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Construction of a noise algorithm-Based three-dimensional porous adsorption model of coal and study of coal-oxygen physisorption kinetics

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MICROPOROUS AND MESOPOROUS MATERIALS 2025年 390卷

作者： Song, Zhifan Dong, Zhiyu Lu, Zhifan Ren, Kaiwen Xie, Peng Wang, Junfeng Taiyuan Univ Technol Coll Min & Technol Taiyuan 030024 Peoples R China Jinneng Holding Coal Grp Yanzishan Mine Taiyuan 030024 Peoples R China

In view of the difficulty of accurately characterizing the dynamic process of coal-oxygen physisorption using existing models, this study constructed three-dimensional (3D) porous adsorption models of coal based on a noise algorithm, and the kinetics of coal oxygen physisorption was investigated based on this model. The degree of metamorphism and pore characteristics of the coal samples were determined via industrial analysis and pore structure characterization, and high-pressure isothermal adsorption experiments were carried out to determine the isothermal adsorption lines of coal oxygen at different temperatures. The experimental data were fitted to construct a mathematical model under the joint influence of temperature and pressure, and a 3D porous adsorption model for coal was constructed based on the experimental results to explore the adsorption kinetic process. Revealing that the amount of oxygen adsorbed was negatively correlated with temperature, and coal samples with less deterioration possessed larger specific surface areas and pore volumes, resulting in a stronger oxygen adsorption ability. The coal-oxygen isothermal adsorption data fitted well with the Langmuir adsorption model, and the fitted oxygen adsorption values were similar to the experimental data. Numerical simulations using the constructed models indicated that the adsorption process of oxygen on the coal particle surface could be divided into three stages: (1) the seepage stage, in which oxygen is adsorbed by the outer surface of the coal body and its surface pores;(2) the surface diffusion stage, in which oxygen diffuses to the inner region of the coal particles through their pores;and (3) the deep adsorption stage, in which oxygen is adsorbed in the deep pores of the coal particles, enabling complete adsorption. Coal-oxygen adsorption reached equilibrium in a short time, the amount of oxygen adsorbed rapidly increased during the initial stage of adsorption, approaching the maximum value at

关键词： Spontaneous coal combustion Physical adsorption Kinetics noise algorithm Numerical simulation

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Trajectory dimensionality reduction and hyperparameter settings of DBSCAN for trajectory clustering

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IET INTELLIGENT TRANSPORT SYSTEMS 2022年第5期16卷 691-710页

作者： Yu, Xiaohong Long, Wei Li, Yanyan Gao, Lin Shi, Xiaoqiu Sichuan Univ Sch Mech Engn Chengdu Peoples R China

The density-based spatial clustering of application with noise (DBSCAN) algorithm has good robustness and is widely employed to cluster vehicle trajectories for vehicle movement pattern recognition. However, the distance or similarity between two trajectories varies from tens to hundreds of thousands, and there is no effective method for determining the values of the hyperparameters eps and MinPts of DBSCAN. In addition, with increasing sizes of trajectory datasets, some trajectory clustering methods that directly analyse points and line segments incur large computational costs and time overhead. To solve these two dilemmas, the authors propose an effective trajectory dimensionality reduction method and a DBSCAN hyperparameter initial value setting method. The trajectory dimensionality reduction algorithm processes trajectories with different lengths into the same dimensionality (the same number of feature points). The reserved points preserve the spatial and temporal information of these trajectories as much as possible. The DBSCAN hyperparameter initial value setting algorithm obtains the effective initial values of eps and MinPts for facilitating subsequent adjustments. Finally, we validate these proposed methods on two trajectory datasets collected from two real-world scenes, and the experimental results are promising and effective.

