The application of improved signal summing method into the spacecraft force limited vibration test

This paper provides an improved signal summing method for the spacecraft force limited vibration test system with eight force transducers. The key point for this method is to change the combination way of the signals coming out of the eight force transducers while the formulas inside the signal conditioning amplifier have been used skillfully. This method had been successfully adopted in the spacecraft force limited vibration test and the accuracy requirements of key force and moment signals have been met. And this method has been proved to be a very powerful tool for providing the critical force and moment data used to determine the force limited profile during the spacecraft dynamic test

Atmospheric CO2 Variability Observed From ASCENDS Flight Campaigns

Significant atmospheric CO2 variations on various spatiotemporal scales were observed during ASCENDS flight campaigns. For example, around 10-ppm CO2 changes were found within free troposphere in a region of about 200x300 sq km over Iowa during a summer 2014 flight. Even over extended forests, about 2-ppm CO2 column variability was measured within about 500-km distance. For winter times, especially over snow covered ground, relatively less horizontal CO2 variability was observed, likely owing to minimal interactions between the atmosphere and land surface. Inter-annual variations of CO2 drawdown over cornfields in the Mid-West were found to be larger than 5 ppm due to slight differences in the corn growing phase and meteorological conditions even in the same time period of a year. Furthermore, considerable differences in atmospheric CO2 profiles were found during winter and summer campaigns. In the winter CO2 was found to decrease from about 400 ppm in the atmospheric boundary layer (ABL) to about 392 ppm above 10 km, while in the summer CO2 increased from 386 ppm in the ABL to about 396 ppm in free troposphere. These and other CO2 observations are discussed in this presentation

Magnetic resonance imaging based deep-learning model: a rapid, high-performance, automated tool for testicular volume measurements

BackgroundTesticular volume (TV) is an essential parameter for monitoring testicular functions and pathologies. Nevertheless, current measurement tools, including orchidometers and ultrasonography, encounter challenges in obtaining accurate and personalized TV measurements.PurposeBased on magnetic resonance imaging (MRI), this study aimed to establish a deep learning model and evaluate its efficacy in segmenting the testes and measuring TV.Materials and methodsThe study cohort consisted of retrospectively collected patient data (N = 200) and a prospectively collected dataset comprising 10 healthy volunteers. The retrospective dataset was divided into training and independent validation sets, with an 8:2 random distribution. Each of the 10 healthy volunteers underwent 5 scans (forming the testing dataset) to evaluate the measurement reproducibility. A ResUNet algorithm was applied to segment the testes. Volume of each testis was calculated by multiplying the voxel volume by the number of voxels. Manually determined masks by experts were used as ground truth to assess the performance of the deep learning model.ResultsThe deep learning model achieved a mean Dice score of 0.926 ± 0.034 (0.921 ± 0.026 for the left testis and 0.926 ± 0.034 for the right testis) in the validation cohort and a mean Dice score of 0.922 ± 0.02 (0.931 ± 0.019 for the left testis and 0.932 ± 0.022 for the right testis) in the testing cohort. There was strong correlation between the manual and automated TV (R2 ranging from 0.974 to 0.987 in the validation cohort; R2 ranging from 0.936 to 0.973 in the testing cohort). The volume differences between the manual and automated measurements were 0.838 ± 0.991 (0.209 ± 0.665 for LTV and 0.630 ± 0.728 for RTV) in the validation cohort and 0.815 ± 0.824 (0.303 ± 0.664 for LTV and 0.511 ± 0.444 for RTV) in the testing cohort. Additionally, the deep-learning model exhibited excellent reproducibility (intraclass correlation >0.9) in determining TV.ConclusionThe MRI-based deep learning model is an accurate and reliable tool for measuring TV

