Center for space microelectronics technology

The 1992 Technical Report of the Jet Propulsion Laboratory Center for Space Microelectronics Technology summarizes the technical accomplishments, publications, presentations, and patents of the center during the past year. The report lists 187 publications, 253 presentations, and 111 new technology reports and patents in the areas of solid-state devices, photonics, advanced computing, and custom microcircuits

NASA patent abstracts bibliography: A continuing bibliography. Section 1: Abstracts (supplement 45)

Abstracts are provided for 137 patents and patent applications entered into the NASA scientific and technical information system during the period Jan. 1994 through Jun. 1994. Each entry consists of a citation, an abstract, and in most cases, a key illustration selected from the patent or patent application

Nonlinear system identification and control using dynamic multi-time scales neural networks

In this thesis, on-line identification algorithm and adaptive control design are proposed for nonlinear singularly perturbed systems which are represented by dynamic neural network model with multi-time scales. A novel on-line identification law for the Neural Network weights and linear part matrices of the model has been developed to minimize the identification errors. Based on the identification results, an adaptive controller is developed to achieve trajectory tracking. The Lyapunov synthesis method is used to conduct stability analysis for both identification algorithm and control design. To further enhance the stability and performance of the control system, an improved . dynamic neural network model is proposed by replacing all the output signals from the plant with the state variables of the neural network. Accordingly, the updating laws are modified with a dead-zone function to prevent parameter drifting. By combining feedback linearization with one of three classical control methods such as direct compensator, sliding mode controller or energy function compensation scheme, three different adaptive controllers have been proposed for trajectory tracking. New Lyapunov function analysis method is applied for the stability analysis of the improved identification algorithm and three control systems. Extensive simulation results are provided to support the effectiveness of the proposed identification algorithms and control systems for both dynamic NN models

Using evolutionary artificial neural networks to design hierarchical animat nervous systems.

The research presented in this thesis examines the area of control systems for robots or animats (animal-like robots). Existing systems have problems in that they require a great deal of manual design or are limited to performing jobs of a single type. For these reasons, a better solution is desired. The system studied here is an Artificial Nervous System (ANS) which is biologically inspired; it is arranged as a hierarchy of layers containing modules operating in parallel. The ANS model has been developed to be flexible, scalable, extensible and modular. The ANS can be implemented using any suitable technology, for many different environments. The implementation focused on the two lowest layers (the reflex and action layers) of the ANS, which are concerned with control and rhythmic movement. Both layers were realised as Artificial Neural Networks (ANN) which were created using Evolutionary Algorithms (EAs). The task of the reflex layer was to control the position of an actuator (such as linear actuators or D.C. motors). The action layer performed the task of Central Pattern Generators (CPG), which produce rhythmic patterns of activity. In particular, different biped and quadruped gait patterns were created. An original neural model was specifically developed for assisting in the creation of these time-based patterns. It is shown in the thesis that Artificial Reflexes and CPGs can be configured successfully using this technique. The Artificial Reflexes were better at generalising across different actuators, without changes, than traditional controllers. Gaits such as pace, trot, gallop and pronk were successfully created using the CPGs. Experiments were conducted to determine whether modularity in the networks had an impact. It has been demonstrated that the degree of modularization in the network influences its evolvability, with more modular networks evolving more efficiently

An early-stage decision-support framework for the implementation of intelligent automation

The constant pressure on manufacturing companies to improve productivity, reduce the lead time and progress in quality requires new technological developments and adoption.The rapid development of smart technology and robotics and autonomous systems (RAS) technology has a profound impact on manufacturing automation and might determine winners and losers of the next generation’s manufacturing competition. Simultaneously, recent smart technology developments in the areas enable an automation response to new production paradigms such as mass customisation and product-lifecycle considerations in the context of Industry 4.0. New paradigms, like mass customisation, increased both the complexity of the tasks and the risk due to smart technology integration. From a manufacturing automation perspective, intelligent automation has been identified as a possible response to arising demands. The presented research aims to support the industrial uptake of intelligent automation into manufacturing businesses by quantifying risks at the early design stage and business case development. An early-stage decision-support framework for the implementation of intelligent automation in manufacturing businesses is presented in this thesis.The framework is informed by an extensive literature review, updated and verified with surveys and workshops to add to the knowledge base due to the rapid development of the associated technologies. A paradigm shift from cost to a risk-modelling perspective is proposed to provide a more flexible and generic approach applicable throughout the current technology landscape. The proposed probabilistic decision-support framework consists of three parts:• A clustering algorithm to identify the manufacturing functions in manual processes from task analysis to mitigate early-stage design uncertainties• A Bayesian Belief Network (BBN) informed by an expert elicitation via the DELPHI method, where the identified functions become the unit of analysis.• A Markov-Chain Monte-Carlo method modelling the effects of uncertainties on the critical success factors to address issues of factor interdependencies after expert elicitation.Based on the overall decision framework a toolbox was developed in Microsoft Excel. Five different case studies are used to test and validate the framework. Evaluation of the results derived from the toolbox from the industrial feedback suggests a positive validation for commercial use. The main contributions to knowledge in the presented thesis arise from the following four points:• Early-stage decision-support framework for business case evaluation of intelligent automation.• Translating manual tasks to automation function via a novel clustering approach• Application of a Markov-Chain Monte-Carlo Method to simulate correlation between decision criteria• Causal relationship among Critical Success Factors has been established from business and technical perspectives.The implications on practise might be promising. The feedback arising from the created tool was promising from the industry, and a practical realisation of the decision-support tool seems to be desired from an industrial point of view.With respect to further work, the decision-support tool might have established a ground to analyse a human task automatically for automation purposes. The established clustering mechanisms and the related attributes could be connected to sensorial data and analyse a manufacturing task autonomously without the subjective input of task analysis experts. To enable such an autonomous process, however, the psychophysiological understanding must be increased in the future.</div

Example Based Caricature Synthesis

The likeness of a caricature to the original face image is an essential and often overlooked part of caricature production. In this paper we present an example based caricature synthesis technique, consisting of shape exaggeration, relationship exaggeration, and optimization for likeness. Rather than relying on a large training set of caricature face pairs, our shape exaggeration step is based on only one or a small number of examples of facial features. The relationship exaggeration step introduces two definitions which facilitate global facial feature synthesis. The first is the T-Shape rule, which describes the relative relationship between the facial elements in an intuitive manner. The second is the so called proportions, which characterizes the facial features in a proportion form. Finally we introduce a similarity metric as the likeness metric based on the Modified Hausdorff Distance (MHD) which allows us to optimize the configuration of facial elements, maximizing likeness while satisfying a number of constraints. The effectiveness of our algorithm is demonstrated with experimental results

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