Sequential Clustering Research Articles

Vision-based trajectory generation and tracking algorithm for maneuvering of a paddy field robot

In this study, we propose a novel visual-based autonomous trajectory-tracking control method for steering a wheeled robot following the lines of crop row in paddy field. A rice crop rows detection method, based on the region growth sequential clustering − random sample consensus (RANSAC) algorithm, is developed to generate trajectory. Concurrently, a dynamics predictive controller is employed to compute the command for the desired steering angle. The controller leverages a model that incorporates slip dynamics and operates on a low power consumption industrial computer. Experimental results show that the developed algorithm can successfully obtain the correct trajectory in more than 96.25 % of the cases, with the angle error consistently below 3°. Furthermore, the single-image processing time is notably swift at 13.98 ms, underscoring the commendable adaptability and real-time performance of the proposed methodology. During movement in the paddy field, the robot exhibits maximum lateral deviations of 4.55 cm, 5.65 cm, and 6.41 cm at speeds of 0.3 m/s, 0.6 m/s, and 0.9 m/s, respectively, accompanied by corresponding heading angle errors of 4.59°, 5.63°, and 7.39°. Notably, while adeptly tracking rice crop rows at all three speeds, the robot consistently maintains a maximum lateral error below one-fourth of the inter-row spacing of rice planting. This study assumes significance in enhancing the stability of ground-traversing agricultural robots, serving as a valuable reference for advancing the research and development of intelligent and efficient agricultural robotic systems.

Single cell expression and chromatin accessibility of the Toxoplasma gondii lytic cycle identifies AP2XII-8 as an essential ribosome regulon driver

Sequential lytic cycles driven by cascading transcriptional waves underlie pathogenesis in the apicomplexan parasite Toxoplasma gondii. This parasite’s unique division by internal budding, short cell cycle, and jumbled up classically defined cell cycle stages have restrained in-depth transcriptional program analysis. Here, unbiased transcriptome and chromatin accessibility maps throughout the lytic cell cycle are established at the single-cell level. Correlated pseudo-timeline assemblies of expression and chromatin profiles maps transcriptional versus chromatin level transition points promoting the cell division cycle. Sequential clustering analysis identifies functionally related gene groups promoting cell cycle progression. Promoter DNA motif mapping reveals patterns of combinatorial regulation. Pseudo-time trajectory analysis reveals transcriptional bursts at different cell cycle points. The dominant burst in G1 is driven largely by transcription factor AP2XII-8, which engages a conserved DNA motif, and promotes the expression of 44 ribosomal proteins encoding regulon. Overall, the study provides integrated, multi-level insights into apicomplexan transcriptional regulation.

Flexible density peak clustering for real-world data

In density based clustering, the density peak algorithm has attracted much attention due to its effectiveness and simplicity, and a vast amount of clustering approaches have been proposed based on this algorithm. Some of these works require manual selection of cluster centers with a decision graph, where human involvement leads to uncertainty in clustering results. In order to avoid human involvement, some other algorithms depend on user-specified number of clusters to determine cluster centers automatically. However, it is well known that accurate estimation of number of clusters is a long-standing difficulty in data clustering. In this paper we present a sequential density peak clustering algorithm to extract clusters one by one, thereby determining the number of clusters automatically and avoiding manual selection of cluster centers in the meanwhile. Starting from a density peak, our algorithm generates an initial cluster surrounding the density peak in the first step, and then obtains the final cluster by expanding the initial cluster based on the relative density relationship among neighboring data points. With a peeling-off strategy, we obtain all the clusters sequentially. Our algorithm works well with clusters of Gaussian distribution and is therefore potential for clustering of real-world data. Experiments with a large number of synthetic and real datasets and comparisons with existing algorithms demonstrate the effectiveness of the proposed algorithm.

