Pomiary Automatyka Robotyka最新文献

Study on the Influence of an UGV Suspension System on Camera Motion of the Teleoperation System 研究无人潜航器悬挂系统对远程操纵系统摄像机运动的影响

Pomiary Automatyka Robotyka Pub Date : 2024-06-14 DOI: 10.14313/par_252/53

Arkadiusz Rubiec, Mirosław Przybysz, M. Łopatka, Ł. Rykała, P. Krogul, K. Cieślik, R. Typiak

{"title":"Study on the Influence of an UGV Suspension System on Camera Motion of the Teleoperation System","authors":"Arkadiusz Rubiec, Mirosław Przybysz, M. Łopatka, Ł. Rykała, P. Krogul, K. Cieślik, R. Typiak","doi":"10.14313/par_252/53","DOIUrl":"https://doi.org/10.14313/par_252/53","url":null,"abstract":"In the following article, the results of a study on the impact of the suspension system used in Unmanned Ground Vehicles (UGVs) on the kinematic excitation of cameras in teleoperation systems are presented. As indicated by preliminary reconnaissance studies, these excitations significantly affect the operator’s ability to perceive the environment and recognize images while driving. Currently, there is a lack of publications and guidelines in the literature regarding the design of UGV suspensions and their evaluation in terms of improving operator perception in teleoperation systems. The studies were conducted in a simulated environment using multibody systems, where various suspension structure variants were developed. The tests were carried out on the ISO 5008 rough test track. The evaluation of the tested suspension structures was carried out using a proprietary method, enabling parametric analysis and the selection of favorable solutions for improving image recognition by the UGV operator. Future research can focus on adjustment of the UGV suspension characteristics which could have significant influence on situational awareness and the operator’s ability to act effectively, especially during dynamic missions.","PeriodicalId":383231,"journal":{"name":"Pomiary Automatyka Robotyka","volume":"48 40","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141339345","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

Non-selective Reference Surfaces for Hyperspectral Remote Sensing in the Long-Wave Infrared Range 用于长波红外范围高光谱遥感的非选择性参考表面

Pomiary Automatyka Robotyka Pub Date : 2024-06-14 DOI: 10.14313/par_252/107

A. Ligienza, M. Kastek, T. Sosnowski, K. Firmanty

引用次数: 0

Generation of Spatial Terrain Using the Hermes Editor 使用赫尔墨斯编辑器生成空间地形

Pomiary Automatyka Robotyka Pub Date : 2024-06-14 DOI: 10.14313/par_252/113

Mateusz Tecław, Maciej Szłapczyński

引用次数: 0

Comparative Analysis of Selected Calibration Methods for a Thermal Imaging Camera with a Cooled Detector Operating in the MWIR Range 对在中波红外范围内工作的带有冷却探测器的热成像仪的选定校准方法进行比较分析

Pomiary Automatyka Robotyka Pub Date : 2024-06-14 DOI: 10.14313/par_252/45

Karol Erd

引用次数: 0

Tuning the Heading Controller for the Ship’s Autopilot 调整船舶自动驾驶仪的航向控制器

Pomiary Automatyka Robotyka Pub Date : 2024-06-14 DOI: 10.14313/par_252/5

Zbigniew Świder

引用次数: 0

Robotic Systems Development Method Based on EARL and MeROS Metamodels 基于 EARL 和 MeROS 元模型的机器人系统开发方法

Pomiary Automatyka Robotyka Pub Date : 2024-06-14 DOI: 10.14313/par_252/11

Tomasz Winiarski, Jan Kaniuka, Jakub Ostrysz

引用次数: 0

A Method of Measurement of Minimum Resolvable Temperature Difference as a Function of Magnification and Defocusing of a Thermal Camera 一种测量红外热像仪放大倍率和散焦时可解决的最小温差的方法

