Sample Problems Research Articles

Estimation of Population Mean using Neutrosophic Exponential Estimators with Application to Real Data

One of the traditional problems in survey sampling is to estimate the population parameter like mean variance etc. This article investigates the mathematical derivations and application of neutrosophic statistics to address the challenges posed by imprecise, indeterminacies or ambiguous data, such as daily stock prices, weather forecast, social media sentiment and temperatures. The suggested estimators are highly useful for computing results while working with unclear, hazy, and neutrosophic-type data. These estimators produce answers that are interval-form rather than single-valued, which may give our population parameter a better chance of being off. We propose three novel neutrosophic exponential ratio-type estimators for the population mean, utilizing information of neutrosophic auxiliary variables. Expressions for bias and mean square error (MSE) of these estimators are derived using first-order approximations to assess their performance in terms of accuracy. To demonstrate their effectiveness, we apply the proposed estimators to real-life neutrosophic data sets. Additionally, a simulation study shows that our estimators outperform existing methods in terms of MSEs and percentage relative efficiency (PREs). This study further expands its originality by including pre-existing estimators into the neutrosophic framework, showcasing its versatility and adaptability. The results suggest that neutrosophic statistics provide a robust framework for analyzing uncertain data, facilitating more reliable decision-making in various applications.

A joint entity and relation extraction framework for handling negative samples problems in named entity recognition

A joint entity and relation extraction framework for handling negative samples problems in named entity recognition

Anomaly-aware summary statistic from data batches

Signal-agnostic data exploration based on machine learning could unveil very subtle statistical deviations of collider data from the expected Standard Model of particle physics. The beneficial impact of a large training sample on machine learning solutions motivates the exploration of increasingly large and inclusive samples of acquired data with resource efficient computational methods. In this work we consider the New Physics Learning Machine (NPLM), a multivariate goodness-of-fit test built on the Neyman-Pearson maximum-likelihood-ratio construction, and we address the problem of testing large size samples under computational and storage resource constraints. We propose to perform parallel NPLM routines over batches of the data, and to combine them by locally aggregating over the data-to-reference density ratios learnt by each batch. The resulting data hypothesis defining the likelihood-ratio test is thus shared over the batches, and complies with the assumption that the expected rate of new physical processes is time invariant. We show that this method outperforms the simple sum of the independent tests run over the batches, and can recover, or even surpass, the sensitivity of the single test run over the full data. Beside the significant advantage for the offline application of NPLM to large size samples, the proposed approach offers new prospects toward the use of NPLM to construct anomaly-aware summary statistics in quasi-online data streaming scenarios.

Circular RNAs as a new perspective in the diagnosis and mechanism of Leishmania infections.

Leishmaniasis is a neglected infectious disease that affects millions of people worldwide. Visceral leishmaniasis (VL) caused by Leishmania infantum and cutaneous leishmaniasis (CL) caused by L. major/ L. tropica are the main clinical forms of this disease, which are life-threatening if not diagnosed and treated properly. Considering the problems in sampling and laboratory diagnosis of leishmaniasis, new molecular markers such as circular RNAs (circRNAs) are needed. circRNAs, a novel class of RNAs, have been one of the most promising targets for the diagnosis and prognosis of diseases. Although the therapeutic and diagnostic role of circRNAs in many diseases and some parasitic diseases are known, not much research has been done in the field of leishmaniasis. We determined the gene expressions of circRNAs in human leukemia monocytic (THP-1) cells after infection with Leishmania. For this, the human cell line THP-1 was differentiated into macrophages by Phorbol 12-myristate 13-acetate (PMA) treatment. Differentiated THP-1 cells were infected with L. infantum and L. tropica promastigotes. After 24 hours, expression levels of circRNAs were determined by RT-qPCR technique. Also, the microRNAs associated with differentially expressed circRNAs were investigated. Then, the molecular pathways associated with expressed circRNAs were obtained by GO and Reactome. The results showed that five circRNAs were differentially expressed in THP1 macrophages infected with L. infantum and L. tropica. These findings suggest that some circRNAs may be potential biomarkers for diagnosis in Leishmania-infected patients. The enrichment analysis revealed that differentially expressed circRNAs are mainly involved in the regulation of protein stability, RNA catabolic process, and P53/PTK6 signaling mechanism. This is the first study to report an overview of Leishmania-induced circRNAs, which can be potential biomarker candidate for diagnosis especially at species level. Notably, expression of some circRNAs in supernatant of Leishmania infected macrophages suggests that these genes are available in body fluids, therefore, can easily be accessed from the patient without invasive methods especially during treatment monitoring.

