Mean Shift Research Articles

Variable Parameters t Chart: An Application in Automobile Engine Piston Rings Manufacturing Process

The [Formula: see text] type charts possess a drawback as they may falsely indicate out-of-control (OOC) situations when the process standard deviation is unstable. They are not robust against estimation errors or variations in the process standard deviation. To overcome this setback, the [Formula: see text] type charts were developed. This research introduces the variable parameters (VP) [Formula: see text] chart for effectively monitoring the process mean. This study encompasses the implementation, optimal designs and performance evaluation of the proposed VP [Formula: see text] chart. The average time to signal (ATS), standard deviation of the time to signal (SDTS) and expected average time to signal (EATS) criteria are used to assess and compare the performance of the VP [Formula: see text] chart with existing charts, such as Shewhart (SH) [Formula: see text], synthetic (Syn) [Formula: see text], side-sensitive group runs (SSGR) [Formula: see text], exponentially weighted moving average (EWMA) [Formula: see text] and variable sampling interval (VSI) EWMA [Formula: see text] charts. From extensive analysis, we observe that the VP [Formula: see text] chart outperforms the SH [Formula: see text], Syn [Formula: see text] and SSGR [Formula: see text] charts for all sizes of mean shifts. However, the EWMA [Formula: see text] and VSI EWMA [Formula: see text] charts exhibit better performance for small shifts. Additionally, to demonstrate the implementation and usage of the proposed VP [Formula: see text] chart, we present a real-world manufacturing example.

Combined Freak Wave, Wind, and Current Effects on the Dynamic Responses of Offshore Triceratops

Offshore structures are exposed to various environmental loads, including extreme and abnormal waves, over their operational lifespan. The existence of wind and current can exacerbate the dynamic response of these structures, posing threats to safety and integrity. This study focuses on the dynamic responses of offshore triceratops under different environmental conditions characterized by the superimposition of freak waves, uniform wind, and current. The free surface profile of the freak wave was generated using the dual superposition model. The numerical model of the offshore platform designed for ultra-deep-water applications was developed using the ANSYS AQWA 2023 R2 modeler. Numerical investigations, including the free decay tests and time-domain analysis under random sea states, including freak waves, were initially carried out. Then, the combined effects of freak waves, wind, and current were studied in detail under different loading scenarios. The results revealed the increase in structural response under the freak wave action at the focus time. Wind action resulted in a mean shift in responses, while the inclusion of current led to a pronounced increase in the total response of the platform, encompassing deck and buoyant legs, alongside the tether tension variation. Notably, considerable variations in the response were observed after freak wave exposure under the combined influence of wind, freak wave, and current. The results underscore the profound effects induced by wind and current in the presence of freak waves, providing valuable insights for analyzing similar offshore structures under ultimate design conditions.

Wood trade regions of Russia: clustering approach development

Network analysis of the Russian timber industry was based on the information about timber transactions between Russian companies in 2020. The data were collected from the Unified State Automated Information System Accounting Timber and Transactions with It. The method of graph clustering was developed. The first step is clustering of the hole graph by Leiden algorithm. The second step is drawing of each Leiden created cluster in abstract space using Fruchterman–Reingold layout and extraction of clusters from layout using meanshift algorithm. Such approach helped us to divide many Leiden-created clusters into components. This algorithm has a problem of resolution limit. It tends to combine some medium and small clusters into large ones. Also, a special method was developed to limit the uncertainty of clustering – both Leiden algorithm and Fruchterman–Reingold layout are non-deterministic algorithms. Wood trade clusters vary in size and content. They are rather autonomous. The average share of intra-cluster trade is 89%. The modularity score of our partition is 0.863. Each cluster has been described using open sources from the Internet. The content of the clusters does not contradict the logic of the wood industry processes. The clusters create rather compact areas on the map – wood trade regions. Their borders often overlap with existing administrative boundaries. Five types of network structure of clusters were defined: vertical monocentric, vertical polycentric, horizontal, dendritic, simple. There are three factors of clustering in wood industry of Russia: production chains (4 types – pulp, plywood, lumber, chipboard chains), demand chains (redistribution of raw material between manufacturers within the cluster, accumulation of wood volumes for some enterprises outside the cluster, wood trade between traders within the cluster), common holder. We have shown that existing approaches to cluster detection based on location quotients often misrepresent economic networks.

