Neural network based predictive control of personalized heating systems

Fossil fuels have traditionally played a crucial role in global energy production, but they cause significant environmental challenges. In response, the scientific community has shifted its focus toward renewable alternative fuels. In this context, biodiesel and alcohols have emerged as promising options for diesel engines. This study is centered on predicting the cylinder pressure of a single-cylinder four-stroke diesel engine fuelled with a diesel fuel-biodiesel (methyl ester)-isopropanol ternary blend using three machine learning algorithms: Gaussian Process Regression (GPR), Artificial Neural Networks (ANN), and Ensembles of Trees (ET). The cylinder pressure data is collected under the full throttle condition and different engine speeds. GPR, ANN, and ET algorithms are trained and compared using root mean square error and regression analysis. GPR exhibits outstanding prediction performance during the validation, with a lower root mean square error of 0.12686, and r2 of 1.00. ANN also exhibits strong prediction performance, with a validation of root mean square error of 0.47081, and a r2 of 1.00. ET, while showing a slightly higher validation root mean square error of 1.73370, maintains strong predictive capability with an r2 of 0.99. However, a comparison between the measured cylinder pressure data and the predicted values reveals a qualitatively and quantitatively closer agreement, particularly for ANN. These findings can suggest the practicality and reliability of these algorithms for predicting cylinder pressure in internal combustion engine studies. In conclusion, this study can contribute to the expanding body of research on alternative fuels and machine learning applications in internal combustion engines.

The Huge vibration data are generated continuously by many sensors in daily high-speed rotating machinery operations. Accurate online prediction based on big vibration data streaming can reduce the risks related to failures and avoid service disruptions. This paper presents a hybrid nonlinear autoregressive network with exogenous inputs (NARX) model to forecast the remaining useful life of ball bearings through health index based on big vibration data streaming. This approach is validated by a real data from PRONOSTIA experimentation platform and industrial test rig compared with backpropagation neural network (BP), Elman and general regression neural network (GRNN) prediction model. Root mean square error, mean absolute error and correlation coefficient were used as performance indexes to evaluate the prediction accuracy of these models. The mean absolute error, the root mean square error and the correlation coefficient of hybrid NARX model evaluation index are 2.04, 2.85 and 0.98 respectively. It shows that the model presented in this paper has higher prediction accuracy. It can meet the needs of actual ball bearing remaining useful life prediction and also provide reference in other fields.

