Honey Badger Algorithm Based Clustering with Routing Protocol for Wireless Sensor Networks

Wireless sensor network(WSN)includes a set of self-organizing and homogenous nodes employed for data collection and tracking applications.It comprises a massive set of nodes with restricted energy and processing abilities.Energy dissipation is a major concern involved in the design of WSN.Clustering and routing protocols are considered effective ways to reduce the quantity of energy dissipation using metaheuristic algorithms.In order to design an energy aware cluster-based route planning scheme,this study introduces a novel Honey Badger Based Clustering with African Vulture Optimization based Routing(HBAC-AVOR)protocol for WSN.The presented HBAC-AVOR model mainly aims to cluster the nodes in WSN effectually and organize the routes in an energy-efficient way.The presented HBAC-AVOR model follows a two stage process.At the initial stage,the HBAC technique is exploited to choose an opti-mal set of cluster heads(CHs)utilizing afitness function involving many input parameters.Next,the AVOR approach was executed for determining the optimal routes to BS and thereby lengthens the lifetime of WSN.A detailed simulation analysis was executed to highlight the increased outcomes of the HBAC-AVOR protocol.On comparing with existing techniques,the HBAC-AVOR model has outperformed existing techniques with maximum lifetime.

Object Detection in Remote Sensing Images Using Picture Fuzzy Clustering and MapReduce

In image processing, one of the most important steps is image segmentation. The objects in remote sensing images often have to be detected in order toperform next steps in image processing. Remote sensing images usually havelarge size and various spatial resolutions. Thus, detecting objects in remote sensing images is very complicated. In this paper, we develop a model to detectobjects in remote sensing images based on the combination of picture fuzzy clustering and MapReduce method (denoted as MPFC). Firstly, picture fuzzy clustering is applied to segment the input images. Then, MapReduce is used to reducethe runtime with the guarantee of quality. To convert data for MapReduce processing, two new procedures are introduced, including Map_PFC and Reduce_PFC.The formal representation and details of two these procedures are presented in thispaper. The experiments on satellite image and remote sensing image datasets aregiven to evaluate proposed model. Validity indices and time consuming are usedto compare proposed model to picture fuzzy clustering model. The values ofvalidity indices show that picture fuzzy clustering integrated to MapReduce getsbetter quality of segmentation than using picture fuzzy clustering only. Moreover,on two selected image datasets, the run time of MPFC model is much less thanthat of picture fuzzy clustering.

SPATIAL CORRELATION DESCRIPTION OF DEFORMATION OBJECT BASED ON FUZZY CLUSTERING AND GEOLOGICAL ANALYSIS

The methods of deformation analysis and modeling at single point are realized easily now,but available approaches do not make full use of the information from monitoring points and can not reveal integrated deformation regularity of a deformable body.This paper presents a fuzzy clusetering method to analyze the correlative relations of multiple points in space,and then the spatial model for a practical dangerous rockmass in the area of Three Gorges,Yangtze River is established,in which the correlation of six points in space is analyzed by geological investigation and fuzzy set theory.

Density-based clustering method in the moving object database

With the rapid advance of wireless communication, tracking the positions of the moving objects is becoming increasingly feasible and necessary. Because a large number of people use mobile phones, we must handle a large moving object database as well as the following problems. How can we provide the customers with high quality service, that means, how can we deal with so many enquiries within as less time as possible? Because of the large number of data, the gap between CPU speed and the size of main memory has increasing considerably. One way to reduce the time to handle enquiries is to reduce the I/O number between the buffer and the secondary storage.An effective clustering of the objects can minimize the I/O cost between them. In this paper, according to the characteristic of the moving object database, we analyze the objects in buffer, according to their mappings in the two dimension coordinate, and then develop a density based clustering method to effectively reorganize the clusters. This new mechanism leads to the less cost of the I/O operation and the more efficient response to enquiries.

COOPERATIVE CLUSTERING BASED ON GRID AND DENSITY

Based on the analysis of features of the grid-based clustering method-clustering in quest （CLIQUE） and density-based clustering method-density-based spatial clustering of applications with noise （DBSCAN）, a new clustering algorithm named cooperative clustering based on grid and density （CLGRID） is presented. The new algorithm adopts an equivalent rule of regional inquiry and density unit identification. The central region of one class is calculated by the grid-based method and the margin region by a density-based method. By clustering in two phases and using only a small number of seed objects in representative units to expand the cluster, the frequency of region query can be decreased, and consequently the cost of time is reduced. The new algorithm retains positive features of both grid-based and density-based methods and avoids the difficulty of parameter searching. It can discover clusters of arbitrary shape with high efficiency and is not sensitive to noise. The application of CLGRID on test data sets demonstrates its validity and higher efficiency, which contrast with tradi- tional DBSCAN with R tree.

