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2nd International Conference on Artificial Intelligence and Information Systems (ICAIIS )

作者： Han, Xiaowei Hu, Wen Weng, Rui Yao, Guilin Zhang, Yuru Hua, Xiaojie Harbin Univ Commerce Sch Comp & Informat Engn Harbin Peoples R China Harbin Univ Commerce Lab Elect Commerce & Informat Proc Harbin Peoples R China Harbin Univ Sci & Technol Sch Software Harbin Peoples R China

ISBN: (纸本)9781450390200

In recent years, with the development of E-commerce economy, growth of multimedia information data is very rapid. The data without image compression processing will be limited in processing and storage, and it is not conducive to the use of machine learning for data mining. In this paper, svd (Singular Value Decomposition) image compression algorithm is used to reduce the dimension of the images in the commodity image data set of Harbin University of Commerce, and preserve the image features. This study mainly uses the scikit-learn open source machine learning library written in Python language, combined with the function of the module, to write the target algorithm. The experimental results show that different commodity images in the dataset can achieve 0.7-0.9 compression ratio through the algorithm, while maintaining good image features. It is significant to improve the efficiency of image data transmission and the machine learning research for this dataset.

关键词： Image compression machine learning svd algorithm dimensionality reduction

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Improved svd algorithm for DOA Estimation of Coherent Signal Sources 36

Improved SVD Algorithm for DOA Estimation of Coherent Signal...

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第36届中国控制会议

作者： Ji Zheng-yan Chen Hui Zhang Jia-jia Key Research Laboratory Air Force Early Warning Academy

ISBN: (纸本)9781538629185

In order to solve the coherent problem of Orthogonally Matched(OMP) Pursuit algorithm in sparse reconstruction,the feature vector corresponding to large eigenvalue of svd is constructed by using received data,and two improved methods are *** methods reconstruct the angle through the feature vector,and can reconstruct the angle information accurately without knowingthe number of the signal *** with the classical OMP algorithm,the operation speed is faster and the sparse reconstruction effect is *** analysis and simulation results verify the good performance of the algorithm.

关键词： OMP algorithm Coherent signal source svd algorithm

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Efficient Information Sharing Methods for Online Education Platforms

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MOBILE NETWORKS & APPLICATIONS 2024年 1-14页

作者： Dong, Xiuying Guo, Zhijun Changchun Univ Finance & Econ Sch Marxism Changchun 130000 Peoples R China Hunan Normal Univ Coll Informat Sci & Engn Changsha 410081 Peoples R China

A large amount of redundant teaching resource interferes with the information sharing process in the online education platform, leading to the problems of information silos and data sparsity. Regarding this, we propose an improved method for information sharing in the application of online education platforms. Firstly, based on the entropy method, we calculate the weight of resource allocation in online education platforms. Upon obtaining the weights for resource allocation in online education platforms, we assess the resource allocation level using the Dagum Gini coefficient test, and analyze the current status of information sharing. Furthermore, we implement information clustering on online education platforms using the K-means algorithm to facilitate effective sharing among different teaching modules, addressing the issue of information silos. Then, using the Singular Value Decomposition (svd) algorithm, we conduct dimensionality reduction of information on online education platforms. By reducing the matrix dimensions and extracting key features from the rating matrix, we aim to mitigate data sparsity and enhance the effectiveness of information mobility and sharing on the platform. Finally, after the determination of keyword consistency, information sharing on the online education platform can be implemented. The experimental results demonstrate that this method achieved a recall rate of 98.32% for grassroots information sharing, with a CPU usage of 10.25%. This effectively enhances the performance of information sharing methods on online education platforms.

关键词： 3D community education Matrix decomposition Collaborative filtering Dimensionality reduction of data svd algorithm Dagum Gini coefficient

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Extraction of pipeline defect feature based on variational mode and optimal singular value decomposition

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Petroleum Science 2023年第2期20卷 1200-1216页

作者： Min Zhang Yan-Bao Guo Zheng Zhang Ren-Bi He De-Guo Wang Jin-Zhong Chen Tie Yin College of Mechanical and Transportation Engineering China University of PetroleumBeijing102249China China Special Equipment Inspection and Research Institute Beijing100029China China Petroleum and Natural Gas Pipeline Research Institute Co.LTD LangfangHebei 065000China

