The identification features of composite pseudocode phase modulation and carry frequency modulation signal include pseudocode and modulation frequency. In this paper,PWD is used to extract these features. First,the fe...The identification features of composite pseudocode phase modulation and carry frequency modulation signal include pseudocode and modulation frequency. In this paper,PWD is used to extract these features. First,the feature of pseudocode is extracted using the amplitude output of PWD and the correlation filter technology. Then the feature of frequency modulation is extracted by way of PWD analysis on the signal processed by anti-phase operation according to the extracted feature of pseudo code,i.e. position information of changed abruptly point of phase. The simulation result shows that both the features of frequency modulation and phase change position caused by the pseudocode phase modulation can be extracted effectively for SNR=3 dB.展开更多
With the new system radar put into practical use, the characteristics of complex radar signals are changing and developing. The traditional analysis method of one-dimensional transformation domain is no longer applica...With the new system radar put into practical use, the characteristics of complex radar signals are changing and developing. The traditional analysis method of one-dimensional transformation domain is no longer applicable to the modern radar signal processing, and it is necessary to seek new methods in the two-dimensional transformation domain. The time-frequency analysis method is the most widely used method in the two-dimensional transformation domain. In this paper, two typical time-frequency analysis methods of short-time Fourier transform and Wigner-Ville distribution are studied by analyzing the time-frequency transform of typical radar reconnaissance linear frequency modulation signal, aiming at the problem of low accuracy and sen-sitivity to the signal noise of common methods, the improved wavelet transform algorithm was proposed.展开更多
Hot-dip aluminizing（HDA） is a proven surface coating technique for improving the oxidation and corrosion resistance of ferrous substrates. Although extensive studies on the HDA of plain carbon steels have been repor...Hot-dip aluminizing（HDA） is a proven surface coating technique for improving the oxidation and corrosion resistance of ferrous substrates. Although extensive studies on the HDA of plain carbon steels have been reported, studies on the HDA of stainless steels are limited. Because of the technological importance of stainless steels in high-temperature applications, studies of their microstructural development during HDA are needed. In the present investigation, the HDA of AISI 321 stainless steel was carried out in a pure Al bath. The microstructural features of the coating were studied using scanning electron microscopy and transmission electron microscopy. These studies revealed that the coating consists of two regions： an Al top coat and an aluminide layer at the interface between the steel and Al. The Al top coat was found to consist of intermetallic phases such as Al_7Cr and Al_3Fe dispersed in an Al matrix. Twinning was observed in both the Al_7Cr and the Al_3Fe phases. Furthermore, the aluminide layer comprised a mixture of nanocrystalline Fe_2Al_5, Al_7Cr, and Al. Details of the microstructural features are presented, and their formation mechanisms are discussed.展开更多
A feature extraction method was proposed to sectorial scan image of Ti-6Al-4V electron beam welding seam based on principal component analysis to solve problem of high-dimensional data resulting in timeconsuming in de...A feature extraction method was proposed to sectorial scan image of Ti-6Al-4V electron beam welding seam based on principal component analysis to solve problem of high-dimensional data resulting in timeconsuming in defect recognition. Seven features were extracted from the image and represented 87. 3% information of the original data. Both the extracted features and the original data were used to train support vector machine model to assess the feature extraction performance in two aspects: recognition accuracy and training time. The results show that using the extracted features the recognition accuracy of pore,crack,lack of fusion and lack of penetration are 93%,90.7%,94.7% and 89.3%,respectively,which is slightly higher than those using the original data. The training time of the models using the extracted features is extremely reduced comparing with those using the original data.展开更多
In audio classification applications, features extracted from the frequency domain representation of signals are typically focused on the magnitude spectral content, while the phase spectral content is ignored. The co...In audio classification applications, features extracted from the frequency domain representation of signals are typically focused on the magnitude spectral content, while the phase spectral content is ignored. The conventional Fourier Phase Spectrum is a highly discontinuous function;thus, it is not appropriate for feature extraction for classification applications, where function continuity is required. In this work, the sources of phase spectral discontinuities are detected, categorized and compensated, resulting in a phase spectrum with significantly reduced discontinuities. The Hartley Phase Spectrum, introduced as an alternative to the conventional Fourier Phase Spectrum, encapsulates the phase content of the signal more efficiently compared with its Fourier counterpart because, among its other properties, it does not suffer from the phase ‘wrapping ambiguities’ introduced due to the inverse tangent function employed in the Fourier Phase Spectrum computation. In the proposed feature extraction method, statistical features extracted from the Hartley Phase Spectrum are combined with statistical features extracted from the magnitude related spectrum of the signals. The experimental results show that the classification score is higher in case the magnitude and the phase related features are combined, as compared with the case where only magnitude features are used.展开更多
