Miracle-dz/UniFAR-FTUs huggingface dataset:
Fault Classification using Deep Learning in a Grid-Connected Photovoltaic Systems*

PV systems are prone to failure owing to aging and external/environmental factors. These failures can affect a range of system components, such as PV modules, connecting lines, and converters/inverters, re-sulting in decreased efficiency, performance, and even system failure. As a result, problem detection and diag-nosis (FDD) is an important issue in high-efficiency grid-connected PV systems. Deep learning techniques are the most well-known data-driven methodologies. The biggest advantage of deep learning algorithms, in diagnosis, are learning effectiveness, intelligent FDD becomes more effective. This paper therefore presents a comparative study of FDD based deep learning. The techniques include the Convolutional Neural Network (CNN) and Long Short Time Memory (LSTM). Finally, the FDD based frameworks are implemented using simulated PV data. The diagnosis results show that the CNN and LSTM-based fault diagnosis methods are able to detect and diagnose faults under different operating modes.
A Performance Study on Deep Learning Covid-19 Prediction through Chest X-Ray Image with ResNet50 Model

The COVID-19 epidemic has claimed many lives throughout the world and constitutes an unprecedented public health concern. The key challenge in early detection of the corona virus is early detection. And the main obstacle was the similarity of COVID-19 symptoms to flu symptoms. With the goal of saving human lives and stemming the spread of a worldwide pandemic, an accurate and speedy analysis of COVID-19-induced pneumonia has now taken centre stage. Responding this urgent concern and to reduce the burden as well as chances of faulty manual diagnosis, several deep learning approaches are developed to conduct early diagnosis. Based on the availability of reliable patient's records, an accepted technique is pre-trained deep learning prediction approach through patient's chest X-Rays. Convenience of this approach led development of a number of novel deep learning-based lung screening technologies. However, little emphasis is placed on ensuring the quality of their output. Pre-trained deep learning systems will be used in this project to evaluate their ability to recognise and diagnose disorders. To categorise COVID and normal pictures, a neural network-based ResNet50 architecture is presented. The implementation is based on the normal, COVID, and lung opacity datasets. For data pre-processing, ImageDataGenerator is used, which rescales, flips, and modifies the range to meet the model. To categorise the x- ray images, the suggested method ResNet50 architecture is used. Performance matrices like precision, accuracy, recall, as well as F1-score are examined to verify the algorithm's usefulness. The suggested technique has an accuracy of 80%, indicating that the proposed model is quite good in classifying COVID and normal x-ray pictures. This research will have a significant influence on real-time since it will accurately diagnose COVID in less time, perhaps lowering the mortality rate.
A Five-Level Unity-Gain Active Neutral-Point-Clamped Inverter Designed Using Half-Bridges

This paper proposes a novel 5-level active neutral-point-clamped (ANPC) inverter that doubles the voltage gain of the conventional topology from half to unity. Each phase of the proposed topology is constituted by three half-bridges that control a flying capacitor to generate 5 symmetrical ac voltage levels. Natural voltage balancing of dc-link and flying capacitors in the proposed topology implies that the sensors and voltage balancing controller commonly used in the conventional ANPC inverter is no longer necessary. In addition to switch count reduction, the most noteworthy merit of the proposed topology is its ease of implementation with commercial half-bridge modules, where the design of dedicated circuit is not needed. The operation of the proposed 5-level unity-gain ANPC (5L-VG-ANPC) inverter is analyzed and validated through simulation and experimental tests.
Quantitative Analysis of Parameters Dependence of PVDF Films Polarization

In poly (vinylidene fluoride) (PVDF) polymer films, α-phase usually dominates but only β-phase exhibits the strongest Ferro- and piezoelectric properties. To explore the influence of different conditions on the piezoelectric property of PVDF, in this paper, parameters dependence of dielectric properties of piezoelectric material poly (vinylidene fluoride) (PVDF) were quantitatively analysed by a piezoelectric resonance method. The dielectric coefficient d33 was used to evaluate the piezoelectricity of PVDF. The results show that poling time has a limited effect on d33, which means there will be no further change in d33 after poling enough time. For poling voltage, the d33 increased with voltage until the breakdown of PVDF material. There’s a linear relationship between d33 and poling voltage, as long as poling time is enough. Poling temperature also has an effect on d33, which can help increase d33. But there will be a reduction in d33 measuring after 24 hours. It also needs to be mentioned that the decrease rate is different while poling in different temperatures.
Voltage Actuated Transmission Line Protection Scheme Applying Signal Processing Techniques