A hybrid prediction model of blast furnace permeability index combining least square support vector machine and artificial neural network

Blast furnace (BF) permeability index is a crucial parameter that can quickly, intuitively, and comprehensively reflect the furnace condition. Accurate prediction of this index is crucial for optimising production efficiency and ensuring the stable operation of the BF. In this study, a hybrid permeability index prediction model is constructed by combining a least squares support vector machine and an artificial neural network, using the mean shift clustering algorithm (MSCA) to classify the BF conditions is applied. The results show that the MSCA algorithm shows remarkable precision in classifying the stable and unstable operating states of BF, achieving an impressive accuracy rate of 93.98%. The hybrid prediction model could accurately predict the permeability index and has a mean absolute error of 0.6877, a mean square error of 0.4721 and an R2 of 0.9215, highlighting its robust predictive performance. These findings underscore the practical significance of our model in enhancing BF operational efficiency and reliability.

Data-Driven Dynamic Security Partition Assessment of Power Systems Based on Symmetric Electrical Distance Matrix and Chebyshev Distance

A rapid dynamic security assessment (DSA) is crucial for online preventive and restoration decision-making. The deep learning-based DSA models have high efficiency and accuracy. However, the complex model structure and high training cost make them hard to update quickly. This paper proposes a dynamic security partition assessment method, aiming to develop accurate and incrementally updated DSA models with simple structures. Firstly, the power grid is self-adaptively partitioned into several local regions based on the mean shift algorithm. The input of the mean shift algorithm is a symmetric electrical distance matrix, and the distance metric is the Chebyshev distance. Secondly, high-level features of operating conditions are extracted based on the stacked denoising autoencoder. The symmetric electrical distance matrix is modified to represent fault locations in local regions. Finally, DSA models are constructed for fault locations in each region based on the radial basis function neural network (RBFNN) and Chebyshev distance. An online incremental updating strategy is designed to enhance the model adaptability. With the simulation software PSS/E 33.4.0, the proposed dynamic security partition assessment method is verified in a simplified provincial system and a large-scale practical system in China. Test results demonstrate that the Chebyshev distance can improve the partition quality of the mean shift algorithm by approximately 50%. The RBFNN-based partition assessment model achieves an accuracy of 98.96%, which is higher than the unified assessment with complex models. The proposed incremental updating strategy achieves an accuracy of over 98% and shortens the updating time to 30 s, which can meet the efficiency of online application.

Economic Openness and the Behaviour of Great Ratios in G20 Countries

Most of the macroeconomic models suggest that great ratios of economics should be stable in the long term. Keeping the theoretical consequences in mind, the present study investigates the order of integration of two great ratios, viz. consumption–output and investment–output ratios. The study employs both mean shift and trend shift unit root tests with a structural break to examine the stationarity property of great ratios. This article attempts to analyse the relationship between economic openness and the behaviour of great ratios. Results of the study indicate a balanced growth path for 10 out of 18 G20 countries considered in our analysis. Findings show that countries that have a deficit trade balance with surplus foreign direct investment are more likely to demonstrate a balanced growth path. The multivariate analysis shows evidence in support of the balanced growth hypothesis in 13 of the 18 G20 countries. Of the 18 countries analysed, 8 internationally open and 6 financially open countries provide support for the balanced growth path. JEL: C20, E20, O50

Multiscale Change Point Detection for Univariate Time Series Data with Missing Value

This paper studies the autoregressive integrated moving average (ARIMA) state space model combined with Kalman smoothing to impute missing values in a univariate time series before detecting change points. We estimate a scale-dependent time-average variance constant that depends on the length of the data section and is robust to mean shifts under serial dependence. The consistency of the proposed estimator is shown under the assumption allowing heavy tailedness. Integrating the proposed estimator with the moving sum and wild binary segmentation procedures to determine the number and locations of change points is discussed. Furthermore, the performance of the proposed methods is evaluated through extensive simulation studies and applied to the Beijing multi-site air quality dataset to impute missing values and detect mean changes in the data.

