scholarly journals Automated Tool Based on Deep Learning to Assess Voltage Dips Validity: Integration in the QuEEN MV network Monitoring System

2021 ◽  
Vol 19 ◽  
pp. 235-240
Author(s):  
M. Zanoni ◽  
◽  
R. Chiumeo ◽  
L. Tenti ◽  
M. Volta
Keyword(s):  
Deep Learning ◽  
Monitoring System ◽  
Network Monitoring ◽  
Quality Analysis ◽  
Voltage Dips ◽  
Measurement Units ◽  
Using Data ◽  
First Time ◽  
Harmonic Measurement ◽  
Automated Tool

This paper presents the development of an automated tool called QuEEN PyService, aimed to the extraction of events voltage signals from the QuEEN distribution network monitoring system database, for advanced Power Quality analysis. The application has allowed the integration of the DELFI classifier (DEep Learning for False voltage dips Identification), recently developed by RSE, making it possible for the first time the intensive validation of the latter on a large number of voltage dips. Thanks to this tool, a comparison between the performance of DELFI and those of an older criterion based on the 2nd voltage harmonic measurement has been performed using data recorded by 61 measurement units in the period 2015-2020 The analysis has been focused on traditional PQ voltage dips counting indices as N2a e N3b. Results show that the usage of the DELFI classifier increases the N2a and the N3b by respectively the 20.6 % and 38.8% with respect to the QuEEN criterion.

Generalizability Improvement of Deep Learning based Non-Intrusive Load Monitoring System Using Data Augmentation

2021 ◽  
pp. 1-1
Author(s):  
Hasan Rafiq ◽  
Xiaohan Shi ◽  
Hengxu Zhang ◽  
Huimin Li ◽  
Manesh K. Ochani ◽  
...  
Keyword(s):  
Deep Learning ◽  
Monitoring System ◽  
Data Augmentation ◽  
Load Monitoring ◽  
Using Data

scholarly journals Power Electric Transformer Fault Diagnosis Based on Infrared Thermal Images Using Wasserstein Generative Adversarial Networks and Deep Learning Classifier

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1161
Author(s):  
Kuo-Hao Fanchiang ◽  
Yen-Chih Huang ◽  
Cheng-Chien Kuo
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Monitoring System ◽  
Absolute Difference ◽  
Generative Adversarial Networks ◽  
Economic Losses ◽  
Maintenance Cost ◽  
Full Time ◽  
Noise Interference ◽  
In Series

The safety of electric power networks depends on the health of the transformer. However, once a variety of transformer failure occurs, it will not only reduce the reliability of the power system but also cause major accidents and huge economic losses. Until now, many diagnosis methods have been proposed to monitor the operation of the transformer. Most of these methods cannot be detected and diagnosed online and are prone to noise interference and high maintenance cost that will cause obstacles to the real-time monitoring system of the transformer. This paper presents a full-time online fault monitoring system for cast-resin transformer and proposes an overheating fault diagnosis method based on infrared thermography (IRT) images. First, the normal and fault IRT images of the cast-resin transformer are collected by the proposed thermal camera monitoring system. Next is the model training for the Wasserstein Autoencoder Reconstruction (WAR) model and the Differential Image Classification (DIC) model. The differential image can be acquired by the calculation of pixel-wise absolute difference between real images and regenerated images. Finally, in the test phase, the well-trained WAR and DIC models are connected in series to form a module for fault diagnosis. Compared with the existing deep learning algorithms, the experimental results demonstrate the great advantages of the proposed model, which can obtain the comprehensive performance with lightweight, small storage size, rapid inference time and adequate diagnostic accuracy.

scholarly journals Feasibility of Continual Deep Learning-Based Segmentation for Personalized Adaptive Radiation Therapy in Head and Neck Area

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 702
Author(s):  
Nalee Kim ◽  
Jaehee Chun ◽  
Jee Suk Chang ◽  
Chang Geol Lee ◽  
Ki Chang Keum ◽  
...  
Keyword(s):  
Deep Learning ◽  
Head And Neck ◽  
Organs At Risk ◽  
Subjective Assessment ◽  
Turing Test ◽  
Similar Rate ◽  
Dice Similarity Coefficient ◽  
Adaptive Planning ◽  
Test Set ◽  
Using Data

