- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Remote sensing of cropping practice in Northern Italy using time-series from Sentinel-2
摘要： Maps of cropping practice, including the level of weed infestation, are useful planning tools e.g. for the assessment of the environmental impact of the crops, and Northern Italy is an important example due to the large and diverse agricultural production and the high weed infestation. Sentinel-2A is a new satellite with a high spatial and temporal resolution which potentially allows the creation of detailed maps of cropping practice including weed infestation. To explore the applicability of Sentinel-2A for mapping cropping practice, we analysed the Normalised Differential Vegetation Index (NDVI) time series from five weed-infested crop fields as well as the areas designated as non-irrigated agricultural land in Corine Land Cover, which also contributed to an increased understanding of the cropping practice in the region. The analysis of the case studies showed that the temporal resolution of Sentinel-2A was high enough to distinguish the gross features of the cropping practice, and that high weed infestations can be detected at this spatial resolution. The analysis of the entire region showed the potential for mapping cropping practice using Sentinel-2. In conclusion, Sentinel-2A is to some extent applicable for mapping cropping practice with reasonable thematic accuracy.
关键词： Clustering,Phenology,Weed infestation,NDVI,Time-series analysis
更新于2025-09-23 15:22:29
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Hypercube States for Sub-Planck Sensing
摘要： Land-cover datasets are crucial for research on eco-hydrological processes and earth system modeling. Many land-cover datasets have been derived from remote-sensing data. However, their spatial resolutions are usually low and their classification accuracy is not high enough, which are not well suited to the needs of land surface modeling. Consequently, a comprehensive method for monthly land-cover classification in the Heihe river basin (HRB) with high spatial resolution is developed. Moreover, the major crops in the HRB are also distinguished. The proposed method integrates multiple classifiers and multisource data. Three types of data including MODIS, HJ-1/CCD, and Landsat/TM and Google Earth images are used. Compared to single classifier, multiple classifiers including thresholding, support vector machine (SVM), object-based method, and time-series analysis are integrated to improve the accuracy of classification. All the data and classifiers are organized using a decision tree. Monthly land-cover maps of the HRB in 2013 with 30-m spatial resolution are made. A comprehensive validation shows great improvement in the accuracy. First, a visual comparison of the land-cover maps using the proposed method and standard SVM method shows the classification differences and the advantages of the proposed method. The confusion matrix is used to evaluate the classification accuracy, showing an overall classification accuracy of over 90% in the HRB, which is quite higher than previous approaches. Furthermore, a ground campaign was performed to evaluate the accuracy of crop classification and an overall accuracy of 84.09% for the crop classification was achieved.
关键词： Crop classification,river basin,multisource remotely sensed data,phenology,time-series analysis,multiple classifiers,multiple scales,HJ-1/CCD,land cover
更新于2025-09-19 17:13:59
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A Gaussian-Gaussian-Restricted-Boltzmann-Machine-based Deep Neural Network Technique for Photovoltaic System Generation Forecasting
摘要： This paper proposes a new Gaussian-Gaussian-Restricted-Boltzmann-Machine-based method for forecasting photovoltaic (PV) system generation forecasting. Although renewable energy such as PV system and wind power generation has been used to suppress greenhouse gases in the world, it has a drawback that weather conditions influence the generation output significantly. Thus, it is not easy to perform Economic Load Dispatch (ELD) and Unit Commitment in power systems smoothly. From a standpoint of power system operation, more accurate predication models are required to deal with predicted values of PV system generation. In this paper, an efficient Deep Neural Network (DNN) model with Gaussian Gaussian Restricted Boltzmann Machine is presented to predict one-step-ahead PV system generation output. The model is based on Restricted Boltzmann Machine as a feature extractor and Multi-Layer Perceptron (MLP) as ANN. The effectiveness of the proposed method is demonstrated for real data of a PV system.
关键词： Solar energy,Forecasting,Time-series analysis,Artificial Intelligence,Power systems
更新于2025-09-19 17:13:59
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[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Irreversible Photobleaching of BAC-Si in Bi/Er Co-Doped Optical Fiber under 830 nm Pumping
摘要： A novel empirical data analysis methodology based on the random matrix theory (RMT) and time series analysis is proposed for the power systems. Among the ongoing research studies of big data in the power system applications, there is a strong necessity for new mathematical tools that describe and analyze big data. This paper used RMT to model the empirical data which also treated as a time series. The proposed method extends traditional RMT for applications in a non-Gaussian distribution environment. Three case studies, i.e., power equipment condition monitoring, voltage stability analysis and low-frequency oscillation detection, illustrate the potential application value of our proposed method for multi-source heterogeneous data analysis, sensitive spot awareness and fast signal detection under an unknown noise pattern. The results showed that the empirical data from a power system modeled following RMT and in a time series have high sensitivity to dynamically characterized system states as well as observability and efficiency in system analysis compared with conventional equation-based methods.
