Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2010. / Committee Chair: Kemp, Charles; Committee Member: Glass, Jonathan; Committee Member: Griffin, Paul; Committee Member: Howard, Ayanna; Committee Member: Thomaz, Andrea. Part of the SMARTech Electronic Thesis and Dissertation Collection.

The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis and other online collaborative media. The distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand, or organisation. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions, in fact, involves a deep understanding of natural language text by machines, from which we are still very far. Hitherto, online information retrieval has been mainly based on algorithms relying on the textual representation of web-pages. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling and counting their words. But when it comes to interpreting sentences and extracting meaningful information, their capabilities are known to be very limited. Existing approaches to opinion mining and sentiment analysis, in particular, can be grouped into three main categories: keyword spotting, in which text is classified into categories based on the presence of fairly unambiguous affect words; lexical affinity, which assigns arbitrary words a probabilistic affinity for a particular emotion; statistical methods, which calculate the valence of affective keywords and word co-occurrence frequencies on the base of a large training corpus. Early works aimed to classify entire documents as containing overall positive or negative polarity, or rating scores of reviews. Such systems were mainly based on supervised approaches relying on manually labelled samples, such as movie or product reviews where the opinionist’s overall positive or negative attitude was explicitly indicated. However, opinions and sentiments do not occur only at document level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a document. In more recent works, text analysis granularity has been taken down to segment and sentence level, e.g., by using presence of opinion-bearing lexical items (single words or n-grams) to detect subjective sentences, or by exploiting association rule mining for a feature-based analysis of product reviews. These approaches, however, are still far from being able to infer the cognitive and affective information associated with natural language as they mainly rely on knowledge bases that are still too limited to efficiently process text at sentence level. In this thesis, common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques on two common sense knowledge bases was exploited to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. The engine was tested on three different resources, namely a Twitter hashtag repository, a LiveJournal database and a PatientOpinion dataset, and its performance compared both with results obtained using standard sentiment analysis techniques and using different state-of-the-art knowledge bases such as Princeton’s WordNet, MIT’s ConceptNet and Microsoft’s Probase. Differently from most currently available opinion mining services, the developed engine does not base its analysis on a limited set of affect words and their co-occurrence frequencies, but rather on common sense concepts and the cognitive and affective valence conveyed by these. This allows the engine to be domain-independent and, hence, to be embedded in any opinion mining system for the development of intelligent applications in multiple fields such as Social Web, HCI and e-health. Looking ahead, the combined novel use of different knowledge bases and of common sense reasoning techniques for opinion mining proposed in this work, will, eventually, pave the way for development of more bio-inspired approaches to the design of natural language processing systems capable of handling knowledge, retrieving it when necessary, making analogies and learning from experience.

Cranes are widely used in material-handling and transportation applications, e.g. in shipyards, construction sites, and warehouses. As they are critical to the economic vitality of modern-day industries, improving crane performance and ease of use are important contributors to industrial productivity, low production costs, and workplace safety. In a typical crane operation, a payload is lifted, moved to its destination, and then lowered into place. This dissertation aims to improve crane performance and reduce task difficulty for the human operator in the movements mentioned above, namely: 1) Moving payloads laterally in the horizontal plane, 2) Lifting payloads off the ground, and 3) Lowering or laying down payloads on the ground. The design of a novel and intuitive human-machine control interface is the focus for improving operations that involve moving payloads laterally. The interface allows operators to drive a crane by simply moving a hand-held device through the desired path. The position of the device, which is tracked by sensors, is used to generate command signals to drive the crane. This command is then input-shaped such that payload oscillations are greatly reduced, making it much easier for the operator to drive the crane. Several facets of this crane control method are examined, such as control structure and stability, usability contexts, modes of operation, and quantitative measures (by means of human operator studies) of performance improvements over standard crane control interfaces. Lifting up a payload can be difficult for the operator, if the hoist is not properly centered above the payload. In these potentially dangerous and costly ``off-centered" lifts, the payload may slide on the ground and/or oscillate in the air after it is hoisted. Newtonian and Coulomb friction models that focus on the stiction-sliding-separation contact dynamics are derived and experimentally verified to study off-centered lifts. Then, with the goal of aiding operators during lift operations, simple but practical, self-centering solutions are proposed and implemented. Laying down or lowering a payload to the ground can also be challenging for operators in certain situations. For example, laying down a long, slender payload from a vertical orientation in the air, to a horizontal position on a flat surface. If the operator does not properly coordinate the motions of the crane in the vertical and horizontal directions simultaneously, then the potential hazards that may occur during these operations include: 1) slipping of the pivot about which the payload rotates, leading to sudden and dangerous payload movements; and 2) excessive hoist cable angles that lead to ``side-pull" problems. Newtonian and Coulomb friction models are derived to describe this lay-down scenario. The forces and motions experienced by the payload are then used to determine the motion trajectories that the crane and payload should follow to execute a successful lay-down maneuver. Finally, a special chapter is included to address the oscillation control of systems that have on-off nonlinear actuators, such as cranes powered by relay-controlled circuits. Due to their simplicity, ruggedness, and long service life, this type of crane can be commonly found in older factories or in applications where precise motion control is not a strict requirement. However, controlling payload oscillations on this type of crane is challenging for two reasons: 1) Relays that can only be turned on or off allow for only limited control over the crane velocity; and 2) These cranes typically have nonlinear asymmetrical acceleration and deceleration properties. Methods are derived for determining the relay switch-times that move single-pendulum and double-pendulum payloads with low residual oscillations.

