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Research on optimization of image fast feature point matching algorithm

EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING 2018年第1期2018卷 1页

作者： Wu, Manyi Wuhan Univ Sch Geodesy & Geomat Luoyu Rd 129 Wuhan Hubei Peoples R China Natl Adm Surveying Mapping & Geoinformat China Topog Surveying Brigade NASG 1 Cehui Rd 4 Xian Shaanxi Peoples R China

The author studied the feature point extraction and matching based on BRISK and ORB algorithms, experimented with the advantages of both algorithms, and ascertained optimal pyramid layer and inter-layer scale parameters used in features extraction and matching for the same scale image and different scale images with BRISK and ORB algorithm, and analyzed the effectiveness of different parameters combinations on the accuracies of feature extraction and matching and proposed method to determine parameters based on the results. In addition, comparing with the traditional algorithm, using the optimal algorithm with the parameters combining Gaussian denoising, graying, and image sharpening, the ratio of feature points for detection improved 3%;the number of effective matching points increased by nearly 2%. Meanwhile, an algorithm experiment on UAV image mosaic was carried out. The transition of mosaic image color was more natural, and there was no clear mosaic joint with the stitching effect, which indicated that the optimized parameters and the extracted feature point pairs can be used for matrix operations and the algorithm is suitable for UAV image mosaic processing.

关键词： BRISK and ORB algorithm Fast feature detection algorithm optimization UAV image mosaic processing

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Neural Network Data Mining Clustering optimization algorithm

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IETE JOURNAL OF RESEARCH 2021年

作者： Jiao, Guie Li, Wang Shanghai Univ Sch Informat Technol Shanghai Jianqiao Coll Shanghai 201300 Peoples R China Shanghai Ocean Univ Sch Informat Shanghai 201300 Peoples R China

Data mining technology is an effective way to solve the problem of rich data and poor knowledge. Cluster analysis is an important content of data mining, including analysis ideas based on partition, hierarchy, density, grid and model. Neural network clustering is a typical clustering method based on model thinking. It is an organic combination of brain cognitive science and data mining. It has a strong theoretical connection with actual brain processing knowledge. This paper uses the K-means algorithm to optimize the neural network clustering data mining algorithm, and designs experiments to verify the neural network data mining clustering optimization algorithm proposed in this paper. The experimental research results in this paper show that on four UCI datasets, the mean MP values of the neural network data mining clustering optimization algorithms are 77.9 and 87.72, respectively, which are greater than the values of the other two algorithms. This paper also applies the algorithm to the study of the distribution of remaining oil. The algorithm has achieved obvious results in the cluster analysis of the degree of flooding.

关键词： algorithm optimization cluster analysis data mining neural network

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optimization of Pipe Network Simulation algorithm for Tree-Shaped Water Injection System in Large-Scale Oilfield

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2021年第13期35卷 2159045-2159045页

作者： Ruan, Yan Zhang, Xuliang Chen, Jiaona Shaanxi Prov Key Lab Oil & Gas Well Measurement & Xian 710065 Shaanxi Peoples R China Xian Shiyou Univ Sch Elect Engn Xian 710065 Shaanxi Peoples R China

As intelligence technology develops, there is a higher requirement for computing speed and accuracy of water injection system simulation. In this paper, aiming at the tree-shaped water injection pipe network system of large-scale oilfields, based on the energy equation for calculating the pressure drop Delta H of pipe section, a mathematical model of the pipeline unit and the node unit is established, and finally, a mathematical model of pipe network for the entire water injection system is established;then, the improved iterative algorithm is used to solve the simulation model of water injection system. In this way, we determine the boundary calculation conditions, take the water injection station as reference node, and use the maximum pressure of water injection well as the initial value of the reference node for calculation, which reduces the number of iterations in model calculation;by comparing the simulation results of different iteration steps, 0.01 is selected as the iteration step size due to its higher calculation accuracy;and the calculation process has also been optimized. The process of solving the characteristic matrix K is combined with the process of calculating the pressure drop Delta H of pipe section, and placed outside the algorithm loop, thereby shortening the calculation time of a single cycle and reducing the calculation amount of the algorithm. The application cases show that the proposed optimization algorithm for water injection system pipe network simulation can be used as an effective method to improve the solution speed and calculation accuracy of the simulation algorithm of tree-shaped water injection system in large-scale oilfields.

