Raw Pressure Data Research Articles

Two-stream bolt preload prediction network using hydraulic pressure and nut angle signals

The non-uniformity of preload between bolts in flange often leads to problems such as loosening, fatigue, and even failure, which raises the safety risk of joint connection. In order to increase the accuracy of initial tightening and improve the uniformity of preload, more accurate tightening control methods are needed contrasted with conventional tightening control methods. This study introduces a multi-view network model for predicting the instant preload during tightening process based on hydraulic tightening system. First, a dataset about bolt tightening is established, and corresponding algorithm for extracting effective tightening time is created to obtain effective hydraulic pressure signals. Based on the operation of the hydraulic system and the transmission law of the wrench, an algorithm is designed to calculate the nut angle data from the raw pressure data. Then, based on the multi-view structure, several two-stream deep neural networks model with different feature extraction blocks and fusion strategies are developed to predict preload during the bolt tightening process. Finally, the suitable network models for preload control method and yield control preload monitoring are selected through comparison of all network structures, and the preload uniformity in test data of preload control method is further evaluated. All experimental results indicate that the two-stream deep learning model has achieved good prediction results in the allowable tightening process of hydraulic system. The preload control model can reduce the deviation of preload from 15% to 20% of conventional methods to about 10%.

A dual fusion recognition model for sleep posture based on air mattress pressure detection

In order to solve the difficult portability problem of traditional non-invasive sleeping posture recognition algorithms arising from the production cost and computational cost, this paper proposes a sleeping posture recognition model focusing on human body structural feature extraction and integration of feature space and algorithms based on a specific air-spring mattress structure, called SPR-DE (SPR-DE is the Sleep Posture Recognition-Data Ensemble acronym form). The model combines SMR (SMR stands for Principle of Spearman Maximal Relevance) with horizontal and vertical division based on the barometric pressure signals in the human body’s backbone region to reconstruct the raw pressure data into strongly correlated non-image features of the sleep postures in different parts and directions and construct the feature set. Finally, the recognit-ion of the two sleep postures is accomplished using the AdaBoost-SVM integrated classifier. SPR-DE is compared with the base and integrated classifiers to verify its performance. The experimental results show that the amount of significant features helps the algorithm to classify different sleeping patterns more accurately, and the f1 score of the SPR-DE model determined by the comparison experiments is 0.998, and the accuracy can reach 99.9%. Compared with other models, the accuracy is improved by 2.9% ~ 7.7%, and the f1-score is improved by 0.029 ~ 0.076. Therefore, it is concluded that the SMR feature extraction strategy in the SPR-DE model and the AdaBoost-SVM can achieve high accuracy and strong robustness in the task of sleep posture recognition in a small area, low-density air-pressure mattress, taking into account the comfort of the mattress structural design and the sleep posture recognition, integrated with the mattress adaptive adjustment system.

Decomposition and spectrum analysis of surge impact pressures on a vertical wall based on the Hilbert–Huang transform

In this study, two individual dam-break induced surge impact pressure datasets are analyzed using the Hilbert–Huang transform method, which is capable of adaptively decomposing the impulsive, oscillatory, and quasi-steady pressure components from the measured raw pressure data. Time-frequency characteristics of these three pressure components are analyzed. Applying the empirical mode decomposition method, the intrinsic mode function (IMF) series extract the local periodic features, including the impulsive and oscillatory pressures, whereas the non-periodic residual series reveal the general variation characteristics of the surge impact pressure, i.e., the quasi-steady pressure. Subsequently, the Hilbert spectrum analysis is conducted to the individual IMF series. The impulsive pressure components are found to be high-frequency (over 5 Hz in this study), and the oscillatory pressure components have frequency ranging from 20 to 60 Hz. It is confirmed that the quasi-steady pressure contributes the dominant amount of the total surge impact energy in both the bottom impact zone and the upper outer zone on the wall. The impulsive pressure-induced instantaneous energy is large in the bottom impact zone shortly after the impact, whereas the oscillatory pressures only lead to a small local instantaneous energy peak. In addition, the impulsive pressure impulses are calculated, which are more test-stable than those of peak pressures. These findings indicate that impulsive pressures induced by surge impact can be rather destructive, especially near the toe of the structure, thus deserve particular attention in the safe design of coastal structures.

