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Significant full reference image segmentation evaluation: a survey in remote sensing field

MULTIMEDIA TOOLS AND APPLICATIONS 2022年第13期81卷 17959-17987页

作者： Pugazhenthi, A. Sebastian, Sruthy Rohith, G. Kumar, Lakshmi Sutha Natl Inst Technol Puducherry Dept Elect & Commun Engn Pondicherry India

Image segmentation is a crucial step in remote sensing application, as it breaks down a larger image into smaller chunks, which contains useful information. Although there are several image segmentation algorithms available, evaluation of the algorithms is challenging. Furthermore, the evaluation of image segmentation can elucidate the best image segmentation algorithm for a single image or a group of image or a whole class of image. This paper explores and evaluates the benefits and the drawbacks of various qualitative and quantitative image segmentation evaluation metrics used in remote sensing applications. For all the metrics, a quantitative set of values for good and bad segmentation is provided. In addition, some image segmentation algorithms such as Multi Otsu Threshold, K Means clustering, Fuzzy C Means clustering, Improved K Means clustering (IFCM), Improved Fuzzy C Means clustering, Naive Bayes classifier, K Nearest Neighbor, Decision Tree (DT) and Random Forest classifier are used in the experimental comparison of metrics. The qualitative and quantitative satellite image segmentation evaluation using the mentioned algorithms is measured. The results are analyzed to strengthen the impact of different metrics on the segmentation algorithms. In both qualitative and quantitative analysis, the IFCM outperformed the other unsupervised machine learning algorithms and the DT outperformed the other supervised machine learning algorithms. The effectiveness of the provided metrics in the remote sensing field is validated.

关键词： Image segmentation INSAT-3D Evaluation metrics clustering algorithm Qualitative evolution Quantitative evolution Machine learning algorithm

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Managing information measures for hesitant fuzzy linguistic term sets and their applications in designing clustering algorithms

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INFORMATION FUSION 2019年 50卷 30-42页

作者： Tang, Ming Liao, Huchang Sichuan Univ Business Sch Chengdu 610064 Sichuan Peoples R China

Recently, the Hesitant Fuzzy Linguistic Term Sets (HFLTSs) have been widely used to address cognitive complex linguistic information because of its advantage in representing vagueness and hesitation in qualitative decision-making process. Information measures, including distance measure, similarity measure, entropy measure, inclusion measure and correlation measure, are used to characterize the relationships between linguistic elements. Many decision-making theories are based on information measures. Up to now, distance, similarity, entropy and correlation measures have been proposed by scholars but there is no paper focuses on inclusion measure. This paper dedicates to filling this gap and the inclusion measure between HFLTSs are proposed. We discuss the relationships among distance, similarity, inclusion and entropy measures of HFLTSs. Given that clustering algorithm is an important application of information measures but there are few papers related to clustering algorithm based on information measures in the environment of HFLTS, in this paper, we propose two clustering algorithms based on correlation measure and distance measure, respectively. After that, a case study concerning water resource bearing capacity is illustrated to verify the applicability of the proposed clustering algorithms.

关键词： Hesitant fuzzy linguistic term set Information measure Inclusion measure clustering algorithm Water resource bearing capacity

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Cluster-Based Routing Protocols for Flying Ad Hoc Networks (FANETs)

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IEEE ACCESS 2022年 10卷 32981-33004页

作者： Abdulhae, Omer T. Mandeep, Jit Singh Islam, M. T. Univ Kebangsaan Malaysia Dept Elect Elect & Syst Engn Fac Engn & Built Environm FKAB Bangi 43600 Selangor Malaysia Univ Kebangsaan Malaysia Space Sci Ctr ANGKASA Inst Climate Change Bangi 43600 Malaysia

