Search NASA⌕ Search

SEARCH · Search NASA

Results for “Sensing error”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Quartz-enhanced conductance spectroscopy for nanomechanical analysis of polymer wire

Quartz-enhanced conductance spectroscopy is developed as an analytical tool to investigate dynamic nanomechanical behaviors of polymer wires, in order to determine the glass transition temperature (T{sub g}). A polymethyl methacrylate (PMMA) microwire with a diameter of 10 μm was bridged across the prongs of a quartz tuning fork (QTF). With the advantage of QTF self-sensing as compared with micro-cantilevers or other resonators, the resonance frequency and Q factor can be directly determined by means of its electrical conductance spectra with respect to the frequency of the external excitation source (dI/dV vs f), and therefore, no optical beam is required. The T{sub g} of the PMMA microwire was determined by the maximum loss modulus of the QTF, calculated from the resonance frequency and the Q factor as a function of temperature. The measured T{sub g} of the PMMA is 103 °C with an error of ±2 °C. Both heating/cooling and physical aging experiments were carried out, demonstrating that the technique is both reversible and reproducible.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Convolutional Neural Networks

Abstract Accurate and timely precipitation estimates are critical for monitoring and forecasting natural disasters such as floods. Despite having high-resolution satellite information, precipitation estimation from remotely sensed data still suffers from methodological limitations. State-of-the-art deep learning algorithms, renowned for their skill in learning accurate patterns within large and complex datasets, appear well suited to the task of precipitation estimation, given the ample amount of high-resolution satellite data. In this study, the effectiveness of applying convolutional neural networks (CNNs) together with the infrared (IR) and water vapor (WV) channels from geostationary satellites for estimating precipitation rate is explored. The proposed model performances are evaluated during summer 2012 and 2013 over central CONUS at the spatial resolution of 0.08° and at an hourly time scale. Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)–Cloud Classification System (CCS), which is an operational satellite-based product, and PERSIANN–Stacked Denoising Autoencoder (PERSIANN-SDAE) are employed as baseline models. Results demonstrate that the proposed model (PERSIANN-CNN) provides more accurate rainfall estimates compared to the baseline models at various temporal and spatial scales. Specifically, PERSIANN-CNN outperforms PERSIANN-CCS (and PERSIANN-SDAE) by 54% (and 23%) in the critical success index (CSI), demonstrating the detection skills of the model. Furthermore, the root-mean-square error (RMSE) of the rainfall estimates with respect to the National Centers for Environmental Prediction (NCEP) Stage IV gauge–radar data, for PERSIANN-CNN was lower than that of PERSIANN-CCS (PERSIANN-SDAE) by 37% (14%), showing the estimation accuracy of the proposed model.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1967

Design of Liquid Propellant Rocket Engines Second Edition

This book intends to build a bridge for the student and the young engineer: to link the rocket propulsion fundamentals and elements (which are well covered in the literature) with the actual rocket engine design and development work as it is carried out in industry (which is very little, if at all covered in literature). The book attempts to further the understanding of the realistic application of liquid rocket propulsion theories, and to help avoid or at least reduce time and money consuming errors and disappointments. In so doing, it also attempts to digest and consolidate numerous closely related subjects, hitherto often treated as separate, bringing them up to date at the same time. The book was written "on the job" for use by those active in all phases of engine systems design, development, and application, in industry as well as government agencies. Since it addresses itself to human beings set out to create new machines, rather than describing machines about to dominate man, the language chosen may not always be "functional" in the strict sense of the word. The book presents sufficient detail to familiarize and educate thoroughly those responsible for various aspects of liquid propellant rocketry, including engine systems design, engine development, and flight vehicle application. It should enable the rocket engineer to conduct, independently, complete or partial engine systems preliminary detail designs and to understand and judge the activities in, and the problems, limitations, and "facts of life~ of the various subsystems making up a complete engine system. It also attempts to educate those ultimately interested in specialized subsystems and component design (thrust chamber, turbopump, control valves, etc.) about their own as well as neighboring subsystems and about the complete engine system. This should enable the student to prepare realistic analytical calculations and design layouts with a long headstart toward the final specialized designs for subsystem production release. Special emphasis has been placed on engine flight application to stimulate engine systems and subsystem designers to think in these terms from the outset. The book is intended as a textbook, with specific consideration of the teacher without industry experience. We hope it will stimulate those desiring to specialize in the area of a rocket engine subsystem by supplying adequate information to enable them to benefit fully from the specialized literature. Thus, it provides a realistic expert introduction for those joining the liquid propellant rocket engine field.

