Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensor Fault Diagnosis Based on Wavelet Analysis and LSTM Neural Network

In order to realize online monitoring and improve the reliability of altitude simulation test equipment, wavelet analysis combining with neural network are proposed for fault diagnosis on sensors in an inlet pressure modulation system. Wavelet transform modulus maximum characteristic and high-frequency energy characteristic extraction obtains two-dimension wavelet decomposition level and type of the output signal from a pressure sensor. The extracted characteristic matrix by the two methods has the characteristics of sequence and is more distinct in discrimination for different signal than the characteristic vector. A LSTM (Long Short-Term Memory) neural network is adopted to diagnose the fault of the sensor. The LSTM neural network prediction modulus is trained by using the sensor characteristic data set for different pressure conditions to improve its generalization ability. Testing and verification simulation show that, wavelet analysis combining the LSTM neural network prediction method can predict the random failures of a pressure sensor with satisfactory accuracy.
Influence of Frequency Components of Short-Circuit Electromagnetic Force on Vibration Characteristics of Power Transformer Windings

The safe operation of power transformers is critical for the stability of power grid. The external short circuit is one of the major threats of the transformers. When an external short circuit occurs, the transformer windings would endure large electromagnetic force and vibrate violently. Accurate calculation of winding vibration is necessary for better designed windings with improved mechanical and insulation strength. The short-circuit electromagnetic force consists of DC component, fundamental frequency component and double frequency component. Different frequency components have different amplitudes and produce different displacement and stress distribution in the windings. In this paper, the influence of frequency components on vibration characteristics is investigated. The displacement distributions under the effect of different components are obtained. According to the results of the case study, it is worth to notice the influence of the fundamental frequency component on the short-circuit vibration process. For the spatial distribution, the amplitudes of vibration parameters are larger at certain positions, which implies that more attention should be paid to these positions in order to improve reliability of power transformers. The distribution characteristics are in accordance with the mode shape theory.
Anisotropy-Based Position Sensorless Control for Safety Functionality Applied to Synchronous Machines in Electric Vehicles

This contribution presents anisotropy-based position sensorless control estimation methods within a modified functional safety concept suitable for rotor position monitoring using a two-level safety concept. The principle includes two position-sensorless algorithms running in parallel, whereby two appropriate modified anisotropy-based algorithms are investigated. The first method, a double high-frequency injection algorithm, is easy to integrate and shows low parameter sensitivity. However, it needs a second injection scheme and subsequent influences the overall system efficiency. In contrast, the second method, an algorithm using the modulation-excitation. , does not influence the overall system, but relies on a high bandwidth of the used sensors. Measurements validate that both methods are suitable as monitoring functions in automotive applications.
A Large-scale Wireless Charging Station for Electric Vehicles

This paper proposes a large-scale, high-power, transformerless, multilevel wireless charging station for electric vehicles (EVs) using a multilevel rectifier, resonant inverters, and excitation coils. The multilevel rectifier converts the medium voltage (MV, 22.9 kVrms in S.Korea) of the electrical grid to 42 levels of 1 kV. Forty-two resonant inverters and excitation coils generate synchronized magnetic fields and induce voltage and current to a transmitter coil. Then, the transmitter coil induces voltages on receiver coils mounted on EVs. The proposed topology has the advantage of balancing the cell voltages of the multilevel converter for arbitrary load conditions. The input impedance seen from each converter is balanced regardless of the load numbers and their conditions. Circuit analysis of the multilevel inductive power transfer (IPT) system with multiple loads is presented to achieve load-independent output voltages. In addition, a novel $dq$ synchronous reference frame controller for the multilevel rectifier is proposed. A 42-level IPT system with ten receiver coils was evaluated with simulation results. The cell balancing of the multilevel converter and the load-independent output voltages were achieved.
Leakage Current Analysis and Mitigation for Modular Multilevel Converter with Floating Submodules

Modular Multilevel Converter (MMC) has been applied in VSC-HVDC system for AC-DC power conversion, with numerous submodules. These submodules are usually mounted in tower with floating ground. The parasitic capacitors between the submodule and the ground provides a path for leakage current caused by dv/dt of the switching actions. This paper has analyzed the model of MMC leakage current with multiple floating submodules, pointing out the leakage current difference for the submodules in different location in the MMC. Besides, the conduction path and distribution law of leakage current in the MMC are analyzed. Furthermore, the leakage current reduction can be obtained by selection of submodules for plug-in/removal in the switching moment. Simulation and experimental results are provided in this paper to validate the analysis and the effect of the proposed leakage current reduction method. This paper provides a new way for Electromagnetic Interference (EMI) for MMC.
Predicting Generalized Contrast-to-Noise Ratios in Frame-Averaged Photoacoustic Images

