Interconnected Services for Time-Series Data Management in Smart Manufacturing Scenarios

xvii, 218 p.The rise of Smart Manufacturing, together with the strategic initiatives carried out worldwide, have promoted its adoption among manufacturers who are increasingly interested in boosting data-driven applications for different purposes, such as product quality control, predictive maintenance of equipment, etc. However, the adoption of these approaches faces diverse technological challenges with regard to the data-related technologies supporting the manufacturing data life-cycle. The main contributions of this dissertation focus on two specific challenges related to the early stages of the manufacturing data life-cycle: an optimized storage of the massive amounts of data captured during the production processes and an efficient pre-processing of them. The first contribution consists in the design and development of a system that facilitates the pre-processing task of the captured time-series data through an automatized approach that helps in the selection of the most adequate pre-processing techniques to apply to each data type. The second contribution is the design and development of a three-level hierarchical architecture for time-series data storage on cloud environments that helps to manage and reduce the required data storage resources (and consequently its associated costs). Moreover, with regard to the later stages, a thirdcontribution is proposed, that leverages advanced data analytics to build an alarm prediction system that allows to conduct a predictive maintenance of equipment by anticipating the activation of different types of alarms that can be produced on a real Smart Manufacturing scenario

Interconnected Services for Time-Series Data Management in Smart Manufacturing Scenarios

xvii, 218 p.The rise of Smart Manufacturing, together with the strategic initiatives carried out worldwide, have promoted its adoption among manufacturers who are increasingly interested in boosting data-driven applications for different purposes, such as product quality control, predictive maintenance of equipment, etc. However, the adoption of these approaches faces diverse technological challenges with regard to the data-related technologies supporting the manufacturing data life-cycle. The main contributions of this dissertation focus on two specific challenges related to the early stages of the manufacturing data life-cycle: an optimized storage of the massive amounts of data captured during the production processes and an efficient pre-processing of them. The first contribution consists in the design and development of a system that facilitates the pre-processing task of the captured time-series data through an automatized approach that helps in the selection of the most adequate pre-processing techniques to apply to each data type. The second contribution is the design and development of a three-level hierarchical architecture for time-series data storage on cloud environments that helps to manage and reduce the required data storage resources (and consequently its associated costs). Moreover, with regard to the later stages, a thirdcontribution is proposed, that leverages advanced data analytics to build an alarm prediction system that allows to conduct a predictive maintenance of equipment by anticipating the activation of different types of alarms that can be produced on a real Smart Manufacturing scenario

Mobile Opera Backdrop

Opera San Luis Obispo requires an acoustically projecting stage backdrop for their future plan of deploying a mobile opera house. The custom stage backdrop must be low-cost, easy to assemble and must project the performance sound to outdoor audiences. Other design requirements include being transportable in a step van, the ability to be assembled quickly by a crew of two, resistance to various outdoor conditions, the ability to mount accessories from the walls, and should meet a budget of $5,000. With the scope of the project understood, the three-quarter project is outlined. During the first quarter, the main deliverables included the scope of work and the preliminary design review. During the design process, a variety of brainstorming sessions were held to develop ideas, while decision matrices yielded the superior full design concept. Based on the ideation activities and evaluations performed, the design will be a collapsible aluminum frame that uses ball-bungees to attach to a grommeted polyester coated vinyl tarps. In preparation for the Preliminary Design Review, analysis of all major components of the design was performed in order to propose a final design with all materials, dimensions, and manufacturing processes identified. Inquiries were made to several manufacturing companies and cost estimates were obtained for the custom manufactured pipes, endplates, connectors, and vinyl tarps required. Parts were then ordered to commence the manufacture phase and testing of components. Manufacturing and testing began, taking advantage of the facilities and equipment available on-campus to cut the pipes to size, drill holes, and water-jet cut the endplates. Throughout the manufacturing process, components and subassemblies were tested to validate the design and mitigate possible safety concerns. Adjustments from these sessions were made to refine the final design and assembly procedure. With the refinements finalized, the final design was presented on June 1, 2018 at the Cal Poly’s Spring 2018 Senior Project Expo

Non-contact torsion transducer based on the measurement of Moire patterns using plastic optical fibres

An angular and displacement sensor that uses a polymer optical fiber and Moire patterns is demonstrated. Moire fringes are generated using two transparent superimposed planar gratings placed in front of an optical mirror. Moire patterns with periods ranging from 0.4 to 2 mm have been obtained in this way with 1mm-diameter plastic optical fibers for torsion angles ranging from 10° to 20° have been compared with theoretical calculations and a good agreement has been confirmed. Measuring the period length and the number of periods, both the relative angle between the gratings and the displacement of the fiber with respect to the mirror are obtained. With this technique very low angles can be measured with a very high resolution. The sensor principle has been successfully checked in the laboratory. Finally, the effect of employing different plastic fibers is also discussed. Besides, other possible applications of this measurement technique are presented and discussed

