Search

Performance degradation assessment methods for rolling bearings under vibration signal monitoring typically involve extracting signal degradation features and inputting them directly into unoptimized assessment models. However, this method often fails to characterize degradation trends and detect early faults in bearings effectively. Moreover, it is susceptible to interference from outliers and false signal fluctuations, posing challenges for accurate performance degradation assessment. To solve the above problems, a novel performance degradation assessment method of rolling bearings based on optimized variational mode decomposition to construct the maximum information degradation feature set and improved hippopotamus optimization algorithm optimized fuzzy support vector data description is proposed. This method effectively suppresses the signal degradation feature pollution caused by the redundancy of irrelevant frequency components, realizes the screening of effective degradation feature sets, and further improves the accuracy of bearing performance degradation assessment. Through experimental verification, this research method uses early healthy rolling bearing samples to establish an assessment model, which can adaptively determine the starting point and degradation trend of bearing degradation. In comparison with other methods for performance degradation assessment under vibration monitoring, it offers distinct advantages. [ABSTRACT FROM AUTHOR]

The high heritability of amyotrophic lateral sclerosis (ALS) contrasts with its low molecular diagnosis rate post-genetic testing, pointing to potential undiscovered genetic factors. To aid the exploration of these factors, we introduced EpiOut, an algorithm to identify chromatin accessibility outliers that are regions exhibiting divergent accessibility from the population baseline in a single or few samples. Annotation of accessible regions with histone chromatin immunoprecipitation sequencing and Hi-C indicates that outliers are concentrated in functional loci, especially among promoters interacting with active enhancers. Across different omics levels, outliers are robustly replicated, and chromatin accessibility outliers are reliable predictors of gene expression outliers and aberrant protein levels. When promoter accessibility does not align with gene expression, our results indicate that molecular aberrations are more likely to be linked to post-transcriptional regulation rather than transcriptional regulation. Our findings demonstrate that the outlier detection paradigm can uncover dysregulated regions in rare diseases. EpiOut is available at github.com/uci-cbcl/EpiOut.

Heart disease increases the strain on the heart by reducing its ability to pump blood throughout the body, which can lead to heart attacks and strokes. Heart disease is becoming a global threat to the world due to people's unhealthy lifestyles, prevalent stroke history, physical inactivity, and current medical background. In predictive analytics, many studies were proposed to get alerts about forthcoming heart disease based on various attributes. However, the performance metrics were good, but the model was trained with few features. This study aims to train the model with all the essential attributes for heart disease prediction on Framingham Heart Study (FHS) dataset. The dataset is pre-processed with IQR (Inter Quartile Range) outlier detection followed by data oversampling using Synthetic Minority Oversampling Technique (SMOTE). We proposed a stack generalization approach, wherein various machine learning classifiers, namely logistic regression, random forest, K-nearest neighbour, Naïve Bayes, support vector machine, XGBoost, and decision tree with optimized hyperparameter trained the model to offer the best learner for the prediction of Coronary Heart disease with improved performance. The proposed model is tested on the original imbalanced and SMOTE-balanced FHS dataset. It is observed that logistic regression on the original (imbalanced) FHS dataset provides 86.51% accuracy, while the support vector machine on the SMOTE (balanced) FHS dataset outperformed the other models with an accuracy of 93.07%. Also, the proposed approach of stacked generalization with cross-validation provided 97.2% accuracy on SMOTE (balanced) dataset, which is remarkable. [ABSTRACT FROM AUTHOR]

Demographic processes can substantially affect a species' response to changing ecological conditions, necessitating the combined consideration of genetic responses to environmental variables and neutral genetic variation. Using a seascape genomics approach combined with population demographic modelling, we explored the interplay of demographic and environmental factors that shaped the current population structure in Indo‐Pacific bottlenose dolphins (Tursiops aduncus) along the Western Australian coastline. We combined large‐scale environmental data gathered via remote sensing with RADseq genomic data from 133 individuals at 19 sampling sites. Using population genetic and outlier detection analyses, we identified three distinct genetic clusters, coinciding with tropical, subtropical and temperate provincial bioregions. In contrast to previous studies, our demographic models indicated that populations occupying the paleo‐shoreline split into two demographically independent lineages before the last glacial maximum (LGM). A subsequent split after the LGM 12—15 kya gave rise to the Shark Bay population, thereby creating the three currently observed clusters. Although multi‐locus heterozygosity declined from north to south, dolphins from the southernmost cluster inhabiting temperate waters had higher heterozygosity in potentially adaptive loci compared to dolphins from subtropical and tropical waters. These findings suggest ongoing adaptation to cold‐temperate waters in the southernmost cluster, possibly linked to distinct selective pressures between the different bioregions. Our study demonstrated that in the marine realm, without apparent physical boundaries, only a combined approach can fully elucidate the intricate environmental and genetic interactions shaping the evolutionary trajectory of marine mammals. [ABSTRACT FROM AUTHOR]

Solar radiation prediction research is a key area of interest in the realm of solar energy utilization and has garnered significant attention in recent times. In order to realize accurate prediction of solar radiation and make solar radiation prediction better serve photovoltaic (PV) power generation, this study proposes a solar radiation prediction method based on sequence model, which integrates two kinds of neural networks, namely, temporal convolutional network (TCN) and neural basis expansion analysis (N‐BEATS). First, the dataset is preprocessed using Pearson's correlation coefficient, outlier detection, and normalized to obtain valid and relevant data; second, the features of TCN feature extraction and N‐BEATS flexible extension are integrated to predict the solar radiation; then, the model's hyperparameters are fine‐tuned using the grid search algorithm to ensure precise predictions; and last, the correctness of the method is verified by comparing the error metrics and the running time. Empirical findings indicate that the TCN‐N‐BEATS sequence model has high prediction accuracy and short time overhead, and it has certain application value in solar radiation prediction, and the model could offer valuable insights for predicting solar radiation. [ABSTRACT FROM AUTHOR]

DNA methylation is a molecular process that mediates gene-environment interactions. Epigenome-wide association studies (EWAS) using the Illumina Human Methylation BeadChip are powerful tools for quantifying the relationship between DNA methylation and phenotypes. Recently, the Illumina Methylation EPICv2 BeadChip (EPICv2) was released, which includes new features, such as duplicated probes and changed probe names. Several published algorithms have been updated to address these features in EPICv2. However, appropriate EPICv2 preprocessing and integration with previous microarray versions remain complex. Therefore, MethylCallR, an open-source R package designed to provide standard procedures for performing EWAS using Illumina methylation microarrays including EPICv2, was developed. MethylCallR can be used to control duplicated probes in EPICv2, by using pre-set data implemented in MethylCallR or new customized data. MethylCallR includes a straightforward conversion function between different types of Illumina Human Methylation BeadChips. Using MethylCallR, potential outlier sample detection and statistical power estimation were conducted and used to select meaningful probes. Publicly available data was analyzed using MethylCallR and the findings were compared to that of a previous study. [ABSTRACT FROM AUTHOR]

