Modeling viscosity of crude oil using k-nearest neighbor algorithm

Oil viscosity is an important factor in every project of the petroleum industry. These processes can range from gas injection to oil reservoirs to comprehensive reservoir simulation studies. Different experimental approaches have been proposed for measuring oil viscosity. However, these methods are often time taking, cumbersome and at some physical conditions, impossible. Therefore, development of predictive models for estimating this parameter is crucial. In this study, three new machine learning based models are developed to estimate the oil viscosity. These approaches are genetic programing, k-nearest neighbor (KNN) and linear discriminant analysis. Oil gravity and temperature were the input parameters of the models. Various graphical and statistical error analyses were used to measure the performance of the developed models. Also, comparison study between the developed models and the well-known previously published models was conducted. Moreover, trend analysis was performed to compare the predictions of the models with the trend of experimental data. The results indicated that the developed models outperform all of the previously published models by showing negligible prediction errors. Among the developed models, the KNN model has the highest accuracy by showing an overall mean absolute error of 8.54%. The results show that the new developed models in this study can be potentially utilized in reservoir simulation packages of the petroleum industry.Cited as: Mahdiani, M.R., Khamehchi, E., Hajirezaie, S., Hemmati-Sarapardeh, A. Modeling viscosity of crude oil using k-nearest neighbor algorithm. Advances in Geo-Energy Research, 2020, 4(4): 435-447, doi: 10.46690/ager.2020.04.0

Semantic Sentiment Analysis of Microblogs

Microblogs and social media platforms are now considered among the most popular forms of online communication. Through a platform like Twitter, much information reflecting people's opinions and attitudes is published and shared among users on a daily basis. This has recently brought great opportunities to companies interested in tracking and monitoring the reputation of their brands and businesses, and to policy makers and politicians to support their assessment of public opinions about their policies or political issues. A wide range of approaches to sentiment analysis on Twitter, and other similar microblogging platforms, have been recently built. Most of these approaches rely mainly on the presence of affect words or syntactic structures that explicitly and unambiguously reflect sentiment (e.g., "great'', "terrible''). However, these approaches are semantically weak, that is, they do not account for the semantics of words when detecting their sentiment in text. This is problematic since the sentiment of words, in many cases, is associated with their semantics, either along the context they occur within (e.g., "great'' is negative in the context "pain'') or the conceptual meaning associated with the words (e.g., "Ebola" is negative when its associated semantic concept is "Virus"). This thesis investigates the role of words' semantics in sentiment analysis of microblogs, aiming mainly at addressing the above problem. In particular, Twitter is used as a case study of microblogging platforms to investigate whether capturing the sentiment of words with respect to their semantics leads to more accurate sentiment analysis models on Twitter. To this end, several approaches are proposed in this thesis for extracting and incorporating two types of word semantics for sentiment analysis: contextual semantics (i.e., semantics captured from words' co-occurrences) and conceptual semantics (i.e., semantics extracted from external knowledge sources). Experiments are conducted with both types of semantics by assessing their impact in three popular sentiment analysis tasks on Twitter; entity-level sentiment analysis, tweet-level sentiment analysis and context-sensitive sentiment lexicon adaptation. Evaluation under each sentiment analysis task includes several sentiment lexicons, and up to 9 Twitter datasets of different characteristics, as well as comparing against several state-of-the-art sentiment analysis approaches widely used in the literature. The findings from this body of work demonstrate the value of using semantics in sentiment analysis on Twitter. The proposed approaches, which consider words' semantics for sentiment analysis at both, entity and tweet levels, surpass non-semantic approaches in most datasets

KAVUAKA: a low-power application-specific processor architecture for digital hearing aids

