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Road Safety is a matter of great concern throughout the world. As number of casualties is increasing more than 4% annually in all age groups. It has been predicted that due to road accidents causality rate will grow around 8% till 2030. It’s entirely admissible and saddening to let citizens get killed in road accidents. As a result, to handle this sort of situation, an in-depth analysis is required. The Data of Road accidents are very heterogeneous in nature so analysis of such type of data is tricky. Segmentation is the main task for analyzing such data. So, K-means clustering method is mainly used for it as proposed in the research work. Second task of this model is to extract the data, images and hidden patterns by using Supervised Machine Learning algorithm that will help to form the policies for the prevention from road accidents. The combination of segmentation machine learning algorithm produces meaning full information.

The observation data is the basis for the global navigation satellite system (GNSS) to provide positioning, navigation and timing (PNT) service, and the observation quality directly determines the performance level of the PNT service. At present, the analysis of GNSS observations quality is partial and can only be based on a single index assessment. GNSS observation quality is difficult to analyze comprehensively by fusing multiple indicators. To solve the above problem, the supervised and unsupervised machine learning algorithms are applied, and a new comprehensive and classification method of GNSS observations quality based on the k-means clustering algorithm (k-means) and K-nearest neighbor algorithm (KNN) was proposed. The four core index features of GNSS observations, including data integrity rate, carrier-to-noise-density ratio (CNR), pseudorange multipath and the number of observations per slip, were selected to construct the sample dataset. The sample set was unsupervised clustered based on the k-means algorithm, and the classification label of GNSS observations quality was obtained. Then KNN algorithm was used to construct a comprehensive classification and evaluation model for GNSS observations quality. The data from 30 MGEX stations in the Asia–Pacific region in 2019 were selected for modeling analysis. The experiment results show that: (1) a strong correlation has been revealed between pseudorange multipath, CNR and the number of observations per slip. (2) The average classification correctness rate of the new model was over 90% by n-fold cross-validation. (3) The new model can effectively realize the automatic evaluation and classification of GNSS observations quality and easily distinguish the superiority and inferiority of the station observations. The relevant results provide a new idea for the automatic classification and assessment of GNSS observation quality.

K-means clustering algorithm is a partitional clustering algorithm that has been used widely in many applications for traditional clustering due to its simplicity and low computational complexity. This clustering technique depends on the user specification of the number of clusters generated from the dataset, which affects the clustering results. Moreover, random initialization of cluster centers results in its local minimal convergence. Automatic clustering is a recent approach to clustering where the specification of cluster number is not required. In automatic clustering, natural clusters existing in datasets are identified without any background information of the data objects. Nature-inspired metaheuristic optimization algorithms have been deployed in recent times to overcome the challenges of the traditional clustering algorithm in handling automatic data clustering. Some nature-inspired metaheuristics algorithms have been hybridized with the traditional K-means algorithm to boost its performance and capability to handle automatic data clustering problems. This study aims to identify, retrieve, summarize, and analyze recently proposed studies related to the improvements of the K-means clustering algorithm with nature-inspired optimization techniques. A quest approach for article selection was adopted, which led to the identification and selection of 147 related studies from different reputable academic avenues and databases. More so, the analysis revealed that although the K-means algorithm has been well researched in the literature, its superiority over several well-established state-of-the-art clustering algorithms in terms of speed, accessibility, simplicity of use, and applicability to solve clustering problems with unlabeled and nonlinearly separable datasets has been clearly observed in the study. The current study also evaluated and discussed some of the well-known weaknesses of the K-means clustering algorithm, for which the existing improvement methods were conceptualized. It is noteworthy to mention that the current systematic review and analysis of existing literature on K-means enhancement approaches presents possible perspectives in the clustering analysis research domain and serves as a comprehensive source of information regarding the K-means algorithm and its variants for the research community.

