Process control of time-varying systems using parameter-less self-organizing maps

Traditional control charts, such as Hotelling’s T², are effective in detecting abnormal patterns. However, most control charts do not take into account a time-varying property in a process. In the present study, we propose a parameter-less self-organizing map-based control chart that can handle a situation in which changes occur in the distribution or parameter of the target observations. The control limits of the proposed chart are determined by estimating the empirical level of significance on the percentile using the bootstrap method. Experimental results obtained by using simulated data and actual process data from the manufacturing process for a thin-film transistor-liquid crystal display demonstrate the effectiveness and usefulness of the proposed algorithm.     

Shape indexing using self-organizing maps

In this paper, we propose a novel approach to generate the topology-preserving mapping of structural shapes using self-organizing maps (SOMs). The structural information of the geometrical shapes is captured by relational attribute vectors. These vectors are quantised using an SOM. Using this SOM, a histogram is generated for every shape. These histograms are treated as inputs to train another SOM which yields a topology-preserving mapping of the geometric shapes. By appropriately choosing the relational vectors, it is possible to generate a mapping that is invariant to some chosen transformations, such as rotation, translation, scale, affine, or perspective transformations. Experimental results using trademark objects are presented to demonstrate the performance of the proposed methodology.     

Practical issues in designing knowledge-based expert systems

This paper examines problems, practical issues, and considerations in the design of knowledge-based expert systems. The state-of-the-art as represented by numerous systems in financial planning, accounting, and capital budgeting domains is summarized.     

Environment classification using Kohonen self-organizing maps

Abstract: This paper describes a new method for classifying three-dimensional environments in real time using Kohonen self-organizing maps (SOMs). The method has been developed to enable autonomous underwater vehicles (AUVs) to navigate without human intervention in previously unexplored subsea environments, but can be generalized to unmanned aircraft equipped with appropriate sensors flying over unchartered terrains, or spacecraft exploring remote planets, subject to appropriate pre-mission training. The method involves a fuzzy comparison between a SOM created in real time using accumulated sensor data and a class atlas of SOMs derived from previously trained and manually classified environments. This enables mission- and environment-appropriate AUV navigation strategies to be selected in real time. Simulation results using real-world, three-dimensional environment data acquired from digital elevation maps are presented, which demonstrate the potential of the method.     

Applying inductive learning to enhance knowledge-based expert systems

This paper describes the use of inductive learning in MARBLE, a knowledge-based expert system I have developed for assisting business loan evaluation. Inductive learning is the process of inferring classification concepts from raw data; I use this technique to generate loan-granting decision rules based on historical and proforma financial information. A learning method is presented in this paper that can induce decision rules from training examples.     

Dynamic muscle fatigue detection using self-organizing maps

Wavelets are used for the processing of signals that are non-stationary and time varying. The electromyogram (EMG) contains transient signals related to muscle activity. Wavelet coefficients are proposed as features for identifying muscle fatigue. By observing the approximation coefficients it is shown that their amplitude follows closely the muscle fatigue development. The proposed method for detecting fatigue is automated by using neural networks. The self-organizing map (SOM) has been used to visualize the variation of the approximation wavelet coefficients and aid the detection of muscle fatigue. The results show that a 2D SOM separates EMG signatures from fresh and fatigued muscles, thus providing a visualization of the onset of fatigue over time. The map is able to detect if muscles have recovered temporarily. The system is adaptable to different subjects and conditions since the techniques used are not subject or workload regime specific.     

A knowledge-based neural network for fusing edge maps of multi-sensor images

With the goal of fusion prescribed as building an edge map that contains as many edges as possible from the given multi-spectral/sensor images, a new fusion scheme, called the knowledge-based neural network fusion (KBNNF), is proposed to fuse edge maps of these images in order to generate a combined edge map that has more complete and reliable edge information than what one can obtain from any single image.The KBNNF is used to fuse edge maps of images having mutually complementary edge information in the following sense: (i) the edges in the images are compatible, i.e., can be interpreted together; and (ii) the edges in the different images reveal different parts of the scene. More complete edge contours of the same object are obtained by linking the edge sections obtained from different images together. The resulting edge map can be used for subsequent study (like object recognition).The proposed scheme bases its confidence and reliability on the analysis of variance (ANOVA)-based edge detector that can address two important issues of edge based image fusion well: (i) the difference in edge position among the images because of the different characteristics of the images and the error in the image registration process; and (ii) the variance existing among the edge test values calculated from different images. The KBNNF has been applied to fuse: (i) radar (SAR)–optical (SPOT), (ii) optical–optical, (iii) infrared–infrared, and (iv) optical–infrared (satellite) image combinations. Comparisons are made with the relevant existing techniques in the literature. The paper concludes with some examples to illustrate the efficacy of the proposed scheme.     

