Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of Violations in the Examination Room Based on Deep Detection Algorithm

In the field of proctoring, cameras play an irreplaceable role, but the traditional invigilation methods for monitoring students during exams require a large number of invigilators, which can be a waste of manpower, resources, and finances. In recent years, the rapid development of deep learning technology has shown potential for recognizing abnormal behavior in various settings, including examination rooms. Test postural assistant detection algorithm in the examination room studied in this paper aims to use computer technology instead of human power to automatically detect behaviors in surveillance video. In this paper, five algorithms are used to identify any violations of test discipline in the examination room and compare the recognition results. The experimental results show that DenseNet can effectively detect video frames containing violations in the video sequence, and the average detection accuracy is 95.62%, which can accurately identify these behaviors such as looking left and right in the examination room and passing items. The study also presents a self-built database to support model training and testing. The experiments were conducted on self-built dataset of examination room videos. The findings of this study have important implications for improving examination security and reducing the need for large numbers of invigilators.
RDIDAS: A Robust Distributed Industrial Data Acquisition Strategy

Industrial data acquisition gateway is a key intermediary between intelligent things and servers. The implementation scenario of limited bandwidth and difficult maintenance in industrial environment puts forward requirements for the robustness of the acquisition gateway. Therefore, ensuring the robustness of the acquisition gateway is a key problem. This paper designs a data acquisition node election algorithm based on MQTT (RDIDAS: Robust Distributed Industrial Data Acquisition Scheme) to realize the deployment of multi-node gateway; designs a multi-node cooperative acquisition algorithm to realize the cooperative work of multi-nodes. The multi-node acquisition strategy is constructed to improve the robustness of industrial data acquisition. The experiment verifies that RDIDAS improves the reliability of data acquisition when encountering single point of failure and completely disconnected state, and effectively ensures the robustness of the industrial data acquisition gateway.
State Estimation of Discrete Events Systems Modeled by P-Timed FCF and BCF Petri Nets

In this paper, P-timed FCF and a BCF Petri nets are studied using a specific state model, allowing an explanation of the system evolution over time, specifically for state estimation. The state model is described using the solid and powerful mathematical formalism allowing modeling P-timed Petri nets. The initial state of the models, their inputs and dynamics during the simulation describe the system evolution in time. Using an illustrative example, it is proved that the system state is well estimated using similar formalism by comparing the obtained results of simulated and estimated states.
Optimization of Secondary Segmented Flux-Switching Linear Motor Designed for Urban Rail Transit

A Flux-switching Linear Motor (FSLM) is suitable for urban rail transit due to its high efficiency and low cost. This paper studies a new FSLM with segmented secondary and the key of this structure is that the secondary stator is composed of separate iron blocks. The topology is introduced, and the operating principle is explained. To ensure that the new FSLM is suitable for the linear traction system, the LIM used in Guangzhou Metro Line 4 is taken as the reference. The electromagnetic characteristics of the new FSLM are studied and compared by means of finite element analysis (FEA). The results reveal that the new FSLM can meet requirements of the linear traction system after multi-objective optimization.
Nonlinear Control Law Design for Overhead Crane to Supress Load Oscillations

The problems of trolley position tracking and stabilization of the suspended load are solved simultaneously for an overhead crane. The model of the trolley-pendulum system is derived. To solve the problem under consideration, a nonlinear control law is designed using the second Lyapunov method, which guarantees asymptotic stability of the control error. The derived law is validated via numerical experiments. The obtained results corroborate the effectiveness of the proposed solution.
Bond Graphs and Bayesian Networks Based Fault Diagnosis: Application to a Photovoltaic Power Supply

Model based fault diagnosis can be applied when sufficient information is available on the components and the structure of the process. The bond graphs, as multidisciplinary graphical modeling approach, are suitable since they help to deduce all the constraints and the causality within the model. The task of monitoring industrial processes does not only concern obtaining the desired performance of the control, but also performing a successful diagnosis of the fault in order to carry out the suitable reconfiguration. New generation of power converters should be fully automated and sustainable to ensure the continuity of service. The objective of this research work is to design an automatic supervisor of the power supply equipped with a reconfigurable interleaved boost converter by association of bond graphs and Bayesian networks approaches. The Bayesian Networks are considered as an artificial intelligence method which improves the decision procedure deduced from bond graph model by integrating reliability data of the components. The simulation results of some scenari proves the efficiency of the proposed method applied to a photvoltaic power supply.
Multi-Objective Optimization of Concentrated Flux Interior Permanent Magnet In-wheel Motor with Distributed Winding