Flying Ad-Hoc Network (FANET) is a set of Unmanned Aerial Vehicles (UAVs) inter-connected wirelessly. FANETs self-organize and provide low-cost, adaptable, and simple-to-implement flying nodes, enabling them to complete complicated tasks more quickly and collectively. The high mobility of nodes and the highly dynamic topology pose challenges to communication design, particularly when creating a routing protocol for UAV networks;this has inspired researchers to contribute and develop this technology. Hierarchical routing technique known as clustering is necessary to offer scalability, survivability, and distribute payload among UAVs to maintain the performance. This study has proposed a comprehensive survey of the cluster-based routing protocols (CBRPs) in terms of their strengths, weaknesses, specific applications, method, number of nodes, and future improvements for serving FANETs. Moreover, 21 CBRPs based FANETs were reviewed in terms of their topology, challenges, scalability, characteristics, clustering strategy, outstanding features, cluster head (CH) selection, routing metrics, and performance measures. In addition, open issues that need to be addressed in future studies in the field of routing protocols for UAV networks were also debated.

关键词： Routing protocols Routing Topology Network topology Ad hoc networks Autonomous aerial vehicles Scalability clustering algorithm flying ad hoc network routing protocol scalability unmanned aerial vehicle

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A Game Approach for Charging Station Placement Based on User Preferences and Crowdedness

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第4期23卷 3654-3669页

作者： Bae, Sangjun Jang, Inmo Gros, Sebastien Kulcsar, Balazs Hellgren, Jonas Chalmers Univ Technol Automat Control Grp Dept Elect Engn S-41296 Gothenburg Sweden Univ Manchester Robot Extreme Environm Grp Manchester M13 9PL Lancs England Norwegian Univ Sci & Technol Dept Engn Cybernet N-7491 Trondheim Norway Volvo Grp Truck Technol Automat S-41755 Gothenburg Sweden

The placement of electric vehicle charging stations (EVCSs), which encourages the rapid development of electric vehicles (EVs), should be considered from not only operational perspective such as minimizing installation costs, but also user perspective so that their strategic and competitive charging behaviors can be reflected. This paper proposes a methodological framework to consider crowdedness and individual preferences of electric vehicle users (EVUs) in the selection of locations for fast-charging stations. The electric vehicle charging station placement problem (EVCSPP) is solved via a decentralized game theoretical decision-making algorithm and k-means clustering algorithm. The proposed algorithm, referred to as k-GRAPE, determines the locations of charging stations to maximize the sum of utilities of EVUs. In particular, we analytically present that 50% of suboptimality of the solution can be at least guaranteed, which is about 17% better than the existing game theoretical based framework. We show a few variants to describe the utility functions that may capture the difference in preferences of EVUs. Finally, we demonstrate the viability of the decision framework via three real-world data-based experiments. The results of the experiments, including a comparison with a baseline method are then discussed.

关键词： Charging stations Games Decision making Optimization clustering algorithms Planning Partitioning algorithms clustering algorithm electric vehicle charging station placement game theory

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clustering-based method for locating critical slip surface using the strength reduction method

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COMPUTERS AND GEOTECHNICS 2023年第1期155卷

作者： Jia, Jinqing Ju, Shiyuan Dalian Univ Technol Sch Civil Engn Dalian 116024 Peoples R China Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Peoples R China

In slope stability analysis, the strength reduction method (SRM) combined with the finite element method (FEM) can directly obtain the factor of safety (FOS) of complex slopes, but it cannot directly locate the critical slip surface in the plastic zone. Current methods for locating the critical slip surface in SRM are approximations based on visualization techniques, which limits the application of SRM. To solve this problem, a cluster-based method for locating the critical slip surface was proposed in the present study, which can make full use of the various parameters calculated by FEM. In the proposed method, each node in the finite element model is treated as a data point, and its parameters such as stress, strain, and displacement are used as the features of this data point. Then all nodes in the model are classified into two classes using the clustering algorithm, and the boundary between the two classes is the critical slip surface. The effectiveness of the proposed method was demonstrated by applying it to several examples and comparing the results with those of other methods.

