The Experts below are selected from a list of 50193 Experts worldwide ranked by ideXlab platform
Liejin Guo - One of the best experts on this subject based on the ideXlab platform.
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intelligent identification of steam jet condensation regime in water pipe flow system by wavelet multiresolution analysis of pressure oscillation and artificial neural network
Applied Thermal Engineering, 2019Co-Authors: Weizhi Liu, Yanshuang Chen, Qiyu Chen, Liejin GuoAbstract:Abstract On-line Recognition of condensation regime of vapor jet in pipe flow systems is a promising approach for flow assurance and intellectualization of industrial processes. However, the selection of distinguishable characteristics from pressure signals associated strongly with various condensation regimes is essential and challenging for satisfactory Recognition purpose. Accordingly, an artificial neural network technique using wavelet multiresolution analysis of pressure oscillation signals for objective identification of jet condensation regimes is presented in this paper. The Recognition Procedure was carried out in two major steps. Statistical features of wavelet multiresolution analysis of pressure signals, i.e., mean of absolute and percentage of energy of each wavelet scale, were chose first. And then artificial neural network was adopted to construct classifiers for forecasting the condensation regimes automatically. The Recognition results illustrated that the proposed method is feasible and effective for identifying vapor jet condensation regime in pipe flow system. Furthermore, it is suggested that statistical features of mean of absolute and percentage of energy at least four or more particular wavelet scales, and also sample length longer than 1.5 s could guarantee a satisfactory Recognition rate above 90%.
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Recognition of steam jet condensation regime in water pipe flow system by statistical features of pressure oscillation
Applied Thermal Engineering, 2017Co-Authors: Liejin GuoAbstract:Abstract Recognition of unstable and harmful condensation regimes in liquid pipe flow system can promote a higher level of flow assurance in liquid propellant rocket engine. However, challenges are encountered in extracting distinguishable characteristics from pressure oscillation signals which commonly contains plentiful information strongly associated with various condensation regimes. This article attempts to set up a simple and practical approach of recognizing the steam jet condensation regime in water pipe flow system based on statistical features of pressure oscillation. The Recognition Procedure was performed in three major steps. Initially, twelve statistical features of pressure oscillation in time-domain (probability density function) and frequency-domain (power spectrum density) were chose. Subsequently, principal component analysis was implemented to obtain the clear interrelations between condensation regimes and statistical features of pressure oscillation signal, and then to extract useful features for establishing condensation regimes clusters for classification in the selected features space. Finally, least squares support vector machine was adopted to the clusters for construction of classifiers to forecast the condensation regimes automatically. The experimental results showed that the proposed approach is feasible and effective for recognizing the steam jet condensation regime in water pipe flow system by statistical features of pressure oscillation.
Susan Johnson - One of the best experts on this subject based on the ideXlab platform.
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card check or mandatory representation vote how the type of union Recognition Procedure affects union certification success
Social Science Research Network, 2003Co-Authors: Susan JohnsonAbstract:Cross-section time-series analysis of nine Canadian jurisdictions over nineteen years is used to identify the effect of mandatory votes/card check on certification success. The results indicate that mandatory votes reduce certification success rates by approximately 9 percentage points below what they would have been under card check. This result is robust across specifications and significant at above the 99% confidence level.
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card check or mandatory representation vote how the type of union Recognition Procedure affects union certification success
The Economic Journal, 2002Co-Authors: Susan JohnsonAbstract:Cross-section time-series analysis of nine Canadian jurisdictions over nineteen years is used to identify the effect of mandatory votes/card check on certification success. The results indicate that mandatory votes reduce certification success rates by approximately 9 percentage points below what they would have been under card check. This result is robust across specifications and significant at above the 99% confidence level. This paper provides empirical evidence on how two alternative union Recognition Procedures, mandatory votes and card check, affect certification success.' Mandatory votes require that to be recognised, a union receive majority support in a secret ballot. In contrast, card check allows Recognition based solely on membership evidence collected by the union and does not necessarily require a vote. In Canada, unions are recognised on the basis of either card check or mandatory representation votes.2 Canada is a federal state consisting of ten provinces and labour law is primarily the responsibility of the provinces. There is considerable variation over time and across jurisdictions in the use of these two forms of union Recognition. I conduct an econometric analysis of cross-section time-series data for
Weizhi Liu - One of the best experts on this subject based on the ideXlab platform.
