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This paper shows an inferential sensor that has been developed to be used in the olive oil industry. This sensor has been designed to measure two variables that appear in the elaboration of olive oil in a mill which are very difficult to be measured on line by a physical sensor. The knowledge of these variables on line is crucial for the optimal operation of the process, since they provide the state...
We describe optimal cue mapping (OCM), a potentially eal-time binaural signal processing method for segregating sound source in the presence of multiple interfering 3D ound sources. Spatial cues are extracted from a multisource inaural mixture and used to train artificial neural etworks (ANNs) to estimate the spectral energy fraction of wanted speech source in the mixture. Once trained, the NN outputs...
This study aims to improve the energy performance of residential buildings. heating load (HL) and cooling load (CL) are considered as a measure of heating ventilation and air conditioning (HVAC) system in this process. In order to achive an effective estimation, hybrid machine learning algorithms including, artificial bee colony-based k-nearest neighbor (abc-knn), genetic algorithm-based knn (ga-knn),...
This paper reports on the development of a novel neural network (NN) based airspeed estimator, and focuses on system design. In particular, a novelty detector, capable of preventing the system from producing an erroneous output during conditions in which it is known that a NN model will be extrapolating, is included within the design. A simulation model of a single main rotor helicopter was used to...
Simultaneous perturbation stochastic approximation (SPSA) is an optimization method which requires only a few objective function evaluations to obtain gradient information. In this paper, a first-order SPSA algorithm is described, which makes use of several numerical artifices, including adaptive gain sequences, gradient smoothing and a step rejection procedure, to enhance convergence and stability...
Recently, various techniques using cyclic redundancy check (CRC) codes for error correction have been proposed. In previous techniques, a small number of unreliable bits in a packet were toggled in order to change negative acknowledgement (NAK) into acknowledgement (ACK). The difficulty of using these techniques is that the worst case complexity is still high because the number of possible error patterns...
Impedance cardiography is a noninvasive technique for estimation of stroke volume (SV), based on monitoring the variation in the thoracic impedance during the cardiac cycle. The current SV calculation methods use parameters obtained by ensemble averaging of the waveform along with equations based on simplified models of the thoracic impedance and aortic blood flow profile. They often result in inconsistent...
This study was aimed at estimating subjects' 3-back working memory task error rate using electroencephalogram (EEG) signals. Firstly, spatio-temporal band power features were selected based on statistical significance of across-subject correlation with the task error rate. Method-wise, ensemble network model was adopted where multiple artificial neural networks were trained independently and produced...
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...
As known, the main cause of the degradation in indirect rotor field oriented induction motor (IM) control (IRFOC) is the time-varying machine parameters, especially the rotor-time constant (Tr) and stator resistance (Rs), more pertinently, in cases of proportional-integral control with speed observation. In this work, a new exponential reaching law (ERL) based sliding mode control (SMC) is introduced...
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...
Prediction of a signal from recorded time series is always a challenging task. In this paper, the R-R intervals behaviour is estimated using linear and non-linear prediction techniques. The value of each sample point is predicted using a certain number of previous samples and the prediction error is computed. The wavelet transform provides multi-resolution analysis and allows accurate time-frequency...
This paper proposes a novel approach for automatic estimation of four important traits of speakers, namely age, height, weight and smoking habit, from speech signals. In this method, each utterance is modeled using the i-vector framework which is based on the factor analysis on Gaussian Mixture Model (GMM) mean supervectors, and the Non-negative Factor Analysis (NFA) framework which is based on a...
In this paper, we propose a new framework to optimize the utilization of the image quality estimation without reference. This framework is based on two principal steps. Features are first extracted from the image to characterize each considered degradation type. From this modeling step, a No Reference Image Quality Metric (NR-IQM) per degradation type is obtained. In the second stage, outputs of the...
State of Charge (SoC) estimation is one of the most important parts of Battery Management System (BMS). Inaccurate estimation of SoC may cause overcharge or overdischarge which could lead permanent damage to battery cells. Neural Network (NN) models can yield quite accurate SoC estimation. However, the computation effort is also quite huge and it takes long time training. To improve the performance...
This paper presents an energy expenditure estimation method that uses a wearable accelerometer sensor, but does not rely on a priori knowledge about the location of the sensor. The sensor can be worn at any of three pre-defined locations, namely, right wrist, right thigh and right ankle. It is shown that once the system is trained, the proposed mechanism can perform sensor location detection in run-time...
One way to predict the behavior of smart home lighting is by using machine learning. Currently many methods of supervised learning that used for this problem, one of them is decision tree method. Very Fast Decision Tree (VFDT) as one of the decision tree method that has advantages in online machine learning that may useful in smart home, but there are still some room of improvisation that can improve...
By the global warming and decreasing fossil fuel, alternative energy sources are looked for future and protecting environment. In the recent years, many studies are made about wind power whereby deteriorating environment will be regarded.
A severe problem that impacts the software project is inaccurate estimation of the effort. Estimation of the software development effort remains an intricate problem. The complexity of the software and its scope are increasing alarmingly which attracts many researchers. Past the decades numerous techniques have been introduced and implemented. Many of them have given good results with acceptable error...
Offline handwritten text recognition requires several preprocessing stages. Many different preprocessing techniques have been proposed in the literature based either on geometrical heuristics or on statistical models. Unfortunately, these approaches usually fail when dealing with short sentences or isolated words. One statistical technique for text line preprocessing is based on the detection and...
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