Multi-Model- and Soft-Transition-Based Height Soft Sensor for an Air Cushion Furnace

The floating height of the strip in an air cushion furnace is a key parameter for the quality and efficiency of production. However, the high temperature and high pressure of the working environment prevents the floating height from being directly measured. Furthermore, the strip has multiple floating states in the whole operation process. It is thus difficult to employ a single model to accurately describe the floating height in different states. This paper presents a multi-model soft sensor to estimate the height based on state identification and the soft transition. First, floating states were divided using a partition method that combined adaptive k-nearest neighbors and principal component analysis theories. Based on the identified results, a hybrid model for the stable state, involving a double-random forest model for the vibration state and a soft-transition model, was created to predict the strip floating height. In the hybrid model for the stable state, a mechanistic model combined thick jet theory and the equilibrium equation of force to cope with the lower floating height. In addition, a novel soft-transition model based on data gravitation that further reflects the intrinsic process characteristic was developed for the transition state. The effectiveness of the proposed approach was validated using a self-developed air cushion furnace experimental platform. This study has important value for the process prediction and control of air cushion furnaces.

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Food in transition: The place of food in the theories of transition

The Sociological Review ◽

10.1177/00380261211009092 ◽

2021 ◽

Vol 69 (3) ◽

pp. 702-724

Author(s):

Jean-Pierre Poulain

Keyword(s):

Social Change ◽

Structural Transformation ◽

Stable State ◽

Transition Theory ◽

Transition Model ◽

Theoretical Frameworks ◽

Progressive Movement ◽

Biological Variables ◽

Multidisciplinary Perspective ◽

Theories Of Transition

Among the different theories used to explain social change, the transition theory holds a special place. It has been applied to subjects such as demography, epidemiology, nutrition, etc. and most often from a multidisciplinary perspective. However, beyond the apparent uniqueness of the transition label there are quite different theoretical frameworks and presuppositions, which can be a source of misunderstanding. A first perspective considers change as the transition from one stable state to another and concentrates on the processes at work in the transition phase. It focuses attention on the interactions between cultural and biological variables and tries to take into account the consequences of the fact that they move at different rates. A second perspective conceives change as a series of stages (more or less stable situations) and proceeds to analyse the structural transformation of the organisation at each stage. The emphasis is placed on the transformations that take place during these stages. This brings us closer to the theory of stages. Finally, a third perspective combines stages and transitions. Change is seen as part of an evolutionary and progressive movement and as being reversible or not. This article studies the cognitive organisation of the different theoretical variants of the transition model in which food plays a more or less important role.

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Variety Identification of Orchids Using Fourier Transform Infrared Spectroscopy Combined with Stacked Sparse Auto-Encoder

Molecules ◽

10.3390/molecules24132506 ◽

2019 ◽

Vol 24 (13) ◽

pp. 2506 ◽

Cited By ~ 1

Author(s):

Yunfeng Chen ◽

Yue Chen ◽

Xuping Feng ◽

Xufeng Yang ◽

Jinnuo Zhang ◽

...

Keyword(s):

Fourier Transform ◽

Principal Component ◽

Fourier Transform Infrared ◽

Spectroscopic Technique ◽

Variety Identification ◽

Support Vector ◽

K Nearest Neighbors ◽

Discriminant Models ◽

The Fourier Transform ◽

Better Than

The feasibility of using the fourier transform infrared (FTIR) spectroscopic technique with a stacked sparse auto-encoder (SSAE) to identify orchid varieties was studied. Spectral data of 13 orchids varieties covering the spectral range of 4000–550 cm−1 were acquired to establish discriminant models and to select optimal spectral variables. K nearest neighbors (KNN), support vector machine (SVM), and SSAE models were built using full spectra. The SSAE model performed better than the KNN and SVM models and obtained a classification accuracy 99.4% in the calibration set and 97.9% in the prediction set. Then, three algorithms, principal component analysis loading (PCA-loading), competitive adaptive reweighted sampling (CARS), and stacked sparse auto-encoder guided backward (SSAE-GB), were used to select 39, 300, and 38 optimal wavenumbers, respectively. The KNN and SVM models were built based on optimal wavenumbers. Most of the optimal wavenumbers-based models performed slightly better than the all wavenumbers-based models. The performance of the SSAE-GB was better than the other two from the perspective of the accuracy of the discriminant models and the number of optimal wavenumbers. The results of this study showed that the FTIR spectroscopic technique combined with the SSAE algorithm could be adopted in the identification of the orchid varieties.

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A ROBUST AND FRIENDLY HUMAN–ROBOT INTERFACE SYSTEM BASED ON NATURAL HUMAN GESTURES

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001410008214 ◽

2010 ◽

Vol 24 (06) ◽

pp. 847-866 ◽

Cited By ~ 5

Author(s):

MING-SHAUNG CHANG ◽

JUNG-HUA CHOU

Keyword(s):

Real Time ◽

Face Detection ◽

Fuzzy Logic Controller ◽

Tracking System ◽

Back Propagation ◽

Principal Component ◽

Working Environment ◽

Expression Recognition ◽

Linear Discriminant ◽

Human Gestures

In this paper, we design a robust and friendly human–robot interface (HRI) system for our intelligent mobile robot based only on natural human gestures. It consists of a triple-face detection method and a fuzzy logic controller (FLC)-Kalman filter tracking system to check the users and predict their current position in a dynamic and cluttered working environment. In addition, through the combined classifier of the principal component analysis (PCA) and back-propagation artificial neural network (BPANN), single and successive commands defined by facial positions and hand gestures are identified for real-time command recognition after dynamic programming (DP). Therefore, the users can instruct this HRI system to make member recognition or expression recognition corresponding to their gesture commands, respectively based on the linear discriminant analysis (LDA) and BPANN. The experimental results prove that the proposed HRI system perform accurately in real-time face detection and tracking, and robustly react to the corresponding gesture commands at eight frames per second (fps).