Identifying outliers in data analysis is a critical task, as outliers can significantly influence the results and conclusions drawn from a dataset. This study explores the use of the Mahalanobis distance metric for detecting outliers in multivariate data, focusing on a novel approach inspired by the work of M. Falk, [On mad and comedians, Ann. Inst. Stat. Math. 49 (1997), pp. 615–644]. The proposed method is rigorously tested through extensive simulation analysis, where it demonstrates high True Positive Rates (TPR) and low False Positive Rates (FPR) when compared to other existing outlier detection techniques. Through extensive simulation analysis, we empirically evaluate the affine equivariance and breakdown properties of our proposed distance measure and it is evident from the outputs that our robust distance measure demonstrates effective results with respect to the measures FPR and TPR. The proposed method was applied to seven different datasets, showing promising true positive rates (TPR) and false positive rates (FPR), and it outperformed several well-known outlier identification approaches. We can effectively use our proposed distance measure in fields demanding outlier detection. [ABSTRACT FROM AUTHOR]

The document outlines the winners of the 2023 Technometrics Prizes, with Ying Jin, Shan Ba, Kungang Zhang, Anh Bui, and Daniel Apley receiving awards for their respective articles. The prizes include a certificate and a cash award. Additionally, the document provides a historical list of past winners of the Technometrics Prizes from 1969 to 2022, showcasing a diverse range of research topics and authors. [Extracted from the article]

We propose the Minimum Regularized Covariance Trace (MRCT) estimator, a novel method for robust covariance estimation and functional outlier detection designed primarily for dense functional data. The MRCT estimator employs a subset-based approach that prioritizes subsets exhibiting greater centrality based on the generalization of the Mahalanobis distance, resulting in a fast-MCD type algorithm. Notably, the MRCT estimator handles high-dimensional datasets without the need for preprocessing or dimension reduction techniques, due to the internal smoothening whose amount is determined by the regularization parameter α > 0 . The selection of α is automated. An extensive simulation study demonstrates the efficacy of the MRCT estimator in terms of robust covariance estimation and automated outlier detection, emphasizing the balance between noise exclusion and signal preservation achieved through appropriate selection of α. The method converges fast in practice and performs favorably when compared to other functional outlier detection methods. [ABSTRACT FROM AUTHOR]

Crater identification aims to match the craters of a planetary body observed in an image to the corresponding entries in a catalogue of known craters of the same body. It is a key step in crater-based navigation algorithms for planetary missions. Many crater identification methods extract descriptors from the observed craters and compare them to an index of descriptors constructed a priori from the crater catalogue. The need for viewpoint invariance motivates descriptors defined over crater tuples (e.g. , crater triads for projective invariance). However, this implies a descriptor index that grows rapidly (e.g. , cubically) with the size of the catalogue, which practically limits the spatial resolution and/or coverage of the catalogue. Another serious drawback is the sensitivity of the descriptors and the matching accuracy to noise and outliers in the crater detection result, which are inevitable due to imperfect crater detection algorithms. In this paper, we propose a novel descriptorless crater identification technique. At its core, our method solves the perspective cone alignment problem with geometric verification iteratively over the crater catalogue. We show that our simple algorithm, with time and space complexities that are linear in the catalogue size, is substantially more accurate than state-of-the-art descriptor-based methods in the presence of noise. Moreover, the availability of multi-core onboard processors raises the prospect of speeding up our method through parallelisation. • This paper introduces the first descriptor-less crater identification method. • It comprises a perspective cone alignment solver and a geometric verification scheme. • It achieves 2–3 orders of magnitude memory improvement over descriptor-based methods. • It demonstrated superior robustness against input noise and challenging conditions. [ABSTRACT FROM AUTHOR]

To implement a fully data driven strategy for identifying outliers in clinical datasets used for formula constant optimisation, in order to achieve proper formula predicted refraction after cataract surgery, and to assess the capabilities of this outlier detection method. 2 clinical datasets (DS1/DS2: N = 888/403) of eyes treated with a monofocal aspherical intraocular lens (Hoya XY1/Johnson&Johnson Vision Z9003) containing preoperative biometric data, power of the lens implant and postoperative spherical equivalent (SEQ) were transferred to us for formula constant optimisation. Original datasets were used to generate baseline formula constants. A random forest quantile regression algorithm was set up using bootstrap resampling with replacement. Quantile regression trees were grown and the 25% and 75% quantile, and the interquartile range were extracted from SEQ and formula predicted refraction REF for the SRKT, Haigis and Castrop formulae. Fences were defined from the quantiles and data points outside the fences were marked and removed as outliers before recalculating the formula constants. N B = 1000 bootstrap samples were derived from both datasets, and random forest quantile regression trees were grown to model SEQ versus REF and to estimate the median and 25% and 75% quantiles. The fence boundaries were defined as being from 25% quantile - 1.5·IQR to 75% quantile + 1.5·IQR, with data points outside the fence being marked as outliers. In total, for DS1 and DS2, 25/27/32 and 4/5/4 data points were identified as outliers for the SRKT/Haigis/Castrop formulae respectively. The respective root mean squared formula prediction errors for the three formulae were slightly reduced from: 0.4370 dpt;0.4449 dpt/0.3625 dpt;0.4056 dpt/and 0.3376 dpt;0.3532 dpt to: 0.4271 dpt;0.4348 dpt/0.3528 dpt;0.3952 dpt/0.3277 dpt;0.3432 dpt for DS1;DS2. We were able to prove that with random forest quantile regression trees a fully data driven outlier identification strategy acting in the response space is achievable. In a real life scenario this strategy has to be complemented by an outlier identification method acting in the parameter space for a proper qualification of datasets prior to formula constant optimisation. [ABSTRACT FROM AUTHOR]

The accuracy and reliability of IoT-based sensor networks depend on validating sensed data, including detecting outliers at the node level. This study proposes an online outlier detection approach using Multiple Linear Regression-based adaptive thresholds for real-time IoT/WSN sensor nodes. IoT sensors experience two outlier types: Errors, from sensor malfunctions or low battery, and Events, from sudden environmental changes. The Adaptive Threshold Based Outlier Detection (ATBOD) approach differentiates errors from events using an adaptive threshold that adjusts to real-time data patterns. Unlike existing methods that are used in literature, which lack automated model evolution and suffer from delays and high computational time, ATBOD enhances outlier detection sensitivity without increasing false alarms, which is crucial for efficient IoT sensor board operation. It also improves sensor board lifespan by discarding errors at the node level, preventing energy wastage from transmitting error data to the cloud. ATBOD outperforms existing algorithms, which are referenced for comparison, such as Enhanced Efficient Outlier Detection and Classification Approach (EEODCA), K Nearest Neighbor approximate outlier detection (KNN), and Modified Local Outlier Factor (LOF), in Error Detection Rate, Error False Positive Rate, and Energy Saving Ratio. These advancements represent a significant leap in performance, making ATBOD a superior method for real-time outlier detection in IoT sensor networks. [ABSTRACT FROM AUTHOR]

