Grid Clustering Research Articles

Intelligent Wireless Charging Path Optimization for Critical Nodes in Internet of Things-Integrated Renewable Sensor Networks.

Wireless sensor networks (WSNs) play a crucial role in the Internet of Things (IoT) for ubiquitous data acquisition and tracking. However, the limited battery life of sensor nodes poses significant challenges to the long-term scalability and sustainability of these networks. Wireless power transfer technology offers a promising solution by enabling the recharging of energy-depleted nodes through a wireless portable charging device (WPCD). While this approach can extend node lifespan, it also introduces the challenge of bottleneck nodes-nodes whose remaining energy falls below a critical value of the threshold. The paper addresses this issue by formulating an optimization problem that aims to identify the optimal traveling path for the WPCD based on ant colony optimization (WPCD-ACO), with a focus on minimizing energy consumption and enhancing network stability. To achieve it, we propose an objective function by incorporating a time-varying z phase that is managed through linear programming to efficiently address the bottleneck nodes. Additionally, a gateway node continually updates the remaining energy levels of all nodes and relays this information to the IoT cloud. Our findings indicate that the outage-optimal distance achieved by WPCD-ACO is 6092 m, compared to 7225 m for the shortest path and 6142 m for Dijkstra's algorithm. Furthermore, the WPCD-ACO minimizes energy consumption to 1.543 KJ, significantly outperforming other methods: single-hop at 4.8643 KJ, GR-Protocol at 3.165 KJ, grid clustering at 2.4839 KJ, and C-SARSA at 2.5869 KJ, respectively. Monte Carlo simulations validate that WPCD-ACO is outshining the existing methods in terms of the network lifetime, stability, survival rate of sensor nodes, and energy consumption.

Planning for green infrastructure by integrating multi-driver: Ranking priority based on accessibility equity

Green infrastructure provides multifarious benefits, improving urban resilience and sustainability amid increasing climate change and urbanization. Traditional green infrastructure planning studies were based on the spatial-equity principle, which usually neglected residents’ aggregation pattern, leading to the conflict with equitable green exposure. Using the sponge city of Zhengzhou as a case, this study proposes a novel priority ranking strategy, namely accessibility equity. We first spatially quantified the regional socioecological conditions on 0.25 km2 grids as green infrastructure planning drivers, including stormwater management, urban thermal environment, air quality, habitat maintenance and water purification. Subsequently we integrated these planning drivers with population density to conduct spatial autocorrelation analysis, priority ranking, and grid clustering. While most planning drivers are positively correlated with population density, except air quality, some areas show opposite trends in terms of local perspective, suggesting that the spatial equity may lead to mismatch between residents’ demand and GI priority. The priority order based on accessibility equity, rises in the city center and falls in suburbs and industrial zones. The study area is divided into four categories using k-means clustering, and we propose the corresponding adaptive green infrastructure development strategy. The framework can be a practical tool for guiding green infrastructure and sponge city projects.

Fuzzy Regional Co-Location Pattern Mining Based on Efficient Density Peak Clustering and Maximal Fuzzy Grid Cliques

Due to the heterogeneity of data distribution in real life and the spatial autocorrelation among spatial instances, traditional spatial co-location pattern mining methods tend to ignore valuable information specific to local regions. To address the limitation, regional co-location pattern mining has been proposed to find patterns that may be hidden within local regions. In this paper, a fuzzy regional co-location pattern mining framework based on efficient density peak clustering and maximal fuzzy grid cliques is presented. By incorporating a grid-splitting method and fuzzy theory, an efficient density peak clustering algorithm is proposed to divide the global area into distinct local regions. Furthermore, we propose a method to materialize the neighbor relationships between instances based on the maximal fuzzy grid cliques, and parallelize the clustering process to improve the algorithm efficiency. Experimental results show that the proposed algorithm can not only reduce the time consumption by about 40%, but also mine meaningful patterns with tighter instance distributions.