This paper addresses an important issue in model combination, that is, model locality. Since usually a global linear model is unable to reflect nonlinearity and to characterize local features, especially in a complex ...This paper addresses an important issue in model combination, that is, model locality. Since usually a global linear model is unable to reflect nonlinearity and to characterize local features, especially in a complex sys-tem, we propose a mixture of local feature models to overcome these weaknesses. The basic idea is to split the entire input space into operating domains, and a recently developed feature-based model combination method is applied to build local models for each region. To realize this idea, three steps are required, which include clustering, local modeling and model combination, governed by a single objective function. An adaptive fuzzy parametric clustering algorithm is proposed to divide the whole input space into operating regimes, local feature models are created in each individual region by applying a recently developed fea-ture-based model combination method, and finally they are combined into a single mixture model. Corre-spondingly, a three-stage procedure is designed to optimize the complete objective function, which is actu-ally a hybrid Genetic Algorithm (GA). Our simulation results show that the adaptive fuzzy mixture of local feature models turns out to be superior to global models.展开更多
The effect of Ce, Co, and B on the formation of 1：13 phase in La（Fe, Si）13 alloys was investigated by XRD, SEM and EDS. The results show that Co can improve the formation of 1：13 phase in as-cast LaFe11.6-xCoxSi1....The effect of Ce, Co, and B on the formation of 1：13 phase in La（Fe, Si）13 alloys was investigated by XRD, SEM and EDS. The results show that Co can improve the formation of 1：13 phase in as-cast LaFe11.6-xCoxSi1.4 alloys, but in as-cast and annealed LaFe11.6Si1.4-xCox alloys, it will hamper the formation of 1：13 phase and help the formation of a-Fe（Co, Si） solid solution. Ce2Fel7 phases will form when x reaches a certain value in as-cast and annealed La1-xCexFe11.5Si1.5 alloys. B can improve the formation of 1：13 phase accompanied with Fe2B phase in as-cast LaFe11.6-xBxSi1.4 alloys. B improves the formation of a-Fe solid solution in LaFe11.6Si1.4-xBx alloys, and there is almost only a-Fe in as-cast and annealed LaFe11.6Si0.9B0.5 alloy. In all, the introduction of Co, B, and Ce cannot eliminate the a-Fe phases in corresponding alloys prepared by the high-temperature and short-time annealing process.展开更多
Ratoon rice,which refers to a second harvest of rice obtained from the regenerated tillers originating from the stubble of the first harvested crop,plays an important role in both food security and agroecology while r...Ratoon rice,which refers to a second harvest of rice obtained from the regenerated tillers originating from the stubble of the first harvested crop,plays an important role in both food security and agroecology while requiring minimal agricultural inputs.However,accurately identifying ratoon rice crops is challenging due to the similarity of its spectral features with other rice cropping systems(e.g.,double rice).Moreover,images with a high spatiotemporal resolution are essential since ratoon rice is generally cultivated in fragmented croplands within regions that frequently exhibit cloudy and rainy weather.In this study,taking Qichun County in Hubei Province,China as an example,we developed a new phenology-based ratoon rice vegetation index(PRVI)for the purpose of ratoon rice mapping at a 30 m spatial resolution using a robust time series generated from Harmonized Landsat and Sentinel-2(HLS)images.The PRVI that incorporated the red,near-infrared,and shortwave infrared 1 bands was developed based on the analysis of spectro-phenological separability and feature selection.Based on actual field samples,the performance of the PRVI for ratoon rice mapping was carefully evaluated by comparing it to several vegetation indices,including normalized difference vegetation index(NDVI),enhanced vegetation index(EVI)and land surface water index(LSWI).The results suggested that the PRVI could sufficiently capture the specific characteristics of ratoon rice,leading to a favorable separability between ratoon rice and other land cover types.Furthermore,the PRVI showed the best performance for identifying ratoon rice in the phenological phases characterized by grain filling and harvesting to tillering of the ratoon crop(GHS-TS2),indicating that only several images are required to obtain an accurate ratoon rice map.Finally,the PRVI performed better than NDVI,EVI,LSWI and their combination at the GHS-TS2 stages,with producer's accuracy and user's accuracy of 92.22 and 89.30%,respectively.These results demonstrate that the proposed PRVI based on HLS data can effectively identify ratoon rice in fragmented croplands at crucial phenological stages,which is promising for identifying the earliest timing of ratoon rice planting and can provide a fundamental dataset for crop management activities.展开更多
Signal decomposition and multiscale signal analysis provide many useful tools for timefrequency analysis.We proposed a random feature method for analyzing time-series data by constructing a sparse approximation to the...Signal decomposition and multiscale signal analysis provide many useful tools for timefrequency analysis.We proposed a random feature method for analyzing time-series data by constructing a sparse approximation to the spectrogram.The randomization is both in the time window locations and the frequency sampling,which lowers the overall sampling and computational cost.The sparsification of the spectrogram leads to a sharp separation between time-frequency clusters which makes it easier to identify intrinsic modes,and thus leads to a new data-driven mode decomposition.The applications include signal representation,outlier removal,and mode decomposition.On benchmark tests,we show that our approach outperforms other state-of-the-art decomposition methods.展开更多
文摘The identification features of composite pseudocode phase modulation and carry frequency modulation signal include pseudocode and modulation frequency. In this paper,PWD is used to extract these features. First,the feature of pseudocode is extracted using the amplitude output of PWD and the correlation filter technology. Then the feature of frequency modulation is extracted by way of PWD analysis on the signal processed by anti-phase operation according to the extracted feature of pseudo code,i.e. position information of changed abruptly point of phase. The simulation result shows that both the features of frequency modulation and phase change position caused by the pseudocode phase modulation can be extracted effectively for SNR=3 dB.
