Machines data supervised

For production plants the following data of machines should be supervised ... 

Two generally different scenarios can be found for applications of machine learning technology so-called supervised and unsupervised learning. The difference is the presence or absence of observation of the desired output on a training data set. 

Recently all of the temperature, pressure, and time controls have been replaced with a single microprocessor-based controller. A number of these are available and they allow for complex pressure and temperature curves to be programmed with multiple soaking levels and variables that can be chosen. Built-in memories recall previous programs and cassettes can store them on the shelf. Interfaces can connect with a central host computer for data collection or actual machine setup and supervision. The result is more flexible, more exacting, and easier to control modern molding equipment (Chapter 3). 

A whole spectrum of statistical techniques have been applied to the analysis of DNA microarray data [26-28]. These include clustering analysis (hierarchical, K-means, self-organizing maps), dimension reduction (singular value decomposition, principal component analysis, multidimensional scaling, or correspondence analysis), and supervised classification (support vector machines, artificial neural networks, discriminant methods, or between-group analysis) methods. More recently, a number of Bayesian and other probabilistic approaches have been employed in the analysis of DNA microarray data [11], Generally, the first phase of microarray data analysis is exploratory data analysis. 

Abstract. Artificial neural networks (ANN) are useful components in today s data analysis toolbox. They were initially inspired by the brain but are today accepted to be quite different from it. ANN typically lack scalability and mostly rely on supervised learning, both of which are biologically implausible features. Here we describe and evaluate a novel cortex-inspired hybrid algorithm. It is found to perform on par with a Support Vector Machine (SVM) in classification of activation patterns from the rat olfactory bulb. On-line unsupervised learning is shown to provide significant tolerance to sensor drift, an important property of algorithms used to analyze chemo-sensor data. Scalability of the approach is illustrated on the MNIST dataset of handwritten digits. 

A Support Vector Machine (SVM) is a class of supervised machine learning techniques. It is based on the principle of structural risk minimization. The ideal of SVM is to search for an optimal hyperplane to separate the data with maximal margin. Let <5 -dimensional input x belong to two classwhich was labeled... 

Logging and collecting the operating data, such as production quantities, material and energy consumption, machine running times and downtimes, time spent on repair or maintenance work, and supervising the stores for spare parts and process materials. Supervision of arrival and departure of personnel ("clocking in/out") can also be computerized. 

---