Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Carbon Concentration Distribution in Carburizing Steel Based on Convolutional Neural Networks

The carburizing and quenching heat treatment process can form a carburized layer with high strength, high hardness and high wear resistance on the surface of the gear. At the same time, it still maintains high toughness in the gear core. According to the carburizing process parameters of specific materials, the accurate and rapid prediction of the surface carbon concentration distribution after carburizing can improve the production efficiency and quality of heat treatment. Therefore, this paper establishes accurate numerical models of the carburizing process in three different shapes of 20CrMnTi material, pentagon, circle and trapezoid, and sets 1440 sets of process parameter combinations for each shape for large-scale calculation. After collecting and processing the simulation data, CBNNet is proposed, which takes the structural geometry and process parameters as input, and the carbon concentration value as output. On the test set, the model showed high accuracy with a mean square error of 2.49x10-5. It shows that the model has strong generalization, and it is expected to improve the efficiency and quality of heat treatment in actual production.
A MW and MMW Shared-Aperture Antenna by Reusing Electromagnetic Band-Gap Structure

A dual-band shared aperture antenna is proposed in this letter, based on the concept of electromagnetic band-gap (EBG) decoupling structure reuse, with two antennas separately operating from 4.8 GHz to 5 GHz and 28.5 GHz to 31.5 GHz. By properly adjusting the EBG dimension, the mutual coupling among two symmetrical planar inverted-F antenna (PIFA) elements operating in the microwave (MW) frequency band can be effectively reduced. Simultaneously, the EBG structure itself plays the role of millimeter-wave (MMW) antenna array, which realizing both excellent radiation performance and beam scanning ability with the help of MW antenna. The mutual coupling level of the E-coupled MW two-element antenna is decreased from -8.7 dB to -24.8 dB, and the MMW arrays can steer the beams from -30° to +30°.
Security Analysis of the Enhanced Cryptographic Algorithm Based on Discrete Logarithm Problem that Uses Multiple Handshakes for Improved Security

This research enhanced the Discrete Logarithm Problem-Based (DLP-based) Algorithm, enabling it to utilize two or more security keys in the encryption process. The enhancement enabled the Algorithm to inject multiple handshakes into the process as a security method. Tests conducted on the enhanced DLP-based Algorithm showed the accuracy of the process, which was recorded at 100% using 1,000 samples. This study used the randomness test to analyze the Algorithm's security. It also determined if there are established patterns in the process, both for Encryption and Decryption, through observations of the inputs and outputs of the method. The Confusion Test checks if there are conversion patterns between the security key and the Ciphertext used in the process, while the Diffusion Test checks for conversion patterns between the plaintext and the Ciphertext. The level of security of the Enhanced DLP-Based Algorithm, as tested by the confusion test, was HIGH, as exhibited by the confusion value, which is 54 %. The Diffusion test yielded a diffusion value of 54.925%, which also manifested a HIGH level of security. The Strict Avalanche Criterion (SAC) also recorded an Avalanche Effect of 54% and 54.8% for Confusion and Diffusion Test results, respectively. This study found the Enhanced DLP-Based Algorithm reliable based on its SAC value, above the minimum of 50% for all test samples. Experiments and simulations also proved that the Enhanced DLP-Based Algorithm allowed successful implementation in a digital data-sharing scheme that utilizes Multiple Handshakes to ensure security.
Design and Analysis of Broadband Vertical Via Interconnection Technology for Multilayer Circuit Board of Heart Rate Sensor

In this paper, a broadband vertical interconnect structure for multilayer circuit board of heart rate sensor is designed. Signal transmission between different layers often requires vertical vias for interconnect transitions. For active devices, the interconnect transition between coplanar waveguides (CPW) and striplines is extremely widely used. As the operating frequency increases, the transmission characteristics of the circuit are reduced. Therefore, this paper designs a broadband vertical interconnect structure based on coaxial vias. A grounding holes are added along the transmission line to suppress the parallel plate pattern. Through modelling analysis and simulation optimization, the return loss S11>20 in the DC-50GHz frequency range, and a wideband, low-loss vertical interconnection structure is realized.
Analysis of Microstrip Patch Antenna Array Using the Sub-Structure Characteristic Modes

