Miracle-dz/UniFAR-FTUs huggingface dataset:
End to End Neural Network Application for Ultrasonic Computed Tomography Image Recognition

Deep learning techniques have known a big revolution in various areas including the industry, robotics, data security, signal processing and especially medical imaging. In this context, a new neural approach has been proposed in the ultrasonic medical imaging area. Thus, we have developed an end-to-end deep learning approach for the recognition of ultrasonic images. Our neural network system consists of an end-to-end variable structure of neural network (VSMN-VGG-Unet) for the segmentation of Ultrasonic Computed Tomographic (USCT) images. The experimental results are performed on our new USCT dataset, proving the efficiency of our proposed VSMN-VGG-Unet approach. The whole system is implemented on GPU Geforce GTX 1050Ti, achieving excellent accuracy results reaching 99.08% for training and 98% for the validation process. As for the test, the segmented USCT images show excellent segmented images with high resolution and a small error equal to 0.05. Moreover, we accelerate the time process speed, reaching 0.01s/image, hence surpassing the state of art.
Understanding Tourist Perspectives of Borobudur through LDA Topic Modelling Using Elbow Method

The primary objective of this research is to unveil the underlying themes and topics within tourist reviews that evaluate the Borobudur temple complex and its surroundings. This goal will be achieved through the application of Latent Dirichlet Allocation (LDA) topic modeling. The method involves collecting English-language reviews from the online platform TripAdvisor and subsequently subjecting them to preprocessing techniques to ensure proper data formatting for subsequent analysis. The optimal number of topics is determined using the elbow method within the Optimize Parameters (Grid) Operator in the RapidMiner platform. The preprocessed data is then subjected to analysis utilizing the Latent Dirichlet Allocation (LDA) model, which enables the extraction and identification of distinct topics. The results reveal a diverse range of topics pertinent to tourists’ experiences, encompassing accessibility, prominent features, guided tours, and activities during sunrise and sunset. These findings offer valuable insights into the key aspects emphasized by tourists and their corresponding sentiments. Such insights can be harnessed to enhance tourism management and services within the Borobudur temple complex.
Puzzle Game Data Analysis Based on Big Data and Neural Network Algorithms

Wordle is a popular daily puzzle game offered by The New York Times, challenging players to guess a five-letter word with iterative feedback. This game has amassed a substantial Twitter following, providing a valuable dataset for analysis. In this study, we leverage a dataset of 10,000 tweets spanning two months to investigate the trends and patterns of Wordle players. We introduce two neural network models to forecast the quantity and distribution of future reported results. The first model is founded on Long Short-Term Memory (LSTM) recurrent neural networks, utilizing the 10-day rolling mean as the primary prediction sequence. The second model employs a Genetic Algorithm-optimized Backpropagation (GA-BP) temporal network to fine-tune the weights and thresholds of the Backpropagation neural network. We benchmark these models against established methods such as ARIMA and linear regression. Our findings reveal that the LSTM recurrent neural network achieves the highest accuracy in forecasting the number of reported results, exhibiting an impressively low Root Mean Square Error (RMSE) value of 1.794. For forecasting the distribution of reported results, the GA-BP temporal network excels with an exceptional R-squared (R-value) of 0.99752. This study underscores the potential and utility of neural network models in the analysis of social media data related to online games. Furthermore, it offers valuable insights into the behaviors and preferences of Wordle players, thereby informing the design and enhancement of similar games in the future.
Enhanced Output Quality of Dual Inverter with Isolated Supplies Using Virtual SVPWM Method

This paper presents a novel virtual SVPWM modulation method aimed at enhancing the output quality of dual inverters with isolated power supplies. At a voltage ratio of 1:0.33, the resulting space vector diagram lacks the necessary active vectors for modulation, leading to additional harmonics. The proposed method creates the missing vectors by synthesizing virtual vectors. Using the proposed method can allow the dual inverter to achieve a perfect five-level operation along with improved output quality. The benefits of this method have been proven in simulation results.
Resonant Coupled Excitation Brushless Synchronous Motor

The Electrically Excited Synchronous Motor (EESM) addresses the requirements of low cost and wide speed range in electric drive systems due to its rare-earth free composition and adjustable magnetic field. However, its brushed structure restricts its application. The present paper proposes a resonant coupled excitation synchronous motor system, which multiplexes the motor stator winding and converter, establishes a dual-frequency magnetic field in the air gap, and achieves brushless excitation through magnetically coupled resonant wireless power transfer. A field-circuit joint simulation model of a 6-pole, 36-slot EESM was constructed.The field current can be obtained in a wide range by high-frequency current modulation. By operating in a dual-frequency magnetic field, the motor and wireless excitation power transfer can synergistically achieve stable torque and power output.
A Dynamic Voltage Decoupling Control Method Based on Vehicle Reluctance Motor