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How to Evaluate Digital Leadership: A Cross-sectional Study

10.21203/rs.3.rs-432739/v1 ◽

2021 ◽

Author(s):

Kevin Claassen ◽

Dominique Rodil Dos Anjos ◽

Jan Kettschau ◽

Horst Christoph Broding

Keyword(s):

Construct Validity ◽

Online Survey ◽

Leadership Skills ◽

Principal Component ◽

Cross Sectional Study ◽

Working Environment ◽

Cross Sectional ◽

One Dimensional ◽

Leadership Competence ◽

Transformation Processes

Abstract Background: With the increasing digitalization of the working environment, the demands on managers are changing fundamentally to the point of an emerging field of research in digital leadership. Municipal administrations are particularly affected by the digital transformation processes. Therefore, a score to measure the construct of digital leadership competence in the context of virtual-based workstation was developed and tested.Methods: Based of an online survey with n = 546 employees at virtual-based workstations in municipal administrations in 2020, the instrument is tested regarding selectivity (coefficients), dimensionality (principal component analysis), homogeneity (inter-product-moment correlations), reliability (Cronbach's α) and construct validity (correlation with general leadership skills).Results: The instrument can be considered selective, one-dimensional, homogeneous, reliable and constructively valid in the sense of the formulated hypotheses. By integrating the employees‘ perspective, the instrument aims to be one of the first of its kind to initiate a scientific further discourse. Among other things, the categorization of the co-determination component as either traditional or digital leadership can be discussed.Conclusions: The developed instrument for measuring digital leadership performs well concerning the aspects of discriminatory power, one-dimensionality, homogeneity, reliability as well as construct validity. It aims to induce further research and a scientific discourse on the topic of health-oriented leadership within the world of work 4.0.

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Selection of Certain Cancer Mediating Genes Using a Hybrid Model Logistic Regression Supported by Principal Component Analysis (PC‐LR)

10.1002/9781119764113.ch3 ◽

2021 ◽

pp. 41-60

Author(s):

Subir Hazra ◽

Alia Nikhat Khurshid ◽

Akriti

Keyword(s):

Principal Component Analysis ◽

Logistic Regression ◽

Hybrid Model ◽

Principal Component ◽

Component Analysis ◽

Selection Of

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Soft sensor design and fault detection using Bayesian network and probabilistic principal component analysis

Journal of Advanced Manufacturing and Processing ◽

10.1002/amp2.10027 ◽

2019 ◽

Vol 1 (4) ◽

Cited By ~ 2

Author(s):

Ahad Mohammadi ◽

Reza Zarghami ◽

Dimitri Lefebvre ◽

Shahab Golshan ◽

Navid Mostoufi

Keyword(s):

Principal Component Analysis ◽

Fault Detection ◽

Bayesian Network ◽

Principal Component ◽

Component Analysis ◽

Soft Sensor ◽

Sensor Design ◽

Probabilistic Principal Component Analysis

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The Implementation of a Hybrid Model for Hilly Sub-Watershed Prioritization Using Morphometric Variables: Case Study in India

Water ◽

10.3390/w11061138 ◽

2019 ◽

Vol 11 (6) ◽

pp. 1138 ◽

Cited By ~ 4

Author(s):

Anurag Malik ◽

Anil Kumar ◽

Daniel Prakash Kushwaha ◽

Ozgur Kisi ◽

Sinan Q. Salih ◽

...

Keyword(s):

Water Resource ◽

Hybrid Model ◽

Water Conservation ◽

Principal Component ◽

Resource Planning ◽

Morphometric Parameters ◽

Study Region ◽

Water Resource Planning ◽

Watershed Prioritization ◽

Planning And Management

Among several components of watershed prioritization, morphometric parameters are considered to be essential elements for appropriate water resource planning and management. In the current study, nine hilly sub-watersheds are prioritized using novel hybrid model based on morphometric variables analysis at Bino Watershed (BW) located in the upper Ramganga basin, India. The proposed model is based on the hybridization of principal component analysis (PCA) with weighted-sum approach (WSA), presenting a single-frame methodology (PCWSA) for sub-watershed prioritization. The prioritization process was conducted based on several morphometric parameters including linear, areal, and shape. The PCA was performed to identify the significant correlated factor-loading matrix whereas WSA was established to provide the weights for the morphometric parameters and fix their priority ranking (PR) to be categorized based on compound factor value. The findings showed that 37.81% of total area is under highly susceptible zone sub-watersheds (SW-6 and SW-7). This is verifying the necessity for appropriate soil and water conservation measures for the area. The proposed hybrid methodology demonstrated a reliable approach for water resource planning and management, agriculture, and irrigation activities in the study region.

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