Our related journal article presents a theoretical framework to relate the performance of photoacoustic-based computer vision systems to the parameters of the underlying photoacoustic imaging systems. However, software-based adjustments were not previously included. In this paper, we extend the previous framework to include software-based noise reduction algorithms (e.g., frame averaging). We derive expressions for generalized contrast-to-noise ratio (gCNR) predictions in frame-averaged photoacoustic images. These gCNR predictions are validated against histogram-based gCNR measurements, with mean absolute errors of 4.4×10−3 and 7.7×10−3 in the phantom and ex vivo environments, respectively. This extension reduced the mean absolute prediction error by 7.74% and 27.15% when compared to predictions with the previous photoacoustic framework applied to frame-averaged photoacoustic images from phantom and ex vivo data, respectively. Results demonstrate that our extended framework more accurately predicts target detectability in frame-averaged photoacoustic images, which is beneficial for photoacoustic-based computer vision systems.
Deep Learning Based Model for Alzheimer's Disease Detection Using Brain MRI Images

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that causes problems with memory, thinking, and behavior. And with time, symptoms become severe enough to interfere with daily activities. Although there is no cure for the disease, a proper management strategy starting at an early stage can help improve quality of life and potentially slow the disease progression. In clinical research, machine learning techniques are frequently being used in different ways to help detect disease conditions and progressions. Magnetic resonance imaging (MRI) is one of the best available tools that is used to diagnose Alzheimer's disease. However, detecting very small changes in AD brain during the early stage of the disease is challenging. In this study, we developed deep learning-based models for Alzheimer's detection using the 6219 MRI images dataset. The dataset consists of images of different degrees of demented and non-demented brains. Four deep learning models that are utilized in this study are Convolutional Neural Network (CNN), ResNet101, DenseNet121, and Visual Geometry Group16 (VGG16). From the analysis, we found that CNN outperformed other models and achieved an accuracy of 97.60%, recall of 97%, and AUC of 99.26%, with a nominal loss of 0.091.
Condition Deterioration Mechanisms of High Voltage Press-Pack IGBT under Power Cycling Test

Modular multilevel converter-based high voltage current (MMC-HVDC) system is widely utilized in power transmission systems. Insulated gate bipolar transistor (IGBT) is one of the core devices in MMC. The degradation mechanism is of significance to the safe and reliable operation of the MMC-HVDC system. Compared with power module IGBTs (PMIs), high voltage press-pack IGBTs (PPIs) have short-circuit failure mode and double-side cooling advantages. This paper uses power cycling tests and microscopic observation methods to study the condition deterioration characteristics and mechanism of PPIs. Power cycling test is performed on PPIs to obtain the evolution rule of parameters like saturation voltage and gate threshold voltage. The deterioration modes of aged chips are analyzed based on the result of micro-optical observation on the chips' emitter surface, and the possible reason for the evolution rule of the measured parameters of the PPIs is explained. The results show that the condition deterioration characteristics of PPIs are dispersed. In the early stage of the power cycling tests, since the fretting wear has little effect on the contact thermal resistance and contact resistance, the device saturation voltage and gate threshold voltage remain unchanged. It is inferred that if fatigue cracks appear on the emitter's surface, the device's state deteriorates faster and causes the increment of the saturation voltage. The gate threshold voltage of the device is less affected by cracks and remains relatively unchanged.
Performance Comparison of Optimized Stator-Mounted Permanent Magnet Machines Focusing on PM Usage

This paper compares the performance of five stator-mounted permanent magnet (PM) machines including conventional, E-core and modular flux-switching PM machine, as well as flux-reversal and doubly salient PM machines. First type of comparison is conducted when the five machines are optimized with the same magnet volumes. The performance of each topology is represented using the Pareto front obtained from genetic algorithm optimization. However, the second type of comparison is conducted when the five machines are optimized with different magnet volumes. Regardless of what condition is used for the comparison, it is concluded that the flux-switching PM machines generally have better torque performance and higher torque to magnet ratio than the flux-reversal and doubly salient PM machines. Secondly, high PM volume could have negative influence on torque performance of the stator-mounted PM machines.
Genetic algorithm based-technique for CIGS solar cell performance optimization

In this work, we investigate an optimal design of CIGS solar cell using a hybrid strategy by combining an analytical model with genetic algorithm technique. The proposed strategy takes into account the electrical and optical properties of the employed materials. Physical and geometrical parameters influences are investigated within a suitable range of variation that considers experimental and physical limits. Besides, the presented strategy not only used to examine the influence of different parameters on the device efficiency but also to captivate the optimal combination of this design parameters that leads to the best CIGS solar cell performance. The findings show that for suitable doping concentrations and thicknesses of ZnO, CdS and CIGS layers an efficiency of 14.6 % can be achieved.
Using Synthetic Data to Train a Conversational Response Generation Model in Low Resource Settings

