The Development of An Adjustable Low-pass Filter for A Partial Discharge Detection System under Testing with The Power Frequency Converter

2019 ◽  
Author(s):  
Banyat Leelachariyakul ◽  
Peerawut Yutthagowith
Keyword(s):  
Detection System ◽  
Frequency Converter ◽  
Partial Discharge ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Power Frequency ◽  
Discharge Detection

Weak Signal Detection System Based on Lock-in Amplifier Technology

2013 ◽  
Vol 389 ◽  
pp. 489-493
Author(s):  
Yong Lv ◽  
Chun Hui Niu ◽  
Yue Qiang Li ◽  
Qing Shan Chen ◽  
Xiao Ying Li ◽  
...  
Keyword(s):  
Signal Detection ◽  
Detection System ◽  
Signal To Noise Ratio ◽  
Weak Signal ◽  
Weak Signal Detection ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Phase Sensitive ◽  
Lock In

In order to detect the weak signal deeply buried in the noise, a weak signal detection system based on lock-in amplifier is proposed. The system includes the preamplifier circuit, active low pass filter circuit, AC amplifying circuit and phase sensitive demodulation circuit. Test results show that it can greatly increase the signal-to-noise ratio (SNR) up to 12.7db.

scholarly journals Parking detection system using background subtraction and HSV color segmentation

2021 ◽  
Vol 10 (6) ◽  
pp. 3211-3219
Author(s):  
Awang Hendrianto Pratomo ◽  
Wilis Kaswidjanti ◽  
Alek Setiyo Nugroho ◽  
Shoffan Saifullah
Keyword(s):  
Real Time ◽  
Background Subtraction ◽  
Detection System ◽  
Threshold Value ◽  
Color Segmentation ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Detection Process ◽  
Average Accuracy ◽  
Low Pass

Manual system vehicle parking makes finding vacant parking lots difficult, so it has to check directly to the vacant space. If many people do parking, then the time needed for it is very much or requires many people to handle it. This research develops a real-time parking system to detect parking. The system is designed using the HSV color segmentation method in determining the background image. In addition, the detection process uses the background subtraction method. Applying these two methods requires image preprocessing using several methods such as grayscaling, blurring (low-pass filter). In addition, it is followed by a thresholding and filtering process to get the best image in the detection process. In the process, there is a determination of the ROI to determine the focus area of the object identified as empty parking. The parking detection process produces the best average accuracy of 95.76%. The minimum threshold value of 255 pixels is 0.4. This value is the best value from 33 test data in several criteria, such as the time of capture, composition and color of the vehicle, the shape of the shadow of the object’s environment, and the intensity of light. This parking detection system can be implemented in real-time to determine the position of an empty place.

scholarly journals PENGEMBANGAN SISTEM DETEKSI KELELAHAN PADA PENGEMUDI MOBIL BERBASIS SINYAL ELECTROMYOGRAPHY (EMG)

2016 ◽  
Vol 3 (1) ◽  
pp. 18
Author(s):  
Sukma Firdaus ◽  
Marlia Adriana
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Surface Emg ◽  
Mean Power ◽  
Mean Power Frequency ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Power Frequency ◽  
High Pass

Peningkatan alat trasportasi khususnya kendaraan roda empat (mobil) mengakibatkan peningkatan volume kendaraan di jalan. Hal ini berdampak pada meningkatnya kemacetan. Selain kemacetan, peningkatan volume kendaraan berdampak juga pada peningkatan kecelakan lalu lintas. Salah satu penyebab kecelakan lalu lintas adalah faktor pengendaranya, yaitu kelelahan. Penelitian ini, merancang sistem pendeteksi kelelahan pengemudi, berdasarkan sinyal biologis pengemudi yaitu sinyal biologis kondisi otot lengan. Sinyal tersebut direkam dengan menggunakan metode surface EMG. Elektroda yang ditempelkan disebelah kanan lengan dihubungkan dengan penguat instrumentasi dan digitalisasi melalui unit pemproses sinyal untuk dapat disimpan kedalam komputer. Kegiatan pengambilan data dilakukan sebanyak 8 kali dengan jarak tempuh pengemudi sebesar 80 km dari Kota Banjarmasin menuju Kota Pelaihari. Akuisisi data menggunakan frekuensi sampling sebesar 4 KHz dan diproses secara filter analog untuk High Pass Filter sebesar 2 KHz dan Low Pass Filter sebesar 500 Hz. Setelah data direkam, sinyal dilakukan proses downsampling menjadi 1 KHz. Pada proess digital, dilakukan lagi proses pemfilteran secara Low Pass Filter sebesar 500 Hz. Proses digital selanjutnya adalah melakukan analisis pada domain frekuensi menggunakan transformasi fourier dengan memakai algoritma fast fourier transform (fft). Hasil dari transformasi fourier diidentifikasi berdasarkan nilai Mean Power Frequency (MPF). Berdasarkan hasil perhitungan MPF yang telah dilakukan, diperoleh saat awal berkendara nilai rata-rata MPF nya adalah sebesar 25,2 Hz sedangkan pada akhir berkendara bernilai sebesar 33,3 Hz. Dengan hasil ini dapat tergambarkan kondisi pengemudi pada awal mengemudi dan tidak terjadi kelelahan maka nilai frekuensi yang dominan cenderung lebih rendah jika dibandingkan dengan setelah berkendara atau saat kelelahan.

scholarly journals Low-Pass Filter for HV Partial Discharge Testing

Sensors ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 482 ◽  
Author(s):  
Vladimir Kindl ◽  
Bohumil Skala ◽  
Roman Pechanek ◽  
Vaclav Kus ◽  
Jaroslav Hornak
Keyword(s):  
Partial Discharge ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass

Design of Programmable Wideband Low Pass Filter with Continuous-Time/Discrete-Time Hybrid Architecture

2017 ◽  
Vol E100.C (10) ◽  
pp. 858-865 ◽  
Author(s):  
Yohei MORISHITA ◽  
Koichi MIZUNO ◽  
Junji SATO ◽  
Koji TAKINAMI ◽  
Kazuaki TAKAHASHI
Keyword(s):  
Discrete Time ◽  
Continuous Time ◽  
Hybrid Architecture ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass

ANALYSIS AND IMPROVEMENT TECHNIQUES FOR THE TRANSFER FUNCTION OF A PLANAR LOW - PASS FILTER

2016 ◽  
Vol 15 (12) ◽  
pp. 2579-2586
Author(s):  
Adina Racasan ◽  
Calin Munteanu ◽  
Vasile Topa ◽  
Claudia Pacurar ◽  
Claudia Hebedean
Keyword(s):  
Transfer Function ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass
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