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New multi-layer data correlation algorithm for multi-passive-sensor location system 认领 引用 被引量：1
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作者 Zhou Li Li Lingyun He You 《Journal of Systems Engineering and Electronics》 SCIE EI 2007年第4期667-672,共6页
Under the scenario of dense targets in clutter, a multi-layer optimal data correlation algorithm is proposed. This algorithm eliminates a large number of false location points from the assignment process by rough corr... Under the scenario of dense targets in clutter, a multi-layer optimal data correlation algorithm is proposed. This algorithm eliminates a large number of false location points from the assignment process by rough correlations before we calculate the correlation cost, so it avoids the operations for the target state estimate and the calculation of the correlation cost for the false correlation sets. In the meantime, with the elimination of these points in the rough correlation, the disturbance from the false correlations in the assignment process is decreased, so the data correlation accuracy is improved correspondingly. Complexity analyses of the new multi-layer optimal algorithm and the traditional optimal assignment algorithm are given. Simulation results show that the new algorithm is feasible and effective. 展开更多
关键词 multi-passive-sensor data correlation multi-layer correlation algorithm location system correlation cost
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Correlation-weighted least squares residual algorithm for RAIM 认领 引用 被引量：10
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作者 Dan SONG Chuang SHI +2 位作者 Zhipeng WANG Cheng WANG Guifei JING 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2020年第5期1505-1516,共12页
The Least Squares Residual(LSR)algorithm,one of the classical Receiver Autonomous Integrity Monitoring(RAIM)algorithms for Global Navigation Satellite System(GNSS),presents a high Missed Detection Risk(MDR)for a large... The Least Squares Residual(LSR)algorithm,one of the classical Receiver Autonomous Integrity Monitoring(RAIM)algorithms for Global Navigation Satellite System(GNSS),presents a high Missed Detection Risk(MDR)for a large-slope faulty satellite and a high False Alarm Risk(FAR)for a small-slope faulty satellite.From the theoretical analysis of the high MDR and FAR cause,the optimal slope is determined,and thereby the optimal test statistic for fault detection is conceived,which can minimize the FAR with the MDR not exceeding its allowable value.To construct a test statistic approximate to the optimal one,the CorrelationWeighted LSR(CW-LSR)algorithm is proposed.The CW-LSR test statistic remains the sum of pseudorange residual squares,but the square for the most potentially faulty satellite,judged by correlation analysis between the pseudorange residual and observation error,is weighted with an optimal-slope-based factor.It does not obey the same distribution but has the same noncentral parameter with the optimal test statistic.The superior performance of the CW-LSR algorithm is verified via simulation,both reducing the FAR for a small-slope faulty satellite with the MDR not exceeding its allowable value and reducing the MDR for a large-slope faulty satellite at the expense of FAR addition. 展开更多
关键词 Correlation analysis Fault detection Least squares residual(LSR)algorithm Receiver autonomous integrity monitoring(RAIM) Slope
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Collaborative Filtering Algorithms Based on Kendall Correlation in Recommender Systems 认领 引用 被引量：3
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作者 YAO Yu ZHU Shanfeng CHEN Xinmeng 《Wuhan University Journal of Natural Sciences》 EI CAS 2006年第5期1086-1090,共5页
In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of consider... In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of considering the relative order of the users＇ ratings. Kendall based algorithm is based upon a more general model and thus could be more widely applied in e-commerce. Another discovery of this work is that the consideration of only positive correlated neighbors in prediction, in both Pearson and Kendall algorithms, achieves higher accuracy than the consideration of all neighbors, with only a small loss of coverage. 展开更多
关键词 Kendall correlation collaborative filtering algorithms recommender systems positive correlation
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DIGITAL SPECKLE CORRELATION METHOD IMPROVED BY GENETIC ALGORITHM 认领 引用 被引量：30
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作者 MaShaopeng JillGuanchang 《Acta Mechanica Solida Sinica》 SCIE 2003年第4期366-373,共8页
