2022 30th Mediterranean Conference on Control and Automation (MED)最新文献_第5页

Comparative Study of Neural Networks Used in Halyomorpha Halys Detection* 神经网络在海苔检测中的应用比较研究*

2022 30th Mediterranean Conference on Control and Automation (MED) Pub Date : 2022-06-28 DOI: 10.1109/MED54222.2022.9837254

D. Popescu, L. Ichim, M. Dimoiu, Raluca Trufelea

{"title":"Comparative Study of Neural Networks Used in Halyomorpha Halys Detection*","authors":"D. Popescu, L. Ichim, M. Dimoiu, Raluca Trufelea","doi":"10.1109/MED54222.2022.9837254","DOIUrl":"https://doi.org/10.1109/MED54222.2022.9837254","url":null,"abstract":"The paper’s purpose was to investigate some methods based on neural networks for the detection and classification of harmful insects for agriculture as the Halyomorpha Halys. The implementation of different object detection networks for image categorization was analyzed. Images from the Maryland Biodiversity database were used for neural network training and testing. Rotation, scaling, blurring, mirroring, and other techniques were employed for data augmentation. For the detection and classification of Halyomorpha Halys, some neural networks that include multiple smaller networks were implemented and investigated. The networks used are the following: YOLOv5s, SSD with different backbones such as MobileNet V1, MobileNet V2, and ResNet-50, Faster R-CNN with ResNet-50 backbone, and EfficientDet-D0. Moreover, neural networks were evaluated and compared based on performance metrics such as accuracy and time. Performances like accuracy between 0.49 – 0.86 and time between 36 ms – 55 ms were obtained. The best results were obtained for YOLOv5s, in terms of accuracy, and EfficientDet-D0, in terms of time.","PeriodicalId":354557,"journal":{"name":"2022 30th Mediterranean Conference on Control and Automation (MED)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114613812","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: 2

Shortest path type classification for real-time three-points Dubins problems 实时三点dubin问题的最短路径类型分类

2022 30th Mediterranean Conference on Control and Automation (MED) Pub Date : 2022-06-28 DOI: 10.1109/MED54222.2022.9837205

Daniela De Palma, G. Parlangeli

引用次数: 0

Distributed Least-Squares over Directed Networks 有向网络上的分布最小二乘

2022 30th Mediterranean Conference on Control and Automation (MED) Pub Date : 2022-06-28 DOI: 10.1109/MED54222.2022.9837278

Mohammad Jahvani, M. Guay

引用次数: 0

Combining Data-driven and Physics-based Process Models for Hybrid Model Predictive Control of Building Energy Systems 结合数据驱动和基于物理的过程模型用于建筑能源系统混合模型预测控制

2022 30th Mediterranean Conference on Control and Automation (MED) Pub Date : 2022-06-28 DOI: 10.1109/MED54222.2022.9837277

Phillip Stoffel, Charlotte S Löffler, Steffen Eser, A. Kümpel, D. Müller

引用次数: 1

Time-delay estimation with non-persistent input * 非持久输入的时延估计*

2022 30th Mediterranean Conference on Control and Automation (MED) Pub Date : 2022-06-28 DOI: 10.1109/MED54222.2022.9837191

A. Medvedev

引用次数: 2

Distributed Asynchronous Projection onto the Intersection of Convex Sets 凸集交点上的分布式异步投影

2022 30th Mediterranean Conference on Control and Automation (MED) Pub Date : 2022-06-28 DOI: 10.1109/MED54222.2022.9837283

Camilla Fioravanti, G. Oliva, S. Panzieri

引用次数: 0

Object Detection and Navigation of a Mobile Robot by Fusing Laser and Camera Information 基于激光与相机信息融合的移动机器人目标检测与导航

2022 30th Mediterranean Conference on Control and Automation (MED) Pub Date : 2022-06-28 DOI: 10.1109/MED54222.2022.9837249