This study investigated the feasibility of deep learning-based segmentation (DLS) and continual training for adaptive radiotherapy (RT) of head and neck (H&N) cancer. One-hundred patients treated with definitive RT were included. Based on 23 organs-at-risk (OARs) manually segmented in initial planning computed tomography (CT), modified FC-DenseNet was trained for DLS: (i) using data obtained from 60 patients, with 20 matched patients in the test set (DLSm); (ii) using data obtained from 60 identical patients with 20 unmatched patients in the test set (DLSu). Manually contoured OARs in adaptive planning CT for independent 20 patients were provided as test sets. Deformable image registration (DIR) was also performed. All 23 OARs were compared using quantitative measurements, and nine OARs were also evaluated via subjective assessment from 26 observers using the Turing test. DLSm achieved better performance than both DLSu and DIR (mean Dice similarity coefficient; 0.83 vs. 0.80 vs. 0.70), mainly for glandular structures, whose volume significantly reduced during RT. Based on subjective measurements, DLS is often perceived as a human (49.2%). Furthermore, DLSm is preferred over DLSu (67.2%) and DIR (96.7%), with a similar rate of required revision to that of manual segmentation (28.0% vs. 29.7%). In conclusion, DLS was effective and preferred over DIR. Additionally, continual DLS training is required for an effective optimization and robustness in personalized adaptive RT.

scholarly journals Deep learning for cephalometric landmark detection: systematic review and meta-analysis

2021 ◽  
Author(s):  
Falk Schwendicke ◽  
Akhilanand Chaurasia ◽  
Lubaina Arsiwala ◽  
Jae-Hong Lee ◽  
Karim Elhennawy ◽  
...  
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Meta Analysis ◽  
High Accuracy ◽  
Risk Of Bias ◽  
Automated Detection ◽  
Reference Test ◽  
Landmark Detection ◽  
Future Studies ◽  
Using Data

Abstract Objectives Deep learning (DL) has been increasingly employed for automated landmark detection, e.g., for cephalometric purposes. We performed a systematic review and meta-analysis to assess the accuracy and underlying evidence for DL for cephalometric landmark detection on 2-D and 3-D radiographs. Methods Diagnostic accuracy studies published in 2015-2020 in Medline/Embase/IEEE/arXiv and employing DL for cephalometric landmark detection were identified and extracted by two independent reviewers. Random-effects meta-analysis, subgroup, and meta-regression were performed, and study quality was assessed using QUADAS-2. The review was registered (PROSPERO no. 227498). Data From 321 identified records, 19 studies (published 2017–2020), all employing convolutional neural networks, mainly on 2-D lateral radiographs (n=15), using data from publicly available datasets (n=12) and testing the detection of a mean of 30 (SD: 25; range.: 7–93) landmarks, were included. The reference test was established by two experts (n=11), 1 expert (n=4), 3 experts (n=3), and a set of annotators (n=1). Risk of bias was high, and applicability concerns were detected for most studies, mainly regarding the data selection and reference test conduct. Landmark prediction error centered around a 2-mm error threshold (mean; 95% confidence interval: (–0.581; 95 CI: –1.264 to 0.102 mm)). The proportion of landmarks detected within this 2-mm threshold was 0.799 (0.770 to 0.824). Conclusions DL shows relatively high accuracy for detecting landmarks on cephalometric imagery. The overall body of evidence is consistent but suffers from high risk of bias. Demonstrating robustness and generalizability of DL for landmark detection is needed. Clinical significance Existing DL models show consistent and largely high accuracy for automated detection of cephalometric landmarks. The majority of studies so far focused on 2-D imagery; data on 3-D imagery are sparse, but promising. Future studies should focus on demonstrating generalizability, robustness, and clinical usefulness of DL for this objective.

scholarly journals Meteor head echo polarization at 930 MHz studied with the EISCAT UHF HPLA radar

2011 ◽  
Vol 29 (6) ◽  
pp. 1197-1208 ◽  
Author(s):  
G. Wannberg ◽  
A. Westman ◽  
A. Pellinen-Wannberg
Keyword(s):  
Angle Of Incidence ◽  
Resonance Effects ◽  
Observation Volume ◽  
Polarization Characteristics ◽  
Radar Measurements ◽  
Head Echo ◽  
Using Data ◽  
Static Head ◽  
First Time ◽  
Underdense Plasma

