Actual Data Sets Research Articles

A multilayer network model of interaction between rumor propagation and media influence.

Rumors spread among the crowd have an impact on media influence, while media influence also has an impact on rumor dissemination. This article constructs a two-layer rumor media interaction network model, in which the rumors spread in the crowd are described using the susceptibility-apathy-propagation-recovery model, and the media influence is described using the corresponding flow model. The rationality of the model is studied, and then a detailed analysis of the model is conducted. In the simulation section, we undertake a sensitivity analysis of the crucial parameters within our model, focusing particularly on their impact on the basic reproduction number. According to data simulation analysis, the following conclusion can be drawn: First, when the media unilaterally influences the crowd and does not accept feedback from the crowd, the influence of the media will decrease to zero over time, which has a negative effect on the spread of rumors among the crowd (the degree of rumor dissemination decreases). Second, when the media does not affect the audience and accepts feedback from the audience, this state is similar to the media collecting information stage, which is to accept rumors from the audience but temporarily not disclose their thoughts. At this time, both the media influence and the spread of rumors in the audience will decrease. Finally, the model is validated using an actual dataset of rumors. The simulation results show an R-squared value of 0.9606, indicating that the proposed model can accurately simulate rumor propagation in real social networks.

Unit Maxwell-Boltzmann Distribution and Its Application to Concentrations Pollutant Data

In the vast statistical literature, there are numerous probability distribution models that can model data from real-world phenomena. New probability models, nevertheless, are still required in order to represent data with various spread behaviors. It is a known fact that there is a great need for new models with limited support. In this study, a flexible probability model called the unit Maxwell-Boltzmann distribution, which can model data values in the unit interval, is derived by selecting the Maxwell-Boltzmann distribution as a base-line model. The important characteristics of the derived distribution in terms of statistics and mathematics are investigated in detail in this study. Furthermore, the inference problem for the mentioned distribution is addressed from the perspectives of maximum likelihood, method of moments, least squares, and maximum product space, and different estimators are obtained for the unknown parameter of the distribution. The derived distribution outperforms competitive models according to different fit tests and information criteria in the applications performed on four actual air pollutant concentration data sets, indicating that it is an effective model for modeling air pollutant concentration data.

Hydraulic Hybrid Cut-to-Length Forest Harvester—Evaluation of Effects on Productivity and Fuel Efficiency

A cut-to-length (CTL) forest harvester is a purpose-built, hydraulically actuated mobile work machine used for felling, delimbing, and cross-cutting trees into dimensions and assortments. In such a machine, the diesel engine load is known to fluctuate greatly over the work cycle. To manage these changing power demands, a hydraulic hybrid system is implemented into a full-scale proof-of-concept machine to discover its functionality and to collect experimental results in real operating conditions. The hydraulic hybrid system installed on a CTL harvester machine was tuned to enable the collection of two actual datasets of timber harvesting, and data collection over a period of production use was carried out. In addition to updating the state-of-the-art research into heavy non-road mobile machinery regarding hybrid systems, this paper discusses the two tests carried out in actual production conditions with the studied hydraulic hybrid system. The adaptations and modifications to the studied and tested system since earlier papers are discussed—especially the improvements in the system dynamics and response as well as the control of the hybrid system. In conclusion, the tested system can be said to operate satisfactorily and shows clear advantages over the conventional system, even though it still has some limitations. The results show that the system has the potential to increase the productivity of the machine through the demonstrated higher peak power, as well as showing improvement in fuel efficiency under highly fluctuating loads, especially with heavy or large-volume tree stems.

Forest damage and its braking effect on the extreme snow avalanche on Mt. Nodanishoji, Japan, in 2021

ABSTRACT An extreme snow avalanche occurred on Mt. Nodanishoji, Japan, in 2021. This avalanche was the second largest ever documented in Japan and destroyed many trees and structures. Despite the increasing interest in avalanche disaster mitigation effects of forests, there are limited opportunities to obtain actual data sets on avalanches and forests. In this study, we reconstructed the avalanche velocity based on the cedar forest damage and simulated the avalanche flow using the dynamical run-out model Titan2D to reveal the avalanche movement and ascertain the braking effect of the forest on the avalanche. We successfully simulated the whole movement of the extreme avalanche with a horizontal runout distance of 2,800 m from the starting zone at an altitude of 1,700 m to the run-out zone considering the effect of the forest by increasing the frictional parameters in the model. Whereas in the case of no forest, the avalanche spread beyond its actual reach, considering the forest, the run-out distance decreased and was consistent with the observed reach. Comparing the results with and without forests, we ascertained the distinct braking effect of the forest on the extreme avalanche and quantify it in terms of a locally increased friction angle.

