Miracle-dz/UniFAR-FTUs huggingface dataset:
Anomalous Current Spikes in the Locally Corrected Nystrom Discretization of Volume Integral Equations

A locally corrected Nystrom (LCN) discretization of a magnetostatic volume integral equation is presented. Anomalous current spikes are observed to occur when the underlying mesh uses tetrahedral cells. The mechanism for the anomalous current spikes is discussed, and mitigation of the current spikes through use of an LCN-to-Moment Method conversion is investigated. Results are presented validating that the LCN-to-Moment Method suppresses the anomalous current spikes.
Doppler Superpulse Processing for Improved Tomographic Characterization of Space Objects

The tomographic perspective of 1D Doppler profiles formed from Range Time Intensity data enables 2D noncoherent imaging of rotating objects over wide aspect diversity. Tomographic reconstruction using these Doppler profiles obtained over a full rotation is known as Doppler Tomography. Doppler Tomography requires the target to be fully unresolved in range (occupy a single range bin). For large bandwidth regimes, the target is well-resolved in range and extends over multiple range bins. In making a single choice of range-bin for forming 1D Doppler profiles for tomographic reconstruction, Doppler tomography does not make use of all available bandwidth in the radar data and fails to address range migration. In this work, we propose Doppler superpulse processing, a strategy for fusing together DTIs formed across all range bins to create an enhanced Doppler versus time characterization data product. Doppler profiles obtained using superpulse processing preserve scatterers that are migrating across range due to the object's rotation. Our experiments with real compact range satellite radar data highlight the superior structural image quality of our Doppler superpulse tomographic reconstructions over those of conventional Doppler tomography.
Why GloVe Shows Negative Effects in Malware Classification

The past decades witness the development of various Machine Learning (ML) models for malware classification. Semantic representation is a crucial basis for these classifiers. This paper aims to assess the effect of semantic representation methods on malware classifier performance. Two commonly-used semantic representation methods including N-gram and GloVe. We utilize diverse ML classifiers to conduct comparative experiments to analyze the capability of N-gram, GloVe and image-based methods for malware classification. We also analyze deeply the reason why the GloVe can produce negative effects on malware static analysis.
An Early diagnosis of diabetic retinopathy using ConvNeXt

Diabetic retinopathy (DR), a frequent complication of diabetes mellitus, is the prime reason for vision loss worldwide. The current method for identifying diabetic retinopathy is a time-consuming and manual process, requiring trained medical professionals to examine digital color fundus photographs of the retina and look for the presence of lesions associated with the disease’s vascular abnormalities. Despite technological advancements, accurately identifying the early stages of diabetic retinopathy remains a challenge. To address this issue, different computer vision-based approaches have been introduced to detect DR and classify various stages from retina images. However, these techniques have limited success, particularly in accurately identifying the early stages of the disease. In this study, we employed a publicly available Kaggle Eyepac dataset to train our model for the detection of DR which is known to have five distinct stages, namely, proliferate DR, severe DR, moderate DR, mild DR, and no DR. The overall accuracy, precision, recall, Fl-score, and AUC score obtained by our proposed model were 88.31± 0.23,89.15± 0.19,90.25± 0.41,89.69± 0.25, and 87.38± 1.45 respectively. The performance of our model is improved by employing a pre-processing technique known as Contrast Limited Adaptive Histogram Equalization (CLAHE) followed by a state-of-the-art deep learning model known as ConvNeXt to achieve promising results.
A Methodology to Diagnose Transformer Faults Based on Principal Components Analysis and Artificial Neural Network

A methodology, combining the principal components analysis (PCA) and the artificial neural network (ANN), to diagnose transformer faults based on dissolved gas analysis (DGA) data is proposed. The main features extracted from DGA data by the PCA are used as the input of the ANN to train and test the classifier. The proposed methodology is tested with practical transformer fault data. The test results show that a higher diagnosis accuracy can be achieved by using the main features extracted by the PCA as compared to that by the original features or the optimal feature combinations obtained by feature selection methods.
Approaches towards Fake News Detection using Machine Learning and Deep Learning

Fake news evolving around us for a very long time. The gradual growth of social media platforms has provided us with an easily accessible and publishable news platform in front of the audience that news may be true or False. The spreading of fake news increased as compared to ancient times. Nowadays Fake news detection become a tough challenge for Both Natural language processing(NLP) and Machine Learning (ML) experts. For detecting fake news fact-checking is also very important. In this paper, we focus on the analysis of recently published papers in this domain and the analysis of different techniques for detecting fake news. Through this survey, we will get inside knowledge of the detection process of fake news using different natural language processing, machine learning, and Deep Learning Techniques.
Deep Facial Expression Recognition Using Transfer Learning and Fine-Tuning Techniques

