DNN-based Method Research Articles

Fault Monitoring and Analysis of Distributed New Energy Grids Using Simulation Data Models

Data analysis is essential for fault identification and detection in smart grids in order to maintain grid monitoring. Many DL algorithms have been developed recently for data analysis applications related to smart grids. To resolve these challenges, the study suggests a Deep Neural Network (DNN) for data-driven fault location detection and type of fault classification by exploiting the Modified Sand Cat Swarm Optimization (MSCSO) optimization. Here, the DNN is used to diagnose the faults and determine the position of the sites. Numerous synthetic field data sets derived from simulated models of different transmission line types are used for training and testing. The position and type of faults are predicted by the DNN classifier based on the fault signal features, in which the DNN weights are ideally tuned using a novel MSCSO optimization method that is the improved concept of Sand Cat Swarm Optimization (SCSO). Lastly, MATLAB/Simulink is used to implement the suggested DNN-based method for fault classification and its localization in transmission line. An intellectual IEEE 6-node network model is utilized to confirm the efficacy and reliability of these methods. The outcomes demonstrated its effectiveness in giving the system operator precise and detailed analytics for fault identification.

Shear-wave velocity prediction of tight reservoirs based on poroelasticity theory: A comparative study of deep neural network and rock physics model

The absence of shear-wave (S-wave) velocity in well log data limits the effective seismic characterization of lithology, petrophysical properties, and fluid distribution of tight reservoirs in the Chang 7 shale oil formations of Ordos Basin, west China. However, conventional rock physics models (RPMs), e.g., the Xu-White model, struggle to accurately obtain the key parameters such as pore aspect ratio in the process of S-wave velocity prediction. This work performs a comparative study of S-wave velocity prediction based on deep neural network (DNN) and RPM for such reservoirs. By employing the poroelasticity equations which are capable of describing elastic wave propagation in the complex reservoirs saturated with viscous fluid, the relation between elastic and petrophysical properties is established with a DNN-based method. This approach quantifies the connection between P-/S-wave velocities and rock properties. On the other hand, we reformulate the traditional Xu-White model by incorporating the decoupled Kuster-Toksöz (K-T) model and the poroelasticity equations with viscoelastic fluid in the modeling process. The variation of pore aspect ratio with depth is also considered to characterize the complex pore structures, which is appropriate for improving the accuracy of prediction. Log data from the three wells are considered for verification, of which results show that the S-wave velocity prediction methods help improve the final result. Aided by the DNN-based method, the prediction exhibits a better performance if the training data is sufficient. In particular, provided a small amount of measured S-wave data (even ten data points), the trained DNN model can also provide reasonable predictions.

DNN-based algorithm for joint SIC ordering and power allocation in downlink NOMA-enabled heterogeneous networks

In the heterogeneous network (HetNet) employing downlink non-orthogonal multiple access (NOMA), we focus on the non-convex optimization problem to optimize the spectral efficiency (SE) while the users satisfy the quality-of-service (QoS) requirement. In the previous work, the optimal joint successive interference cancellation and power allocation (JSPA) algorithm for maximizing SE is proposed to solve the mixed-integer non-linear programming (MINLP) problem in NOMA-enabled HetNet. However, the optimal solution requires exponential complexity by the number of base stations (BSs). Therefore, we present a deep neural network (DNN)-based algorithm for JSPA to reduce the complexity. In particular, to deal with the MINLP-based JSPA problem, we reformulate it into an equivalently simple problem that optimizes only the power consumption of BSs. Then, we introduce the unsupervised DNN-based method for JSPA to handle the simplified problem. The presented scheme yields improved SE and outage performance compared with traditional DNN-based methods. Additionally, we propose a user selection scheme with low complexity to enhance the SE of the proposed DNN-based power allocation. Through simulations, we illustrate that the suggested DNN-based scheme can attain SE performance similar to that of the optimal scheme.