Dieter K Huzel↗
NASA NTRS1961

Study of a Solar Sensor for use in Space Vehicle Orientation Control Systems

The solar sensor described herein may be used for a variety of space operations requiring solar orientation. The use of silicon solar cells as the sensing elements provides the sensor with sufficient capability to withstand the hazards of a space environment. A method of arranging the cells in a sensor consists simply of mounting them at a large angle to the base. The use of an opaque shield placed between the cells and perpendicular to the base enhances the small-angle sensitivity while adding slightly to the bulk of the sensor. The difference in illumination of these cells as the result of an oblique incidence of the light rays from the reference source causes an electrical error signal which, when used in a battery-bridge circuit, requires a minimum of electrical processing for use in a space-vehicle orientation control system. An error which could occur after prolonged operation of the sensor is that resulting from asymmetrical aging of opposite cells. This could be periodically corrected with a balance potentiometer. A more routine error in the sensor is that produced by reflected earth radiation. This error may be eliminated over a large portion of the operation time by restricting the field of view and, consequently, the capture capability. A more sophisticated method of eliminating this error is to use separate sensors, for capture and fine pointing, along with a switching device. An experimental model has been constructed and tested to yield an output sensitivity of 1.2 millivolts per second of arc with a load resistance of 1,000 ohms and a reference light source of approximately 1,200 foot-candles delivered at the sensor.

Spencer, Paul R.↗
NASA NTRS1994

Nonvolatile random access memory

A nonvolatile magnetic random access memory can be achieved by an array of magnet-Hall effect (M-H) elements. The storage function is realized with a rectangular thin-film ferromagnetic material having an in-plane, uniaxial anisotropy and inplane bipolar remanent magnetization states. The thin-film magnetic element is magnetized by a local applied field, whose direction is used to form either a 0 or 1 state. The element remains in the 0 or 1 state until a switching field is applied to change its state. The stored information is detcted by a Hall-effect sensor which senses the fringing field from the magnetic storage element. The circuit design for addressing each cell includes transistor switches for providing a current of selected polarity to store a binary digit through a separate conductor overlying the magnetic element of the cell. To read out a stored binary digit, transistor switches are employed to provide a current through a row of Hall-effect sensors connected in series and enabling a differential voltage amplifier connected to all Hall-effect sensors of a column in series. To avoid read-out voltage errors due to shunt currents through resistive loads of the Hall-effect sensors of other cells in the same column, at least one transistor switch is provided between every pair of adjacent cells in every row which are not turned on except in the row of the selected cell.

Wu, Jiin-Chuan↗
DOE OSTI2020

Assessing Historical Variability of South Asian Monsoon Lows and Depressions With an Optimized Tracking Algorithm

