Mean Shift Research Articles

The performance of the one-sided truncated exponentially weighted moving average X ¯ control chart in the presence of measurement errors

When the direction of a potential mean shift can be anticipated, the one-sided exponentially weighted moving average (EWMA) X ¯ control chart using the truncation method (namely, the one-sided TEWMA X ¯ chart) is more efficient than those conventional one- and two-sided EWMA X ¯ schemes for process monitoring. Although attractive, there are no studies on designing the one-sided TEWMA X ¯ chart by taking measurement errors into account. In this context, we investigate the effect of measurement errors on the performance of the one-sided TEWMA X ¯ chart based on the linear covariate error model. Additionally, a Markov chain model is established to evaluate the run length properties of the scheme in the presence of measurement errors. Then, an optimal design procedure is developed for searching the optimal design parameters of the scheme. Based on these mentioned studies, several tables and figures are presented to evaluate the detecting performance of the scheme under different parameters of the linear covariate error model, and then a conventional one-sided EWMA X ¯ chart with reflecting boundary is introduced to further study the effect of the presence and absence of measurement errors on control chart comparison studies. Simulation results show that although the detecting performance of the proposed scheme is significantly affected by measurement errors, its performance is still superior to the classic competing chart under the same comparison conditions. Finally, an illustrative example is given to show the implementation of the recommended scheme.

Improved shewhart-type control charts based on weak runs in multistate trials

ABSTRACT In this work we introduce two new classes of Shewhart-type control charts with supplementary runs rules using a three or four region partition of the observed characteristic’s range. The study of the control charts’ characteristics (run length distribution, average run length) is carried over by considering an extension of the concept of r -weak runs (which was recently introduced for sequences of binary trials) under a multistate framework. Our numerical experimentation provides useful hints for the implementation of the new control charts by a practitioner and reveals that the proposed charts significantly improve the detection power (as compared to other existing charts) especially for small shifts in the process mean.

An integrated optimal-GICP design for the SPRT control chart with estimated process parameters based on the average number of observations to signal

ABSTRACT As a variable-sample-size control scheme, the sequential probability ratio test (SPRT) chart is favoured due to its sensitivity and high sampling efficiency. It is frequently documented that the SPRT chart inspects only a small number of observations at each sampling stage, making the said chart extremely appealing to quality engineers. In the current literature, most developments of the SPRT chart are established based on the average run length and average time to signal metrics. However, these metrics do not consider sampling efficiency. In this paper, we develop two optimal designs of the SPRT chart, based on (i) the average value of the average number of observations to signal (AANOS) and (ii) the expected value of the AANOS, with consideration of Phase-I process parameter estimation. We develop a model that integrates the concept of optimization and the guaranteed in-control performance (GICP) framework to neutralize the adverse effects of parameter estimation. Results show that the proposed optimal-GICP design preserves satisfactory out-of-control performances for moderate and large mean shifts, while keeping the false alarm rates at reasonably low levels. Finally, we illustrate an example of the SPRT chart with estimated process parameters for monitoring loop height measurements from a wire bonding dataset.

Convergence of the CUSUM estimation for a mean shift in linear processes with random coefficients

Convergence of the CUSUM estimation for a mean shift in linear processes with random coefficients