StresSense: Real-Time detection of stress-displaying behaviors

BackgroundWrist-worn gadgets like smartphones are ideal for unobtrusively gathering user data, in various fields such as health and fitness monitoring, communication, and productivity enhancement. They seamlessly integrate into users' daily lives, providing valuable insights and features without the need for constant attention or disruption. In sensitive domains like mental health, these devices provide user-friendly, privacy-protected means of diagnosis and treatment, offering a secure and cost-effective avenue for seeking help. ObjectivesThis study addresses the limitations of traditional mental health assessment techniques, such as intrusive sensing and subjective self-reporting, by harnessing the unobtrusive data collection capabilities of smartphones. Equipped with accelerometers and other sensors, these devices offer a novel approach to mental health research. Our objective was to develop methods for real-time detection of stress and boredom behavior markers using smart devices and machine learning algorithms. MethodologyBy leveraging data from accelerometers (A), gyroscopes (G), and magnetometers (M), we compiled a dataset indicative of stress-related behaviors and trained various machine-learning models for predictive accuracy. The methodology involved collecting data from motion sensors (A, G, and M) on the dominant arm's wrist-worn smartphone, followed by data preprocessing, transformation from time series format, and training a Deep Neural Network (DNN) model for activity recognition. FindingsRemarkably, the DNN achieved an accuracy of 93.50% on test data, outperforming traditional and ensemble machine learning methods across different window sizes, and demonstrated real-time accuracy of 77.78%, validating its practical application. ConclusionIn conclusion, this research presents a novel dataset for detecting stress and boredom behaviors using smartphones, reducing reliance on costly devices and offering a more objective assessment. It also proposes a DNN-based method for wrist-worn devices to accurately identify complex activities associated with stress and boredom, with benefits in terms of privacy and user convenience. This advancement represents a significant contribution to the field of mental health research, providing a less intrusive and more user-friendly approach to monitoring mental well-being.

Fast Trajectory Generation with a Deep Neural Network for Hypersonic Entry Flight

Optimal entry flight of hypersonic vehicles requires achieving specific mission objectives under complex nonlinear flight dynamics constraints. The challenge lies in rapid generation of optimal or near-optimal flight trajectories with significant changes in the initial flight conditions during entry. Deep Neural Networks (DNNs) have shown the capability to capture the inherent nonlinear mapping between states and optimal actions in complex control problems. This paper focused on comprehensive investigation and evaluation of a DNN-based method for three-dimensional hypersonic entry flight trajectory generation. The network is designed using cross-validation to ensure its performance, enabling it to learn the mapping between flight states and optimal actions. Since the time-consuming training process is conducted offline, the trained neural network can generate a single optimal control command in about 0.5 milliseconds on a PC, facilitating onboard applications. With the advantages in mapping capability and calculating speed of DNNs, this method can rapidly generate control action commands based on real-time flight state information from the DNN model. Simulation results demonstrate that the proposed method maintains a high level of accuracy even in scenarios where the initial flight conditions (including altitude, velocity, and flight path angle) deviate from their nominal values, and it has certain generalization ability.

Deep transfer learning approach for digital circuits vulnerability analysis

Hardware Trojans (HT) are the most malicious components attacking modern integrated circuits (ICs). Information leakage, incorrect functionality, and overheating are the major effects of HT insertion. Due to the outsourcing of multi-stage digital circuit design and fabrication, HTs can be inserted by rogues at every stage. The types of HTs and insertion methods evolve continuously and their detection becomes more complex. Consequently, designing integrated circuits robust to HT insertion seems efficient. A prerequisite for such a strategy is the evaluation of integrated circuit vulnerability to HT insertion. There are several factors involved in the vulnerability of fabricated integrated circuits such as the distribution of white spaces in the IC layout, the ratio of unutilized routing resources, the amount of activity of the circuit’s nodes, the geometrical properties of the circuit’s gates, the delay of various paths, and etc. The interrelation of these diverse factors makes IC vulnerability modeling a challenging problem. In this paper, a deep neural network (DNN) based approach is developed to combine all the effective factors in IC vulnerability analysis. Firstly, we generate a comprehensive dataset containing various types and sizes of hardware Trojans inserted into benchmark circuits. The benchmark circuits used in this study are ISCAS 85, ISCAS 89, and ITC 99. Then using the transfer learning concept seven state-of-the-art deep convolutional neural networks are trained and verified using the constructed dataset to classify every integrated circuit into three vulnerability levels (low, moderate, and highly vulnerable classes). Simulation results show that our proposed method achieves almost 95 % accuracy in the most successful case (VGG16). The lowest accuracy belongs to Inception V3 (∼87.65 %) which is much better than the most successful previous studies (72 %). This is mainly because the DNN-based method handles shortcomings (such as incomplete modeling of vulnerability-related factors) in the previous state-of-the-art methods efficiently.