An outlier probability is the probability that an observation is an outlier. Typically, outlier detection algorithms calculate real-valued outlier scores to identify outliers. Converting outlier scores into outlier probabilities increases the interpretability of outlier scores for domain experts and makes outlier scores from different outlier detection algorithms comparable. Although several transformations to convert outlier scores to outlier probabilities have been proposed in the literature, there is no common understanding of good outlier probabilities and no standard approach to evaluate outlier probabilities. We require that good outlier probabilities be sharp, refined, and calibrated. To evaluate these properties, we adapt and propose novel measures that use ground-truth labels indicating which observation is an outlier or an inlier. The refinement and calibration measures partition the outlier probabilities into bins or use kernel smoothing. Compared to the evaluation of probability in supervised learning, several aspects are relevant when evaluating outlier probabilities, mainly due to the imbalanced and often unsupervised nature of outlier detection. First, stratified and weighted measures are necessary to evaluate the probabilities of outliers well. Second, the joint use of the sharpness, refinement, and calibration errors makes it possible to independently measure the corresponding characteristics of outlier probabilities. Third, equiareal bins, where the product of observations per bin times bin length is constant, balance the number of observations per bin and bin length, allowing accurate evaluation of different outlier probability ranges. Finally, we show that good outlier probabilities, according to the proposed measures, improve the performance of the follow-up task of converting outlier probabilities into labels for outliers and inliers. [ABSTRACT FROM AUTHOR]

The air quality index (AQI) is a commonly employed metric for evaluating air quality across diverse locations and temporal spans. Similar to other environmental datasets, AQI data can exhibit outliers data points markedly divergent from the norm, signifying instances of exceptionally favorable or adverse air quality. This becomes crucial in identifying and comprehending severe pollution episodes with far‐reaching environmental and public health implications. This study utilizes air quality data from January 1, 2014, to January 31, 2021, collected at daily intervals in Shanghai City, China, as the experimental dataset. The dataset includes daily AQI measurements, along with six pollutant concentrations: particulate matter (PM2.5 and PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO). Each pollutant's concentration is measured in micrograms per cubic meter (μ$$ \upmu $$g/m 3$$ {}^3 $$). The dataset is then preprocessed by cleaning and normalizing it before using K‐means clustering to discover different patterns. A stacked ensemble machine learning model that incorporates K‐means clustering, random forest (RF) and gradient boosting classifier (GBC) is developed and compared to decision tree, support vector machine, K‐nearest neighbor and Naive Bayes algorithms to evaluate its performance in identifying outliers using accuracy, precision, recall, and F1‐score. The stacked model outperformed all other established models based on the accuracy, precision, recall, and F1‐score of 0.99, 0.99, 0.97, and 0.99, respectively. [ABSTRACT FROM AUTHOR]

The objective of this study was to identify the manufacturer of Hanji, Korean handmade paper widely used in conservation science. To achieve this, machine learning models utilizing attenuated total reflectance–infrared spectroscopy (ATR–IR) were developed to assess the robustness and effectiveness of the computed models. Principal component analysis (PCA), partial least squares–discriminant analysis (PLS–DA), decision tree (DT), and k-NN models were constructed using IR spectral data, with the spectral region between 1800 and 1500 cm⁻1 identified as the critical input variable through Variable Importance in Projection (VIP) scores. The transformation of the obtained spectra into second derivative spectra proved beneficial in this key spectral region, leading to significant improvements in model performance. Additionally, the application of DBSCAN for outlier detection was effective in refining the dataset, further enhancing the performance of the models. Specifically, the k-NN model, when applied to the selected variables and preprocessed with the second derivative transformation, achieved an F1 score of 0.92. These findings underscore the importance of focusing on the 1800–1500 cm⁻1 spectral range and applying outlier detection techniques, such as DBSCAN, to enhance the robustness and accuracy of the Hanji classification models by eliminating the influence of atypical data points. [ABSTRACT FROM AUTHOR]

AbstractAccurate model performance assessment in survival analysis is imperative for robust predictions and informed decision-making. Traditional residual diagnostic tools like martingale and deviance residuals lack a well-characterized reference distribution for censored regression, making numerical statistical tests based on these residuals challenging. Recently, the introduction of Z-residuals for diagnosing survival models addresses this limitation. However, concerns arise from conventional methods that utilize the entire dataset for both model parameter estimation and residual assessment, which may cause optimistic biases. This paper introduces cross-validatory Z-residuals as an innovative approach to address these limitations. Employing a cross-validation (CV) framework, the method systematically partitions the dataset into training and testing sets to reduce the optimistic bias. Our simulation studies demonstrate that, for goodness-of-fit tests and outlier detection, cross-validatory Z-residuals are significantly more powerful (e.g. power increased from 0.2 to 0.6). and more discriminative (e.g. AUC increased from 0.58 to 0.85) than Z-residuals without CV. We also compare the performance of Z-residuals with and without CV in identifying outliers in a real application that models the recurrence time of kidney infection patients. Our findings suggest that cross-validatory Z-residuals can identify outliers, which Z-residuals without CV fail to identify. The CV Z-residual is a more powerful tool than the No-CV Z-residual for checking survival models, particularly in goodness-of-fit tests and outlier detection. We have published a generic function, which is collected in an R package called Zresidual, for computing CV Z-residual for the output of the widely used survival R package. [ABSTRACT FROM AUTHOR]

Outlier detection identifies the objects which are deviated from the other objects in the dataset. However, outlier detection is challenged due to data scarcity and abnormal system behaviour. These limitations are resolved with Generative Adversarial Networks (GANs) which has significant attention with the capacity of new data generation. In this paper, the novel outlier detection approach Adaptive Mine Blast Optimization (AMBO) GAN is proposed for differentiating the fake data from the original one. GAN improves the data quality and efficiency by correcting errors and reducing the noise. GAN involves iterative backup loop between generator and descriminator. The network loss of GAN is minimized with AMBO algorithm which outperforms other metaheuristic approaches in terms of convergence and solution. In AMBO, the function values are minimized to near optimal solution during early iterations. The proposed approach is analysed with abalone age dataset in which the classification related metrics are considered for performance evaluation. In addition to that, the sensitivity, specificity, accuracy, AUC, and G-mean performance obtained with the proposed approach is 95%, 96%, 98%, 0.92, and 0.9564 respectively. The numerical outcomes show that the model is best suited for real-time environment and outperforms the functionality of the existing systems. Proposed appraoch is appropriate for real-time applications as it learns the dataset's internal data representations and complex data. [ABSTRACT FROM AUTHOR]

