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International Conference on Application of Materials Science and Environmental Materials (AMSEM)

作者： Chen, QiGong Wang, YongZhi Wei, LiSheng Gao, WenGen Anhui Polytech Univ Anhui Prov Detect Lab Wuhu 241000 Anhui Peoples R China

ISBN: (纸本)9783038352709

Energy consumption is a hot issue in WSNs (Wireless Sensor Networks). In this paper, we present an improved clustering algorithm. By changing the order of traditional WSNs clustering algorithm, this algorithm uses k-means clustering firstly base on optimal number of cluster head is determined;Then selects cluster head by an improved LEACH (Low Energy Adaptive clustering Hierarchy) algorithm;Finally, Our experimental results demonstrate that this approach can reduces energy consumption and increases the lifetime of the WSNs.

关键词： energy consumption WSNs k-means LEACH clustering algorithm

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A Greedy Dynamic clustering algorithm of Joint Transmission In Dense Small Cell Deployment

A Greedy Dynamic Clustering Algorithm of Joint Transmission ...

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IEEE 11th Consumer Communications and Networking Conference (CCNC)

作者： Qin, Cheng Tian, Hui Beijing Univ Posts & Telecommun State Key Lab Neworking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9781479923557

In a dense small cell deployment scenario, users are always prone to suffer severe interferences from neighbor base stations (BS) because the BSs are usually located closely. Coordinated Multi-Point (CoMP) can be introduced to alleviate these interferences and improve the system performance. It is necessary to determine coordination areas (CA) before implementation. In this paper, a novel dynamic clustering algorithm in CoMP joint transmission system is proposed based on graph theory. Firstly a feedback procedure is designed for interference reports mainly based on large scale fading. By building a graph according to the interferences, the clustering problem is equivalent to dividing the graph into several subgraphs. Each subgraph represents a CoMP cluster. It can be solved through a greedy strategy that each BS searches its best coordinated BSs. Compared with some other dynamic algorithms, the complexity of the proposed scheme is lower because it can be implemented in a decentralized way. Therefore this method is suitable in dense cell deployment with a large number of BSs. The simulation results show that the novel clustering algorithm performs better in user capacity than other traditional dynamic schemes. The influences of some parameters in this method are also considered and evaluated in the simulation.

关键词： Small cell deployment clustering algorithm Joint Transmission

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Adaptive state augmented clustering-based fuzzy learning control of a passive torque simulator

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INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2022年第3期10卷 917-929页

作者： Saadat, Seyyed Amirhossein Fateh, Mohammad Mehdi Keighobadi, Javad Shahrood Univ Technol Control & Robot Fac Shahrood Iran

This paper presents a newclustering-based fuzzy learning controller for a passive torque simulator (PTS) system in the presence of nonlinear friction and disturbance. An adaptive network-based fuzzy inference system is integrated with clustering algorithm to deal with unknown terms. Besides, a state-augmented technique is also employed in the framework of the backstepping method to improve the performance of system. The simplicity of design, fast learning speed and robust behavior are the main properties of the proposed controller for PTS system. In addition, the online computational burden is also alleviated due to employing the clustering algorithm. The stability of the closed-loop system is confirmed by the Lyapunov theorem. Furthermore, different simulation results are provided to validate the potential of the proposed control system in comparison with previous related research.

关键词： Passive torque simulator clustering algorithm Adaptive backstepping method Network-based fuzzy inference system Learning-based controller

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Application of computer information management system in universities in the information age

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第3期24卷 1557-1569页

作者： Yin, Li Chen, Yijun Nantong Univ Informatizat Ctr Nantong 226019 Jiangsu Peoples R China

The application of computer information management system (IMS for short here) in university management faces problems such as incomplete system software and complex system design. Applying clustering algorithms (CA for short here) to computer student IMS can help optimize the system's overall effectiveness. This article constructed a computer student IMS based on computer technology and applied it to the management of college students. This article also combined CA to conduct relevant effectiveness tests on the system, in order to optimize the overall effectiveness of the system. Under the algorithm in this article, the average connection speed for each user accessing the system was 9.17 Mbps. The average reaction time was 0.34 seconds, the average security level was 92.47%, and the highest memory usage rate of the system was 34.27%;Under the decision tree algorithm, the average connection speed of each user accessing the system was 8.82 Mbps, and the average reaction time reached 0.64 s. The average security level was 88.41%, and the highest memory usage rate was 42.58%. Under the artificial neural network algorithm, the average connection speed of the system was 8.47 Mbps, the average response time was 0.86 s, and the highest memory usage rate was 45.97%. Analyzing the data reveals that the algorithm introduced in this paper significantly enhances system connection speed and reduces reaction time. This improvement not only enhances security measures but also minimizes memory usage, effectively optimizing the overall efficiency of the system.

