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Design of Intelligent Robot Platform based on Multi-sensor Fusion

Journal of Physics: Conference Series 2020年第1期1606卷

作者： Ya Dajin Lei Xiaochun Li Yue Chen Junyan Huang Rongcun Lan Lin School of computer and information security Guilin University of Electronic Technology Guilin 541004 China Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin 541004 China Guangxi Cooperative Innovation Center of cloud computing and Big Data Guilin University of Electronic Technology Guilin 541004 China

The mobile robot adapts to the more complicated indoor and outdoor environments, and can expand its scope of application. In order to reduce the influence of the cumulative error caused by navigation in complex environments, the indoor mobile robot that combines Inertial Measurement Unit (IMU) and encoder fusion is designed and implemented. In view of the limitations of the traditional single lidar scheme, a Multi-sensor Fusion scheme is proposed to achieve indoor map construction, path planning, multi-point navigation and other functions, and a MSIF KartoSLAM (Multi-sensor information Fusion) algorithm is proposed, which combines the KartoSLAM algorithm and Multi-sensor information to achieve map construction in complex environments. Through comprehensive testing in the indoor environment, the results show that the Multi-sensor Fusion scheme is superior to the traditional single lidar scheme, and can achieve higher accuracy in mapping and navigation. At the same time, the robot platform can also be combined with the Internet of Things technology and integrated into intelligent housing system.

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Simple Iterative Clustering on Graphs for Robust Model Fitting

Simple Iterative Clustering on Graphs for Robust Model Fitti...

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IEEE Visual Communications and Image processing (VCIP)

作者： Hailing Luo Guobao Xiao Hanzi Wang Fujian Key Laboratory of Sensing and Computing for Smart City School of Information Science and Engineering Xiamen University Xiamen China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

ISBN: (纸本)9781538644591;9781538644584

In this paper, we propose a novel method, simple iterative clustering on graphs (SICG), to deal with robust model fitting problems. Specifically, we first construct a graph, where each vertex denotes a model hypothesis and each edge represents the similarity between two model hypotheses, for model fitting. We then propose a simple iterative clustering algorithm, which adapts the k-medoids clustering algorithm, to intuitively estimate model instances in data. The proposed SICG method is able to effectively fit and segment multiple-structure data contaminated with a large number of outliers and noises. Experimental results show that SICG achieves superior fitting results over several state-of-the-art model fitting methods on real images.

关键词： Computational modeling Clustering algorithms Data models Iterative algorithms Adaptation models Image segmentation Mathematical model

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Design of People Flow Monitoring System in Public Place based on MD-MCNN

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Journal of Physics: Conference Series 2020年第1期1606卷

作者： Xiong Tianmin Lei Xiaochun Zheng Xingwen Chen Junyan Feng Yizhou School of computer and information security Guilin University of Electronic Technology Guilin 541004 China Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin 541004 China Guangxi Cooperative Innovation Center of cloud computing and Big Data Guilin University of Electronic Technology Guilin 541004 China

Due to the limitation of hardware resources, the traditional people flow monitoring system based on computer vision in public places can't meet different crowd-scale scenarios. Therefore, a people flow monitoring system based on MD-MCNN algorithm is designed, which is an application system combining the improved SSD object detection algorithm MNSSD and MCNN density map regression algorithm. In the initial stage, the system uses MNSSD for accurate detection and counting. If the people flow gradually reaches a certain threshold, the system automatically uses MCNN to estimate people flow until the people flow falls below the threshold. Through the experimental verification, the system can realize the people flow statistics of low-density and high-density people in different scenarios, and can be applied on the existing embedded platform. This scheme can be extended to smart cities, smart scenic spots, smart transportation and other fields.

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Machine learning dynamical phase transitions in complex networks

arXiv

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arXiv 2019年

作者： Ni, Qi Tang, Ming Liu, Ying Lai, Ying-Cheng School of Information Science and Technology East China Normal University Shanghai200241 China School of Mathematical Sciences Shanghai Key Laboratory of PMMP East China Normal University Shanghai200241 China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China School of Computer Science Southwest Petroleum University Chengdu610500 China Big Data Research center University of Electronic Science and Technology of China Chengdu610054 China School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States