The digital speckle correlation method is an important optical metrology for sur- face displacement and strain measurement.With this technique,the whole field deformation in- formation can be obtained by tracking the ... The digital speckle correlation method is an important optical metrology for sur- face displacement and strain measurement.With this technique,the whole field deformation in- formation can be obtained by tracking the geometric points on the speckle images based on a correlation-matching search technique.However,general search techniques suffer from great com- putational complexity in the processing of speckle images with large deformation and the large random errors in the processing of images of bad quality.In this paper,an advanced approach based on genetic algorithms (GA) for correlation-matching search is developed.Benefiting from the abilities of global optimum and parallelism searching of GA,this new approach can complete the correlation-matching search with less computational consumption and at high accuracy.Two experimental results from the simulated speckle images have proved the efficiency of the new approach. 展开更多
关键词 digital speckle correlation method genetic algorithm deformation measurement
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Parameter optimization of gravity density inversion based on correlation searching and the golden section algorithm 认领 引用 被引量：1
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作者 Sun Lu-Ping Liu Zhan +1 位作者 Shou Hao Zhang Yu-Hua 《Applied Geophysics》 SCIE CSCD 2012年第2期131-138,233,共8页
For density inversion of gravity anomaly data,once the inversion method is determined,the main factors affecting the inversion result are the inversion parameters and subdivision scheme.A set of reasonable inversion p... For density inversion of gravity anomaly data,once the inversion method is determined,the main factors affecting the inversion result are the inversion parameters and subdivision scheme.A set of reasonable inversion parameters and subdivision scheme can,not only improve the inversion process efficiency,but also ensure inversion result accuracy.The gravity inversion method based on correlation searching and the golden section algorithm is an effective potential field inversion method.It can be used to invert 2D and 3D physical properties with potential data observed on flat or rough surfaces.In this paper,we introduce in detail the density inversion principles based on correlation searching and the golden section algorithm.Considering that the gold section algorithm is not globally optimized.we present a heuristic method to ensure the inversion result is globally optimized.With a series of model tests,we systematically compare and analyze the inversion result efficiency and accuracy with different parameters.Based on the model test results,we conclude the selection principles for each inversion parameter with which the inversion accuracy can be obviously improved. 展开更多
关键词 Density inversion correlation searching golden section algorithm inversion parameter optimization
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An algorithm to solve autocorrelation matrix singular value based on SNR estimation 认领 引用
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作者 赵继军 张曙光 赵文玉 《Optoelectronics Letters》 EI 2009年第1期41-44,共4页
SNR estimation of communication signals is important to improve demodulation performance and channel quality of communication system,thus it is an important research issue of communication field.According to the core ... SNR estimation of communication signals is important to improve demodulation performance and channel quality of communication system,thus it is an important research issue of communication field.According to the core problem of autocorrelation matrix singular value in SNR estimation process,through making use of householder transforming autocorrelation matrix into tridiagonal matrix,and by using the relation of corresponding characteristic equation coefficients and singular value,a numerical algorithm is gi... 展开更多
关键词 Acoustic intensity Algorithms Channel estimation Communication systems Correlation detectors Estimation Matrix algebra
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A blind modulation recognition algorithm based on cyclic spectral correlation 认领 引用
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作者 高玉龙 Zhang Zhongzhao 《High Technology Letters》 EI CAS 2007年第2期160-163,共4页
Cyclic spectral correlation above the bifrequency plane for the received signal was calculated by the strip spectral correlation algorithm （SSCA）and then was normalized. The result was expressed by matrix. The sum o... Cyclic spectral correlation above the bifrequency plane for the received signal was calculated by the strip spectral correlation algorithm （SSCA）and then was normalized. The result was expressed by matrix. The sum of error-square was computed between corresponding elements for the theoretical sampling matrix of all kinds of modulated signals and calculated matrix. The modulation type was recognized by exploiting the minimum value of the sum of error-square. No extracted characteristic parameter and prior information are needed for identifying the modulation type compared to the conventional methods. In addition, the new method extends the recognition scope and has high recognition probability at low SNR. The simulation results obtained by means of Monter-Carlo method proved the presented algorithm. 展开更多
