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1	EKF-Based Enhanced Performance Controller Design for Nonlinear Stochastic Systems Zhou, Y., Zhang, Qichun, Wang, H., Zhou, P., Chai, T. 03 October 2019 (has links) Yes / In this paper, a novel control algorithm is presented to enhance the performance of the tracking property for a class of nonlinear and dynamic stochastic systems subjected to non-Gaussian noises. Although the existing standard PI controller can be used to obtain the basic tracking of the systems, the desired tracking performance of the stochastic systems is difficult to achieve due to the random noises. To improve the tracking performance, an enhanced performance loop is constructed using the EKF-based state estimates without changing the existing closed loop with a PI controller. Meanwhile, the gain of the enhanced performance loop can be obtained based upon the entropy optimization of the tracking error. In addition, the stability of the closed loop system is analyzed in the mean-square sense. The simulation results are given to illustrate the effectiveness of the proposed control algorithm. / This work was supported in part by the PNNL Control of Complex Systems Initiative and in part by the National Natural Science Foundation of China under Grants 61621004,61573022 and 61333007. Extended Kalman filter EKF Minimum entropy criterion Tracking performance enhancement
2	How Well Do Commodity Based ETFs Track Underlying Assets? Neff, Tyler Wesley 08 June 2018 (has links) Exchange Traded Funds are growing in popularity and volume, however academic literature related to their performance is limited. This study analyzes how well the CORN, WEAT, SOYB, USO, and UGA commodity ETFs track their respective futures assets during the period of January 2012 to October 2017. Tracking error in this study is evaluated through 4 approaches to measure error, bias, systematic risk, and error magnitude. Additionally, a mispricing analysis is conducted as an alternative form of error measurement Results indicate that tracking error is small on average, however CORN shows average excess returns significantly smaller than zero. The CORN ETF is returning a smaller positive value compared to the asset basket when asset basket returns are greater than zero and a larger negative value compared to the asset basket when asset basket returns are less than zero. The CORN, WEAT, USO, and UGA ETFs are found to move less aggressively than the respective asset baskets they track. While errors were small on average, large tracking errors were present across ETFs. The size of errors were found to be impacted by large price moves, as well as seasonality on a monthly and yearly level. USDA reports impacted the size of errors for CORN, WEAT and SOYB while EIA reports had no impact on error size. The mispricing analysis concluded that CORN and SOYB trade at a discount to Net Asset Value on average while WEAT trades at a premium. / Master of Science Exchange Traded Funds Tracking Performance Agriculture Energy CORN SOYB WEAT USO UGA
3	A pilot study: Effect of a novel dual-task treadmill walking program on balance, mobility, gaze and cognition in community dwelling older adults Nayak, Akshata 31 August 2015 (has links) A growing body of literature suggests that aging causes restrictions in mobility, gaze, and cognitive functions, increasing the risk of falls and adverse health events. A novel Dual-Task Treadmill walking (DT-TW) program was designed to train balance, gaze, cognition, and gait simultaneously. Eleven healthy community-dwelling older adults (age 70-80 yrs) were recruited to play a variety of computer games while standing on a sponge surface and walking on a treadmill. Data on centre of pressure (COP) excursion for core balance, spatio-temporal gait variability parameters, head tracking performances, and neuropsychological tests were collected pre and post intervention. A significant improvement in balance, gaze, cognition, and gait performance was observed under dual-task conditions. The study thus concludes that DT-TW is a novel intervention program which combines interactive games with exercises to train dual-task abilities in community dwelling older adults. / October 2015 Treadmill Rehabilitation Platform (TRP) Useful Field of View (UFOV) Rehabilitation Computer-games Head tracking performance Spatio-temporal gait parameters Dual-task treadmill walking Executive function Core balance
4	Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms Vestin, Albin, Strandberg, Gustav January 2019 (has links) Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms. evaluation target tracking multiple sensors non-causal smoother smoothing tracking vehicle tracking camera lidar estimate estimation prediction vehicle dynamics sensor fusion real-time tracking extended kalman filter filter validation validation position estimation velocity estimation dynamic model model complexity multi object tracking multiple object tracking single object tracking data association tracking fundamentals iterated kalman filter track management gnn global nearest neighbour mahalanobis mahalanobis distance performance evaluation differential gps dgps roi ego several sensors sensors rmse root mean square error invertible motion anti-causal motion anti-causal tracking constant velocity gnn imu tfs two filter smoother ekf rts radar inertial measurement unit nonlinear nonlinear systems mono camera monocular camera noise model tracking performance fixed interval smoothing m/n logic centralized fusion non-causal object tracker car tracking car dynamics automotive active safety object tracking automotive industry thesis master reverse dynamics reverse tracking reverse sequence sequence tracking data propagation ground truth estimating ground truth additional sensors mounted sensors true estimates environment comparison algorithm independent targets overlapping measurements occluded track switch improve lower uncertainty more certain state process noise covariance sampling image sprt adas cnn cv pdf track target ego tracker tentative track observatiom online tracking offline tracking online offline recorded sequences robust self driving self-driving car traffic trajectory true state scenario scenarios future accurate output advanced driver assistance systems non-linear complex noise pedestrian truck bus maneuvering vehicles processed measurement frame state correction probability density function tuning likelihood transition measurement motion model recursion gaussian approximation distribution linear jacobian multiplicative noise ratio ad hoc ad hoc state space approach backward auction euclidean distance statistical threshold gating association margin normalize covariance matrix fusion confirmed rejected tentative history absolute error modular ego motion parameters variables logg hardware specification fused causal factorization independent uncorrelated transform moving rotation translation oncoming overtaking Control Engineering Reglerteknik

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