Research Notes “THE INDUSTRIAL INSTRUMENT—MASTER OF INDUSTRY, SERVANT OF MANAGEMENT”: AUTOMATIC CONTROL IN THE PROCESS INDUSTRIES, 1900-1940 STUART BENNETT Herbert H. Dow, founder of the Dow Chemical Company, opined in 1930 that industrial development rested on three basic technical ad­ vances: “firstly the steam engine inaugurating power production,” then interchangeable parts “making possible mass production,” and third, an “advance still in its infancy, automatic operation by automatic con­ trol ofcontinuous processes.” He thought the latter occupied a position in the process industries akin to that of interchangeable parts in the assembly industries.1 From the 1890s onward, Dow sought to reduce the number of people employed in the production process. His strat­ egy was to increase plant size, use automatic analysis and automatic operation of the equipment governed by automatic analysis, and de­ velop automatically controlled and operated continuous processes. By automatic control Dow meant closed-loop feedback control, and the Dow Chemical Company developed its own electrically operated controllers during the 1920s.2 During this same period, specialist Dr. Bennett, a lecturer in the Department of Control Engineering, University of Sheffield, completed the work for this article during his tenure as a senior postdoctoral fellow at the National Museum of American History (NMAH), the Smithsonian Institution. He is grateful for financial support from the Smithsonian Institution fellowships and grants program and from the University of Sheffield, and to the NMAH Division of Engineering and Industry for providing a base from which to work. He thanks Carlene Stephens and Barney Finn, his fellowship advisers, for their support and encouragement; Carlene Stephens, Jeffrey Stine, Steven Lubar, Maureen Whitebrook , and Peter Liebhold for reading the manuscript and suggesting improvements; and Jim Roan of the museum library and the division staff for research assistance. ‘Herbert H. Dow, “Economic Trend in the Chemical Industry,” Industrial and Engineering Chemistry 22 (1930): 113-16. Dow’s views were summarized in the January 1933 issue of the Industrial Bulletin of Arthur D. Little, Inc., and an extract from this summary was quoted in an editorial entitled “Industry’s Third Horizon?” Instruments 6 (February 1933): A5. ’The Dow Chemical Company first attempted to introduce automatic control in 1894; see Dow, p. 115. It continued to develop its own controllers until the early 1930s;© 1991 by the Society for the History of Technology. All rights reserved. 0040-165X/91/3201-0005$01.00 69 70 Stuart Bennett instrument companies extended their product range from indicators and recorders to include electrically and pneumatically operated feedback controllers. These instruments were the forerunners of the pneumatic and electronic controllers that were to play a major role in automating a whole range of industries during the 1950s.3 The process industries to which Dow’s techniques for reducing labor could be applied include metals, power generation, petroleum, chemicals, food, textiles, paper and pulp, glass, ceramics, brewery and distillery, sugar, lumber, and paint and varnish. The Works Progress Administration (WPA) surveyed these industries for the period 1909—35 for a project that culminated in a report, Reemployment Opportunities and Recent Changes in Industrial Techniques. Published in 1938, this WPA report found that there was a very rapid growth, in both absolute terms and relative to other forms of machinery, in the sales of industrial instruments between 1919 and 1929.4 And although the absolute value of sales of instruments fell sharply during the Depression, instrument sales relative to sales for all machinery continued to increase, moving from approximately 0.4 percent of machinery sales in 1919 to 1.4 percent in 1935, with a peak of almost 1.6 percent in 1933. The instruments included in the survey were indicators, recorders, and automatic feedback controllers, and, by 1935, the sales were roughly one-third indicators, one-third record­ ers, and one-third controllers. There was a rapid increase in the see John J. Grebe, Ray H. Boundy, and Robert W. Cermak, “The Control of Chemical Processes,” Transactions of the American Institution of Chemical Engineers 29 (1933): 211-55. This was one of the first technical papers on industrial process control. ’To introduce automatic feedback control, three components are needed: a measur­ ing instrument to convert some physical quantity to an electrical or...

Asthma is a common respiratory disease that is affected by air pollutants and meteorological factors. In this study, we developed models that predict the daily number of patients receiving treatment for asthma using air pollution and meteorological data. A neural network with long short-term memory (LSTM) and fully connected (FC) layers was used. The daily number of asthma patients in the city of Seoul, the capital of South Korea, was collected from the National Health Insurance Service. The data from 2015 to 2018 were used as the training and validation datasets for model development. Unseen data from 2019 were used for testing. The daily number of asthma patients per 100,000 inhabitants was predicted. The LSTM-FC neural network model achieved a Pearson correlation coefficient of 0.984 (P < 0.001) and root mean square error of 3.472 between the predicted and original values on the unseen testing dataset. The factors that impacted the prediction were the number of asthma patients in the previous time step before the predicted date, type of day (regular day and day after a holiday), minimum temperature, SO2, daily changes in the amount of cloud, and daily changes in diurnal temperature range. We successfully developed a neural network that predicts the onset and exacerbation of asthma, and we identified the crucial influencing air pollutants and meteorological factors. This study will help us to establish appropriate measures according to the daily predicted number of asthma patients and reduce the daily onset and exacerbation of asthma in the susceptible population.

