Lunar Orbiter 3 - Photography

Photographic mission planning and photograph interpretation for Lunar Orbiter

Rotorcraft aviation icing research requirements: Research review and recommendations

The status of rotorcraft icing evaluation techniques and ice protection technology was assessed. Recommendations are made for near and long term icing programs that describe the needs of industry. These recommended programs are based on a consensus of the major U.S. helicopter companies. Specific activities currently planned or underway by NASA, FAA and DOD are reviewed to determine relevance to the overall research requirements. New programs, taking advantage of current activities, are recommended to meet the long term needs for rotorcraft icing certification

Analyzing and Predicting Railway Operational Accidents Based on Fishbone Diagram and Bayesian Networks

The prevention of railway operational accidents has become one of the leading issues in railway safety. Identifying the impact factors which significantly affect railway operating is critical for decreasing the occurrence of railway accidents. In this study, 8440 samples of accident data are selected as the datasets for analyzing. Fishbone diagram is applied to obtain the factors which cause the accident from the perspective of human-equipment-environment-management system theory. Then, the Bayesian network method was selected to establish a railway operation safety accident prediction model, and the sensitivity analysis method was used to obtain the sensitivity of each variable factor to the accident level. The results show that season, location, trouble maker and job function have a significant impact on railway safety, and their sensitivity was 0.4577, 0.4116, 0.3478 and 0.3192, respectively. Research helps the railway sector to understand the fundamental causes of accidents, and provides an effective reference for accident prevention, which is conducive to the long-term development of railway transportation

Drilling data quality improvement and information extraction with case studies

Data analytics is a process of data acquiring, transforming, interpreting, modelling, displaying and storing data with an aim of extracting useful information, so that decision-making, actions executing, events detecting and incidents managing can be handled in an efficient and certain manner. However, data analytics also meets some challenges, for instance, data corruption due to noises, time delays, missing and external disturbances, etc. This paper focuses on data quality improvement to cleanse, improve and interpret the post-well or real-time data to preserve and enhance data features, like accuracy, consistency, reliability and validity. In this study, laboratory data and field data are used to illustrate data issues and show data quality improvements with using different data processing methods. Case study clearly demonstrates that the proper data quality management process and information extraction methods are essential to carry out an intelligent digitalization in oil and gas industry.publishedVersio

ANALYSIS OF THE PILE LOAD TESTS AT THE US 68/KY 80 BRIDGE OVER KENTUCKY LAKE

Large diameter piles are widely used as foundations to support buildings, bridges, and other structures. As a result, it is critical for the field to have an optimized approach for quality control and efficiency purposes to measure the suggested number of load tests and the required measured capacities driven piles. In this thesis, an analysis of a load test program designed for proposed bridge replacements at Kentucky Lake is performed. It includes a detailed site exploration study with in-situ and laboratory testing. The pile load test program included monitoring of a steel H-pile and steel open ended pipe pile during driving and static loading. The pile load test program included static and dynamic testing at both pile testing locations. Predictions of both pile capacities were estimated using commonly applied failure criterion, and a load transfer analysis was carried out on the dynamic and static test data for both piles. The dynamic tests were then compared to the measured data from the static test to examine the accuracy. This thesis concludes by constructing t-z and q-z curves and comparing the load transfer analyses of the static and dynamic tests

A novel strategy to fit and validate physiological models: a case study of acardiorespiratory model for simulation of incremental aerobic exercise

Applying complex mathematical models of physiological systems is challenging due to the large number of parameters. Identifying these parameters through experimentation is difficult, and although procedures for fitting and validating models are reported, no integrated strategy exists. Additionally, the complexity of optimization is generally neglected when the number of experimental observations is restricted, obtaining multiple solutions or results without physiological justification. This work proposes a fitting and validation strategy for physiological models with many parameters under various populations, stimuli, and experimental conditions. A cardiorespiratory system model is used as a case study, and the strategy, model, computational implementation, and data analysis are described. Using optimized parameter values, model simulations are compared to those obtained using nominal values, with experimental data as a reference. Overall, a reduction in prediction error is achieved compared to that reported for model building. Furthermore, the behavior and accuracy of all the predictions in the steady state were improved. The results validate the fitted model and provide evidence of the proposed strategy’s usefulness.Peer ReviewedPostprint (published version

