Numerical modeling and optimization of waterjet based surface decontamination

The mission of this study is to investigate the high-pressure waterjet based surface decontamination. Our specific objective is to develop a practical procedure for selection of process conditions at given constraints and available knowledge. This investigation is expected to improve information processing in the course of material decontamination and assist in the implementation of the waterjet decontamination technology into practice. The development of a realistic procedure for processing of a chaotic and non-accurate information constitutes the main accomplishment of this study. The research involved acquisition of representative information about removal of brittle, elastic and viscous deposits. As a result an extended database representing jet based decoating has been compiled and feasibility of the damage free decontamination of various surfaces including highly sensitive ones is demonstrated. Artificial Intelligence techniques (Fuzzy Logic, Artificial Neural Networks, Genetic Computing) have been applied for processing of the acquired information and a realistic procedure of such an application has been developed and demonstrated. This procedure enables us to integrate available information about surface in question and existing numerical models. The developed procedure allows a user to incorporate both qualitative (linguistic) and quantitative (crisp) information into a process model and to predict operational conditions for treatment of an unknown surface using a readily detectable single experimental parameter that characterizes a deposit/substrata combination. The suggested technique is shown to perform reliably in the case of incomplete and chaotic information, where the traditional regression based methods fail. Numerical simulations of the two-phase flow inside a waterjet nozzle are conducted. Numerical solutions of the partial differential equations of the two-phase turbulent jet flow are obtained using FLUENT package. The numerical prediction of jet velocity profiles and the interface between the two phases (water - air) inside a nozzle are in good agreement with experimental data available in the literature. Thus the current problem setup and the results of simulations can be applied to improvement in the nozzle design. A realistic procedure for the design of the jet based surfaces decontamination developed, as a result of this study, is applied for optimization of the removal of the paint, rust, tar and rubber from the steel surface

PROGNOSIS - Historical Pattern Matching for Economic Forecasting and Trading

In recent years financial markets have become complex environments that continuously change and they change quickly. The strong link between the continuous change in the markets and the danger of losing money when trading in them, has made financial studies a domain that concentrates increasing scientific and business attention. In this context, the development of computational techniques that can monitor recent financial events can process them according to their similarity with historical data recordings, and can support financial decision making, is a challenging problem. In this work, the principal idea for tackling this problem is the integration of 'current' market information as derived from the market's recent past and historical information. A robust technique which is based on flexible pattern matching, segmented data representations, time warping, and time series embedding dimension measures is proposed. Complementary time series derived features, concerning trend structures, temporal considerations and statistical measures are systematically combined in this technique. All these components have been integrated into a software package, which I called PROGNOSIS, that can selectively monitor its application and allows systematic evaluation in terms of financial forecasting and trading performance. In addition, two other topics are discussed in this thesis. Firstly, in chapter 3, a neural network, that is known as the Growing Neural Gas network, is employed for financial forecasting and trading. To my knowledge, this network has never been applied before to financial problems. Based on this a neural network forecasting and trading benchmark was constructed for comparison purposes. Secondly, a novel method of approaching the well established co-integraton theory is proposed in the last chapter of the thesis. This method enhances the co-integration theory by integrating into it local time relations between two time series. These local time dependencies are identified using dynamic time warping. The hypothesis that is tested is that local time shifts, delays, shrinks or stretches, if identified, may help to reveal co-integrating movement between the two time series. I called this type of co-integration time-warped co-integration. To this end, the time-warped co-integration framework is presented as an error correction model and it is tested on arbitrage trading opportunities within PROGNOSIS

Classification of Inter-Organizational Knowledge Mechanisms and their Effects on Networking Capability:A Multi-Layer Decision Making Approach

