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2021 ◽  
Vol 2021 ◽  
pp. 1-12
Magdiel Jiménez-Guarneros ◽  
Jonas Grande-Barreto ◽  
Jose de Jesus Rangel-Magdaleno

Early detection of fault events through electromechanical systems operation is one of the most attractive and critical data challenges in modern industry. Although these electromechanical systems tend to experiment with typical faults, a common event is that unexpected and unknown faults can be presented during operation. However, current models for automatic detection can learn new faults at the cost of forgetting concepts previously learned. This article presents a multiclass incremental learning (MCIL) framework based on 1D convolutional neural network (CNN) for fault detection in induction motors. The presented framework tackles the forgetting problem by storing a representative exemplar set from past data (known faults) in memory. Then, the 1D CNN is fine-tuned over the selected exemplar set and data from new faults. Test samples are classified using nearest centroid classifier (NCC) in the feature space from 1D CNN. The proposed framework was evaluated and validated over two public datasets for fault detection in induction motors (IMs): asynchronous motor common fault (AMCF) and Case Western Reserve University (CWRU). Experimental results reveal the proposed framework as an effective solution to incorporate and detect new induction motor faults to already known, with a high accuracy performance across different incremental phases.

2021 ◽  
Vol 54 (6) ◽  
Mykola Shopa ◽  
Nazar Ftomyn ◽  
Yaroslav Shopa

A high-accuracy polarimetric technique has been used for the characterization of a lead germanate ferroelectric single crystal. The measurement results of the linear and circular birefringence in the [010] direction at a wavelength of 633 nm under the influence of an electric field are presented. Gyration–electric field hysteresis loops at alternative crystal positions in the polarization system have been used to determine the ellipticity of the eigenwaves. A temperature dependence of the gyration tensor component g 11 in the temperature range of 300–450 K was found.

2021 ◽  
M.S Roobini ◽  
M. Lakshmi

Abstract Alzheimer diseases are very hard to identify at beginning stage and also medication is available. So, the only way to protect those people is to predict the Alzheimer disease before it reaches the peak. More studies in diabetes show that there is a link between Diabetes and Alzheimer. Initially the prediction of diabetes is done using most relevant parameters, which detects the Diabetes. Then the severity level of diabetes is identified using some scoring levels. Based on scoring levels of diabetes, it is classified in to Type1 and Type2 using Machine Learning algorithms. When Diabetes reaches a worst case, it may affect any organs in the human body, whereas Type 2 Diabetes has associated with rare diseases which literally affects the brain and leads to cognitive impairment. After predicting the patients having cognitive impairment by applying classification algorithms are further examined to check whether it leads to Alzheimer disease. For this prediction the most relevant parameters which are common to Diabetes and Alzheimer is identified. Further identified parameters are used for prediction of Alzheimer disease with high accuracy which is helpful for taking precaution measures. In this proposed work, the most relevant features are selected using Pearson correlation-based feature elimination method and the diagnosis of the diabetes are carried using the Graph convolutional neural network (GCN). The measures of performance of the proposed work are calculated with various factors like Sensitivity measure, Recall, Precision, F-Measure. Proposed has achieved highest of 98.91%, 97.01%, 98.62%, 98.91% in above metrics.

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1397
Shu-Ya Chen ◽  
Thi-Chinh Nguyen ◽  
Ching-Yuang Huang

FORMOSAT-7/COSMIC-2 (FS7/C2) satellite was successfully launched in June 2019. The satellite provides about 5000 radio occultation (RO) soundings daily over the tropical and partial subtropical regions. Such plentiful RO soundings with high accuracy and vertical resolution could be used to improve model initial analysis for typhoon prediction. In this study, assimilation experiments with and without the RO data were conducted with the WRFDA hybrid system for the prediction of Typhoon Haishen (2020). The experimental results show a remarkable improvement in typhoon track prediction with RO data assimilation, especially when using a nonlocal refractivity operator. Results in cycling DA and forecast are analyzed and verified for the RO data impact. Diagnostics of potential vorticity (PV) tendency budget helps explain the typhoon translation induced by different physical processes in the budget. The typhoon translation is essentially dominated by horizontal PV advection, but the track deviation can increase due to the vertical PV advection with opposite effects in the absence of RO data. Sensitivity experiments for different model initial times, physics schemes, and RO observation amounts show positive RO data impacts on typhoon prediction, mainly contributed from FS7. Complementary, an improved forecast of Typhoon Hagupit (2020) is also illustrated for the RO data impact.