关键词： Computer information management system university management work student information management system safety clustering algorithm

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A Vertex-clustering algorithm Based on the Cluster-Clique

A Vertex-Clustering Algorithm Based on the Cluster-Clique

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14th International Conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Wang, Deqiang Zhang, Bin Wang, Kelun Dalian Maritime Univ Inst Naut Sci & Technol Dalian Peoples R China Dalian Maritime Univ Dept Math Dalian Peoples R China

ISBN: (纸本)9783319111940;9783319111933

The vertex-clustering algorithm based on intra connection ratio (MV-ICR algorithm) is a graph-clustering algorithm proposed by Moussiades and Vakali[clustering dense graph: A web site graph paradigm. Information Processing and Management, 2010, 46: 247-267]. In this paper, we propose a new conception called cluster-clique for vertex-clustering of graphs. And based on the cluster-clique and the intra connection ratio, a new vertex-clustering algorithm is proposed. This algorithm is more reasonable and effective than MV-ICR algorithm for some clusters which have the same maximum intra connection ratio.

关键词： graph clustering algorithm cluster-clique intra connection ratio

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A NONLINEAR CONVOLUTIONAL NEURAL NETWORK algorithm FOR AUTONOMOUS VEHICLE LANE LINE DETECTION

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第5期25卷 4237-4245页

作者： Lyu, Kanhui Zhejiang Financial Coll Inst Informat Technol Hangzhou 310018 Zhejiang Peoples R China

The traditional lane line detection algorithm relies on artificial design features, which has poor robustness and cannot cope with the complex urban street background. With the rise of deep learning technology, the algorithm model with convolutional neural network as the mainstream is widely used in the field of computer vision, which provides a new idea for lane line detection. In order to improve the disadvantages of traditional lane line detection methods that are vulnerable to environmental impact and poor robustness, a nonlinear convolution neural network algorithm for driverless lane line detection is proposed. Firstly, the pretreatment method of extracting the region of interest and enhancing the contrast of lane lines is used to reduce the unnecessary image background and enhance the feature details of the image. Existing deep learning-based lane line detection algorithms still have difficulties. First, accumulated wear and tear will cause lane line to fade and fade;roadside trees and buildings can interfere with the performance of lane line detection algorithm. In addition, compared with the pixels of the whole picture, the lane line pixels are too few, and the deep convolution neural network of layer convolution is easy to lead to the loss of details. In addition, when the traffic flow is large, the lane line is easily blocked, which makes it more difficult to detect the lane line. Then the model is built based on the lane line image features extracted by CNN, and the DBSCAN clustering algorithm is used to post-process the lane line segmentation model;Finally, the least square method is used to fit the quadratic curve of the pixel peak points of the lane line, and the fitting results are regressed to the original image. The experimental results show that the accuracy and recall of the lane line detection model verification set are 91.3% and 90.6%, respectively, indicating that the model has a good segmentation effect. It is proved that the lane line detect

关键词： Lane line detection Convolution neural network Deep learning clustering algorithm

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A Cluster-Oriented Bayesian Network Approach for Mixed-Type Event Prediction With Application in Order Logistics

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第10期19卷 10069-10078页

作者： Yang, Xing Cao, Hui Zhang, Chen Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Yarton Technol Beijing 100083 Peoples R China

Temporal representation and reasoning for probabilistic events describe temporal causal relationships between events. This has been widely used in several applications to predict events accurately. However, there are two challenges: the occurrence time points of events may have distinct types, distributed randomly or at several fixed time points;only limited historical data are available in some cases. This article presents a mixed-type event prediction algorithm based on a cluster-oriented Bayesian network (BN) model to address the highlighted challenges. The proposed model categorizes events as random events or timing events based on their temporal features. The similarity between events is measured according to event types and features. A clustering algorithm for events is further implemented to help reduce the model size and build a simpler and more accurate BN. The experimental results show that the proposed model significantly improves performance under small data sizes.