Quality development of vocational education in the context of big data

Abstract Taking vocational education as an entry point, this paper utilizes factor analysis to deal with the problem of multicollinearity of vocational education indicators so that the synthesized factors are no longer correlated and lay a good foundation for vocational education cluster analysis. The distance measure and similarity coefficient describe the relationship between vocational education indicators. To increase computational efficiency and handle large and multidimensional data sets more effectively, the advantages of K-mean and grid clustering algorithms are combined to create the GBKM cluster analysis algorithm. Higher education institutions in Province H are chosen as representative samples in order to examine the variables influencing the development of high-quality vocational education, categorize the degree of that development, and forecast the trend of that development. The findings demonstrate that the GBKM cluster analysis algorithm’s accuracy difference between the predicted and actual data on the development trend of vocational education ranges from [0,0.1]. The algorithm’s absolute error value is 0.182, which falls within an acceptable error range and suggests that the algorithm proposed in this paper is capable of more accurately predicting the development trend of vocational education and may have some theoretical and practical implications for fostering high-quality development of vocational education. It has theoretical and practical implications for advancing the development of vocational education to a high standard.

Sample of Groups: A New Strategy to Find a Representative Point for Each Undisclosed Cluster

Some problems involving the selection of samples from undisclosed groups are relevant in various areas such as health, statistics, economics, and computer science. For instance, when selecting a sample from a population, well-known strategies include simple random and stratified random selection. Another related problem is selecting the initial points corresponding to samples for the K-means clustering algorithm. In this regard, many studies propose different strategies for choosing these samples. However, there is no consensus on the best or most effective approaches, even when considering specific datasets or domains. In this work, we present a new strategy called the Sample of Groups (SOG) Algorithm, which combines concepts from grid, density, and maximum distance clustering algorithms to identify representative points or samples located near the center of the cluster mass. To achieve this, we create boxes with the right size to partition the data and select the representatives of the most relevant boxes. Thus, the main goal of this work is to find quality samples or seeds of data that represent different clusters. To compare our approach with other algorithms, we not only utilize indirect measures related to K-means but also employ two direct measures that facilitate a fairer comparison among these strategies. The results indicate that our proposal outperforms the most commonly used algorithms.

Tensor and Confident Information Coverage Based Reliability Evaluation for Large-Scale Intelligent Transportation Wireless Sensor Networks

Wireless sensor network (WSN) plays an important role in information collection and processing in Intelligent Transportation Systems (ITS) recently. With rapid development of ITS, the size and complexity of WSN is rapidly increasing. Thus, how to evaluate the reliability of large-scale WSN has attracted more and more attention. In this paper, reliability is defined as the probability that the WSN is functional. A confident information coverage (CIC) model-based reliability algorithm (CICRA) is proposed to comprehensively consider coverage reliability and connectivity reliability. Especially, to determine the impact of failing nodes on connectivity in large-scale WSN, a grid clustering connectivity algorithm (GCCA) is proposed to reduce the complexity of computing the connectivity between large-scale wireless sensor nodes, which transforms the connectivity problem between nodes into a grid connectivity problem. In addition, a 3-order tensor modeling is proposed to uniformly represent and model node states, node combinations, and coverage patterns. Furthermore, the network reliability combines coverage reliability and connectivity reliability is evaluated by tensor modeling. Simulation results indicate that the proposed CICRA can improve the network reliability and accuracy of reliability evaluation.

A Novel Clustering Method Based on Adjacent Grids Searching.

Clustering is used to analyze the intrinsic structure of a dataset based on the similarity of datapoints. Its widespread use, from image segmentation to object recognition and information retrieval, requires great robustness in the clustering process. In this paper, a novel clustering method based on adjacent grid searching (CAGS) is proposed. The CAGS consists of two steps: a strategy based on adaptive grid-space construction and a clustering strategy based on adjacent grid searching. In the first step, a multidimensional grid space is constructed to provide a quantization structure of the input dataset. The noise and cluster halo are automatically distinguished according to grid density. Moreover, the adaptive grid generating process solves the common problem of grid clustering, in which the number of cells increases sharply with the dimension. In the second step, a two-stage traversal process is conducted to accomplish the cluster recognition. The cluster cores with arbitrary shapes can be found by concealing the halo points. As a result, the number of clusters will be easily identified by CAGS. Therefore, CAGS has the potential to be widely used for clustering datasets with different characteristics. We test the clustering performance of CAGS through six different types of datasets: dataset with noise, large-scale dataset, high-dimensional dataset, dataset with arbitrary shapes, dataset with large differences in density between classes, and dataset with high overlap between classes. Experimental results show that CAGS, which performed best on 10 out of 11 tests, outperforms the state-of-the-art clustering methods in all the above datasets.

