Miracle-dz/UniFAR-FTUs huggingface dataset:
An Online Inductance Current Modeling Method for SS-Compensated WPT System

The inductance current is usually used for ZVS control, parameters identification, power control, etc. Acquisition of its time-domain model is the key to achieving the above objectives. This article studies the time-domain modeling method of inductance current and proposes an online inductance current modeling method for the SS-compensated WPT system. Firstly, the output voltage harmonic components of the high-frequency inverter using phase shift control and the filtering characteristics of SS topology are analyzed. Then, the principle of amplitude and phase extraction of inductance current based on orthogonal modulation and demodulation method is introduced; Finally, the difference between the simulation current and the calculation current is analyzed. The accuracy of the proposed method is verified through simulation.
Back-EMF optimization of a Rotor Permanent Magnet Flux-Switching Machine Through Air-gap Harmonic Design

Unlike the stator permanent magnet (PM) flux-switching (FS) machine, the sinusoidal degree of the no-load back electromotive force (back-EMF) in the rotor PMFS machine is not high, which will decrease the utilization rate of the inverter voltage, and the torque performances. Hence, this paper presents a method to sinusoidal the back-EMF of the rotor PMFS machine with a T-shaped rotor module by optimizing the key harmonics of the air-gap flux density $(B_{r})$. The presented method has the benefit of optimizing the back emf without sacrificing the output torque performance of the machine. Firstly, the magnetomotive force (MMF)-permeance model is analyzed to investigate the no-load $B_{r}$, which contributes to the back-EMF, and is obtained by introducing the winding function theory. Then, based on the Maxwell stress tensor method, it is possible to obtain the devotion of the harmonics of the $B_{r}$ to the torque. After that, the design method to reduce the back-EMF is realized by minimizing the harmonics of $B_{r}$ which generates more to the third harmonic of the back-EMF and maximizing $B_{r}$ which generates more to the torque.
Features of Synthesis of Neural Network Simulators with Limiters

One of the problems of the modern theory of automatic control is the construction of verified mathematical models of objects of regulation. In some cases, obtaining such models is not possible. This paper discusses approaches to the use of neural networks as regulators. Due to the presence of the adaptivity property, neuroregulators in some cases show a better quality of control in comparison with classical ones. The most common architectures of neural networks and methods of their training are considered. A comparative analysis was carried out. The first stage in the design of neural network regulators is the creation of a neuroimitator. An example of the synthesis of a neural network simulator is given. The proposed scheme has been worked out on systems with saturation type limiters and a mechanical stop.
Business Process Digitalization on Authentic Culinary Palembang

Micro, small and medium enterprises (MSMEs) contributed 60.34% to Indonesia's economic growth where the number reached 93.4% with a labor absorption rate of 97.22%. The large level of labor absorption indicates that the MSME sector is one of the foundations of economic stability through labor-intensive populist economic activities. Furthermore, the Ministry of Cooperatives and Small and Medium Enterprises announced that as many as 3.79 million micro, small and medium enterprises (MSMEs) have used information technology to digitize their businesses. This number is only around 8 percent of the total MSMEs in Indonesia, namely 59.2 million. In the province of South Sumatra there are 75,569 MSMEs and only 9,114 or 12% of MSMEs use the internet to support their business digital business processes. Therefore, this study aims to design and build an information system to support digital business processes in village SMEs. Research data were obtained from surveys, interviews and FGDs with MSMEs, the government, industry associations, and MSME sector experts. While secondary data will be obtained through interviews, UMKM and village profiles, the Central Bureau of Statistics. The research sample was obtained from the village MSME population which has superior products in several districts of South Sumatra province. The results of this research investigation are used to assist MSMEs in improving service to the level of international fast-food restaurants.
Evaluation of Land Use, Habitat, and Water Quality Parameters on Macroinvertebrate Index Metrics by Polynomial Regression Analysis

Habitat parameters, land use characteristics, and water chemistry data from sample locations within watersheds of Massachusetts were analyzed to assess their influences on macroinvertebrate metrics and indices of biotic integrity. The habitat parameters and macroinvertebrate metrics were obtained from the Massachusetts Department of Environmental Protection Division of Watershed Management (MA-DEP-DWM). The water quality data was obtained from a different database also obtained from MA-DEPDWM in ArcGIS 9.1. Care was taken to assure that sample locations and biotic, chemical, and habitat parameters coincided. The disparity in the sample date for the water chemistry and the macroinvertebrate data was not greater than 30 days but was primarily within one week of each other. Land use characteristics for the watershed and 30-m riparian buffer were calculated using ArcGIS 9.1. A 30-m riparian buffer was used because of the Wetland Protection Act of Massachusetts prohibiting development within 30-m of the water body. Once all of the data was combined into one database, principal components analysis and polynomial regression analysis was performed to evaluate the relationships between explanatory (environmental) and response (species) variables. Polynomial regression equations were developed to calculate species metrics and assemblages. The most significant explanatory variable was deemed to be natural land/undisturbed vegetation (riparian area and watershed area) for the three response variables of HBI, EPT Index, and richness. The polynomial regression equations developed show a good agreement (R values ranging between 0.4 and 0.7) between the measured EPT Index and richness variables and those predicted by the model.
CoDex: Cross-Tactic Correlation System for Data Exfiltration Detection

