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Sectorisation of airspace based on balanced workloads

AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY 2020年第2期92卷 213-221页

作者： Soh, S. W. Zhong, Z. W. Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore Singapore

Purpose Given the ever-growing air travel industry, there is an increasing strain on the systems that provide safe flights. Different methods have to be adopted to help to cope with the increasing demand, especially in Southeast Asia. The purpose of this study is to sectorise one existing airspace to better manage sector workloads. Design/methodology/approach Cambodia's airspace was chosen for this study because it had only one sector and it was quickly approaching its limit. In this paper, after characterising the airspace, it was first bi-partitioned using the spectral clustering algorithm. The weights of the resulting subgraphs were then balanced through a weight-balancing algorithm. Also, a post-processing algorithm established the sector boundary to be drawn. The method was first carried out on one test airspace. Following the successful sectorisation of the test airspace, the actual Cambodian airspace was sectorised. The resulting two new sectors were then calculated to be able to last for approximately five years before they would reach their capacity. Hence, a further sectorisation was carried out. This resulted in four sectors, which were projected to last more than 10 years. Findings The method produced satisfactory results. The methodology presented is proven to be effective in achieving the sectorisation. The workloads of the new sectors obtained are balanced, and the sector boundaries are at least 15 km away from the air routes and nodes. The methodology is also general and can be applied to different scenarios. This means that applications to other airspace in the region are possible, which can further help to increase the safety, efficiency and capacity of the air traffic movement in this region. Originality/value This paper presents one of the approaches for airspace sector designs. The problems are clearly presented with references. The authors discuss the advantages and disadvantages of subdividing airspace and the need to sectorise Cambodia's airs

关键词： Airspace Sector workloads Sectorization spectral clustering algorithm Weight balancing algorithm

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A 3D Non-Stationarity and Consistency Model for Cooperative Multi-Vehicle Channels

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第9期72卷 11095-11110页

作者： Huang, Ziwei Bai, Lu Sun, Mingran Cheng, Xiang Peking Univ Sch Elect State Key Lab Adv Opt Commun Syst & Networks Beijing 100871 Peoples R China Dept Broadband Commun Peng Cheng Lab Shenzhen 518055 Peoples R China Shandong Univ Joint SDU NTU Ctr Artificial Intelligence Res C F Jinan 250100 Peoples R China

In this paper, based on the irregular shaped geometry-based stochastic modeling (IS-GBSM) approach, a novel three-dimensional (3-D) model for cooperative multi-vehicle communication channels is developed. By employing the cooperative communication technology, multi-vehicles can form a platoon to cooperatively transmit data streams, which can achieve distributed multiple-input multiple-output (MIMO) system performance. The proposed cooperative IS-GBSM distinguishes static and dynamic clusters, where continuously arbitrary vehicular movement trajectories (VMTs) of dynamic clusters and multi-vehicles are imitated. A new method, which combines frequency-dependent path gain and spectral clustering algorithm in machine learning (ML), is proposed to model cooperative space-time-frequency (S-T-F) non-stationarity with cooperative time-space (T-S) consistency. Based upon Poisson and Bernoulli processes, the developed method can also model the disappearance of links/sub-channels due to the destruction of transceivers by missiles in an optional manner. Therefore, the proposed cooperative IS-GBSM can further mimic an actual military cooperative communication scenario, i.e., cooperative multiple-unmanned ground vehicle (multi-UGV) communications for Mosaic Warfare. From the proposed cooperative IS-GBSM, typical channel statistics are derived. Simulation results show that cooperative S-T-F non-stationarity with cooperative T-S consistency is mimicked and the influences of vehicular traffic density (VTD) and VMT on channel statistics are investigated. Finally, simulation results fit well with the ray-tracing-based results, confirming the accuracy of the proposed cooperative IS-GBSM.