In this article, the multi-objective optimization method is proposed to improve the torque density for the concentrated flux double-layer interior permanent magnet (IPM) motor, which enhances the efficiency of the optimization processes. Firstly, the sensitivity analysis of stator and rotor parameters is performed, and some key optimization parameters are obtained. Then, the average torque and torque ripple agent models are constructed by using the response surface method (RSM). Besides, the optimal parameters are acquired based on the non-dominated sorting genetic algorithm (NSGA-II). In addition, the electromagnetic performance of the initial and optimal motors are compared, including air-gap flux density, back electromotive force (back-EMF), average torque, and torque ripple. Finally, a 75kW prototype is manufactured and tested to verify the proposed optimization method.
Contactless Palmprint Recognition for Children

Effective distribution of nutritional and healthcare aid for children, particularly infants and toddlers, in the world's least developed and most impoverished countries, is a major problem due to lack of reliable identification documents. We present a mobile-based contactless palmprint recognition system, Child Palm-ID, which meets the requirements of usability, cost, and accuracy for child recognition. On a contactless child palmprint database, Child-PalmDB1, with 1,020 unique palms (age range of 6 mos. to 48 mos.), Child Palm-ID achieves a TAR=94.8% at FAR=0.1 %. Child Palm-ID is also able to recognize adults, achieving a TAR=99.5% on the CASIA contactless palmprint database and a TAR=100% on the COEP contactless adult palmprint database, both at FAR=0.1 %. For child palmprint images captured at an interval of five months with differences in standoff distance, illumination and motion blur, the TAR drops to 80.5% at FAR=0.1 %. This indicates that more research opportunities remain in contactless child palmprint recognition.
Design and Analysis of Core Reactor Based on Magnetic Powder Material

In order to solve the problem of noise and hot spot caused by air gap in traditional silicon steel reactor, a series reactor is designed using Fe-Si core material with low permeability. The magnetic properties of Fe-Si material with a magnetic permeability of 60 and the design method of Fe-Si reactor are given. The magnetic field, thermal field, vibration and noise characteristics of Fe-Si series reactor and traditional silicon steel reactor are analyzed by three-dimensional finite element method. The comparison results show that the reactance value of Fe-Si series reactor has a certain attenuation, the winding temperature is slightly lower, the core temperature is higher, and the noise is obviously reduced. A prototype of Fe-Si series reactor is made. The experimental results are in good agreement with the calculated results, which proves the feasibility of using low permeability Fe-Si power material to develop power frequency reactor for power system.
Winds and Meltwater Together Lead to Southern Ocean Surface Cooling and Sea Ice Expansion

Southern Ocean surface cooling and Antarctic sea ice expansion from 1979 through 2015 have been linked both to changing atmospheric circulation and melting of Antarctica's grounded ice and ice shelves. However, climate models have largely been unable to reproduce this behavior. Here we examine the contribution of observed wind variability and Antarctic meltwater to Southern Ocean sea surface temperature (SST) and Antarctic sea ice. The free‐running, CMIP6‐class GISS‐E2.1‐G climate model can simulate regional cooling and neutral sea ice trends due to internal variability, but they are unlikely. Constraining the model to observed winds and meltwater fluxes from 1990 through 2021 gives SST variability and trends consistent with observations. Meltwater and winds contribute a similar amount to the SST trend, and winds contribute more to the sea ice trend than meltwater. However, while the constrained model captures much of the observed sea ice variability, it only partially captures the post‐2015 sea ice reduction.
Alzheimer's Disease Detection Based on Features Level Selection for CNN-SVM Combination

Neurodegenerative disease especially dementia are reported as disease that leads to death, Alzheimer's Disease (AD) is kind dementia that cause progressive and irreversible brain disorder loss which leads to death. AD shows no symptoms in its early stages which makes diagnosing it its beginning a challenge and helpful for doctors as they can slow down its progress in its early stages. Computer-aided approaches such as machine learning which come up with several techniques to detect AD by extracting features from the given image data and use them to build a classifier. Recently, a subcategory of machine learning called deep learning has widely been employed to enhance the medical diagnosis by attempting notable performance. In fact, these approaches avoid the tricky manual feature extraction using Convolutional Neural Network (CNN) considered as a reference in the field of computer vision. This paper proposes a combination of machine-deep learning technics for early diagnosis of AD from positron emission tomography (PET). We first train our CNN on PET images to extract the most relevant features, then we select the most appropriate CNN's level from where the features will be extracted, which will be feed in a second step as input to a Support Vector Machine based classifier (SVM). The proposed approach achieves notable results that exceeded the performance obtained by various existing approaches.
Efficient Regression with Feature Selection Based on L12-Norm