Detecting Oriented Text in Natural Images by Linking Segments

Most state-of-the-art text detection methods are specific to horizontal Latin text and are not fast enough for real-time applications. We introduce Segment Linking (SegLink), an oriented text detection method. The main idea is to decompose text into two locally detectable elements, namely segments and links. A segment is an oriented box covering a part of a word or text line; A link connects two adjacent segments, indicating that they belong to the same word or text line. Both elements are detected densely at multiple scales by an end-to-end trained, fully-convolutional neural network. Final detections are produced by combining segments connected by links. Compared with previous methods, SegLink improves along the dimensions of accuracy, speed, and ease of training. It achieves an f-measure of 75.0% on the standard ICDAR 2015 Incidental (Challenge 4) benchmark, outperforming the previous best by a large margin. It runs at over 20 FPS on 512x512 images. Moreover, without modification, SegLink is able to detect long lines of non-Latin text, such as Chinese.Comment: To Appear in CVPR 201

Predictive maintenance in hydropower plants : a case study of valves and servomotors

Digitalization has opened the opportunity for a fourth industrial revolution and the hydropower industry is taking charge of enabling digitalization in their operation. There are a lot of studies on predictive maintenance, however, there are, to our knowledge no studies on system-specific predictive maintenance for hydropower. To bridge this gap, the idea of system-specific, Machine Learning driven Predictive Maintenance is explored. Two systems are chosen as a use-case for this thesis: valves and servomotors. With the increasing amount of intermittent renewable energy resources entering the power system, the need for flexibility in the power grid is unequivocal. Valves and servomotors are key components of hydropower control and thus will play a pivotal role in securing flexibility to the grid. The first system assessed is the main valve. In order to make this analysis easily applicable, the data that is already being collected at Nore 1 hydropower plant is analyzed in order to assess the possibility of maintenance prediction from limited data. Unfortunately, this did not achieve the desired results for the data collected from the valve sensors. This is due to the fact that only one variable was measured, in this case, the opening and closing time-lag of the valve. However, this thesis presents a framework for data collection that allows the use of Machine Learning for predictive maintenance. Various sensors are suggested based on several published works on predictive maintenance. The second system assessed is the servomotor that controls the guiding vanes in a Francis turbine. Servomotors are key components of hydropower control. Due to the data not being collected by Statkraft at the time of the study, this data was provided by one of Statkrafts suppliers. By making use of the historical data of pressure as a function of the piston position, a boundary for where new values should be expected is computed by making use of One Class Support Vector Machine. Another embodiment of this case is presented where force is given as a function of piston position, which yielded better results. When new values are being measured, the data is presented as a bullet chart that visualizes the distance of new values compared to the boundary computed by the One Class Support Vector Machine. This tool could easily be applied to other servomotors which perform other tasks such as controlling water injection to a Pelton turbine or opening and closing of the valve, whether they are butterfly or ball valves. Suggestions for further data collection are presented in order to make use of more data for the use of Machine Learning in Predictive Maintenance.Digitalisering har ledet frem til en fjerde industriell revolusjon og vannkraft bransjen er i ferd med å digitalisere sin operasjon. Under literaturstudien er det ikke funnet noen publiseringer innen systemspesifikk maskinlæringsdrevet predikativ vedlikehold. I denne masteroppgaven blir muligheten for bruk av systemspesifikk, maskinlæringdrevet predikativ vedlikehold innen vannkraftverk utforsket for å vekke interesse innen dette feltet. To av vannkraftverkenes maskiner er brukt som eksempler og utforsket: ventiler og servomotor. Økende mengder uregulerbar strøm er introdusert i kraftnettet og behovet for fleksibilitet øker. Ventiler og servomotor er nøkkeldeler av vannkraftverk regulering og spiller en stor rolle i å sikre flexibilitet til strømnettet. Det første systemet som ble analysert er ventiler. For å gjøre analysen og resultatene enkelt anvendbare, blir data som allerede er innsamlet analysert for å utforske muligheten for predikativ vedlikehold med begrenset data. Analysene basert på data samlet inn fra sensorne montert på ventilene ble dessverre ikke konklusive. Det er utfordrende å forutsi fremtiden når man bare har en variabel å ta utgangspunkt i. Likevel presenteres det et prinsipielt rammeverk for innsamling av data som gjør det mulig å ta i bruk maskinlæring for predikativ vedlikehold. Ulike sensorer er foreslått, basert på relevant litteratur innen ventiler og maskinlæring drevet predikativ vedlikehold. Det andre systemet analysert under studien er servomotorer som styrer vannet i en Francis turbin ved å regulere vinklingen til skovlene. Dataen innsamlet om servomotoren er en god indikator på tilstanden til servomotoren. Ettersom dataen var ikke samlet inn av Statkraft da studien ble utført, ble dataen hentet fra en av Statkraft sine leverandører. En One Class Support Vector Machine ble brukt for å beregne foventet verdi av differansetrykk over stempelkamrene, som funksjon av stempel posisjon. En kulegraf som viser avstanden mellom grensen og nye verdier er visualisert. En annen metode er også presentert hvor man regner ut kraft på begge sider av stempelkamrene gjennom trykk for å vise kraft som funksjon av stempel posisjon. Dette ga bedre valideringsresultater i forventet differansekraft over tempelkamrene. Verktøyet kan enkelt bli anvendt til andre servomotorer som styrer vannmengden i en Pelton turbin eller åpning og lukking av ventilene, uavhengig av om det er spjeld- eller kuleventiler. Forslag til videre data innsamling er presentert for å ta i bruk maskinlæring for predikativ vedlikehold.StatkraftsubmittedVersionM-M