A GAN-based method for diagnosing bodywork spot welding defects in response to small sample condition

Due to the hidden nature and complexity of resistance spot welding weld nugget formation, how to avoid the time-consuming and money-consuming problem of traditional defect diagnosis methods and accurately grasp the weld nugget status is still an urgent problem. In this paper, an improved GAN model is proposed to solve the corresponding problem by combining the weld nugget defects with the dynamic resistance curve. Aiming at the problem that traditional GAN algorithms are prone to pattern collapse, this paper utilizes a variational autoencoder integrated with a channel attention mechanism as the generator part of the generative adversarial network, which helps the model pay better attention to the high-weight part of the defective sample data and combines the encoding and decoding processes to highlight defective features, thus reconstructing the defective samples with higher quality. Convolutional neural networks are then utilized to identify the features of the generated samples and diagnose the type of weldment defects. The test results show that the proposed scheme is highly reliable and the model outperforms other schemes in diagnosing welded nugget defects under the same conditions, avoiding undesirable effects such as underfitting. The validation of the actual dataset shows that, compared with other diagnostic methods that generally have an accuracy rate of less than 75%, the accuracy of the weld nugget defects diagnosis of this paper's method reaches more than 94%, which is a positive impetus to the development of auto body welding diagnosis.

An improved class of estimators for estimation of population distribution functions under stratified random sampling

The main objective of the current study is to suggest an enhanced family of log ratio-exponential type estimators for population distribution function (DF) using auxiliary information under stratified random sampling. Putting different choices in our suggested generalized class of estimators, we found some Specific estimators. The bias and MSE expressions of the estimators have been approximated up to the first order. By using the actual and simulated data sets, we measured the performance of estimators. Based on the results, the suggested estimators for DF show better performance as compared to the preliminary estimators considered here. The suggested estimators have a advanced efficiency than the other estimators examined with the estimators F‾ˆlogPR(st)2, and F‾ˆlogPR(st)4 for both the actual and simulated data sets. The magnitude of the improvement in efficiency is noteworthy, indicating the superiority of the proposed estimators in terms of MSE.

Novel statistical method for data drift detection in satellite telemetry

SummaryAutonomy is becoming a prime requirement for satellite mission control operations. Data‐driven methods like Machine Learning models are playing a key role in bringing in autonomy. Health keeping data from satellite telemetry is a key ingredient in these data‐driven methods. In real‐world satellite operations, the health‐keeping telemetry data gradually drifts due to adverse space weather effects and wear and tear of electronic and mechanical components. The key question that arises is how to detect and quantify the data drift which is generally a gradual phenomenon. This paper discusses a novel statistical method for detecting data drift occurring in satellite telemetry. For the purpose of experimental work in this paper, an actual telemetry data set of the BUS CURRENT sensor which is part of the Electrical Power System of a Low Earth Orbit Satellite was considered. Data drift detection test was carried out using this sensor data using the developed novel statistical method and with Kolmogorov Smirnov test which is a probabilistic method. Both results are analysed and compared. Thereafter novel statical method was used to check its efficacy using a synthetic data set with induced drift.

Vibration anomaly detection of wind turbine based on temporal convolutional network and support vector data description

Due to the complex working conditions and harsh environment, wind turbines often encounter abnormalities, resulting in great operation and maintenance difficulties. As nacelle vibration signals reveal the structure's dynamic characteristics and the interaction between components, vibration anomaly detection has a strong potential. However, vibration anomaly detection is challenging due to the complex characteristics and non-stationarity of high dynamic nacelle vibration signals. To solve this problem, this paper proposed a semi-supervised vibration anomaly detection approach for wind turbines, combining deep learning and one-class classification. Firstly, a healthy behavior model (HBM) for predicting wind turbine nacelle vibration based on the temporal convolutional network (TCN) is developed. To use all available information, a Hilbert spectrum fusion technology (HSFT) was proposed to enhance model performance. Then, based on the support vector data description (SVDD) algorithm, we established a one-class classifier of vibration prediction residual and realized the vibration anomaly detection. The proposed approach can be trained on the healthy dataset to provide accurate detection of different abnormal types. The effectiveness of the proposed anomaly detection approach was verified on simulated and actual monitoring datasets.

