Diversification and Generalized Tracking Errors For Correlated Non-Normal Returns

2002 ◽  
Author(s):  
Mark B. Wise ◽  
Vineer Bhansali
Keyword(s):  
Tracking Errors

Portfolio Management under Constraints on Ex-Ante and Ex-Post Tracking-Errors

2012 ◽  
Author(s):  
Riadh Belhaj ◽  
Didier Maillard ◽  
Roland Portait
Keyword(s):  
Portfolio Management ◽  
Tracking Errors ◽  
Ex Post ◽  
Ex Ante

Quantile Tracking Errors (QuTE)

2020 ◽  
Author(s):  
Mike Aguilar ◽  
Ruyang Chengan ◽  
Anessa Custovic
Keyword(s):  
Tracking Errors

Sliding Mode Control of an Actuated Knee Joint Orthosis

2021 ◽  
Vol 15 ◽  
Author(s):  
Imen Saidi ◽  
Asma Hammami
Keyword(s):  
Sliding Mode Control ◽  
Lower Limb ◽  
Sliding Mode ◽  
Position Tracking ◽  
Sliding Mode Controller ◽  
Mechanical Device ◽  
Tracking Errors ◽  
Mode Control ◽  
Speed Tracking ◽  
Human Morphology

Introduction: In this paper, a robust sliding mode controller is developed to control an orthosis used for rehabilitation of lower limb. Materials and Methods: The orthosis is defined as a mechanical device intended to physically assist a human subject for the realization of his movements. It should be adapted to the human morphology, interacting in harmony with its movements, and providing the necessary efforts along the limbs to which it is attached. Results: The application of the sliding mode control to the Shank-orthosis system shows satisfactory dynamic response and tracking performances. Conclusion: In fact, position tracking and speed tracking errors are very small. The sliding mode controller effectively absorbs disturbance and parametric variations, hence the efficiency and robustness of our applied control.

scholarly journals Deep-Learning-Based Pupil Center Detection and Tracking Technology for Visible-Light Wearable Gaze Tracking Devices

2021 ◽  
Vol 11 (2) ◽  
pp. 851
Author(s):  
Wei-Liang Ou ◽  
Tzu-Ling Kuo ◽  
Chin-Chieh Chang ◽  
Chih-Peng Fan
Keyword(s):  
Deep Learning ◽  
Visible Light ◽  
Tracking System ◽  
Recall Rate ◽  
Detection Accuracy ◽  
Learning Technology ◽  
Tracking Errors ◽  
Gaze Tracking ◽  
Detection Technology ◽  
Pupil Tracking

In this study, for the application of visible-light wearable eye trackers, a pupil tracking methodology based on deep-learning technology is developed. By applying deep-learning object detection technology based on the You Only Look Once (YOLO) model, the proposed pupil tracking method can effectively estimate and predict the center of the pupil in the visible-light mode. By using the developed YOLOv3-tiny-based model to test the pupil tracking performance, the detection accuracy is as high as 80%, and the recall rate is close to 83%. In addition, the average visible-light pupil tracking errors of the proposed YOLO-based deep-learning design are smaller than 2 pixels for the training mode and 5 pixels for the cross-person test, which are much smaller than those of the previous ellipse fitting design without using deep-learning technology under the same visible-light conditions. After the combination of calibration process, the average gaze tracking errors by the proposed YOLOv3-tiny-based pupil tracking models are smaller than 2.9 and 3.5 degrees at the training and testing modes, respectively, and the proposed visible-light wearable gaze tracking system performs up to 20 frames per second (FPS) on the GPU-based software embedded platform.

scholarly journals Visual Tracking Control of Cable-Driven Hyper-Redundant Snake-Like Manipulator

2021 ◽  
Vol 11 (13) ◽  
pp. 6224
Author(s):  
Qisong Zhou ◽  
Jianzhong Tang ◽  
Yong Nie ◽  
Zheng Chen ◽  
Long Qin
Keyword(s):  
Visual Tracking ◽  
Tracking Control ◽  
Control Method ◽  
Adaptive Optimization ◽  
Tracking Errors ◽  
Kinematics Model ◽  
Compound Control ◽  
Original Intention ◽  
Control Scheme ◽  
Specific Geometry

The cable-driven hyper-redundant snake-like manipulator (CHSM) inspired by the biomimetic structure of vertebrate muscles and tendons, which consists of numerous joint units connected adjacently driven by elastic materials with hyper-redundant DOF, performs flexible kinematic skills and competitive compound capability under complicated working circumstances. Nevertheless, the drawback of lacking the ability to perceive the environment to perform intelligently in complex scenarios leaves a lot to be improved, which is the original intention to introduce visual tracking feedback acting as an instructor. In this paper, a cable-driven snake-like robotic arm combined with a visual tracking technique is introduced. A visual tracking approach based on dual correlation filter is designed to guide the CHSM in detecting the target and tracing after its trajectory. Specifically, it contains an adaptive optimization for the scale variation of the tracking target via pyramid sampling. For the CHSM, an explicit kinematics model is derived from its specific geometry relationships and followed by a simplification for the inverse kinematics based on some assumption or limitation. A control scheme is brought up to combine the kinematics with visual tracking via the processing tracking errors. The experimental results with a practical prototype validate the availability of the proposed compound control method with the derived kinematics model.