关键词： Bayesian network (BN) clustering algorithm event prediction probabilistic inference temporal reasoning

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A Design of Granular Classifier Based on Granular Data Descriptors

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第3期53卷 1790-1801页

作者： Zhu, Xiubin Wang, Dan Pedrycz, Witold Li, Zhiwu Xidian Univ Sch Electromech Engn Xian 710054 Peoples R China Univ Sci & Technol Coll Comp Sci & Technol Xian 710054 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada King Abdulaziz Univ Fac Engn Jeddah 21589 Saudi Arabia Macau Univ Sci & Technol Inst Syst Engn Taipa Macau Peoples R China

Designing effective and efficient classifiers is a challenging task given the facts that data may exhibit different geometric structures and complex intrarelationships may exist within data. As a fundamental component of granular computing, information granules play a key role in human cognition. Therefore, it is of great interest to develop classifiers based on information granules such that highly interpretable human-centric models with higher accuracy can be constructed. In this study, we elaborate on a novel design methodology of granular classifiers in which information granules play a fundamental role. First, information granules are formed on the basis of labeled patterns following the principle of justifiable granularity. The diversity of samples embraced by each information granule is quantified and controlled in terms of the entropy criterion. This design implies that the information granules constructed in this way form sound homogeneous descriptors characterizing the structure and the diversity of available experimental data. Next, granular classifiers are built in the presence of formed information granules. The classification result for any input instance is determined by summing the contents of the related information granules weighted by membership degrees. The experiments concerning both synthetic data and publicly available datasets demonstrate that the proposed models exhibit better prediction abilities than some commonly encountered classifiers (namely, linear regression, support vector machine, Naive Bayes, decision tree, and neural networks) and come with enhanced interpretability.

关键词： Granular computing clustering algorithms Data models Support vector machines Data analysis Task analysis Semantics Classification clustering algorithm granular computing (GrC) information granule principle of justifiable granularity

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Inner areas matter: A place-sensitive approach for the identification of the Daily Life Spaces in the Italian Abruzzo region

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SOCIO-ECONOMIC PLANNING SCIENCES 2024年 93卷

作者： Compagnucci, Fabiano Morettini, Gabriele Gran Sasso Sci Inst GSSI Social Sci Viale F Crispi 7 I-67100 Laquila Italy Univ Politecn Marche Dept Econ & Social Sci Piazzale Martelli8 I-60100 Ancona Italy

Despite the institutionalization of the issue of inner areas with the National Strategy for Inner Areas (Strategia Nazionale per le Aree Interne, SNAI), a reflection on their spatial organisation is still missing. Our paper aims at filling this gap by providing a methodology for identifying the citizens' Daily Life Spaces (DLS) in the Italian Abruzzo region, which can be used as the spatial unit of analysis of cohesion policies. Their identification results from a multi-step algorithm based on an original definition of central places and on the notions of geographical and organised proximity which consider both citizens' isochrones and commuting flows. Our methodology is able to provide the internal spatial organisation of both Labour Market Areas and Project Areas and is consistent with a historical perspective. Finally, it questions the SNAI classification, calling for a revision of its methodology of identification.

关键词： Inner areas Functional areas Daily life spaces clustering algorithm Organised and geographical Sub-regional centres

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Modified SDSA clustering algorithm

Modified SDSA Clustering Algorithm

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IEEE Workshop on Electronics, Computer and Applications (IWECA)

作者： Zhang, Qing Li, Danong Huanggang Normal Univ Sch Comp Huanggang Peoples R China

ISBN: (纸本)9781479945658

An effective clustering algorithm, named SDSA algorithm, is developed recently by Wei Li, Haohao Li and Jianye Chen. The algorithm based on the concept of the short distance of the consecutive points and the small angle between the consecutive vectors formed by three adjacent points. In this paper, we present a modification of the newly developed SDSA algorithm (MSDS). The MSDS algorithm is suitable for almost all test data sets used by Chung and Liu for point symmetry based K-means (PSK) algorithm and SDSA algorithm. Also, its much more effective than SDSA algorithm, since the computational effort per iteration required by MSDS algorithm is a lot less than that required by SDSA algorithm. Experimental results demonstrate that our proposed MSDS algorithm is rather encouraging.

关键词： clustering algorithm Pattern recognition Data clustering PSK algorithm SDSA algorithm

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