关键词 cyclic spectral correlation strip spectral correlation algorithm recognition perfor-mance bifrequency plane
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Employment of predictive search algorithm in digital image correlation 认领 引用
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作者 马志峰 王昊 韩福海 《Journal of Beijing Institute of Technology》 EI CAS 2014年第2期254-259,共6页
A predictive search algorithm to estimate the size and direction of displacement vectors was presented.The algorithm decreased the time of calculating the displacement of each pixel.In addition,the updating reference ... A predictive search algorithm to estimate the size and direction of displacement vectors was presented.The algorithm decreased the time of calculating the displacement of each pixel.In addition,the updating reference image scheme was used to update the reference image and to decrease the computation time when the displacement was larger than a certain number.In this way,the search range and computational complexity were cut down,and less EMS memory was occupied.The capability of proposed search algorithm was then verified by the results of both computer simulation and experiments.The results showed that the algorithm could improve the efficiency of correlation method and satisfy the accuracy requirement for practical displacement measuring. 展开更多
关键词 machine vision predictive search algorithm digital image correlation sub-pixel displacement measurement
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Optimizing slope safety factor prediction via stacking using sparrow search algorithm for multi-layer machine learning regression models 认领 引用 被引量：5
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作者 SHUI Kuan HOU Ke-peng +2 位作者 HOU Wen-wen SUN Jun-long SUN Hua-fen 《Journal of Mountain Science》 SCIE CSCD 2023年第10期2852-2868,共17页
The safety factor is a crucial quantitative index for evaluating slope stability.However,the traditional calculation methods suffer from unreasonable assumptions,complex soil composition,and inadequate consideration o... The safety factor is a crucial quantitative index for evaluating slope stability.However,the traditional calculation methods suffer from unreasonable assumptions,complex soil composition,and inadequate consideration of the influencing factors,leading to large errors in their calculations.Therefore,a stacking ensemble learning model(stacking-SSAOP)based on multi-layer regression algorithm fusion and optimized by the sparrow search algorithm is proposed for predicting the slope safety factor.In this method,the density,cohesion,friction angle,slope angle,slope height,and pore pressure ratio are selected as characteristic parameters from the 210 sets of established slope sample data.Random Forest,Extra Trees,AdaBoost,Bagging,and Support Vector regression are used as the base model(inner loop)to construct the first-level regression algorithm layer,and XGBoost is used as the meta-model(outer loop)to construct the second-level regression algorithm layer and complete the construction of the stacked learning model for improving the model prediction accuracy.The sparrow search algorithm is used to optimize the hyperparameters of the above six regression models and correct the over-and underfitting problems of the single regression model to further improve the prediction accuracy.The mean square error(MSE)of the predicted and true values and the fitting of the data are compared and analyzed.The MSE of the stacking-SSAOP model was found to be smaller than that of the single regression model(MSE=0.03917).Therefore,the former has a higher prediction accuracy and better data fitting.This study innovatively applies the sparrow search algorithm to predict the slope safety factor,showcasing its advantages over traditional methods.Additionally,our proposed stacking-SSAOP model integrates multiple regression algorithms to enhance prediction accuracy.This model not only refines the prediction accuracy of the slope safety factor but also offers a fresh approach to handling the intricate soil composition and other influencing factors,making it a precise and reliable method for slope stability evaluation.This research holds importance for the modernization and digitalization of slope safety assessments. 展开更多
关键词 Multi-layer regression algorithm fusion Stacking gensemblelearning Sparrow search algorithm Slope safety factor Data prediction
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Effects of initial states on the quantum correlations in the generalized Grover search algorithm 认领 引用 被引量：1
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作者 Zhen-Yu Chen Tian-HuiQiu +1 位作者 Wen-Bin Zhang Hong-Yang Ma 《Chinese Physics B》 SCIE EI CAS CSCD 2021年第8期145-151,共7页