关键词： Channel models Vehicle dynamics Transceivers Millimeter wave communication Missiles clustering algorithms Solid modeling Cooperative multi-vehicle channel model vehicular communications cooperative channel non-stationarity cooperative channel consistency spectral clustering algorithm

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Capturing Spatial-Temporal Traffic Patterns: A Dynamic Partitioning Strategy for Heterogeneous Traffic Networks

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IEEE ACCESS 2024年 12卷 131982-131992页

作者： Peng, Xianyue Wang, Hao Southeast Univ Sch Transportat Nanjing 211189 Peoples R China

Macroscopic fundamental diagram (MFD) has become a popular model used in developing network traffic controls for a roughly homogeneous traffic network, encounters limitations when applied to the inherently heterogeneous nature of real-world transportation networks, affecting its predictive accuracy and applicability. To address these challenges, this paper proposes a dynamic network partitioning strategy for heterogeneous traffic networks. We devise a spatial-temporal dual-form graph to accurately represent the road network's spatial-temporal traffic patterns. To solve the dynamic network partitioning problem, we employ a spectral theory technique known as RatioCut. Our evaluation of the partitioning methods' effectiveness relies on fitting performance to the MFD and network modularity as metrics. A case study set in Yangzhou, China, executed with the Simulation of Urban Mobility (SUMO), demonstrates the performance of our dynamic partitioning approach. The results highlight our method's ability to capture the spatial and temporal evolution of the congestion in the road network and cluster the heterogeneous network into multiple quasi-homogeneous regions. Moreover, it shows that speed is potentially a more accessible indicator for network partitioning as it performs similar to density and is easier to collect.

关键词： Roads Vehicle dynamics Traffic control Measurement Partitioning algorithms Heuristic algorithms clustering algorithms Spatiotemporal phenomena Network partitioning time-varying traffic spatial-temporal graph spectral clustering algorithm

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Automatic determination of types number of mixed binary protocols

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IET COMMUNICATIONS 2019年第12期13卷 1769-1775页

作者： Zhang, Chunrui Wang, Shen Zhan, Dechen Yang, Yonghui Harbin Inst Technol Dept Comp Sci & Technol Harbin 150001 Heilongjiang Peoples R China China Acad Engn Phys Inst Comp Applicat Mianyang 621900 Sichuan Peoples R China

In the absence of prior knowledge, it is a challenge to determine the number of protocol types in frames, which are completely unknown. These frames might be mixed with multiple protocols from the data link layer to the application layer. In this study, the authors combine the spectral clustering algorithm with a method of determining the number of protocol frame types, and further more design the refinement clustering of different hierarchical protocols based on the eigenvectors of the Laplace matrix. They use three clustering validity indices, which are Calinski-Harabasz index, Davies-Bouldinn index and Silhouette index, to quantify the clustering effect in order to calculate the number of protocol types. Extensive experiments on several open datasets obtain relatively satisfying results without prior knowledge and demonstrate the significant advantages of their methods clearly.

关键词： eigenvalues and eigenfunctions protocols multiple protocols spectral clustering algorithm protocol frame types refinement clustering clustering validity indices clustering effect mixed binary protocols hierarchical protocols Calinski-Harabasz index Davies-Bouldinn index

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Clusters partition and zonal voltage regulation for distribution networks with high penetration of PVs

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第22期12卷 6041-6051页

作者： Ding, Jinjin Zhang, Qian Hu, Shijun Wang, Qunjing Ye, Qiubo Anhui Univ Dept Elect Engn & Automat Hefei 230061 Anhui Peoples R China Anhui Elect Power Sci Res Inst Hefei 230601 Anhui Peoples R China Anhui Univ Collaborat Innovat Ctr Ind Energy Saving & Power Hefei 230601 Anhui Peoples R China Jimei Univ Coll Informat Engn Xiamen 361021 Fujian Peoples R China

Integration of distributed generation (DG) at large scale with high penetration challenges the radial structure of the traditional distribution networks and the effectiveness of the conventional voltage regulation methods. In this study, the clusters partitioning and voltage regulation are researched. The modified electrical distance is introduced. An effective method, based on spectral clustering algorithm, is proposed for the partitioning of the DG network via the judgement of critical load buses. Two-stage voltage regulation optimisation is realised in each sub-community. The optimal objects are the minimal voltage fluctuation and the network loss of the distributed network. The independent variables are reactive-power absorption and active-power curtailment for each controllable photovoltaic node. An advanced particle swarm optimisation algorithm is applied to the voltage regulation for the sub-communities. After a case study of the IEEE 33-bus system, a regional distribution network in Anhui province of China is analysed. Simulation results indicate that the node voltages are stabilised with the improvement of power quality employing the proposed clusters partitioning method and zonal power control scheme.

