Selection of input features across subjects for classifying crewmember workload using artificial neural networks

The issue of crewmember workload is important in complex system operation because operator overload leads to decreased mission effectiveness. Psychophysiological research on mental workload uses measures such as electroencephalogram (EEG), cardiac, eye-blink, and respiration measures to identify mental workload levels. This paper reports a research effort whose primary objective was to determine if one parsimonious set of salient psychophysiological features can be identified to accurately classify mental workload levels across multiple test subjects performing a multiple task battery. To accomplish this objective, a stepwise multivariate discriminant analysis heuristic and artificial neural network feature selection with a signal-to-noise ratio (SNR) are used. In general, EEG power in the 31-40-Hz frequency range and ocular input features appeared highly salient. The second objective was to assess the feasibility of a single model to classify mental workload across different subjects. A classification accuracy of 87% was obtained for seven independent validation subjects using neural network models trained with data from other subjects. This result provides initial evidence for the potential use of generalized classification models in multitask workload assessment.     

Predicting the performance measures of a message-passing multiprocessor architecture using artificial neural networks

In this paper, we develop multi-layer feed-forward artificial neural network (MFANN) models for predicting the performance measures of a message-passing multiprocessor architecture interconnected by the simultaneous optical multiprocessor exchange bus (SOME-Bus), which is a fiber-optic interconnection network. OPNET Modeler is used to simulate the SOME-Bus multiprocessor architecture and to create the training and testing datasets. The performance of the MFANN prediction models is evaluated using standard error of estimate (SEE) and multiple correlation coefficient (R). Also, the results of the MFANN models are compared with the ones obtained by generalized regression neural network (GRNN), support vector regression (SVR), and multiple linear regression (MLR). It is shown that MFANN models perform better (i.e., lower SEE and higher R) than GRNN-based, SVR-based, and MLR-based models for predicting the performance measures of a message-passing multiprocessor architecture.     

Modelling deforestation using GIS and artificial neural networks

This study aims to predict the spatial distribution of tropical deforestation. Landsat images dated 1974, 1986 and 1991 were classified in order to generate digital deforestation maps which locate deforestation and forest persistence areas. The deforestation maps were overlaid with various spatial variables such as the proximity to roads and to settlements, forest fragmentation, elevation, slope and soil type to determine the relationship between deforestation and these explanatory variables. A multi-layer perceptron was trained in order to estimate the propensity to deforestation as a function of the explanatory variables and was used to develop deforestation risk assessment maps. The comparison of risk assessment map and actual deforestation indicates that the model was able to classify correctly 69% of the grid cells, for two categories: forest persistence versus deforestation. Artificial neural networks approach was found to have a great potential to predict land cover changes because it permits to develop complex, non-linear models.     

Contingency evaluation and monitorization using artificial neural networks

In this paper, different neural network-based solutions to the contingency analysis problem are presented. Contingency analysis is examined from two perspectives: as a functional approximation problem obtaining a numerical evaluation and ranking contingencies; and as a graphical monitoring problem, obtaining an easy visualization system of the relative severity of the contingencies. For the functional evaluation problem, we analyze the use of different supervised feed-forward artificial neural networks (multilayer perceptron and radial basis function networks). The proposed systems produce a very accurate evaluation and ranking, and so present a high applicability. For the graphical monitoring problem, unsupervised artificial neural networks such as self-organizing maps by Kohonen have been used. This solution allows both a rapid, easy and simultaneous visualization of the severity level of the complete contingency set. The proposed solutions avoid the main drawbacks of previous neural network approaches to this problem, which are explicitly analyzed here.     

Gradientless shape optimization using artificial neural networks

In this paper a new zero order method of structural shape optimization, in which material shrinks or grows perpendicular to the design boundary, has been proposed in order to satisfy fully stressed design criteria. To avoid mesh distortion that results in undesirable shape, design element concept and for nodal movement and convergence checking, fuzzy set theory have been used. To accelerate the convergence, artificial neural networks are employed. The proposed approach, named as GSN technique, has been incorporated in a FORTRAN software GSOANN. Using this software shape optimization of four structures are carried out. It is demonstrated that proposed technique overcomes most of the shortcomings of mundane zero order methods.     

