Nothing Special   »   [go: up one dir, main page]

Chou et al., 2006 - Google Patents

Economic design of variable sampling intervals T2 control charts using genetic algorithms

Chou et al., 2006

Document ID
13596440810247926011
Author
Chou C
Chen C
Chen C
Publication year
Publication venue
Expert Systems with Applications

External Links

Snippet

Control charting is a graphical expression and operation of statistical hypothesis testing. In this paper, we develop the economic design of the variable sampling intervals (VSI) T2 control chart to determine the values of the five test parameters of the chart (ie the sample …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/12Computer systems based on biological models using genetic models
    • G06N3/126Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F7/00Methods or arrangements for processing data by operating upon the order or content of the data handled
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F2207/00Indexing scheme relating to methods or arrangements for processing data by operating upon the order or content of the data handled
    • G06F2207/72Indexing scheme relating to groups G06F7/72 - G06F7/729
    • G06F2207/7219Countermeasures against side channel or fault attacks

Similar Documents

Publication Publication Date Title
Chou et al. Economic design of variable sampling intervals T2 control charts using genetic algorithms
Lin et al. Economic design of autoregressive moving average control chart using genetic algorithms
Wang et al. Quality-related fault detection using linear and nonlinear principal component regression
Wang et al. Comparison of variable selection methods for PLS-based soft sensor modeling
Pariyani et al. Dynamic risk analysis using alarm databases to improve process safety and product quality: Part II—Bayesian analysis
Hache et al. Reverse engineering of gene regulatory networks: a comparative study
Apeland et al. Quantifying uncertainty under a predictive, epistemic approach to risk analysis
Chen et al. Detection and analysis of real-time anomalies in large-scale complex system
Chou et al. Economic design of variable sampling intervals EWMA charts with sampling at fixed times using genetic algorithms
Bučar et al. A neural network approach to describing the scatter of S–N curves
Abbas et al. On monitoring of linear profiles using Bayesian methods
Park et al. A new multivariate EWMA control chart via multiple testing
Chen et al. Economic statistical design of non-uniform sampling scheme X bar control charts under non-normality and Gamma shock using genetic algorithm
Zhang et al. Economic design of cumulative count of conforming charts under inspection by samples
Ganguly et al. A teaching–learning based optimization approach for economic design of X-bar control chart
Chou et al. Joint economic design of variable sampling intervals (x) and r charts using genetic algorithms
Wang et al. Contamination source identification based on sequential Bayesian approach for water distribution network with stochastic demands
Katebi et al. Optimal economic statistical design of combined double sampling and variable sampling interval multivariate T 2 control charts
Lin et al. Economic design of variable sampling intervals X charts with A&L switching rule using genetic algorithms
Xie A knowledge-based multi-dimension discrete common cause failure model
Sheu et al. Monitoring process mean and variability with generally weighted moving average control charts
Singh et al. Performance of CUSUM and EWMA charts for serial correlation
Koh et al. Machine learning-based sensitivity of steel frames with highly imbalanced and high-dimensional data
Chen Economic design of T 2 control charts with the VSSI sampling scheme
Rasheed et al. Designing efficient dispersion control charts under various ranked-set sampling approaches