We investigate the correlations between two qubits in the Grover search algorithm with arbitrary initial states by numerical simulation.Using a set of suitable bases,we construct the reduced density matrix and give th... We investigate the correlations between two qubits in the Grover search algorithm with arbitrary initial states by numerical simulation.Using a set of suitable bases,we construct the reduced density matrix and give the numerical expression of correlations relating to the iterations.For different initial states,we obtain the concurrence and quantum discord compared with the success probability in the algorithm.The results show that the initial states affect the correlations and the limit point of the correlations in the searching process.However,the initial states do not influence the whole cyclical trend. 展开更多
关键词 Grover search algorithm quantum correlations initial states the success probability
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SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm 认领 引用
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作者 Jiahui Liu Lang Li +1 位作者 Di Li Yu Ou 《Computers, Materials & Continua》 SCIE EI 2024年第6期4641-4657,共17页
Correlation power analysis(CPA)combined with genetic algorithms(GA)now achieves greater attack efficiency and can recover all subkeys simultaneously.However,two issues in GA-based CPA still need to be addressed:key de... Correlation power analysis(CPA)combined with genetic algorithms(GA)now achieves greater attack efficiency and can recover all subkeys simultaneously.However,two issues in GA-based CPA still need to be addressed:key degeneration and slow evolution within populations.These challenges significantly hinder key recovery efforts.This paper proposes a screening correlation power analysis framework combined with a genetic algorithm,named SFGA-CPA,to address these issues.SFGA-CPA introduces three operations designed to exploit CPA characteris-tics:propagative operation,constrained crossover,and constrained mutation.Firstly,the propagative operation accelerates population evolution by maximizing the number of correct bytes in each individual.Secondly,the constrained crossover and mutation operations effectively address key degeneration by preventing the compromise of correct bytes.Finally,an intelligent search method is proposed to identify optimal parameters,further improving attack efficiency.Experiments were conducted on both simulated environments and real power traces collected from the SAKURA-G platform.In the case of simulation,SFGA-CPA reduces the number of traces by 27.3%and 60%compared to CPA based on multiple screening methods(MS-CPA)and CPA based on simple GA method(SGA-CPA)when the success rate reaches 90%.Moreover,real experimental results on the SAKURA-G platform demonstrate that our approach outperforms other methods. 展开更多
关键词 Side-channel analysis correlation power analysis genetic algorithm crossover mutation
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A Robust Damage Identification Method Based on Modified Holistic Swarm Optimization Algorithm and Hybrid Objective Function 认领 引用
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作者 Xiansong Xie Xiaoqian Qian 《Structural Durability & Health Monitoring》 EI 2026年第2期235-259,共25页
Correlation function of acceleration responses-based damage identificationmethods has been developed and employed,while they still face the difficulty in identifying local orminor structural damages.To deal with this ... Correlation function of acceleration responses-based damage identificationmethods has been developed and employed,while they still face the difficulty in identifying local orminor structural damages.To deal with this issue,a robust structural damage identification method is developed,integrating a modified holistic swarm optimization(MHSO)algorithm with a hybrid objective function.The MHSO is developed by combining Hammersley sequencebased population initialization,chaotic search around the worst solution,and Hooke-Jeeves pattern search around the best solution,thereby improving both global exploration and local exploitation capabilities.A hybrid objective function is constructed by merging acceleration correlation function-based and strain correlation function-based objective functions,effectively leveraging the complementary sensitivities of global and local responses.To further suppress spurious solutions and promote sparsity in parameter estimation,an additional L0.5 regularization term is introduced.The effectiveness of the proposed method is validated through numerical simulations on a simply supported beam and a steel girder benchmark structure.Comparative studies with sequential quadratic programming,genetic algorithm,andHSO demonstrate that theMHSOachieves superior accuracy and convergence efficiency,even with limited sensors and 20%noise-contaminated measurements.Results highlight that the hybrid objective function significantly enhances the detection of both major and minor damages,while the inclusion of sparse regularization improves robustness against noise and model uncertainties.The findings indicate that the proposed framework provides a reliable and computationally efficient solution for simultaneous localization and quantification of structural damages,offering promising applicability to real-world structural health monitoring scenarios. 展开更多
