in this manuscript a compact Ultra Wide Band Antenna is presented. Antenna is working with extra wide frequency band from 2.7GHz to 30.1 GHz which makes this module suitable for 5 G, and Robotics and Devices in the wide area for Internet of Things which uses a very wide of frequency band. The design begins with design circular planer patch and then to enhance the performance of the antenna it was made an inserting of Split, Ring Resonators for improving the frequency band and to deny the radiation in cretin frequency band. UWB antenna was simulated using HFSS Simulator. Simulation showed extra wide frequency band with stop band in the band 3.41GHz -3.76GHz to work with WiMAX communication systems. The simulated model was implemented and tested for Return Loss ( S11 parameter ) and the Gain of the antenna .The results of analysis showed a very good agreement between simulated and implemented planner UWB antennas.
{"title":"Ultra Wide Band Antenna Design for Robotics & Sensors Environment","authors":"J. M. Rasool","doi":"10.1109/DeSE.2019.00125","DOIUrl":"https://doi.org/10.1109/DeSE.2019.00125","url":null,"abstract":"in this manuscript a compact Ultra Wide Band Antenna is presented. Antenna is working with extra wide frequency band from 2.7GHz to 30.1 GHz which makes this module suitable for 5 G, and Robotics and Devices in the wide area for Internet of Things which uses a very wide of frequency band. The design begins with design circular planer patch and then to enhance the performance of the antenna it was made an inserting of Split, Ring Resonators for improving the frequency band and to deny the radiation in cretin frequency band. UWB antenna was simulated using HFSS Simulator. Simulation showed extra wide frequency band with stop band in the band 3.41GHz -3.76GHz to work with WiMAX communication systems. The simulated model was implemented and tested for Return Loss ( S11 parameter ) and the Gain of the antenna .The results of analysis showed a very good agreement between simulated and implemented planner UWB antennas.","PeriodicalId":6632,"journal":{"name":"2019 12th International Conference on Developments in eSystems Engineering (DeSE)","volume":"6 1","pages":"668-672"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79220429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Saeidi, M. Soufian, A. Elkurdi, S. Nefti-Meziani
In this work, a predictive maintenance system is discussed as a modern solution for reducing downtimes in complex systems such as airplanes’ jet engines. The developed predictive maintenance system is based on prognostic and predictive algorithms which will be constructed by using machine learning techniques. Bayesian theorem is specially studied and employed for this purpose in this paper. The design and implementation of a Naïve Bayesian classifier will be presented to demonstrate and challenge the practicality of the method. A turbofan jet engine health check system was chosen as a complex and live industrial testbed example. We also demonstrate that the system in question has a high entropy and despite this, the Bayesian approach is sufficient enough to eliminate the critical errors as well as maintain a satisfactory overall accuracy.
{"title":"A Jet Engine Prognostic and Diagnostic System Based on Bayesian Classifier","authors":"M. Saeidi, M. Soufian, A. Elkurdi, S. Nefti-Meziani","doi":"10.1109/DeSE.2019.00181","DOIUrl":"https://doi.org/10.1109/DeSE.2019.00181","url":null,"abstract":"In this work, a predictive maintenance system is discussed as a modern solution for reducing downtimes in complex systems such as airplanes’ jet engines. The developed predictive maintenance system is based on prognostic and predictive algorithms which will be constructed by using machine learning techniques. Bayesian theorem is specially studied and employed for this purpose in this paper. The design and implementation of a Naïve Bayesian classifier will be presented to demonstrate and challenge the practicality of the method. A turbofan jet engine health check system was chosen as a complex and live industrial testbed example. We also demonstrate that the system in question has a high entropy and despite this, the Bayesian approach is sufficient enough to eliminate the critical errors as well as maintain a satisfactory overall accuracy.","PeriodicalId":6632,"journal":{"name":"2019 12th International Conference on Developments in eSystems Engineering (DeSE)","volume":"87 1","pages":"975-977"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83435948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. H. Teng, Ibijoke A. Idowu, P. Kot, A. Shaw, M. Muradov
A novel non-destructive electromagnetic wave (EM) sensor for rapid identification of biological material is presented in this paper. Biological treats could be defined as biological agents such as bacteria spores, viruses and toxins. Spores can disable or kill people, animals and crops. Therefore, it is important to identify the hazard in rapid and nondestructive manner to make a safer environment. In this research, a 2.45 GHz microwave resonator was used to detect the dipliconic acid (DPA), which is the bio-maker of bacillus spores. A promising results were obtained by detecting the DPA from 0.001M – 0.3M concentration at frequency of 2.4 GHz, which are the fundamental mode (TM101) of the designed cavity. In addition, different species of bacillus spores was detected at frequency approximate at 2.36 GHz. The results concluded that electromagnetic wave sensors may have the potential for use as a non-destructive and real time sensor to detect bacillus spores. The EM principle could be extended to detect different hazardous biological materials by identify the “finger print” of specific biological materials on different surfaces.