For the protection of a two-terminal transmission line, this research developed a method for fault recognition by analyzing voltage data using Stockwell transform (ST) and Hilbert transform (HT). The HT is used to process the voltage signals and compute a Hilbert amplitude frequency index (HAFI). ST is also used to get the Stockwell summation median index from the voltage signals (SSMI). The combined fault index is calculated by multiplication of HAFI and SSMI (CFI). Hilbert Stockwell Derivative Fault Index (HSDFI) is computed by differentiating the CFI and comparing it to a pre-set threshold value for identifying problematic phases. Numbers of faulty phases are utilized to classify defective occurrences. The Hilbert Stockwell derivative ground fault index (HSDGFI), which is generated by processing the zero sequence currents, is used to detect ground involvement during a failure. Line to ground (LG) faults, double line (LL) faults, double line to ground (LLLG) faults, and three phase faults with ground (LLLG) are among the faults investigated. Results of this study have been verified in MATLAB.
Synthesized Optimal Control for Mecanum-wheeled Robot

The problem of optimal control of Mecanum-wheeled robot is considered. Mecanum wheels allow to move along the plane in any direction without turning the body of the robot. A feature of this robot model as a control object is that the control vector dimension is greater than the state vector. As a result, the same optimal robot movement can be realized by different control means. To solve the problem of optimal control, two numerical methods are used: direct control and synthesized. To check the efficiency of the obtained control functions, the mathematical model of the control object with both control functions was simulated under disturbances. The evaluation criterion was the change in the value of the quality criterion of the optimal solution depending on the level of perturbation. Experiments have shown that the synthesized optimal control for the Mecanum-wheeled robot is less sensitive to disturbances than the direct optimal control.
Geographically distributed real-time co-simulation of electric vehicle

The present paper shows the capabilities of a distributed real-time co-simulation environment merging simulation models and testing facilities for developing and verifying electric vehicles. This environment has been developed in the framework of the XILforEV project and the presented case is focused on a ride control with a real suspension installed on a test bench in Spain, which uses real-time information from a complete vehicle model in Germany. Given the long distance between both sites, it has been necessary to develop a specific delay compensation algorithm. This algorithm is general enough to be used in other real-time co-simulation frameworks. In the present work, the system architecture including the communication compensation is described and successfully experimentally validated.
Information security culture guidelines to improve employee’s security behavior: a review of empirical studies

This paper reviews Information Security Culture (ISC) studies published in six leading databases from year 2000 until 2016 to investigate empirical findings that could support the relationship between ISC and employee’s security behavior as well as to identify the findings that could be applied as guidelines to cultivate ISC in the organization. This review discovered that there is lack of comprehensive empirical studies have been done to provide sufficient empirical findings in supporting the relationship between ISC and security behavior. The approaches of the studies in terms of conceptualization and operationalization of ISC concept also limit the applicability of the findings to be used as the guidelines for ISC cultivation. This paper provides clear justifications on these issues and indicated a clear direction on the future of ISC research to be taken.
A Short-Distance Running Algorithm Based MPPT Control Strategy for PV Power Systems Under Partial Shading Conditions

This paper presents a new simple approach based on description of a short-distance running race in athletics to find maximum power point (MPP) from PV energy systems, which can improve tracking speed and searching accuracy. The operating principle of this approach is based on the division of the active area of contestants at the starting line. Each position of the contestant corresponds to each duty ratio value to control the DC boost converter. Based on the proposed athletics run-ning algorithm (ARA), a suitable control signal will be found where the load will receive the best electrical power from the PV energy system. With new ideas in terms of regional division as well as updated positions, the global MPP can be found pre-cisely, the positions quickly converge to the global maximum power region. As a result, the proposed method has excellent tracking ability with high accuracy and zero oscillation ampli-tude. Simulation results proved that the proposed algorithm provided high efficiency, especially in the case of partial shading influences.
A Dual-channel Attention Model for Optical Microscope Pollen Classification

The effective classification of pollen is a critical method to the prevention of pollen allergy. The conventional pollen classification primarily relies on manual handling under the microscope, which does not only require a lot of manpower but also has low classicization accuracy on results. In this paper, the optical microscope was used to scan the slides and the pollen image dataset was made for classification. We found from the pollen dataset that most of the images have clear contours, but there were also many pollen images with the following problems. One is that the pollen was covered by impurities like dust and pebbles, and the other is that the pollen images stain unevenly and the pollen image was blurred and the number of pollens was unbalanced. This paper proposes a Dual-Channel Attention method, we call it DCANet, which combines the advantages of channel attention and channel self-attention to improve the classification accuracy. The results of classification activation map analysis showed that DCANet paid more attention to pollen images.
Hexagonal Voltage Modification Scheme to Improve Torque Capability of Low-Cost Drives

