DOCSLIB.ORG

O'reilly G 20728328.Pdf (2.828Mb)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
O'reilly G 20728328.Pdf (2.828Mb) Decision-making tools for establishment of improved monitoring of water purification processes G O’Reilly orcid.org 0000-0002-7576-1723 Thesis accepted in fulfilment of the requirements for the degree Doctor of Philosophy in Environmental Sciences at the North-West University Promoter: Prof CC Bezuidenhout Assistant Promoter: Dr JJ Bezuidenhout Graduation May 2020 20728328 ACKNOWLEDGEMENTS I would like to express my sincerest gratitude and appreciation to the following persons for their contributions and support towards the successful completion of this study: The National Research Foundation (NRF) for their financial support. Prof Carlos Bezuidenhout, words will never be enough to express my gratitude towards you Prof. Thank you for all the support, patience, constructive criticism and words of encouragement throughout the years. Thank you for always treating me with respect and never giving up on me, even during the tough times. You are truly the best at what you do and I was blessed to have you as my promotor. Dr. Jaco Bezuidenhout, thank you for all your assistance with the statistics. Thank you for always having an open-door policy and for always being willing to help wherever you can. It has only been a pleasure to work with you. Prof Nico Smit, thank you Prof Nico, not only for the financial support, but also for the words of encouragement, your kind heart and for always being understanding whenever I came to see you. Prof. Sarina Claassens, thank you for all your support and words of encouragement throughout the past years. I have always looked up to you. Thank you for being a great role model and friend. My parents, Stanley & Lynda O’Reilly. There are not enough words to say how thankful I am for all that you have done for me. Thank you for your unconditional love, your support in everything that I do and for being the best parents that anyone could ask for. I love you so much and I will be forever grateful for all your support throughout the years. My fiancé, Marco van Staden. Thank you for all your love and support babs, especially during the last few months. Thank you for always believing in me and teaching me to never give up. I have learned so much from you and I thank God every day for giving me a life partner so understanding and loving to walk this journey with me. I couldn’t have asked for anyone better. I love you with all my heart!! i To my family, Granny (ouma Sarie), Zynéldi, Stanley, Roos, Ryan, Chanté, Gavin, Blom, Owen, Chani-mae, Joe, Lychél, Liam, Luhan, Hanlie, OD, Tannie Anne-Marie and Anneme. Thank you for all your love and support throughout my studies. I am so blessed and grateful to have such an awesome family and support network. I love you all very much. Roelof Coertze, Roels, thank you for your assistance with some of the statistical work. More importantly, thank you for always being there for me and for your friendship and support throughout the years. I really appreciate everything that you have done for me. Keeping it in the family… Tannie Linda and Oom Dirk, tannie Linda, firstly, thank you for your assistance with the proofreading. I really appreciate the help on such short notice. Secondly, thank you both for all your love and support during my studies. I really appreciate it. To all my friends and work colleagues at the Department of Microbiology, thank you for all the words of encouragement, laughs and support throughout the years. A special thank you to Clarissa and Bianca. We started this journey together…and we have obviously not finished together.. hahaha… but nonetheless, thank you for your friendship, motivation, support and just for always being there when I needed you. To Vasti, thank you for being my best friend for the past 17 years. I really appreciate your friendship and support throughout my studies. Thank you for always being there for me and for always phoning me. Hehe. Dankie maat. To my work colleagues and friends, Adéle (Êdêlle) and Adriaan(nus). A HUGE thank you for all your support the past few months. Thank you for all the laughs and words of encouragement and understanding when things got a bit rough. I really appreciate all that you have done for me. ii TABLE OF CONTENTS ACKNOWLEDGEMENTS ........................................................................................... i Abstract ................................................................................................................... vii Preface ................................................................................................................... viii List of abbreviations ............................................................................................... ix List of Figures ......................................................................................................... xi List of Tables ......................................................................................................... xiv CHAPTER 1 ............................................................................................................... 1 Introduction and problem statement ...................................................................... 1 1.1 The HACCP concept ........................................................................................ 2 1.2 Water Safety Plans .......................................................................................... 3 1.3 Artificial Neural Networks (ANNs) and Evolutionary Algorithms (EAs) ...... 4 1.4 Unconventional microbial monitoring ........................................................... 