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The International Conference on Future Communication,Information and Computer Science(ICFCICS 2014)

作者： X.Gao L.Liu X.Guo Equipment Academy

In order to ensure the reliable transmission of important service information,that meets the requirements of accessing at any time,this paper puts forward the improved adaptive weighted clustering *** simulation and verification,this algorithm can be applied to the maneuvering communication network which can realize the effective supplement and reasonable extension to existing network.

关键词： maneuvering communication network clustering algorithm

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DE-FR: Differential Evolution algorithm Based on DBSCAN-FR clustering Method 23

DE-FR: Differential Evolution Algorithm Based on DBSCAN-FR C...

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Proceedings of the 2023 9th International Conference on Communication and Information Processing

作者： Xiaolin Hu Lingyu Wu Zenglin Qiao Yunyun Chong Yujie Chen Xinchao Zhao School of Science Beijing University of Posts and Telecommunications China

ISBN: (纸本)9798400708909

In recent years, evolutionary algorithms (EAs) have gained attention among scholars and have been applied to optimization engineering with various degrees of success. Concurrently, machine learning methods have rapidly developed in the field of artificial intelligence and have been increasingly integrated with other domains. This paper introduces a novel multi-population differential evolution algorithm called DE-FR, based on the proposed DBSCAN-FR clustering algorithm. This paper contributes to the improvement of the differential evolution algorithm in the following aspects. Firstly, it presents an enhanced clustering algorithm, DBSCAN-FR, which incorporates a forward distance filtering mechanism to divide the population into several groups successfully in high dimensional space. Secondly, it introduces a novel differential evolution algorithm named DE-FR, which builds upon the DBSCAN-FR clustering algorithm aims to solve complex single-objective optimization problems. Lastly, the proposed algorithm is compared with other classical differential evolution variants on CEC2014 benchmarks, and experimental results demonstrate its competitive performance.

关键词： clustering algorithm differential evolution evolutionary algorithm machine learning

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Research on multi-source POI data fusion based on ontology and clustering algorithms (Jul, 10.1007/s10489-021-02561-6, 2021)

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APPLIED INTELLIGENCE 2022年第5期52卷 4775-4775页

作者： Cai, Li Zhu, Longhao Jiang, Fang Zhang, Yihan He, Jing Yunnan Univ Sch Software Kunming 650091 Yunnan Peoples R China Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China

Traditional point-of-interest (POI) data are collected by professional surveying and mapping organizations and are distributed in electronic maps. With the booming Internet and the development of crowdsourcing, the POI data defined in various formats are issued by some Internet companies and non-profit organizations. Due to the multiple sources and diverse formats of POI data, some problems occur in the data fusion process, such as conceptual definition differences, inconsistent classification, inefficient fusion algorithms, inaccurate fusion results, etc. To overcome the challenges of multi-source POI data fusion, this paper proposes a standardized POI data model and an ontology-based POI category system. Furthermore, a fusion framework and a fusion algorithm based on a two-stage clustering approach are proposed. The proposed method is compared with existing algorithms using datasets of different sizes, including POI surveying and mapping data from Kunming, China, Weibo check-in POI data, and real estate POI data. The experimental results demonstrate that the fusion effects of the proposed algorithm are superior to those of existing algorithms in terms of different evaluation indexes and operational efficiency.

关键词： Point of interest Data fusion clustering algorithm Ontology Text similarity

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Research on Region-Based Vehicular Ad Hoc Network clustering algorithm

Research on Region-Based Vehicular Ad Hoc Network Clustering...

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2015 International Conference on Automation,Mechanical Control and Computational Engineering(AMCCE 2015)

作者： Yang Tao Na Li Wenxuan Zang School of Communication and Information Engineering University of Posts and Telecommunications School of Computer Science and Technology University of Posts and Telecommunications

ISBN: (纸本)9781510803084

Aiming at the complicated environment of Vehicular Ad Hoc Networks(VANET),in this paper,a region-based adaptive clustering algorithm(ZACA) is *** classifying the road environment into segment and intersection, respectively calculating the connectivity according to the different road environments, and combine it with the regional location and the traditional synthesis weights of the Weighted clustering algorithm,the election of the cluster head under different road models and the maintenance of cluster based on the network area are *** result shows this method can adapt to vehicular network environment effectively, and also can improve the stability of the cluster and reduce the clustering overhead.

