The document summarizes a web-based weather monitoring system in Palermo, Italy that collects temperature and other data from several weather stations around the city. It uses the data to analyze trends like the urban heat island effect and creates forecasts using artificial neural networks. The forecasts showed good accuracy within a 1-2 degree range when predicting temperatures a week in advance for different areas around Palermo. Future work will focus on longer-term forecasting to study thermal comfort conditions in the city.
Estimation of global solar radiation by using machine learning methodsmehmet şahin
In this study, global solar radiation (GSR) was estimated based on 53 locations by using ELM, SVR, KNN, LR and NU-SVR methods. Methods were trained with a two-year data set and accuracy of the mentioned methods was tested with a one-year data set. The data set of each year was consisting of 12 months. Whereas the values of month, altitude, latitude, longitude, vapour pressure deficit and land surface temperature were used as input for developing models, GSR was obtained as output. Values of vapour pressure deficit and land surface temperature were taken from radiometry of NOAA-AVHRR satellite. Estimated solar radiation data were compared with actual data that were obtained from meteorological stations. According to statistical results, most successful method was NU-SVR method. The RMSE and MBE values of NU-SVR method were found to be 1,4972 MJ/m2 and 0,2652 MJ/m2, respectively. R value was 0,9728. Furthermore, worst prediction method was LR. For other methods, RMSE values were changing between 1,7746 MJ/m2 and 2,4546 MJ/m2. It can be seen from the statistical results that ELM, SVR, k-NN and NU-SVR methods can be used for estimation of GSR.
At present, with the development of wind power project in China, there are more and more projects located at the complex terrain and complex environment. At the same time, since the large planned area of project, the complex mountain area, and limited number of met mast, even without met mast, in order to the reliable development of the wind power project, it is important that how to do the wind resource assessment without actual measurement wind data and other conditions such as less reliable wind data, and the met mast was not considered representative. This paper will use the atmospheric model to do mesoscale simulation calculation of wind resources, and then combine with CFD technology to downscaling computation to get high resolution wind power assessment result. Finally, in order to confirm the validity of this application in the actual project, the comparison between calculation values and measurement values is carried out. The verification result through the actual data of different met mast shows that the wind resource assessment method which combines the CFD and mesoscale technologies is reliable. The main contribution of the article is to provide the reference model and approach for regional planning and large scale wind resource assessment when there isn’t enough adequate and effective wind data.
CLEARMiner: Mining of Multitemporal Remote Sensing ImagesEditor IJCATR
A new unsupervised algorithm, called CLimate and rEmote sensing Association patterns Miner, for mining association patterns on
heterogeneous time series from climate and remote sensing data, integrated in a remote sensing information system is developed to improve the
monitoring of sugar cane fields. The system, called RemoteAgri, consists of a large database of climate data and low-resolution remote sensing
images, an image pre-processing module, a time series extraction module, and time series mining methods. The time series mining method
transforms series to symbolic representation in order to identify patterns in a multitemporal satellite images and associate them with patterns in
other series within a temporal sliding window. The validation process was achieved with agro climatic data and NOAA-AVHRR images of
sugar cane fields. Rules generated by the new algorithm show the association patterns in different periods of time in each time series, pointing to
a time delay between the occurrences of patterns in the series analyzed, corroborating what specialists usually forecast without having the burden
of dealing with many data charts. This new method can be used by agro meteorologists to mine and discover knowledge from their long time
series of past and forecasting data, being a valuable tool to support their decision-making process.
Determination of wind energy potential of campus area of siirt universitymehmet şahin
In this study, wind energy potential of Siirt
University campus area is statistically examined by using the mean hourly wind speed data between 2014
and 2015 years which are measured by Vantage Pro2 device, located at the roof of the Engineering Faculty building with 6 m altitude. Weibull distribution
function and Rayleigh distribution function are used
as statistical approach to evaluate the wind data. Weibull distribution function is examined by using two different methods that are maximum likelihood estimation and Rayleigh method. The determination
coefficient (R2) and Root Mean Square Error (RMSE) values of these methods are compared. According the error analysis, it is indicated that the Rayleigh method
gives better results. Wind speed and wind power density are calculated in pursuance of Weibull distribution parameters. The results are evaluated as
monthly and annually. Hence, this preliminary study is made to determine the wind energy potential of Siirt University campus area.
