scholarly journals University student's preference on job finding: Using paired comparison and monitoring information acquisition method

Author(s):  
Shigetaka OKUBO ◽  
Takashi IDENO ◽  
Kazuhisa TAKEMURA
Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 416
Author(s):  
Hui Wang ◽  
Jie Song

Aiming at the problem of insufficient integration and sharing of forestry information resources under the current communication network and the lack of the concept set of forestry information attributes, which leads to poor information retrieval performance, a fast retrieval method of forestry information features based on symmetry function is studied in depth, and the method is implemented by PDA (Personal Digital Assistant)-BA (Buliding Automation). Using the SED (Stream Editor) forestry information acquisition method under a communication network to collect forestry information, a forestry signal noise cancellation method based on symmetric function method is obtained. In order to improve the accuracy of forestry information acquisition, denoising of the signal in the information was carried out. Constructing forestry information data ontology, integrating forestry resources, establishing a conceptual set of forestry information attributes, distinguishing forestry information attributes, establishing a fast retrieval model of forestry information features based on the synonym library, and completing the fast retrieval of forestry information features. The experimental results show that the recall and precision of this method are 99.25% and 99.24%, respectively, and the retrieval performance is superior, which has a certain application value.


Magnetic Resonance Imaging (MRI) has been utilized broadly for clinical purposes to portray human anatomy due to its non-intrusive nature. The information acquisition method in MRI naturally picks up encoded signals (Fourier transformed) instead of pixel values and is called k-space information. Sparse reconstruction techniques can be executed in MRI for producing an image from fewer measurements. Compressive sensing (CS) technique samples the signals at a rate lower than traditional Nyquist’s rate and thereby reduces the data acquisition time in MRI. This paper investigates a new proposed sampling scheme along with radial sampling and 1D Cartesian variable density sampling. For various sampling percentages, subjective and quantitative analyses are carried out on the reconstructed Magnetic Resonance image. Experimental results depicts that the high sampling density near the center of k-space gives a better reconstruction of compressing sensing MRI.


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