This paper presents a dynamic voltage decoupling control method based on vehicle reluctance motor. Through real-time detection of current error and identification of inductance parameters, the separation and adjustment model of coupling voltage of reluctance motor is constructed, so as to carry out dynamic voltage compensation control on current loop output and realize decoupling linearization control of reluctance motor voltage. Finally, the optimized performance is verified by simulation and load test.
Overview of Image Classification Methods Based on Deep Learning

Image classification is a method of classifying different categories of objects based on the different characteristics of objects in an image. Most of the traditional image classification algorithms use shallow structures, which have obvious deficiencies in performance and generalization ability. The image classification algorithm based on convolutional neural network has good performance indicators and can solve the image classification problem well.
Implementing a Wide Frequency Range Tightly Coupled Wireless Charging System Using Hybrid Topology

In response to the issue of coupling coefficient and frequency variation leading to system output power fluctuations in wireless power transmission systems, this paper proposes a tightly coupled wireless charging system with a wide frequency range based on hybrid topology. Firstly, output characteristics of SS and LCC-LCC topology circuits are analyzed in this paper, then a hybrid topology structure with a DDQ coil structure is proposed. Secondary, a parameter optimization method is proposed to optimize the parameters of the compensation network. Finally, build the system circuit in MATLAB simulation software to verify the feasibility of theoretical analysis. The simulation results show that when the system frequency changes between 78kHz and 96kHz, the output power of the system remains approximately 11kW, and when the coupling coefficient changes, the output power of the system can also maintain stationarity.
Characterizing Equivalent Electrical Circuit Model of Lithium-Ion Battery with Grey Wolf Optimizer

The advancement of Electric Vehicles (EV s) has fostered extensive research into battery technology. Among these, the Lithium-ion battery has emerged as a pivotal component within EV s, serving as the primary energy source for these vehicles. When designing EV s, it is important to consider the characteristics of Lithium-ion batteries to ensure their durability and safety. Constructing a battery model stands as an efficient means to gain a comprehensive understanding of battery cells. Such a model offers the advantage of circumventing time-consuming experimental tests across a wide array of conditions. Furthermore, it provides a secure platform for observing battery behavior under extreme test conditions through simulation. We introduced an efficient and systematic method for determining parameters in the Battery Equivalent Electric Circuit (EEC) model. Based on a second-order RC (2RC) circuit, an Extended Hybrid Pulse Power Characterization was implemented to analyze the dynamic response of battery cells in the time domain. To ascertain the values of RC parameters under various States of Charge (SOCs) and temperatures, a method using the Grey Wolf Optimizer was applied. The 2RC EEC model accurately simulated battery voltage responses, with a Root Mean Squared Error (RMSE) of 0.0021 and minimal voltage discrepancies as low as 0.15 V.
Meta-Research: The effect of the COVID-19 pandemic 1 on the gender gap in research productivity within 2 academia 3

23 Measuring research productivity to gauge academic performance is flawed because it is biased 24 against certain groups of researchers. Women researchers are disadvantaged because gender roles 25 and unconscious biases, operating both at home and in academia, can affect research 26 productivity. Here, we bring together existing studies to show that the gender gap in research 27 productivity increased during the COVID-19 pandemic, with the relative research productivity of 28 women declining compared to the pre-pandemic period.
Guided Learning Based on Bezier Curves with Selective Weight Adjustment

Although CNN-based classifiers have been successfully used for image classification, they still have several problems. Recognition performance may suffer when input images are heavily resized, rotated or shifted. Another critical problem is that slightly modified images, which are imperceptible to human eyes, can be incorrectly classified. These results indicate that CNN-based classifiers may fail to use robust and valid features for pattern classification. In order to force the CNN to use relevant features, we first apply feature extraction based on Bezier curves and then use neural networks to classify the projected data. The adjustments of the Bezier curves and neural network parameters are intertwined along with selective weight adjustment and adaptive expanding structures. The proposed method provided robust performance and correctly classified most adversarial images produced by conventional CNN-based classifiers.
Performance Comparison of 20 MW Double-Stator Superconducting Exciting Wind Generator with Modular Dewar and Integrated Dewar

Superconducting electrical machines have the advantages of higher power density and higher efficiency than the permanent magnet electrical machines, but the cost of high-temperature superconducting (HTS) wire, such as ReBCO wire and BSCCO wire is relatively high at present, which makes the commercial application of HTS electrical machines difficult. However, the price of the MgB2 wire or the low temperature superconducting (LTS) wire is much lower than the HTS wire. In addition, the development of cryogenic refrigeration system offers the possible to use the MgB2 or LTS wire in the electrical machine. Therefore, the aim of this paper is to analyze two types of topologies of 20 MW double-stator superconducting exciting wind generator, one of which uses the modular dewar and the other uses the integrated dewar, and compare comprehensive performance of them.
Neutral-Point Voltage Balancing Control of Active NPC Converter for The High-Power Flywheel Energy Storage System