Graph Joint Representation Clustering via Penalized Graph Contrastive Learning.

Graph clustering based on graph contrastive learning (GCL) is one of the dominant paradigms in the current graph clustering research field. However, those GCL-based methods often yield false negative samples, which can distort the learned representations and limit clustering performance. In order to alleviate this issue, we propose the idea of maintaining mutual information (MI) between the representations and the inputs to mitigate the loss of semantic information of false negative samples. We demonstrate the validity of this proposal through relevant experiments. Since maximizing MI can be approximately replaced by minimizing reconstruction error, we further propose a graph clustering method based on GCL penalized by reconstruction error, in which our carefully designed reconstruction decoder, as well as reconstruction error term, improve the clustering performance. In addition, we use a pseudo-label-guided strategy to improve the GCL process and further alleviate the problem of false negative samples. Our experiment results demonstrate the superiority and great potential of our proposed graph clustering method compared with state-of-the-art algorithms.

An improved RRT* drone three-dimensional path-planning algorithm based on point cloud maps

Given the problems of slow convergence and blind sampling of the Rapidly-exploring Random Trees (RRT*) algorithm in 3D path-planning of UAVs, this paper proposes an improved bidirectional probabilistic target bias RRT* algorithm for 3D path-planning of UAVs based on point cloud maps. Initially, 3D point cloud maps and the dynamic step size necessary for the expansion of the RRT* algorithm are derived through the implementation of 3D map reconstruction and point cloud analysis techniques. Subsequently, a double sampling mechanism of random and target sampling is combined with the expansion strategy of the Bi-RRT* and RRT*-Connect algorithms to generate a bidirectional random tree, which improves the search speed and shortens the path. Furthermore, the influence of a gravitational mechanism, generated by the goal, is considered to direct the growth of the random tree toward the target. A collision avoidance strategy based on KD-tree is also introduced to enhance collision detection efficiency and mitigate collision risks. Additionally, cubic spline optimization is employed to achieve a smoother path. Finally, in order to illustrate the viability and efficacy of the proposed algorithm, a comparative analysis is performed between the enhanced algorithm and the RRT*, P-RRT*, and Bi-RRT* algorithms across two distinct environments.

Optimal mixing via tensorization for random independent sets on arbitrary trees

Abstract We study the mixing time of the single-site update Markov chain, known as the Glauber dynamics, for generating a random independent set of a tree. Our focus is obtaining optimal convergence results for arbitrary trees. We consider the more general problem of sampling from the Gibbs distribution in the hard-core model where independent sets are weighted by a parameter $\lambda \gt 0$ ; the special case $\lambda =1$ corresponds to the uniform distribution over all independent sets. Previous work of Martinelli, Sinclair and Weitz (2004) obtained optimal mixing time bounds for the complete $\Delta$ -regular tree for all $\lambda$ . However, Restrepo, Stefankovic, Vera, Vigoda, and Yang (2014) showed that for sufficiently large $\lambda$ there are bounded-degree trees where optimal mixing does not hold. Recent work of Eppstein and Frishberg (2022) proved a polynomial mixing time bound for the Glauber dynamics for arbitrary trees, and more generally for graphs of bounded tree-width. We establish an optimal bound on the relaxation time (i.e., inverse spectral gap) of $O(n)$ for the Glauber dynamics for unweighted independent sets on arbitrary trees. We stress that our results hold for arbitrary trees and there is no dependence on the maximum degree $\Delta$ . Interestingly, our results extend (far) beyond the uniqueness threshold which is on the order $\lambda =O(1/\Delta )$ . Our proof approach is inspired by recent work on spectral independence. In fact, we prove that spectral independence holds with a constant independent of the maximum degree for any tree, but this does not imply mixing for general trees as the optimal mixing results of Chen, Liu, and Vigoda (2021) only apply for bounded-degree graphs. We instead utilize the combinatorial nature of independent sets to directly prove approximate tensorization of variance via a non-trivial inductive proof.

Machine Learning Clustering Algorithm for Water Environmental Monitoring

Machine learning based on genetic algorithms is an important application. The multi-dimensional ordered sample clustering problem is often solved using Fisher’s optimal segmentation method. However, this method has obvious shortcomings when encountering long sample problems due to its high storage requirements during the computation process. Therefore, Fisher’s optimal two-segmentation method is generally used in practical problems instead, which avoids storage problems. But it is prone to local optima. Based on the analysis of the shortcomings of the Fisher optimal segmentation and optimal two-segmentation algorithms, this paper proposes a genetic-based machine learning clustering algorithm, which overcomes the problem of Fisher’s optimal two-segmentation algorithm being prone to local optima and also solves the problem of high storage requirements during the computation process of Fisher’s optimal segmentation method. The application of this algorithm in the optimization system of water environment monitoring points shows that it is effective.