Deliberate premarket underpricing: New evidence on IPO pricing using machine learning

We propose a nonlinear approach based on stochastic frontier and Deep Neural Networks (DNN) to estimate the pricing efficiency and the level of premarket inefficiencies for IPOs, using information available before the IPO day and without any distributional assumptions. We apply the proposed approach in the US IPO market to estimate deliberate (premarket) underpricing and find that the IPO offer prices are about 12.43% less than the estimated maximum offer prices on average. We further show that only a few determinants of the value of firms impact the pricing and deliberate underpricing of IPOs. Negative net income and EBITDA play the most important roles in determining the IPO maximum offer price, among various pricing variables. Proceeds followed by underwriter reputation negatively impact the premarket underpricing, and the IPO market activity, measured by the number of new issues, is the most important market cycle proxy that influences the premarket underpricing. We show that aftermarket mispricing is attributed more to offer size and underwriter reputation. The proposed DNN-based method is an easy to implement approach and can be used by academics and practitioners to estimate maximum offer prices and disentangle initial returns into deliberate premarket underpricing and aftermarket mispricing.

Tree Segmentation and Parameter Measurement from Point Clouds Using Deep and Handcrafted Features

Accurate measurement of the geometric parameters of trees is a vital part of forest inventory in forestry management. Aerial and terrestrial Light Detection and Ranging (LiDAR) sensors are currently used in forest inventory as an effective and efficient means of forest data collection. Many recent approaches to processing and interpreting this data make use of supervised machine learning algorithms such as Deep Neural Networks (DNNs) due to their advantages in accuracy, robustness and the ability to adapt to new data and environments. In this paper, we develop new approaches to deep-learning-based forest point cloud analysis that address key issues in real applications in forests. Firstly, we develop a point cloud segmentation framework that identifies tree stem points in individual trees and is designed to improve performance when labelled training data are limited. To improve point cloud representation learning, we propose a handcrafted point cloud feature for semantic segmentation which plays a complementary role with DNNs in semantics extraction. Our handcrafted feature can be integrated with DNNs to improve segmentation performance. Additionally, we combine this feature with a semi-supervised and cross-dataset training process to effectively leverage unlabelled point cloud data during training. Secondly, we develop a supervised machine learning framework based on Recurrent Neural Networks (RNNs) that directly estimates the geometric parameters of individual tree stems (via a stacked cylinder model) from point clouds in a data-driven process, without the need for a separate procedure for model-fitting on points. The use of a one-stage deep learning algorithm for this task makes the process easily adaptable to new environments and datasets. To evaluate our methods for both the segmentation and parameter estimation tasks, we use four real-world datasets of different tree species collected using aerial and terrestrial LiDAR. For the segmentation task, we extensively evaluate our method on the three different settings of supervised, semi-supervised, and cross-dataset learning, and the experimental results indicate that both our handcrafted point cloud feature and our semi-supervised and cross-dataset learning framework can significantly improve tree segmentation performance under all three settings. For the tree parameter estimation task, our DNN-based method performs comparably to well-established traditional methods and opens up new avenues for DNN-based tree parameter estimation.

Online Phase Reconstruction via DNN-Based Phase Differences Estimation

This paper presents a two-stage online phase reconstruction framework using causal deep neural networks (DNNs). Phase reconstruction is a task of recovering phase of the short-time Fourier transform (STFT) coefficients only from the corresponding magnitude. However, phase is sensitive to waveform shifts and not easy to estimate from the magnitude even with a DNN. To overcome this problem, we propose to use DNNs for estimating differences of phase between adjacent time-frequency bins. We show that convolutional neural networks are suitable for phase difference estimation, according to the theoretical relation between partial derivatives of STFT phase and magnitude. The estimated phase differences are used for reconstructing phase by solving a weighted least squares problem in a frame-by-frame manner. In contrast to existing DNN-based phase reconstruction methods, the proposed framework is causal and does not require any iterative procedure. The experiments showed that the proposed method outperforms existing online methods and a DNN-based method for phase reconstruction.

