Modification of Box-Jenkins methodology by injecting genetic algorithm technique

The Box-Jenkins(BJ) methodology has four stages in modeling forecast time series data. The stages are model identification, model estimation, model validation and model forecast. The difficulties in modeling BJ is determining the right order in model identification and identifying the right parameter in model estimation. This study, genetic algorithm (GA) is proposed to solve the problem of model identification and model estimation. International tourist arrival to Malaysia is used as a case study to illustrate the effectiveness of this proposed model. The forecast result generated from this proposed model outperform single BJ mode

Employment of an auto-regressive model for knock detection supported by 1D and 3D analyses

In this work, experimental data, carried out on a twin-cylinder turbocharged engine at full load operations and referred to a spark advance of borderline knock, are used to characterize the effects of cyclic dispersion on knock phenomena. 200 consecutive incylinder pressure signals are processed through a refined Auto-Regressive Moving Average (ARMA) mathematical technique, adopted to define the percentage of knocking cycles, through a prefixed threshold level. The heuristic method used for the threshold selection is then verified by 1D and 3D analyses. In particular, a 1D model, properly accounting for cycle-by-cycle variations, and coupled to a reduced kinetic sub-model, is used to reproduce the measured cycles, in terms of statistical distribution of a theoretical knock index. In addition, few individual cycles, representative of the whole dataset, are selected in a single operating condition in order to perform a more detailed knock analysis by means of a 3D CFD approach, coupled to a tabulated chemistry technique for auto-ignition modeling. Outcomes of 1D and 3D models are compared to the ARMA results and a substantial coherence of the numerical and experimental results is demonstrated. The integrated 1D and 3D analyses can hence help in supporting the choice of the experimental threshold level for knock identification, following a more standardized theoretical approach

diagnostic process by using vibrational sensors for monitoring cavitation phenomena in a getoror pump used for automotive applications

Abstract A full experimental investigation on a Gerotor pump used for the lubrication of engines is described in this paper. These pumps, as well known, are widely used on engines for all hydraulic circuits and, for this reason, often they work in some conditions (such as at high speeds and pressure value) which are very challenging. In this paper one of the most unwanted phenomena that often occurs during the pump operation has been investigated: the cavitation. The cavitation can be triggered by many multiple factors such as the sloshing in the tank (translational and rotational motions), high percentage of gas dissolved in the fluid and pressure too low at the pump suction port. Therefore, the characterization of a Gerotor pump in cavitation condition is really interesting. In order to replay the cavitating conditions a pump has been installed on a dedicated test bench of the Department of Industrial Engineering of the university of Naples "Federico II". The pump has been forced to cavitate by placing calibrated orifices on the suction side of the pump. Many decreasing diameters have been located in an aluminum connection block, to measure all the working parameters like the flow-rate, pressure (at the suction and delivery ports), pump speeds and pressure ripple. Cavitating and no-cavitating conditions have been investigated by using an accelerometer sensor in proximity of the pump suction chamber with the aim of monitoring the phenomena in terms of vibration amplitude. As afore mentioned, the pump under investigation has been studied in all operative conditions with and without cavitation phenomena by using a non-intrusive sensor like accelerometer in order to monitoring if cavitation is present. More precisely, the accelerometer sensor has been located close to the pump suction chamber and the vibrations have been acquired contemporarily with pressure signals (intake and outgoing discharge) and properly triggered with tachometer signal by using a multichannel acquisition system (Siemens™). A spectral vibration analysis has been used as diagnostic tool for accurately detecting pump degradation. The results coming from the analysis have shown that in presence of cavitation phenomena the non-intrusive monitoring technique represent a good diagnostic method for assessing pump operability

Parametric Modelling of EEG Data for the Identification of Mental Tasks

Electroencephalographic (EEG) data is widely used as a biosignal for the identification of different mental states in the human brain. EEG signals can be captured by relatively inexpensive equipment and acquisition procedures are non-invasive and not overly complicated. On the negative side, EEG signals are characterized by low signal-to-noise ratio and non-stationary characteristics, which makes the processing of such signals for the extraction of useful information a challenging task.peer-reviewe

Forecasting future energy production using hybrid artificial neural network and arima model

The objective of this research is to obtain an accurate forecasting model for the amount of electricity (in kWh) that is generated from different primary energy sources in the U.S. In this research, Artificial Neural Network (ANN) and hybrid ARIMA and ANN algorithms were developed that can be used for forecasting the amount of energy production in the short, as well as, in the long run. Based on the inferences made from the available data provided by Energy Information Administration from January 2004 to December 2014, two different forecasting models for each primary energy source were constructed. These two models were validated with available data from January 2015 to November 2017, and their performance, as measured by forecasting errors computed, were compared. The results show that ANN algorithm is good for fossil fuels sources such as coal, petroleum, and natural gas. However, ARIMA - ANN hybrid works more accurately for renewable energy sources such as geothermal, hydroelectric, solar, and wind. Finally, the best predictor was selected for each primary energy source which provides valuable information regarding the future electricity generation, and future dominant energy source to generate electricity. This information will hopefully influence energy sector forecasting models and help the government to develop future regulations to shift toward dominant energy sources of the future

Automation of Energy Demand Forecasting

Automation of energy demand forecasting saves time and effort by searching automatically for an appropriate model in a candidate model space without manual intervention. This thesis introduces a search-based approach that improves the performance of the model searching process for econometrics models. Further improvements in the accuracy of the energy demand forecasting are achieved by integrating nonlinear transformations within the models. This thesis introduces machine learning techniques that are capable of modeling such nonlinearity. Algorithms for learning domain knowledge from time series data using the machine learning methods are also presented. The novel search based approach and the machine learning models are tested with synthetic data as well as with natural gas and electricity demand signals. Experimental results show that the model searching technique is capable of finding an appropriate forecasting model. Further experimental results demonstrate an improved forecasting accuracy achieved by using the novel machine learning techniques introduced in this thesis. This thesis presents an analysis of how the machine learning techniques learn domain knowledge. The learned domain knowledge is used to improve the forecast accuracy

Modeling of the head-related transfer functions for reduced computation and storage

The synthesis of three-dimensional sound via headphones generally requires the implementation of rather complex filters based on the head-related transfer functions (HRTFs), direction-specific transfer functions which simulate the transformation of sound pressure between a sound source and the eardrums of the listener. Current implementations generally rely on FIR filtering techniques, resulting in high computational complexity. The main objective of this work was to develop a set of computationally efficient filters which would be capable of emulating the head-related transfer functions. To accomplish this objective, a modification of conventional system modeling techniques through the application of psychoacoustic principles has been applied to the design of low-order IIR filters, resulting in the reduction of computation and storage requirements without significantly sacrificing perceptual performance. Results presented will include objective measurements based on a critical band distance measure and subjective measurements based on sound localization tests