Search results for " OPT"
showing 10 items of 13620 documents
Periodic Controls in Step 2 Strictly Convex Sub-Finsler Problems
2020
We consider control-linear left-invariant time-optimal problems on step 2 Carnot groups with a strictly convex set of control parameters (in particular, sub-Finsler problems). We describe all Casimirs linear in momenta on the dual of the Lie algebra. In the case of rank 3 Lie groups we describe the symplectic foliation on the dual of the Lie algebra. On this basis we show that extremal controls are either constant or periodic. Some related results for other Carnot groups are presented. peerReviewed
A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision
2020
Computer vision based indoor localization methods use either an infrastructure of static cameras to track mobile entities (e.g., people, robots) or cameras attached to the mobile entities. Methods in the first category employ object tracking, while the others map images from mobile cameras with images acquired during a configuration stage or extracted from 3D reconstructed models of the space. This paper offers an overview of the computer vision based indoor localization domain, presenting application areas, commercial tools, existing benchmarks, and other reviews. It provides a survey of indoor localization research solutions, proposing a new classification based on the configuration stage…
Iterated greedy with variable neighborhood search for a multiobjective waste collection problem
2020
Abstract In the last few years, the application of decision making to logistic problems has become crucial for public and private organizations. Efficient decisions clearly contribute to improve operational aspects such as cost reduction or service improvement. The particular case of waste collection service considered in this paper involves a set of economic, labor and environmental issues that translate into difficult operational problems. They pose a challenge to nowadays optimization technologies since they have multiple constraints and multiple objectives that may be in conflict. We therefore need to resort to multiobjective approaches to model and solve this problem, providing efficie…
Crowd-Averse Robust Mean-Field Games: Approximation via State Space Extension
2016
We consider a population of dynamic agents, also referred to as players. The state of each player evolves according to a linear stochastic differential equation driven by a Brownian motion and under the influence of a control and an adversarial disturbance. Every player minimizes a cost functional which involves quadratic terms on state and control plus a cross-coupling mean-field term measuring the congestion resulting from the collective behavior, which motivates the term “crowd-averse.” Motivations for this model are analyzed and discussed in three main contexts: a stock market application, a production engineering example, and a dynamic demand management problem in power systems. For th…
Thermo-Hydraulic Modelling and Experimental Validation of an Electro-Hydraulic Compact Drive
2021
Electro-hydraulic compact drives (ECDs) are an emerging technology for linear actuation in a wide range of applications. Especially within the low power range of 5–10 kW, the plug-and-play capability, good energy efficiency and small space requirements of ECDs render this technology a promising alternative to replace conventional valve-controlled linear drive solutions. In this power range, ECDs generally rely on passive cooling to keep oil and system temperatures within the tolerated range. When expanding the application range to larger power classes, passive cooling may not be sufficient. Research investigating the thermal behaviour of ECDs is limited but indeed required for a successful …
Adjusted bat algorithm for tuning of support vector machine parameters
2016
Support vector machines are powerful and often used technique of supervised learning applied to classification. Quality of the constructed classifier can be improved by appropriate selection of the learning parameters. These parameters are often tuned using grid search with relatively large step. This optimization process can be done computationally more efficiently and more precisely using stochastic search metaheuristics. In this paper we propose adjusted bat algorithm for support vector machines parameter optimization and show that compared to the grid search it leads to a better classifier. We tested our approach on standard set of benchmark data sets from UCI machine learning repositor…
Robust Mean Field Games with Application to Production of an Exhaustible Resource
2012
International audience; In this paper, we study mean field games under uncertainty. We consider a population of players with individual states driven by a standard Brownian motion and a disturbance term. The contribution is three-fold: First, we establish a mean field system for such robust games. Second, we apply the methodology to an exhaustible resource production. Third, we show that the dimension of the mean field system can be significantly reduced by considering a functional of the first moment of the mean field process.
Opinion dynamics in social networks through mean field games
2016
Emulation, mimicry, and herding behaviors are phenomena that are observed when multiple social groups interact. To study such phenomena, we consider in this paper a large population of homogeneous social networks. Each such network is characterized by a vector state, a vector-valued controlled input, and a vector-valued exogenous disturbance. The controlled input of each network aims to align its state to the mean distribution of other networks' states in spite of the actions of the disturbance. One of the contributions of this paper is a detailed analysis of the resulting mean-field game for the cases of both polytopic and $mathcal L_2$ bounds on controls and disturbances. A second contrib…
DESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization
2021
Interactive multiobjective optimization methods incorporate preferences from a human decision maker in the optimization process iteratively. This allows the decision maker to focus on a subset of solutions, learn about the underlying trade-offs among the conflicting objective functions in the problem and adjust preferences during the solution process. Incorporating preference information allows computing only solutions that are interesting to the decision maker, decreasing computation time significantly. Thus, interactive methods have many strengths making them viable for various applications. However, there is a lack of existing software frameworks to apply and experiment with interactive …
Dissipativity-Based Small-Gain Theorems for Stochastic Network Systems
2016
In this paper, some small-gain theorems are proposed for stochastic network systems which describe large-scale systems with interconnections, uncertainties and random disturbances. By the aid of conditional dissipativity and showing times of stochastic interval, small-gain conditions proposed for the deterministic case are extended to the stochastic case. When some design parameters are tunable in practice, we invaginate a simpler method to verify small-gain condition by selecting one subsystem as a monitor. Compared with the existing results, the existence-and-uniqueness of solution and ultimate uniform boundedness of input are removed from requirements of input-to-state stability and smal…