Describing Strong Correlation with Block-Correlated Coupled Cluster Theory

A block-correlated coupled cluster (BCCC) method based on the generalized valence bond (GVB) wave function (GVB-BCCC in short) is proposed and implemented at the ab initio level, which represents an attractive multireference electronic structure method for strongly correlated systems. The GVB-BCCC method is demonstrated to provide accurate descriptions for multiple bond breaking in small molecules, although the GVB reference function is qualitatively wrong for the studied processes. For a challenging prototype of strongly correlated systems, tridecane with all 12 single C-C bonds at various distances, our calculations have shown that the GVB-BCCC2b method can provide highly comparable results as the density matrix renormalization group method for potential energy surfaces along simultaneous dissociation of all C-C bonds

An Automatic Head Surface Temperature Extraction Method for Top-View Thermal Image with Individual Broiler

Surface temperature variation in a broiler’s head can be used as an indicator of its health status. Surface temperatures in the existing thermograph based animal health assessment studies were mostly obtained manually. 2185 thermal images, each of which had an individual broiler, were captured from 20 broilers. Where 15 broilers served as the experimental group, they were injected with 0.1mL of pasteurella inoculum. The rest, 5 broilers, served as the control group. An algorithm was developed to extract head surface temperature automatically from the top-view broiler thermal image. Adaptive K-means clustering and ellipse fitting were applied to locate the broiler’s head region. The maximum temperature inside the head region was extracted as the head surface temperature. The developed algorithm was tested in Matlab® (R2016a) and the testing results indicated that the head region in 92.77% of the broiler thermal images could be located correctly. The maximum error of the extracted head surface temperatures was not greater than 0.1 °C. Different trend features were observed in the smoothed head surface temperature time series of the broilers in experimental and control groups. Head surface temperature extracted by the presented algorithm lays a foundation for the development of an automatic system for febrile broiler identification

An Automatic Head Surface Temperature Extraction Method for Top-View Thermal Image with Individual Broiler

Surface temperature variation in a broiler's head can be used as an indicator of its health status. Surface temperatures in the existing thermograph based animal health assessment studies were mostly obtained manually. 2185 thermal images, each of which had an individual broiler, were captured from 20 broilers. Where 15 broilers served as the experimental group, they were injected with 0.1mL of pasteurella inoculum. The rest, 5 broilers, served as the control group. An algorithm was developed to extract head surface temperature automatically from the top-view broiler thermal image. Adaptive K-means clustering and ellipse fitting were applied to locate the broiler's head region. The maximum temperature inside the head region was extracted as the head surface temperature. The developed algorithm was tested in Matlab® (R2016a) and the testing results indicated that the head region in 92.77% of the broiler thermal images could be located correctly. The maximum error of the extracted head surface temperatures was not greater than 0.1 °C. Different trend features were observed in the smoothed head surface temperature time series of the broilers in experimental and control groups. Head surface temperature extracted by the presented algorithm lays a foundation for the development of an automatic system for febrile broiler identification.status: publishe

Advanced ESD coregistration of inteferometric processing for Sentinel-1 TOPS data

The TOPS mode images of Sentinel-1 satellite usually use the overlapping regions between bursts to achieve high-precision coregistration by using enhanced spectral diversity (ESD) technique after carrying out geometric coregistration. The geometric coregistration relies on the use of satellite orbit parameters which limiting the registration accuracy. Consequently, the key of sentinel-1 image registration is to accurately estimate the residual offset after geometric registration through enhanced spectral diversity. However, the ESD suffers from incoherent noise making it difficult to meet the requirement of 0.001 of the pixels spacing. In this study, we first improve the ESD performance for single interferometric pair in following aspects: ①develop ESD multi-looking theory, optimized the ESD workflow by using early-multi-looking process; ②apply weighted periodogram to improve the parameter estimation on the basis of the residual offset equal weight observations of ESD phase; ③propose the ESD weighted estimation that exploits range overlapping areas, and an increased number of overlapping areas contribute to the estimated accuracy. On this basis of single-baseline co-registration, we proposed the optimized solutions for time-series image processing: ①use the redundant ESD observations from interferometric pairs to conduct multi-baseline coregistration; ②improve the interferogram quality by distributed scatterers technique so that increase the accuracy of the ESD estimation. The above improvement schemes are mutually complemental and can also be implemented independently. The experiment results show that the above improvement methods can improve the registration accuracy

Construction of sheep forage intake estimation models based on sound analysis

Forage intake is one of the most important indicators of health and productivity of ruminant livestock, such as sheep. Forage intake estimation can also make an important contribution to the design and implementation of rotational grazing systems. In this paper, sheep forage intake estimation models were developed based on acoustic analysis from data gathered by an audio recorder mounted on a sheep. A data set with 114 pieces of audio was constructed by collecting ingestion sound of eight female sheep (2 years old with 50 ± 3 kg body mass). A Gaussian kernel-based support vector machine (SVM) classifier was trained to identify chewing sound segments from sheep ingestion audio. Seven explanatory variables were extracted from each chewing sound segment. These variables were used to establish single variable and multiple variable-based forage estimation models. Least squares regression and elastic network approach were employed to determine the coefficients of the single variable and multiple variable-based forage intake estimation models, respectively. Validation results showed that the best single variable and multiple variable-based model could explain 71.02% and 80.94% of forage intake changes, respectively. The average accuracy of the two best models was 86.13% and 89.32%, respectively. The results suggested that an automatic system to estimate the forage intake of sheep based on a wearable audio-recorder could be developed in the future. This could contribute to sheep health disorder identification, decisions on the number of sheep for a given grassland unit, and so on. The latter is the foundation of sheep rotational grazing.status: publishe