Estimation of global solar radiation by using machine learning methodsmehmet şahin
In this study, global solar radiation (GSR) was estimated based on 53 locations by using ELM, SVR, KNN, LR and NU-SVR methods. Methods were trained with a two-year data set and accuracy of the mentioned methods was tested with a one-year data set. The data set of each year was consisting of 12 months. Whereas the values of month, altitude, latitude, longitude, vapour pressure deficit and land surface temperature were used as input for developing models, GSR was obtained as output. Values of vapour pressure deficit and land surface temperature were taken from radiometry of NOAA-AVHRR satellite. Estimated solar radiation data were compared with actual data that were obtained from meteorological stations. According to statistical results, most successful method was NU-SVR method. The RMSE and MBE values of NU-SVR method were found to be 1,4972 MJ/m2 and 0,2652 MJ/m2, respectively. R value was 0,9728. Furthermore, worst prediction method was LR. For other methods, RMSE values were changing between 1,7746 MJ/m2 and 2,4546 MJ/m2. It can be seen from the statistical results that ELM, SVR, k-NN and NU-SVR methods can be used for estimation of GSR.
At present, with the development of wind power project in China, there are more and more projects located at the complex terrain and complex environment. At the same time, since the large planned area of project, the complex mountain area, and limited number of met mast, even without met mast, in order to the reliable development of the wind power project, it is important that how to do the wind resource assessment without actual measurement wind data and other conditions such as less reliable wind data, and the met mast was not considered representative. This paper will use the atmospheric model to do mesoscale simulation calculation of wind resources, and then combine with CFD technology to downscaling computation to get high resolution wind power assessment result. Finally, in order to confirm the validity of this application in the actual project, the comparison between calculation values and measurement values is carried out. The verification result through the actual data of different met mast shows that the wind resource assessment method which combines the CFD and mesoscale technologies is reliable. The main contribution of the article is to provide the reference model and approach for regional planning and large scale wind resource assessment when there isn’t enough adequate and effective wind data.
CLEARMiner: Mining of Multitemporal Remote Sensing ImagesEditor IJCATR
A new unsupervised algorithm, called CLimate and rEmote sensing Association patterns Miner, for mining association patterns on
heterogeneous time series from climate and remote sensing data, integrated in a remote sensing information system is developed to improve the
monitoring of sugar cane fields. The system, called RemoteAgri, consists of a large database of climate data and low-resolution remote sensing
images, an image pre-processing module, a time series extraction module, and time series mining methods. The time series mining method
transforms series to symbolic representation in order to identify patterns in a multitemporal satellite images and associate them with patterns in
other series within a temporal sliding window. The validation process was achieved with agro climatic data and NOAA-AVHRR images of
sugar cane fields. Rules generated by the new algorithm show the association patterns in different periods of time in each time series, pointing to
a time delay between the occurrences of patterns in the series analyzed, corroborating what specialists usually forecast without having the burden
of dealing with many data charts. This new method can be used by agro meteorologists to mine and discover knowledge from their long time
series of past and forecasting data, being a valuable tool to support their decision-making process.
Determination of wind energy potential of campus area of siirt universitymehmet şahin
In this study, wind energy potential of Siirt
University campus area is statistically examined by using the mean hourly wind speed data between 2014
and 2015 years which are measured by Vantage Pro2 device, located at the roof of the Engineering Faculty building with 6 m altitude. Weibull distribution
function and Rayleigh distribution function are used
as statistical approach to evaluate the wind data. Weibull distribution function is examined by using two different methods that are maximum likelihood estimation and Rayleigh method. The determination
coefficient (R2) and Root Mean Square Error (RMSE) values of these methods are compared. According the error analysis, it is indicated that the Rayleigh method
gives better results. Wind speed and wind power density are calculated in pursuance of Weibull distribution parameters. The results are evaluated as
monthly and annually. Hence, this preliminary study is made to determine the wind energy potential of Siirt University campus area.
