PCA is most effective for distributions which are close to Gaussian. However, typical ST segments are not nearly symmetric. Nonlinear principal component analysis (NLPCA) is a rather new technique for nonlinear featur...
详细信息
PCA is most effective for distributions which are close to Gaussian. However, typical ST segments are not nearly symmetric. Nonlinear principal component analysis (NLPCA) is a rather new technique for nonlinear feature extraction which is usually implemented by a 5-layer neural network. It has been observed to have better performance, compared to PCA, in complex problems where the relationships between the variables are not linear. The authors apply NLPCA techniques for ST segment feature extraction and they use the NLPCA features to classify each segment into one of 4 classes: normal, ST+, ST-, or artefact. The authors' results from the European ST-T database show that using only 2 nonlinear components trained on a set of 1000 normal samples from each file they are often capable of achieving a classification rate of more than 90% with a false alarm rate of less than 10%, while the classification rate rarely falls below 80%. This is an encouraging result which can be further improved with the use of more nonlinear component features or more complex classifiers.
Adaptive Control Distributed Parameter Systems (ACDPS) with adaptive learning algorithms based on orthogonal neural network methodology are presented in this paper. We discuss a modification of orthogonal least square...
详细信息
Adaptive Control Distributed Parameter Systems (ACDPS) with adaptive learning algorithms based on orthogonal neural network methodology are presented in this paper. We discuss a modification of orthogonal least squares learning to find appropriate efficient algorithms for solution of ACDPS problems. A two times problem linked with the real time of plant control dynamic processes and the learning time for adjustment of parameters in adaptive control of unknown distributed systems is discussed. The simulation results demonstrate that the orthogonal learning algorithms on a neural network concept allow to find perfectly tracked output control distributed parameters in ACDPS and have rather a good perspective in the development of generalised ACDSP theory and practice in the future.
The load balancing scheme of a massively parallel computer system named PISMA is analysed in this paper. The theory behind the load balancing scheme is presented and several variations of the same scheme, which differ...
详细信息
This paper presents the design and implementation of an open hypermedia system using the Postgres extended relational database management system. Openness, extensibility and support for views and computations are amon...
详细信息
Although Object-Oriented Database Managemen-t Systems provide a direct mapping between problem domain and the way data are stored, they are not yet as popular as Relational Database Management Systems. Storing objects...
详细信息
In this paper, the solution used in the context of SEPDS (a Software Development Environment) to the problem of combining interactive behavior specification with functionality description of a distributed interactive ...
详细信息
This paper discusses a functional approach [4] to specifying a computer system's requirements. The method allows a system's requirements to be documented whether the system is implemented using a computer and ...
详细信息
This paper describes an algorithm for two-level logic minimization. The approach differs from most of the procedures presented in the past;in fact, rather than generating all the prime implicants of the function to be...
详细信息
This volume constitutes the refereed proceedings of the confederated international conferences: Cooperative Information Systems (CoopIS 2013), Distributed Objects and Applications (DOA-Trusted Cloud 2013), and Ontolog...
详细信息
ISBN:
(数字)9783642410307
ISBN:
(纸本)9783642410291
This volume constitutes the refereed proceedings of the confederated international conferences: Cooperative Information Systems (CoopIS 2013), Distributed Objects and Applications (DOA-Trusted Cloud 2013), and Ontologies, Data Bases and Applications of SEmantics (ODBASE 2013) held as part of OTM 2013 in September 2013 in Graz, Austria. The 47 revised full papers presented together with 6 short papers and 5 keynotes were carefully reviewed and selected from a total of 137 submissions. The papers are organized in topical sections on business process management; process modelling; service management; social networking; models and schemas; technical advances in cloud computing; towards trusted cloud computing; privacy for the cloud; querying and mining semantic information; semantic matching and mapping; semantic information management; semantics in use.
暂无评论