A real-time bird detection system with species identification for prevention of wind turbine bird strikes

2016 ◽  
Vol 2016.26 (0) ◽  
pp. 431
Author(s):  
Yuichi MURAI ◽  
Yasushi TAKEDA ◽  
Hiroyuki KUMENO ◽  
Yuji TASAKA ◽  
Yoshihiko OISHI
Keyword(s):  
Wind Turbine ◽  
Real Time ◽  
Species Identification ◽  
Detection System ◽  
Bird Detection

scholarly journals Species identification of Anguilla japonica by real-time PCR based on a sequence detection system: a practical application to eggs and larvae

2009 ◽  
Vol 66 (9) ◽  
pp. 1915-1918 ◽  
Author(s):  
Yuki Minegishi ◽  
Tatsuki Yoshinaga ◽  
Jun Aoyama ◽  
Katsumi Tsukamoto
Keyword(s):  
Real Time ◽  
Species Identification ◽  
Developmental Stages ◽  
Detection System ◽  
Anguilla Japonica ◽  
Sequence Detection ◽  
Target Region ◽  
System A ◽  
Sequence Variations ◽  
Eggs And Larvae

Abstract Minegishi, Y., Yoshinaga, T., Aoyama, J., and Tsukamoto, K. 2009. Species identification of Anguilla japonica by real-time PCR based on a sequence detection system: a practical application to eggs and larvae. – ICES Journal of Marine Science, 66: 1915–1918. To develop a practical method for identifying Japanese eel Anguilla japonica eggs and larvae to species by a sequence detection system using a real-time polymerase chain reaction (PCR), we examined (i) the sensitivity of the system using samples at various developmental stages, and (ii) influences of intra- and interspecific DNA sequence variations in the PCR target region. PCR amplifications with extracted DNA solution at 7.0 ng µl−1 or lower were efficient at distinguishing A. japonica from other anguillids. A single egg at the gastrula or later developmental stages could also be identified. Two sequence variations in the PCR target region were observed in 2 out of 35 A. japonica collected from three localities, and from four year classes at a single locality. These mutations, however, did not affect the result of species identification achieved by A. japonica-specific PCR primers and probe. The accuracy of this PCR-based method of species identification will help in field surveys of the species.

Design of Icing Prober Configuration for Horizontal Axis Wind Turbine

2012 ◽  
Vol 608-609 ◽  
pp. 658-661
Author(s):  
Xian Yi ◽  
Kun Chen ◽  
Kai Chun Wang ◽  
Hong Lin Ma
Keyword(s):  
Wind Turbine ◽  
Real Time ◽  
Reference Frame ◽  
Detection System ◽  
Horizontal Axis ◽  
Future Research ◽  
Ice Accretion ◽  
Horizontal Axis Wind Turbine ◽  
Ice Detection ◽  
Multiple Reference

A design approach of ice detection system for wind turbine is presented in this paper. Basic steps for design are proposed. Numerical arithmetic used for design configuration and shape of the icing prober is given. The arithmetic is composed of the Multiple Reference Frame (MRF) method to calculate flowfield of air, a Lagrangian method to compute droplet trajectories and a technique for fast computing ice accretion. Icing prober configuration for a 1.5 MW horizontal axis wind turbine is then obtained with the approach. The state of wind turbine icing can be reflected by the prober in real time. All these achievements build a good base for future research.

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

scholarly journals Validation of a Novel Diagnostic Approach Combining the VersaTREK™ System for Recovery and Real-Time PCR for the Identification of Mycobacterium chimaera in Water Samples

2021 ◽  
Vol 9 (5) ◽  
pp. 1031
Author(s):  
Roberto Zoccola ◽  
Alessia Di Blasio ◽  
Tiziana Bossotto ◽  
Angela Pontei ◽  
Maria Angelillo ◽  
...  
Keyword(s):  
Real Time ◽  
Real Time Pcr ◽  
Limit Of Detection ◽  
Detection System ◽  
Bacterial Load ◽  
Microbial Control ◽  
Hospital Setting ◽  
Sample Volume ◽  
Analytical Sensitivity ◽  
Initial Water

Mycobacterium chimaera is an emerging pathogen associated with endocarditis and vasculitis following cardiac surgery. Although it can take up to 6–8 weeks to culture on selective solid media, culture-based detection remains the gold standard for diagnosis, so more rapid methods are urgently needed. For the present study, we processed environmental M. chimaera infected simulates at volumes defined in international guidelines. Each preparation underwent real-time PCR; inoculates were placed in a VersaTREK™ automated microbial detection system and onto selective Middlebrook 7H11 agar plates. The validation tests showed that real-time PCR detected DNA up to a concentration of 10 ng/µL. A comparison of the isolation tests showed that the PCR method detected DNA in a dilution of ×102 CFU/mL in the bacterial suspensions, whereas the limit of detection in the VersaTREK™ was <10 CFU/mL. Within less than 3 days, the VersaTREK™ detected an initial bacterial load of 100 CFU. The detection limit did not seem to be influenced by NaOH decontamination or the initial water sample volume; analytical sensitivity was 1.5 × 102 CFU/mL; positivity was determined in under 15 days. VersaTREK™ can expedite mycobacterial growth in a culture. When combined with PCR, it can increase the overall recovery of mycobacteria in environmental samples, making it potentially applicable for microbial control in the hospital setting and also in environments with low levels of contamination by viable mycobacteria.

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