Abstract Cyclonic low‐pressure systems (LPS) produce abundant rainfall in South Asia, where they are traditionally categorized as monsoon lows, monsoon depressions, and more intense cyclonic storms. The India Meteorological Department (IMD) has tracked monsoon depressions for over a century, finding a large decline in their number in recent decades, but their methods have changed over time and do not include monsoon lows. This study presents a fast, objective algorithm for identifying monsoon LPS and uses it to assess interannual variability and trends in reanalyses. Variables and thresholds used in the algorithm are selected to best match a subjectively analyzed LPS data set while minimizing disagreement between four reanalyses in a training period. The stream function of 850 hPa horizontal wind is found to be optimal in this sense; it is less noisy than vorticity and represents the complete nondivergent wind, even when flow is not geostrophic. Using this algorithm, LPS statistics are computed for five reanalyses, and none show a detectable trend in monsoon depression counts since 1979. Both the Japanese 55‐year Reanalysis (JRA‐55) and the IMD data set show a step‐like reduction in depression counts when they began using geostationary satellite data, in 1979 and 1982, respectively; the 1958–2018 linear trend in JRA‐55, however, is smaller than in the IMD data set, and its error bar includes 0. There are more LPS in seasons with above‐average monsoon rainfall and in La Niña years, but few other large‐scale modes of interannual variability are found to modulate LPS counts, lifetimes, or track length consistently across reanalyses.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1985

Evaluation and interpretation of Thematic Mapper ratios in equations for estimating corn growth parameters

Reflectance and agronomic Thematic Mapper (TM) data were analyzed to determine possible data transformations for evaluating several plant parameters of corn. Three transformation forms were used: the ratio of two TM bands, logarithms of two-band ratios, and normalized differences of two bands. Normalized differences and logarithms of two-band ratios responsed similarly in the equations for estimating the plant growth parameters evaluated in this study. Two-term equations were required to obtain the maximum predictability of percent ground cover, canopy moisture content, and total wet phytomass. Standard error of estimate values were 15-26 percent lower for two-term estimates of these parameters than for one-term estimates. The terms log(TM4/TM2) and (TM4/TM5) produced the maximum predictability for leaf area and dry green leaf weight, respectively. The middle infrared bands TM5 and TM7 are essential for maximizing predictability for all measured plant parameters except leaf area index. The estimating models were evaluated over bare soil to discriminate between equations which are statistically similar. Qualitative interpretations of the resulting prediction equations are consistent with general agronomic and remote sensing theory.

Dardner, B. R.↗
NASA NTRS1991

Spaceborne observation of columnar water vapor - SSMI observations and algorithm

An analysis of the transfer of radiation with a frequency between 10 and 40 GHz through a cloud-free atmosphere is given, and the limitations of employing observations at 22.235 GHz for sensing the columnar water vapor (CWV) in the moist atmosphere (CWV above 40 kg/sq m) are shown. It is because of this limitation that a new method for the retrieval of columnar water vapor is described. This method uses both the horizontally and vertically polarized brightness temperatures measured at 19.35 GHz by the special sensor microwave imager (SSMI) which was launched as part of the Defense Military Satellite Program. The water vapor derived from the SSMI observations is validated against radiosonde observations that are near coincident in time and space with an rms error of 6 kg/sq m. This large rms is in part due to sensitivity of the retrieval method to the surface emission. Distributions of CWV over the oceans were obtained for a 3-month period starting September, 1987. These results are compared to other water vapor retrievals for the same period using the same microwave radiance data; they show close agreement except over the warmest water in the equatorial tropics.

Tjemkes, Stephen A.↗
DOE OSTI2018

Occupancy prediction through machine learning and data fusion of environmental sensing and Wi-Fi sensing in buildings

Occupancy information is crucial to building facility design, operation, and energy efficiency. Many studies propose the use of environmental sensors (such as carbon dioxide, air temperature, and relative humidity sensors) and radio-frequency sensors (Wi-Fi networks) to monitor, assess, and predict occupancy information for buildings. As many methods have been developed and a variety of sensory data sources are available, establishing a proper selection of model and data source is critical to the successful implementation of occupancy prediction systems. This study compared three popular machine learning algorithms, including k-nearest neighbors (kNN), support vector machine (SVM), and artificial neural network (ANN), combined with three data sources, including environmental data, Wi-Fi data, and fused data, to optimize the occupancy models’ performance in various scenarios. Three error measurement metrics, the mean average error (MAE), mean average percentage error (MAPE), and root mean squared error (RMSE), have been employed to compare the models’ accuracies. Examined with an on-site experiment, the results suggest that the ANN-based model with fused data has the best performance, while the SVM model is more suitable with Wi-Fi data. The results also indicate that, comparing with independent data sources, the fused data set does not necessarily improve model accuracy but shows a better robustness for occupancy prediction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1977