Identification of trends from patents using self-organizing maps

Patent users such as governments, inventors, and manufacturing organizations strive to identify the directions in which new technology is advancing, and their goal is to outline the boundaries of existing knowledge. The paper analyzes patent knowledge to identify research trends. A model based on knowledge extraction from patents and self-organizing maps for knowledge representation is presented. The model was tested on patents from the United States Patent and Trademark Office. The experiments show that the method provides both an overview of the directions of the trends and a drill-down perspective of current trends.     

Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation

This study presents an image segmentation system that automatically segments and labels T1-weighted brain magnetic resonance (MR) images. The method is based on a combination of unsupervised learning algorithm of the self-organizing maps (SOM) and supervised learning vector quantization (LVQ) methods. Stationary wavelet transform (SWT) is applied to the images to obtain multiresolution information for distinguishing different tissues. Statistical information of the different tissues is extracted by applying spatial filtering to the coefficients of SWT. A multidimensional feature vector is formed by combining SWT coefficients and their statistical features. This feature vector is used as input to the SOM. SOM is used to segment images in a competitive unsupervised approach and an LVQ system is used for fine-tuning. Results are evaluated using Tanimoto similarity index and are compared with manually segmented images. Quantitative comparisons of our system with the other methods on real brain MR images using Tanimoto similarity index demonstrate that our system shows better segmentation performance for the gray matter while it gives average results for white matter.     

Content-based image collection summarization and comparison using self-organizing maps

Progresses made on content-based image retrieval have reactivated the research on image analysis and a number of similarity-based methods have been established to assess the similarity between images. In this paper, the content-based approach is extended towards the problem of image collection summarization and comparison. For these purposes we propose to carry out clustering analysis on visual features using self-organizing maps, and then evaluate their similarity using a few dissimilarity measures implemented on the feature maps. The effectiveness of these dissimilarity measures is then examined with an empirical study.     

Recognizing environments from action sequences using self-organizing maps

In this paper, we describe development of a mobile robot which does unsupervised learning for recognizing an environment from action sequences. We call this novel recognition approach action-based environment modeling (AEM). Most studies on recognizing an environment have tried to build precise geometric maps with high sensitive and global sensors. However such precise and global information may be hardly obtained in a real environment, and may be unnecessary to recognize an environment. Furthermore unsupervised-learning is necessary for recognition in an unknown environment without help of a teacher. Thus we attempt to build a mobile robot which does unsupervised-learning to recognize environments with low sensitive and local sensors. The mobile robot is behavior-based and does wall-following in enclosures (called rooms). Then the sequences of actions executed in each room are transformed into environment vectors for self-organizing maps. Learning without a teacher is done, and the robot becomes able to identify rooms. Moreover, we develop a method to identify environments independent of a start point using a partial sequence. We have fully implemented the system with a real mobile robot, and made experiments for evaluating the ability. As a result, we found out that the environment recognition was done well and our method was adaptive to noisy environments.     

Variants of self-organizing maps

Self-organizing maps have a bearing on traditional vector quantization. A characteristic that makes them more closely resemble certain biological brain maps, however, is the spatial order of their responses, which is formed in the learning process. A discussion is presented of the basic algorithms and two innovations: dynamic weighting of the input signals at each input of each cell, which improves the ordering when very different input signals are used, and definition of neighborhoods in the learning algorithm by the minimal spanning tree, which provides a far better and faster approximation of prominently structured density functions. It is cautioned that if the maps are used for pattern recognition and decision process, it is necessary to fine tune the reference vectors so that they directly define the decision borders.     