In spite of the abundance of clustering techniques and algorithms, clustering mixed interval (continuous) and categorical (nominal and/or ordinal) scale data remain a challenging problem. In order to identify the most effective approaches for clustering mixed-type data, we use both theoretical and empirical analyses to present a critical review of the strengths and weaknesses of the methods identified in the literature. Guidelines on approaches to use under different scenarios are provided, along with potential directions for future research.

We investigated the relationship between heavy rainfall events (HREs) and the Pacific Decadal Oscillation (PDO) occurring in Korea over Far East Asia for 40 years (1981–2020). Using K‐means clustering on the low‐level jet, we identified four clusters (C1–C4), with C1 being characterized by weaker synoptic conditions. Out of the four clusters, C1 represented localized extreme HREs compared with the other clusters. Interestingly, only the HRE frequency of C1 was found to have a strong negative correlation with PDO. During the negative‐PDO, sea surface temperature increased above 30°N, which decreased the meridional temperature gradient. This weakened the atmospheric circulation and created thermodynamic instability (i.e., weakened upper jet, increased low‐level temperature, higher atmospheric water capacity), creating a favorable environment for HRE in C1. However, this negative‐PDO environment provided somewhat unfavorable conditions for other clusters (C2–C4), so the PDO impact was insignificant.

Gradient Boosting Trees (GBT) is a powerful machine learning technique that is based on ensemble learning methods that leverage the idea of boosting. GBT combines multiple weak learners sequentially to boost its prediction power proving its outstanding efficiency in many problems, and hence it is now considered one of the top techniques used to solve prediction problems. In this paper, a hybrid approach is proposed that combines GBT with K-means and Bisecting K-means clustering to enhance the predictive power of the approach on regression datasets. The proposed approach is applied on 40 regression datasets from UCI and Kaggle websites and it achieves better efficiency than using only one GBT model. Statistical tests are applied, namely, Friedman and Wilcoxon signed-rank tests showing that the proposed approach achieves significant better results than using only one GBT model.

This work presents a novel and automatic approach to process data from computational fluid dynamics at runtime, to identify and separate different regions of wind turbine wakes. The methodology is based on partitional clustering, in particular k‐Means, and applied to large eddy simulation (LES) computations of the wake of a DTU‐10‐MW wind turbine, simulated with the actuator line method. Unlike other methods that are based on the definition of turbulent quantities, like Q $$ Q $$or λ2 $$ {\\lambda}_2 $$ , the one proposed here is developed on a robust and statistically relevant decomposition of the whole flow field and does not require to manually set values of, for example, Q $$ Q $$to differentiate the regions of the wind turbine wake. Details of the computations used to establish a numerical dataset are discussed and validated, then relevant features are selected and their preprocessing and normalization are discussed. Then a clustering approach is selected and tested to tune the hyperparameters of the method. Results are then discussed providing an interpretation with comparison to qualitative description of the wake available in literature and further linked on the original quantities that were used as input features for k‐Means. The relevance of these quantities in the clustering results is discussed and then the robustness of the method is assessed against temporal propagation of the model to 108 time steps, corresponding to three rotor revolutions. To further assess the robustness of the full methodology, the effects of grid refinements and coarsening are discussed and compared to other classic wake decomposition methods like Q $$ Q $$ ‐criterion.

