Anytime Fourier Analysis

Anytime signal processing algorithms are to improve the overall performance of larger scale embedded digital signal processing (DSP) systems. In such systems there are cases where due to abrupt changes within the environment and/or the processing system temporal shortage of computational power and/or loss of some data may occur. It is an obvious requirement that even in such situations the actual processing should be continued to insure appropriate performance. This means that signal processing of somewhat simpler complexity should provide outputs of acceptable quality to continue the operation of the complete embedded system. The accuracy of the processing will be temporarily lower but possibly still enough to produce data for qualitative evaluations and supporting decisions. In this paper a new anytime Fourier transformation algorithm is introduced. The presented method reduces the delay problem caused by the block-oriented fast algorithms and at the same time keeps the computational complexity on relatively low level. It also makes possible the availability of partial results or estimates in case of abrupt reaction need, long or possibly infinite input data sequences

intelligent neural network design for nonlinear control using simultaneous perturbation stochastic approximation spsa optimization

Simultaneous Perturbation Stochastic Approximation (SPSA) Optimization Adrienn Dineva*, Annamaria R. Varkonyi-Koczy**, Jozsef K. Tar*** and Vincenzo Piuri**** *Doctoral School of Applied Informatics and Applied Mathematics, Obuda University, Budapest, Hungary *Doctoral School of Computer Science, Universita' degli Studi di Milano, Crema, Italy **Institute of Mechatronics & Vehicle Engineering, Obuda University, Budapest Hungary ** Department of Mathematics and Informatics, J. Selye University, Komarno, Slovakia ***Institute of Applied Mathematics, Obuda University, Budapest, Hungary **** Department of Computer Science, Universita' degli Studi di Milano, Crema, Italy E-mail: * [email protected], ** [email protected], ***[email protected], ****[email protected]

Modeling pan evaporation using Gaussian Process Regression, K-Nearest Neighbors, Random Forest, and Support Vector Machines: Comparative analysis

Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters

The cooling effect of large-scale urban parks on surrounding area thermal comfort

This empirical study investigates large urban park cooling effects on the thermal comfort of occupants in the vicinity of the main central park, located in Madrid, Spain. Data were gathered during hot summer days, using mobile observations and a questionnaire. The results showed that the cooling effect of this urban park of 125 ha area at a distance of 150 m could reduce air temperatures by an average of 0.63 °C and 1.28 °C for distances of 380 m and 665 meters from the park. Moreover, the degree of the physiological equivalent temperature (PET) index at a distance of 150 meters from the park is on average 2 °C PET and 2.3 °C PET less compared to distances of 380 m and 665 m, respectively. Considering the distance from the park, the correlation between occupant perceived thermal comfort (PTC) and PET is inverse. That is, augmenting the distance from the park increases PET, while the extent of PTC reduces accordingly. The correlation between these two factors at the nearest and furthest distances from the park is meaningful (p-value < 0.05). The results also showed that large-scale urban parks generally play a significant part in creating a cognitive state of high-perceived thermal comfort spaces for residents

Developing an ANFIS-PSO model to predict mercury emissions in combustion flue gases

Accurate prediction of mercury content emitted from fossil-fueled power stations is of the utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model included coal characteristics and the operational parameters of the boilers. The dataset was collected from 82 sample points in power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of the ANFIS-PSO, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between the acquired data and predicted values were between −0.25% and 0.1%, which confirm the accuracy of the model to deal non-linearity and represent the dependency of flue gas mercury content into the specifications of coal and the boiler type