PurposeThis study constructs time series model, artificial neural networks (ANNs) and statistical topologies to examine the volatility and forecast foreign exchange rates. The Mauritian forex market has been utilized as a case study, and daily data for nominal spot rate (during a time period of five years spanning from 2014 to 2018) for EUR/MUR, GBP/MUR, CAD/MUR and AUD/MUR have been applied for the predictions.Design/methodology/approachAutoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) models are used as a basis for time series modelling for the analysis, along with the non-linear autoregressive network with exogenous inputs (NARX) neural network backpropagation algorithm utilizing different training functions, namely, Levenberg–Marquardt (LM), Bayesian regularization and scaled conjugate gradient (SCG) algorithms. The study also features a hybrid kernel principal component analysis (KPCA) using the support vector regression (SVR) algorithm as an additional statistical tool to conduct financial market forecasting modelling. Mean squared error (MSE) and root mean square error (RMSE) are employed as indicators for the performance of the models.FindingsThe results demonstrated that the GARCH model performed better in terms of volatility clustering and prediction compared to the ARIMA model. On the other hand, the NARX model indicated that LM and Bayesian regularization training algorithms are the most appropriate method of forecasting the different currency exchange rates as the MSE and RMSE seemed to be the lowest error compared to the other training functions. Meanwhile, the results reported that NARX and KPCA–SVR topologies outperformed the linear time series models due to the theory based on the structural risk minimization principle. Finally, the comparison between the NARX model and KPCA–SVR illustrated that the NARX model outperformed the statistical prediction model. Overall, the study deduced that the NARX topology achieves better prediction performance results compared to time series and statistical parameters.Research limitations/implicationsThe foreign exchange market is considered to be instable owing to uncertainties in the economic environment of any country and thus, accurate forecasting of foreign exchange rates is crucial for any foreign exchange activity. The study has an important economic implication as it will help researchers, investors, traders, speculators and financial analysts, users of financial news in banking and financial institutions, money changers, non-banking financial companies and stock exchange institutions in Mauritius to take investment decisions in terms of international portfolios. Moreover, currency rates instability might raise transaction costs and diminish the returns in terms of international trade. Exchange rate volatility raises the need to implement a highly organized risk management measures so as to disclose future trend and movement of the foreign currencies which could act as an essential guidance for foreign exchange participants. By this way, they will be more alert before conducting any forex transactions including hedging, asset pricing or any speculation activity, take corrective actions, thus preventing them from making any potential losses in the future and gain more profit.Originality/valueThis is one of the first studies applying artificial intelligence (AI) while making use of time series modelling, the NARX neural network backpropagation algorithm and hybrid KPCA–SVR to predict forex using multiple currencies in the foreign exchange market in Mauritius.

Microwave remote sensing instrument like Soil Moisture Active Passive ranging from 1 cm to 1 m has provided spatial soil moisture information over the entire globe. However, Soil Moisture Active Passive satellite soil moisture products have a coarse spatial resolution (36km x 36km), limiting its application at the basin scale. This research, subsequently plans to; (1) Evaluate the capability of SAR for the retrieval of surface roughness variables in the Awash River basin; (2) Measure the performance of Random Forest (RF) regression model to downscale SMAP satellite soil moisture over the Awash River basin; (3) validate downscaled soil moisture data with In-situ measurements in the river basin. Random Forest (RF) based downscaling approach was applied to downscale satellite-based soil moisture product (36km x 36km) to fine resolution (1km x 1km). Fine spatial resolution (1km) soil moisture data for the Awash River basin was generated. The downscaled soil moisture product also has a strong spatial correlation with the original one, allowing it to deliver more soil moisture information than the original one. In-situ soil moisture and downscaled soil moisture had a 0.69 Pearson correlation value, compared to a 0.53 correlation between the original and In-situ soil moisture. In-situ soil moisture measurements were obtained from the Middle and Upper Awash sub-basins for validation purposes. In the case of Upper Awash, downscaled soil moisture shows a variation of 0.07 cm3 /cm3, -0.036 cm3 /cm3, and 0.112 cm3 /cm3 with Root Mean Square Error, Bias error, and Unbiased Root Mean Square Error respectively. Following that, the accuracy of downscaled soil moisture against the Middle Awash Sub-basin reveals a variance of 0.1320 cm3 /cm3, -0.033 cm3 /cm3, and 0.148 cm3 /cm3 with Root Mean Square Error, Bias error, and Unbiased Root Mean Square Error respectively. Future studies should take into account the temporal domain of Soil Moisture Active Passive satellite soil moisture product downscaling over the study region.