5 1.5 Description of study sites ............................................................................... 7 1.5.1 Plant A ...................................................................................................... 7 1.5.2 Plant B ...................................................................................................... 8 1.5.3 Plant C ...................................................................................................... 8 1.6 Outline of thesis ............................................................................................. 10 CHAPTER 2 ............................................................................................................. 12 Comparative analysis of the HACCP concept at drinking water purification plants ....................................................................................................................... 12 2.1 Introduction .................................................................................................... 12 2.2 Materials and Methods .................................................................................. 16 2.2.1 Evaluation of historical data .................................................................... 16 2.2.2 Evaluation of measured data .................................................................. 16 2.3 Results ............................................................................................................ 17 2.3.1 Evaluation of historical turbidity data ....................................................... 17 2.3.2 Evaluation of measured turbidity data ..................................................... 20 iii 2.4 Discussion ...................................................................................................... 25 2.5 Conclusion ..................................................................................................... 27 2.6 Recommendations ......................................................................................... 28 CHAPTER 3 ............................................................................................................. 29 Artificial Neural Networks: Applications in the drinking water sector .............. 29 3.1 Introduction .................................................................................................... 29 3.2 Principles of ANNs ........................................................................................ 31 3.3 Application of ANNs in the drinking water sector ...................................... 32 ......................................................................................................................... 33 3.3.1 Pipes/infrastructure ................................................................................. 33 3.3.2 Coagulation/flocculation dosage ............................................................. 43 3.3.3 Filtration efficacy ..................................................................................... 45 3.3.4 Municipal water demand ......................................................................... 46 3.3.5 Disinfection residuals .............................................................................. 47 3.3.6 Water quality ........................................................................................... 48 3.3.7 Disinfection by-products .......................................................................... 49 3.3.8 Organic matter removal .......................................................................... 50 3.3.9 Contamination events ............................................................................. 51 3.3.10 Organic & inorganic pollutants .............................................................. 51 3.3.11 Residual aluminium, cost & performance efficiency .............................. 52 3.4 Application of ANNs in the water sector: Scenario in South Africa .......... 53 3.5 Conclusion ....................................................................................................
Load more

Recommended publications
  • Affective Computing and Deep Learning to Perform Sentiment Analysis
    Affective computing and deep learning to perform sentiment analysis NJ Maree orcid.org 0000-0003-0031-9188 Dissertation accepted in partial fulfilment of the requirements for the degree Master of Science in Computer Science at the North-West University Supervisor: Prof L Drevin Co-supervisor: Prof JV du Toit Co-supervisor: Prof HA Kruger Graduation October 2020 24991759 Preface But as for you, be strong and do not give up, for your work will be rewarded. ~ 2 Chronicles 15:7 All the honour and glory to our Heavenly Father without whom none of this would be possible. Thank you for giving me the strength and courage to complete this study. Secondly, thank you to my supervisor, Prof Lynette Drevin, for all the support to ensure that I remain on track with my studies. Also, thank you for always trying to help me find the golden thread to tie my work together. To my co-supervisors, Prof Tiny du Toit and Prof Hennie Kruger, I appreciate your pushing me to not only focus on completing my dissertation, but also to participate in local conferences. Furthermore, thank you for giving me critical and constructive feedback throughout this study. A special thanks to my family who supported and prayed for me throughout my dissertation blues. Thank you for motivating me to pursue this path. Lastly, I would like to extend my appreciation to Dr Isabel Swart for the language editing of the dissertation, and the Telkom Centre of Excellence for providing me with the funds to attend the conferences. i Abstract Companies often rely on feedback from consumers to make strategic decisions.
