A novel compound neural network for fault sources recognition

Electrocardiogram (ECG) is a nonstationary signal; therefore, the disease indicators may occur at random in the time scale. This may require the patient be kept under observation for long intervals in the intensive care unit of hospitals for accurate diagnosis. The present study examined the classification of the states of patients with certain diseases in the intensive care unit using their ECG and an Artificial Neural Networks (ANN) classification system. The states were classified into normal, abnormal and life threatening. Seven significant features extracted from the ECG were fed as input parameters to the ANN for classification. Three neural network techniques, namely, back propagation, self-organizing maps and radial basis functions, were used for classification of the patient states. The ANN classifier in this case was observed to be correct in approximately 99% of the test cases. This result was further improved by taking 13 features of the ECG as input for the ANN classifier.

A thorough investigation of the electroencephalograph (EEG) information may support an enriched awareness of the mechanism of understanding different learning styles patterns. Wavelet analysis is a powerful technique that uniquely permits the decomposition of complex information of trends, discontinuities, a repeated pattern. The purpose of such methods is to be able to assign simple segments at diverse locations and scales, to be remodelled afterward effectively. In this paper, we attempt to classify individual cognitive learning styles using artificial neural networks and unsupervised learning. First, we apply Independent component analysis (ICA) to extract relevant features (artefacts removal) of the EEG records. We analyse the ICA-based EEG channels data using inter-quartiles to show the degree of dispersion and skewness. Next, self-organising maps (SOM) are then created to characterise different cognitive learning styles from selected ICA-based channel data.

In this paper, a novel independent component analysis (ICA) approach is proposed, which is robust against the interference of impulse noise. To implement ICA in a noisy environment is a difficult problem, in which traditional ICA may lead to poor results. We propose a method that consists of noise detection and image signal recovery. The proposed approach includes two procedures. In the first procedure, we introduce a self-organizing map (SOM) network to determine if the observed image pixels are corrupted by noise. We will mark each pixel to distinguish normal and corrupted ones. In the second procedure, we use one of two traditional ICA algorithms (fixed-point algorithm and Gaussian moments-based fixed-point algorithm) to separate the images. The fixed-point algorithm is proposed for general ICA model in which there is no noise interference. The Gaussian moments-based fixed-point algorithm is robust to noise interference. Therefore, according to the mark of image pixel, we choose the fixed-point or the Gaussian moments-based fixed-point algorithm to update the separation matrix. The proposed approach has the capacity not only to recover the mixed images, but also to reduce noise from observed images. The simulation results and analysis show that the proposed approach is suitable for practical unsupervised separation problem.

Nonlinear independent component analysis (NICA) is a powerful method for analyzing nonlinear and nongaussian data. Artificial neural network (ANN), especially self-organizing map (SOM) based on unsupervised learning, is an excellent tool for pattern clustering and recognition. A novel multi-NICA network is proposed for feature extraction of different mechanical patterns, followed by a typical ANN that is one of Multi-Layer Perceptron (MLP), or Radial Basis Function Network (RBFN), or self-organizing map (SOM), which implements the final classification. Using NICA and appropriate strategies for further feature extraction, nonlinear and higher than second order features embedded in multi-channel vibration measurements can be captured effectively. Mechanical fault patterns can be recognized correctly. Results from the contrast classification experiments show that the new compound ICA-SOM classifier can be constructed in a simpler way and it can classify various fault patterns with high accuracy, both of which imply a great potential in health condition monitoring of machine systems.

This paper presents the technique of fault diagnosis using independent component analysis (ICA) and demonstrates applications of ICA-based RBF networking in the diagnostic system. The ICA with reference is proposed to incorporate additional requirements and prior information as constraints into the ICA constraints into the ICA contrast function. The adaptive solutions using the RBF network learning are proposed to solve the constrained optimization problem. A radial-basis-function (RBF) neural network based fault detection method is developed. The application illustrate the versatility of the method of the paper by separating the subspace of independent components according to density types and extracting a set of desired sources when rough templates are available. The experiments using an unbalance rotor of rotating systems demonstrate the efficacy of the method

Extending the Kohonen self-organizing map networks for clustering analysis

Concrete pavement joint evaluation involves a number of assessment criteria, such as deflection near the joint, load transfer efficiency of the dowels and severity of voids under the slab. Although there are well defined thresholds for each of these parameters, often there arises a situation where each of the considered parameters lends contradictory assessment that leads to a considerable subjectivity in the evaluation process. A Self-Organizing Map (SOM), an unsupervised learning procedure in artificial neural network, is utilised for the first time to map the joint condition of concrete pavements from Falling Weight Deflectometer (FWD) deflection bowls. A novel methodology is proposed for labelling the network, whereby pavement engineering expertise can be directly used in a SOM for consistent deflection data classification in joint evaluation. The effectiveness of the trained network is demonstrated by using joint assessment parameters; namely, load transfer efficiency (LTE), void intercepts and absolute deflection. The joints were classified as good, marginal or poor. For the three parameters based SOM classification, an accuracy of 65-70% was obtained; this improves to 87.5% when the SOM was trained with 2-parameters (LTE and absolute deflection). However, when the SOM was tested with the data classified as 'good', accuracy improves to around 90%. Therefore, a SOM can be a powerful supplementary tool for a consistent and non-subjective evaluation of concrete pavement joints.

