Error Uncertainty Analysis in Planar Closed-Loop Structure with Joint Clearances

For planar closed-loop structures with clearances, the angular and positional error uncertainties are studied. By using the vector translation method and geometric method, the boundaries of the errors are analyzed. The joint clearance is considered as being distributed uniformly in a circle area. A virtual link projection method is proposed to deal with the clearance affected length error probability density function (PDF) for open-loop links. The error relationship between open loop and closed loop is established. The open-loop length PDF and the closed-loop angular error PDF both approach being Gaussian distribution if there are many clearances. The angular propagation error of multi-loop structures is also investigated by using convolution. The positional errors of single and multiple loops are both discussed as joint distribution functions. Monte Carlo simulations are conducted to verify the proposed methods.

Support Directional Shifting Vector: A Direction Based Machine Learning Classifier

Machine learning models have been very popular nowadays for providing rigorous solutions to complicated real-life problems. There are three main domains named supervised, unsupervised, and reinforcement. Supervised learning mainly deals with regression and classification. There exist several types of classification algorithms, and these are based on various bases. The classification performance varies based on the dataset velocity and the algorithm selection. In this article, we have focused on developing a model of angular nature that performs supervised classification. Here, we have used two shifting vectors named Support Direction Vector (SDV) and Support Origin Vector (SOV) to form a linear function. These vectors form a linear function to measure cosine-angle with both the target class data and the non-target class data. Considering target data points, the linear function takes such a position that minimizes its angle with target class data and maximizes its angle with non-target class data. The positional error of the linear function has been modelled as a loss function which is iteratively optimized using the gradient descent algorithm. In order to justify the acceptability of this method, we have implemented this model on three different standard datasets. The model showed comparable accuracy with the existing standard supervised classification algorithm. Doi: 10.28991/esj-2021-01306 Full Text: PDF

Analysis of the Impact of Positional Accuracy When Using a Block of Pixels for Thematic Accuracy Assessment

Pixels, blocks (i.e., grouping of pixels), and polygons are the fundamental choices for use as assessment units for validating per-pixel image classification. Previous research conducted by the authors of this paper focused on the analysis of the impact of positional accuracy when using a single pixel for thematic accuracy assessment. The research described here provided a similar analysis, but the blocks of contiguous pixels were chosen as the assessment unit for thematic validation. The goal of this analysis was to assess the impact of positional errors on the thematic assessment. Factors including the size of a block, labeling threshold, landscape characteristics, spatial scale, and classification schemes were also considered. The results demonstrated that using blocks as an assessment unit reduced the thematic errors caused by positional errors to under 10% for most global land-cover mapping projects and most remote-sensing applications achieving a half-pixel registration. The larger the block size, the more the positional error was reduced. However, there are practical limitations to the size of the block. More classes in a classification scheme and higher heterogeneity increased the positional effect. The choice of labeling threshold depends on the spatial scale and landscape characteristics to balance the number of abandoned units and positional impact. This research suggests using the block of pixels as an assessment unit in the thematic accuracy assessment in future applications.

Coordinating With a Robot Partner Affects Neural Processing Related to Action Monitoring

Robots start to play a role in our social landscape, and they are progressively becoming responsive, both physically and socially. It begs the question of how humans react to and interact with robots in a coordinated manner and what the neural underpinnings of such behavior are. This exploratory study aims to understand the differences in human-human and human-robot interactions at a behavioral level and from a neurophysiological perspective. For this purpose, we adapted a collaborative dynamical paradigm from the literature. We asked 12 participants to hold two corners of a tablet while collaboratively guiding a ball around a circular track either with another participant or a robot. In irregular intervals, the ball was perturbed outward creating an artificial error in the behavior, which required corrective measures to return to the circular track again. Concurrently, we recorded electroencephalography (EEG). In the behavioral data, we found an increased velocity and positional error of the ball from the track in the human-human condition vs. human-robot condition. For the EEG data, we computed event-related potentials. We found a significant difference between human and robot partners driven by significant clusters at fronto-central electrodes. The amplitudes were stronger with a robot partner, suggesting a different neural processing. All in all, our exploratory study suggests that coordinating with robots affects action monitoring related processing. In the investigated paradigm, human participants treat errors during human-robot interaction differently from those made during interactions with other humans. These results can improve communication between humans and robot with the use of neural activity in real-time.

Accuracy of pedicle localization using a 3D ultrasound navigator on vertebral phantoms for posterior spinal surgery

Severe adolescent idiopathic scoliosis (AIS) requires surgery to halt curve progression. Accurate insertion of pedicle screws is important. This study reports a newly developed 3D ultrasound (3DUS) to localize pedicles intraoperatively and register a pre-op 3D vertebral model to the surface to be displayed for navigation. The objective was to determine speed of the custom 3DUS navigator and accuracy of pedicle probe placement. The developed 3DUS navigator integrated an ultrasound scanner with motion capture cameras. Two adolescent 3D printed spine models T2-T8 and T7-T11 were modified to include pedicle holes with known trajectory and be mounted on a high precision LEGO pegboard in a water bath for imaging. Calibration of the motion cameras and the 3DUS were conducted prior to the study. A total of 27 scans from T3 to T11 vertebrae with 3 individual scans were performed to validate the repeatability. Three accuracy tests that varied vertebral a) orientation, b) position and c) a combination of location and orientation were completed. Based on all experiments, the acquisition-to-display time was 18.9±3.1s. The repeatability of the trajectory error and positional error were 0.5±0.2° and 0.3±0.1mm, respectively. The a) center orientation, b) position and c) orientation/position on trajectory and positional error were for a) 1.4±0.9° and 0.5±0.4mm, b) 1.4±0.8° and 0.3±0.3mm and c) 2.0±0.8° and 0.5±0.5mm, respectively. These results demonstrated that a high precision real-time 3DUS navigator for screw placement in scoliosis surgery is feasible. The next step will study the effect of surrounding soft tissues on navigation accuracy.

MAPPING BARLEY LODGING WITH UAS MULTISPECTRAL IMAGERY AND MACHINE LEARNING

Unmanned Aircraft Systems (UAS) are demonstrated cost- and time-effective remote sensing platforms for precision agriculture applications and crop damage monitoring. In this study, lodging damage on barley crops has been mapped from UAS imagery that was acquired over multiple barley fields with extensive lodging damages in two aerial surveys. A Random Forests classification model was trained and tested for the discrimination of lodged barley with an overall accuracy of 99.7% on the validation dataset. The crop areas with lodging were automatically delineated by vector analysis and compared to manually delineated areas using two spatial accuracy metrics, the Area Goodness of Fit (AGoF) and the Boundary Mean Positional Error (BMPE). The average AGoF was 97.95% and the average BMPE was 0.235 m.