BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/Warsaw BEGIN:DAYLIGHT DTSTART:20250330T030000 TZOFFSETFROM:+0100 TZOFFSETTO:+0200 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST END:DAYLIGHT BEGIN:STANDARD DTSTART:20251026T020000 TZOFFSETFROM:+0200 TZOFFSETTO:+0100 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20250912T200247Z UID:C96AF95D-2764-4C43-B085-307EB160E069 DTSTART;TZID=Europe/Warsaw:20250513T100000 DTEND;TZID=Europe/Warsaw:20250513T140000 DESCRIPTION:Wykład 1: “Can You Identify an Electromagnetic Photo? – EM C Analysis Enhanced by Artificial Intelligence” godzina 10.00 do 11.30\n \nWykład 2: “Practical EMI Measurements and EMI Probes Design” godzin a 12.00 do 13.30\n\nWykłady z serii „IEEE EMC Society Distinguished Lec turer Program” finansowane są przez „centralę” IEEE EMC Society (T owarzystwo Kompatybilności Elektromagnetycznej) z USA i odbędą się w s iedzibie Łukasiewicz - Poznańskim Instytucie Technologicznym\, ul. Estko wskiego 6\, Poznań\n\nTalk 1: Can You Identify an Electromagnetic Photo? – EMC Analysis Enhanced by Artificial Intelligence\nAbstract: In recent years\, the artificial intelligence (AI) technology provides a powerful to ol for solving electromagnetic problems\, and there has been many successf ul stories for their applications on microwave device and antenna designs. The radiated near-field can be taken as an electromagnetic photo of an un known EMI radiation source. This photo contains a lot of intrinsic informa tion about the radiation source\, such as its 3-meter radiation\, coupling characteristics with nearby sensitive devices\, as well as information ab out the position and polarization of the radiation source itself. But due to the inability of the human eye to see electromagnetic waves\, our abili ty to identify electromagnetic photos is much lower than that of ordinary photos. AI has achieved significant results in facial recognition. This al lows us to use AI to process electromagnetic photos and extract the useful information for EMI analysis from the features of the photos\, such as 3- meter far-field and far-field radiation pattern.\n\nThis talk will start w ith a brief overview of AI and its applications in the EMC area. Then seve ral different ways to enhance the near-field scanning by AI are presented. The Green’s function hybrid with artificial neural network (ANN) is dev eloped for EMI estimation. The Green’s function of a dipole array with f ixed source points is taken as input and the radiated field at any given o bservation point is taken as the output of ANN. We use the powerful mappin g ability of ANN to replace the matrix-vector multiplication between Green ’s function and dipole moments in the traditional dipole model\, so that the ANN can be used to predict the near-field from unknown EMI source. Ne xt\, a deep convolutional neural network (DCNN) hybrid with the plane wave spectrum is proposed. By leveraging plane wave expansion\, the spatial ma gnetic near-field data are converted into the spectrum domain\, serving as the input for the DCNN model. DCNN’s output is the 3-meter electric far field. It enables the output of DCNN (3-meter far-field) insensitive to v ariations in the near-field scanning height. Finally\, the physics-informe d neural network (PINN) is introduced for near-field prediction\, where th e wave equation is integrated with the deep neural network. Therefore\, th e PINN is capable of efficiently interpolating and extrapolating the scann ed near-field fields.\n\nTalk 2: Practical EMI Measurements and EMI Probes Designs\nAbstract: Electromagnetic interference (EMI) modeling and debugg ing require different suitable measurement methods. For high-speed circuit s and integrated circuits\, the IEC published a series of radiated emissio n and immunity testing methods.