Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Stability of Grid-Following Inverter for Renewables

The increasing penetration of wind and solar power sources in power networks has led to the need for advanced converters' control technologies to support the integration of these renewable energy sources into the grid. Inverters play a critical role in power networks, and understanding the benefits and limitations of different inverter types can facilitate the effective management of renewable energy sources and contribute to a more stable and sustainable power system. With multiple inverters connected to the grid, different instability phenomena can occur (ranging from low to medium to high frequencies). The main objective of this paper is to explore the instability phenomena related to grid-following inverters in weak power networks with high penetration of wind and solar power sources. These inverters have electronic circuits, such as the phase-locked loop (PLL), that provide information about the phase angle of the grid voltage waveform. This paper provides insight into the advantages and disadvantages of this type of inverter control and its impact on grid stability and intermittency management of renewable energy sources. It will also discuss the challenges associated with stability and propose a possible improvement of the grid-following control approach by introducing additional control circuitry emulating synchronverter [1]. The presented simulation results demonstrate the quality of the proposed modification of the grid-following control approach.
Single-pixel Imaging Classification Method Based on Improved Lenet-5 Network

Single-pixel imaging, as a new imaging technique, requires a large amount of data processing when reconstructing images. If the classification is based on the reconstructed images of single-pixel imaging, a large amount of data redundancy will be generated. In view of this defect, the “image-free classification” method can significantly reduce the data throughput in the step of image reconstruction in single-pixel imaging, and directly send the value of the single-pixel detector before image recovery into the convolutional neural network for classification. However, there is still some room for improvement in the classification accuracy and time of MNIST dataset. This paper combines Lenet-5 network with “image-free classification”, innovatively adjusts Lenet-5 network to a structure suitable for “image-free classification”, completes the classification of MNIST and Fashion-MNIST dataset using “image-free classification”, and adds residual structure to improve accuracy. Using the structural illumination trained by the improved neural network to project the target can increase the test accuracy from 89% to 99%, reduce the number of projection times of digital micromirror devices from 15 to 6, and reduce the total time of testing and training from 2 hours to 4.7 minutes, which indicates that the improved convolutional neural network reduces the classification time and improves the classification accuracy.
Implementation of Convolutional Neural Network in the Development of Object Recognition System

Education, which is the key to success, is often the most valuable inheritance from parents to children. Not infrequently parents are willing to spend a lot of money for the best education for their children. Early Childhood Education which is early education for children does not escape special attention for parents. Rapid child development begins at this stage. In the final era of the Covid-19 pandemic, digital learning applications are expected to be a bridge and a solution to improve children’s education, especially in the preschool period. One of the abilities of early childhood that needs to be honed is the introduction of objects around them. This object socialization activity also needs to be supported by object recognition applications to help parents or teachers improve children’s abilities. Therefore, we propose an Object Recognition System (OR-Sys) that is connected to a camera to help children recognize objects around them. The image recognition process of surrounding objects requires a reliable classification method. Convolutional Neural Network (CNN), which is the latest development of the Neural Network method, can adopt and improve the advantages possessed by previous methods, especially in terms of classification. In this study, we choose to use two CNN architectures that are quite popular in use, namely GoogLeNet and ResNet. Both architectures play an important role in the success of OR-Sys in carrying out its duties. This can be seen from the high level of accuracy generated by two architectures, which are 91,4% for GoogLeNet and 94,8% for ResNet.
An Empirical Test of Momentum Effect on the Chinese Market during COVID period

Our research analyzes momentum effects in the Chinese stock market during the COVID period from 2020 to 2023. We studied over 5000 Chinese A-share stocks categorized into 306 industry sectors to identify momentum and reversal effects in both individual stocks and industries. Our primary research follows the traditional construction of winner-loser groups. Results indicate that individual stock portfolios exhibit profits only when the formation period is one week, with diminishing earnings as the formation and holding periods increase. Reversal effects are observed in most cases, with the winner group showing a stronger impact than the loser group. In contrast, industry portfolios show different patterns of changes between momentum and reversal. Specifically, industry portfolios display short-term momentum effects, generating profits within one month, followed by reversal effects over longer periods. However, the magnitude of gains and losses in industry portfolios is smaller compared to individual stocks.
Multi-objective Optimization of Square Magnetic Coupler for Undersea Inductive Power Transfer

