Angle Data Research Articles

Investigation the structural and surface characteristics of irradiated flexible polymeric nanocomposites films

This study is to investigate the surface and structural characteristics of the pure and irradiated novel PEO/NiO composite by subjecting the films to argon ions with different ion beam fluencies. The structural characteristics were studied by the EDX and FTIR techniques, while the surface was investigated by SEM technique. The FTIR showed a notable decrease in the peak intensity for the bombarded composite, due to the functional groups with hydrophilic characteristics and the occurrence of chain scission processes. The PEO/NiO composite demonstrates a consistent structure without any nanoparticle clusters, as depicted in the SEM image of PEO/NiO. Moreover, the electrical conductivity for the pure and the irradiated samples were determined. Exposing the composite PEO/NiO to a fluence of 15×1016 ions.cm-2, increasing the conductivity from 7.5×10–8 S/cm to 8.4×10–7 S/cm. By increasing ion fluence from 5×1016 to 15×1016 ions.cm-2. The contact angle is decreased from 81.15o to 72.22o for water, while is decreased from 74.32o to 62.20o for diiodomethane. Moreover, the surface wettability and the adhesion force were determined from the data of the contact angle. The work of adhesion of water increases from 84.37 to 94.16 mJ/m2 and for dioodomethane from 64.52 to 74.49 mJ/m2 , respectively, by increasing ion fluence from 5×1016 to 15×1016 ions.cm-2. This suggests that, in comparison to a unirradiated surface, the increase in 𝑊𝑊𝑎𝑎 is the result of surface cleanliness following radiationThe results of this study show the opportunities for utilizing these irradiated materials in the fields of coating and printing applications.

Mechanical, water contact angle and fiber thickness data for Insulin-like Growth Gactor-1 (IGF-1) incorporated in electrospun random DegraPol® fibers and IGF-1 impact on tenocyte aspect ratio and gene expression data

Mechanical, water contact angle and fiber thickness data for Insulin-like Growth Gactor-1 (IGF-1) incorporated in electrospun random DegraPol® fibers and IGF-1 impact on tenocyte aspect ratio and gene expression data

Evaluating functional ability in older adults’ object retrieval behavior from kitchen furniture using OpenPose and REBA

The purpose of this study is to evaluate, through analysis, the ability of older persons to retrieve items from kitchen cabinets. To achieve this goal, data were collected from 128 valid questionnaires and supplemented with field research and user interviews. The study revealed that the elderly’s behavior in retrieving items from kitchen spaces is characterized by both high frequency and difficulty. For this experiment, a total of 42 participants, comprising 21 males and 21 females from the self-care elderly population in the Yangtze River Delta region, were recruited. Two different experimental settings were arranged: one with kitchen utensils arranged in a straight line and another with a purpose-made chest of drawers with varying heights. Video recordings using the Logitech C930C were utilized to capture the gestures and behaviors of the elderly while retrieving objects from the kitchen cupboards (cabinets). By employing a combination of the OpenPose model and the Rapid Entire Body Assessment (REBA) method, which involves calculating human posture angles, REBA scores, and determining the risk level of Work-Related Musculoskeletal Disorders (WMSDs), a risk assessment framework for manual operations associated with WMSDs was developed. Using the angle data acquired from the user operation experiment as parameters, a gradient model of the elderly user’s operational capability was established. The findings indicated a significant impact of neck, trunk, and knee movements on the subjects (P < 0.001). The participants were able to distinguish between different levels of exertion, categorizing movements as ‘easy’, ‘moderate,’ or ‘strenuous.’ These results form the basis for a comfort gradient model for leaning over and retrieving items. Given the prevalent conditions of bone and joint degeneration and osteoporosis among the elderly population, it is evident that they face challenges when accessing items in the kitchen. Therefore, investigating the elderly’s execution abilities during the retrieval process becomes crucial. Understanding how different cabinet heights impact the joint angles of the elderly can be instrumental in optimizing cabinet designs for elderly users, thereby reducing their physical exertion in the kitchen and enhancing their comfort levels. This research holds significant value in improving the quality of life for the elderly population at home and fostering the advancement of elderly-friendly design principles.