Median Filter Data Analysis of Bowel Activity using Wireless Intracolonic Sensor

HypothesisThe mechanical and neurological mechanisms contributing to intractable chronic constipation and fecal incontinence are not yet fully understood, in part because of challenges in measuring the slow actions of the bowel. To enable multimodal data collection with a route to endoscopic implantation, we developed the Colonic Monitor of Conscious Activity (ColoMOCA). The ColoMOCA was designed for use with electrophysiology tools and to be affixed to the colon wall. After implant, the ColoMOCA wirelessly transmits colonic pressures and electrical properties of stool over multiple days. In this analysis, we are determining the capability of the ColoMOCA device in qualitatively observing slower bowel activity through the use of a median filter analysis.Study Design and MethodsThe ColoMOCA included battery‐powered pressure‐sensing electronics, stainless‐steel electrodes for measuring salinity of bowel contents. The ColoMOCA measured pressure from two locations 63 mm apart, and electrically measured stool conductivity in 3 overlapping regions (approximately 36, 28, and 20 cm long). The ColoMOCA transmitted data 10 times per second to a pager‐like wearable radio for ambulatory data recording.Colonic pressures and electrode readings were made with animals fully conscious and untethered. Data was received by a wireless radio worn by the animal. The radio recorded data to an onboard memory card, and forwarded data over Bluetooth to a computer that plotted data in real time. During recordings the animal ate food, walked, urinated, and defecated freely.Recorded raw pressure and electrode data were analyzed in MATLAB. The raw pressure data was downsampled to match the sampling rate of the electrode data. Following this, all data were passed through a 13th order median filter in order to observe slower colonic activity (occurring over the span of several seconds) in the animal bowel. Peak locations were then annotated by eye, where the annotator had no prior knowledge of physiological expectations of colonic activity when making annotations. The difference between the peaks was taken as period data for colonic slow waves and placed into histograms containing 10 bins.ResultsAfter applying a downsample and median filter analysis to the data, slower waves of colon activity were visible in both the pressure and electrode data. Within these plots, there exist portions of time where the slow wave activity peaks seem to align relatively well between sensor probes. Once the slow waves were available, manual annotation by eye (Figure 1) showed a median pressure wave period that ranged from roughly 48‐92 sec and a median length of 76 sec and 59 sec for the respective pressure probes: P1 and P2 (Figure 2). Electrode wave activity showed longer activity and ranged from 48‐142 sec, with a median period length of roughly 83 sec, 93 sec, 99 sec for electrode probes: E1, E2, and E3.ConclusionThese results display the viability of qualitatively observing longer trends in the ColoMOCA pressure and electrode data to observe median filter analysis. These results provide a prelude to a more quantitative analysis with regards the ColoMOCA device capability of observing slower wave activity of the bowel.

Low-Cost Pressure Gauges for Measuring Water Waves

Temple, N.A.; Webb, B.M.; Sparks, E.L., and Linhoss, A.C., 2020. Low-cost pressure gauges for measuring water waves. Journal of Coastal Research, 36(3), 661–667. Coconut Creek (Florida), ISSN 0749-0208.Waves have profound effects on coastal geomorphology, but the understanding of wave climate effects on coastal ecology is limited due, in part, to the high cost of commercial wave gauges. High-cost gauges also limit the scope of coastal wave models and the ability of coastal land managers to design effective restoration, conservation and enhancement projects. To address these limitations, a low-cost do-it-yourself (DIY) wave gauge was constructed using commercial plumbing parts, a pressure sensor, an Arduino© microcontroller and adapted accessories. Details on gauge construction, coding and an instructional video tutorial are provided. Performance of the DIY gauge was determined by measuring the agreement of raw pressure data recorded by the DIY gauge to a comparable commercial gauge in both a laboratory wave channel study featuring a series of wave tests and in a complementary field test. Agreement of raw pressure data among gauges in the wave channel study was assessed using paired t-tests and by fitting linear models. Field test data agreement was assessed by comparing the total wave field energy recorded by each gauge and by fitting a linear model to recorded raw pressure data. Pressure data agreement between the gauges was excellent in all wave channel tests with mean differences between pressure readings consistently near zero and with 95% of all differences lying within ±63 Pascals (<1 cm static water depth), on average. The greatest variability between readings occurred within tests featuring high-frequency waves, mirroring results reported by others. Still, raw DIY wave gauge data explained, on average, 91% of the variance in raw commercial gauge data in wave channel tests. Field performance testing indicated similar gauge responses with 92% total wave energy agreement. Thus, the DIY wave gauge is a viable alternative to high-cost gauges that could improve the understanding and management of coastal environments.