scholarly journals Window dressing in the Active Share scores in publicly reported portfolios

2021 ◽  
pp. 234094442110246
Author(s):  
Laura Andreu ◽  
Carlos Forner ◽  
José Luis Sarto
Keyword(s):  
Positive Relationship ◽  
Tracking Error ◽  
Jel Classification ◽  
Tracking Errors ◽  
Window Dressing ◽  
Future Performance ◽  
Money Flows

Using a unique database that includes publicly disclosed fund holdings at the end of the quarter as well as the holdings in all non-publicly disclosed months, we found that some funds could alter their portfolios in publicly disclosed months to artificially increase their Active Share scores and consequently appear more active and take advantage of the positive relationship between Active Share and money flows. We show how, consistent with non-informed trades, these funds erode their future performance. However, these funds reach their objective of increasing future money flows. Moreover, we find that window-dresser funds can be identified by controlling the level of tracking error. The funds with high Active Share scores and low tracking errors have the highest levels of Active Share window dressing and the worst future returns. However, compared with less active funds, they are able to capture higher money flows. JEL CLASSIFICATION G23; G11

Adaptive attitude-tracking control of spacecraft considering on-orbit refuelling

2020 ◽  
pp. 014233122097313
Author(s):  
Yiqi Xu
Keyword(s):  
Tracking Control ◽  
Closed Loop ◽  
Closed Loop System ◽  
Tracking Errors ◽  
Attitude Tracking ◽  
Control Scheme ◽  
Inertia Model ◽  
And Performance ◽  
Attitude Tracking Control ◽  
The Stability

This paper studies the attitude-tracking control problem of spacecraft considering on-orbit refuelling. A time-varying inertia model is developed for spacecraft on-orbit refuelling, which actually includes two processes: fuel in the transfer pipe and fuel in the tank. Based upon the inertia model, an adaptive attitude-tracking controller is derived to guarantee the stability of the resulted closed-loop system, as well as asymptotic convergence of the attitude-tracking errors, despite performing refuelling operations. Finally, numerical simulations illustrate the effectiveness and performance of the proposed control scheme.

Reducing Culture Reporting Errors in the Microbiology Laboratory

2019 ◽  
Vol 152 (Supplement_1) ◽  
pp. S131-S132
Author(s):  
Kathryn Hogan ◽  
Beena Umar ◽  
Mohamed Alhamar ◽  
Kathleen Callahan ◽  
Linoj Samuel
Keyword(s):  
Real Time ◽  
Error Rate ◽  
Reporting System ◽  
Error Rates ◽  
Tracking Errors ◽  
Corrective Measures ◽  
Safety Awareness ◽  
Before And After ◽  
Set Up ◽  
System Errors

Abstract Objectives There are few papers that characterize types of errors in microbiology laboratories and scant research demonstrating the effects of interventions on microbiology lab errors. This study aims to categorize types of culture reporting errors found in microbiology labs and to document the error rates before and after interventions designed to reduce errors and improve overall laboratory quality. Methods To improve documentation of error incidence, a self-reporting system was changed to an automatic reporting system. Errors were categorized into five types Gram stain (misinterpretations), identification (incorrect analysis), set up labeling (incorrect patient labels), procedures (not followed), and miscellaneous. Error rates were tracked according to technologist, and technologists were given real-time feedback by a manager. Error rates were also monitored in the daily quality meeting and frequently detected errors were discussed at staff meetings. Technologists attended a year-end review with a manager to improve their performance. To maintain these changes, policies were developed to monitor technologist error rate and to define corrective measures. If a certain number of errors per month was reached, technologists were required to undergo retraining by a manager. If a technologist failed to correct any error according to protocol, they were also potentially subject to corrective measures. Results In 2013, we recorded 0.5 errors per 1,000 tests. By 2018, we recorded only 0.1 errors per 1,000 tests, an 80% decrease. The yearly culture volume from 2013 to 2018 increased by 32%, while the yearly error rate went from 0.05% per year to 0.01% per year, a statistically significant decrease (P = .0007). Conclusion This study supports the effectiveness of the changes implemented to decrease errors in culture reporting. By tracking errors in real time and using a standardized process that involved timely follow-up, technologists were educated on error prevention. This practice increased safety awareness in our micro lab.

scholarly journals Tracking Errors, Assessing Patients, Redesigning Processes—Itʼs All About Safe Care

2017 ◽  
Vol 117 (10) ◽  
pp. 7
Author(s):  
Maureen Shawn Kennedy
Keyword(s):  
Tracking Errors ◽  
Safe Care
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