Numerical Simulation and Computational Fluid Dynamics Analysis of Two-Dimensional Lid-Driven Cavity Flow Within the Weapon Bay of an Autonomous Fighter Drone

The efficient deployment of weapons in military operations is critical for mission success, and the flow of air within the weapon bay of an autonomous fighter drone plays a vital role in achieving this objective. In this paper, we present a comprehensive numerical simulation and computational fluid dynamics (CFD) analysis of the three-dimensional lid-driven cavity flow within the weapon bay of an autonomous fighter drone. To address this challenging problem, we employ CFD analysis and a multigrid approach to solve the Navier-Stokes equations for the aerodynamic problem. Our simulations include high Reynolds numbers of up to 10,000, which demonstrates the potential of CFD analysis in optimizing the design of autonomous fighter drones for military operations. We evaluate the effectiveness of the linked strongly implicit multigrid technique in estimating high-Re fine-mesh flow solutions using the vorticity-stream function formulation of the two-dimensional incompressible Navier-Stokes equations. The model issue is the driven flow in a square cavity, and we consider meshes up to 1024 x 1024 points and combinations with Reynolds numbers as high as 1000. To further improve the accuracy of our simulations, we employ one-dimensional grid clustering coordinate transformations instead of uniform mesh refinement, as the flow field exhibits one or more secondary vortices. Our findings demonstrate that CFD analysis can provide valuable insights into the complex flow dynamics within the weapon bay of autonomous fighter drones, which can lead to the optimization of their design for enhanced mission capabilities. Overall, our study highlights the significance of numerical simulations and CFD analysis in the design and optimization of autonomous fighter drones for military applications. Our results can serve as a basis for future research in the field of UAV aerodynamics and contribute to the development of more efficient and effective military operations.

Isoparametric Finite Element Method to Generate Structured Grid for Numerical Flow Simulation

Eight-nodded quadrilateral finite element is used to generate structured grid for a numerical simulation. The computational domain is sub-divided into number of quadrilateral regions that display the geometry of each sub-domain. The inner and the outer boundaries of the computational domain are described in terms of the surface coordinates. An algebraic homotopy procedure is used to generate grid clustering in order to resolve boundary layer. The structured grid is linked with the flow solver based on finite volume of space discretiztion scheme with multi-stage Runge-Kutta time stepping technique. Examples are illustrated to demonstrate the grid generation procedure and data processing for a forward facing aero-disc spike attached to a hemispherical blunt body at Mach 6 and over a heat shield of a satellite launch vehicle at Mach 0.8 at an angle of attack 5 deg. The present grid generation method is convenient for checking the grid independence test by varying the stretching factor. The quadrilateral grid generated by finite element, vector plot of velocity and contour plots of the computed flow field data are easily drawn with the help of MATLAB.

Some Clustering Methods, Algorithms and their Applications

Clustering is a type of unsupervised learning [15]. When no target values are known, or "supervisors," in an unsupervised learning task, the purpose is to produce training data from the inputs themselves. Data mining and machine learning would be useless without clustering. If you utilize it to categorize your datasets according to their similarities, you'll be able to predict user behavior more accurately. The purpose of this research is to compare and contrast three widely-used data-clustering methods. Clustering techniques include partitioning, hierarchy, density, grid, and fuzzy clustering. Machine learning, data mining, pattern recognition, image analysis, and bioinformatics are just a few of the many fields where clustering is utilized as an analytical technique. In addition to defining the various algorithms, specialized forms of cluster analysis, linking methods, and please offer a review of the clustering techniques used in the big data setting.