Balanced and Unbalanced Components of Moist Atmospheric Flows with Phase Changes

Atmospheric variables (temperature, velocity, etc.) are often decomposed into balanced and unbalanced components that represent low-frequency and high-frequency waves, respectively. Such decompositions can be defined, for instance, in terms of eigen-modes of a linear operator. Traditionally these decompositions ignore phase changes of water since phase changes create a piecewise-linear operator that differs in different phases (cloudy versus non-cloudy). Here we investigate the following question: How can a balanced-unbalanced decomposition be performed in the presence of phase changes? A method is described here motivated by the case of small Froude and Rossby numbers, in which case the asymptotic limit yields precipitating quasi-geostrophic equations with phase changes. Facilitated by its zero-frequency eigenvalue, the balanced component can be found by potential vorticity (PV) inversion, by solving an elliptic partial differential equation (PDE), which includes Heaviside discontinuities due to phase changes. The method is also compared with two simpler methods: one which neglects phase changes, and one which simply treats the raw pressure data as a streamfunction. Tests are shown for both synthetic, idealized data and data from Weather Research and Forecasting (WRF) model simulations. In comparisons, the phase-change method and no-phase-change method produce substantial differences within cloudy regions, of approximately 5 K in potential temperature, due to the presence of clouds and phase changes in the data. A theoretical justification is also derived in the form of a elliptic PDE for the differences in the two streamfunctions.

Convolutional neural network based combustion mode classification for condition monitoring in the supersonic combustor

Supersonic combustor is one of the core components in the scramjet, so it is of great significance to monitor the combustion modes in the combustor to ensure the safe and stable operation of the scramjet engine. Traditionally, several key parameters or manually-engineered features are selected as the indicators to evaluate the operation conditions, which usually heavily depends on the professional experience and carries considerable limitations. Convolutional neural networks have been proved to be effective in automatic feature extraction and have shown better generalization performance. Hence, it is attractive and promising to apply Convolutional neural networks to condition monitoring in mechanical systems due to their excellent ability of pattern recognition. To accomplish the classification of combustion modes, a convolutional neural network based method is proposed, which can learn features directly from the raw pressure data collected during supersonic combustion experiments. Meanwhile, the proposed method is compared with the traditional machine learning methods, such as multilayer perceptron, k-nearest neighbor, single-hidden layer feedforward neural network, and support vector machine. Furthermore, feature data is constructed by manual statistical features from time domain and frequency domain. The raw data and feature data are both considered to study the influence of feature extraction methods on the performance of different models. The results show that the proposed convolutional neural network based method is able to reveal intrinsic features from raw data and effectively complete the classification of four main combustion modes occurring in the combustor. The novel approach achieves a higher classification accuracy and better generalization performance than other comparative methods.

A leak detection method for oil pipeline based on markov feature and two-stage decision scheme

In the domain of oil and gas transmission, a high-performance leak detection method is of great significance. In this paper, a novel leak detection method based on Markov feature extraction and two-stage decision scheme is proposed to detect pipeline leak. Different from the traditional feature extraction methods, Markov feature is introduced to extract leak information. By means of a transformation, pressure data can be transformed into a Markov chain. By extracting its dynamic feature, raw pressure data can be effectively represented. Furthermore, a two-stage decision scheme is designed. Utilizing a switching rule, short-term and long-term detection models can be correctly selected to identify pipeline status rapidly and precisely. The proposed method is verified by pipeline pressure data collected from the industrial site and experimental field. Experimental results indicate that the proposed leak detection method has a high accuracy and low false positive rate.