On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration

Abstract. Deep learning (DL) rainfall–runoff models outperform conceptual, process-based models in a range of applications. However, it remains unclear whether DL models can produce physically plausible projections of streamflow under climate change. We investigate this question through a sensitivity analysis of modeled responses to increases in temperature and potential evapotranspiration (PET), with other meteorological variables left unchanged. Previous research has shown that temperature-based PET methods overestimate evaporative water loss under warming compared with energy budget-based PET methods. We therefore assume that reliable streamflow responses to warming should exhibit less evaporative water loss when forced with smaller, energy-budget-based PET compared with temperature-based PET. We conduct this assessment using three conceptual, process-based rainfall–runoff models and three DL models, trained and tested across 212 watersheds in the Great Lakes basin. The DL models include a Long Short-Term Memory network (LSTM), a mass-conserving LSTM (MC-LSTM), and a novel variant of the MC-LSTM that also respects the relationship between PET and evaporative water loss (MC-LSTM-PET). After validating models against historical streamflow and actual evapotranspiration, we force all models with scenarios of warming, historical precipitation, and both temperature-based (Hamon) and energy-budget-based (Priestley–Taylor) PET, and compare their responses in long-term mean daily flow, low flows, high flows, and seasonal streamflow timing. We also explore similar responses using a national LSTM fit to 531 watersheds across the United States to assess how the inclusion of a larger and more diverse set of basins influences signals of hydrological response under warming. The main results of this study are as follows: The three Great Lakes DL models substantially outperform all process-based models in streamflow estimation. The MC-LSTM-PET also matches the best process-based models and outperforms the MC-LSTM in estimating actual evapotranspiration. All process-based models show a downward shift in long-term mean daily flows under warming, but median shifts are considerably larger under temperature-based PET (−17 % to −25 %) than energy-budget-based PET (−6 % to −9 %). The MC-LSTM-PET model exhibits similar differences in water loss across the different PET forcings. Conversely, the LSTM exhibits unrealistically large water losses under warming using Priestley–Taylor PET (−20 %), while the MC-LSTM is relatively insensitive to the PET method. DL models exhibit smaller changes in high flows and seasonal timing of flows as compared with the process-based models, while DL estimates of low flows are within the range estimated by the process-based models. Like the Great Lakes LSTM, the national LSTM also shows unrealistically large water losses under warming (−25 %), but it is more stable when many inputs are changed under warming and better aligns with process-based model responses for seasonal timing of flows. Ultimately, the results of this sensitivity analysis suggest that physical considerations regarding model architecture and input variables may be necessary to promote the physical realism of deep-learning-based hydrological projections under climate change.

Experimental study of laser spot tracking for underwater optical wireless communication.

In this paper, a novel laser spot tracking algorithm that incorporates the Kalman filter with the continuously adaptive Meanshift algorithm (Cam-Kalm) is proposed and employed in an underwater optical wireless communication (UOWC) system. Since the Kalman filter has the advantage of predicting the state information of the target spot based on its spatial motion features, the proposed algorithm can improve the accuracy and stability of the moving laser spot tracking. A 2 m optical wireless communication experimental system with auto-tracking based on a green laser diode (LD) is built to evaluate the tracking performance of different algorithms. Experimental results verify that the proposed algorithm outperforms conventional tracking algorithms in aspects of tracking accuracy, interference resistance, and response time. With the proposed Cam-Kalm algorithm, the experimental system can establish an effective communication link, while the maximum tracking speed is 20 mm/s given the forward-error-correction (FEC) threshold.

Joint Sample Position Based Noise Filtering and Mean Shift Clustering for Imbalanced Classification Learning

Joint Sample Position Based Noise Filtering and Mean Shift Clustering for Imbalanced Classification Learning

On the performance and comparison of various memory-type control charts

Several versions of the exponentially weighted moving average (EWMA) control chart, such as the generally weighted moving average (GWMA), the double, triple, and quadruple EWMA (DEWMA, TEWMA, and QEWMA), and the homogeneously weighted moving average (HWMA) charts, have been developed to enhance its detection ability, especially for small and moderate shifts. These charts have been criticized as it is considered that an appropriately designed EWMA chart provides as good or better zero-state and steady-state average run-length (ARL) performance. In this article, we perform a careful comparison study of the above control charts under similar in-control (IC) run-length properties. We show that the “new” memory-type control charts have better out-of-control (OOC) run-length characteristics, especially for small to moderate shifts in the process mean for both the zero-state and steady-state cases, except for the HWMA chart where its steady-state OOC performance is very poor. We also present a comparison study based on the worst-case ARL measure. Finally, the extensions of the EWMA chart have better IC robustness ability than the EWMA and HWMA charts.