This work proposes a Convolutional neural network (CNN)-based framework for estimating basic facial expressions, which achieves state-of-the-art results and runs in real-time on an android-based device to fulfill the target of intelligent human-computer interaction (HCI). A new CNN architecture is modeled using MobileNetV1 for the facial expression recognition task. CNN training often necessitates a large amount of data. However, most current facial expression datasets fall short of this requirement, making it difficult to train a deep CNN with insufficient data. Using a model pre-trained on ImageNet, and a supplementary dataset (such as FER-2013) in supervised two-stage transfer learning addresses the issue of small datasets (such as JAFFE and CK+) to a great extent. Furthermore, to improve the framework's performance, a center loss is used in conjunction with the softmax loss as a joint supervision signal for CNN training optimization. Validation is carried out on two publicly available datasets, JAFFE and CK+. The model is evaluated based on the average accuracy, precision, recall, F1-score, and confusion matrix. An evaluation baseline for the CK+ dataset includes eight scenarios. As a whole, the proposed method outperforms current state-of-the-art methods on both JAFFE and CK+.
Quick Search Algorithm of Finding Similar Sequence

Basing on the extended definition of time series distance, first presents a new algorithm of computing the time series distance on time domain. The time complexity of the algorithm is O(n×m). The algorithm can solve the problems brought by the data scaling and shifting on the Y axis. Then presents a new algorithm of computing the time series distance on frequency domain. The algorithm, with the time complexity of O(n×f c), is very effective, so it can be implemented online. The algorithm can also adaptive to the extended distance definition. This paper also gives and proves an important part of the new algorithm: a increment DFT algorithm on time series.
Improvement of Continuity Condition on the PEC-Dielectric Interfaces for the Solution of Volume-Surface Integral Equation

In the solution of the volume-surface integral equation (VSIE) by the method of moments (MoM), the continuity condition (CC) of electric flux is improved and enforced on the perfect electric conductor (PEC)-dielectric interfaces to reduce the volumetric unknowns. For the open PEC structures embedded within dielectric substrates, the proposed CC scheme can reduce the volumetric unknowns in a simple way. Efficiency and validity of the proposed CC-based VSIE are verified by reliable numerical experiments involving anisotropic materials.
High-index Resonator Supercavity with Different Background Contrast

Our detailed study aims to analyze the effect of background contrast on the excitation of supercavity modes in high index resonators (HIR) for microstrip-HIR systems. This goal is achieved by designing a model in the microwave range; a high contrast background permittivity is defined depending on the system geometry and resonant frequency. We show that the Q-factor increases up to 80% of the material quality factor for optimized contrast and background.
Attack-and-Defend Game Simulation Considering Strategic Bidding and Market Power Regulation

Generators’ strategic bidding behaviors in deregulated electricity markets have been generally studied. However, market operators' abilities to identify and manage market power is gradually improved. This paper proposed a simulation framework around market power simulation, identification. The dynamic balance of market power identifications and strategic bidding behaviors has been studied through simulations.
Stability Analysis of Grid Connected System under the Influence of Grid Impedance

New energy power generation widely relies on grid connected inverters to connect with the power grid, and the traditional double closed-loop control strategy has been widely used. Due to the fluctuation of power grid impedance, when it changes, it may cause resonance in the system, and the grid connected waveform is seriously distorted, which poses a threat to the stability of the system. In this paper, based on the equivalent model of grid connected inverter, the concept of harmonic resonance is proposed, and the difference and connection with traditional circuit resonance are analyzed. The simulation verifies that with the gradual increase of grid impedance, the stability of the system gradually decreases, the harmonic resonance gradually appears.
Design-based analysis of factorial designs embedded in probability samples

At national statistical institutes experiments embedded in ongoing sample surveys are frequently conducted, for example to test the effect of modifications in the survey process on the main parameter estimates of the survey, to quantify the effect of alternative survey implementations on these estimates, or to obtain insight into the various sources of non-sampling errors. A design-based analysis procedure for factorial completely randomized designs and factorial randomized block designs embedded in probability samples is proposed in this paper. Design-based Wald statistics are developed to test whether estimated population parameters, like means, totals and ratios of two population totals, that are observed under the different treatment combinations of the experiment are significantly different. The methods are illustrated with a real life application of an experiment embedded in the Dutch Labor Force Survey.
Wide-band Harmonic Coupling Characteristic Analysis of Thyristor Rectifier

The wide frequency range of harmonic components introduced by the extensive access of power electronic equipment to the power grid will induce the phenomenon of under-damped high-frequency oscillation and harmonic amplification in the increasingly power electronic power grid system, which threatens the safety and reliability operation of the system. With the continuous improvement of the response frequency band of the power electronic grid system, it is necessary to conduct a more comprehensive research and description of the broadband port characteristics of the thyristor rectifier, to provide technical support for the broadband interactive response analysis of power electronic devices. So this paper analyzes the relationship between the port voltage and current of the thyristor rectifier, and establishes a small-signal model based on the harmonic transfer function matrix (HTF). The model can accurately describe the frequency coupling characteristics in a wider frequency range. On this basis, an on-demand truncated HTF description method suitable for usage scenarios is proposed. At last, the accuracy of the proposed HTF modeling results is verified by simulation and experiments.
Transfer Learning-based Rich Feature Analysis on Leather Images for Species Prediction