文摘With the new system radar put into practical use, the characteristics of complex radar signals are changing and developing. The traditional analysis method of one-dimensional transformation domain is no longer applicable to the modern radar signal processing, and it is necessary to seek new methods in the two-dimensional transformation domain. The time-frequency analysis method is the most widely used method in the two-dimensional transformation domain. In this paper, two typical time-frequency analysis methods of short-time Fourier transform and Wigner-Ville distribution are studied by analyzing the time-frequency transform of typical radar reconnaissance linear frequency modulation signal, aiming at the problem of low accuracy and sen-sitivity to the signal noise of common methods, the improved wavelet transform algorithm was proposed.
文摘Hot-dip aluminizing（HDA） is a proven surface coating technique for improving the oxidation and corrosion resistance of ferrous substrates. Although extensive studies on the HDA of plain carbon steels have been reported, studies on the HDA of stainless steels are limited. Because of the technological importance of stainless steels in high-temperature applications, studies of their microstructural development during HDA are needed. In the present investigation, the HDA of AISI 321 stainless steel was carried out in a pure Al bath. The microstructural features of the coating were studied using scanning electron microscopy and transmission electron microscopy. These studies revealed that the coating consists of two regions： an Al top coat and an aluminide layer at the interface between the steel and Al. The Al top coat was found to consist of intermetallic phases such as Al_7Cr and Al_3Fe dispersed in an Al matrix. Twinning was observed in both the Al_7Cr and the Al_3Fe phases. Furthermore, the aluminide layer comprised a mixture of nanocrystalline Fe_2Al_5, Al_7Cr, and Al. Details of the microstructural features are presented, and their formation mechanisms are discussed.
基金Sponsored by the National Natural Science Foundation of China(Grant Nos.51575134 and 51205083)
文摘A feature extraction method was proposed to sectorial scan image of Ti-6Al-4V electron beam welding seam based on principal component analysis to solve problem of high-dimensional data resulting in timeconsuming in defect recognition. Seven features were extracted from the image and represented 87. 3% information of the original data. Both the extracted features and the original data were used to train support vector machine model to assess the feature extraction performance in two aspects: recognition accuracy and training time. The results show that using the extracted features the recognition accuracy of pore,crack,lack of fusion and lack of penetration are 93%,90.7%,94.7% and 89.3%,respectively,which is slightly higher than those using the original data. The training time of the models using the extracted features is extremely reduced comparing with those using the original data.
文摘In audio classification applications, features extracted from the frequency domain representation of signals are typically focused on the magnitude spectral content, while the phase spectral content is ignored. The conventional Fourier Phase Spectrum is a highly discontinuous function;thus, it is not appropriate for feature extraction for classification applications, where function continuity is required. In this work, the sources of phase spectral discontinuities are detected, categorized and compensated, resulting in a phase spectrum with significantly reduced discontinuities. The Hartley Phase Spectrum, introduced as an alternative to the conventional Fourier Phase Spectrum, encapsulates the phase content of the signal more efficiently compared with its Fourier counterpart because, among its other properties, it does not suffer from the phase ‘wrapping ambiguities’ introduced due to the inverse tangent function employed in the Fourier Phase Spectrum computation. In the proposed feature extraction method, statistical features extracted from the Hartley Phase Spectrum are combined with statistical features extracted from the magnitude related spectrum of the signals. The experimental results show that the classification score is higher in case the magnitude and the phase related features are combined, as compared with the case where only magnitude features are used.