This paper proposes a multi-objective design and optimization methodology for square magnetic couplers concerning power density and efficiency for undersea inductive power transfer (IPT). To establish the objective functions, an analytical model of mutual inductance computation for square coils with ferrite core, and a loss prediction model via backpropagation (BP) neural network based on finite element models (FEM) are proposed. Objectives’ sensitivities analysis is conducted to select design and operating parameters. Based on analytical calculations and FEM-based data-driven models, valid design guidelines for a scaled 1 kW prototype magnetic coupler are presented. A magnetic coupler with an efficiency of 96.07% at a power density of 0.4045 kW/d$\text{m}^{2}$, of which the length is less than 170 mm with a 60-mm air gap, is designed to validate the methodology.
The Effectiveness of SPBE Application and Infrastructure Audit Tools in Enhancing Information Technology Governance Using COBIT 2019

Information Technology (IT) has become the core of nearly every aspect of business and organizations. Effective IT governance is vital for achieving organizational goals and managing risks. This study uses COBIT 2019 to assess the SPBE application and infrastructure audit tool effectiveness. SPBE Application and Infrastructure Audit Tools are used in Indonesian central and local government agencies. SPBE aims to create a clean, efficient, transparent government system and trusted public services. The SPBE Audit Tools assist in auditing SPBE applications and infrastructures. SPBE Audit tools play a crucial role in monitoring and evaluating SPBE applications and infrastructure in Indonesia, contributing to improved IT Governance. However, effective governance requires risk management to address potential threats and vulnerabilities. COBIT 2019 is designed to support digital transformation and adapt to the dynamic changes in the IT landscape. This study uses COBIT 2019 as a framework for assessing the effectiveness of SPBE application and infrastructure audit tools in enhancing information technology governance because COBIT has more advantages compared to other frameworks. Based on the data, applications that conduct audits using SPBE Audit Tools show an improvement in the maturity level acquired each year. This suggests that SPBE Audit Tools contribute to achieving SPBE monitoring and evaluation objectives, including measuring progress, enhancing SPBE quality, and improving public services at IPPD.
Development of a Low Cost Elecromyogram (EMG) Circuit for EMG Tracking

Electromyography (EMG) is a technique used to evaluate and record the electrical activity produced by the human body's muscles. The electromyograph circuit can be used in various situations such as neurology, medical rehabilitation, and many more applications. The purpose of this work is the design and implementation of a low-cost electromyogram (EMG) circuit for EMG tracking with the enhancement of the acquired signal. The overall topology consists of four seven stages based on discrete operational and instrumentation amplifiers. The basic acquisition circuit consists of four stages for capturing the EMG signal, removing the interference noise, and amplifying the final processed signal. The rest of the topology makes a further signal enhancement by processing it with the exponential or the logarithmic functions, depending on the signal form. The output signal is suitable for direct driving a monitoring device or for acquisition by an analog-to-digital converter (ADC) for further digital analysis. The proposed circuit simplicity makes it suitable to be employed in a low-cost EMG system.
Influence of Stator Core Materials on Electromagnetic Performance of YASA Type Axial Flux Permanent Magnet Machines

Stator core materials have important impacts on the electromagnetic performance of electric machines. In order to meet the different design requirements of machine, it is very important to understand these impacts in the initial design phase. In this paper, based on these four stator core materials of grain-oriented silicon steel (GO), non-oriented silicon steel (NO), soft magnetic composite (SMC) and amorphous alloy (AA), the air-gap magnetic density, no-load Back-EMF, output torque, torque ripple, inductance, core loss and efficiency of a Yolkless And Segmented Armature (YASA) type axial flux permanent magnet machine are analyzed by the finite element analysis (FEA), in order to provide a design reference for selecting the appropriate stator core materials. The results show that choosing GO as the stator core material can maximize the output torque, no-load back-EMF amplitude and inductance of the YASA machine, choosing SMC can minimize the no-load back-EMF harmonic distortion and torque ripple, and choosing AA can maximize the efficiency of YASA machine. It provides an initial design reference for YASA machine.
Optimized Inductance Method Based on Neural Networks for Wireless Power Transfer Applications in Implantable Medical Devices

Wireless Power Transfer (WPT) is a trendy technology that enables the charging of batteries without physical connection. Medical implants have benefited from this technology, and consequently rechargeable devices can be implemented, improving the patient's quality of life. In this work, a method to optimize multi-layer planar squared coils for a WPT application is proposed. This method involves the utilization of Artificial Neural Networks (ANN), which predict the efficiency of a system from its geometrical parameters. This ANN is trained by a dataset, which is obtained through Finite Elements Analysis (FEA). The proposed method reduces the computational cost of a pure FEA optimization, providing accurate results.
An Intelligent Recognition Method and System of “Phubber” based on Object Detection and Pose Estimation Algorithm

