Engineering Commons^™

Safety Intelligence And Legal Machine Language: Do We Need Three Laws Of Robotics?, Chien Hsun Chen, Y. H. Weng, C. T. Sun

Chien Hsun Chen

In this chapter we will describe a legal framework for Next Generation Robots (NGRs) that has safety as its central focus. The framework is offered in response to the current lack of clarity regarding robot safety guidelines, despite the development and impending release of tens of thousands of robots into workplaces and homes around the world. We also describe our proposal for a safety intelligence (SI) concept that addresses issues associated with open texture risk for robots that will have a relatively high level of autonomy in their interactions with humans. Whereas Isaac Asimov’s Three Laws of Robotics are frequently …

Developing Large-Scale Bayesian Networks By Composition: Fault Diagnosis Of Electrical Power Systems In Aircraft And Spacecraft, Ole J. Mengshoel, Scott Poll, Tolga Kurtoglu

Ole J Mengshoel

In this paper, we investigate the use of Bayesian networks to construct large-scale diagnostic systems. In particular, we consider the development of large-scale Bayesian networks by composition. This compositional approach reflects how (often redundant) subsystems are architected to form systems such as electrical power systems. We develop high-level specifiations, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems. The largest among these 24 Bayesian networks contains over 1,000 random variables. Another BN represents the real-world electrical power system ADAPT, which is representative of electrical power systems deployed in aerospace vehicles. In addition to demonstrating the scalability …

Diagnosis And Reconfiguration Using Bayesian Networks: An Electrical Power System Case Study, W. Bradley Knox, Ole J. Mengshoel

Ole J Mengshoel

Automated diagnosis and reconfiguration are important computational techniques that aim to minimize human intervention in autonomous systems. In this paper, we develop novel techniques and models in the context of diagnosis and reconfiguration reasoning using causal Bayesian networks (BNs). We take as starting point a successful diagnostic approach, using a static BN developed for a real-world electrical power system. We discuss in this paper the extension of this diagnostic approach along two dimensions, namely: (i) from a static BN to a dynamic BN; and (ii) from a diagnostic task to a reconfiguration task.

More specifically, we discuss the auto-generation of …

Strongly Coupled Computation Of Material Response And Nonequilibrium Flow For Hypersonic Ablation, Alexandre Martin, Iain D. Boyd

Alexandre Martin

A one-dimensional material response implicit solver with surface ablation and pyrolysis is strongly coupled to LeMANS, a CFD code for the simulation of weakly ionized hypersonic flows in thermo-chemical non-equilibrium. Using blowing wall boundary conditions and a moving mesh algorithm, the results of a strongly coupled solution of a re-entry problem are presented, using the well defined case of the IRV-2 vehicle. Results are compared to other coupled codes and show good agreement with published numerical results.

The Diagnostic Challenge Competition: Probabilistic Techniques For Fault Diagnosis In Electrical Power Systems, Brian W. Ricks, Ole J. Mengshoel

Ole J Mengshoel

An Immune Self-Adaptive Differential Evolution Algorithm With Application To Estimate Kinetic Parameters For Homogeneous Mercury Oxidation, Chunping Hu, Xuefeng Yan

Chunping Hu

A new version of differential evolution (DE) algorithm, in which immune concepts and methods are applied to determine the parameter setting, named immune self-adaptive differential evolution (ISDE), is proposed to improve the performance of the DE algorithm. During the actual operation, ISDE seeks the optimal parameters arising from the evolutionary process, which enable ISDE to alter the algorithm for different optimization problems and improve the performance of ISDE by the control parameters’ self-adaptation. The performance of the proposed method is studied with the use of nine benchmark problems and compared with original DE algorithm and other well-known self-adaptive DE algorithms. …

Kpca-Rvm Modeling Method And Its Application For Soft Sensor, Xuefeng Yan, Jia Chen, Chunping Hu, Feng Qian

Chunping Hu

A novel modeling method integrated KPCA with RVM is proposed. Firstly, kernel primary component analysis (KPCA) is employed to identify the principal components from the nonlinear transform data of independent variables, which are regarded as character variables. Then, regression between character variables and dependent variables is done based on RVM, and the optimal number of the character variables is adaptively determined according to the generalization performance of the regression model. Thus, KPCA-RVM method can eliminate the disturbance of redundant information and achieve the best nonlinear model with good generalization performance. Finally, the method of KPCA-RVM is demonstrated by a 4-CBA's …

Toward The Human-Robot Co-Existence Society: On Safety Intelligence For Next Generation Robots, Chien Hsun Chen, Y. H. Weng, C. T. Sun

Chien Hsun Chen

Technocrats from many developed countries, especially Japan and South Korea, are preparing for the human-robot co-existence society that they believe will emerge by 2030. Regulators are assuming that within the next two decades, robots will be capable of adapting to complex, unstructured environments and interacting with humans to assist with the performance of daily life tasks. Unlike heavily regulated industrial robots that toil in isolated settings, Next Generation Robots will have relative autonomy, which raises a number of safety issues that are the focus of this article. Our purpose is to describe a framework for a legal system focused on …

Constraint Handling Using Tournament Selection: Abductive Inference In Partly Deterministic Bayesian Network, Severino F. Galan, Ole J. Mengshoel

Ole J Mengshoel

Constraints occur in many application areas of interest to evolutionary computation. The area considered here is Bayesian networks (BNs), which is a probability-based method for representing and reasoning with uncertain knowledge. This work deals with constraints in BNs and investigates how tournament selection can be adapted to better process such constraints in the context of abductive inference. Abductive inference in BNs consists of finding the most probable explanation given some evidence. Since exact abductive inference is NP-hard, several approximate approaches to this inference task have been developed. One of them applies evolutionary techniques in order to find optimal or close-to-optimal …

Methods For Probabilistic Fault Diagnosis: An Electrical Power System Case Study, Brian Ricks, Ole J. Mengshoel

Ole J Mengshoel

Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case …