Search results for "modeling"
showing 10 items of 4489 documents
Enabling peer-to-peer User-Preference-Aware Energy Sharing Through Reinforcement Learning
2020
Renewable, heterogeneous and distributed energy resources are the future of power systems, as envisioned by the recent paradigm of Virtual Power Plants (VPPs). Residential electricity generation, e.g., through photovoltaic panels, plays a fundamental role in this paradigm, where users are able to participate in an energy sharing system and exchange energy resources among each other. In this work, we study energy sharing systems and, differently from previous approaches, we consider realistic user behaviors by taking into account the user preferences and level of engagement in the energy trades. We formulate the problem of matching energy resources while contemplating the user behavior as a …
INTEGRATED SURVEYING AND MODELING TECHNIQUES FOR THE DOCUMENTATION AND VISUALIZATION OF THREE ANCIENT HOUSES IN THE MEDITERRANEAN AREA
2015
The paper is focused on the layout and testing of a workflow for the documentation of archeological remains, addressed to the study, visualization and information data input. Topographic, laser scanning and photogrammetric data have been used to build up 3D textured models of three ancient houses built in Sicily and in Tunisia in the Hellenistic and Roman age. 3D models have been used to extract conventional representations (plans and sections), analyze the geometric and proportional features and propose a virtual reconstruction of the original layout. The final step of the research work has been addressed to the creation of a web-based tools for the visualization of the models addressed to…
A Reinforcement Learning Approach for User Preference-aware Energy Sharing Systems
2021
Energy Sharing Systems (ESS) are envisioned to be the future of power systems. In these systems, consumers equipped with renewable energy generation capabilities are able to participate in an energy market to sell their energy. This paper proposes an ESS that, differently from previous works, takes into account the consumers’ preference, engagement, and bounded rationality. The problem of maximizing the energy exchange while considering such user modeling is formulated and shown to be NP-Hard. To learn the user behavior, two heuristics are proposed: 1) a Reinforcement Learning-based algorithm, which provides a bounded regret and 2) a more computationally efficient heuristic, named BPT- ${K}…
A Methodology for Graphical Modeling of Business Rules
2011
This work proposes a novel methodology based on the Business Process Modeling Notation (BPMN) standard capable of graphically modeling business rules. A set of new representation patterns allows business analysts to map processes described through BPMN into conditions and actions of business rules. Our approach exploits Domain Specific Language techniques in order to make the methodology independent from the programming language supported by the specific rule engine. Moreover, this work proposes a web graphical editor, instantiated on a specific sample scenario, where the selected rule engine is Drools, one of the most used open source products. The developed editor allows business analysts…
A cognitive architecture for ambient intelligence systems
2018
Nowadays, the use of intelligent systems in homes and workplaces is a well-established reality. Research efforts are moving towards increasingly complex Ambient Intelligence (AmI) systems that exploit a wide variety of sensors, software modules and stand-alone systems. Unfortunately, using more data often comes at a cost, both in energy and computational terms. Finding the right trade-off between energy savings, information costs and accuracy of results is a major challenge, especially when trying to integrate many heterogeneous modules. Our approach fits into this scenario by proposing an ontology-based AmI system with a cognitive architecture, able to perceive the state of the surrounding…
Wi-Dia: Data-Driven Wireless Diagnostic Using Context Recognition
2018
The recent densification of Wi-Fi networks is exacerbating the effects of well-known pathologies including hidden nodes and flow starvation. This paper provides an automatic diagnostic tool for detecting the source roots of performance impairments by recognizing the wireless operating context. Our tool for Wi-Fi diagnostic, named Wi-Dia, exploits machine learning methods and uses features related to network topology and channel utilization, without impact on regular network operations and working in real-time. Real-time per-link Wi-Fi diagnosis enables recovering actions for context-specific treatments. Wi-Dia classifier recognizes different classes of interference; it is jointly trained us…
Evaluating Correlations in IoT Sensors for Smart Buildings
2021
International audience; In this paper we introduce a dataset of environmental information obtained via indoor and outdoor sensors deployed in the SMART Infrastructure Facility of the University of Wollongong (Australia). The acquired dataset is also made open-sourced along with this paper. We also propose a novel approach based on an evolutionary algorithm to determine pairs of correlated sensors. We compare our approach with three other standard techniques on the same dataset: on average, the accuracy of the evolutionary method is about 62,92%. We also evaluate the computational time, assessing the suitability of the proposed pipeline for real-time applications.
WSNs for structural health monitoring of historical buildings
2009
Monitoring structural health of historical heritage buildings may be a daunting task for civil engineers due to the lack of a pre-existing model for the building stability, and to the presence of strict constraints on monitoring device deployment. This paper reports on the experience maturated during a project regarding the design and implementation of an innovative technological framework for monitoring critical structures in Sicily, Italy. The usage of wireless sensor networks allows for a pervasive observation over the sites of interest in order to minimize the potential damages that natural phenomena may cause to architectural or engineering works. Moreover, the system provides real-tim…
Verification of Symbolic Distributed Protocols for Networked Embedded Devices
2020
The availability of versatile and interconnected embedded devices makes it possible to build low-cost networks with a large number of nodes running even complex applications and protocols in a distributed manner. Common tools used for modeling and verification, such as simulators, present some limitations as application correctness is checked off-board and only focuses on source code. Execution in the real network is thus excluded from the early stages of design and verification. In this paper, a system for modeling and verification of symbolic distributed protocols running on embedded devices is introduced. The underlying methodology is rooted in a symbolic programming paradigm that makes …
An FPGA Implementation of a Quadruple-Based Multiplier for 4D Clifford Algebra
2008
Geometric or Clifford algebra is an interesting paradigm for geometric modeling in fields as computer graphics, machine vision and robotics. In these areas the research effort is actually aimed at finding an efficient implementation of geometric algebra. The best way to exploit the symbolic computing power of geometric algebra is to support its data types and operators directly in hardware. However the natural representation of the algebra elements as variable-length objects causes some problems in the case of a hardware implementation. This paper proposes a 4D Clifford algebra in which the variable-length elements are mapped into fixed-length elements (quadruples). This choice leads to a s…