The theory of characteristic modes is widely used in antenna design, but in the analysis of sub-structure, the theory of sub-structure characteristic mode (SCM) is more effective for practical applications. This paper initially introduces a method to solve the electromagnetic field problem in a space with a dielectric substrate by combining the mixed-potential integral equation (MPIE) and the discrete complex image method (DCIM). Then, we derived a method for calculating the SCM for the microstrip antenna array. Finally, the correctness of this method is verified through numerical examples.
Metaverse Trend and Digital Transformation: A Bibliometric Analysis

The metaverse, a virtual reality-based successor of the internet, is revolutionizing the way we interact and conduct business, and is a key aspect of digital transformation. This study aimed to investigate the trend of metaverse and digital transformation by conducting a bibliometric analysis using VOSviewer mapping. The study utilized a reference manager application to collect data from 2012 to 2022 using the keywords "metaverse" and "digital transformation". The results revealed that research on metaverse and digital transformation has been conducted from 2012 to 2022. However, the number of research articles on the topic decreased over time. The most articles were published in 2022, with 683 articles. The findings of this study demonstrate the importance of bibliometric analysis in understanding the development and popularity of research topics. This study can serve as a reference for researchers in determining research topics and direction in the field of metaverse and digital transformation. Additionally, this study is prospective and can assist in decision making for future research.
An Investigation into the Prediction of Annual Income Levels Through the Utilization of Demographic Features Employing the Modified UCI Adult Dataset

Predicting an individual's annual income is crucial and has many uses for the planning of government, policymakers, and development partners. This paper uses the UCI Adult Dataset to examine demographic-based annual income predictions. The 16-column dataset classifies “Income” into two categories: $< =50\mathrm{K}$ and $> 50\mathrm{K}$, where 14 characteristics provide personal information. Appropriate Methods to Handle Categorical Data guidelines govern the study's preprocessing, categorical data management, and missing value strategies. Eleven machine learning models are then studied, including classification, clustering, neural networks, Logistic Regression, Naive Bayes (standard and Gaussian), K-Nearest Neighbors, SVMs, Decision Trees, Random Forest, XGBoost, and Artificial Neural Networks. This study emphasizes optimization, evaluation, and model comparison and how optimization strategies and parameters affect model performance. Comparative analyses compare model performance within a category. The results imply that demographic characteristics help some models predict income levels better. Logistic Regression provides essential knowledge; whereas XGBoost and Neural Networks improve accuracy, Clustering adds knowledge. This study's findings inform revenue-anticipation-related decision-making with an accuracy of 87% from XGBoost and XGBoost-ANN Ensembling. This study emphasizes the need for data pretreatment, algorithm selection, optimization, and evaluation for accurate predictions and valuable insights.
Design and Implementation of Sliding Mode Controlled Closed Loop Three Level ZVS Controlled Resonant Converter

This study presents an innovative approach to the switched capacitance-based resonant convertor by using two half bridges, and a series resonance circuit. The goal of this project is to reduce the MOSFET turn-off current and achieve zero voltage turn-on. To achieve this, the proposed method regulates the switching frequency, phase-shift angle, and dead time. By doing so, it effectively reduces switching power losses, regardless of whether the system operates under light or heavy load conditions. The research also focuses on simulating a closed-loop three-level switched capacitor resonant converter using two different control strategies: Sliding Mode Controller (SMC) and Hysteresis Controller (HC). The simulation results were compared on the basis of settling time, steady-state error. The findings indicate that the closed loop three level switched capacitor resonant converter with Sliding Mode Controller demonstrates superior performance compared to the Hysteresis Controller. This work introduces a new design for a switched capacitor-based resonant converter, optimizing the turn-off and turn-on processes of MOSFETs. The simulation-based evaluation of closed-loop control strategies reveals the advantages of utilizing the Sliding Mode Controller in terms of steady-state error and settling time.
Unveiling Sentiments in Javanese Text: A Study on Sentiment Analysis for the Javanese Language

