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In this chapter, we present the details of Kramers–Moyal (KM) expansion and prove the Pawula theorem. The Fokker–Planck equation is then introduced and its short-term propagator is presented. Finally, we derive the master equation from the Chapman–Kolmogorov equation. © 2019, Springer Nature Switzerland AG  

In this chapter, we present the details of Kramers–Moyal (KM) expansion and prove the Pawula theorem. The Fokker–Planck equation is then introduced and its short-term propagator is presented. Finally, we derive the master equation from the Chapman–Kolmogorov equation. © 2019, Springer Nature Switzerland AG  

This paper presents a new model to short-term prediction of air pollution using a new structure is based on the intelligent neural networks. A new structure known as Time Delay Cerebellar Model Arithmetic Computer (TD-CMAC), an extension to the CMAC, it requires fewer memory sizes. The new model is demonstrated and validated with three primary air pollutants known as carbon monoxide (CO), sulfur dioxide (SO2), and nitrogen dioxide (NO 2). The simulation results for the half an hour ahead-prediction of the air pollutant data set show that the suggested new model is suitable for our purpose  

One of the requirements for the operation and planning activities of an electrical utility is the prediction of load for the next hour to several days out, known as short term load forecasting (STLF). This paper presents the STLF of the Iranian national power system (INPS) using artificial neural networks (ANN) and fuzzy expert systems (FES). The ANN is trained with the load patterns corresponding to the forecasting hours and the forecasted load is obtained. The FES modifies the initial forecasted load for the special holidays and also in the case sudden changes in temperature. A data analyser and a temperature forecaster are also included in the NRI STLF (NSTLF) package. The program has... 

Background: Many studies have been conducted on the changes in the balance capabilities of stroke patients. However, results regarding the effects of dual-task activities on postural control in these patients have been variable. Objective: To evaluate the effects of a short-term memory task on the sway characteristics of stroke patients. Method: Center of pressure (COP) fluctuations were measured in three levels of postural difficulty (rigid surface with closed and open eyes and foamsurface with closed eyes), aswell as two levels of cognitive difficulty (easy and difficult). COP parameters included mean velocity, standard deviation of velocity in both medial-lateral (M.L) and... 

In Iranian Sign Language (ISL), alongside the movement of fingers/arms, the dynamic movement of lips is also essential to perform/recognize a sign completely and correctly. In a follow up of our previous studies in empowering the RASA social robot to interact with individuals with hearing problems via sign language, we have proposed two automated lip-reading systems based on DNN architectures, a CNN-LSTM and a 3D-CNN, on the robotic system to recognize OuluVS2 database words. In the first network, CNN was used to extract static features, and LSTM was used to model temporal dynamics. In the second one, a 3D-CNN network was used to extract appropriate visual and temporal features from the... 

A controversial issue in artificial intelligence is human emotion recognition. This paper presents a fuzzy parallel cascades (FPC) model for predicting the continuous subjective emotional appraisal of music by time-varying spectral content of electroencephalogram (EEG) signals. The EEG, along with an emotional appraisal of 15 subjects, was recorded during listening to seven musical excerpts. The emotional appraisement was recorded along the valence and arousal emotional axes as a continuous signal. The FPC model was composed of parallel cascades with each cascade containing a fuzzy logic-based system. The FPC model performance was evaluated using linear regression (LR), support vector... 

Cascaded Short Term Hydro-Thermal Scheduling problem (CSTHTS) is a single objective, non-linear multi-modal or convex (depending upon the cost function of thermal generation) type of Short Term Hydro-Thermal Scheduling (STHTS), having complex hydel constraints. It has been solved by many metaheuristic optimization algorithms, as found in the literature. Recently, the authors have published the best-achieved results of the CSTHTS problem having quadratic fuel cost function of thermal generation using an improved variant of the Accelerated PSO (APSO) algorithm, as compared to the other previously implemented algorithms. This article discusses and presents further improvement in the results... 