关键词： low frequency oscillation,non-Gaussian,static voltage stability,Random matrix theory,time series analysis,data mining,condition monitoring
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Experimental Implementation of Non-uniformity Effects in Artificial Media : (Invited)
摘要： Land-cover datasets are crucial for research on eco-hydrological processes and earth system modeling. Many land-cover datasets have been derived from remote-sensing data. However, their spatial resolutions are usually low and their classification accuracy is not high enough, which are not well suited to the needs of land surface modeling. Consequently, a comprehensive method for monthly land-cover classification in the Heihe river basin (HRB) with high spatial resolution is developed. Moreover, the major crops in the HRB are also distinguished. The proposed method integrates multiple classifiers and multisource data. Three types of data including MODIS, HJ-1/CCD, and Landsat/TM and Google Earth images are used. Compared to single classifier, multiple classifiers including thresholding, support vector machine (SVM), object-based method, and time-series analysis are integrated to improve the accuracy of classification. All the data and classifiers are organized using a decision tree. Monthly land-cover maps of the HRB in 2013 with 30-m spatial resolution are made. A comprehensive validation shows great improvement in the accuracy. First, a visual comparison of the land-cover maps using the proposed method and standard SVM method shows the classification differences and the advantages of the proposed method. The confusion matrix is used to evaluate the classification accuracy, showing an overall classification accuracy of over 90% in the HRB, which is quite higher than previous approaches. Furthermore, a ground campaign was performed to evaluate the accuracy of crop classification and an overall accuracy of 84.09% for the crop classification was achieved.
关键词： land cover,river basin,time-series analysis,multisource remotely sensed data,phenology,Crop classification,HJ-1/CCD,multiple scales,multiple classifiers
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC) - Singapore, Singapore (2019.11.25-2019.11.28)] 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC) - A Comparative Study of Flexible Power Point Tracking Algorithms in Photovoltaic Systems
摘要： Land-cover datasets are crucial for research on eco-hydrological processes and earth system modeling. Many land-cover datasets have been derived from remote-sensing data. However, their spatial resolutions are usually low and their classification accuracy is not high enough, which are not well suited to the needs of land surface modeling. Consequently, a comprehensive method for monthly land-cover classification in the Heihe river basin (HRB) with high spatial resolution is developed. Moreover, the major crops in the HRB are also distinguished. The proposed method integrates multiple classifiers and multisource data. Three types of data including MODIS, HJ-1/CCD, and Landsat/TM and Google Earth images are used. Compared to single classifier, multiple classifiers including thresholding, support vector machine (SVM), object-based method, and time-series analysis are integrated to improve the accuracy of classification. All the data and classifiers are organized using a decision tree. Monthly land-cover maps of the HRB in 2013 with 30-m spatial resolution are made. A comprehensive validation shows great improvement in the accuracy. First, a visual comparison of the land-cover maps using the proposed method and standard SVM method shows the classification differences and the advantages of the proposed method. The confusion matrix is used to evaluate the classification accuracy, showing an overall classification accuracy of over 90% in the HRB, which is quite higher than previous approaches. Furthermore, a ground campaign was performed to evaluate the accuracy of crop classification and an overall accuracy of 84.09% for the crop classification was achieved.
关键词： HJ-1/CCD,multiple classifiers,phenology,river basin,multiple scales,time-series analysis,Crop classification,land cover,multisource remotely sensed data
更新于2025-09-16 10:30:52
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Multi-Temporal Cliff Erosion Analysis Using Airborne Laser Scanning Surveys
摘要： Rock cli?s are a signi?cant component of world coastal zones. However, rocky coasts and factors contributing to their erosion have not received as much attention as soft cli?s. In this study, two rocky-cli? systems in the southern Baltic Sea were analyzed with Airborne Laser Scanners (ALS) to track changes in cli? morphology. The present contribution aimed to study the volumetric changes in cli? pro?les, spatial distribution of erosion, and rate of cli? retreat corresponding to the cli? exposure and rock resistance of the Jasmund National Park chalk cli?s in Rugen, Germany. The study combined multi-temporal Light Detection and Ranging (LiDAR) data analyses, rock sampling, laboratory analyses of chemical and mechanical resistance, and along-shore wave power ?ux estimation. The spatial distribution of the active erosion areas appear to follow the cli? exposure variations; however, that trend is weaker for the sections of the coastline in which structural changes occurred. The rate of retreat for each cli?–beach pro?le, including the cli? crest, vertical cli? base, and cli? base with talus material, indicates that wave action is the dominant erosive force in areas in which the cli? was eroded quickly at equal rates along the cli? pro?le. However, the erosion proceeded with di?erent rates in favor of cli? toe erosion. The e?ects of chemical and mechanical rock resistance are shown to be less prominent than the wave action owing to very small di?erences in the measured values, which proves the homogeneous structure of the cli?. The rock resistance did not follow the trends of cli? erosion revealed by volume changes during the period of analysis.
