Density-based Spatial Clustering Of Applications Research Articles

Widom Line in Supercritical Water in Terms of Changes in Local Structure: Theoretical Perspective.

Performing molecular dynamics simulations with the TIP4P/2005 water model along 9 isobars (from 175 to 375 bar) in the temperature range between 300 and 1100 K, we have found that the loci of the extrema in the rate of change of specific structural properties can be used to define purely structure-based Widom lines. We have examined several parameters that describe the local structure of water, such as the tetrahedral arrangement, nearest neighbor distance, local density around the molecules, and the size of the largest dense domain. The last two parameters were determined using the Voronoi polyhedral and density-based spatial clustering of applications with noise methods, respectively. By analyzing the moments of the associated distributions, we show that along a given isobar, the temperature at which we observe a maximum in the fluctuation, the rate of change of the average values, or in the skewness values unambiguously determines the Widom line that is in agreement with the experimentally detected, thermodynamic response function-based ones.

Hybrid Feature Selection with Chaotic Rat Swarm Optimization-Based Convolutional Neural Network for Heart Disease Prediction from Imbalanced Datasets

Introduction: Early diagnosis of Cardiovascular Disease (CVD) is vital in reducing mortality rates. Artificial intelligence and machine learning algorithms have increased the CVD prediction capability of clinical decision support systems. However, the shallow feature learning in machine learning and incompetent feature selection methods still pose a greater challenge. Consequently, deep learning algorithms are needed to improvise the CVD prediction frameworks. Methods: This paper proposes an advanced CDSS for CVD detection using a hybrid DL method. Initially, the Improved Hierarchical Density-based Spatial Clustering of Applications with Noise (IHDBSCAN), Adaptive Class Median-based Missing Value Imputation (ACMMVI) and Clustering Using Representatives-Adaptive Synthetic Sampling (CURE-ADASYN) approaches are introduced in the pre-processing stage for enhancing the input quality by solving the problems of outliers, missing values and class imbalance, respectively. Then, the features are extracted, and optimal feature subsets are selected using the hybrid model of Information gain with Improved Owl Optimization algorithm (IG-IOOA), where OOA is improved by enhancing the search functions of the local search process. These selected features are fed to the proposed Chaotic Rat Swarm Optimization-based Convolutional Neural Networks (CRSO-CNN) classifier model for detecting heart disease. Results: Four UCI datasets are used to validate the proposed framework, and the results showed that the OOA-DLSO-ELM-based approach provides better heart disease prediction with high accuracy of 97.57%, 97.32%, 96.254% and 97.37% for the four datasets. Conclusions: Therefore, this proposed CRSO-CNN model improves the heart disease classification with reduced time complexity for all four UCI datasets.

A novel framework and validation for the dynamic characterization of civil structures via ground-based lidar

While there has been substantial research conducted on the use of Ground-Based Lidar (GBL; i.e., Terrestrial Laser Scanners) in monitoring the static deformation of civil structures, its application in monitoring dynamic vibrations of structures, which is critical for structural health monitoring (SHM), has only been studied to a limited extent. Traditional contact-based SHM frameworks are constrained by a limited number of sensors and the need for physical access for instrumentation placement. Although recent case studies demonstrate GBL's potential in quantifying dynamic displacements, there is a need for more comprehensive research to validate the accuracy of GBL-based dynamic measurements under various GBL- and structure-based parameters in an autonomous and scalable framework. Therefore, the main objective of this study is to develop and comprehensively validate a novel end-to-end framework to monitor the dynamic vibrations of structures using GBL through extensive experimentation in a controlled laboratory environment. In this study, a novel two-step spatio-temporal algorithm was developed to extract the dynamic vibrations of structures from the dynamic point clouds. The framework leverages the Density-based Spatial Clustering of Applications with Noise (DBSCAN) and change detection algorithms. The impact of several GBL-based parameters on the accuracy of the operational modal analysis results was investigated across six single-degree-of-freedom structures with unique natural frequencies. The GBL-based parameters included the resolution, quality, and point-to-point distance of the dynamic point clouds. Accelerometers and infrared-based sensors were used for the validation of GBL measurements and operational modal analysis results. To validate GBL's full-field mode shapes across the tested specimen configurations, analytical and finite element models were constructed to provide high-fidelity mode shapes as ground-truth data. The results show that the GBL can detect sub-millimeter structural vibrations, and that the resulting natural frequencies and operational deflected shapes closely match those of traditional sensing modalities, and the analytical and finite element models. This study concludes that GBL can be used reliably for remotely monitoring the dynamic response of structures at a high spatial resolution. However, further research is warranted to evaluate the full extents of the proposed framework.

