This article deals with a Hub Location Problem arising inTelecommunication Network Design. The considered network presents two different kinds of nodes: access nodes, that represent source and destination of traffic demands but cannot be directly connected, and transit nodes, that have no own traffic demand but collect traffic from access nodes and route it through the network. Transit nodes are supposed to be fully connected. Given a set of access nodes and a set of potential locations for the transit nodes, the problem is to decide number and positions of the transit nodes to guarantee that all access nodes are allocated to a transit node, satisfying capacity constraints. The goal is to minimize the total cost of the network, which is the sum of connection costs and nodes fixed costs. The problem is a Hub Location Problem, which is known to be NP-hard. A local search approach is proposed, and different metaheuristic algorithms, such as tabu search, iterated local search and random multistart, have been developed, based on such local search. [A preliminary procedure has been developed in a research project joint with Telecom Italia (Turin Research & Innovation Laboratories) and a patent application has been filed to cover this issue.]
In this paper, we present a novel distributed framework for real time management and co-simulation of Demand Response (DR) in smart grids. Our solution provides a (near-) real-time co-simulation platform to validate new DR-policies exploiting Internet-of-Things approach performing software-in-the-loop. Hence, the behavior of real-world power systems can be emulated in a very realistic way and different DR-policies can be easily deployed and/or replaced in a plugand-play fashion, without affecting the rest of the framework. In addition, our solution integrates real internet-connected smart devices deployed at customer premises and along the Smart Grid to retrieve energy information and send actuation commands. Thus, the framework is also ready to manage DR in a real-world Smart Grid. This is demonstrated on a realistic smart grid with a test case DR-policy.
A novel didactic sequence is proposed
for the teaching of chemical
equilibrium. This teaching sequence takes into account the historical
and epistemological evolution of the concept, the alternative conceptions
and learning difficulties highlighted by teaching science and research
in education, and the need to focus on both the students’ learning
process and the knowledge to learn.
This paper deals with the control of the sit-to-stand transfer of a biped robotic device (either an autonomous biped robot or a haptic assistive exoskeleton for postural rehabilitation). The control has been synthesized, instead of considering the physiology, analyzing the basic laws of dynamics. The transfer of a human from sitting on a chair to an erect posture is an interesting case study, because it treats biped balance in a two-phase dynamic setting, with an external force disturbance (the chair–pelvis contact) affecting the center of pressure under the feet. At the beginning, a body is sitting, with a fixed pelvis moving with the hips going toward the supporting feet and, contemporaneously, releasing the load from the chair with ankles and knee torques. Then, after lift-off, it reaches and maintains an erect posture. The paper objectives are threefold: identifying the major dynamical determinants of the exercise; sythesizing an automatic control for an autonomous device; proposing an innovative approach for the rehabilitation process with an exoskeleton. For this last objective, the paper extends the idea of the authors of a haptic exoskeleton for rehabilitation. It is driven to control the joints by electromiographical signals from the patient. The two spaces, cartesian (world) and joint, where, respectively, the automatic control and the patient operate, are considered and a technique to blend the two actions is proposed. The exoskeleton is programed to perform the exercise autonomously. Then, during the evolution of the phases of rehabilitation, we postulated to seamlessly move the control from one space (purely autonomous) to another (completely driven by the patient), choosing and keeping the postural tasks and joints (heaps, knees, or ankles) on which to apply each one of the two actions without interaction.
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