关键词 Damage identification holistic swarm optimization algorithm combined correlation function hybrid objective function sparse regularization grid structure
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Night Vision Object Tracking System Using Correlation Aware LSTM-Based Modified Yolo Algorithm 认领 引用
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作者 R.Anandha Murugan B.Sathyabama 《Intelligent Automation & Soft Computing》 SCIE 2023年第4期353-368,共16页
Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and diffe... Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions,such as mist,fog,dust etc.The pictures then shift in intensity,colour,polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient envir-onments.In recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance capabilities.However,the iden-tification of objects in the night world also poses further problems because of the distorted backdrop and dim light.The Correlation aware LSTM based YOLO(You Look Only Once)classifier method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this work.In order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerfilter for removing the noise in the image.The process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set Segmentation.Finally,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(LSTM)with YOLO method for object tracking system.A range of experimentalfindings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mechanism.Our approach may therefore satisfy the true demands of night scene target detection applications.We very much believe that our method will help future research. 展开更多
关键词 Object monitoring night vision image SSAN dataset adaptive internal linear embedding uplift linear discriminant analysis recurrent-phase level set segmentation correlation aware LSTM based yolo classifier algorithm
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Using Genetic Algorithms to Improve the Search of the Weight Space in Cascade-Correlation Neural Network 认领 引用 被引量：1
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作者 E.A.Mayer, K. J. Cios, L. Berke & A. Vary 《Journal of Systems Engineering and Electronics》 EI 1995年第2期9-21,共13页
In this paper, we use the global search characteristics of genetic algorithms to help search the weight space of the neurons in the cascade-correlation architecture. The cascade-correlation learning architecture is a ... In this paper, we use the global search characteristics of genetic algorithms to help search the weight space of the neurons in the cascade-correlation architecture. The cascade-correlation learning architecture is a technique of training and building neural networks that starts with a simple network of neurons and adds additional neurons as they are needed to suit a particular problem. In our approach, instead ofmodifying the genetic algorithm to account for convergence problems, we search the weight-space using the genetic algorithm and then apply the gradient technique of Quickprop to optimize the weights. This hybrid algorithm which is a combination of genetic algorithms and cascade-correlation is applied to the two spirals problem. We also use our algorithm in the prediction of the cyclic oxidation resistance of Ni- and Co-base superalloys. 展开更多
关键词 Genetic algorithm Cascade correlation Weight space search Neural network.
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Hybrid adaptive machine learning approach for detection and mitigation of GNSS spoofing through enhanced osprey optimization algorithm 认领 引用
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作者 KOTI Sushmitha SANDHYA Rachamalla 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第3期1059-1080,共22页
Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,veloci... Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,velocity,and time(PVT).Several GNSS devices are exposed to detect spoofing attacks due to the use of unsafe locations.In addition,misleading signals are intentionally used to generate timing and position,and GNSS signal spoofing provides a constant risk to consumers.In past works,the implementation of the Global Positioning System(GPS)in autonomous vehicle navigation might be endangered by spoofing.To mitigate these issues,this task develops a hybrid machine-learning method for mitigating and detecting GNSS spoofing attacks.The developed model is processed with three phases:data collection,feature extraction,and detection.Initially,the required data is taken from the standard resource.Then,the data is given to the feature extraction phase.The features of the data are retrieved using the principal component analysis(PCA)and t-distributed stochastic neighbor embedding(t-SNE)model.The features obtained from the collected data are transferred to the detection phase.In the final phase,the GNSS spoofing detection and mitigation is executed using a machine learning method called as hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP).Enhanced osprey optimization algorithm(EOOA)is utilized for optimizing the variables to enhance the efficacy of models and achieves greater performance than other standard models. 展开更多
