Imaging time series for the classification of EMI discharge sources

In this work, we aim to classify a wider range of Electromagnetic Interference (EMI) discharge sources collected from new power plant sites across multiple assets. This engenders a more complex and challenging classification task. The study involves an investigation and development of new and improved feature extraction and data dimension reduction algorithms based on image processing techniques. The approach is to exploit the Gramian Angular Field technique to map the measured EMI time signals to an image, from which the significant information is extracted while removing redundancy. The image of each discharge type contains a unique fingerprint. Two feature reduction methods called the Local Binary Pattern (LBP) and the Local Phase Quantisation (LPQ) are then used within the mapped images. This provides feature vectors that can be implemented into a Random Forest (RF) classifier. The performance of a previous and the two new proposed methods, on the new database set, is compared in terms of classification accuracy, precision, recall, and F-measure. Results show that the new methods have a higher performance than the previous one, where LBP features achieve the best outcome

Review of soil salinity assessment for agriculture across multiple scales using proximal and/or remote sensors

Mapping and monitoring soil spatial variability is particularly problematic for temporally and spatially dynamic properties such as soil salinity. The tools necessary to address this classic problem only reached maturity within the past 2 decades to enable field- to regional-scale salinity assessment of the root zone, including GPS, GIS, geophysical techniques involving proximal and remote sensors, and a greater understanding of apparent soil electrical conductivity (ECa) and multi- and hyperspectral imagery. The concurrent development and application of these tools have made it possible to map soil salinity across multiple scales, which back in the 1980s was prohibitively expensive and impractical even at field scale. The combination of ECa-directed soil sampling and remote imagery has played a key role in mapping and monitoring soil salinity at large spatial extents with accuracy sufficient for applications ranging from field-scale site-specific management to statewide water allocation management to control salinity within irrigation districts. The objective of this paper is: (i) to present a review of the geophysical and remote imagery techniques used to assess soil salinity variability within the root zone from field to regional scales; (ii) to elucidate gaps in our knowledge and understanding of mapping soil salinity; and (iii) to synthesize existing knowledge to give new insight into the direction soil salinity mapping is heading to benefit policy makers, land resource managers, producers, agriculture consultants, extension specialists, and resource conservation field staff. The review covers the need and justification for mapping and monitoring salinity, basic concepts of soil salinity and its measurement, past geophysical and remote imagery research critical to salinity assessment, current approaches for mapping salinity at different scales, milestones in multi-scale salinity assessment, and future direction of field- to regional-scale salinity assessment

Ocean Color Instrument Integration and Testing

This paper describes the plans, flows, key facilities, components and equipment necessary to fully integrate, functionally test, qualify and calibrate the Ocean Color Instrument (OCI) on the Plankton, Aerosols, Clouds, and oceans Ecosystem (PACE) observatory. PACE is currently in the design phase of mission development. It is scheduled to launch in 2022, extending and improving NASA's twenty-year record of satellite observations of global ocean biology, aerosols and clouds. PACE will advance the assessment of ocean health by measuring the distribution of phytoplankton, which are small plants and algae that sustain the marine food web. It will also continue systematic records of key atmospheric variables associated with air quality and the Earth's climate. PACE's primary sensor, the OCI, is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. The color of the ocean is determined by the interaction of sunlight with substances or particles present in seawater such as chlorophyll. By monitoring global phytoplankton distribution and abundance with unprecedented detail, the OCI will contribute to a better understanding of the complex systems that drive ocean ecology and it's impacts on global fisheries. This paper will focus on the Integration and Test (I&T) activities for OCI while it is at the NASA Goddard Space Flight Center. The OCI integration consists of assembly and alignment of the rotating telescope, electronics box integration, fixed deck assembly integration, thermal systems integration and the final assembly and testing. This I&T phase will be followed by the OCI calibration and characterization, environmental tests which include electromagnetic interference (EMI)/electromagnetic compatibility (EMC), vibration with sine sweep, acoustics, shock, thermal balance, thermal vacuum, mass properties and center of gravity. This paper will briefly discuss OCI shipment and delivery to the spacecraft vendor for observatory level I&T as well as some launch preparation activities

Characteristics and Outcomes of Patients Discharged Home from an Emergency Department with Acute Kidney Injury: A Population-Based Cohort Study

