A Province-Scale Maize Yield Estimation Method Based on TM and Modis Time-Series Interpolation

2010 ◽  
Vol 8 (1) ◽  
pp. 2-5 ◽  
Author(s):  
Xin He ◽  
Yuanshu Jing ◽  
Xiaohe Gu ◽  
Wenjiang Huang
Keyword(s):  
Time Series ◽  
Estimation Method ◽  
Maize Yield ◽  
Yield Estimation

scholarly journals An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks

2021 ◽  
Author(s):  
Cong Gao ◽  
Ping Yang ◽  
Yanping Chen ◽  
Zhongmin Wang ◽  
Yue Wang
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Estimation Method ◽  
Feature Representation ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Data Sets ◽  
Edge Node

AbstractWith large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

scholarly journals Local stress fields in solids estimated by acoustical emission method

2021 ◽  
Vol 2103 (1) ◽  
pp. 012064
Author(s):  
V L Hilarov ◽  
E E Damaskinskaya
Keyword(s):  
Time Series ◽  
Acoustic Emission ◽  
Stress Field ◽  
Estimation Method ◽  
Local Stress ◽  
Stress Fields ◽  
Emission Method ◽  
Macroscopic Fracture ◽  
Stress Estimation ◽  
Kinetic Concept

Abstract Based on the Zhurkov’s kinetic concept of solids’ fracture a local internal stress estimation method is introduced. Stress field is computed from the time series of acoustic emission intervals between successive signals. For the case of two structurally different materials the time evolution of these stresses is examined. It is shown that temporal changes of these stresses’ accumulation law may serve as a precursor of incoming macroscopic fracture.

scholarly journals Detecting long-memory: Monte Carlo simulations and application to daily streamflow processes

2006 ◽  
Vol 3 (4) ◽  
pp. 1603-1627 ◽  
Author(s):  
W. Wang ◽  
P. H. A. J. M. van Gelder ◽  
J. K. Vrijling ◽  
X. Chen
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Maximum Likelihood ◽  
Long Memory ◽  
Positive Relationship ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Large Data ◽  
Daily Streamflow ◽  
Maximum Likelihood Estimation Method

Abstract. The Lo's R/S tests (Lo, 1991), GPH test (Geweke and Porter-Hudak, 1983) and the maximum likelihood estimation method implemented in S-Plus (S-MLE) are evaluated through intensive Mote Carlo simulations for detecting the existence of long-memory. It is shown that, it is difficult to find an appropriate lag q for Lo's test for different AR and ARFIMA processes, which makes the use of Lo's test very tricky. In general, the GPH test outperforms the Lo's test, but for cases where there is strong autocorrelations (e.g., AR(1) processes with φ=0.97 or even 0.99), the GPH test is totally useless, even for time series of large data size. Although S-MLE method does not provide a statistic test for the existence of long-memory, the estimates of d given by S-MLE seems to give a good indication of whether or not the long-memory is present. Data size has a significant impact on the power of all the three methods. Generally, the power of Lo's test and GPH test increases with the increase of data size, and the estimates of d with GPH test and S-MLE converge with the increase of data size. According to the results with the Lo's R/S test (Lo, 1991), GPH test (Geweke and Porter-Hudak, 1983) and the S-MLE method, all daily flow series exhibit long-memory. The intensity of long-memory in daily streamflow processes has only a very weak positive relationship with the scale of watershed.

An Efficient Yield Estimation Method for Layouts of High Dimensional and High Sigma SRAM Arrays

2021 ◽  
Author(s):  
Yue Shen ◽  
Changhao Yan ◽  
Sheng-Guo Wang ◽  
Dian Zhou ◽  
Xuan Zeng
Keyword(s):  
Estimation Method ◽  
High Dimensional ◽  
Yield Estimation

scholarly journals Maize yield estimation in West Africa from crop process-induced combinations of multi-domain remote sensing indices

2019 ◽  
Vol 108 ◽  
pp. 11-26 ◽  
Author(s):  
Louise Leroux ◽  
Mathieu Castets ◽  
Christian Baron ◽  
Maria-Jose Escorihuela ◽  
Agnès Bégué ◽  
...  
Keyword(s):  
Remote Sensing ◽  
West Africa ◽  
Maize Yield ◽  
Yield Estimation ◽  
Remote Sensing Indices

scholarly journals Spatial Pattern of Agricultural Productivity Trends in Malawi

2020 ◽  
Vol 12 (4) ◽  
pp. 1313
Author(s):  
Leah M. Mungai ◽  
Joseph P. Messina ◽  
Sieglinde Snapp
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Agricultural Productivity ◽  
Household Survey ◽  
Maize Yield ◽  
Series Data ◽  
Maize Crop

This study aims to assess spatial patterns of Malawian agricultural productivity trends to elucidate the influence of weather and edaphic properties on Moderate Resolution Imaging Spectroradiometer (MODIS)-Normalized Difference Vegetation Index (NDVI) seasonal time series data over a decade (2006–2017). Spatially-located positive trends in the time series that can’t otherwise be accounted for are considered as evidence of farmer management and agricultural intensification. A second set of data provides further insights, using spatial distribution of farmer reported maize yield, inorganic and organic inputs use, and farmer reported soil quality information from the Malawi Integrated Household Survey (IHS3) and (IHS4), implemented between 2010–2011 and 2016–2017, respectively. Overall, remote-sensing identified areas of intensifying agriculture as not fully explained by biophysical drivers. Further, productivity trends for maize crop across Malawi show a decreasing trend over a decade (2006–2017). This is consistent with survey data, as national farmer reported yields showed low yields across Malawi, where 61% (2010–11) and 69% (2016–17) reported yields as being less than 1000 Kilograms/Hectare. Yields were markedly low in the southern region of Malawi, similar to remote sensing observations. Our generalized models provide contextual information for stakeholders on sustainability of productivity and can assist in targeting resources in needed areas. More in-depth research would improve detection of drivers of agricultural variability.

scholarly journals Biomass limit reference points are sensitive to estimation method, time‐series length and stock development

2020 ◽  
Vol 22 (1) ◽  
pp. 18-30
Author(s):  
Mikael Deurs ◽  
Mollie E. Brooks ◽  
Martin Lindegren ◽  
Ole Henriksen ◽  
Anna Rindorf
Keyword(s):  
Time Series ◽  
Estimation Method ◽  
Reference Points ◽  
Series Length
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