关键词 Global Navigation Satellite System(GNSS) detection and mitigation of GNSS t-distributed stochastic neighbor embedding(t-SNE) enhanced osprey optimization algorithm principal component analysis(PCA) hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP)
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Covariance Matrix Learning Differential Evolution Algorithm Based on Correlation 认领 引用
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作者 Sainan Yuan Quanxi Feng 《International Journal of Intelligence Science》 2021年第1期17-30,共14页
Differential evolution algorithm based on the covariance matrix learning can adjust the coordinate system according to the characteristics of the population, which makes the search move in a more favorable direction. ... Differential evolution algorithm based on the covariance matrix learning can adjust the coordinate system according to the characteristics of the population, which makes the search move in a more favorable direction. In order to obtain more accurate information about the function shape, this paper proposes covariance matrix learning differential evolution algorithm based on correlation (denoted as RCLDE) to improve the search efficiency of the algorithm. First, a hybrid mutation strategy is designed to balance the diversity and convergence of the population;secondly, the covariance learning matrix is constructed by selecting the individual with the less correlation;then, a comprehensive learning mechanism is comprehensively designed by two covariance matrix learning mechanisms based on the principle of probability. Finally, the algorithm is tested on the CEC2005, and the experimental results are compared with other effective differential evolution algorithms. The experimental results show that the algorithm proposed in this paper is an effective algorithm. 展开更多
关键词 Differential Evolution Algorithm Correlation Covariance Matrix Parameter Self-Adaptive Technique
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An Improved Apriori Algorithm Based on Matrix and Double Correlation Profit Constraint 认领 引用
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作者 Yuan Liu Ya Li +3 位作者 Jian Yang Yan Ren Guoqiang Sun Quansheng Li 《国际计算机前沿大会会议论文集》 EI 2018年第1期27-27,共1页
In view of the performance bottlenecks of Apriori algorithm associ- ation rules, this paper puts forward a new type of profit constraint extensional association rules mining algorithm named MDCApriori. The algorithm c... In view of the performance bottlenecks of Apriori algorithm associ- ation rules, this paper puts forward a new type of profit constraint extensional association rules mining algorithm named MDCApriori. The algorithm constructs a 0-1 transaction matrix through scanning the database once, consid- ering establishing the AE arrays to weight each column in matrix and calculating the completely-weighted support for frequent itemsets. Frequent itemsets and negative itemsets are pruned by the minimum support and the strategy of double correlation, finally using the correlation coefficient to mine the profit constraint extensional association rules and determine the strength degree of the correlation. Experiments show that the method has a good effect of pruning, effectively reduce the number of generated redundant frequent itemsets and association rules, mean- while improving the operational efficiency of the algorithm. 展开更多
关键词 Constraint association rulesMDC_apriori algorithm double correlation Weighted support
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A Super-High-Efficiency Algorithm for the Calculation of the Correlation Integral 认领 引用
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作者 Zhuping Gong 《Journal of Data Analysis and Information Processing》 2015年第4期128-135,共8页
When the chaotic characteristics of manufacturing quality level are studied, it is not practical to use chaotic methods because of the low speed of calculating the correlation integral. The original algorithm used to ... When the chaotic characteristics of manufacturing quality level are studied, it is not practical to use chaotic methods because of the low speed of calculating the correlation integral. The original algorithm used to calculate the correlation integral is studied after a computer hardware upgrade. The result is that calculation of the correlation integral can be sped up only by improving the algorithm. This is accomplished by changing the original algorithm in which a single distance threshold-related correlation integral is obtained from one traversal of all distances between different vectors to a high-efficiency algorithm in which all of the distance threshold-related correlation