Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Implementation of a Secure Login Method for RESTful Server Based on Hybrid Programming and AES Algorithm

In order to prevent malicious network sniffing and reverse engineering of JavaScript source code during the login process for a RESTful server, this paper proposes a secure login method based on hybrid programming and the AES algorithm. The method encrypts the password digest using C# to ensure the protection of the login process. Firstly, a COM interface of type AutoDual is constructed in C# to implement AES cryptographic functions, which are then injected into the web view. Subsequently, the aforementioned interface can be accessed through the global object named "chrome.webview. hostObjects" within the web page. Prior to logging in to the RESTful server, the encryption function is called to compute the MD5 digest of the password. Finally, upon receiving the information, the RESTful server utilizes the corresponding decryption function to decrypt the login password and retrieve the password summary, thereby completing the user authentication process. Experimental results demonstrate that, under public network conditions, the proposed method effectively protects the password digest, prevents network sniffing, and ensures the confidentiality of JavaScript source code during the login process. This secure login method, integrating hybrid programming and the AES algorithm, conceals the encryption algorithm used by the RESTful server, thereby addressing a class of network security issues.
An blind identification algorithm of key nodes for UAV swarm communication network

In the action of counteracting UAV swarm, accurate and efficient identification of key nodes of UAV swarm communication network is of great significance for reducing the scope of monitoring and reconnaissance, reducing the amount of information processing and improving the efficiency of attack. In this paper, under non cooperative conditions, based on the intercepted electromagnetic wave signals of UAV swarm communication network, the communication event sequence is constructed, and the sequence is regarded as a multi-dimensional Hawkes process. Through estimating the model parameters of the multi-dimensional Hawkes process, the communication network topology of UAV swarm is deduced. On this basis, the importance of each node in the perceived network topology is evaluated and ranked by using the betweenness centrality index, and finally the efficient blind identification of key nodes in the UAV swarm communication network is realized. Meanwhile, some typical simulation scenario are constructed to verify and evaluate the effectiveness of the proposed algorithm.
Activation Function Comparison On Potato Leaf Disease Classification Performance

Indonesia is a country with agriculture and food crops potential. The agricultural production index in 2021 has grown in comparison to 2020 based on these three plant categories, with food crops reaching 92.51, horticultural crops at 121.39, and plantation crops at 161.85. A food crop called the potato (Solanum tuberosum L.) is utilized in Indonesia as a vegetable and is frequently used in processed food, homes, fast food company, and flour and chip company. Attacks from pests and diseases are one of the contributing elements to the current fall in potato crop productivity. The ability to diagnose illnesses more quickly and with greater accuracy has been made possible by technological advancements. One method for detecting potato leaf disease is artificial intelligence (AI). Unbalanced data is another issue, where the relative data sets proportionate class sizes diverge by a significant enough margin. Data processing for imbalanced or oversampled data is necessary to avoid this. A method for combining various data classes to get a balanced data collection is oversampling. This method is known as data pre-processing. Convolutional Neural Network (CNN) method will be used to process the data after it has been balanced. CNN are a type of deep learning algorithm. The CNN approach uses many processing layers to identify and extract data features. More than one convolution layer can be used by CNN to build complicated features from the network’s initial simple features. The goal of the study was to use potato leaf image data based on three class, including potato plants with healthy, early blight, and late blight to diagnose illnesses with the greatest accuracy to farmers or agriculture department. The steps in the methodology are choosing the optimal activation, optimizer, and epoch functions to compare, selecting the best potato dataset, pre-processing, splitting the data for training and testing, determining the classification using CNN, and accuracy. The proposed method delivers accuracy on the potato leaf diseases with an accuracy of 98.33% with a 3X3 kernel, ADAM optimizer, activation function ReLu, and epoch 250.
An FPGA Implementation of Turbo Decoding in VDES

VDES (Very High Frequency Data Exchange System), as an enhancement and upgrading of AIS, further enhances the development of maritime communications and has a wide range of application prospects. The ASM and VDE integrated in VDES use Turbo code to enhance the receiver's error correction capability, while the Turbo code corresponds to different Link IDs of ASM or VDE with multiple coding rates, and different Link IDs in the frame structure also make some data in the coding process differences, these problems bring challenges to the realization of Turbo decoding in VDES. Based on this, this paper proposes a Turbo decoding implementation method for the nature of Turbo codes in VDES system, and verifies the feasibility of the method in FPGA. In this paper, the theory of Turbo decoding is introduced in detail, the applicability of Turbo decoding based on Max-Log-MAP component decoder in VDES is determined through simulation, and the adaptation of Turbo decoding scheme to different Link IDs is realized in FPGA logic design, and the validation results and test data prove that the Turbo decoding scheme has the ability to adapt to different Link IDs in VDES.
A Three-coil Wireless Power Transfer System Based on Orthogonal Relay Structure