Guided by the principles of geometric independent component analysis (ICA), we present a new approach (SOM-ICA) to linear geometric ICA using self-organizing map (SOM). We observe a considerable improvement in separation quality of different distributions, albeit at high computational costs. The SOMICA algorithm is therefore primarily interesting from a theoretical point of view bringing together ICA and SOMs; this intersection could lead to new proofs in geometric ICA based on similar theorems in the SOM theory.

Spiking Self-organizing Maps for Classification Problem

Face recognition has always been a potential research area because of its demand for reliable identification of a human being especially in government and commercial sectors, such as security systems, criminal identification, border control, etc. where a large number of people interact with each other and/or with the system. The last two decades have witnessed many supervised and unsupervised learning techniques proposed by different researchers for the face recognition system. Principal component analysis (PCA), self‐organizing map (SOM), and independent component analysis (ICA) are the most widely used unsupervised learning techniques reported by research community. This article presents an analysis and comparison of these techniques. The article also includes two SOM processing methods global SOM (GSOM) and local SOM (LSOM) for performance evaluation along with PCA and ICA. We have used two different databases for our analysis. The simulation result establishes the supremacy of GSOM in general among all the unsupervised techniques. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 261–267, 2010

Bringing artificial intelligence to embedded devices has become a central research topic in many scientific domains (environment, agriculture, sociology, health…). For Human Activity Recognition, Artificial Neural Networks (ANNs) have shown their capability to provide better performance compared to other machine learning methods. However, ANNs suffer from two major limitations. First, ANNs are often trained using supervised learning requiring labelled databases, which are often difficult to build in real applications. Then, those algorithms are usually very expensive in terms of computing power. For that reason, their integration into low-power microcontrollers has been so far only evaluated to a limited extent. In this paper, we propose to evaluate quantitatively and qualitatively the embedded implementation of different neural networks for human activity recognition. First, supervised learning approaches are presented, followed by an exploratory study of unsupervised learning approaches using Self-Organizing Maps. Finally, some aspects of embedded unsupervised online learning are investigated to improve classification results using subject-specific data over a more general training. Each neural network is tested on a Human Activity Recognition dataset acquired from a smartphone using accelerometer and gyroscope sensing information (UCI HAR) and deployed on the SparkFun Edge board. This board hosts a low-power ARM Cortex-M4F-based microcontroller.

In a previous study {Takagi, T. et al., Chem. Pharm. Bull., 52(12), 1427-1432 (2004)}, we applied a slightly revised neural Independent Component Analysis (ICA) for profiling illegally distributed methamphetamine. Using ICA and an hourglass type Hierarchical Neural Network (HNN), we obtained better classification results than by using Principal Component Analysis (PCA), CATegorical PCA (CATPCA) and the MultiDimensional Scaling method (MDS). The HNN is a nonlinear machine learning method, and the ICA applied in that study exhibited nonlinear characteristics. The results indicated that nonlinear analysis is more efficient than linear analysis for profiling confiscated methamphetamine. Consequently, in this study, we applied Self-Organizing Maps (SOMs) to impurity profiling of methamphetamine. While SOM is currently a frequently employed nonlinear classification method, the ordinary SOM uses only that information contained by the winner neuron for classification and the information of other grid points is neglected. We therefore attempted to simultaneously utilize the information of loser neurons in order to avoid information loss. First, we visualized the resultant reference vectors using a contour map of each sample. Although considerable information can be visually compared using the SOM contour maps, metric comparisons are difficult. We therefore used MDS to construct a similarity matrix using the data of the resultant reference vectors to visualize metric data. To assess the results, we assumed that there are four synthetic routes (Nagai, Leuckart, Emde and reductive amination methods), and that each of these can be identified by comparing route-specific impurities.

This paper describes the time variant changes in satellite images using Self Organizing Feature Map (SOFM) technique associated with Artificial Neural Network. In this paper, we take a satellite image and find the time variant changes using above technique with the help of MATLAB. This paper reviews remotely sensed data analysis with neural networks. First, we present an overview of the main concepts underlying Artificial Neural Networks (ANNs), including the main architectures and learning algorithms. Then, the main tasks that involve ANNs in remote sensing are described. We first make a brief introduction to models of networks, for then describing in general terms Artificial Neural Networks (ANNs). As an application, we explain the back propagation algorithm, since it is widely used and many other algorithms are derived from it. There are two techniques that are used for classification in pattern recognition such as Supervised Classification and Unsupervised Classification. In supervised learning technique the network knows about the target and it has to change accordingly to get the desired output corresponding to the presented input sample data. Most of the previous work has already been done on supervised classification. In this study we are going to present the classification of satellite images using unsupervised classification method of ANN.