A multi sensor-information_fusion_method_based_on_factor_graph_for_integrated...Ashish Sharma
The current navigation systems used in many autonomous mobile robotic applications, like
unmanned vehicles, are always equipped with various sensors to get accurate navigation results. The
key point is to fuse the information from different sensors efciently. However, different sensors provide
asynchronous measurements, some of which even appear to be nonlinear. Moreover, some sensors are
vulnerable in specic environments, e.g., GPS signal is likely to work poorly in interior space, underground,
and tall buildings. We propose a multi-sensor information fusion method based on a factor graph to fuse
all available asynchronous sensor information and efciently and accurately calculate a navigation solution.
Assuming the sensor measurements and navigation states in a navigation system as factor nodes and variable
nodes in a factor graph, respectively, the update of the states can be implemented in the framework of the
factor graph. The proposed method is experimentally validated using two different datasets. A comparison
with Federated Filter, which has been widely used in integrated navigation systems, demonstrates the
proposed method's effectiveness. Additionally, analyzing the navigation results with data loss that
the proposed method could achieve sensor plug and play in software.INDEX TERMS Integrated navigation, multi-sensor, information fusion, factor graph, plug and play.
Nonlinear filtering approaches to field mapping by sampling using mobile sensorsijassn
This work proposes a novel application of existing powerful nonlinear filters, such as the standard
Extended Kalman Filter (EKF), some of its variants and the standard Unscented Kalman Filter (UKF), to
the estimation of a continuous spatio-temporal field that is spread over a wide area, and hence represented
by a large number of parameters when parameterized. We couple these filters with the powerful scheme of
adaptive sampling performed by a single mobile sensor, and investigate their performances with a view to
significantly improving the speed and accuracy of the overall field estimation. An extensive simulation work
was carried out to show that different variants of the standard EKF and the standard UKF can be used to
improve the accuracy of the field estimate. This paper also aims to provide some guideline for the user of
these filters in reaching a practical trade-off between the desired field estimation accuracy and the
required computational load.
Digital Heritage Documentation Via TLS And Photogrammetry Case Studytheijes
In the last decade, several manual tradition measurement techniques were used to document the heritage buildings around the word; however, some of these techniques take a long time, often lack completeness, and may sometimes give unreliable information. In contrast, terrestrial laser scanning “TLS” surveys and Photogrammetry have already been undertaken in several heritage sites in the United Kingdom and other countries of Europe as a new method of documenting heritagesites. This paper focuses on using the TLS and Photogrammetry methods to document one of the important houses in Historic Jeddah, Saudi Arabia, which is Nasif Historical House, as an example of Digital Heritage Documentation (DHD).
Coal-Fired Boiler Fault Prediction using Artificial Neural Networks IJECEIAES
Boiler fault is a critical issue in a coal-fired power plant due to its high temperature and high pressure characteristics. The complexity of boiler design increases the difficulty of fault investigation in a quick moment to avoid long duration shut-down. In this paper, a boiler fault prediction model is proposed using artificial neural network. The key influential parameters analysis is carried out to identify its correlation with the performance of the boiler. The prediction model is developed to achieve the least misclassification rate and mean squared error. Artificial neural network is trained using a set of boiler operational parameters. Subsequenlty, the trained model is used to validate its prediction accuracy against actual fault value from a collected real plant data. With reference to the study and test results, two set of initial weights have been tested to verify the repeatability of the correct prediction. The results show that the artificial neural network implemented is able to provide an average of above 92% prediction rate of accuracy.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
AUTOMATIC IDENTIFICATION OF CLOUD COVER REGIONS USING SURF ijcseit
Weather forecasting has become an indispensable application to predict the state of the atmosphere for a
future time based on cloud cover identification. But it generally needs the experience of a well-trained
meteorologist. In this paper, a novel method is proposed for automatic cloud cover estimation, typical to
Indian Territory Speeded Up Robust Feature Transform(SURF) is applied on the satellite images to obtain
the affine corrected images. The extracted cloud regions from the affine corrected images based on Otsu
threshold are superimposed on the artistic grids representing latitude and longitude over India. The
segmented cloud and grid composition drive a look up table mechanism to identify the cloud cover regions.
