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According to privatization and deregulation of power system, accurate electric load forecasting has come into prominence recently. The new energy market and the smart grid paradigm ask for both better demand side management policies and for more reliable forecasts from single end-users, up to system scale. However, it is complex to predict the electric demand owing to the influencing factors such...
A fast, yet accurate nanoscale IC energy estimation is a design-time desideratum for area-delay-power-reliability optimized circuits and architectures. This paper introduces an IC energy estimation approach, which instead of sequentially propagating workload vectors throughout the circuit, relies on an one time propagation of the workload statistics. To this end, the basic gates need be SPICE pre-characterized...
The relevance of this study is stipulated by the necessity of designing algorithms allowing to improve the efficiency of human face detection and emotions recognition on images with complex background. Purpose: Development of algorithms and software system allowing to improve the efficiency of human face detection and in addition facial expression classification on images with complex background,...
Diabetes is one of the most common metabolic diseases and the statistics show that one in eleven adults has diabetes, but one in two adults with diabetes is undiagnosed, and in 2040 one in 10 adults will have diabetes. In this paper is proposed a hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) model for classifying patients with diabetes based on data sets with diabetic patients (Pima Indians...
One of the most challenging tasks for energy domain stakeholders is to have a better preview of the electricity consumption. Having a more trustable expectation of electricity consumption can help minimizing the cost of electricity and also enable a better control on the electricity tariff. This paper presents a study using a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative...
A nonlinear adaptive controller for an unmanned aerial vehicle (UAV) has been developed using Echo State Network (ESN), which is a form of three-layered recurrent neural network (RNN). Online learning is used to train the ESN in real-time starting from randomized weights. The ESN is integrated into ArduPilot, an open source autopilot, for complex flight simulations. Software-in-the-loop and hardware-...
In this paper the practical issues of automotive surface identification system development are considering. The novelty of this work is the combining of different training algorithms, neural network structures and methods to increase the classification accuracy and avoid overfitting of real-world data. The obtained results thereby demonstrate that the use of proposed system architecture and statistical...
This paper presents a unique and efficient artificial neural network (ANN) based fault detection, classification and location on part of the Nigerian 132kV transmission line. The objective is to evaluate the performance of ANN based relays connected at both ends of the lines using feed-forward non-linear supervised back propagation algorithm with Levenberg-marguardt network topology. Using the PSCAD/EMTP...
This paper uses time-frequency methods and neural networks for the analysis and forecasting of indoor temperature time series. In a first phase, the time series are processed by means of the Fourier transform and the empirical mode decomposition methods to unveil temporal patterns embedded in the data. In a second phase, neural networks are adopted for forecasting future values. The results obtained...
This work presents the development and evaluation of a new scheme based on Artificial Neural Network (ANN) for fault detection and fault location in distribution systems with distributed generator. Two different ANNs are used, where the first one is able to detect which part of the distribution system the fault occurred and the second one is able to precisely locate the fault along the faulty line...
With the increase in the availability of information regarding energy use, there is an increase of forecasting software on the energy market, that can forecast on the short, medium and long term. In the paper, there is presented a software solution for load forecasting using Artificial Neural Networks (ANN) method. In the case study, we have written an application for a consumer which is engaged in...
The characterisation of topographical features of fitness landscapes can provide significant insight into the nature of underlying optimisation problems and the behaviour of metaheuristic search algorithms. Neutrality as a landscape feature is often overlooked in continuous problems, but researchers have theorised that the presence of neutral regions on neural network error surfaces may be an impediment...
We propose distributed deep neural networks (DDNNs) over distributed computing hierarchies, consisting of the cloud, the edge (fog) and end devices. While being able to accommodate inference of a deep neural network (DNN) in the cloud, a DDNN also allows fast and localized inference using shallow portions of the neural network at the edge and end devices. When supported by a scalable distributed computing...
Chronic kidney failure (chronic kidney disease ‘CKD’) is a serious disease that related to the gradual loss of kidney function. It is considered one of the health threats in the developing and undeveloped countries At early stages, few symptoms can be detected, where the CKD may not become obvious until significant kidney function impaired occur. CKD treatment focuses on reducing the kidney damage...
The paper aims at synthesis of an adaptive controller of the distillate output flow rate of a binary distillation column. The disturbance of the process is the change of concentration of the inlet compound. The Adaptive Critic Design (ACD) approach was applied to predict on time the future effect of disturbance and to adapt the distillate output flow rate in order to prevent deviations from the desired...
In this paper, the single-channel EEG based classification systems using simple extracted features are investigated. Each classification system contains the following stages: data acquisition, signal decomposition, feature extraction, and classification. In addition to using the filter bank and empirical mode decomposition (EMD) methods for signal decomposition, a sparse discrete wavelet packet transform...
A plethora of disorders are found in human oral mucosa. A variety and huge number of lesions and diseases in human oral mucosa have been clinically identified and classified. Most lesions have the possibility to develop into oral cancer. The initial diagnosis of oral cancer is to inspect the ocular regions carefully and register the oral cavity of the patient as true-color digital images. The decision...
Corneal diseases are increasing year by year in the world, because of the lack of cornea donation. Thus the management of artificial corneal transplantation becomes much more necessary. As an important stage of medical device research projects, the animal experiments of artificial cornea should be completed before the clinical trials. In this paper, the corneas is prepared from pig eyes. Firstly,...
ADP is an effective optimal method. However, the optimality depends on its network structure and training algorithm. This paper adopts RBF neural network to realize its critic and action networks after a detailed analysis on ADP. The LSM method is introduced as training algorithm, and a novel basis function is defined, which achieves global optimization and online control. The validity is verified...
In the practice applications of defect detecting, large amounts of data need to be analyzed. In this paper, a new analysis method is developed based on adaboost algorithm. By using neural networks with a fixed structure, a series of models are built which may be not accurate. Error rates of the models are computed to gain and adjust the weights of every model. A higher accurate model is built by the...
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