Internet-based remote supervision of industrial processes using self-organizing maps

In upcoming years, the strategies for maintenance, traceability, management and operation of productive processes will demand the use of novel information and communication technologies. Supervisory systems in these new scenarios will have to be able to integrate large volumes of information and knowledge coming both from local and remote points of large processes. These systems will therefore require new tools for management and integration of information and knowledge. In this work, the authors present an internet-based remote supervision system of industrial processes that incorporates powerful data and knowledge visualization tools based on self-organizing maps (SOM). This architecture adds an intermediate layer (database) to the well-known client and server layers, that isolates the client part from the industrial process, allowing to incorporate the required data management and neural network processing tasks. Remote users have access to advanced information visualization tools based on SOM, including both static visualizations, such as component planes or distance maps, and dynamical ones, such as residuals and state trajectory, allowing the interpretation of knowledge extracted by the SOM as well as the analysis and detection of possible abnormal conditions. This architecture has been validated through the supervision of an industrial pilot plant.     

Filtering multilingual Web content using fuzzy logic and self-organizing maps

Effective multilingual information filtering is required to alleviate users burden of information overload resulting from the increasing flood of multilingual textual content available extensively over the World-Wide Web. This paper proposes a content-based self-organizing approach to multilingual information filtering using fuzzy logic and the self-organizing map. This approach screens and evaluates multilingual documents based on their semantic contents. Correlated multilingual documents are disseminated according to their corresponding themes or topics, thus enabling language-independent content-based information access efficiently and effectively. A Web-based multilingual online news-filtering system is developed to illustrate how the approach works.     

Managing complexity in large data bases using self-organizing maps

The amount of financial information in today's sophisticated large data bases is substantial and makes comparisons between company performance—especially over time—difficult or at least very time consuming. The aim of this paper is to investigate whether neural networks in the form of self-organizing maps can be used to manage the complexity in large data bases. We structure and analyze accounting numbers in a large data base over several time periods. By using self-organizing maps, we overcome the problems associated with finding the appropriate underlying distribution and the functional form of the underlying data in the structuring task that is often encountered, for example, when using cluster analysis. The method chosen also offers a way of visualizing the results. The data base in this study consists of annual reports of more than 120 world wide pulp and paper companies with data from a five year time period.     

Video object segmentation using color-component-selectable learning for self-organizing maps

In this article, self-organizing-map-based video object segmentation is proposed, assuming that either Y-quantification or HSV-quantification can be systematically selected. Given a video sequence, the value of the probability density function for each component value is calculated according to a kernel estimation at the first frame. Some areas randomly chosen from the background are then examined, using each component value, to judge whether or not they include the target object. The quantification is determined so that the frequency of occurrence of false extractions can be reduced. The data presented to the maps are generated based on the selected quantification. Experimental results show that the proposed method recognizes the target object well.     

Qualitative analysis of goat and sheep production data using self-organizing maps

Abstract: The aim of this study was to analyse the relationship between different small ruminant livestock production systems with different levels of specialization. The analysis is carried out by using the self-organizing map. This tool allows high-dimensional input spaces to be mapped into much lower-dimensional spaces, thus making it much more straightforward to understand any set of data. These representations enable the visual extraction of qualitative relationships among variables (visual data mining), converting the data to maps. The data used in this study were obtained from surveys completed by farmers who are principally dedicated to goat and sheep production. With the self-organizing map we found a relationship between qualitative and quantitative variables showing that more specialized farms have greater milk incomes per goat, highlighting farms that have a greater number of animals, better facilities (including milking machines) or animals fed with elaborated diets. The use of self-organizing maps for the analysis of this kind of data has proven to be highly valuable in extracting qualitative conclusions and in guiding improvements in farm performance.     

Quality-oriented optimization of wave soldering process by using self-organizing maps

In this paper, the optimal process parameters of a wave soldering process were defined. The optimization was performed in respect to soldering quality by minimizing a cost function describing the total repairing cost of a wave-soldered printed circuit board (PCB). The data analysis stages were as follows. First, the process data were coded into inputs for a self-organizing map (SOM). Next, a function for the repairing cost was constructed and used to find the optimal map neurons. At the last phase, the optimal parameters were approximated on the basis of the reference vectors of the optimal neurons. The results showed clearly potential in the optimization of the wave soldering process, especially in the visualization of the optimal process conditions. Therefore, it would be useful to exploit the method more widely in the electronics industry.     

Visualization of high-dimensional data using two-dimensional self-organizing piecewise-smooth Kohonen maps

To make visualization of high-dimensional data more accurate, we offer a method of approximating two-dimensional Kohonen maps lying in a multiple-dimensional space. Cubic parametric spline-based least-defect surfaces can be used as an approximation function to minimize approximation errors.