{"title":"Non-destructive Electromagnetic Wave Sensor for Hazardous Biological Materials","authors":"K. H. Teng, Ibijoke A. Idowu, P. Kot, A. Shaw, M. Muradov","doi":"10.1109/DeSE.2019.00122","DOIUrl":"https://doi.org/10.1109/DeSE.2019.00122","url":null,"abstract":"A novel non-destructive electromagnetic wave (EM) sensor for rapid identification of biological material is presented in this paper. Biological treats could be defined as biological agents such as bacteria spores, viruses and toxins. Spores can disable or kill people, animals and crops. Therefore, it is important to identify the hazard in rapid and nondestructive manner to make a safer environment. In this research, a 2.45 GHz microwave resonator was used to detect the dipliconic acid (DPA), which is the bio-maker of bacillus spores. A promising results were obtained by detecting the DPA from 0.001M – 0.3M concentration at frequency of 2.4 GHz, which are the fundamental mode (TM101) of the designed cavity. In addition, different species of bacillus spores was detected at frequency approximate at 2.36 GHz. The results concluded that electromagnetic wave sensors may have the potential for use as a non-destructive and real time sensor to detect bacillus spores. The EM principle could be extended to detect different hazardous biological materials by identify the “finger print” of specific biological materials on different surfaces.","PeriodicalId":6632,"journal":{"name":"2019 12th International Conference on Developments in eSystems Engineering (DeSE)","volume":"11 1","pages":"651-655"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87401963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Poly (methyl methacrylate) doped with (0, 0.3, 0.5, 0.7, and 0.9) wt. % of bromine additives conductive polymers films were prepared by electrospinning technique. The polymeric solution investigation lead that increasing the bromine weight addition decrease viscosity and increase both surface tension and electrical conductivity .The morphology of the prepared films was characterized by scanning electron microscopy ,the average fiber diameter measured statistically from the SEM image was equal to 800 nm for PMMA with 0.7 wt.% bromine thin film, and 400 nm for the pure PMMA thin film. The Bromine addition improved the electrical conductivity of the prepared films with increasing bromine concentration, from (29.5518× 10-9 S/cm) for pure PMMA film to (224.870 × 10-9 S/cm) for PMMA with 0.9 wt. % of bromine additives at room temperature. Activation energy of the polymeric film decreased from 0.4483 e.V for pure PMMA film to 0.3783 eV for PMMA with 0.9 wt. % bromine film. Key words; Electro spinning, Electrical conductivity. Bromine ,PMMA ,Activation energy
{"title":"Bromine Addition Improving Activation Energy and Electrical Conductivity of Electrospun Poly Methyl Meth Acrylate (PMMA) Thin Film Nanofibers Conductive Polymers","authors":"A. R. Jabur, Ali A. Hameed","doi":"10.1109/DeSE.2019.00140","DOIUrl":"https://doi.org/10.1109/DeSE.2019.00140","url":null,"abstract":"Poly (methyl methacrylate) doped with (0, 0.3, 0.5, 0.7, and 0.9) wt. % of bromine additives conductive polymers films were prepared by electrospinning technique. The polymeric solution investigation lead that increasing the bromine weight addition decrease viscosity and increase both surface tension and electrical conductivity .The morphology of the prepared films was characterized by scanning electron microscopy ,the average fiber diameter measured statistically from the SEM image was equal to 800 nm for PMMA with 0.7 wt.% bromine thin film, and 400 nm for the pure PMMA thin film. The Bromine addition improved the electrical conductivity of the prepared films with increasing bromine concentration, from (29.5518× 10-9 S/cm) for pure PMMA film to (224.870 × 10-9 S/cm) for PMMA with 0.9 wt. % of bromine additives at room temperature. Activation energy of the polymeric film decreased from 0.4483 e.V for pure PMMA film to 0.3783 eV for PMMA with 0.9 wt. % bromine film. Key words; Electro spinning, Electrical conductivity. Bromine ,PMMA ,Activation energy","PeriodicalId":6632,"journal":{"name":"2019 12th International Conference on Developments in eSystems Engineering (DeSE)","volume":"21 1","pages":"750-754"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87356754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Social media are a way for people to build their reputation or to promote an idea. Twitter, in contrast with other social media sources, is a generator of real-time textual information, and it is mainly used to share ideas, opinions and breaking news. It is meant for short, quick, compelling statements that reach out millions of users around the world. Posting something inappropriate may affect the public image, privacy of celebrities, politicians as well as ordinary Twitter users. If