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Load prediction is a necessity in a deregulated electrical energy sector. It is important financially and technically. In order to cope with nonlinear and non stationary character of a load signal, an efficient adaptive predictor should be employed. Also, power utilities manage load information as a complex-valued signal. To this cause, performance of a class of complex-valued gradient descent (GD)...
Energy consumption predictions are essential and are required in the studies of capacity expansion, energy supply strategy, capital investment, revenue analysis and market research management. In the recent years artificial neural networks (ANN) have attracted much attention and many interesting ANN applications have been reported in power system areas, due to their computational speed, their ability...
Advancements in digital electronics and signal processing algorithms for various purposes generated the possibility and need for designing artificial neural networks in hardware. The selected platform, FPGA, enables fulfillment of their demands and provides comfortable work and test environment. This paper presents development cycle and specifics for implementation of high speed Hamming artificial...
Influence of the interaction time-delay on the noise induced system size resonance in a system of all-to-all electrically coupled FitzHugh-Nagumo excitable neurons is studied. It is observed that small time-lags decrease and that large time-lags increase the coherence of spiking. Bifurcations of the system's stationary state are used to explain the observed non-monotonic dependence of coherence on...
Nonnegative tensor factorization (NTF) is a recent multiway (multilinear) extension of negative matrix factorization (NMF), where nonnegativity constraints are mainly imposed on CANDECOMP/PARAFAC model and recently, also, on Tucker model. Nonnegative tensor factorization algorithms have many potential applications, including multiway clustering, multi-sensory or multidimensional data analysis and...
This paper addresses the problem of vowels recognition in patients after total laryngectomy using combined visual and acoustic features. The linear prediction coefficients were estimated from speech signal using weighted recursive least squares algorithm. Ten cross-sectional areas of vocal tract model were calculated. Face expression parameters related to the spoken vowel were extracted from video...
In this paper we present a method for optimization of spatial selectivity of multi-pad electrode during transcutaneous Functional Electrical Stimulation (FES). The presented method is based on measurement of individual muscle twitches using Micro-Electro-Mechanical Systems (MEMS) accelerometers positioned on hand, while stimulating with low frequency electrical stimulation via pads within multi-pad...
This paper focuses on grid performance optimization in large scale workflow applications with an intelligent workflow scheduling mechanism. Utility Management Systems (UMS) are managing very large numbers of workflows with very high resource requirements. This paper proposes a UMS scheduling architecture which dynamically executes a scheduling algorithm using near real-time feedback about the current...
This paper discusses the problem of object area detection of video frames. The goal is to design a pixel accurate detector for grass, which could be used for object adaptive video enhancement. A boosting neural network is used for creating such a detector. The resulted detector uses both textural features and color features of the frames.
Due to high penetration of wind generation in modern power systems, the influence of wind power production over the efficient operation of the power system is increasingly complex. Hence, an increasing interest is shown by different actors in the wind energy market to develop and enhance existent forecasting methods for power generated by wind farms. This paper presents the experience with wind power...
This paper proposes novel approach in coding single phonemes based on mel-frequency cepstral coefficients (MFCC) in order to simplify the neural network used to recognize those phonemes. The efficiency and effectiveness of proposed algorithm are demonstrated for both male and female speakers.
We present the method for classifying kinematical data required for control of a rehabilitation robot for upper extremities. The classification to two cases (success, no-success) was analyzed by two methods: Bayes estimation and artificial neural network (ANN). The results are presented for an example being envisioned for rehabilitation: playing the Wii bowling with the specially constructed pantograph...
The following topics are dealt with: neural networks for rehabilitation of sensory-motor systems; wind power forecast based on neural networks; neural adaptive FIR filters; and neural networks based signal processing.
This paper presents machine learning (ML) techniques for development of a control scheme to be used in functional electrical stimulation (FES) of hemiplegic walking. The goal is to make an electrical stimulation pattern by mapping the sensors signals acquired during walking (input) to activities of muscles (output) acting around knee and ankle joints. Two machine learning techniques with ability of...
Alternative and more efficient approaches for microwave applicators modeling, in respect to conventional numerical techniques, are presented in the paper. These approaches are based on artificial neural networks incorporating previously acquired partially knowledge about problem domain. Neural models, created using the suggested approaches, provide the similarly accuracy as numerical methods but performing...
Summary form only given. The contemporary robotics technology is broadening its applications from factory to more general-purpose applications in domestic and public use, e.g., partner to the elderly, rehabilitations, search and rescue, etc. If robotics technology is to be successful in such complex, unstructured, dynamic environments with high level of uncertainties, it will need to meet new levels...
The aim of this paper is to discuss and compare two neural approaches applied in small-signal modelling of microwave FETs. One of them is completely based on artificial neural networks, while the other is a hybrid model putting together artificial neural networks and an equivalent circuit representation of a microwave transistor. Devices with different gate width are considered in this paper. Different...
The aim of this paper is to show that the data stored in companies data warehouses can be used in order to improve business. By application of data mining method, neural clustering, we investigate age structure of employees and its influence on business companies. This would enable improvement in employment policy for small and medium-sized companies. The criteria while employing new people in retail...
In this paper, the author's previous work is extended and a new neural network is utilized to solve the stability problem of multidimensional systems. In the original authors work the problem is transformed into an optimization problem. Using the DeCarlo-Strintzis Theorem one has to check if |B(Z1,..., 1, Zm)| ≠ 0 for |Z1| = ... = |Zm| = 1 or equivalently if the min |B(Z1, ..., 1, Zm)| is 0 or not,...
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