Ariel Tankus ◽  
Lior Solomom ◽  
Yotam Aharony ◽  
Achinoam Faust-Socher ◽  
Iso Strauss

Abstract Objective. The goal of this study is to decode the electrical activity of single neurons in the human subthalamic nucleus (STN) to infer the speech features that a person articulated, heard or imagined. We also aim to evaluate the amount of subthalamic neurons required for high accuracy decoding suitable for real-life speech brain-machine interfaces. Approach. We intraoperatively recorded single-neuron activity in the STN of 21 neurosurgical patients with Parkinson's disease undergoing implantation of deep brain stimulator (DBS) while patients produced, perceived or imagined the five monophthongal vowel sounds. Our decoder is based on machine learning algorithms that dynamically learn specific features of the speech-related firing patterns. Main results. In an extensive comparison of algorithms, our sparse decoder ("SpaDe"), based on sparse decomposition of the high dimensional neuronal feature space, outperformed the other algorithms in all three conditions: production, perception and imagery. For speech production, our algorithm, Spade, predicted all vowels correctly (accuracy: 100%; chance level: 20%). For perception accuracy was 96%, and for imagery: 88%. The accuracy of Spade showed a linear behavior in the amount of neurons for the perception data, and even faster for production or imagery. Significance. Our study demonstrates that the information encoded by single neurons in the STN about the production, perception and imagery of speech is suitable for high-accuracy decoding. It is therefore an important step towards brain-machine interfaces for restoration of speech faculties that bears an enormous potential to alleviate the suffering of completely paralyzed ("locked-in") patients and allow them to communicate again with their environment. Moreover, our research indicates how many subthalamic neurons may be necessary to achieve each level of decoding accuracy, which is of supreme importance for a neurosurgeon planning the implantation of a speech brain-machine interface.

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1401
Haq Nawaz ◽  
Ahsen Tahir ◽  
Nauman Ahmed ◽  
Ubaid U. Fayyaz ◽  
Tayyeb Mahmood ◽  

Global navigation satellite systems have been used for reliable location-based services in outdoor environments. However, satellite-based systems are not suitable for indoor positioning due to low signal power inside buildings and low accuracy of 5 m. Future smart homes demand low-cost, high-accuracy and low-power indoor positioning systems that can provide accuracy of less than 5 m and enable battery operation for mobility and long-term use. We propose and implement an intelligent, highly accurate and low-power indoor positioning system for smart homes leveraging Gaussian Process Regression (GPR) model using information-theoretic gain based on reduction in differential entropy. The system is based on Time Difference of Arrival (TDOA) and uses ultra-low-power radio transceivers working at 434 MHz. The system has been deployed and tested using indoor measurements for two-dimensional (2D) positioning. In addition, the proposed system provides dual functionality with the same wireless links used for receiving telemetry data, with configurable data rates of up to 600 Kbauds. The implemented system integrates the time difference pulses obtained from the differential circuitry to determine the radio frequency (RF) transmitter node positions. The implemented system provides a high positioning accuracy of 0.68 m and 1.08 m for outdoor and indoor localization, respectively, when using GPR machine learning models, and provides telemetry data reception of 250 Kbauds. The system enables low-power battery operation with consumption of <200 mW power with ultra-low-power CC1101 radio transceivers and additional circuits with a differential amplifier. The proposed system provides low-cost, low-power and high-accuracy indoor localization and is an essential element of public well-being in future smart homes.

2021 ◽  
Vol 13 (21) ◽  
pp. 4290
Andreas Baumann-Ouyang ◽  
Jemil Avers Butt ◽  
David Salido-Monzú ◽  
Andreas Wieser

Terrestrial Radar Interferometry (TRI) is a measurement technique capable of measuring displacements with high temporal resolution at high accuracy. Current implementations of TRI use large and/or movable antennas for generating two-dimensional displacement maps. Multiple Input Multiple Output Synthetic Aperture Radar (MIMO-SAR) systems are an emerging alternative. As they have no moving parts, they are more easily deployable and cost-effective. These features suggest the potential usage of MIMO-SAR interferometry for structural health monitoring (SHM) supplementing classical geodetic and mechanical measurement systems. The effects impacting the performance of MIMO-SAR systems are, however, not yet sufficiently well understood for practical applications. In this paper, we present an experimental investigation of a MIMO-SAR system originally devised for automotive sensing, and assess its capabilities for deformation monitoring. The acquisitions generated for these investigations feature a 180∘ Field-of-View (FOV), distances of up to 60 m and a temporal sampling rate of up to 400 Hz. Experiments include static and dynamic setups carried out in a lab-environment and under more challenging meteorological conditions featuring sunshine, fog, and cloud-cover. The experiments highlight the capabilities and limitations of the radar, while allowing quantification of the measurement uncertainties, whose sources and impacts we discuss. We demonstrate that, under sufficiently stable meteorological conditions with humidity variations smaller than 1%, displacements as low as 25m can be detected reliably. Detecting displacements occurring over longer time frames is limited by the uncertainty induced by changes in the refractive index.