The outliers in dam safety monitoring data can have an impact on the correctness and timeliness of dam safety analysis and decision-making. In order to accurately and efficiently detect outliers in dam safety monitoring data, an anomaly data detection algorithm based on IF Encoder was proposed. The target sequence was rebuilt based on the correlation between time series, and the residual size between the re- built sequence and the target sequence was compared to identify outliers. In addition, according to regulatory requirements, a correlation based outlier identification method was proposed, which divided the detected outliers into true and false anomalies, and removed false anomalies while retaining the true outliers. The results show that compared with the quartile method, Rayda criterion, KNN nearest neighbor method, and DBSCAN clustering method, the IF-Encoder algorithm has improved recall, precision, and accuracy in detecting outliers, and its recognition of outliers is more reliable and effective. The correlation based outlier identification method has an accuracy rate of 92% for identifying true anomalies and 100% for identifying false anomalies, which can effectively classify outliers. [ABSTRACT FROM AUTHOR]

Excessive carbon dioxide ( CO 2 ) emissions pose a formidable challenge, driving global climate change and necessitating urgent attention. Striking a balance between curbing CO 2 emissions and fostering economic growth hinges upon the ability to reliably forecast CO 2 emissions. Such forecasts are indispensable for policymakers as they endeavor to make informed decisions and proactively implement mitigation measures. In this research, we introduce an innovative deep ensemble prediction model for CO 2 emissions. This model is constructed around four parallel Long Short-Term Memory (LSTM) neural networks, complemented by a novel Multi-Layer Perception (MLP)-based ensemble framework, equipped with an outlier detection mechanism and an order-invariant ranking module. To enhance prediction accuracy and stability, a k-nearest neighbor (KNN)-based outlier detection module is employed to identify non-outliers and reasonable predictions for the ensemble models. Additionally, a novel feature ranking module is proposed to mitigate prediction fluctuations. The performance evaluation of our model is conducted using historical CO 2 emission data spanning from 1971 to 2021, encompassing six representative countries. Our findings demonstrate that the proposed methodology outperforms existing approaches across various evaluation metrics, offering considerably reduced prediction variances and greater stability. Moreover, long-term CO 2 emission predictions for the corresponding six countries have been provided, which might offer policymakers some basis for making decisions. [ABSTRACT FROM AUTHOR]

During the development of a field, many fluid samples are taken from wells. Selecting a robust fluid sample as the reservoir representative helps to have a better field characterization, reliable reservoir simulation, valid production forecast, efficient well placement and finally achieving optimized ultimate recovery. First, this paper aims to detect and separate the samples that have been collected under poor conditions or analyzed in a non-standard way. Moreover, it introduces a novel ranking method to score the samples based on the amount of coordination with other fluid samples in the region. The dataset includes 136 fluid samples from five reservoirs in Iranian fields, each of them consisting of 21 key parameters. Five acknowledged machine learning based anomaly detection techniques are implemented to compare fluid samples and detect those whose results deviate from others, indicating non-standard samples. To ensure the proper detection of outlier data, the results are compared with the traditional validation method of gas-oil ratio estimation. All five outlier detection methods demonstrate acceptable performance with average accuracy of 79% compared to traditional validation. Furthermore, the fluid samples with the highest scores in scoring-based algorithms are introduced as the best reservoir's representative fluid. Finally, fuzzy logic is used to obtain a final score for each sample, taking the results of the six methods as input and ranking the samples based on their output score. The study confirms the robustness of the novel approach for fluid validation using outlier detection techniques and the value of machine learning and fuzzy logic for sample ranking, excelling in considering all critical fluid parameters simultaneously over traditional methods. [ABSTRACT FROM AUTHOR]

This paper provides a review and comparative analysis of trajectory outlier detection methods. It presents the definition of outliers in trajectory data and the existing types to further examine the advanced approaches. Basic steps for detecting an outlier, which include data preprocessing, feature extraction, modeling, and similar, have been presented. Moreover, advanced methods such as autoencoders and the use of deep learning for outlier detection have been explored. In the end, this paper evaluates the techniques and compares them using common metrics, mainly focusing on the techniques based on autoencoders or deep learning. It covers applications in real life and practice along with any limitations, challenges, and perspective ideas for the future. Ultimately, it can be a useful resource for expanding the understanding of domain researchers and practitioners. [ABSTRACT FROM AUTHOR]

Outlier detection has garnered considerable attention in recent years due to its wide-ranging applications across various research domains. This surge in interest has led to the development of numerous detection techniques, predominantly based on distance or density metrics. A notable limitation of these existing methods is their reliance on parameter adjustments, significantly affecting the outcome. Additionally, these methods exhibit intrinsic flaws: distance-based approaches struggle with clusters with varying local densities, while density-based methods fail to identify patterns within low-density areas. Moreover, most prior techniques are adept at identifying only one kind of outlier—local, global, or group of outliers. Addressing these challenges, we introduce the Adaptive Radius Density-Based Outlier Detection (ARDOD) method, which departs from the traditional parameter-dependent approach. ARDOD is a novel parameter-free algorithm that dynamically determines the necessary parameters based on the data distribution within the feature space. This innovative method demonstrates robust performance in detecting all three categories of outliers. The efficacy and superior performance of ARDOD are validated through an extensive experimental analysis involving various synthetic and real-world datasets. This analysis showcases ARDOD's advantages over seven established methods: Local Outlier Factor (LOF), Angle-Based Outlier Detection (ABOD), Robust Distance-Based Outlier Score (RDOS), Directed density ratio Changing Rate-based outlier detection (DCROD), Empirical-Cumulative-distribution-based Outlier Detection(ECOD), mean-shift outlier detector(MOD +),and Local–Global Outlier Detection (LGOD), underscoring its potential as a versatile tool in outlier detection research. [ABSTRACT FROM AUTHOR]

Climate change is having an impact on locally adapted species, including the cork oak (Quercus suber L.), a key tree species in the Mediterranean region especially due to its role in carbon sequestration. Notably, the Maâmora forest in Morocco stands as the largest forest in the Mediterranean basin with the highest diversity compared to similar forests across Morocco. In this study, we genotyped 240 samples from natural populations of cork oak in the Maâmora forest using seven microsatellite markers, to investigate signatures of local adaptation. Using a genomic scan approach, we identified outliers loci experiencing divergent natural selection pressures, which were later correlated with climatic, edaphic, and geographic conditions of the Maâmora forest. Statistical analysis of the data shows a high allelic variation. The detection of outliers, carried out using BayeScan v2.1 software, revealed the presence of two significant outliers loci ZAG20-171 (qvalue = 0.0004) and MSQ4-211 (qvalue = 0.04). The result of the environmental association analysis revealed a significant correlation between locus ZAG20-171 with mean diurnal range, isothermality, annual precipitation, precipitation of the driest month, precipitation of the wettest quarter and altitude. This work aims to help understanding the genetic basis of adaptation in Q. suber L., and thus the selection of suitable provenances for further breeding efforts. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System fast precise positioning can be achieved with accurate ionospheric corrections computed from an adequate number of GNSS stations in a local region. In low-latitude regions, the presence of electron density gradients over short distances can lead to outliers in the map of ionospheric corrections and decrease its accuracy. In this study, we explored outlier detection in ionospheric correction mapping through statistical residuals during a four-month test in 2021. Our findings indicate that the residuals of the local ionospheric model conform to the Laplace distribution. To determine outliers, we use an empirical rule for the Laplace distribution, setting thresholds at μ ± 3b, μ ± 3.5b, and μ ± 5.8b for data retention rates of 95%, 97%, and 99.7%, respectively. Here, μ represents the location parameter, which corresponds to the median of data, and b is the scale parameter, calculated as the medium absolute deviation. We found that while removing outliers can improve model accuracy, it may result in unavailable prediction due to a lack of data in a spare network. For example, applying a μ ± 3.5b threshold for outlier removal led to approximately 2.5% of recording time having no ionospheric corrections map in low-latitude regions, however, the local model has the potential to improve its mean accuracy by up to 50% for both low and mid-latitudes. Therefore, choosing the appropriate percentile threshold depends on the network configuration and the desired accuracy. Removing erroneous satellite data to improve ionospheric accuracy brings positive impacts on precise positioning. [ABSTRACT FROM AUTHOR]

