Reduction of linear dynamic systems using generalized approach of pole clustering method

2021 ◽  
pp. 014233122110633
Author(s):  
Arvind Kumar Prajapati ◽  
Rajendra Prasad
Keyword(s):  
Large Scale ◽  
Original System ◽  
Clustering Methods ◽  
Clustering Method ◽  
Large Scale Systems ◽  
Linear Dynamic Systems ◽  
Clustering Technique ◽  
Cluster Centre ◽  
Performance Error ◽  
Poles And Zeros

A new model order abatement method based on the clustering of poles and zeros of a large-scale continuous time system is proposed. The clustering of poles and zeros are used for finding the cluster centres. The abated model is identified from the cluster centres, which reflect the effectiveness of the dominant poles of the clusters. The cluster centre is determined by taking [Formula: see text] root of the sum of the inverse of [Formula: see text] power of poles (zeros) in a particular cluster. It is famous that the magnitude of the pole cluster centre plays an important role in the clustering technique for the simplification of large-scale systems. The magnitude of the cluster centres computed by the modified pole clustering method or some other methods based on the pole clustering techniques is large as compared to the proposed technique. The less magnitude of pole cluster centre reflects the better approximations and proper matching of the abated model with the original system. Therefore, the proposed method offers better approximations matching between actual and abated systems during the transient period compared to some other clustering methods, which supports the replacement of large-scale systems by proposed abated systems. The proposed technique is a generalized version of the standard pole clustering technique. The proposed method guarantees the retention of dominant poles, stability and other fundamental control properties of the actual plant in the abated model. The proposed algorithm is illustrated by the five standard systems taken from the literature. The accuracy and effectiveness of the proposed method are verified by comparing the time responses and various performance error indices.

EdClust: A heuristic sequence clustering method with higher sensitivity

2021 ◽  
Author(s):  
Ming Cao ◽  
Qinke Peng ◽  
Ze-Gang Wei ◽  
Fei Liu ◽  
Yi-Fan Hou
Keyword(s):  
Large Scale ◽  
Sequence Data ◽  
Clustering Algorithms ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Clustering Method ◽  
Cluster Number ◽  
Sequence Clustering ◽  
Downstream Analysis ◽  
Heuristic Clustering

The development of high-throughput technologies has produced increasing amounts of sequence data and an increasing need for efficient clustering algorithms that can process massive volumes of sequencing data for downstream analysis. Heuristic clustering methods are widely applied for sequence clustering because of their low computational complexity. Although numerous heuristic clustering methods have been developed, they suffer from two limitations: overestimation of inferred clusters and low clustering sensitivity. To address these issues, we present a new sequence clustering method (edClust) based on Edlib, a C/C[Formula: see text] library for fast, exact semi-global sequence alignment to group similar sequences. The new method edClust was tested on three large-scale sequence databases, and we compared edClust to several classic heuristic clustering methods, such as UCLUST, CD-HIT, and VSEARCH. Evaluations based on the metrics of cluster number and seed sensitivity (SS) demonstrate that edClust can produce fewer clusters than other methods and that its SS is higher than that of other methods. The source codes of edClust are available from https://github.com/zhang134/EdClust.git under the GNU GPL license.

scholarly journals Effects of Heterogeneity and Global Dynamics of Weakly Connected Subpopulations

2021 ◽  
Author(s):  
Derdei M. Bichara ◽  
Abderrahman Iggidr ◽  
Souad Yacheur
Keyword(s):  
Epidemic Model ◽  
Host Species ◽  
Large Scale ◽  
Original System ◽  
Connectivity Matrix ◽  
Global Dynamics ◽  
Vector Species ◽  
Large Scale Systems ◽  
Vector Borne ◽  
Stability Results

We develop a method that completely characterizes the global dynamics of models with multiple subpopulations that are weakly interconnected. The method is applied on two classes of models with multiple subpopulations: an epidemic model that involves multiple host species and multiple vector species and a patchy vector-borne model. The method consists of two main steps: reducing the system using tools of large scale systems and studying the dynamics of an auxiliary system related the original system. The developed method determines the underlying dynamics and the ``weight" of each subpopulations with respect to the dynamics of the whole population, and how the topology of the connectivity matrix alters the dynamics of the overall population. The method provides global stability results for all types of equilibria, namely trivial, boundary or interior equilibria.

scholarly journals An Improved Fuzzy Trajectory Clustering Method for Exploring Urban Travel Patterns

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fang Liu ◽  
Wei Bi ◽  
Wei Hao ◽  
Fan Gao ◽  
Jinjun Tang
Keyword(s):  
Large Scale ◽  
Spatial Clustering ◽  
Urban Traffic ◽  
Clustering Methods ◽  
Travel Patterns ◽  
Trajectory Clustering ◽  
Trajectory Data ◽  
Clustering Method ◽  
Urban Travel ◽  
Fuzzy Clustering Method

Exploring urban travel patterns can analyze the mobility regularity of residents to provide guidance for urban traffic planning and emergency decision. Clustering methods have been widely applied to explore the hidden information from large-scale trajectory data on travel patterns exploring. How to implement soft constraints in the clustering method and evaluate the effectiveness quantitatively is still a challenge. In this study, we propose an improved trajectory clustering method based on fuzzy density-based spatial clustering of applications with noise (TC-FDBSCAN) to conduct classification on trajectory data. Firstly, we define the trajectory distance which considers the influence of different attributes and determines the corresponding weight coefficients to measure the similarity among trajectories. Secondly, membership degrees and membership functions are designed in the fuzzy clustering method as the extension of the classical DBSCAN method. Finally, trajectory data collected in Shenzhen city, China, are divided into two types (workdays and weekends) and then implemented in the experiment to explore different travel patterns. Moreover, three indices including Silhouette Coefficient, Davies–Bouldin index, and Calinski–Harabasz index are used to evaluate the effectiveness among the proposed method and other traditional clustering methods. The results also demonstrate the advantage of the proposed method.