Basic Statistical Methods in Determining Criteria Weights

The proliferation of technology has facilitated data accessibility, leading to an expansion in the range of criteria employed in decision problem design. This situation offers an advantage for making precise and rational decisions, but when it comes to managing spending, it becomes a disadvantage. Specifically, the expense of acquiring expert views utilized in the computation of criteria weights by subjective approaches experiences a substantial rise. Hence, decision-makers may employ objective methodologies to determine criterion weights. Nevertheless, objective methods provide a more limited range of choices compared to subjective methods. The study aims to utilize two widely recognized fundamental statistical approaches in order to enhance the capabilities of objective methods. One of the suggested approaches is the dissimilarity-based weighting method, which calculates the differentiation of values within the criteria. Another approach is the weighting method, which relies on the interquartile range. The methods were adapted as means of weighting criteria. Explanatory examples were provided, simulation-based comparisons were conducted, and ultimately applied to an actual data set. The data from each scenario were compared using the factorial analysis of variance method. The findings produced demonstrate that the proposed methods align with other objective methodologies. Furthermore, the proposed approaches were observed to take more time to finish the procedure compared to the Entropy and Standard Deviation methods, but less time compared to the Critic and Merec methods. Consequently, the suggested techniques are introduced as alternative approaches derived from established fundamental statistical procedures, which are straightforward to comprehend and valuable for professionals.

Weighted Strategy for Estimation of Finite Population Mean in Presence of Auxiliary Information

ABSTRACT In sample designs, it is commonly recognized that using auxiliary information significantly increases an estimator’s precision. This manuscript introduces an weighted strategy for computing the finite population mean using auxiliary information in sample surveys. The equations for the mean squared error (MSE) of the proposed estimator are derived under large sampling approximation. A graphical representation of efficiency comparison is provided to show the ranges where the proposed estimator performs more efficiently. Additionally, a numerical analysis is conducted to evaluate the performance of the suggested estimator using actual datasets.

Health monitoring of CNC machining processes using machine learning and wavelet packet transform

Monitoring the health of a machining process plays a crucial role in advancing fully automated manufacturing. Faulty machining processes (abnormal process) leads to part rejection and can damage the CNC machines. In this context, data-driven machine learning (ML) models with wavelet packet transform (WPT)-based feature extraction method have gained significant importance in health monitoring. The study of different ML models, mother wavelets, and decomposition levels on real production data is important for effective process health monitoring but are not explored well in the literature. This study analyzes the performance of fifty mother wavelets, two decomposition levels (L2 and L3), and five ML models using actual production facility CNC machine dataset to achieve accurate process health monitoring. Various performance indicators obtained from the confusion matrix have been used to evaluate the ML models. The findings indicate that the Random Forest (RF) and Light-Gradient boosting machine (GBM), using the coif8 and db14 mother wavelet, at decomposition level (L3), deliver the best classification performance. Furthermore, the RF model consistently achieves good precision, sensitivity, accuracy, and F1 scores across most classes, making it a reliable and efficient option for the classification of normal and abnormal machining processes. The SVC, MLP, and CNN models, however, also exhibit competitive performance.

Multilayer adaptive critic design with digital twin for data-driven optimal tracking control and industrial applications

In this paper, an optimal trajectory tracking control problem for general nonlinear systems is investigated. An adaptive critic control method with the digital twin (DT) theory is developed. Divergent from the existing tracking control methods, the advantages of adaptive dynamic programming (ADP) and the theory of DT are combined in this paper, and the novel multilayer artificial system structure is constructed. The action-critic structure is employed by each artificial system to obtain an approximate optimal control policy. The model network (MN) is built by using the actual input and output data sets of the controlled system, which means the dependence on the dynamics of the system is overcame. Then, the weights of the trained action network (AN) and MN are passed to the real system to realize the optimal tracking control. The feasibility of the algorithm is proved by theoretical analysis. Finally, the algorithm is applied to a simple nonlinear torsional pendulum system and an industrial wastewater treatment system (WWTS), and the effectiveness of the algorithm is verified. The algorithm effectively realizes the tracking control of nonlinear systems.

Enhanced exponential ratio-cum-ratio estimator in ranked set sampling using transformed auxiliary information

Estimation of population mean is a determined subject issue in sampling surveys and many efforts have been paid by various researchers to enhance the precision of the estimates by utilizing the correlated auxiliary information. In connection with this, we suggest an improved exponential ratio-cum-ratio estimator using transformed auxiliary variables under ranked set sampling scheme. Theoretical comparison between estimators is made in terms of mean square errors (MSEs), percentage relative efficiencies (PREs), and percentage relative root mean squared error (PRRMSE). The numerical expression for the bias and MSE of the suggested estimator is derived up to first order of approximation. Based on the results of actual data sets and a simulation study, it is found that the suggested estimator perform well as compared to its existing counterparts.

