Thesis (M.S.)--Ohio University, March, 1993. / Title from PDF t.p.

This research concerns the integration of Adaptive Search (AS) technique such as the Genetic Algorithms (GA) with knowledge based software to develop a research prototype of an Adaptive Search Manager (ASM). The developed approach allows to utilise both quantitative and qualitative information in engineering design decision making. A Fuzzy Expert System manipulates AS software within the design environment concerning the preliminary design of gas turbine blade cooling systems. Steady state cooling hole geometry models have been developed for the project in collaboration with Rolls Royce plc. The research prototype of ASM uses a hybrid of Adaptive Restricted Tournament Selection (ARTS) and Knowledge Based Hill Climbing (KBHC) to identify multiple "good" design solutions as potential design options. ARTS is a GA technique that is particularly suitable for real world problems having multiple sub-optima. KBHC uses information gathered during the ARTS search as well as information from the designer to perform a deterministic hill climbing. Finally, a local stochastic hill climbing fine tunes the "good" designs. Design solution sensitivity, design variable sensitivities and constraint sensitivities are calculated following Taguchi's methodology, which extracts sensitivity information with a very small number of model evaluations. Each potential design option is then qualitatively evaluated separately for manufacturability, choice of materials and some designer's special preferences using the knowledge of domain experts. In order to guarantee that the qualitative evaluation module can evaluate any design solution from the entire design space with a reasonably small number of rules, a novel knowledge representation technique is developed. The knowledge is first separated in three categories: inter-variable knowledge, intra-variable knowledge and heuristics. Inter-variable knowledge and intra-variable knowledge are then integrated using a concept of compromise. Information about the "good" design solutions is presented to the designer through a designer's interface for decision support.

The k-path problem is to find a simple path of length k. This problem is NP-complete and has applications in bioinformatics for detecting signaling pathways in protein interaction networks and for biological subnetwork matching. There are algorithms implemented to solve the problem for k up to 13. The fastest implementation has running time O^*(4.32^k), which is slower than the best known algorithm of running time O^*(4^k). To implement the best known algorithm for the k-path problem, we need to construct (n,k)-universal set. In this thesis, we study the practical algorithms for constructing the (n,k)-universal set problem. We propose six algorithm variants to handle the increasing computational time and memory space needed for k=3, 4, ..., 8. We propose two major empirical techniques that cut the time and space tremendously, yet generate good results. For the case k=7, the size of the universal set found by our algorithm is 1576, and is 4611 for the case k=8. We implement the proposed algorithms with the OpenMP parallel interface and construct universal sets for k=3, 4, ..., 8. Our experiments show that our algorithms for the (n,k)-universal set problem exhibit very good parallelism and hence shed light on its MPI implementation. Ours is the first implementation effort for the (n,k)-universal set problem. We share the effort by proposing an extensible universal set construction and retrieval system. This system integrates universal set construction algorithms and the universal sets constructed. The sets are stored in a centralized database and an interface is provided to access the database easily. The (n,k)-universal set have been applied to many other NP-complete problems such as the set splitting problems and the matching and packing problems. The small (n,k)-universal set constructed by us will reduce significantly the time to solve those problems.

Large-scale bed features are often encountered in coastal waters, and include sandbanks and spoil heaps. The morphodynamic development of such features involves complicated nonlinear interactions between the flow hydrodynamics, sediment transport, and bed profile. Numerical modelling of the morphodynamic evolution and migration of large-scale bed features is necessary in order to understand their long-term behaviour in response to changing environmental conditions. This thesis describes detailed measurements of the morphodynamics of sand mounds in unidirectional and oscillatory (tidal) flows, undertaken at the U.K. Coastal Research Facility (UKCRF). High quality data were collected, including water velocities, water levels and overhead images. The parameters tested are: three types of mound shape (circular and elliptical in plan shape, and Gaussian, cosine and triangular in cross-section); underlying fixed or mobile bed conditions; and initial crest height (submerged, surface-touching and surface-piercing). Peak flow velocities are about 0.5 m/s, the sand median grain size is 0.454 mm, and transport occurring mostly as bedload. When analysing the data, the bed contours are determined by digitising the shoreline at different water levels. From these plots, the volume, height, and centroid position of the mound are calculated. A large-scale fit method, based on a Gaussian function has been used to separate small-scale ripples from the large-scale bed structure during the evolution of an isolated sand mound or spoil heap. The bed profile after the ripples are removed is comparable to typical predictions by shallow-flow numerical solvers. The UKCRF experiments investigated the morphodynamic response of a bed mound to hydrodynamic forcing: shape changes, migration rates, volume decay and sediment transport rates. The measured migration rate and decay of a submerged sand mound in the UKCRF are found to be in satisfactory agreement with results from various theoretical models, such as the analytical solution derived by De Vriend. Numerical predictions of mound evolution by a commercial code, PISCES, are also presented for a fully submerged sand mound; the bed evolution is reasonably similar to that observed in the UKCRF. The data provided as a result of the research reported in this thesis provide insight into the behaviour of sand mounds in steady and unsteady flows at laboratory scale, and should also be useful for benchmark (validation) purposes to numerical modellers of large-scale morphodynamics.

