Procedures for Applying Ada Quality Prediction Models

1992 ◽  
Author(s):  
D. D. Murphy ◽  
W. M. Thomas ◽  
W. M. Evanco ◽  
W. W. Agresti
Keyword(s):  
Prediction Models ◽  
Quality Prediction

scholarly journals A tool for finding inclusion clusters in steel SEM specimens

2020 ◽  
Vol 10 (1) ◽  
pp. 642-648
Author(s):  
Anna-Mari Wartiainen ◽  
Markus Harju ◽  
Satu Tamminen ◽  
Leena Määttä ◽  
Tuomas Alatarvas ◽  
...  
Keyword(s):  
Prediction Models ◽  
Microscopic Analysis ◽  
Steel Specimen ◽  
Sem Analysis ◽  
Quality Prediction ◽  
Automatic Production ◽  
Automatic Interpretation ◽  
Metallic Inclusions ◽  
Purity Analysis ◽  
Inclusions In Steel

AbstractNon-metallic inclusions, especially large or clustered inclusions, in steel are usually harmful. Thus, the microscopic analysis of test specimens is an important part of the quality control. This steel purity analysis produces a large amount of individual inclusion information for each test specimen. The interpretation of the results is laborious and the comparison of larger product groups practically impossible. The purpose of this study was to develop an easy-to-use tool for automatic interpretation of the SEM analysis to differentiate clustered and large inclusions information from the manifold individual inclusion information. Because of the large variety of the potential users, the tool needs to be applicable for any steel grade and application, both for liquid and final product specimen, to analyse automatically steel specimen inclusions, especially inclusion clusters, based on the INCA Feature program produced data from SEM/EDS. The developed tool can be used to improve the controlling of the steel purity or for automatic production of new inclusion cluster features that can be utilised further in quality prediction models, for example.

An Intelligent Integrated Method for Quality Prediction in Lead-Zinc Sintering Process

2011 ◽  
Vol 52-54 ◽  
pp. 674-679
Author(s):  
Chun Sheng Wang ◽  
Min Wu ◽  
Qi Lei
Keyword(s):  
Large Time ◽  
Prediction Models ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Quality Prediction ◽  
Sintering Process ◽  
Grey System ◽  
Integrated Method ◽  
Lead Zinc ◽  
Weighting Coefficients

Based on some features in lead-zinc sintering process (LZSP), such as large time delay and strong non-linearity, an intelligent integrated method for quality prediction based on back-propagation neural network (BPNN) and improved grey system (IGS) is presented. First, the compositions of agglomerate are predicted by BPNN and IGS models. Then, a recursive entropy algorithm for the weighting coefficients is devised from the viewpoint of the information theory and an intelligent integrated prediction model (IIPM) is established. The compositions of sinter agglomerate are predicted by integrating the two prediction models. Application results show that the IIPM has higher prediction precision than that of single model and the proposed intelligent integrated method settles the modeling problem of the quality in the LZSP.

Voice quality prediction models and their application in VoIP networks

2006 ◽  
Vol 8 (4) ◽  
pp. 809-820 ◽  
Author(s):  
Lingfen Sun ◽  
E.C. Ifeachor
Keyword(s):  
Prediction Models ◽  
Voice Quality ◽  
Quality Prediction

scholarly journals Water Quality Prediction Method Based on Multi-Source Transfer Learning for Water Environmental IoT System

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7271
Author(s):  
Jian Zhou ◽  
Jian Wang ◽  
Yang Chen ◽  
Xin Li ◽  
Yong Xie
Keyword(s):  
Water Quality ◽  
Transfer Learning ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Prediction Method ◽  
Water Area ◽  
Quality Information ◽  
Quality Prediction ◽  
Monitoring Point ◽  
Water Quality Prediction

Water environmental Internet of Things (IoT) system, which is composed of multiple monitoring points equipped with various water quality IoT devices, provides the possibility for accurate water quality prediction. In the same water area, water flows and exchanges between multiple monitoring points, resulting in an adjacency effect in the water quality information. However, traditional water quality prediction methods only use the water quality information of one monitoring point, ignoring the information of nearby monitoring points. In this paper, we propose a water quality prediction method based on multi-source transfer learning for a water environmental IoT system, in order to effectively use the water quality information of nearby monitoring points to improve the prediction accuracy. First, a water quality prediction framework based on multi-source transfer learning is constructed. Specifically, the common features in water quality samples of multiple nearby monitoring points and target monitoring points are extracted and then aligned. According to the aligned features of water quality samples, the water quality prediction models based on an echo state network at multiple nearby monitoring points are established with distributed computing, and then the prediction results of distributed water quality prediction models are integrated. Second, the prediction parameters of multi-source transfer learning are optimized. Specifically, the back propagates population deviation based on multiple iterations, reducing the feature alignment bias and the model alignment bias to improve the prediction accuracy. Finally, the proposed method is applied in the actual water quality dataset of Hong Kong. The experimental results demonstrate that the proposed method can make full use of the water quality information of multiple nearby monitoring points to train several water quality prediction models and reduce the prediction bias.

ANFIS-based quality prediction models for AMR telephony in public 2G/3G mobile networks

2012 ◽  
Author(s):  
Charalampos N. Pitas ◽  
Dimitris E. Charilas ◽  
Athanasios D. Panagopoulos ◽  
Periklis Chatzimisios ◽  
Philip Constantinou
Keyword(s):  
Mobile Networks ◽  
Prediction Models ◽  
Quality Prediction

scholarly journals Video Quality Prediction Models Based on Video Content Dynamics for H.264 Video over UMTS Networks

2010 ◽  
Vol 2010 ◽  
pp. 1-17 ◽  
Author(s):  
Asiya Khan ◽  
Lingfen Sun ◽  
Emmanuel Ifeachor ◽  
Jose-Oscar Fajardo ◽  
Fidel Liberal ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Prediction Models ◽  
Linear Regression Analysis ◽  
Video Quality ◽  
Quality Prediction ◽  
Application Layer ◽  
Loss Models ◽  
The Impact ◽  
Umts Networks

The aim of this paper is to present video quality prediction models for objective non-intrusive, prediction of H.264 encoded video for all content types combining parameters both in the physical and application layer over Universal Mobile Telecommunication Systems (UMTS) networks. In order to characterize the Quality of Service (QoS) level, a learning model based on Adaptive Neural Fuzzy Inference System (ANFIS) and a second model based on non-linear regression analysis is proposed to predict the video quality in terms of the Mean Opinion Score (MOS). The objective of the paper is two-fold. First, to find the impact of QoS parameters on end-to-end video quality for H.264 encoded video. Second, to develop learning models based on ANFIS and non-linear regression analysis to predict video quality over UMTS networks by considering the impact of radio link loss models. The loss models considered are 2-state Markov models. Both the models are trained with a combination of physical and application layer parameters and validated with unseen dataset. Preliminary results show that good prediction accuracy was obtained from both the models. The work should help in the development of a reference-free video prediction model and QoS control methods for video over UMTS networks.

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