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Purpose – The role of inter-organizational knowledge mechanisms (IOKMs) in learning networks is increasing so that the competition of business networks in providing innovations is highly dependent on the effective selection and application of these mechanisms. This study aims to argue that recognizing the classification of IOKMs and understanding their impact on networking capability (NC) makes the selection of mechanismsmore effective. Design/methodology/approach – With a systematic review of literature, a comprehensive list of IOKMs, their main characteristics and NCs have been extracted. The authors have used a focus group for data gathering and a hybrid multi-layer decision-making approach for data analysis. Finally, the impact of IOKMs onNC was determined. Findings – By implementing a multi-layer decision-making approach, four categories of IOKMs including person-to-person, co-creation, team-oriented and informational are illustrated and their effects of NC are determined. Therefore, the findings of this research provide latecomer firms (LCFs) managers with a clear framework for selecting IOKMs. Originality/value – The literature review shows that the number of knowledge mechanisms, especially their inter-organizational types, is increasing. It has made it difficult for LCFs managers to select effective and efficient mechanisms. Most of these mechanisms are listed, and few studies have classified them. Besides, research shows that fewer studies have investigated how IOKMs relate to NC. Furthermore, most studies on IOKMs have been conducted in the context of leading firms and LCFs have been neglected

An Empirical Analysis of Takeover Predictions in the UK: Application of Artificial Neural Networks and Logistic Regression

Merged with duplicate record 10026.1/656 on 27.03.2017 by CS (TIS)This study undertakes an empirical analysis of takeover predictions in the UK. The objectives of this research are twofold. First, whether it is possible to predict or identity takeover targets before they receive any takeover bid. Second, to test whether it is possible to improve prediction outcome by extending firm specific characteristics such as corporate governance variables as well as employing a different technique that has started becoming an established analytical tool by its extensive application in corporate finance field. In order to test the first objective, Logistic Regression (LR) and Artificial Neural Networks (ANNs) have been applied as modelling techniques for predicting target companies in the UK. Hence by applying ANNs in takeover predictions, their prediction ability in target classification is tested and results are compared to the LR results. For the second objective, in addition to the company financial variables, non-financial characteristics, corporate governance characteristics, of companies are employed. For the fist time, ANNs are applied to corporate governance variables in takeover prediction purposes. In the final section, two groups of variables are combined to test whether the previous outcomes of financial and non-financial variables could be improved. However the results suggest that predicting takeovers, by employing publicly available information that is already reflected in the share price of the companies, is not likely at least by employing current techniques of LR and ANNs. These results are consistent with the semi-strong form of the efficient market hypothesis

Neural networks in economic modelling:An empirical study

This dissertation addresses the statistical aspects of neural networks and their usability for solving problems in economics and finance. Neural networks are discussed in a framework of modelling which is generally accepted in econometrics. Within this framework a neural network is regarded as a statistical technique that implements a model-free regression strategy. Model-free regression seems particularly useful in situations where economic theory cannot provide sensible model specifications. Neural networks are applied in three case studies: modelling house prices; predicting the production of new mortgage loans; predicting the foreign exchange rates. From these case studies is concluded that neural networks are a valuable addition to the econometrician's toolbox, but that they are no panacea

The influence of variable selection methods on the accuracy of bankruptcy prediction models

Over the last four decades, bankruptcy prediction has given rise to an extensive body of literature, the aim of which was to assess the conditions under which forecasting models perform effectively. Of all the parameters that may influence model accuracy, one has rarely been discussed: the influence of the variable selection method. The aim of our research is to evaluate the prediction accuracy of models designed with various classification techniques and variables selection methods. As a result, we demonstrate that a search strategy cannot be designed without considering the characteristics of the modeling technique and that the fit between the variable selection method and the technique used to design models is a key factor in performance

The influence of variable selection methods on the accuracy of bankruptcy prediction models

Over the last four decades, bankruptcy prediction has given rise to an extensive body of literature, the aim of which was to assess the conditions under which forecasting models perform effectively. Of all the parameters that may influence model accuracy, one has rarely been discussed: the influence of the variable selection method. The aim of our research is to evaluate the prediction accuracy of models designed with various classification techniques and variables selection methods. As a result, we demonstrate that a search strategy cannot be designed without considering the characteristics of the modeling technique and that the fit between the variable selection method and the technique used to design models is a key factor in performance

