28,257 research outputs found
Intelligent systems in manufacturing: current developments and future prospects
Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS
The Evolution of Neural Network-Based Chart Patterns: A Preliminary Study
A neural network-based chart pattern represents adaptive parametric features,
including non-linear transformations, and a template that can be applied in the
feature space. The search of neural network-based chart patterns has been
unexplored despite its potential expressiveness. In this paper, we formulate a
general chart pattern search problem to enable cross-representational
quantitative comparison of various search schemes. We suggest a HyperNEAT
framework applying state-of-the-art deep neural network techniques to find
attractive neural network-based chart patterns; These techniques enable a fast
evaluation and search of robust patterns, as well as bringing a performance
gain. The proposed framework successfully found attractive patterns on the
Korean stock market. We compared newly found patterns with those found by
different search schemes, showing the proposed approach has potential.Comment: 8 pages, In proceedings of Genetic and Evolutionary Computation
Conference (GECCO 2017), Berlin, German
Multivariate Statistical Process Control Charts: An Overview
In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.quality control, process control, multivariate statistical process control, Hotelling's T-square, CUSUM, EWMA, PCA, PLS
Application of machine learning and expert systems to Statistical Process Control (SPC) chart interpretation
Statistical Process Control (SPC) charts are one of several tools used in quality control. Other tools include flow charts, histograms, cause and effect diagrams, check sheets, Pareto diagrams, graphs, and scatter diagrams. A control chart is simply a graph which indicates process variation over time. The purpose of drawing a control chart is to detect any changes in the process signalled by abnormal points or patterns on the graph. The Artificial Intelligence Support Center (AISC) of the Acquisition Logistics Division has developed a hybrid machine learning expert system prototype which automates the process of constructing and interpreting control charts
Warranty Data Analysis: A Review
Warranty claims and supplementary data contain useful information about product quality and reliability. Analysing such data can therefore be of benefit to manufacturers in identifying early warnings of abnormalities in their products, providing useful information about failure modes to aid design modification, estimating product reliability for deciding on warranty policy and forecasting future warranty claims needed for preparing fiscal plans. In the last two decades, considerable research has been conducted in warranty data analysis (WDA) from several different perspectives. This article attempts to summarise and review the research and developments in WDA with emphasis on models, methods and applications. It concludes with a brief discussion on current practices and possible future trends in WDA
Implementing statistics in a diagnostic coaching structure for rugby
Statistics are having an increased influence in the rugby-coaching environment. Many of the statistics
used are exposed to changeable match constraints and conditions, reducing the practical significance of
these data.
Multivariate statistical techniques allow meaningful statistics to be created that can summarise
individual performance and negate the variability in match involvement. This increases the power of the
statistical tool to coaches by enabling deficient or superior performances to be identified and put into
context.
The steps required to create a stable measure of overall performance are outlined, before it is shown
how this same process can be used to identify inferior/superior performance on single physical tasks. The
same simple process of constructing an overall rating and then deconstructing the rating to diagnose the
causes of abnormal performance is applicable to other sports, but provides the coach with an easier to use
data set that can be explored using graphical techniques
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