Lossless Compression of Industrial Time Series With Direct Access

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] The new opportunities generated by the data-driven economy in the manufacturing industry have causedmany companies opt for it. However, the size of time series data that need to be captured creates theproblem of having to assume high storage costs. Moreover, these costs, which are constantly growing,begin to have an impact on the profitability of companies. Thus, in this scenario, the need arises to developtechniques that allow obtaining reduced representations of the time series. In this paper, we present alossless compression method for industrial time series that allows an efficient access. That is, our aim goesbeyond pure compression, where the usual way to access the data requires a complete decompressionof the dataset before processing it. Instead, our method allows decompressing portions of the dataset,and moreover, it allows direct querying the compressed data. Thus, the proposed method combines theefficient access, typical of lossy methods, with the lossless compression.Xunta de Galicia; ED431G 2019/01Xunta de Galicia; IG240. 2020.1.185Xunta de Galicia; IN852A 2018/14Gobierno Vasco; IT1330-19For the A Coruña team: This work was supported by CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia”, supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014-2020, and the remaining 20% by “Secretaría Xeral de Universidades” (Grant ED431G 2019/01), Xunta de Galicia/FEDER-UE under Grants [IG240.2020.1.185; IN852A 2018/14] and Ministerio de Ciencia, Innovación under Grants [TIN2016-78011-C4-1-R; RTC-2017-5908-7]. For the Basque team: Ministerio de Ciencia, Innovación y Universidades under Grant [FEDER/TIN2016-78011-C4-2-R] and the Basque Government under Grant No. [IT1330-19]. Funding for open access charge: Universidade da Coruña/CISUG

Applying Latent Class Analysis on Cancer Registry Data to Identify and Compare Health Disparity Profiles in Colorectal Cancer Surgical Treatment Delay

Context: Colorectal cancer (CRC) surgical treatment delay (TD) has been associated with mortality and morbidity; however, disparities by TD profiles are unknown. Objectives: This study aimed to identify CRC patient profiles of surgical TD while accounting for differences in sociodemographic, health insurance, and geographic characteristics. Design: We used latent class analysis (LCA) on 2005-2015 Tennessee Cancer Registry data of CRC patients and observed indicators that included sex/gender, age at diagnosis, marital status (single/married/divorced/widowed), race (White/Black/other), health insurance type, and geographic residence (non-Appalachian/Appalachian). Setting: The state of Tennessee in the United States that included both Appalachian and non-Appalachian counties. Participants: Adult (18 years or older) CRC patients (N = 35 412) who were diagnosed and surgically treated for in situ (n = 1286) and malignant CRC (n = 34 126). Main Outcome Measure: The distal outcome of TD was categorized as 30 days or less and more than 30 days from diagnosis to surgical treatment. Results: Our LCA identified a 4-class solution and a 3-class solution for in situ and malignant profiles, respectively. The highest in situ CRC patient risk profile was female, White, aged 75 to 84 years, widowed, and used public health insurance when compared with respective profiles. The highest malignant CRC patient risk profile was male, Black, both single/never married and divorced/separated, resided in non-Appalachian county, and used public health insurance when compared with respective profiles. The highest risk profiles of in situ and malignant patients had a TD likelihood of 19.3% and 29.4%, respectively. Conclusions: While our findings are not meant for diagnostic purposes, we found that Blacks had lower TD with in situ CRC. The opposite was found in the malignant profiles where Blacks had the highest TD. Although TD is not a definitive marker of survival, we observed that non-Appalachian underserved/underrepresented groups were overrepresented in the highest TD profiles. The observed disparities could be indicative of intervenable risk

Alarm system of optical fibre using the thermal-optical sensibility of the PNIPAAm polymer

An alarm system as extrinsic sensor on optical fibers for detecting and controlling inflammable liquids based on thermosensitive proprieties of the PNIPAAm hydrogel is presented. The changes on the optical proprieties of the PNIPAAm with the temperature (being its LCST 32ºC), induce abrupt changes on the light intensity and they act as an alarm signal, which is transmitted by optical fibers and after they will be processed by an optoelectronic circuit, responsible to active an alarm. An appropriate system consists of the hydrogel connected between its ends to two segments of plastic optical fibers (source and receiver) and they turn on the alarm when a photo detector does not receive light when the hydrogel becomes when it reaches threshold of temperature. The characterization of the hydrogel and the experimental results are presented for a prototype