Explainable AI refers to techniques by which the reasons underlying decisions taken by intelligent artifacts are single out and provided to users. Outlier detection is the task of individuating anomalous objects within a given data population they belong to. In this paper we propose a new technique to explain why a given data object has been singled out as anomalous. The explanation our technique returns also includes counterfactuals, each of which denotes a possible way to "repair" the outlier to make it an inlier. Thus, given in input a reference data population and an object deemed to be anomalous, the aim is to provide possible explanations for the anomaly of the input object, where an explanation consists of a subset of the features, called choice, and an associated set of changes to be applied, called mask, in order to make the object "behave normally". The paper presents a deep learning architecture exploiting a features choice module and mask generation module in order to learn both components of explanations. The learning procedure is guided by an ad-hoc loss function that simultaneously maximizes (minimizes, resp.) the isolation of the input outlier before applying the mask (resp., after the application of the mask returned by the mask generation module) within the subspace singled out by the features choice module, all that while also minimizing the number of features involved in the selected choice. We consider also the case in which a common explanation is required for a group of outliers provided together in input. We present experiments on both artificial and real data sets and a comparison with competitors validating the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Unsupervised learning methods are well established in the area of anomaly detection and achieve state of the art performances on outlier datasets. Outliers play a significant role, since they bear the potential to distort the predictions of a machine learning algorithm on a given dataset. Especially among PCA-based methods, outliers have an additional destructive potential regarding the result: they may not only distort the orientation and translation of the principal components, they also make it more complicated to detect outliers. To address this problem, we propose the robust outlier detection algorithm CoMadOut, which satisfies two required properties: (1) being robust towards outliers and (2) detecting them. Our CoMadOut outlier detection variants using comedian PCA define, dependent on its variant, an inlier region with a robust noise margin by measures of in-distribution (variant CMO) and optimized scores by measures of out-of-distribution (variants CMO*), e.g. kurtosis-weighting by CMO+k. These measures allow distribution based outlier scoring for each principal component, and thus, an appropriate alignment of the degree of outlierness between normal and abnormal instances. Experiments comparing CoMadOut with traditional, deep and other comparable robust outlier detection methods showed that the performance of the introduced CoMadOut approach is competitive to well established methods related to average precision (AP), area under the precision recall curve (AUPRC) and area under the receiver operating characteristic (AUROC) curve. In summary our approach can be seen as a robust alternative for outlier detection tasks. [ABSTRACT FROM AUTHOR]

Accurate determination of physio‐chemical properties of polyethylene glycol as a green non‐toxic solvent is vital for any chemical and energy process involving polyethylene glycol. Machine learning algorithms are known as robust methods for prediction purposes. This study put forward least squares support vector machine scheme optimized with either particle swarm optimization, genetic algorithm and coupled simulated annealing optimization methods to precisely carry out the prediction task of polyethylene glycol viscosity in terms of its influencing parameters of pressure, temperature and polyethylene glycol molecular weight using a dataset containing experimental pertinent values. An outlier detection algorithm is made use of to confirm the data reliability for model development. In addition, sensitivity study is done to ascertain the relative impacts of each input factor on polyethylene glycol viscosity. The results showed that least squares support vector machine optimized with coupled simulated annealing is the most accurate model for the PEG viscosity prediction task with coefficient of determination of 0.997, average absolute relative error percent of 0.653 and mean square error of 8.005 for all datapoints. In addition, it was found that unlike temperature, we observe an indirect correlation of pressure and polyethylene glycol molecular weight with the pertinent viscosity data. The robustness of the developed least squares support vector machine optimized with coupled simulated annealing is further approved as it outperforms a renowned correlation that is often used for general viscosity prediction. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Exploring the data quality problems in the context of federated cooperation and adopting corresponding governance countermeasures can facilitate the smooth progress of federated cooperation and obtain high-performance models. However, previous studies have rarely focused on quality issues in federated cooperation. To this end, this paper analyzes the quality problems in the federated cooperation scenario and innovatively proposes a "Two-stage" data quality governance framework for the federated collaboration scenarios. The first stage is mainly local data quality assessment and optimization, and the evaluation is performed by constructing a metrics scoring formula, and corresponding optimization measures are taken at the same time. In the second stage, the outlier processing mechanism is introduced, and the Data Quality Federated Averaging (Abbreviation DQ-FedAvg) aggregation method for model quality problems is proposed, so as to train high-quality global models and their own excellent local models. Finally, experiments are conducted in real datasets to compare the model performance changes before and after quality governance, and to validate the advantages of the data quality governance framework in a federated learning scenario, so that it can be widely applied to various domains. The governance framework is used to check and govern the quality problems in the federated learning process, and the accuracy of the model is improved. [ABSTRACT FROM AUTHOR]

As lithium-ion batteries are widely used in electric vehicles, safety accidents caused by battery failures emerge one after another. Nevertheless, failures caused by changes in the internal structure or characteristics of the battery, such as sudden and progressive failures, are still a serious problem for electric vehicles, challenging existing fault diagnosis methods. This paper first performs wavelet packet decomposition on the battery's raw voltage signal to obtain high-quality low-frequency and high-frequency characteristic signal components. Then performs singular value decomposition on the characteristic signal components to extract the corresponding singular value characteristic parameters, and introduces the Manhattan average distance algorithm to battery faults. Diagnosing and locating faulty battery units using the Laida criterion (3-σ criterion) outlier detection method. Finally, actual vehicle data were used to verify the reliability, stability, accuracy of the method, and compared with the traditional Manhattan distance, correlation coefficient, information entropy methods. The method in this paper has good fault detection effects on vehicles with sudden and progressive faults vehicles. [ABSTRACT FROM AUTHOR]