‘Start-Up Nation’: The Making and Performativity of an Empty Signifier

The article sheds light on the process of fabrication of a polysemous, ambiguous, and mocking French entrepreneurial expression – the ‘start-up nation’ – construed as an empty signifier. The fabrication of such empty signifiers in the discourses of entrepreneurship and management, what creates them and what they create, remains little explored questions. This article addresses the following question: how do repeated quotations of an empty signifier enable it to perform entrepreneurship? We trace the circulation of the expression from its first utterance in the political sphere by Emmanuel Macron, then French minister of the economy, through to the media and the scientific sphere, using a communicative analysis of Emmanuel Macron’s speeches (n = 4), press articles (n = 210) and academic productions (n = 30). We show the shifts in meaning and values that take place, in particular the way in which the ‘start-up nation’ takes on denunciatory and pejorative values, and is transformed from a political formula into a pejorative, decontextualized little phrase. Our results enrich the critical literature on management and entrepreneurship, particularly the analysis of the performativity of entrepreneurial discourse. By describing the manufacture of an empty signifier through its circulation in social space, the study reveals the counter-power potential of performativity. The results also highlight the surprising absence of an academic critical dimension.

Process Capability Indices for Processes when the Underlying Data are Interval-Valued

One of the important activities of process quality management is to see that the processes of interest are, in fact, stable and capable. In this paper, the problem of obtaining process capability indices (PCIs) for the processes when the underlying data are interval-valued is considered. Since interval-valued data such as systolic and diastolic readings have specifications for both lower and upper values, drawing PCIs cannot be straightforward. In this paper, we attempted to build connections between the lower and upper specifications limits based on which the resulting PCIs are drawn. This is done by considering the coefficients of inflation and the mean shift values of distributions of both lower and upper values of the interval-valued data. The new expressions for the proposed PCIs are determined. We have considered the systolic and diastolic data to demonstrate the computations of PCIs.

Vines, Vineyards, and John 15

It has traditionally been thought that in the discourse of John 15 Jesus identifies himself as the vine and his followers as branches. Recently a different interpretation, in which Jesus is the vineyard and his disciples the vines, has begun to catch on. This article investigates the arguments of its original advocate, Chrys Caragounis, and finds them wanting. It shows that the new interpretation fails to appreciate that, whatever the evidence from more popular sources, any shifts of meaning for the two Greek terms involved cannot be clearly demonstrated from the LXX. Indeed, this critique demonstrates that the new proposal fails to identify correctly the LXX source of the imagery in John 15 and to acknowledge the significance of the evidence for an unbroken chain of interpretation of the two terms that runs from its source in Ezekiel through John 15 to the major Christian writers of the early centuries CE. It also refutes the alleged difficulties for traditional exegesis brought forward in the case for a shift of meaning and concludes that there is no convincing reason for abandoning the more appropriate reading of Jesus as the vine and his followers as branches.

An LSTM-based predictive monitoring method for data with time-varying variability

The recurrent neural network and its variants have shown great success in processing sequences in recent years. However, these neural networks have not aroused much attention in the process monitoring literature. Whereas the flexibility and outstanding performance of these models enable them to be solutions to many problem settings within process monitoring. One such problem setting is process data with inherent time-varying variability, an aspect of data that is often ignored. Therefore, this paper proposes a prospective monitoring method for detecting structural changes in data with inherent time-varying variability. We integrate long short-term memory (LSTM) prediction intervals with a neural network (NN) for variability prediction to detect structural changes in data online. We compare our proposed method to benchmarks in a simulation study, showing that the proposed model outperforms other NN-based predictive monitoring methods for mean shift detection. The proposed method is also applied in two case studies to sensor data, which confirms that the proposed method is an effective technique for detecting shifts.

