Predicting Port Tugboat Operations for Arriving and Departing Vessels Using Machine Learning

Today, predicting the number of tugs required to assist in a towing operation many days in advance is difficult. Towing operations, being a complicated process, are prone to human errors and conflicts, which can have severe financial consequences for all parties involved. In this thesis, a method for extracting port tugboat operations for incoming and outgoing vessels is proposed. Using the obtained tugboat operations dataset, a machine learning model is built in order to predict the number of tugs required to assist in a towing operation. The data used is a year of historical Baltic Sea AIS data and weather data from nearby weather stations near the two analysis ports. The recommended ideas and their implementation were a success from a performance standpoint. The proposed method for extracting towing operations detected the vast majority of towing operations within the analysis area. The obtained tugboat operations dataset was then used during the model construction phase. The obtained models are port-specific. One of the models achieved an overall accuracy of 87.0\%, while the other achieved an accuracy of 91.5\%. The results demonstrated that it is possible to develop a viable predictive tool for tugboat operations. When deployed, the proposed method will enable port and tugboat operators to make faster and more efficient decisions, resulting in increased operational efficiency in the port area

Human-Robot Collaboration for Effective Bridge Inspection in the Artificial Intelligence Era

Advancements in sensor, Artificial Intelligence (AI), and robotic technologies have formed a foundation to enable a transformation from traditional engineering systems to complex adaptive systems. This paradigm shift will bring exciting changes to civil infrastructure systems and their builders, operators and managers. Funded by the INSPIRE University Transportation Center (UTC), Dr. Qin’s group investigated the holism of an AI-robot-inspector system for bridge inspection. Dr. Qin will discuss the need for close collaboration among the constituent components of the AI-robot-inspector system. In the workplace of bridge inspection using drones, the mobile robotic inspection platform rapidly collected big inspection video data that need to be processed prior to element-level inspections. She will illustrate how human intelligence and artificial intelligence can collaborate in creating an AI model both efficiently and effectively. Obtaining a large amount of expert-annotated data for model training is less desirable, if not unrealistic, in bridge inspection. This INSPIRE project addressed this annotation challenge by developing a semi-supervised self-learning (S3T) algorithm that utilizes a small amount of time and guidance from inspectors to help the model achieve an excellent performance. The project evaluated the improvement in job efficacy produced by the developed AI model. This presentation will conclude by introducing some of the on-going work to achieve the desired adaptability of AI models to new or revised tasks in bridge inspection as the National Bridge Inventory includes over 600,000 bridges of various types in material, shape, and age

A Genetic Programming Approach for Computer Vision: Classifying High-level Image Features from Convolutional Layers with an Evolutionary Algorithm

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceComputer Vision is a sub-field of Artificial Intelligence that provides a visual perception component to computers, mimicking human intelligence. One of its tasks is image classification and Convolutional Neural Networks (CNNs) have been the most implemented algorithm in the last few years, with few changes made to the fully-connected layer of those neural networks. Nonetheless, recent research has been showing their accuracy could be improved in certain cases by implementing other algorithms for the classification of high-level image features from convolutional layers. Thus, the main research question for this document is: To what extent does the substitution of the fully-connected layer in Convolutional Neural Networks for an evolutionary algorithm affect the performance of those CNN models? The proposed two-step approach in this study does the classification of high-level image features with a state-of-the-art GP-based algorithm for multiclass classification called M4GP. This is conducted using secondary data with different characteristics, to better benchmark the implementation and to carefully investigate different outcomes. Results indicate the new learning approach yielded similar performance in the dataset with a low number of output classes. However, none of the M4GP models was able to surpass the results of the fully-connected layers in terms of test accuracy. Even so, this might be an interesting route if one has a powerful computer and needs a very light classifier in terms of model size. The results help to understand in which situation it might be beneficial to perform a similar experimental setup, either in the context of a work project or concerning a novel research topic

