The frequent missing values in radar-derived time-series tracks of aerial targets(RTT-AT)lead to significant challenges in subsequent data-driven tasks.However,the majority of imputation research focuses on random mis...The frequent missing values in radar-derived time-series tracks of aerial targets(RTT-AT)lead to significant challenges in subsequent data-driven tasks.However,the majority of imputation research focuses on random missing(RM)that differs significantly from common missing patterns of RTT-AT.The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation.Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss.In this paper,a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed.Our model consists of two probabilistic sparse diagonal masking self-attention(PSDMSA)units and a weight fusion unit.It learns missing values by combining the representations outputted by the two units,aiming to minimize the difference between the missing values and their actual values.The PSDMSA units effectively capture temporal dependencies and attribute correlations between time steps,improving imputation quality.The weight fusion unit automatically updates the weights of the output representations from the two units to obtain a more accurate final representation.The experimental results indicate that,despite varying missing rates in the two missing patterns,our model consistently outperforms other methods in imputation performance and exhibits a low frequency of deviations in estimates for specific missing entries.Compared to the state-of-the-art autoregressive deep learning imputation model Bidirectional Recurrent Imputation for Time Series(BRITS),our proposed model reduces mean absolute error(MAE)by 31%~50%.Additionally,the model attains a training speed that is 4 to 8 times faster when compared to both BRITS and a standard Transformer model when trained on the same dataset.Finally,the findings from the ablation experiments demonstrate that the PSDMSA,the weight fusion unit,cascade network design,and imputation loss enhance imputation performance and confirm the efficacy of our design.展开更多
Q value and optimal exciting energy of hypothetical superheavy nuclei in cold fusion reaction are calculated with relativistic mean field model and semiemperical shell model mass equation (SSME) and the validity of th...Q value and optimal exciting energy of hypothetical superheavy nuclei in cold fusion reaction are calculated with relativistic mean field model and semiemperical shell model mass equation (SSME) and the validity of the two models is tested. To give useful references for the experiments in the superheavy nuclei synthesized in cold fusion reactions,the Q value, fusion barrier and optimal exciting energy for the possible target plus projectile combinations suggested by Gupta et al. are calculated and the most possible target plus projectile combinations are pointed out according to our calculations.展开更多
An animation works to attract the audience＇ s attention, arouse the audience＇ s emotional needs, in the design process of rational use of color to reflect the characters inner feelings, show the conflict between the...An animation works to attract the audience＇ s attention, arouse the audience＇ s emotional needs, in the design process of rational use of color to reflect the characters inner feelings, show the conflict between the story and the plot and characters. In the animation design color is designed based on the needs of the story, art processing with subjective wishes, these art is in order to better serve the characters, plot, and services for the final theme of the. Therefore, designers need to continue to explore, better grasp the law of animation colors, so that the color of animation works to play a unique artistic charm.展开更多
As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery...As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn＇t require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor＇s own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.展开更多
Cardiac diseases are one of the greatest global health challenges.Due to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent years.This article proposes a hy...Cardiac diseases are one of the greatest global health challenges.Due to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent years.This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases.The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms.An ensemble of classifiers is then applied to the fusion’s results.The proposed model classifies the arrhythmia dataset from the University of California,Irvine into normal/abnormal classes as well as 16 classes of arrhythmia.Initially,at the preprocessing steps,for the miss-valued attributes,we used the average value in the linear attributes group by the same class and the most frequent value for nominal attributes.However,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized classifiers.The preprocessing step led to 161 out of 279 attributes(features).Thereafter,a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection algorithms.In short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive model.Our proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art techniques.It achieves 98.5%accuracy for binary class mode and 98.9%accuracy for categorized class mode.展开更多
为了从微观角度更深入地分析引文网络中知识的融合过程和价值传递机制,以及从量化的角度发现和评估引文网络中文献的价值、作用与贡献,本文从引文网络中分解出知识收敛融合和知识扩散融合的基本结构单元,通过表征和量化知识存量、知识...为了从微观角度更深入地分析引文网络中知识的融合过程和价值传递机制,以及从量化的角度发现和评估引文网络中文献的价值、作用与贡献,本文从引文网络中分解出知识收敛融合和知识扩散融合的基本结构单元,通过表征和量化知识存量、知识流量、知识融合量与知识融合度,构建知识引用价值、知识流量价值和知识融合价值的分析方法;利用Web of Science数据库,选取具有代表性的文献进行实验分析;通过量化数据,计算知识引用价值、知识流量价值和知识融合价值的增值率,分析文献在知识融合中产生的价值、作用和贡献;结合引用位置和论文功能结构,发现提供创新性和引证性的主要文献,以及契合度高、关联性强的被引文献和施引文献。展开更多
A fusion approach is proposed to refine the resolution of urban multi-spectral images using the corresponding high-resolution panchromatic （PAN） images. Firstly, the two images are decomposed by wavelet transformati...A fusion approach is proposed to refine the resolution of urban multi-spectral images using the corresponding high-resolution panchromatic （PAN） images. Firstly, the two images are decomposed by wavelet transformation, and five texture features are extracted from high-frequency detailed sub-images. Then a multi-characteristics fusion rule is used to merge wavelet coefficients from the two images according to the extracted features. Experimental results indicate that, comparing with the non-characteristic methods, the proposed method can efficiently preserve the spectral information while improving the spatial resolution of the urban remote sensing images.展开更多
基金supported by Graduate Funded Project(No.JY2022A017).