Optimizing Business-to-Business Customer Satisfaction Analysis through Advanced Two-Stage Clustering: Insights from Industrial Parks

Traditional research on customer satisfaction (CS) estimation has focused on the business-to-customer (BTC) business mode. Customers in the BTC mode have been assumed to be familiar with the full range of services or products and to be able to make estimations of their CS. However, in the business-to-business (BTB) mode, diverse services have often been required and provided. It may be difficult to find members who have experience with all kinds of services or to generate common CS estimation results supported by different members. In this study, the difference between BTC and BTB was verified using structural equation modeling (SEM), and a model of CS estimation was developed with respect to BTB. The empirical results show that perceived service quality has no direct impact on enterprise satisfaction, indicating that traditional models are limited. A two-stage clustering algorithm was adopted to optimize the traditional CS evaluation model based on SEM, i.e., (1) K-nearest neighbor (KNN) classification and (2) density-based spatial clustering of applications with noise (DBSCAN). In order to verify the feasibility of the proposed model, CS with respect to six industrial parks was estimated empirically. The results show that the proposed model can improve the results of CS estimation compared with the results obtained using traditional methods. During the clustering process, each park generated and eliminated a certain number of noise points to optimize the satisfaction evaluation results. Specifically, park A generated and eliminated seven noise points, while park C generated and eliminated five noise points. The results of the satisfaction evaluation of each park obtained using the proposed model are more realistic, i.e., park A > park B > park C > park E > park D > park F. The proposed model extends the existing research on CS estimation in theory and can support applications in the BTB business mode.

An exploratory study on spatiotemporal clustering of suicide in Korean adolescents

BackgroundAdolescent suicides are more likely to form clusters than those of other age groups. However, the definition of a cluster in the space–time dimension has not been established, neither are the factors contributing to it well known. Therefore, this study aimed to identify space–time clusters in adolescent suicides in Korea and to examine the differences between clustered and non-clustered cases using novel statistical methods.MethodsFrom 2016 to 2020, the dates and locations, including specific addresses from which the latitude and longitude of all student suicides (aged 9–18 years) in Korea were obtained through student suicide reports. Sociodemographic characteristics of the adolescents who died by suicide were collected, and the individual characteristics of each student who died by suicide were reported by teachers using the Strengths and Difficulties Questionnaire (SDQ). Density-Based Spatial Clustering of Applications with Noise (DBSCAN) analysis was used to assess the clustering of suicides.ResultsWe identified 23 clusters through the data analysis of 652 adolescent suicides using DBSCAN. By comparing the size of each cluster, we identified 63 (9.7%) spatiotemporally clustered suicides among adolescents, and the temporal range of these clusters was 7–59 days. The suicide cluster group had a lower economic status than the non-clustered group. There were no significant differences in other characteristics between the two groups.ConclusionThis study has defined the space–time cluster of suicides using a novel statistical method. Our findings suggest that when an adolescent suicide occurs, close monitoring and intervention for approximately 2 months are needed to prevent subsequent suicides. Future research using DBSCAN needs to involve a larger sample of adolescents from various countries to further corroborate these findings.