Hit screening, which involves the identification of compounds or targets capable of modulating disease-relevant processes, is an important step in drug discovery. Some assays, such as image-based high-content screenings, produce complex multivariate readouts. To fully exploit the richness of such data, advanced analytical methods that go beyond the conventional univariate approaches should be employed. In this work, we tackle the problem of hit identification in multivariate assays. As with univariate assays, a hit from a multivariate assay can be defined as a candidate that yields an assay value sufficiently far away in distance from the mean or central value of inactives. Viewed another way, a hit is an outlier from the distribution of inactives. A method was developed for identifying multivariate hit in high-dimensional data sets based on principal components and robust Mahalanobis distance (the multivariate analogue to the Z- or T-statistic). The proposed method, termed mROUT (multivariate robust outlier detection), demonstrates superior performance over other techniques in the literature in terms of maintaining Type I error, false discovery rate and true discovery rate in simulation studies. The performance of mROUT is also illustrated on a CRISPR knockout data set from in-house phenotypic screening programme. [ABSTRACT FROM AUTHOR]

Coronavirus disease (COVID-19) is a respiratory condition that has quickly expanded to pandemic levels, affecting people in over 192 countries around the world. Estimating the number of new COVID-19 cases is never an easy undertaking. This study explores the application of Auto-Regressive Integrated Moving Average (ARIMA) modelling combined with Discrete Wavelet Transform (DWT) for analyzing and predicting time series data of Covid-19 patients. It aims to comprehend the disease progression and forecast future trends. The methodology makes use of the ARIMA model, a statistical tool that captures temporal correlations, K-means clustering for outlier detection and removal, and the discrete wavelet transform (DWT) for feature selection. This combination allows for a more thorough evaluation of the patterns and deviations in the COVID-19 patient data. The Enhanced ARIMA (EARIMA) model aims to enhance the current ARIMA-based time series prediction methods. By employing this hybrid approach, the study aims to provide a robust framework for understanding the temporal dynamics of Covid-19 patient data, offering more accurate and interpretable predictions for future trends in the progression of the disease. It improves on the current ARIMA based time-series prediction algorithms. The EARIMA was evaluated using COVID-19 data set gotten from the WHO official website and was compared with various state-of-the-art algorithms of today. The EARIMA received an RSS score of 0.326 and an accuracy of 98.7%, which is better than the existing ARIMA model, which has an RSS score of 0.659 and an accuracy of 93%. The findings contribute to enhancing forecasting accuracy and aiding in decision-making processes for healthcare resource allocation and pandemic management strategies. [ABSTRACT FROM AUTHOR]

Discriminant analysis (DA) is one of the most popular methods for classification due to its conceptual simplicity, low computational cost, and often solid performance. In its standard form, DA uses the arithmetic mean and sample covariance matrix to estimate the center and scatter of each class. We discuss and illustrate how this makes standard DA very sensitive to suspicious data points, such as outliers and mislabeled cases. We then present an overview of techniques for robust DA, which are more reliable in the presence of deviating cases. In particular, we review DA based on robust estimates of location and scatter, along with graphical diagnostic tools for visualizing the results of DA. This article is categorized under:Statistical and Graphical Methods of Data Analysis > Robust MethodsStatistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification [ABSTRACT FROM AUTHOR]

Froth flotation, a widely used mineral beneficiation technique, generates substantial volumes of data, offering the opportunity to extract valuable insights from these data for production line analysis. The quality of flotation data is critical to designing accurate prediction models and process optimisation. Unfortunately, industrial flotation data are often compromised by quality issues such as outliers that can produce misleading or erroneous analytical results. A general approach is to preprocess the data by replacing or imputing outliers with data values that have no connection with the real state of the process. However, this does not resolve the effect of outliers, especially those that deviate from normal trends. Outliers often occur across multiple variables, and their values may occur in normal observation ranges, making their detection challenging. An unresolved challenge in outlier detection is determining how far an observation must be to be considered an outlier. Existing methods rely on domain experts' knowledge, which is difficult to apply when experts encounter large volumes of data with complex relationships. In this paper, we propose an approach to conduct outlier analysis on a flotation dataset and examine the efficacy of multiple machine learning (ML) algorithms—including k-Nearest Neighbour (kNN), Local Outlier Factor (LOF), and Isolation Forest (ISF)—in relation to the statistical 2 σ rule for identifying outliers. We introduce the concept of "quasi-outliers" determined by the 2 σ threshold as a benchmark for assessing the ML algorithms' performance. The study also analyses the mutual coverage between quasi-outliers and outliers from the ML algorithms to identify the most effective outlier detection algorithm. We found that the outliers by kNN cover outliers of other methods. We use the experimental results to show that outliers affect model prediction accuracy, and excluding outliers from training data can reduce the average prediction errors. [ABSTRACT FROM AUTHOR]

The problem of Power Quality analysis is becoming crucial to ensuring the proper functioning of complex systems and big plants. In this regard, it is essential to rapidly detect anomalies in voltage and current signals to ensure a prompt response and maximize the system's availability with the minimum maintenance cost. In this paper, anomaly detection algorithms based on machine learning, such as One Class Support Vector Machine (OCSVM), Isolation Forest (IF), and Angle-Based Outlier Detection (ABOD), are used as a first tool for rapid and effective clustering of the measured voltage and current signals directly on-line on the sensing unit. If the proposed anomaly detection algorithm detects an anomaly, further investigations using suitable classification algorithms are required. The main advantage of the proposed solution is the ability to rapidly and efficiently detect different types of anomalies with low computational complexity, allowing the implementation of the algorithm directly on the sensor node used for signal acquisition. A suitable experimental platform has been established to evaluate the advantages of the proposed method. All the different models were tested using a consistent set of hyperparameters and an output dataset generated from the principal component analysis technique. The best results achieved included models reaching 100% recall and a 92% F1 score. [ABSTRACT FROM AUTHOR]

BACKGROUND Outpatient visits for nonmelanoma skin cancer (NMSC) and actinic keratoses (AK) have risen steadily in the United States, notably among Medicare beneficiaries. Individuals may delay seeking care for minimally symptomatic conditions until they qualify for Medicare coverage, indicating potential delay of nonurgent screening interventions for uninsured or underinsured patients younger than 65 years. OBJECTIVE This study investigates whether an atypical increase in outpatient visits for NMSC, AK, or actinic cheilitis (AC) occurs at the age of Medicare transition by utilizing the National Ambulatory Care Survey from 1993 to 2019. MATERIALS AND METHODS The National Ambulatory Care Survey data were analyzed for patients aged within 5 years of 65 years. Diagnoses were identified using International Classification of Diseases codes. Linear regression and outlier detection were used to identify a relationship between Medicare eligibility and outpatient visits for NMSC and AK/AC. RESULTS Predicted visits for AK/AC and NMSC increased with age. However, there was no evidence of a disproportionate increase in outpatient visits for NMSC and AK/AC at the age of Medicare eligibility. CONCLUSION Outside evidence indicates health care utilization increases after Medicare transition. This study's data do not support a corresponding rise in outpatient visits for NMSC and AK/AC at the age of Medicare eligibility. [ABSTRACT FROM AUTHOR]