关键词： vehicular ad hoc clustering algorithm zone connectivity

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AI-enabled cluster head selection through modified density based clustering in Aeronautical Ad Hoc Networks✩

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AD HOC NETWORKS 2023年 148卷

作者： Shahbazi, Mohsen Simsek, Murat Kantarci, Burak Univ Ottawa Sch Elect Engn & Comp Sci 800 King Edward Ave Ottawa ON K1N 6N5 Canada

Offering in-flight connectivity has emerged as an essential demand for everyday flights. Such aircraft's high speed and dynamic characteristics make this task challenging, particularly in distant areas with no ground-to-air link. Aeronautical Ad Hoc Network (AANET), a network of commercial airplanes with air-to-air connectivity, is a viable solution to this problem. However, the instability of air-to-air links results in poor performance of such ad hoc networks. A cluster-based topology formation is an initial step to boost the performance and connectivity in such a network. Selecting a well-connected cluster head is the next stage in enhancing connection and stability. This study presents a new cluster head selection technique for AANETs that calculates the neighbor nodes within a given distance of each node and selects the node with the most connections as the new cluster head. A gateway node is chosen to facilitate connections with other clusters. According to simulations, the proposed method increases packet delivery ratio by 3%, end-to-end delay by 9%, and throughput by up to 10%. In addition, the proposed method reduces cluster head replacements by 17% and increases cluster head longevity by 8%.

关键词： In-flight connectivity AANET Ad hoc network AANET clustering clustering algorithm Cluster head selection Self organizing maps

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Design of clustering Techniques in Cognitive Radio Sensor Networks

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Computer Systems Science & Engineering 2023年第1期44卷 441-456页

作者： R.Ganesh Babu D.Hemanand V.Amudha S.Sugumaran Department of Electronics and Communication Engineering SRM TRP Engineering College621105Tamil NaduIndia Department of Computer Science and Engineering Sriram Engineering College602024Tamil NaduIndia Department of Electronics and Communication Engineering Saveetha School of Engineering602105Tamil NaduIndia Department of Electronics and Communication Engineering Vishnu Institute of TechnologyBhimavaram534202Andhra PradeshIndia

In recent decades,several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during trans-mission to a shorter distance while restricting the Primary Users(PUs)*** Cognitive Radio(CR)system is based on the Adaptive Swarm Distributed Intelligent based clustering algorithm(ASDIC)that shows better spectrum sensing among group of multiusers in terms of sensing error,power sav-ing,and convergence *** this research paper,the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their *** this research,multiple random Sec-ondary Users(SUs),and PUs are considered for ***,the pro-posed ASDIC algorithm improved the convergence speed by combining the multi-users clustered communication compared to the existing optimization *** results showed that the proposed ASDIC algorithm reduced the node power of 9.646%compared to the existing ***,ASDIC algorithm reduced 24.23%of SUs average node power compared to the existing *** of detection is higher by reducing the Signal-to-Noise Ratio(SNR)to 2 dB *** proposed ASDIC delivers low false alarm rate compared to other existing optimization algorithms in the primary *** results showed that the proposed ASDIC algorithm effectively solves the multimodal optimization problems and maximizes the performance of net-work capacity.

关键词： Adaptive swarm distributed clustering cognitive radio clustering algorithm distributed swarm intelligent energy efficient distributed cluster-based sensing multi modal optimization

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A clustering Offloading Decision Method for Edge Computing Tasks Based on Deep Reinforcement Learning

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NEW GENERATION COMPUTING 2023年第1期41卷 85-108页

作者： Zhang, Zhen Li, Huanzhou Tang, Zhangguo Gu, Dinglin Zhang, Jian Sichuan Normal Univ Sch Phys & Elect Engn Chengdu 610101 Sichuan Peoples R China Sichuan Normal Univ Inst Cyber & Commun Technol Chengdu 610101 Sichuan Peoples R China

In many IoT scenarios, the resources of terminal devices are limited, and it is difficult to provide services with low latency and low energy consumption. Mobile edge computing is an effective solution by offloading computing tasks to edge server processing. There are some problems in the existing offloading decision algorithms: the offloading decision method based on heuristic algorithms cannot dynamically adjust the policy in the changing environment;the offloading algorithm based on deep reinforcement learning will lead to slow convergence and poor exploration effect due to the problem of dimension explosion. To solve the above problems, this paper designs an offloading decision algorithm to make dynamic decisions in a mobile edge computing network with multi-device access. The algorithm comprehensively considers the energy consumption of terminal equipment, offloading overhead, average delay and success rate of task completion, aiming to achieve the highest total revenue of the whole system in a period of time. In this work, the online offloading problem is abstracted as a Markov decision process. Based on the Double Dueling Deep Q-Network (D3QN) algorithm, the offloading decision is designed to adapt to the highly dynamic environment of the edge computing network and solve the problem of high state space complexity. In addition, this paper innovatively introduces a clustering algorithm into deep reinforcement learning (DRL) to preprocess the action space and solve the explosion problem of the action space dimension caused by the increase of terminal devices. The experimental results show that the proposed algorithm is superior to the baseline strategies such as Deep Q-Network (DQN) algorithm in convergence speed and total reward.