Renewable energy is connected to the grid on a large scale, and the frequency stability of the grid is reduced. The high-power flywheel energy storage system (FESS) can reduce the power fluctuation of renewable energy and improve the frequency stability of power grid. The three-level (3L) active neutral point clamped (ANPC) voltage-source converter is widely used in high-power medium-voltage applications. But, the neutral point (NP) voltage unbalance has always been a disadvantage of this topology. This paper analyzes the principle of the back-to-back ANPC converter in the high-power flywheel energy storage system, studied the NP voltage fluctuation problem of the back-to-back ANPC converter. Then aiming at the problem of the NP voltage fluctuation, an algorithm based on small vector coefficient adjustment of capacitor voltage difference is designed. Finally, the effectiveness of the NP voltage balancing algorithm is verified in the simulation and experiments.
Progressive Dunhuang Murals Inpainting Based on Attention Mechanism

In this paper, we propose a novel approach for inpainting damaged areas of Dunhuang murals based on Recurrent Feature Reasoning Network (RFR-Net) with several innovative contributions. The Dunhuang murals are a precious cultural heritage that requires careful restoration and conservation. To address this challenge, we introduce an Efficient Attention (EA) mechanism on shallow features to capture more detailed information of the murals. Moreover, we propose a Attentional Residual Merging module (ARM) to merge intermediate feature maps for more effective feature fusion. And two new loss functions, HSV loss based on the HSV color space and edge loss, are introduced to improve color consistency and preserve the edges of murala during the inpainting process. Experimental results on a dataset of Dunhuang murals demonstrate that our proposed method achieves state-of-the-art performance in terms of both quantitative and qualitative evaluation. Our method effectively restores the missing areas of the murals while maintaining the color consistency and preserving the edges. The proposed ARM module effectively integrates intermediate features from different recursions, improving the accuracy of the inpainting results. The HSV loss function ensures that the inpainted areas match the surrounding colors of the mural, while the edge loss preserves the edges of the murals, which is crucial for maintaining the integrity of the artwork. Experimental results, both qualitatively and quantitatively, indicate that our proposed method achieves competitive outcomes compared to the contrastive algorithms.
Automated Hardware and Software Intelligent System for Infrared Thermometry

This article presents an automated system for infrared thermography of biological tissues. The purpose of the system development is to provide noninvasive and rapid temperature measurements of biological tissues. The system is based on the principles of infrared thermography and uses an infrared sensor to record the temperature of biological tissue. The temperature information is then used to identify areas of tissue with abnormally high or low temperatures, which provides valuable diagnostic information. The system is also capable of creating heat maps of biological tissues, which are used for further intelligent analysis of temperature distribution. Finally, the performance of the system is evaluated in terms of accuracy and speed. The accuracy of binary classification of thermometric data using a neural network reaches 87.2 percent.
Dynamic Knowledge Graph Algorithm for Electricity Knowledge

When employees of electricity companies want to know relevant information in the electricity field in real time, such as the latest policies, news, standards, cases, etc., traditional information retrieval alone cannot meet people's needs for fast and accurate knowledge acquisition. This paper introduces electricity knowledge graph as auxiliary information to help knowledge recommendation. To maintain the freshness and completeness of knowledge recommendation, it is necessary to improve the freshness of knowledge graph, thus we study the dynamic updating method of knowledge graph before knowledge recommendation in electricity field. First, we study the dynamic updating method of the knowledge graph in the electricity field, and then continues to study the knowledge recommendation method based on the knowledge graph that can be dynamically updated. In the automatic updating part of the knowledge graph, the missing information is supplemented according to the existing information in the knowledge graph to achieve internal updating. When the known knowledge is limited and it is difficult to further supplement by inferring the missing knowledge, the hot new knowledge is discovered according to the network new word discovery, and the domain entity is identified, the dynamic multivariate attribute labeling algorithm (DWAL) is added to the knowledge graph to update the knowledge. Considering the dynamic of knowledge recommendation, this paper introduces a time correction factor in the process of user interest modeling to dynamically reflect the changes of users' interests, which successfully reflects the dynamic changes of users' interests and ultimately improves the quality of recommendation.
The Empirical Analysis of College Students’ Behavior Based on SSAS