Patient handling activity recognition through pressure-map manifold learning using a footwear sensor

Abstract The risk of overexertion injury caused by patient handling and movement activities causes chronic pain and other physical and social impairments among the nursing force. The accurate recognition of patient handling activities (PHA) is the first step to reduce injury risk for caregivers. The current practice on workplace activity recognition is neither accurate nor convenient to perform. In this paper, we propose a novel solution comprising a smart footwear device and an action manifold learning framework to address the challenge. The wearable device, called Smart Insole, is equipped with a rich set of sensors and can provide an unobtrusive approach to obtain and characterize the action information of patient handling activities. Our proposed action manifold learning (AML) framework extracts the intrinsic signature structure by projecting raw pressure data from a high-dimensional input space to a low-dimensional manifold space. This framework not only performs dimension reduction but also reduces motion artifacts, which is robust against the noise and inter-class/intra-class variation in PHA recognition. To validate the effectiveness of the proposed framework, we perform a pilot study with eight subjects including eight common activities in a nursing room. The intrinsic dimensionality of the manifold is estimated by comparing the residual variances of different dimensionality settings. The experimental results show the overall classification accuracy achieves 86.6%. Meanwhile, the qualitative profile and load level can also be classified with accuracies of 98.9% and 88.3%, respectively.

Effect of prolonged sitting on body-seat contact pressures among quay crane operators: A pilot study.

Quay crane operators are specialized in moving containers to and from vessels while adopting constrained sitting postures for prolonged periods (4-6 consecutive hours) in a very challenging environment. Thus, they are exposed to discomfort or pain that may result in deterioration of their performance with consequent reduction of operational safety levels. Such discomfort can be indirectly and partially assessed by measuring contact pressure at the body-seat interface. The aim of the study was to assess the feasibility, usefulness, and effectiveness of monitoring the variations in seat-body interface pressure during a regular work shift of 4 hours performed in a simulated environment. Eight professional operators performed a four-hour shift in a realistic control station set inside a quay crane simulator. Seat-body contact pressures were measured at 10 Hz using two pressure-sensitive mats placed on the seat pan and the backrest. Raw pressure data were processed to extract pressure vs. time curves related to the whole seat surface and, for the seat pan only, values associated with four anatomical regions (i.e. left and right thighs and buttocks). During the work shift, the mean backrest pressure was low and fairly constant. Seat pan pressure increased by 10%, rising from 7.4 (1.5) to 8.2 (2.3) kPa over the simulated shift. Detailed analysis of the four sub-regions revealed that as the trial progressed the mean contact pressure on buttocks decreased (-6% at the end of the trial) while thigh pressures increased (by 10 and 20% for right and left sides, respectively). Although further studies with larger samples are needed, long-term monitoring of the body-seat contact pressures of crane operators in a simulated environment appears to be a useful tool to identify specific postural strategies to reduce discomfort originated by prolonged sitting posture.

Troughing after Scarf Osteotomy Leads to Minimal Gait Alterations

We aimed to investigate whether troughing during scarf osteotomy for hallux valgus can be determined using plantar pressure analysis. In order to quantify the foot print area of a patient at 7 and 12 weeks after the surgery, the raw pressure data were exported to two matrixes. Each matrix has the size of the force distribution platform and contains the plantar force data of the patient in gait movement. These large matrixes containing both left and right pressure data were divided in small size matrixes and averaged. The computation was done in Matlab. The matrixes were flipped up in order to have the natural position of the footprint. The two were plotted as contours in Z axis but kept the external contours only. This difference was measured by subtracting the areas of the two contours. The results are typical footprints for left and right legs, at predetermined follow-up points.

Effects of Different Imaging Models on Least-Squares Image Reconstruction Accuracy in Photoacoustic Tomography

In the classic formulation of photoacoustic tomography (PAT), two distinct descriptions of the imaging model have been employed for developing reconstruction algorithms. We demonstrate that the numerical and statistical properties of unweighted least-squares reconstruction algorithms associated with each imaging model are generally very different. Specifically, some PAT reconstruction algorithms, including many of the iterative algorithms previously explored, do not work directly with the raw measured pressure wavefields, but rather with an integrated data function that is obtained by temporally integrating the photoacoustic wavefield. The integration modifies the statistical distribution of the data, introducing statistical correlations among samples. This change is highly significant for iterative algorithms, many of which explicitly or implicitly seek to minimize a statistical cost function. In this work, we demonstrate that iterative reconstruction by least-squares minimization yields better resolution-noise tradeoffs when working with the raw pressure data than with the integrated data commonly employed. In addition, we demonstrate that the raw-data based approach is less sensitive to certain deterministic errors, such as dc offset errors.