To improve a motor's torque capability, most existing methods use the vertices of the voltage hexagon. However, it is inappropriate for a low-cost drive system where a bootstrap circuit is embedded. This paper proposes a control scheme with hexagonal voltage modification (HVM) to improve a torque capability of a low-cost drive. In the proposed method, the current controller's output is modified to a hexagon by adopting ideas of well-known overmodulation methods. With the hexagonally modified voltage, flux weakening is performed by a PI-based controller. The torque capability can be improved through the proposed scheme without significantly changing the conventional control structure. Simulation and experimental results are presented to show the effectiveness of the proposed method. The proposed scheme increases the base speed by 8.1% compared to the conventional method.
Speaker Recognition of Fiber-Optic External Fabry-Perot Interferometric Microphone Based on Deep Learning

In order to realize speaker recognition in flammable, explosive and strong electromagnetic interference environments, the speaker recognition technology of fiber-optic external Fabry-Perot interferometric (EFPI) microphone based on Deep learning is studied in this paper. The combination of convolutional neural network and long short-term memory (CNN-LSTM) model is proposed for speaker recognition in EFPI acoustic sensing system. The intensity self-compensation method is used to demodulate the voice signals of different speakers, then, the spectral features of voice signals are divided into training set and test set. The training set are input into the established CNN-LSTM model for model training, and the test set are input into the trained model for testing. The experimental results show that the CNN-LSTM model proposed in this paper has good training accuracy and test accuracy, reaching 100% and 94.0% respectively. Comparing with CNN and LSTM recurrent neural network (LSTM-RNN), the test accuracy of this model is 11.1% and 13.4% higher respectively.
Design and implementation of pulse rate monitoring over internet using thingSpeak and ESP8266

Heart disease-related deaths are growing at an alarming rate year after year. The global death rate of 7.57 deaths per 1000 people grew by 0.44 % in 2019 compared to 2018. As technology advances, varieties of smart medical sensors that can frequently monitor and evaluate patients' health conditions have been produced. This article describes the design and implementation of a pulse rate and temperature monitoring system via the internet by using ThingSpeak and the ESP8266 as a microcontroller. A thorough review of the various approaches and technologies employed in the monitoring and regulation of cardiac rate is conducted. The suggested scheme was designed and executed with the help of different system modules that are coupled and tested. The experimental outcomes produced by the proposed system were determined to be precise, as the method was capable of sensing and reading the user's pulse rate and transmitting the detected data through the internet.This paper provides a flexible, stable, and secure strategy for pulse rate monitoring utilizing sensor networks and IoT technologies that may be used in the medical industry to help health professionals accomplish their jobs more efficiently.
Integrated Scratch Marker for Wafer Defect Diagnosis

The scratch defect type is difficult to recognize because the position, shape, size and curvature vary widely from one scratch to another. Discontinuity points within scratches also contribute to the low recognition rate, and such points are often hidden defective dies that become reliability threat. The recognition rate for scratches is among the lowest in all patterns even if the overall accuracy is high. In this paper, we propose a novel scratch pattern recognition method. The method is validated by real products. Experimental results show that the average recall, precision and accuracy achieved by the proposed method are 97.22%, 98.81%, and 99.92%, respectively. Furthermore, the proposed method is based on image processing techniques alone with low processing time. In contrast to machine-learning based methods, there is no need to train a complicated prediction model.
Quantifying the Probability of Partial Discharge in VFD Fed Electric Motors under Voltage Harmonics Concentration

Partial discharges (PD) diagnostics are considered as reliable techniques for the insulation health assessment of various electrical components of the power system. Generally, PD diagnostic tests are performed in laboratory by ignoring the effect of harmonic pollution in test voltage waveform. However, the operation of electric motors (EMs) fed by variable frequency drives (VFD) produces harmonic distortion in the sinusoidal test voltage waveform. During low speed operation of VFD, the influence of harmonic distortion in test voltage waveform is increased. The additional distortion parameters in the test voltage waveform can significantly increase the PD activity in the EM insulation. Therefore, for correct understanding of PD data, it is essential to consider the actual harmonic spectrum of the test voltage waveform during online PD measurement. This paper presents a methodology for the quantification of the probability of PD in VFD-fed EMs under different concentration of voltage harmonics. For the experimental work, PD diagnostic tests have been performed under variable operating conditions of eight EMs and PD severity is investigated by calculating the PD characteristic parameters at different harmonic distortion levels. Also, the probability density functions and cumulative distribution functions for several probability distributions were evaluated to statistically process the PD data at different harmonic levels. Finally, the distribution fitting tools have been implemented to find an appropriate distribution that can precisely characterize the probability of PD under different concentration of voltage harmonics. The proposed method may be utilized for studying the effect of harmonic distortion on PD activity and estimating the lifetime of stator insulation in EMs.
Reducing Routing Overhead by Self-Enabling Functional Path Ring Oscillators

