Thesis (M.S.)--West Virginia University, 2004. / Title from document title page. Document formatted into pages; contains xi, 143 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 138-143).

To improve the safety and comfort of intelligent vehicles, advanced driver models offer promising solutions. However, several shortcomings of these models prevent them from being widely applied in reality. To address these shortcomings, advanced artificial intelligence algorithms in conjunction with the sufficient driving environmental factors are proposed based on real-life driving data. More specifically, three typical problems will be addressed in this thesis: Mandatory Lane Changing (MLC) suggestion at the highway entrance; Discretionary Lane Changing (DLC) intention prediction; Car-Following gap model considering the effect of cuts-in from the adjacent lanes. For the MLC suggestion system, in which the main challenges are efficient decision making and high prediction accuracy of both non-merge and merge events, an additional gated branch neural network (GBNN) is proposed. The proposed GBNN algorithm not only achieves the highest accuracy among conventional binary classifiers in terms of great performance on the non-merge accuracy, the merge accuracy, and receiver operating characteristic score but also takes less time. For the DLC, we propose a recurrent neural network (RNN)-based time series classifier with a gated recurrent units (GRU) architecture to predict the surrounding vehicles’ intention. It can predict the surrounding vehicles’ lane changing maneuver 0.8 s in advance at a recall and precision of 99.5% and 98.7%, respectively, which outperforms conventional algorithms such as the Hidden Markov Model (HMM). Finally, drivers are typically faced with two competing challenges when following a preceding vehicle. A method is proposed to address the problem through an overall objective function of car-following gap and velocity. Based on this, seeking the strategic car-following gap translates to finding the optimal solution that minimizes the overall objective function. With the support of field data, the method along with concrete models are instantiated and the application of the method is elaborated. / Thesis / Doctor of Philosophy (PhD) / Lane changing and car following are the two most frequently encountered driving behaviours for intelligent vehicles. Substantial research has been carried out and several prototypes have been developed by universities as well as companies. However, the low accuracy and high computational cost prevent the existing lane changing models from providing safer and more reliable decisions for intelligent vehicles. In the existing car-following models, there are also few models that consider the effects of cut-ins from adjacent lanes which may result in their poor accuracy and efficiency. To address these obstacles, advanced artificial intelligence algorithms combined with sufficient driving environmental factors are proposed due to their promise of providing accurate, efficient, and robust lane changing and car-following models. The main part of this thesis is composed of three journal papers. Paper 1 proposed a gated branch neural network for a mandatory lane changing suggestion system at the on-ramps of highways; paper 2 developed a recurrent neural network time-series algorithm to predict the surrounding vehicles’ discretionary lane changing intention in advance; paper 3 researched the strategic car-following gap model considering the effect of cut-ins from adjacent lanes.

Thesis (M. Phil.)--University of Hong Kong, 2010. / Includes bibliographical references (leaves 93-103). Also available in print.

The application of Evolutionary Algorithms in hierarchical model partitioning for parallel system simulation in VLSI design processes has proven to be successful. Thereby, individuals embody partitions of hardware designs. On the basis of a formal model of parallel cycle simulation a fitness function is chosen combining load balancing and interprocessor communication aspects. As supplement to the concept of superposition we introduce a Local Search Operator to achieve a fast decreasing fitness function during evolution. This operator is based on a modification of a classical iterative partitioning algorithm by Fiduccia-Mattheyses. Results are shown for the partitioning of two real processor models, representing the PowerPC 604 and an IBM S/390 processor.

Q-learning as well as other learning paradigms depend strongly on the representation of the underlying state space. As a special case of the hidden state problem we investigate the effect of a self-organizing discretization of the state space in a simple control problem. We apply the neural gas algorithm with adaptation of learning rate and neighborhood range to a simulated cart-pole problem. The learning parameters are determined by the ambiguity of successful actions inside each cell.

Automated decision making is becoming the norm across large parts of society, which raises interesting liability challenges when human control over technical systems becomes increasingly limited. This article defines "quasi-automation" as inclusion of humans as a basic rubber-stamping mechanism in an otherwise completely automated decision-making system. Three cases of quasi- automation are examined, where human agency in decision making is currently debatable: self- driving cars, border searches based on passenger name records, and content moderation on social media. While there are specific regulatory mechanisms for purely automated decision making, these regulatory mechanisms do not apply if human beings are (rubber-stamping) automated decisions. More broadly, most regulatory mechanisms follow a pattern of binary liability in attempting to regulate human or machine agency, rather than looking to regulate both. This results in regulatory gray areas where the regulatory mechanisms do not apply, harming human rights by preventing meaningful liability for socio-technical decision making. The article concludes by proposing criteria to ensure meaningful agency when humans are included in automated decision-making systems, and relates this to the ongoing debate on enabling human rights in Internet infrastructure.

Thesis (Ph.D. in Bioinformatics) -- University of Colorado at Denver and Health Sciences Center, 2005. / Typescript. Includes bibliographical references (leaves 99-106).

This thesis deals with the Travelling salesman problem with time windows. The problem is that the travelling salesman must pass through each defined location exactly once and finally return to the original place for the lowest possible price. The time windows in this problem are that each place can only be visited in a given time range, or it can happen that in a certain period of time there will be no path between some places. The thesis deals with an overview of this problem and problems similar to it. It also deals with the description of various methods by which this problem can be solved. As part of this thesis, an application in the Python programming language was also created, which is used to test selected methods for finding solutions. Finally, the given experiments are evaluated and the effectiveness of the given strategies is compared.

M. Tech. (Department of Information Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology. / The increasing demand for knowledge representation and exchange on the semantic web has resulted in an increase in both the number and size of ontologies. This increased features in ontologies has made them more complex and in turn difficult to select, reuse and maintain them. Several ontology evaluations and ranking tools have been proposed recently. Such evaluation tools provide a metrics suite that evaluates the content of an ontology by analysing their schemas and instances. The presence of ontology metric suites may enable classification techniques in placing the ontologies in various categories or classes. Machine Learning algorithms mostly based on statistical methods used in classification of data makes them the perfect tools to be used in performing classification of ontologies. In this study, popular Machine Learning algorithms including K-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forest, Naïve Bayes, Linear Regression and Logistic Regression were used in the classification of ontologies based on their complexity metrics. A total of 200 biomedical ontologies were downloaded from the Bio Portal repository. Ontology metrics were then generated using the OntoMetrics tool, an online ontology evaluation platform. These metrics constituted the dataset used in the implementation of the machine learning algorithms. The results obtained were evaluated with performance evaluation techniques, namely, precision, recall, F-Measure Score and Receiver Operating Characteristic (ROC) curves. The Overall accuracy scores for K-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forest, Naïve Bayes, Logistic Regression and Linear Regression algorithms were 66.67%, 65%, 98%, 99.29%, 74%, 64.67%, and 57%, respectively. From these scores, Decision Trees and Random Forests algorithms were the best performing and can be attributed to the ability to handle multiclass classifications.