Target Rotation Research Articles

Experimental Characterization of Inverse Dynamics Guidance in Docking with a Rotating Target

The performance of an inverse dynamics guidance and control strategy is experimentally evaluated for the planar maneuver of a “chaser” spacecraft docking with a rotating “target.” The experiments were conducted on an air-bearing proximity maneuver testbed. The chaser spacecraft simulator consists of a three-degree-of-freedom autonomous vehicle floating via air pads on a granite table and actuated by thrusters. The target consists of a docking interface mounted on a rotational stage with the rotation axis perpendicular to the plane of motion. Given a preassigned trajectory, the guidance and control strategy computes the required maneuver control forces and torque via an inverse dynamics operation. The recorded data of 150 experimental test runs were analyzed using two-way analysis of variance and post hoc Tukey tests. The metrics were maneuver success, vehicle mass change, maneuver duration, thruster duty cycle, and maneuver work. The results showed that the guidance and control algorithm provided robust performance over a range of target rotation rates from 1 to . The effects of the rate estimation errors are measurable but not dominant.

Time-Resolved Intrafraction Target Translations and Rotations During Stereotactic Liver Radiation Therapy: Implications for Marker-based Localization Accuracy

Image guided liver stereotactic body radiation therapy (SBRT) often relies on implanted fiducial markers. The target localization accuracy decreases with increased marker-target distance. This may occur partly because of liver rotations. The aim of this study was to examine time-resolved translations and rotations of liver marker constellations and investigate if time-resolved intrafraction rotational corrections can improve localization accuracy in liver SBRT. Twenty-nine patients with 3 implanted markers received SBRT in 3 to 6 fractions. The time-resolved trajectory of each marker was estimated from the projections of 1 to 3 daily cone beam computed tomography scans and used to calculate the translation and rotation of the marker constellation. In all cone beam computed tomography projections, the time-resolved position of each marker was predicted from the position of another surrogate marker by assuming that the marker underwent either (1) the same translation as the surrogate marker; or (2) the same translation as the surrogate marker corrected by the rotation of the marker constellation. The localization accuracy was quantified as the root-mean-square error (RMSE) between the estimated and the actual marker position. For comparison, the RMSE was also calculated when the marker's position was estimated as its mean position for all the projections. The mean translational and rotational range (2nd-98th percentile) was 2.0mm/3.9° (right-left), 9.2mm/2.9° (superior-inferior), 4.0mm/4.0° (anterior-posterior), and 10.5mm (3-dimensional). Rotational corrections decreased the mean 3-dimensional RMSE from 0.86mm to 0.54mm(P<.001) and halved the RMSE increase per millimeter increase in marker distance. Intrafraction rotations during liver SBRT reduce the accuracy of marker-guided target localization. Rotational correction can improve the localization accuracy with a factor of approximately 2 for large marker-target distances.

Target rotation parameter estimation for ISAR imaging via frame processing

Frame processing method offers a model-based approach to Inverse Synthetic Aperture Radar (ISAR) imaging. It also provides a way to estimate the rotation rate of a non-cooperative target from radar returns via the frame operator properties. In this paper, the relationship between the best achievable ISAR image and the reconstructed image from radar returns was derived in the framework of Finite Frame Processing theory. We show that image defocusing caused by the use of an incorrect target rotation rate is interpreted under the FP method as a frame operator mismatch problem which causes energy dispersion. The unknown target rotation rate may be computed by optimizing the frame operator via a prominent point. Consequently, a prominent intensity maximization method in FP framework was proposed to estimate the underlying target rotation rate from radar returns. In addition, an image filtering technique was implemented to assist searching for a prominent point in practice. The proposed method is justified via a simulation analysis on the performance of FP imaging versus target rotation rate error. Effectiveness of the proposed method is also confirmed from real ISAR data experiments.

Teacher burnout : construct equivalence and the role of union membership

The objective in this study was to investigate its structure and validate the Maslach Burnout Inventory for educators in the Goldfields region of the northern Free State province of South Africa. A cross-sectional survey design was used, where a sample of educators was drawn from the total population (N=468). An adapted version of the Maslach Burnout Inventory and a biographical questionnaire were administered. Exploratory factor analysis with target rotations confirmed construct equivalence of burnout dimensions for an Afrikaans and English subgroup and an African Languages subgroup. Burnout is described as consisting of exhaustion, cynicism, depersonalisation, and professional efficacy. However, the depersonalisation construct showed better fit across language groups than the cynicism construct. Item bias analysis was carried out for the cynicism items. For biographical differences, it was found that union membership presented an important distinction in educators ' experience of burn out. South African Journal of Education Vol. 26 (4) 2006: pp. 541-551

Fuzzy system for calculating the Target Rotation Time in Profibus networks

Abstract: Communication on Profibus networks is accomplished through the exchange of data between masters and slave devices. This communication is performed in cycles, and each cycle must be performed in a preset time interval called Token Rotation Time. The precise calculation of this value is complex and in practice, the user performs it in a simplified manner, thus, obtaining an approximated value. In order to simplify the mechanism to obtain this value, this research proposes a Fuzzy system, which automatically indicates to the user an ideal value to the target rotation time variable. The project was validated by data obtained from concrete Profibus networks, and the results were satisfactory, proving the great strength and versatility that intelligent computer systems have when applied to the purposes outlined in this work.