We designed a population-based cohort study to describe the characteristics and outcomes of 6346 adults discharged home from an emergency department (ED) with acute kidney injury (AKI). Within 30 days of discharge, 149 (2.3%) patients died (stage 1: 2.1%, stage 2: 5.2%, and stage 3 AKI: 15.9%). We also compared 30-day mortality to patients hospitalized with AKI and patients discharged home with no AKI in two separate propensity score-matched analyses. An ED discharge versus hospitalization was associated with lower 30-day mortality (3.0% vs. 11.9%, relative risk (RR): 0.25, 95% confidence interval (CI): 0.21-0.30). An ED discharge home with AKI versus no AKI was associated with higher 30-day mortality (2.2% vs. 1.4%, RR: 1.56, 95% CI: 1.20-2.04). Although sicker patients are appropriately hospitalized, patients discharged home from the ED with AKI remain at risk of adverse outcomes. A better understanding of care appears warranted, as is testing strategies to improve care

Deep-Gap: A deep learning framework for forecasting crowdsourcing supply-demand gap based on imaging time series and residual learning

Mobile crowdsourcing has become easier thanks to the widespread of smartphones capable of seamlessly collecting and pushing the desired data to cloud services. However, the success of mobile crowdsourcing relies on balancing the supply and demand by first accurately forecasting spatially and temporally the supply-demand gap, and then providing efficient incentives to encourage participant movements to maintain the desired balance. In this paper, we propose Deep-Gap, a deep learning approach based on residual learning to predict the gap between mobile crowdsourced service supply and demand at a given time and space. The prediction can drive the incentive model to achieve a geographically balanced service coverage in order to avoid the case where some areas are over-supplied while other areas are under-supplied. This allows anticipating the supply-demand gap and redirecting crowdsourced service providers towards target areas. Deep-Gap relies on historical supply-demand time series data as well as available external data such as weather conditions and day type (e.g., weekday, weekend, holiday). First, we roll and encode the time series of supply-demand as images using the Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF) and the Recurrence Plot (REC). These images are then used to train deep Convolutional Neural Networks (CNN) to extract the low and high-level features and forecast the crowdsourced services gap. We conduct comprehensive comparative study by establishing two supply-demand gap forecasting scenarios: with and without external data. Compared to state-of-art approaches, Deep-Gap achieves the lowest forecasting errors in both scenarios.Comment: Accepted at CloudCom 2019 Conferenc

The development of the toner density sensor for closed-loop feedback laser printer calibration

A new infrared (IR) sensor was developed for application in closed-loop feedback printer calibration as it relates to monochrome (black toner only) laser printers. The toner density IR sensor (TDS) was introduced in the early 1980’s; however, due to cost and limitation of technologies at the time, implementation was not accomplished until within the past decade. Existing IR sensor designs do not discuss/address: • EMI (electromagnetic interference) effects on the sensor due to EP (electrophotography) components • Design considerations for environmental conditions • Sensor response time as it affects printer process speed The toner density sensor (TDS) implemented in the Lexmark E series printer reduces these problems and eliminates the use of the current traditional “open-loop” (meaning feedback are parameters not directly affecting print darkness such as page count, toner level, etc.) calibration process where print darkness is adjusted using previously calculated and stored EP process parameters. The historical process does not have the ability to capture cartridge component variation and environmental changes which affect print darkness variation. The TDS captures real time data which is used to calculate EP process parameters for the adjustment of print darkness; as a result, greatly reducing variations uncontrolled by historical printer calibration. Specifically, the first and primary purpose of this research is to reduce print darkness variation using the TDS. The second goal is to mitigate the TDS EMI implementation issue for reliable data accuracy

Acoustic Emission and X-Ray Diffraction Techniques for the In Situ Study of Electrochemical Energy Storage Materials

Current demands on lithium ion battery (LIB) technology include high capacity retention over a life time of many charge and discharge cycles. Maximizing battery longevity is still a major challenge partly due to electrode degradation as a function of repeated cycling. The intercalation of lithium ions into an active material causes the development of stress and strain in active electrode materials which can result in fracture and shifting that can in turn lead to capacity fade and eventual cell failure. The processes leading to active material degradation in cycling LIBs has been studied using a combination of acoustic emission (AE) and in situ X-ray diffraction (XRD) techniques. Safe, low cost custom electrochemical cells were designed and developed for use in battery AE and XRD experiments. These tools were used to monitor the time of material fracture through AE and link these events to lattice strain and phase composition as determined by XRD. Both anode and cathode materials were studied with an emphasis on graphite, silicon, and Li(Mn1.5Ni0.5)O4, and tin. A thermal analogy model for lithiation/delithiation induced fracture of spherical particles capable of predicting when AE should be detected in a cell containing a composite silicon electrode. The results of this work were used to develop an understanding of when and how active materials are degrading as well as to suggest methods of improving their performance and operational longevity