Streaming Potential Collection and Data Processing Techniques

To date, no comprehensive comparison of streaming potential coupling coefficient collection or processing techniques has been made. Here, time-varying streaming potential and dc streaming potential data collection and processing techniques are presented and compared. The time-varying streaming potential data include sinusoidal and transient data. The collection techniques include acquiring dc streaming potentials at various pressures, acquiring time-varying streaming potentials at varying pressure, acquiring streaming potentials as a function of frequency, and collecting time-varying raw data. The processing techniques include dc filtering, rms processing, cross-correlation, spectral analysis, and plotting of raw time-varying streaming potential versus raw pressure data. The results show that all processing methods yield the same coupling coefficient within 3%. The analysis also shows that if there is a good signal-to-noise ratio, all processing methods perform satisfactorily. If the signal-to-noise ratio is poor, then the spectral analysis outperforms the other processing methods. The data collection methods are all adequate, but individual applications may make one method superior to another.

Hydrophobic interaction chromatography coupled with dynamic surface tension detection for the determination of surface active species in protein formulations

The measurement and analysis of surface active species (surfactants) in protein formulations by hydrophobic interaction chromatography (HIC) coupled with gradient elution and dynamic surface tension detection (DSTD) is presented. The DSTD is based upon the measurement of the pressure inside a repeating drop formed when the eluate of a liquid chromatographic separation flows through the end of a pointed stainless steel capillary tube. The DSTD provides a measure of the dynamic surface tension at the air–liquid interface, and in some applications, information on adhesive interactions at the solid–liquid interface. A method for extraction of the desired surface tension signal from the raw pressure data is reported. High-salt gradient HIC separations are demonstrated and shown to be compatible with DSTD. With the gradient pumping system, optimum signal sensitivity and stability (low noise) were achieved when the pressure measurement is taken when the drop size is a minimum. Implementation of HIC–DSTD with salt gradient elution revealed that the noise of DSTD was slightly higher in the gradient mode, due to higher flow-rate imprecision with a dual pump system, as compared to employing isocratic elution. The combination of HIC with DSTD provides a selective methodology for the analysis of surface active species in commercially available protein formulations. Calibration of the HIC–DSTD instrument was achieved using surface active and/or surface tension lowering standards (2-propanol, 1-hexanesulfonic acid, and N-decyl- N, N-dimethyl-3-ammonio-1-propane-sulfonate), that were separated by gradient HIC with sequential ultraviolet (UV) absorbance detection (at 250 nm) and DSTD. The standards were detected only by DSTD. A commercial ribonuclease A formulation was analyzed. The data revealed one primary peak by UV absorbance detection, while two significant peaks with retention times earlier than the primary UV absorbance peak were observed with the DSTD. The identity of the primary UV absorbing peak was confirmed as ribonuclease A by enzyme assay. The first two surface active species observed with the DSTD show no enzyme activity. The utility of the DSTD coupled with HIC separations is demonstrated, opening several new avenues of investigation for the researcher interested in gaining a new perspective on the surface active species in complex biological samples.

On the Response of Superpressure Balloons to Displacements from Equilibrium Density Level

The response of a superpressure balloon to an initial displacement from its constant-density floating level is examined. An approximate solution is obtained to the governing vertical equation of motion for constant-density superpressure balloons. This solution is used to filter out neutrally buoyant oscillations in balloon records despite the nonlinear behavior of the balloon. The graph depicting the pressure data after deconvolution between the raw pressure data and the normalized balloon wavelet shows clearly the strong filtering-out of the neutral buoyancy oscillations.

Measurement of Seiche Activity in San Diego Bay

Recordings of bottom-pressure fluctuations were made in San Diego Bay in April 1960 and in January 1961 to study the seiche phenomenon. A solion infrasonic hydrophone was utilized as a bottom-pressure transducer to detect pressure fluctuations over the range of 5- to 1600 sec periods. Analysis of the bottom-pressure records consists of computing relative power spectra estimates and autocorrelation functions after the raw pressure data are handpass filtered. From the relative power spectra and limited theoretical considerations, it was determined that there is an active longitudinal seiche with a second harmonic period of 1450 sec. A description of the solion infrasonic hydrophone is presented along with the recording and analysis system.