Wireless Power Transfer (WPT) technology uses high-frequency magnetic fields as space power transfer medium, achieving power transmission through the mutual conversion of high-frequency alternating current and high-frequency magnetic fields. Adding a new coil as a relay between the transmitting and receiving coils in traditional WPT systems is a way to improve the transmission distance and efficiency of the system. Currently, research on relay coils focuses on factors such as optimizing the position and quantity of relay coils, and the free degree for optimization is relatively low. To address this issue, this paper proposes an orthogonal relay coil structure based on the relay concept of vertical turn transmission. By modifying the structure of the relay coil and designing reasonable parameters, the transmission power of the system can be increased as needed. Build a 100W orthogonal three-coil relay system simulation model to verify the proposed conclusions. The accuracy of the proposed system is verified through simulation.
Improvement Design of Outer-Rotor PMSM with Additive Manufactured Heat Exchanger

A heat exchanger (HE) formed by additive manufacturing (AM) is designed inside an outer-rotor permanent magnet synchronous motor (PMSM) to solve the problem of heat accumulation in the stator components. The introduction of HE makes the optimization process of the motor more complicated, and the balance between heat dissipation and output torque capacity needs to be considered. This paper reveals the variation law of the volume on cooling effect of the proposed HE. Through the coupling calculation of the traditional electromagnetic field and computational fluid dynamics (CFD) model, the optimization process of the motor equipped with HE is carried out. Three optimized motor models with similar electromagnetic and thermal characteristics are obtained, which shows the validity of the optimization theory discussed.
Linear Controller Design using Pole Placement Method for Nonholonomic Mobile Robot Trajectory Tracking

This work proposes a stable tracking control rule for nonholonomic mobile robot. A closed-loop linearized system model is presented. The feedback controller gains (driving, lateral, and angular) are computed using the pole placement method, where the desired characteristic polynomial equation is compared with the system characteristic polynomial. The poles of the desired characteristic polynomial are located in the stable region of the complex plane. The time varying controller parameters are proposed to overcome undesired system response due to high gain in the lateral direction. The proposed method has been simulated. Satisfactory performances of the trajectory tracking have been verified from simulation results.
Improving Coal Commodity Stock Price Forecasting Using Combined Linear Models via Artificial Neural Network

As a consequence of the geopolitical issues that have developed as a direct result of the conflict between Russia and Ukraine, such as the energy crisis that has afflicted a number of European nations, coal stocks have become one of the most profitable investment sectors. This study aims to develop a combined forecast based on Triple Exponential Smoothing (TES) and Seasonal Autoregressive Integrated Moving Average (SARIMA) via Artificial Neural Network (ANN) in the coal stock price. This research utilized the daily stock of ADRO, UNTR, and ITMG from January 1, 2022, to October 20, 2022. The result showed that the combined model via ANN always outperforms TES and SARIMA for each variable in the 22-day forecast horizon. Meanwhile, for the individual model, TES has better accuracy performance than SARIMA in ADRO and UNTR with SMAPE 2.81% and 5.48%. More investigations ought to explore the other combined techniques, such as the Bayesian moving average and regression. It might develop into a significant subject of research in the future.
Determinants of E-Wallet Continuous Usage Intention in Post-Covid 19 Era

This paper seeks to understand e-wallet users’ motivation and perspective on the continuous use of the payment mode in post COVID 19 era. Technology Acceptance Model (TAM) factors: Perceived Usefulness (PU) and Perceived Ease of Use (PEU) were used as the backbone of theoretical framework of the research because of its established strong explanatory power observed in previous researches. Other factor used in uncovering this rationale and perspectives include: Social Influence, e-wallet awareness (AW) and Perceived Risk (PR). To achieve the aforementioned objective, survey method with aid of online questionnaires was used in collecting data from 159 respondents comprising of different personalities across Indonesia. The findings from the results indicate that PEU, PR and SI strongly and significantly influence e-wallet continuous use intention. On the other hand, awareness was found to be insignificant in determining e-wallet continuous intention among survey respondents in Indonesia. However, perceived usefulness shows a weak significance level on its relationship to influence e-wallet continuous use intention. Lastly, it is recommended that non-bank several server-based should devise means of marketing their product through network marketing among peers, friend, colleagues and family members because social influence is among the top influencing factors found towards e-wallet continuous use intention in post COVID 19 era
Wordle result prediction based on machine learning algorithm LSTM model