文摘The frequent missing values in radar-derived time-series tracks of aerial targets(RTT-AT)lead to significant challenges in subsequent data-driven tasks.However,the majority of imputation research focuses on random missing(RM)that differs significantly from common missing patterns of RTT-AT.The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation.Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss.In this paper,a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed.Our model consists of two probabilistic sparse diagonal masking self-attention(PSDMSA)units and a weight fusion unit.It learns missing values by combining the representations outputted by the two units,aiming to minimize the difference between the missing values and their actual values.The PSDMSA units effectively capture temporal dependencies and attribute correlations between time steps,improving imputation quality.The weight fusion unit automatically updates the weights of the output representations from the two units to obtain a more accurate final representation.The experimental results indicate that,despite varying missing rates in the two missing patterns,our model consistently outperforms other methods in imputation performance and exhibits a low frequency of deviations in estimates for specific missing entries.Compared to the state-of-the-art autoregressive deep learning imputation model Bidirectional Recurrent Imputation for Time Series(BRITS),our proposed model reduces mean absolute error(MAE)by 31%~50%.Additionally,the model attains a training speed that is 4 to 8 times faster when compared to both BRITS and a standard Transformer model when trained on the same dataset.Finally,the findings from the ablation experiments demonstrate that the PSDMSA,the weight fusion unit,cascade network design,and imputation loss enhance imputation performance and confirm the efficacy of our design.
文摘Q value and optimal exciting energy of hypothetical superheavy nuclei in cold fusion reaction are calculated with relativistic mean field model and semiemperical shell model mass equation (SSME) and the validity of the two models is tested. To give useful references for the experiments in the superheavy nuclei synthesized in cold fusion reactions,the Q value, fusion barrier and optimal exciting energy for the possible target plus projectile combinations suggested by Gupta et al. are calculated and the most possible target plus projectile combinations are pointed out according to our calculations.
文摘An animation works to attract the audience＇ s attention, arouse the audience＇ s emotional needs, in the design process of rational use of color to reflect the characters inner feelings, show the conflict between the story and the plot and characters. In the animation design color is designed based on the needs of the story, art processing with subjective wishes, these art is in order to better serve the characters, plot, and services for the final theme of the. Therefore, designers need to continue to explore, better grasp the law of animation colors, so that the color of animation works to play a unique artistic charm.
基金supported by National Hi-tech Research and Development Program of China (863 Program, Grant No. 2007AA04Z433)Hunan Provincial Natural Science Foundation of China (Grant No. 09JJ8005)Scientific Research Foundation of Graduate School of Beijing University of Chemical and Technology,China (Grant No. 10Me002)
文摘As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn＇t require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor＇s own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.
文摘Cardiac diseases are one of the greatest global health challenges.Due to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent years.This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases.The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms.An ensemble of classifiers is then applied to the fusion’s results.The proposed model classifies the arrhythmia dataset from the University of California,Irvine into normal/abnormal classes as well as 16 classes of arrhythmia.Initially,at the preprocessing steps,for the miss-valued attributes,we used the average value in the linear attributes group by the same class and the most frequent value for nominal attributes.However,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized classifiers.The preprocessing step led to 161 out of 279 attributes(features).Thereafter,a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection algorithms.In short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive model.Our proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art techniques.It achieves 98.5%accuracy for binary class mode and 98.9%accuracy for categorized class mode.
文摘为了从微观角度更深入地分析引文网络中知识的融合过程和价值传递机制,以及从量化的角度发现和评估引文网络中文献的价值、作用与贡献,本文从引文网络中分解出知识收敛融合和知识扩散融合的基本结构单元,通过表征和量化知识存量、知识流量、知识融合量与知识融合度,构建知识引用价值、知识流量价值和知识融合价值的分析方法;利用Web of Science数据库,选取具有代表性的文献进行实验分析;通过量化数据,计算知识引用价值、知识流量价值和知识融合价值的增值率,分析文献在知识融合中产生的价值、作用和贡献;结合引用位置和论文功能结构,发现提供创新性和引证性的主要文献,以及契合度高、关联性强的被引文献和施引文献。
基金This work was supported by the National "863" Projects (No. 2001AA135091) the Shanghai Key Project (No. 02DZ15001), and the China Aviation Science Foun-dation (No. 02D57003).
文摘A fusion approach is proposed to refine the resolution of urban multi-spectral images using the corresponding high-resolution panchromatic （PAN） images. Firstly, the two images are decomposed by wavelet transformation, and five texture features are extracted from high-frequency detailed sub-images. Then a multi-characteristics fusion rule is used to merge wavelet coefficients from the two images according to the extracted features. Experimental results indicate that, comparing with the non-characteristic methods, the proposed method can efficiently preserve the spectral information while improving the spatial resolution of the urban remote sensing images.