Remote sensing of atmospheric water vapor, liquid water and wind speed at the ocean surface by passive microwave techniques from the Nimbus-5 satellite

The microwave brightness temperature measurements for Nimbus-5 electrically scanned microwave radiometer and Nimbus E microwave spectrometer are used to retrieve the atmospheric water vapor, liquid water and wind speed by a quasi-statistical retrieval technique. It is shown that the brightness temperature can be utilized to yield these parameters under various weather conditions. Observations at 19.35 GHz, 22.235 GHz and 31.4 GHz were input to the regression equations. The retrieved values of these parameters for portions of two Nimbus-5 orbits are presented. Then comparison between the retrieved parameters and the available observations on the total water vapor content and the surface wind speed are made. The estimated errors for retrieval are approximately 0.15 g/sq cm for water vapor content, 6.5 mg/sq cm for liquid water content and 6.6 m/sec for surface wind speed.

Chang, A. T. C.↗
NASA NTRS2019

Updated MISR Over-Water Research Aerosol Retrieval Algorithm - Part 2: A Multi-Angle Aerosol Retrieval Algorithm for Shallow, Turbid, Oligotrophic, and Eutrophic Waters

Coastal waters serve as transport pathways to the ocean for all agricultural and other runoff from terrestrial sources, and many are the sites for upwelling of nutrient rich, deep water; they are also some of the most biologically productive on Earth. Estimating the impact coastal waters have on the global carbon budget requires relating satellite-based remote-sensing retrievals of biological productivity (e.g., chlorophyll alpha concentration) to in situ measurements taken in near-surface waters. The Multi-angle Imaging SpectroRadiometer (MISR) can uniquely constrain the "atmospheric correction" needed to derive ocean color from remote-sensing imagers. Here, we retrieve aerosol amount and type from MISR over all types of water. The primary limitation is an upper bound on aerosol optical depth (AOD), as the algorithm must be able to distinguish the surface. This updated MISR research aerosol retrieval algorithm (RA) also assumes that light reflection by the underlying ocean surface is Lambertian. The RA computes the ocean surface reflectance (R (sub rs)) analytically for a given AOD, aerosol optical model, and wind speed. We provide retrieval examples over shallow, turbid, and eutrophic waters and introduce a productivity and turbidity index (PTI), calculated from retrieved spectral R (sub rs), that distinguished water types (similar to the the normalized difference vegetation index, NDVI, over land). We also validate the new algorithm by comparing spectral AOD and Angstrom exponent (ANG) results with 2419 collocated AErosol RObotic NETwork (AERONET) observations. For AERONET 558 nanometer-interpolated AOD less than 1.0, the root-mean square error (RMSE) is 0.04 and linear correlation coefficient is 0.95. For the 502 cloud-free MISR and AERONET collocations with an AERONET AOD greater than 0.20, the ANG RMSE is 0.25 and r is 0.89. Although MISR RA AOD retrieval quality does not appear to be substantially impacted by the presence of turbid water, the MISR-RA-retrieved Angstrom exponent seems to suffer from increased uncertainty under such conditions. MISR supplements current ocean color sources in regions where sunglint precludes retrievals from single-view-angle instruments. MISR atmospheric correction should also be more robust than that derived from single-view instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS). This is especially true in regions of shallow, turbid, and eutrophic waters, locations where biological productivity can be high, and single-view-angle retrieval algorithms struggle to separate atmospheric from oceanic features.