A Combined Capacity Planning and Simulation Approach for the Optimization of AGV Systems in Complex Production Logistics Environments

Background: The capacity planning of production systems is one of the most fundamental strategic problems in the creation of a production plant. However, the implementation of increasingly complex production systems combined with sophisticated automated material handling justifies the development of novel approaches to solve the combined capacity planning and material handling problem, which is also the objective of the current study. Methods: The presented approach combines the use of capacity planning formulas and discrete event simulation for optimizing extensive automated guided vehicle (AGV) systems from the aspect of the number of required vehicles. Extensive series of simulation experiments are applied in the case of each model variant for optimal results and to account for machine failures in the system. Results: The application of the proposed method is demonstrated through a realistic sample problem in a plastic industry setting with the use of the Siemens Tecnomatix Plant Simulation software (version 2302.0003, Educational license). Conclusions: The results from the sample problem demonstrate the usefulness of the approach, as a non-intuitive solution proved to be the most efficient. Additionally, the main advantage of the method is that it provides a standardized framework for the simulation-based optimization of AGV systems starting out from the comprehensive production capacity parameters.

Causal discovery from nonstationary time series

AbstractThis paper introduces a new causal structure learning method for nonstationary time series data, a common data type found in fields such as finance, economics, healthcare, and environmental science. Our work builds upon the constraint-based causal discovery from nonstationary data algorithm (CD-NOD). We introduce a refined version (CD-NOTS) which is designed specifically to account for lagged dependencies in time series data. We compare the performance of different algorithmic choices, such as the type of conditional independence test and the significance level, to help select the best hyperparameters given various scenarios of sample size, problem dimensionality, and availability of computational resources. Using the results from the simulated data, we apply CD-NOTS to a broad range of real-world financial applications in order to identify causal connections among nonstationary time series data, thereby illustrating applications in factor-based investing, portfolio diversification, and comprehension of market dynamics.

Quantum walks advantage on the dihedral group for uniform sampling problem

Random walk algorithms are crucial for sampling and approximation problems in statistical physics and theoretical computer science. The mixing property is necessary for Markov chains to approach stationary distributions and is facilitated by walks. Quantum walks show promise for faster mixing times than classical methods but lack universal proof, especially in finite group settings. Here, we investigate the continuous-time quantum walks on Cayley graphs of the dihedral group D 2n for odd n, generated by the smallest inverse closed symmetric subset. We present a significant finding that, in contrast to the classical mixing time on these Cayley graphs, which typically takes at least order Ω(n2log(1/2ϵ)) , the continuous-time quantum walk mixing time on D 2n is of order O(n(logn)5log(1/ϵ)) , achieving a quadratic improvement over the classical case. Our paper advances the general understanding of quantum walk mixing on Cayley graphs, highlighting the improved mixing time achieved by continuous-time quantum walks on D 2n . This work has potential applications in algorithms for a class of sampling problems based on non-abelian groups.

Host-Guest Binding Free Energies à la Carte: An Automated OneOPES Protocol.

Estimating absolute binding free energies from molecular simulations is a key step in computer-aided drug design pipelines, but the agreement between computational results and experiments is still very inconsistent. Both the accuracy of the computational model and the quality of the statistical sampling contribute to this discrepancy, yet disentangling the two remains a challenge. In this study, we present an automated protocol based on OneOPES, an enhanced sampling method that exploits replica exchange and can accelerate several collective variables to address the sampling problem. We apply this protocol to 37 host-guest systems. The simplicity of setting up the simulations and producing well-converged binding free energy estimates without the need to optimize simulation parameters provides a reliable solution to the sampling problem. This, in turn, allows for a systematic force field comparison and ranking according to the correlation between simulations and experiments, which can inform the selection of an appropriate model. The protocol can be readily adapted to test more force field combinations and study more complex protein-ligand systems, where the choice of an appropriate physical model is often based on heuristic considerations rather than systematic optimization.

Calculating linear and nonlinear multi-ensemble slow collective variables for protein folding.