关键词： clustering algorithm Strength reduction method The critical slip surface Dimensionality reduction Slope stability

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Effectively Filtering Images for Better Multi-modal Knowledge Graph 6th

Effectively Filtering Images for Better Multi-modal Knowledg...

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6th Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data (APWeb-WAIM)

作者： Peng, Huang Xu, Hao Tang, Jiuyang Wu, Jibing Huang, Hongbin Natl Univ Def Technol Lab Big Data & Decis Changsha 410000 Peoples R China

ISBN: (纸本)9789819913534;9789819913541

The existing multi-modal knowledge graph construction techniques have become mature for processing text modal data, but lack effective processing methods for other modal data such as visual modal data. Therefore, the focus of multi-modal knowledge graph construction lies in image and image and text fusion processing. At present, the construction of multi-modal knowledge graph often does not filter the image quality, and there are noises and similar repetitive images in the image set. To solve this problem, this paper studies the quality control and screening of images in the construction process of multi-modal knowledge graph, and proposes an image refining framework of multi-modal knowledge graph, which is divided into three modules. The final experiment proves that this framework can provide higher quality images for multi-modal knowledge graphs, and in the benchmark task of multi-modal entity alignment, the effect of entity alignment based on the multi-modal knowledge graphs constructed in this paper has been improved compared with previous models.

关键词： Multi-modal knowledge graph clustering algorithm Image-text matching Entity alignment

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Product Family Formation for Reconfigurable Manufacturing Systems 2nd

Product Family Formation for Reconfigurable Manufacturing Sy...

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3rd International Conference on Digital Technologies and Applications

作者： Bahtat, Chaymae El Barkany, Abdellah Jabri, Ahdelouahhah Sidi Mohammed Ben Abdellah Univ Fac Sci & Technol Mech Engn Lab BP 2202 Route Imouzzer Fes Morocco

ISBN: (纸本)9783031298561;9783031298578

Reconfigurable manufacturing systems, or RMS, are a new concept in production systems. Their goal is to assure the feasibility of numerous generations of products by making quick adjustments to existing systems in order to meet the increasing evolution of markets while maintaining a high quality of products at a low cost. An RMS is designed around a family of products with enough flexibility to manufacture them all. These products are grouped into families based on certain common characteristics, such as modularity, components, sequence of manufacturing operations, etc. These characteristics differ depending on the production system studied and the products that need to be integrated in order to have an optimal formation of product families. These characteristics differ according to the production system studied and the products that must be integrated in order to have an optimal formation of product families. With this objective, this paper presents a structured and updated literature review on product family formation for RMS, highlighting key application areas and tools. Through this literature review, we were able to construct a general clustering algorithm that can be applied to any production system.

关键词： Reconfigurable Manufacturing Systems product family formation clustering algorithm

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Spatiotemporal correlation analysis of the dynamic response of supertall buildings under ambient wind conditions

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2022年第4期31卷 e1914-e1914页

作者： Yang, Bin Pan, Licheng Zhu, Haitao Sun, Siyuan Zhang, Qilin Tongji Univ Coll Civil Engn 1239 Siping Rd Shanghai Peoples R China

The structural health monitoring systems of supertall buildings are typical big data systems with abundant and high-dimensional data. Correlation analysis is a vital technique for data mining and analysis. This study aims to reveal the spatial and temporal correlation of 4 years of structural health monitoring data from the 632-m-high Shanghai Tower under normal wind and typhoon conditions. In this paper, the root mean square (RMS) value of the acceleration response is used to characterize the structural vibration intensity. The maximal information coefficient (MIC) and Pearson product-moment correlation coefficient (PPMCC) are adopted to measure the spatial correlations along Shanghai Tower under different wind conditions. The temporal correlation of structural vibration is investigated with kernel density estimation and the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The results show that the structural vibrations of Shanghai Tower in different spatial positions are correlated. The correlation of the structural vibrations of the same floor is at a high level under both normal wind and typhoons, while that of different floors rises with increasing mean wind speed. Furthermore, the MIC is more stable than the PPMCC in measuring this spatial correlation. Based on temporal correlation analysis, a circadian rhythm tendency of the structural vibration intensity of Shanghai Tower on the daily scale is identified, and it is verified that the structural response is temporally correlated. These findings can provide a structural health state estimation index based on the correlation coefficient and provide a basis for the screening of modeling data.