Pomiary Automatyka Robotyka Pub Date : 2024-06-14 DOI: 10.14313/par_252/99

S. Gogler, Krzysztof Sawicki, A. Ligienza, Mariusz Mścichowski

引用次数: 0

The Use of Machine Learning Algorithms to Forecast Energy Production in a Small PV System 使用机器学习算法预测小型光伏系统的发电量

Pomiary Automatyka Robotyka Pub Date : 2024-06-14 DOI: 10.14313/par_252/37

Adam Idźkowski, Mateusz Sumorek

{"title":"The Use of Machine Learning Algorithms to Forecast Energy Production in a Small PV System","authors":"Adam Idźkowski, Mateusz Sumorek","doi":"10.14313/par_252/37","DOIUrl":"https://doi.org/10.14313/par_252/37","url":null,"abstract":"The article presents data analysis for predicting energy production in photovoltaic (PV) power plant systems. The purpose of long-term forecasts is to determine the effectiveness of preventive actions and manage the power system effectively. Climate variables affecting the production of electricity in photovoltaic systems were analyzed. Forecasting methods using machine learning techniques such as Multi-Layer Perceptron (MLP) neural networks and Support Vector Machine (SVM) were compared. In addition, metrics were selected to determine the quality of forecasts. Determining the quality of forecasts was based on the actual varying conditions, not on the weather forecast data. The way of data preparation to create forecasting models were presented and the models with the best metrics were selected. For this purpose, the Scikit-learn library was used to create scripts in Python. The best results were obtained for regression models: MLPRegressor, CatBoostRegressor and Support Vector Regression. Actual measurement data from a system of optimally-positioned panels with a power of 3.0 kWp were used. For the MLPRegressor model, the highest coefficient of determination 0.605 was achieved with the smallest root-mean-square error of 1.79 KWh.","PeriodicalId":383231,"journal":{"name":"Pomiary Automatyka Robotyka","volume":"5 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141343745","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

Examples of Urban Furniture Based on Arm-Z System 基于 Arm-Z 系统的城市家具实例

Pomiary Automatyka Robotyka Pub Date : 2024-06-14 DOI: 10.14313/par_252/73

Ela Zawidzka, Jacek Szklarski, J. Kobaka, M. Zawidzki

引用次数: 0

The ECG Signal Monitoring System Using Machine Learning Methods and LoRa Technology 使用机器学习方法和 LoRa 技术的心电信号监测系统

Pomiary Automatyka Robotyka Pub Date : 2024-06-14 DOI: 10.14313/par_252/21

S. Śmigiel, Tomasz Topoliński, D. Ledziński, Tomasz Andrysiak

{"title":"The ECG Signal Monitoring System Using Machine Learning Methods and LoRa Technology","authors":"S. Śmigiel, Tomasz Topoliński, D. Ledziński, Tomasz Andrysiak","doi":"10.14313/par_252/21","DOIUrl":"https://doi.org/10.14313/par_252/21","url":null,"abstract":"An electrocardiogram (ECG) is the first step in diagnosing heart disease. Heart rhythm abnormalities are among the early signs of heart disease, which can contribute to a patient’s heart attack, stroke, or sudden death. The importance of the ECGs has increased with the development of technologies based on machine learning and remote monitoring of vital signs. In particular, early detection of arrhythmias is of great importance when it comes to diagnosing a patient with heart disease. This is made possible through recognizing and classifying pathological patterns in the ECG signal. This paper presents a system for mobile monitoring of ECG signals enriched with the results of the study of the application of machine learning models from the group of Tree-based ML techniques and Neural Networks in the context of heart disease classification. The research was carried out through the use of the publicly available PTB-XL database of the ECG signals. The results were analyzed in the context of classification accuracy for 2, 5 and 15 classes of heart disease. Moreover, a novelty in the work is the proposal of machine learning techniques and architectures neural networks, which, have been selected to be applicable to IoT devices. It has been proven that the proposed solution can run in real time on IoT devices.","PeriodicalId":383231,"journal":{"name":"Pomiary Automatyka Robotyka","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141343985","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