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An efficient iterative algorithm for laser ranging signals extraction based on multi-frame overlaying

OPTICS COMMUNICATIONS 2025年 583卷

作者： Zhou, Chengkai Qian, Qiang Li, Hui Han, Xida Wu, Xianlin Li, Ming Sun Yat Sen Univ Tianqin Res Ctr Gravitat Phys Frontiers Sci Ctr Tianqin Gravitat Wave Res Ctr CNSAMOE Key Lab Tianqin Mis Zhuhai Campus Zhuhai 519082 Peoples R China Sun Yat Sen Univ Frontiers Sci Ctr Tianqin Gravitat Wave Res Ctr CNSA Sch Phys & Astron Zhuhai Campus Zhuhai 519082 Peoples R China

At present, the commonly used signal extraction algorithms face limitations in low signal-to-noise ratio and prediction data deviation, leading to restricted interpretability. This paper presents an algorithm for extracting laser ranging signals based on the concept of multi-frame overlaying. The statistical dimension is enhanced from 1D to 2D by considering the cumulative effect of laser ranging signals over time and utilizing a portion of the ranging data to create a template. The physical significance of the algorithm parameters is examined by an elucidation of their calculation method. The algorithm is utilized for processing the observation data from Tianqin laser ranging station, and the results demonstrate its effective signal extraction capability. In the simulation data, our algorithm demonstrates higher accuracy across various signal-to-noise ratios. Thus, our algorithm offers a solution to mitigate the low signal-to-noise ratio and prediction data deviation.

关键词： Laser ranging Signal extraction Multi-frame overlaying algorithm evaluation

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BP Neural Network and Cloud algorithm in Marketing Big Data 14th

BP Neural Network and Cloud Algorithm in Marketing Big Data

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14th International Conference on Frontier Computing, FC 2024

作者： Zhang, Cuiping Yin, Shuai School of Economics and Management Weifang Engineering Vocational College Shandong Weifang China

ISBN: (纸本)9789819623907

Aiming at the issues of long data analysis cycle and slow market response in the analysis and application of marketing big data, this article applies predictive analysis, data mining and other methods based on cloud-based back propagation neural network (BPNN), so as to solve the problems in the traditional marketing big data analysis, aiming to improve the effectiveness and implementation efficiency of marketing strategies. In this article, it is found that optimization of marketing strategies and precision marketing can be achieved by combining the learning capabilities of BPNN with the efficient processing capabilities of cloud algorithms. The resource consumption of the cloud-based BPNN used in the study is 10% higher than that of the autoencoder model;the accuracy of the cloud-based BPNN is 25% higher than that of the autoencoder model;the resource utilization of cloud-based BPNN is 15% higher than that of autoencoder models. Therefore, the cloud-based BPNN has the advantages of low resource consumption and high accuracy, which can be more effectively applied in marketing big data analysis and decision-making. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： algorithm evaluation BP Neural Network Cloud Computing Marketing Big Data Precision marketing

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Data Mining MRO-BP Network-Based evaluation Effectiveness of Music Teaching

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第1期16卷 180-189页

作者： Fan, Yifan Henan Inst Sci & Technol Sch Mus & Dance Xinxiang 453003 Henan Peoples R China

This study addresses the need for data analysis in evaluating the teaching outcomes of higher music education. It proposes a solution using data-driven algorithms to measure and analyze these outcomes. This study focuses on the issue of measuring and evaluating the outcomes of music education teaching. It analyzes the process of measuring and assessing these outcomes, designs a program for doing so, and introduces key technologies such as music education teaching process analysis, measurement of music teaching outcomes, construction of an assessment model for music teaching outcomes, and application of the assessment model. The study selects teaching content, practical skills, and social practice ability as the three aspects to evaluate. The results demonstrate that this method achieves higher assessment accuracy and requires less time, effectively addressing the challenge of measuring and evaluating the teaching outcomes of higher music education using big data. The findings demonstrate that the technique exhibits a high level of assessment accuracy and is less time-consuming. Additionally, it effectively addresses the challenge of measuring and evaluating the teaching accomplishments in higher music education from the viewpoint of big data.