Predicting grinding burn using artificial neural networks

This paper introduces a method for predicting grinding burn using artificial neural networks (ANN). First, the way to model grinding burn via ANN is presented. Then, as an example, the prediction of grinding burn of ultra-strength steel 300M via ANN is given. Very promising results were obtained.     

Microstrip antenna design using artificial neural networks

Neural‐network computational modules have recently gained recognition as an unconventional and useful tool for RF and microwave modeling and design. Neural networks can be trained to learn the behavior of passive/active components/circuits. This work describes the fundamental concepts in this emerging area aimed at teaching RF/microwave engineers what neural networks are, why they are useful, when they can be used, and how to use them to model microstrip patch antenna. This work studies in‐depth different designs and analysis methods of microstrip patch antenna using artificial neural‐network and different network structure are also described from the RF/microwave designer's perspective. This article also illustrates two examples of microstrip antenna design and validating the utility of ANN in the area of microstrip antenna design. © 2009 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.     

最佳拟合与神经网络相结合实现传感器特性线性化

提出了一种传感器特性线性化的方法.该方法把传感器特性分为线性和非线性段,用一种改进的BP神经网络映射传感器特性非线性段的反函数作为校正环节,用最佳拟合方法得到线性段的直线方程,从而实现传感器特性的线性化.经过仿真试用表明,这种方法可使传感器的非线性误差减小近十倍.最后,给出了一些仿真实验和仿真结果.     

Robust radar target classifier using artificial neural networks

In this paper an artificial neural network (ANN) based radar target classifier is presented, and its performance is compared with that of a conventional minimum distance classifier. Radar returns from realistic aircraft are synthesized using a thin wire time domain electromagnetic code. The time varying backscattered electric field from each target is processed using both a conventional scheme and an ANN-based scheme for classification purposes. It is found that a multilayer feedforward ANN, trained using a backpropagation learning algorithm, provides a higher percentage of successful classification than the conventional scheme. The performance of the ANN is found to be particularly attractive in an environment of low signal-to-noise ratio. The performance of both methods are also compared when a preemphasis filter is used to enhance the contributions from the high frequency poles in the target response.     

A hybrid recommender system using artificial neural networks

In the context of recommendation systems, metadata information from reviews written for businesses has rarely been considered in traditional systems developed using content-based and collaborative filtering approaches. Collaborative filtering and content-based filtering are popular memory-based methods for recommending new products to the users but suffer from some limitations and fail to provide effective recommendations in many situations. In this paper, we present a deep learning neural network framework that utilizes reviews in addition to content-based features to generate model based predictions for the business-user combinations. We show that a set of content and collaborative features allows for the development of a neural network model with the goal of minimizing logloss and rating misclassification error using stochastic gradient descent optimization algorithm. We empirically show that the hybrid approach is a very promising solution when compared to standalone memory-based collaborative filtering method.     

Real-time speech-driven face animation with expressions using neural networks

A real-time speech-driven synthetic talking face provides an effective multimodal communication interface in distributed collaboration environments. Nonverbal gestures such as facial expressions are important to human communication and should be considered by speech-driven face animation systems. In this paper, we present a framework that systematically addresses facial deformation modeling, automatic facial motion analysis, and real-time speech-driven face animation with expression using neural networks. Based on this framework, we learn a quantitative visual representation of the facial deformations, called the motion units (MUs). A facial deformation can be approximated by a linear combination of the MUs weighted by MU parameters (MUPs). We develop an MU-based facial motion tracking algorithm which is used to collect an audio-visual training database. Then, we construct a real-time audio-to-MUP mapping by training a set of neural networks using the collected audio-visual training database. The quantitative evaluation of the mapping shows the effectiveness of the proposed approach. Using the proposed method, we develop the functionality of real-time speech-driven face animation with expressions for the iFACE system. Experimental results show that the synthetic expressive talking face of the iFACE system is comparable with a real face in terms of the effectiveness of their influences on bimodal human emotion perception.     

