Convergence analysis of Nesterov’s accelerated gradient method has attracted significant attention over the past decades. While extensive work has explored its theoretical properties and elucidated the intuition behi...
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作者：
El Haouari, OussamaMourad, HanaKhrissi, LahbibEl Akkad, NabilLASET
Laboratory of Applied Sciences and Emerging Technologies National School of Applied Sciences of Fez Sidi Mohamed Ben Abdellah University Fez Morocco LIPI
Laboratory of Interdisciplinary Computer Science and Physics Normal Superior School of Fez Sidi Mohamed Ben Abdellah University Fez Morocco
Clustering remains a critical task in image analysis, yet traditional K-means methods frequently suffer from local optima issues, leading to suboptimal clustering, particularly in complex datasets. In this study, we p...
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Proximal policy optimization (PPO) is one of the most popular state-of-the-art on-policy algorithms that has become a standard baseline in modern reinforcement learning with applications in numerous fields. Though it ...
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In this paper we present a unifying framework for continuous optimization methods grounded in the concept of generalized convexity. Utilizing the powerful theory of Φ-convexity, we propose a conceptual algorithm that...
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Feature selection is an important task in data-driven control applications to identify relevant features and remove non-informative ones, for example residual selection for fault diagnosis. For multi-class data, the o...
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Feature selection is an important task in data-driven control applications to identify relevant features and remove non-informative ones, for example residual selection for fault diagnosis. For multi-class data, the objective is to find a minimal set of features that can distinguish data from all different classes. A distributed feature selection algorithm is derived using convex optimization and the Alternating Direction Method of Multipliers. The distributed algorithm scales well with increasing number of classes by utilizing parallel computations. Two case studies are used to evaluate the developed feature selection algorithm: fault classification of an internal combustion engine and the MNIST data set to illustrate a larger multi-class classification problem.
Purpose: To assess the performance of a new optimization system, VOLO, for CyberKnife MLC-based SBRT plans in comparison with the existing Sequential optimizer. Methods: MLC-plans were created for 25 SBRT cases (liver...
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Purpose: To assess the performance of a new optimization system, VOLO, for CyberKnife MLC-based SBRT plans in comparison with the existing Sequential optimizer. Methods: MLC-plans were created for 25 SBRT cases (liver, prostate, pancreas and spine) using both VOLO and Sequential. Monitor units (MU), delivery time (DT), PTV coverage, conformity (nCI), dose gradient (R50%) and OAR doses were used for comparison and combined to obtain a mathematical score (MS) of plan quality for each solution. MS strength was validated by changing parameter weights and by a blinded clinical plan evaluation. The optimization times (OT) and the average segment areas (SA) were also compared. Results: VOLO solutions offered significantly lower mean DT (-19%) and MU (-13%). OT were below 15 min for VOLO, whereas for Sequential, values spanned from 8 to 160 min. SAs were significantly larger for VOLO: on average 10 cm(2) versus 7 cm(2). VOLO optimized plans achieved a higher MS than Sequential for all tested parameter combinations. PTV coverage and OAR sparing were comparable for both groups of solutions. Although slight differences in R50% and nCI were found, the parameters most affecting MS were MU and DT. VOLO solutions were selected in 80% of cases by both physicians with 88% inter-observer agreement. Conclusions: The good performance of the VOLO optimization system, together with the large reduction in OT, make it a useful tool to improve the efficiency of CK SBRT planning and delivery. The proposed methodology for comparing different planning solutions can be applied in other contexts.
Currently, educational data mining act as a major part of student performance prediction approaches and their applications. However, more ensemble methods are needed to improve the student performance prediction, and ...
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Currently, educational data mining act as a major part of student performance prediction approaches and their applications. However, more ensemble methods are needed to improve the student performance prediction, and also which helps increase the learning quality of the Student's performance. The usage of an ensemble classifier with rule mining to predict students' academic success is proposed. In response to this need, this research mainly concentrated on an ensemble classifier with rule mining to predict students' academic success. The feature mining is performed using the weighted Rough Set Theory method, in which the proposed meta-heuristic algorithm optimizes the weight function. The variable optimization of the ensemble classifier is accomplished with the help of a combination of Harris Hawks optimization (HHO), and Krill Herd Algorithm (KHA) known as Escape Energy Searched Krill Herd-Harris Hawks optimization (EES-KHHO) for maximizing the prediction rate. Extensive tests are carried out on various datasets, and the findings show that our technique outperforms conventional approaches. Throughout the result analysis, the offered method attains a 92.77% accuracy rate, and also it attains a sensitivity rate of 94.87%. Therefore, the offered student performance prediction model achieves better effectiveness regarding various performance metrics.
This paper introduces two novel criteria: one for feature selection and another for feature elimination in the context of best subset selection, which is a benchmark problem in statistics and machine learning. From th...
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This paper studies the combined maneuver of flying and sailing for a robotic system which is referred to as a flying+sailing drone. Due to the emergence of hybrid systems behavior in tasks which involve both the flyin...
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This paper studies the combined maneuver of flying and sailing for a robotic system which is referred to as a flying+sailing drone. Due to the emergence of hybrid systems behavior in tasks which involve both the flying and sailing modes, a hybrid systems formulation of the robotic system is presented. Key characteristics of the system are (i) changes in the dimension of the state space as the system switches from flying to sailing and vice versa and (ii) the presence of autonomous switchings triggered only upon the landing of the drone on the water surface. For the scenario in which the drone’s initial state is given in the flying mode and a fixed terminal state is specified in the sailing mode, the associated optimal control problems are studied within the vertical plane passing through the given points, hence the dynamics of the drone in the flying mode are represented in a five-dimensional state space (associated with three degrees-of-freedom) and in a three-dimensional state space in the sailing mode (associated with two degrees-of-freedom). In particular, the optimal control problems for the minimization of time and the minimization of the control effort are formulated, the associated necessary optimality conditions are obtained from the hybrid minimum principle (HMP), and the associated numerical simulations are presented.
Detecting scour is possible by locating sensors on a railway carriage and processing these signals. In the first stage of the simulation phase of this study, the bridge ‘apparent profile’(AP) is computed by a Finite...
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Detecting scour is possible by locating sensors on a railway carriage and processing these signals. In the first stage of the simulation phase of this study, the bridge ‘apparent profile’(AP) is computed by a Finite Element model. The model is used to derive the Moving Reference Influence Lines. Reducing support spring stiffnesses will correspond to the scoured case. In the second phase, off-bridge conditions will be taken into account. Then, measured AP, i.e., signals from the instrumented in-service train carriage both for scoured and scour-repaired cases, will be processed. Lastly, the foundation stiffnesses of the bridge will be computed by minimizing the sum of squared differences between the calculated AP and the corresponding measured AP. Loss of foundation compared to a healthy condition will prove the existence of scour.
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