Estimation and Optimal Censoring Plan for a New Unit Log-Log Model via Improved Adaptive Progressively Censored Data

To gather enough data from studies that are ongoing for an extended duration, a newly improved adaptive Type-II progressive censoring technique has been offered to get around this difficulty and extend several well-known multi-stage censoring plans. This work, which takes this scheme into account, focuses on some conventional and Bayesian estimation missions for parameter and reliability indicators, where the unit log-log model acts as the base distribution. The point and interval estimations of the various parameters are looked at from a classical standpoint. In addition to the conventional approach, the Bayesian methodology is examined to derive credible intervals beside the Bayesian point by leveraging the squared error loss function and the Markov chain Monte Carlo technique. Under varied settings, a simulation study is carried out to distinguish between the standard and Bayesian estimates. To implement the proposed procedures, two actual data sets are analyzed. Finally, multiple precision standards are considered to pick the optimal progressive censoring scheme.

Exploration and prediction of high pressure dynamic water hidden collapse column in coal mines

Hidden collapse column associated with high pressure dynamic water is a main cause of major water inrush accidents in North China type coal fields. Taking the structural abnormality area discovered in 11603 working face of Daizhuang Coal Mine as an example, underground three-dimensional high-density electrical method, advanced exploration of underground drilling and curtain grouting were used to detect the existence of collapse column, and analyzed the water conductivity of collapse columns based on the hydraulic connection analysis of the 13th limestone and Ordovician limestone aquifers. Finally, it is determined that this abnormal area is a strong water filling collapse column originating from the upper Ordovician strata runoff zone (inferred to be within a range of 30 to 100 m below the Ordovician limestone top interface), developed to a height of 12th limestone. Based on the fact that the water yield and water pressure of underground directional drilling, the grouting pressure of curtain grouting, and the amount of cement injected are external quantitative factors that reflect the existence of hidden karst collapse columns during the process of detecting hidden karst collapse columns, and in combination with the feature that deep learning can fully independently learn abstract knowledge expression, a prediction model based on convolutional neural networks is constructed. According to the established network model, it was found that among the 12 sets of actual measurement data, only one data point indicated the absence of a collapse column. The prediction accuracy reached 91.6%, which meets the practical needs.

SDE-based software reliability additive models with masked data using ELS algorithm

The growing complexity of contemporary software systems, with multiple sub-modules, demands innovative modeling approaches, especially in addressing intricate failures and masked data generation. This paper proposed a stochastic differential equation (SDE)-based software reliability additive model tailored for multi-component systems dealing with masked data. However, in the presence of masked data, the challenge of parameter estimation arises due to the inherent complexity of the objective function and numerous parameters. To overcome this, we proposed the Expectation Least Square (ELS) algorithm, providing a strategic solution for the intricate task of parameter estimation with masked data. In order to verify the effectiveness of our proposed model, we conducted a comparative analysis with traditional reliability models using three actual failure data sets. The experimental results emphasize the superior performance of the proposed model and highlight the effectiveness of the ELS algorithm in accurately estimating software reliability.

Neuroimaging data repositories and AI-driven healthcare-Global aspirations vs. ethical considerations in machine learning models of neurological disease.

Neuroimaging data repositories are data-rich resources comprising brain imaging with clinical and biomarker data. The potential for such repositories to transform healthcare is tremendous, especially in their capacity to support machine learning (ML) and artificial intelligence (AI) tools. Current discussions about the generalizability of such tools in healthcare provoke concerns of risk of bias-ML models underperform in women and ethnic and racial minorities. The use of ML may exacerbate existing healthcare disparities or cause post-deployment harms. Do neuroimaging data repositories and their capacity to support ML/AI-driven clinical discoveries, have both the potential to accelerate innovative medicine and harden the gaps of social inequities in neuroscience-related healthcare? In this paper, we examined the ethical concerns of ML-driven modeling of global community neuroscience needs arising from the use of data amassed within neuroimaging data repositories. We explored this in two parts; firstly, in a theoretical experiment, we argued for a South East Asian-based repository to redress global imbalances. Within this context, we then considered the ethical framework toward the inclusion vs. exclusion of the migrant worker population, a group subject to healthcare inequities. Secondly, we created a model simulating the impact of global variations in the presentation of anosmia risks in COVID-19 toward altering brain structural findings; we then performed a mini AI ethics experiment. In this experiment, we interrogated an actual pilot dataset (n = 17; 8 non-anosmic (47%) vs. 9 anosmic (53%) using an ML clustering model. To create the COVID-19 simulation model, we bootstrapped to resample and amplify the dataset. This resulted in three hypothetical datasets: (i) matched (n = 68; 47% anosmic), (ii) predominant non-anosmic (n = 66; 73% disproportionate), and (iii) predominant anosmic (n = 66; 76% disproportionate). We found that the differing proportions of the same cohorts represented in each hypothetical dataset altered not only the relative importance of key features distinguishing between them but even the presence or absence of such features. The main objective of our mini experiment was to understand if ML/AI methodologies could be utilized toward modelling disproportionate datasets, in a manner we term "AI ethics." Further work is required to expand the approach proposed here into a reproducible strategy.