Satellite Remote Sensing↗
NASA NTRS1990

Radiometer system requirements for microwave remote sensing from satellites

An area of increasing interest is the establishment of a significant research program in microwave remote sensing from satellites, particularly geosynchronous satellites. Due to the relatively small resolution cell sizes, a severe requirement is placed on beam efficiency specifications for the radiometer antenna. Geostationary satellite microwave radiometers could continuously monitor several important geophysical parameters over the world's oceans. These parameters include the columnar content of atmospheric liquid water (both cloud and rain) and water vapor, air temperature profiles, and possibly sea surface temperature. Two principle features of performance are of concern. The first is the ability of the radiometer system to resolve absolute temperatures with a very small absolute error, a capability that depends on radiometer system stability, on frequency bandwidth, and on footprint dwell time. The second is the ability of the radiometer to resolve changes in temperature from one resolution cell to the next when these temperatures are subject to wide variation over the overall field-of-view of the instrument. Both of these features are involved in the use of the radiometer data to construct high-resolution temperature maps with high absolute accuracy.

Juang, Jeng-Nan↗
NASA NTRS1992

An algorithm for the estimation of bounds on the emissivity and temperatures from thermal multispectral airborne remotely sensed data

The effective flux incident upon the detectors of a thermal sensor, after it has been corrected for atmospheric effects, is a function of a non-linear combination of the emissivity of the target for that channel and the temperature of the target. The sensor system cannot separate the contribution from the emissivity and the temperature that constitute the flux value. A method that estimates the bounds on these temperatures and emissivities from thermal data is described. This method is then tested with remotely sensed data obtained from NASA's Thermal Infrared Multispectral Scanner (TIMS) - a 6 channel thermal sensor. Since this is an under-determined set of equations i.e. there are 7 unknowns (6 emissivities and 1 temperature) and 6 equations (corresponding to the 6 channel fluxes), there exist theoretically an infinite combination of values of emissivities and temperature that can satisfy these equations. Using some realistic bounds on the emissivities, bounds on the temperature are calculated. These bounds on the temperature are refined to estimate a tighter bound on the emissivity of the source. An error analysis is also carried out to quantitatively determine the extent of uncertainty introduced in the estimate of these parameters. This method is useful only when a realistic set of bounds can be obtained for the emissivities of the data. In the case of water the lower and upper bounds were set at 0.97 and 1.00 respectively. Five flights were flown in succession at altitudes of 2 km (low), 6 km (mid), 12 km (high), and then back again at 6 km and 2 km. The area selected with the Ross Barnett reservoir near Jackson, Mississippi. The mission was flown during the predawn hours of 1 Feb. 1992. Radiosonde data was collected for that duration to profile the characteristics of the atmosphere. Ground truth temperatures using thermometers and radiometers were also obtained over an area of the reservoir. The results of two independent runs of the radiometer data averaged 7.03 plus or minus .70 for the first run and 7.31 plus or minus .88 for the second run. The results of the algorithm yield a temperature of 7.68 for the low altitude data to 8.73 for the high altitude data.

Jaggi, S.↗
NASA NTRS1996

A Full Multi-Grid Method for the Solution of the Cell Vertex Finite Volume Cauchy-Riemann Equations

The system of inhomogeneous Cauchy-Riemann equations defined on a square domain and subject to Dirichlet boundary conditions is considered. This problem is discretised by using the cell vertex finite volume method on quadrilateral meshes. The resulting algebraic problem is overdetermined and the solution is defined in a least squares sense. By this approach a consistent algebraic problem is obtained which differs from the original one by O(h(exp 2)) perturbations of the right-hand side. A suitable cell-based convergent smoothing iteration is presented which is naturally linked to the least squares formulation. Hence, a standard multi-grid algorithm is reported which combines the given smoother and cell-based transfer operators. Some remarkable reduction properties of these operators are shown. A full multi-grid method is discussed which solves the discrete problem to the level of truncation error by employing one multi-grid cycle at each current level of discretisation. Experiments and applications of the full multi-grid scheme are presented.