We propose and experimentally demonstrate visible-light positioning (VLP) systems using silicon photovoltaic cells (Si-PVCs) and machine learning and neural network algorithms. Both angle-of-arrival (AOA)-based and received-signal-strength (RSS)-based VLP systems are evaluated and compared. The Si-PVC could also provide energy harvesting to store received optical power for the mobile unit. Here, second-order linear regression machine learning (RML) model and two-layer neural network are implemented in both AOA-based and RSS-based VLP systems to enhance the positioning accuracy. The root mean square (RMS) average positioning error of the AOA-based VLP system is reduced from 7.22 to 3.46 and to 2.99 cm when using the RML and neural network, respectively. The RMS average positioning error of the RSS-based VLP system is reduced from 7.07 to 3.01 and to 2.60 cm when using the RML and neural network, respectively. The experimental results clearly illustrate that the proposed schemes can significantly improve the positioning accuracy.

Force and vibration generated in apical direction by three endodontic files of different kinematics during simulated canal preparation: An in vitro analytical study.

Neural network time series (NNTS) tool was used to predict municipal solid waste composition in Iasi, Romania. The nonlinear input output (NIO) time series model and nonlinear autoregressive model with external (exogenous) input (NARX) included in this tool were selected. The coefficient of determination (R2) and root mean square error (RMSE) were chosen for evaluation. By applying NIO, the optimum model is 4-11-6 artificial neural network (ANN, R2 = 0.929) in the case of testing as for the validation, with all 0.849 and 0.885, respectively. Applying NARX, the suitable model became 4-13-6 ANN model, with R2 = 0.999 for training, 0.879 for testing, and 0.931, respectively 0.944 for validation and all. The resulted RMSE is zero for training and 0.0109 for validation in the case of this model which had 4 inputs, 13 neurons and 6 outputs. The four input variables were: number of residents, population aged 15–59 years, urban life expectancy, total municipal solid waste (ton/year). The suitable ANN model revealed the lowest root mean square error and the highest coefficient of determination. Results indicate that NNTS tool is a complex instrument, NARX is more accurate than NIO model, and can be used and applied easily.

Uniqueness in economies and stock markets has given rise to an interesting domain of exploring data mining techniques across global indices. Previously, very few studies have attempted to compare the performance of data mining techniques in diverse markets. The current study adds to the understanding regarding the variations in performance of data mining techniques across the global stock indices. We compared the performance of Neural Networks and Support Vector Machines using accuracy measures Mean Absolute Error (MAE) and R­­­­oot Mean Square Error (RMSE) across seven major stock markets. For prediction purpose, technical analysis has been employed on selected indicators based on daily values of indices spanning a period of 12 years. We created 196 data sets spanning different time periods for model building such as 1 year, 2 years, 3 years, 4 years, 6 years and 12 years for selected seven stock indices. Based on prediction models built using Neural Networks and Support Vector Machines, the findings of the study indicate there is a significant difference, both for MAE and RMSE, across the selected global indices. Also, Mean Absolute Error and Root Mean Square Error of models built using NN were greater than Mean Absolute Error and Root Mean Square Error of models built using SVM.

