Pub Date : 2024-11-27DOI: 10.1109/temc.2024.3493458
Yue Zhang, Liang Zhou, Fayang Pan, Jun-Fa Mao
{"title":"Design and Failure Mechanism Analysis of a High-Power Limiter at DC-6 GHz With GaAs PIN Technology","authors":"Yue Zhang, Liang Zhou, Fayang Pan, Jun-Fa Mao","doi":"10.1109/temc.2024.3493458","DOIUrl":"https://doi.org/10.1109/temc.2024.3493458","url":null,"abstract":"","PeriodicalId":55012,"journal":{"name":"IEEE Transactions on Electromagnetic Compatibility","volume":"9 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142753105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-20DOI: 10.1109/TEMC.2024.3467039
Harry H. Hodes;Din D. Ng
Traditional statistical procedures used for analyzing quantitative electromagnetic compatibility (EMC) proficiency test (PT) data are overly simplistic, given that they are implicitly based upon the following erroneous assumptions: first, PT data in a given round constitute a random sample drawn from an underlying population that is distributed normally, and therefore, the normality criteria can used to set the pass/fail threshold for PT participants based upon an arbitrary, predefined choice of Z -value; and second the maximum permissible error values are unimportant and can be ignored. Those two fundamental errors produce misclassified PT pass/fail results, which can have serious economic consequences for both EMC test laboratories and their customers. This article first reviews the published literature on quantitative EMC PT from the standpoint of assessing the statistical methodologies used and how the pass/fail criteria were applied. Next, this article discusses the effects of these statistical procedure errors. Finally, it details a practical, more accurate alternative Bayesian method that incorporates the maximum permissible error as the a priori information in its analysis model. This method treats the quantitative EMC PT data as a population with no specific presumed distribution.
{"title":"On the Correct Approach to the Modeling and Analysis of Quantitative EMC Proficiency Testing Data for the Purpose of Evaluating Test Laboratory Claims of Competency","authors":"Harry H. Hodes;Din D. Ng","doi":"10.1109/TEMC.2024.3467039","DOIUrl":"10.1109/TEMC.2024.3467039","url":null,"abstract":"Traditional statistical procedures used for analyzing quantitative electromagnetic compatibility (EMC) proficiency test (PT) data are overly simplistic, given that they are implicitly based upon the following erroneous assumptions: first, PT data in a given round constitute a random sample drawn from an underlying population that is distributed normally, and therefore, the normality criteria can used to set the pass/fail threshold for PT participants based upon an arbitrary, predefined choice of \u0000<italic>Z</i>\u0000-value; and second the maximum permissible error values are unimportant and can be ignored. Those two fundamental errors produce misclassified PT pass/fail results, which can have serious economic consequences for both EMC test laboratories and their customers. This article first reviews the published literature on quantitative EMC PT from the standpoint of assessing the statistical methodologies used and how the pass/fail criteria were applied. Next, this article discusses the effects of these statistical procedure errors. Finally, it details a practical, more accurate alternative Bayesian method that incorporates the maximum permissible error as the \u0000<italic>a priori</i>\u0000 information in its analysis model. This method treats the quantitative EMC PT data as a population with no specific presumed distribution.","PeriodicalId":55012,"journal":{"name":"IEEE Transactions on Electromagnetic Compatibility","volume":"66 6","pages":"1780-1792"},"PeriodicalIF":2.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142678477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1109/TEMC.2024.3488058
Jiarui Qiu;Hanzhi Ma;Fengzhao Zhang;Zengyi Sun;Er-Ping Li
Accurate and efficient signal integrity modeling methods are crucial in the iterative design process of high-speed links. While data-driven deep learning exhibits robust capabilities for temporal transient modeling, it often ignores the correlations among high-speed links sharing similar structures, necessitating separate retraining for each distinct case. In this study, we propose a new transient modeling approach for high-speed links employing a transfer learning-enhanced deep simple recurrent unit (TL-DSRU) method. The deep simple recurrent unit architecture overcomes the challenges in handling sequential data and parallel processing found in traditional recurrent neural networks, enabling efficient modeling. Our technique leverages the initialization principles of transfer learning, utilizing a pretrained model of a basic high-speed link to enhance the comprehension of more intricate cases. The proposed TL-DSRU model combines parallelization and transfer learning initialization methods to balance training speed and accuracy, thereby enhancing the practicality and generalization potential of neural network-based transient simulation for high-speed links. Comparative experiments demonstrate that the transfer learning-based initialization method substantially outperforms typical neural network random initialization techniques, delivering markedly improved time-domain waveform prediction accuracy across various channels and equalization in high-speed links, as well as yielding more precise predictions of eye diagram parameters.