Borzi, A.↗
NASA NTRS1999

GPS Altimetry

The advent of satellite altimetry has greatly improved our ability to observe global ocean circulation. However, the swath of a single, nadir-viewing satellite altimeter is only a few km and the track spacing is several hundred km to resolve the two-dimensional structure of ocean eddies. Our goal is to increase spatial and temporal coverage by monitoring Global Positioning System (GPS) signals reflected from the ocean. A constellation of spacecraft would each carry a GPS receiver capable of recording 8 reflections simultaneously. The reflections are well distributed in azimuth and elevation and can be tracked continuously while the satellite is in view, and another is then acquired, as illustrated below. The diagram depicts a new approach at altimetry measurements where ocean surface reflected GPS signals are simultaneously tracked and processed in a GPS flight receiver in space. The reflected GPS signals from the ocean must be compared precisely with the direct GPS signals in order to infer the characteristics of the ocean from the combined data set. Understanding the features and accuracy of GPS altimetry measurement is crucial to establishing its suitability for oceanography. Preliminary work has enabled us to theoretically model the signal output of the correlator for a variety of system parameters such as wind speed (sea roughness), receiver height, incidence angle, receiver range and Doppler filter bandwidth and antenna gain. Expected signal-to-noise ratio has been estimated from which we have inferred, to a first approximation, the basic receiver gain requirements for a space-based altimeter and the expected range raw error. In 1998, work on a different task led to the extraction of the first reflected GPS signal observed from a spaceborne receiver during the 1995 Space Transportation System-68 (STS-68) Shuttle Radar Laboratory-2 (SRL-2) high resolution synthetic aperture radar mission. Good comparisons with our signal models have been obtained. Having established that only modest signal-to-noise ratios are obtainable unless very high gain antennas are used, we expect that single measurements might not provide an estimate of sea state parameters as accurate as that obtainable with traditional remote sensing instruments, if costs are limited. Therefore spatial and temporal averaging of many measurements is required. Since the receiving satellite tracks do not repeat, measurements in a given area will be used to refine the local solution as a function of time, and will define the spatial resolution. In order to make this instrument viable in space, we need to detect and process many scattered signals. In 1998 we made a first step at understanding what configurations of antenna gain and orientations capture the largest number of viable signals to be used in the subsequent spatial/temporal averaging process. Additional information is contained in the original.

Lichten, Stephen M.↗
NASA NTRS2000

Validation and Development of the GPCP Experimental One-Degree Daily (1DD) Global Precipitation Product

The One-Degree Daily (1DD) precipitation dataset has been developed for the Global Precipitation Climatology Project (GPCP) and is currently in beta test preparatory to release as an official GPCP product. The 1DD provides a globally-complete, observation-only estimate of precipitation on a daily 1 deg. x 1 deg. grid for the period 1997 through early 2000 (by the time of the conference). In the latitude band 40N-40S the 1DD uses the Threshold-Matched Precipitation Index (TMPI), a GPI-like IR product with the pixel-level T(sub b) threshold and (single) conditional rain rate determined locally for each month by the frequency of precipitation in the GPROF SSM/I product and by, the precipitation amount in the GPCP monthly satellite-gauge (SG) combination. Outside 40N-40S the 1DD uses a scaled TOVS precipitation estimate that has month-by-month adjustments based on the TMPI and the SG. Early validation results are encouraging. The 1DD shows relatively large scatter about the daily validation values in individual grid boxes, as expected for a technique that depends on cloud-sensing schemes such as the TMPI and TOVS. On the other hand, the time series of 1DD shows good correlation with validation in individual boxes. For example, the 1997-1998 time series of 1DD and Oklahoma Mesonet values in a grid box in northeastern Oklahoma have the correlation coefficient = 0.73. Looking more carefully at these two time series, the number of raining days for the 1DD is within 7% of the Mesonet value, while the distribution of daily rain values is very similar. Other tests indicate that area- or time-averaging improve the error characteristics, making the data set highly attractive to users interested in stream flow, short-term regional climatology, and model comparisons. The second generation of the 1DD product is currently under development; it is designed to directly incorporate TRMM and other high-quality precipitation estimates. These data are generally sparse because they are observed by low-orbit satellites, so a fair amount of work must be devoted to analyzing the effect of data boundaries. This work is laying, the groundwork for effective use of the NASA Global Precipitation Mission, which will have full Global coverage by low-orbit passive microwave satellites every three hours.