WEIGH-IN-MOTION DATA-DRIVEN PAVEMENT PERFORMANCE PREDICTION MODELS

The effective functioning of pavements as a critical component of the transportation system necessitates the implementation of ongoing maintenance programs to safeguard this significant and valuable infrastructure and guarantee its optimal performance. The maintenance, rehabilitation, and reconstruction (MRR) program of the pavement structure is dependent on a multidimensional decision-making process, which considers the existing pavement structural condition and the anticipated future performance. Pavement Performance Prediction Models (PPPMs) have become indispensable tools for the efficient implementation of the MRR program and the minimization of associated costs by providing precise predictions of distress and roughness based on inventory and monitoring data concerning the pavement structure\u27s state, traffic load, and climatic conditions. The integration of PPPMs has become a vital component of Pavement Management Systems (PMSs), facilitating the optimization, prioritization, scheduling, and selection of maintenance strategies. Researchers have developed several PPPMs with differing objectives, and each PPPM has demonstrated distinct strengths and weaknesses regarding its applicability, implementation process, and data requirements for development. Traditional statistical models, such as linear regression, are inadequate in handling complex nonlinear relationships between variables and often generate less precise results. Machine Learning (ML)-based models have become increasingly popular due to their ability to manage vast amounts of data and identify meaningful relationships between them to generate informative insights for better predictions. To create ML models for pavement performance prediction, it is necessary to gather a significant amount of historical data on pavement and traffic loading conditions. The Long-Term Pavement Performance Program (LTPP) initiated by the Federal Highway Administration (FHWA) offers a comprehensive repository of data on the environment, traffic, inventory, monitoring, maintenance, and rehabilitation works that can be utilized to develop PPPMs. The LTPP also includes Weigh-In-Motion (WIM) data that provides information on traffic, such as truck traffic, total traffic, directional distribution, and the number of different axle types of vehicles. High-quality traffic loading data can play an essential role in improving the performance of PPPMs, as the Mechanistic-Empirical Pavement Design Guide (MEPDG) considers vehicle types and axle load characteristics to be critical inputs for pavement design. The collection of high-quality traffic loading data has been a challenge in developing Pavement Performance Prediction Models (PPPMs). The Weigh-In-Motion (WIM) system, which comprises WIM scales, has emerged as an innovative solution to address this issue. By leveraging computer vision and machine learning techniques, WIM systems can collect accurate data on vehicle type and axle load characteristics, which are critical factors affecting the performance of flexible pavements. Excessive dynamic loading caused by heavy vehicles can result in the early disintegration of the pavement structure. The Long-Term Pavement Performance Program (LTPP) provides an extensive repository of WIM data that can be utilized to develop accurate PPPMs for predicting pavement future behavior and tolerance. The incorporation of comprehensive WIM data collected from LTPP has the potential to significantly improve the accuracy and effectiveness of PPPMs. To develop artificial neural network (ANN) based pavement performance prediction models (PPPMs) for seven distinct performance indicators, including IRI, longitudinal crack, transverse crack, fatigue crack, potholes, polished aggregate, and patch failure, a total of 300 pavement sections with WIM data were selected from the United States of America. Data collection spanned 20 years, from 2001 to 2020, and included information on pavement age, material properties, climatic properties, structural properties, and traffic-related characteristics. The primary dataset was then divided into two distinct subsets: one which included WIMgenerated traffic data and another which excluded WIM-generated traffic data. Data cleaning and normalization were meticulously performed using the Z-score normalization method. Each subset was further divided into two separate groups: the first containing 15 years of data for model training and the latter containing 5 years of data for testing purposes. Principal Component Analysis (PCA) was then employed to reduce the number of input variables for the model. Based on a cumulative Proportion of Variation (PoV) of 96%, 12 input variables were selected. Subsequently, a single hidden layer ANN model with 12 neurons was generated for each performance indicator. The study\u27s results indicate that incorporating Weigh-In-Motion (WIM)-generated traffic loading data can significantly enhance the accuracy and efficacy of pavement performance prediction models (PPPMs). This improvement further supports the suitability of optimized pavement maintenance scheduling with minimal costs, while also ensuring timely repairs to promote acceptable serviceability and structural stability of the pavement. The contributions of this research are twofold: first, it provides an enhanced understanding of the positive impacts that high-quality traffic loading data has on pavement conditions; and second, it explores potential applications of WIM data within the Pavement Management System (PMS)