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.

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.

A Novel Sampling Technique for Probabilistic Static Coverage Problems

2016 ◽  
Vol 138 (3) ◽  
Author(s):  
Binbin Zhang ◽  
Nagavenkat Adurthi ◽  
Rahul Rai ◽  
Puneet Singla
Keyword(s):  
Resource Allocation ◽  
San Francisco ◽  
Systems Engineering ◽  
Real Life ◽  
Sampling Technique ◽  
Superior Performance ◽  
Coverage Problem ◽  
Area Of Interest ◽  
Wide Range ◽  
Coverage Problems

Resource allocation in the presence of constraints is an important activity in many systems engineering problems such as surveillance, infrastructure planning, environmental monitoring, and cooperative task performance. The resources in many important problems are agents such as a person, machine, unmanned aerial vehicles (UAVs), infrastructures, and software. Effective execution of a given task is highly correlated with effective allocation of resources to execute the task. An important class of resource allocation problem in the presence of limited resources is static coverage problem. In static coverage problems, it is necessary to allocate resources (stationary configuration of agents) to cover an area of interest so that an event or spatial property of the area can be detected or monitored with high probability. In this paper, we outline a novel sampling algorithm for the static coverage problem in presence of probabilistic resource intensity allocation maps (RIAMs). The key intuition behind our sampling approach is to use the finite number of samples to generate an accurate representation of RIAM. The outlined sampling technique is based on an optimization framework that approximates the RIAM with piecewise linear surfaces on the Delaunay triangles and optimizes the sample placement locations to decrease the difference between the probability distribution and Delaunay triangle surface. Numerical results demonstrate that the algorithm is robust to the initial sample point locations and has superior performance in a wide range of theoretical problems and real-life applications. In a real-life application setting, we demonstrate the efficacy of the proposed algorithm to predict the position of wind stations for monitoring wind speeds across the U.S. The algorithm is also used to give recommendations on the placement of police cars in San Francisco and weather buoys in Pacific Ocean.

A Comparative Study of Computational Intelligence Algorithms for Sensor Localization

2019 ◽  
Vol 9 (2) ◽  
pp. 224-236
Author(s):  
Vaishali R. Kulkarni ◽  
Veena Desai ◽  
Raghavendra Kulkarni
Keyword(s):  
Firefly Algorithm ◽  
Heuristic Algorithms ◽  
Computing Time ◽  
Sensor Nodes ◽  
Convergence Time ◽  
Localization Error ◽  
Location Awareness ◽  
Cultural Algorithm ◽  
Localization Accuracy ◽  
Multi Stage

Background & Objective: Location of sensors is an important information in wireless sensor networks for monitoring, tracking and surveillance applications. The accurate and quick estimation of the location of sensor nodes plays an important role. Localization refers to creating location awareness for as many sensor nodes as possible. Multi-stage localization of sensor nodes using bio-inspired, heuristic algorithms is the central theme of this paper. Methodology: Biologically inspired heuristic algorithms offer the advantages of simplicity, resourceefficiency and speed. Four such algorithms have been evaluated in this paper for distributed localization of sensor nodes. Two evolutionary computation-based algorithms, namely cultural algorithm and the genetic algorithm, have been presented to optimize the localization process for minimizing the localization error. The results of these algorithms have been compared with those of swarm intelligence- based optimization algorithms, namely the firefly algorithm and the bee algorithm. Simulation results and analysis of stage-wise localization in terms of number of localized nodes, computing time and accuracy have been presented. The tradeoff between localization accuracy and speed has been investigated. Results: The comparative analysis shows that the firefly algorithm performs the localization in the most accurate manner but takes longest convergence time. Conclusion: Further, the cultural algorithm performs the localization in a very quick time; but, results in high localization error.