Huffman, George J.↗
DOE OSTI2015

SU-E-T-258: Development of a New Patient Set-Up Monitoring System Using Force Sensing Resistor (FSR) Sensor for the Radiation Therapy

Purpose: The purpose of this work is to develop a new patient set-up monitoring system using force sensing resistor (FSR) sensors that can confirm pressure of contact surface and evaluate its feasibility. Methods: In this study, we focused on develop the patient set-up monitoring system to compensate for the limitation of existing optical based monitoring system, so the developed system can inform motion in the radiation therapy. The set-up monitoring system was designed consisting of sensor units (FSR sensor), signal conditioning devices (USB cable/interface electronics), a control PC, and a developed analysis software. The sensor unit was made by attaching FSR sensor and dispersing pressure sponge to prevent error which is caused by concentrating specific point. Measured signal from the FSR sensor was sampled to arduino mega 2560 microcontroller, transferred to control PC by using serial communication. The measured data went through normalization process. The normalized data was displayed through the developed graphic user interface (GUI) software. The software was designed to display a single sensor unit intensity (maximum 16 sensors) and display 2D pressure distribution (using 16 sensors) according to the purpose. Results: Changes of pressure value according to motion was confirmed by the developed set-up monitoring system. Very small movement such as little physical change in appearance can be confirmed using a single unit and using 2D pressure distribution. Also, the set-up monitoring system can observe in real time. Conclusion: In this study, we developed the new set-up monitoring system using FSR sensor. Especially, we expect that the new set-up monitoring system is suitable for motion monitoring of blind area that is hard to confirm existing optical system and compensate existing optical based monitoring system. As a further study, an integrated system will be constructed through correlation of existing optical monitoring system. This work was supported by the Industrial R&D program of MOTIE/KEIT. [10048997, Development of the core technology for integrated therapy devices based on real-time MRI guided tumor tracking] and the Mid-career Researcher Program (2014R1A2A1A10050270) through the National Research Foundation of Korea funded by the Ministry of Science, ICT&Future Planning.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

A New Research Approach for Observing and Characterizing Land–Atmosphere Feedback

Forecast errors with respect to wind, temperature, moisture, clouds, and precipitation largely correspond to the limited capability of current earth system models to capture and simulate and-atmosphere feedback. To facilitate its realistic simulation in next generation models, an improved process understanding of the related complex interactions is essential. To this end, accurate 3D observations of key variables in the land-atmosphere (L-A) system with high vertical and temporal resolution from the surface to the free troposphere are indispensable. Recently, we developed a synergy of innovative ground-based, scanning active remote sensing systems for 2D to 3D measurements of wind, temperature, and water vapor from the surface to the lower troposphere that is able to provide comprehensive data sets for characterizing L-A feedback independently of any model input. Several new applications are introduced such as the mapping of surface momentum, sensible heat, and latent heat fluxes in heterogeneous terrain, the testing of Monin-Obukhov similarity theory and turbulence parameterizations, the direct measurement of entrainment fluxes, and the development of new flux-gradient relationships. An experimental design taking advantage of the sensors’ synergy and advanced capabilities was realized for the first time during the Land Atmosphere Feedback Experiment (LAFE), conducted at the Atmospheric Radiation Measurement Program Southern Great Plains site in August 2017. The scientific goals and the strategy of achieving them with the LAFE data set are introduced. We envision the initiation of innovative L-A feedback studies in different climate regions to improve weather forecast, climate, and earth system models worldwide.

54 ENVIRONMENTAL SCIENCES↗