Nowadays , nonlinear time series and artificial neural networks (ANN) models are used for forecasting in the field of business, agriculture and soon. Recent studies have shown, ANN have been successfully used for forecasting of financial and agriculture data series The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. ANN have more advantages that can approximate to model both linear and nonlinear structures in time series, they are not able to handling both structures equally well. The autoregressive integrated moving average (ARIMA) model and two ANN models namely, Radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) methods were applied to Hyderabad airport traffic data. The data obtained for 15 years from 2002–2003 to 2016–2017 about domestic and international passenger of International Airport of Hyderabad, India. In this research paper, we compared the performances of ARIMA, RBFNN and ERNN were based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results showed that RBFNN obtained the smallest MAE, MAPE and RMSE in both the modeling and forecasting processes. The performances of the three models ranked in ascending order were: ARIMA, ERNN and the RBFNN model. Keywords: T ime series, forecasting, artificial neural networks, ARIMA models, radial basis function neural networks, and Elman recurrent neural networks Cite this Article R. Ramakrishna, Berhe Aregay, Tewodros Gebregergs. The Comparison in Time Series Forecasting of Air Traffic Data by Autoregressive Integrated Moving Average Model, Radial Basis Function and Elman Recurrent Neural Networks. Research & Reviews: Journal of Statistics . 2018; 7(3): 75–90p.

Three-Dimensional Quantitative Comparative Analysis of Trapezial-Metacarpal Joint Surface Curvatures in Human Populations

Visible-NIR spectral characterization and grade inversion modelling study of the Derni copper deposit

In this paper we use the property of diatoms as bioindicators, to indentify which physical-chemical parameters are contained in the taken sample using machine learning algorithm – CN2. Important physical-chemical parameters such as conductivity, saturated oxygen, pH, organic chemical parameters and metals are important in the process of environmental monitoring. These physical-chemical parameters have influence on the entire lake web food chain, thus disturbing the organism’s patterns and interactions between them, such as diatoms community. These communities have high coefficient of indication on certain process such as eutrophication and presence or absence of certain physical-chemical parameters, which means that they can be used as bio-indicators of water quality. The machine learning algorithm – CN2 can produce rules in a form IF-THEN which is suitable for organizing knowledge from diatoms abundance data. In literature the diatoms have ecological preference organized in the same manner. The experimental setup is build to satisfy not only the algorithm properties, but also the ecological knowledge of the diatoms community. We used several modifications of the algorithm, from which then we compare the compactness and coverage of the induced rule. Nevertheless, for regression problems we compare the correlation coefficient, root mean square error (RMSE) and relative root mean square error (RRMSE) or rule quality to point which experiment proved to be most accuracy and more general. Several of the rules are presented in this paper together with the evaluation performance.

The aim of the present research is to calculate the Relative Accuracy (R.A) and Root Mean Square Error (RMSE) for the location of Technical Institute, Mosul in Nineveh Governorate, Iraq. at the northern part of Mosul city by using surveying Instruments (GPS 1230 , Total Station 06 & Level NAK2), to calculate the coordinates and elevations of (16) Ground Control Points which covered the study area. The field data, remote sensing techniques and GIS software (Arc Map GIS10.3) are used to check the digital map of study area that helped the administrator to take the correct decision, determining positions of the future expansionist for buildings, roads, measuring lengths, areas and there coordinates, directions as well as the total costs estimation needed for any development from the final map. The gradient percentage between the highest and lowest points is calculated to be (9.954%). Relative accuracy for the based measured points (R.A) = 1:60000), and Root Mean Square error in Easting coordinates (RMSE) = ± 0.102m, in Northing coordinates (RMSN) = ± 0.096m, the Root Mean Square for the resultant (RMSR) = ± 0.099m, and Root Mean Square for Elevations ( RMSRL)= ± 0.005m. We Matched and correlated (10) measured points from study area with their graphic data positions on the aerial photo, determining the offsets for position and elevations as follows: (RMSE = ± 0.076m), (RMSN = ± 0.125m), (RMSR = ± 0.088m), and (Root Mean Square for Elevations = RMSRL= ± 0.045m). This research enable us after checking the results to produce new maps from Aerial photograph or Digital Image to use it for land uses and future extensions in whole study area.