关键词： cluster vehicle trajectories spatial information trajectory dimensionality reduction algorithm DBSCAN hyperparameter initial value setting method pattern clustering data preparation reserved points noise algorithm MinPts feature points trajectory clustering methods vehicle movement pattern recognition DBSCAN hyperparameter initial value setting algorithm effective initial values pattern recognition temporal information eps

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HRRP multi-target recognition in a beam using prior-independent DBSCAN clustering algorithm

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IET RADAR SONAR AND NAVIGATION 2019年第8期13卷 1366-1372页

作者： Guo, Peng-cheng Liu, Zheng Wang, Jing-jing Xidian Univ Natl Lab Radar Signal Proc Xian Shaanxi Peoples R China Xian Elect Engn Res Inst Xian Shaanxi Peoples R China

During the operation of monopulse radar, the high-resolution range profiles (HRRPs) of multiple targets may be overlapped, which will reduce the target recognition performance. In this study, a novel multi-target recognition method is proposed based on prior-independent density-based spatial clustering of applications with noise (PI-DBSCAN) algorithm. In the training phase, various features of training samples are extracted, among which the distribution of strong range cells is utilised to obtain the parameters of PI-DBSCAN algorithm while the others are used to train the support vector machine (SVM) classifier. In the test phase, PI-DBSCAN algorithm is exploited at the radial distance-azimuth plane to segment the test multi-target HRRP. Afterward, the features of the segmented HRRPs are extracted and fed to the SVM classifier to be recognised. The proposed method has no constraint on the target motion and does not need to set any parameters manually, which benefits its application. The experiment results of real measured data show that the proposed method is robust against noise and the increasing target HRRP overlap ratio comparing with traditional methods.

关键词： radar resolution support vector machines radar target recognition pattern clustering radar signal processing radar computing HRRP multitarget recognition prior-independent DBSCAN clustering algorithm monopulse radar high-resolution range profiles multiple targets target recognition performance noise algorithm training phase training samples PI-DBSCAN algorithm support vector machine classifier test phase radial distance-azimuth plane test multitarget HRRP SVM classifier target motion

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Data driven optimisation of small cell depolyment

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ELECTRONICS LETTERS 2020年第1期56卷 48-+页

作者： Chen, Bozhong Zhang, Zitian Chu, Xiaoli Zhang, Jie Univ Sheffield Dept Elect & Elect Engn Sheffield S10 2TN S Yorkshire England East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China

Increasingly popular online social networks contribute to the massive growth in mobile traffic. The authors find that social network data, which is easier to obtain than carrier data, can be used to optimise the deployment of small base station (SBS). In this Letter, the authors propose a framework based on analysing social network data to identify and cluster mobile traffic hot-spots by using the density-based spatial clustering of applications with noise algorithm. By comparing the clustered hot-spots with the existing macro base stations' locations, they identify where additional SBSs need to be deployed and show that such deployment can effectively improve the quality of service. The proposed simulation results show that the SBSs deployment around hot-spot has relatively better user received signal power on mobile communication network than random SBS deployment following the normal distribution or the Poisson distribution.

关键词： telecommunication traffic social networking (online) Poisson distribution pattern clustering cellular radio mobility management (mobile radio) quality of service normal distribution telecommunication computing base station social network data mobile traffic hot-spots noise algorithm clustered hot-spots macro base stations SBS deployment mobile communication network random SBS deployment data driven optimisation cell depolyment carrier data online social networks density-based spatial clustering quality of service normal distribution Poisson distribution

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Procedural Generation of 3D Planetary-Scale Terrains 7

Procedural Generation of 3D Planetary-Scale Terrains

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7th IEEE International Conference on Space Mission Challenges for Information Technology (SMC-IT)

作者： Vitacion, Ryan J. Liu, Li Calif State Univ Northridge Coll Engn & Comp Sci Northridge CA 91330 USA Calif State Univ Northridge Dept Comp Sci Northridge CA 91330 USA

ISBN: (纸本)9781728115443

The creation of three-dimensional geographical surface has been a primary concern at the forefront of the fields of space mission simulation. This paper introduces how to apply procedural terrain generation techniques to the creation of 3D terrains for a spherical object. The paper first identifies algorithms that can be used to generate terrains on a spherical surface. Then, the paper compares computational complexity and scalability of using these algorithms in 3D planetary scale simulation. The paper uses a benchmarking program created in virtual reality (VR) to evaluate the performance of these algorithms in the simulation and imaging of planetary bodies in VR including execution time, quality and memory usage.