Rotational Risley prisms: Fast and high-accuracy inverse solution and application based on back propagation neural networks

In the application of rotational Risley prism systems, there is a conflict between inverse solution time and accuracy. This paper proposes an inverse solution method based on back propagation neural networks to solve this problem. The training set data for prism rotation, azimuth, and elevation angles are produced based on the beam deflection model of the actual application. After that, two independent networks are trained individually. One network is used to calculate the angular difference, while the other network calculates the synchronous rotation angle to satisfy the solutions of elevation and azimuth angles of target. The azimuth and elevation root mean square errors for the inverse solution of the spiral trajectory using the trained network were 8.04e-04 arcsec and 2.60e-3 arcsec, respectively. At a solution time of 80 µs, the pointing accuracy in the experiment was less than 1 arcsec. The experimental results demonstrate that the proposed inverse solution method can obtain high-accuracy analytical solutions with low time consumption.

Gear Classification in Skating Cross-Country Skiing Using Inertial Sensors and Deep Learning.

The aim of this current work is to identify three different gears of cross-country skiing utilizing embedded inertial measurement units and a suitable deep learning model. The cross-country style studied was the skating style during the uphill, which involved three different gears: symmetric gear pushing with poles on both sides (G3) and two asymmetric gears pushing with poles on the right side (G2R) or to the left side (G2L). To monitor the technique, inertial measurement units (IMUs) were affixed to the skis, recording acceleration and Euler angle data during the uphill tests performed by two experienced skiers using the gears under study. The initiation and termination points of the tests were controlled via Bluetooth by a smartphone using a custom application developed with Android Studio. Data were collected on the smartphone and stored on the SD memory cards included in each IMU. Convolutional neural networks combined with long short-term memory were utilized to classify and extract spatio-temporal features. The performance of the model in cross-user evaluations demonstrated an overall accuracy of 90%, and it achieved an accuracy of 98% in the cross-scene evaluations for individual users. These results indicate a promising performance of the developed system in distinguishing between different ski gears within skating styles, providing a valuable tool to enhance ski training and analysis.

Precision encoder grating mounting: a near-sensor computing approach.

The rotary motor plays a pivotal role in various motion execution mechanisms. However, an inherent issue arises during the initial installation of the encoder grating, namely, eccentricity between the centers of the encoder grating and motor shaft. This eccentricity substantially affects the accuracy of motor angle measurements. To address this challenge, we proposed a precision encoder grating mounting system that automates the encoder grating mounting process. The proposed system mainly comprises a near-sensor detector and a push rod. With the use of a near-sensor approach, the detector captures rotating encoder grating images, and the eccentricity is computed in real-time. This approach substantially reduces the time delays in image data transmission, thereby enhancing the speed and accuracy of eccentricity calculation. The major contribution of this article is a method for real-time eccentricity calculation that leverages an edge processor within the detector and an edge-vision baseline detection algorithm. This method enables real-time determination of the eccentricity and eccentricity angle of the encoder grating. Leveraging the obtained eccentricity and eccentricity angle data, the position of the encoder grating can be automatically adjusted by the push rod. In the experimental results, the detector can obtain the eccentricity and eccentricity angle of the encoder grating within 2.8 s. The system efficiently and precisely completes a encoder grating mounting task in average 25.1 s, and the average eccentricity after encoder grating mounting is 3.8 µm.