Despite complex fluctuations, missing data, and maintenance costs of detectors, traffic volume forecasting at intersections is still a challenge. Moreover, most existing forecasting methods consider an isolated intersection instead of multiple adjacent ones. By accurately forecasting the volume of short-term traffic, a low-cost method can be provided to solve the problems of congestion, delay, and breakdown of detectors in the road transport system. This paper outlines a novel hybrid method based on deep learning to estimate short-term traffic volume at three adjacent intersections. The gated recurrent unit (GRU) and long short-term memory (LSTM) bilayer network with wavelet transform (WL)... 

Real-time electricity prices along with demand-side potentials can provide distribution companies (DisCos) with considerable financial and technical benefits compared to the conventional flat prices. This paper incorporates demand response in DisCos' short-term decision model in a real-time pricing (RTP) environment wherein consumers are charged based on hourly varying prices. Besides the hourly RTP sale prices, the established model deals with other DisCo's short-term activities including hourly purchases from the grid, commitment of distributed generation (DG) units, dispatch of shunt compensators, and invocation of load curtailments (LCs). The stochastic nature of wholesale market prices... 

This chapter describes and discusses Langevin dynamics and the Fokker–Planck equation in higher dimension, and discrete-time evolution and discrete-time approximation of stochastic evolution equations. We close the chapter with calculations of short-time propagators of d-dimensional Fokker–Planck equation. © 2019, Springer Nature Switzerland AG  

This chapter describes and discusses Langevin dynamics and the Fokker–Planck equation in higher dimension, and discrete-time evolution and discrete-time approximation of stochastic evolution equations. We close the chapter with calculations of short-time propagators of d-dimensional Fokker–Planck equation. © 2019, Springer Nature Switzerland AG  

Short term hydrothermal scheduling (STHTS) is a non-linear, multi-modal and very complex constrained optimization problem which has been solved using several conventional and modern metaheuristic optimization algorithms. A number of research articles have been published addressing STHTS using different techniques. This article presents a comprehensive review of research published for solving the STHTS problem in the last four decades. © 2013 IEEE  

One of the most important requirements for the operation and planning activities of an electrical utility is the prediction of load for the next hour to several days out, known as short term load forecasting (STLF). This paper presents STLF of Iran national power system (INPS) using artificial neural network (ANN). The developed program is based on a four-layered perceptron ANN building block. The optimum inputs were selected for the ANN considering historical data of the INPS. Instead of conventional back propagation (BP) methods, Levenberg-Marquardt BP (LMBP) method has been used for the ANN training to increase the speed of convergence. A data analyzer and a temperature forecaster are... 

The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques to solve different engineering problems. Besides, Short Term Electrical Load Forecasting (STLF) is one of the important concerns of power systems and accurate load forecasting is vital for managing supply and demand of electricity. This study estimates short term electricity loads of Iran by means of Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN) and Genetic Algorithm (GA) which are the most successful AI techniques in this field. In order to improve forecasting accuracy, all AI techniques are equipped with preprocessing concept, and effects... 

The performance of a vertical ground source heat pump system (GSHPS) largely depends on the fluid temperature leaving the borehole heat exchanger (BHE) that may be affected by the short-term behavior of the BHE. Although considerable research has been carried out to analyze the short-term transient response of the BHEs, few studies have investigated its impact on dynamic simulation of GSHPS. Therefore, this paper presents a numerical approach based on a transient BHE model to evaluate the performance of a residential GSHPS over short and long timescales. The numerical results are compared with the results of EnergyPlus software. It is shown that the proposed model can appropriately predict... 

Electricity demand forecasting has been a real challenge for power system scheduling in different levels of energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for short-term load forecasting, although scant evidence is available about the feasibility of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationales behind short-term load forecasting methodologies based on works of previous researchers in the energy field. Fundamental benefits and drawbacks of these methods are discussed to represent the efficiency of each approach in various...