Owing to its simplicity, the proposed method processes the test images faster and provides accurate
segmentation for cloud cover regions.
Current & Future Services - EUMETCast User Forum 2014EUMETSAT
Sally Wannop, User Relations Manager, describes the current and future data services at EUMETSAT. The main focus us on those which will be available via EUMETCast. These include Meteosat Third Generation, EPS-SG, Jason and Sentinel.
Floweather, environment monitoring system based on sensor networks — 2010Domenico Schillaci
http://www.floweather.com/
floweather is a GLOSS (Green, Low cost, Open Source, Sustainable) project,
floweather is more than an embedded system or the node of a wireless sensor network
floweather is not just a little, modular, wiireless station for weather and air quality measurements,
floweather is the hi-tech flowerpot designed for your balcony,
floweather is Arduino based.
develop team:
Salvatore Di Dio
Stefano Manni
Domenico Schillaci
A multi sensor-information_fusion_method_based_on_factor_graph_for_integrated...Ashish Sharma
The current navigation systems used in many autonomous mobile robotic applications, like
unmanned vehicles, are always equipped with various sensors to get accurate navigation results. The
key point is to fuse the information from different sensors efciently. However, different sensors provide
asynchronous measurements, some of which even appear to be nonlinear. Moreover, some sensors are
vulnerable in specic environments, e.g., GPS signal is likely to work poorly in interior space, underground,
and tall buildings. We propose a multi-sensor information fusion method based on a factor graph to fuse
all available asynchronous sensor information and efciently and accurately calculate a navigation solution.
Assuming the sensor measurements and navigation states in a navigation system as factor nodes and variable
nodes in a factor graph, respectively, the update of the states can be implemented in the framework of the
factor graph. The proposed method is experimentally validated using two different datasets. A comparison
with Federated Filter, which has been widely used in integrated navigation systems, demonstrates the
proposed method's effectiveness. Additionally, analyzing the navigation results with data loss that
the proposed method could achieve sensor plug and play in software.INDEX TERMS Integrated navigation, multi-sensor, information fusion, factor graph, plug and play.
Nonlinear filtering approaches to field mapping by sampling using mobile sensorsijassn
This work proposes a novel application of existing powerful nonlinear filters, such as the standard
Extended Kalman Filter (EKF), some of its variants and the standard Unscented Kalman Filter (UKF), to
the estimation of a continuous spatio-temporal field that is spread over a wide area, and hence represented
by a large number of parameters when parameterized. We couple these filters with the powerful scheme of
adaptive sampling performed by a single mobile sensor, and investigate their performances with a view to
significantly improving the speed and accuracy of the overall field estimation. An extensive simulation work
was carried out to show that different variants of the standard EKF and the standard UKF can be used to
improve the accuracy of the field estimate. This paper also aims to provide some guideline for the user of
these filters in reaching a practical trade-off between the desired field estimation accuracy and the
required computational load.
Digital Heritage Documentation Via TLS And Photogrammetry Case Studytheijes
In the last decade, several manual tradition measurement techniques were used to document the heritage buildings around the word; however, some of these techniques take a long time, often lack completeness, and may sometimes give unreliable information. In contrast, terrestrial laser scanning “TLS” surveys and Photogrammetry have already been undertaken in several heritage sites in the United Kingdom and other countries of Europe as a new method of documenting heritagesites. This paper focuses on using the TLS and Photogrammetry methods to document one of the important houses in Historic Jeddah, Saudi Arabia, which is Nasif Historical House, as an example of Digital Heritage Documentation (DHD).