Advanced Persistence Threats (APTs) have become one of the major threats to enterprise security. In the past three years, over 70% of APT campaigns involved Data Exfiltration for double and even triple extortion, causing tremendous financial lost. The data exfiltration tactic illustrated in the MITRE Cybersecurity Framework are often detected based on a large amount of data being transferred. However, the malicious behaviors of Data Exfiltration are similar to either web browsing or system backups, leading to a high false positive rate for conventional detection methods. In this paper, we proposed a cross-tactic correlation system for Data Exfiltration detection, named CoDex, which detects and correlates potential cross-tactics malicious behaviors related to Data Exfiltration, such as Discovery and Data Collection. In our experiments, we reproduced 3 popular APT campaigns to evaluate the detection accuracy. On average, CoDex achieved 98.5% of detection accuracy, increased 60% of F1 score, and reduced the false positive rate from 7.1% to 0.5%.
Robotic Tactile Sensing System Based on Electrical Impedance Tomography

With the rapid development of robotics technology, it has become crucial for robots to effectively perceive the sense of touch. This helps intelligent robots better understand their surroundings, improves their perception and interaction capabilities, and is directly related to the robot's motion decision-making and human-robot interaction safety. Electrical impedance tomography is a non-invasive medical imaging technique that can reconstruct the conductivity distribution of a conductive domain using a small amount of boundary voltage data. It demonstrates the unique advantages of large-area tactile sensors for human-computer interaction. In this paper, we present a tactile sensor system based on electrical impedance tomography. A robotic arm sensor with 16 electrodes was designed and prepared using 3D printing technology, a data acquisition hardware system and an upper computer image reconstruction software system were built. The sensor was applied to the robotic arm as an "electronic skin", and several physical interaction experiments were conducted. The experimental results show that the sensor is of great significance in realizing the tactile sensing function of robots and human-computer interaction.
Fast Calculation of Electromagnetic Force and Vibration for Surface Mounted PMSM Considering Spatial Harmonic Suppression

Electromagnetic vibration (EMV) is a key indicator for the performance analysis of permanent magnet synchronous motor (PMSM), and when using finite element method (FEM) for EMV prediction analysis, the results are greatly affected by the mesh quality and calculation steps. Based on the traditional accurate subdomain method (SDM) of surface-mounted PMSM, this paper considers the space harmonic weakening method of air gap flux density (AGFD) of magnet shaping, skewed magnet, skewed slot and auxiliary slot structure, and establishes the analytical model of radial electromagnetic force (REMF) calculation and the fast prediction model of EMV. Based on the 24-slot/28-pole fractional-slot motor, the harmonic components and corresponding vibration components of the REMF of each spatial order under several structures are compared and analyzed at the main vibration frequency of $2f_{e}. $ The accuracy of the AGFD waveform calculation is verified by the FEM results, and the effectiveness of the EMV prediction model is verified by the vibration test results of the prototype.
Flame Detection Based on Improved Yolov5 Network Model

Traditional flame detection often requires professional equipment and personnel, which is inefficient and prone to false positives, making it difficult to meet the needs of daily fire safety detection. At the same time, current flame detection methods based on neural network image recognition are also difficult to meet the requirements of real-time and accuracy. In order to improve the performance of flame detection, this paper proposes a flame target detection algorithm based on improved YOLOv5. First, changing the activation function of the model to FReLU can not only improve the feature expression ability of the model, but also enhance the nonlinear fitting ability of the model and reduce the occurrence of Vanishing gradient problem. Secondly, replace the part of the backbone network of yolov5 with the Fasternet network, and extract spatial features more effectively by reducing redundant computing and memory access at the same time. The improved algorithm has an average inspection accuracy improvement of 3.2% compared to the original YOLOv5s algorithm, while reducing the number of model parameters by 11% and increasing the detection speed by 41%.
A Framework for Cognitive Load Recognition Based on Machine Learning and Multimodal Physiological Signals by Wearable Sensors

