This paper analyses the security of dirty paper trellis (DPT) watermarking schemes which use both informed coding and informed em- bedding. After recalling the principles of message embedding with DPT watermarking, the secret parameters of the scheme are highlighted. The security weaknesses of DPT watermarking are then presented: in the wa- termarked contents only attack (WOA) setup, the watermarked data-set exhibits clusters corresponding to the different patterns attached to the arcs of the trellis. The K-means clustering algorithm is used to estimate these patterns and a co-occurrence analysis is performed to retrieve the connectivity of the trellis. Experimental results demonstrate that it is possible to accurately estimate the trellis configuration, which enables to perform attacks much more efficient than simple additive white Gaus- sian noise (AWGN).

Mobile networks produce a huge amount of spatio-temporal data. The data consists of parameters of base stations and quality information of calls. The self-organizing map (SOM) is an efficient tool for visualization and clustering of multidimensional data. It transforms the input vectors on a two-dimensional grid of prototype vectors and orders them. The ordered prototype vectors are easier to visualize and explore than the original data. There are two possible ways to start the analysis. We can build either a model of the network using state vectors with parameters from all mobile cells or a general one cell model trained using one cell state vector from all cells. In both methods, further analysis is needed. In the first method the distributions of parameters of one cell can be compared with the others and in the second it can be compared how well the general model represents each cell.

The self-organizing map (SOM) is an efficient tool for visualization and clustering of multidimensional data. It transforms the input vectors on two-dimensional grid of prototype vectors and orders them. The ordered prototype vectors are easier to visualize and explore than the original data. Mobile networks produce a huge amount of spatiotemporal data. The data consists of parameters of base stations (BS) and quality information of calls. There are two alternatives in starting the data analysis. We can build either a general one-cell-model trained using state vectors from all cells, or a model of the network using state vectors with parameters from all mobile cells. In both methods, further analysis is needed to understand the reasons for various operational states of the entire network.

In this paper, three process monitoring methods based on the self-organizing map (SOM) are presented: trajectory display, fuzzy response and probabilistic response. These approaches are compared with each other and also demonstrated in two case studies: continuous pulping and hot rolling of steel strips

The Self-Organizing Map (SOM) is a powerful neural network method for analysis and visualization of high-dimensional data. It maps nonlinear statistical dependencies between high-dimensional measurement data into simple geometric rela- tionships on a usually two-dimensional grid. The mapping roughly preserves the most important topological and metric relationships of the original data elements and, thus, inherently clusters the data. The need for visualization and clustering occurs, for instance, in the analysis of various engineering problems. In this paper, the SOM has been applied in monitoring and modeling of complex industrial processes. Case studies, including pulp process, steel production, and paper industry are described.

The Self-Organizing Map (SOM) is a powerful tool in visualization and analysis of high-dimensional data in engineering applications. The SOM maps the data on a two-dimensional grid which may be used as a base for various kinds of visual approaches for clustering, correlation and novelty detection. In this paper, the methods are discussed and applied to analysis of hot rolling of steel, continuous pulping process and technical data from world's pulp and paper mills.

The self-organizing map (SOM) is a neural network algorithm which is especially suitable for the analysis and visualization of high-dimensional data. It maps nonlinear statistical relationships between high-dimensional input data into simple geometric relationships, usually on a two-dimensional grid. The mapping roughly preserves the most important topological and metric relationships of the original data elements and, thus, inherently clusters the data. The need for visualization and clustering occurs in various engineering applications, in the analysis of complex processes or systems. In addition, SOM allows easy data fusion enabling visualization and analysis of large databases of industrial systems. As a case study, the SOM has been used to cluster the pulp and paper mills of the world.

Industrial Applications of Neural Networks, pp. 397-402 (1998) No AccessPATIENT GROUPING USING SELF-ORGANIZING MAPT PESSI, J KANGAS, and O SIMULAT PESSIDatawell Oy, P.O. Box 15, FIN-02131 Espoo, FinlandHelsinki University of Technology, TKK-F, FIN-02150 Espoo, Finlandtel: +358 9 70017970, J KANGAS, and O SIMULAhttps://doi.org/10.1142/9789812816955_0047Cited by:0 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: In hospitals, patient grouping is done for administrative purposes, e.g. in order to design treatment and financial activities. Patient grouping, especially automatic grouping, is a difficult task mainly due to the large number of different possibilities in diagnosis, treatment, and other patient information. Neural network algorithms have successfully been applied in various classification problems. They have the characteristics of learning the classification rules from examples which helps in handling complicated or incompletely defined cases. In this paper, the Self-Organizing Map algorithm has been used in developing a patient grouping system which has been implemented in the Department of Pediatrics at Helsinki University Central Hospital. FiguresReferencesRelatedDetails Industrial Applications of Neural NetworksMetrics History PDF download

