Framework for a space shuttle main engine health monitoring system

A framework developed for a health management system (HMS) which is directed at improving the safety of operation of the Space Shuttle Main Engine (SSME) is summarized. An emphasis was placed on near term technology through requirements to use existing SSME instrumentation and to demonstrate the HMS during SSME ground tests within five years. The HMS framework was developed through an analysis of SSME failure modes, fault detection algorithms, sensor technologies, and hardware architectures. A key feature of the HMS framework design is that a clear path from the ground test system to a flight HMS was maintained. Fault detection techniques based on time series, nonlinear regression, and clustering algorithms were developed and demonstrated on data from SSME ground test failures. The fault detection algorithms exhibited 100 percent detection of faults, had an extremely low false alarm rate, and were robust to sensor loss. These algorithms were incorporated into a hierarchical decision making strategy for overall assessment of SSME health. A preliminary design for a hardware architecture capable of supporting real time operation of the HMS functions was developed. Utilizing modular, commercial off-the-shelf components produced a reliable low cost design with the flexibility to incorporate advances in algorithm and sensor technology as they become available

An interactive human centered data science approach towards crime pattern analysis

The traditional machine learning systems lack a pathway for a human to integrate their domain knowledge into the underlying machine learning algorithms. The utilization of such systems, for domains where decisions can have serious consequences (e.g. medical decision-making and crime analysis), requires the incorporation of human experts' domain knowledge. The challenge, however, is how to effectively incorporate domain expert knowledge with machine learning algorithms to develop effective models for better decision making. In crime analysis, the key challenge is to identify plausible linkages in unstructured crime reports for the hypothesis formulation. Crime analysts painstakingly perform time-consuming searches of many different structured and unstructured databases to collate these associations without any proper visualization. To tackle these challenges and aiming towards facilitating the crime analysis, in this paper, we examine unstructured crime reports through text mining to extract plausible associations. Specifically, we present associative questioning based searching model to elicit multi-level associations among crime entities. We coupled this model with partition clustering to develop an interactive, human-assisted knowledge discovery and data mining scheme. The proposed human-centered knowledge discovery and data mining scheme for crime text mining is able to extract plausible associations between crimes, identifying crime pattern, grouping similar crimes, eliciting co-offender network and suspect list based on spatial-temporal and behavioral similarity. These similarities are quantified through calculating Cosine, Jacquard, and Euclidean distances. Additionally, each suspect is also ranked by a similarity score in the plausible suspect list. These associations are then visualized through creating a two-dimensional re-configurable crime cluster space along with a bipartite knowledge graph. This proposed scheme also inspects the grand challenge of integrating effective human interaction with the machine learning algorithms through a visualization feedback loop. It allows the analyst to feed his/her domain knowledge including choosing of similarity functions for identifying associations, dynamic feature selection for interactive clustering of crimes and assigning weights to each component of the crime pattern to rank suspects for an unsolved crime. We demonstrate the proposed scheme through a case study using the Anonymized burglary dataset. The scheme is found to facilitate human reasoning and analytic discourse for intelligence analysis

Robot localization in symmetric environment

The robot localization problem is a key problem in making truly autonomous robots. If a robot does not know where it is, it can be difficult to determine what to do next. Monte Carlo Localization as a well known localization algorithm represents a robot\u27s belief by a set of weighted samples. This set of samples approximates the posterior probability of where the robot is located. Our method presents an extension to the MCL algorithm when localizing in highly symmetrical environments; a situation where MCL is often unable to correctly track equally probable poses for the robot. The sample sets in MCL often become impoverished when samples are generated in several locations. Our approach incorporates the idea of clustering the samples and organizes them considering to their orientation. Experimental results show our method is able to successfully determine the position of the robot in symmetric environment, while ordinary MCL often fails

LONGITUDINAL CLONAL LINEAGE DYNAMICS AND FUNCTIONAL CHARACTERIZATION OF PANCREATIC CANCER CHEMO-RESISTANCE AND METASTASIZATION

In recent years, technological advancements, such as next-generation sequencing and single-cell interrogation techniques, have enriched our understanding in tumor heterogeneity. By dissecting tumors and characterizing clonal lineages, we are better understanding the intricacies of tumor evolution. Tumors are represented by the presence of and dynamic interactions amongst clonal lineages. Each lineage and each cell contributes to tumor dynamics through intrinsic and extrinsic mechanisms, and the variable responses of clones to perturbations in the environment, especially therapeutics, underlie disease progression and relapse. Thus, there exists a pressing need to understand the molecular mechanisms that determine the functional heterogeneity of tumor sub-clones to improve clinical outcomes. Clonal replica tumors (CRTs) is an in vivo platform created specifically to enable robust tracing and functional study of clones within a tumor. The establishment of CRTs is built upon our current concept of tumor heterogeneity, intrinsic cancer cell hierarchy and clonal self-renewal properties. The model allows researchers to create large cohorts of tumors in different animals that are identical in their clonal lineage composition (clonal correlation amongst tumors \u3e0.99). CRTs allow simultaneously tracking of tens of thousands of clonal lineages in different animals to provide a high level of resolution and biological reproducibility. CRTs are comprised of barcoded cells that can be identified and quantified. A critical feature is that we have developed a systematic method to isolate and expand essentially any of the clonal lineages present within a CRT in their naïve state; that is, we can characterize each sub-clonal lineage at the molecular and functional levels and correlate these findings with the behavior of the same lineage in vivo and in response to drugs. Here, based on the CRT model and its concept, we studied differential chemo-resistance among clones, where we identified pre-existing upregulation in DNA repair as a mechanism for chemo-resistance. Furthermore, through stringent statistical testing, we demonstrated orthotopic CRTs to be a powerful and robust model to quantitatively track clonal evolution. Specifically, we longitudinally tracked clones in models of pancreatic ductal adenocarcinoma (PDAC) from primary tumor expansion through metastasization, where we captured unexpected clonal dynamics and “alternating clonal dominance” naturally occurring in unperturbed tumors. Moreover, by characterizing pro- and none-metastasizing clones, we were able to identified key clonal intrinsic factors that determined the nature of tumor metastases. Finally, I will discuss distinct clonal evolution patterns that emerged under different environmental pressures, leading to the hypothesis of “tumor clonal fingerprint”, where the characteristic of a tumor could be defined by actively maintained ratio of different tumor lineages, which could provide measurable insights to how we approach treatments

Reconstructing Textual File Fragments Using Unsupervised Machine Learning Techniques

This work is an investigation into reconstructing fragmented ASCII files based on content analysis motivated by a desire to demonstrate machine learning\u27s applicability to Digital Forensics. Using a categorized corpus of Usenet, Bulletin Board Systems, and other assorted documents a series of experiments are conducted using machine learning techniques to train classifiers which are able to identify fragments belonging to the same original file. The primary machine learning method used is the Support Vector Machine with a variety of feature extractions to train from. Additional work is done in training committees of SVMs to boost the classification power over the individual SVMs, as well as the development of a method to tune SVM kernel parameters using a genetic algorithm. Attention is given to the applicability of Information Retrieval techniques to file fragments, as well as an analysis of textual artifacts which are not present in standard dictionaries

Applications of aerospace technology to petroleum exploration. Volume 2: Appendices

Participants in the investigation of problem areas in oil exploration are listed and the data acquisition methods used to determine categories to be studied are described. Specific aerospace techniques applicable to the tasks identified are explained and their costs evaluated