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A novel low-cost visual ear tag based identification system for precision beef cattle livestock farming
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作者 Andrea Pretto Gianpaolo Savio +2 位作者 Flaviana Gottardo Francesca Uccheddu Gianmaria Concheri 《Information Processing in Agriculture》 EI CSCD 2024年第1期117-126,共10页
The precision livestock farming(PLF)has the objective to maximize each animal's performance while reducing the environmental impact and maintaining the quality and safety of meat production.Among the PLF technique... The precision livestock farming(PLF)has the objective to maximize each animal's performance while reducing the environmental impact and maintaining the quality and safety of meat production.Among the PLF techniques,the personalised management of each individual animal based on sensors systems,represents a viable option.It is worth noting that the implementation of an effective PLF approach can be still expensive,especially for small and medium-sized farms;for this reason,to guarantee the sustainability of a customized livestock management system and encourage its use,plug and play and cost-effective systems are needed.Within this context,we present a novel low-cost method for identifying beef cattle and recognizing their basic activities by a single surveillance camera.By leveraging the current state-of-the-art methods for real-time object detection,(i.e.,YOLOv3)cattle's face areas,we propose a novel mechanism able to detect the ear tag as well as the water ingestion state when the cattle is close to the drinker.The cow IDs are read by an Optical Character Recognition(OCR)algorithm for which,an ad hoc error correction algorithm is here presented to avoid numbers misreading and correctly match the IDs to only actually present IDs.Thanks to the detection of the tag position,the OCR algorithm can be applied only to a specific region of interest reducing the computational cost and the time needed.Activity times for the areas are outputted as cattle activity recognition results.Evaluation results demonstrate the effectiveness of our proposed method,showing a mAP@0.50 of 89%. 展开更多
关键词 Precision livestock farming Deep learning Cattlei dentification Low-cost sensors Computer vision
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Modeling Rogowski Coils for Monitoring Surge Arrester Discharge Currents
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作者 Nehmdoh A.Sabiha Hend I.Alkhammash 《Computer Systems Science & Engineering》 SCIE EI 2022年第8期439-449,共11页
Rogowski coils(RCs)are widely used to measure power or high frequency currents based on their design.In this paper,two types of RCs that are circular(traditional)and cylindrical shapes wound using wire covered by varn... Rogowski coils(RCs)are widely used to measure power or high frequency currents based on their design.In this paper,two types of RCs that are circular(traditional)and cylindrical shapes wound using wire covered by varnish are constructed.This construction is carried out to be suitable for monitoring the discharge current of the surge arrester installed in the distribution system.Concerning high frequency RC modeling for both types considering transfer function is introduced.Self-integrating for both types is attained.Therefore,the experimental tests using function generator for both coils are carried out to identify the parameters of the transfer function representing the introduced model.The measured signals for current and induced voltages are denoised for the parameter identification process.The denoised process is achieved using the MATLAB code‘wdenoise’while the parameters are estimated using the system identification toolbox.Verification of the proposed model is achieved using experimental results for the two coils.The sensitivity of the two coils is investigated based on the induced output voltage.The application concerning the two coils for monitoring the discharge current of the surge arrester is done.The results confirm the accuracy of the introduced RC model,as well as the performance of the cylindrical shape,is better than the traditional one.The simulation is carried out using MATLAB and ATPDraw programs. 展开更多
关键词 Rogowski coil surge arrester discharge current frequency response parameters dentification denoised signal
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