Investigating the Effect of Motion Capture Suits on the Test–Retest Reliability of Gait Parameters

When collecting marker-based motion capture data from clinical populations, speed of collection and comfort for the participant is a priority. This could be achieved by attaching markers to motion capture Velcro suits, as opposed to the skin. This study aimed to ascertain the reliability of sagittal-plane gait parameters estimated using Plug-in Gait (PiG) and Conventional Gait Model 2 (CGM2) marker sets from data collected in Suited and Non-suited (markers placed onto skin) conditions. For ten participants, markers were placed based on PiG and CGM2 models and data captured during a 2-min treadmill walk. Trials were repeated in suited and non-suited conditions. PiG ankle flexion/extension measurements had poor/moderate reliability (Non-suited ICC = 0.531, Suited ICC = 0.435). CGM2 ankle flexion/extension measurements had good/excellent reliability (Non-suited ICC = 0.916, Suited ICC = 0.900). There were significant differences in minimal detectable change (MDC) between conditions at the ankle for PiG (Non-suited MDC = 2.32°, Suited MDC = 18.90°), but not for CGM2 (Non-suited MDC = 0.63°, Suited MDC = 0.95°). When using CGM2, knee (Non-suited ICC = 0.878, Suited ICC = 0.855) and hip (Non-suited ICC = 0.897, Suited ICC = 0.948) showed good/excellent reliability in both conditions. A motion capture suit is not a reliable solution when collecting joint angle data using the PiG model but is reliable enough to consider when using the CGM2 model.

Evaluating Joint Angle Data for Clinical Assessment Using Multidimensional Inverse Kinematics with Average Segment Morphometry.

Movement analysis is a critical tool in understanding and addressing various disabilities associated with movement deficits. By analyzing movement patterns, healthcare professionals can identify the root causes of these alterations, which is essential for preventing, diagnosing, and rehabilitating a broad spectrum of medical conditions, disabilities, and injuries. With the advent of affordable motion capture technologies, quantitative data on patient movement is more accessible to clinicians, enhancing the quality of care. Nonetheless, it is crucial that these technologies undergo rigorous validation to ensure their accuracy in collecting and monitoring patient movements, particularly for remote healthcare services where direct patient observation is not possible. In this study, motion capture technology was used to track upper extremity movements during a reaching task presented in virtual reality. Kinematic data was then calculated for each participant using a scaled dynamic inertial model. The goal was to evaluate the accuracy of joint angle calculations using inverse kinematics from motion capture relative to the typical movement redundancy. Shoulder, elbow, radioulnar, and wrist joint angles were calculated with models scaled using either direct measurements of each individual's arm segment lengths or those lengths were calculated from individual height using published average proportions. The errors in joint angle trajectories calculated using the two methods of model scaling were compared to the inter-trial variability of those trajectories. The variance of this error was primarily within the normal range of variability between repetitions of the same movements. This suggests that arm joint angles can be inferred with good enough accuracy from motion capture data and individual height to be useful for the clinical assessment of motor deficits.

Development of an Automatic Feature Point Classification Method for Three-Dimensional Mapping Around Slewing and Derricking Cranes

Crane automation requires a three-dimensional (3D) map around cranes that should be reconstructed and updated quickly.In this study, a high-precision classification method was developed to distinguish stationary objects from moving objects in moving images captured by a monocular camera to stabilize 3D reconstruction. To develop the method, a moving image was captured while the crane was slewed with a monocular camera mounted vertically downward at the tip of the crane. The boom length and angle data were output from a control device, a controller area network. For efficient development, a simulator that imitated the environment of an actual machine was developed and used. The proposed method uses optical flow to track feature points. The classification was performed successfully, independent of derricking motion. Consequently, the proposed method contributes to stable 3D mapping around cranes in construction sites.

A dataset of optical camera and IMU sensor derived kinematics of thirty transtibial prosthesis wearers

Motion analysis has played a crucial role in providing gait analysis for prosthetic users. Understanding kinematics in motion analysis allows for the evaluation of the effects of prostheses and the development of a prosthetic component design that aids in walking within the community. However, there are currently limited open datasets available to study the locomotion of individuals using transtibial prostheses, and most research studies involve a limited number of participants. This dataset shows 30 transtibial prosthesis users walking comfortably on a 10-meter walkway inside a laboratory. We offer a set of joint ankles obtained from the inertial measurement unit (IMU) signals in CSV file format. We also provide the optical motion capture (OMC) system’s raw trajectory marker data in C3D file format and joint angle in CSV file format. This open dataset will provide resources for professionals interested in amputee gait for research in amputee gait detection and tracking algorithms. Moreover, the data will be the control data for comparison in developing advanced prosthetic components.

