A Comparative Analysis of Design Techniques for the Construction of an Expert System for Aircraft Engine Diagnostics

The lack of knowledge and understanding of diagnostic aircraft propulsion systems causes inappropriate problem diagnosis. Because of increasing complexity, technicians are incapable of performing the necessary tasks in accordance with standard regulations. More sophisticated systems are needed today to assist the user technician in decision-making. This work provided a study of rule-based and frame-based expert system techniques to determine the most appropriate solution in the domain of complex diagnosis using large amounts of deterministic data. The study produced a framework that facilitates the diagnosing of faults on aircraft engines, thus reducing the burden on the aircraft mechanic regardless of experience level. An intelligent system, the Virtually Automated Maintenance Analysis System (V AMAS), was created as a test model. It was used to compare the relative efficiency of the different expert systems techniques and the effectiveness of expert systems. One aviation malfunction problem was identified. Information collected for the Main Ignition Malfunction was developed into question sets and coded. Six specific subsets of problems were addressed. This research compared the rule-based and frame-based knowledge representation techniques using a set of evaluation factors: computational efficiency, correctness, expressiveness, and consistency. From the analysis it was concluded that the frame based knowledge representation technique ranked higher than the rule-based representation, and is suitable for use with an expert system to represent an aircraft propulsion system \u27s deterministic data

Parameters Identification for a Composite Piezoelectric Actuator Dynamics

This work presents an approach for identifying the model of a composite piezoelectric (PZT) bimorph actuator dynamics, with the objective of creating a robust model that can be used under various operating conditions. This actuator exhibits nonlinear behavior that can be described using backlash and hysteresis. A linear dynamic model with a damping matrix that incorporates the Bouc–Wen hysteresis model and the backlash operators is developed. This work proposes identifying the actuator’s model parameters using the hybrid master-slave genetic algorithm neural network (HGANN). In this algorithm, the neural network exploits the ability of the genetic algorithm to search globally to optimize its structure, weights, biases and transfer functions to perform time series analysis efficiently. A total of nine datasets (cases) representing three different voltage amplitudes excited at three different frequencies are used to train and validate the model. Four cases are considered for training the NN architecture, connection weights, bias weights and learning rules. The remaining five cases are used to validate the model, which produced results that closely match the experimental ones. The analysis shows that damping parameters are inversely proportional to the excitation frequency. This indicates that the suggested hysteresis model is too general for the PZT model in this work. It also suggests that backlash appears only when dynamic forces become dominant

Variable Scale Statistics For Cardiac Segmentation and Shape Analysis

A novel framework for medical image analysis, known as Shells and Spheres, has been developed by our research lab. This framework utilizes spherical operators of variable radius, centered at each image pixel and sized to reach, but not cross, the nearest boundary. Statistical population tests are performed on the populations of pixels within adjacent spheres to compare image regions across boundaries, delineating bothindependent image objects and the boundaries between them. This research has focused on developing the Shells and Spheres frameworkand applying it to the problem of segmentation of anatomical objects. Furthermore, we have rigorously studied the framework and its applications to clinical segmentation, validating and improving our n-dimensional segmentation algorithm. To this end, we have enhanced the original Shells and Spheres segmentation algorithm by adding a priori information, developing techniques for optimizing algorithm parameters, implementing a software platform for experimentation, and performing validation experiments using real 3D ovine cardiac MRI data. The system developed provides automated 3D segmentation given a priori information in the form of a trivial 2D manual training procedure, which involves tracing a single 2D contour from which 3D algorithm parameters are then automatically derived. We apply this system tosegmentation of the Right Ventricular Outflow Tract (RVOT) to aid in research toward the creation of a Tissue Engineered Pulmonary Valve(TEPV). Experimental methods are presented for the development and validation of the system, as well as a detailed description of the Shells and Spheres framework, our segmentation algorithm, and the clinical significance of this work

Cultural evolution in Vietnam’s early 20th century: a Bayesian networks analysis of Franco-Chinese house designs

The study of cultural evolution has taken on an increasingly interdisciplinary and diverse approach in explicating phenomena of cultural transmission and adoptions. Inspired by this computational movement, this study uses Bayesian networks analysis, combining both the frequentist and the Hamiltonian Markov chain Monte Carlo (MCMC) approach, to investigate the highly representative elements in the cultural evolution of a Vietnamese city’s architecture in the early 20th century. With a focus on the façade design of 68 old houses in Hanoi’s Old Quarter (based on 78 data lines extracted from 248 photos), the study argues that it is plausible to look at the aesthetics, architecture, and designs of the house façade to find traces of cultural evolution in Vietnam, which went through more than six decades of French colonization and centuries of sociocultural influence from China. The in-depth technical analysis, though refuting the presumed model on the probabilistic dependency among the variables, yields several results, the most notable of which is the strong influence of Buddhism over the decorations of the house façade. Particularly, in the top 5 networks with the best Bayesian Information Criterion (BIC) scores and p\u3c0.05, the variable for decorations (DC) always has a direct probabilistic dependency on the variable B for Buddhism. The paper then checks the robustness of these models using Hamiltonian MCMC method and find the posterior distributions of the models’ coefficients all satisfy the technical requirement. Finally, this study suggests integrating Bayesian statistics in the social sciences in general and for the study of cultural evolution and architectural transformation in particular