Deep learning for hetero–homo conversion in channel-domain for phase aberration correction in ultrasound imaging

Echo imaging in ultrasound computed tomography (USCT) using the synthetic aperture technique is performed with the assumption that the speed of sound is constant in the system. However, tissue heterogeneity causes a mismatch between the predicted arrival time and the actual arrival time of the echo signal, which will result in phase aberration, leading to the quality degradation of the reconstructed B-mode image. The conventional correction methods that use the correlation of each different channel require the presence of strong point scatterers and involve the problem of local solutions due to excessive correction. In this study, we propose a novel approach to correcting the signal distortion due to sound speed heterogeneity using a deep neural network (DNN). The DNN was trained to convert the distorted radio frequency (RF) inputs for the heterogeneous medium to the distortion-free RF outputs for the homogeneous medium. The network with U-net architecture using ResNet-34 as a backbone was trained using the hetero–homo corresponding channel-domain RF data generated via numerical simulations. The trained network performed phase aberration correction in the channel-domain RF, with the B-mode images reconstructed with the corrected RF demonstrating a higher contrast and an improved resolution compared with uncorrected cases. It was also demonstrated that the DNN model is robust to both varied reflection intensities and varied sound speed heterogeneities. The successful results demonstrated that the proposed DNN-based method is effective for phase aberration correction in US imaging.

An unsupervised convolutional neural network method for estimation of intravoxel incoherent motion parameters

Objective. Intravoxel incoherent motion (IVIM) imaging obtained by fitting a biexponential model to multiple b-value diffusion-weighted magnetic resonance imaging (DW-MRI) has been shown to be a promising tool for different clinical applications. Recently, several deep neural network (DNN) methods were proposed to generate IVIM imaging. Approach. In this study, we proposed an unsupervised convolutional neural network (CNN) method for estimation of IVIM parameters. We used both simulated and real abdominal DW-MRI data to evaluate the performance of the proposed CNN-based method, and compared the results with those obtained from a non-linear least-squares fit (TRR, trust-region reflective algorithm) and a feed-forward backward-propagation DNN-based method. Main results. The simulation results showed that both the DNN- and CNN-based methods had lower coefficients of variation than the TRR method, but the CNN-based method provided more accurate parameter estimates. The results obtained from real DW-MRI data showed that the TRR method produced many biased IVIM parameter estimates that hit the upper and lower parameter bounds. In contrast, both the DNN- and CNN-based methods yielded less biased IVIM parameter estimates. Overall, the perfusion fraction and diffusion coefficient obtained from the DNN- and CNN-based methods were close to literature values. However, compared with the CNN-based method, both the TRR and DNN-based methods tended to yield increased pseudodiffusion coefficients (55%–180%). Significance. Our preliminary results suggest that it is feasible to estimate IVIM parameters using CNN.

A Deep Neural Network-Based Method for Building a Professional Farmer Training Model

In this paper, we analyzed the deep neural network (DNN) model and proposed a DNN-based method to build the training model for professional farmers. This paper aims to construct the vocational farmer cultivation model and seek a better path for transforming farmers into new experienced farmers by analyzing each training body involved in the cultivation of vocational farmers and studying their respective problems and reasons. The conceptual and logical structures of the system database were designed, and MySQL was selected for database implementation to complete the information of professional farmers. The system network topology and logical architecture are created, and the functions of view, control, business logic and data access layers are divided. This paper combines the DNN with the vocational farmer training enhancement decision tree. The experimental results of this model are most intuitive and accurate. This paper reconstructs the neural network model using the global average pooling layer to better model the vocational farmer training model to replace the fully connected layer in the original convolutional neural network. At the same time, to make the network model produce a lower probability of overfitting, a dropout layer is added to the layer after the fully connected layer to improve the efficiency of the neural network further to enhance vocational farmer training. The experimental content of this paper provides a new research direction for the estimation of vocational farmer cultivation. The above modeling is used to improve the vocational farmer cultivation model and accelerate the process of vocational farmer cultivation.

Predicting gas-bearing distribution using DNN based on multi-component seismic data: Quality evaluation using structural and fracture factors

The tight-fractured gas reservoir of the Upper Triassic Xujiahe Formation in the Western Sichuan Depression has low porosity and permeability. This study presents a DNN-based method for identifying gas-bearing strata in tight sandstone. First, multi-component composite seismic attributes are obtained. The strong nonlinear relationships between multi-component composite attributes and gas-bearing reservoirs can be constrained through a DNN. Therefore, we identify and predict the gas-bearing strata using a DNN. Then, sample data are fed into the DNN for training and testing. After optimized network parameters are determined by the performance curves and empirical formulas, the best deep learning gas-bearing prediction model is determined. The composite seismic attributes can then be fed into the model to extrapolate the hydrocarbon-bearing characteristics from known drilling areas to the entire region for predicting the gas reservoir distribution. Finally, we assess the proposed method in terms of the structure and fracture characteristics and predict favorable exploration areas for identifying gas reservoirs.

