scholarly journals Grid & Force Based Sensor Deployment Methods in WSN using PSO

2018 ◽  
Vol 10 (1) ◽  
pp. 271
Author(s):  
Aparna Pradeep Laturkar ◽  
Sridharan Bhavani ◽  
DeepaliParag Adhyapak
Keyword(s):  
Probabilistic Approach ◽  
Optimization Method ◽  
Sensor Deployment ◽  
Discrete Version ◽  
Binary Particle Swarm Optimization ◽  
Packet Size ◽  
Local Optima ◽  
Wide Range ◽  
Battery Capacity ◽  
Coverage Problems

<span>Wireless Sensor Network (WSN) is emergingtechnology and has wide range of applications, such as environment monitoring, industrial automation and numerous military applications. Hence, WSN is popular among researchers. WSN has several constraints such as restricted sensing range, communication range and limited battery capacity. These limitations bring issues such as coverage, connectivity, network lifetime and scheduling &amp; data aggregation. There are mainly three strategies for solving coverage problems namely; force, grid and computational geometry based. PSO is a multidimensional optimization method inspired from the social behavior of birds called flocking. Basic version of PSO has the drawback of sometimes getting trapped in local optima as particles learn from each other and past solutions. This issue is solved by discrete version of PSO known as Modified Discrete Binary PSO (MDBPSO) as it uses probabilistic approach. This paper discusses performance analysis of random; grid based MDBPSO (Modified Discrete Binary Particle Swarm Optimization), Force Based VFCPSO and Combination of Grid &amp; Force Based sensor deployment algorithms based on interval and packet size. </span><span>From the results of Combination of Grid &amp; Force Based sensor deployment algorithm, it can be concluded that its performance is best for all parameters as compared to rest of the three methods when interval and packet size is varied.</span>

scholarly journals Random, PSO & MDBPSO based Sensor Deployment in WSN

2018 ◽  
Vol 10 (1) ◽  
pp. 286
Author(s):  
Aparna Pradeep Laturkar ◽  
Sridharan Bhavani ◽  
DeepaliParag Adhyapak
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Region Of Interest ◽  
Optimization Methods ◽  
Sensor Deployment ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Wide Range ◽  
Battery Capacity ◽  
Coverage Problems

Wireless Sensor Network (WSN) is emergingtechnology and has wide range of applications, such as environment monitoring, industrial automation and numerous military applications. Hence, WSN is popular among researchers. WSN has several constraints such as restricted sensing range, communication range and limited battery capacity. These limitations bring issues such as coverage, connectivity, network lifetime and scheduling and data aggregation. There are mainly three strategies for solving coverage problems namely; force, grid and computational geometry based. This paper discusses sensor deployment using Random; Particle Swarm Optimization (PSO) and grid based MDBPSO (Modified Discrete Binary Particle Swarm Optimization) methods. This paper analyzes the performance of Random, PSO based and MDBPSO based sensor deployment methods by varying different grid sizes and the region of interest (ROI). PSO and MDBPSO based sensor deployment methods are analyzed based on number of iterations. From the simulation results; it can be concluded that MDBPSO performs better than other two methods.

scholarly journals Grid and Force Based Sensor Deployment Methods in Wireless Sensor Network using Particle Swarm Optimization

2018 ◽  
Vol 10 (3) ◽  
pp. 1287
Author(s):  
Aparna Pradeep Laturkar ◽  
Sridharan Bhavani ◽  
DeepaliParag Adhyapak
Keyword(s):  
Particle Swarm Optimization ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Sensor Deployment ◽  
Packet Size ◽  
Swarm Optimization ◽  
Wide Range ◽  
Battery Capacity

Wireless Sensor Network (WSN) is emergingtechnology and has wide range of applications, such as environment monitoring, industrial automation and numerous military applications. Hence, WSN is popular among researchers. WSN has several constraints such as restricted sensing range, communication range and limited battery capacity. These limitations bring issues such as coverage, connectivity, network lifetime and scheduling &amp; data aggregation. There are mainly three strategies for solving coverage problems namely; force, grid and computational geometry based. PSO is a multidimensional optimization method inspired from the social behavior of birds called flocking. Basic version of PSO has the drawback of sometimes getting trapped in local optima as particles learn from each other and past solutions. This issue is solved by discrete version of PSO known as Modified Discrete Binary PSO (MDBPSO) as it uses probabilistic approach. This paper discusses performance analysis of random; grid based MDBPSO (Modified Discrete Binary Particle Swarm Optimization), Force Based VFCPSO and Combination of Grid &amp; Force Based sensor deployment algorithms based on interval and packet size. From the results of Combination of Grid &amp; Force Based sensor deployment algorithm, it can be concluded that its performance is best for all parameters as compared to rest of the three methods when interval and packet size is varied.