Sensitivity of TBL Wall-Pressure over the Flat Plate on Numerical Turbulence Model Parameter Variations

A two-fold sensitivity of the zero-pressure gradient (ZPG) turbulent boundary layer (TBL) wall-pressure spectrum to different RANS model parameters is investigated for a flat plate case, which is a close approximation to the aircraft fuselage or wing. The alteration in the mean square pressure fluctuations due choice of semi-empirical pressure model and the choice of computational model parameters like solver, near wall grid clustering, measuring location, and flow velocity are separately studied. The underlying effect of different TBL parameters in the said sensitivity has been studied while numerically replicating wind tunnel experiments and in-flight tests considering different RANS configurations. Initially, the best-predicting pressure spectrum models are selected by comparing them with available in-flight and wind tunnel test data. Subsequently, the accuracy of all the individual model parameters in predicting mean TBL flow quantities like wall shear stress, boundary layer thickness, displacement thickness, momentum thickness, etc., and eventually mean square pressure (MSP) is estimated. The sensitivity of the mean square pressure fluctuations value to the TBL flow quantities and the near-wall grid clustering is observed to be significant. In general, family of models is found to be best in terms of numerical convergence and closeness when compared to the experimental MSP values. family of models is suggested to be avoided while estimating MSP in flat plate TBL case

Fast and Accurate Framework for Ontology Matching in Web of Things

The Web of Things (WoT) can help with knowledge discovery and interoperability issues in many Internet of Things (IoT) applications. This article focuses on semantic modeling of WoT and proposes a new approach called Decomposition for Ontology Matching (DOM) to discover relevant knowledge by exploring correlations between WoT data using decomposition strategies. The DOM technique adopts several decomposition techniques to order highly linked ontologies of WoT data into similar groups. The main idea is to decompose the instances of each ontology into similar groups and then match instances of similar groups instead of entire instances of two ontologies. Three main algorithms for decomposition have been developed. The first algorithm is based on radar scanning, which determines the distribution of distances between each instance and all other instances to determine the cluster centroid. The second algorithm is based on adaptive grid clustering, where it focuses on distribution information and the construction of spanning trees. The third algorithm is based on split index clustering, where instances are divided into groups of cells from which noise is removed during the merging process. Several studies were conducted with different ontology databases to illustrate the use of the DOM technique. The results show that DOM outperforms state-of-the-art ontology matching models in terms of computational cost while maintaining the quality of the matching. Moreover, these results demonstrate that DOM is capable of handling various large datasets in WoT contexts.

Muscle magnetic resonance characterization of STIM1 tubular aggregate myopathy using unsupervised learning.

Congenital myopathies are a heterogeneous group of diseases affecting the skeletal muscles and characterized by high clinical, genetic, and histological variability. Magnetic Resonance (MR) is a valuable tool for the assessment of involved muscles (i.e., fatty replacement and oedema) and disease progression. Machine Learning is becoming increasingly applied for diagnostic purposes, but to our knowledge, Self-Organizing Maps (SOMs) have never been used for the identification of the patterns in these diseases. The aim of this study is to evaluate if SOMs may discriminate between muscles with fatty replacement (S), oedema (E) or neither (N). MR studies of a family affected by tubular aggregates myopathy (TAM) with the histologically proven autosomal dominant mutation of the STIM1 gene, were examined: for each patient, in two MR assessments (i.e., t0 and t1, the latter after 5 years), fifty-three muscles were evaluated for muscular fatty replacement on the T1w images, and for oedema on the STIR images, for reference. Sixty radiomic features were collected from each muscle at t0 and t1 MR assessment using 3DSlicer software, in order to obtain data from images. A SOM was created to analyze all datasets using three clusters (i.e., 0, 1 and 2) and results were compared with radiological evaluation. Six patients with TAM STIM1-mutation were included. At t0 MR assessments, all patients showed widespread fatty replacement that intensifies at t1, while oedema mainly affected the muscles of the legs and appears stable at follow-up. All muscles with oedema showed fatty replacement, too. At t0 SOM grid clustering shows almost all N muscles in Cluster 0 and most of the E muscles in Cluster 1; at t1 almost all E muscles appear in Cluster 1. Our unsupervised learning model appears to be able to recognize muscles altered by the presence of edema and fatty replacement.