Online nonparametric monitoring for asynchronous processes with serial correlation

Existing multivariate statistical process control methods commonly require all data streams have the same sampling interval. In practice, this assumption may not be valid, as different sensors can have different sampling intervals. In this article, we first propose a generic nonparametric monitoring scheme to online monitor the asynchronous data streams without considering serial correlation. Then the proposed scheme is extended such that it can handle serially correlated data streams. Specifically, we construct a nonparametric local statistic for each data stream, which is sensitive to mean shifts. To eliminate the influence of different sampling intervals, our innovative idea is to transform the local statistics into time-related statistics according to the sampling intervals. A global monitoring scheme is then constructed based on the sum of top- r time-related statistics. To extend the proposed method for serially correlated data streams, we further propose a novel estimation method for the pairwise covariance functions and the data streams can be decorrelated accordingly. Numerical simulations and a case study are conducted, showing the effectiveness of the proposed method in handling asynchronous data streams with serial correlation.

The epistemic conditional in polar questions as an argumentative strategy

Abstract This paper accounts for the epistemic use of the conditional in polar questions in French. It is argued that polar questions modalised by the epistemic conditional are biased questions that serve an argumentative function, and that the conditional contributes a different commitment update in interrogatives and in questioning declaratives. The bias type depends on whether the question is interrogative or declarative. In interrogatives, the epistemic conditional is generally associated with the subject clitic-verb inversion pattern. This pattern is argued to routinely convey evidential bias, while evidential bias is determined by the context in est-ce que questions. As biased polar questions in the conditional do not undergo interrogative flip, the conjectural meaning conveyed by the combination of the conditional and questioning is reported to be an evidential extension in keeping with evidentiality in non-flip languages (Bhadra, Diti. 2020. The semantics of evidentials in questions. Journal of Semantics 37. 367–423). This might shed new light on the apparent conceptual puzzle posed by the shift in meaning of the epistemic conditional in assertions and in questions.

An adaptive EWMA chart for Poisson process

ABSTRACT This paper introduces an adaptive EWMA (AEWMA) chart designed for monitoring Poisson processes. This chart offers a streamlined implementation process, eliminating the need for additional parameters. Notably, it demonstrates the ability to effectively detect a range of the mean shift sizes. Using Monte Carlo simulations, this study provides estimates for various performance metrics, including the zero-state and steady-state average run-length (ARL), expected weighted run-length, and expected relative ARL profiles for existing and proposed charts. The results indicate that the AEWMA chart surpasses the performances of existing EWMA, CUSUM, and two other AEWMA charts in detecting multiple shifts in the process mean. Additionally, an illustrative example is presented to showcase the practical implementation of the considered charts.

Nucleic acid amplification-free biosensor for sensitive and specific cfDNA detection based on CRISPR-Cas12a and single nanoparticle dark-field microscopy (DFM) imaging

Circulating cell-free DNA (cfDNA) is known to be an attractive biomarker for liquid biopsy. In this work, a rapid and sensitive biosensor was achieved for cfDNA detection based on CRISPR-associated nuclease 12a (CRISPR-Cas12a) and single nanoparticle dark-field microscopy (DFM) imaging. The biosensor employed gold nanoparticles (AuNPs) as signal source, DFM as readout system, and Meanshift algorithms as the image processing systems, respectively. The presence or absence of cfDNA caused the different existing states (monomer or aggregated state) of AuNPs. AuNPs monomers could be effectively distinguished from the aggregated ones under DFM since the AuNPs aggregation could induced the green-to-yellow or green-to-red changing of scattering color. The monomer ratio easily obtained by Meanshift and partial least-square (PLS) algorithms was used for quantitative analysis. Using this strategy, breast cancer gene-1 (BRCA-1), a representative of cfDNA biomarker for breast cancer could be measured with high sensitivity in 40 min and has a low detection limit of 0.081 fM. It is hoped that this nucleic acid amplification-free sensor could be also utilized to detect other nucleic acid biomarkers, and thus, provides a novel strategy for biomedical image analysis.

A Deep-Learning-Based Method for Extracting an Arbitrary Number of Individual Power Lines from UAV-Mounted Laser Scanning Point Clouds

