Classes of decision analysis

The ultimate task of an engineer consists of developing a consistent decision procedure for the planning, design, construction and use and management of a project. Moreover, the utility over the entire lifetime of the project should be maximized, considering requirements with respect to safety of individuals and the environment as specified in regulations. Due to the fact that the information with respect to design parameters is usually incomplete or uncertain, decisions are made under uncertainty. In order to cope with this, Bayesian statistical decision theory can be used to incorporate objective as well as subjective information (e.g. engineering judgement). In this factsheet, the decision tree is presented and answers are given for questions on how new data can be combined with prior probabilities that have been assigned, and whether it is beneficial or not to collect more information before the final decision is made. Decision making based on prior analysis and posterior analysis is briefly explained. Pre-posterior analysis is considered in more detail and the Value of Information (VoI) is defined

MobiHealth-Innovative 2.5/3G mobile services and applications for health care

MobiHealth aims at introducing new mobile value added services in the area of healthcare, based on 2.5 (GPRS) and 3G (UMTS) technologies, thus promoting the use and deployment of GPRS and UMTS. This will be achieved by the integration of sensors and actuators to a Wireless Body Area Network (BAN). These sensors and actuators will continuously measure and transmit vital constants along with audio and video to health service providers and brokers, improving on one side the life of patients and allowing on the other side the introduction of new value-added services in the areas of disease prevention and diagnostic, remote assistance, para-health services, physical state monitoring (sports) and even clinical research. Furthermore, the MobiHealth BAN system will support the fast and reliable application of remote assistance in case of accidents by allowing the paramedics to send reliable vital constants data as well as audio and video directly from the accident site

Dielectric Elastomers for Energy Harvesting

Dielectric elastomers are a type of electroactive polymers that can be conveniently used as sensors, actuators or energy harvesters and the latter is the focus of this review. The relatively high number of publications devoted to dielectric elastomers in recent years is a direct reflection of their diversity, applicability as well as nontrivial electrical and mechanical properties. This chapter provides a review of fundamental mechanical and electrical properties of dielectric elastomers and up-to-date information regarding new developments of this technology and it’s potential applications for energy harvesting from various vibration sources explored over the past decade

Mobile Health Care over 3G Networks: the MobiHealth Pilot System and Service

Health care is one of the most prominent areas for the application of wireless technologies. New services and applications are today under research and development targeting different areas of health care, from high risk and chronic patients’ remote monitoring to mobility tools for the medical personnel. In this direction the MobiHealth project developed and trailed a system and a service that is using UMTS for the continuous monitoring and transmission of vital signals, like Pulse Oximeter sensor , temperature, Marker, Respiratory band, motion/activity detector etc., to the hospital. The system, based on the concept of the Body Area Network, is highly customisable, allowing sensors to be seamlessly connected and transmit the monitored vital signal measurements. The system and service was trialed in 4 European countries and it is presently under market validation

Updatable Probabilistic Evaluation of Failure Rates of Mechanical Components in Power Take-Off Systems of Tidal Stream Turbines

This paper presents a method for the probabilistic evaluation of the failure rates of mechanical components in a typical power take-off (PTO) system of a horizontal-axis tidal stream turbine (HATT). The method is based on a modification of the method of the influence factors, when base failure rates, relevant influence factors and, subsequently, resulting failure rates are treated as random variables. The prior (i.e., initial) probabilistic distribution of the failure rates of a HATT component is generated using data for similar components from other industries, while taking into account actual characteristics of the component and site-specific operating and environmental conditions of the HATT. A posterior distribution of the failure rate is estimated numerically based on a Bayesian approach as new information about the component performance in an operating HATT becomes available. The posterior distribution is then employed to obtain the updated mean and lower and upper confidence limits of the failure rate. The proposed method is illustrated by applying it to the evaluation of the failure rates of two key components of the PTO system of a typical HATT—main seal and main bearing. In particular, it is shown that uncertainty associated with the method itself has a major influence on the failure rate evaluation. The proposed method is useful for the reliability assessment of both PTO designs of new HATTs and PTO systems of operating HATTs

An application of augmented MDA for the extended healthcare enterprise

Mobile health systems extend the enterprise computing system of the healthcare provider by bringing services to the patient any time and anywhere. We propose a methodology for the development of such extended enterprise computing systems which applies a model-driven design and development approach augmented with formal validation and verification to address quality and correctness and to support model transformation. At the University of Twente we develop context aware m-health systems based on Body Area Networks (BANs). A set of deployed BANs are supported by a server. We refer to this distributed system as a BAN System. Development of such distributed m-health systems requires a sound software engineering approach and this is what we target with the proposed methodology. The methodology is illustrated with reference to modelling activities targeted at real implementations. BAN implementations are being trialled in a number of clinical settings including epilepsy management and management of chronic pain