Determination of thin metal film’s thickness and optical constants based on SPR phase detection by simulated annealing particle swarm optimization

Yue, Chong; Qin, Zirui; Lang, Yaopu; Liu, Qinggang

doi:10.1016/j.optcom.2018.08.051

Cited by 16 publications

(6 citation statements)

References 33 publications

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“…A swarm of particles is initially generated randomly, and the particle's position indicates a possible solution point in the problem space. Each particle has its velocity and position as well as a degree of fitness specified by the objective function [23]. In this search space, all particles fly at a certain velocity and seek global optimization by following the current optimal value(s).…”

Section: Mopso-asa Scheduling Algorithmmentioning

confidence: 99%

A Fault-tolerant Scheduling Strategy through Proactive and Clustering Techniques for Scientific Workflows in Cloud Computing

Farhood,

Khorsand,

Hussein

et al. 2024

Preprint

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Scientific workflow scheduling allocates many fine computational granularity tasks to the best appropriate cloud resources. The prevalence of failures in cloud computing is augmented by the substantial quantity of servers and components burdened with resource-intensive workloads. In addition, workflow tasks may face a higher failure risk than a job with the single task. To mitigate the likelihood of these potential failures, the workflow scheduling system should exhibit fault tolerance. In this paper, a fault-tolerant scheduling strategy through proactive and clustering techniques for scientific workflows is proposed in cloud computing. First, the problem of task clustering is formulated by combining several short-duration tasks into a single job to minimize scheduling overhead and enhance the runtime performance of workflow executions. Then, an autonomous framework for workflow scheduling is introduced based on the MAPE-K control model with four essential steps: monitoring, analyzing, planning, and executing, all supported by a shared knowledge base. In the monitoring step, clustered jobs and capabilities of available cloud resources are monitored. In the analyzing step, the failure prediction accuracy is increased by applying the group method of data handling (GMDH) neural network before fault /failure occurrence. In the planning step, (1) the reliability of application execution is assured through a re-clustering technique after fault /failure occurrence; (2) a new hybrid multi-objective algorithm is proposed based on MOPSO and adaptive SA, called MOPSO-aSA, to facilitate workflow scheduling in faulty execution environments. Last, according to the experimental results, it can be concluded that the suggested strategy outperforms other approaches in terms of makespan, total cost, energy consumption, and failure rate.

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Section: Mopso-asa Scheduling Algorithmmentioning

confidence: 99%

A Fault-tolerant Scheduling Strategy through Proactive and Clustering Techniques for Scientific Workflows in Cloud Computing

Farhood,

Khorsand,

Hussein

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…End % Update globe values (16) If max(func(p g )) > max(func(x i )) then (17) [fmax,r]= max(func(p g )); bestx=x_pso(r,:);() (18) else [fmax,r]= max(func(x i )); bestx=x i (r,:); ( 19)…”

Section: Differential Evolution Particle Swarm Optimizationmentioning

confidence: 99%

“…In order to promote the sensitivity and resolution of SPR sensors, phase-sensitivity configuration has been proposed in several studies in the literature [13][14][15], based on the fact that the phase of the TM-polarized component of the incident light wave undergoes drastic changes, while the phase of the TE-polarized component remains essentially unchanged [16]. The conventional phase-modulated SPR sensor, which employs the Kretschmann prism coupler, typically utilizes monolayer gold (Au) [17], silver (Ag) [18], aluminum (Al) [19], or copper (Cu) [20] film as the plasmonic metal.…”

Section: Introductionmentioning

confidence: 99%

Differential Evolution Particle Swarm Optimization for Phase-Sensitivity Enhancement of Surface Plasmon Resonance Gas Sensor Based on MXene and Blue Phosphorene/Transition Metal Dichalcogenide Hybrid Structure

Yue,

Ding,

Tao

et al. 2023

Sensors

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A differential evolution particle swarm optimization (DEPSO) is presented for the design of a high-phase-sensitivity surface plasmon resonance (SPR) gas sensor. The gas sensor is based on a bilayer metal film with a hybrid structure of blue phosphorene (BlueP)/transition metal dichalcogenides (TMDCs) and MXene. Initially, a Ag-BlueP/TMDCs-Ag-MXene heterostructure is designed, and its performance is compared with that of the conventional layer-by-layer method and particle swarm optimization (PSO). The results indicate that optimizing the thickness of the layers in the gas sensor promotes phase sensitivity. Specifically, the phase sensitivity of the DEPSO is significantly higher than that of the PSO and the conventional method, while maintaining a lower reflectivity. The maximum phase sensitivity achieved is 1.866 × 106 deg/RIU with three layers of BlueP/WS2 and a monolayer of MXene. The distribution of the electric field is also illustrated, demonstrating that the optimized configuration allows for better detection of various gases. Due to its highly sensitive characteristics, the proposed design method based on the DEPSO can be applied to SPR gas sensors for environmental monitoring.

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“…Because phase sensitivity configuration is less sensitive to external impacts, it may be utilized to improve the sensitivity of SPR biosensors [ 13 , 14 , 15 ]. The fact that the phase of the incident light wave’s transverse magnetic (TM) polarized component varies significantly while the phase of the transverse electric (TE) stays mostly constant serves as the foundation for this configuration [ 16 , 17 ]. Silver (Ag) is favored as an active metal for SPR sensors due to its lower D-electron energy bands and bulk plasma frequency [ 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

An Improved Seeker Optimization Algorithm for Phase Sensitivity Enhancement of a Franckeite- and WS2-Based SPR Biosensor for Waterborne Bacteria Detection

Yue,

Zhao,

Tao

et al. 2024

Micromachines

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For the purpose of detecting waterborne bacteria, a high-phase-sensitivity SPR sensor with an Ag–TiO2–Franckeite–WS2 hybrid structure is designed using an improved seeker optimization algorithm (ISOA). By optimizing each layer of sensor construction simultaneously, the ISOA guarantees a minimum reflectance of less than 0.01 by Ag (20.36 nm)–TiO2 (6.08 nm)–Franckeite (monolayer)–WS2 (bilayer) after 30 iterations for E. coli. And the optimal phase sensitivity is 2.378 × 106 deg/RIU. Sensor performance and computing efficiency have been greatly enhanced using the ISOA in comparison to the traditional layer-by-layer technique and the SOA method. This will enable sensors to detect a wider range of bacteria with more efficacy. As a result, the ISOA-based design idea could provide SPR biosensors with new applications in environmental monitoring.

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Determination of thin metal film’s thickness and optical constants based on SPR phase detection by simulated annealing particle swarm optimization

Cited by 16 publications

References 33 publications

A Fault-tolerant Scheduling Strategy through Proactive and Clustering Techniques for Scientific Workflows in Cloud Computing

A Fault-tolerant Scheduling Strategy through Proactive and Clustering Techniques for Scientific Workflows in Cloud Computing

Differential Evolution Particle Swarm Optimization for Phase-Sensitivity Enhancement of Surface Plasmon Resonance Gas Sensor Based on MXene and Blue Phosphorene/Transition Metal Dichalcogenide Hybrid Structure

An Improved Seeker Optimization Algorithm for Phase Sensitivity Enhancement of a Franckeite- and WS2-Based SPR Biosensor for Waterborne Bacteria Detection

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