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Optimization of Neural Networks using Swarm Intelligence Techniques for Achieving Energy Efficiency in Smart Building architecture 1

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Optimization of Neural Networks using Swarm Intelligence Tec...

1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Raja, M. Karthick Shermatova, Makhbuba Al Ansari, Mohammed Saleh Abdufattokhov, Shokhjakhon Rao, Vuda Sreenivasa Raj, I. Infant Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Turin Polytechnic University in Tashkent Civil Engineering and Architecture Department Tashkent Uzbekistan University of Bahrain College of Engineering Department of Chemical Engineering Bahrain Turin Polytechnic University Automatic Control and Computer Engineering Dept Italy Tashkent Intl. University of Education Dept of Information Technologies Tashkent Uzbekistan Koneru Lakshmaiah Education Fou. Green Fields Department of Computer Science and Engineering A.P Vaddeswaram522302 India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy621 112 India

ISBN: (纸本)9798350372748

The increasing prevalence of smart building architectures, driven by the integration of Internet of Things (IoT) devices and automation systems, has led to a surge in energy consumption. This research explores the application of swarm intelligence techniques as an innovative approach to optimize neural networks, aiming to strike a balance between maintaining the desired performance levels and minimizing energy consumption. The study investigates the integration of swarm-based optimization algorithms, such as Particle Swarm Optimization (PSO) into the training and operation of neural networks. These algorithms enable the networks to dynamically adapt and optimize their parameters in response to changing environmental conditions and user requirements. The research focuses on developing a comprehensive framework that considers the specific challenges posed by smart building architectures, including real-time data processing, sensor integration, and adaptive control. The proposed approach aims to achieve optimal neural network configurations that minimize energy consumption while ensuring reliable and responsive operation of smart building systems. The results demonstrate the potential of swarm intelligence to significantly improve the energy efficiency of neural network-enabled smart building architectures, providing a promising avenue for sustainable and intelligent infrastructure. The proposed model has an accuracy of 98.23% which is 7.64% higher than that of the traditional approaches. © 2024 IEEE.

关键词： Energy efficiency

automatic detection of vehicles in outdoor parking lots from zenith perspective using neural networks

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Automatic detection of vehicles in outdoor parking lots from...

2020 Summer computer Simulation Conference, SCSC 2020, Held at the 2020 Summer Simulation Multi-Conference, SummerSim 2020

作者： García, Juan Manuel Carrera Piorno, Joaquín Recas Mata-Garcia, María Guijarro Department of Computer Architecture and Automatic Control Faculty of Computer Science Universidad Complutense de Madrid Spain

ISBN: (纸本)9781713819462

Nowadays there are a variety of methods to assist parking users in finding free sites in parking lots. However, there is no automatic system that takes into account the size of the car looking for a space or whether the cars adjacent to the free spaces are correctly parked. This paper presents a new method for detecting and calculating the area of vehicles in images taken from a zenith plane using computer vision and machine learning techniques that will help to create a vehicle-oriented search algorithm dedicated to finding the optimal spaces for vehicles entering an outdoor parking lot based on its characteristics. Results with scaled-down and real vehicles show that this new method can detect the area of the vehicles in an image with an average accuracy of 97.98%. ©2020 Society for Modeling & Simulation International (SCS)

关键词： Vehicles

Data-Driven Reachability Analysis for Piecewise Affine System

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arXiv 2025年

作者： Xie, Peng Betz, Johannes Raimondo, Davide M. Alanwar, Amr TUM School of Computation Information and Technology Department of Computer Engineering Technical University of Munich Heilbronn74076 Germany TUM School of Engineering and Design Department of Autonomous Vehicles Systems Technical University of Munich Garching85748 Germany School of Automatic Control Department of Engineering and Architecture University of Trieste Via Valerio 10 Trieste34127 Italy

Hybrid systems play a crucial role in modeling real-world applications where discrete and continuous dynamics interact, including autonomous vehicles, power systems, and traffic networks. Safety verification for these systems requires determining whether system states can enter unsafe regions under given initial conditions and uncertainties—a question directly addressed by reachability analysis. However, hybrid systems present unique difficulties because their state space is divided into multiple regions with distinct dynamic models, causing traditional data-driven methods to produce inadequate over-approximations of reachable sets at region boundaries where dynamics change abruptly. This paper introduces a novel approach using hybrid zonotopes for data-driven reachability analysis of piecewise affine systems. Our method addresses the boundary transition problem by developing computational algorithms that calculate the family of set models guaranteed to contain the true system trajectories. Additionally, we extend and evaluate three methods for set-based estimation that account for input-output data with measurement noise. © 2025, CC BY.

关键词： Convergence of numerical methods

Towards an improvement of the forecast of wind resources in europe: application of unsupervised machine learning on future projections of polar vortex

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Towards an improvement of the forecast of wind resources in ...

2020 Summer computer Simulation Conference, SCSC 2020, Held at the 2020 Summer Simulation Multi-Conference, SummerSim 2020

作者： Montes, María Rodríguez Ayarzagüena, Blanca Mata-García, María Guijarro Complutense University of Madrid Spain Dept. Earth Physics and Astrophysics Complutense University of Madrid Spain Dept. Computer Architecture and Automatic Control Complutense University of Madrid Spain

ISBN: (纸本)9781713819462

Studying the impact of climate change on wintertime polar stratosphere is of particular relevance not only for climate knowledge but also for tropospheric projections. Machine learning provides a way to extract information from different climate models and combine the data in a such way that patterns are clearer, so predictions can be inducted. The methods used in this study have been region growing algorithm, K-means and combination of predictions. The final results show three clusters of response trends for the intensity and location of the stratospheric polar night jet. The prediction shows an increase in zonal wind intensity over the 2xCO2 and 4xCO2 concentration points and a decrease in the related latitude. Our methods can be extended for more climate models and simulation periods, and will allow not only to map the behavior of the polar night jet, but other stratospheric and tropospheric features and interactions between them. ©2020 Society for Modeling & Simulation International (SCS)

关键词： Climate models

Optimization of Neural Networks using Swarm Intelligence Techniques for Achieving Energy Efficiency in Smart Building architecture

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Optimization of Neural Networks using Swarm Intelligence Tec...