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intelligent identification of steam jet condensation regime in water pipe flow system by wavelet multiresolution analysis of pressure oscillation and artificial neural network
Applied Thermal Engineering, 2019Co-Authors: Weizhi Liu, Yanshuang Chen, Qiyu Chen, Liejin GuoAbstract:Abstract On-line Recognition of condensation regime of vapor jet in pipe flow systems is a promising approach for flow assurance and intellectualization of industrial processes. However, the selection of distinguishable characteristics from pressure signals associated strongly with various condensation regimes is essential and challenging for satisfactory Recognition purpose. Accordingly, an artificial neural network technique using wavelet multiresolution analysis of pressure oscillation signals for objective identification of jet condensation regimes is presented in this paper. The Recognition Procedure was carried out in two major steps. Statistical features of wavelet multiresolution analysis of pressure signals, i.e., mean of absolute and percentage of energy of each wavelet scale, were chose first. And then artificial neural network was adopted to construct classifiers for forecasting the condensation regimes automatically. The Recognition results illustrated that the proposed method is feasible and effective for identifying vapor jet condensation regime in pipe flow system. Furthermore, it is suggested that statistical features of mean of absolute and percentage of energy at least four or more particular wavelet scales, and also sample length longer than 1.5 s could guarantee a satisfactory Recognition rate above 90%.
Patrick Argos - One of the best experts on this subject based on the ideXlab platform.
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knowledge based protein secondary structure assignment
Proteins, 1995Co-Authors: Dmitrij Frishman, Patrick ArgosAbstract:We have developed an automatic algorithm STRIDE for protein secondary structure assignment from atomic coordinates based on the combined use of hydrogen bond energy and statistically derived backbone torsional angle information. Parameters of the pattern Recognition Procedure were optimized using designations provided by the crystallographers as a standard-of-truth. Comparison to the currently most widely used technique DSSP by Kabsch and Sander (Biopolymers 22:2577-2637, 1983) shows that STRIDE and DSSP assign secondary structural states in 58 and 31% of 226 protein chains in our data sample, respectively, in greater agreement with the specific residue-by-residue definitions provided by the discoverers of the structures while in 11% of the chains, the assignments are the same. STRIDE delineates every 11th helix and every 32nd strand more in accord with published assignments.
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knowledge based protein secondary structure assignment
Proteins, 1995Co-Authors: Dmitrij Frishman, Patrick ArgosAbstract:We have developed an auto- matic algorithm STRIDE for protein secondary structure assignment from atomic coordinates based on the combined use of hydrogen bond energy and statistically derived backbone tor- sional angle information. Parameters of the pattern Recognition Procedure were optimized using designations provided by the crystallog- raphers as a standard-of-truth. Comparison to the currently most widely used technique DSSP by Kabsch and Sander (Biopolymers 222577- 2637, 1983) shows that STRIDE and DSSP as- sign secondary structural states in 58 and 31% of 226 protein chains in our data sample, re- spectively, in greater agreement with the spe- cific residue-by-residue definitions provided by the discoverers of the structures while in 11% of the chains, the assignments are the same. STRIDE delineates every 11 th helix and every 32nd strand more in accord with published assignments. Q 1995 Wiley-Liss, Inc.
Yanshuang Chen - One of the best experts on this subject based on the ideXlab platform.
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intelligent identification of steam jet condensation regime in water pipe flow system by wavelet multiresolution analysis of pressure oscillation and artificial neural network
Applied Thermal Engineering, 2019Co-Authors: Weizhi Liu, Yanshuang Chen, Qiyu Chen, Liejin GuoAbstract:Abstract On-line Recognition of condensation regime of vapor jet in pipe flow systems is a promising approach for flow assurance and intellectualization of industrial processes. However, the selection of distinguishable characteristics from pressure signals associated strongly with various condensation regimes is essential and challenging for satisfactory Recognition purpose. Accordingly, an artificial neural network technique using wavelet multiresolution analysis of pressure oscillation signals for objective identification of jet condensation regimes is presented in this paper. The Recognition Procedure was carried out in two major steps. Statistical features of wavelet multiresolution analysis of pressure signals, i.e., mean of absolute and percentage of energy of each wavelet scale, were chose first. And then artificial neural network was adopted to construct classifiers for forecasting the condensation regimes automatically. The Recognition results illustrated that the proposed method is feasible and effective for identifying vapor jet condensation regime in pipe flow system. Furthermore, it is suggested that statistical features of mean of absolute and percentage of energy at least four or more particular wavelet scales, and also sample length longer than 1.5 s could guarantee a satisfactory Recognition rate above 90%.