Spatiotemporal Evolution and Risk Analysis of Land Use in the Coastal Zone of the Yangtze River Delta Region of China

The extensive accumulation of big data, along with the development of a high-performance platform, bridge the gap between the previous inability to provide long-term time series and broad-scale coastal zone monitoring and risk warnings with remote sensing techniques. Based on 20 years of Landsat images from the Google Earth Engine platform, the time series land cover in the coastal zone of the Yangtze River Delta in China was classified. Then, a spatiotemporal clustering method based on grid segmentation was proposed to analyze the spatiotemporal evolution details of artificial surface expansion and the risks of cropland loss and ecological degradation caused by this. The results showed that significant changes have taken place in the quantitative structure and spatial morphology of coastal land use in the past 20 years. The artificial surface maintained a growth trend, increasing by 229%, while cropland decreased by 19%. Natural land showed a fluctuation pattern of “up→down→up”. The spatiotemporal details of land use obtained through 1km grid segmentation and clustering analysis were more significant. The artificial surface mainly underwent a progressive spatial expansion along the central urban area and important transportation axes (types III and IV), with the most dramatic changes occurring from 2010 to 2013. Type III cropland loss was the most significant, falling from 75.02% in 2000 to 38.23% in 2020. At the same time, the change in type III water body corresponds to the newly increased area of reclamation, which has decreased by 17% in the past 20 years, indicating that the degradation of coastal natural wetlands was significant. This paper provided a comprehensive diagnosis of coastal land use change, which could help policy makers and implementers to propose more targeted and differentiated coastal development and protection policies.

Anomaly detection for streaming data based on grid-clustering and Gaussian distribution

A massive amount of real-time and evolving streaming data are produced from various devices and applications. Anomaly detection is one of the main tasks of streaming data mining with many practical applications. However, without prior knowledge, it is difficult to detect the anomaly accurately and quickly. In this paper, we propose an unsupervised anomaly detection algorithm (GC-ADS), which is based on the idea of grid clustering and Gaussian distribution. Specifically, the data space is first segmented using the grid structure, then the data points are mapped to grids and finally grids are clustered. The anomaly can be preliminarily judged according to the cluster density. To solve the problem that clustering cannot distinguish between noise and anomaly, based on the idea of data similarity and Gaussian distribution, a noise recognition model is designed. In addition, a data filtering model based on grid and sliding window is designed to save memory and retain valid information. The proposed method is compared with the state-of-the-art methods on the Numenta anomaly benchmark. Experimental results indicate that GC-ADS detects anomalies more accurately than other methods with less time cost.

Efficient energy management for a plug-in hybrid electric vehicle considering motor current alert mechanism

Plug-in hybrid electric vehicle (PHEV), due to its energy cleanliness and sustainability, has gained plenty of research in cleaner production. Energy management strategy (EMS) can govern the energy flow and reduce fossil fuels wasting. Most PHEVs prefer their motors working prolonged time to pursue better fuel-saving performance, and the overcurrent protection (OCP) would easily occur due to the motor taking additional demand power, and it would be a challenge to develop self-examination of EMS and ensure the efficient energy distribution. Thus, this paper proposes an efficient energy management for a PHEV considering motor current alert (MCA) mechanism. First, an MCA mechanism is originally established for PHEV to prevent large motor currents that persist for a long time, which improves the current sensitivity of EMS. Second, a multi-step Markov chain is used to predict future velocities and a new clustering method for Markov states is designed, which improves the grid clustering method. Third, the cooperative game theory (CGT) of the energy optimization problem is formulated, and its calculation process is implemented by model predictive control (MPC) method. This CGT-MPC can optimize both group profit and personal profit. Finally, the proposed strategy is validated against other baseline strategies in both simulation and bench test. Comparison results show that the proposed strategy can reduce the frequency and duration of large motor current occurrences by 71% under complex driving conditions, while at most reducing the fuel consumption by 10.28% and electricity consumption bias within 1%.