Accelerated Sequential Data Clustering

Accelerated Sequential Data Clustering

A Sequential Clustering Method With Improved Iteration and Its Application to Plug-In Hybrid Electric Vehicle: Theoretical Design and Experiment Implementation

A Sequential Clustering Method With Improved Iteration and Its Application to Plug-In Hybrid Electric Vehicle: Theoretical Design and Experiment Implementation

Prediction of wind energy location by parallel programming using MPI-based KMEANS clustering algorithm

ABSTRACT Renewable energy resources, like the power of the wind, are the essential sources of energy in today’s world. To keep down the greenhouse energy degasification and stop global warming, it is very important to predict the exact location where maximum wind energy is generated. The total amount of electrical power produced by a turbine relies more on the speed of the wind, pressure created by the wind, and weather conditions. The proposed method predicts the maximum power generated in a particular location using the current conditions of the weather, pressure generated through wind, and its speed. The speed of the wind and pressure of the wind are clustered using the Message Passing Interface (MPI) based KMEANS clustering algorithm. The system minimizes the amount of time required for clustering and it is done by MPI. Clustering is formed with the help Euclidean distance of each point. The wind data is collected and formed into three clusters such as low, medium, and high based on the speed of the wind. The parameters for evaluation are considered as the speed of the wind, and the direction of the wind are determined. The results show that the speed of the wind varies up to 25 km/s with the power of 2000 watts. The proposed method compares the execution time of sequential and MPI-based KMEANS clustering for different numbers of clusters. The sequential KMEANS clustering algorithm takes 3% to 7% more time compared to MPI-based KMEANS clustering algorithm. For the maximum cluster size of 5, the MPI-based KMEANS clustering algorithm produces the result in 0.42 s.

Rapid sequential clustering of NMDARs, CaMKII, and AMPARs upon activation of NMDARs at developing synapses.

Rapid, synapse-specific neurotransmission requires the precise alignment of presynaptic neurotransmitter release and postsynaptic receptors. How postsynaptic glutamate receptor accumulation is induced during maturation is not well understood. We find that in cultures of dissociated hippocampal neurons at 11 days in vitro (DIV) numerous synaptic contacts already exhibit pronounced accumulations of the pre- and postsynaptic markers synaptotagmin, synaptophysin, synapsin, bassoon, VGluT1, PSD-95, and Shank. The presence of an initial set of AMPARs and NMDARs is indicated by miniature excitatory postsynaptic currents (mEPSCs). However, AMPAR and NMDAR immunostainings reveal rather smooth distributions throughout dendrites and synaptic enrichment is not obvious. We found that brief periods of Ca2+ influx through NMDARs induced a surprisingly rapid accumulation of NMDARs within 1 min, followed by accumulation of CaMKII and then AMPARs within 2-5 min. Postsynaptic clustering of NMDARs and AMPARs was paralleled by an increase in their mEPSC amplitudes. A peptide that blocked the interaction of NMDAR subunits with PSD-95 prevented the NMDAR clustering. NMDAR clustering persisted for 3 days indicating that brief periods of elevated glutamate fosters permanent accumulation of NMDARs at postsynaptic sites in maturing synapses. These data support the model that strong glutamatergic stimulation of immature glutamatergic synapses results in a fast and substantial increase in postsynaptic NMDAR content that required NMDAR binding to PSD-95 or its homologues and is followed by recruitment of CaMKII and subsequently AMPARs.

Improved Fingerprint-Based Localization Based on Sequential Hybridization of Clustering Algorithms

The localization accuracy of a fingerprint-based localization system is dependent on several factors, one of which is the accuracy and efficiency at which the fingerprint database is clustered. Most highly efficient and accurate clustering algorithms have high time-dependent computational complexity (CC), which tends to limit their practical applicability. A technique that has yet to be explored is the sequential hybridization of multiple low-time CC clustering algorithms to produce a single moderate-time CC clustering algorithm with high localization accuracy. As a result, this paper proposes a clustering algorithm with a moderate time CC that is based on the sequential hybridization of the closest access point (CAP) and improved k-means clustering algorithms. The performance of the proposed sequential hybrid clustering algorithm is determined and compared to the modified affinity propagation clustering (m-APC), fuzzy c-mean (FCM), and 2-CAP algorithms presented in earlier research works using four experimentally generated and publicly available fingerprint databases. The performance metrics considered for the comparisons are the position root mean square error (RMSE) and clustering time based on big O notation. The simulation results show that the proposed sequential hybrid clustering algorithm has improved localization accuracy with position RMSEs of about 54%, 77%, and 52%, respectively, higher than those of the m-APC, FCM, and 2-CAP algorithms. In terms of clustering time, it is 99% and 79% faster than the m-APC and FCM algorithms, respectively, but 90% slower than the 2-CAP algorithm. The results have shown that it is possible to develop a clustering algorithm that has a moderate clustering time with very high localization accuracy through sequential hybridization of multiple clustering algorithms that have a low clustering time with poor localization accuracy. Doi: 10.28991/ESJ-2024-08-02-02 Full Text: PDF