Open-Source Data Analysis Tool for Spectral Small-Angle X-ray Scattering Using Spectroscopic Photon-Counting Detector.

Spectral small-angle X-ray scattering (sSAXS) is a powerful technique for material characterization from thicker samples by capturing elastic X-ray scattering data in angle- and energy-dispersive modes at small angles. This approach is enabled by the use of a 2D spectroscopic photon-counting detector that provides energy and position information of scattered photons when a sample is irradiated by a polychromatic X-ray beam. Here, we describe an open-source tool with a graphical interface for analyzing sSAXS data obtained from a 2D spectroscopic photon-counting detector with a large number of energy bins. The tool takes system geometry parameters and raw detector data to output 1D scattering patterns and a 2D spatially-resolved scattering map in the energy range of interest. We validated these features using data from samples of caffeine powder with well-known scattering peaks. This open-source tool will facilitate sSAXS data analysis for various material characterization applications.

Impact of Modeling Assumptions on Muon Scattering Images of Loaded Dry Storage Casks

Using cosmic muons allows for a noninvasive imaging approach to examine nuclear fuel in sealed dry storage casks. By assessing muons both before and after passing through the cask, one can infer details about the cask’s interior by analyzing scattering angle data. The effective scattering angles of muons depend on the characteristics of the interacting material, such as the atomic number (Z). This allows for the deduction of the material and geometric composition of the cask’s inventory. When employing simulations to forecast muon paths within the cask, it is essential to scrutinize the impact of modeling assumptions and simplifications on the scattering angle distribution. In this study, we examine the influence of modeling assumptions and simplifications on the effective scattering angle. Additionally, the significance of the number of particles used is shown. We evaluate four GEANT4 cask models of a CASTOR® V/19, each incorporating varying degrees of simplification, and analyze their impact on the projected muon scattering angle. These simplifications include both the simplification of individual geometric components of the cask and the complete exclusion of specific components. We assess and prioritize the various model simplifications in terms of their effect on the observed scattering angle. We recognize the importance of thoughtfully considering the degree of simplification used in the model to ensure accurate and reliable results for the scattering angle distribution.

Quantitative Measurements in the Exhaust Flow of a Rotating Detonation Combustor Using Rainbow Schlieren Deflectometry

Abstract This study employs Rainbow Schlieren Deflectometry (RSD) to characterize the unsteady, supersonic/subsonic exhaust plume of a Rotating Detonation Combustor (RDC). Firstly, RSD images are analyzed to quantify the frequency and strength of flow oscillations and their relationship to the detonation wave. Secondly, a 3D tomographic algorithm is used to obtain the local 3D density field across the whole region of interest (ROI). The tomographic analysis relies upon wave rotation to infer projection data of the 3D exhaust plume at multiple view angles using a single RSD/camera system and was previously validated using phantom data from computational fluid dynamics analysis of an RDC. The annular RDC operated on methane and 2/3 O2 - 1/3 N2 oxidizer mixture is equipped with a converging nozzle to pressurize the combustion chamber. The product flow exiting the nozzle throat expands across an unoptimized conical aerospike attached to the center body of the RDC. RSD images provide a temporal resolution of 369 ns and spatial resolution of 100 ?m in a 6.4 high and 25.6 mm wide ROI of the exhaust plume. A rainbow filter is calibrated to convert hue in color schlieren images into deflection angle data. This data is used to characterize the unsteady flow oscillations that show excellent agreement with PCB pressure probe measurements acquired inside the combustion chamber. Tomographic analysis yields a 3D local density field that shows distinct features, consistent with published numerical simulations of the RDC exhaust plume.