Determining animal species in leather plays a significant role in biodiversity preservation and consumer protection. Unlike the traditional methods, the present work employs digital leather image analysis for automatic leather species prediction. However, it is challenging to derive species-distinct features from the complex leather image data. Hence, this paper proposes transfer learning-based rich feature analysis. In addition, it proposed to use Generative Adversarial Network (GAN) to pre-process the leather images with plausible pore pixels. The combination of adaptive learning in GAN and transfer learning led to the extraction of distinguishable features for efficient classification. Finally, it trains a support vector machine (SVM) for the classification of rich features extracted from AlexNet, VGG16, GoogLeNet, and ResNetl8, respectively. The comparative study affirms ResNetl8, with 99.58% prediction accuracy, as an appropriate choice for rich feature extraction. It elevated leather species prediction over the existing hand-crafted feature analysis. Thus, the proposed work adaptively learned the diverse behavior of leather images for accurate species prediction.
Multi Spectral Feature Extraction to Improve Lung Sound Classification using CNN

The principal method for screening and diagnosing lung disorders is auscultation of respiratory sounds. In the medical field, automated auscultation is a new research field that helps with faster disease diagnosis. The task of automated auscultation is to classify the respiratory sound, which requires a dataset to build a classification model. Deep learning networks are a popular method for producing such classification models, which require a large number of samples to train. Unfortunately, even the largest publicly accessible respiratory sound dataset (ICBHI 2017) of 5.5 hours total length contains only 6898 respiratory cycles, which is still insufficient for training deep learning networks. In this research work, data augmentation and smart padding techniques are utilised to account for the ICBHI dataset’s class imbalance. Further, we have suggested to extract several features from the audio signal, such as MFCC, Chroma STFT, Mel-Spectrogram, Spectral Contrast, and Tonnetz, for better representation of the samples in a class. Further, we have obtained the statistical features such as mean and standard deviation for developing the classification model. We have utilised a deep learning-based CNN architecture for the classification of the learned features. The proposed method achieved a score of 74.08% for a 60-40 train-test split and 75.04% for an 80-20 train-test split, outperforming all state-of-the-art lung sound classification methods.
Investigating Manifold Learning Algorithms Based on Magnification Factors and Principal Spread Directions

As a new unsupervised learning technique, manifold learning has captured the attention of many researchers in the field of machine learning and cognitive sciences. The major algorithms include Isometric mapping (ISOMAP) and Locally Linear Embedding (LLE). The approaches can be used for discovering the intrinsic dimensions of nonlinear high-dimensional data effectively and aim researchers to analyze the data better. How to quantitatively analyze the relationship between the intrinsic dimensions and the observation space, however, has fewer reports. And thus further works in manifold learning may have suffered some difficulties. The paper focuses on two kinds of manifold learning algorithms (ISOMAP, LLE), and discusses magnification factors and principal spread directions from the observation space to the intrinsic low-dimensional space. Also the corresponding algorithm is proposed. Experiments show the effectiveness and advantages of the research.
Optimal Bidding Strategy for Combined-Cycle Unit in Day-ahead Energy and Reserve Markets

In this paper, a bidding strategy model is established to analyze the bidding behavior of the combined-cycle (CC) unit in the day-ahead energy and reserve electricity markets. The CC unit can flexibly change its operation modes, take advantage of the mode transfer characteristics, and adjust the output of the CC unit to maximize its profit. Case studies are given to illustrate the effectiveness of the method.
Solar Powered Sensors for Efficient Routing without Power Constraints

This paper proposes an EH-WSN multi-hop (MH) cluster routing protocol (Energy-balanced MH cluster routing, EBMCR) to obtain solar energy, to achieve network energy balance during intra-cluster transmission and inter-cluster transmission. The main contributions of this paper are as the protocol balances the energy consumption (EC) of cluster head (C-H)nodes in different units by dividing the network area, calculates the number of nodes in each unit according to mathematical derivation, and minimizes the total EC of Tdata, improve the HEED C-H selection method, select the node with high energy distribution and neighbor distribution weight in the unit as the C-H, and effectively balance the EC of the nodes in the cluster, based on the PSO algorithm, the inter-cluster routing strategy is designed to improve the energy efficiency of Tdata, ensure the energy balance of nodes on the transmission path, and avoid nodes with lower energy being selected as routing nodes.
Coordinated Scheduling of Distribution Network and Virtual Power Plants Considering Controllable Thermal Loads and Distributed Photovoltaics

