2022 International Symposium on Electromagnetic Compatibility – EMC Europe最新文献_第4页

On the Feasibility of the Three-Antenna Technique for Estimating Antenna Radiation Efficiency in Bluetest Reverberation Test Systems 三天线技术估算蓝牙混响测试系统天线辐射效率的可行性研究

2022 International Symposium on Electromagnetic Compatibility – EMC Europe Pub Date : 2022-09-05 DOI: 10.1109/EMCEurope51680.2022.9900982

Samar Hosseinzadegan, M. Kristoffersen, P. Svedjenas, Sara Catteau, J. Kvarnstrand

{"title":"On the Feasibility of the Three-Antenna Technique for Estimating Antenna Radiation Efficiency in Bluetest Reverberation Test Systems","authors":"Samar Hosseinzadegan, M. Kristoffersen, P. Svedjenas, Sara Catteau, J. Kvarnstrand","doi":"10.1109/EMCEurope51680.2022.9900982","DOIUrl":"https://doi.org/10.1109/EMCEurope51680.2022.9900982","url":null,"abstract":"In wireless communications, antenna characteristics, especially antenna efficiency, are critical for connectivity and reliability. Despite the importance of this parameter, there are few methods for determining antenna efficiency without relying on a reference antenna with known efficiency. The three-antenna technique, an approach without a reference antenna, has already been proposed to measure antenna efficiency using the reverberation chamber at the National Institute of Standards and Technology (NIST). In this paper, we focus on the implementation and evaluation of this technique to determine the radiation efficiency of antennas in the reverberation test systems (RTS) of Bluetest AB. We show that the radiation efficiency estimated using the three-antenna method is comparable to the corresponding values obtained from standard radiation efficiency measurements, as well as to the efficiencies reported by antenna manufacturers. In addition, we report the decay times for two RTS chambers avail-able at Bluetest's laboratory center. The enhanced backscatter parameter, which indicates how well a reverberation chamber is mode-stirred, is also compared for our systems.","PeriodicalId":268262,"journal":{"name":"2022 International Symposium on Electromagnetic Compatibility – EMC Europe","volume":"789 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134589425","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

Correlation Between HF Interference at Low and High Elevation Angle 低、高仰角高频干扰的相关性研究

2022 International Symposium on Electromagnetic Compatibility – EMC Europe Pub Date : 2022-09-05 DOI: 10.1109/EMCEurope51680.2022.9901047

A. Constantinides, H. Haralambous

{"title":"Correlation Between HF Interference at Low and High Elevation Angle","authors":"A. Constantinides, H. Haralambous","doi":"10.1109/EMCEurope51680.2022.9901047","DOIUrl":"https://doi.org/10.1109/EMCEurope51680.2022.9901047","url":null,"abstract":"Interference from other users in the High Frequency (HF) spectrum (3–30 MHz) is an established consequence as a result of the long distance propagation of HF signals and the high level of the HF spectrum usage on a global scale. One crucial factor affecting HF interference conditions is the angle of arrival of HF signals which depends on antenna radiation pattern characteristics. In this study we perform an analysis based on an extended dataset of 280 days of electric field measurements within an interval of more than a year (Jan 2021-Feb 2022) collected at low and high incident elevation angles by a R&S HE016 antenna installed in Nicosia, Cyprus. This extended dataset enables the examination of the correlation of signals received by these two different antenna components and how this correlation varies as a function of frequency across the HF spectrum. The ultimate aim is to indicate the possible benefit from the possible introduction of elevation angle as a model parameter to existing HF interference model formulations in order to better characterize the HF interference environment over the eastern Mediterranean region.","PeriodicalId":268262,"journal":{"name":"2022 International Symposium on Electromagnetic Compatibility – EMC Europe","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115559464","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

Estimation of Effectiveness of EMI Gaskets by using Results of Standardized Measurements 利用标准化测量结果估计电磁干扰衬垫的有效性

2022 International Symposium on Electromagnetic Compatibility – EMC Europe Pub Date : 2022-09-05 DOI: 10.1109/EMCEurope51680.2022.9901097