With big data technology, the potential and valuable rules can be mined from the behavioural data of college students to provide scientific decision-making for teaching and management, which is conducive to comprehensively improving management and education service level of college students. Based on the data collection, processing and mining algorithm of SSAS (SQL Server Analysis Services), this paper conducts intelligent analysis and research on college students’ behaviours. The massive data of students’ learning, consumption, socializing, surfing and borrowing, and wireless network access data accumulated are from the Smart campus of Jianghan University. To realize the analysis, prediction and early warning management of student behaviour, to provide scientific support for the precision of management.
Diversity and Abundance of Crustacean Zooplankton Community in Gilgel Gibe Reservoir, Southwestern Ethiopian Highland

Aims: The study was conducted to assess spatio-temporal dynamics of diversity and abundance of crustacean zooplankton in Gilgel Gibe Reservoir. Place and Duration of Study: The reservoir is located in Omo-Gibe catchment in southwestern Ethiopian highland. Samples were collected fortnightly at three major localities representing riverine, transition and lacustrine zones from March to August 2013. Methodology: Water samples for zooplankton were collected with plankton net and fish were sampled using gillnets. Zooplankton identification and counting were made in laboratory. Chlorophyll-a concentration and major physico-chemical parameters (dissolved oxygen, pH, water temperature, and electric conductivity) were measured in situ using standard meters. Water transparency was measured using Secchi disc. The data were analyzed using two-way analysis of variance (ANOVA). The association between abundance of crustacean zooplankton and the measured environmental variables was explored using canonical correspondence analysis (CCA). Results: Fifty-one species of crustacean zooplankton in three major categories were identified. The abundance of crustacean zooplankton varied spatio-temporally (P = 0.01). The crustacean zooplankton diversity was higher during the dry season while abundance was higher during wet season. While most of the crustacean zooplankton species (63%) were distributed virtually across all the sampling sites of the reservoir, a few species turned to be habitat specialists occurring only at a particular site. The dynamics of cyclopoids largely related to chlorophyll-a, water temperature, and electric conductivity while the dynamics of calanoids largely associated with Secchi depth, and partly with amount of dissolved oxygen and water pH. The dynamics of cladocerans remained unaccounted for by environmental variables measured during study period. Fish predation appeared to have largely structured the large sized cladoceran zooplankton as these were the most frequent and highest contributors to Oreochromis niloticus and Labeobarbus intermedius diets, the two dominant fish species in the reservoir. Conclusion: This indicates that dynamics of the crustacean zooplankton community of reservoir is regulated by various factors including primary productivity, major physico-chemical parameters and fish predation.
Voltage Regulation of Buck Converter in DC microgrid using Energy Valley Optimizer

Buck converter is basic component and main control of DC voltage regulator in DC microgrid. In this paper, an energy valley optimizer (EVO) used to tune gains of proportional integral (PI) controller for voltage regulation of buck converter is proposed. The new metaheuristic algorithm EVO is inspired by stability and various particle decay modes in advanced physics. To verify the superiority of the proposed method, numerical simulation is carried out. Furthermore, the proposed method is compared by several metaheuristic methods and assessed by two system tests including input voltage and load variation. Simulation results indicate the proposed method has the better performance such as lower overshoot and shorter recovery time than other metaheuristic methods. Additionally, integral absolute error (IAE) of proposed method is smaller compared to others.
A Full-bridge LLC Resonant Converters with Auxiliary Switches for Hold-up Operation

To address the problem of narrow input voltage range of conventional frequency controlled LLC resonant converter, a high efficiency improved LLC converter is proposed to this paper. The auxiliary switches are kept off in the normal state and since the proposed converter always operates around the resonant frequency. During the hold-up time, the converter can significantly widen the voltage gain range of the converter through the auxiliary switch conduction, so that the LLC converter can adapt to a wider range of voltage input. Meanwhile, the control structure is simple and easy to realize. The correctness of the theoretical analysis was finally verified on the Matlab/Simulink platform.
Internal Temperature Estimation of Power Lithium Batteries Based on GA-EKF Algorithm

Accurate estimation of internal battery temperature is vital for the secure functioning of power batteries. In order to obtain real-time and precise internal battery temperature, A technique for estimating the internal temperature of lithium-ion batteries is put forward, based on a combination of genetic optimization algorithm and extended Kalman filter algorithm. By combining the Bernardi battery heat generation model with the thermal circuit model, an equivalent thermal model for lithium-ion batteries is formulated. The correlation between internal and external temperatures of the battery is determined using the state equation analysis method. The genetic algorithm (GA) is employed to identify the parameters of the thermal model and validate the model accuracy. The estimation of internal battery temperature is achieved by integrating the GA algorithm with the extended Kalman filter (EKF) algorithm. Experimental trials performed on power batteries featuring embedded temperature sensors illustrate the efficacy of the proposed approach in accurately estimating the internal temperature of lithium-ion power batteries. with an estimation error of less than 1°C. This method provides a robust guarantee for real-time safety monitoring of power batteries.