Plant pathogens are constantly under selection pressure for host resistance adaptation. Soybean cyst nematode (SCN, Heterodera glycines) is a major pest of soybean primarily managed through resistant cultivars; however, SCN populations have evolved virulence in response to selection pressures driven by repeated monoculture of the same genetic resistance. Resistance to SCN is mediated by multiple epistatic interactions between Rhg (for resistance to H. glycines) genes. However, the identity of SCN virulence genes that confer the ability to overcome resistance remains unknown. To identify candidate genomic regions showing signatures of selection for increased virulence, we conducted whole genome resequencing of pooled individuals (Pool‐Seq) from two pairs of SCN populations adapted on soybeans with Peking‐type (rhg1‐a, rhg2, and Rhg4) resistance. Population differentiation and principal component analysis‐based approaches identified approximately 0.72–0.79 million SNPs, the frequency of which showed potential selection signatures across multiple genomic regions. Chromosomes 3 and 6 between population pairs showed the greatest density of outlier SNPs with high population differentiation. Conducting multiple outlier detection tests to identify overlapping SNPs resulted in a total of 966 significantly differentiated SNPs, of which 285 exon SNPs were mapped to 97 genes. Of these, six genes encoded members of known stylet‐secreted effector protein families potentially involved in host defence modulation including venom‐allergen‐like, annexin, glutathione synthetase, SPRYSEC, chitinase, and CLE effector proteins. Further functional analysis of identified candidate genes will provide new insights into the genetic mechanisms by which SCN overcomes soybean resistance and inform the development of molecular markers for rapidly screening the virulence profile of an SCN‐infested field. [ABSTRACT FROM AUTHOR]

This study introduces an enhanced recursive least-squares algorithm that applies the adaptive accelerated proximal gradient method to identify Auto-Regressive Exogenous models with output outliers. First, the outlier problem was converted into a robust principal component analysis problem. The adaptive accelerated proximal gradient method is then introduced to recover the information matrix, and the recursive least squares algorithm is applied to estimate the model parameters. Furthermore, we demonstrated that the parameter estimation is unbiased and that the parameter estimation error converges to zero under persistent excitation. A series of bench tests consistently highlighted the practicality and effectiveness of the proposed algorithm. • The accelerated proximal gradient algorithm is introduced for effective outlier recovery. • The proposed adaptive accelerated proximal gradient algorithm can adaptively adjust the step-size. • A comprehensive framework is established by integrating accelerated proximal gradient and recursive least squares algorithms. • The proposed algorithm is validated through a state of charge estimation problem, demonstrating its effectiveness. [ABSTRACT FROM AUTHOR]

Statistical depths provide a fundamental generalization of quantiles and medians to data in higher dimensions. This paper proposes a new type of globally defined statistical depth, based upon control theory and eikonal equations, which measures the smallest amount of probability density that has to be passed through in a path to locations on the boundary of the support of the distribution or spatial infinity for distributions with unbounded support. This depth is easy to interpret and compute, expressively captures multi-modal behavior, and extends naturally to data that is non-Euclidean. We prove various properties of this depth, and provide discussion of computational considerations. In particular, we demonstrate that this notion of depth is robust under an approximate isometrically constrained adversarial model, a property which is not enjoyed by the Tukey depth. Finally we give some illustrative examples in the context of two-dimensional mixture models and MNIST. [ABSTRACT FROM AUTHOR]

In order to improve the detection accuracy of CO2 and other gases in the flue gas emitted from thermal power plants, a CO2 concentration detection model based on tunable semiconductor laser absorption spectroscopy was proposed. First, a variational mode decomposition model was used to filter the harmonic signal after removing the outliers to reduce the influence of noise on the detection results. Suitable absorption lines and concentration characteristics were then selected according to the gas absorption properties and correlation theory. Finally, the CO2 concentration inversion was completed using long short-term memory networks, and a Bayesian optimization algorithm was introduced to optimize the hyperparameters of the network. The experimental results showed that the R 2 and RMSE of the test set were 0.998 84 and 0.116 08, respectively, in the concentration range of 1%–12%. In addition, the Allan analysis of variance revealed that the maximum measurement error of CO2 was only 0.005 619% when the integration time was 38 s. Compared to the traditional CO2 detection schemes, the detection accuracy and stability are significantly improved, which provides a feasible scheme for flue gas detection in thermal power plants. [ABSTRACT FROM AUTHOR]

Being the parent distribution in Romania, the Pearson III distribution (PE3) is one of the statistical distributions that is most frequently used in flood frequency analysis (FFA). All the components required for the simple implementation of PE3 distribution in FFA are presented in this manuscript. The estimated methods and exact and approximate relationships for estimating the parameters and frequency factors particular to the distribution are described. All of these factors are used to identify maximum flows with various annual exceedance probabilities, using data collected at the 6 rivers with different morphometric characteristics and different lengths of data series. Five parameter estimation techniques are used in this comparative analysis, i.e. the method of ordinary moments (MOM), the method of linear moments (L-moments), the high-order linear moments method (LH-moments), the method of maximum likelihood (MLE) and the method of least squares (LSM). Given the results, it can be assumed that the L-moments approach is more reliable, stable, robust and less sensitive to variations in recorded data lengths, as well as to the presence of outliers. [ABSTRACT FROM AUTHOR]

Recently, the commercialization of autonomous vehicles has increased the importance of verifying vehicle safety through autonomous trials. Autonomous driving trials are conducted in limited areas within artificially constructed test roads and pilot districts and directly explore road sections and areas with similar environments to ensure the safety of AVs driving on real roads. Many previous studies have evaluated the complex response potential of AVs by deriving edge scenarios to ensure their safety. However, difficulties arise in exploring real roads with traffic accident factors and configurations similar to those in edge scenarios, making validation on real roads uncertain. This paper proposes a novel method for exploring pilot zones using traffic accident data to verify the safety of autonomous vehicles (AVs). The method employs a CNN + BiGRU model trained on DMV dataset data to classify traffic accidents as AV- or human-caused. The model's classification accuracy was evaluated using recall, precision, F1 score, and accuracy, achieving 100.0%, 97.8%, 98.9, and 99.5%, respectively. The trained model was applied to the KNPA dataset, identifying 562 out of 798 cases as AV-like, indicating potential areas of high accident density due to AV operation. Outlier detection and DBSCAN clustering were utilized to identify compact pilot zones, effectively reducing the area size compared to raw data clusters. This approach significantly lowers the cost and time associated with selecting test roads and provides a viable alternative for countries lacking real AV accident data. The proposed method's effectiveness in identifying pilot zones demonstrates its potential for advancing AV safety validation. [ABSTRACT FROM AUTHOR]