The existing research works regard the recognition of “phubber” as single task of object detection or human pose recognition. Its disadvantage is that its accuracy is insufficient, and it cannot effectively solve the problems of human body behavior understanding and human body overlaps. As a result, this paper provides an intelligent “phubber” recognition method that integrates object detection and pose estimation algorithms. The basic idea is to use the human pose estimation network to predict key points of each human body in video frame image, and then comprehensively determine whether the human body is a “phubber” according to the defined body behavior judgment rules, the type of hand-held object and distance of the hand-held objects from body. The experimental results show that this method outperforms existing baseline models of object detection in terms of recognition accuracy. As well, a system for cameras-based recognizing phubber” is developed based proposed method, and a verification of phubber” recognition on campus is conducted, reaching an overall recognition accuracy of 89.7%.
Analysis of Wordle Game Mechanism Based on Multi-task Model

Wordle is a popular word-guessing game on Twitter, which can stimulate people’s interest in word structure and has pedagogical significance. In this paper, we analyze the game mechanics through a large amount of game data deposited in the backend of Wordle, and provide corresponding decision support for game managers. In this paper, a multi-task model (MMCL) is firstly established to combine MASK masking with contrast learning to encode words as a matrix with deep word construction features by using letters as the smallest unit; secondly, a specialized decoder is trained to realize the mapping between encoded words and difficulty coefficients by using difficulty histogram as supervision; finally, in this paper, through a systematic clustering algorithm, the words are mapped by Finally, this paper classifies the words into difficult, medium and easy levels through the difficulty coefficients mapped out by a systematic clustering algorithm. Based on this model, this paper well completes our research on word structure patterns and the psychological logic of human word construction.
Lung CT image segmentation based on improved ResUnet

Pneumonia image segmentation studies for pneumonia-infected regions involve relatively few, and accurate segmentation of infected regions is a challenging task.Firstly, because of the low contrast between the boundary between the infection and the surrounding environment, and secondly, because of the large variation in the infected area and the lack of large-scale annotated data, which is a big challenge for research. Therefore, we propose a method based on the combination of Unet and residual network with the attention mechanism and spatial convolutional pooling for lung CT image segmentation. The residual connection can provide more low-level features to assist the model to accurately determine the infected region, the attention mechanism can focus on a certain region, which will increase the feature extraction in this part of the region and make the feature weight of this region larger, and the Atrous Spatial Pyramid Pooling (ASPP) can obtain multi-scale information.
Hybrid Taguchi-Artificial Neural Network Method for Design and Optimization of Microfluidic Biosensor

A Taguchi $L_{32}(2^{8})$ orthogonal design combined with an analysis of variance ANOVA will be used to optimize the microfluidic biosensor with confinement. For this, eight factors such as Reynolds number (Re), Damkohler number (Da), relative adsorption capacity $(\boldsymbol{\sigma})$, equilibrium dissociation constant (KD), Schmidt number (Sc), confinement coefficient $(\boldsymbol{\alpha})$, confinement position (X) and inclination angle $(\boldsymbol{\theta})$ have been analyzed. In order to accurately predict the results, a predictive model based on the ANN artificial neural network was developed. Taguchi's analysis suggests that the optimal moment is obtained when position X is at level 1 and the other factors are at level 2. This combination gave a response time of 9.6. ANOVA analysis shows that the Damkohlar number × relative adsorption capacity interaction (41.35% contribution), the Reynolds number*confinement coefficient interaction (11.35% contribution), the Damkohler number (11.35% contribution) of 9.55%) and the equilibrium dissociation constant (contribution of 6.81%) are the interactions and the most influencing factors on the response time. Response time was predicted by ANN with 99% accuracy. The ANN prediction was more precise than the regression model and the numerical simulation data.
Recurrent Deep Learning to Predict Personality Types from Social Media Posts

The analysis of personality behavior has become important in many applications such as educational hypermedia systems, psychotherapy, and business recommendation systems. With the development of social media platforms (SMPs), people can express their feelings and opinions through their personal posts, comments and/or tweets. This study aims to analyze texts on SMPs to identify users' personalities according to the Myers-Briggs Type Indicator (MBTI) model. It also sought to enhance prediction accuracy and compare the findings with various deep learning techniques and previous literature. This study analyzes personality through a dataset that includes 8667 participants. Two deep learning techniques are implemented namely, long short-term memory (LSTM) and convolutional neural network (CNN). Unlike earlier literature, this research helps improve the prediction accuracy of users' personalities by building a multi-layer deep learning network and integrating the random search optimization approach. The results suggest that LSTM with the application of random search optimization outperformed previous findings. The average prediction accuracy of the four dimensions namely, Introversion-Extroversion (IE), Feeling-Thinking (FT), Intuition-Sensing (NS), and Judging-Perceiving (JP) is 85.02%.
Design Options and Considerations of a Linear Flux Switching Machine for Dry Gravity Energy Storage.