    [Show full text]
  • A Timeline of Artificial Intelligence
    A Timeline of Artificial Intelligence by piero scaruffi | www.scaruffi.com All of these events are explained in my book "Intelligence is not Artificial". TM, ®, Copyright © 1996-2019 Piero Scaruffi except pictures. All rights reserved. 1960: Henry Kelley and Arthur Bryson invent backpropagation 1960: Donald Michie's reinforcement-learning system MENACE 1960: Hilary Putnam's Computational Functionalism ("Minds and Machines") 1960: The backpropagation algorithm 1961: Melvin Maron's "Automatic Indexing" 1961: Karl Steinbuch's neural network Lernmatrix 1961: Leonard Scheer's and John Chubbuck's Mod I (1962) and Mod II (1964) 1961: Space General Corporation's lunar explorer 1962: IBM's "Shoebox" for speech recognition 1962: AMF's "VersaTran" robot 1963: John McCarthy moves to Stanford and founds the Stanford Artificial Intelligence Laboratory (SAIL) 1963: Lawrence Roberts' "Machine Perception of Three Dimensional Solids", the birth of computer vision 1963: Jim Slagle writes a program for symbolic integration (calculus) 1963: Edward Feigenbaum's and Julian Feldman's "Computers and Thought" 1963: Vladimir Vapnik's "support-vector networks" (SVN) 1964: Peter Toma demonstrates the machine-translation system Systran 1965: Irving John Good (Isidore Jacob Gudak) speculates about "ultraintelligent machines" (the "singularity") 1965: The Case Institute of Technology builds the first computer-controlled robotic arm 1965: Ed Feigenbaum's Dendral expert system 1965: Gordon Moore's Law of exponential progress in integrated circuits ("Cramming more components
    [Show full text]
  • A New Concept of Convex Based Multiple Neural Networks Structure
    Session 5A: Learning Agents AAMAS 2019, May 13-17, 2019, Montréal, Canada A New Concept of Convex based Multiple Neural Networks Structure Yu Wang Yue Deng Samsung Research America Samsung Research America Mountain View, CA Mountain View, CA [email protected] [email protected] Yilin Shen Hongxia Jin Samsung Research America Samsung Research America Mountain View, CA Mountain View, CA [email protected] [email protected] ABSTRACT During the last decade, there are lots of progresses on build- In this paper, a new concept of convex based multiple neural net- ing a variety of different neural network structures, like recurrent works structure is proposed. This new approach uses the collective neural networ (RNN), long short-term memory (LSTM) network, information from multiple neural networks to train the model. convolutional neural network (CNN), time-delayed neural network From both theoretical and experimental analysis, it is going to and etc. [12, 13, 31]. Training these neural networks, however, are demonstrate that the new approach gives a faster training speed of still heavily to be relied upon back-propagation [40] by computing convergence with a similar or even better test accuracy, compared the loss functions’ gradients, then further updating the weights of to a conventional neural network structure. Two experiments are networks. Despite the effectiveness of using back-propagation, it conducted to demonstrate the performance of our new structure: still has several well known draw-backs in many real applications. the first one is a semantic frame parsing task for spoken language For example, one of the main issues is the well known gradient understanding (SLU) on ATIS dataset, and the other is a hand writ- vanishing problem [3], which limits the number of layers in a deep ten digits recognition task on MNIST dataset.
    [Show full text]
  • Table of Contents
    69 TABLE OF CONTENTS DETAILED PROGRAM (IJCNN 2014) ........................................................................ 151 Monday, July 7, 1:30PM-3:30PM .............................................................................. 151 Special Session: MoN1-1 Neuromorphic Science & Technology for Augmented Human Performance in Cybersecurity, Chair: Tarek Taha and Helen Li, Room: 308 ..................................................................... 