Background and purpose Post‐stroke fatigue commonly presents alongside several comorbidities. The interaction between comorbidities and their relationship to fatigue is not known. In this study, we focus on physical and mood comorbidities, alongside lesion characteristics. We predict the emergence of distinct fatigue phenotypes with distinguishable physical and mood characteristics. Methods In this cross‐sectional observational study, in 94 first time, non‐depressed, moderate to minimally impaired chronic stroke survivors, the relationship between measures of motor function (grip strength, nine‐hole peg test time), motor cortical excitability (resting motor threshold), Hospital Anxiety and Depression Scale and Fatigue Severity Scale‐7 (FSS‐7) scores, age, gender and side of stroke was established using Spearman's rank correlation. Mood and motor variables were then entered into a k‐means clustering algorithm to identify the number of unique clusters, if any. Post hoc pairwise comparisons followed by corrections for multiple comparisons were performed to characterize differences among clusters in the variables included in k‐means clustering. Results Clustering analysis revealed a four‐cluster model to be the best model (average silhouette score of 0.311). There was no significant difference in FSS‐7 scores among the four high‐fatigue clusters. Two clusters consisted of only left‐hemisphere strokes, and the remaining two were exclusively right‐hemisphere strokes. Factors that differentiated hemisphere‐specific clusters were the level of depressive symptoms and anxiety. Motor characteristics distinguished the low‐depressive left‐hemisphere from the right‐hemisphere clusters. Conclusion The significant differences in side of stroke and the differential relationship between mood and motor function in the four clusters reveal the heterogenous nature of post‐stroke fatigue, which is amenable to categorization. Such categorization is critical to an understanding of the interactions between post‐stroke fatigue and its presenting comorbid deficits, with significant implications for the development of context‐/category‐specific interventions.

This paper presents a compensation method for unbalanced voltage through active and reactive power control by utilizing a smart inverter that improves the voltage unbalance index and detects an unbalanced state of voltage magnitude and phase, and thus enhances power quality by minimizing the voltage imbalance. First of all, this paper presents an analysis of a mathematical approach, which demonstrates that the conventional voltage unbalanced factor (VUF) using the symmetrical component cannot correctly detect the imbalanced state from index equations; and by only minimizing the VUF value, it cannot establish a balanced condition for an unbalanced state of the voltage profile. This paper further discusses that intermittent photovoltaic (PV) output power and diversified load demand lead to an unexpected voltage imbalance. Therefore, considering the complexity of unbalanced voltage conditions, a specific load and an PV profile were extracted from big data and applied to the distribution system model. The effectiveness of the proposed scheme was verified by comparing VUF indices and controlling the active and reactive power of a smart inverter through a numerical simulation.

Recently, the proliferation of distributed generation (DG) has been intensively increased in distribution systems worldwide. In distributed systems, DGs and utility‐owned electric vehicle (EV) to grid aggregators have to be efficiently scaled for cost‐effective network operation. Accordingly, with the penetration of power systems, demand response (DR) is considered an advanced step towards a smart grid. To cope with these advancements, this study aims to develop an innovative solution for the day‐ahead sizing approach of energy storage systems of EVs parking lots and DGs in smart distribution systems complying with DR and minimizing the pertinent costs. The unique feature of the proposed approach is to allow interactive customers to participate effectively in power systems. To accurately solve this optimization model, two probabilistic self‐adjusted modified particle swarm optimization (SAPSO) algorithms are developed and compared for minimizing the total operational costs addressing all constraints of the distribution system, DG units, and energy storage systems of EV parking lots. The K‐means clustering and the Naive Bayes approach are utilized to determine the EVs that are ready to participate efficiently in the DR program. The obtained results on the IEEE‐24 reliability test system are compared to the genetic algorithm and the conventional PSO to verify the effectiveness of the developed algorithms. The results show that the first SAPSO algorithm outperforms the algorithms in terms of minimizing the total running costs. The finding demonstrates that the proposed near‐optimal day‐ahead scheduling approach of DG units and EV energy storage systems in a simultaneous manner can effectively minimize the total operational costs subjected to generation constraints complying with DR. Graphical An optimal simultaneous hourly scheduling strategy for energy storage systems of electric vehicle (EV) parking lots and distributed generators in smart distribution networks that conform with demand response (DR) are reported here. The suggested solution is unique in that it allows interactive consumers to engage successfully in power systems. Two self‐adjusted particle swarm optimization methods are devised and compared to minimize overall operational costs while addressing all restrictions of the distribution system, distributed generation units, and energy storage systems of EV parking lots. The K‐means clustering and the Naive Bayes approach are utilized to determine the EVs that are ready to participate efficiently in the DR program.