Because the magnetic signal information of pipeline defects obtained by magnetic flux leakage detection contains interference signals, it is difficult to accurately extract the features. Therefore, a novel pipeline defect feature extraction method based on VMD-Osvd (variational modal decomposition - optimal singular value decomposition) is proposed to promote the signal to noise ratio (SNR) and reduce aliasing in the frequency domain. By using the VMD method, the sampled magnetic signal is decomposed, and the optimal variational mode is selected according to the rate of relative change (VMK) of Shannon entropy (SE) to reconstruct the signal. After that, svd algorithm is used to filter the reconstructed signal again, in which the H-matrix is optimized with the phase-space matrix to enhance SNR and decrease the frequency domain aliasing. The results show that the method has excellent denoising ability for defect magnetic signals, and SNR is increased by 21.01%, 24.04%, 0.96%, 32.14%, and 20.91%, respectively. The improved method has the best denoising effect on transverse mechanical scratches, but a poor denoising effect on spiral welding position. In the frequency domain, the characteristics of different defects are varied, and their corresponding frequency responses are spiral weld corrosion > transverse mechanical cracking > girth weld > deep hole > normal pipe. The high-frequency band is the spiral weld corrosion with f1 = 153.37 Hz. The low-frequency band is normal with f2 = 1 Hz. In general, the VMD-Osvd method is able to improve the SNR of the signal and characterize different pipe defects. And it has a certain guiding significance to the application of pipeline inspection in the field of safety in the future.

关键词： VMD method VMK svd algorithm Phase space matrix Signal characteristics Pipeline defect Reconstruct

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FPGA-Based Hardware Accelerator for Matrix Inversion

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SN Computer Science 2023年第2期4卷 147页

作者： Kokkiligadda, Venkata Siva Kumar Naikoti, Vijitha Patkotwar, Gaurao Sunil Sabat, Samrat L. Peesapati, Rangababu Centre for Advanced Studies in Electronics Science and Technology University of Hyderabad Telangana Hyderabad 500046 India Department of Electronics and Communications Engineering NIT Meghalaya Meghalaya Shillong 793001 India

Matrix inversion is a computationally expensive operation in many scientific applications. Performing matrix inversion of rank deficient large order matrices is still a challenge due to its computational overhead. This paper presents the hardware implementation of matrix inversion with (i) singular value decomposition (svd) based on Lanczos and implicit triQR algorithms, (ii) QR method that uses modified Gram–Schmidt (MGS) technique and (iii) inbuilt linear algebra package QR inverse using Xilinx Vivado high level synthesis platform. All the three algorithms are implemented on Pynq-Z1 Field Programmable Gate Array (FPGA) using System on Chip (SoC) approach. The resource utilization along with accuracy, hardware execution time with and without loop optimization are reported for the aforementioned matrix inversion techniques of different matrix sizes. We achieved the hardware acceleration factor with loop optimization as 111x and 104x, respectively, for the matrix inversion of the MGS algorithm and svd based on Lanczos algorithm with respect to the software implementation execution time on Pynq-Z1 FPGA for the matrix size 90 × 90. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Implicit triQR Lanczos algorithm Matrix inversion Modified Gram–Schmidt QR decomposition svd algorithm

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A Dual Quaternion-Based Approach for Coordinate Calibration of Dual Robots in Collaborative Motion

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IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第3期5卷 4086-4093页

作者： Fu, Zhongtao Pan, Jiabin Spyrakos-Papastavridis, Emmanouil Chen, Xubing Li, Miao Wuhan Inst Technol Sch Mech & Elect Engn Wuhan 430205 Peoples R China Kings Coll London Ctr Robot Res London WC2R 2LS England Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Imperial Coll London Dyson Sch Design Engn London SW7 2AZ England Wuhan Univ Sch Power & Mech Engn Wuhan 430072 Peoples R China

Coordinate calibration accuracy is a prerequisite for the achievement of collaborative motion of dual robots, and visual positioning. In the case of AXB=YCZ, the associated hand-eye (X), robot-robot (Y), and tool-flange (Z) coordinate relationships need to be determined in order to provide accurate data for dual-robot manipulation. The classical independent estimations and separable solving of rotational and translational components, inevitably induce certain levels of inaccuracy and inconsistency. To this end, the work presented herein describes an efficient approach based on dual quaternion, to the calibration of the hand-eye (X), robot-robot (Y), tool-flange (Z) transformations, corresponding to the dual-robot collaborative motion. After establishing a closed-form kinematic equation for dual robots in form, the calibration process is divided into two steps: 1) the hand-eye transformation (X) calibration is performed by letting the one robot with the visual sensor move freely, while keeping the other robot with the end-tool in a fixed configuration, thereby allowing the calibration result to be obtained by solving the -based linear equations with the svd algorithm;2) the simultaneous calibration of robot-robot (Y) and tool-flange (Z) is transformed into a -based equation, and the results are obtained in a similar way. A simulation analysis is carried out, accounting for the presence of noise levels and different data pairs, while calibration experiments are also conducted to verify the accuracy and efficacy of the proposed method.

关键词： Coordinate calibration dual quaternions svd algorithm robots collaborative motion

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Research on The Anonymous Customer Segmentation Model of Telecom 5

Research on The Anonymous Customer Segmentation Model of Tel...

