A Novel Approach to Determine the Enriched Gas Recycling during the Enriched Gas Flooding Process

A novel approach was proposed for calculating the enriched gas recovery factor based on the theory of two-phase isothermal flash calculations. First, define a new parameter, pseudo formation volume factor of enriched gas, to represent the ratio of the surface volume of produced mixture gas to underground volume of enriched gas. Two logarithmic functions were obtained by matching the flash calculation data, to characterize the relationships between pseudo formation volume factor and the produced gas-oil ratio. These two functions belong to the proportion of liquefied petroleum gas in enriched gas; the proportion is greater than 50% and less than 50%, respectively. Given measured gas-oil ratio and produced gas volume, underground volume of produced enriched gas can be calculated. Injection volume of enriched gas is known; therefore, recovery factor of enriched gas is the ratio of produced to injected volume of enriched gas. This approach is simply to calculate enriched gas recovery factor, because of only needs three parameters, which can be measured directly. New approach was compared to numerical simulation results; mean error is 12%. In addition, new approach can effectively avoid the influence of lean gas on the calculation of enriched gas recycling. Three stages of enriched gas recovery factors in field Z were calculated, and the mean error is 5.62% compared to the field analysis, which proves that the new approach’s correctness and practicability.

Video-Based Pose Estimation for Gait Analysis in Stroke Survivors during Clinical Assessments: A Proof-of-Concept Study

Recent advancements in deep learning have produced significant progress in markerless human pose estimation, making it possible to estimate human kinematics from single camera videos without the need for reflective markers and specialized labs equipped with motion capture systems. Such algorithms have the potential to enable the quantification of clinical metrics from videos recorded with a handheld camera. Here we used DeepLabCut, an open-source framework for markerless pose estimation, to fine-tune a deep network to track 5 body keypoints (hip, knee, ankle, heel, and toe) in 82 below-waist videos of 8 patients with stroke performing overground walking during clinical assessments. We trained the pose estimation model by labeling the keypoints in 2 frames per video and then trained a convolutional neural network to estimate 5 clinically relevant gait parameters (cadence, double support time, swing time, stance time, and walking speed) from the trajectory of these keypoints. These results were then compared to those obtained from a clinical system for gait analysis (GAITRite®, CIR Systems). Absolute accuracy (mean error) and precision (standard deviation of error) for swing, stance, and double support time were within 0.04 ± 0.11 s; Pearson’s correlation with the reference system was moderate for swing times (<i>r</i> = 0.4–0.66), but stronger for stance and double support time (<i>r</i> = 0.93–0.95). Cadence mean error was −0.25 steps/min ± 3.9 steps/min (<i>r</i> = 0.97), while walking speed mean error was −0.02 ± 0.11 m/s (<i>r</i> = 0.92). These preliminary results suggest that single camera videos and pose estimation models based on deep networks could be used to quantify clinically relevant gait metrics in individuals poststroke, even while using assistive devices in uncontrolled environments. Such development opens the door to applications for gait analysis both inside and outside of clinical settings, without the need of sophisticated equipment.

Validation of the final monthly Integrated Multi-satellitE Retrievals for GPM (IMERG) Version 05 and Version 06 with ground-based precipitation gauge measurements across the Canadian Arctic

To assess the performance of the most recent versions of the Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG), namely V05 and V06, in Arctic regions, comparisons with Environment and Climate Change Canada (ECCC) Climate Network stations north of 60°N were performed. This study focuses on the IMERG monthly final products. The mean bias and mean error-weighted bias were assessed in comparison with twenty-five precipitation gauge measurements at ECCC Climate Network stations. The results of this study indicate that IMERG generally detects higher precipitation rates in the Canadian Arctic than ground-based gauge instruments, with differences ranging up to 0.05 mm h−1 and 0.04 mm h−1 for the mean bias and the mean error-weighted bias, respectively. Both IMERG versions perform similarly, except for a few stations, where V06 tends agree slightly better with ground-based measurements. IMERG’s tendency to detect more precipitation is in good agreement with findings indicating that weighing gauge measurement suffer from wind undercatch and other impairing factors, leading to lower precipitation estimates. Biases between IMERG and ground-based stations were found to be slightly larger during summer and fall, which is likely related to the increased precipitation rates during these seasons. Correlations of both versions of IMERG with the ground-based measurements are considerably lower in winter and spring than during summer and fall, which might be linked to issues that Passive Microwave (PMW) sensors encounter over ice and snow. However, high correlation coefficients with medians of 0.75-0.8 during summer and fall are very encouraging for potential future applications.