关键词： distribution networks power distribution control optimisation pattern clustering voltage control distributed power generation power grids voltage regulators load flow power supply quality particle swarm optimisation power control clusters partition zonal voltage regulation high penetration distributed generation radial structure traditional distribution networks conventional voltage regulation methods clusters partitioning modified electrical distance effective method spectral clustering algorithm DG network critical load buses two-stage voltage regulation optimisation minimal voltage fluctuation network loss distributed network reactive-power absorption active-power curtailment advanced particle swarm optimisation algorithm regional distribution network node voltages zonal power control scheme

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DOE-AND-SCA: A Novel SCA Based on DNN With Optimal Eigenvectors and Automatic Cluster Number Determination

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IEEE ACCESS 2018年 6卷 20764-20778页

作者： Chen, Jinyin Wu, Yangyang Lin, Xiang Xuan, Qi Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Zhejiang Peoples R China

spectral clustering algorithm (SCA) is one of the widely used clustering algorithms (CAs), which is proved to be efficient in many applications including unsupervised image identification and gene prediction. However, most SCAs are confronted with several problems: 1) It is difficult for SCAs to handle multi-scale data sets;2) It is difficult to set cluster number in advance for various applications;3) It is also difficult to choose the most appropriate eigenvectors to reflect the data distribution;and 4) Moreover, SCAs are sensitive to the parameters. To handle these problems, we propose a novel SCA based on dynamic nearest-neighbors (DNN) with optimal eigenvector and automatic cluster number determination, namely DOE-AND-SCA. There into, first, we design a novel similarity function based on DNN for multi-scale data, making the similarity metric more accurate;Second, the cluster number is automatically determined, and the cluster centers are also automatically determined by normal fitting, based on the density and minimum distance distribution of the data points;Third, the optimal eigenvectors are selected on the basis of global and local features of the data set for more accurate data distribution reflection;Fourth, two main parameters, including the optimal density difference threshold and the number of intervals, are self-adaptive. The efficiency of DOE-AND-SCA is testified on abundant of simulation data sets, by comparing with other outstanding algorithms. And finally, DOE-AND-SCA is also applied to image recognition problems.

关键词： spectral clustering algorithm dynamic nearest-neighbors automatic cluster number determination optimal eigenvector parameter self-adaptive image recognition

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A method for ontology construction derived from folksonomy

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INTERNATIONAL JOURNAL OF SERVICES TECHNOLOGY AND MANAGEMENT 2012年第1-2期18卷 88-101页

作者： Dou, Yongxiang He, Jiyuan Liu, Dongsu Xidian Univ Dept Informat Management 2 South Taibai Rd Xian 710071 Shaanxi Peoples R China Xidian Univ Dept Informat Management Informat Xian 710071 Shaanxi Peoples R China Xidian Univ Sch Econ & Management Xian 710071 Shaanxi Peoples R China

In this paper, we propose a comprehensive approach for deriving ontology from folksonomy. In our method, we mainly utilise spectral clustering algorithm (SCA) based on the concept of similarity between points instead of the distance. The details are as follows. Firstly, we filter tags that are wrong or non-English words. Secondly, overlap similarity is used to measure similarity between co-occurrence tags. And SCA is applied to cluster tags with semantic relations. Moreover, another algorithm is adopted to obtain hierarchical structure of tags by running SCA repeatedly. Finally, online ontology resource WordNet is applied to add definitions and other semantic relations between tags to finish the work. We also conduct an experiment in which we use the tags from delicious website as the external knowledge source for the domains of the tags. And the result shows that this method is appropriate for ontology construction of emerging areas due to that folksonomy covers the latest concepts.