The Javanese language faces challenges due to its complexity and decreasing usage as Indonesian becomes more prevalent. This study highlights the importance of preserving Javanese as a crucial cultural heritage by developing a sentiment analysis model tailored to the language. Two datasets, Indonesian hotel reviews, and “kuliah online” (Online Lectures) comments/tweets, were manually translated into Javanese by three bilingual speakers, creating the Javanese sentiment analysis corpus. Through text preprocessing, word embedding, and utilizing Bi-LSTM and BERT methods. While Bi-LSTM and Bi- LSTM + fastText achieved similar results in predicting negative and positive sentiments. The sentiment data comprising natural cases, mixed sentiment polarities, and conflicting or similar target sentiment face challenges for the models. The most successful model, BERT, achieved an F1-score of 77%. The result obtained from the study will contribute to the preservation of Javanese language and literature on NLP for the Javanese language, with future prospects involving training and testing on original Javanese datasets.
Research on Image Captioning Based on Vision-language Pre-trained Models

Image captioning is a vision-language task that targets at describing an image by generating a coherent sentence automatically. This technology allows computers to understand and describe images like humans, enabling further processing of images. It combines computer vision and natural language processing, which has broad applications. This paper investigates the use of vision-language pre-trained models for image captioning. In this article, an encoder-decoder hybrid model based on PVT and BERT is pre-trained on a large number of image-text pairs, possessing both understanding and generation capabilities. It consists of three components: 1) an image encoder and a text encoder to extract image and text features respectively; 2) a multi-modal encoder to align the two modalities; and 3) a text decoder to generate text descriptions. This paper focuses on enhancing the image encoder of the model, which is crucial for image captioning models as it extracts high-resolution image features. Reducing training time and computational costs can also improve the applicability of pre-trained models. Improvements are made to the original model by modifying the ViT model architecture of the image encoding module. A feature pyramid structure and a spatial reduction attention mechanism is introduced to extract high-resolution image features while reducing computational complexity and memory usage, thereby enhancing the universality of the model. The model adopts a pure Transformer architecture, discarding the convolutional structure, and achieves excellent performance.
A TCAD Simulation Analysis of NADGFET Oscillator with Metal and Dielectric Combination

This paper analyzes the design and simulation results of a 5-stage ring oscillator using a 180 nm length non-aligned double gate inverter (NADGI). The NADGI consists of p-channel and n-channel field effect transistors i.e., NADGPFET and NADGNFET, with a channel length of 40 nm. The performance of a 5-stage ring oscillator was investigated by taking a combination of metal work functions (<tex>$\phi$</tex>) and gate oxide dielectrics (<tex>$k$</tex>). In this work, three metals namely, gold (<tex>$\phi=5 \text{eV}$</tex>), molybdenum (<tex>$\phi=4.75 \text{eV}$</tex>), tungsten (<tex>$\phi= 4.5 \text{eV}$</tex>), and three gate oxide namely, HfO<inf>2</inf> (<tex>$k =24$</tex>), SiO<inf>2</inf> (<tex>$k = 3.9$</tex>), Si<inf>3</inf>N<inf>4</inf> (<tex>$k= 7.2$</tex>), are taken. The product of dielectric constant and metal work function (<tex>$\phi$</tex>) is taken as a characterizing parameter. The structure of the NADGI and ring oscillator was designed and tested in Sentaurus sdevice mixed-mode environment. The simulation result shows, at a supply voltage of 1 V, the NADGI device with low-<tex>$k$</tex> dielectric material, and metal work function of 4.5 eV, gives a highest frequency of oscillations i.e., 85 GHz, when compared to the other combinations. Thus, this paper concludes that NADGI with low-<tex>$k$</tex> dielectric material and work function is suitable choice to get a larger frequency of oscillations.
Audio to Sign Language Translator