Diabetes mellitus (DM) is a global health challenge that requires advanced strategies for its early detection and prevention. This study evaluates the South Korean population using the Korea National Health and Nutrition Examination Survey (KNHANES) dataset from 2015 to 2021, provided by the Korea Disease Control and Prevention Agency (KDCA), focusing on improving diabetes prediction models. Outlier removal was implemented using Mahalanobis distance (MAH), and feature selection was based on multicollinearity (MC) and reliability analysis (RA). The proposed Extreme Gradient Boosting (XGBoost) model demonstrated exceptional performance, achieving an accuracy of 98.04% (95% CI: 97.89~98.59), an F1-score of 98.24%, and an Area Under the Curve (AUC) of 98.71%, outperforming other state-of-the-art models. The study highlights the significance of rigorous outlier detection and feature selection in enhancing the predictive power of diabetes risk models. Notably, a significant increase in diabetes cases was observed during the COVID-19 pandemic, particularly linked to male sex, older age, rural location, hypertension, and obesity, underscoring the need for enhanced public health strategies for early intervention and targeted prevention. [ABSTRACT FROM AUTHOR]

The imminent depletion of oil resources and increasing environmental pollution have driven the use of clean energy, particularly wind energy. However, wind turbines (WTs) face significant challenges, such as critical component failures, which can cause unexpected shutdowns and affect energy production. To address this challenge, we analyzed the Supervisory Control and Data Acquisition (SCADA) data to identify significant differences between the relationship of variables based on data reconstruction errors between actual and predicted values. This study proposes a hybrid short- and long-term memory autoencoder model with multihead self-attention (LSTM-MA-AE) for WT converter fault detection. The proposed model identifies anomalies in the data by comparing the reconstruction errors of the variables involved. However, more is needed. To address this model limitation, we developed a fault prediction system that employs an adaptive threshold with an Exponentially Weighted Moving Average (EWMA) and a fixed threshold. This system analyzes the anomalies of several variables and generates fault warnings in advance time. Thus, we propose an outlier detection method through data preprocessing and unsupervised learning, using SCADA data collected from a wind farm located in complex terrain, including real faults in the converter. The LSTM-MA-AE is shown to be able to predict the converter failure 3.3 months in advance, and with an F1 greater than 90% in the tests performed. The results provide evidence of the potential of the proposed model to improve converter fault diagnosis with SCADA data in complex environments, highlighting its ability to increase the reliability and efficiency of WTs. [ABSTRACT FROM AUTHOR]

This paper proposes a method for assessing differential item functioning (DIF) in item response theory (IRT) models. The method does not require pre-specification of anchor items, which is its main virtue. It is developed in two main steps: first by showing how DIF can be re-formulated as a problem of outlier detection in IRT-based scaling and then tackling the latter using methods from robust statistics. The proposal is a redescending M-estimator of IRT scaling parameters that is tuned to flag items with DIF at the desired asymptotic type I error rate. Theoretical results describe the efficiency of the estimator in the absence of DIF and its robustness in the presence of DIF. Simulation studies show that the proposed method compares favorably to currently available approaches for DIF detection, and a real data example illustrates its application in a research context where pre-specification of anchor items is infeasible. The focus of the paper is the two-parameter logistic model in two independent groups, with extensions to other settings considered in the conclusion. [ABSTRACT FROM AUTHOR]

While field research in indoor air quality (IAQ) increasingly uses large numbers of low-cost sensors, detailed validation of each sensor is becoming infeasible, ignored, or undeclared. Questions remain about whether these sensors meet manufacturer or third-party specifications and whether their utility improves with some validation, even under imperfect conditions. This study details the outcomes of a co-location experiment with 87 low-cost IAQ monitors, conducted with the objective of verifying their performance in measuring total volatile organic compounds (tVOCs), particulate matter 2.5 (PM2.5), carbon dioxide (CO2), temperature, and relative humidity. The monitors were installed next to professional-grade reference IAQ instruments in an office with controlled ventilation. A mixed validation methodology was used, involving outlier detection, uncertainty quantification, and performance metric calculations. Results showed that CO2, temperature, and humidity sensors reliably met manufacturer specifications, while tVOC sensors had significant accuracy issues, deviating by up to 79% from reference measurements—substantially more than the stated uncertainty of ±20%. PM2.5 sensors were more consistent but had an error margin of ±27%, compared to the stated ±15%. A total of 5 of the 87 sensors (5.7%) were outliers for at least one IAQ indicator. Despite the need for further long-term validation, this study highlights the importance of performing an experimental evaluation of low-cost IAQ monitors before field deployment. [ABSTRACT FROM AUTHOR]

Safety monitoring of hydraulic structures is a critical task in the field of hydraulic engineering construction. This study developed a method for preprocessing and classifying monitoring data for the identification of gross errors in hydraulic structures. By utilizing linear regression and wavelet analysis techniques, it effectively differentiated various waveform characteristics in data sets, such as Sinusoidal Wave Cyclical, Triangular Wave Cyclical, Seasonal Cyclical, and Weakly Cyclical growth types. In the experiments for gross error identification, the 3σ algorithm, K-medoids algorithm, and Isolation Forest algorithm were applied to test the data. The results showed that the K-medoids algorithm excelled in processing Sinusoidal Wave Cyclical Data Sets; the 3σ algorithm adapted better to Triangular Wave Cyclical Data Sets; the Isolation Forest algorithm performed well in handling data sets with significant anomalies or atypical fluctuations and excelled in scenarios with strong seasonality and large data fluctuations; and for complex Weakly Cyclical Growth Data Sets, all three algorithms were less effective, indicating the potential need for more advanced analysis methods or a combination of multiple techniques. Testing on actual engineering data further confirmed the importance of using specific gross error identification techniques for special data types after data set pre-classification, providing a more effective technical solution for the safety monitoring of hydraulic structures. [ABSTRACT FROM AUTHOR]

Intrusion detection systems (IDS) are an important tool for securing computer networks from various types of cyberattacks. The increasing complexity of network attacks demands more sophisticated approaches to intrusion detection. This paper presents an innovative method for IDS that involves combining Z-Score outlier detection, KMeans clustering, and Random Forest classification techniques. We tested our methodology using the CICIDS2017 dataset, which is a standardization dataset for intrusion detection that is frequently utilized. Our proposed approach first uses Z-Score outlier detection to identify abnormal traffic flows in the network. Next, KMeans clustering is used to group the traffic flows into different clusters based on their similarity. Finally, Random Forest classification is used to classify each traffic flow into normal or abnormal categories. Based on our experimental results, our approach for intrusion detection shows superior performance compared to several other state-of-the-art methods in terms of accuracy and precision. Our proposed method achieved an accuracy rate of 95.75% and a precision of 95.76%, surpassing the performance of KNN, SVM, and decision trees approaches. In conclusion, the proposed Z-K-R approach offers a promising solution for IDS by leveraging the strengths of Z-Score outlier detection, KMeans clustering, and Random Forest classification techniques. This strategy has the potential to increase the efficiency of IDS and boost network security in applications that take place in the real world. [ABSTRACT FROM AUTHOR]