Heavy-lifting machines such as cranes are widely used at ports, construction sites, and manufacturing plants in a variety of material-transporting applications. However, cranes possess inherent flexible dynamics that make fast and precise operation challenging. Most cranes are driven by human operators, which adds another element of complexity. The goal of this thesis is to develop controllers that allow human operators to easily and efficiently control machines with flexible dynamics. To improve the ease of human operation of these machines, various control structures are developed and their effectiveness in aiding the operator are evaluated. Cranes are commonly used to swing wrecking balls that demolish unwanted structures. To aid the operator in such tasks, swing-amplifying controllers are designed and their performance are evaluated through simulations and experiments with real operators. To make maneuvering of these machines in material-transporting operations easier, input-shaping control is used to reduce oscillation induced by operator commands. In the presence of external disturbances, input shaping is combined with a low-authority feedback controller to eliminate unwanted oscillations, while maintaining the human operator as the primary controller of the machine. The performance and robustness of the proposed controllers are thoroughly examined via numerical simulations and a series of experiments and operator studies on a small-scale mobile boom crane and a two-ton dual-hoist bridge crane.

Automation surprises are examples of poor Human-Machine Interaction (HMI) where pilots were surprised by actions of the automation, which lead to dangerous situations during which pilots had to counteract the autopilot. To be able to identify problems that may arise between pilots and automation before implementation, methods are needed that can uncover potentially dangerous HMI early in the design process. In this work, two such methods, simulation and model checking, have been combined and compared to leverage the benefits of both. In the past, model checking has been successful at uncovering known automation surprises. Simulation, on the other hand, has been successful in the aviation domain and human factor issues. To be able to compare these two approaches, this work focused on a common case study involving a known automation surprise. The automation surprise that was examined, is linked to the former Airbus speed protection logic that caused aircraft on approach to change the flight mode, resulting in a sudden climb. The results provided by the model checking with SAL (Symbolic Analysis Laboratory) in a previous work, have been used to provide input for simulation. In this work, this automation surprise was simulated with the simulation platform WMC (Work Models that Compute) and compared to the corresponding results from SAL. By using the case study, this work provides a method to examine system behavior, such as automation surprises, using model checking and simulation in conjunction to leverage the benefits of both.

Thesis (D.Ph.) (Communication Arts)--University of Wisconsin-Madison, 2006. / Includes bibliographical references (p. 263-277). Also available on the Internet.

Thesis (MFineArt)--University of Melbourne, VCA Art, The Faculty of the Victorian College of the Arts, 2009. / Typescript. Includes bibliographical references (p. 41-42)

Thesis (D.Ph.) (Communication Arts)--University of Wisconsin-Madison, 2006. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (p. 263-277).

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Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2005. / This thesis explores the design and implementation of an intelligent semantic machine control system with specific reference to human-machine interface design. The term "intelligent" refers to machines that can execute some level of decision taking in context. The term "semantic" refers to a structured language that allows user and machine to communicate. This study will explore all the key concepts about an intelligent semantic machine control system with human-machine interface. The key concepts to be investigated will include Artificial Intelligence, Intelligent Control, Semantics, Intelligent Machine Architecture, Human-Machine Interaction, Information systems and Graphical User Interface. The primary purpose of this study is to develop a methodology for designing a machine control system and its related human-machine interface.