Cognitive Robotics and Intelligent Systems (ICC - ROBINS), International Conference on

作者： M. Karthick Raja Makhbuba Shermatova Mohammed Saleh Al Ansari Shokhjakhon Abdufattokhov Vuda Sreenivasa Rao I. Infant Raj Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Civil Engineering and Architecture Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan College of Engineering Department of Chemical Engineering University of Bahrain Bahrain Automatic Control and Computer Engineering Dept Turin Polytechnic University Dept of Information Technologies Tashkent Intl. University of Education Tashkent Uzbekistan Department of Computer Science and Engineering Koneru Lakshmaiah Education Fou. Green Fields Vaddeswaram A.P India Department of Computer Science and Engineering K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

关键词： Training Energy consumption Smart buildings architecture Heuristic algorithms Neural networks Energy efficiency

Distributed formation maneuver control by manipulating the complex Laplacian

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arXiv 2020年

作者： de Marina, Hector Garcia Department of Computer Architecture Automatic Control Faculty of Physics Universidad Complutense Madrid Spain

This paper proposes a novel maneuvering technique for the complex-Laplacian-based formation control. We show how to modify the original weights that build the Laplacian such that a designed steady-state motion of the desired shape emerges from the local interactions among the agents. These collective motions can be exploited to solve problems such as the shaped consensus (the rendezvous with a particular shape), the enclosing of a target, or translations with controlled speed and heading to assist mobile robots in area coverage, escorting, and traveling missions, respectively. The designed steady-state collective motions correspond to rotations around the centroid, translations, and scalings of a reference shape. The proposed modification of the weights relocates one of the Laplacian's zero eigenvalues while preserving its associated eigenvector that constructs the desired shape. For example, such relocation on the imaginary or real axis induces rotational and scaling motions, respectively. We will show how to satisfy a sufficient condition to guarantee the global convergence to the desired shape and motions. Finally, we provide simulations and comparisons with other maneuvering techniques. Copyright © 2020, The Authors. All rights reserved.

关键词： Laplace transforms

Stability of Linear Systems under Extended Weakly-Hard Constraints

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arXiv 2021年

作者： Vreman, Nils Pazzaglia, Paolo Magron, Victor Wang, Jie Maggio, Martina The Department of Automatic Control Lund University Sweden The Department of Computer Science Saarland University Germany The Laboratory for Analysis and Architecture of Systems CNRS France The Academy of Mathematics and Systems Science CAS China

control systems can show robustness to many events, like disturbances and model inaccuracies. It is natural to speculate that they are also robust to sporadic deadline misses when implemented as digital tasks on an embedded platform. This paper proposes a comprehensive stability analysis for control systems subject to deadline misses, leveraging a new formulation to describe the patterns experienced by the control task under different handling strategies. Such analysis brings the assessment of control systems robustness to computational problems one step closer to the controller implementation. Copyright © 2021, The Authors. All rights reserved.

关键词： Networked control systems

Maneuvering and robustness issues in undirected displacement-consensus-based formation control

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arXiv 2020年

作者： de Marina, Hector Garcia Department of Computer Architecture and Automatic Control at the Faculty of Physics Universidad Complutense de Madrid Spain

n this paper, we first propose a novel maneuvering technique compatible with displacement-consensus-based formation controllers. We show that the formation can be translated with an arbitrary velocity by modifying the weights in the consensus Laplacian matrix. In fact, we demonstrate that the displacement-consensus-based formation control is a particular case of our more general method. We then uncover robustness issues with undesired steady-state motions and resultant distorted shapes in undirected displacement-consensus-based formation control. In particular, these issues are triggered when neighboring agents mismeasure their relative positions, e.g., their onboard sensors are misaligned and have different scale factors. We will show that if all the sensing is close to perfect but different among the agents, then the stability of the system is compromised. Explicit expressions for the eventual non-desired velocity and shape’s distortion are given as functions of the scale factors and misalignments for formations based on tree graphs. Copyright © 2020, The Authors. All rights reserved.

关键词： System stability

Securing isosceles triangular formations under heterogeneous sensing and mixed constraints

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arXiv 2021年

作者： Chan, Nelson P.K. Jayawardhana, Bayu de Marina, Hector Garcia Engineering and Technology Institute Groningen University of Groningen Nijenborgh 4 Groningen9747AG Netherlands Department of Computer Architecture and Automatic Control Faculty of Physics Universidad Complutense de Madrid Madrid28040 Spain

This paper focuses on securing a triangular shape (up to translation) for a team of three mobile robots that uses heterogeneous sensing mechanism. Based on the available local information, each robot employs the popular gradient-based control law to attain the assigned individual task(s). In the current work, robots are assigned either distance and signed area task(s) or bearing task(s). We provide a sufficient condition on the gain ratio RAd between the signed area and the distance control term such that the desired formation shape, an isosceles triangle, is reached from all feasible starting positions. Numerical simulations are provided to support the theoretical *** Codes 93D99 © 2021, CC BY-NC-SA.

关键词： Robots