Effects of artificial neural networks characterization on prediction of diesel engine emissions

More than a century after its invention, diesel remains the fuel of choice for buses and freight trucks. Diesel exhaust contains three gases that are regulated by the United States Environmental Protection Agency (EPA), as well as particulate matter (PM). There is a societal need both to lower emissions and to predict or model emissions more accurately for inventory purposes. Engine modeling, and real time control are the most indispensable steps towards lowering engine emissions, and it is argued that this modeling can be achieved by implementation of Artificial Neural Networks (ANN). Effects of ANN design, architecture, and learning parameters on the accuracy of emissions predictions were studied along with the variation of embedded activation functions. An optimization strategy was followed to attain the most suitable network in the defined framework for five emissions of NOx, PM, HC, CO, and CO2. The emissions data were obtained from five engine transient test schedules, namely the E-CSHVR, ETC, FTP, E-Highway and E-WVU-5 Peak schedules. These were performed on a 550 hp General Electric DC engine dynamometer-testing unit at the West Virginia University Alternative Fuels, Engine and Emissions Research Center. The 3-Layer and Jump Connection networks were the most promising architectures and it was found that the radial basis functions such as the Gaussian and Gaussian Complement functions outperform the sigmoidal functions in all of the examined architectures. The accuracy of an excellent typical instance of CO2 prediction was as good as 0.009% error of accumulated value over the course of a FTP cycle

An Extensive Analysis of Machine Learning Based Boosting Algorithms for Software Maintainability Prediction

Software Maintainability is an indispensable factor to acclaim for the quality of particular software. It describes the ease to perform several maintenance activities to make a software adaptable to the modified environment. The availability & growing popularity of a wide range of Machine Learning (ML) algorithms for data analysis further provides the motivation for predicting this maintainability. However, an extensive analysis & comparison of various ML based Boosting Algorithms (BAs) for Software Maintainability Prediction (SMP) has not been made yet. Therefore, the current study analyzes and compares five different BAs, i.e., AdaBoost, GBM, XGB, LightGBM, and CatBoost, for SMP using open-source datasets. Performance of the propounded prediction models has been evaluated using Root Mean Square Error (RMSE), Mean Magnitude of Relative Error (MMRE), Pred(0.25), Pred(0.30), & Pred(0.75) as prediction accuracy measures followed by a non-parametric statistical test and a post hoc analysis to account for the differences in the performances of various BAs. Based on the residual errors obtained, it was observed that GBM is the best performer, followed by LightGBM for RMSE, whereas, in the case of MMRE, XGB performed the best for six out of the seven datasets, i.e., for 85.71% of the total datasets by providing minimum values for MMRE, ranging from 0.90 to 3.82. Further, on applying the statistical test and on performing the post hoc analysis, it was found that significant differences exist in the performance of different BAs and, XGB and CatBoost outperformed all other BAs for MMRE. Lastly, a comparison of BAs with four other ML algorithms has also been made to bring out BAs superiority over other algorithms. This study would open new doors for the software developers for carrying out comparatively more precise predictions well in time and hence reduce the overall maintenance costs