Wordle, a novel word puzzle courtesy of the New York Times, is becoming popular on the Internet. Since players are willing to share their scores on Twitter, this adds a social attribute to the game. We first conducted a multi-factor ANOVA on the attributes of the words and the percentage of scores in the difficult mode. The results showed that the letters in the first four positions of the word and the number of repeated letters in the word had an effect on the percentage of scores. Next, we proposed a multiple-input-multiple-output long and short-term memory (LSTM) model to predict the distribution of reported outcomes. To incorporate the internal effects of the data into the study, we designed a six-input-seven-output LSTM regression prediction model and trained it with the dataset. Then, the trained network was used to predict the distribution of "EERIE" scores. To predict the difficulty of words, a random forest model was designed to determine which of these attributes were associated with each classification by using several attributes carefully selected from the words. Finally, the difficulty of the word "EERIE" was predicted.
Modeling and Analysis of Thermal-mechanical Stress in Motor Insulation

High temperature generates thermal-mechanical stress (TMS), which causes aging of the electrical insulation system in the stator windings. In this paper, a TMS analytical model for motor insulation is proposed, and the model is modified by using a shallow neural network. Then, the accuracy of the TMS analytical model was verified by finite element simulation, and finally verified by experiments. The average error obtained after comparison with the analytical model is less than 5%.
A Step-up DC-DC Converter with Self-boost Charge-Pump-Type Switched capacitor

To meet the demand for high-gain DC/DC converters in application scenarios such as fuel cells and photovoltaic power generation systems, this paper proposes a new type of non-isolated DC/DC converter topology. By integrating charge pump type switching capacitors into the active switch inductor converter, the boosting capability is enhanced, the switch tube voltage stress is reduced, and zero input current ripple is achieved. To verify the accuracy of the proposed converter, we constructed a 200W, 50kHz switching frequency experimental prototype for testing.
Position Sensorless Control for Electromagnetic Launch system based on Linear Extended Kalman Filter

Considering high acceleration, intense vibration shock and aperiodic transient operation characteristics of the electromagnetic launch system, which will reduce the reliability of position sensor, thus affecting both stability and safety of the system. Therefore, a position sensorless control with strong robustness is desirable. Robustness and fast response are main features that make extended Kalman filter (EKF) position observation scheme superior to other methods. However, the conventional EKF algorithm suffers from the shortcomings of nonlinearity and heavy computational effort. To address above drawbacks, an EKF model is constructed by using a linearization algorithm based on substitution method, which realizes the position sensorless control of dual three-phase permanent magnet linear synchronous motor (DTP-PMLSM) for electromagnetic launch. The effectiveness and robustness of the presented algorithm is verified by both field circuit combined simulation and experimental platform based on electromagnetic launch.
Deep neural network-based (BMI) brain-computer interface system design : Development and evaluation of a BMI brain-computer interface learning framework for rehabilitation

Based on the brain-computer interface is an important way of existing and future medical rehabilitation medicine, based on the very important role played by motor rehabilitation after stroke, the research framework based on reinforcement learning (RL) deep neural networks (BMI) brain-computer interface decoder utilizes an internal reward decoding framework and metrics to transform the mapping relationship between motor and rehabilitation. The brain is allowed to travel a customized set of learning mechanisms to identify the rewarding process with the correct behavior in a new task. This internal guidance can be used as an external reference. Data systems research can improve rehabilitation efficiency by 9%. The accuracy rate is up to 92%. And as a separate research and treatment modality. This study is based on the design of a deep neural network-based (BMI) brain-machine interface system, which is experimented with in both online and offline research directions, with 9 subjects (5 male, 4 female), aged between 25 and 34 years old, wearing 32-guided joint electrode caps on their heads, all with a resistance of 5kΩ or less. The glasses were 40cm~90cm away from the screen, the chief operating range and procedure were set in advance, and the sampling frequency of the Neuroscan-NuAmps EEG acquisition instrument was set at 250Hz. the tests were verified and improved. The research results of this paper have been applied for a patent and granted.
Deep Learning for the Recognition of Skin Cancer

The health industry has kept growing over the years with breakthroughs. However, in skin disease recognition such as cancer, there is still a battle to improve how recognition is implemented to get the desired result. The paper focuses on skin cancer recognition using deep learning. In addition, a cloud-based model-based architecture that uses deep learning algorithms in its core applications to create models that help predict skin cancer with improved accuracy is considered. System analysis and specification are also done in conjunction with a description of procedures and requirements. Implementation and testing are also done and finalized with the conclusion and future work. The main outcomes of the results and conclusion will be drawn from the implementation and testing that are carried out on the data. In addition to the fact that the implications of the findings would become valuable and further used as a benchmark for future research work in the research domain.
The Software Complex for the Selection and Analysis of Algorithms Predicting Key Quality Indicators of Polymer Film Materials of Industrial Production

