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59 results on '"Wu, Haotian"'

13. Robust Multipoint-Sets Registration for Free-Form Surface Based on Probability.

Author

Peng, Weixing, Wang, Yaonan, Zhang, Hui, Chen, Yurong, Wu, Haotian, and Zhao, Jiawen

Subjects
SIMULATED annealing, MAXIMUM likelihood statistics, POINT cloud, LIE algebras, RECORDING & registration
Abstract

Free-form surface reconstruction using point clouds is a common issue in manufacturing. In this article, a robust joint registration approach for multiview point clouds is proposed to address the problems brought by coarse initialization, outliers, and noise. The basic idea is that minimizing the L2 distance between probability distributions of integrated and standard models, such that a robust initialization is provided for fine registration to avoid local minima. The fine registration is formulated as a joint closet point problem, which is implicitly constrained by closed-loop consistency. In addition, a Lie algebra solution is derived to enforce rigid transformations. The robust initialization is judged by the simulated annealing algorithm. Finally, a probabilistic distance is defined and a maximum likelihood estimation of multiview transformations is designed to resist noise. The experiment on simulated and real data illustrate better robustness of our method to initial errors, outliers, and noise. [ABSTRACT FROM AUTHOR]

Published
2022
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16. Geometric Inlier Selection for Robust Rigid Registration With Application to Blade Surfaces.

Author

Wu, Haotian, Wang, Yaonan, Vela, Patricio A., Zhang, Hui, Yuan, Xiaofang, and Zhou, Xianen

Subjects
*GEOMETRIC distribution, *RECORDING & registration, *TURBINE blades, *SURFACE defects, *POINT cloud
Abstract

Robotic 3-D measurement for evaluating the final shape of blades can improve product quality and reduce fabrication costs. However, large or complex objects require multiple scans from different viewpoints to capture the entire object. They must then be registered to a common frame. Errors in registration artificially introduce defects and surface discontinuities in the reconstructed object model. To address the sources of inaccurate registration for methods relying on pairwise associated points, this manuscript describes an inlier selection approach based on the geometric properties of rigid registration, called RiGID. It readily applies to registration strategies that use 3-D interest points and descriptors to associate data across overlapping point clouds. RiGID converts geometric constraint violations into distributions over geometric error values. Analysis of the distributions provides a deterministic and data adaptive way to identify inlier sets. Given the inlier set, standard registration approaches may be applied. RiGID is an efficient preprocessor to rigid registration. It reduces the amount of input data and improves the inlier ratio, which translates to more efficient and accurate registration. Evaluation of the method shows a higher inlier rate and lower registration error compared to baseline outlier rejection methods. The approach is well suited to surfaces with smooth regions and a high percentage of outliers (up to 90%), which engine and turbine blades exhibit. [ABSTRACT FROM AUTHOR]

Published
2022
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17. Trading Decision Making Based on Hybrid Neural Network

Author

Wu Haotian

Subjects
021110 strategic, defence & security studies, 050208 finance, Artificial neural network, business.industry, Stochastic process, Computer science, Deep learning, 05 social sciences, 0211 other engineering and technologies, 02 engineering and technology, Machine learning, computer.software_genre, Profit (economics), Hybrid neural network, Competition (economics), Dominance (economics), 0502 economics and business, Value (economics), Artificial intelligence, business, computer
Abstract

Now, with the dominance of electronic stock trading, it is possible to find and make profit from the price difference in real time. Machine learning has been applied in stock trading for years by companies. Yet as the rising of deep learning, price forecasting models become more accurate, which create more opportunities to gain higher profits. In this work, a novel hybrid neural network is proposed to deal with the stock trading decision making challenge. After properly training with labeled stock trading data, the hybrid neural network model proposed in this paper has been proved to be able to assist stock trading decisions better and achieve higher profits. The proposed hybrid neural network is evaluated on the stock trading data of Jane Street data set provided by the Kaggle competition. It is shown in the experiments that the proposed hybrid neural network outperforms other neural networks. Our neural network achieves as high profit value as 11417, which reveals the efficiency of the proposed method.

Published
2021

22. Pistis: Issuing Trusted and Authorized Certificates With Distributed Ledger and TEE.

Author

Li, Zecheng, Wu, Haotian, Lao, Lap Hou, Guo, Songtao, Yang, Yuanyuan, and Xiao, Bin

Subjects
*CONTRACTS, *BLOCKCHAINS
Abstract

The security of HTTPS fundamentally relies on SSL/TLS certificates issued by Certificate Authorities (CAs), which, however, are vulnerable to be compromised to issue unauthorized certificates (i.e., certificates issued without domains’ permission). Current countermeasures such as Certificate Transparency (CT) can only detect unauthorized certificates rather than preventing them. In this article, we present Pistis, a framework for issuing authorized and trusted certificates with the distributed ledger and Trusted Execution Environment (TEE) technology. In Pistis, TEE nodes validate whether the domain in a requested certificate passes the domain ownership validation (i.e., under corresponding applicants’ control) and submit attested results to a smart contract in the distributed ledger. The smart contract issues a certificate to the applicant when an attested result shows a pass. Therefore, Pistis can ensure its issued certificates are authorized due to the domain ownership validation mechanism in the TEE. Furthermore, as the issued certificates are stored in a Merkle Patricia Tree (MPT) in Pistis, they are trusted and can be verified by a normal user easily. The security of Pistis is formally proved in the Universally Composable (UC) framework. Compared with state-of-the-art, Pistis avoids potential damages by preventing unauthorized certificates from issuing. [ABSTRACT FROM AUTHOR]

Published
2022
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28. Rigid Shape Matching for 3-D Robotic Grinding Measurement With Applications to Blades.