A deep inverse convolutional neural network-based semantic classification method for land cover remote sensing images

The imbalance of land cover categories is a common problem. Some categories appear less frequently in the image, while others may occupy the vast majority of the proportion. This imbalance can lead the classifier to tend to predict categories with higher frequency of occurrence, while the recognition effect on minority categories is poor. In view of the difficulty of land cover remote sensing image multi-target semantic classification, a semantic classification method of land cover remote sensing image based on depth deconvolution neural network is proposed. In this method, the land cover remote sensing image semantic segmentation algorithm based on depth deconvolution neural network is used to segment the land cover remote sensing image with multi-target semantic segmentation; Four semantic features of color, texture, shape and size in land cover remote sensing image are extracted by using the semantic feature extraction method of remote sensing image based on improved sequential clustering algorithm; The classification and recognition method of remote sensing image semantic features based on random forest algorithm is adopted to classify and identify four semantic feature types of land cover remote sensing image, and realize the semantic classification of land cover remote sensing image. The experimental results show that after this method classifies the multi-target semantic types of land cover remote sensing images, the average values of Dice similarity coefficient and Hausdorff distance are 0.9877 and 0.9911 respectively, which can accurately classify the multi-target semantic types of land cover remote sensing images.

G2P-DDM: Generating Sign Pose Sequence from Gloss Sequence with Discrete Diffusion Model

The Sign Language Production (SLP) project aims to automatically translate spoken languages into sign sequences. Our approach focuses on the transformation of sign gloss sequences into their corresponding sign pose sequences (G2P). In this paper, we present a novel solution for this task by converting the continuous pose space generation problem into a discrete sequence generation problem. We introduce the Pose-VQVAE framework, which combines Variational Autoencoders (VAEs) with vector quantization to produce a discrete latent representation for continuous pose sequences. Additionally, we propose the G2P-DDM model, a discrete denoising diffusion architecture for length-varied discrete sequence data, to model the latent prior. To further enhance the quality of pose sequence generation in the discrete space, we present the CodeUnet model to leverage spatial-temporal information. Lastly, we develop a heuristic sequential clustering method to predict variable lengths of pose sequences for corresponding gloss sequences. Our results show that our model outperforms state-of-the-art G2P models on the public SLP evaluation benchmark. For more generated results, please visit our project page: https://slpdiffusier.github.io/g2p-ddm.

Factorization and resummation for sequential recombination jet cross sections

We extend the class of factorization theorems for non-global observables from fixed angular constraints to cross sections defined in terms of sequential jet clustering. The associated hard and soft functions depend not only on the directions of the hard partons, but also on their energy fractions. We derive the one-loop anomalous dimension of the hard functions that drives the leading-logarithmic resummation. The anomalous dimension imposes energy ordering, which simplifies the clustering sequence. We perform resummations for gap-between-jet observables defined with different jet algorithms and explain the effects of the clustering on the importance of secondary emissions and on the effective gap size.