The power consumption of digital hearing aids is very restricted due to their small physical size and the available hardware resources for signal processing are limited. However, there is a demand for more processing performance to make future hearing aids more useful and smarter. Future hearing aids should be able to detect, localize, and recognize target speakers in complex acoustic environments to further improve the speech intelligibility of the individual hearing aid user. Computationally intensive algorithms are required for this task. To maintain acceptable battery life, the hearing aid processing architecture must be highly optimized for extremely low-power consumption and high processing performance.The integration of application-specific instruction-set processors (ASIPs) into hearing aids enables a wide range of architectural customizations to meet the stringent power consumption and performance requirements. In this thesis, the application-specific hearing aid processor KAVUAKA is presented, which is customized and optimized with state-of-the-art hearing aid algorithms such as speaker localization, noise reduction, beamforming algorithms, and speech recognition. Specialized and application-specific instructions are designed and added to the baseline instruction set architecture (ISA). Among the major contributions are a multiply-accumulate (MAC) unit for real- and complex-valued numbers, architectures for power reduction during register accesses, co-processors and a low-latency audio interface. With the proposed MAC architecture, the KAVUAKA processor requires 16 % less cycles for the computation of a 128-point fast Fourier transform (FFT) compared to related programmable digital signal processors. The power consumption during register file accesses is decreased by 6 %to 17 % with isolation and by-pass techniques. The hardware-induced audio latency is 34 %lower compared to related audio interfaces for frame size of 64 samples.The final hearing aid system-on-chip (SoC) with four KAVUAKA processor cores and ten co-processors is integrated as an application-specific integrated circuit (ASIC) using a 40 nm low-power technology. The die size is 3.6 mm2. Each of the processors and co-processors contains individual customizations and hardware features with a varying datapath width between 24-bit to 64-bit. The core area of the 64-bit processor configuration is 0.134 mm2. The processors are organized in two clusters that share memory, an audio interface, co-processors and serial interfaces. The average power consumption at a clock speed of 10 MHz is 2.4 mW for SoC and 0.6 mW for the 64-bit processor.Case studies with four reference hearing aid algorithms are used to present and evaluate the proposed hardware architectures and optimizations. The program code for each processor and co-processor is generated and optimized with evolutionary algorithms for operation merging,instruction scheduling and register allocation. The KAVUAKA processor architecture is com-pared to related processor architectures in terms of processing performance, average power consumption, and silicon area requirements

Intrinsic Functions for Securing CMOS Computation: Variability, Modeling and Noise Sensitivity

A basic premise behind modern secure computation is the demand for lightweight cryptographic primitives, like identifier or key generator. From a circuit perspective, the development of cryptographic modules has also been driven by the aggressive scalability of complementary metal-oxide-semiconductor (CMOS) technology. While advancing into nano-meter regime, one significant characteristic of today\u27s CMOS design is the random nature of process variability, which limits the nominal circuit design. With the continuous scaling of CMOS technology, instead of mitigating the physical variability, leveraging such properties becomes a promising way. One of the famous products adhering to this double-edged sword philosophy is the Physically Unclonable Functions (PUFs), which extract secret keys from uncontrollable manufacturing variability on integrated circuits (ICs). However, since PUFs take advantage of microscopic process variations, thus many specialized issues including variability, modeling attacks and noise sensitivity need to be considered and addressed. In this dissertation, we present our recent work on PUF based secure computation from three aspects: variability, modeling and noise sensitivity, which are deemed the foundations of our study. Moreover, we found that the three factors coordinate with each other in our study, for example, the modeling technique can be utilized to improve the unsatisfied reliability caused by noise sensitivity, quantifying the variability can effectively eliminate the impact from noise, and modeling can help with characterizing the physical variability precisely

Computationally Linking Chemical Exposure to Molecular Effects with Complex Data: Comparing Methods to Disentangle Chemical Drivers in Environmental Mixtures and Knowledge-based Deep Learning for Predictions in Environmental Toxicology