Data-Driven Method for Nonlinear Optical Fiber Channel Modeling Based on Deep Neural Network

Recently, data-driven fiber channel modeling methods based on deep learning have been proposed in optical communication system simulations. We investigate a new data-driven method based on the deep neural network (DNN) to model the nonlinear fiber channel with the characteristics of attenuation, chromatic dispersion, amplified spontaneous emission noise, self-phase modulation (SPM), and cross-phase modulation (XPM). Demonstration in multiple dimensions, including constellations, optical waveforms, spectra, and the normalized mean square error, shows that DNN can approach the transfer function of the fiber channel accurately. Additionally, the DNN shows good generalization for modulation formats and wavelength schemes. Besides, the time complexity of DNN-based method for modeling nonlinear fiber channel is reduced significantly (96.5%) compared to the conventional model-driven method, which is based on the split-step Fourier method. This work demonstrates that the DNN can model accurately the nonlinear fiber channel that takes account of both SPM and XPM. Therefore, it can contribute to the application of data-driven methods in modern optical communication system simulations and designs.

Data-informed deep optimization.

Motivated by the impressive success of deep learning in a wide range of scientific and industrial applications, we explore in this work the application of deep learning into a specific class of optimization problems lacking explicit formulas for both objective function and constraints. Such optimization problems exist in many design problems, e.g., rotor profile design, in which objective and constraint values are available only through experiment or simulation. They are especially challenging when design parameters are high-dimensional due to the curse of dimensionality. In this work, we propose a data-informed deep optimization (DiDo) approach emphasizing on the adaptive fitting of the the feasible region as follows. First, we propose a deep neural network (DNN) based adaptive fitting approach to learn an accurate DNN classifier of the feasible region. Second, we use the DNN classifier to efficiently sample feasible points and train a DNN surrogate of the objective function. Finally, we find optimal points of the DNN surrogate optimization problem by gradient descent. To demonstrate the effectiveness of our DiDo approach, we consider a practical design case in industry, in which our approach yields good solutions using limited size of training data. We further use a 100-dimension toy example to show the effectiveness of our approach for higher dimensional problems. Our results indicate that, by properly dealing with the difficulty in fitting the feasible region, a DNN-based method like our DiDo approach is flexible and promising for solving high-dimensional design problems with implicit objective and constraints.

Efficient and accurate atomistic modeling of dopant migration using deep neural network

This paper proposes an efficient and accurate method to model atomistic dopant migration, by leveraging the emerging deep neural network (DNN). By performing nudged elastic band (NEB) simulations of three prototype systems (B-doped Si, Li-doped Si, and C-doped GaN), it is shown that the proposed DNN-based method runs about 104-105 times faster than the widely-used atomistic dopant migration modeling method based on density functional theory (DFT), meanwhile keeping DFT-level high accuracy. Active learning is used to reduce training set redundancy, and the DNN model is further optimized for more accurate NEB calculation. As a result, the dopant atomic position in saddle-point and the dopant migration energy barrier in the migration energy path (MEP) predicted by the proposed DNN-based NEB deviate merely about 10−2 Å and 10−2 eV, respectively, from those predicted by the established DFT-based NEB. Given its efficiency and accuracy, the proposed DNN-based method might be useful to develop future-generation atomic-scale technology computer-aided design (TCAD) tools.

DNN With Similarity Constraint for GEO SA-BSAR Moving Target Imaging

A GEOsynchronous Spaceborne-Airborne Bistatic Synthetic Aperture Radar (GEO SA-BSAR) system has been proved to be a significant tool for moving targets monitoring. Due to the special geometry model of the GEO SA-BSAR system, there is a complex relative movement between the moving target and the bistatic radar, leading to an additional phase modulation of the echo and further, causing moving targets to be smeared in the SAR image. Recently, Deep Neural Network (DNN) shows great potential in rapid image recovery. However, most image recovery methods based on DNN concentrate on the whole image, which limits the imaging performance of sparse targets. In this letter, we propose a DNN framework with similarity constraints for GEO SA-BSAR moving target imaging. This DNN-based method optimizes the cosine similarity of azimuth signals between the ground-truth image and the predicted image in the loss function to recover the azimuth position and focusing characteristics of the sparse targets. Extensive experimental results prove that the proposed model can quickly obtain GEO SA-BSAR moving target images with small training datasets compared with some counterparts.

