2023 31st Mediterranean Conference on Control and Automation (MED)最新文献_第10页

Anisotropy-based Approach of Estimating for Sensors Network with Nonzero Mean of Input 基于各向异性的非零输入均值传感器网络估计方法

2023 31st Mediterranean Conference on Control and Automation (MED) Pub Date : 2023-06-26 DOI: 10.1109/MED59994.2023.10185867

A. Yurchenkov, A. Kustov

引用次数: 0

Bibliometric Analysis on Applications of Digital Twins in Autonomous Vehicles 数字孪生在自动驾驶汽车中的应用文献计量学分析

2023 31st Mediterranean Conference on Control and Automation (MED) Pub Date : 2023-06-26 DOI: 10.1109/MED59994.2023.10185874

Nikolaos Sarantinoudis, George J. Tsinarakis, L. Doitsidis, N. Tsourveloudis, G. Arampatzis

引用次数: 0

Control of Isolated AC Microgrids with Constant Power Loads: A Set Invariance Approach 恒负荷隔离型交流微电网控制:一种集不变性方法

2023 31st Mediterranean Conference on Control and Automation (MED) Pub Date : 2023-06-26 DOI: 10.1109/MED59994.2023.10185703

Grigoris Michos, George C. Konstantopoulos, P. Trodden, V. Kadirkamanathan

引用次数: 0

Reviewing Deep Learning-Based Feature Extractors in a Novel Automotive SLAM Framework 基于深度学习的特征提取器在汽车SLAM框架中的应用综述

2023 31st Mediterranean Conference on Control and Automation (MED) Pub Date : 2023-06-26 DOI: 10.1109/MED59994.2023.10185780

Christos Anagnostopoulos, A. Lalos, P. Kapsalas, Duong Nguyen Van, C. Stylios

引用次数: 0

Evaluation of Deep Learning and Machine Learning Algorithms for Building Occupancy Classification on Open Datasets 基于开放数据集的建筑占用分类的深度学习和机器学习算法评价

2023 31st Mediterranean Conference on Control and Automation (MED) Pub Date : 2023-06-26 DOI: 10.1109/MED59994.2023.10185804

Georgiana Cretu, Iulia Stamatescu, G. Stamatescu

{"title":"Evaluation of Deep Learning and Machine Learning Algorithms for Building Occupancy Classification on Open Datasets","authors":"Georgiana Cretu, Iulia Stamatescu, G. Stamatescu","doi":"10.1109/MED59994.2023.10185804","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185804","url":null,"abstract":"Accurately estimating and forecasting building occupancy represents an important tasks for higher level indoor energy management and control routines. Extended availability of public and open datasets reflecting indoor conditions through various sensor measurement and indirect proxies of human activity enable reliable benchmarking of new techniques for pre-processing and learning of occupancy patterns. In this work we present a comparative study between deep learning, such as convolutional neural networks, and conventional machine learning approaches, such as decision trees and random forests, on an a reference occupancy dataset. The various design decision and parametrisation options are discussed. The building occupancy classification task involves generating model outputs for various discrete occupancy categories. Standardised metrics such as accuracy, precision, recall and the F1-score are used for replicable benchmarking of the results. Main finding of the study is that, though generally the deep learning methods offer better overall results, the addition of relevant features (sensors) to the input dataset can yield better results for the conventional machine learning models with significantly lower training time and model size. This results in suitable, fast-inference, models for embedded deployment in physical proximity to the process.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123483287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Achieving Prescribed Performance for Uncertain Impulsive Systems in Brunovsky Canonical Form* 不确定脉冲系统在Brunovsky标准形式下的性能实现

2023 31st Mediterranean Conference on Control and Automation (MED) Pub Date : 2023-06-26 DOI: 10.1109/MED59994.2023.10185769

Andreas P. Kechagias, G. Rovithakis

引用次数: 0

Nonlinear MPC for Fuel Cell Air Path Control with Experimental Validation 非线性MPC在燃料电池空气路径控制中的实验验证

2023 31st Mediterranean Conference on Control and Automation (MED) Pub Date : 2023-06-26 DOI: 10.1109/MED59994.2023.10185785

L. Schmitt, D. Abel

引用次数: 0

Relaxed fault estimation conditions for fuzzy systems subject to time varying actuator and sensor faults 时变致动器和传感器故障模糊系统的松弛故障估计条件

2023 31st Mediterranean Conference on Control and Automation (MED) Pub Date : 2023-06-26 DOI: 10.1109/MED59994.2023.10185670

Salama Makni, A. Hajjaji, M. Chaabane

引用次数: 0

Fault Tolerant Control Using Sliding Modes for Scale Model of a High Altitude Long Endurance Aircraft 基于滑模的高空长航时飞机比例模型容错控制

2023 31st Mediterranean Conference on Control and Automation (MED) Pub Date : 2023-06-26 DOI: 10.1109/MED59994.2023.10185758

S. S. Rawikara, H. Alwi, C. Edwards

引用次数: 0

RL-based path planning for controller performance validation 基于rl的控制器性能验证路径规划

2023 31st Mediterranean Conference on Control and Automation (MED) Pub Date : 2023-06-26 DOI: 10.1109/MED59994.2023.10185811

Lukas Schichler, Karin Festl, M. Stolz, D. Watzenig

{"title":"RL-based path planning for controller performance validation","authors":"Lukas Schichler, Karin Festl, M. Stolz, D. Watzenig","doi":"10.1109/MED59994.2023.10185811","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185811","url":null,"abstract":"Autonomous vehicles (AVs) will be part of everyday life in the near future. In order to accelerate this process, many subsystems need to be optimised and validated. One of the most important subsystem of AVs is the steering controller. It’s task is to keep the vehicle on track, which is the reason, why many steering controllers have been designed for a large variety of applications. However, the validation of such controllers is a labour-intensive task, which is why in this paper, an Artificial Intelligence (AI) is trained to find an edge case path that brings the steering controller to its limits. This path is a sufficient substitute for a large set of paths and enables fast validation of steering controllers. This contribution describes the development of a reinforcement learning (RL) based path planner using the PPO-Algorithm to train a so called agent. Comparing the resulting key feature maps shows that the agent adapts to each controllers characteristics during the learning process. The result is demonstrated for three different state of the art path tracking controllers. For each controller the agent finds a path that leads to the controllers failure within seconds.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114574675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0