Artificial Neural Network Approaches For Slope Stability

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2007Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2007Bu çalışmada 170 tane lokal bölgenin şev profili dataları kullanılarak yapay zeka mantığı yaklaşımlarından beş tane yapay sinir ağı mimarisi kullanılmıştır. Bunlar BPNN, geri yayılmalı sinir ağı mimarisi ve GRNN, genel regresyonlu yapay sinir ağı mimarisi, GMDH, gruplama methodu, Kohonen ve PNN, olasılık yöntemidir. Ancak sadece BPNN, geri yayılmalı sinir ağı mimarisi ve GRNN, genel regresyonlu yapay sinir ağı mimarisi model oluşturmakta kullanılmıştır. Bu yaklaşımlarda 9 adet girdi ve 1 tane çıkış parametreleri verilmiştir. Çıkış parametresi şev güvenlik katsayısı olup, girdi parametreleri şev yüksekliği ( H ), şev eğimi ( β ), yeraltı suyu derinliği ( Hw ), sağlam zemin derinliği ( Hb ), kohezyon ( c ), zemin içsel sürtünme açısı ( Φ ), kuru birim hacim ağırlığı ( γ ), düşey ve yatay sismik zemin katsayıları ( Kh , Kv )‘dır. Bu çalışmadaki amaç sismik zemin katsayılarının şev stabilitesindeki önemlerinin incelenmesidir. Sonuç olarak genel regresyon yapay sinir ağı modelinin daha başarılı olduğu ve % 92.5 başarı yüzdesine sahip olduğu görülmüş, düşey ve yatay sismik zemin katsayılarının şev yüksekliği, şev eğimi ve yeraltı suyu derinliğinden sonra şev stabilitesindeki etkisinin önemli olduğu görülmüştür.In this study 170 slope data and their properties are used by Artificial Intelligence approach five neural network approaches architecture These approaches are Back propagation neural network architecture ( BPNN ), General regression neural network ( GRNN ), Group method of data handling ( GMDH ), Kohonen learning paradigm and Probabilistic neural network ( PNN ) architectures. But only 2 of them used, these are the back propagation neural network architecture ( BPNN ) and the general regression neural network ( GRNN ). There are 9 input parameters and 1 output parameter. The output parameter is the factor of the safety of the slopes ( F.S. ), the input parameters are the height of slope ( H ), the inclination of slope ( β ), the height of water level ( Hw ), the depth of firm base ( Hb ), the cohesion of soil ( c ), the friction angle of soil ( Φ ), the unit weight of soil ( γ ), but the important input parameters are horizontal and vertical seismic coefficients ( kh , kv ).Trying to be obtained in this study is to see the importance of the seismic coefficients for a slope stability safety. In conclusion this study shows that general regression neural network (GRNN) approach is more useful model and have % 92.5 success rate for seeing the effect of earthquake for slope stability safety and generally horizontal and vertical seismic coefficients importance seen after the height of the slope ( H ), the inclination of slope ( β ), the height of water level (Hw) importance.Yüksek LisansM.Sc

DEVELOPMENT AND TESTING OF UNIVERSAL PRESSURE DROP MODELS IN PIPELINES USING ABDUCTIVE AND ARTIFICIAL NEURAL NETWORKS

Determination of pressure drop in pipeline system is difficult. Conventional methods (empirical correlations and mechanistic methods) were not successful in providing accurate estimate. Artificial Neural Networks and polynomial Group Method of Data Handling techniques had received wide recognition in terms of discovering hidden and highly nonlinear relationships between input and output patterns. The potential of both Artificial Neural Networks (ANN) and Abductory Induction Mechanism (AIM) techniques has been revealed in this study by generating generic models for pressure drop estimation in pipeline systems that carry multiphase fluids (oil, gas, and water) and with wide range of angles of inclination. No past study was found that utilizes both techniques in an attempt to solve this problem. A total number of 335 data sets collected from different Middle Eastern fields have been used in developing the models. The data covered a wide range of variables at different values such as oil rate (2200 to 25000 bbl/d), water rate (up to 8424 bbl/d), angles of inclination (-52 to 208 degrees), length of the pipe (500 to 26700 ft) and gas rate (1078 to 19658 MSCFD). For the ANN model, a ratio of 2: 1: 1 between training, validation, and testing sets yielded the best training/testing performance. The ANN model has been developed using resilient back-propagation learning algorithm. The purpose for generating another model using the polynomial Group Method of Data Handling technique was to reduce the problem of dimensionality that affects the accuracy of ANN modeling. It was found that (by the Group Method of Data Handling algorithm), length of the pipe, wellhead pressure, and angle of inclination have a pronounced effect on the pressure drop estimation under these conditions. The best available empirical correlations and mechanistic models adopted by the industry had been tested against the data and the developed models. Graphical and statistical tools had been utilized for comparing the performance of the new models and other empirical correlations and mechanistic models. Thorough verifications have indicated that the developed Artificial Neural Networks model outperforms all tested empirical correlations and mechanistic models as well as the polynomial Group Method of Data Handling model in terms of highest correlation coefficient, lowest average absolute percent error, lowest standard deviation, lowest maximum error, and lowest root mean square error. The study offers reliable and quick means for pressure drop estimation in pipelines carrying multiphase fluids with wide range of angles of inclination using Artificial Neural Networks and Group Method of Data Handling techniques. Graphical User Interface (GUI) has been generated to help apply the ANN model results while an applicable equation can be used for Group Method of Data Handling model. While the conventional methods were not successful in providing accurate estimate of this property, the second approach (Group Method of Data Handling technique) was able to provide a reliable estimate with only three-input parameters involved. The modeling accuracy was not greatly harmed using this technique