Changes in Callosal Angle and Evans Index After Shunt Surgery in Patients with Idiopathic Normal Pressure Hydrocephalus

BackgroundIdiopathic normal pressure hydrocephalus (iNPH) is a disease characterized by gait disturbance, cognitive impairment and urinary incontinence. For those patients who do not respond to shunt surgery, it lacks objective radiological findings for the diagnosis of shunt malfunction. Here we aimed to evaluate whether Evans index and callosal angle change during a prospective long-term follow-up of patients with iNPH submitted to shunt surgery. MethodsClinical (NPH Japanese Scale) and radiological (Evans index, callosal angle) data were collected pre- and postoperatively (3, 6, 12 months) in 19 patients with iNPH. Imaging tests were evaluated by the same neuroradiologist during the follow-up. ResultsPatients had lower scores on NPH Japanese Scale over time (p< 0.001). There was no significant difference among Evans index values during the follow-up (p= 0.24). Preoperative average callosal angle was 72 ± 15, which increased to 91 ± 18 in 6 months (p= 0.003). ConclusionsIn this sample, patients with iNPH submitted to a programmable valve shunt had an increase in callosal angle concomitant to neurological improvement. Evans index did not change during follow-up.

The correlation analysis between incisal guidance angle and occlusal plane angles and temporomandibular joint morphology.

The correlations between the incisal guidance angle (IGA) and occlusal plane angles and temporomandibular joint (TMJ) morphology were investigated in adults with skeletal Class II division II malocclusion. CBCT images of 37 patients were analyzed. It included 19 cases of skeletal Class II division II malocclusion with low angle (study group) and 18 cases of skeletal Class I average angle (control group). The Invivo Dental 5 software was employed to acquire the data of the incisal guidance angle (IGA), occlusal plane angle (FH-OP), anterior occlusal plane angle (FH-AOP) and the TMJ measurement items. The results of IGA, FH-AOP angle and FH-OP angle showed the study group > the control group (P < 0.05). There were statistically difference in the condylar mediolateral diameters, articular eminence inclination and height, and posterior joint spaces between two groups. No differences were revealed in the condylar anteroposterior diameters, the condylar inclination angle, condylar head width and height, condylar length, glenoid fossa depth and width between two groups. In the study group, IGA showed a moderate correlation with FH-AOP, a weak correlation with FH-OP and condylar mediolateral diameters. Meanwhile, there was a correlation between FH-AOP, FH-OP, and TMJ indicators. The IGA was not only related to FH-AOP and FH-OP, but also to the condylar mediolateral diameters. In addition, there was a correlation between the occlusal plane angles and TMJ morphology in skeletal Class II division II low angle malocclusion. For patients with skeletal Class II division II low angle malocclusion, adjusting the IGA and the occlusal plane angles could improve the esthetic appearance of the anterior teeth, occlusal function, and TMJ morphology.

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%.

Study of the contact angle and roughness effects on mercury intrusion porosimetry (MIP) analysis in natural/real carbonate rocks using massive pure minerals and synthetic carbonate rocks

Studying pore networks in rocks is critical for understanding reservoir properties, with mercury intrusion porosimetry (MIP) being a key laboratory method. MIP involves capillary intrusion of mercury, with intrusion pressure (P) related to capillary diameter (d) by the Washburn equation, incorporating mercury properties like contact angle (θ) and surface tension (γ). While a constant contact angle of 140° is typically assumed for MIP, it can vary based on rock mineralogy and surface roughness. This study aims to analyze how contact angle variations and surface roughness affect MIP analysis of carbonate rocks. Initially, contact angle data was obtained for calcite, dolomite, and quartz with varying roughness levels. Synthetic rocks were then constructed using these minerals. MIP was performed using the typical 140° contact angle and a calculated contact angle from the Cassie model, which considers surface composition fraction. Natural rock MIP analysis followed, with contact angles varying due to mineral composition and roughness. The study found higher contact angles than the standard 140°, affecting pore size distributions up to 30%. Natural rock pore network curves were adjusted based on synthetic rock data analysis. These findings emphasize the need for accurate contact angle selection in MIP for precise pore distribution analysis and contribute to the further study of carbonate rocks.