Analysis of Precipitation Variability using Memory Based Artificial Neural Networks

This article analyzes the variability in precipitation of the Barak river basin using memory-based ANN models called Gamma Memory Neural Network(GMNN) and genetically optimized GMNN called GMNN-GA for precipitation downscaling precipitation. GMNN having adaptive memory depth is capable techniques in modeling time varying inputs with unknown input characteristics, while an integration of the model with GA can further improve its performances. NCEP reanalysis and HadCM3A2 (a) scenario data are used for downscaling and forecasting precipitation series for Barak river basin. Model performances are analyzed by using statistical criteria, RMSE and mean error and are compared with the standard SDSM model. Results obtained by using 24 years of daily data sets show that GMNN-GA is efficient in downscaling daily precipitation series with maximum daily annual mean error of 6.78%. The outcomes of the study demonstrate that execution of the GMNN-GA model is superior to the GMNN and similar with that of the standard SDSM.

RFTemp

Microwave ovens have been widely used in recent years to heat food quickly and efficiently. Users estimate the time to heat the food by prior knowledge or by trial and error process. However, this often results in the food being over-heated or under-heated, destroying the nutrients. In this paper, we present RFTemp, a system that can monitor microwave oven leakage to estimate the temperature of the food that is being heated and thus estimate the accurate time when the food has reached the targeted temperature. To design such a system, we propose an innovative microwave leakage sensing procedure and a novel water-equivalent food model to estimate food temperature. To evaluate the real-world performance of RFTemp we build a prototype using software defined radios and conducted experiments on various food items using household microwave ovens. We show that RFTemp can estimate the temperature of the food with a mean error of 5°C, 2x improvement over contactless infrared thermometer and sensors.

Wits: An Efficient Wi-Fi Based Indoor Positioning and Tracking System

With the development of wireless communication technology, indoor tracking technology has been rapidly developed. Wits presents a new indoor positioning and tracking algorithm with channel state information of Wi-Fi signals. Wits tracks using motion speed. Firstly, it eliminates static path interference and calibrates the phase information. Then, the maximum likelihood of the phase is used to estimate the radial Doppler velocity of the target. Experiments were conducted, and two sets of receiving antennas were used to determine the velocity of a human. Finally, speed and time intervals were used to track the target. Experimental results show that Wits can achieve the mean error of 0.235 m in two different environments with a known starting point. If the starting point is unknown, the mean error is 0.410 m. Wits has good accuracy and efficiency for practical applications.

New Methodology of Designation the Precise Aircraft Position Based on the RTK GPS Solution

The paper presents the results of research on improving the accuracy of aircraft positioning using RTK-OTF (Real Time Kinematic–On The Fly) technique in air navigation. The paper shows a new solution of aircraft positioning for the application of the differential RTK-OTF technique in air navigation. In particular, a new mathematical model is presented which makes it possible to determine the resultant position of an aircraft based on the solution for the method of least squares in a stochastic process. The developed method combines in the process of alignment of GPS (Global Positioning System) observations, three independent solutions of the aircraft position in OTF mode for geocentric coordinates XYZ of the aircraft. Measurement weights as a function of the vector length and the mean vector length error, respectively, were used in the calculations. The applied calculation method makes it possible to determine the resultant position of the aircraft with high accuracy: better than 0.039 m with using the measurement weight as a function of the vector length and better than 0.009 m with the measurement weight as a function of the mean error of the vector length, respectively. In relation to the classical RTK-OTF solution as a model of the arithmetic mean, the proposed method makes it possible to increase the accuracy of determination of the aircraft position by 45–46% using the measurement weight as a function of the vector length, and 86–88% using the measurement weight as a function of the mean error of the vector length, respectively. The obtained test results show that the developed method improves to significantly improve the accuracy of the RTK-OTF solution as a method for determining the reference position in air navigation.