FUZZY IDENTIFICATION METHOD BASED ON A NEW OBJECTIVE FUNCTION

A method of fuzzy identification based on a new objective function is proposed. The method could deal with the issue that input variables of a system have an effect on the input space while output variables of the system do not exert an influence on the input space in the proposed objective functions of fuzzy clustering. The method could simultaneously solve the problems about structure identification and parameter estimation; thus it makes the fuzzy model become optimal. Simulation example demonstrates that the method could identify non linear systems and obviously improve modeling accuracy.

Automatic Video Segmentation Algorithm by Background Model and Color Clustering

In order to detect the object in video efficiently, an automatic and real time video segmentation algorithm based on background model and color clustering is proposed. This algorithm consists of four phases: background restoration, moving objects extract, moving objects region clustering and post processing. The threshold of the background restoration is not given in advanced. It can be gotten automatically. And a new object region cluster algorithm based on background model and color clustering to remove significance noise is proposed. An efficient method of eliminating shadow is also used. This approach was compared with other methods on pixel error ratio. The experiment result indicates the algorithm is correct and efficient.

An Algorithm for Mining Gradual Moving Object Clusters Pattern From Trajectory Streams

The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment,which leverages new applications and services.Since the trajectory streams is rapidly evolving,continuously created and cannot be stored indefinitely in memory,the existing approaches designed on static trajectory datasets are not suitable for discovering gradual moving object clusters pattern from trajectory streams.This paper proposes a novel algorithm of gradual moving object clusters pattern discovery from trajectory streams using sliding window models.By processing the trajectory data in current window,the mining algorithm can capture the trend and evolution of moving object clusters pattern.Firstly,the density peaks clustering algorithm is exploited to identify clusters of different snapshots.The stable relationship between relatively few moving objects is used to improve the clustering efficiency.Then,by intersecting clusters from different snapshots,the gradual moving object clusters pattern is updated.The relationship of clusters between adjacent snapshots and the gradual property are utilized to accelerate updating process.Finally,experiment results on two real datasets demonstrate that our algorithm is effective and efficient.

Realization of R-tree for GIS on hybrid clustering algorithm

The characteristic of geographic information system（GfS） spatial data operation is that query is much more frequent than insertion and deletion, and a new hybrid spatial clustering method used to build R-tree for GIS spatial data was proposed in this paper. According to the aggregation of clustering method, R-tree was used to construct rules and specialty of spatial data. HCR-tree was the R-tree built with HCR algorithm. To test the efficiency of HCR algorithm, it was applied not only to the data organization of static R-tree but also to the nodes splitting of dynamic R-tree. The results show that R-tree with HCR has some advantages such as higher searching efficiency, less disk accesses and so on.

Cluster Model of Formation of Subnuclear and Subatomic Objects

The paper describes the development results on one-dimensional (1D) asymptotic model of the formation kinetics for the objects (clusters) of subnuclear (quark) and subatomic (nuclear) matters. A concept of the objects distribution density wave φ(a, t) in space of sizes a lies in the basis for analytical description of the processes under consideration. The proposed formalism makes it possible to describe in an adequate way the final outcomes of the well-known catastrophic phenomena in the world of elementary particles. Mass characteristics of different processes of approach to equilibrium in nuclear reactions are calculated.

The Coalition Cooperative Game Method and Its Application in Multi-objective Optimization Design

This paper proposes a multi-objective optimization design method based on the coalition cooperative game theory where the three design goals have been seen as three game players. By calculating the affecting factors and fuzzy clustering, the design variables are divided into different strategic spaces which belong to each player, then it constructs a payoff function based on the coalition mechanism. Each game player takes its own revenue function as a target and obtains the best strategy versus other players. The best strategies of all players consist of the strategy permutation of a round game and it obtains the final game solutions through multi-round games according to the convergence criterion. A multi-objective optimization example of the luff mechanism of compensative sheave block shows the effectiveness of the coalition cooperative game method.

