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Human geography is a concept used to indicate the augmentation of standard geographic layers of information about an area with behavioral variations of the people in the area. In particular, the actions of people can be attributed to both local and regional variations in physical (i.e., terrain) and human (e.g., income, political, cultural) variables. In this paper, we study the utility of a human...
The snag in a DTC scheme based induction motors is the presence of high content of torque ripples in the output torque. This has been reduced by modifying the three-level torque controller to five-level torque controller. Moreover, the controllability of the torque in motor with no overshoot and minimal ripples, good transient and steady state responses forms the basis of performance analysis. This...
This article presents a methodology for detection of high impedance faults (HIF). HIF occurs when e.g. a cable makes contact with objects of high electric resistance, resulting in a nonsignificant increase in current. Such faults cannot be detected by traditional protection devices that operate due to overcurrent. The developed methodology is based on making use of variables from a power quality meter...
In this paper, space vector pulse width modulation (SVPWM) scheme for voltage fed inverter (VFI) using conventional method and artificial neural network (ANN) based approach are presented separately. In the conventional method, the difficulty of explicitly expressing cross-over and holding-angle as a function of modulation factor in overmodulation mode-I and mode-II respectively are overcome by introducing...
The popular i-vector approach to speaker recognition represents a speech segment as an i-vector in a low-dimensional space. It is well known that i-vectors involve both speaker and session variances, and therefore additional discriminative approaches are required to extract speaker information from the ‘total variance’ space. Among various methods, the probabilistic linear discriminant analysis (PLDA)...
Character Recognition has become an interesting and a challenge topic research in the field of pattern recognition in recent decade. It has numerous applications including bank cheques, address sorting and conversion of handwritten or printed character into machine-readable form. Artificial neural network including self-organization map and multilayer perceptron network with the learning ability could...
Adaptive networks (ANs) rely on local adaptive filters (AFs) and a cooperation protocol to achieve a common goal, e.g., estimating a set of parameters. This protocol fuses the information from the rest of the network based on local combiners whose design impacts directly the network performance. Indeed, although diffusion schemes improve network performance on average, heterogeneity in signal statistics...
The brain-inspired neural networks have demonstrated great potential in big data analysis. The spiking neural network (SNN), which encodes the real world data into spike trains, promises great performance in computational ability and energy efficiency. Moreover, it is much more biologically plausible than the traditional artificial neural network (ANN), which keeps the input data in its original form...
Classification and decision systems in data analysis are mostly based on accuracy optimization. This criterion is only a conditional informative value if the data are imbalanced or false positive/negative decisions cause different costs. Therefore more sophisticated statistical quality measures are favored in medicine, like precision, recall etc‥ Otherwise, most classification approaches in machine...
Applying weight regularisation to gradient-descent based neural network training methods such as backpropagation was shown to improve the generalisation performance of a neural network. However, the existing applications of weight regularisation to particle swarm optimisation are very limited, despite being promising. This paper proposes adding a regularisation penalty term to the objective function...
The realization of robotic systems that understands human intentions and produces accordingly complex behaviors is needed particularly for disabled persons, and would consequently benefit the aged. For this purpose, a control technique that recognizes human intentions from neural responses called brain machine interface (BMI) have been suggested. The unique ability to communicate with machines by...
Deep architectures have been used in transfer learning applications, with the aim of improving the performance of networks designed for a given problem by reusing knowledge from another problem. In this work we addressed the transfer of knowledge between deep networks used as classifiers of digit and shape images, considering cases where only the set of class labels, or only the data distribution,...
Credit rating prediction using clustering algorithms has become more and more important in the financial literature. Expanding the ideas of [4] and [5], we propose an approach to generate models for automated credit rating prediction based on support vector domain description (SVDD) and linear regression (LR). The models include the prediction for sovereign and corporate bonds. Another advantage is,...
Active attacks are studied on noise-free graphical multicast networks. A malicious adversary may enter the network and arbitrarily corrupt transmissions. A very general model is adopted for the scope of attack: a collection of sets of edges is specified, and the adversary may control any one set of edges in this collection. The adversary is assumed to be omniscient but causal, such that the adversary...
Voting Advice Applications (VAAs) are online tools that match the policy preferences of voters' with the policy positions of political parties or candidates. A recent, innovative extension of VAAs has been to draw on the field of computer science to introduce a social vote recommendation borrowing the basic principles of collaborative filtering. The latter takes advantage of the community of VAA users...
This paper describes the methodology for implementation of artificial neural networks with adaptable parameters (weights, connections, number of neurons) on fixed-point embedded systems. Components of neuron unit and interconnecting matrix are discussed. Particular example of implementation on PIC18F46K80 is given. Results are discussed in appropriate part.
Self-organizing map (SOM), an unsupervised learning way of artificial neural network, plays a very important role for classification and clustering of inputs. The property of SOM, also called topology-preserving maps or self-organizing feature map (SOFM), is observed in human brain which is not found in other artificial neural networks. Aircrafts' crossing points between two airports may generate...
Solving the non-linear distortion problems in wireless communications is often based on developing the behavioral models of non-linear components. In this paper, a non-linear Volterra model up to third order is developed by using an artificial neural network (ANN) approach. The Volterra kernels are derived from the parameters of a feed-forward time delay neural network with a suitable activation function...
Here we have discussed how the training data set should be selected for the Approximate Internal Model-based Neural Control (AIMNC) applied to the typical industrial processes. In the considered control strategy only one neural network (NN), Multi Layer NN (MLNN), which is the neural model of the plant, should be trained off-line. An inverse neural controller can be directly obtained from the neural...
Measurement of cognitive load using brain signalsis an important area of research in human behavior and psychology. Recently, there have been attempts to use low cost, commercially available Electroencephalogram (EEG) devices for the analysis of the cognitive load. Due to the reduced number of leads, these low resolution devices pose major challenges in signal processing as well as in feature extraction...
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