Cognitive load recognition is of great significance to the study of emotional health, as excessive cognitive load not only impairs work and learning efficiency but also leads to mental pressure and fatigue, affecting overall well-being. Advances in wearable technology and understanding the role of physiological signals in assessing human health have enabled the possibility of cognitive load recognition using these signals. This work proposes a framework that combines multiple physiological signals (ECG, EDA, and PPG) collected through wearable sensors for cognitive load recognition. Heart rate variability (HRV) features are extracted from ECG signals, while statistical characteristics are extracted from EDA and PPG signals. The RFECV feature selection algorithm is utilized in the feature selection module to achieve optimal feature subset selection and redundant feature removal. The classification of cognitive load states is implemented based on the ASAPSO-LightGBM(Adaptive Simulated Annealing Particle Swarm Optimization-Light Gradient Boosting Machine) machine learning model. We validated the effectiveness of the framework on two publicly available datasets (MAUS and WESAD). Experimental comparisons of feature combinations and machine learning models demonstrate that the proposed framework achieves superior performance, with recognition accuracy of 98.52% and 88.38% on the two datasets, respectively.
Nonlinear Post Equalizer for Visible Light Communication System Based on Deep Residual Convolutional Neural Network with Feature Concatenation

Visible light communication (VLC) is a promising wireless communication technology but its performance is limited by linear and nonlinear distortions. In this paper, we proposed a post-equalization method based on deep residual convolutional neural network (DRCNN) with feature concatenation and demonstrated the effectiveness of the residual structure and the excellent compensation performance of DRCNN through experiments on a PAM-8 VLC system. The residual structure reduces the bit error rate (BER) by 46.7% on severely distorted data collected with direct current (DC) bias of 140mA, voltage peak-to-peak (VPP) of 0.8V, and bit rate of 450Mbps. Compared to the conventional linear FIR equalizer, the DRCNN improves the Q factor by 1.4 dB at maximum and the operating current range and voltage range of the system below the hard decision forward error correction (HD-FEC) threshold of 3.8 × 10−3 by 43.5% and 19.5%, respectively.
Design of Electric Vehicle Wireless Charging System Suitable for 11kw and 22kw Receiving End

The interoperability of electric vehicle wireless charging means that the same ground transmitter can match the receiving end of electric vehicles with different input power levels, different ground clearance and different circuit topologies for safe and efficient wireless charging. However, how to meet the optimal transmission efficiency in different input power levels, different ground clearance and different circuit topologies is a difficult problem in interoperation design. Aiming at the LCC-S electric vehicle wireless charging system, this paper takes the given size parameter of the transmitter end in GB/T38755.6 as the standard, and takes the compensation inductance and system coupling coefficient of the transmitter end as the parameter variables to optimize the transmission efficiency and the electrical parameters of the system device, and designs two receiving ends with different power of 11kW and 22kW.The interoperability of the design is preliminarily verified by simulation. Finally, two sets of experimental devices, 11kW and 22kW, were built to verify the feasibility of the design method for the interoperability of the wireless charging system of electric vehicles.
On the Issue of Developing an Agent- Based Model of the Hydrolithosphere of a Resort and Recreational Region

The model of an agent-oriented system of a balneological resort, which also produces mineral water, is described by a number of equations that describe the processes occurring in this system. Creation of an agent-based model is a rather difficult task that requires the use of specialized software tools for modeling hydrological and geological processes. The purpose of the study is to select mathematical and software for the developed agent-based model of the hydrolithosphere of the Caucasian Mineral Waters region. The use of an agent-based approach in modeling will make it possible to take into account various interactions between agents, such as the migration of water and porous media components, as well as the processes of mineral water extraction. In addition, this approach will make it possible to take into account the influence of changing conditions on the behavior of agents and, accordingly, on the dynamics of the hydrolithosphere of the resort as a whole.
Comparative Analysis of the Rate of Convergence of the Methods of Gradient Descent and Natural Gradient Descent in Regression Analysis Problems

This article provides a comparative analysis of gradient descent and natural gradient descent in its own implementation in terms of accuracy and convergence rate on various data sets. The results of the experiments obtained make it possible to compare the effectiveness of both methods both in terms of classical metrics for evaluating mathematical models, and in terms of convergence rate and learning time. The findings of the study help to choose the optimal optimization method, depending on the requirements for accuracy and speed of work when building mathematical models
Modeling and Simulation of a Two-Phase Interleaved Boost Converter for Photovoltaic Applications

As the world clamors for renewable energy sources, DC/DC converters are increasingly indispensable tools for transforming DC voltage into the required output voltage with minimal energy loss. This research paper showcases the design and simulation of a two-phase Interleaved Boost Converter for unconventional energy sources that utilize Matlab/Simulink software. With its capacity to raise output voltage to 380V. Furthermore, the paper provides the behavior of interleaved boost converters under variable duty cycles with maximum power point tracker control. The results show the efficiency of the proposed converter for both low and high-power applications such as PV systems.
Overview of High Overload Motors