关键词： Water injection pipe network mathematical model algorithm optimization solving speed calculation accuracy

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Research and optimization of Apriori algorithm Based on Cloud Computing and Medical Large Data

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AGRO FOOD INDUSTRY HI-TECH 2017年第1期28卷 697-700页

作者： Song, Menghua Tianjin Maritime Coll Tianjin Peoples R China

With the rapid development of the Internet, the variety and quantity of various medical data resources have experienced large-scale growth. The algorithmic organization structure of fragmented medical data resources can accelerate the standardization of local resources and improve resource utilization rate. Thus, the optimization of Apriori algorithm based on cloud computing and medical data was studied. This paper introduced an overview of cloud computing and medical data and described Apriori algorithm based on the cloud computing and medical data optimization process. Results show that the application of Apriori algorithm based on cloud computing and medical data was realistic and reasonable.

关键词： Cloud computing medical large data Apriori algorithm algorithm optimization

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Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle-based network coverage optimization

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2022年第3期18卷

作者： Ruzicka, Marek Volosin, Marcel Gazda, Juraj Maksymyuk, Taras Han, Longzhe Dohler, Mischa Tech Univ Kosice Dept Comp & Informat Letna 9 Kosice 04001 Slovakia Lviv Polytech Natl Univ Dept Telecommun Lvov Ukraine Nanchang Inst Technol Sch Informat Engn Nanchang Jiangxi Peoples R China Kings Coll London Ctr Telecommun Res London England

The challenge of dynamic traffic demand in mobile networks is tackled by moving cells based on unmanned aerial vehicles. Considering the tremendous potential of unmanned aerial vehicles in the future, we propose a new heuristic algorithm for coverage optimization. The proposed algorithm is implemented based on a conditional generative adversarial neural network, with a unique multilayer sum-pooling loss function. To assess the performance of the proposed approach, we compare it with the optimal core-set algorithm and quasi-optimal spiral algorithm. Simulation results show that the proposed approach converges to the quasi-optimal solution with a negligible difference from the global optimum while maintaining a quadratic complexity regardless of the number of users.

关键词： Generative adversarial network unmanned aerial vehicle algorithm optimization coverage machine learning

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optimization of Levenberg Marquardt algorithm Applied to Nonlinear Systems

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PROCESSES 2023年第6期11卷 1794页

作者： Huang, Xinyi Cao, Hao Jia, Bingjing Anhui Sci & Technol Univ Coll Informat & NetWork Engn Chuzhou 233100 Peoples R China

As science and technology advance, industrial manufacturing processes get more complicated. Back Propagation Neural Network (BPNN) convergence is comparatively slower for processing nonlinear systems. The nonlinear system used in this study to evaluate the optimization of BPNN based on the LM algorithm proved the algorithm's efficacy through a MATLAB simulation analysis. This paper examined the application impact of the enhanced approach using the Continuous stirred tank reactor (CSTR) control system as an example. The study's findings demonstrate that the LM optimization algorithm's identification error exceeds 10-5. The research's suggested control approach for reactant concentration CA in CSTR systems provides a better tracking effect and a stronger anti-interference capacity. Compared to the PI control method, the overall control effect is superior. As a result, the optimization model for nonlinear systems has a greatly improved processing accuracy. With some data support for the accuracy study of neural network models and the application of nonlinear systems, the suggested LM-BP optimization algorithm is evidently more appropriate for nonlinear systems.

关键词： LM algorithm BP neural network nonlinear systems algorithm optimization CSTR control system

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Fouling Prediction of a Heat Exchanger Based on Wavelet Neural Network Optimized by Improved Particle Swarm optimization algorithm

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PROCESSES 2024年第11期12卷 2412页

作者： Liang, Yandong Zhu, Lipeng Wang, Yang Wu, Hao Zhang, Junwei Guan, Jing Wang, Jianguo Northeast Elect Power Univ Sch Automat Engn Jilin 132012 Peoples R China Northeast Elect Power Univ Sch Econ & Management Jilin 132012 Peoples R China