With the transformation of clean heating and the construction of distributed photovoltaics (PVs), a large number of virtual power plants (VPPs) have appeared in the distribution network (DN). Moreover, the strong randomness and volatility of distributed PVs also bring many challenges to the operation of the power system. Considering the increasing proportion of controllable thermal loads (TLs) in the power grids, this paper proposes a coordinated optimization method for the DN and VPPs considering these flexible resources. Finally, case studies are carried out on the DN69-VPP4 system, verifying that the proposed method can effectively improve the power grid's operation level on the premise of ensuring users' comfort.
MC-NET: Spectral-Spatial Feature Reduction for Hyperspectral Image Classification with Optimized Technique Series

The use of hyperspectral images (HSI) in a variety of practical applications has become widespread. High correlations between and among classes, the curse of dimensionality, and overfitting have drawn attention. Deep learning-based algorithms with dimensionality reduction methods have been proposed as a feasible alternative for hyperspectral image analysis owing to their effective feature extraction and high overall performance. Principal component analysis (PCA) and Sparse PCA (SPCA) are frequently employed in the fields of dimensionality reduction. Since principal component analysis (PCA) is hard to interpret because each principal component is a linear mixture of the original variables and SPCA cannot select the most important features. Dimensionality reduction via SPCA-MIFS (mutual information feature selection) is utilized to address the problem. The 2D and 3D Convolutional Neural Network (CNN) methods provide the backbone of many of the deep learning combination techniques. A fused 3D-2D CNN makes the model less complicated than a 3D-CNN alone, and it can also work well in noisy environments and when there aren't many training samples. Overfitting is addressed and classification accuracy is enhanced by using several optimization techniques. To evaluate the efficacy of this proposed strategy performance results compared highly reputed models with the less numbers of characteristics.
Improved SHGM Robust State Estimation for Power System Considering Complementary Non-smooth Constraints

The state estimation accuracy will be greatly affected when the measurement gross error exists. Schweppe-type generalized M-estimator with Huber psi-function (SHGM) can not completely eliminate the influence of bad data which exists in leverage point. On-load tap changer (OLTC) model has non-smooth characteristics such as limits, which will bring non-smooth complementary constraints. The existing SHGM robust state estimation is limited to deal with complementary constraints. To solve the above problems, this paper proposes an improved SHGM model considering complementary non-smooth constraints. In order to deal with the limits constraints introduced by the voltage control strategy of OLTC, the Fischer-Burmeister (FB) smoothing function is introduced to convert the complementary constraints into equality constraints, which improves the processing ability of SHGM state estimation for non-smooth characteristics. The correctness and practicability of the improved SHGM model are verified by 3-bus and IEEE 18-bus system. The case studies shows that considering the detailed control characteristics of OLTC and introducing FB smoothing function to deal with complementary constraints can improve the ability of SHGM robust state estimation to deal with bad data.
A NOVEL DYNAMIC CLUSTERING ALGORITHM AND ITS APPLICATION IN FUZZY MODELING FOR THERMAL PROCESSES

A novel dynamic evolutionary clustering algorithm(DECA) is proposed in this paper to overcome the shortcomings of fuzzy modeling method based on general clustering algorithms that fuzzy rule number should be determined beforehand. DECA searches for the optimal cluster number by using the improved genetic techniques to optimize string lengths of chromosomes; at the same time, the convergence of cluster center parameters is expedited with the help of Fuzzy C-Means(FCM) algorithm. Moreover, by introducing memory function and vaccine inoculation mechanism of immune system, DECA can converge to the optimal solution rapidly and stably. The proper fuzzy rule number and exact premise parameters are obtained simultaneously when using this efficient DECA to identify fuzzy models. The effectiveness of the proposed fuzzy modeling method based on DECA is demonstrated by simulation examples, and the accurate nonlinear fuzzy models can be obtained when the method is applied to the thermal processes.
Research on Economic Operation of Distribution Network Considering Energy Storage and Electric Vehicle Charging Station

The penetration of electric vehicles and energy storage devices in the power system is increasing, and they can participate in the optimal dispatch of the distribution network as adjustable resources. Firstly, based on the tidal model, energy storage operation model and electric vehicle cluster charging station model, a distribution network economic dispatching model is established, with minimization of network loss and minimization of charging cost of energy storage and charging stations as the day-ahead dispatching objectives. Secondly, a distributed solution scheme based on alternating direction multiplication algorithm with master-slave structure is designed for this optimization model to give full play to the autonomous coordination of resources within the distribution network. Finally, the simulation shows that the participation of energy storage and charging stations in the distribution network dispatch is beneficial to the energy storage operator and the distribution network in both directions, and the distributed solution scheme based on alternating direction multiplication algorithm is effective.