Long-term Satellite-derived Bathymetry of Arctic Supraglacial Lake from ICESat-2 and Sentinel-2

Abstract. The polar ice sheets serve as natural thermostats, regulating Earth’s temperatures. The Greenland Ice Sheet (GrIS), the second-largest ice sheet, is a critical indicator of climate change and global warming. Estimating the volume of supraglacial lakes on the GrIS, which is directly linked to the extent of melting in the Arctic ice sheet, requires information on both lake area and water depth. Conventional bathymetric methods (i.e., airborne bathymetric LiDAR, shipborne echo-sounder) are commonly used for accurate water depth measurement. However, polar supraglacial lakes face challenging conditions, leading to uncertainties in their spatial and temporal distribution. To overcome the limitations, this study combines Sentinel-2 and ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) to estimate bathymetry and detect changes in lake volume on the GrIS from 2019 to 2023. Firstly, Sentinel-2 images were pre-processed, and ICESat-2 single-photon LiDAR points were extracted using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method, followed by the bathymetric corrections as the training data. Subsequently, three bathymetry models (i.e., log-linear, log-ratio, and BP (Back Propagation) neural network) were constructed using Sentinel-2 images and ICESat-2 data. Lastly, the highresolution ArcticDEM (Arctic Digital Elevation Model) was used as the validation data to assess the satellite-derived bathymetry accuracy. In this study, the log-ratio model yielded the best results with the R2, RMSE, and MAE of 0.92, 0.79 m (lower than 10% of the maximum depth), and 0.62 m. The results demonstrate the feasibility of the integrated active and passive remote sensing approach for bathymetry in Arctic supraglacial lakes.

Estimation of the Living Vegetation Volume (LVV) for Individual Urban Street Trees Based on Vehicle-Mounted LiDAR Data

The living vegetation volume (LVV) can accurately describe the spatial structure of greening trees and quantitatively represent the relationship between this greening and its environment. Because of the mostly line shape distribution and the complex species of street trees, as well as interference from artificial objects, current LVV survey methods are normally limited in their efficiency and accuracy. In this study, we propose an improved methodology based on vehicle-mounted LiDAR data to estimate the LVV of urban street trees. First, a point-cloud-based CSP (comparative shortest-path) algorithm was used to segment the individual tree point clouds, and an artificial objects and low shrubs identification algorithm was developed to extract the street trees. Second, a DBSCAN (density-based spatial clustering of applications with noise) algorithm was utilized to remove the branch point clouds, and a bottom-up slicing method combined with the random sampling consistency iterative method algorithm (RANSAC) was employed to calculate the diameters of the tree trunks and obtain the canopy by comparing the variation in trunk diameters in the vertical direction. Finally, an envelope was fitted to the canopy point cloud using the adaptive AlphaShape algorithm to calculate the LVVs and their ecological benefits (e.g., O2 production and CO2 absorption). The results show that the CSP algorithm had a relatively high overall accuracy in segmenting individual trees (overall accuracy = 95.8%). The accuracies of the tree height and DBH extraction based on vehicle-mounted LiDAR point clouds were 1.66~3.92% (rRMSE) and 4.23~15.37% (rRMSE), respectively. For the plots on Zijin Mountain, the LVV contribution by the maple poplar was the highest (1049.667 m3), followed by the sycamore tree species (557.907 m3), and privet’s was the lowest (16.681 m3).

A multi-target detection and position tracking algorithm based on mmWave-FMCW radar data

Detecting and tracking the position of multiple targets indoors is a challenging measurement problem due to the inherent difficulty to cluster correctly the sensor data associated to a given target and to track the position of each cluster with adequate accuracy. This problem is critical especially in rooms filled with fixed or moving objects hampering target detection and whenever the paths of different targets cross one another. In this paper, a robust Multiple Targets Tracking (MTT) algorithm exploiting the clouds of points collected from a mmWave-FMCW radar is presented. The proposed solution consists of four main steps. First, the possible outliers of a raw radar data set are removed using a neural network model. Next, the cleaned-up radar data are clustered using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Then, a Kalman Filter (KF) is used to track the position of the centroid of each cluster. Finally, a Structured Branching Multiple Hypothesis Testing (SBMHT) algorithm is applied and updated over reasonably short time intervals to decide which detected tracks are supposed to be confirmed and which ones instead should be discarded. The proposed MTT technique was validated experimentally using the data sets collected from a 60-GHz TI IWR6843 radar platform. The reported results show that the developed algorithm, if properly tuned, is faster and returns more accurate results than other MTT techniques. In particular, the percentage of detection errors is negligible and the planar positioning accuracy is within about 30 cm with 90% probability when up to five targets move freely within the same room.

