Autonomous Strategic Defense: An Adaptive Clustering Approach to Capture Order Optimization

2022 ◽  
Author(s):  
Noah D. Zepp ◽  
Han Fu ◽  
Hugh H. Liu
Keyword(s):  
Adaptive Clustering ◽  
Strategic Defense ◽  
Clustering Approach

Featureless Data Clustering

2010 ◽  
pp. 141-164 ◽  
Author(s):  
Wilson Wong
Keyword(s):  
Data Clustering ◽  
Dominant Role ◽  
Clustering Algorithms ◽  
Adaptive Clustering ◽  
Feature Based ◽  
Clustering Approach ◽  
Semantic Computation ◽  
The Many ◽  
Time Required ◽  
Existing Data

Feature-based semantic measurements have played a dominant role in conventional data clustering algorithms for many existing applications. However, the applicability of existing data clustering approaches to a wider range of applications is limited due to issues such as complexity involved in semantic computation, long pre-processing time required for feature preparation, and poor extensibility of semantic measurement due to non-incremental feature source. This chapter first summarises the many commonly used clustering algorithms and feature-based semantic measurements, and then highlights the shortcomings to make way for the proposal of an adaptive clustering approach based on featureless semantic measurements. The chapter concludes with experiments demonstrating the performance and wide applicability of the proposed clustering approach.

Designing a Fast Adaptive Clustering Approach for Traffic Wave Simulation

2015 ◽  
Author(s):  
Lijun Lan ◽  
Xian Wu ◽  
Ying Liu
Keyword(s):  
Flow Simulation ◽  
Traffic Data ◽  
Simulation Approach ◽  
Traffic Demand ◽  
Adaptive Clustering ◽  
Traffic Flow Simulation ◽  
Proposed Model ◽  
Clustering Approach ◽  
Traffic Wave ◽  
Traffic Optimization

Traffic wave, also known as stop wave or traffic shockwave, is travelling disturbance in the distribution of vehicles on the highways. In this paper, we attempt to study this problem using a simulation approach. Largely inspired by an interesting observation from ant chain movement, we explore how such a vivid pattern can be mathematically modeled and whether the similar way of behavior is helpful for dealing traffic wave issue in our highway systems. Therefore, a decentralized fast-adaptive clustering approach is proposed jointly with considerations for traffic optimization. To validate the proposed approach and to better understand its mechanism in lifting traffic flow, simulation study is carried out using real-world traffic data. Results have revealed the applicability and effectiveness of the proposed approach and have also indicated that both road configuration and traffic demand affect the effectiveness of the proposed model.

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