Coal-Fired Boiler Fault Prediction using Artificial Neural Networks IJECEIAES
Boiler fault is a critical issue in a coal-fired power plant due to its high temperature and high pressure characteristics. The complexity of boiler design increases the difficulty of fault investigation in a quick moment to avoid long duration shut-down. In this paper, a boiler fault prediction model is proposed using artificial neural network. The key influential parameters analysis is carried out to identify its correlation with the performance of the boiler. The prediction model is developed to achieve the least misclassification rate and mean squared error. Artificial neural network is trained using a set of boiler operational parameters. Subsequenlty, the trained model is used to validate its prediction accuracy against actual fault value from a collected real plant data. With reference to the study and test results, two set of initial weights have been tested to verify the repeatability of the correct prediction. The results show that the artificial neural network implemented is able to provide an average of above 92% prediction rate of accuracy.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
AUTOMATIC IDENTIFICATION OF CLOUD COVER REGIONS USING SURF ijcseit
Weather forecasting has become an indispensable application to predict the state of the atmosphere for a
future time based on cloud cover identification. But it generally needs the experience of a well-trained
meteorologist. In this paper, a novel method is proposed for automatic cloud cover estimation, typical to
Indian Territory Speeded Up Robust Feature Transform(SURF) is applied on the satellite images to obtain
the affine corrected images. The extracted cloud regions from the affine corrected images based on Otsu
threshold are superimposed on the artistic grids representing latitude and longitude over India. The
segmented cloud and grid composition drive a look up table mechanism to identify the cloud cover regions.
Owing to its simplicity, the proposed method processes the test images faster and provides accurate
segmentation for cloud cover regions.
Current & Future Services - EUMETCast User Forum 2014EUMETSAT
Sally Wannop, User Relations Manager, describes the current and future data services at EUMETSAT. The main focus us on those which will be available via EUMETCast. These include Meteosat Third Generation, EPS-SG, Jason and Sentinel.
Floweather, environment monitoring system based on sensor networks — 2010Domenico Schillaci
http://www.floweather.com/
floweather is a GLOSS (Green, Low cost, Open Source, Sustainable) project,
floweather is more than an embedded system or the node of a wireless sensor network
floweather is not just a little, modular, wiireless station for weather and air quality measurements,
floweather is the hi-tech flowerpot designed for your balcony,
floweather is Arduino based.
develop team:
Salvatore Di Dio
Stefano Manni
Domenico Schillaci
Reliability analysis of pmu using hidden markov modelamaresh1234
As modern electric power systems are transforming into smart grids, real time wide area monitoring system (WAMS) has become an essential tool for operation and control. With the increasing applications of WAMS for on-line stability analysis and control in smart grids, phasor measurement unit (PMU) is becoming a key element in wide area measurement system and the consequence of the failure of PMU is very severe and may cause a black out. Therefore reliable operation of PMU is very much essential for smooth functioning of the power system. This thesis is focused mainly on evaluating the reliability of PMU using hidden Markov model. Firstly, the probability of given observation sequence is obtained for the individual modules and PMU as a whole using forward and backward algorithm. Secondly, the optimal state sequence each module passes through is found. Thirdly, the parameters of the hidden Markov model are re-estimated using Baum-Welch algorithm.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
The concepts related of the New Model of River Adige, and especially an analysys of the existing OMS components ready and their interpretation on the basis of travel time approaches
Wireless Technology for Monitoring Site-specific Landslide in Vietnam IJECEIAES
Climate change has caused an increasing number of landslides, especially in the mountainous provinces of Vietnam, resulting in the destruction of vital transport and other infrastructure. Current monitoring and forecasting systems of the meteorology department cannot deliver accurate and reliable forecasts for weather events and issue timely warnings. This paper describes the development of a simple, low cost, and efficient system for monitoring and warning landslide in real-time. The authors focus on the use of wireless and related technologies in the implementation of a technical solution and some of the problems of the wireless sensor network (WSN) related to power consumption. Promising compressed sensing (CS) based solution for landslide monitoring is discussed and evaluated in the paper.