we could in advance alarm the user of the potential vulnerability in the message to be posted we could protect his/her identity from being compromised. So, automatic identification of the message with the content causing it to be deleted in the future is a promising area of research. In this paper, we are analyzing Twitter messages in English language with the objective to build a classifier to predict whether a particular post will be deleted by the user or not. We apply the Recurrent Neural Networks (RNN) model that relies on the context-based information of tweets while doing the classification. An additional contribution of the work is the construction of a rich set of features including twitter metadata, user information and tweets’ text to train classical machine learning algorithms on Twitter data.
{"title":"Prediction of Twitter Message Deletion","authors":"A. Gazizullina, M. Mazzara","doi":"10.1109/DeSE.2019.00031","DOIUrl":"https://doi.org/10.1109/DeSE.2019.00031","url":null,"abstract":"Social media are a way for people to build their reputation or to promote an idea. Twitter, in contrast with other social media sources, is a generator of real-time textual information, and it is mainly used to share ideas, opinions and breaking news. It is meant for short, quick, compelling statements that reach out millions of users around the world. Posting something inappropriate may affect the public image, privacy of celebrities, politicians as well as ordinary Twitter users. If we could in advance alarm the user of the potential vulnerability in the message to be posted we could protect his/her identity from being compromised. So, automatic identification of the message with the content causing it to be deleted in the future is a promising area of research. In this paper, we are analyzing Twitter messages in English language with the objective to build a classifier to predict whether a particular post will be deleted by the user or not. We apply the Recurrent Neural Networks (RNN) model that relies on the context-based information of tweets while doing the classification. An additional contribution of the work is the construction of a rich set of features including twitter metadata, user information and tweets’ text to train classical machine learning algorithms on Twitter data.","PeriodicalId":6632,"journal":{"name":"2019 12th International Conference on Developments in eSystems Engineering (DeSE)","volume":"47 1","pages":"117-122"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90585122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article describes the architecture of the training simulator designed for invasive operations in virtual reality, which is based on users interaction with virtual surgical instruments and a virtual patient using controllers. Particular attention is paid to game mechanics, that synchronized with real practitioners used in open operations. Types of possible user actions are described.
{"title":"Virtual Reality-Based Immersive Simulation Mechanics for Invasive Surgery Training","authors":"Rumia Sultanova, R. Sharaeva","doi":"10.1109/DeSE.2019.00171","DOIUrl":"https://doi.org/10.1109/DeSE.2019.00171","url":null,"abstract":"This article describes the architecture of the training simulator designed for invasive operations in virtual reality, which is based on users interaction with virtual surgical instruments and a virtual patient using controllers. Particular attention is paid to game mechanics, that synchronized with real practitioners used in open operations. Types of possible user actions are described.","PeriodicalId":6632,"journal":{"name":"2019 12th International Conference on Developments in eSystems Engineering (DeSE)","volume":"33 1","pages":"924-928"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90699560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Danilin, S. Shchanikov, A. Zuev, I. Bordanov, D. Korolev, A. Belov, A. Pimashkin, A. Mikhaylov, V. Kazantsev
A key problem at hardware implementation of artificial neural networks based on memristors (ANNM) is to ensure the required accuracy of their operation at the transition from models to real fabricated memristive devices. Due to a number of factors, such as the imperfections in stateof- the-art memristors and memristive arrays, ANNM design and tuning methods, additional computation errors occur during the process of ANNM hardware implementation. The article proposes a general approach to the simulation and design of a multilayer perceptron (MLP) network implemented with original cross-bar arrays of metal-oxide memristive devices. The proposed approach is based on the theory of engineering tolerances, simulation and the design of experiments. The authors present the research results for the ANNM trained to solve the problem of nonlinear classification for a bidirectional adaptive neural interface.