Feature selection is one of the most important components of many machine learning applications. Most of the existing sparse coding-based methods only concerned with the regularization of the transformation matrix, but ignoring the sparsity of projected features in the intrinsic feature space. In this paper, we introduce a novel regularization with ℓ12-norm, called class specificity distribution term. It aims to ensure that samples with the same label could obey a specific distribution, while data from the different categories should have different distributions in the intrinsic feature space. Then we propose a novel embedded feature selection framework based on regression and class specificity distribution term measured by ℓ12-norm, termed RL12. An efficient augmented Lagrange multipliers based optimization algorithm is proposed to solve the ℓ12-norm regularization iteratively. Experimental results demonstrate the effectiveness of our method.
Common Mode Voltage Suppression Method of H14 Inverter Based on Hybrid Modulation

By adding two extra switches, the H14 three-level inverter suppresses the common-mode voltage (CMV) in a very small range, but the common-mode voltage does not eliminate completely. In response of this problem, a new virtual space vector PWM (VSVPWM) based CMV eliminate method of H14 inverter is proposed. This method uses space vectors with zero CMV to synthesize a new virtual vector, designs the synthesis rules and implementation process of the VSVPWM. Due to the zero CMV of the virtual vector, the proposed method in this paper can eliminate the CMV generated by the H14 inverter. In addition, considering the neutral-point voltage balance of the H14 inverter, this paper proposes a hybrid modulation method, which can balance the neutral point voltage and CMV of the H14 inverter within the set value range by the modulation method.
A Cross-Sampling Method for Hidden Structure Extraction to Improve Imbalanced Multiclass Classification Accuracy

Class prediction problems in classification cases are often faced with irregular data conditions. This research contributes to providing a more structured hidden pattern extraction approach to data sets that already have class labels. The process of taking samples from data collection in this study is called the cross-sampling (CS) technique. The basic idea of this technique is to regroup the data using the appropriate clustering method. This study applied the Mini Batch K-Mean and Spectral algorithms to four public datasets with an imbalanced multiclass distribution. The grouping results are then assigned a label based on the original label reference using the pattern-matching concept on the first principal component through the PCA procedure. The labelling results are then used as external validation for the actual data labels to represent all classes. The validation results produce a confusion matrix used as the basis for the cross-sampling method. The dataset before cross-sampling and the results of cross-sampling were compared to the performance of the classification prediction accuracy using the F1-Score and Area Under Curve (AUC) measurements. The statistical hypothesis testing results show a significant difference in performance before and after the cross-sampling procedure. This difference is demonstrated by the accuracy of all the classification algorithms used, which increased significantly from the average performance value of 82.09% to 96.7%.
Deep Learning-Based Fish Classification with Data Augmentation

Accurate identification and recording of different fish species' distribution and abundance provide deeper insights into local marine ecosystems and aid in the formulation and implementation of effective conservation strategies. However, due to the influence of fish similarity and underwater optical characteristics, the identification of fish needs professional knowledge and experience. Therefore, it is necessary to develop an effective underwater fish species recognition system using artificial intelligence. In this study, we employed the YOLOv8 neural network architecture for model training and recognition. Additionally, various data augmentation techniques such as color channel adjustment, perspective transformation, image overlay, and flipping were used to improve the recognition accuracy for fish species with limited training data. The proposed model achieves a mean Average Precision (mAP) of 93.8% for 30 different fish species.
Fault image recognition method of industrial equipment based on GWO-GoogleNet and transfer learning

Aiming at the problem of how to recognize the difficulty of identifying industrial equipment faults in the field of industrial production, this paper proposes an image recognition method for industrial equipment faults based on GWO-GoogleNet and transfer learning. First, the original signals of different types of faults of industrial equipment are time-frequency converted into images to generate corresponding data sets. Secondly, the deep learning network with the same parameter level is selected for noise resistance test, and the GoogleNet network with the best relative noise resistance is selected. Then, with the final error of the network as the optimization goal, the parameters of batch sampling number and learning rate are optimized, and the optimized network framework is completed. Finally, based on this network framework, its trained parameters are transferred to the target domain, and a small amount of target domain data is used to fine-tune the network. The method is verified on the bearing dataset of Western Reserve University, and the results show that the method has good recognition ability on bearing fault images, and still has good fault detection ability under migration conditions.
Analysis and Compensation of Nonlinear Dynamics in Optical Fiber Transmission with the Optoelectronic Reservoir Computing

