Optimal bounding ellipsoid (OBE) algorithms provide feasible parameter sets at each time increment of an identification process. These sets contain information that can enhance tracking and classification, and provide...
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Optimal bounding ellipsoid (OBE) algorithms provide feasible parameter sets at each time increment of an identification process. These sets contain information that can enhance tracking and classification, and provide a framework for incorporating difficult-to-quantify criteria such as perceptual quality. Some example signal processing applications of OBE parameter sets are presented in this paper.
One of the important applications of high frequency-ground wave radar (HFGWR) is to detect offshore ships. A proper method should be used to obtain the ship radar cross section (RCS), which is a key parameter of the s...
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One of the important applications of high frequency-ground wave radar (HFGWR) is to detect offshore ships. A proper method should be used to obtain the ship radar cross section (RCS), which is a key parameter of the ship. This paper proposes amethod based on an automatic information system (AIS). The relationship of the ship RCS versus bearing for different frequencies is analyzed by processing multifrequency HFGWR data. With this new method, bearing information is taken into consideration, which is not the case in traditional empirical formulas. The results provide prior knowledge for ship detection and tracking;therefore, the probability of detection is significantly improved.
This paper aims to provide a method to represent the virtual Arabidopsis plant at each growth stage. It includes simulating the shape and providing growth parameters. The shape is described with elliptic Fourier descr...
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This paper aims to provide a method to represent the virtual Arabidopsis plant at each growth stage. It includes simulating the shape and providing growth parameters. The shape is described with elliptic Fourier descriptors. First, the plant is segmented from the background with the chromatic coordinates. With the segmentation result, the outer boundary series are obtained by using boundary tracking algorithm. The elliptic Fourier analysis is then carried out to extract the coefficients of the contour. The coefficients require less storage than the original contour points and can be used to simulate the shape of the plant. The growth parameters include total area and the number of leaves of the plant. The total area is obtained with the number of the plant pixels and the image calibration result. The number of leaves is derived by detecting the apex of each leaf. It is achieved by using wavelet transform to identify the local maximum of the distance signal between the contour points and the region centroid. Experiment result shows that this method can record the growth stage of Arabidopsis plant with fewer data and provide a visual platform for plant growth research.
We consider how breakdown of the quasistatic approximation for attractors can lead to rate-induced tipping, where a qualitative change in tracking/tipping behaviour of trajectories can be characterised in terms of a c...
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We consider how breakdown of the quasistatic approximation for attractors can lead to rate-induced tipping, where a qualitative change in tracking/tipping behaviour of trajectories can be characterised in terms of a critical rate. Associated with rate-induced tipping (where tracking of a branch of quasistatic attractors breaks down), we find a new phenomenon for attractors that are not simply equilibria: partial tipping of the pullback attractor where certain phases of the periodic attractor tip and others track the quasistatic attractor. For a specific model system with a parameter shift between two asymptotically autonomous systems with periodic attractors, we characterise thresholds of rate-induced tipping to partial and total tipping. We show these thresholds can be found in terms of certain periodic-to-periodic and periodic-to-equilibrium connections that we determine using Lin's method for an augmented system. Published by AIP Publishing.
The 6-hourly 1948-2010 NCEP 1 reanalyses have been dynamically downscaled for the region of the North Pacific. With a detecting-and-tracking algorithm, the climatology of North Pacific Polar Lows has been constructed....
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The 6-hourly 1948-2010 NCEP 1 reanalyses have been dynamically downscaled for the region of the North Pacific. With a detecting-and-tracking algorithm, the climatology of North Pacific Polar Lows has been constructed. This derived climatology is consistent with the limited observational evidence in terms of frequency and spatial distribution. The climatology exhibits strong year-to-year variability but weak decadal variability and a small positive trend. A canonical correlation analysis describes the conditioning of the formation of Polar Lows by characteristic seasonal mean flow regimes, which favor, or limit, cold air outbreaks and upper air troughs.
For the non-Gaussian singular time-delayed stochastic distribution control (SDC) system with unknown external disturbance where the output probability density function (PDF) is approximated by the rational square-root...
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For the non-Gaussian singular time-delayed stochastic distribution control (SDC) system with unknown external disturbance where the output probability density function (PDF) is approximated by the rational square-root B-spline basis function, a robust fault diagnosis and fault tolerant control algorithm is presented. A full-order observer is constructed to estimate the exogenous disturbance and an adaptive observer is used to estimate the fault size. A fault tolerant tracking controller is designed using the feedback of distribution tracking error, fault, and disturbance estimation to let the postfault output PDF still track desired distribution. Finally, a simulation example is included to illustrate the effectiveness of the proposed algorithms and encouraging results have been obtained.
We address an iterative learning control (ILC) method for overcoming initial value problem caused by local convergence methods. Introducing a feedback recursive form of tracking errors into iterative learning law, thi...
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We address an iterative learning control (ILC) method for overcoming initial value problem caused by local convergence methods. Introducing a feedback recursive form of tracking errors into iterative learning law, this algorithm can avoid a crude linear approximation to nonlinear plants to reach global convergence property. The algorithm's structure is entirely illustrated. Under assumptions, it is guaranteed that tracking errors of the closed-loop system converge to zero. Besides, we discuss the roles of parameters in iterative learning law for algorithm realization, and a nonlinear case study is presented to demonstrate the effectiveness and tracking performance of the proposed algorithm.
Purpose: The present study reviews the available scientific literature on artificial intelligence (AI)-assisted ultrasound-guided regional anaesthesia (UGRA) and evaluates the reported intraprocedural parameters and p...
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Purpose: The present study reviews the available scientific literature on artificial intelligence (AI)-assisted ultrasound-guided regional anaesthesia (UGRA) and evaluates the reported intraprocedural parameters and postprocedural outcomes. Methods: A literature search was performed on 19 September 2023, using the Medline, EMBASE, CINAHL, Cochrane Library and Google Scholar databases by experts in electronic searching. All study designs were considered with no restrictions regarding patient characteristics or cohort size. Outcomes assessed included the accuracy of AI-model tracking, success at the first attempt, differences in outcomes between AI-assisted and unassisted UGRA, operator feedback and case-report data. Results: A joint adaptive median binary pattern (JAMBP) has been applied to improve the tracking procedure, while a particle filter (PF) is involved in feature extraction. JAMBP combined with PF was most accurate on all images for landmark identification, with accuracy scores of 0.83, 0.93 and 0.93 on original, preprocessed and filtered images, respectively. Evaluation of first-attempt success of spinal needle insertion revealed first-attempt success in most patients. When comparing AI application versus UGRA alone, a significant statistical difference (p < 0.05) was found for correct block view, correct structure identification and decrease in mean injection time, needle track adjustments and bone encounters in favour of having AI assistance. Assessment of operator feedback revealed that expert and nonexpert operator feedback was overall positive. Conclusion: AI appears promising to enhance UGRA as well as to positively influence operator training. AI application of UGRA may improve the identification of anatomical structures and provide guidance for needle placement, reducing the risk of complications and improving patient outcomes. Level of EvidenceLevel IV.
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