In recent years, laser scanners integrated with Unmanned Aerial Vehicles (UAVs) have exhibited great potential in conducting power line inspections in harsh environments. The point clouds collected for power line inspections have numerous advantages over remote image data. However, point cloud-based individual power line extraction, which is a crucial technology required for power line inspections, still poses several challenges such as massive 3D points, imbalanced category points, etc. Moreover, in various power line scenarios, previous studies often require manual setup and careful adjustment of different thresholds to separate different power lines, which is inefficient for practical applications. To handle these challenges, in this paper, we propose a multi-branch network to automatically extract an arbitrary number of individual power lines from point clouds collected by UAV-based laser scanners. Specifically, to handle the massive 3D point clouds in complex outdoor scenarios, we propose to leverage deep neural network for efficient and rapid feature extraction in large-scale point clouds. To mitigate imbalanced data quantities across different categories, we propose to design a weighted cross-entropy loss function to measure the varying importance of each category. To achieve the effective extraction of an arbitrary number of power lines, we propose leveraging a loss function to learn the discriminative features that can differentiate the points belonging to different power lines. Once the discriminative features are learned, the Mean Shift method can distinguish the individual power lines by clustering without supervision. The evaluations are executed on two datasets, which are acquired at different locations with UAV-mounted laser scanners. The proposed method has been thoroughly tested and evaluated, and the results and discussions confirm its outstanding ability to extract an arbitrary number of individual power lines in point clouds.

Sufficient Reduction Method for Bivariate Zero-Inflated Poisson Process

The sufficient reduction (SR) method was developed for detecting a mean shift in a bivariate zero-inflated Poisson process. The derived sequence of statistics from the reduction was monitored with the EWMA and EWMA-SN charts for monitoring a mean shift in a process. The detection performance was compared against other SR methods developed for a Poisson process and evaluated via the simulations under the different shift sizes and proportions of zero in the process. The results showed that the presence of zeros in the process influenced the performance of SR methods by delaying shift detection and reducing the detection accuracy, especially when shift size was small. The proposed method with the EWMA chart gave the shortest delay for detecting a small to moderate shift and gave the highest true alarm rate and the lowest non-detection rate for detecting a small shift compared to other methods.

Hybrid Clustering Algorithm Based on Improved Density Peak Clustering

In the era of big data, unsupervised learning algorithms such as clustering are particularly prominent. In recent years, there have been significant advancements in clustering algorithm research. The Clustering by Density Peaks algorithm is known as Clustering by Fast Search and Find of Density Peaks (density peak clustering). This clustering algorithm, proposed in Science in 2014, automatically finds cluster centers. It is simple, efficient, does not require iterative computation, and is suitable for large-scale and high-dimensional data. However, DPC and most of its refinements have several drawbacks. The method primarily considers the overall structure of the data, often resulting in the oversight of many clusters. The choice of truncation distance affects the calculation of local density values, and varying dataset sizes may necessitate different computational methods, impacting the quality of clustering results. In addition, the initial assignment of labels can cause a ‘chain reaction’, i.e., if one data point is incorrectly labeled, it may lead to more subsequent data points being incorrectly labeled. In this paper, we propose an improved density peak clustering method, DPC-MS, which uses the mean-shift algorithm to find local density extremes, making the accuracy of the algorithm independent of the parameter dc. After finding the local density extreme points, the allocation strategy of the DPC algorithm is employed to assign the remaining points to appropriate local density extreme points, forming the final clusters. The robustness of this method in handling uncertain dataset sizes adds some application value, and several experiments were conducted on synthetic and real datasets to evaluate the performance of the proposed method. The results show that the proposed method outperforms some of the more recent methods in most cases.

Change point detection via feedforward neural networks with theoretical guarantees

This article mainly studies change point detection for mean shift change point model. An estimation method is proposed to estimate the change point via feedforward neural networks. The complete f-moment consistency of the proposed estimator is obtained. Numerical simulation results show that the performance of the proposed estimator is better than that of cumulative sum type estimator which is widely used in the change point detection, especially when the mean shift signal size is small. Finally, we demonstrate the proposed method by empirically analyzing a stock data set.