Recent years have witnessed a growing interest in using machine learning to predict and identify critical dynamical phase transitions in physical systems (e.g., many body quantum systems). The underlying lattice structures in these applications are generally regular. While machine learning has been adopted to complex networks, most existing works concern about the structural properties. To use machine learning to detect phase transitions and accurately identify the critical transition point associated with dynamical processes on complex networks thus stands out as an open and significant problem. Here we develop a framework combining supervised and unsupervised learning, incorporating proper sampling of training data set. In particular, using epidemic spreading dynamics on complex networks as a paradigmatic setting, we start from supervised learning alone and identify situations that degrade the performance. To overcome the difficulties leads to the idea of exploiting confusion scheme, effectively a combination of supervised and unsupervised learning. We demonstrate that the scheme performs well for identifying phase transitions associated with spreading dynamics on homogeneous networks, but the performance deteriorates for heterogeneous networks. To strive to meet this challenge leads to the realization that sampling the training data set is necessary for heterogeneous networks, and we test two sampling methods: one based on the hub nodes together with their neighbors and another based on k-core of the network. The end result is a general machine learning framework for detecting phase transition and accurately identifying the critical transition point, which is robust, computationally efficient, and universally applicable to complex networks of arbitrary size and topology. Extensive tests using synthetic and empirical networks verify the virtues of the articulated framework, opening the door to exploiting machine learning for understanding, detection, prediction, a

关键词： Complex networks

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Petri Net Control Method for Pipe-line Systems and Its Implementation via CIF3 ⁎

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IFAC-PapersOnLine 2019年第24期52卷 261-266页

作者： YuHao Fu JiLiang Luo Huifeng Wu Jianhong Ye Yi-sheng Huang College Information Science and Engineering Huaqiao University Xiamen 361021 China College Information Science and Engineering Huaqiao University Xiamen 361021 China and also with Fujian Engineering Research Center of Motor Control and System Optimal Schedule Xiamen 361021 China Institute of Intelligent and Software Technology Hangzhou Dianzi University Hangzhou 310018 China Computer Science and Technology Huaqiao University Xiamen 361021 China and also with Fujian Provincial Key Laboratory of Data Intensive Computing Quanzhou 362000 China Department of Electrical Engineering National Ilan University Taiwan

As for a pipe-line system subjected to complex operations, an approach is proposed to synthesize the controller via Petri nets (PNs) such that the plant is run as concurrently as possible and the loads of equipments are balanced. A P-timed and labeled PN is designed to model the whole process of a pipe-line system, where tasks, including transporting material from one tank to another and cleaning tank, are represented by operational places, and level sensors amounted in tanks are represented by labels assigned to transitions. Further, monitor places are designed to resolve the conflict relations among tasks due to the shared valves and pipes. An method is presented to translate an PN controller into a CIF3 (Compositional Interchange Format) model, which can be converted into a PLC program in the CIF3 tool. A beer filtration plant is taken as an example to illustrate the approach, and its simulation experiments are carried out to verify the theoretic results.

关键词： Chemical plant Valve control Petri net CIF3

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A beam search approach based on action space for the 2D rectangular packing problem 1

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9th International Symposium on Intelligence Computation and Applications, ISICA 2017

作者： Yin, Aihua Wang, Lei Hu, Dongping Rao, Hao Deng, Song School of Software and Internet of Things Engineering Jiangxi University of Finance and Economics NanchangJiangxi330023 China College of Computer Science and Technology Wuhan University of Science and Technology WuhanHubei430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System WuhanHubei430081 China

ISBN: (数字)9789811316517

ISBN: (纸本)9789811316500

A beam search algorithm is presented to solve the 2D rectangular packing problem. The basic algorithms work according to 7 rule vectors of heuristic selection rules designed to select a corner-sticking action. Furthermore, the trade-off scheme of breadth first search (BFS) and the depth first search (DFS) increases the algorithm’s effectiveness and efficiency. The improved version of the algorithm adopts a rough phase to get a height for the stripe and a refine phase to obtain better solution for the problem. Computational experiments run on two sets of well-known benchmark instances and the computational results show that the algorithm outperforms the current best algorithms. Especially, for two benchmark instances ZDF6 and ZDF7, our algorithm finds the best packing configurations so far. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Benchmarking

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Accelerated nuclear magnetic resonance spectroscopy with deep learning

arXiv

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arXiv 2019年

作者： Qu, Xiaobo Huang, Yihui Lu, Hengfa Qiu, Tianyu Guo, Di Agback, Tatiana Orekhov, Vladislav Chen, Zhong Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance State Key Laboratory of Physical Chemistry of Solid Surfaces Xiamen University Xiamen361005 China School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Department of Molecular Sciences Swedish University of Agricultural Sciences Uppsala Sweden Department of Chemistry and Molecular Biology University of Gothenburg Box 465 Gothenburg40530 Sweden

Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental time. We present a proof-of-concept of application of deep learning and neural network for highquality, reliable, and very fast NMR spectra reconstruction from limited experimental data. We show that the neural network training can be achieved using solely synthetic NMR signal, which lifts the prohibiting demand for large volume of realistic training data usually required in the deep learning approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Nuclear magnetic resonance spectroscopy

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Granular Computing: An Augmented Scheme of Degranulation Through a Modified Partition Matrix

arXiv

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arXiv 2020年

作者： Xu, Kaijie Pedrycz, Witold Li, Zhiwu Xing, Mengdao School of Electro-Mechanical Engineering Xidian University Xi’an710071 China Department of remote sensing science and technology School of Electronic Engineering Xidian University Xi’an710071 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Institute of Systems Engineering Macau University of Science and Technology Macau999078 China National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China Collaborative Innovation Center of Information Sensing and Understanding Xidian University Xi’an710071 China National Key Laboratory of Microwave Imaging Technology Institute of Electronics Chinese Academy of Sciences Beijing100190 China

As an important technology in artificial intelligence Granular Computing (GrC) has emerged as a new multi-disciplinary paradigm and received much attention in recent years. information granules forming an abstract and efficient characterization of large volumes of numeric data have been considered as the fundamental constructs of GrC. By generating centroids (prototypes) and partition matrix, fuzzy clustering is a commonly encountered way of information granulation. As a reverse process of granulation, degranulation involves data reconstruction completed on a basis of the granular representatives (decoding information granules into numeric data). Previous studies have shown that there is a relationship between the reconstruction error and the performance of the granulation process. Typically, the lower the degranulation error is, the better performance of granulation process becomes. However, the existing methods of degranulation usually cannot restore the original numeric data, which is one of the important reasons behind the occurrence of the reconstruction error. To enhance the quality of reconstruction (degranulation), in this study, we develop an augmented scheme through modifying the partition matrix. By proposing the augmented scheme, we dwell on a novel collection of granulation-degranulation mechanisms. In the constructed approach, the prototypes can be expressed as the product of the dataset matrix and the partition matrix. Then, in the degranulation process, the reconstructed numeric data can be decomposed into the product of the partition matrix and the matrix of prototypes. Both the granulation and degranulation are regarded as generalized rotation between the data subspace and the prototype subspace with the partition matrix and the fuzzification factor. By modifying the partition matrix, the new partition matrix is constructed through a series of matrix operations. We offer a thorough analysis of the developed scheme. Copyright © 2020, The Authors. Al

关键词： Granulation

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Research on the key technology of Safety Protection in Application Environment of Blockchain

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IOP Conference Series: Materials Science and Engineering 2019年第3期569卷

作者： Hua Zhang Jiong Wang Ge Xu Zhiqiang Ruan College of Computer and Control Engineering Mmjiang University Fuzhou 350108 china Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University)

This paper analyzes the current situation of blockchain application security. Then, makes a concrete analysis on the problems existing in the application safety of blockchain, including the attack of consensus algorithm, the leakage of privacy, and the programming vulnerability of blockchain and hash collisions. Then it introduces in detail the three key technologies of security protection under the blockchain application environment, P2P network technology, asymmetric encryption technology and consensus mechanism technology. In order to make more relevant people know more about the key technologies of safety protection in the application environment of blockchain.

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Optimal protein structure extraction with clustering algorithm

Optimal protein structure extraction with clustering algorit...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Jiafu Zhao Xiaolong Zhang Min Cao Hubei Hubei Key Laboratory of Intelligent Information Wuhan University of Science and Technology Wuhan China Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China. 430065

In this paper, we use K-means++ and AP algorithm to cluster the five protein similarity measures of RMSD, TM, MaxSub, GDT-TS and GDT-HA. As for the selection of the number of clusters, using the measures of Scikit-learn to value the cluster result to attain an optimal number of clusters. Also, we optimize the AP algorithm through the change of the preference value, whose value changes from the general mean value, max value, min value to mean value of 4 neighbour at the corresponding position. Moreover, we propose a cluster center selection algorithm based on mean distance from data points to cluster center and number of data points in a cluster, which could automatically delete exception value, thus improve the accuracy of selection cluster center. After the cluster and selection of cluster center, we get better similarity between protein structure and natural protein compare to the earlier results, which means a lot to the protein structure prediction.

关键词： Proteins Clustering algorithms Predictive models Amino acids Indexes Prediction algorithms Bioinformatics

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