\n\nIn this talk\, we will briefly introduc e EMI measurement methods\, including the TEM/GTEM\, surface scan\, 1/150 Ohm method\, reverberation chamber\, and anechoic chamber. Next\, we will focus on the practice of designing electric and magnetic probes for EMI te sting. What EMI testing requires is not a simple loop or monopole antenna as a suitable probe. In practical applications\, one should consider probe ’s sensitivity\, effective center\, spatial resolution\, bandwidth and i mmunity to unwanted field components. A compact multi-components EzHxHy pr obe is proposed. In comparing with available single component probes\, the EzHxHy probe can greatly save the engineer’s time. Furthermore\, a wide band EzEx probe is proposed through the integration of a strip line magic- T. The magic-T can effectively streamline the complex data processing\, en abling the proposed probe to offer a flexible instrument selection and ach ieve high measurement efficiency. After that\, an Ex probe covering a wide frequency band (1.4–7.0 GHz) based on common mode absorbing is presente d\, where the self-resonance of a traditional Ex probe is eliminated by an absorbing-type balun. Meanwhile\, other probes including the high spatial resolution Ez probe and high frequency (up to 70GHz) Hx probe will also b e discussed.\n\nCo-sponsored by: Lukasiewicz - PIT\, POLLAB\n\nLukasiewicz -PIT\, Estkowskiego 6\, Poznan\, Wielkopolskie\, Poland\, 61-755 LOCATION:Lukasiewicz-PIT\, Estkowskiego 6\, Poznan\, Wielkopolskie\, Poland \, 61-755 ORGANIZER:Krzysztof.Sieczkarek@pit.lukasiewicz.gov.pl SEQUENCE:16 SUMMARY:IEEE EMC-S PL reported activity no. 04/2025: "IEEE EMC Society Dist inguished Lecturer - prof. Xing Chang Wei" URL;VALUE=URI:https://events.vtools.ieee.org/m/500800 X-ALT-DESC:Description: <br /><p class="MsoNormal"><strong><span lang="EN-G B" style="mso-ansi-language: EN-GB\;">Wykład 1: &ldquo\;Can You Identify an Electromagnetic Photo? &ndash\; EMC Analysis Enhanced by Artificial Int elligence</span></strong><span lang="EN-GB" style="mso-ansi-language: EN-G B\;">&rdquo\; godzina 10.00 do 11.30</span></p>\n<p class="MsoNormal"><spa n lang="EN-GB" style="mso-ansi-language: EN-GB\;">&nbsp\;</span></p>\n<p c lass="MsoNormal"><strong><span lang="EN-GB" style="mso-ansi-language: EN-G B\;">Wykład 2: &ldquo\;Practical EMI Measurements and EMI Probes Design</ span></strong><span lang="EN-GB" style="mso-ansi-language: EN-GB\;">&rdquo \; godzina 12.00 do 13.30</span></p>\n<p class="MsoNormal"><span lang="EN- GB" style="mso-ansi-language: EN-GB\;">&nbsp\;</span></p>\n<p class="MsoNo rmal">Wykłady z serii &bdquo\;IEEE EMC Society Distinguished Lecturer Pro gram&rdquo\; finansowane są przez &bdquo\;centralę&rdquo\; IEEE EMC Soci ety (Towarzystwo Kompatybilności Elektromagnetycznej) z USA i odbędą si ę w siedzibie Łukasiewicz - Poznańskim Instytucie Technologicznym\, ul. Estkowskiego 6\, Poznań</p>\n<p class="MsoNormal">&nbsp\;</p>\n<p class= "MsoNormal">&nbsp\;</p>\n<p class="MsoNormal"><span style="mso-ligatures: none\;"><img style="height: 3.266in\; width: 6.458in\;" src="https://event s.vtools.ieee.org/vtools_ui/media/display/a64978d5-275c-4909-a8e8-bec374bc 583c" width="620" height="314" border="0"></span></p>\n<p class="MsoNormal "><strong><span lang="EN-GB" style="mso-ansi-language: EN-GB\;">Talk 1: Ca n You Identify an Electromagnetic Photo? &ndash\; EMC Analysis Enhanced by Artificial Intelligence <br>Abstract: </span></strong><span lang="EN-GB" style="mso-ansi-language: EN-GB\;">In recent years\, the artificial intell igence (AI) technology provides a powerful tool for solving electromagneti c problems\, and there has been many successful stories for their applicat ions on microwave device and antenna designs. The radiated near-field can be taken as an electromagnetic photo of an unknown EMI radiation source. T his photo contains a lot of intrinsic information about the radiation sour ce\, such as its 3-meter radiation\, coupling characteristics with nearby sensitive devices\, as well as information about the position and polariza tion of the radiation source itself. But due to the inability of the human eye to see electromagnetic waves\, our ability to identify electromagneti c photos is much lower than that of ordinary photos. AI has achieved signi ficant results in facial recognition. This allows us to use AI to process electromagnetic photos and extract the useful information for EMI analysis from the features of the photos\, such as 3-meter far-field and far-field radiation pattern.