Intelligent mapping of geochemical anomalies: Adaptation of DBSCAN and mean-shift clustering approaches

Cluster analysis can be used to organize samples and generate ideas regarding the multivariate geochemistry of given dataset. Traditional clustering techniques have the drawbacks of high computational complexity and poor adaptability to big data. Hence, there has recently been much focus on creating better clustering algorithms. Although many clustering algorithms have been applied, and some produce notable clustering results, the performance efficiency of algorithms is often highly dependent on the values the user chooses for the parameters. Currently, density-based spatial clustering of applications with noise clustering (DBSCAN) is widely utilized in image processing, bioinformatics, and social network analysis owing to its ability to detect clusters of various shapes. Even though partitional clustering techniques may be effective when the number of clusters K is known in advance, they cannot implement non-convex clustering and rapidly return to a local optimum. This study proposes the concept of DBSCAN clustering for stream sediment geochemical data. In this respect, the geochemical data collected from Varcheh district, SW Iran, were processed using the clr transformation before applying DBSCAN. Then, PCA was used to minimize the dimension of variables and specify the mineralization-related elements. In the following, one of the PCs connected with mineralization (PC2) was chosen for further analysis. DBSCAB, Mean-shift and Fuzzy K-means algorithms were used to monitor the multi-element geochemical anomalies linked to MVT PbZn deposits in the study area. According to Davies-Bouldin and Silhouette as two validation metrics, it can be deduced that the three SCB models are advantageous, however, the model generated by DBSCAN is preferable to the model generated by Mean-shift and Fuzzy K-means.

«Жирный» и «полный» в «Преступлении и наказании». К апологии Порфирия Петровича

The article is devoted to the analysis of the image of Porfiry Petrovich. The analysis is based on the lexical drawing of the text created by Dostoevsky in Crime and Punishment by distributing the synonymous words “zhirnyi” (“fat”) and “polnyi” (“full” + “plump”) in it. “Fat,” taking a place in the characteristics of “positive” characters and displacing the more expected in this case “polnyi,” shifts the perception of the word “polniy” to another segment of its semantic field: it is displaced from the reader’s consciousness as related to physical complexion (“plump”) and actualizes the general meaning of completeness (integrity, fulfillment). This shift in meaning is supported by the repeated use of various words in the text with the root “poln” precisely with the meaning of final completion. If we proceed from this point, the author’s characteristics of Porfiry Petrovich, which at first glance seem disjointed, strange, derogatory, and marginalizing, begin to gather into a completely coherent whole. They gather into a unity that is fully described by the character’s name, which in Russian reads like “the red stone” or “the red stage in stone making.”

A Study of the Impact of Integrating Artificial Intelligence with the Archival Profession under Data Mining

Abstract This paper integrates data mining technology into the technical processing layer of the “Artificial Intelligence +” archive intelligent service system to provide technical support for massive archive data. Using association rules, archive classification, Mean-Shift clustering, and other techniques to reveal the relationship between archives and users, archives, archives, and various practical activities so as to play the role of the information of these archive data at a deeper level. Applying the “Artificial Intelligence +” archive wisdom service system to the actual operation of the business management of college archive professional teachers, the results show that the speed of “Artificial Intelligence +” database entry (4.83s) is significantly higher than that of the traditional database (38.62s) the speed is higher. The contour coefficients of the classification results of the experimental group are all above 0.80, realizing high-precision classification of text data, image data, and audio data in digitized archives.2018-2023 The number of business declarations is rising, the total score of the scientific research type varies considerably, and the total score of the teaching type is concentrated in the distribution, with most of them being between 70-80. Mean-shift clustering algorithms can make the scientific research type, the teaching type, and the Research and Teaching and heavy type three data types to achieve a good clustering effect.

Social Spider Optimization Based Optimally Weighted Otsu Thresholding for Image Enhancement

Image enhancement corresponds to processing an image to obtain an image with more perceptual details. In this paper, a social spider optimization (SSO) based scheme is proposed to generate an enhanced image which contains higher contrast and minimum change of entropy with respect to the original image. The proposed method employs histogram equalization with a modified cumulative distribution function to obtain a mapping function. A three-step process is followed to modify the original histogram. The first step is to segment the image histogram into two parts using the Otsu's thresholding method. Then both of the upper and lower histograms are weighted as well as thresholded to control the level of enhancement. The constraint parameters for modification are obtained by SSO. After applying the constrain parameters on the histograms, mean shift correction is performed to ensure there is a minimum level of mean shift from input image to output image. The results indicate that proposed method achieves better color preservation, and balanced contrast enhancement in comparison to existing techniques. The proposed scheme also leads to significant feature enhancement, low contrast boosting, and brightness preservation in the enhanced image, while preserving the natural feel of the original image.