Physical Activities Recommender System Based on Sequential Data Use K-Mean Clustering

Physical activities such as Exercise are essential in maintaining health and fitness, especially for those who adopt a healthy lifestyle. Irregularity in doing Exercise can hurt the body and health, especially if it is not done according to one's physical capacity. In the framework of this research, we developed a Recommender System that aims to provide exercise suggestions according to the user's preferences, especially in the categories of cycling, running, walking, and horse riding. The primary considerations of the variables include heart rate (Average Heart Rate) and pace (Speed Rate). This research approach uses the FitRec Dataset and applies the K-Mean Clustering Algorithm, with the support of APACHE SPARK, for large-scale data processing, given the large data size in the FitRec dataset. Grouping is done using the FitRec dataset and K-Mean. Users are grouped according to heart rate and pace information; this provides appropriate Exercise for users. The test results show that the proposed system performs well, as indicated by the silhouette score = 0.596, calinzski-harabaz score = 2133.09, and davies bouldin score = 0.480. These test metrics reflect the system's ability to cluster. Indirectly, the accuracy performance of the system is assessed through these metrics, showing good accuracy test results.

Does Rounding Order Bias Discharge Efficiency? Predictors of Discharge Timing on an Academic Urology Service.

No previous works have analyzed whether the order in which surgical teams see patients on morning rounds affects discharge efficiency at teaching hospitals. We obtained perioperative urologic surgery timing data at our academic institution from 2014 to 2019. We limited the analysis to routine postoperative day 1 discharges. Univariate and multivariate analyses were performed to determine whether various hospital and patient factors were associated with discharge timing. We analyzed 1,494 patients. Average discharge order time was 11:22 a.m. and hospital discharge 1:24 p.m. Univariate regression revealed earlier discharge order time for patients seen later in rounds by 4 minutes per sequential room cluster (p = .013) and by 12 minutes per cluster when excluding short-stay patients. Multivariate analysis revealed discharge order placement did not vary significantly by rounding order. However, time of hospital discharge did (p < .001), likely due to speed of discharge in the designated short-stay units. Attending physician was the most consistent predictor in variations of discharge timing, with statistical significance across all measured outcomes. Patients seen later in rounding progression received earlier discharge orders, but this relationship does not remain in multivariate modeling or translate to earlier discharge. These findings have helped guide quality improvement efforts focused on discharge efficiency.

Collaborative dialogue patterns of pair programming and their impact on programming self‐efficacy and coding performance

AbstractThe purpose of this study was to investigate the collaborative dialogue patterns of pair programming and their impact on programming self‐efficacy and coding performance for both slow‐ and fast‐paced students. Forty‐six postgraduate students participated in the study. The students were asked to solve programming problems in pairs; those pairs' conversations were recorded when they worked on their tasks. Data analysis methods, including lag sequential analysis, cluster analysis and paired t‐test, were employed, and the results showed that (1) four collaborative dialogue patterns emerged: Lecture, Guide, Question and Answer (Q&amp;A), and Inquiry patterns; (2) Guide and Inquiry patterns significantly increased programming self‐efficacy for both fast‐ and slow‐paced students while Lecture and Q&amp;A patterns significantly increased programming self‐efficacy for slow‐paced students but not for fast‐paced students; (3) Guide and Inquiry patterns played a significant role in improving coding performance for slow‐paced students. The study reveals a complex relationship between collaborative dialogue patterns with programming self‐efficacy and coding performance, critically affecting students' pair programming quality. Further details of the findings are also discussed. Practitioner notesWhat is already known about this topic Pair programming is promising in promoting problem solving and knowledge transfer and is widely used in programming education. There are different patterns observed in the pair programming process. Collaborative dialogue patterns found in “expert‐novice” pair programming were derived from a single programming task. What this paper adds Four collaborative dialogue patterns of pair programming emerged by increasing the different tasks and experiment duration and expanding the sample size, which further verified the stability of the similar patterns in previous studies. Four collaborative dialogue patterns showed different significant impacts on different students' programming self‐efficacy and coding performance. This study presents the finer‐grained characteristics of collaborative interaction in programmer pairs and contributes to the explanation regarding the different effects of pair programming reported in previous studies. Implications for practice and/or policy Four collaborative dialogue patterns can help the teachers understand the collaborative process of pair programming between fast‐ and slow‐paced students. The collaborative dialogue patterns can be used to formulate effective intervention strategies to stimulate the process of collaboration in pairs and applied in future programming education to provide a new path for cultivating and promoting students' programming abilities. It is worth further investigating the impact of collaborative dialogue patterns on students' computational thinking.