Learning From Mistakes: Machine Learning Enhanced Human Expert Effort Estimates

In this paper, we introduce a novel approach to predictive modeling for software engineering, named Learning From Mistakes (LFM). The core idea underlying our proposal is to automatically learn from past estimation errors made by human experts, in order to predict the characteristics of their future misestimates, therefore resulting in improved future estimates. We show the feasibility of LFM by investigating whether it is possible to predict the type, severity and magnitude of errors made by human experts when estimating the development effort of software projects, and whether it is possible to use these predictions to enhance future estimations. To this end we conduct a thorough empirical study investigating 402 maintenance and new development industrial software projects. The results of our study reveal that the type, severity and magnitude of errors are all, indeed, predictable. Moreover, we find that by exploiting these predictions, we can obtain significantly better estimates than those provided by random guessing, human experts and traditional machine learners in 31 out of the 36 cases considered (86%), with large and very large effect sizes in the majority of these cases (81%). This empirical evidence opens the door to the development of techniques that use the power of machine learning, coupled with the observation that human errors are predictable, to support engineers in estimation tasks rather than replacing them with machine-provided estimates

Interactive Optimisation in Marine Propeller Design

Marine propeller design is a complex engineering problem that depends on the collaboration of several scientific disciplines. During the design process, the blade designers need to consider contradicting requirements and come up with one optimal propeller design as a solution to the specific problem. This solution is usually the trade-o between the stakeholders\u27 requirements and the objectives and constraints of the problem.The significant amount of design variables related to blade design problems requires a systematic search in a large design space. Automated optimisation has been utilised for a number of blade design applications, as it has the advantage of creating a large set of design alternatives in a short period of time. However, automated optimisation has failed to be used in industrial applications, due to its complex set-up and the fact that in more complex scenarios the majority of the non-dominated design alternatives are infeasible. This necessitates a way of enabling the blade designers to interact with the algorithm during the optimisation process.The purpose of this thesis is to develop a methodology that supports the blade designers during the design process and to enable them to interact with the design tools and assess design characteristics during the optimisation. The overall aim is to improve the design performance and speed. According to the proposed methodology, blade designers are called during intermediate stages of the optimisation to provide information about the designs, and then this information is input in the algorithm. The goal is to steer the optimisation to an area of the design space with feasible Pareto designs, based on the designer\u27s preference. Since there are objectives and constraints that cannot be quantified with the available computational tools, keeping the "human in the loop" is essential, as a means to obtain feasible designs and quickly eliminate designs that are impractical or unrealistic.The results of this research suggest that through the proposed methodology the designers have more control over the whole optimisation procedure and they obtain detailed Pareto frontiers that involve designs that are characterised by high performance and follow the user preference

Player Behavior Modeling In Video Games

Player Behavior Modeling in Video Games In this research, we study players’ interactions in video games to understand player behavior. The first part of the research concerns predicting the winner of a game, which we apply to StarCraft and Destiny. We manage to build models for these games which have reasonable to high accuracy. We also investigate which features of a game comprise strong predictors, which are economic features and micro commands for StarCraft, and key shooter performance metrics for Destiny, though features differ between different match types. The second part of the research concerns distinguishing playing styles of players of StarCraft and Destiny. We find that we can indeed recognize different styles of playing in these games, related to different match types. We relate these different playing styles to chance of winning, but find that there are no significant differences between the effects of different playing styles on winning. However, they do have an effect on the length of matches. In Destiny, we also investigate what player types are distinguished when we use Archetype Analysis on playing style features related to change in performance, and find that the archetypes correspond to different ways of learning. In the final part of the research, we investigate to what extent playing styles are related to different demographics, in particular to national cultures. We investigate this for four popular Massively multiplayer online games, namely Battlefield 4, Counter-Strike, Dota 2, and Destiny. We found that playing styles have relationship with nationality and cultural dimensions, and that there are clear similarities between the playing styles of similar cultures. In particular, the Hofstede dimension Individualism explained most of the variance in playing styles between national cultures for the games that we examined