In this study, we analyze two different design-options of the linear flux switching machine. These are used as the main drive unit for a dry gravity energy storage application, taking into account the technical requirements of the linear electric machine gravity energy storage system. Detailed finite element analysis of the two different optimized design-options are carried out, and the electromagnetic performance of the machines compared to determine the most suitable design that offers the best utilization and performance for a given piston size. Through a series of comparative analysis, the paper reveals the strengths and weaknesses of both designs. Design one offers a modular assembly while design two offers no end-windings. Both designs offer unique advantages.
New Inductance Calculating Formulas of Planar Symmetrical Square Inductors

The paper presents new analytical formulas for calculating the inductance of planar symmetrical square inductors. The formulas are obtained by using the modified method of inductors layout decomposition. The inductors layout is divided into parts (inner incomplete turn, full turns, and leads) and the total inductances of each part and the mutual inductances between the parts are calculated. Comparison with the results of 3D finite element modeling in ADS CAD showed that the proposed formulas have an error of no more than 2 per cent for inductance value from 100 pH to 10 nH.
Electromagnetic Vibration and Acoustic Noise Analysis of Single Phase Flux Switching Motor

Single phase flux switching motors (FSM) usually has the problem of large torque ripple and obvious vibration and acoustic noise, which are often regarded as the major drawback of this type of motor. In this paper, the analytical expression of the radial force in single phase FSM is derived in terms of spatial order and frequency, and the influence of different rotor structures on the frequency distribution of radial force is analyzed. The experimental platform was built to test the vibration and acoustic noise of the motor, and compared with the radial electromagnetic force spectrum obtained from the finite element analysis (FEA) and the inherent frequency of the whole machine from the modal test.
Hybrid Approaches for Customer Segmentation and Product Recommendation

The trend of influencer marketing is increasing massively in the digital age. Despite this, the majority of marketing strategies are still not optimally using this trend. Marketing strategies can be implemented by applying customer segmentation and product recommendations through the implementation of artificial intelligence technology. Customer segmentation can be implemented using clustering techniques, and product recommendations can be applied using association rules. The clustering methods K-Means and K-Medoids will be compared to find the best clusters, and then each clustering method will be combined with the Apriori association rules method. The objective of this research is to identify the combination of clustering methods and association rules that produces the best performance. The elbow method is used to determine the optimal number of clusters. Based on three used evaluation metrics, it is found that between the two clustering methods, K-Means has a better evaluation metric. After the associations are combined, the result shows that K-Medoids and Apriori have a slightly higher confidence score than K-Means and Apriori with 39.265s for runtime and 244.175Mb for memory. This research will help companies identify various groups of customer characteristics and products that have strong ties to these groups. Therefore, this will help companies gain the necessary business insights.
High Performance Modem for Underwater Communication Systems

The algorithm of operation of the modem with increased energy efficiency with quadrature intrapulse phase modulation (QIPPM) in radio lines using extremely low frequencies for underwater communication is described. In this case, the increase in energy efficiency of the modem with QIPPM is achieved by reducing its frequency efficiency and introducing additional code redundancy. This allows for a 2-fold increase in depth for underwater communication. At the same time the probability of erroneous reception of code combinations is reduced to a minimum.
Design of Frequency Adaptive Sensorless Observer for Reduced DC-Link Capacitance IPMSM Drives

The interior permanent magnet synchronous motor (IPMSM) drive system using small-capacity film capacitors has the advantages of long operating life, low cost, and high integration. However, due to the small capacitance value of the dc-link capacitor, the dc-link voltage exhibits twice the frequency fluctuation of the grid-side voltage, which will lead to additional harmonics in the back electromotive forces (BEMFs). Eventually, this could result in a large position estimation error, thus deteriorating the performance of the sensorless control. Aiming at that, a composite complex coefficient filter (CCCF) applied to sensorless control is studied in this paper. Based on the CCCF, an improved disturbance observer is designed by adopting the reverse thinking, which is used to realize the accurate extraction of the fundamental component of the BEMF. CCCF-based disturbance observer has the band-pass characteristic, and it can realize the frequency adaptive control. Hence, the amplitude attenuation and phase lag of the BEMF can be avoided. Finally, the effectiveness of the proposed method is verified by simulation and experimental results.