In this thesis, we present three main contributions to laser-based detection and tracking of dynamic objects, from both a model-based point of view and a model-free point of view, with an emphasis on applications to autonomous driving. A segmentation-based detector is first proposed to provide an end-to-end detection of the classes car, pedestrian and bicyclist in 3D laser data amongst significant background clutter. We postulate that, for the particular classes considered, solving a binary classification task outperforms approaches that tackle the multi-class problem directly. This is confirmed using custom and third-party datasets gathered of urban street scenes. The sliding window approach to object detection, while ubiquitous in the Computer Vision community, is largely neglected in laser-based object detectors, possibly due to its perceived computational inefficiency. We give a second thought to this opinion in this thesis, and demonstrate that, by fully exploiting the sparsity of the problem, exhaustive window searching in 3D can be made efficient. We prove the mathematical equivalence between sparse convolution and voting, and devise an efficient algorithm to compute exactly the detection scores at all window locations, processing a complete Velodyne scan containing 100K points in less than half a second. Its superior performance is demonstrated on the KITTI dataset, and compares commensurably with state of the art vision approaches. A new model-free approach to detection and tracking of moving objects with a 2D lidar is then proposed aiming at detecting dynamic objects of arbitrary shapes and classes. Objects are modelled by a set of rigidly attached sample points along their boundaries whose positions are initialised with and updated by raw laser measurements, allowing a flexible, nonparametric representation. Dealing with raw laser points poses a significant challenge to data association. We propose a hierarchical approach, and present a new variant of the well-known Joint Compatibility Branch and Bound algorithm to handle large numbers of measurements. The system is systematically calibrated on real world data containing 7.5K labelled object examples and validated on 6K test cases. Its performance is demonstrated over an existing industry standard targeted at the same problem domain as well as a classical approach to model-free tracking.

This biomedical engineering thesis explores the opportunities and challenges of 2D+t contrast echocardiography for left ventricle functional analysis, both clinically and within a computer vision atlas-based deformable template model framework. A database was created for the experiments in this thesis, with 21 studies of contrast Dobutamine Stress Echo, in all 4 principal planes. The database includes clinical variables, human expert hand-traced myocardial contours and visual scoring. First the problem is studied from a clinical perspective. Quantification of endocardial global and local function using standard measures shows expected values and agreement with human expert visual scoring, but the results are less reliable for myocardial thickening. Next, the problem of segmenting the endocardium with a computer is posed in a standard landmark and atlas-based deformable template model framework. The underlying assumption is that these models can emulate human experts in terms of integrating previous knowledge about the anatomy and physiology with three sources of information from the image: texture, geometry and kinetics. Probabilistic atlases of contrast echocardiography are computed, while noting from histograms at selected anatomical locations that modelling texture with just mean intensity values may be too naive. Intensity analysis together with the clinical results above suggest that lack of external boundary definition may preclude this imaging technique for appropriate measuring of myocardial thickening, while endocardial boundary definition is appropriate for evaluation of wall motion. Geometry is presented in a Principal Component Analysis (PCA) context, highlighting issues about Gaussianity, the correlation and covariance matrices with respect to physiology, and analysing different measures of dimensionality. A popular extension of deformable models ---Active Appearance Models (AAMs)--- is then studied in depth. Contrary to common wisdom, it is contended that using a PCA texture space instead of a fixed atlas is detrimental to segmentation, and that PCA models are not convenient for texture modelling. To integrate kinetics, a novel spatio-temporal model of cardiac contours is proposed. The new explicit model does not require frame interpolation, and it is compared to previous implicit models in terms of approximation error when the shape vector changes from frame to frame or remains constant throughout the cardiac cycle. Finally, the 2D+t atlas-based deformable model segmentation problem is formulated and solved with a gradient descent approach. Experiments using the similarity transformation suggest that segmentation of the whole cardiac volume outperforms segmentation of individual frames. A relatively new approach ---the inverse compositional algorithm--- is shown to decrease running times of the classic Lucas-Kanade algorithm by a factor of 20 to 25, to values that are within real-time processing reach.