BSEC Method for Unveiling Open Clusters and its Application to Gaia DR3: 83 New Clusters

Open clusters (OCs) are common in the Milky Way, but most of them remain undiscovered. There are numerous techniques, including some machine-learning algorithms, available for the exploration of OCs. However, each method has its limitations and therefore, different approaches to discovering OCs hold significant values. We develop a comprehensive approach method to automatically explore the data space and identify potential OC candidates with relatively reliable membership determination. This approach combines the techniques of Hierarchical Density-Based Spatial Clustering of Applications with Noise, Gaussian mixture model, and a novel cluster member identification technique, color excess constraint. The new method exhibits efficiency in detecting OCs while ensuring precise determination of cluster memberships. Because the main feature of this technique is to add an extra constraint (EC) for the members of cluster candidates using the homogeneity of color excess, compared to typical blind search codes, it is called Blind Search-Extra Constraint (BSEC) method. It is successfully applied to the Gaia Data Release 3, and 83 new OCs are found, whose color–magnitude diagrams (CMDs) are fitted well to the isochrones. In addition, this study reports 621 new OC candidates with discernible main sequence or red giant branch. It is shown that BSEC technique can discard some false negatives of previous works, which takes about three percentage of known clusters. It shows that as an EC, the color excess (or two-color) constraint is useful for removing fake cluster member stars from the clusters that are identified from the positions and proper motions of stars, and getting more precise CMDs, when differential reddening of member stars of a cluster is not large (e.g., ΔE(G BP − G RP) < 0.5 mag). It makes the CMDs of 15% clusters clearer (in particular for the region near turnoff) and therefore is helpful for CMD and stellar population studies. Our result suggests that the color excess constraint is more appropriate for clusters with small differential reddening, such as globular clusters or older OCs, and clusters that the distances of member stars cannot be determined accurately.

A hybrid clustering approach integrating first-principles knowledge with data for fault detection in HVAC systems

The building sector, primarily through heating, ventilation, and air conditioning (HVAC) systems, accounts for 30% of global final energy consumption and 26% of energy-related emissions, highlighting the urgency for efficient energy management and effective fault detection. Optimizing HVAC system performance is crucial for energy conservation and sustainability. This study introduces a hybrid modeling methodology to enhance HVAC systems’ fault detection and isolation (FDI). Using feature extraction through principal component analysis (PCA) and autoencoder (AE), the proposed approach integrates first-principles knowledge with data to improve the performance of different clustering algorithms (K-means, density-based spatial clustering of applications with noise (DBSCAN), and ordering points to identify the clustering structure (OPTICS) to distinguish datasets of different operating conditions (normal and faulty conditions). The proposed approach is applied to detect common faults in HVAC systems, demonstrating superior performance compared to purely data-driven methods.

GB-DBSCAN: A fast granular-ball based DBSCAN clustering algorithm

Density-Based Spatial Clustering of Applications with Noise (DBSCAN) identifies high-density connected areas as clusters, so that it has advantages in discovering arbitrary-shaped clusters. However, it has difficulty in adjusting parameters and since it needs to scan all data points in turn, its time complexity is O(n2). Granular-ball (GB) is a coarse grained representation of data. It is on the basis of the assumption that an object and its local neighbors have similar distribution and they have high possibility of belonging to the same class. It has been introduced into supervised learning by Xia et al. to improve the efficiency of supervised learning. Inspired by the idea of granular-ball, we introduce it into unsupervised learning and use it to improve the efficiency of DBSCAN, called GB-DBSCAN. The main idea of the proposed algorithm GB-DBSCAN is to employ granular-ball to represent a set of data points and then clustering on granular-balls, instead of the data points. Firstly, we use k-nearest neighbors (KNN) to generate granular-balls, which is a bottom-up strategy, and describe granular-balls according to their centers and radius. Then, the granular-balls are divided into Core-GBs and Non-Core-GBs according to their density. After that, the Core-GBs are merged into clusters according to the idea of DBSCAN and the Non-Core-GBs are assigned to the appropriate clusters. Since the granular-balls' number is much smaller than the size of the objects in a dataset, the running time of DBSCAN is greatly reduced. By comparing with KNN-BLOCK DBSCAN, RNN-DBSCAN, DBSCAN, K-means, DP and SNN-DPC algorithms, the proposed algorithm can get similar or even better clustering result in much less running time.