关键词： Mushroom propagation optimisation algorithm BP neural network higher music education teaching outcomes measurement algorithm evaluation

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evaluation on algorithm reliability and efficiency for an image flame detection technology

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JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY 2023年第11期148卷 5063-5070页

作者： Yang, Yi Wang, Xue-Feng Pan, Meng-Yi Li, Pu Tsai, Yun-Ting Xian Univ Sci & Technol Coll Safety Sci & Engn Xian 710054 Peoples R China Zhengzhou Airport Econ Zone Fire Brigade Zhengzhou 450000 Peoples R China Nanjing Tech Univ Coll Safety Sci & Engn Nanjing 211816 Peoples R China

Image-based flame detection (IBFD) algorithms are effective in flame detection, and their advantages and disadvantages directly affect the accuracy and timeliness of flame detection. However, based on the existing research in China, the IBFD algorithm still lacks a clear standard for distinguishing advantages and disadvantages. This study adopted a fuzzy comprehensive hierarchical analysis method to establish an evaluation method for IBFD and to define the rating standards for 29 third-level evaluation indexes. A three-level index system was installed for algorithm evaluation, and the masses of needles at all levels were determined. An index system was used to assign indexes to the two flame identification algorithms in the YOLOv3 series. The algorithm evaluation was completed according to the mass of the algorithm index, which indicated the reliability and validity of the algorithm evaluation method. Therefore, this study established a set of methods for the performance evaluation of IBFD and thus laid a theoretical foundation for improving relevant standards for image-based flame detectors.

关键词： IBFD algorithm Fuzzy comprehensive hierarchical analysis method evaluation indexes algorithm evaluation Image-based flame detectors

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An evaluation of SAR ATR algorithm performance sensitivity to MSTAR extended operating conditions

An evaluation of SAR ATR algorithm performance sensitivity t...

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SPIE Conference on algorithm for Synthetic Aperture Radar Imagery V

作者： Mossing, JC Ross, TD Sverdrup Technol Inc Adv Syst Grp Beavercreek OH 45431 USA

ISBN: (纸本)0819428191

Testing a SAR Automatic Target Recognition (ATR) algorithm at or very near its training conditions often yields near perfect results as we commonly see in the literature. This paper describes a series of experiments near and not so near to ATR algorithm training conditions. Experiments are setup to isolate individual Extended Operating Conditions (EOCs) and performance is reported at these points. Additional experiments are setup to isolate specific combinations of EOCs and the SAR ATR algorithm's performance is measured here also. The experiments presented here are a by-product of a DARPA/AFRL Moving and Stationary Target Acquisition and Recognition (MSTAR) program evaluation conducted in November of 1997. Although the tests conducted here are in the domain of EOCs, these tests do not encompass the "real world" (i.e., what you might see on the battlefield) problem. In addition to performance results this paper describes an evaluation methodology including the Extended Operating Condition concept, as well as, data;algorithm, and figures of merit. In summary, this paper highlights the sensitivity that a baseline Mean Squared Error (MSE) ATR algorithm has to various operating conditions both near and varying degrees away from the training conditions.

关键词： performance sensitivity automatic target recognition synthetic aperture radar extended operating conditions algorithm evaluation mean square error classifier MSTAR

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Challenging situations for background subtraction algorithms

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APPLIED INTELLIGENCE 2019年第5期49卷 1771-1784页

作者： Sanches, Silvio R. R. Oliveira, Claiton Sementille, Antonio C. Freire, Valdinei Univ Tecnol Fed Parana Cornelio Procopio PR Brazil Univ Estadual Paulista Bauru Brazil Univ Sao Paulo Sao Paulo Brazil

Background subtraction is the prerequisite for a wide range of applications including video surveillance, smart environments and content retrieval. Real environments present some challenging situations even for the most recent algorithms, such as shadows, illumination changes, dynamic background, among others. If a real environment is previously known and the challenging situations of this environment can be predicted, the choice of an appropriate algorithm to deal with such situations may be essential for obtaining better segmentation results. In our work, we identify the main situations that affect the performance of background subtraction algorithms and present a classification of these challenging situations. In addition, we present a solution that uses videos and ground-truths from existing datasets to evaluate the performance of segmentation algorithms when they need to deal with a specific challenging situation.

关键词： Background subtraction Foreground extraction algorithm evaluation Challenging situation

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Evaluating Lesion Segmentation on Breast Sonography as Related to Lesion Type

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JOURNAL OF ULTRASOUND IN MEDICINE 2013年第9期32卷 1659-1670页

作者： Pons, Gerard Marti, Joan Marti, Robert Ganau, Sergi Caries Vilanova, Joan Noble, J. Alison Univ Girona Dept Comp Architecture & Technol Girona 17071 Spain Corp Parc Tauli UDIAT Diagnost Ctr Dept Radiol Sabadell Spain Univ Girona Girona Magnet Resonance Imaging Ctr Girona 17071 Spain Univ Oxford Dept Engn Sci Oxford OX1 3PJ England

Breast sonography currently provides a complementary diagnosis when other modalities are not conclusive. However, lesion segmentation on sonography is still a challenging problem due to the presence of artifacts. To solve these problems, Markov random fields and maximum a posteriori-based methods are used to estimate a distortion field while identifying regions of similar intensity inhomogeneity. In this study, different initialization approaches were exhaustively evaluated using a database of 212 B-mode breast sonograms and considering the lesion types. Finally, conclusions about the relationship between the segmentation results and lesions types are described.