关键词： Mobile edge computing Offloading decisions Deep reinforcement learning Dimension explosion clustering algorithm

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BRAS syslog pattern generation method: a preliminary experiment on clustering algorithms 22

BRAS syslog pattern generation method: a preliminary experim...

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22nd Asia-Pacific Network Operations and Management Symposium (APNOMS)

作者： Li Yun-Jie Li Jhao-Yin Chunghwa Telecom Network Management Tech Lab Telecommun Labs Taoyuan Taiwan

ISBN: (纸本)9784885523328

Internet service provider (ISP) uses Broadband remote access server (BRAS) to connect its customers called subscribers. Due to the usage of the Internet had grown up rapidly, the ISP company have to pay more attention to manage BRAS device. In general, the ISP administrators set up several suspicious Syslog extraction patterns into the operation support system (OSS) to extract Syslogs. The administrators will check out the detail of the Syslog matched the suspicious Syslog extraction pattern to find out if any problem exists on devices while they are notified by OSS that there are some words in Syslog are matching the extraction pattern. However, it is difficult to define the proper extract pattern that the administrators do not see before. Especially, after configuration had been changed, e.g. after an upgraded new version of the software or after adjusted the topology of the network, etc, there are a few new words related anomaly in Syslogs and those words are not match the Syslog extraction pattern because the administrators do not see those before. Furthermore, we found that BRAS Syslog data have a special feature that the other log data would not have is that there is a lot of content-related digits. Note that, those contents carry unique information and can make the extraction algorithm misleading. According to that, how to leverage the automatically extract algorithm to deal with this problem is another issue we have to take care of. In this paper, we proposed the BRAS Syslog pattern generation methodology and conduct a preliminary experiment to evaluate the performance. The result shows that the method with the combination of tfidf and EM or tfidf and HCA can produce better performance compared with the other clustering algorithms.

关键词： Machine Learning Unsupervised Learning clustering algorithm Network Diagonosis

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Minimum Mutual Information Based Level Set clustering algorithm for Fast MRI Tissue Segmentation

Minimum Mutual Information Based Level Set Clustering Algori...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Shuanglu Dai Hong Man Shu Zhan M.S. student of Electrical Engineering Stevens Institute of Technology 1 Castle Point on Hudson Hoboken NJ07030 U.S.A the Department of Electrical and Computer Engineering Stevens Institute of Technology 1 Castle Point on Hudson Hoboken NJ07030 U.S.A School of Computer and Information Hefei University of Technology 193Tunxi Rd. Hefei 230009 China

ISBN: (纸本)9781424492695

Accurate and accelerated MRI tissue recognition is a crucial preprocessing for real-time 3d tissue modeling and medical diagnosis. This paper proposed an information de-correlated clustering algorithm implemented by variational level set method for fast tissue segmentation. The key idea is to design a local correlation term between original image and piecewise constant into the variational framework. The minimized correlation will then lead to de-correlated piece-wise regions. Firstly, by introducing a continuous bounded variational domain describing the image, a probabilistic image restoration model is assumed to modify the distortion. Secondly, regional mutual information is introduced to measure the correlation between piecewise regions and original images. As a de-correlated description of the image, piecewise constants are finally solved by numerical approximation and level set evolution. The converged piecewise constants automatically clusters image domain into discriminative regions. The segmentation results show that our algorithm performs well in terms of time consuming, accuracy, convergence and clustering capability.

关键词： clustering algorithm Fast MRI Tissue Segmentation image clustering algorithms Level set images Imagery (Psychotherapy) original image Piecewise Image numerical approximation image restoration technique segmentation Segmentation algorithms

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An accurate measurement method of lamb wave phase velocity based on clustering algorithms

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MEASUREMENT 2022年 195卷

作者： Gao, Xiang Tian, Ye Jiao, Jingpin Gao, Jie Li, Chenxu Beijing Univ Technol Fac Mat & Mfg Beijing Peoples R China

Due to the large detection range and high sensitivity to damages, Lamb waves are widely used in the localization and quantification of structural damages in a plate structure. The dispersion curve measurement is significant in the applications of Lamb waves, especially in a material with unknown properties. In the study, a method was proposed to measure dispersion curves based on clustering. Compared with traditional methods, the proposed method could realize more accurate and reliable measurement results with less measured data. This method was experimentally verified with an isotropic aluminum plate and an anisotropic CFRP plate. The relative error between measured and real values in an aluminum plate was less than 1%. With this method, A0 mode phasevelocity dispersion curves in CFRP plate with various braiding angles were experimentally obtained. This method facilitated Lamb wave defect detection and material parameter inversion.

关键词： Lamb waves Phase-velocity dispersion evaluation clustering algorithm CFRP plate

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