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Hand-eye calibration for flexible manipulator

Journal of Physics: Conference Series 2019年第3期1187卷

作者： Jiahao Li Xiuzhi Li Ao Dun Songmin Jia Yanjun Sun Faculty of Information Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System.

This paper studies the determination of the pose relationship between camera which is embedded in the Barrett hand and end-effector of the UR5. It is fundamental for subsequent visual control. Least-square method and Lie group is used in this paper, and we also come up with an algorithm to increase the quantity of data for least-square to improve the accuracy of eye-in-hand calibration result. Firstly, through camera calibration, the intrinsic and external parameters of the camera are obtained. Secondly, we obtain equation of CX = XD by eye-in-hand calibration, and X not only is the matrix we want to compute but also represents the transformation relationship between camera and end-effector. Finally, to evaluate the proposed method, an experiment was designed, and the test result demonstrates the effectiveness of the proposed approach.

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An Online Method Based on Random Forest for Air Pollutant Concentration Forecasting

An Online Method Based on Random Forest for Air Pollutant Co...

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Chinese Control Conference (CCC)

作者： Jiangeng Li Xingyang Shao Huihong Zhao Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Clean Energy Research and Technology Promotion Center Dezhou University Dezhou Shandong China

With rapid modernization, air quality is becoming gradually deteriorate. To avoid the adverse effects of severe air pollution on human health, we need accurate real-time air quality prediction. The factors relevant to air pollutant concentration forecasting contain simultaneously numeric type (temperature etc.) and non-numeric type (wind direction etc.). Random Forest has many advantages, which includes it can deal with numeric features and non-numeric features. In this study, an online forecasting method based on Random Forest is proposed to predict the concentrations of three kinds of air pollutants (PM 2.5 , NO 2 , SO 2 ), 24 hours in advance. The sliding window is used to take the recent data to retrain Random Forest prediction model and the well-trained models is used to predict the dependent variable at target moment. Before prediction model is trained, a variable selection method based on Random Forests (VSURF) is used to select the factors that are relevant to the forecast of air pollutant concentrations. We evaluate our method with dataset from Microsoft Research. Comparison with baseline methods shows that our method achieve state-of-art performance on air pollutant concentration forecasting. Experimental results also indicate that the features we selected using VSURF method are most important predictors for the prediction of three kinds of air pollutant concentrations.

关键词： Forestry Air pollution Forecasting Predictive models Data models Microsoft Windows Atmospheric modeling

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Design of Adaptive Sliding Mode Controller for Four-Mecanum Wheel Mobile Robot

Design of Adaptive Sliding Mode Controller for Four-Mecanum ...

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第37届中国控制会议

作者： Xingyang Lu Xiangying Zhang Guoliang Zhang Songmin Jia Faulty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education

In this paper, an adaptive sliding mode controller is designed for the omnidirectional mobile robot with four Mecanum wheels to track the trajectory. The Mecanum wheel mobile robot has been widely used in factories, hospitals, families and other workspaces because of its omnidirectional ability. Consider the dynamic model of the omnidirectional mobile robot based on Mecanum wheel with uncertainties and external disturbances, a neural network adaptive sliding mode control(NNASMC) strategy is proposed, which combines the techniques of Radial Basis Function(RBF) neural network adaptive control method and sliding mode control method. Finally, some simulation experiments are conducted in the Matlab environment. Simulation results demonstrate the effectiveness of the proposed control method.

关键词： adaptive sliding mode control Mecanum wheel mobile robot Radial Basis Function(RBF)

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Implementation and Application of Video Distribution Technology Based on OpenGL

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IOP Conference Series: Materials Science and Engineering 2019年第4期563卷

作者： Jiangeng Li Minjie Fan Guangsheng Wang Rihui Sun Faculty of Information Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124 China

The panoramic video contains a wealth of information that vividly reproduces the surrounding scene. However, the larger video information puts forward higher requirements for data transmission, interactivity and real-time. All the video information directly shows that the phenomenon of stagnation and black screen is easy to occur, and the user's traffic is greatly consumed, so that the payment cost of the terminal is large. In a traditional live video broadcast, the information displayed by the client is information with a fixed perspective. The client has no right to select the information, but only displays the obtained information. In order to solve the above problems, this paper has improved the traditional live broadcast mode. In this new live broadcast mode, the host side synthesizes the collected surrounding scene into a super wide-angle video. Then with the OpenGL technology, the host side performs the texture mapping, projection and other steps on the panoramic video. The host then distributes the video to the client. As a result, the client not only gets undistorted video, but also interacts with the server in real time. This approach greatly enhances the visual experience of the client.