D. Tsyanenka, E. Sinkevich, Y. Arlou, Alexey Galenko, Xie Ma, Wen-Qing Guo

引用次数: 1

Combining 2oo3 Voting and Hamming Error Correction to Reduce the Occurrence of False Negatives in Wired Communication Lines under Continuous-Wave Electromagnetic Disturbances 结合2303投票和Hamming纠错减少连续波电磁干扰下有线通信线路误负的发生

2022 International Symposium on Electromagnetic Compatibility – EMC Europe Pub Date : 2022-09-05 DOI: 10.1109/EMCEurope51680.2022.9900946

Mohammad Kameli, T. Claeys, D. Pissoort

引用次数: 1

Detection of fault location in branching power distribution network using deep learning algorithm 基于深度学习算法的分支配电网故障定位检测

2022 International Symposium on Electromagnetic Compatibility – EMC Europe Pub Date : 2022-09-05 DOI: 10.1109/EMCEurope51680.2022.9901205

Daiki Nagata, Shunya Fujioka, Tohlu Matshushima, H. Kawano, Y. Fukumoto

{"title":"Detection of fault location in branching power distribution network using deep learning algorithm","authors":"Daiki Nagata, Shunya Fujioka, Tohlu Matshushima, H. Kawano, Y. Fukumoto","doi":"10.1109/EMCEurope51680.2022.9901205","DOIUrl":"https://doi.org/10.1109/EMCEurope51680.2022.9901205","url":null,"abstract":"An immediate response is desired when any failure on overhead power distribution systems has occurred, and the TDR(Time Domain Reflecting) method has been introduced to detect fault detection locations. However, accurate detection of the fault point is difficult due to waveform distortion and decrease in amplitude of TDR pulse in complex networks with multiple branches and electric power distribution equipment such as transformers and switchgear. A method for detecting fault points from TDR waveforms using deep learning is proposed in this study. The proposed method can be applied to detect fault locations and fault types in branching power distribution networks where multiple reflected waves are observed. Since the deep learning algorithm requires a large amount of waveform data, we developed a fast simulation method to create the data. To simulate the circuit rapidly, the power distribution line was treated as a transmission line, thereby deriving the fundamental matrix of the transmission line. Additionally, the equivalent circuit model of the power distribution network was represented by cascading the fundamental matrix. The TDR waveform data was obtained rapidly by calculating the equivalent circuit using MATLAB. We used this to create many TDR waveform data of an overhead distribution network model with multiple branches and performed fault locations detection using a deep learning algorithm. As a result, it was shown that the location of accident was identified with 96.8% accuracy.","PeriodicalId":268262,"journal":{"name":"2022 International Symposium on Electromagnetic Compatibility – EMC Europe","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115788327","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

Predicting the EMI Induced Offset of a Differential Amplifier Stage using a Neural Network Model 用神经网络模型预测差分放大器级的电磁干扰诱发偏置

2022 International Symposium on Electromagnetic Compatibility – EMC Europe Pub Date : 2022-09-05 DOI: 10.1109/EMCEurope51680.2022.9900922

Dominik Zupan, Daniel Kircher, N. Czepl

{"title":"Predicting the EMI Induced Offset of a Differential Amplifier Stage using a Neural Network Model","authors":"Dominik Zupan, Daniel Kircher, N. Czepl","doi":"10.1109/EMCEurope51680.2022.9900922","DOIUrl":"https://doi.org/10.1109/EMCEurope51680.2022.9900922","url":null,"abstract":"In this paper we present a concept for predicting offset changes on a differential amplifier stage that is exposed to electromagnetic interference (EMI) on its inputs. We do this by using methods that are commonly used in the field of artificial intelligence (AI). To be more precise we develop a regression model based on a neural network topology. In the course of this we first create independent training and test data sets from simulations. The training data is then used to train prediction models, that are different in their structure and complexity. The test data is used to validate these models and to choose the best fitting model. Finally, we show that the model predictions match the real labels well, both for test data within and outside of the training data range, i.e. for higher frequencies than we trained for. Furthermore we provide the code as well as the data needed for the fitting algorithm, that was implemented by using the Tensorflow Python library. This work can be understood as a proof of concept, that can be applied to more complex regression problems to predict EMI induced offset changes.","PeriodicalId":268262,"journal":{"name":"2022 International Symposium on Electromagnetic Compatibility – EMC Europe","volume":"373 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115905757","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