scholarly journals CLUSTERING METHODS IN LARGE-SCALE SYSTEMS

2020 ◽  
Vol 6 (5) ◽  
pp. 21-24
Author(s):  
Denis V. Gadasin ◽  
◽  
Andrey V. Shvedov ◽  
Alyona A. Yudin ◽  
◽  
...  
Keyword(s):  
Social Network ◽  
Complex Networks ◽  
Large Scale ◽  
Physical Nature ◽  
Clustering Methods ◽  
Large Scale Systems ◽  
Practical Applications ◽  
System Properties ◽  
Definition Of ◽  
And Training

Interactions between people, groups, organizations, and biological cells have a relationship character that can be represented as a network. The system properties of such networks, regardless of their physical nature, but clearly determining the performance of networks, create the totality of the real world. Complex networks – are naturally existing networks (graphs) that have complex topological properties. The researchers who participate and also make discoveries in this field come from various Sciences such as mathematics, computer science, physics, sociology, and engineering. Therefore, the results of research carry both theoretical knowledge and practical applications in these Sciences. This paper discusses the definition of complex networks. The main characteristics of complex networks, such as clustering and congestion, are considered. A popular social network is considered as a complex network. The calculation of nodes and links of the considered social network is made. The main types of AI development and training are highlighted.

scholarly journals MDST-DBSCAN: A Density-Based Clustering Method for Multidimensional Spatiotemporal Data

2021 ◽  
Vol 10 (6) ◽  
pp. 391
Author(s):  
Changlock Choi ◽  
Seong-Yun Hong
Keyword(s):  
Large Scale ◽  
Complex Structure ◽  
Real Data ◽  
Spatiotemporal Data ◽  
Clustering Methods ◽  
Computationally Efficient ◽  
Clustering Method ◽  
Efficient Manner ◽  
Data Set ◽  
Urban Dynamics

The increasing use of mobile devices and the growing popularity of location-based ser-vices have generated massive spatiotemporal data over the last several years. While it provides new opportunities to enhance our understanding of various urban dynamics, it poses challenges at the same time due to the complex structure and large-volume characteristic of the spatiotemporal data. To facilitate the process and analysis of such spatiotemporal data, various data mining and clustering methods have been proposed, but there still needs to develop a more flexible and computationally efficient method. The purpose of this paper is to present a clustering method that can work with large-scale, multidimensional spatiotemporal data in a reliable and efficient manner. The proposed method, called MDST-DBSCAN, is applied to idealized patterns and a real data set, and the results from both examples demonstrate that it can identify clusters accurately within a reasonable amount of time. MDST-DBSCAN performs well on both spatial and spatiotemporal data, and it can be particularly useful for exploring massive spatiotemporal data, such as detailed real estate transactions data in Seoul, Korea.

scholarly journals Fault Detection for Large Scale Systems Using Dynamic Principal Components Analysis with Adaptation

2007 ◽  
Vol 2 (2) ◽  
pp. 185 ◽  
Author(s):  
Jesús Mina ◽  
Cristina Verde
Keyword(s):  
Fault Detection ◽  
Large Scale ◽  
Principal Component ◽  
False Alarms ◽  
Large Scale Systems ◽  
Linear Dynamic Systems ◽  
Dynamic Principal Component Analysis ◽  
On Line ◽  
Components Analysis ◽  
Monitoring Phase

The Dynamic Principal Component Analysis is an adequate tool for the monitoring of large scale systems based on the model of multivariate historical data under the assumption of stationarity, however, false alarms occur for non-stationary new observations during the monitoring phase. In order to reduce the false alarms rate, this paper extends the DPCA based monitoring for non-stationary data of linear dynamic systems, including an on-line means estimator to standardize new observations according to the estimated means. The effectiveness of the proposed methodology is evaluated for fault detection in a interconnected tanks system.

scholarly journals Algebraic Condition for Decomposition of Large-Scale Linear Dynamic Systems

2009 ◽  
Vol 19 (1) ◽  
pp. 107-112 ◽  
Author(s):  
Henryk Górecki
Keyword(s):  
Characteristic Polynomial ◽  
Dynamic Systems ◽  
General Condition ◽  
Large Scale ◽  
Symmetric Functions ◽  
Original System ◽  
Algebraic Condition ◽  
Equivalent Problem ◽  
Linear Dynamic Systems ◽  
Linear Dynamic

Algebraic Condition for Decomposition of Large-Scale Linear Dynamic SystemsThe paper concerns the problem of decomposition of a large-scale linear dynamic system into two subsystems. An equivalent problem is to split the characteristic polynomial of the original system into two polynomials of lower degrees. Conditions are found concerning the coefficients of the original polynomial which must be fulfilled for its factorization. It is proved that knowledge of only one of the symmetric functions of those polynomials of lower degrees is sufficient for factorization of the characteristic polynomial of the original large-scale system. An algorithm for finding all the coefficients of the decomposed polynomials and a general condition of factorization are given. Examples of splitting the polynomials of the fifth and sixth degrees are discussed in detail.

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