Convergence Analysis of Mean Shift.

The mean shift (MS) algorithm seeks a mode of the kernel density estimate (KDE). This study presents a convergence guarantee of the mode estimate sequence generated by the MS algorithm and an evaluation of the convergence rate, under fairly mild conditions, with the help of the argument concerning the Łojasiewicz inequality. Our findings extend existing ones covering analytic kernels and the Epanechnikov kernel. Those are significant in that they cover the biweight kernel, which is optimal among non-negative kernels in terms of the asymptotic statistical efficiency for the KDE-based mode estimation.

Delayed Maxillary Reconstruction with Free Osteocutaneous Fibula Flap Using CAD-CAM Technology

Abstract Background Maxillary reconstruction poses unique challenges for the reconstructive surgeon because of the complex three-dimensional (3D) anatomy of the maxilla. Undertaking this endeavor on secondary reconstruction makes it more difficult due to problems in recreating the true defect. This study is an attempt to demonstrate the role of virtual surgical planning (VSP), 3D printing, and mock surgery in reconstructing such defects using free fibula flaps. Materials and Methods This was a prospective study involving 10 patients of maxillary defects who underwent delayed reconstruction with a free fibula flap. The planning was done preoperatively using computer-aided design and computer-aided manufacturing (CAD-CAM) technology. A mock surgery with 3D printed models was done before the surgery. After the surgery, the accuracy results were obtained by overlapping and measuring fixed point distances between preoperative virtual planning and postoperative computed tomography (CT) scan data. Results and Discussion Nine patients underwent successful reconstruction and were satisfied with the outcome. One patient had flap loss. The mean shift along the horizontal, vertical, and 3D axes was less than 5 mm between the preoperative virtual planning and postoperative CT scan data, indicating accurate reconstruction. We also suggest strategies for soft-tissue and bony inset including inferolateral pedicle origin, anteriorly facing lateral fibular surface, and two bony struts for the alveolus. Conclusion VSP and CAD-CAM technology in maxillary reconstructions help achieve an anatomically accurate neo-maxilla. The addition of mock surgery to the routine and the use of cutting guide avoid unpredictability and reduce the need for adaptation activities on the operating table. CAD-CAM technology despite its limitations is invaluable in maxillary reconstruction and is an important tool for a reconstructive plastic surgeon.

Deep Learning Driven Real-Time Airspace Monitoring Using Satellite Imagery

Detecting aircraft in remote sensing images poses a formidable challenge due to the diverse characteristics of aircraft, including type, size, pose, and intricate backgrounds. Traditional algorithms encounter difficulties in manually extracting features from numerous candidate regions. This paper introduces an innovative aircraft detection approach that combines corner clustering with a diverse set of Deep Learning (DL) models. The proposed method involves two main stages: region proposal and classification. In the region proposal stage, initial candidate regions are generated using a mean-shift clustering algorithm applied to corners detected on binary images. Subsequently, a comprehensive set of classifiers, encompassing CNN, DenseNet, MobileNetV2, Inception v3, Random Forest (R.F), ResNet50, ResNeXT, Support Vector Machine (SVM), VGG16, Xception, EfficientNet, and InceptionResNetv2, is employed for feature extraction and classification. The presented approach demonstrates superior accuracy and efficiency compared to conventional methods. By leveraging the autonomous learning capabilities of CNN and DL models on extensive datasets, the methodology generates a reduced yet high-quality set of candidate regions. Inspired by the detection methodology employed by image analysts, the approach adopts a coarse-to-fine strategy using CNN and DL models. The first CNN proposes coarse candidate regions, and the second identifies individual airplanes within these regions in finer detail. This framework results in a decreased number of candidate regions compared to existing literature while extracting distinctive deep features. Experimental evaluations on Google Earth images validate the efficiency of the proposed method, underscoring its potential for practical applications in both civilian and military contexts.