Development of Detection and Volumetric Methods for the Triceps of the Lower Leg Using Magnetic Resonance Images with Deep Learning

Purpose: A deep learning technique was used to analyze the triceps surae muscle. The devised interpolation method was used to determine muscle’s volume and verify the usefulness of the method. Materials and Methods: Thirty-eight T1-weighted cross-sectional magnetic resonance images of the triceps of the lower leg were divided into three classes, i.e., gastrocnemius lateralis (GL), gastrocnemius medialis (GM), and soleus (SOL), and the regions of interest (ROIs) were manually defined. The supervised images were classified as per each patient. A total of 1199 images were prepared. Six different datasets separated patient-wise were prepared for K-fold cross-validation. A network model of the DeepLabv3+ was used for training. The images generated by the created model were divided as per each patient and classified into each muscle types. The model performance and the interpolation method were evaluated by calculating the Dice similarity coefficient (DSC) and error rates of the volume of the predicted and interpolated images, respectively. Results: The mean DSCs for the predicted images were >0.81 for GM and SOL and 0.71 for GL. The mean error rates for volume were approximately 11% for GL, SOL, and total error and 23% for GL. DSCs in the interpolated images were >0.8 for all muscles. The mean error rates of volume were <10% for GL, SOL, and total error and 18% for GM. There was no significant difference between the volumes obtained from the supervised images and interpolated images. Conclusions: Using the semantic segmentation of the deep learning technique, the triceps were detected with high accuracy and the interpolation method used in this study to find the volume was useful.

Studying the Accuracy and Function of Different Thermometry Techniques for Measuring Body Temperature

The purpose of this study was to determine which thermometry technique is the most accurate for regular measurement of body temperature. We compared seven different commercially available thermometers with a gold standard medical-grade thermometer (Welch-Allyn): four digital infrared thermometers (Wellworks, Braun, Withings, MOBI), one digital sublingual thermometer (Braun), one zero heat flux thermometer (3M), and one infrared thermal imaging camera (FLIR One). Thirty young healthy adults participated in an experiment that altered core body temperature. After baseline measurements, participants placed their feet in a cold-water bath while consuming cold water for 30 min. Subsequently, feet were removed and covered with a blanket for 30 min. Throughout the session, temperature was recorded every 10 min with all devices. The Braun tympanic thermometer (left ear) had the best agreement with the gold standard (mean error: 0.044 °C). The FLIR One thermal imaging camera was the least accurate device (mean error: −0.522 °C). A sign test demonstrated that all thermometry devices were significantly different than the gold standard except for the Braun tympanic thermometer (left ear). Our study showed that not all temperature monitoring techniques are equal, and suggested that tympanic thermometers are the most accurate commercially available system for the regular measurement of body temperature.

Ray tracing intraocular lens calculation performance improved by AI-powered postoperative lens position prediction

AimsTo assess whether incorporating a machine learning (ML) method for accurate prediction of postoperative anterior chamber depth (ACD) improves cataract surgery refraction prediction performance of a commonly used ray tracing power calculation suite (OKULIX).Methods and analysisA dataset of 4357 eyes of 4357 patients with cataract was gathered at the Kellogg Eye Center, University of Michigan. A previously developed machine learning (ML)–based method was used to predict the postoperative ACD based on preoperative biometry measured with the Lenstar LS900 optical biometer. Refraction predictions were computed with standard OKULIX postoperative ACD predictions and ML-based predictions of postoperative ACD. The performance of the ray tracing approach with and without ML-based ACD prediction was evaluated using mean absolute error (MAE) and median absolute error (MedAE) in refraction prediction as metrics.ResultsReplacing the standard OKULIX postoperative ACD with the ML-predicted ACD resulted in statistically significant reductions in both MAE (1.7% after zeroing mean error) and MedAE (2.1% after zeroing mean error). ML-predicted ACD substantially improved performance in eyes with short and long axial lengths (p<0.01).ConclusionsUsing an ML-powered postoperative ACD prediction method improves the prediction accuracy of the OKULIX ray tracing suite by a clinically small but statistically significant amount, with the greatest effect seen in long eyes.