Optimization of Microchannel Heat Sinks Using Prey-Predator Algorithm and Artificial Neural Networks

Hamadneh, Nawaf N.; Khan, Waqar A.; Tilahun, Surafel Luleseged

doi:10.3390/machines6020026

Cited by 29 publications

(17 citation statements)

References 51 publications

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“…When the rod of the pneumatic cylinder (2) The opening mechanism (10) is mounted on the frame (7) opposite the gripping mechanism (1). It consists of a bilateral pneumatic cylinder (11) and a vacuum gripper (12), which is installed in a inverse manner to the central bars (4) of the vacuum gripper (3) of the gripping mechanism (1), attached to the U-shaped lever (13) and connected to the pneumatic cylinder (11). The stack of empty flexible containers (14), unstitched from their neck side, is located on the elevating platform (15) inside the frame (7).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

The Quality Control of the Automatic Manipulating Process of a Flexible Container When Bulk Materials are Packaged

et al. 2019

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The quality control of a flexible container (FC) gripped and held by a gripping device during the entire cycle of packaging is an important task in the packaging process of bulk materials in a soft package. Noncontact optical methods of control have been developed and researched for the diagnostics of the automatic manipulating process of a flexible container when bulk materials are packaged in a soft package. Diagnostics of the FC gripping and opening accuracy was carried out herein with the help of machine vision. Processing of the image obtained when the neck of the FC was photographed was carried out by a neural network algorithm, which was made according to a scheme of a perceptron. An automated diagnostics system of the FC gripping and opening accuracy was developed in terms of the obtained algorithm. A control technique based on the algorithm of comparison with a reference was used to reveal the FC gripping and opening defects. This technique consists of preliminary processing of the image obtained from the camera and automatic search for deviations in FC gripping and opening. As a result, a report of defects in the process of FC gripping and opening was obtained.

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Section: Methodsmentioning

confidence: 99%

“…Both immediate pixel comparison of the test image with the reference pattern and picking out and following a comparison of informational attributes of elements are possible when the image is compared with the reference. A neural network can implement the comparison process of real images with the reference [12][13][14][15][16][17][18].…”

mentioning

confidence: 99%

The Quality Control of the Automatic Manipulating Process of a Flexible Container When Bulk Materials are Packaged

et al. 2019

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“…At first, the main problem is training the ANN, via an optimization algorithm. Several optimization algorithms for the heat transfer processes such as genetic algorithm (GA) [26], Heat transfer search (HTS) [27], particle swarm optimization (PSO) algorithm [28], the modified teaching-learning-based optimization algorithm [29], simulated annealing [30], generative design algorithm (GDA) [31], prey-predator algorithm [32], Global Best Algorithm [33] etc.…”

Section: The Modeling Approach; Principlesmentioning

confidence: 99%

Modeling the free convection in an open round cavity using a hybrid approach of Jaya optimization algorithm and neural network

Akbari¹,

Karami²,

Ashjaee³

2018

IJHT

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The current study highlights the application of a hybrid model in which the Jaya optimization is employed to train the artificial neural network (ANN), to model the free convection in an open round cavity. As a matter of fact, the present research attempts to demonstrate the capability of the aforementioned hybrid network to model the free convection in the cavity against the decision parameters. The decision parameters are the Rayleigh number (Ra) and ratio of the nonconductor barrier distance from the bottom of the cavity to the cavity diameter (H/D). Then, the obtained hybrid model is applied to predict the average Nusselt number in the cavity. In the next step, the experimentally obtained data by using a Mach-Zehnder interferometer is used to train the hybrid model. The accuracy of the hybrid model is evaluated through the calculation of average testing and checking errors. According to the obtained results, there is an optimum ratio (H/D), in which the heat transfer is maximum. Also, this maximum value increases by increasing the Rayleigh number (Ra).

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“…Neural networks (NN) are AI structures that include some mathematical and graphical models. ANN has been successfully applied to solve various applications, such as classification, function approximation, and optimisation [1]. According to connectivity, the NN has two main topologies.…”

Section: Introductionmentioning

confidence: 99%

Radial basis function neural network for 2 satisfiability programming

Alzaeemi

Mansor

Kasihmuddin

et al. 2020

IJEECS

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<span>Radial Basis Function Neural Network (RBFNN) is very prominent in data processing. However, improving this technique is vital for the NN training process. This paper presents an integrated 2 Satisfiability in radial basis function neural network (RBFNN-2SAT). There are two different types of training in RBFNN, namely no-training technique and half-training technique. The performance of the solutions via Genetic Algorithm (GA) training was investigated by comparing the Radial Basis Function Neural Network No-Training Technique (RBFNN- 2SATNT) and Radial Basis Function Neural Network Half-Training Technique (RBFNN- 2SATHT). The comparison of both techniques was examined on 2 Satisfiability problem by using a C# software that was developed for this experiment. The performance of the RBFNN-2SATNT and RBFNN-2SATHT in performing 2SAT is discussed in terms of root mean squared error (RMSE), sum squared error (SSE), mean absolute percentage error (MAPE), mean absolute error (MAE), number of the hidden neurons and CPU time. Results obtained from a computer simulation showed that RBFNN-2SATHT outperformed RBFNN-2SATNT.</span>

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Optimization of Microchannel Heat Sinks Using Prey-Predator Algorithm and Artificial Neural Networks

Cited by 29 publications

References 51 publications

The Quality Control of the Automatic Manipulating Process of a Flexible Container When Bulk Materials are Packaged

The Quality Control of the Automatic Manipulating Process of a Flexible Container When Bulk Materials are Packaged

Modeling the free convection in an open round cavity using a hybrid approach of Jaya optimization algorithm and neural network

Radial basis function neural network for 2 satisfiability programming

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