Real-time torque control using discrete-time recurrent high-order neural networks

This paper presents a discrete-time direct current (DC) motor torque tracking controller, based on a recurrent high-order neural network to identify the plant model. In order to train the neural identifier, the extended Kalman filter (EKF) based training algorithm is used. The neural identifier is in series-parallel configuration that constitutes a well approximation method of the real plant by the neural identifier. Using the neural identifier structure that is in the nonlinear controllable form, the block control (BC) combined with sliding modes (SM) control techniques in discrete-time are applied. The BC technique is used to design a nonlinear sliding manifold such that the resulting sliding mode dynamics are described by a desired linear system. For the SM control technique, the equivalent control law is used in order to the plant output tracks a reference signal. For reducing the effect of unknown terms, it is proposed a specific desired dynamics for the sliding variables. The control problem is solved by the indirect approach, where an appropriate neural network (NN) identification model is selected; the NN parameters (synaptic weights) are adjusted according to a specific adaptive law (EKF), such that the response of the NN identifier approximates the response of the real plant for the same input. Then, based on the designed NN identifier a stabilizing or reference tracking controller is proposed (BC combined with SM). The proposed neural identifier and control applicability are illustrated by torque trajectory tracking for a DC motor with separate winding excitation via real-time implementation.     

Inversion of a velocity model using artificial neural networks

We present a velocity model inversion approach using artificial neural networks (NN). We selected four aftershocks from the 2000 Tottori, Japan, earthquake located around station SMNH01 in order to determine a 1D nearby underground velocity model. An NN was trained independently for each earthquake-station profile. We generated many velocity models and computed their corresponding synthetic waveforms. The waveforms were presented to NN as input. Training consisted in associating each waveform to the corresponding velocity model. Once trained, the actual observed records of the four events were presented to the network to predict their velocity models. In that way, four 1D profiles were obtained individually for each of the events. Each model was tested by computing the synthetic waveforms for other events recorded at SMNH01 and at two other nearby stations: TTR007 and TTR009.     

Prediction of sulphur dioxide concentration using artificial neural networks

A three-layer neural network model with a hidden recurrent layer is used to predict sulphur dioxide concentration and the predicted values are compared with the measured concentrations at three sites in Delhi. The Levenberg–Marquardt algorithm is used to train the network. The neural network is used to simulate the behaviour of the system. A multivariate regression model is also used for comparison with the results obtained by using the neural network model. The study results indicate that the neural network is able to give better predictions with less residual mean square error than those given by multivariate regression models.     

Classification of remotely-sensed image data using artificial neural networks

Abstract

Abstract. Artificial neural networks have been used recently for speech and character recognition. Their application for the classification of remotely-sensed images is reported in this Letter. Remotely sensed image data are usually large in size and spectral overlaps among classes of ground objects are common. This results in low convergence performance of the Back-Propagation Algorithm in a neural network classifier. A Blocked Back-Propagation (BB-P) algorithm was proposed arid described in this Letter. It improved convergence performance and classification accuracy.     

Prediction of urban stormwater quality using artificial neural networks

There are a vast number of complex, interrelated processes influencing urban stormwater quality. However, the lack of measured fundamental variables prevents the construction of process-based models. Furthermore, hybrid models such as the buildup-washoff models are generally crude simplifications of reality. This has created the need for statistical models, capable of making use of the readily accessible data. In this paper, artificial neural networks (ANN) were used to predict stormwater quality at urbanized catchments located throughout the United States. Five constituents were analysed: chemical oxygen demand (COD), lead (Pb), suspended solids (SS), total Kjeldhal nitrogen (TKN) and total phosphorus (TP). Multiple linear regression equations were initially constructed upon logarithmically transformed data. Input variables were primarily selected using a stepwise regression approach, combined with process knowledge. Variables found significant in the regression models were then used to construct ANN models. Other important network parameters such as learning rate, momentum and the number of hidden nodes were optimized using a trial and error approach. The final ANN models were then compared with the multiple linear regression models. In summary, ANN models were generally less accurate than the regression models and more time consuming to construct. This infers that ANN models are not more applicable than regression models when predicting urban stormwater quality.     

Evaluating of traumatic brain injuries using artificial neural networks

The purpose of this study is to develop a diagnostic system to detect the severity of traumatic brain injuries using artificial neural networks. Three layered back propagation neural network with an input layer of 10 nodes whose output providing the inputs to a hidden layer was used. Thirty-two patients with traumatic brain injuries in different age and gender were taken in the study. Electroencephalography, Trauma and Glasgow coma scores were used for evaluating the data. The results obtained from the system were compared with the findings of neurologists. We found a significant relationship between the findings of neurologists and systems output for normal, mild, moderate and severe electroencephalography tracing data. Getting this system in routine use will lead to make a rapid decision for the degree of trauma with electroencephalography and revised trauma score.