Bank note classification using neural networks

Prediction of laser butt joint welding parameters using back propagation and learning vector quantization networks

The history of patient classification in nursing dates back to the period of Florence Nightingale. The first and the foremost condition for providing quality nursing care, which is measured by care standards, and determined by number of hours of actual care, is the appropriate number of nurses. Patient classification criteria are discussed in this paper. Hybrid classification model based on learning vector quantization (LVQ) networks and self-organising maps (SOM) are purposed. It is possible to discus three types of experimental results. First result could be assessment of Braden scale and Mors scale by LVQ. Second result, the time for nursing logistics activities. The third is possibility to predict appropriate number of nurses for providing quality nursing care. This research was conducted on patients from Institute of Neurology, Clinical Centre of Vojvodina.

Facial electromyography is myoelectric signals that formed by human facial muscles. This signal can be acquired by attaching an electrode to the facial muscle that has been connected with an electromyography sensor. When human say certain words, the articulation muscles contract and facial electromyography signals appear in the muscles. This study aims to recognize patterns in facial electromyography signals by classifying signals using naïve bayes and learning vector quantization classifier. Feature extraction used one-dimensional discrete wavelet transforms. Wavelet transform type used wavelet daubechies2 level 5. The transformation produces a level 5 approximation coefficient called a5 and five detail coefficients called d1, d2, d3, d4, and d5. The result of this study show that the average classification accuracy for ho neuk jak sentence using naïve bayes and LVQ classifier was 62.5% and 92.5% respectively. The average classification accuracy for ja’ word using naïve bayes and LVQ classifier was 70% and 92.5% respectively. The average classification accuracy for ja’ wo sentence using naïve bayes and LVQ classifier was 52.5% and 90% respectively. The average classification accuracy for pane word using naïve bayes and LVQ was 70% and 90% respectively. The average classification accuracy for soe word using naïve bayes and LVQ classifier is 85% and 95% respectively. Thus, this study shows that when humans say the words, facial electromyography signals that appear on facial muscles difference for each subject.

Unnatural control chart patterns (CCPs) are associated with a particular set of assignable causes for process variation. Hence, effectively recognizing CCPs can substantially narrow down the set of possible causes to be examined, and accelerate the diagnostic search. Recently, machine-learning techniques, especially the artificial neural network (ANN), have been widely used as an effective tool for CCP recognition (CCPR) tasks. Most ANN applications in CCPR have been using static supervised ANNs, such as back propagation networks (BPNs) and learning vector quantization (LVQ) networks. The false recognition problem (i.e. the patterns are misclassified) commonly encountered for these ANN-based CCPR models is mainly due to the fact that the static ANNs cannot appropriately deal with dynamic patterns, such as CCPs. In this research, a dynamic training algorithm is designed to provide an LVQ network-based CCPR model the capability to on-line recognize the dynamic CCPs that vary over time. The numerical results using simulation show that the dynamically trained LVQ network-based model proposed in this research performs much better than other ANN-based models reported in literature with respective to recognition accuracy and speed. Although this research considers the specific application of a real-time CCPR model based on an LVQ network, the proposed dynamic training algorithm could be applied to CCPR systems based on other ANN architectures in general.

The use of neural networks to create adaptive models of users and communications channels for use in designing system response characteristics is discussed. Two types of neural networks that will be useful for this type of task are considered. One, the Kohonen learning vector quantization (LVQ) network, is a clustering network. It can adjust the vector element values of a set of quantizing vectors in order to create exemplar vectors which represent clusters in a set of data. The other, the Kohonen self-organizing topology-preserving map (SOTPM), is a more advanced and powerful network that uses similar principles as the LVQ. It can create a topographic mapping of a set of vector data which creates a data clustering visible in a reduced dimensionality space from the original data. This will facilitate interpretation of the data clusters describing different user types. The use of a rather highly evolved form of neural networks to create more powerful system models is discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This paper used fuzzy logic theory to study the behavior of a simple pulse width modulated three phase voltage controlled VSI, which feeds a weak AC network with power produced from an offshore wind farm (WF) of induction generators. Its control system, which is based on fuzzy controllers, manages to offer very satisfactory performance. Using Matlab simulation, the study was performed under both steady-state and transient conditions. The results have proven an excellent performance and verified the validity of the proposed system. The studies have also demonstrated the ability of the advanced inverter to assist the system keeping the ac voltage fluctuations in the point of common coupling at an acceptable level.

In this paper we have done analysis of supervised and unsupervised learning. We have classified different images by supervised and unsupervised learning methods. 250 object images are used for classification. The techniques used for classification are competitive learning, self-organizing map (SOM) and learning vector quantization (LVQ) networks.

To improve real-time control performance and reduce possible negative impacts of photovoltaic (PV) systems, an accurate forecasting of PV output is required, which is an important function in the operation of an energy management system (EMS) for distributed energy resources. In this paper, a weather-based hybrid method for 1-day ahead hourly forecasting of PV power output is presented. The proposed approach comprises classification, training, and forecasting stages. In the classification stage, the self-organizing map (SOM) and learning vector quantization (LVQ) networks are used to classify the collected historical data of PV power output. The training stage employs the support vector regression (SVR) to train the input/output data sets for temperature, probability of precipitation, and solar irradiance of defined similar hours. In the forecasting stage, the fuzzy inference method is used to select an adequate trained model for accurate forecast, according to the weather information collected from Taiwan Central Weather Bureau (TCWB). The proposed approach is applied to a practical PV power generation system. Numerical results show that the proposed approach achieves better prediction accuracy than the simple SVR and traditional ANN methods.