scholarly journals Random, PSO and MDBPSO based Sensor Deployment in Wireless Sensor Network

2018 ◽  
Vol 10 (3) ◽  
pp. 1278 ◽  
Author(s):  
Aparna Pradeep Laturkar ◽  
Sridharan Bhavani ◽  
DeepaliParag Adhyapak
Keyword(s):  
Particle Swarm Optimization ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Sensor Deployment ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Wide Range ◽  
Battery Capacity

Wireless Sensor Network (WSN) is emergingtechnology and has wide range of applications, such as environment monitoring, industrial automation and numerous military applications. Hence, WSN is popular among researchers. WSN has several constraints such as restricted sensing range, communication range and limited battery capacity. These limitations bring issues such as coverage, connectivity, network lifetime and scheduling and data aggregation. There are mainly three strategies for solving coverage problems namely; force, grid and computational geometry based. This paper discusses sensor deployment using Random; Particle Swarm Optimization (PSO) and grid based MDBPSO (Modified Discrete Binary Particle Swarm Optimization) methods. This paper analyzes the performance of Random, PSO based and MDBPSO based sensor deployment methods by varying different grid sizes and the region of interest (ROI). PSO and MDBPSO based sensor deployment methods are analyzed based on number of iterations. From the simulation results; it can be concluded that MDBPSO performs better than other two methods.

scholarly journals Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 82
Author(s):  
Ahmad O. Aseeri
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Probabilistic Approach ◽  
Classification Model ◽  
Gating Mechanism ◽  
Uncertainty Estimates ◽  
Wide Range

Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.

An Enhanced One-Layer Passive Microfluidic Mixer With an Optimized Lateral Structure With the Dean Effect

2015 ◽  
Vol 137 (9) ◽  
Author(s):  
Teng Zhou ◽  
Yifan Xu ◽  
Zhenyu Liu ◽  
Sang Woo Joo
Keyword(s):  
Reynolds Number ◽  
Topology Optimization ◽  
Optimization Method ◽  
Mixing Efficiency ◽  
Boolean Operation ◽  
Microfluidic Mixer ◽  
Wide Range ◽  
The One ◽  
Lateral Structure ◽  
Topology Optimization Method

Topology optimization method is applied to a contraction–expansion structure, based on which a simplified lateral flow structure is generated using the Boolean operation. A new one-layer mixer is then designed by sequentially connecting this lateral structure and bent channels. The mixing efficiency is further optimized via iterations on key geometric parameters associated with the one-layer mixer designed. Numerical results indicate that the optimized mixer has better mixing efficiency than the conventional contraction–expansion mixer for a wide range of the Reynolds number.

Benchmarking and Field-Testing of the Distributed Quasi-Newton Derivative-Free Optimization Method for Field Development Optimization

2021 ◽  
Author(s):  
Faruk Alpak ◽  
Yixuan Wang ◽  
Guohua Gao ◽  
Vivek Jain
Keyword(s):  
Optimization Problems ◽  
Field Testing ◽  
Optimization Method ◽  
Training Data ◽  
Local Optima ◽  
Field Development ◽  
Derivative Free Optimization ◽  
Derivative Free ◽  
Data Points ◽  
Quasi Newton

Abstract Recently, a novel distributed quasi-Newton (DQN) derivative-free optimization (DFO) method was developed for generic reservoir performance optimization problems including well-location optimization (WLO) and well-control optimization (WCO). DQN is designed to effectively locate multiple local optima of highly nonlinear optimization problems. However, its performance has neither been validated by realistic applications nor compared to other DFO methods. We have integrated DQN into a versatile field-development optimization platform designed specifically for iterative workflows enabled through distributed-parallel flow simulations. DQN is benchmarked against alternative DFO techniques, namely, the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method hybridized with Direct Pattern Search (BFGS-DPS), Mesh Adaptive Direct Search (MADS), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). DQN is a multi-thread optimization method that distributes an ensemble of optimization tasks among multiple high-performance-computing nodes. Thus, it can locate multiple optima of the objective function in parallel within a single run. Simulation results computed from one DQN optimization thread are shared with others by updating a unified set of training data points composed of responses (implicit variables) of all successful simulation jobs. The sensitivity matrix at the current best solution of each optimization thread is approximated by a linear-interpolation technique using all or a subset of training-data points. The gradient of the objective function is analytically computed using the estimated sensitivities of implicit variables with respect to explicit variables. The Hessian matrix is then updated using the quasi-Newton method. A new search point for each thread is solved from a trust-region subproblem for the next iteration. In contrast, other DFO methods rely on a single-thread optimization paradigm that can only locate a single optimum. To locate multiple optima, one must repeat the same optimization process multiple times starting from different initial guesses for such methods. Moreover, simulation results generated from a single-thread optimization task cannot be shared with other tasks. Benchmarking results are presented for synthetic yet challenging WLO and WCO problems. Finally, DQN method is field-tested on two realistic applications. DQN identifies the global optimum with the least number of simulations and the shortest run time on a synthetic problem with known solution. On other benchmarking problems without a known solution, DQN identified compatible local optima with reasonably smaller numbers of simulations compared to alternative techniques. Field-testing results reinforce the auspicious computational attributes of DQN. Overall, the results indicate that DQN is a novel and effective parallel algorithm for field-scale development optimization problems.

