipulability domain adaptation method for the transfer of manipulability information to the domain of another kinematic system. As manipulability matrices/ellipsoids are symmetric positive-definite (SPD) they can be viewed as points on the Riemannian manifold of SPD matrices. We are the first to address the problem of manipulability transfer from the perspective of point cloud registration. We propose a manifold-aware iterative closest point algorithm (ICP) with parallel transport initialization. Furthermore, we introduce a correspondence matching heuristic for manipulability ellipsoids based on inherent geometric features. We confirm our method in simulation experiments with 2-DoF manipulators as well as 7-DoF models representing the human-arm kinematics.

关键词： point cloud compression Manifolds Adaptation models Symmetric matrices iterative closest point algorithm Kinematics Manipulators

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A Review of point Cloud Registration Methods Based on Laser SLAM

A Review of Point Cloud Registration Methods Based on Laser ...

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Electrical Engineering, Big Data and algorithms (EEBDA), IEEE International Conference on

作者： Boxiang Wang Jianggui Han Jianing Huang Naval University of Engineering Wuhan China

point cloud registration is one of the key technologies for Simultaneous Localization and Mapping of LiDAR. In the SLAM process, the LiDAR pose change leads to different poses of the point cloud data, resulting in motion distortion, and the multi-frame data point cloud is accurately registered to the same coordinate system through point cloud registration to form a completed point cloud. In this paper, the principle analysis of two different point cloud registration methods, iterative closest point (ICP) algorithm and Normal Distributions Transform (NDT) algorithm, summarizes and analyzes the corresponding improved algorithms and development status, and simulates the two algorithms in MATLAB. The results show that the accuracy and robustness of the NDT algorithm are higher than those of the ICP algorithm.

关键词： point cloud compression Simultaneous localization and mapping Laser radar iterative closest point algorithm Lasers Process control Transforms

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INDFORG: Industrial Forgery Detection Using Automatic Rotation Angle Detection and Correction

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第5期17卷 3630-3639页

作者： Hurrah, Nasir N. Loan, Nazir A. Parah, Shabir A. Sheikh, Javaid A. Muhammad, Khan de Macedo, Antonio Roberto L. de Albuquerque, Victor Hugo C. Univ Kashmir Dept Elect & IT Srinagar 190006 India Sungkyunkwan Univ Sch Convergence Coll Comp & Informat Visual Analyt Knowledge Lab VIS2KNOW Lab Seoul South Korea Univ Fortaleza BR-60811905 Fortaleza Ceara Brazil

Internet and other online media networks have emerged as the most important platforms for the sharing of digital information. However, the readily available editing tools provide an easy way for adversaries to manipulate the data and affect decision-making in various industrial applications. This malicious modification of the content, which has reduced the credibility of information delivery, is a commonly prevalent issue and hence needs serious attention. It also initiates an extreme need for industrial cyber-physical systems (ICPS), which can compare the transferred and received images for correct orientation to ensure that it conveys meaningful information and assists in correct decision-making in industrial automation. In this article, we propose "INDFORG", which employs a novel and highly accurate automatic rotation angle detection and correction algorithm (ARADC) for intelligent detection of forgery in industrial images. ARADC uses basic geometrical concepts, such as Pythagorean theorem and intensity correlation computation and works without any digital signature or watermark. It performs accurately even under several simultaneous signal-processing manipulations. The proposed framework detects the rotation angles blindly with a 99% accuracy rate for rotation up to +/- 89 degrees. Experimental results prove that the proposed algorithm is highly efficient compared to various state-of-the-art approaches and is a preferred ICPS for trustworthy media delivery in industrial automation.

