Manufacturing Process Causal Knowledge Discovery using a Modified Random Forest-based Predictive Model

A Modified Random Forest algorithm (MRF)-based predictive model is proposed for use in man-ufacturing processes to estimate the e˙ects of several potential interventions, such as (i) altering the operating ranges of selected continuous process parameters within specified tolerance limits,(ii) choosing particular categories of discrete process parameters, or (iii) choosing combinations of both types of process parameters. The model introduces a non-linear approach to defining the most critical process inputs by scoring the contribution made by each process input to the process output prediction power. It uses this contribution to discover optimal operating ranges for the continuous process parameters and/or optimal categories for discrete process parameters. The set of values used for the process inputs was generated from operating ranges identified using a novel Decision Path Search (DPS) algorithm and Bootstrap sampling.The odds ratio is the ratio between the occurrence probabilities of desired and undesired process output values. The e˙ect of potential interventions, or of proposed confirmation trials, are quantified as posterior odds and used to calculate conditional probability distributions. The advantages of this approach are discussed in comparison to fitting these probability distributions to Bayesian Networks (BN).The proposed explainable data-driven predictive model is scalable to a large number of process factors with non-linear dependence on one or more process responses. It allows the discovery of data-driven process improvement opportunities that involve minimal interaction with domain expertise. An iterative Random Forest algorithm is proposed to predict the missing values for the mixed dataset (continuous and categorical process parameters). It is shown that the algorithm is robust even at high proportions of missing values in the dataset.The number of observations available in manufacturing process datasets is generally low, e.g. of a similar order of magnitude to the number of process parameters. Hence, Neural Network (NN)-based deep learning methods are generally not applicable, as these techniques require 50-100 times more observations than input factors (process parameters).The results are verified on a number of benchmark examples with datasets published in the lit-erature. The results demonstrate that the proposed method outperforms the comparison approaches in term of accuracy and causality, with linearity assumed. Furthermore, the computational cost is both far better and very feasible for heterogeneous datasets

Assertion level proof planning with compiled strategies

This book presents new techniques that allow the automatic verification and generation of abstract human-style proofs. The core of this approach builds an efficient calculus that works directly by applying definitions, theorems, and axioms, which reduces the size of the underlying proof object by a factor of ten. The calculus is extended by the deep inference paradigm which allows the application of inference rules at arbitrary depth inside logical expressions and provides new proofs that are exponentially shorter and not available in the sequent calculus without cut. In addition, a strategy language for abstract underspecified declarative proof patterns is developed. Together, the complementary methods provide a framework to automate declarative proofs. The benefits of the techniques are illustrated by practical applications.Die vorliegende Arbeit beschäftigt sich damit, das Formalisieren von Beweisen zu vereinfachen, indem Methoden entwickelt werden, um informale Beweise formal zu verifizieren und erzeugen zu können. Dazu wird ein abstrakter Kalkül entwickelt, der direkt auf der Faktenebene arbeitet, welche von Menschen geführten Beweisen relativ nahe kommt. Anhand einer Fallstudie wird gezeigt, dass die abstrakte Beweisführung auf der Fakteneben vorteilhaft für automatische Suchverfahren ist. Zusätzlich wird eine Strategiesprache entwickelt, die es erlaubt, unterspezifizierte Beweismuster innerhalb des Beweisdokumentes zu spezifizieren und Beweisskizzen automatisch zu verfeinern. Fallstudien zeigen, dass komplexe Beweismuster kompakt in der entwickelten Strategiesprache spezifiziert werden können. Zusammen bilden die einander ergänzenden Methoden den Rahmen zur Automatisierung von deklarativen Beweisen auf der Faktenebene, die bisher überwiegend manuell entwickelt werden mussten

Assertion level proof planning with compiled strategies

This book presents new techniques that allow the automatic verification and generation of abstract human-style proofs. The core of this approach builds an efficient calculus that works directly by applying definitions, theorems, and axioms, which reduces the size of the underlying proof object by a factor of ten. The calculus is extended by the deep inference paradigm which allows the application of inference rules at arbitrary depth inside logical expressions and provides new proofs that are exponentially shorter and not available in the sequent calculus without cut. In addition, a strategy language for abstract underspecified declarative proof patterns is developed. Together, the complementary methods provide a framework to automate declarative proofs. The benefits of the techniques are illustrated by practical applications.Die vorliegende Arbeit beschäftigt sich damit, das Formalisieren von Beweisen zu vereinfachen, indem Methoden entwickelt werden, um informale Beweise formal zu verifizieren und erzeugen zu können. Dazu wird ein abstrakter Kalkül entwickelt, der direkt auf der Faktenebene arbeitet, welche von Menschen geführten Beweisen relativ nahe kommt. Anhand einer Fallstudie wird gezeigt, dass die abstrakte Beweisführung auf der Fakteneben vorteilhaft für automatische Suchverfahren ist. Zusätzlich wird eine Strategiesprache entwickelt, die es erlaubt, unterspezifizierte Beweismuster innerhalb des Beweisdokumentes zu spezifizieren und Beweisskizzen automatisch zu verfeinern. Fallstudien zeigen, dass komplexe Beweismuster kompakt in der entwickelten Strategiesprache spezifiziert werden können. Zusammen bilden die einander ergänzenden Methoden den Rahmen zur Automatisierung von deklarativen Beweisen auf der Faktenebene, die bisher überwiegend manuell entwickelt werden mussten

Large space structures and systems in the space station era: A bibliography with indexes (supplement 03)

Bibliographies and abstracts are listed for 1221 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1, 1991 and June 30, 1991. Topics covered include large space structures and systems, space stations, extravehicular activity, thermal environments and control, tethering, spacecraft power supplies, structural concepts and control systems, electronics, advanced materials, propulsion, policies and international cooperation, vibration and dynamic controls, robotics and remote operations, data and communication systems, electric power generation, space commercialization, orbital transfer, and human factors engineering

Reports to the President

A compilation of annual reports for the 1985-1986 academic year, including a report from the President of the Massachusetts Institute of Technology, as well as reports from the academic and administrative units of the Institute. The reports outline the year's goals, accomplishments, honors and awards, and future plans

Together structures

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (leaves 140-147).by Michael de la Maza.Ph.D