Dynamics of weighted flexible ribbons in a uniform flow

This study explores the dynamics of flexible ribbons with an added weight $G$ at the tail in uniform flow, considering key parameters like inflow Reynolds number ( $Re_u$ ), mass ratio ( $M_t$ ) and aspect ratio ( ${A{\kern-4pt}R}$ ). For two-dimensional ribbons, a simplified theoretical model accurately predicts equilibrium configurations and forces. Inspired by Barois &amp; De Langre (J. Fluid Mech., vol. 735, 2013, R2), we introduce an important control parameter ( $C_G$ ) that effectively collapses normalized forces and angle data. Vortex-induced vibration is observed, and Strouhal number ( $St$ ) scaling laws with $C_G$ are identified. In three-dimensional scenarios, the model effectively predicts lift, but its accuracy in predicting drag is limited to situations with small $Re_u$ values. The flow along the side edges mitigates pressure differences, thereby suppressing vibration and uplift, particularly noticeable in the case of narrow ribbons. This study offers new insights into the dynamics of flexible bodies in uniform flow.

Random Forest Model-Based Inversion of Aerosol Vertical Profiles in China Using Orbiting Carbon Observatory-2 Oxygen A-Band Observations

Aerosol research is important for the protection of the ecological environment, the improvement of air quality, and as a response to climate change. In this study, a random forest (RF) estimation model of aerosol optical depth (AOD) and extinction coefficient vertical profiles was, respectively, established using Orbiting Carbon Observatory-2 (OCO-2) oxygen-A band (O2 A-band) data from China and its surrounding areas in 2016, combined with geographical information (longitude, latitude, and elevation) and viewing angle data. To address the high number of OCO-2 O2 A-band channels, principal component analysis (PCA) was employed for dimensionality reduction. The model was then applied to estimate the aerosol extinction coefficients for the region in 2017, and its validity was verified by comparing the estimated values with the Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) Level 2 extinction coefficients. In the comprehensive analysis of overall performance, an AOD model was initially constructed using variables, achieving a correlation coefficient (R) of 0.676. Subsequently, predictions for aerosol extinction coefficients were generated, revealing a satisfactory agreement between the predicted and the actual values in the vertical direction, with an R of 0.535 and a root mean square error (RMSE) of 0.107 km−1. Of the four seasons of the year, the model performs best in autumn (R = 0.557), while its performance was relatively lower in summer (R = 0.442). Height had a significant effect on the model, with both R and RMSE decreasing as height increased. Furthermore, the accuracy of aerosol profile inversion shows a dependence on AOD, with a better accuracy when AOD is less than 0.3 and RMSE can be less than 0.06 km−1.

Fault-tolerant joint state feedback adaptive control for snake robots that move through random pole environments

ABSTRACT In unstructured terrain, snakes can move more efficiently by actively pushing and avoiding obstacles, a capability believed to be realized through a decentralized mechanism. Moreover, considering harsh and unpredictable rescue environments where snake robots are expected to be deployed, such as post-earthquake scenarios, some joint sensors are prone to failure. We propose a decentralized adaptation mechanism for snake robots to navigate through random pole environments. This mechanism integrates Fault-Tolerant Joint State Feedback Adaptive Control (FT-JSFAC) with Extended Curvature Derivatives control (ECD) and Angular Damping control (AD), leveraging the FT-JSFAC method to maintain operational robustness even when some joint sensors fail. To meet critical goals of adaptability, we utilize real-time angle and electrical current data from the robot's internal servo motors. Using this data, FT-JSFAC determines whether an obstacle is beneficial or detrimental to propulsion and then allows for decentralized torque adjustment to adapt to the environment. FT-JSFAC also employs historical data from functioning joints to compensate for erroneous sensor data, ensuring operational robustness even with joint sensor failure. Simulation experiments demonstrate that the proposed mechanism effectively adapts to environmental obstacles and maintains adaptability when one or two joint sensors fail.