{"title":"Design of Multilayer Perceptron Network Based on Metal-Oxide Memristive Devices","authors":"S. Danilin, S. Shchanikov, A. Zuev, I. Bordanov, D. Korolev, A. Belov, A. Pimashkin, A. Mikhaylov, V. Kazantsev","doi":"10.1109/DeSE.2019.00103","DOIUrl":"https://doi.org/10.1109/DeSE.2019.00103","url":null,"abstract":"A key problem at hardware implementation of artificial neural networks based on memristors (ANNM) is to ensure the required accuracy of their operation at the transition from models to real fabricated memristive devices. Due to a number of factors, such as the imperfections in stateof- the-art memristors and memristive arrays, ANNM design and tuning methods, additional computation errors occur during the process of ANNM hardware implementation. The article proposes a general approach to the simulation and design of a multilayer perceptron (MLP) network implemented with original cross-bar arrays of metal-oxide memristive devices. The proposed approach is based on the theory of engineering tolerances, simulation and the design of experiments. The authors present the research results for the ANNM trained to solve the problem of nonlinear classification for a bidirectional adaptive neural interface.","PeriodicalId":6632,"journal":{"name":"2019 12th International Conference on Developments in eSystems Engineering (DeSE)","volume":"22 1","pages":"533-538"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91077651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Volodarskaya, A. S. Grishina, L. I. Pechinskaya
The paper examines problems related to development of lexical skills in non-language students when learning foreign languages exemplified by the English language. The authors analyse common aspects of active vocabulary expansion techniques, specify and clarify the necessity of Moodle virtual learning environment implementation as a tool for self-directed vocabulary learning. Based on the study findings, the authors conclude that lexical skills development can be improved, particularly active vocabulary expansion, transfer of words from the passive to the active vocabulary and overcoming a fear of producing monologues and participating in dialogues due to an active vocabulary deficit.
{"title":"Virtual Learning Environment in Lexical Skills Development for Active Vocabulary Expansion in Non-Language Students Who Learn English","authors":"E. Volodarskaya, A. S. Grishina, L. I. Pechinskaya","doi":"10.1109/DeSE.2019.00077","DOIUrl":"https://doi.org/10.1109/DeSE.2019.00077","url":null,"abstract":"The paper examines problems related to development of lexical skills in non-language students when learning foreign languages exemplified by the English language. The authors analyse common aspects of active vocabulary expansion techniques, specify and clarify the necessity of Moodle virtual learning environment implementation as a tool for self-directed vocabulary learning. Based on the study findings, the authors conclude that lexical skills development can be improved, particularly active vocabulary expansion, transfer of words from the passive to the active vocabulary and overcoming a fear of producing monologues and participating in dialogues due to an active vocabulary deficit.","PeriodicalId":6632,"journal":{"name":"2019 12th International Conference on Developments in eSystems Engineering (DeSE)","volume":"32 1","pages":"388-392"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89792254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Danilin, S. Shchanikov, A. Zuev, I. Bordanov, A. E. Sakulin
A general approach to the development of memristor-based artificial neural networks (ANNM) operated with specified fault tolerance (FT) is formulated and applied in the paper. It is shown that ensuring the required ANNM FT is related to ensuring the required accuracy of their operation at all the structural and functional hierarchy levels. The paper proposes a quantitative FT criterion that can be used to create ANNM reliability block diagrams, calculate and optimize reliability in accord with the actual Russian and international standards. The application of the proposed algorithm is considered on the example of the ANN performing an approximation of mathematical functions, the synapses of which are implemented with memristors. It is found that a potentially high ANNM FT cannot be achieved by itself only because of the massive parallelism of artificial neural networks. Instead it depends on many factors and requires the application of special physical and information technologies at all the ANNM life cycle stages.