WATERSHED MODELING USING ARTIFICIAL NEURAL NETWORKS IAEME Publication
Artificial Neural Networks analysis was used for modeling rainfall-runoff relationship. A new Instantaneous ANN watershed model was built and tried herein using Walnut Gulch watershed (catchment) area. For modeling the instantaneous response of a catchment to a rainfall event an ANN model was built shown herein. The built model can represent the actual response using descritized
rainfall-runoff values, over a selected time interval (∆t). As this time interval decreases the actual response is more accurately modeled. This model was applied to one of the sub-catchment of Walnut Gulch watershed (sub-catchment No.9 (flume 11)). The model was found successful to represent the lag-time and time of runoff related to the hyetograph properties
Forecasting Electric Energy Demand using a predictor model based on Liquid St...Waqas Tariq
Electricity demand forecasts are required by companies who need to predict their customers’ demand, and by those wishing to trade electricity as a commodity on financial markets. It is hard to find the right prediction method for a given application if not a prediction expert. Recent works show that Liquid State Machines (LSM’s) can be applied to the prediction of time series. The main advantage of the LSM is that it projects the input data in a high-dimensional dynamical space and therefore simple learning methods can be used to train the readout. In this paper we present an experimental investigation of an approach for the computation of time series prediction by employing Liquid State Machines (LSM) in the modeling of a predictor in a case study for short-term and long-term electricity demand forecasting. Results of this investigation are promising, considering the error to stop training the readout, the number of iterations of training of the readout and that no strategy of seasonal adjustment or preprocessing of data was achieved to extract non-correlated data out of the time series.
Forecasting Electric Energy Demand using a predictor model based on Liquid St...Waqas Tariq
Electricity demand forecasts are required by companies who need to predict their customers’ demand, and by those wishing to trade electricity as a commodity on financial markets. It is hard to find the right prediction method for a given application if not a prediction expert. Recent works show that Liquid State Machines (LSM’s) can be applied to the prediction of time series. The main advantage of the LSM is that it projects the input data in a high-dimensional dynamical space and therefore simple learning methods can be used to train the readout. In this paper we present an experimental investigation of an approach for the computation of time series prediction by employing Liquid State Machines (LSM) in the modeling of a predictor in a case study for short-term and long-term electricity demand forecasting. Results of this investigation are promising, considering the error to stop training the readout, the number of iterations of training of the readout and that no strategy of seasonal adjustment or preprocessing of data was achieved to extract non-correlated data out of the time series.
Prediction of Extreme Wind Speed Using Artificial Neural Network ApproachScientific Review SR
Prediction of an accurate wind speed of wind farms is necessary because of the intermittent nature
of wind for any region. Number of methods such as persistence, physical, statistical, spatial correlation, artificial
intelligence network and hybrid are generally available for prediction of wind speed. In this paper, ANN based
methods viz., Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks are used. The
performance of the networks applied for prediction of wind speed is evaluated by model performance indicators
viz., Correlation Coefficient (CC), Model Efficiency (MEF) and Mean Absolute Percentage Error (MAPE).
Meteorological parameters such as maximum and minimum temperature, air pressure, solar radiation and
altitude are considered as input units for MLP and RBF networks to predict the extreme wind speed at Delhi.
The study shows the values of CC, MEF and MAPE between the observed and predicted wind speed (using
MLP) are computed as 0.992, 95.4% and 4.3% respectively while training the network data. For RBF network,
the values of CC, MEF and MAPE are computed as 0.992, 95.9% and 3.0% respectively. The model
performance analysis indicates the RBF is better suited network among two different networks studied for
prediction of extreme wind speed at Delhi.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Joint State and Parameter Estimation by Extended Kalman Filter (EKF) techniqueIJERD Editor
In order to increase power system stability and reliability during and after disturbances, power grid
global and local controllers must be developed. SCADA system provides steady and low sampling density. To
remove these limitation PMUs are being rapidly adopted worldwide. Dynamic states of power system can be
estimated using EKF. This requires field excitation as input which may not available. As a result, the EKF with
unknown inputs proposed for identifying and estimating the states and the unknown inputs of the synchronous
machine.