关键词： cli? retreat,time-series analysis,cli? coastlines,airborne laser scanner
更新于2025-09-16 10:30:52
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Time series forecasting of solar power generation for large-scale photovoltaic plants
摘要： Accurate solar power forecasting is essential for grid-connected photovoltaic (PV) systems especially in case of fluctuating environmental conditions. The prediction of PV power output is critical to secure grid operation, scheduling and grid energy management. One of the key elements in PV output prediction is time series analysis especially in locations where the historical solar radiation measurements or other weather parameters have not been recorded. In this work, several time series prediction methods including the statistical methods and those based on artificial intelligence are introduced and compared rigorously for PV power output prediction. Moreover, the effect of prediction time horizon variation for all the algorithms is investigated. Hourly solar power forecasting is carried out to verify the effectiveness of different models. The data utilized in the current work comprises 3640 hours of operation data taken from a 20 MW grid-connected PV station in China.
关键词： neural network,statistical methods,PV power forecasting,time series analysis,deep learning,grid-connected PV plant
更新于2025-09-12 10:27:22
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Optical Cavity-Less 40-GHz Picosecond Pulse Generator in the Visible Wavelength Range
摘要： A method combined ensemble empirical mode decomposition, Volterra model and decision acyclic graph support vector machine was proposed to improve adaptability, feature resolution, and identification accuracy when diagnosing mechanical faults in an on-load tap changer of a transformer. In detail, the ensemble empirical mode decomposition algorithm was applied to decompose the multi-channel vibration signals in the switchover process of the on-load tap changer. Then, a Volterra model for the mechanical state of the on-load tap changer was established based on time-frequency characteristics obtained through the use of the ensemble empirical mode decomposition algorithm. Moreover, a matrix of coefficient vectors was also used in the Volterra model. This method will not only reduce the aliasing effect of empirical mode decomposition but also obtain high-resolution characteristics of nonstationary vibration signals. Furthermore, taking the singular values of the Volterra coefficient matrix as the fault characteristic, the data states of the model for diagnosing the on-load tap changer were automatically classified and identified by establishing a rapid, multi-classification decision acyclic graph support vector machine model with a low misjudgment rate. Finally, based on a certain on-load tap changer, the test platform for simulating mechanical faults was built. On this basis, by using the proposed method, the vibration signals generated due to typical mechanical faults, such as loosening of moving contacts, lessening of transition contact, and motor jam were acquired and analyzed, thus validating the effectiveness of the method through case studies. Compared with other methods, the new method could overcome many defects in existing methods and it has higher fault identification accuracy.
关键词： signal processing algorithms,Mechanical variables measurement,power transformers,fault diagnosis,electromechanical devices,time series analysis,support vector machines,switches
更新于2025-09-11 14:15:04
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Electric field dependence of dc conductivity in As2Te3 (in) thin films
摘要： The time evolutions of transient current data for As2Te3(In) thin films were measured at different electric fields for the dc voltages of 0.001 V, 0.002 V, 0.005 V, 0.01 V, 0.1 V, 0.5 V and 1 V at room temperature (296 K). Transient current was analyzed by means of time series analysis in order to identify different conduction regimes. The maximal Lyapunov exponents for the transient currents were calculated. Positive maximal Lyapunov exponents reflected electric field dependence with positive Lyapunov exponents. Existence of a positive Lyapunov exponent means sensitive dependence on initial conditions and As2Te3(In) is known to have memory effects hence different initial conditions for each dc current measurement. Detrended Fluctuation Analysis was utilized to characterize the behavior of dc current time series which is invariant to initial conditions. Detrended Fluctuation Analysis identified three different conduction regimes with multiple conduction mechanisms.
关键词： Weak chaotic systems,Conduction mechanisms,Time Series Analysis,Detrended Fluctuation Analysis,Scaling exponent
更新于2025-09-10 09:29:36