PSYCHOMETRIC PROPERTIES OF THE OBSESSIVE-COMPULSIVE INVENTORY-REVISED IN A TURKISH ANALOGUE SAMPLE.

The Obsessive-Compulsive Inventory-Revised (OCI-R) assesses distress associated with the symptoms of obsessive-compulsive disorder (OCD). This study reports on the psychometric properties of the Turkish version of the OCI-R as a widely known measure. The sample consisted of 319 Turkish university students (67.1% women; M age = 21.5, SD = 2.0). The questionnaire battery included measures of OCD symptoms, specific cognitions, thought control, and personality characteristics. A target rotation analysis supported the factorial validity of the Turkish OCI-R as indicated by its replicability with the original factor structure (i.e., checking, washing, obsessing, hoarding, ordering, and mental neutralizing). High-scoring OCD symptom groups also significantly differed on the Turkish OCI-R and thus presented preliminary evidence for its criterion validity. Correlational analysis supported convergent and divergent validity of the measure, with significant correlations between the Turkish OCI-R and OCD symptoms, OCD-specific beliefs, two thought control strategies (e.g., worry and punishment), and neuroticism, but not with psychoticism or extraversion. The current findings provide initial evidence of sound psychometric properties for the Turkish OCI-R in a nonclinical sample.

Monte Carlo Singular Spectrum Analysis (SSA) Revisited: Detecting Oscillator Clusters in Multivariate Datasets

Abstract Singular spectrum analysis (SSA) along with its multivariate extension (M-SSA) provides an efficient way to identify weak oscillatory behavior in high-dimensional data. To prevent the misinterpretation of stochastic fluctuations in short time series as oscillations, Monte Carlo (MC)–type hypothesis tests provide objective criteria for the statistical significance of the oscillatory behavior. Procrustes target rotation is introduced here as a key method for refining previously available MC tests. The proposed modification helps reduce the risk of type-I errors, and it is shown to improve the test’s discriminating power. The reliability of the proposed methodology is examined in an idealized setting for a cluster of harmonic oscillators immersed in red noise. Furthermore, the common method of data compression into a few leading principal components, prior to M-SSA, is reexamined, and its possibly negative effects are discussed. Finally, the generalized Procrustes test is applied to the analysis of interannual variability in the North Atlantic’s sea surface temperature and sea level pressure fields. The results of this analysis provide further evidence for shared mechanisms of variability between the Gulf Stream and the North Atlantic Oscillation in the interannual frequency band.

Joint ISAR Imaging and Cross-Range Scaling Method Based on Compressive Sensing With Adaptive Dictionary

Compressive sensing (CS) is successfully applied in inverse synthetic aperture radar (ISAR) imaging. But, as target rotation rate is not concerned in the CS-based imaging methods, the obtained image cannot be scaled in the cross-range dimension. Consequently, difficulties arise in extracting the target geometrical information from the CS ISAR image. But, target geometrical size is an important parameter in automatic radar target recognition. To remedy this problem, a joint ISAR imaging and cross-range scaling method is proposed. In the proposed method, an adaptive parametric dictionary, comprising chirp rate parameter, is used to represent the observed data. By minimizing the reconstruction error, sparsity-constrained optimization, combined with the chirp-rate parameter and target reflective coefficient, is established. To find a solution to the nonlinear and nonconvex optimization problem, an iterative procedure is developed. Finally, with the help of the chirp-rate, target rotation rate can be estimated by the least square method, and the ISAR image can be scaled in cross-range. Experimental results show that the proposed method can fit the observed data better than the method using a fixed Fourier dictionary. Besides, cross-range scaled ISAR images can be obtained with limited pulses.

Image-based displacement and rotation detection using scale invariant features for 6 degree of freedom ICF target positioning

Precise online ending supervision for target positioning plays an important role in inertial confinement fusion (ICF) experiments. This paper presents a 6 degree of freedom (6DOF) ICF target displacement and rotation detection method using a scale invariant feature transform (SIFT) algorithm. A SIFT algorithm can extract image key points, which are localized to subpixel accuracy and can perform reliable matching between different views of an object. For testing the accuracy offline, precise and known displacement was given by an electromotor on a calibrated optical grating-vision platform. When the platform moves along its x axis, the image of each step is captured with corresponding grating sensor values recorded. Offline experimental results prove that the different values between the image points and grating sensor are less than 1 μm (1/10 pixel). In online conditions, a 6DOF parallel robot subsystem is used as an ending driver, and a target alignment sensor is used to capture four orthogonal images. The feedback information is obtained by SIFT features. Due to the excellent matching ability and 1/10 subpixel localization accuracy of the SITF features, the proposed method can accurately and reliably online detect the displacement and rotation of target. The experimental results demonstrate the effectiveness of the method.