Author

Wu, Haotian, Wang, Yaonan, Zhang, Hui, Yuan, Xiaofang, and Zhou, Xianen

Subjects
*ROBOTICS, *PRODUCT quality, *PRODUCT improvement, *SURFACE preparation, *MEASUREMENT
Abstract

Robotic grinding is a promising method to form the surfaces of industrial components, which improves product quality, lowers machining costs, and reduces processing time. Allowance optimization is a surface matching process, which is important to grinding measurement. The grinding efficiency highly depends on the results of the surface matching between measured points and the design model. However, existing matching methods are usually based on distance and variance minimization, ignoring the grinding requirements to different surfaces, which leads to high processing time and unstable grinding quality. In this article, we proposed a novel surface matching method for robotic grinding. The objective function is constructed via time minimization and involving weights for allowance requirements to different surfaces. This method also balances all measured points’ contributions, avoiding matching distortion due to uneven points’ distribution. Experimental results and comparisons verify the effectiveness of our methods. Comparing to existing matching methods, our method is robust to uneven point distribution and has advantages in reducing the grinding time. [ABSTRACT FROM AUTHOR]

Published
2021
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34. Study of a 6DOF robot assisted ultrasound scanning system and its simulated control handle

Author

Xiong Linfei, Hou Xilong, Guan Xiaolong, Wang Ban, Wu Haotian, Teng Qing, Zhang Jiantao, Wei Shiyou, Jiang Tao, and Yang Jialin

Subjects
Robot kinematics, Diagnostic methods, Computer science, business.industry, Remote diagnosis, education, 020208 electrical & electronic engineering, Ultrasound, 02 engineering and technology, Kinematics, Robotic systems, 0202 electrical engineering, electronic engineering, information engineering, Robot, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business, Ultrasound image
Abstract

Ultrasound scanning is a commonly used diagnostic method in hospital because of its' high efficiency and limited side effects. In this paper, a robot assisted ultrasound scanning system was developed to help doctors do the remote diagnosis. Doctor could control the ultrasound probe through the Internet from a remote place and receive the ultrasound image in real time. Simultaneously, the robot system does not need to change the doctors' habits for the ultrasound diagnosis and it could be used by doctors quickly.

Published
2017

35. Adaptive Real-Time Scheduling for Mixed Task Sets Based on Total Bandwidth Server

Author

Wu Haotian, Peng Azhen, Zheng Liaomo, Guo Ruifeng, and Deng Changyi

Subjects
Computer science, 020208 electrical & electronic engineering, Real-time computing, Processor scheduling, 020206 networking & telecommunications, 02 engineering and technology, Parallel computing, Scheduling (computing), Prediction algorithms, Aperiodic graph, Server, 0202 electrical engineering, electronic engineering, information engineering, ComputerSystemsOrganization_SPECIAL-PURPOSEANDAPPLICATION-BASEDSYSTEMS, Real-time operating system
Abstract

In order to shorten the response times of aperiodic tasks in mixed task sets in real-time systems, this paper proposes an adaptive total bandwidth server (ATBS) algorithm based on aperiodic tasks with varying executing times. This method of reducing response times of aperiodic execution by using predictive execution times instead of worst-case execution times for deadline calculations in the total bandwidth server to obtain shorter deadlines, while ensuring the integrity of periodic tasks. Then, an adaptive total bandwidth server with resource reclaiming which can obtain much shorter response times of aperiodic tasks is proposed. In the evaluation by simulation, the proposed ATBS and ATBSRR techniques shorten average response times for aperiodic tasks, by up to 15.17% and 48.68% compared with the original total bandwidth server technique respectively.

Published
2017

36. A Low Power Scheduling Algorithm with Balanced Factor for Periodic Tasks

Author

Zheng Liaomo, Guo Ruifeng, Deng Changyi, Peng Azhen, and Wu Haotian

Subjects
0209 industrial biotechnology, Schedule, Least slack time scheduling, Computer science, Real-time computing, Clock rate, 02 engineering and technology, Energy consumption, 020202 computer hardware & architecture, Scheduling (computing), Dynamic voltage scaling, Running time, 020901 industrial engineering & automation, Critical speed, 0202 electrical engineering, electronic engineering, information engineering
Abstract

Energy consumption is one of the key issues in real-time system scheduling. In this paper, a new low power algorithm (LPABOBF) is proposed for the periodic task model of real-time systems. The algorithm uses dynamic voltage scaling (DVS) to calculate the optimal static speed at the offline stage, and updates the processor speed at running time by using the slack time of both high priority tasks and low priority tasks. When the task speed is lower than the critical speed, the balance factor is used to analyze whether the critical speed or traditional DVS strategy is used to schedule tasks. Simulation results show that LPABOBF algorithm saves 8.9%~26.19% energy compared with DRA algorithm, and saves 1.9%~20.74% energy consumption compared with DSTRA algorithm.

Published
2017

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