A Progressive Approach to Generic Object Detection: A Two-Stage Framework for Image Recognition

Object detection in images has been identified as a critical area of research in computer vision image processing.Research has developed several novel methods for determining an object’s location and category from an image.However,there is still room for improvement in terms of detection effi-ciency.This study aims to develop a technique for detecting objects in images.To enhance overall detection performance,we considered object detection a two-fold problem,including localization and classification.The proposed method generates class-independent,high-quality,and precise proposals using an agglomerative clustering technique.We then combine these proposals with the relevant input image to train our network on convolutional features.Next,a network refinement module decreases the quantity of generated proposals to produce fewer high-quality candidate proposals.Finally,revised candidate proposals are sent into the network’s detection process to determine the object type.The algorithm’s performance is evaluated using publicly available the PASCAL Visual Object Classes Challenge 2007(VOC2007),VOC2012,and Microsoft Common Objects in Context(MS-COCO)datasets.Using only 100 proposals per image at intersection over union((IoU)=0.5 and 0.7),the proposed method attains Detection Recall(DR)rates of(93.17%and 79.35%)and(69.4%and 58.35%),and Mean Average Best Overlap(MABO)values of(79.25%and 62.65%),for the VOC2007 and MS-COCO datasets,respectively.Besides,it achieves a Mean Average Precision(mAP)of(84.7%and 81.5%)on both VOC datasets.The experiment findings reveal that our method exceeds previous approaches in terms of overall detection performance,proving its effectiveness.

Improving Class Cohesion Measurement: Towards a Novel Approach Using Hierarchical Clustering

Class cohesion is considered as one of the most important object-oriented software attributes. High cohesion is, in fact, a desirable property of software. Many different metrics have been suggested in the last several years to measure the cohesion of classes in object-oriented systems. The class of structural object-oriented cohesion metrics is the most in-vestigated category of cohesion metrics. These metrics measure cohesion on structural information extracted from the source code. Empirical studies noted that these metrics fail in many situations to properly reflect cohesion of classes. This paper aims at exploring the use of hierarchical clustering techniques to improve the measurement of cohesion of classes in object-oriented systems. The proposed approach has been evaluated using three particular case studies. We also used in our study three well-known structural cohesion metrics. The achieved results show that the new approach appears to better reflect the cohesion (and structure) of classes than traditional structural cohesion metrics.

基于匝道合流数据的自动驾驶汽车安全性测试评价方法

针对自动驾驶汽车测试场景不明确、评价模型主观性强等问题,研究了高速匝道汇入场景下的典型测试场景提取方法和自动驾驶汽车匝道汇入安全性客观评价方法。深入分析了匝道汇入功能场景下的逻辑场景要素,对自然驾驶数据中的自车速度、车间距离、前车车速等逻辑场景要素进行聚类,得到两类典型的匝道汇入测试场景用于自动驾驶汽车的仿真测试。构建多层次自动驾驶汽车评价体系,引入基于自然驾驶数据的核密度估计模型来获取指标最优阈值,建立以最优阈值为参考序列、以层次分析法(AHP)和客观赋权法(CRITIC)为权重输入的灰色关联理评价模型,对自动驾驶汽车在汇入过程中的安全性进行客观评价。评价结果表明:基于核密度估计的灰色关联理论模型评价结果与主观模糊综合分析模型的评价结果相似率达98.01%,验证了客观模型的有效性。

基于改进YOLOv8n模型的多品种葡萄簇检测方法

葡萄簇目标的精准检测是实现估产、采摘等作业的前提,现有方法难以实现多品种葡萄簇的轻量化精准检测。为提高复杂自然场景下多品种葡萄簇检测准确性、鲁棒性与泛化性,提出一种基于改进YOLOv8n模型的多品种葡萄簇检测模型ESIC-YOLOv8n,该模型在YOLOv8n的Backbone和Neck网络中分别添加EMA和SA注意力模块,以加强网络的特征提取和多尺度特征融合能力,降低因遮挡或重叠对葡萄簇检测的干扰,提高检测精度和召回率;在Head把CIoU替换成Inner-CIoU,利用辅助框提高重叠目标检测的准确性,从而提升模型整体的检测准确性和泛化性。ESIC-YOLOv8n模型的检测精度为87.00%,召回率为81.60%,mAP为88.90%,F1值为84.21%,较原YOLOv8n模型分别提高1.05%、2.90%、1.48%和2.00%。结果表明,ESIC-YOLOv8n模型具有准确率高、泛化性好、轻量化等优点,可为葡萄产量估计、采摘等研究提供技术支持。