PD-0231: Time-resolved internal target translation and rotation during liver SBRT treatments

PD-0231: Time-resolved internal target translation and rotation during liver SBRT treatments

Iteration of Partially Specified Target Matrices: Applications in Exploratory and Bayesian Confirmatory Factor Analysis

We describe and evaluate a factor rotation algorithm, iterated target rotation (ITR). Whereas target rotation (Browne, 2001) requires a user to specify a target matrix a priori based on theory or prior research, ITR begins with a standard analytic factor rotation (i.e., an empirically informed target) followed by an iterative search procedure to update the target matrix. In Study 1, Monte Carlo simulations were conducted to evaluate the performance of ITR relative to analytic rotations from the Crawford-Ferguson family with population factor structures varying in complexity. Simulation results: (a) suggested that ITR analyses will be particularly useful when evaluating data with complex structures (i.e., multiple cross-loadings) and (b) showed that the rotation method used to define an initial target matrix did not materially affect the accuracy of the various ITRs. In Study 2, we: (a) demonstrated the application of ITR as a way to determine empirically informed priors in a Bayesian confirmatory factor analysis (BCFA; Muthén & Asparouhov, 2012) of a rater-report alexithymia measure (Haviland, Warren, & Riggs, 2000) and (b) highlighted some of the challenges when specifying empirically based priors and assessing item and overall model fit.

Common Factor Analysis versus Principal Component Analysis: A Comparison of Loadings by Means of Simulations

Common factor analysis (CFA) and principal component analysis (PCA) are widely used multivariate techniques. Using simulations, we compared CFA with PCA loadings for distortions of a perfect cluster configuration. Results showed that nonzero PCA loadings were higher and more stable than nonzero CFA loadings. Compared to CFA loadings, PCA loadings correlated weakly with the true factor loadings for underextraction, overextraction, and heterogeneous loadings within factors. The pattern of differences between CFA and PCA was consistent across sample sizes, levels of loadings, principal axis factoring versus maximum likelihood factor analysis, and blind versus target rotation.

Research on semi-supervising learning algorithm for target model updating in target tracking

Target detection and tracking technique is one of the hot subjects in image processing and computer vision fields, which has significant research value not only in military areas such as imaging guidance and military target tracking, but also for civil use such as security and monitoring and the intelligent man-machine interaction. In this paper, for target deformation, scale changing, rotation, and other issues in the long-term stable target tracking, a bootstrapping feedback learning algorithm is proposed, which may improve the target model and the classifier discriminating capacity as well as the fault tolerance ability; and it also makes fewer errors during the updating, and then the proof of convergence of the algorithm is given. Experimental results show that among the same tracking algorithms, utilization of the learning method to update the target model and classifier is more stable and more adaptable than unusing it in the processes of target scale changing, deformation, rotation, perspective changing and fuzzy. And compared with the existing conventional method, this method has a better robustness, and a high value in practical application and research.

Supervised SOM Based ATR Method with Circular Polarization Basis of Full Polarimetric Data

Satellite-borne or aircraft-borne synthetic aperture radar (SAR) is useful for high resolution imaging analysis for terrain surface monitoring or surveillance, particularly in optically harsh environments. For surveillance application, there are various approaches for automatic target recognition (ATR) of SAR images aiming at monitoring unidentified ships or aircraft. In addition, various types of analyses for full polarimetric data have been developed recently because it can provide significant information to identify structure of targets, such as vegetation, urban, sea surface areas. ATR generally consists of two processes, one is target feature extraction including target area determination, and the other is classification. In this paper, we propose novel methods for these two processes that suit full polarimetric exploitation. As the target area extraction method, we introduce a peak signal-to noise ratio (PSNR) based synthesis with full polarimetric SAR images. As the classification method, the circular polarization basis conversion is adopted to improve the robustness especially to variation of target rotation angles. Experiments on a 1/100 scale model of X-band SAR, demonstrate that our proposed method significantly improves the accuracy of target area extraction and classification, even in noisy or target rotating situations.

ISAR Cross-Range Scaling by Using Sharpness Maximization

This letter presents a new method of cross-range scaling in inverse synthetic aperture radar (ISAR) imaging. The effective rotational velocity (ERV), being the crucial factor for scaling, is generally unknown for noncooperative objects. By considering the degradation from target rotation, the proposed scheme estimates ERV based on image sharpness maximization. A range deviator induced by the center shift is also embedded in the estimation process. The cross-range scaling factor with an enhanced ISAR image can be obtained by an efficient Gauss-Newton method. The results acquired from both the simulations and real data experiments validate the effectiveness and robustness of the proposed method.

