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This paper describes the problems and an adaptive solution for process control in rubber industry. We show that the human and economical benefits of an adaptive solution for the approximation of process parameters are very attractive. The modeling of the industrial problem is done by the means of artificial neural networks. For the example of the extrusion of a rubber profile in tire production our method shows good results even using only a few training samples.
The encoding of images by semantic entities is still an unresolved task. This paper proposes the encoding of images by only a few important components or image primitives. Classically, this can be done by the Principal Component Analysis (PCA). Recently, the Independent Component Analysis (ICA) has found strong interest in the signal processing and neural network community. Using this as pattern primitives we aim for source patterns with the highest occurrence probability or highest information. For the example of a synthetic image composed by characters this idea selects the salient ones. For natural images it does not lead to an acceptable reproduction error since no a-priori probabilities can be computed. Combining the traditional principal component criteria of PCA with the independence property of ICA we obtain a better encoding. It turns out that the Independent Principal Components (IPC) in contrast to the Principal Independent Components (PIC) implement the classical demand of Shannon’s rate distortion theory.
This paper proposes a new approach for the encoding of images by only a few important components. Classically, this is done by the Principal Component Analysis (PCA). Recently, the Independent Component Analysis (ICA) has found strong interest in the neural network community. Applied to images, we aim for the most important source patterns with the highest occurrence probability or highest information called principal independent components (PIC). For the example of a synthetic image composed by characters this idea selects the salient ones. For natural images it does not lead to an acceptable reproduction error since no a-priori probabilities can be computed. Combining the traditional principal component criteria of PCA with the independence property of ICA we obtain a better encoding. It turns out that this definition of PIC implements the classical demand of Shannon’s rate distortion theory.
Classically, encoding of images by only a few, important components is done by the Principal Component Analysis (PCA). Recently, a data analysis tool called Independent Component Analysis (ICA) for the separation of independent influences in signals has found strong interest in the neural network community. This approach has also been applied to images. Whereas the approach assumes continuous source channels mixed up to the same number of channels by a mixing matrix, we assume that images are composed by only a few image primitives. This means that for images we have less sources than pixels. Additionally, in order to reduce unimportant information, we aim only for the most important source patterns with the highest occurrence probabilities or biggest information called „Principal Independent Components (PIC)“. For the example of a synthetic picture composed by characters this idea gives us the most important ones. Nevertheless, for natural images where no a-priori probabilities can be computed this does not lead to an acceptable reproduction error. Combining the traditional principal component criteria of PCA with the independence property of ICA we obtain a better encoding. It turns out that this definition of PIC implements the classical demand of Shannon’s rate distortion theory.
Im Zeitraum 1. 11. 1993 bis 30. 3. 1997 wurden 1149 allgemeinchirurgische Intensivpatienten prospektiv erfaßt, von denen 114 die Kriterien des septischen Schocks erfüllten. Die Letalität der Patienten mit einem septischen Schock betrug 47,3%. Nach Training eines neuronalen Netzes mit 91 (von insgesamt n = 114) Patienten ergab die Testung bei den verbleibenden 23 Patienten bei der Berücksichtigung von Parameterveränderungen vom 1. auf den 2. Tag des septischen Schocks folgendes Ergebnis: Alle 10 verstorbenen Patienten wurden korrekt als nicht überlebend vorhergesagt, von den 13 Überlebenden wurden 12 korrekt als überlebend vorhergesagt (Sensitivität 100%; Spezifität 92,3%).
Diese Arbeit plädiert für eine rationale Behandlung von Patientendaten und untersucht dazu die Analyse der Daten mit Hilfe neuronale Netze etwas näher. Erfolgreiche Beispielanwendungen zeigen, daß die menschlichen Diagnosefähigkeiten deutlich schlechter sind als neuronale Diagnosesysteme. Für das Beispiel der neueren Architektur mit RBF-Netzen wird die Funktionalität näher erläutert und gezeigt, wie menschliche und neuronale Expertise miteinander gekoppelt werden kann. Der Ausblick deutet Anwendungen und Praxisproblematik derartiger Systeme an.
This paper describes the use of a Radial Basis Function (RBF) neural network in the approximation of process parameters for the extrusion of a rubber profile in tyre production. After introducing the rubber industry problem, the RBF network model and the RBF net learning algorithm are developed, which uses a growing number of RBF units to compensate the approximation error up to the desired error limit. Its performance is shown for simple analytic examples. Then the paper describes the modelling of the industrial problem. Simulations show good results, even when using only a few training samples. The paper is concluded by a discussion of possible systematic error influences, improvements and potential generalisation benefits. Keywords: Adaptive process control; Parameter estimation; RBF-nets; Rubber extrusion
The prevention of credit card fraud is an important application for prediction techniques. One major obstacle for using neural network training techniques is the high necessary diagnostic quality: Since only one financial transaction of a thousand is invalid no prediction success less than 99.9% is acceptable. Due to these credit card transaction proportions complete new concepts had to be developed and tested on real credit card data. This paper shows how advanced data mining techniques and neural network algorithm can be combined successfully to obtain a high fraud coverage combined with a low false alarm rate.
For the efficient management of large image databases, the automated characterization of images and the usage of that characterization for searching and ordering tasks is highly desirable. The purpose of the project SEMACODE is to combine the still unsolved problem of content-oriented characterization of images with scale-invariant object recognition and modelbased compression methods. To achieve this goal, existing techniques as well as new concepts related to pattern matching, image encoding, and image compression are examined. The resulting methods are integrated in a common framework with the aid of a content-oriented conception. For the application, an image database at the library of the university of Frankfurt/Main (StUB; about 60000 images), the required operations are developed. The search and query interfaces are defined in close cooperation with the StUB project “Digitized Colonial Picture Library”. This report describes the fundamentals and first results of the image encoding and object recognition algorithms developed within the scope of the project.
The prevention of credit card fraud is an important application for prediction techniques. One major obstacle for using neural network training techniques is the high necessary diagnostic quality: Since only one financial transaction of a thousand is invalid no prediction success less than 99.9% is acceptable. Due to these credit card transaction proportions complete new concepts had to be developed and tested on real credit card data. This paper shows how advanced data mining techniques and neural network algorithm can be combined successfully to obtain a high fraud coverage combined with a low false alarm rate.