The accurate prediction of solar irradiation has been
a leading problem for better energy scheduling approach.
Hence in this paper, an Artificial neural network based solar
irradiance is proposed for five days duration the data is
obtained from National Renewable Energy Laboratory, USA
and the simulation were performed using MATLAB 2013. It
was found that the neural model was able to predict the solar
irradiance with a mean square error of 0.0355.
System for Prediction of Non Stationary Time Series based on the Wavelet Radi...IJECEIAES
This paper proposes and examines the performance of a hybrid model called the wavelet radial bases function neural networks (WRBFNN). The model will be compared its performance with the wavelet feed forward neural networks (WFFN model by developing a prediction or forecasting system that considers two types of input formats: input9 and input17, and also considers 4 types of non-stationary time series data. The MODWT transform is used to generate wavelet and smooth coefficients, in which several elements of both coefficients are chosen in a particular way to serve as inputs to the NN model in both RBFNN and FFNN models. The performance of both WRBFNN and WFFNN models is evaluated by using MAPE and MSE value indicators, while the computation process of the two models is compared using two indicators, many epoch, and length of training. In stationary benchmark data, all models have a performance with very high accuracy. The WRBFNN9 model is the most superior model in nonstationary data containing linear trend elements, while the WFFNN17 model performs best on non-stationary data with the non-linear trend and seasonal elements. In terms of speed in computing, the WRBFNN model is superior with a much smaller number of epochs and much shorter training time.
A Land Data Assimilation System Utilizing Low Frequency Passive Microwave Rem...drboon
To address the gap in bridging global and smaller modelling scales, downscaling approaches have been reported as an appropriate solution. Downscaling on its own is not wholly adequate in the quest to produce local phenomena, and in this paper we use a physical downscaling method combined with data assimilation strategies, to obtain physically consistent land surface condition prediction. Using data assimilation strategies, it has been demonstrated that by minimizing a cost function, a solution utilizing imperfect models and observation data including observation errors is feasible. We demonstrate that by assimilating lower frequency passive microwave brightness temperature data using a validated theoretical radiative transfer model, we can obtain very good predictions that agree well with observed conditions.
Analyzing and assessing ecological transition in building sustainable citiesBeniamino Murgante
"Analyzing and assessing ecological transition in building sustainable cities" Keynote presentation at "International Conference on Sustainable Environment and Technologies" 23 September 2022, Nicolas Tesla University Union, Belgrade, Serbia
Smart Cities: New Science for the Cities
Beniamino Murgante
School of Engineering, University of Basilicata
Lecture at the Department of Community and Regional Planning
Smart Cities course - Professor Alenka Poplin
Keynote at the 24th International Conference on Urban Planning and Regional Development in the Information Society
GeoMultimedia 2019, 2-4 April 2019
Karlsruhe Institute of Technology, Germany
Involving citizens in smart energy approaches: the experience of an energy pa...Beniamino Murgante
Involving citizens in smart energy approaches: the experience of an energy park in Calvello municipality
4th International Conference on Urban e-Planning, University of Lisbon, 23-24 April 2019
Programmazione per la governance territoriale in tema di tutela della biodive...Beniamino Murgante
Programmazione per la governance territoriale in tema di tutela della biodiversità - Sabrina Lai - Regione Sardegna, Direzione generale della difesa dell’ambiente slai@regione.sardegna.it
Università degli Studi di Cagliari, DICAAR, sabrinalai@unica.it
RISCHIO TERRITORIALE NEL GOVERNO DEL TERRITORIO: Ricerca e formazione nelle s...Beniamino Murgante
RISCHIO TERRITORIALE NEL GOVERNO DEL TERRITORIO: Ricerca e formazione nelle scuole di ingegneria
Giuseppe Las Casas, Beniamino Murgante, Francesco Scorza
UrbIng 2016
GEOGRAPHIC INFORMATION – NEED TO KNOW (GI-N2K) Towards a more demand-driven g...Beniamino Murgante
GEOGRAPHIC INFORMATION – NEED TO KNOW (GI-N2K) Towards a more demand-driven geospatial workforce education/training system
Mauro Salvemini, Giuliana Vitiello, Monica Sebillo, Sergio Farruggia. Beniamino Murgante
Focussing Energy Consumers’ Behaviour Change towards Energy Efficiency and Lo...Beniamino Murgante
Focussing Energy Consumers’ Behaviour Change towards Energy Efficiency and Low Carbon Economy: Perspective for Policy Making, Transnational Cooperation and Research.