{"title":"The Research of Fault Tolerance of Memristor-Based Artificial Neural Networks","authors":"S. Danilin, S. Shchanikov, A. Zuev, I. Bordanov, A. E. Sakulin","doi":"10.1109/DeSE.2019.00104","DOIUrl":"https://doi.org/10.1109/DeSE.2019.00104","url":null,"abstract":"A general approach to the development of memristor-based artificial neural networks (ANNM) operated with specified fault tolerance (FT) is formulated and applied in the paper. It is shown that ensuring the required ANNM FT is related to ensuring the required accuracy of their operation at all the structural and functional hierarchy levels. The paper proposes a quantitative FT criterion that can be used to create ANNM reliability block diagrams, calculate and optimize reliability in accord with the actual Russian and international standards. The application of the proposed algorithm is considered on the example of the ANN performing an approximation of mathematical functions, the synapses of which are implemented with memristors. It is found that a potentially high ANNM FT cannot be achieved by itself only because of the massive parallelism of artificial neural networks. Instead it depends on many factors and requires the application of special physical and information technologies at all the ANNM life cycle stages.","PeriodicalId":6632,"journal":{"name":"2019 12th International Conference on Developments in eSystems Engineering (DeSE)","volume":"70 1","pages":"539-544"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90937357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Zubaidi, H. Al-Bugharbee, Y. R. Muhsen, K. Hashim, R. Alkhaddar, Wisam H. Hmeesh
Accurate prediction of short-term water demand plays an important role for water suppliers as well as for government's water plan. This paper aims to predict a municipal water demand for an upcoming year based on previous water consumption in Baghdad city. We have investigated various signal processing approaches to address the noisy time series data of water consumption, while a new methodology for short-term prediction of municipal water consumption has been proposed. This would enable us to forecast the short-term municipal water demand using different windows and multi-stages of hybrid univariate singular spectrum analysis and autoregressive model (SSA-AR model). First, different windows and multi-stages of SSA are utilised to analyse and clean the original water time series from noise. Then, the autoregressive (AR) model is employed to predict water demand based on the treated water consumption time series. In this study, monthly water consumption data from (2006-2015) for Al-Wehda treatment plant in Baghdad city, Iraq is selected to assess the model. The findings show that (SSA-AR model) can predict water demand with high accuracy from high noisy raw data.
{"title":"The Prediction of Municipal Water Demand in Iraq: A Case Study of Baghdad Governorate","authors":"S. Zubaidi, H. Al-Bugharbee, Y. R. Muhsen, K. Hashim, R. Alkhaddar, Wisam H. Hmeesh","doi":"10.1109/DeSE.2019.00058","DOIUrl":"https://doi.org/10.1109/DeSE.2019.00058","url":null,"abstract":"Accurate prediction of short-term water demand plays an important role for water suppliers as well as for government's water plan. This paper aims to predict a municipal water demand for an upcoming year based on previous water consumption in Baghdad city. We have investigated various signal processing approaches to address the noisy time series data of water consumption, while a new methodology for short-term prediction of municipal water consumption has been proposed. This would enable us to forecast the short-term municipal water demand using different windows and multi-stages of hybrid univariate singular spectrum analysis and autoregressive model (SSA-AR model). First, different windows and multi-stages of SSA are utilised to analyse and clean the original water time series from noise. Then, the autoregressive (AR) model is employed to predict water demand based on the treated water consumption time series. In this study, monthly water consumption data from (2006-2015) for Al-Wehda treatment plant in Baghdad city, Iraq is selected to assess the model. The findings show that (SSA-AR model) can predict water demand with high accuracy from high noisy raw data.","PeriodicalId":6632,"journal":{"name":"2019 12th International Conference on Developments in eSystems Engineering (DeSE)","volume":"65 1","pages":"274-277"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90938619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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