Automotive Microcontrollers (MCUs) are extensively tested to guarantee zero-defect quality. Performance screening is one of the critical factors to ensure that MCUs meet quality requirements. Ring Oscillator (RO) structures are used for this performance screening. Such RO structures usually cause routing overhead on the chip. The routing overhead increases, especially when many ROs are implemented. This paper presents a novel self-enabling technique that significantly reduces the routing overhead for functional path ROs. We present a proof of concept on a large automotive MCU. The routing overhead can be reduced by over 80% compared to traditional approaches.
Fuzzy Region Merging using Fuzzy Similarity Measurement on Image Segmentation

Some image’s regions have unbalance information, such as blurred contour, shade, and uneven brightness. Those regions are called as ambiguous regions. Ambiguous region cause problem during region merging process in interactive image segmentation because that region has double information, both as object and background. We proposed a new region merging strategy using fuzzy similarity measurement for image segmentation. The proposed method has four steps; the first step is initial segmentation using mean-shift algorithm. The second step is giving markers manually to indicate the object and background region. The third step is determining the fuzzy region or ambiguous region in the images. The last step is fuzzy region merging using fuzzy similarity measurement. The experimental results demonstrated that the proposed method is able to segment natural images and dental panoramic images successfully with the average value of misclassification error (ME) 1.96% and 5.47%, respectively.
Lab Investigation of Thermal Anemometers for Mass Flow Measurements in Harsh Operating Conditions

Argon purging is a process in steel production where liquid metal is flushed with argon gas to remove impurities. It is an essential processing part for high quality steel production. For process control, reliable flow measurements of the amount of purging gas are required. However, due to the situation in the steelworks, the measurement of the flow must be done directly at the injection. At this position an energy autarkic measurement system is required, which puts energy constraints to the application of flow metering techniques. The harsh operating conditions at this position make this measurement even more challenging. Currently, no off-the-shelf measurement system exists. In this work, we investigate the feasibility of lower power thermal anemometry for flow metering in the present application. Different sensing elements and operational modes for thermal anemometry are studied and analysed by means of test rig measurements. Finally, an uncertainty evaluation is presented, which provides an approach for the setting of the operational conditions of such a sensor.
Face Mask Detection System using Convolutional Neural Network

According to the World Health Organization, the coronavirus also known as the COVID-19 pandemic, is causing a global health crisis, WHO and Other health organizations recommended wearing face masks as a protection measurement especially in work, school, university, malls, and any public places. The proposed system consists of a Convolutional Neural Network-based system that can recognize faces with masks and without a mask, The dataset used for this project consists of 3835 images of two types of images, people with masks and people without masks. By utilizing the TensorFlow framework we’re able to get a 98.80% accuracy rate in training time while training the model with 200 epochs.
Electromagnetic Design of Dual Winding Permanent Magnet Synchronous Motor for Electromechanical Actuators of Flight Control Systems

This paper deals with PM motors using multi-pole anisotropic ring magnet for electro mechanic actuators (EMAs) of future aircraft flight control systems. The electromagnetic design via FEM software and evaluations of motor torque, losses in healthy and failure state are carried out. The results showed that even if one-phase open circuit of an inverter occurred or one-side of the 3-phase current supply system failed, the PM motor could operate at 100% of the average torque of 5.5 Nm or over 14.6 Nm, which is over 97 % of the maximum torque of 15.0 Nm.
Battery Package Fused Bus Bar Design for Cylindrical Battery Cells

This paper presents a method for designing fused bus bars of a cylindrical battery cell based battery package. The testing environment covered in this paper can be adapted to test any fused bus bar. The bus bars are made as cut-outs of copper and nickel sheets that were designed in the AutoCAD drawing environment. Comsol’s simulation environment was used to simulate the fuse’s melting points in regard with different current levels. After the simulation, the bus bars were cut out and sent to the testing bench for the purpose of comparing the measured values with the simulated ones. The purpose of the measurement is to find the cut off temperature and electrical current values at which the different fuse materials break the battery pack circuit. The fuses are thoroughly monitored with temperature sensors and a thermal camera. The test bench is made out of a bidirectional DC power supply that is connected to a single fuse, two temperature sensors that measure the temperature of the area around the fuse and a thermal camera to verify the temperature readings.