Algorithms for Fault Detection and Diagnosis

Due to the increasing demand for security and reliability in manufacturing and mechatronic systems, early detection and diagnosis of faults are key points to reduce economic losses caused by unscheduled maintenance and downtimes, to increase safety, to prevent the endangerment of human beings involved in the process operations and to improve reliability and availability of autonomous systems. The development of algorithms for health monitoring and fault and anomaly detection, capable of the early detection, isolation, or even prediction of technical component malfunctioning, is becoming more and more crucial in this context. This Special Issue is devoted to new research efforts and results concerning recent advances and challenges in the application of “Algorithms for Fault Detection and Diagnosis”, articulated over a wide range of sectors. The aim is to provide a collection of some of the current state-of-the-art algorithms within this context, together with new advanced theoretical solutions

The validity of percent body fat estimates by Jackson & Pollock skinfold equation, near infrared, bioelectrical impedance and body mass index

This study compared the validity of percent fat estimates by the Jackson & Pollock sum of four skinfold equation (JPSF), bioeletrical impedance (BIA), near infrared (NIR) and body mass index (BMI) when compared to the criterion method of underwater weighing (UWW). Skinfolds were measured at four sites, the triceps, ilium, abdomen and thigh using the Harpenden skinfold caliper. The Jackson & Pollock sum of four skinfold equation was used to calculate percent body fat. Infrared interactance was determined on a Futrex 5000 measured at the biceps halfway between the axillary fold and the anticubital space. Bioelectrical impedance was determined with the Bio-analgenics ELG analyzer using the four electrode placement technique. Electrodes were placed on the dorsal surfaces of the ankle and wrist and the distal surfaces of the metacarpals and metatarsals. Underwater weighing was determined in a seated position with functional residual volume measured in the tank at the time of weighing. Statistical analysis was determined using an repeated measures ANOVA, Pearson correlation coefficient (r), standard error of estimate (SEE), R{dollar}\sp2{dollar}, and total error (TE). (Abstract shortened by UMI.)