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Visual Loop Closure Detection Based on Stacked Convolutional and Autoencoder Neural Networks

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IOP Conference Series: Materials Science and Engineering 2019年第5期563卷

作者： Fei Wang Xiaogang Ruan Jing Huang Faculty of Information Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124 China

Simultaneous localization and mapping is the basis for solving the problem of robotic autonomous movement. Loop closure detection is vital for visual simultaneous localization and mapping. Correct detection of closed loops can effectively reduce the accumulation error of the robot poses, which plays an important role in building a globally consistent environment map. Traditional loop closure detection adopts the method of extracting handcrafted image features, which are sensitive to dynamic environments and are poor in robustness. In this paper, a method called stacked convolutional and autoencoder neural networks is proposed to automatically extract image features and perform dimensionality reduction processing. These features have multiple invariances in image transformation. Therefore, this method is robust to environmental changes. Experiments on public datasets show that the proposed method is superior to traditional methods in terms of accuracy, recall, and average accuracy, thereby validating the effectiveness of the proposed method.

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Prediction of Effluent Ammonia Nitrogen Using FNN-based CBR

Prediction of Effluent Ammonia Nitrogen Using FNN-based CBR

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Chinese Control Conference (CCC)

作者： Limin Quan Xudong Ye Cuili Yang Junfei Qiao Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Huludao Power Co. Ltd. Liaoning Power Co. Ltd. Huludao

Due to the complex dynamic behavior in wastewater treatment process, online measurement of ammonia nitrogen value is very difficult. In this paper, a case-based reasoning (CBR) prediction model based on a feedforward neural network (FNN) is introduced to predict the effluent ammonia nitrogen value. First, easily measured feature variables which have great effect on effluent ammonia nitrogen value were selected. Next, the prediction model was established, and attribute weights in case retrieval were determined by the connection weights of a trained FNN. Finally, based on the data in a real wastewater treatment process, simulation experiments were carried out. The results show that the prediction model using FNN-based CBR is effective and has better prediction accuracy than some other methods.

关键词： Neurons Predictive models Effluents Object oriented modeling Nitrogen Feedforward neural networks Artificial neural networks

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Multi-source Mechanical Signal Analysis Based on Linear Latent Structure Model for Mill Load Parameter Forecasting

Multi-source Mechanical Signal Analysis Based on Linear Late...

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第30届中国控制与决策会议

作者： Jian Tang Zhuo Liu Xiaoli Li Gang Yu Jianjun Zhao Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System State Key Laboratory of Synthetical Automation for Process Industries Northeaster University State Key Laboratory of Process Automation in Mining & Metallurgy Beijing Key Laboratory of Process Automation in Mining & Metallurgy

Accurate detect mill load inside the heavy rotating ball mill is one of the key factors for realizing operation optimization and control of the mineral grinding process. By using multi-source mechanical signals, such as vibration and acoustical signals at different location of the ball mill system, to construct mill load parameter forecasting(MLPF) model has been a hot and focus recently. However, a few researches about the relative accuracy and reliability contribution ratio of these different multi-source signals are addressed. It is necessary to select the suitable mechanical signal at different industrial applications. Aim to this problem, multi-source mechanical signal analysis based on linear latent structure MLPF model is studied in this paper. At first, Fast Fourier transformer(FFT) is used to obtain mechanical frequency spectrum of the time domain vibration and acoustic signals with characteristics of high dimension and strong co-linearity. Then, project to latent structure(PLS) models are build based on these spectra data. Finally, a new defined metric is used to estimate relative accuracy and reliability contribution ratio of these multi-source signals based on generalization performance and structure parameter of different prediction model. Experiments based on a laboratory-scale ball mill are used to valid the proposed method.

关键词： Mechanical frequency spectrum ball mill mill load parameter forecasting latent structure model

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An Early Stage Researcher's Primer on systems Medicine Terminology

Network and Systems Medicine

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Network and systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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MPPR: Multi Perspective Page Rank for User Influence Estimation

MPPR: Multi Perspective Page Rank for User Influence Estimat...

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International Conference on Big Data and Smart computing (BIGCOMP)

作者： Lifang Wu Bowen Yang Meng Jian Jitao Sang Dai Zhang Lei Zhang Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China

ISBN: (纸本)9781538636503

With the rapid development of social networks, it is important to identify users with high influence. In content curation social networks (CCSNs), there are two kinds of user relations. The one is the explicit user relations from user's following behavior. And the other is the content based user relations from user's repin behavior. Based on these observation, we propose multi perspective page rank (MPPR) to estimate user influence. The proposed algorithm integrates both user relations to calculate influence scores of the users automatically. User influences are computed based on the transition matrix of following and repin relations. When the iteration is convergent, every user will get a fixed influence value. Experiments on the dataset containing 11990 users, 920610 following relations and 39321016 repin relations show that the proposed algorithm outperforms the typical PageRank algorithm.

关键词： Conferences Big Data

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Optimal Fractional Order of Self-propelled Particles in the Synchronization Motion 36

Optimal Fractional Order of Self-propelled Particles in the ...

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第36届中国控制会议

作者： Xiangyin Zhang Meng Jian Songmin Jia Xiuzhi Li Faulty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education

This paper proposed a novel fractional-order Vicsek model(FOVM),which can effectively improve the convergence performance of the original Vicsek *** experiment results show that there is the optimal value of the fract... 详细信息

ISBN: (纸本)9781538629185

关键词： Fractional Calculus Vicsek Model Synchronization

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