A framework to maximise the communicative power of knowledge visualisations

Knowledge visualisation, in the field of information systems, is both a process and a product, informed by the closely aligned fields of information visualisation and knowledg management. Knowledge visualisation has untapped potential within the purview of knowledge communication. Even so, knowledge visualisations are infrequently deployed due to a lack of evidence-based guidance. To improve this situation, we carried out a systematic literature review to derive a number of “lenses” that can be used to reveal the essential perspectives to feed into the visualisation production process.We propose a conceptual framework which incorporates these lenses to guide producers of knowledge visualisations. This framework uses the different lenses to reveal critical perspectives that need to be considered during the design process. We conclude by demonstrating how this framework could be used to produce an effective knowledge visualisation

Tool Tracking in a Laparoscopic Virtual Reality Training System

Minimally invasive surgery (MIS) is replacing the open surgical approach to reduce pain, expedite patient recovery, shorten hospital stays and improve cosmetic results for many abdominal procedures. It is widely accepted that the increased technical difficulty of MIS approaches (difficult visualization, tool fulcrum effect, etc.) requires specialized surgical training. The fundamentals of laparoscopic surgery (FLS) program has become a de facto standard to evaluate surgical skills for laparoscopic MIS Simulation-based surgical training has been growing not only as an innovative way to teach surgery [2] but also as a method to assess the skill of a surgeon in performing a procedure, without risk to patients We use cameras to track tool motion [5], a general concept of the design and determination of appropriate workspace visualization were discussed. The fabrication process was explained and the first prototype was presented. In this paper, the computer vision and image processing aspects of the surgical simulator are described; these are used to detect the motion of the tool in the physical simulator and map these movements into a virtual surgical environment (Vizard, WorldViz, Santa Barbara, CA) for visualization. Pilot virtual simulation tasks are also described. Methods 2.1 Image-Based Tracking. An image-based method is used to send grasper motion data into the simulator by using video from a pair of cameras, which are calibrated using toolboxes available with MATLAB software. A pose estimation algorithm A four-colored marker is attached to the end of the grasper as shown in The red-blue-green color space is used to find the position of the markers against a white background as frames are streamed from the cameras. The differently colored markers are arranged to minimize occluded views and detect rotation about the tool shaft. The color threshold module is used to segment the colored dots in the images by using the MATLAB image processing toolbox. The colored dots are extracted by subtracting the channel color of the image from the grayscale of the images and extracting the pixels whose value exceeds the threshold. After calibrating the position of the cameras, the 3D positions of the markers are identified with respect to the 2D color-filtered images by applying a triangulation method. In this method, the nearest point from the projection line to the 3D position of the object is calculated, as illustrated in In the first step, we determine the direction of the 3D rays by calculating the unit vector pointing from the camera center to the object

Survey of maps of dynamics for mobile robots

Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area

ESSE 2017. Proceedings of the International Conference on Environmental Science and Sustainable Energy

Environmental science is an interdisciplinary academic field that integrates physical-, biological-, and information sciences to study and solve environmental problems. ESSE - The International Conference on Environmental Science and Sustainable Energy provides a platform for experts, professionals, and researchers to share updated information and stimulate the communication with each other. In 2017 it was held in Suzhou, China June 23-25, 2017

Exploiting general-purpose background knowledge for automated schema matching

The schema matching task is an integral part of the data integration process. It is usually the first step in integrating data. Schema matching is typically very complex and time-consuming. It is, therefore, to the largest part, carried out by humans. One reason for the low amount of automation is the fact that schemas are often defined with deep background knowledge that is not itself present within the schemas. Overcoming the problem of missing background knowledge is a core challenge in automating the data integration process. In this dissertation, the task of matching semantic models, so-called ontologies, with the help of external background knowledge is investigated in-depth in Part I. Throughout this thesis, the focus lies on large, general-purpose resources since domain-specific resources are rarely available for most domains. Besides new knowledge resources, this thesis also explores new strategies to exploit such resources. A technical base for the development and comparison of matching systems is presented in Part II. The framework introduced here allows for simple and modularized matcher development (with background knowledge sources) and for extensive evaluations of matching systems. One of the largest structured sources for general-purpose background knowledge are knowledge graphs which have grown significantly in size in recent years. However, exploiting such graphs is not trivial. In Part III, knowledge graph em- beddings are explored, analyzed, and compared. Multiple improvements to existing approaches are presented. In Part IV, numerous concrete matching systems which exploit general-purpose background knowledge are presented. Furthermore, exploitation strategies and resources are analyzed and compared. This dissertation closes with a perspective on real-world applications

Dynamic modelling of demand risk in PPP infrastructure projects : “The case of toll roads”

Infrastructure is the main driver of prosperity and economic development. To fill the gap between increasing demand for infrastructure and supply, the role of the private financing has become increasingly critical. Concession contracts in which the investment cost is recovered via payments from the end users are the most dominant among all PPP types. Although this mechanism has been seen as an efficient way to achieve infrastructure projects in terms of realising the project on time and to budget, the demand risk faced in the operation stage has heavily limited this efficiency. Evidence has shown that shortfall in demand can seriously jeopardize the scheme’s viability. Demand is dependent on a range of interrelated, dynamic factors such as economic conditions, willingness to pay and tariff for using the facility. In addition, uncertainty is an inherent aspect of most demand-underlying factors which makes demand estimation subject to high level of uncertainty. However, this uncertainty is largely ignored by modellers and planners and single demand estimate is often used when evaluating the facility. Given the threat to the project success resulting from potential variation between predicted and actual demand, it is believed that a demand risk assessment model is essential. This research is therefore devoted to developing a system dynamics model to assess demand risk by capturing the factors affecting demand and their relationships and simulating their change over time. A system dynamics based conceptual model was developed for mapping factors affecting demand for service provided by a typical PPP concession project. The model has five Causal Loop Diagrams (CLDs) which include: socio-economic, public satisfaction, willingness to pay, competition and level of fee. Based on the developed conceptual model, a quantitative simulation model for assessing traffic demand in toll road projects was developed. This model has six sub-models which are: socio-economic, public satisfaction, willingness to pay, competition, toll and expansion factors sub-models. With the use of case study of M6 toll roads (UK), it was demonstrated the potential application of SD as a tool for the assessment of demand risk in toll roads. Univariate and multivariate sensitivity analysis, as well as risk analysis using Monte Carlo approach, were conducted using the developed SD model. Univariate sensitivity analysis helps identify the significance of the demand underlying factors when they change individually. Toll was identified as the most critical factor affecting toll traffic demand followed by congestion on the alternative un-tolled facility. Multivariate sensitivity analysis showed how demand changes when several factors change. Four scenarios were developed to show the impact of change in conditions and policies on the level of traffic. Monte Carlo simulation, on the other hand, provided level of demand with a range of confidence intervals. Providing such estimates of the expected value and the confidence level offers useful information throughout their ranges and creates overall risk profiles by providing the probability of achieving a specific result. The main contribution of the research is in the development of a system dynamics model as a tool for assessing demand in PPP projects and informing decision making, which is new to the area of demand risk modelling