scholarly journals Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion

2021 ◽  
Vol 13 (5) ◽  
pp. 978
Author(s):  
Abdul Mounem Mouazen ◽  
Zhou Shi
Keyword(s):  
Data Fusion ◽  
Soil Properties ◽  
Soil Management ◽  
Proximal Sensing ◽  
Valuable Source ◽  
Recent Advances ◽  
Source Data ◽  
Source Of Information

Recent advances in remote and proximal sensing technologies provide a valuable source of information for enriching our geo-datasets, which are necessary for soil management and the precision application of farming input resources [...]

scholarly journals Binding modes of methyl α-d-glucopyranoside to an artificial receptor in crystalline complexes

RSC Advances ◽  
2021 ◽  
Vol 11 (36) ◽  
pp. 22221-22229
Author(s):  
Linda Köhler ◽  
Conrad Hübler ◽  
Wilhelm Seichter ◽  
Monika Mazik
Keyword(s):  
Binding Modes ◽  
Carbohydrate Recognition ◽  
Valuable Source ◽  
Molecular Features ◽  
Artificial Receptor ◽  
Source Of Information

Complexes formed between methyl α-d-glucopyranoside and an artificial receptor represent a valuable source of information about the basic molecular features of carbohydrate recognition.

A Semi-Automated, Multi-Source Data Fusion Update of a Wetland Inventory for East-Central Minnesota, USA

Wetlands ◽  
2015 ◽  
Vol 35 (2) ◽  
pp. 335-348 ◽  
Author(s):  
Steven M. Kloiber ◽  
Robb D. Macleod ◽  
Aaron J. Smith ◽  
Joseph F. Knight ◽  
Brian J. Huberty
Keyword(s):  
Data Fusion ◽  
East Central ◽  
Source Data ◽  
Wetland Inventory

Application of the Multi-Source Data Fusion Algorithm in the Hail Identification

2021 ◽  
Author(s):  
Yonghua Zhu ◽  
Yongqing Wang ◽  
Zhiqun Hu ◽  
Fansen Xu ◽  
Renqiang Liu
Keyword(s):  
Data Fusion ◽  
Fusion Algorithm ◽  
Source Data

The royal mummies of ancient Egypt:An odyssey of discoveries and a fascinating source of data

1970 ◽  
Vol 80 (1) ◽  
Author(s):  
Frédéric Bauduer
Keyword(s):  
Ancient Egypt ◽  
Age At Death ◽  
Valuable Source ◽  
Ancient Egyptian ◽  
Egyptian History ◽  
Source Of Information

Thanks to mummification, the physical remains of many rulers of ancient Egypt are still observable today and constitute a valuable source of information. By evaluating the age at death and sometimes elucidating the degree of kinship and circumstances of death, our knowledge of ancient Egyptian history becomes more precise. Different pathologic conditions have been found and the evolution of the mummification process can be seen through time.The most spectacular discovery was that of Tutankhamen’s mummy, the single totally undisturbed tomb, associated with a fabulous treasure.The mummy of Ramses II has been extensively studied, the only one that flew to Paris where an irradiation was delivered in order to eradicate a destructive fungal infection.The identification of Akhenaten’s mummy and the explanation for his peculiar appearance are still unsolved problems.Noticeably, many Royal mummies remain of uncertain identity or undiscovered hitherto.

Rock hardness identification based on optimized PNN and multi-source data fusion

2021 ◽  
pp. 095440622110420
Author(s):  
Ying He ◽  
Muqin Tian ◽  
Jiancheng Song ◽  
Junling Feng
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Differential Evolution Algorithm ◽  
Vibration Signal ◽  
Optimization Method ◽  
Rock Wall ◽  
Rock Hardness ◽  
Source Data

To solve the problem that it is difficult to identify the cutting rock wall hardness of the roadheader in coal mine, a recognition method of cutting rock wall hardness is proposed based on multi-source data fusion and optimized probabilistic neural network. In this method, all kinds of cutting signals (the vibration signal of cutting arm, the pressure signal of hydraulic cylinders and current signal of cutting motor) are analyzed by wavelet packet to extract the feature vector, and the multi feature signal sample database of rock cutting with different hardness is established. To solve the problems of uncertain spread and complex network structure of probabilistic neural network (PNN), a PNN optimization method based on differential evolution algorithm (DE) and QR decomposition was proposed, and the rock hardness was identified based on multi-source data fusion by optimizing PNN. Then, based on the ground test monitoring data of a heavy longitudinal roadheader, the method is applied to recognize the cutting rock hardness, and compared with other common pattern recognition methods. The experimental results show that the cutting rock hardness recognition based on multi-source data fusion and optimized PNN has higher recognition accuracy, and the overall recognition error is reduced to 6.8%. The recognition of random cutting rock hardness is highly close to the actual. The method provides theoretical basis and technical premise for realizing automatic and intelligent cutting of heading face.

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