A sequential sampling algorithm that adapts to the uncertain sparsity in signal environment

2011 ◽  
Author(s):  
Karen M. Guan ◽  
Jonathan P. Krauss ◽  
Emilio Sovero ◽  
Gilbert Tseng ◽  
May Tan
Keyword(s):  
Sequential Sampling ◽  
Sampling Algorithm ◽  
Signal Environment

A Sequential Sampling Algorithm for Multistage Static Coverage Problems

2018 ◽  
Vol 18 (2) ◽  
Author(s):  
Binbin Zhang ◽  
Jida Huang ◽  
Rahul Rai ◽  
Hemanth Manjunatha
Keyword(s):  
Optimization Problems ◽  
Sequential Sampling ◽  
Minimum Energy ◽  
Pattern Search ◽  
Coverage Problem ◽  
Sampling Algorithm ◽  
System Designer ◽  
Area Of Interest ◽  
Coverage Problems ◽  
Multiple Stages

In many system-engineering problems, such as surveillance, environmental monitoring, and cooperative task performance, it is critical to allocate limited resources within a restricted area optimally. Static coverage problem (SCP) is an important class of the resource allocation problem. SCP focuses on covering an area of interest so that the activities in that area can be detected with high probabilities. In many practical settings, primarily due to financial constraints, a system designer has to allocate resources in multiple stages. In each stage, the system designer can assign a fixed number of resources, i.e., agents. In the multistage formulation, agent locations for the next stage are dependent on previous-stage agent locations. Such multistage static coverage problems are nontrivial to solve. In this paper, we propose an efficient sequential sampling algorithm to solve the multistage static coverage problem (MSCP) in the presence of resource intensity allocation maps (RIAMs) distribution functions that abstract the event that we want to detect/monitor in a given area. The agent's location in the successive stage is determined by formulating it as an optimization problem. Three different objective functions have been developed and proposed in this paper: (1) L2 difference, (2) sequential minimum energy design (SMED), and (3) the weighted L2 and SMED. Pattern search (PS), an efficient heuristic algorithm has been used as optimization algorithm to arrive at the solutions for the formulated optimization problems. The developed approach has been tested on two- and higher dimensional functions. The results analyzing real-life applications of windmill placement inside a wind farm in multiple stages are also presented.

scholarly journals A Novel Sequential Sampling Algorithm for Reliability Assessment of Microgrids

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 134468-134479
Author(s):  
Xiaotong Song ◽  
Yi Sun ◽  
Fei Wang ◽  
Wenyue Xu
Keyword(s):  
Sequential Sampling ◽  
Reliability Assessment ◽  
Sampling Algorithm

A Sequential Sampling Algorithm for Multi-Stage Static Coverage Problems

2016 ◽  
Author(s):  
Hemanth Manjunatha ◽  
Jida Huang ◽  
Binbin Zhang ◽  
Rahul Rai
Keyword(s):  
Heuristic Algorithms ◽  
Sequential Sampling ◽  
Minimum Energy ◽  
Pattern Search ◽  
Coverage Problem ◽  
Sampling Algorithm ◽  
System Designer ◽  
Area Of Interest ◽  
Multi Stage ◽  
Coverage Problems

It is critical in many system-engineering problems (such as surveillance, environmental monitoring, and cooperative task performance) to optimally allocate resources in the presence of limited resources. Static coverage problem is an important class of the resource allocation problems that focuses on covering an area of interest so that the activities in the area of interest can be detected/monitored with higher probability. In many practical settings (primarily due to financial constraints) a system designer has to allocate resources in multiple stages. In each stage, the system designer can assign a fixed number of resources (agents). In the multi-stage formulation, the agents locations for the next stage are dependent on all the agents location in the previous stages. Such multi-stage static coverage problems are non-trivial to solve. In this paper, we propose a robust and efficient sequential sampling algorithm to solve the multi-stage static coverage problem in the presence of probabilistic resource intensity allocation maps (RIAMs). The agents locations are determined by formulating this problem as an optimization problem in the successive stage . Three different objective functions are compared and discussed from the aspects of decreasing L2 difference and Sequential Minimum Energy Design (SMED). It is shown that utilizing SMED objective function leads to a better approximation of the RIAMs. Two heuristic algorithms, i.e. cuckoo search, and pattern search, are used as optimization algorithms. Numerical functions and real-life applications are provided to demonstrate the robustness and efficiency of the proposed approach.

Versatile sequential sampling algorithm using Kernel Density Estimation

2020 ◽  
Vol 284 (1) ◽  
pp. 201-211 ◽  
Author(s):  
Pamphile T. Roy ◽  
Lluís Jofre ◽  
Jean-Christophe Jouhaud ◽  
Bénédicte Cuenot
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Sequential Sampling ◽  
Kernel Density ◽  
Sampling Algorithm
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