Research on noise reduction and data mining techniques for pavement dynamic response signals

Purpose This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals. Design/methodology/approach The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently. Findings The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present. Originality/value The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

Efficient Target Classification Based on Vehicle Volume Estimation in High-Resolution Radar Systems

In this paper, we propose a method for efficient target classification based on the spatial features of the point cloud generated by using a high-resolution radar sensor. The frequency-modulated continuous wave radar sensor can estimate the distance and velocity of a target. In addition, the azimuth and elevation angle of the target can be estimated by using a multiple-input and multiple-output antenna system. Using the estimated distance, velocity, and angle, the 3D point cloud of target can be generated. From the generated point cloud, we extract the point cloud for each individual target using the density-based spatial clustering of application with noise method and a camera mounted on the radar sensor. Then, we define the convex hull boundaries that enclose these point clouds in both 3D and 2D spaces obtained by orthogonally projecting onto the xy, yz, and zx planes. Using the vertices of convex hull, we calculate the volume of the targets and the areas in 2D spaces. Several feature points, including the calculated spatial information, are numerized and configured into feature vectors. We design an uncomplicated deep neural network classifier based on minimal input information to achieve fast and efficient classification performance. As a result, the proposed method achieved an average accuracy of 97.1%, and the time required for training was reduced compared to the method using only point cloud data and the convolutional neural network-based method.

A Hierarchical Heuristic Architecture for Unmanned Aerial Vehicle Coverage Search with Optical Camera in Curve-Shape Area

This paper focuses on the problem of dynamic target search in a curve-shaped area by an unmanned aerial vehicle (UAV) with an optical camera. Our objective is to generate an optimal path for UAVs to obtain the maximum detection reward by a camera in the shortest possible time, while satisfying the constraints of maneuverability and obstacle avoidance. First, based on prior qualitative information, the original target probability map for the curve-shaped area is modeled by Parzen windows with 1-dimensional Gaussian kernels, and then several high-value curve segments are extracted by density-based spatial clustering of applications with noise (DBSCAN). Then, given an example that a target floats down river at a speed conforming to beta distribution, the downstream boundary of each curve segment in the future time is expanded and predicted by the mean speed. The rolling self-organizing map (RSOM) neural network is utilized to determine the coverage sequence of curve segments dynamically. On this basis, the whole path of UAVs is a successive combination of the coverage paths and the transferring paths, which are planned by the Dubins method with modified guidance vector field (MGVF) for obstacle avoidance and communication connectivity. Finally, the good performance of our method is verified on a real river map through simulation. Compared with the full sweeping method, our method can improve the efficiency by approximately 31.5%. The feasibility is also verified through a real experiment, where our method can improve the efficiency by approximately 16.3%.

TBM tunneling strata automatic identification and working conditions decision support

It has become an important research topic to ensure the safe and efficient tunnel boring machine (TBM) tunneling in the construction of long distance and deep buried tunnels. This study aims to construct an assistant decision support system that integrates surrounding rock classification identification and tunneling parameters prediction optimization, providing quantitative decision guidance for driving TBM for main drivers. For this purpose, this study takes a TBM diversion tunnel project in Xinjiang as the research background, collects 8633 m length TBM on-site tunneling data, and constructs a TBM tunneling data sample database; The DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm was improved using kernel density estimation to achieve TBM tunnel surrounding rock classification based on tunneling performance and automatic identification. Compared with the traditional DBSCAN model, the Sk parameter average decreased by 30.0%, and the Ku parameter average increased by 181.9%; Adopting the opposition-based learning strategy and β-chaotic sequence method improved the MOGWO (multi-objective grey wolf optimization) algorithm to achieve optimal decision-making of TBM tunneling parameters under 12 working conditions. Compared to the traditional MOGWO model, the Pareto optimality solved by the improved MOGWO model increasing the improvement efficiency of the original value and sample mean by 14.82% and 11.46%, respectively. The on-site application results indicate that the assistant decision support system proposed in this study can provide construction guidance for TBM main drivers under different working conditions, improving the tunneling efficiency and safety of TBM.