关键词： algorithm evaluation breast lesion segmentation Markov random fields maximum a posteriori sonography

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A COMPUTATIONAL COMPARISON OF THE ELLIPSOID algorithm WITH SEVERAL NONLINEAR-PROGRAMMING algorithmS

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SIAM JOURNAL ON CONTROL AND OPTIMIZATION 1985年第5期23卷 657-674页

作者： ECKER, JG KUPFERSCHMID, M RENSSELAER POLYTECH INST VOORHEES COMP CTRTROYNY 12181

A computational comparison of several general purpose nonlinear programming algorithms is presented. This study was motivated by the preliminary results in [12] which show that the recently developed ellipsoid algorithm is competitive with a widely used augmented Lagrangian algorithm. To provide a better perspective on the value of ellipsoid algorithms in nonlinear programming, the present study includes some of the most highly regarded nonlinear programming algorithms and is a much more comprehensive study than [12]. The algorithms considered here are chosen from four distinct classes and 50 well-known test problems are used. The algorithms used represent augmented Lagrangian, ellipsoid, generalized reduced gradient, and iterative quadratic programming methods. Results regarding robustness and relative efficiency are presented.

关键词： nonlinear programming algorithm evaluation ellipsoid algorithm

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Competition system for automatically evaluating edge detection algorithms based on the evolutionary game theory

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The Journal of China Universities of Posts and Telecommunications 2019年第5期26卷 49-59页

作者： Peng Hong Ding Guangtai Zhang Huiran Hu Dongli School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Materials Genome Institute of Shanghai University Shanghai 200444China School of Materials Science and Engineering Shanghai 200444China

An ensemble learning algorithm based on game theory is proposed to evaluate algorithms of image analysis and image feature extraction.A competition system is established to implement the algorithm for evaluating the applicability and efficiency of different edge detection *** the game in the algorithm competition system,the most suitable algorithm as a winner in the competition can be selected.A group of optimal parameters for the corresponding edge detection can also be ***,based on the evolutionary game theory,a strategy of the competition of edge extraction algorithms is ***,after selecting the most suitable algorithm from the candidates,the overall parameters are *** show that for a specific class of images,several candidate algorithms can be used as a class of preference algorithms based on the final evolutionary *** analyzing the images,the priority algorithm can be recommended as the best edge detection algorithm from these reference *** is more effective than traditional methods in determining an algorithm and choosing parameters.

关键词： algorithm evaluation competition system game theory edge detection

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Multiparameter method for analysis and selection of motion estimation algorithms for video compression

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MULTIMEDIA TOOLS AND APPLICATIONS 2008年第1期38卷 119-146页

作者： Kaminsky, Evgeny Hadar, Ofer Ben Gurion Univ Negev Dept Elect & Comp Engn IL-84105 Beer Sheva Israel Ben Gurion Univ Negev Commun Syst Engn Dept IL-84105 Beer Sheva Israel

This paper proposes a new multiparameter method for analysis and selection of motion estimation algorithms for video compression. We present motion estimation algorithms, results of computer simulations and illustrate the analysis with tables, PSNR and performance plots. Numerous algorithms and tests for analysis of algorithm performance for video compression have recently been suggested, which has resulted in a need for effective evaluation methods. A highly qualified expert is also needed to evaluate the test results. The more input parameters used the more complex and subjective the evaluation will be. Our multiparameter method for algorithm analysis and selection eliminates subjectivity and provides a qualitative and quantitative evaluation of the tested algorithms for any number of algorithms and parameters. We propose two new methods of evaluation: (1) a quality method-a graphic method using the Pareto approach, and (2) a quantity method which obtains an integrated parameter composed of numerous evaluation parameters. In addition, we evaluate various motion estimation algorithms accordingly to two different implementation strategies: (a) using a software video encoder that depends on available processing resources using a computational complexity-rate-distortion (C-R-D) evaluation framework and (b) using a power-limited video encoder implemented on mobile or handheld computing platform by using energy-rate-distortion (E-R-D) behavior.

关键词： fast motion estimation (ME) block matching algorithms (BMA) video analysis algorithm evaluation algorithm selection multiparameter analysis Pareto approach

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