关键词： clustering algorithm correlation analysis structural health monitoring supertall building wind effect

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RETRACTED: Hybrid cluster made content forwarding and light weight sign encryption IoT based Named Data Network using African Buffalo optimization algorithm (Retracted Article)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第3期43卷 2891-2905页

作者： Karthik, S. V. Vijaya Selvi, J. Arputha Vijaya Kings Coll Engn Dept ECE Pudukkottai Tamil Nadu India

Information Centric Network (ICN) is a newer technology in handling web content distribution that has recently emerged in order to tackle the risk of data security. For handling content distribution, ICN provides data security via a name-based approach. Named Data Networking (NDN) is an ongoing ICN realisation that was incorporated recently. Named Data Networking (NDN) has recently grown in popularity and significance as a new internet design that solves certain limitations in traditional internet communications. NDN is perfectly adapted for the Internet of Things (IoT), which is today dominated by huge, and emerging applications. In this work, we propose an IoT enabled hybrid cluster-based routing protocol with mitigation of content poisoning attack for information-centric Wireless Sensor Network (WSN)-NDN. In this method, hybrid K-medoids clustering is used with African Buffalo Optimization algorithm (ABOA), which is to find an optimal shortest path between the cluster heads, and light weight encryption. It is developed by using Hyperelliptic Curve Cryptography (HCC) to mitigate content poisoning. Our proposed system has effective data security as it has encrypted data in the cluster head. The smart health care monitoring system has been used for our proposed method. The proposed method has been subjected to extensive analysis by comparing with other existing methods that should improve performance justified in terms of several metrics by introducing the malicious nodes (10%, 20%, and 30%).

关键词： Named data network clustering algorithm content poisoning african buffalo optimization hyperelliptic curve cryptography

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Designing an Optimal GNSS Receiver Network for Space Weather Studies Using Unsupervised Machine Learning algorithm

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RADIO SCIENCE 2022年第12期57卷 1-20页

作者： Dashora, N. Harsha, P. Babu Sree Natl Atmospher Res Lab Gadanki India

Ionospheric maps from an optimal GNSS (global navigation satellite system) receiver network may lead to better understanding and prediction of the equatorial ionospheric gradients. An unsupervised machine learning framework is proposed using the k-means clustering approach to optimize the available number of GNSS receivers. Beginning with k = 15 receivers, the simulations are performed to obtain distribution of the ionospheric pierce points (IPPs) in different grid resolutions up to k = 1,000. The algorithm uses a reference grid and subsequently optimizes the locations according to the available IPPs in a cluster. The IPP cluster centers are iteratively optimized based on minimizing the "within cluster sum of squares" error. The IPPs from the optimal receiver locations are used to generate a 2-D vertical total electron content (VTEC) map using NeQuick-G model. A one-sigma confidence limit derived from hourly histogram of the IPPs from the k locations is used as decisive criteria for useable grid cells. The number of empty bins thus obtained in a given scenarios provides a percentage normalized empty grid ratio (NEG(ratio)). A NEG(ratio) below 10% is used as a threshold to make a decision over eligibility of a scenario to produce a desired VTEC map. Several scenarios are thus tested for varying temporal and spatial grid resolutions, geographical boundaries, GNSS and NavIC satellite configurations, considering the diurnal and annual variation in year 2019. The framework is found to be successful in designing a GNSS network for any geographical region to obtain high resolution 2-D VTEC maps for space weather applications.

关键词： space weather total electron content unsupervised machine learning clustering algorithm GNSS NavIC ionospheric maps optimization

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