In this paper, an innovative technique for communicating with people who have vocal and hearing impairments is presented. It discusses a more effective technique for speech-to-sign translation and sign recognition. Millions of individuals all over the world have hearing impairments. Deaf people never have the opportunity that hearing people do, whether it be to play computer games, communicate, go to seminars, or participate in video meetings. Since everyone cannot understand sign language, communication is the main difficulty they encounter while interacting with regular people. The primary objective of this paper is to develop a translating system composed of multiple modules that can translate English audio to English text. The structured grammar representation obtained after the parsing of the English text is then subjected to the Indian Sign Language grammar rules. The communication gap between the hearing and the deaf is being reduced thanks to technology.
On Zero-Dynamics Stealthy Attacks with Learned State Space Models

Zero-dynamics stealthy attacks are a subset of false data injection attacks (FDIAs) that can be catastrophic as they are designed to be undetectable by traditional residual based anomaly detectors. The attacker’s ability to successfully attack a system relies heavily on their capacity to learn an accurate state space model. Utilizing a grey box approach to system identification, we show that even when the attacker is able to learn a state space model close enough to have a high probability of a successful attack, making small improvements to the system’s anomaly detector causes the probability of a successful attack to drop drastically. Finally, we study the trade-offs between making the system less susceptible to zero-dynamics attacks and maintaining its controllability, by increasing the sampling time of the system, thus providing the attacker fewer samples to learn a state space model.
Port and Radiation Pattern Decoupled Microstrip Antennas Using Shorting Vias

In this paper, a microstrip antenna array using shorting vias is presented with port and radiation pattern decoupling. Shorting vias on the passive patch introduce an opposite current component, canceling the original coupling currents and obtaining the decoupling of antenna elements. To verify the decoupling method, a sample is designed at 3.6 GHz. The measured port isolation of the decoupled antenna array is more than 25.7 dB. It should be highlighted that the radiation patterns of decoupled antenna elements are all along the broadside direction, defined as the radiation pattern decoupling effect.
Analysis of Load Restoration Capacity Considering Transient Frequency Constraints

In the process of power system recovery, it is necessary to determine the load recovery capability of the power supply. The research on load recovery capability of conventional power supply is usually based on steady-state frequency constraints. On this basis, this paper studies the load recovery capability of new energy power supply and its networking with conventional power supply, and adds transient frequency constraints. Firstly, based on the active-frequency control mode of new energy power supply, the order reduction of new energy power supply related modules is carried out. Secondly, the system frequency response model (SFR) of single new energy power supply and multi-type power supply network is established. Finally, a simulation model is built in matlab/simulink to analyze the load recovery ability of single power supply and power supply network in different scenarios.
Generative AI: A Review on Models and Applications

Generative Artificial Intelligence (AI) stands as a transformative paradigm in machine learning, enabling the creation of complex and realistic data from latent representations. This review paper comprehensively surveys the landscape of Generative AI, encompassing its foundational concepts, diverse models, training methodologies, applications, challenges, recent advancements, evaluation metrics, and ethical dimensions. The paper begins by introducing Generative AI's significance across various domains, presenting its pivotal role in producing synthetic data with applications spanning image synthesis, text generation, music composition, drug discovery, and more. The objectives lie in elucidating the foundational concepts, delving into model intricacies, unveiling the training procedures, exploring its application landscape, addressing challenges, envisioning future directions, and discussing ethical ramifications. The foundational section elucidates the diverse array of generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based models, Generative Reinforcement Learning (GRL), and advanced hybrid architectures. Subsequently, evaluation metrics ranging from Inception Score to perceptual similarity metrics and human evaluations are surveyed to assess generative model performance. Finally, ethical considerations underscore the necessity for addressing biases, misuse, intellectual property concerns, and the call for responsible AI development and regulation in the Generative AI landscape.
Comparative Study of Face Detection Using Cascaded Haar, Hog and MTCNN Algorithms