关键词： folksonomy ontology ontology construction spectral clustering algorithm SCA overlap similarity hierarchical structure

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Regional-level prediction model with difference equation model and fine particulate matter (PM2.5) concentration data

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MATHEMATICAL METHODS IN THE APPLIED SCIENCES 2024年第5期47卷 3171-3181页

作者： Han, Xiaoling Lei, Ceyu Northwest Normal Univ Dept Math Lanzhou 730070 Peoples R China

Accurate reporting and prediction of PM2.5 concentration is very important for improving public health. In this article, we use spectral clustering algorithm to cluster 15 cities in the Pearl River Delta. On this basis, we propose a special difference equation model, especially the use of nonlinear diffusion equations to characterize the temporal and spatial dynamic characteristics of PM2.5 propagation between and within clusters for real-time prediction. For example, through the analysis of PM2.5 concentration data for 91 consecutive days in the Pearl River Delta, and according to different accuracy definitions, the average prediction accuracy of the difference equation model in all city clusters is 97% or 88%. The mean absolute error (MAE) of the forecast data for each urban agglomeration is within 7 units ( mu g/m3). Experimental results show that the difference equation model can effectively reduce the prediction time and improve the prediction accuracy. Therefore, based on the spectral clustering algorithm and the difference equation model, the fastest prediction speed and the best prediction result can be obtained, and the problem of PM2.5 concentration prediction can be effectively solved. The research can provide decision support for local air pollution early warning and urban integrated management.

关键词： difference equation model mean absolute error prediction accuracy spectral clustering algorithm

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A clustering Analysis Method for Massive Music Data

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Modern Electronic Technology 2021年第1期5卷 24-31页

作者： Yanping Xu Sen Xu School of Information Engineering Yancheng Institute of TechnologyYanchengJiangSu224051China

clustering analysis plays a very important role in the field of data mining,image segmentation and pattern *** method of cluster analysis is introduced to analyze NetEYun music *** addition,different types of music data are clustered to find the commonness among the same kind of music.A music data-oriented clustering analysis method is proposed:Firstly,the audio beat period is calculated by reading the audio file data,and the emotional features of the audio are extracted;Secondly,the audio beat period is calculated by Fourier ***,a clustering algorithm is designed to obtain the clustering results of music data.

关键词： spectral clustering algorithm K-mean Music similarity Audio period extraction

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Functional Module Mining in Uncertain PPI Network Based on Fuzzy spectral clustering

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电脑学刊 2020年第4期31卷 91-106页

作者： Yi-min Mao Yin-ping Liu

Aiming at the lack of accuracy, sensitivity and low time efficiency of protein function module mining methods based on spectral clustering and fuzzy C-means (FCM) clustering, a new algorithm named FSC-FM (functional module mining in uncertain PPI network based on fuzzy spectral clustering) was proposed. In the clustering process, in order to overcome the effect of false positives on the experimental results, the uncertain protein-protein interaction (PPI) network was constructed, in which each protein-protein interaction was assigned with an existence probability by using edge aggregation coefficient. At the same time, FEC (flow distance of edge clustering coefficient) measure was proposed to solve the problem that the spectral clustering is sensitive to the scale parameters in similarity matrix. Furthermore, based on density theory, a probability clustering center strategy was used to design an optimal selection method (DPCS) to improve accuracy and time efficiency of the algorithm. Finally, an improved EED (edge-expected density) metric was studied to filter out the functional modules to improve the precision of the algorithm. We compared our FSC-FM approach on yeast PPI data to the state-of-the-art functional modules prediction algorithms: CDUN, DCU, EA and MGPPA. The experimental results show the superiority of the FSC-FM algorithm in accuracy, sensitivity and time efficiency.

关键词： FCM functional module expected density uncertain data protein-protein interaction (PPI) spectral clustering algorithm

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