Estimating Seebeck Coefficient of a p-Type High Temperature Thermoelectric Material Using Bee Algorithm Multi-layer Perception

Uysal, Fatih; Kılınç, Enes; Çelik, Erdal; Düğenci, Muharrem; Sağiroğlu, Selami

doi:10.1007/s11664-017-5497-6

Cited by 11 publications

(5 citation statements)

References 21 publications

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“…From formula (1), we can see that there is an energy connection between each visible layer node and the hidden layer node. Based on formula (1), when all parameters are determined, the joint probability distribution of (v, h) can be obtained: v,h|θ) .…”

Section: Deep Learningmentioning

confidence: 99%

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Image Self-Coding Algorithm Based on IoT Perception Layer

2022

Mobile Information Systems

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In fact, with the quick growth of IoT-related industries in recent years, multimedia contents such as digital image videos have also shown explosive growth. In the sensing layer of the three-layer IoT architecture, sensors are the most critical part, which mainly sense the state of the environment. In this paper, an image self-coding algorithm based on the IoT perception layer is proposed. There is no specific encoding algorithm for the pictures collected by the current Internet of Things network perception layer. This results in poor search results for the network images collected by the sensor at the perception layer. A deep convolutional neural network image self-coding algorithm based on the IoT perception layer combines prior knowledge with deep involutional aural networks to increase the discriminative ability of images while preserving the message of the images themselves and improving the goal of image search accuracy. The experimental results show that the block search algorithm is used for image registration to reduce energy consumption, the absolute difference sum algorithm is improved to improve the accuracy of image registration, and the progressively out weighted average algorithm is used to stitch the images. After image stitching, the communication volume with the base station is reduced, which can effectively reduce the network load by 60%.

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Section: Deep Learningmentioning

confidence: 99%

“…e IoT is mainly used for the purpose of perception [1,2]. Like skin, the awareness level can be employed to collect information, identify the messages collected, and evaluate the impact of information interactions on the IoT [3].…”

Section: Introductionmentioning

confidence: 99%

Image Self-Coding Algorithm Based on IoT Perception Layer

2022

Mobile Information Systems

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“…This algorithm is based on the behavior of the bees to find the appropriate flower for gathering nectar [51]. Bee algorithm is one of the algorithms based on collective intelligence and the result of the relationship of bees with each other [52].…”

Section: Bee Colony Optimization (Bco)mentioning

confidence: 99%

Prediction of tunnel boring machine penetration rate using ant colony optimization, bee colony optimization and the particle swarm optimization, case study: Sabzkooh water conveyance tunnel

Afradi¹,

Ebrahimabadi²,

Hallajian³

2020

Min. miner. depos.

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The purpose of this study is to use a novel approach to estimate the tunnel boring machine (TBM) penetration rate in diverse ground conditions. Methods. The methods used in this study include ant colony optimization (ACO), bee colony optimization (BCO) and the particle swarm optimization (PSO). Moreover, a comprehensive database was created based on machine performance using penetration rate (m/h) as an output parameteras well as intact rock and rock mass parameters including uniaxial compressive strength (UCS) (MPa), Brazilian tensile strength (BTS) (MPa), rock quality designation (RQD) (%), cohesion (MPa), elasticity modulus (GPa), Poisson's ratio, density(g/cm 3 ), joint angle (deg.) and joint spacing (m) as input parameters. Findings. Results showed that the analyses yielded several realistic and reliable models for predicting penetration rate of TBMs. ACO model has R 2 = 0.8830 and RMSE = 0.6955, BCO model has R 2 = 0.9367 and RMSE = 0.5113 and PSO model has R 2 = 0.9717 and RMSE = 0.3418. Originality. Prediction of TBM penetration rate using these methods has been carried out in the Sabzkooh water conveyance tunnel for the first time. Practical implications. According to the results, all three approaches are very effective but PSO yields more precise and realistic findings than other methods.

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“…While there are some situations where ANN gives better results than mathematical model installation systems, bee algorithm (BA) gives better results than ANN [12,13]. Therefore, some AI algorithms were applied to optimise the weights of ANNs to achieve the appropriate results [7].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the lapping parameters of silicon wafers were studied to optimise the lapping duration and minimise lapping materials consumed during lapping process [20]. Moreover, the BA-based ANN also was used to estimate Seebeck coefficient and other parameters of the thermoelectric materials [12].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence solution to extract the dielectric properties of materials at sub‐THz frequencies

Guneser¹

2019

IET Science, Measurement & Technology

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Estimating Seebeck Coefficient of a p-Type High Temperature Thermoelectric Material Using Bee Algorithm Multi-layer Perception

Cited by 11 publications

References 21 publications

Image Self-Coding Algorithm Based on IoT Perception Layer

Image Self-Coding Algorithm Based on IoT Perception Layer

Prediction of tunnel boring machine penetration rate using ant colony optimization, bee colony optimization and the particle swarm optimization, case study: Sabzkooh water conveyance tunnel

Artificial intelligence solution to extract the dielectric properties of materials at sub‐THz frequencies

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