Response generation is a task in natural language processing where a model is trained to respond to human statements. Conversational response generators take this one step further with the ability to respond within the context of previous responses. While techniques exist for training such models, they all require large amounts of conversational data, which are not always available for low-resource languages. In this research, we make three contributions. First, we released the first Filipino conversational dataset collected from a popular Philippine online forum called the “PEx Conversations Dataset. ” Second, we introduce a data augmentation methodology for Filipino data by employing a Tagalog RoBERTa model to increase the size of the existing corpora. Lastly, we published the first Filipino conversational response generator capable of generating responses related to the previous three responses. With the supplementary synthetic data, we were able to improve the performance of the response generator by up to 10.7% in perplexity, and 11.7% in content word usage as compared to training with zero synthetic data.
Development of Ultrasonic Shrimp Monitoring System Based on Machine Learning Approaches

The rising of smart aquaculture offers the possibility to maintain or even increase the production of shrimps with less cost and environmental impact. The most critical factors that affect the growth rate of shrimp is the efficiency of feeding. Therefore, identify the location and number of shrimps are important for optimizing feeding approach. Recently, computer vision has been applied in many fields to extract useful information from images. To increase the production with less cost, computer vision is also used to monitor the products of aquaculture. Usually, to ensure the accuracy of computer vision, a relatively high-quality picture is required. Since shrimp is benthic animal and the water of shrimp pond is usually turbidity, the quality of underwater photos is usually poor. Compared to optical photos, the imaging mechanism of ultrasound is less affected by the above effects. Therefore, in this study, an innovative white shrimp monitoring system combining ultrasound imaging technology and YOLOv4 is proposed. The database of shrimps' ultrasonic images is built by imaging the different parts of the shrimps from various viewing angles with a linear ultrasonic probe. Through moving the ultrasound probe by a 2-axis motor, the images could be captured from two orthogonal viewing angles within a 12.69cm x 12.69cm region of interest. After 260 images are captured, the locations of shrimps in each image would be recognized and the centers of bounding boxes will be marked. After that, a custom grouping method based on spectral clustering algorithm would be applied to distinguish the number of shrimps. Finally, the length and location of shrimps would be calculated from the best corresponding fitting line. After examining the proposed system with 30 sets of different number, size, and location of shrimps, the accuracy of identifying the number of shrimps is 97.3% along with 8.99% averaged length error and 0.97 cm averaged position error. These results demonstrate the feasibility of monitoring shrimp farms using proposed system.
Nonlinear Simulation of Modulation Pulse Sequencing for Contrast-Enhanced Ultrasound (CEUS) Imaging

Microbubble nonlinearity based contrast-enhanced ultrasound (CEUS) imaging has received extensive attentions due to its high scattering and resonance for facilitating blood flow imaging. In contrast to soft tissues, microbubble echoes are rich in harmonic components because of periodic expansion and contraction movements. Different Contrast Pulse Sequence (CPS) strategies have been developed for nonlinear ultrasound imaging. In present study, a modified Rayleigh-Plesset equation based numerical simulation method is applied to investigate performances of nonlinear ultrasound imaging with different CPS strategies.
Performance Improvement in Cellular V2X (CV2X) by Using Low Density Parity Check (LDPC) Code

Cellular Vehicle-to-Everything (CV2X) has recently piqued the interest of both industry and academia. CV2X extends communication coverage by using the cellular infrastructure as a standard for vehicular communication. CV2X also enables the communication between vehicles and pedestrians, vehicles and the cellular network, along with vehicle to vehicle communication. The CV2X standard’s expectations are gradually rising as the number of vehicles, cell phone users, smart devices connected to vehicles, and other factors increase. A more efficient CV2X framework is needed to fulfill these expectations. We focused on the PHY layer of the CV2X standard and proposed a model featuring Low Density Parity Check (LDPC) code. Our experimental results suggest that our proposed model is able to improve reliability by reducing the bit error rate compared to the existing PHY layer framework. In addition, our proposed model can increase the throughput up to 1.4 times and 2 times at 10 MHz and 20 MHz channels, respectively.
Feasibility of Attenuation Coefficient and Envelope Signal-to-Noise Ratio Estimation for Tissue Characterization of Liver Steatosis