A short-term prediction-based efficient optimization power control strategy for heavy-duty hybrid electric vehicle

This study proposes a short-term prediction-based power control strategy using the modified iteration sequential clustering quadratic programming (MISCQP) algorithm for heavy-duty series hybrid electric vehicles (SHEVs). In this strategy, a power preconditioning method is established on the basis of demand power prediction which guarantees the stable power output under transient high-power condition. Through the prediction sequence, MISCQP algorithm is proposed to solve receding horizon problem and achieve real-time control by improving the iteration efficiency. For this purpose, the clustering algorithm is designed to skip the unnecessary short step in the iteration which is too few to obtain sufficient descent. The iteration points in various iteration domains are clustered and the corresponding cluster centers are obtained. Next, the aforementioned clustering results are introduced to improve the termination criterion. The updated criterion turns to skip the short step when the distance of cluster centers of various iteration domains varies within the set threshold. Finally, the performance of the proposed strategy is validated both in simulation and hardware-in-loop tests. The results reveal that the proposed strategy achieves 5.00 %, 5.86 %, 6.27 % less fuel consumption while maintaining stable power output under all the driving cycles. And the average iteration number of proposed strategy is decreased by 10.36 %, 8.47 %, 9.21 %, respectively.

Fully automatic training sample collection for detecting multi-decadal inland/seaward urban sprawl

Accurate and timely monitoring of urban sprawl using remote sensing provides valuable insights for urban management and sustainable development. While existing studies have explored methods for large-scale monitoring of urban sprawl, most of them rely on the collection of manually labeled training samples for supervised classification. Additionally, previous studies generally focused on inland urban sprawl, neglecting the process of urban sprawl towards sea areas, known as seaward urban sprawl, which has profound ecological implications for coastal ecosystems. Here we develop a fully automatic algorithm for detecting urban sprawl, named Fully Automatic Detection of Urban Sprawl (FADUS), using the time-series Landsat imagery without manually collecting training samples or existing urban maps for reference. FADUS automatically generates initial samples through a sequential binary unsupervised clustering technique and then updates them by removing the spectral outliers to obtain a high-quality training set for subsequent supervised classification. A tidal module is included in FADUS to avoid pseudo-urban changes caused by tidal fluctuations so that both inland and seaward urban sprawl can be detected. By applying the algorithm to 75 coastal cities in China, the most rapidly urbanizing region in the world, we uncovered a neglected but dramatic seaward urban sprawl process that has converted sea areas to urban space since 1985. Our results indicate that 9904.36 km2 of coastal wetlands and nearshore seawater areas have been converted to land area, of which 44.83% is currently covered by built-up area. With our method, the fully automatic detection of urban dynamics is now possible for both coastal and inland cities worldwide. Our derived map of seaward urban sprawl in China provides valuable references for coastal land-cover change monitoring, wetland protection and restoration, and integrated coastal management.

Spatial and Temporal Variation in Vegetation Cover and Its Response to Topography in the Selinco Region of the Qinghai-Tibet Plateau

In recent years, the vegetation cover in the Selinco region of the Qinghai-Tibet Plateau has undergone significant changes due to the influence of global warming and intensified human activity. Consequently, comprehending the distribution and change patterns of vegetation in this area has become a crucial scientific concern. To address this concern, the present study employed MODIS-NDVI and elevation data, integrating methodologies such as trend analysis, Hurst exponent analysis, and sequential cluster analysis to explore vegetation cover changes over the past 21 years and predict future trends, while examining their correlation with topographic factors. The study findings indicate a fluctuating upward trend in vegetation cover, with a notable decrease in 2015. Spatially, the overall fractional vegetation cover (FVC) in the study area showed a basic stability with a percentage of 78%. The analysis of future trends in vegetation cover revealed that the majority of areas (68.26%) exhibited an uncertain trend, followed by stable regions at 15.78%. The proportion of areas showing an increase and decrease in vegetation cover accounted for only 9.63% and 5.61%, respectively. Elevation and slope significantly influence vegetation cover, with a trend of decreasing vegetation cover as elevation increases, followed by an increase, and then another decrease. Likewise, as the slope increases, initially, there is a rise in vegetation cover, followed by a subsequent decline. Notably, significant abrupt changes in vegetation cover are observed within the 4800 m elevation band and the 4° slope band in the Selinco region. Moreover, aspect has no significant effect on vegetation cover. These findings offer comprehensive insights into the spatial and temporal variations of vegetation cover in the Selinco region and their association with topographic factors, thus serving as a crucial reference for future research.