151 1:30PM STDP Learning Rule Based on Memristor with STDP Property Ling Chen, Chuandong Li, Tingwen Huang, Xing He, Hai Li and Yiran Chen 1:50PM An Adjustable Memristor Model and Its Application in Small-World Neural Networks Xiaofang Hu, Gang Feng, Hai Li, Yiran Chen and Shukai Duan 2:10PM Efficacy of Memristive Crossbars for Neuromorphic Processors Chris Yakopcic, Raqibul Hasan and Tarek Taha 2:30PM Enabling Back Propagation Training in Memristor Crossbar Neuromorphic Processors Raqibul Hasan and Tarek Taha 2:50PM Ferroelectric Tunnel Memristor-Based Neuromorphic Network with 1T1R Crossbar Architecture Zhaohao Wang, Weisheng Zhao, Wang Kang, Youguang Zhang, Jacques-Olivier Klein and Claude Chappert Special Session: MoN1-2 Artificial Neural Networks and Learning Techniques towards Intelligent Transport Systems, Chair: David Elizondo and Benjamin Passow, Room: 305A ......................................................... 152 1:30PM Traffic Flow Prediction Using Orthogonal Arrays and Takagi-Sugeno Neural Fuzzy Models Kit Yan Chan and Tharam Dillon 1:50PM Optimal Design of Traffic Signal
    [Show full text]
  • Reconocimiento Facial: Facenet Y AWS Rekognition
    Facultad de Ingeniería y Ciencias Agrarias Ingeniería Informática Trabajo Final Reconocimiento facial: FaceNet y AWS Rekognition Marzo 2020 Alumno: Juan Francisco Diaz y Diaz ​ Tutor: Ricardo Hector Di Pasquale ​ Reconocimiento facial: FaceNet vs AWS Recognition | Juan F. Diaz y Diaz Índice Índice 1 Resumen 4 Introducción 6 Motivación 8 Historia de Machine Learning 9 1943 Neural networks, Warren McCulloch y Walter Pitts 9 1957 Perceptron, Frank Rosenblatt 9 1974 Backpropagation, Paul Werbos 10 1986 Learning representations by backpropagating errors, Rumelhart et al. 10 1989 a 1998: LeNet-5, Convolutional Neural Networks (CNNs) 10 1995 NIST Special Database 19 10 1998 Gradient-Based Learning Applied to Document Recognition, LeCun et al. 11 1999 SIFT & Object Recognition, David Lowe 11 2001 Face Detection, Viola & Jones 11 2005 y 2009 Human recognition 11 2005 a 2012 PASCAL Visual Object Challenge, Everingham et al. 11 2007 The MNIST database of handwritten digits 12 2009 ImageNet, Deng, Dong, Socher, Li, Li, & Fei-Fei, 12 2014 DeepFace: Closing the Gap to Human-Level Performance in Face Verification 13 2016 AlphaGo, Mastering the game of Go with deep neural networks and tree search 13 2017 Capsule Networks (CapsNet), Geoffrey E. Hinton et al. 13 2018 BERT (Bidirectional Encoder Representations from Transformers), Google AI Language 13 2019 Language Models are Unsupervised Multitask Learners (GPT-2), OpenAI 14 Machine Learning (ML) 15 Definición 15 Tarea, T 15 Medición de performance, P 15 Experiencia, E 16 Unsupervised Learning 16 Supervised Learning 16 Reinforcement Learning 16 Capacidad, Overfitting & Underfitting 17 Regularización (Regularization) 19 1 Reconocimiento facial: FaceNet vs AWS Recognition | Juan F.
    [Show full text]
  • Neural Natural Language Processing for Unstructured Data in Electronic
    Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review Irene Li, Jessica Pan, Jeremy Goldwasser, Neha Verma, Wai Pan Wong Muhammed Yavuz Nuzumlalı, Benjamin Rosand, Yixin Li, Matthew Zhang, David Chang R. Andrew Taylor, Harlan M. Krumholz and Dragomir Radev {irene.li,jessica.pan,jeremy.goldwasser,neha.verma,waipan.wong}@yale.edu {yavuz.nuzumlali,benjamin.rosand,yixin.li,matthew.zhang,david.chang}@yale.edu {richard.taylor,harlan.krumholz,dragomir.radev}@yale.edu. Yale University, New Haven CT 06511 Abstract diagnoses, medications, and laboratory values in fixed nu- Electronic health records (EHRs), digital collections of pa- merical or categorical fields. Unstructured data, in contrast, tient healthcare events and observations, are ubiquitous in refer to free-form text written by healthcare providers, such medicine and critical to healthcare delivery, operations, and as clinical notes and discharge summaries. Unstructured data research. Despite this central role, EHRs are notoriously dif- represent about 80% of total EHR data, but unfortunately re- ficult to process automatically. Well over half of the informa- main very difficult to process for secondary use[255]. In this tion stored within EHRs is in the form of unstructured text survey paper, we focus our discussion mainly on unstruc- (e.g. provider notes, operation reports) and remains largely tured text data in EHRs and newer neural, deep-learning untapped for secondary use. Recently, however, newer neural based methods used to leverage this type of data. network and deep learning approaches to Natural Language In recent years, artificial neural networks have dramati- Processing (NLP) have made considerable advances, outper- cally impacted fields such as speech recognition, computer forming traditional statistical and rule-based systems on a vision (CV), and natural language processing (NLP) within variety of tasks.