GAN-based statistical process control for the time series data

The cumulative sum (CUSUM) control chart and the multivariate anomaly detection with the generative adversarial network (MAD-GAN) were compared for monitoring the time series data. However, the control boundaries constructed in terms of the one-class classification with only the normal data for the training phase are inappropriate for the test phase because the normal data and the abnormal data should be classified for the test phase. In this regard, we first propose this GAN-based statistical process control (SPC) framework to compare them in terms of detecting the process mean shift based on the perspective of SPC. Second, we propose the residual MAD-GAN in order to improve the detection performance. Third, we develop the loss function of the MAD-GAN. Finally, we find that the maximum mean discrepancy (MMD) as well as the nash equilibrium is useful for the MAD-GAN. Our experiments demonstrate that the residual MAD-GAN is more effective than the residual CUSUM control chart in terms of the run lengths for the time series data. Therefore, we propose SPC practitioners to consider the residual MAD-GAN for detecting the process mean shift in time series data.

Elbow kinematics with increased lengthening of a radial head arthroplasty evaluated with dynamic radiostereometric analysis.

The aim of this study was to evaluate the kinematics of the elbow following increasing length of the radius with implantation of radial head arthroplasties (RHAs) using dynamic radiostereometry (dRSA). Eight human donor arms were examined by dRSA during motor-controlled flexion and extension of the elbow with the forearm in an unloaded neutral position, and in pronation and supination with and without a 10 N valgus or varus load, respectively. The elbows were examined before and after RHA with stem lengths of anatomical size, + 2 mm, and + 4 mm. The ligaments were maintained intact by using a step-cut lateral humeral epicondylar osteotomy, allowing the RHAs to be repeatedly exchanged. Bone models were obtained from CT scans, and specialized software was used to match these models with the dRSA recordings. The flexion kinematics of the elbow were described using anatomical coordinate systems to define translations and rotations with six degrees of freedom. The greatest kinematic changes in the elbows were seen with the longest, + 4 mm, implant, which imposed a mean joint distraction of 2.8 mm in the radiohumeral joint and of 1.1 mm in the ulnohumeral joint, an increased mean varus angle of up to 2.4° for both the radius and the ulna, a mean shift of the radius of 2.0 mm in the ulnar direction, and a mean shift of the ulna of 1.0 mm posteriorly. The kinematics of the elbow deviated increasingly from those of the native joint with a 2 mm to a 4 mm lengthening of the radius. This confirms the importance of restoring the natural length of the radius when undertaking RHA.

A weighted dual cumulative sum chart for monitoring the process mean

AbstractThe cumulative sum (CUSUM) charts are well‐structured and widely used for detecting small‐to‐moderate changes in the process parameters. However, their performance can be limited when the shift size is unknown or varies over time. Various methods have been developed to address this issue, including dual CUSUM (DCUSUM), adaptive CUSUM, adaptive EWMA, and weighted CUSUM charts, all of which offer better sensitivity compared to traditional CUSUM charts. In this study, we propose a new weighted DCUSUM chart designed to effectively detect the mean shifts in a normally distributed process. Monte Carlo simulations are used to estimate the zero‐state and steady‐state average run‐length (ARL) profiles of the control charts. The ARL performances of the control charts are evaluated in terms of expected relative ARL (ERARL) and expected weighted run‐length (EWRL). Our results show that the proposed chart may outperform existing counterparts in terms of the ARL, ERARL, and EWRL measures, indicating superior performance in detecting a range of the mean shift sizes. Finally, we apply the existing and proposed charts to a real dataset, showcasing their practical utility in process monitoring.

