This paper deals with the joint estimation of the unknown input and the fractional differentiation orders of a linear fractional order system. A two-stage algorithm combining the modulating functions with a first-orde...
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(纸本)9781479977871
This paper deals with the joint estimation of the unknown input and the fractional differentiation orders of a linear fractional order system. A two-stage algorithm combining the modulating functions with a first-order Newton method is applied to solve this estimation problem. First, the modulating functions approach is used to estimate the unknown input for a given fractional differentiation orders. Then, the method is combined with a first-order Newton technique to identify the fractional orders jointly with the input. To show the efficiency of the proposed method, numerical examples illustrating the estimation of the neural activity, considered as input of a fractional model of the neurovascular coupling, along with the fractional differentiation orders are presented in both noise-free and noisy cases.
This paper describes our system, called ITC-UT, for the task-2 (on-line reputation management task) in WePS-3. Our idea is to categorize each query into 3 or 4 classes according to how much the tweets retrieved by the...
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This paper describes our system, called ITC-UT, for the task-2 (on-line reputation management task) in WePS-3. Our idea is to categorize each query into 3 or 4 classes according to how much the tweets retrieved by the query contain the "true" entity names that refer to the target entity, and then categorize each tweet by the rules defined for each class of queries. We show the evaluation results for our system along with the details of results of query categorization.
Recently, prefabricated construction has been vigorously promoted, resulting in high demand for precast concrete (PC) components. The transportation scheduling optimization problem of PC components with various kinds ...
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Recently, prefabricated construction has been vigorously promoted, resulting in high demand for precast concrete (PC) components. The transportation scheduling optimization problem of PC components with various kinds from multiple projects arises. Unlike conventional cargo, PC components are characterized by shape heterogeneity, large volume, and strict delivery time limits. Based on three characteristics, a heterogeneous fixed fleet vehicle routing problem (HFFVRP) for PC components is introduced, where heterogeneous vehicles, allocation of PC components to size-matching vehicles, and hybrid time windows are considered. Then a two-stage solution strategy based on the improved ant colony optimization (ACO) and Dijkstra algorithm is designed to obtain optimal vehicle routes under minimum transportation costs. The results indicate that the improved ACODijkstra algorithm outperforms in obtaining optimal transportation plans for heterogeneous vehicles compared with manual decision-making and other heuristic algorithms. Sensitivity analysis denotes that utilizing heterogeneous vehicles contributes to reductions in transportation costs, and vehicle configuration should be adjusted along with demand scales. The proposed model and algorithm extend the theoretical basis of construction industrial applications.
Anticipating the forthcoming integration of shared autonomous vehicles (SAVs) into urban networks, the imperative of devising an efficient real-time scheduling and routing strategy for these vehicles becomes evident i...
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Anticipating the forthcoming integration of shared autonomous vehicles (SAVs) into urban networks, the imperative of devising an efficient real-time scheduling and routing strategy for these vehicles becomes evident if one is to maximize their potential in enhancing travel efficiency. In this study, we address the problem of jointly scheduling and routing SAVs across an urban network with the possibility of platooning the vehicles at intersections to reduce their travel time. We argue that this is especially useful in large urban areas. We introduce a novel vehicle scheduling and routing method that allows a specific number of SAVs to converge at the intersections of urban corridors within designated time intervals, facilitating the formation of SAV platoons. Dedicated lanes and signal priority control are activated to ensure that these platoons go through the corridors efficiently. Based on the above concept, we propose a linear integer programming model to minimize the total travel time of SAVs and the delays experienced by the conventional vehicles due to SAV priority, thereby optimizing the overall performance of the road network. For large instances, we develop a two-stage heuristic algorithm to solve it faster. In the first stage, leveraging an evaluation index that manifests the compatibility of each vehicle-torequest combination, we allocate passenger requests to a fleet of SAVs. In the second stage, a customized genetic algorithm is designed to coordinate the paths of various SAVs, thus achieving the desired vehicle platooning effect. The optimization method is tested on a real-world road network in Shanghai, China. The results display a remarkable reduction of 15.76 % in the total travel time of the SAVs that formed platoons. The overall performance of the road network could be improved with the total travel time increase of conventional vehicles significantly smaller than the reduction observed in SAVs' total travel time.
Introduction: Identifying predictors of patient outcomes evaluated over time may require modeling interactions among variables while addressing within-subject correlation. Generalized linear mixed models (GLMMs) and g...
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Introduction: Identifying predictors of patient outcomes evaluated over time may require modeling interactions among variables while addressing within-subject correlation. Generalized linear mixed models (GLMMs) and generalized estimating equations (GEEs) address within-subject correlation, but identifying interactions can be difficult if not hypothesized a priori. We evaluate the performance of several variable selection approaches for clustered binary outcomes to provide guidance for choosing between the methods. Methods: We conducted simulations comparing stepwise selection, penalized GLMM, boosted GLMM, and boosted GEE for variable selection considering main effects and two-way interactions in data with repeatedly measured binary outcomes and evaluate a two-stage approach to reduce bias and error in parameter estimates. We compared these approaches in real data applications: hypothermia during surgery and treatment response in lupus nephritis. Results: Penalized and boosted approaches recovered correct predictors and interactions more frequently than stepwise selection. Penalized GLMM recovered correct predictors more often than boosting, but included many spurious predictors. Boosted GLMM yielded parsimonious models and identified correct predictors well at large sample and effect sizes, but required excessive computation time. Boosted GEE was computationally efficient and selected relatively parsimonious models, offering a compromise between computation and parsimony. The two-stage approach reduced the bias and error in regression parameters in all approaches. Conclusion: Penalized and boosted approaches are effective for variable selection in data with clustered binary outcomes. The two-stage approach reduces bias and error and should be applied regardless of method. We provide guidance for choosing the most appropriate method in real applications.
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