    [Show full text]
  • Singularity Wikibook
    Singularity Wikibook PDF generated using the open source mwlib toolkit. See http://code.pediapress.com/ for more information. PDF generated at: Tue, 16 Oct 2012 09:05:57 UTC Contents Articles Technological singularity 1 Artificial intelligence 19 Outline of artificial intelligence 47 AI-complete 55 Strong AI 57 Progress in artificial intelligence 68 List of artificial intelligence projects 70 Applications of artificial intelligence 74 Augmented reality 78 List of emerging technologies 89 Connectome 108 Computational neuroscience 114 Artificial brain 120 Artificial life 122 Biological neural network 126 Cybernetics 129 Connectionism 139 Mind uploading 143 Systems science 154 Systems biology 158 Biology 162 Transhumanism 177 Outline of transhumanism 194 References Article Sources and Contributors 203 Image Sources, Licenses and Contributors 208 Article Licenses License 210 Technological singularity 1 Technological singularity The technological singularity is the theoretical emergence of greater-than-human superintelligence through technological means.[1] Since the capabilities of such intelligence would be difficult for an unaided human mind to comprehend, the occurrence of a technological singularity is seen as an intellectual event horizon, beyond which events cannot be predicted or understood. Proponents of the singularity typically state that an "intelligence explosion",[2][3] where superintelligences design successive generations of increasingly powerful minds, might occur very quickly and might not stop until the agent's cognitive
    [Show full text]
  • My First Deep Learning System of 1991+ Deep Learning Timeline
    My First Deep Learning System of 1991 + Deep Learning Timeline 1962-2013 Jurgen¨ Schmidhuber The Swiss AI Lab IDSIA, Galleria 2, 6928 Manno-Lugano University of Lugano & SUPSI, Switzerland 20 December 2013 Abstract Deep Learning has attracted significant attention in recent years. Here I present a brief overview of my first Deep Learner of 1991, and its historic context, with a timeline of Deep Learning highlights. Note: As a machine learning researcher I am obsessed with proper credit assignment. This draft is the result of an experiment in rapid massive open online peer review. Since 20 September 2013, subsequent revisions published under www.deeplearning.me have absorbed many suggestions for improvements by experts. The abbreviation “TL” is used to refer to subsections of the timeline section. arXiv:1312.5548v1 [cs.NE] 19 Dec 2013 Figure 1: My first Deep Learning system of 1991 used a deep stack of recurrent neural networks (a Neural Hierarchical Temporal Memory) pre-trained in unsupervised fashion to accelerate subsequent supervised learning [79, 81, 82]. 1 In 2009, our Deep Learning Artificial Neural Networks became the first Deep Learners to win official international pattern recognition competitions [40, 83] (with secret test sets known only to the organisers); by 2012 they had won eight of them (TL 1.13), including the first contests on object detection in large images (ICPR 2012) [2, 16] and image segmentation (ISBI 2012) [3, 15]. In 2011, they achieved the world’s first superhuman visual pattern recognition results [20, 19]. Others have implemented very similar techniques, e.g., [51], and won additional contests or set benchmark records since 2012, e.g., (TL 1.13, TL 1.14).
    [Show full text]
  • Demystifying Differentiable Programming: Shift/Reset the Penultimate Backpropagator
    Demystifying Differentiable Programming: Shift/Reset the Penultimate Backpropagator FEI WANG, Purdue University, USA XILUN WU, Purdue University, USA GREGORY ESSERTEL, Purdue University, USA JAMES DECKER, Purdue University, USA TIARK ROMPF, Purdue University, USA Deep learning has seen tremendous success over the past decade in computer vision, machine translation, and gameplay. This success rests in crucial ways on gradient-descent optimization and the ability to “learn” parameters of a neural network by backpropagating observed errors. However, neural network architectures are growing increasingly sophisticated and diverse, which motivates an emerging quest for even more general forms of differentiable programming, where arbitrary parameterized computations can be trained by gradient descent. In this paper, we take a fresh look at automatic differentiation (AD) techniques, and especially aim to demystify the reverse-mode form of AD that generalizes backpropagation in neural networks. We uncover a tight connection between reverse-mode AD and delimited continuations, which permits implementing reverse-mode AD purely via operator overloading and without any auxiliary data structures. We further show how this formulation of AD can be fruitfully combined with multi-stage programming (staging), leading to a highly efficient implementation that combines the performance benefits of deep learning frameworks based on explicit reified computation graphs (e.g., TensorFlow) with the expressiveness ofpure library approaches (e.g., PyTorch). 1 INTRODUCTION Under the label deep learning, artificial neural networks have seen a remarkable renaissance over the last decade. After a series of rapid advances, they are now matching or surpassing human performance in computer vision, machine translation, and gameplay. Common to all these break- throughs is the underlying dependency on optimization by gradient descent: a neural network “learns” by adjusting its parameters in a direction that minimizes the observed error on a training set.
    [Show full text]