Enhancing Recommendation Diversity and Novelty with Bi-LSTM and Mean Shift Clustering

In the digital age, personalized recommendation systems have become crucial for information dissemination and user experience. While traditional systems focus on accuracy, they often overlook diversity, novelty, and serendipity. This study introduces an innovative recommendation system model, Time-based Outlier Aware Recommender (TOAR), designed to address the challenges of content homogenization and information bubbles in personalized recommendations. TOAR integrates Neural Matrix Factorization (NeuMF), Bidirectional Long Short-Term Memory Networks (Bi-LSTM), and Mean Shift clustering to enhance recommendation accuracy, novelty, and diversity. The model analyzes temporal dynamics of user behavior and facilitates cross-domain knowledge exchange through feature sharing and transfer learning mechanisms. By incorporating an attention mechanism and unsupervised clustering, TOAR effectively captures important time-series information and ensures recommendation diversity. Experimental results on a news recommendation dataset demonstrate TOAR’s superior performance across multiple metrics, including AUC, precision, NDCG, and novelty, compared to traditional and deep learning-based recommendation models. This research provides a foundation for developing more intelligent and personalized recommendation services that balance accuracy with content diversity.

Meaning Shifts of Culture-Specific Expressions: A Case Study of Facebook

This research aimed to examine the effects of meaning shifts in culture-specific content on Facebook. Specifically, it sought to explore the linguistic, cultural, and grammatical similarities between English and Kiswahili, while highlighting the significant mismatches between these two languages. The study used the theory of Principle of Cultural Translation to provide the theoretical relevance. The target population comprised Facebook pages with a significant following and a focus on CSIs posted within the context of conversations on those pages. The study aimed to examine CSIs posted on Facebook pages based in Kenya on foods, fashion and cultural architecture where English-Kiswahili translations were assessed for errors. The study employed purposive sampling to select a sample of 11 Facebook pages for content analysis. Primary data was gathered through the content analysis method, which involved the systematic preparation, organization, and reporting of the data collected. The study found that lexical, rhetorical, and grammatical mis-matches contributed to misunderstandings and misrepresentations in a variety of domains, including food, fashion, and cultural architecture. Based on the analysis, 80% of the Facebook sources revealed food items like "ugali" and "Nyama Choma" were often reduced to overly simplified translations like "cornmeal" and "grilled meat," failing to capture their rich cultural significance. With regards of fashion, the study found that 70% of the Facebook pages mistranslated the garments such as "kanga" led to a loss of their deeper meanings, contributing to cultural appropriation. Similarly, architectural terms suffered from meaning shifts; 55% of the sources depicted that terms describing unique Kenyan architectural styles were reduced to words like "huts," ignoring the cultural and historical narratives they encapsulate. The study concludes that the meaning shifts of culture-specific expressions related to food, fashion, and architecture in Kenya on Facebook have various negative implications as it leads to misunderstandings that go beyond mere words. The study thus recommends that stickers that contain only the most basic information should be made more useful by scaling them down or making them smaller while figurative language, such as idioms, should be used to describe complex ideas. Keywords: Culture-specific expressions, Meaning shifts, Mistranslation, Lexical mismatches, Facebook content

Performance evaluation of extended EWMA control chart in the presence of measurement error

In any manufacturing or service industry, substantial effort is made to keep a process under control. However, factors such as measurement error are bound to create distortions. Many researchers have studied the effect of measurement error on various types of control charts including the EWMA control chart. In this paper, we study the effect of measurement error on the competency of the extended EWMA control chart. We examine the performance of the chart in the case of a shift in mean taking into account the model with linear covariates. The consequence of making multiple measurements on each sampled unit is also investigated. Last but not the least, we examine the case of linearly increasing variance. Through a simulation study, we demonstrate that the performance of the chart for detecting a shift in the mean is substantially reduced in the presence of measurement error. The important finding, however, is that, in spite of the existence of measurement error, the EEWMA control chart performs better than the EWMA control chart. A practical example is provided to elucidate this finding.