Rapid earthquake loss assessment based on machine learning and representative sampling

2021 ◽  
pp. 875529302110423
Author(s):  
Zoran Stojadinović ◽  
Miloš Kovačević ◽  
Dejan Marinković ◽  
Božidar Stojadinović
Keyword(s):  
Machine Learning ◽  
Classification Model ◽  
Local Network ◽  
Loss Assessment ◽  
Damage Classification ◽  
Sampling Algorithm ◽  
Representative Sampling ◽  
Random Forest Classification ◽  
Area Of Interest ◽  
Earthquake Loss

This article proposes a new framework for rapid earthquake loss assessment based on a machine learning damage classification model and a representative sampling algorithm. A random forest classification model predicts a damage probability distribution that, combined with an expert-defined repair cost matrix, enables the calculation of the expected repair costs for each building and, in aggregate, of direct losses in the earthquake-affected area. The proposed building representation does not include explicit information about the earthquake and the soil type. Instead, such information is implicitly contained in the spatial distribution of damage. To capture this distribution, a sampling algorithm, based on K-means clustering, is used to select a minimal number of buildings that represent the area of interest in terms of its seismic risk, independently of future earthquakes. To observe damage states in the representative set after an earthquake, the proposed framework utilizes a local network of trained damage assessors. The model is updated after each damage observation cycle, thus increasing the accuracy of the current loss assessment. The proposed framework is exemplified using the 2010 Kraljevo, Serbia earthquake dataset.

scholarly journals A novel measurement-based procedure for dynamic equivalents of electric power systems for stability studies using improved sine cosine algorithm

2019 ◽  
Vol 70 (6) ◽  
pp. 454-464
Author(s):  
Omar Benmiloud ◽  
Salem Arif
Keyword(s):  
Power Systems ◽  
Dynamic Properties ◽  
Specific Area ◽  
Pattern Search ◽  
Fine Tuning ◽  
Large Power ◽  
Area Of Interest ◽  
Starting Point ◽  
Sine Cosine Algorithm ◽  
Dynamic Equivalent

Abstract Dynamic equivalent (DE) is an important process of multi-area interconnected power systems. It allows to perform stability assessment of a specific area (area of interest) at minimum cost. This study is intended to investigate the dynamic equivalent of two relatively large power systems. The fourth-order model of synchronous generators with a simplified excitation system is used as equivalent to the group of generators in the external system. To improve the accuracy of the estimated model, the identification is carried in two stages. First, using the global search Sine Cosine Algorithm (SCA) to find a starting set values, then this set is used as starting point for the fine-tuning made through the Pattern Search (PS) algorithm. To increase the reliability of the model’s parameters, two disturbances are used to avoid the identification based on a specific event. The developed program is applied on two standard power systems, namely, the New England (NE) system and the Northeast Power Coordinating Council (NPCC) system. Simulation results confirm the ability of the optimized model to preserve the main dynamic properties of the original system with accuracy.

scholarly journals Flexible Wi-Fi Communication among Mobile Robots in Indoor Industrial Environments

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Jetmir Haxhibeqiri ◽  
Elnaz Alizadeh Jarchlo ◽  
Ingrid Moerman ◽  
Jeroen Hoebeke
Keyword(s):  
Mobile Robots ◽  
Ad Hoc ◽  
Mobile Node ◽  
Mesh Network ◽  
Coverage Problem ◽  
Problem Detection ◽  
Speed Up ◽  
Coverage Problems ◽  
The Impact ◽  
Configurable Architecture

In order to speed up industrial processes and to improve logistics, mobile robots are getting important in industry. In this paper, we propose a flexible and configurable architecture for the mobile node that is able to operate in different network topology scenarios. The proposed solution is able to operate in presence of network infrastructure, in ad hoc mode only, or to use both possibilities. In case of mixed architecture, mesh capabilities will enable coverage problem detection and overcoming. The solution is based on real requirements from an automated guided vehicle producer. First, we evaluate the overhead introduced by our solution. Since the mobile robot communication relies in broadcast traffic, the broadcast scalability in mesh network is evaluated too. Finally, through experiments on a wireless testbed for a variety of scenarios, we analyze the impact of roaming, mobility and traffic separation, and demonstrate the advantage of our approach in handling coverage problems.

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