Selected Computing Research Papers Volume 2 June 2013

An Evaluation of Current Innovations for Solving Hard Disk Drive Vibration Problems (Isiaq Adeola) ........................................................................................................ 1 A Critical Evaluation of the Current User Interface Systems Used By the Blind and Visually Impaired (Amneet Ahluwalia) ................................................................................ 7 Current Research Aimed At Improving Bot Detection In Massive Multiplayer Online Games (Jamie Burnip) ........................................................................................................ 13 Evaluation Of Methods For Improving Network Security Against SIP Based DoS Attacks On VoIP Network Infrastructures (David Carney) ................................................ 21 An Evaluation of Current Database Encryption Security Research (Ohale Chidiebere) .... 29 A Critical Appreciation of Current SQL Injection Detection Methods (Lee David Glynn) .............................................................................................................. 37 An Analysis of Current Research into Music Piracy Prevention (Steven Hodgson) .......... 43 Real Time On-line Analytical Processing: Applicability Of Parallel Processing Techniques (Kushatha Kelebeng) ....................................................................................... 49 Evaluating Authentication And Authorisation Method Implementations To Create A More Secure System Within Cloud Computing Technologies (Josh Mallery) ................... 55 A Detailed Analysis Of Current Computing Research Aimed At Improving Facial Recognition Systems (Gary Adam Morrissey) ................................................................... 61 A Critical Analysis Of Current Research Into Stock Market Forecasting Using Artificial Neural Networks (Chris Olsen) ........................................................................... 69 Evaluation of User Authentication Schemes (Sukhdev Singh) .......................................... 77 An Evaluation of Biometric Security Methods for Use on Mobile Devices (Joe van de Bilt) .................................................................................................................. 8

Koneoppimiskehys petrokemianteollisuuden sovelluksille

Machine learning has many potentially useful applications in process industry, for example in process monitoring and control. Continuously accumulating process data and the recent development in software and hardware that enable more advanced machine learning, are fulfilling the prerequisites of developing and deploying process automation integrated machine learning applications which improve existing functionalities or even implement artificial intelligence. In this master's thesis, a framework is designed and implemented on a proof-of-concept level, to enable easy acquisition of process data to be used with modern machine learning libraries, and to also enable scalable online deployment of the trained models. The literature part of the thesis concentrates on studying the current state and approaches for digital advisory systems for process operators, as a potential application to be developed on the machine learning framework. The literature study shows that the approaches for process operators' decision support tools have shifted from rule-based and knowledge-based methods to machine learning. However, no standard methods can be concluded, and most of the use cases are quite application-specific. In the developed machine learning framework, both commercial software and open source components with permissive licenses are used. Data is acquired over OPC UA and then processed in Python, which is currently almost the de facto standard language in data analytics. Microservice architecture with containerization is used in the online deployment, and in a qualitative evaluation, it proved to be a versatile and functional solution.Koneoppimisella voidaan osoittaa olevan useita hyödyllisiä käyttökohteita prosessiteollisuudessa, esimerkiksi prosessinohjaukseen liittyvissä sovelluksissa. Jatkuvasti kerääntyvä prosessidata ja toisaalta koneoppimiseen soveltuvien ohjelmistojen sekä myös laitteistojen viimeaikainen kehitys johtavat tilanteeseen, jossa prosessiautomaatioon liitettyjen koneoppimissovellusten avulla on mahdollista parantaa nykyisiä toiminnallisuuksia tai jopa toteuttaa tekoälysovelluksia. Tässä diplomityössä suunniteltiin ja toteutettiin prototyypin tasolla koneoppimiskehys, jonka avulla on helppo käyttää prosessidataa yhdessä nykyaikaisten koneoppimiskirjastojen kanssa. Kehys mahdollistaa myös koneopittujen mallien skaalautuvan käyttöönoton. Diplomityön kirjallisuusosa keskittyy prosessioperaattoreille tarkoitettujen digitaalisten avustajajärjestelmien nykytilaan ja toteutustapoihin, avustajajärjestelmän tai sen päätöstukijärjestelmän ollessa yksi mahdollinen koneoppimiskehyksen päälle rakennettava ohjelma. Kirjallisuustutkimuksen mukaan prosessioperaattorin päätöstukijärjestelmien taustalla olevat menetelmät ovat yhä useammin koneoppimiseen perustuvia, aiempien sääntö- ja tietämyskantoihin perustuvien menetelmien sijasta. Selkeitä yhdenmukaisia lähestymistapoja ei kuitenkaan ole helposti pääteltävissä kirjallisuuden perusteella. Lisäksi useimmat tapausesimerkit ovat sovellettavissa vain kyseisissä erikoistapauksissa. Kehitetyssä koneoppimiskehyksessä on käytetty sekä kaupallisia että avoimen lähdekoodin komponentteja. Prosessidata haetaan OPC UA -protokollan avulla, ja sitä on mahdollista käsitellä Python-kielellä, josta on muodostunut lähes de facto -standardi data-analytiikassa. Kehyksen käyttöönottokomponentit perustuvat mikropalveluarkkitehtuuriin ja konttiteknologiaan, jotka osoittautuivat laadullisessa testauksessa monipuoliseksi ja toimivaksi toteutustavaksi