Nonalcoholic fatty liver disease (NAFLD) has become the most common cause of chronic liver disease worldwide. In addition, conducting large-scale screening studies is still challenging due to the absence of reliable and affordable noninvasive tools for NAFLD staging. Conventional ultrasound is commonly the preferred first imaging modality used to evaluate liver steatosis. However, it is mainly a qualitative technique that depends on operator's skill and experience, resulting in variable results and reproducibility. In this study, we assess the application of attenuation coefficient slope (ACS) estimation by the regularized spectral log difference (RSLD) technique and computation of the envelope signal-to-noise ratio (SNR) for tissue characterization of NAFLD in a murine animal model. This approach was tested using rats that were fed either a control or methionine and choline deficient (MCD) diet. Ultrasound measurements were then performed at 0 (baseline), 2, and 6 weeks. After the final imaging session, animals were humanely euthanized and livers excised for histological processing. Results showed that ACS and SNR increased by approximately 0.2 dB/cm/MHz and 0.2 a.u. , respectively, for each 25% increment in fat fraction percentage in diet group over the weeks. Moreover, control and diet groups were statistically different at weeks 2 $(p < 0.05)$ and 6 $(p < 0.01)$ for both parameters. Overall, ACS and SNR were found to be encour-aging for determining relative contents of liver fat and noninvasive grading of steatosis.
Dual Band Cup-shaped Microstrip Monopole Antenna for WLAN/WiMAX Application

In this article, a novel design of a compact dual band planar monopole antenna has been presented. The antenna radiating patch consists of a slotted cup-shaped structure operating in the two bands covering the 3.5/5.5 WiMAX and 5.2/5.8 WLAN applications. The proposed radiating patch is fed by means of 50 Ω micro strip line. For better impedance matching, an inset is inserted. The dual band operation is achieved by loading two symmetrical rectangular open slots on the a wide band radiator patch. FR-4 substrate with relative permittivity of 4.3, loss tangent of 0.017 and thickness is used to fabricate the structure. The total size of the proposed design is 26x22x1.6 mm3. The measured return less results are found to be in good agreement with the simulated ones. Moreover, the H-plane simulated radiation patterns, at both operating bands, are omnidirectional, while bidirectional radiation patterns are obtained in the E-plane at both operating bands. Accordingly, the slotted-cup shaped proposed structure is well suited to be used in wireless applications.
Performance Benchmarking of PyScript and Comparative Results with Javascript

In the last two decades that there has been an explosion in the use of web applications. Given the ever-expanding number of web and mobile apps, extensive work has been done in developing and using efficient core technologies to support the performance goals of the apps. In particular, front-end developer languages such as Javascript are highly popular. In this paper, we perform a detailed study of the recently introduced Python-based web development framework PyScript and evaluate the performance of PyScript in comparison with Javascript using standard benchmarks. The research contributions of this work are in the benchmarking of code in three different categories (i) DOM manipulation, (ii) Operations involving the network layer, and (iii)cpu-intensive computations. The benchmarks come from one of the standards and carefully implemented to perform a comparative evaluation. Results show that native Javascript code runs faster on all DOM and networking sets of benchmarks by a factor of 1.5 to 5 when compared with PyScript, with the exception of two of the networking benchmarks. It is in the computationally intensive benchmarks that Javascript trumps PyScript by a factor of 25 to 35 depending the algorithm. We offer architectural insights into performance issues and conclude with directions on further research.
LIPUS Stimulation of the knee cartilage: in-vitro to in-vivo translation

A promising application of LIPUS is the possibility to regenerate damaged cartilage in the knee joint by stimulating in-situ adipose-derived stem cells and inducing chondrogenesis. Preclinical trials are mandatory to validate the protocol, but unfortunately it is often difficult to set the right set-up for in-vivo scenarios in order to stimulate the target in the correct way. The objective of this work is to give general guidelines for applying a controlled US stimulation to the knee joint and in particular to: select suitable transducer dimension, properly position it with respect to the selected joint and determine the transducer input signal amplitude to induce the wanted pressure at target accounting for tissues attenuation.
Human observer sensitivity to temporal noise in ultrasound imaging

The impact of temporal noise on ultrasound image quality can be decreased with use of higher acoustic output. Acoustic output increases amplify receive signal strength relative to the temporal noise floor, improving the temporal signal-to-noise ratio (SNR). When acoustic output has been increased to the point that observers are no longer sensitive to the noise, the noise perceptibility threshold has been reached. In this work, a simulation study was performed to evaluate the effect of motion and imaging frequency on the perceptibility threshold. Motion decreases the temporal SNR at which the perceptibility cutoff is reached, while frequency seems to have minimal impact. Applying one of these noise perceptibility thresholds to fetal imaging data yields acoustic output recommendations within safety limits and lower than scanner default values.
Design of a time-truncated double sampling plan for a general life distribution

A double sampling plan based on truncated life tests is proposed and designed under a general life distribution. The design parameters such as sample sizes and acceptance numbers for the first and the second samples are determined so as to minimize the average sample number subject to satisfying the consumer's and producer's risks at the respectively specified quality levels. The resultant tables can be used regardless of the underlying distribution as long as the reliability requirements are specified at two risks. In addition, Gamma and Weibull distributions are particularly considered to report the design parameters according to the quality levels in terms of the mean ratios.