To improve the assessment capability of power transformers, this paper proposes a new intelligent decision support system based on fuzzy learning vector quantization (HVQ) networks. In constructing the system, a fuzzy-based classifier is designed to divide the historical data for dissolved gas analysis (DGA) into various categories with different levels of gas attributes. For each category of gas attributes, a learning vector quantization (LVQ) network is trained to be responsible for the classification of the potential faults due to insulation deterioration. The assessment approach has been tested on the DGA data from Taiwan Power Company (TPC) and compared with the previous fuzzy diagnosis system and the existing multi-layered backpropagation based artificial neural networks (BPANN) methods. Remarkable classification accuracy and far less training efforts of the proposed approach are achieved in this paper.

Stereology and stochastic geometry can be used as auxiliary tools for diagnostic purposes in tumour pathology. Whether first-order parameters or stochastic-geometric functions are more important for the classification of the texture of biological tissues is not known. In the present study, volume and surface area per unit reference volume, the pair correlation function and the centred quadratic contact density function of epithelium were estimated in three case series of benign and malignant lesions of glandular tissues. The information provided by the latter functions was summarized by the total absolute areas between the estimated curves and their horizontal reference lines. These areas are considered as indicators of deviation of the tissue texture from a completely uncorrelated volume process and from the Boolean model with convex grains, respectively. We used both areas and the first-order parameters for the classification of cases using artificial neural networks (ANNs). Learning vector quantization and multilayer feedforward networks with backpropagation were applied as neural paradigms. Applications included distinction between mastopathy and mammary cancer (40 cases), between benign prostatic hyperplasia and prostatic cancer (70 cases) and between chronic pancreatitis and pancreatic cancer (60 cases). The same data sets were also classified with linear discriminant analysis. The stereological estimates in combination with ANNs or discriminant analysis provided high accuracy in the classification of individual cases. The question of which category of estimator is the most informative cannot be answered globally, but must be explored empirically for each specific data set. Using learning vector quantization, better results could often be obtained than by multilayer feedforward networks with backpropagation.

One example of the implementations of digital image processing in biomedical field is to identify the gender of the fetus on the ultrasound image. To identify the gender of the fetus, a fetal must attain the age of at least 5 months of pregnancy. Before the process of identification, there are three steps that must be done, i.e. image preprocessing, image segmentation, and feature extraction (shape description). Having obtained the value of the feature extraction stage, the next step is the classification by utilizing one of the artificial neural network (ANN) methods, namely the learning vector quantization (LVQ). Prior to the LVQ process, the training datasets process is conducted beforehand with 3 iterations using the learning rate of 0.05 and the learning rate reduction of 0.02 per iteration. Then the training process is followed by a classification stage. The obtained test results show that the LVQ classification gives poor results. The less optimal results are generated due to the quality of the dataset used. The quality of this dataset is affected by the results of the digitization process, the stage of preprocessing, segmentation, and feature extraction.

This paper presents a new hybrid intelligent algorithm for assessing and enhancing voltage security. The problem is decomposed into two stages. Firstly, the security of current operating point is assessed by learning vector quantization (LVQ) network according to the pre-specified criterion. Throughout the paper, continuation power flow (CPF) is adopted as a tool for defining voltage security margin (VSM). If an insecure state is indicated by the LVQ, the power system operator is given the suggestion for appropriate control settings so as to maintain the specified security level. In the second stage, the optimal reactive power dispatch (ORPD) problem is formulated and VSM is considered as an additional constraint. To include VSM determined by CPF along the optimization process, feed-forward neural network (FFNN) is trained to learn and perform similarly to CPF to relieve the intensive computing requirement. Ant colony optimization (ACO) is applied to handle the VSM constrained ORPD problem. The proposed method was tested on IEEE 30-bus system and successful results were obtained.

The learning vector quantization (LVQ) network was used to classify the ECG ST segment into different morphological categories. Due to the lack of data in the ST elevation categories, the classifier was only trained to identify different types of ST depressions (horizontal, upsloping and downsloping). The accuracies were 91%, 85% and 65% respectively for the training, validation and testing data respectively. Despite the low accuracy for the testing data, most of the mis-classifications were downsloping ST depression being classified as horizontal ST depression. We concluded that more data and more training are needed in order to train the LVQ to recognize other morphological types of ST deviation and to improve the accuracy.

A distributed diagnosis system combining the immune network (IN) and learning vector quantization (LVQ) is proposed for accurately detecting faulty sensor outputs in control plants. The system has two execution modes, namely, its training mode, where the LVQ extracts a correlation between each two sensors from their outputs when they work properly, and its diagnosis mode, where the LVQ contributes to testing each two sensors using the extracted correlation, and the IN contributes to determining faulty sensors by integrating the local testing results obtained from the LVQ. With the proposed method, faulty sensors, such as age deteriorated ones, which have been difficult to be detected only by checking each sensor output independently, can be specified.

The concept of Concealed Weapon and Explosive (CWE) detection by the analysis of the Late Time Response (LTR) of the complex human-CWE object in UWB Radar, has been presented in [1,2]. As the overall reflected human signal depends on the human stance and orientation with respect to the radar system, this paper investigates whether the resonant frequencies can be classified according to the illuminated simple i.e. human or complex i.e. human-CWE object. This classification yields that the human frequencies do not overlap with the CWE signature frequencies therefore the CWE frequencies can be obtained and the body-worn CWE detection is realised. The resonant frequency classification is achieved via a Learning Vector Quantization (LVQ) network.