Parallel processing and expert systems

Whether it be monitoring the thermal subsystem of Space Station Freedom, or controlling the navigation of the autonomous rover on Mars, NASA missions in the 1990s cannot enjoy an increased level of autonomy without the efficient implementation of expert systems. Merely increasing the computational speed of uniprocessors may not be able to guarantee that real-time demands are met for larger systems. Speedup via parallel processing must be pursued alongside the optimization of sequential implementations. Prototypes of parallel expert systems have been built at universities and industrial laboratories in the U.S. and Japan. The state-of-the-art research in progress related to parallel execution of expert systems is surveyed. The survey discusses multiprocessors for expert systems, parallel languages for symbolic computations, and mapping expert systems to multiprocessors. Results to date indicate that the parallelism achieved for these systems is small. The main reasons are (1) the body of knowledge applicable in any given situation and the amount of computation executed by each rule firing are small, (2) dividing the problem solving process into relatively independent partitions is difficult, and (3) implementation decisions that enable expert systems to be incrementally refined hamper compile-time optimization. In order to obtain greater speedups, data parallelism and application parallelism must be exploited

Experimental L-band SST satellite communications/surveillance terminal study. Volume 1 - Study summary

Study of design for experimental L band supersonic transport communications/surveillance termina

Doctor of Philosophy

dissertationFocused ultrasound (FUS) is a promising noninvasive and radiation-free cancer therapy that selectively delivers high-intensity acoustic energy to a small target volume. This dissertation presents original research that improves the speed, safety, and efficacy of FUS therapies under magnetic resonance imaging (MRI) guidance. First, a new adaptive model-predictive controller is presented that leverages the ability of MRI to measure temperature inside the patient at near real-time speeds. The controller uses MR temperature feedback to dynamically derive and update a patient-specific thermal model, and optimizes the treatment based on the model's predictions. Treatment safety is a key element of the controller's design, and it can actively protect healthy tissue from unwanted damage. In vivo and simulation studies indicate the controller can safeguard healthy tissue and accelerate treatments by as much as 50%. Significant tradeoffs exist between treatment speed, and safety, which makes a real-time controller absolutely necessary for carrying out efficient, effective, and safe treatments while also highlighting the importance of continued research into optimal treatment planning. Next, two new methods for performing 3D MR acoustic radiation force imaging (MR-ARFI) are presented. Both techniques measure the tissue displacement induced by short bursts of focused ultrasound, and provide a safe way to visualize the ultrasound beam's location. In some scenarios, ARFI is a necessity for proper targeting since traditional MR thermometry cannot measure temperature in fat. The first technique for performing 3D ARFI introduces a novel unbalanced bipolar motion encoding gradient. The results demonstrate that this technique is safe, and that 3D displacement maps can be attained time-efficiently even in organs that contain fat, such as breast. The second technique measures 3D ARFI simultaneously with temperature monitoring. This method uses a multi-contrast gradient recalled echo sequence which makes multiple readings of the data without increasing scan time. This improves the signal to noise ratio and makes it possible to separate the effects of tissue heating vs displacement. Both of the 3D MR-ARFI techniques complement the presented controllersince proper positioning of the focal spot is critical to achieving fast and safe treatments

‘Mind the Gap’: Extending Outcome Measurement for Accountability and Meaningful Innovation

\ua9 The Author(s) 2024. Published by Oxford University Press on behalf of The British Association of Social Workers.We examine the outcome measurement landscape in care leaver innovation, where many innovations to support transitions of young people leaving care fail to sustain beyond a fixed-term pilot, and fewer impact wider transition policies. Our empirical qualitative study comprises interviews with 31 senior UK children’s social care policy and practice professionals, 103 interviews across five innovation-focused case studies within England with a range of public and private providers. We consider these data in relation to evaluations from a nationally diffused social care innovation. We identified three measurement landscape challenges. First, we highlight the limits of the economically oriented measurement and identify an overlooked outcome measurement demand. Second, we emphasise a need to stratify care leaver population outcomes to better reflect individuals transition through different domains of life and trajectory. Third, we identify areas of precarity around the intended use of care leaver experience. We conclude that tensions exist between the pull towards a unified approach to outcome measurement and the reality of decoupled outcome requirements and legitimacy-seeking priorities which differ according to stakeholder. These tensions entrench stagnant innovation. Recognition of roles and legitimacies that exist across the process of care leaver innovation is warranted. Opportunities for action are discussed