Lossless compression of industrial time series with direct access

[EN]The new opportunities generated by the data-driven economy in the manufacturing industry have caused many companies opt for it. However, the size of time series data that need to be captured creates the problem of having to assume high storage costs. Moreover, these costs, which are constantly growing, begin to have an impact on the profitability of companies. Thus, in this scenario, the need arises to develop techniques that allow obtaining reduced representations of the time series. In this paper, we present a lossless compression method for industrial time series that allows an efficient access. That is, our aim goes beyond pure compression, where the usual way to access the data requires a complete decompression of the dataset before processing it. Instead, our method allows decompressing portions of the dataset, and moreover, it allows direct querying the compressed data. Thus, the proposed method combines the efficient access, typical of lossy methods, with the lossless compression.For the A Coruna team: This work was supported by CITIC, as Research Center accredited by Galician University System, is funded by "Conselleria de Cultura, Educacion e Universidade from Xunta de Galicia", supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014-2020, and the remaining 20% by "Secretaria Xeral de Universidades" (Grant ED431G 2019/01) , Xunta de Galicia/FEDER-UE under Grants [IG240.2020.1.185; IN852A 2018/14] and Ministerio de Ciencia, Innovacion under Grants [TIN2016-78011-C4-1-R; RTC-2017-5908-7] . For the Basque team: Ministerio de Ciencia, Innovacion y Universidades under Grant [FEDER/TIN2016-78011-C4-2-R] and the Basque Government under Grant No. [IT1330-19] . Funding for open access charge: Universidade da Coruna/CISUG

A National Study of Colorectal Cancer Survivorship Disparities: A Latent Class Analysis Using SEER (Surveillance, Epidemiology, and End Results) Registries

Introduction: Long–standing disparities in colorectal cancer (CRC) outcomes and survival between Whites and Blacks have been observed. A person–centered approach using latent class analysis (LCA) is a novel methodology to assess and address CRC health disparities. LCA can overcome statistical challenges from subgroup analyses that would normally impede variable–centered analyses like regression. Aim was to identify risk profiles and differences in malignant CRC survivorship outcomes. Methods: We conducted an LCA on the Surveillance, Epidemiology, and End Results data from 1975 to 2016 for adults ≥18 (N = 525,245). Sociodemographics used were age, sex/gender, marital status, race, and ethnicity (Hispanic/Latinos) and stage at diagnosis. To select the best fitting model, we employed a comparative approach comparing sample-size adjusted BIC and entropy; which indicates a good separation of classes. Results: A four–class solution with an entropy of 0.72 was identified as: lowest survivorship, medium-low, medium-high, and highest survivorship. The lowest survivorship class (26% of sample) with a mean survival rate of 53 months had the highest conditional probabilities of being 76–85 years–old at diagnosis, female, widowed, and non-Hispanic White, with a high likelihood with localized staging. The highest survivorship class (53% of sample) with a mean survival rate of 92 months had the highest likelihood of being married, male with localized staging, and a high likelihood of being non-Hispanic White. Conclusion: The use of a person–centered measure with population-based cancer registries data can help better detect cancer risk subgroups that may otherwise be overlooked

Sociodemographic and Geographic Disparities of Prostate Cancer Treatment Delay in Tennessee: A Population-Based Study

The relationship of social determinants of health, Appalachian residence, and prostate cancer treatment delay among Tennessee adults is relatively unknown. We used multivariate logistic regression on 2005-2015 Tennessee Cancer Registry data of adults aged ≥18 diagnosed with prostate cancer. The outcome of treatment delay was more than 90 days without surgical or nonsurgical intervention from date of diagnosis. Social determinants in the population-based registry were race (White, Black, Other) and marital status (single, married, divorced/separated, widow/widower). Tennessee residence was classified as Appalachian versus non-Appalachian (urban/rural). Covariates include age at diagnosis (18-54, 54-69, ≥70), health insurance type (none, public, private), derived staging of cancer (localized, regional, distant), and treatment type (non-surgical/surgical). We found that Black and divorced/separated patients had 32% (95% confidence interval [CI]: 1.22-1.42) and 15% (95% CI: 1.01-1.31) increased odds to delay prostate cancer treatment. Patients were at decreased odds of treatment delay when living in an Appalachian county, both urban (odds ratio [OR] = 0.89, 95% CI: 0.82-0.95) and rural (OR = 0.83, 95% CI: 0.78-0.89), diagnosed at ≥70 (OR = 0.59, 95% CI: 0.53-0.66), and received surgical intervention (OR = 0.72, 95% CI: 0.68-0.76). Our study was among the first to comprehensively examine prostate cancer treatment delay in Tennessee, and while we do not make clinical recommendations, there is a critical need to further explore the unique factors that may propagate disparities. Prostate cancer treatment delay in Black patients may be indicative of ongoing health and access disparities in Tennessee, which may further affect quality of life and survivorship among this racial group. Divorced/separated patients may need tailored interventions to improve social support