scholarly journals Set-up of integrated system for real-time detection and recording of many engine parameters of agricultural machines during dyno tests

Author(s):  
Marco Bietresato ◽  
Matteo Malavasi ◽  
Fabrizio Mazzetto
Author(s):  
Pablo Koch ◽  
Francisco Bravo ◽  
Sebastian Riquelme ◽  
Jorge G. F. Crempien

ABSTRACT Recent efforts have been made to model the rupture process of large earthquakes in near‐real time (NRT) in Chile. In this study, we propose an automated procedure using strong‐motion data in an integrated system, which can characterize large earthquakes with a finite‐fault model (FFM) in NRT. We developed several heuristic rules using the preliminary W‐phase solutions to automatically set up the search ranges of the finite‐fault inversions. The results show using strong‐motion data and a W‐phase magnitude, it is possible to obtain a rapid kinematic FFM in just a few minutes after the earthquake origin time.


2021 ◽  
Vol 38 (2) ◽  
pp. 443-449
Author(s):  
Wei Liu

During fruit production, the robots must walk stably across the orchard, and detect the obstacles in real time on its path. With the rapid process of deep convolutional neural network (CNN), it is now a hot topic to enable orchard robots to detect obstacles through image semantic segmentation. However, most such obstacle detection schemes are under performing in the complex environment of orchards. To solve the problem, this paper proposes an image semantic fusion network for real-time detection of small obstacles. Two branches were set up to extract features from red-green-blue (RGB) image and depth image, respectively. The information extracted by different modules were merged to complement the image features. The proposed network can operate rapidly, and support the real-time detection of obstacles by orchard robots. Experiments on orchard scenarios show that our network is superior to the latest image semantic segmentation methods, highly accurate in the recognition of high-definition images, and extremely fast in reasoning.


2012 ◽  
Author(s):  
Anthony D. McDonald ◽  
Chris Schwarz ◽  
John D. Lee ◽  
Timothy L. Brown

2019 ◽  
pp. 60-66
Author(s):  
Viet Quynh Tram Ngo ◽  
Thi Ti Na Nguyen ◽  
Hoang Bach Nguyen ◽  
Thi Tuyet Ngoc Tran ◽  
Thi Nam Lien Nguyen ◽  
...  

Introduction: Bacterial meningitis is an acute central nervous infection with high mortality or permanent neurological sequelae if remained undiagnosed. However, traditional diagnostic methods for bacterial meningitis pose challenge in prompt and precise identification of causative agents. Aims: The present study will therefore aim to set up in-house PCR assays for diagnosis of six pathogens causing the disease including H. influenzae type b, S. pneumoniae, N. meningitidis, S. suis serotype 2, E. coli and S. aureus. Methods: inhouse PCR assays for detecting six above-mentioned bacteria were optimized after specific pairs of primers and probes collected from the reliable literature resources and then were performed for cerebrospinal fluid (CSF) samples from patients with suspected meningitis in Hue Hospitals. Results: The set of four PCR assays was developed including a multiplex real-time PCR for S. suis serotype 2, H. influenzae type b and N. meningitides; three monoplex real-time PCRs for E. coli, S. aureus and S. pneumoniae. Application of the in-house PCRs for 116 CSF samples, the results indicated that 48 (39.7%) cases were positive with S. suis serotype 2; one case was positive with H. influenzae type b; 4 cases were positive with E. coli; pneumococcal meningitis were 19 (16.4%) cases, meningitis with S. aureus and N. meningitidis were not observed in any CSF samples in this study. Conclusion: our in-house real-time PCR assays are rapid, sensitive and specific tools for routine diagnosis to detect six mentioned above meningitis etiological agents. Key words: Bacterial meningitis, etiological agents, multiplex real-time PCR


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