<br><br>This talk will start with a brief overview of A I and its applications in the EMC area. Then several different ways to enh ance the near-field scanning by AI are presented. The Green&rsquo\;s funct ion hybrid with artificial neural network (ANN) is developed for EMI estim ation. The Green&rsquo\;s function of a dipole array with fixed source poi nts is taken as input and the radiated field at any given observation poin t is taken as the output of ANN. We use the powerful mapping ability of AN N to replace the matrix-vector multiplication between Green&rsquo\;s funct ion and dipole moments in the traditional dipole model\, so that the ANN c an be used to predict the near-field from unknown EMI source. Next\, a dee p convolutional neural network (DCNN) hybrid with the plane wave spectrum is proposed. By leveraging plane wave expansion\, the spatial magnetic nea r-field data are converted into the spectrum domain\, serving as the input for the DCNN model. DCNN&rsquo\;s output is the 3-meter electric far fiel d. It enables the output of DCNN (3-meter far-field) insensitive to variat ions in the near-field scanning height. Finally\, the physics-informed neu ral network (PINN) is introduced for near-field prediction\, where the wav e equation is integrated with the deep neural network. Therefore\, the PIN N is capable of efficiently interpolating and extrapolating the scanned ne ar-field fields.<br><br><strong>Talk 2: Practical EMI Measurements and EMI Probes Designs </strong><br><strong>Abstract: </strong>Electromagnetic in terference (EMI) modeling and debugging require different suitable measure ment methods. For high-speed circuits and integrated circuits\, the IEC pu blished a series of radiated emission and immunity testing methods.<br><br >In this talk\, we will briefly introduce EMI measurement methods\, includ ing the TEM/GTEM\, surface scan\, 1/150 Ohm method\, reverberation chamber \, and anechoic chamber. Next\, we will focus on the practice of designing electric and magnetic probes for EMI testing. What EMI testing requires i s not a simple loop or monopole antenna as a suitable probe. In practical applications\, one should consider probe&rsquo\;s sensitivity\, effective center\, spatial resolution\, bandwidth and immunity to unwanted field com ponents. A compact multi-components EzHxHy probe is proposed. In comparing with available single component probes\, the EzHxHy probe can greatly sav e the engineer&rsquo\;s time. Furthermore\, a wideband EzEx probe is propo sed through the integration of a strip line magic-T. The magic-T can effec tively streamline the complex data processing\, enabling the proposed prob e to offer a flexible instrument selection and achieve high measurement ef ficiency. After that\, an Ex probe covering a wide frequency band (1.4&nda sh\;7.0 GHz) based on common mode absorbing is presented\, where the self- resonance of a traditional Ex probe is eliminated by an absorbing-type bal un. Meanwhile\, other probes including the high spatial resolution Ez prob e and high frequency (up to 70GHz) Hx probe will also be discussed.</span> </p>\n<p class="MsoNormal"><span lang="EN-GB" style="mso-ansi-language: EN -GB\;">&nbsp\;</span></p>\n<p class="MsoNormal"><span lang="EN-GB" style=" mso-ansi-language: EN-GB\;">&nbsp\;</span></p> END:VEVENT END:VCALENDAR