关键词： Forgery Estimation Streaming media Forensics iterative closest point algorithm Multimedia systems Distortion Authentication cyber– physical systems forensics industrial forgery detection multimedia security privacy protection rotational attacks

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Evaluation of the ICP algorithm in 3D point Cloud Registration

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IEEE ACCESS 2020年 8卷 68030-68048页

作者： Li, Peng Wang, Ruisheng Wang, Yanxia Tao, Wuyong Chuzhou Univ Sch Geog Informat & Tourism Chuzhou 239000 Peoples R China Univ Calgary Dept Geomat Engn Calgary AB T2N 1N4 Canada Guangzhou Univ Sch Geog Sci Guangzhou 510006 Peoples R China Wuhan Univ Sch Geodesy & Geomat Wuhan 430079 Peoples R China

The iterative closest point (ICP) algorithm is widely used in three-dimensional (3D) point cloud registration, and it is very stable and robust. However, its biggest drawback is being easily trapped in a local optimal solution, which results in the incorrect registration result. Currently, there is neither a clear effective range to define whether the ICP algorithm will fall into a local optimum nor a study providing a comprehensive evaluation of the ICP algorithm. In this paper, we take the overlap ratio, angle, distance, and noise as the influencing factors of ICP and evaluate the validity, robustness, accuracy, and efficiency of point-to-point and point-to-plane ICP by using four datasets. We first analyze the effective ranges of the two ICP algorithms with respect to overlap ratio, angle, and distance and then propose a universal effective range for the three factors. Next, the effect of Gaussian noise on the validity and accuracy of two ICPs is evaluated. We also analyze the factors influencing ICP accuracy and explain their changing rules. We finally study the effect of different parameters on the efficiency. All results are compared by using point-to-point and point-to-plane ICP algorithms. The results show that the overlap ratio has no effect on validity, but it has a significant influence on accuracy. The angle has a great impact on the validity and efficiency of ICP but has no effect on accuracy. The distance only affects validity, has a limited effect on efficiency and no effect on accuracy. Meanwhile, Gaussian noise has a little effect on the validity. In addition, the general effective range of point-to-point ICP is larger than that of point-to-plane ICP, but the point-to-plane ICP algorithm presents a better efficiency. The point-to-point ICP is more robust to Gaussian noise with respect to validity, while the point-to-plane is more resistant in terms of accuracy.

关键词： iterative closest point algorithm Three-dimensional displays Robustness Convergence Shape Gaussian noise Search problems ICP point cloud registration validity accuracy efficiency

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Registration of Multi-View point Sets Under the Perspective of Expectation-Maximization

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2020年 29卷 9176-9189页

作者： Zhu, Jihua Guo, Rui Li, Zhongyu Zhang, Jing Pang, Shanmin Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Peoples R China Zhongnan Univ Econ & Law Sch Math & Stat Wuhan 430073 Peoples R China

Multi-view registration plays a critical role in 3D model reconstruction. To solve this problem, most previous methods align point sets by either partially exploring available information or blindly utilizing unnecessary information, which may lead to undesired results or extra computation complexity. Accordingly, we propose a novel solution for the multi-view registration under the perspective of Expectation-Maximization (EM). The proposed method assumes that each data point is generated from one unique Gaussian Mixture Model (GMM), where its corresponding points in other point sets are regarded as Gaussian centroids with equal covariance and membership probabilities. As it is difficult to obtain real corresponding points in the registration problem, they are approximated by the nearest neighbor in each other aligned point sets. Based on this assumption, it is reasonable to define the likelihood function including all rigid transformations, which require to be estimated for multi-view registration. Subsequently, the EM algorithm is derived to estimate rigid transformations with one Gaussian covariance by maximizing the likelihood function. Since the GMM component number is automatically determined by the number of point sets, there is no trade-off between registration accuracy and efficiency in the proposed method. Finally, the proposed method is tested on several benchmark data sets and compared with state-of-the-art algorithms. Experimental results demonstrate its superior performance on the accuracy, efficiency, and robustness for multi-view registration.

关键词： iterative closest point algorithm Robustness Three-dimensional displays Solid modeling Gaussian mixture model Computational modeling Gaussian distribution Gaussian mixture model expectation-maximization point set registration

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