The following algorithms for constructing predictive models of key quality indicators of polymer film materials are considered and implemented: adaptive boosting of decision trees (AdaBoost), recurrent neural network of long short-term memory (LSTM). The characteristics of production data and the stages of their preparation for the construction of predictive models are presented. The comparison of trained predictive models for the defect by the number of black dots per m2 of the film web is carried out by various algorithms according to the main metrics: precision, recall, f1-score, to identify the best depending on the production data.
Research on ARIMA Model for Short-Term Traffic Flow Prediction based on Time Series

Short-term traffic flow prediction is of great significance for urban traffic management and planning. To efficiently utilize traffic flow data for prediction, it is crucial to establish a reasonable short-term traffic flow prediction model. In this study, the ARIMA method is adopted for traffic flow trend prediction. Firstly, time series data obtained from electronic police are processed to determine the stationarity of the sequence. Then, the maximum likelihood estimation method is used to estimate the model parameters, and the residuals are subjected to a white noise test. Static prediction method is used for real-time dynamic traffic flow prediction, and finally the model prediction results are evaluated. The experimental results show that the ARIMA model (4,1,5) has higher accuracy and reliability in predicting short-term traffic flow, thus it has important application value in actual traffic control and planning
Accurate Calculation and Reduction of Losses in Fractional Slot Concentrated Winding Permanent Magnet Machines

Fractional slot concentrated winding permanent magnet (FSCW-PM) machines face challenges of increased losses and computational complexity due to their unique structure and the presence of harmonics in the air gap. The accurate calculation and reduction of the losses in different parts of a FSCW-PM machine are investigated. Initially, two-dimensional (2D) and three-dimensional (3D) models of the machine are constructed to study the impact of segmented magnets on permanent magnet (PM) eddy current loss of and core loss. Subsequently, a detailed model of all individual wires within a single slot is performed to accurately calculate the AC copper loss and separate the factors contributing to the loss. Finally, based on the preceding analysis, the machine structure is modified to reduce losses. By conducting a comprehensive study of loss characteristics, this paper aims to contribute to the design and optimization of high-efficiency FSCW-PM machines.
Federated Learning Algorithm Handling Missing Attributes

Federated learning has gained considerable attention as the solution for handling distributed and privacy-sensitive data. This includes scenarios such as predicting conversion history on smartphones or utilizing electronic medical records held by hospitals within the machine learning context. Essentially, federated learning is a form of decentralized machine learning, where data is distributed across multiple clients/organizations. This approach eliminates the necessity for direct data exchange with a central server. However, prevailing research assumes uniformity in data attributes held by each organization, disregarding the missingness attribute. As a result, this method falls short when it comes to effectively learning from data that exhibit missingness. To address this limitation, our study introduces a federated learning algorithm designed to achieve high accuracy even when dealing with data that demonstrate missingness. This algorithm incorporates a machine learning model tailored for attribute imputation within a federated learning framework. Through a series of validation experiments, we showcase the algorithm's effectiveness.
Applying Deep Learning Technology for Modulation Signals Classification in Underwater Acoustic Communication

Deep learning (DL) technology has considerable potential in wireless communication particularly in spectrum monitoring and signal collection for military applications. These operations entail the interception of enemy signals in highly complex environments and the rapid detection, identification, interpretation, and acquisition of real-time information. In this study, a bidirectional long short-term memory (BiLSTM) model was used for the automatic classification of modulation signals in underwater acoustic communication in the eastern open sea and southwestern coastal sea of Taiwan. The strengths and weaknesses of various DL techniques in classifying signals in different water bodies were examined, too. Underwater signals are easily distorted by multiple factors, including the Doppler effect, multipath interference, and Gaussian white noise. In this study, 11 common underwater modulation techniques (binary-phase-shift keying; quadrature-phase-shift keying; 8-phase-shift keying; 16-quadrature amplitude modulation; 64-quadrature amplitude modulation; 4-level pulse amplitude modulation; Gauss-frequency-shift keying; continuous-phase, frequency-shift keying; broadcast frequency modulation; double-sideband amplitude modulation; and single-sideband amplitude modulation) were investigated to explore the effect of underwater channel characteristics on the classification accuracy of different DL techniques for different modulation signals. On the basis of the obtained results, an optimal DL technique was identified. The simulation results indicated that the designed BiLSTM model accurately classified modulation signals in complex underwater channels in which the signal-to-noise ratio was low (-14 dB). Specifically, the mean accuracy rate of the designed BiLSTM was 99.61% in the eastern open sea and 99.43% in the southwestern coastal sea.