Regional and Global Atmospheric CO2 Measurements Using 1.57 Micron IM-CW Lidar

Atmospheric CO2 is a critical forcing for the Earth's climate, and knowledge of its distribution and variations influences predictions of the Earth's future climate. Accurate observations of atmospheric CO2 are also crucial to improving our understanding of CO2 sources, sinks and transports. To meet these science needs, NASA is developing technologies for the Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) space mission, which is aimed at global CO2 observations. Meanwhile an airborne investigation of atmospheric CO2 distributions as part of the NASA Suborbital Atmospheric Carbon and Transport " America (ACT-America) mission will be conducted with lidar and in situ instrumentation over the central and eastern United States during all four seasons and under a wide range of meteorological conditions. In preparing for the ASCENDS mission, NASA Langley Research Center and Exelis Inc./Harris Corp. have jointly developed and demonstrated the capability of atmospheric CO2 column measurements with an intensity-modulated continuous-wave (IM-CW) lidar. Since 2005, a total of 14 flight campaigns have been conducted. A measurement precision of approx.0.3 ppmv for a 10-s average over desert and vegetated surfaces has been achieved, and the lidar CO2 measurements also agree well with in-situ observations. Significant atmospheric CO2 variations on various spatiotemporal scales have been observed during these campaigns. For example, around 10-ppm CO2 changes were found within free troposphere in a region of about 200A-300 sq km over Iowa during a summer 2014 flight. Results from recent flight campaigns are presented in this paper. The ability to achieve the science objectives of the ASCENDS mission with an IM-CW lidar is also discussed in this paper, along with the plans for the ACT-America aircraft investigation that begins in the winter of 2016

A pressure-temperature dual sensing methodology for injection molding monitoring

Injection molding is a widely employed manufacturing process in modern industry for mass production of plastic parts. Of the various parameters that affect the quality of injection molded products, the pressure and temperature distribution within the mold cavity are the most critical factors, as improper setting of the filling pressure or cooling speed may lead to defects such as sinks, flashes, or short shots. With increasing mold complexity and the related high cost to structurally modify the mold for sensor accommodation, the ability to integrate multiple, miniaturized sensors within the mold cavity for on- line, multi-point pressure and temperature distribution measurement while minimizing mold structure modification becomes highly attractive to the industry. This thesis presented the design of a self-energized, acoustic wireless sensing methodology for the simultaneous measurement of pressure and temperature right at the mold cavity, within one sensor package. The sensor harvests energy from the pressure variation of the polymer melt during the injection molding process, and utilizes ultrasonic pulses as the carrier to wirelessly transmit pressure and temperature information through the injection mold. A significant advantage of the designed sensing method is that it avoids drilling through-holes in the injection molds or holding plates, which are costly to implement but necessary for installing traditional wired sensors, thereby allowing multiple sensors to be embedded within a complicated mold structure to improve the observability of the polymer state during the injection molding processes. Research conducted in this thesis addresses four aspects of the complete solution: (1) Design of a dual-parameter modulation method suited for injection molding environment, (2) Structural optimization of the energy harvesting component, known as a piezoceramic stack, for a minimal volume of the stack while maintaining the minimum Signal-to-Noise Ratio required for reliable signal reception, (3) Design of a multi-layer ultrasound transmitter for effective transmission of the ultrasonic pulses through injection mold steel, and (4) The investigation of signal propagation within the mold cavity and the optimal localization of the receiver to obtain maximum signal strength on the back surface of the injection mold. The developed sensing methodology was systematically evaluated through experiments using a sensor prototype on a Milacron T100 injection molding machine. States of the melt at multiple locations in the mold cavity were retrieved from the sensor data to provide real- time process feedback for the machine controllers. This information will enable the development and widespread implementation of state variable control algorithms for injection molding to reduce the time required for process set-up and stabilization, and improve part quality and consistency. The energy harvesting and signal modulation mechanisms developed from this research provide a new means to power sensors for the condition monitoring and health diagnosis of dynamic systems and processes in a broad range of applications

Rolling Element Bearing Fault Diagnosis Based on Multiscale General Fractal Features

Nonlinear characteristics are ubiquitous in the vibration signals produced by rolling element bearings. Fractal dimensions are effective tools to illustrate nonlinearity. This paper proposes a new approach based on Multiscale General Fractal Dimensions (MGFDs) to realize fault diagnosis of rolling element bearings, which are robust to the effects of variation in operating conditions. The vibration signals of bearing are analyzed to extract the general fractal dimensions in multiscales, which are in turn utilized to construct a feature space to identify fault pattern. Finally, bearing faults are revealed by pattern recognition. Case studies are carried out to evaluate the validity and accuracy of the approach. It is verified that this approach is effective for fault diagnosis of rolling element bearings under various operating conditions via experiment and data analysis