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IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Luo, Bin Lin, Shaofu Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Beijing Univ Technol Beijing Inst Smart City Beijing Peoples R China

ISBN: (纸本)9781728143231;9781728143224

According to the ministry of industry and information technology's 2018 statistical bulletin on the communications industry, the net increase of mobile phone users reached 149 million in the year, bringing the total number to 1.57 billion. How to classify customers according to their value and put forward corresponding business strategies for different value customers has become one of the key issues in the field of telecommunications research. In order to overcome the shortcomings of traditional manual classification, this paper adopts k-means clustering algorithm and svd algorithm to conduct customer clustering in combination with operator data, and achieve the best classification of customers in the clustering process, and analyzes the behavioral characteristics of various groups. The results show that the algorithm can effectively improve the classification efficiency and accuracy of telecom users and reduce the errors caused by traditional classification. Finally, the conclusion and further work are given.

关键词： svd algorithm K-means algorithm customer value analysis data dimension reduction

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On GPU-CUDA as preprocessing of fuzzy-rough data reduction by means of singular value decomposition

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SOFT COMPUTING 2018年第5期22卷 1525-1532页

作者： Cuomo, Salvatore Galletti, Ardelio Marcellino, Livia Navarra, Guglielmo Toraldo, Gerardo Univ Naples Federico II Complesso Univ Monte St Angelo Dept Math & Applicat R Caccippoli Via Cintia Naples Italy Univ Naples Parthenope Dept Sci & Technol Ctr Direz Isola c4 Naples Italy

Data reduction algorithms often produce inaccurate results for loss of relevant information. Recently, the singular value decomposition (svd) method has been used as preprocessing method in order to deal with high-dimensional data and achieve fuzzy-rough reduct convergence on higher dimensional datasets. Despite the well-known fact that svd offers attractive properties, its high computational cost remains a critical issue. In this work, we present a parallel implementation of the svd algorithm on graphics processing units using CUDA programming model. Our approach is based on an iterative parallel version of the QR factorization by means of Givens rotations using the Sameh and Kuck scheme. Our results show significant improvements in terms of performances with respect to the CPU version that encourage its usability for this expensive processing of data.

关键词： svd algorithm GPU computing Performance evaluation

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Tunable Floating-Point for Embedded Machine Learning algorithms Implementation 15

Tunable Floating-Point for Embedded Machine Learning Algorit...

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15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD) / 14th Conference on PhD Research in Microelectronics and Electronics (PRIME)

作者： Franceschi, Marta Nannarelli, Alberto Valle, Maurizio Univ Genoa DITEN Cosm Lab Genoa Italy Tech Univ Denmark DTU Compute Lyngby Denmark

ISBN: (纸本)9781538651537

The development of embedded and real-time systems for Machine Learning data processing is challenging (e.g. IoT). Low latency, low power consumption and reduced hardware complexity should be the characteristics of such systems. Considering prosthetic applications, which are error-tolerant, a technique that tunes the precision of operands and operations has been chosen for a Machine Learning algorithm used for tactile data processing. This paper presents the implementation of a Tunable Floating-Point (TFP) representation into a Singular-Value Decomposition (svd) algorithm based on the One-Sided Jacobi method. The TFP representations demonstrate high performance and efficiency improvements of the svd algorithm.

关键词： Simulation power efficiency svd algorithm

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Blind estimation of PN sequence based on FLO joint M estimation for short-code DSSS signals 7

Blind estimation of PN sequence based on FLO joint M estimat...

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7th International Conference on Digital Home (ICDH)

作者： Sun, Xiyan Fan, Zhuo Ji, Yuanfa Yan, Suqing Wang, Shouhua Zhen, Weimin Guilin Univ Elect Technol Guangxi Key Lab Precis Nav Technol & Applicat Guilin 541004 Peoples R China Natl & Local Joint Engn Res Ctr Satelite Nav & Lo Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Expt Ctr Informat Sci Guilin 541004 Peoples R China Key Lab Radio Wave Environm & Modeling Technol Qingdao 266000 Shandong Peoples R China

ISBN: (纸本)9781538694961

When using a singular value decomposition (svd) algorithm to estimate the pseudo code sequence of short code direct sequence spread spectrum (DSSS) signals directly under impulse noise, the pseudo code information extracted by the algorithm will be seriously interfered, and the estimation performance will deteriorate obviously. In this paper, proposed is a pseudo code sequence blind estimation algorithm based on fractional low order(FLO) joint M estimation. Under the condition of known pseudo code rate and pseudo code period, the received signal is segmented by the size of double PN period, and the fractional low order matrix of the received signal is constructed by using this algorithm in order to reduce the noise component, and then the matrix is decomposed by the svd algorithm. By taking the summation and subtraction operation between the absolute value of the principal component and its complement sets to estimate the position of the out-of-step point of the pseudo code. Finally, the blind estimation of a pseudo code sequence is realized. Simulation results show that the proposed algorithm can greatly improve the performance of pseudo code sequence blind estimation in an impulse noise channel. When the signal-to-noise ratio (SNR) is about-5 db, the accuracy of the]pseudo code estimation can be kept above 90%.

关键词： Direct Sequence Spread Spectrum (DSSS) signal Pseudo-code sequence Fractional lower order M estimation svd algorithm Impulse noise

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