In addition to rated operations, there is a recognized need for the high overload capacity of electric motors, particularly in scenarios of electric ship/aircraft propulsion, heavy mining vehicles, robotics, and machining tools. Through existing studies and practices, motor designers and engineers commonly acknowledge that the overload capacity of motors for extended periods of time not only benefits drive systems from reduced volume and low cost, but also challenges the reliability and safety threshold. To develop high overload motors is a timely topic but related overviews to discuss technical routes are hardly found. This review article observes three routes to high overload motors, i.e., electrical, magnetic, and thermal, while summarizes emerging technologies to achieve the routes. The observations of this review will promote the efficient and reliable operation of heavy machinery in a wider range of industries
Homomorphic Encryption for Secure Computation of White Patch Area in Binarized Medical Images

In specific categories of medical images, pathological abnormalities appear as white patches where the area of the white patches plays a vital role in the diagnosis of the associated disorders. Currently, most medical records are hosted in Cloud Servers for low-cost storage and efficient dissemination. Invariably, the medical images are encrypted and then stored in the Cloud for security and privacy. Moreover, in many cases, homomorphic encryption is adopted to take advantage of the powerful computing capabilities of the Cloud Servers. In such a scenario, a novel homomorphic encryption method that enables the secured computation of the white patch area of a binary image is presented. For this specific task, the target plaintext image is represented in its binary format to demark the white patches. The secured calculation of the white patch area of the specified image in the Cloud server is achieved based on homomorphic matrix multiplications in the cipher domain. The simulated results show that the performance is better compared to its nearest competitor.
Research on multi-model ensemble machine learning methods for temperature forecasting

To improve the accuracy of short-term and imminent surface air temperature forecast in Beijing, a multi-model ensemble rolling temperature forecasting model was developed using two machine learning methods: ridge regression and support vector machines. The model was based on 2 m temperature numerical forecast products from four weather forecast models with 24-hour lead times from the European Centre for Medium-Range Weather Forecasts (EC), the US National Centers for Environmental Prediction in the United States (GFS), the Japan Meteorological Agency (JMA), and the Beijing Meteorological Bureau Rapid-refresh Multi-scale Analysis and Prediction System (RMAPS), as well as observations from 20 national-level stations in Beijing. The results showed that the machine learning-based multi-model ensemble effectively corrected the systematic bias in the model forecasts. The absolute mean bias of temperature forecasts was less than 0.3 °C for the 20 stations, and the root mean square error of the forecasts was smaller than that of any individual model and its equally weighted ensemble mean. The accuracy of the forecasts was significantly improved. Compared to the equal-weight ensemble mean results, the multi-model ensemble method based on ridge regression and support vector machines significantly improved the skill of temperature forecasts in the mountainous areas of Beijing. During the operation period, the forecast accuracy of temperature in FoYeDing is improved by over 30%, which has better operational application value.
Performance Evaluation of Consequent-pole Permanent Magnet Machine with Halbach Array Arrangement

This paper presents a performance evaluation of the surfaced-inset consequent-pole permanent magnet (CPPM) machines with different Halbach array arrangements. To illustrate the torque enhancement mechanism, the torque contributions of the various air-gap field working harmonics are identified and quantified by maxwell stress tensor method. It reveals and confirms a fact that the main-order field harmonic exhibits an improved torque component for Halbach array structure compared with conventional counterpart. Besides, the total magnetic field modulated harmonics contributed a nonnegligible torque component on Halbach array structures. As a result, the Halbach array magnet configuration shows a high torque capability. In addition, the performance of the Halbach array design with different PM arrangements are investigated and compared by finite element analysis.
Comparison of Fasttext and Word2Vec Weighting Techniques for Classification of Multiclass Emotions Using the Conv-LSTM Method

Emotions are one aspect of human psychology which includes the ability to control feelings and behaviors that create ways of self-expression. The classification of emotion in text is an area of linguistic research. The purpose of this study is to solve the problem of how to compare the FastText and Word2Vec weighting techniques with the Conv-LSTM combination to the emotion detection text classification process and how the results of the performance analysis are. In addition, it aims to implement and obtain performance analysis results on comparisons of the FastText and Word2Vec algorithms. The method used is a combination of the Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) methods which are then referred to as Conv-LSTM. The Conv-LSTM combination is usually used for image data, but in this study it was applied to sequential text. Sequential text has its own challenges when viewed from the characteristics of the text in this research dataset which tends to be short. Text classification process in detecting labels on keyword research objects "Anies Baswedan". A total of 98,664 tweet data with 6 multiclass classifications, namely happy, sad, angry, disgusted, shocked, and afraid. The highest accuracy results using the Word2Vec Skipgram method with a Max Pooling of 0.8499. In short, this study shows that the word2vec method is better in terms of accuracy compared to the FastText method.