Spyridon Syntakas, K. Vlachos, A. Likas

{"title":"Object Detection and Navigation of a Mobile Robot by Fusing Laser and Camera Information","authors":"Spyridon Syntakas, K. Vlachos, A. Likas","doi":"10.1109/MED54222.2022.9837249","DOIUrl":"https://doi.org/10.1109/MED54222.2022.9837249","url":null,"abstract":"While state-of-the-art YOLO approaches have revolutionized real time object detection in mobile robotics, most of the publicly available models are trained on datasets with a small number of available classes. In addition, the difficulty in creating large datasets with many available classes for 2D object detection sets limitations to real world robotic applications and specialized use cases. This paper presents a solution that tackles these limitations by approaching object detection via fusion of 2D laser and RGB camera information resulting to a detector with 1000 learned classes. Object localization is performed in the 3D world by clustering the point cloud provided by the 2D laser scanner using the DBSCAN algorithm. The clusters are projected onto the image plane providing Regions of Interest (ROI), where proposed object bounding boxes are obtained, that are labeled with distance information. Object recognition is achieved using a pretrained, on the ImageNet dataset, ResNet and a voting schema among proposed bounding boxes, that also estimates the objects height. The detection system is used in combination with a navigation system that employs artificial potential field. The combination of the two, makes the robot’s perception easily adaptable to specialized applications and the robot’s behaviour adjustable to the complexity and variability of unstructured and unknown workspaces. The method has been implemented in ROS and tested both in simulation as well as in real case scenarios using the mobile robot Pioneer 3-DX. The work is aimed at robots with limited hardware and sensor capabilities and tries to enable detection via fusion, despite the limitations.","PeriodicalId":354557,"journal":{"name":"2022 30th Mediterranean Conference on Control and Automation (MED)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122404678","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: 1

Interval State Estimation of Switched Takagi-Sugeno Systems with Metzler-Lipschitz Features 具有Metzler-Lipschitz特征的切换Takagi-Sugeno系统的区间状态估计

2022 30th Mediterranean Conference on Control and Automation (MED) Pub Date : 2022-06-28 DOI: 10.1109/MED54222.2022.9837265

D. Krokavec, A. Filasová

引用次数: 0

Actuator Fault Diagnosis Of Nonlinear Systems Based On Unknown Input Root-Mean-Square Cubature Kalman Filter * 基于未知输入根均方立方卡尔曼滤波的非线性系统执行器故障诊断*

2022 30th Mediterranean Conference on Control and Automation (MED) Pub Date : 2022-06-28 DOI: 10.1109/MED54222.2022.9837264

Huaming Qian, Shuya Yan, Pengheng Ding, Shuai Chu

引用次数: 0

NARX Models of Two-Phase Microchannels Flow in Comparison 两相微通道流动的NARX模型比较

2022 30th Mediterranean Conference on Control and Automation (MED) Pub Date : 2022-06-28 DOI: 10.1109/MED54222.2022.9837185

Giovanna Stella, S. Gagliano, M. Bucolo

{"title":"NARX Models of Two-Phase Microchannels Flow in Comparison","authors":"Giovanna Stella, S. Gagliano, M. Bucolo","doi":"10.1109/MED54222.2022.9837185","DOIUrl":"https://doi.org/10.1109/MED54222.2022.9837185","url":null,"abstract":"In this work, two structures of data-driven models have been optimized and compared for the identification and tracking of fast two-phase flows in microchannels. Two-phase flow, consisted of an interlaced sequence of two fluids, as water and air, traveling in a microchannel is defined slug flow and it can be generated by their interaction at a junction. An extensive experimental campaign was performed to collect data and the processes was optically monitored. Two structures of Nonlinear AutoRegressive with eXogenous (NARX) input models, by using Neural Networks (NN) and Wavelet Networks (WN), were compared for modeling the slug flow passage. Two types of patterns were chosen to train and test the networks: single-flow pattern, one per experiment, and multi-flow patterns containing more experimental conditions. The test on single flow patterns highlights the robustness of the models in tracking the slug flow passage and the test on multiple flows patterns confirms the possibility to have one model for different experimental conditions. To underline the potential of these models, some indices were considered to evaluate their performance. The proposed models can represent an important step towards the development of predictive control for real-time System-on-Chip applications.","PeriodicalId":354557,"journal":{"name":"2022 30th Mediterranean Conference on Control and Automation (MED)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124106985","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