A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN.

Positioning based on Global Navigation Satellite Systems (GNSSs) in urban environments always suffers from multipath and Non-Line-of-Sight (NLoS) effects. In such conditions, the GNSS pseudorange measurements can be affected by biases disrupting the GNSS-based applications. Many efforts have been devoted to detecting and mitigating the effects of multipath/NLoS, but the identification and classification of such events are still challenging. This research proposes a method for the post-processing estimation of pseudorange biases resulting from multipath/NLoS effects. Providing estimated pseudorange biases due to multipath/NLoS effects serves two main purposes. Firstly, machine learning-based techniques can leverage accurately estimated pseudorange biases as training data to detect and mitigate multipath/NLoS effects. Secondly, these accurately estimated pseudorange biases can serve as a benchmark for evaluating the effectiveness of the methods proposed to detect multipath/NLoS effects. The estimation is achieved by extracting the multipath/NLoS biases from pseudoranges using a clustering algorithm named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The performance is demonstrated using two real-world data collections in multipath/NLoS scenarios for both static and dynamic conditions. Since there is no ground truth for the pseudorange biases due to the multipath/NLoS scenarios, the proposed method is validated based on the positioning performance. Positioning solutions are computed by subtracting the estimated biases from the raw pseudoranges and comparing them to the ground truth.

Automatically Discovering Mechanical Functions From Physical Behaviors via Clustering

Abstract Computational design methods provide opportunities to discover novel and diverse designs that traditional optimization approaches cannot find or that use physical phenomena in ways that engineers have overlooked. However, existing methods require supervised objectives to search or optimize for explicit behaviors or functions—e.g., optimizing aerodynamic lift. In contrast, this article unpacks what it means to discover interesting behaviors or functions we do not know about a priori using data from experiments or simulation in a fully unsupervised way. Doing so enables computers to invent or re-invent new or existing mechanical functions given only measurements of physical fields (e.g., fluid velocity fields) without directly specifying a set of objectives to optimize. This article explores this approach via two related parts. First, we study clustering algorithms that can detect novel device families from simulation data. Specifically, we contribute a modification to the hierarchical density-based spatial clustering of applications with noise algorithm via the use of the silhouette score to reduce excessively granular clusters. Second, we study multiple ways by which we preprocess simulation data to increase its discriminatory power in the context of clustering device behavior. This leads to an insight regarding the important role that a design’s representation has in compactly encoding its behavior. We test our contributions via the task of discovering simple fluidic devices and show that our proposed clustering algorithm outperforms other density-based algorithms, but that K-means clustering outperforms density-based algorithms, as measured by adjusted Rand score. However, the device types may have an even stronger impact on the clustering. This opens up new avenues of research wherein computers can automatically derive new device functions, behaviors, and structures without the need for human labels or guidance.

Developing hybrid machine learning models to assign health score to railcar fleets for optimal decision making

A large amount of data is generated during the operation of a railcar fleet, which can easily lead to dimensional disaster and reduce the resiliency of the railcar network. To solve these issues and offer predictive maintenance, this research introduces a hybrid fault diagnosis expert system method that combines density-based spatial clustering of applications with noise (DBSCAN) and principal component analysis (PCA). Firstly, the DBSCAN method is used to cluster categorical data that are similar to one another within the same group. Secondly, PCA algorithm is applied to reduce the dimensionality of the data and eliminate redundancy in order to improve the accuracy of fault diagnosis. Finally, we explain the engineered features and evaluate the selected models by using the Gain Chart and Area Under Curve (AUC) metrics. We use the hybrid expert system model to enhance maintenance planning decisions by assigning a health score to the railcar system of the North American Railcar Owner (NARO). The model is based on data from one specific railcar type. We utilize the results of data-driven models to assign a health rate to each railcar. Finally, we use data from NARO to evaluate and verify our proposed framework.