Traditional molecular dynamics simulation of biomolecules suffers from the conformational sampling problem. It is often difficult to produce enough valid data for post analysis such as free energy calculation and transition path construction. To improve the sampling, one practical solution is putting an adaptive bias potential on some predefined collective variables. The quality of collective variables strongly affects the sampling ability of a molecule in the simulation. In the past, collective variables were built with the sampling data at a constant temperature. This is insufficient because of the same sampling problem. In this work, we apply the standard weighted histogram analysis method to calculate the multi-ensemble averages of pairs of time-lagged features for the construction of both linear and nonlinear slow collective variables. Compared to previous single-ensemble methods, the presented method produces averages with much smaller statistical uncertainties. The generated collective variables help a peptide and a miniprotein fold to their near-native states in a short simulation time period. By using the method, enhanced sampling simulations could be more effective and productive.

A novel intelligent bearing fault diagnosis method based on image enhancement and improved convolutional neural network

Bearing fault diagnostic is crucial for ensuring the normal operation of equipment. However, the working environment of mechanical equipment is often very harsh, the collection of fault data is limited in the actual industrial process. The problem of insufficient fault samples occurs, which makes the feature extraction ability of the deep learning models drop dramatically. To solve this terrible problem, an intelligent diagnosis framework based on image enhancement and an improved convolutional neural network (IEICNN) is outlined in this article. First, a signal conversion method based on feature fusion is employed to convert multi-channel one-dimensional vibration signals into red − green − blue (RGB) images to obtain more comprehensive fault data representation. In addition, a new image enhancement method is proposed, which uses the improved Squeeze and Excitation-Residual Network (SE-ResNet) to learn and extract the advanced feature map, and uses deconvolution neural networks to reconstruct RGB image. Finally, the Softmax classifier is applied to identify the health conditions of the bearing. The superiority and robustness of IEICNN are validated using two bearing datasets. By comparing with several progressive comparison approaches, it is proven that the proposed approach has better robustness and effectiveness in the case of limited data scarcity.

Automated Surface Defect Detection Based on CycleGAN Model

Abstract Defect detection occupies an increasingly important position in the manufacturing industry, and most of approaches for the traditional defect detection are based on manual extraction of defective region traits and labeling work. This paper presents a novel defect detection approach based on Generative Adversarial Network (GAN) to automatically detect and extract defects from the target dataset. The method extends the samples using GAN model to solve the problem of insufficient samples in reality, and also provides paired samples for the second stage of defective pixel accumulation, after which the defective pixel images are output as binarized defect maps using difference accumulation and threshold segmentation. The experimental results verify that the proposed method can very accurately highlight the defects at the defect locations, and can be generated without manual labeling of defect traits.

Dynamic model-based intelligent fault diagnosis method for fault detection in a rod fastening rotor

A complete fault sample database is of great significance for the intelligent fault diagnosis method of rod fastening rotor. However, the lack of fault samples makes the fault diagnosis results unbelievable. To solve this issue, the dynamic model-based intelligent fault diagnosis method is established for a rod fastening rotor, and the fault sample database is enriched by numerical simulations. First, the lumped parameter model of the rod fastening rotor system is constructed and further updated using Euclidean Distance between measurement and numerical simulation of the intact system. Second, mathematical models of various fault types are incorporate into the updated model to obtain numerical simulation fault samples. Thirdly, the utilization of numerical simulation fault samples is severed as training data to the artificial intelligence (AI) models and the unknown measurement test samples will be finally classified. In this paper, Support Vector Machine, Random Forest, Bayesian Network and Decision Tree are selected as the typical AI models. Subsequently, the feasibility of classification is validated by the test bench of the rod fastening rotor system, and the problem of insufficient fault samples can be solved.

Small-Sample Data Pricing Based on Data Augmentation and Meta-Learning

Data trading platforms play a crucial role in facilitating data circulation and promoting the sustainable allocation of data resources. Establishing a transparent, fair, and efficient pricing mechanism is key to ensuring the long-term stability and development of such platforms. However, these platforms face challenges in pricing due to the small sample problem, as traditional machine learning methods typically rely on large amounts of data. To address this issue, this paper proposes a data resource pricing model that combines WGAN-GP data augmentation and the Reptile algorithm. Data augmentation generates related datasets to increase sample size, enhancing the renewability of data resources, while meta-learning transfers knowledge across tasks, improving the model’s ability to quickly adapt to new tasks and efficiently utilize resources. Validation using actual trading data from the data trading platform shows that the proposed model accurately predicts data resource prices under small-sample conditions, outperforming other models. This study addresses the limitations of existing pricing methods in small-sample scenarios, providing a sustainable pricing solution for small-sample data resources and improving the accuracy and long-term stability of data pricing in the market.