On the Decoupling of Integrals in the Surface PEEC Method 曲面PEEC方法中积分的解耦

2022 International Symposium on Electromagnetic Compatibility – EMC Europe Pub Date : 2022-09-05 DOI: 10.1109/EMCEurope51680.2022.9901250

M. De Lauretis, Elena Haller, D. Romano, Giulio Antonini, J. Ekman, Ivana Kovačević-Badstübner, U. Grossner

引用次数: 0

Experimental Prediction of the Radiated Emission and Final Measurement Process Optimization based on Deep Neural Networks According to EN 55032 根据en55032基于深度神经网络的辐射发射实验预测及最终测量过程优化

2022 International Symposium on Electromagnetic Compatibility – EMC Europe Pub Date : 2022-09-05 DOI: 10.1109/EMCEurope51680.2022.9901041

Hussam Elias, Ninovic Perez, H. Hirsch

{"title":"Experimental Prediction of the Radiated Emission and Final Measurement Process Optimization based on Deep Neural Networks According to EN 55032","authors":"Hussam Elias, Ninovic Perez, H. Hirsch","doi":"10.1109/EMCEurope51680.2022.9901041","DOIUrl":"https://doi.org/10.1109/EMCEurope51680.2022.9901041","url":null,"abstract":"Electromagnetic interference (EMI) is the presence of unwanted electromagnetic emission which has the potential to cause disturbances in electronic and electronic devices. Therefore, any equipment must be certified that it meets electromagnetic compatibility (EMC) requirements. To meet these requirements the equipment must be tested for conducted and radiated emissions in a certified EMC testing company. Most of these tests are time-consuming, thus cost and test time are big challenges when performing an EMI test. In this paper, an approach is proposed to find effectively the worth-case positions during the final measurement phase on critical frequencies in EMI measurements according to the norm EN 55032 in the range 30MHz to 1GHz by using a developed measurement software and deep neural networks (DNN). Firstly, because of its advantage of relatively simple model structure and strong data features extraction, a one-dimensional convolution neural network (1D CNN) was used to predict the positions that meet the maximum radiation emission level. The effectiveness and prediction accuracy of CNNs for the high input variance emission levels were low, therefore a hybrid deep learning neural network framework, that combines CNN with long short term memory(LSTM) was adopted to forecast the worst-case of the high variance emission levels. The DNNs were trained using real EMI measurements for different types of equipment under test (EUTs) in a Semi Anechoic Chamber (SAC) by Cetecom GmbH in Essen, Germany. Secondly, the results from our developed software were compared with the target labeled results from Rode& Schwarz EMC32 Software to evaluate our proposed measurement. By determining the worth-case position by predicting the azimuth of the turntable and the height of the antenna, the required time to perform the final measurement phase is effectively decreased.","PeriodicalId":268262,"journal":{"name":"2022 International Symposium on Electromagnetic Compatibility – EMC Europe","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124839910","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

A fast and efficient Model Extraction Method to predict the transient Response of ESD Protection Devices 一种快速有效的模型提取方法预测ESD保护器件的瞬态响应

2022 International Symposium on Electromagnetic Compatibility – EMC Europe Pub Date : 2022-09-05 DOI: 10.1109/EMCEurope51680.2022.9901148

F. Ruffat, F. Caignet, A. Boyer, F. Escudié, G. Mejecaze, F. Puybaret

引用次数: 2

Distance Characteristics of Field Peak Value of Transient Electric Field Caused by Sphere-Gap ESD Using a Optical E-Field Probe 球隙静电放电引起的瞬态电场峰值距离特性研究

2022 International Symposium on Electromagnetic Compatibility – EMC Europe Pub Date : 2022-09-05 DOI: 10.1109/EMCEurope51680.2022.9901110

K. Kawamata, S. Ishigami, O. Fujiwara

引用次数: 1