This paper compares a few effective and widely used face identification methods. In the area of image processing, automatic face detection is a highly difficult problem, and countless methods and algorithms have been proposed to solve it. We regularly observe face detection approaches being used to accurately identify faces in pictures, videos, and live video streams. There have been numerous face detection methods proposed till date. A brief overview of three face detection systems is provided in this comparison study. We have made an effort to describe a few key algorithms, such as the Haar Cascade Classifier, HOG, and MTCNN. A detailed comparative analysis shows that MTCNN is highly effective technique for face detection with efficiency of 99.7% and it detects faces from all views (i.e. frontal and side pose) as well as faces with accessories(glasses etc.). However Haar Cascade Classifier and HOG are simpler techniques and are easy to implement.
Short-term Prediction of Wind Power Based on Convolutional Gated Recurrent Unit Model

Stochastic and fluctuating winds bring challenges to the accurate prediction of wind power (WP). With the aim of improving the accuracy of the forecasting and reducing the complexity of the model, this paper considers the temporal correlation and multidimensional features (analogous to spatial information) for short-term prediction of WP based on the convolutional gated recurrent unit (Conv-GRU) neural network. With constant and nondestructive equipment, the strength of sea breeze directly determines WP. Wind is usually influenced by meteorological information. Thus, convolution is taken into account in the prediction model to achieve the goal of fitting realistic scenarios and efficient extraction of multidimensional features. GRU is utilized to capture the temporal evolution patterns. Moreover, the RMSE of the proposed model at a 6-h lag is 12.1703, which proves the effectiveness of the model. Finally, the comparison results with five networks demonstrate that the Conv-GRU model has high forecasting accuracy and superior stability.
Beauty Influencer on Social Media: Credibility, Perceived Value, Trust and Purchase Intention of Skin Care Products

Beauty influencers have emerged as a novel force within the beauty sector by effectively harnessing the potential of social media platforms to endorse products and engage with their followers. Nonetheless, there remains a need for further exploration into the role of beauty influencers in order to enhance the beauty industry's understanding of consumer purchasing behavior. Consequently, this study endeavors to investigate the impact of influencer credibility, perceived trust, and perceived value on the intention to purchase skin care products. The focus of this research is on active followers who have bought Skin care products as a result of information disseminated by beauty influencers via social media. The empirical analysis of the data utilizes component-based structural equation modeling (PLS-SEM). The findings of this study have demonstrably established that influencer credibility exerts a positive and significant influence on perceived trust and purchase intention. Moreover, perceived trust and value also exert a positive and significant influence on purchase intention. Beauty influencers have become a potent and indispensable strategy within the beauty industry, as they possess the ability to cultivate trust and expand their reach to a broader audience.
Design of 560GHz Subharmonic Mixer Based on GaAs Monolithic Integration Technology

Based on GaAs monolithic integration technology, a 560GHz subharmonic mixer is designed on a 15um GaAs substrate by half-global designed method. The simulation results show that with fixed IF frequency of 2GHz, the SSB conversion loss is less than 8dB across the RF frequency range from 520GHz to 615GHz, with the best conversion loss occurring at 7.2dB@593GHz. The return loss of the RF port is better than 10dB from 523GHz to 615GHz, while the return loss of the LO port is better than 7dB from 260GHz to 300GHz. With fixed LO frequency of 280GHz, within the RF frequency band from 520GHz to 600GHz, the SSB conversion loss is less than 8.5dB, with the best conversion loss occurring at 7.6dB@559GHz.