Enhancing automated defect detection through sequential clustering and classification: An industrial case study using the Sine-Cosine Algorithm, Possibilistic Fuzzy c-means, and Artificial Neural Network

Most existing inspection models solely classify defects as either good or bad, focusing primarily on separating flaws from perfect ones. The sequential clustering and classification technique (SCC) is used in this work to not only identify and categorize the defects but also investigate their root causes. Conventional clustering techniques like k-means, fuzzy c-means, and self-organizing map are employed in the first stage to find the defects in the finished products. Then, a novel clustering method, that combines a sine-cosine algorithm and possibilistic fuzzy c-means (SCA-PFCM), is proposed to classify the detected defects into multiple groups to identify the defect categories and analyze the root causes of failures. In the second stage, the ground truth labels taken from the clustering technique are used to construct an automated inspection system using back propagation neural networks (BPNN). The proposed approach is applicable for detecting and identifying the causes of errors in manufacturing industry. This study applies a case study in nipper manufacture. The SCA-PFCM algorithm can detect 97 % of defects and classify them into four types while BPNN shows a predicted accuracy of up to 96 %. Additionally, an automated inspection system is developed to reduce the time and cost of the inspection process.

Acoustic emission monitoring of naturally developed damage in large-scale low-speed roller bearings

This article presents an approach to identify naturally developed damage in low-speed bearings using waveform-similarity-based clustering of acoustic emissions (AEs) under fatigue loading. The approach is motivated by the notation that each recorded AE signal from a particular damage is defined by the convolution of the source signal, transfer function of the propagation path and transfer function of the utilised sensor, and may thusly be used to identify consistent AE sources, for example due to crack growth. A sequential clustering procedure is proposed, that is based on waveform cross-correlation. The supporting theoretical background of waveform similarity, rooted in an analytical formulation of waveform propagation and transmission in complex structures, is discussed. The presented methodology is evaluated through application to AE data obtained in a low-speed run-to-failure experiment utilising a densely instrumented purpose-built linear bearing segment. The implemented sensor system comprises arrays of three types of AE transducers, that is relatively low - (40–100 kHz), mid - (95–180 kHz) and high-frequency (180–580 kHz), that are situated on both the raceways and supporting substructures of either side of the bearing. Over the course of 225,000 cycles of extension and retraction, wear has been developed. A total of about ∼2,300,000 AE signals have been recorded. Analysis of the recorded data suggests the rate of degradation increases from around 70,000 cycles onwards. Highly consistent structures of clusters indicative of a localised defect in the raceway have been identified from around 170,000 cycles onwards. These clusters are characterised by hit-rates in the range of 1–2 hits per cycle and an average similarity of 93%, they comprise about half the AE activity for the periods they have been identified for. These results highlight that the proposed cross-correlation-based clustering of AE waveforms and identification of multi-channel formations in said clusters compose a suitable methodology for assessment of damage in low-speed roller bearings.

Canonical Monte Carlo multispin cluster method

We present a new Canonical Multispin-flip Cluster Monte Carlo algorithm for Ising model and describe efficient implementations for hybrid supercomputer. Our method takes advantage of the computing architecture for parallel and multi-threaded operations and uses a sequential cluster state update scheme. Due to the peculiarity of the implementation, the method is more effective for models with a restricted radius of interaction. It is based on combining a random selection of spin cluster by the Monte Carlo method with a complete enumeration of the all states of the selected cluster. To show how it works we applied our method to models of interacting magnetic Ising-moments: ferromagnetic Ising model, the Edwards–Anderson spin glass model, dipolar spin ice on hexagonal and pentagonal Cairo lattices.