Optical fiber communication systems play an important role in broadband signal transmissions, where the nonlinear dynamics in the long-haul optical fiber transmissions greatly degrade the transmission performance and limit the fiber capacity and reach. This paper analyzes and compensates for the nonlinear dynamics in the long-haul optical fiber transmission with the delay-based optoelectronic reservoir computing (RC) scheme, which provides benefits like time-adaptive tracking, low-complexity and hardware implementation potentials. The generalization of the analysis and compensation schemes from the traditional amplitude-modulated PAM signal to the amplitude-phase-modulated QAM signal is achieved by signal preprocessing. In the numerical study, the proposed RC-based fiber dynamics compensation scheme, with fiber reach from 1400km to 3000km, shows considerable performance with the digital backpropagation scheme, which is always used as the benchmark for nonlinear compensation of long-haul fiber transmissions. The realization complexity is much reduced with the RC-based schemes, which has provided a new dawn for optics-based nonlinear analysis and compensations.
An Active Electric Field Energy Harvester For Overhead Lines

This paper analyzes the energy harvesting principle of the capacitive voltage-divider harvester. And introduces the concept of actively adjusting the equivalent impedance of the subsequent circuit through a full-bridge configuration to match the harvester’s impedance, thereby achieving maximum power transmission. Furthermore, a maximum power point tracking algorithm is proposed, which eliminates the need for voltage transformers and enhances the harvester’s usability. Additionally, a harvester capable of harvesting 2. 7W of power is designed. Simulation results validate that impedance matching between the harvester and the load can be achieved by actively adjusting the equivalent load resistance through the full-bridge configuration.
A Detection Method for Interturn Short-circuit Fault of Five-phase Surface Mounted PMSM

In this paper, the inductance parameters of five-phase PMSM without electromagnetic coupling between windings is discussed before and after the interturn short-circuit fault occurs. The mathematical model of the motor when the interturn short-circuit fault occurs is established. An online detection method for interturn short-circuit fault is proposed, which takes the product of short circuit turns ratio and short circuit current as the fault indicator. The fault indicator can be obtained by using differential voltage balance equation of stator windings. Various factors that may affect the fault indicator quantity are simulated and analyzed. Simulation results show that the proposed method is feasible.
Methodology of Synthesis of Tracking Neural Network Control Systems for Objects with Limiters

One of the significant issues in using artificial neural networks in closed-loop automatic control systems (ACS) is the lack of simple and effective algorithms for synthesizing such controllers. Another unsolved problem is to ensure the stability of the designed ACS in a certain region of the phase space. Therefore, this paper recommends the use of the direct Lyapunov method and the application of artificial neural networks for the approximation of Lyapunov functions. The joint training of the neurocontroller and the neural network Lyapunov function enables the synthesis of systems with verifiable stability guarantees. Using mixed integer programming, we can also ensure that stability conditions are satisfied in specified domains. Nevertheless, this approach entails synthesizing autonomous systems. In this paper, we investigate how this method can be extended for tracking control systems. An additional neural network is proposed to calculate the current equilibrium point while considering the input signal. Several nonlinearities frequently encountered in practice, such as limiters, are considered. The effectiveness of the proposed methodology is demonstrated through the synthesis of a tracking control system for a hydraulic drive, compared to a quasi-optimal controller in terms of speed.
The One-vs-Rest Method for a Multilabel Patent Classification Machine Learning Approach using a Regression Model

Redundancy in patent abstracts, that is, the repetition of an International Patent Classification tag or patent class information across documents, poses a challenge in patent classification, which is essential for efficiently managing the growing number of patent documents. This study aims to develop a model to analyze the weights and index values of summary sentences to improve the efficiency and accuracy of patent classification. This model can predict relevant patent data types. Utilizing logistic regression and the one-vs-rest method, this study focuses on the development of models to analyze the weights and index values of summary sentences, with the goal of enhancing the precision and efficiency of patent classification. The main contribution of this research is the creation of predictive models for new patent documents to streamline patent citations based on document type. This study also serves as a reference for evaluating Indonesian patent document classification models, thereby facilitating the patent evaluation process.
Statistical study of the H.266/VVC Low-Frequency Non-Separable Transform

The Versatile Video Coding (H.266/VVC) offers a promising enhancement in coding efficiency compared to the High Efficiency Video Coding (H.265/HEVC). However, this improvement comes at the cost of increased complexity in the encoder. To address this, a deeper understanding of the complexity of the H.266/VVC, particularly related to the new encoding tools, is crucial. In this paper, a statistical study has been conducted under the reference software VTM 16.2, with a focus on the Low-Frequency Non-Separable Transform (LFNST) to promote the most used transform matrices.