scholarly journals Reconstructing LAI Series by Filtering Technique and a Dynamic Plant Model

10.5772/16446 ◽  
2012 ◽  
Author(s):  
Meng Zhen ◽  
Thomas Corpetti ◽  
Jing Hua ◽  
Philippe de
Keyword(s):  
Plant Model ◽  
Dynamic Plant ◽  
Filtering Technique

Design of dynamic plant model and model-based controller for a heat recovery system with a swirling flow incinerator

Energy ◽  
2018 ◽  
Vol 147 ◽  
pp. 1016-1029 ◽  
Author(s):  
Jaeyoung Cho ◽  
Yongtae Kim ◽  
Jeongwoo Song ◽  
Tae Kyung Lee ◽  
Han Ho Song
Keyword(s):  
Heat Recovery ◽  
Swirling Flow ◽  
Plant Model ◽  
Model Based ◽  
Dynamic Plant ◽  
Recovery System ◽  
Heat Recovery System

Alias Filtering Technique in Alias Resolution

2009 ◽  
Vol 20 (8) ◽  
pp. 2280-2288
Author(s):  
Hong-Hua ZHAO ◽  
Hua-Li BAI ◽  
Ming CHEN ◽  
Zhen-Han WEI
Keyword(s):  
Filtering Technique

A Plant Model for Smart Projectiles

2011 ◽  
Author(s):  
Robert J. Yager
Keyword(s):  
Plant Model

LNTP-MDBN: Big Data Integrated Learning Framework for Heterogeneous Image Set Classification

2019 ◽  
Vol 15 (2) ◽  
pp. 227-236
Author(s):  
D. Franklin Vinod ◽  
V. Vasudevan
Keyword(s):  
Big Data ◽  
Medical Image ◽  
Research Study ◽  
Big Data Analytics ◽  
Learning Framework ◽  
Image Set Classification ◽  
Filtering Technique ◽  
Image Set ◽  
Hidden Patterns ◽  
Global Data

Background: With the explosive growth of global data, the term Big Data describes the enormous size of dataset through the detailed analysis. The big data analytics revealed the hidden patterns and secret correlations among the values. The major challenges in Big data analysis are due to increase of volume, variety, and velocity. The capturing of images with multi-directional views initiates the image set classification which is an attractive research study in the volumetricbased medical image processing. Methods: This paper proposes the Local N-ary Ternary Patterns (LNTP) and Modified Deep Belief Network (MDBN) to alleviate the dimensionality and robustness issues. Initially, the proposed LNTP-MDBN utilizes the filtering technique to identify and remove the dependent and independent noise from the images. Then, the application of smoothening and the normalization techniques on the filtered image improves the intensity of the images. Results: The LNTP-based feature extraction categorizes the heterogeneous images into different categories and extracts the features from each category. Based on the extracted features, the modified DBN classifies the normal and abnormal categories in the image set finally. Conclusion: The comparative analysis of proposed LNTP-MDBN with the existing pattern extraction and DBN learning models regarding classification accuracy and runtime confirms the effectiveness in mining applications.

ROI Based Post Image Quality Assessment Technique on Multiple Localized Filtering Method On Kinect HD Sensor Mobile Robot

2018 ◽  
Vol 3 (2) ◽  
pp. 145
Author(s):  
Kholilatul Wardani ◽  
Aditya Kurniawan
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Similarity Index ◽  
Assessment Model ◽  
Sensor Data ◽  
Edge Region ◽  
Kinect Sensor ◽  
Image Region ◽  
Filtering Technique

 The ROI (Region of Interest) Image Quality Assessment is an image quality assessment model based on the SSI (Structural Similarity Index) index used in the specific image region desired to be assessed. Output assessmen value used by this image assessment model is 1 which means identical and -1 which means not identical. Assessment model of ROI Quality Assessment in this research is used to measure image quality on Kinect sensor capture result used in Mobile HD Robot after applied Multiple Localized Filtering Technique. The filter is applied to each capture sensor depth result on Kinect, with the aim to eliminate structural noise that occurs in the Kinect sensor. Assessment is done by comparing image quality before filter and after filter applied to certain region. The kinect sensor will be conditioned to capture a square black object measuring 10cm x 10cm perpendicular to a homogeneous background (white with RGB code 255,255,255). The results of kinect sensor data will be taken through EWRF 3022 by visual basic 6.0 program periodically 10 times each session with frequency 1 time per minute. The results of this trial show the same similar index (value 1: identical) in the luminance, contrast, and structural section of the edge region or edge region of the specimen. The value indicates that the Multiple Localized Filtering Technique applied to the noise generated by the Kinect sensor, based on the ROI Image Quality Assessment model has no effect on the image quality generated by the sensor.

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