2021 ◽  
Mutlu Cukurova ◽  
Madiha Khan-Galaria ◽  
Eva Millan ◽  
Rose Luckin

One-to-one online tutoring provided by human tutors can improve students’ learning outcomes. However, monitoring the quality of such tutoring is a significant challenge. In this paper, we propose a learning analytics approach for monitoring online one-to-one tutoring quality. The approach analyses teacher behaviours and classifies tutoring sessions into those that are effective and those that are not effective. More specifically, we use sequential behaviour pattern mining to analyse tutoring sessions using the CM-SPAM algorithm and classify tutoring sessions into effective and less effective using the J-48 and JRIP decision tree classifiers. To show the feasibility of the approach, we analysed data from 2250 minutes of online one-to-one primary Maths tutoring sessions with 44 tutors from 8 schools. The results showed that the approach can classify tutors’ effectiveness with high accuracy (F measures of 0.89 and 0.98 were achieved). The results also showed that effective tutors present significantly more frequent hint provision and proactive planning behaviours than their less effective colleagues in these online one-to-one sessions. Furthermore, effective tutors sequence their monitoring actions with appropriate pauses and initiations of students’ self-correction behaviours. We conclude that the proposed approach is feasible to monitor the quality of online one-to-one primary Maths tutoring sessions.

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258880
Chang Hee Han ◽  
Eal Kim ◽  
Tan Nhu Nhat Doan ◽  
Dongil Han ◽  
Seong Joon Yoo ◽  

Background Diseases and pests have a profound effect on a yearly harvest and productivity in agriculture. A precise and accurate detection of the diseases and pests could facilitate timely treatment and management of the diseases and pests and lessen the resultant loss in economy and health. Herein, we propose an improved design of the disease detection system for plant images. Methods Built upon the two-stage framework of object detection neural networks such as Mask R-CNN, the proposed network involves three types of extensions, including the addition of additional level of feature pyramids to improve the exploration and proposal of candidate regions, the aggregation of feature maps from all levels of feature pyramids per candidate region to fully exploit the information from feature pyramids, and the introduction of a squeeze-and-excitation block to the construction of feature pyramids and the aggregated feature maps to improve the representation of feature maps. Results The proposed network was evaluated using 74 images of infected apple fruits. In 3-fold cross-validation, the proposed network achieved averaged precision (AP) of 72.26, AP at 0.5 threshold of 88.51 and AP at 0.75 threshold of 82.30. In the comparative experiments, the proposed network outperformed the other competing networks. The utility of the three extensions was also demonstrated in comparison to Mask R-CNN. Conclusions The experimental results suggest that the proposed network could identify and localize the symptom of the disease with high accuracy, leading to an early diagnosis and treatment of the disease, and thus holding the potential for improving crop yield and quality.

Agriculture ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1043
Mohammad Askari ◽  
Yousef Abbaspour-Gilandeh ◽  
Ebrahim Taghinezhad ◽  
Ahmed Mohamed El El Shal ◽  
Rashad Hegazy ◽  

This study aimed to evaluate the ability of the response surface methodology (RSM) approach to predict the tractive performance of an agricultural tractor during semi-deep tillage operations. The studied parameters of tractor performance, including slippage (S), drawbar power (DP) and traction efficiency (TE), were affected by two different types of tillage tool (paraplow and subsoiler), three different levels of operating depth (30, 40 and 50 cm), and four different levels of forward speed (1.8, 2.3, 2.9 and 3.5 km h−1). Tractors drove a vertical load at two levels (225 kg and no weight) in four replications, forming a total of 192 datapoints. Field test results showed that all variables except vertical load, and different combinations of this and other variables, were effective for the S, DP and TE. Increments in speed and depth resulted in an increase and decrease in S and TE, respectively. Additionally, the RSM approach displayed changes in slippage, drawbar power and traction efficiency, resulting from alterations in tine type, depth, speed and vertical load at 3D views, with high accuracy due to the graph’s surfaces, with many small pixels. The RSM model predicted the slippage as 6.75%, drawbar power as 2.23 kW and traction efficiency as 82.91% at the optimal state for the paraplow tine, with an operating depth of 30 cm, forward speed of 2.07 km h−1 and a vertical load of 0.01 kg.

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