Real-Time Forecasting/Control of Water Resource Systems; Selected Papers from an IIASA Workshop, October 18-21,1976

When water resource systems are not under control, the consequences can be devastating. In the United States alone, flood damage cost approximately $1.5 billion annually. These losses can be avoided by building more reservoirs to hold the flood waters, but such construction is very expensive, especially because reservoirs have already been built on the best sites. A better and less expensive alternative is the development of more effective management methods for existing water resource systems, which commonly waste approximately 20 percent of their capacities through mismanagement. Statistical models first appeared in hydrology at the beginning of the 1970s. Hydrologists began to use the techniques of time series analysis and system identification in their models, which seemed to give better results than the earlier, deterministic simulation models. In addition, real-time control of water resources was being developed at the practical level and on-line measurements of rainfall and runoff from a catchment were becoming available. The conceptual models then in use could not take advantage of measurements from the catchment, but on-line measurements now allow an operator to anticipate flood waters upstream or a water shortage downstream. This book contains selected papers from a workshop devoted to the consolidation of international research on statistically estimated models for real-time forecasting and control of water resource systems. The book is divided into three parts. The first part presents several methods of forecasting for water resource systems: distributed lag models, maximum likelihood identification, nonlinear catchment models, Kalman filtering, and self-tuning predictors. The papers in the second part present methods for controlling stream quality and stream flow, and the third part describes forecasting in the United States, the United Kingdom, and Poland

DEVELOPMENT AND TESTING OF UNIVERSAL PRESSURE DROP MODELS IN PIPELINES USING ABDUCTIVE AND ARTIFICIAL NEURAL NETWORKS

Determination of pressure drop in pipeline system is difficult. Conventional methods (empirical correlations and mechanistic methods) were not successful in providing accurate estimate. Artificial Neural Networks and polynomial Group Method of Data Handling techniques had received wide recognition in terms of discovering hidden and highly nonlinear relationships between input and output patterns. The potential of both Artificial Neural Networks (ANN) and Abductory Induction Mechanism (AIM) techniques has been revealed in this study by generating generic models for pressure drop estimation in pipeline systems that carry multiphase fluids (oil, gas, and water) and with wide range of angles of inclination. No past study was found that utilizes both techniques in an attempt to solve this problem. A total number of 335 data sets collected from different Middle Eastern fields have been used in developing the models. The data covered a wide range of variables at different values such as oil rate (2200 to 25000 bbl/d), water rate (up to 8424 bbl/d), angles of inclination (-52 to 208 degrees), length of the pipe (500 to 26700 ft) and gas rate (1078 to 19658 MSCFD). For the ANN model, a ratio of 2: 1: 1 between training, validation, and testing sets yielded the best training/testing performance. The ANN model has been developed using resilient back-propagation learning algorithm. The purpose for generating another model using the polynomial Group Method of Data Handling technique was to reduce the problem of dimensionality that affects the accuracy of ANN modeling. It was found that (by the Group Method of Data Handling algorithm), length of the pipe, wellhead pressure, and angle of inclination have a pronounced effect on the pressure drop estimation under these conditions. The best available empirical correlations and mechanistic models adopted by the industry had been tested against the data and the developed models. Graphical and statistical tools had been utilized for comparing the performance of the new models and other empirical correlations and mechanistic models. Thorough verifications have indicated that the developed Artificial Neural Networks model outperforms all tested empirical correlations and mechanistic models as well as the polynomial Group Method of Data Handling model in terms of highest correlation coefficient, lowest average absolute percent error, lowest standard deviation, lowest maximum error, and lowest root mean square error. The study offers reliable and quick means for pressure drop estimation in pipelines carrying multiphase fluids with wide range of angles of inclination using Artificial Neural Networks and Group Method of Data Handling techniques. Graphical User Interface (GUI) has been generated to help apply the ANN model results while an applicable equation can be used for Group Method of Data Handling model. While the conventional methods were not successful in providing accurate estimate of this property, the second approach (Group Method of Data Handling technique) was able to provide a reliable estimate with only three-input parameters involved. The modeling accuracy was not greatly harmed using this technique