scholarly journals Optimization Design of the Over-All Dimensions of Centrifugal Compressor Stage

1988 ◽  
Author(s):  
Wang Qinghuan ◽  
Sun Zhiqin
Keyword(s):  
Centrifugal Compressor ◽  
Computer Aided Design ◽  
Optimization Design ◽  
Optimization Method ◽  
Complex Method ◽  
Compressor Stage ◽  
Local Optima ◽  
Centrifugal Compressor Stage ◽  
Optimum Solution ◽  
Nonlinear Objective Function

A new procedure employed in computer-aided design of centrifugal compressor stage to determine its over-all dimensions is described in this paper. By the use of the COMPLEX METHOD, the arbitrary number of variables to be optimized can be specified to remove the hidden danger of the local optima which stems from adopting a few, for example two or three, variables to be optimized. This procedure is available for any complicated implicit nonlinear objective function and ensures establishment of a true optimum solution. Numerical calculations have been carried out by using the computer program described here to check the ability of the optimization method. The results obtained by the calculations agree fairly well with that obtained by experiments.

Multiscale unified prediction of size/scale and Hall-Petch effects in the mechanics of polycrystalline materials

2013 ◽  
Vol 22 (1-2) ◽  
pp. 67-71 ◽  
Author(s):  
George N. Frantziskonis
Keyword(s):  
Size Effects ◽  
Probabilistic Approach ◽  
Higher Dimensions ◽  
Spatial Dimension ◽  
Polycrystalline Materials ◽  
Material Failure ◽  
Average Grain Size ◽  
Wide Range ◽  
Physical Information ◽  
Polycrystalline Solids

AbstractMaterials show size effects in their strength, i.e., improved strength as size decreases. Size effects have been studied extensively at a wide range of scales, from atomistic to continuum. Size effects depend on the scale of reference, as the physics change with increasing or decreasing scale. The work reported herein concentrates at scales near the average grain size in polycrystalline solids, where they are examined in conjunction with Hall-Petch effects. It presents a process for isolating physical information on a problem at specific spatial or temporal scales and applies it to Hall-Petch and size effects in one spatial dimension, extendable to higher dimensions. Importantly, the scale-isolated information captures the interactions among scales. As material failure and Hall-Petch effects are highly stochastic, a probabilistic approach to the present work is more appropriate than a deterministic one.

scholarly journals Radioecological assessment of forage agricultural land in the southwestern areas of the Bryansk region based on a set of statistical models

2021 ◽  
Vol 30 (2) ◽  
pp. 38-49
Author(s):  
S.I. Spiridonov ◽  
◽  
V.V. Ivanov ◽  
I.E. Titov ◽  
V.E. Nushtaeva ◽  
...  
Keyword(s):  
Soil Contamination ◽  
Statistical Models ◽  
Agricultural Land ◽  
Sandy Loam ◽  
Probabilistic Approach ◽  
Point Of View ◽  
Content Standards ◽  
Organic Soils ◽  
137Cs Content ◽  
Wide Range

This paper presents a radioecological assessment of forage agricultural land in the southwestern districts of the Bryansk region based on data characterizing the variability of the radionuclides content in the soil. Concentration of 137Cs in forage was calculated taking into account the proba-bility distributions of 137Cs soil contamination density and the soil to plant transfer factor. The pro-cessing data of the radioecological survey has shown the soil contamination density with 137Cs of agricultural lands in the southwestern areas of the Bryansk region obeys a lognormal law. The authors have used statistical models and software modules for the radioecological assessment of forage lands. Risks of exceeding the 137Cs content standards in forage obtained on soils with different texture have been calculated. The limiting levels of contamination of pastures and hay-fields with 137Cs, ensuring compliance with the specified risks for forage, have been estimated. The lowest limiting soil contamination density is characteristic of organic soils, which can be con-sidered “critical” from the point of view of 137Cs intake into forage. The authors have predicted the time of remediation of forage lands in the southwestern districts of the Bryansk region in the ab-sence of protective measures based on a probabilistic approach. The time period during which the risk of forage contamination for sandy, sandy loam and clay loam soils will decrease to 10% varies for the areas under consideration in a wide range, not exceeding 64 years. It is concluded that it is advisable to substantiate the value of the acceptable risk of forage contamination, taking into account radiological and socio-economic aspects.

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