integrals are obtained from one traversal of all of the distances between different vectors. For a time series with 3000 data points, this high-efficiency algorithm offers a 3.7-fold increase in speed over the original algorithm. Further study of the high-efficiency algorithm leads to the development of a super-high-efficiency algorithm, which is accomplished by changing the original and high-efficiency algorithms, in which the add-one operation of the Heaviside function is executed n times, such that the execution of the add-one operation occurs only once. The super-high-efficiency algorithm results in increases in the calculation speed by up to 109 times compared with the high-efficiency algorithm and by approximately 404 times compared with the original algorithm. The calculation speed of the super-high-efficiency algorithm is suitable for practical use with the chaotic method. 展开更多
关键词 Super-High-Efficiency Algorithm Correlation Integral Chaotic Time Series
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Correlation analysis between characteristics under gastroscope and image information of tongue in patients with chronic gastritis 认领 引用 被引量：8
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作者 SHANG Zimeng DU Zhengguang +4 位作者 GUAN Bei JI Xingyu CHEN Longchang WANG Yongji MA Yun 《Journal of Traditional Chinese Medicine》 SCIE CSCD 2022年第1期102-107,共6页
OBJECTIVE:To explore the correlation between diagnostic information of tongue and gastroscopy results of patients with chronic gastritis.METHODS:Frequent pattern growth(FP-Growth),SPSS Modeler was used to analyze the ... OBJECTIVE:To explore the correlation between diagnostic information of tongue and gastroscopy results of patients with chronic gastritis.METHODS:Frequent pattern growth(FP-Growth),SPSS Modeler was used to analyze the correlation rules between the image information of tongue parameters and the characteristics of the stomach and duodenum seen under gastroscopy.RESULTS:Ranking in order of confidence:cyanotic tongue,slippery fur,yellow fur and spotted tongue were sequently associated with both gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/macula.L,one value of tongue coating color,which counted among(30,60),tooth-marked tongue and b,one value of tongue coating color,which counted in the range of(5,20)were sequently associated with gastric antrum mucosal erythema/macula.A,one value of tongue body color,which counted in the range of(0,20),was related to both gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/macula.a,one value of tongue coating color,which counted in the range of(15,35),was associated with gastric antrum mucosal erythema/macula.There are a total of 9 strong correlation rules.CONCLUSIONS:Cyanotic tongue,slippery fur,yellow fur,the CIE Lab value of tongue coating,a,the value of tongue body color,spotted tongue,and tooth-marked tongue are all related to the gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/macula.The conditions of gastric mucosa could be predicted by the examination of the above related image information of tongue. 展开更多
关键词 gastritis tongue stomach duodenum correlation of data frequent pattern growth algorithm
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Identification of low-resistivity-low-contrast pay zones in the feature space with a multi-layer perceptron based on conventional well log data 认领 引用 被引量：6
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作者 Lun Gao Ran-Hong Xie +2 位作者 Li-Zhi Xiao Shuai Wang Chen-Yu Xu 《Petroleum Science》 SCIE CAS CSCD 2022年第2期570-580,共11页
In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water zones.Existing identification methods are model-driven and ca... In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water zones.Existing identification methods are model-driven and cannot yield satisfactory results when the causes of LRLC pay zones are complicated.In this study,after analyzing a large number of core samples,main causes of LRLC pay zones in the study area are discerned,which include complex distribution of formation water salinity,high irreducible water saturation due to micropores,and high shale volume.Moreover,different oil testing layers may have different causes of LRLC pay zones.As a result,in addition to the well log data of oil testing layers,well log data of adjacent shale layers are also added to the original dataset as reference data.The densitybased spatial clustering algorithm with noise(DBSCAN)is used to cluster the original dataset into 49 clusters.A new dataset is ultimately projected into a feature space with 49 dimensions.The new dataset and oil testing results are respectively treated as input and output to train the multi-layer perceptron(MLP).A total of 3192 samples are used for stratified 8-fold cross-validation,and the accuracy of the MLP is found to be 85.53%. 展开更多
关键词 Low-resistivity-low-contrast(LRLC)pay zones Conventional well logging Machine learning DBSCAN algorithm Multi-layer perceptron
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