Chemical exposures affect the environment and may lead to adverse outcomes in its organisms. Omics-based approaches, like standardised microarray experiments, have expanded the toolbox to monitor the distribution of chemicals and assess the risk to organisms in the environment. The resulting complex data have extended the scope of toxicological knowledge bases and published literature. A plethora of computational approaches have been applied in environmental toxicology considering systems biology and data integration. Still, the complexity of environmental and biological systems given in data challenges investigations of exposure-related effects. This thesis aimed at computationally linking chemical exposure to biological effects on the molecular level considering sources of complex environmental data. The first study employed data of an omics-based exposure study considering mixture effects in a freshwater environment. We compared three data-driven analyses in their suitability to disentangle mixture effects of chemical exposures to biological effects and their reliability in attributing potentially adverse outcomes to chemical drivers with toxicological databases on gene and pathway levels. Differential gene expression analysis and a network inference approach resulted in toxicologically meaningful outcomes and uncovered individual chemical effects — stand-alone and in combination. We developed an integrative computational strategy to harvest exposure-related gene associations from environmental samples considering mixtures of lowly concentrated compounds. The applied approaches allowed assessing the hazard of chemicals more systematically with correlation-based compound groups. This dissertation presents another achievement toward a data-driven hypothesis generation for molecular exposure effects. The approach combined text-mining and deep learning. The study was entirely data-driven and involved state-of-the-art computational methods of artificial intelligence. We employed literature-based relational data and curated toxicological knowledge to predict chemical-biomolecule interactions. A word embedding neural network with a subsequent feed-forward network was implemented. Data augmentation and recurrent neural networks were beneficial for training with curated toxicological knowledge. The trained models reached accuracies of up to 94% for unseen test data of the employed knowledge base. However, we could not reliably confirm known chemical-gene interactions across selected data sources. Still, the predictive models might derive unknown information from toxicological knowledge sources, like literature, databases or omics-based exposure studies. Thus, the deep learning models might allow predicting hypotheses of exposure-related molecular effects. Both achievements of this dissertation might support the prioritisation of chemicals for testing and an intelligent selection of chemicals for monitoring in future exposure studies.:Table of Contents ... I Abstract ... V Acknowledgements ... VII Prelude ... IX 1 Introduction 1.1 An overview of environmental toxicology ... 2 1.1.1 Environmental toxicology ... 2 1.1.2 Chemicals in the environment ... 4 1.1.3 Systems biological perspectives in environmental toxicology ... 7 Computational toxicology ... 11 1.2.1 Omics-based approaches ... 12 1.2.2 Linking chemical exposure to transcriptional effects ... 14 1.2.3 Up-scaling from the gene level to higher biological organisation levels ... 19 1.2.4 Biomedical literature-based discovery ... 24 1.2.5 Deep learning with knowledge representation ... 27 1.3 Research question and approaches ... 29 2 Methods and Data ... 33 2.1 Linking environmental relevant mixture exposures to transcriptional effects ... 34 2.1.1 Exposure and microarray data ... 34 2.1.2 Preprocessing ... 35 2.1.3 Differential gene expression ... 37 2.1.4 Association rule mining ... 38 2.1.5 Weighted gene correlation network analysis ... 39 2.1.6 Method comparison ... 41 Predicting exposure-related effects on a molecular level ... 44 2.2.1 Input ... 44 2.2.2 Input preparation ... 47 2.2.3 Deep learning models ... 49 2.2.4 Toxicogenomic application ... 54 3 Method comparison to link complex stream water exposures to effects on the transcriptional level ... 57 3.1 Background and motivation ... 58 3.1.1 Workflow ... 61 3.2 Results ... 62 3.2.1 Data preprocessing ... 62 3.2.2 Differential gene expression analysis ... 67 3.2.3 Association rule mining ... 71 3.2.4 Network inference ... 78 3.2.5 Method comparison ... 84 3.2.6 Application case of method integration ... 87 3.3 Discussion ... 91 3.4 Conclusion ... 99 4 Deep learning prediction of chemical-biomolecule interactions ... 101 4.1 Motivation ... 102 4.1.1Workflow ...105 4.2 Results ... 107 4.2.1 Input preparation ... 107 4.2.2 Model selection ... 110 4.2.3 Model comparison ... 118 4.2.4 Toxicogenomic application ... 121 4.2.5 Horizontal augmentation without tail-padding ...123 4.2.6 Four-class problem formulation ... 124 4.2.7 Training with CTD data ... 125 4.3 Discussion ... 129 4.3.1 Transferring biomedical knowledge towards toxicology ... 129 4.3.2 Deep learning with biomedical knowledge representation ...133 4.3.3 Data integration ...136 4.4 Conclusion ... 141 5 Conclusion and Future perspectives ... 143 5.1 Conclusion ... 143 5.1.1 Investigating complex mixtures in the environment ... 144 5.1.2 Complex knowledge from literature and curated databases predict chemical- biomolecule interactions ... 145 5.1.3 Linking chemical exposure to biological effects by integrating CTD ... 146 5.2 Future perspectives ... 147 S1 Supplement Chapter 1 ... 153 S1.1 Example of an estrogen bioassay ... 154 S1.2 Types of mode of action ... 154 S1.3 The dogma of molecular biology ... 157 S1.4 Transcriptomics ... 159 S2 Supplement Chapter 3 ... 161 S3 Supplement Chapter 4 ... 175 S3.1 Hyperparameter tuning results ... 176 S3.2 Functional enrichment with predicted chemical-gene interactions and CTD reference pathway genesets ... 179 S3.3 Reduction of learning rate in a model with large word embedding vectors ... 183 S3.4 Horizontal augmentation without tail-padding ... 183 S3.5 Four-relationship classification ... 185 S3.6 Interpreting loss observations for SemMedDB trained models ... 187 List of Abbreviations ... i List of Figures ... vi List of Tables ... x Bibliography ... xii Curriculum scientiae ... xxxix Selbständigkeitserklärung ... xlii