关键词： Procedural Generation noise algorithm Planetary Terrain Simulation Imaging Virtual Reality

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A hybrid genetic-fuzzy ant colony optimization algorithm for automatic K-means clustering in urban global positioning system

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartB期137卷

作者： Ran, Xiaojuan Suyaroj, Naret Tepsan, Worawit Ma, Jianghong Zhou, Xiangbing Deng, Wu Sichuan Tourism Univ Sch Informat & Engn Chengdu 610100 Peoples R China Chiang Mai Univ Int Coll Digital Innovat Chiang Mai 50200 Thailand Yibin Univ Sch Big Data & Artificial Intelligence Yibin 644000 Peoples R China Civil Aviat Univ China Coll Elect Informat & Automat Tianjin 300300 Peoples R China

This paper introduces an innovative automatic K-means clustering algorithm, namely HGA-FACO, which seamlessly integrates the noise algorithm, Genetic algorithm (GA), Ant Colony Optimization (ACO), and Adaptive Fuzzy System (AFS). The rationale behind the HGA-FACO algorithm is to mitigate the shortcomings of traditional K-means, particularly the reliance on pre-determined cluster centers and the need for specifying the number of clusters in advance. By optimizing the search strategy, HGA-FACO efficiently circumvents local optima and effectively explores the global optimal solution, resulting in more accurate and stable clustering outcomes. To validate the superiority of the HGA-FACO over conventional K-Means Clustering (KMeans) and other intelligent clustering approaches such as ACO-KMeans, GA-KMeans (GAK), particle swarm optimization KMeans (PSOK), and ACO-GAK, we conducted comprehensive experiments on taxi Global Positioning System (GPS) datasets sourced from four distinct cities. Employing rigorous evaluation metrics including Silhouette Coefficient (SC), Partition Coefficient (PBM), Davies-Bouldin Index (DBI), and Sum of Squared Errors (SSE), the experimental results convincingly demonstrate that the HGA-FACO significantly outperforms its counterparts across all metrics, highlighting its exceptional performance in clustering effectiveness and compactness. While the HGAFACO faces challenges related to computational complexity and the necessity for initial parameter tuning, its performance limitations on small-sized or unevenly distributed datasets are acknowledged. Nevertheless, the algorithm's advancements in the field of clustering algorithms are undeniable and hold immense potential for practical applications, notably in city hotspot identification.

关键词： Automatic clustering Genetic algorithm Adaptive fuzzy system noise algorithm K -means Evaluation

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DBSCAN-based coherency identification of generators using complex wavelet transform

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JOURNAL OF ENGINEERING-JOE 2019年第18期2019卷 4780-4784页

作者： Singh, Ajeet Kumar Fozdar, Manoj Malaviya Natl Inst Technol Dept Elect Engn Jaipur Rajasthan India

The coherency of generators in a multi-machine power system is identified by determining the instantaneous phase difference between their rotor angles using a complex wavelet transform. A new phase difference matrix is proposed that represents the relative phase difference between the dominant frequency components of each pair of oscillating rotor angles and maps it into a higher dimensional space where data points corresponding to coherent generators lie in proximity. Next, density-based spatial clustering of applications with noise (DBSCAN) algorithm is used for automated grouping of coherent generators. A new objective function is proposed for the determination of control parameters of the clustering algorithm. The proposed approach is tested on the New England (NE) 39-bus test system and IEEE 145-bus test system.

关键词： matrix algebra power system measurement oscillations power system stability electric generators rotors power system transient stability pattern clustering wavelet transforms power system control complex wavelet multimachine power system instantaneous phase difference phase difference matrix relative phase difference dominant frequency components oscillating rotor angles higher dimensional space coherent generators noise algorithm New England 39-bus test system IEEE 145-bus test system

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