{"title":"Transient Modeling of High-Speed Links Using Transfer Learning-Based Neural Network Initialization","authors":"Jiarui Qiu;Hanzhi Ma;Fengzhao Zhang;Zengyi Sun;Er-Ping Li","doi":"10.1109/TEMC.2024.3488058","DOIUrl":"10.1109/TEMC.2024.3488058","url":null,"abstract":"Accurate and efficient signal integrity modeling methods are crucial in the iterative design process of high-speed links. While data-driven deep learning exhibits robust capabilities for temporal transient modeling, it often ignores the correlations among high-speed links sharing similar structures, necessitating separate retraining for each distinct case. In this study, we propose a new transient modeling approach for high-speed links employing a transfer learning-enhanced deep simple recurrent unit (TL-DSRU) method. The deep simple recurrent unit architecture overcomes the challenges in handling sequential data and parallel processing found in traditional recurrent neural networks, enabling efficient modeling. Our technique leverages the initialization principles of transfer learning, utilizing a pretrained model of a basic high-speed link to enhance the comprehension of more intricate cases. The proposed TL-DSRU model combines parallelization and transfer learning initialization methods to balance training speed and accuracy, thereby enhancing the practicality and generalization potential of neural network-based transient simulation for high-speed links. Comparative experiments demonstrate that the transfer learning-based initialization method substantially outperforms typical neural network random initialization techniques, delivering markedly improved time-domain waveform prediction accuracy across various channels and equalization in high-speed links, as well as yielding more precise predictions of eye diagram parameters.","PeriodicalId":55012,"journal":{"name":"IEEE Transactions on Electromagnetic Compatibility","volume":"66 6","pages":"2065-2073"},"PeriodicalIF":2.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142673446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-18DOI: 10.1109/temc.2024.3490953
Ahmed Abdullah, Francesco Musolino, Paolo Stefano Crovetti
{"title":"Detection and Suppression of Intentional EMI Attacks to Smart Speakers","authors":"Ahmed Abdullah, Francesco Musolino, Paolo Stefano Crovetti","doi":"10.1109/temc.2024.3490953","DOIUrl":"https://doi.org/10.1109/temc.2024.3490953","url":null,"abstract":"","PeriodicalId":55012,"journal":{"name":"IEEE Transactions on Electromagnetic Compatibility","volume":"250 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 10.1109/TEMC.2024.3490699
Brahim El Mokhtari;Cédric Chauviere;Pierre Bonnet
Physics-informed neural networks are a powerful approach that combines deep learning with physical principles to solve complex problems. However, like any method, they do have some drawbacks. The first one is hyperparameter sensitivity such as learning rates, network architectures, and activation functions. Many researchers have devoted their time and energy to design efficient neural network models by searching optimal hyperparameters. In this article, we follow another path by showing that the mathematical formulation of the problem to be solved, has a critical influence on the performance of the model. Electrostatic examples illustrate this.
{"title":"On the Importance of the Mathematical Formulation to Get PINNs Working","authors":"Brahim El Mokhtari;Cédric Chauviere;Pierre Bonnet","doi":"10.1109/TEMC.2024.3490699","DOIUrl":"10.1109/TEMC.2024.3490699","url":null,"abstract":"Physics-informed neural networks are a powerful approach that combines deep learning with physical principles to solve complex problems. However, like any method, they do have some drawbacks. The first one is hyperparameter sensitivity such as learning rates, network architectures, and activation functions. Many researchers have devoted their time and energy to design efficient neural network models by searching optimal hyperparameters. In this article, we follow another path by showing that the mathematical formulation of the problem to be solved, has a critical influence on the performance of the model. Electrostatic examples illustrate this.","PeriodicalId":55012,"journal":{"name":"IEEE Transactions on Electromagnetic Compatibility","volume":"66 6","pages":"2142-2149"},"PeriodicalIF":2.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 10.1109/temc.2024.3485887
Pei Wang, Yongpin Chen, Xiaofeng Que, Jun Hu
{"title":"A PEEC-Based Fast Direct Solver for Interconnect L&R Extraction","authors":"Pei Wang, Yongpin Chen, Xiaofeng Que, Jun Hu","doi":"10.1109/temc.2024.3485887","DOIUrl":"https://doi.org/10.1109/temc.2024.3485887","url":null,"abstract":"","PeriodicalId":55012,"journal":{"name":"IEEE Transactions on Electromagnetic Compatibility","volume":"1 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 10.1109/temc.2024.3492541
Zhao Zhang, Yang Zhou, Yang Zhang, Baoliang Qian
{"title":"Investigation on the Effect of HPM Pulse Width and Repetition Frequency on the AAV","authors":"Zhao Zhang, Yang Zhou, Yang Zhang, Baoliang Qian","doi":"10.1109/temc.2024.3492541","DOIUrl":"https://doi.org/10.1109/temc.2024.3492541","url":null,"abstract":"","PeriodicalId":55012,"journal":{"name":"IEEE Transactions on Electromagnetic Compatibility","volume":"5 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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