Industrial Applications of Neural Networks, pp. 303-309 (1998) No AccessNEURAL NETWORK BASED CLOUD CLASSIFIERA. VISA, J. IIVARINEN, K. VALKEALAHTI, and O. SIMULAA. VISAHelsinki University of Technology Laboratory of Computer and Information Science Rakentajanaukio 2 C, FIN-02150 Espoo, Finland, J. IIVARINENHelsinki University of Technology Laboratory of Computer and Information Science Rakentajanaukio 2 C, FIN-02150 Espoo, Finland, K. VALKEALAHTIHelsinki University of Technology Laboratory of Computer and Information Science Rakentajanaukio 2 C, FIN-02150 Espoo, Finland, and O. SIMULAHelsinki University of Technology Laboratory of Computer and Information Science Rakentajanaukio 2 C, FIN-02150 Espoo, Finlandhttps://doi.org/10.1142/9789812816955_0035Cited by:2 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: It has become popular to use neural network classifiers in remote sensing. The reported results are usually based on a limited data set. This paper concerns the experiences from the cloud classification scheme based on the Self-Organizing Maps (SOM), which are fine-tuned by the Learning Vector Quantization (LVQ). The experiences are based on a data set of several hundred images. The classifier is capable of classifying satellite images taken round the year, during day and night. The classifier is fully automatic, and it can be adapted to changing situations with new examples. The benefits of this approach are rapid prototyping, adaptivity, and in high degree unsupervised learning. FiguresReferencesRelatedDetailsCited By 2Data-Driven Cloud Clustering via a Rotationally Invariant AutoencoderTakuya Kurihana, Elisabeth Moyer, Rebecca Willett, Davis Gilton and Ian Foster1 Jan 2022 | IEEE Transactions on Geoscience and Remote Sensing, Vol. 60Extracting cloud motion from satellite image sequencesR. Brad and I.A. Letia Industrial Applications of Neural NetworksMetrics History PDF download

The Self-Organizing Map (SOM) is a powerful neural network method for the analysis and visualisation of high-dimensional data. In this paper, the SOM algorithm is applied to the analysis of the technology of world paper and pulp industry. It is seen that the method can be used on environmental, technological and financial data to produce a comprehensive view of the industry as a whole.

Real communication channels with multipath propagation, interference and possible nonlinearities pose a difficult problem to the detecting receiver. This paper deals with neural approaches to solve those difficulties. Two types of neural networks, self-organizing map and radial basis functions have been studied. The results show that, while there are no actual benefits in using neural receivers in simple white noise Gaussian channels, the performance in nonlinear channels is much better with these new approaches than with the traditional ones.

This paper discusses the performance of neural receiver structures in fading (frozen) multipath channels. In addition to noise, non-Gaussian interference is present in system. The modulation under study has been 16QAM. Especially, nonlinear channel models have been investigated. The performance of two receiver structures based on the Self-Organizing Map have been evaluated via simulations. Encouraging results have been achieved in nonlinear channels, albeit interference cancellation leaves room for improvement.

The self-organizing map (SOM) method is a new, powerful software teal for the visualization of high-dimensional data. It concerts complex, nonlinear statistical relationships between high-dimensional data into simple geometric relationships on a low-dimensional display. As if thereby compresses information while preserving the most important topological and metric relationships of the primary data elements on the display, it may also be thought ro produce some kind of abstractions. These two aspects, visualization and abstraction, occur in a number of complex engineering tasks such as process analysis, machine perception, control, and communication.The term self-organizing map signifies a class of mappings defined by error-theoretic consideration. In practice they result in certain unsupervised, competitive learning processes, computed by simple-looking SOM algorithms,The first SOM algorithms were conceived around 1981-1982, and the popularity of the more advanced SOM methods is growing at a steady pace. Many industries have found the SOM-based software tools useful. The most important property of the SOM, orderliness of the input-output mapping, can be utilized for many tasks: reduction of the amount of training data, speeding up learning, nonlinear interpolation and extrapolation, generalization, and effective compression of information for its transmission.

Novel receiver structures combining traditional transversal equalizers and neural networks have been introduced for adaptive discrete-signal detection to improve the equalizer performance especially in compensating nonlinear distortions. In addition to noise and nonlinearities, various interfering signals may be present. In this paper, the behavior of the neural receivers in the presence of random interference has been investigated. New adaptive structures for compensating interference are presented.

In this paper, a traffic shaping for optimal system utilization in telecommunication applications is introduced. The method uses the self-organizing map and a decoding method (called SOM-D) to construct an adaptive resource management for certain systems. The simulation results show that system performance can be significantly improved by using SOM-D

this paper, the self-organizing map was used to visualise thespeech signal and the speech signal variations in time, and from the mappingcreated by the SOM process it was further possible to construct some quantitativemeasures of the voice quality. In the studies the voice quality of vowels wasanalyzed.In [15] another application of the self-organizing map algorithm forspeech signal analysis is described. In this paper the self-organized mapwas used to distinguish between the /s/ samples ...

The performance characteristics of the neural network assisted decision-feedback equalizer (DFE) have been investigated by extensive simulations using a two-path channel model and 16QAM (quadrature amplitude modulation). The authors report some of the simulation results on the start-up behavior of the novel equalizer structure under distorted channel conditions. The results show that, especially in difficult channels, including both linear multipath and nonlinear distortions, the neutral network assisted DFE structures are superior to the traditional DFE with equal computational complexity.< >

Self-organizing map algorithm has the ability to create a model for a system that is not exactly known a priori. Using this kind of model we can classify the system states and detect the abnormal ones. In this paper, an application of the feature map to detect operational states of a device is presented. The features used in the map are the measurements from the device describing its operational and environmental parameters.