Abstract. The polarization characteristics of 930-MHz meteor head echoes have been studied for the first time, using data obtained in a series of radar measurements carried out with the tristatic EISCAT UHF high power, large aperture (HPLA) radar system in October 2009. An analysis of 44 tri-static head echo events shows that the polarization of the echo signal recorded by the Kiruna receiver often fluctuates strongly on time scales of tens of microseconds, illustrating that the scattering process is essentially stochastic. On longer timescales (> milliseconds), more than 90 % of the recorded events show an average polarization signature that is independent of meteor direction of arrival and echo strength and equal to that of an incoherent-scatter return from underdense plasma filling the tristatic observation volume. This shows that the head echo plasma targets scatter isotropically, which in turn implies that they are much smaller than the 33-cm wavelength and close to spherically symmetric, in very good agreement with results from a previous EISCAT UHF study of the head echo RCS/meteor angle-of-incidence relationship. Significant polarization is present in only three events with unique target trajectories. These all show a larger effective target cross section transverse to the trajectory than parallel to it. We propose that the observed polarization may be a signature of a transverse charge separation plasma resonance in the region immediately behind the meteor head, similar to the resonance effects previously discussed in connection with meteor trail echoes by Herlofson, Billam and Browne, Jones and Jones and others.

What drives housing prices, rent and new construction in China

2017 ◽  
Vol 10 (5) ◽  
pp. 662-686
Author(s):  
Dimitrios Staikos ◽  
Wenjun Xue
Keyword(s):  
Real Estate ◽  
Empirical Test ◽  
Housing Prices ◽  
Panel Cointegration ◽  
Real Estate Market ◽  
Estimation Methods ◽  
Content Type ◽  
New Construction ◽  
Using Data ◽  
First Time

Purpose With this paper, the authors aim to investigate the drivers behind three of the most important aspects of the Chinese real estate market, housing prices, housing rent and new construction. At the same time, the authors perform a comprehensive empirical test of the popular 4-quadrant model by Wheaton and DiPasquale. Design/methodology/approach In this paper, the authors utilize panel cointegration estimation methods and data from 35 Chinese metropolitan areas. Findings The results indicate that the 4-quadrant model is well suited to explain the determinants of housing prices. However, the same is not true regarding housing rent and new construction suggesting a more complex theoretical framework may be required for a well-rounded explanation of real estate markets. Originality/value It is the first time that panel data are used to estimate rent and new construction for China. Also, it is the first time a comprehensive test of the Wheaton and DiPasquale 4-quadrant model is performed using data from China.

Azolla Biomass as Liquid Biofertilizer to Improve Yield and Productivity of Paddy Plant Using Efficient Monitoring System

2021 ◽  
Vol 1025 ◽  
pp. 104-109
Author(s):  
Nur Husnina Iffah Bakar ◽  
Noor Suhana Adzahar ◽  
Thong Chuan Lee ◽  
Rama Yusvana ◽  
Raha Ahmad Raus
Keyword(s):  
Monitoring System ◽  
Inductively Coupled Plasma ◽  
Initial Step ◽  
Quality Analysis ◽  
High Yield ◽  
Automated Control ◽  
Computing Device ◽  
Efficient System ◽  
Inductively Coupled ◽  
Software Monitoring

Azolla Filliculoides has been utilized as biofertilizer to increase productivity and yield of paddy. Azolla was used as a good source of nutrient to the paddy plant. In this study, we investigated the growth and productivity of paddy plant supplemented with liquid Azolla biofertilizer. The preparation of Azolla was monitored under several parameters such as pH, dissolved oxygen, temperature, and salinity in an aquaponics system. Water quality analysis of the aquaponic system was monitored by using the Arduino system which the customized design that consists of a microcontroller to record the parameters directly to the computing device in a single optimum and efficient system. The nutrient composition of liquid Azolla biofertilizer was analyzed by using Inductively Coupled Plasma Mass Spectrometry (ICP/MS) and CHNS elemental analysers. hows that liquid Azolla contents 0.958% nitrogen, 15.5 ppm phosphorus and 159.8 ppm potassium. After four months, the yield of paddy on Azolla was 149.12 kg/he with the productivity of 63.157 kg/he/year. This application helped to an efficient monitoring system for measure high yield and productivity of biofertilizer by Arduino software monitoring. This study can act as an initial step for the web-based automated control and monitoring of the food production system.

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