基于改进YOLOv5的高速公路隧道车辆和人员检测

针对高速公路隧道内光线昏暗、图像受灯光影响及远距离小目标检测困难等问题,提出了一种改进的YOLOv5高速公路隧道车辆和人员检测算法。首先,使用高斯混合聚类来获得更加匹配数据集目标的一组锚框,提高了模型的检测精度;其次,在特征融合部分引入内容感知重组特征(content-aware ReAssembly of FEatures, CARAFE)上采样算子,扩大感受野,降低上采样过程特征细节损失;最后,通过向网络中插入坐标注意力(coordinate attention, CA),进一步增强模型对图像各位置特征的提取能力。为验证算法的有效性,在浙江温丽高速公路隧道数据集上进行实验,结果表明:所提算法的平均检测精度(mean average precision, mAP)达到了95.7%,较原模型提升3.8%,对于远距离小目标和受严重灯光影响的目标能够实现更加精准检测,为复杂环境下高速公路隧道内车辆和人员检测提供了一种有效的解决方案。

基于改进YOLOv5s算法的禁捕期长江渔船识别及应用研究

长江实行十年禁渔是长江生态环境修复的关键环节,针对禁渔期间长江非法捕捞渔船目标小、背景复杂、流动大等问题,提出了一种基于改进YOLOv5s的目标检测算法。该算法优化多尺度自适应锚框模块,采用改进的K-means++聚类算法,重新匹配适合长江船舶尺寸的锚框;使用轻量高效的坐标注意力(coordinate attention,CA)机制,提升模型关注目标通道信息特征的能力;采用SPPCSPPC(spatial pyramid pooling and context-aware spatial pyramid pooling combination)对特征图进行池化,提高小目标检测能力;通过构建长江船舶数据集训练得到最优权值模型。结果显示,改进后的模型在准确率、召回率、mAP0.5、mAP0.5∶0.9和原模型相比分别提高了1.5%、3.0%、2.4%、7.7%,且训练过程损失收敛更快,收敛值更低,能够准确快速识别出长江上的渔船目标。研究结果可为长江十年禁渔提供技术支持。

基于演化多目标聚类的SAR图像变化检测

基于合成孔径雷达(SAR)图像的变化检测是遥感领域中一项具有挑战性的任务,如何在噪声鲁棒性和有效保留细节之间取得平衡是一个迫切需要解决的问题。然而,大多数SAR图像变化检测方法为了更好地抑制斑点噪声,不可避免地会在一定程度上丢失图像细节。为了解决这一问题,提出了一种基于演化多目标聚类的SAR图像变化检测多目标聚类算法,将变化检测问题转化为一个多目标优化问题。该方法同时构建了两个相互冲突的目标,即分别基于原始差异图与噪声滤波后差异图的聚类能量函数,并用基于分解的演化多目标优化算法MOEA/D对以上目标函数进行优化,实现对差异图不变区域与变化区域的聚类。利用该技术可得到一组变化检测图,用户可以根据自己的需求选择合适的结果。最后,在两个SAR图像数据集上进行了充分的实验,结果表明了该方法的有效性。

地铁列车牵引系统状态评估方法研究

充分挖掘现场故障统计数据,提出一种地铁列车牵引系统状态评估方法。首先基于牵引系统故障树建立层次分析模型,构建各层级评判矩阵并确定权重,然后计算牵引系统各模块基本事件的灰色聚类系数,完成对系统模块层的状态评估,最后利用各模块聚类系数构建牵引系统模糊综合评判矩阵,采用模糊综合评判法对牵引系统整体的健康状态进行评估。结果表明,牵引逆变模块和牵引控制单元板卡是该车型地铁列车牵引系统的薄弱环节,应作为检修与维护中的重点对象。该评估方法综合利用故障树—层次分析法确定权重,降低了人为因素的影响,其评估结果可为地铁列车牵引系统主动维护和检修提供有效依据。

基于先验特征聚类的目标检测优化方法

针对显著目标检测问题在没有任何先验信息的情况下,通过特征聚类和紧致性先验方案实现目标检测优化。优化后的方法包括四个步骤:首先采用超像素预处理将图像分割成超像素,以抑制噪声并降低计算复杂度;其次应用改进的虾群聚类算法对颜色特征进行分类;接着利用二维熵来衡量每个簇的紧密度,并构建背景模型;最后以背景区域与其他区域之间的对比度作为显著特征,并通过设计高斯滤波器增强其显著性。为了更好地评价显著目标检测的精度,本文通过多维评价指标进行优劣性实验分析,实验结果表明,文中算法具有较好的实时性与鲁棒性。