Beniamino Murgante, Francesco Scorza,
Alessandro Attolico, Federico Amato
Presented at the REAL CORP 2016 - 21st International Conference on Urban Planning
and Regional Development in the Information Society
GEOGRAPHIC INFORMATION – NEED TO KNOW (GI-N2K) Towards a more demand-driven g...Beniamino Murgante
GEOGRAPHIC INFORMATION – NEED TO KNOW (GI-N2K) Towards a more demand-driven geospatial workforce education/training system
Mauro Salvemini, Francesco Di Massa, Monica Sebillo, Sergio Farruggia. Beniamino Murgante
Garden in motion. An experience of citizens involvement in public space regen...Beniamino Murgante
Garden in motion. An experience of citizens involvement in public space regeneration.
Sara Lorusso, Gerardo Sassano, Michele Scioscia, Antonio Graziadei, Pasquale Passannante, Sara Bellarosa, Francesco Scaringi, Beniamino Murgante
Fino alla fine degli anni '80 un urbanista che cercava di supportare dei ragionamenti di piano con l'informatica riusciva ad ottenere, nel migliore dei casi, qualche dato statistico sulla popolazione. Con il trascorrere degli anni si è assistito ad un incremento dell'utilizzo delle tecnologie per la costruzione dei quadri conoscitivi a supporto del processo di piano, fino a raggiungere l'attuale Information Explosion Era.
Il contenuto dell'intervento si baserà su aspetti teorici ed applicativi a partire dall'esperienza di Ian McHarg fino all'ultima "moda" delle Smart Cities.
Introduzione
Andreina Maahsen-Milan
Università di Bologna
Tecnologie, Territorio, Smartness
Beniamino Murgante
Università della Basilicata
Facoltà Ingegneria Edile di Ravenna - Università di Bologna
Via Tombesi dall'Ova 55, 48121 Ravenna
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
AI for Every Business: Unlocking Your Product's Universal Potential by VP of ...
Marvuglia
1. Session: Geographical Analysis, Urban Modeling, Spatial statistics A web-based autonomous weather monitoring system of the town of Palermo and its utilization for temperature nowcasting By: Giorgio Beccali, Maurizio Cellura, Simona Culotta, Valerio Lo Brano, Antonino Marvuglia UNIVERSITY OF PALERMO
6. Area: 6.25 Km 2 Area: 13.5 Km 2 Area: 45.6 Km 2 Installed in March 2008 July, 10 th 2007 MeteoPalermo1 March, 4 th 2008 Albunea May, 21 st 2007 Pizia February, 20 th 2007 Amaltea November, 30 th 2006 Cassandra November, 13 rd 2006 Morgana September, 26 th 2006 Merlino Installation date Weather station
7. Data acquisition and web publishing system The Linux server of DREAM connects to the shared folder of this PC where the file is stored and copies it into a local folder. The procedure is automated by a bash script and is repeated for each weather station. Re-formatting of the ASCII file through a Perl script A web server (Apache) is connected with the database server (MySQL) and an http service is available over TCP/IP network . Graphs and data digest : publicly available. Statistic elaborations and data download : protected by login and password. Every 30 minutes each weather station automatically generates an ASCII file containing the last 336 collected data and immediately transfers it (via GSM) to a MS Windows PC located at the DREAM building, in which a proprietary software is installed.