A fuzzy front end model for concurrent specification in new product development

This research reports on the development of a new model for an early design stage in new product development (NPD) programmes called the Fuzzy Front End (FFE). The new FFE model aims at overcoming two kinds of limitations identified in previous FFE models. The first limitation concerns current trends in FFE model improvement including the need for a data-driven model, and to address agile development, incremental and radical NPDs, balanced explicitness and responsiveness characteristics, and balanced procedural and performative structures. The second limitation concerns deficiencies in the performance structure and operating mechanism regarding contextual performance and concurrent collaboration. This means that performances in the FFE do not systematically link with each other, either in a single functional domain or multidimensionally across diverse functional domains, but instead exist independently. A pragmatic-prescriptive model has been functionally embodied by analysing real-world FFE scenarios using inductive reasoning. The model is data-driven with a performative structure wherein parameters can interlock for contextual performance and concurrent collaboration throughout the entire FFE process. With this interlocking structure, once an initial parameter is produced, all remaining parameters considered from both perspectives can be obtained successively. This model allows performers to explicitly understand the purpose and roles of parameters and their relationships from both perspectives when processing parameters. The model thus leads to more agile FFE execution by reducing the iterative work needed to correct defective parameters which have not been handled with contextual performance and concurrent collaboration in mind but instead exist independently. A theoretical-descriptive model, produced by validating the developed pragmatic-prescriptive model, using deductive reasoning, consists of mathematical formulas, providing the underlying concept of an overall FFE as well as that of its parts. Consequently, the pragmatic-prescriptive model can serve as functional performance guidance, while the theoretical-descriptive model can serve as conceptual performance guidance when employing the pragmatic-prescriptive model.Open Acces

Exploring the application of ultrasonic phased arrays for industrial process analysis

This thesis was previously held under moratorium from 25/11/19 to 25/11/21Typical industrial process analysis techniques require an optical path to exist between the measurement sensor and the process to acquire data used to optimise and control an industrial process. Ultrasonic sensing is a well-established method to measure into optically opaque structures and highly focussed images can be generated using multiple element transducer arrays. In this Thesis, such arrays are explored as a real-time imaging tool for industrial process analysis. A novel methodology is proposed to characterise the variation between consecutive ultrasonic data sets deriving from the ultrasonic hardware. The pulse-echo response corresponding to a planar back wall acoustic interface is used to infer the bandwidth, pulse length and sensitivity of each array element. This led to the development of a calibration methodology to enhance the accuracy of experimentally generated ultrasonic images. An algorithm enabling non-invasive through-steel imaging of an industrial process is demonstrated using a simulated data set. Using principal component analysis, signals corresponding to reverberations in the steel vessel wall are identified and deselected from the ultrasonic data set prior to image construction. This facilitates the quantification of process information from the image. An image processing and object tracking algorithm are presented to quantify the bubble size distribution (BSD) and bubble velocity from ultrasonic images. When tested under controlled dynamic conditions, the mean value of the BSD was predicted within 50% at 100 mms-1 and the velocity could be predicted within 30% at 100 mms-1. However, these algorithms were sensitive to the quality of the input image to represent the true bubble shape. The consolidation of these techniques demonstrates successful application of ultrasonic phased array imaging, both invasively and noninvasively, to a dynamic process stream. Key to industrial uptake of the technology are data throughput and processing, which currently limit its applicability to real-time process analysis, and low sensitivity for some non-invasive applications.Typical industrial process analysis techniques require an optical path to exist between the measurement sensor and the process to acquire data used to optimise and control an industrial process. Ultrasonic sensing is a well-established method to measure into optically opaque structures and highly focussed images can be generated using multiple element transducer arrays. In this Thesis, such arrays are explored as a real-time imaging tool for industrial process analysis. A novel methodology is proposed to characterise the variation between consecutive ultrasonic data sets deriving from the ultrasonic hardware. The pulse-echo response corresponding to a planar back wall acoustic interface is used to infer the bandwidth, pulse length and sensitivity of each array element. This led to the development of a calibration methodology to enhance the accuracy of experimentally generated ultrasonic images. An algorithm enabling non-invasive through-steel imaging of an industrial process is demonstrated using a simulated data set. Using principal component analysis, signals corresponding to reverberations in the steel vessel wall are identified and deselected from the ultrasonic data set prior to image construction. This facilitates the quantification of process information from the image. An image processing and object tracking algorithm are presented to quantify the bubble size distribution (BSD) and bubble velocity from ultrasonic images. When tested under controlled dynamic conditions, the mean value of the BSD was predicted within 50% at 100 mms-1 and the velocity could be predicted within 30% at 100 mms-1. However, these algorithms were sensitive to the quality of the input image to represent the true bubble shape. The consolidation of these techniques demonstrates successful application of ultrasonic phased array imaging, both invasively and noninvasively, to a dynamic process stream. Key to industrial uptake of the technology are data throughput and processing, which currently limit its applicability to real-time process analysis, and low sensitivity for some non-invasive applications

Simulating crowd evacuation with socio-cultural, cognitive, and emotional elements

In this research, the effects of culture, cognitions, and emotions on crisis management and prevention are analysed. An agent-based crowd evacuation simulation model was created, named IMPACT, to study the evacuation process from a transport hub. To extend previous research, various socio-cultural, cognitive, and emotional factors were modelled, including: language, gender, familiarity with the environment, emotional contagion, prosocial behaviour, falls, group decision making, and compliance. The IMPACT model was validated against data from an evacuation drill using the existing EXODUS evacuation model. Results show that on all measures, the IMPACT model is within or close to the prescribed boundaries, thereby establishing its validity. Structured simulations with the validated model revealed important findings, including: the effect of doors as bottlenecks, social contagion speeding up evacuation time, falling behaviour not affecting evacuation time significantly, and travelling in groups being more beneficial for evacuation time than travelling alone. This research has important practical applications for crowd management professionals, including transport hub operators, first responders, and risk assessors