Modelling of tool wear and metal flow behaviour in friction stir welding (FSW)

Friction Stir Welding (FSW) is a solid-state joining process that was invented in 1991; it is particularly useful for joints difficult to make using fusion techniques. Significant advances in FSW have been achieved in terms of process modelling since its inception. However, until now experimental work has remained the primary method of investigating tool wear in FSW. In this project, two main objectives were set; the first one was to produce a numerical approach that can be used as a useful tool to understand the effect that worn tool geometry has on the material flow and resultant weld quality. The second objective was to provide a modelling methodology for calculating tool wear in FSW based on a CFD model. Initially, in this study, a validated model of the FSW process was generated using the CFD software FLUENT, with this model then being used to assess in detail the differences in flow behaviour, mechanically affected zone (MAZ) size and strain rate distribution around the tool for both unworn and worn tool geometries. Later, a novel methodology for calculating tool wear in FSW is developed. Here a CFD model is used to predict the deformation of the highly viscous flow around the tool, with additional analysis linking this deformation to tool wear. A validation process was carried out in this study in order to obtain robust results when using this methodology. Once satisfied with the tool wear methodology results, a parametric study considering different tool designs, rotation speeds and traverse speeds was undertaken to predict the wear depth. In this study, three workpiece materials were used which were aluminium 6061, 7020 and AISI 304 stainless steel, while the materials used for the tools used were of H13 steel and tungsten-rhenium carbide (WRe-HfC) with different tool designs. The study shows that there are significant differences in the flow behaviour around and under the tool when the tool is worn and it shows that the proposed approach is able to predict tool wear associated with high viscous flow around the FSW tool. With a simple dome shaped tool, the results shows that the tool was worn radially and vertically and insignificant wear was predicted during welding near the pin tip. However, in other regions the wear increased as the weld distance increased. Additionally, from the parametric study that was undertaken for the two tool designs - a dome and a conical shape- the study has found that for both tool designs, wear depth increases with increasing tool rotation speed and traverse speed. It was also shown that, generally, the wear depth was higher for the conical tool design than the dome tool in the pin tip zone. The research concludes that a proposed methodology is able to calculate tool wear associated with high viscous flow around the FSW tool, which could be used as a method for calculating tool wear without the need for experimental trials. The CFD model has provided a good tool for prediction and assessment of the flow differences between un-worn and worn tools, which may be used to give an indication of the weld quality and of tool lifetime. Furthermore, from the results, it can be concluded that this approach is capable of predicting tool wear for different process parameters and tool designs and it is possible to obtain a low wear case by controlling the process parameters