A Novel Frequency Domain Iterative Image Registration Algorithm Based on Local Region Extraction

Because of the differences of imaging time, position between sensor and target position, scaling, rotation, translation, and other transformations between the series of images will be generated by the imaging system. The conventional phase correlation algorithm has been widely applied because of its advantages of high speed, precision, and weak influence of the geometric distortion when computing these changing parameters. However, when the scaling factor and the rotation angle are too large, it is difficult to use the conventional phase correlation method for high precision registration. To solve this problem, this paper presents a novel method, which combines the speeded up robust features algorithm and the phase correlation method under the log polar. Through local region extraction and reusing a two-step iterative phase correlation algorithm, this method avoids excessive computation and the demand of characteristics of the image and effectively improves the accuracy of registration. A plurality of visible light image simulation verifies that this is a fast, accurate, and robust algorithm, even when the image has large angle rotation and large multiple scaling.

Changes of the Video Head Impulse Test Gains by the Directions of Head Rotation at Different Target Distances and Rotation Speeds

Chan Il Song, Yeong Eun Kim, Eun Hye Cha, Myung Hoon Yoo, Je Yeon Lee, and Hong Ju Park Department of Otolaryngology-Head and Neck Surgery, Jeju National University School of Medicine, Jeju; and Department of Otolaryngology-Head and Neck Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul; and Department of Otolaryngology-Head and Neck Surgery, Korea University Ansan Hospital, Ansan, Korea

Synthesis of Ti-based metallic glass thin film in high vacuum pressure on 316 L stainless steel

Surface modification of ordinary alloys by advanced coating provides a means to maintain the bulk properties of the alloy and thereby its utility in sensitive applications. In this paper, we describe the deposition of a Ti-based metallic glass thin film on 316L stainless steel using a high vacuum pulse laser deposition (HVPLD) process without a bulk metallic glass (MG) source as target. A predominant amorphous single phase film was obtained in the proper chemical composition (Ti57Cu28{Zr0.95–Hf0.05}5Si10) by controlling several process parameters, including the target rotation speed and vacuum pressure. Our HPLD configuration entailed high vacuum pressure (less than 13.3×10−6Pa), a target rotation speed in the range of 1500–2000rotation per minute (rpm) and a substrate temperature between 450 and 600°C. These parameters provide excellent control over the amorphicity of the film matrix. Our work opens up interesting opportunities to improve the poor surface properties of metallic alloys specially biometallic alloys by depositing a thin film of Ti-based MG on their surface, which can also overcome the problem of MG bulk form production as we just deposited a thin film of MG.

Recovering Substantive Factor Loadings in the Presence of Acquiescence Bias: A Comparison of Three Approaches

Researchers are often advised to write balanced scales (containing an equal number of positively and negatively worded items) when measuring psychological attributes. This practice is recommended to control for acquiescence bias (ACQ). However, little advice has been given on what to do with such data if the researcher subsequently wants to evaluate a 1-factor model for the scale. This article compares 3 approaches for dealing with the presence of ACQ bias, which make different assumptions: an ipsatization approach based on the work of Chan and Bentler (CB; 1993), a confirmatory factor analysis (CFA) approach that includes an ACQ factor with equal loadings (Billiet & McClendon, 2000; Mirowsky & Ross, 1991), and an exploratory factor analysis (EFA) approach with a target rotation (Ferrando, Lorenzo-Seva, & Chico, 2003). We also examine the “do nothing” approach which fits the 1-factor model to the data ignoring the presence of ACQ bias. Our main findings are that the CFA method performs best overall and that it is robust to the violation of its assumptions, the EFA and the CB approaches work well when their assumptions are strictly met, and the “do nothing” approach can be surprisingly robust when the ACQ factor is not very strong.

Face Tracking Based on Particle Filtering and α-β-γ Filtering

In view of the traditional particle filter algorithm cannot guarantee effective tracking in the case of target rotation or obscured. The study proposes a tracking method based on α-β-γ filter and particle filter. The algorithm uses α-β-γ filtering prediction position as the next frame image target candidate model of computing center of particle filter algorithm. The algorithm uses α-β-γ filtering prediction position as the next frame image target candidate model of computing center of particle filter. To reduce the number of iterations of particle filter algorithm, strengthen the real-time tracking of moving face. When detect the face is obscured, with α-β-γ filter prediction point as facial movement position, so as to realize the continuity of the movement. The experimental results show that the proposed algorithm improves the traditional particle filter for real-time face tracking, enhancing the ability of anti-occlusion.