文摘This paper addresses an important issue in model combination, that is, model locality. Since usually a global linear model is unable to reflect nonlinearity and to characterize local features, especially in a complex sys-tem, we propose a mixture of local feature models to overcome these weaknesses. The basic idea is to split the entire input space into operating domains, and a recently developed feature-based model combination method is applied to build local models for each region. To realize this idea, three steps are required, which include clustering, local modeling and model combination, governed by a single objective function. An adaptive fuzzy parametric clustering algorithm is proposed to divide the whole input space into operating regimes, local feature models are created in each individual region by applying a recently developed fea-ture-based model combination method, and finally they are combined into a single mixture model. Corre-spondingly, a three-stage procedure is designed to optimize the complete objective function, which is actu-ally a hybrid Genetic Algorithm (GA). Our simulation results show that the adaptive fuzzy mixture of local feature models turns out to be superior to global models.
基金Project(51176050)supported by the National Natural Science Foundation of ChinaProject(12ZB073)supported by the Research Projects in Sichuan Province Education Office,China
文摘The effect of Ce, Co, and B on the formation of 1：13 phase in La（Fe, Si）13 alloys was investigated by XRD, SEM and EDS. The results show that Co can improve the formation of 1：13 phase in as-cast LaFe11.6-xCoxSi1.4 alloys, but in as-cast and annealed LaFe11.6Si1.4-xCox alloys, it will hamper the formation of 1：13 phase and help the formation of a-Fe（Co, Si） solid solution. Ce2Fel7 phases will form when x reaches a certain value in as-cast and annealed La1-xCexFe11.5Si1.5 alloys. B can improve the formation of 1：13 phase accompanied with Fe2B phase in as-cast LaFe11.6-xBxSi1.4 alloys. B improves the formation of a-Fe solid solution in LaFe11.6Si1.4-xBx alloys, and there is almost only a-Fe in as-cast and annealed LaFe11.6Si0.9B0.5 alloy. In all, the introduction of Co, B, and Ce cannot eliminate the a-Fe phases in corresponding alloys prepared by the high-temperature and short-time annealing process.
基金supported by the National Natural Science Foundation of China(42271360 and 42271399)the Young Elite Scientists Sponsorship Program by China Association for Science and Technology(CAST)(2020QNRC001)the Fundamental Research Funds for the Central Universities,China(2662021JC013,CCNU22QN018)。
文摘Ratoon rice,which refers to a second harvest of rice obtained from the regenerated tillers originating from the stubble of the first harvested crop,plays an important role in both food security and agroecology while requiring minimal agricultural inputs.However,accurately identifying ratoon rice crops is challenging due to the similarity of its spectral features with other rice cropping systems(e.g.,double rice).Moreover,images with a high spatiotemporal resolution are essential since ratoon rice is generally cultivated in fragmented croplands within regions that frequently exhibit cloudy and rainy weather.In this study,taking Qichun County in Hubei Province,China as an example,we developed a new phenology-based ratoon rice vegetation index(PRVI)for the purpose of ratoon rice mapping at a 30 m spatial resolution using a robust time series generated from Harmonized Landsat and Sentinel-2(HLS)images.The PRVI that incorporated the red,near-infrared,and shortwave infrared 1 bands was developed based on the analysis of spectro-phenological separability and feature selection.Based on actual field samples,the performance of the PRVI for ratoon rice mapping was carefully evaluated by comparing it to several vegetation indices,including normalized difference vegetation index(NDVI),enhanced vegetation index(EVI)and land surface water index(LSWI).The results suggested that the PRVI could sufficiently capture the specific characteristics of ratoon rice,leading to a favorable separability between ratoon rice and other land cover types.Furthermore,the PRVI showed the best performance for identifying ratoon rice in the phenological phases characterized by grain filling and harvesting to tillering of the ratoon crop(GHS-TS2),indicating that only several images are required to obtain an accurate ratoon rice map.Finally,the PRVI performed better than NDVI,EVI,LSWI and their combination at the GHS-TS2 stages,with producer's accuracy and user's accuracy of 92.22 and 89.30%,respectively.These results demonstrate that the proposed PRVI based on HLS data can effectively identify ratoon rice in fragmented croplands at crucial phenological stages,which is promising for identifying the earliest timing of ratoon rice planting and can provide a fundamental dataset for crop management activities.
基金supported in part by the NSERC RGPIN 50503-10842supported in part by the AFOSR MURI FA9550-21-1-0084the NSF DMS-1752116.
文摘Signal decomposition and multiscale signal analysis provide many useful tools for timefrequency analysis.We proposed a random feature method for analyzing time-series data by constructing a sparse approximation to the spectrogram.The randomization is both in the time window locations and the frequency sampling,which lowers the overall sampling and computational cost.The sparsification of the spectrogram leads to a sharp separation between time-frequency clusters which makes it easier to identify intrinsic modes,and thus leads to a new data-driven mode decomposition.The applications include signal representation,outlier removal,and mode decomposition.On benchmark tests,we show that our approach outperforms other state-of-the-art decomposition methods.