The relevant experimental data of the fouling formation process of a heat exchanger were obtained through the fouling monitoring experimental platform. Whereafter, with regard to the conventional particle swarm optimization (PSO) algorithm, this study commenced from the iteration formula and innovatively presented an optimization approach for improving the inertia weight, thereby obtaining the improved particle swarm optimization (IPSO) algorithm. The wavelet neural network (WNN) was optimized through the application of the IPSO-WNN algorithm, resulting in the development of the IPSO-WNN model. Utilizing this model, a predictive model for fouling thermal resistance was constructed, incorporating input variables such as conductivity, pH, dissolved oxygen, average wall temperature, and bulk temperature, while the output variable represented fouling thermal resistance. Comparative analyses demonstrated that the IPSO-WNN model exhibited superior prediction accuracy and robust generalization capabilities to that of the conventional WNN and PSO-WNN models, as evidenced by significantly lower values across all indicators, including MAPE, MAE, and RMSE. The IPSO algorithm effectively optimized the initial parameters of the WNN, addressing the challenge of local minimum and enhancing the model's overall capacity to identify optimal solutions. This model effectively captures the dynamic trends of fouling thermal resistance during its growth stage and approaches the asymptotic value in the stable stage. Precise prediction models for heat exchanger fouling contribute valuable insights for its prediction in practical industrial applications.

关键词： fouling prediction heat exchanger algorithm optimization wavelet neural network improved particle swarm optimization

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optimization of Matrix Multiplication algorithm based on FT-M7002 Chip

International Core Journal of Engineering

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International Core Journal of Engineering 2023年第3期9卷 83-90页

作者： Yuli Qian Guodong Ma

With the continuous improvement of domestic processor performance and the continuous improvement of software ecology, the domestic e‐government field is increasing the promotion of localization, information products based on domestic processors have been applied in batches, and the key information infrastructure of related key industries is carrying out domestic processor applications. Based on the domestic DSP chip FT‐M7002, C language and assembly language code are implemented and performance optimized for functions of basic algorithms such as matrix factorization, solving linear equations and filters. Huawei Kunpeng Server Project has developed image processing and signal processing function libraries such as HMPP on Huawei Kunpeng Server, and has obtained good research results.

关键词： Chip algorithm optimization Database

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A Method for Predicting Ground Pressure in Meihuajing Coal Mine Based on Improved BP Neural Network by Immune algorithm-Particle Swarm optimization

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PROCESSES 2024年第1期12卷 147页

作者： Lai, Xingping Tu, Yuhang Yan, Baoxu Wu, Longquan Liu, Xiaoming Xian Univ Sci & Technol Coll Energy Engn Xian 710054 Peoples R China CHN Energy Ningxia Coal Ind Co Ltd Meihuajing Coal Mine Yinchuan 751400 Peoples R China

Based on the background of dynamic mining pressure monitoring and pressure prediction research on the No. 232205 working face of the Meihuajing coal mine, this study systematically investigates the predictive model of mining pressure manifestation on the working face of the Meihuajing coal mine by integrating methods such as engineering investigation, theoretical analysis, and mathematical modeling. A mining pressure manifestation prediction method based on IA-PSO-BP is proposed. The IA-PSO optimization algorithm is applied to optimize the hyperparameters of the BP neural network, and the working face mining pressure prediction model based on IA-PSO-BP is established. The mean absolute error (MAE), mean square error (MSE), and coefficient of determination (R2) are selected as evaluation indicators to compare the prediction performance of the BP model, PSO-BP model, and IA-PSO-BP model. The experimental results of the model show that the convergence speed of the IA-PSO-BP model is about eight times faster than that of the BP model and two times faster than that of the PSO-BP model. Compared with the BP and PSO-BP models, the IA-PSO-BP model has the smallest MAE and MSE and the largest R2 on the three different data sets of the test set, indicating significantly improved prediction accuracy. The predicted results conform to the periodic variation pattern of mining pressure data and are consistent with the actual situation in the coal mine.

关键词： BP IA-PSO-BP algorithm optimization ground pressure prediction

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optimization and Realization of G.729 Speech Coding algorithm

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Semiconductor Photonics and Technology 2009年第2期15卷 111-116页

作者： ZHOU Xin MIAO Chang-yun LI Yan-feng WU Zhi-gang Information and Communication College Tianjin Polytechnic University Tianjin 300160 CHN

The principles of G.729 algorithm are analyzed. It proposes an optimal approach of adaptive codebook search. Realized on fixed point DSP TMS320VC5410,the searching time of the optimal algorithm is thus significantly d... 详细信息

关键词： G. 729 CS-ACELP algorithm optimization TMS320VC5410

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