این مطالعه به منظور بررسی پیش بینی شاخص های عملکرد تولیدی در مرغان تخم گذار با استفاده از شبکه های عصبی مصنوعی و رگرسیون غیرخطی چندگانه انجام شد. بررسی بر روی اطلاعات چهار دوره متوالی پرورش در یک واحد پرورش مرغ تخم گذار صورت گرفت. روش های داده-کاوی شامل رگرسیون خطی و غیر خطی، شبکه عصبی پرسپترون سه لایه، شبکه عصبی پرسپترون چهار لایه و شبکه عصبی با تابع پایه ای شعاعی بود. در این مدل ها از متغیرهای سن گله، میزان خوراک مصرفی و فصل تولید به عنوان متغیر پیشگو و شاخص های عملکرد تولیدی شامل درصد تخم-گذاری، وزن توده ای تخم مرغ تولیدی و ضریب تبدیل غذایی به عنوان متغیر پاسخ استفاده شد. نتایج نهایی رگرسیون های خطی نشان داد که برای تمامی متغیرهای وابسته مورد مطالعه متغیر مستقل سن گله معنی دار می باشد. بنابراین رگرسیون غیر خطی شاخص های عملکرد تولیدی در مقابل سن برای مقایسه با شبکه های عصبی مختلف مورد بررسی قرار گرفت و برای مقایسه کلیه مدل ها از ضریب تعیین (R2) و میانگین قدر مطلق خطا (MAE) استفاده شد. نتایج نشان داد بین شبکه های عصبی مصنوعی مختلف مورد مطالعه، شبکه با تابع پایه ای شعاعی بهتر از سایر مدل های در پیش‌بینی شاخص های عملکرد تولیدی مرغان تخم گذار عمل می کند.

Recent advances in intelligence technology have boosted the development of micro- Unmanned Air Vehicles (UAVs) including Sliver Fox, Shadow, and Scan Eagle for various surveillance and reconnaissance applications. These affordable and reusable devices have to fit a series of size, weight, and power constraints. Cameras used on such micro-UAVs are therefore mounted directly at a fixed angle without any motion-compensated gimbals. This mounting scheme has resulted in the so-called <i>jitter effect</i> in which jitter is defined as sub-pixel or small amplitude vibrations. The jitter blur caused by the jitter effect needs to be corrected before any other processing algorithms can be practically applied. Jitter restoration has been solved by various optimization techniques, including Wiener approximation, maximum <i>a-posteriori</i> probability (MAP), etc. However, these algorithms normally assume a spatial-invariant blur model that is not the case with jitter blur. Szu et al. developed a smart real-time algorithm based on auto-regression (AR) with its natural generalization of unsupervised artificial neural network (ANN) learning to achieve restoration accuracy at the sub-pixel level. This algorithm resembles the capability of the human visual system, in which an agreement between the pair of eyes indicates "signal", otherwise, the jitter noise. Using this non-statistical method, for each single pixel, a deterministic blind sources separation (BSS) process can then be carried out independently based on a deterministic minimum of the Helmholtz free energy with a generalization of Shannon's information theory applied to open dynamic systems. From a hardware implementation point of view, the process of jitter restoration of an image using Szu's algorithm can be optimized by pixel-based parallelization. In our previous work, a parallelly structured independent component analysis (ICA) algorithm has been implemented on both Field Programmable Gate Array (FPGA) and Application-Specific Integrated Circuit (ASIC) using standard-height cells. ICA is an algorithm that can solve BSS problems by carrying out the all-order statistical, decorrelation-based transforms, in which an assumption that neighborhood pixels share the same but unknown mixing matrix <b>A</b> is made. In this paper, we continue our investigation on the design challenges of firmware approaches to smart algorithms. We think two levels of parallelization can be explored, including pixel-based parallelization and the parallelization of the restoration algorithm performed at each pixel. This paper focuses on the latter and we use ICA as an example to explain the design and implementation methods. It is well known that the capacity constraints of single FPGA have limited the implementation of many complex algorithms including ICA. Using the reconfigurability of FPGA, we show, in this paper, how to manipulate the FPGA-based system to provide extra computing power for the parallelized ICA algorithm with limited FPGA resources. The synthesis aiming at the pilchard re-configurable FPGA platform is reported. The pilchard board is embedded with single Xilinx VIRTEX 1000E FPGA and transfers data directly to CPU on the 64-bit memory bus at the maximum frequency of 133MHz. Both the feasibility performance evaluations and experimental results validate the effectiveness and practicality of this synthesis, which can be extended to the spatial-variant jitter restoration for micro-UAV deployment.