A Grid-Based Density Peaks Clustering Algorithm

This work focuses on the improvement of density peaks clustering (DPC, also known as clustering by fast search and find of density peaks, FSFDP) by the introduction of the grid clustering. A grid division is used to divide the data space into grid cells. The global characteristic of grid cell replaces the characteristic of the data points. The local density of DPC is replaced by the density of the grid cell. The cut-off distance of DPC is no longer needed. Then, the cluster centers are determined adaptively based on the quantities. Finally, a two-stage allocation strategy (neighborhood searching and border grid cell assigning) is introduced to obtain the final clustering results. Six datasets are used to experimentally verify the effectiveness and performance of the proposed algorithm. Experimental results show that the improvement of DPC is successful. Compared with DPC, the proposed algorithm is more efficient with less manual intervention.

Cooperative survey of seabed ROIs using multiple USVs with coverage path planning

A fine-grained offshore seabed map is critical to the site selection and construction for offshore wind farms, which can be obtained by the cooperative survey with multiple unmanned surface vehicles (USVs). To accomplish the mission of surveying multiple regions of interest (ROIs) by cooperative coverage path planning of USVs, a sequential algorithm is proposed to facilitate the generating the ROIs’ boundary, decomposing concave polygons, and planning coverage path for multiple USVs. First, in order to extract the multiple ROIs and generate accurate polygon boundaries, the grid clustering method and the α -shapes method are adopted with a prior seabed distribution map information. Second, an improved Bayazit decomposition algorithm is presented to reduce the number of sub-regions and turnings by USVs during the coverage planning. Third, aiming at routing the traveling order of every sub-region, a novel discrete group teaching optimization algorithm with an adaptive neighborhood radius model is proposed, which performs robustly compared with five classical multiple traveling salesman algorithms. Finally, numerical simulation results show that the proposed algorithm integrating the ROIs determination and coverage path planning methods is effective for cooperative surveying of multiple USVs.

Deep Belief Neural Network (DBNN)-Based Categorization of Uncertain Data Streams

In the data mining era, the research field is paying attention to data stream mining, which offers a substantial influence on a variety of applications such as networking, wireless communications, education, economics, weather prediction, financial sector, and so on. Moreover, processing of this uncertain data stream faces two major challenges, which are computational difficulty and long processing time of data. Thus, to overcome this, this work proposes a technique that employs a deep belief neural network to categorize uncertain data streams. Initially, this work utilized a hybrid method that combines ensemble, grid, and density-dependent clustering approaches to acquire the local optimum value in uncertain data streams. Furthermore, for classification, a deep belief neural network (DBNN) has been used. As a result of mining, target semantics or chunks will be obtained from the classified data. The suggested technique performs well, and its effectiveness has been assessed in terms of time and accuracy. Thus, the proposed method outperforms the existing techniques.

Personalized design strategy of vehicle steer-by-wire characteristics considering driving style

In order to meet the needs of drivers with different driving styles and improve driving comfort and safety, a personalized design strategy of vehicle steer-by-wire (SBW) characteristics considering driving style is proposed in this paper. Firstly, based on the driver hardware-in-the-loop test bench, the driver’s driving characteristic data is collected under double lane change condition. Then, the K-means clustering algorithm is adopted to classify the driver’s driving style into cautious type, general type and radical type. Based on this, combined with the clustering results and grid search method, the support vector machine algorithm is used to construct the driving style identification model. Finally, considering the driving style, vehicle stability and steering wheel alignment characteristics, the driver personalized variable steering ratio strategy is designed based on the ideal transmission characteristics, and the personalized SBW performance is analyzed under step condition and sinusoidal condition. The results indicate that the proposed personalized design strategy of vehicle SBW characteristics considering driving style can not only accurately identify driving style, but also meet the needs of drivers with different driving styles for vehicle response.