A comparative evaluation of clustering methods and data sampling techniques in the prediction of reservoir landslide deformation state

ABSTRACT Landslides exhibiting step-wise deformation characteristics are extensively dispersed throughout the Three Gorges Reservoir (TGR) region of China. Predicting the deformation state of landslides in TGR holds paramount significance in landslide early warning and risk management. Machine learning-based landslide deformation state prediction is a combination of clustering and imbalanced classification. This paper compares the efficacy of three prevalent clustering methods, namely K-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Gaussian Mixture Model (GMM), in the clustering analysis process. Furthermore, the paper evaluates the performance of three widely-used data sampling technologies, namely Synthetic Minority Oversampling Technique (SMOTE), SMOTE-Edited Nearest Neighbors (SMOTE-ENN), and ADAptive SYNthetic Sampling (ADASYN), in the imbalanced classification process. The Baijiabao and Bazimen landslides in the TGR region, which exhibit step-wise deformation characteristics, are used as case studies. Results indicate that DBSCAN and GMM exhibit significant advantages in the clustering process. Meanwhile, the mixture models that integrate oversampling technologies and classification algorithms perform exceptionally well in imbalanced classification. The aforementioned algorithms are recommended for predicting the deformation states of step-wise landslides in the TGR region. The machine learning-based predictive models can serve as potent instruments in facilitating the implementation of early warning systems aimed at mitigating landslide risks.

Automated real-time prediction of geological formation tops during drilling operations: an applied machine learning solution for the Norwegian Continental Shelf

AbstractAccurate prediction of geological formation tops is a crucial task for optimizing hydrocarbon exploration and production activities. This research investigates and conducts a comprehensive comparative analysis of several advanced machine learning approaches tailored for the critical application of geological formation top prediction within the complex Norwegian Continental Shelf (NCS) region. The study evaluates and benchmarks the performance of four prominent machine learning models: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest ensemble method, and Multi-Layer Perceptron (MLP) neural network. To facilitate a rigorous assessment, the models are extensively evaluated across two distinct datasets - a dedicated test dataset and a blind dataset independent for validation. The evaluation criteria revolve around quantifying the models' predictive accuracy in successfully classifying multiple geological formation top types. Additionally, the study employs the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm as a baseline benchmarking technique to contextualize the relative performance of the machine learning models against a conventional clustering approach. Leveraging two model-agnostic feature importance analysis techniques - Permutation Feature Importance (PFI) and Shapley Additive exPlanations (SHAP), the investigation identifies and ranks the most influential input variables driving the predictive capabilities of the models. The comprehensive analysis unveils the MLP neural network model as the top-performing approach, achieving remarkable predictive accuracy with a perfect score of 0.99 on the blind validation dataset, surpassing the other machine learning techniques as well as the DBSCAN benchmark. However, the SVM model attains superior performance on the initial test dataset, with an accuracy of 0.99. Intriguingly, the PFI and SHAP analyses converge in consistently pinpointing depth (DEPT), revolution per minute (RPM), and Hook-load (HKLD) as the three most impactful parameters influencing model predictions across the different algorithms. These findings underscore the potential of sophisticated machine learning methodologies, particularly neural network-based models, to significantly enhance the accuracy of geological formation top prediction within the geologically complex NCS region. However, the study emphasizes the necessity for further extensive testing on larger datasets to validate the generalizability of the high performance observed. Overall, this research delivers an exhaustive comparative evaluation of state-of-the-art machine learning techniques, offering critical insights to guide the optimal selection, development, and real-world deployment of accurate and reliable predictive modeling strategies tailored for hydrocarbon exploration and reservoir characterization endeavors in the NCS. Graphical abstract