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

On the induction of temporal structure by recurrent neural networks

Language acquisition is one of the core problems in artificial intelligence (AI) and it is generally accepted that any successful AI account of the mind will stand or fall depending on its ability to model human language. Simple Recurrent Networks (SRNs) are a class of so-called artificial neural networks that have a long history in language modelling via learning to predict the next word in a sentence. However, SRNs have also been shown to suffer from catastrophic forgetting, lack of syntactic systematicity and an inability to represent more than three levels of centre-embedding, due to the so-called 'vanishing gradients' problem. This problem is caused by the decay of past input information encoded within the error-gradients which vanish exponentially as additional input information is encountered and passed through the recurrent connections. That said, a number of architectural variations have been applied which may compensate for this issue, such as the Nonlinear Autoregressive Network with exogenous inputs (NARX) network and the multi-recurrent network (MRN). In addition to this, Echo State Networks (ESNs) are a relatively new class of recurrent neural network that do not suffer from the vanishing gradients problem and have been shown to exhibit state-of-the-art performance in tasks such as motor control, dynamic time series prediction, and more recently language processing. This research re-explores the class of SRNs and evaluates them against the state-of-the-art ESN to identify which model class is best able to induce the underlying finite-state automaton of the target grammar implicitly through the next word prediction task. In order to meet its aim, the research analyses the internal representations formed by each of the different models and explores the conditions under which they are able to carry information about long term sequential dependencies beyond what is found in the training data. The findings of the research are significant. It reveals that the traditional class of SRNs, trained with backpropagation through time, are superior to ESNs for the grammar prediction task. More specifically, the MRN, with its state-based memory of varying rigidity, is more able to learn the underlying grammar than any other model. An analysis of the MRN’s internal state reveals that this is due to its ability to maintain a constant variance within its state-based representation of the embedded aspects (or finite state machines) of the target grammar. The investigations show that in order to successfully induce complex context free grammars directly from sentence examples, then not only are a hidden layer and output layer recurrency required, but so is self-recurrency on the context layer to enable varying degrees of current and past state information, that are integrated over time

Modelling of tool wear and metal flow behaviour in friction stir welding (FSW)

Friction Stir Welding (FSW) is a solid-state joining process that was invented in 1991; it is particularly useful for joints difficult to make using fusion techniques. Significant advances in FSW have been achieved in terms of process modelling since its inception. However, until now experimental work has remained the primary method of investigating tool wear in FSW. In this project, two main objectives were set; the first one was to produce a numerical approach that can be used as a useful tool to understand the effect that worn tool geometry has on the material flow and resultant weld quality. The second objective was to provide a modelling methodology for calculating tool wear in FSW based on a CFD model. Initially, in this study, a validated model of the FSW process was generated using the CFD software FLUENT, with this model then being used to assess in detail the differences in flow behaviour, mechanically affected zone (MAZ) size and strain rate distribution around the tool for both unworn and worn tool geometries. Later, a novel methodology for calculating tool wear in FSW is developed. Here a CFD model is used to predict the deformation of the highly viscous flow around the tool, with additional analysis linking this deformation to tool wear. A validation process was carried out in this study in order to obtain robust results when using this methodology. Once satisfied with the tool wear methodology results, a parametric study considering different tool designs, rotation speeds and traverse speeds was undertaken to predict the wear depth. In this study, three workpiece materials were used which were aluminium 6061, 7020 and AISI 304 stainless steel, while the materials used for the tools used were of H13 steel and tungsten-rhenium carbide (WRe-HfC) with different tool designs. The study shows that there are significant differences in the flow behaviour around and under the tool when the tool is worn and it shows that the proposed approach is able to predict tool wear associated with high viscous flow around the FSW tool. With a simple dome shaped tool, the results shows that the tool was worn radially and vertically and insignificant wear was predicted during welding near the pin tip. However, in other regions the wear increased as the weld distance increased. Additionally, from the parametric study that was undertaken for the two tool designs - a dome and a conical shape- the study has found that for both tool designs, wear depth increases with increasing tool rotation speed and traverse speed. It was also shown that, generally, the wear depth was higher for the conical tool design than the dome tool in the pin tip zone. The research concludes that a proposed methodology is able to calculate tool wear associated with high viscous flow around the FSW tool, which could be used as a method for calculating tool wear without the need for experimental trials. The CFD model has provided a good tool for prediction and assessment of the flow differences between un-worn and worn tools, which may be used to give an indication of the weld quality and of tool lifetime. Furthermore, from the results, it can be concluded that this approach is capable of predicting tool wear for different process parameters and tool designs and it is possible to obtain a low wear case by controlling the process parameters