Uncalibrated stereo vision with deep learning for 6-DOF pose estimation for a robot arm system

This paper proposes a novel method for six degrees of freedom pose estimation of objects for the application of robot arm pick and place. It is based on the use of a stereo vision system, which does not require calibration. Using both cameras, four corner points of the object are detected. A deep-neural-network (DNN) is trained for the prediction of the 6 DOF pose of the object from the four detected corner points’ coordinates in each image of both cameras. The stereo vision used is a low-end vision system placed in a custom-made setup. Before the training phase of the DNN, the robot is set to auto collect data in a predefined workspace. This workspace is defined dependently on the spatial feasibility of the robot arm and the shared field of view of the stereo vision system. The collected data represent images of a 2D marker attached to the robot arm gripper. The 2D marker is used for data collection to ease the detection of the four corner points. The proposed method succeeds in estimating the six degrees of freedom pose of the object, without the need for the determination of neither the intrinsic nor the extrinsic parameters of the stereo vision system. The optimum design of the proposed DNN is obtained after comparing different activation functions and optimizers associated with the DNN.The proposed uncalibrated DNN-based method performance is compared to that of the traditional calibration-based method. In the calibration-based method, the rotational matrix relating the robot coordinates to the stereo vision coordinates is computed using two approaches. The first approach uses Singular Value Decomposition (SVD) while the second approach uses a novel proposed modification of particle swarm optimization (PSO) called Hyper particle Scouts optimization (HPSO). HPSO outperforms other metaheuristic optimization algorithms such as PSO and genetic algorithm (GA).Exhaustive tests are performed, and the proposed DNN-based method is shown to outperform all tested alternatives.

Deep Gaussian process based multi-speaker speech synthesis with latent speaker representation

This paper proposes deep Gaussian process (DGP)-based frameworks for multi-speaker speech synthesis and speaker representation learning. A DGP has a deep architecture of Bayesian kernel regression, and it has been reported that DGP-based single speaker speech synthesis outperforms deep neural network (DNN)-based ones in the framework of statistical parametric speech synthesis. By extending this method to multiple speakers, it is expected that higher speech quality can be achieved with a smaller number of training utterances from each speaker. To apply DGPs to multi-speaker speech synthesis, we propose two methods: one using DGP with one-hot speaker codes, and the other using a deep Gaussian process latent variable model (DGPLVM). The DGP with one-hot speaker codes uses additional GP layers to transform speaker codes into latent speaker representations. The DGPLVM directly models the distribution of latent speaker representations and learns it jointly with acoustic model parameters. In this method, acoustic speaker similarity is expressed in terms of the similarity of the speaker representations, and thus, the voices of similar speakers are efficiently modeled. We experimentally evaluated the performance of the proposed methods in comparison with those of conventional DNN and variational autoencoder (VAE)-based frameworks, in terms of acoustic feature distortion and subjective speech quality. The experimental results demonstrate that (1) the proposed DGP-based and DGPLVM-based methods improve subjective speech quality compared with a feed-forward DNN-based method, and (2) even when the amount of training data for target speakers is limited, the DGPLVM-based method outperforms other methods, including the VAE-based one. Additionally, (3) by using a speaker representation randomly sampled from the learned speaker space, the DGPLVM-based method can generate voices of non-existent speakers.

Discussion of Training Samples-Oreinted Sampling Methods for Data-driven Power Systems Analysis

Data-driven methods such as deep neural networks (DNNs) and Gaussian processes (GPs), have been a promising way to achieve the balance of the calculation accuracy and efficiency in power system analysis. For instance, studies are utilizing the DNN-based method to achieve the fast and accurate calculation of probabilistic power flow. These methods rely on the sample data for training, which is mainly obtained by sampling. However, there is still no guidance on how to select the suitable sampling method to effectively generate the representative training samples. To address this problem, this paper explores the impact of different sampling methods on data-driven methods through theoretical analysis and simulation. Then some reasonable conclusions are drawn in this paper: i) Random sampling (RS) is suitable for data-driven methods that require large-scale training samples. ii) Latin hypercube sampling (LHS) is suitable for data-driven methods that only work well under small-scale training samples. iii) The assumption that the training samples generated by uniform sampling (US), which covers all sampling intervals, could not lead to good performance. Finally, simulations are implemented on the IEEE 39-bus system and the results demonstrate the effectiveness of the proposed conclusions