Enhancing Dynagraph Card Classification in Pumping Systems Using Transfer Learning and the Swin Transformer Model

The dynagraph card plays a crucial role in evaluating oilfield pumping systems’ performance. Nevertheless, classifying dynagraph cards can be quite difficult because certain operating conditions may exhibit similar patterns. Conventional classification approaches mainly involve labor-intensive manual analysis of these cards, leading to subjectivity, prolonged processing times, and vulnerability to human prejudices. In response to this challenge, our study introduces a novel approach that leverages transfer learning and the Swin Transformer model for classifying dynagraph cards across various operating conditions in rod pumping systems. Initially, the Swin Transformer model undergoes pre-training using the ImageNet-22k dataset. Subsequently, we fine-tune the model’s weights using actual dynagraph card datasets, facilitating direct classification analysis with dynagraph cards as input variables. The adoption of transfer learning significantly reduces the training time while enhancing the accuracy of condition diagnosis. To assess the effectiveness of our proposed method, we conducted a comparative evaluation against conventional models like ResNet50, DenseNet121, LeNet, and ViT. The findings demonstrate that our approach outperforms other methods, achieving an accuracy of 96%, thereby improving classification accuracy by 3–4%. Therefore, our approach, based on transfer learning and the Swin Transformer model, provides a better solution for practical problems involving similar dynagraph cards. It meets the requirements of oil field operations, enhancing economic benefits and work efficiency.

Deep Learning-Based Detection of Defects in Wafer Buffer Zone During Semiconductor Packaging Process

Effectively detecting defects in wafer buffer zones is crucial in semiconductor manufacturing processes. If defects are not detected in the middle of the semiconductor manufacturing process, the semiconductor eventually becomes a defective product after all processes are completed. To solve this problem, rule-based vision algorithms have been used to identify defects in the wafer buffer zone. After photographing the wafer buffer zone using a high-speed camera, defects are detected using the pixel values of the images. However, because of the resin bleed in the wafer, which is an epoxy compound, it is difficult to detect defects. Therefore, we introduced a deep learning method for semiconductor inspection and created a high-performance semiconductor inspection algorithm. The defects in the wafer buffer zone should be detected accurately and quickly. Furthermore, the approximate size of the defects must be extracted. We modified the Xception model to fit the wafer data characteristics considering both accuracy and speed. We proposed to extract the size of the defect using class activation mapping (CAM). We obtained an accuracy of 96.9% from the actual wafer dataset through this framework, and then managed to extract the size of the defect through CAM.

A novel deep learning technique to detect electricity theft in smart grids using AlexNet

AbstractElectricity theft (ET), which endangers public safety, interferes with the regular operation of grid infrastructure, and increases revenue losses, is a significant issue for power companies. To find ET, numerous machine learning, deep learning, and mathematically based algorithms have been published in the literature. However, these models do not yield the greatest results due to issues like the dimensionality curse, class imbalance, inappropriate hyper‐parameter tuning of machine learning, deep learning models etc. A hybrid DL model is presented for effectively detecting electricity thieves in smart grids while considering the abovementioned concerns. Pre‐processing techniques are first employed to clean up the data from the smart meters, and then the feature extraction technique, AlexNet is used to address the curse of dimensionality. An actual dataset of Chinese smart meters is used in simulations to assess the efficacy of the suggested approach. To conduct a comparative analysis, various benchmark models are implemented as well. This proposed model achieves accuracy, precision, recall, and F1‐score, up to 86%, 89%, 86%, and 84%, respectively.