Aero-engine defect detection by integrating attention and multi-scale features

The structural integrity of aircraft engines is related to flight safety. At present, aircraft engine defect detection based on borescope technology is mainly manual operation. In order to improve the detection accuracy and efficiency, an intelligent aircraft engine defect detection algorithm that integrates attention and multi-scale features is proposed to assist borescope work. First, to address the class imbalance problem of defect samples in the original borescope image, a multi- sample fusion data enhancement method based on geometric transformation and Poisson image editing is used to enrich small sample images and construct a defect dataset. Then, the coordinated attention module (CA) is integrated into the baseline network YOLOv5 to emphasize the extraction of defect features and enhance the network's distinction between defect targets and complex backgrounds. A weighted bidirectional feature pyramid structure (BiFPN) is constructed in the neck network to complete higher-level feature fusion and improve the expression ability of multi-scale targets. Finally, the bounding box regression loss function is defined as the EIOU loss to achieve fast and accurate positioning and identification of defect targets. Experimental results show that the average accuracy of defect detection of the proposed algorithm reaches 89.7%, which is improved compared with the baseline network 6.3%, and the size of the trained model is only 14.0 M. Therefore, the proposed method can effectively detect the main defects of aircraft engines.

A fault diagnosis method for bearings and gears in rotating machinery based on data fusion and transfer learning

Abstract Rotating machinery is a crucial component of industrial equipment, and the fault diagnosis of bearings and gears, as vital elements of rotating machinery, is essential since they often fail under harsh working conditions, leading to significant property losses and serious personal safety problems. However, fault data for gears and bearings are often sparse in actual condition, and it is a challenge to ensure the reliability and stability of fault diagnosis results by extracting the features of a single data. To solve the above problems, this paper proposes a fault diagnosis method that combines Transfer Learning and data fusion techniques. Firstly, in this method, two kinds of fault signals are transformed into Gramian Angular Difference Fields and Recurrence Plot. Next, a U-shaped feature fusion dual discriminator generative adversarial network is used to fuse two-dimensional images from multiple sensor data. Its feature fusion module deeply integrates the features of the two images, thereby solving the impact of single data on the reliability and stability of fault diagnosis. Moreover, open-source datasets are used for Transfer Learning training to tackle the small sample problem. Finally, a decision-level information fusion classifier, the Dual-Branch Dempster-Shafer Classifier (DB-DSC), classifies the fused images. This classifier incorporates an improved soft threshold function and D-S evidence theory to achieve adaptive gradient changes and improve the robustness and accuracy of classification results. The experimental results show the effectiveness and stability of the proposed method, and the generated images get high score in several metrics. The average classification accuracy of the classification network reaches 93% and 92.5% on the two datasets, Therefore, the proposed method exhibits strong fault diagnosis capabilities under the small sample conditions of bearings and gears.

Convolutional variational autoencoder and multi-scale attention convolutional neural network based diagnostics on filament current sensors for mass spectrometers

Fault detection of the filament current sensor of the spaceborne mass spectrometer is of great significance to the safe operation of the mass spectrometer. Neglecting sensor failures may affect the normal operation of the mass spectrometer, which in turn affects the health of the astronauts and the space missions. This paper proposes an enhanced intelligent diagnosis method based on filament current sensor for mass spectrometer via improved deep learning network, aiming to improve the accuracy and reliability of the filament current sensor when the fault samples are insufficient. The improved convolutional variational autoencoder (CVAE) model not only enhances the data for four typical sensor fault signals, namely bias, drift, missing and random, but also solves the problem of insufficient samples when spike, Precision degradation and stuck fault occur in sensors. Short-time Fourier transform (STFT) is utilized to transform one-dimensional data into two-dimensional spectrograms, which gives more fault characteristics to the data set. The improved multi-scale attention mechanism convolutional neural network (MSAM-CNN) model is constructed to perform fault diagnosis on the generated spectrogram, which solves the problem of low accuracy of fault identification in traditional convolutional neural network (CNN) models. The results of ablation and comparison experiments show that the samples generated by CVAE have smaller Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE), the enhanced samples improve the classification and clustering of MSAM-CNN, and compared to other diagnostic models, MSAM-CNN achieves the highest accuracy, precision, recall, and F1-score of 99.2%, 99.3%, 98.8%, and 0.991.