• Journal of Magnetics
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• v.21 no.4
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• pp.570-576
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• 2016

The shape optimization of the stator and the rotor is important for electrical motor design. Among many motor design parameters, the stator tooth and yoke width are a few of the determinants of noload back-EMF and load torque. In this study, we proposed an equivalent magnetic circuit of motor stator for efficient stator tooth and yoke width shape optimization. Using the proposed equivalent magnetic circuit, we found the optimal tooth and yoke width for minimal magnetic resistance. To verify if load torque is truly maximized for the optimal tooth and yoke width indicated by the proposed method, we performed finite element analysis (FEA) to calculate load torque for different tooth and yoke widths. From the study, we confirmed reliability and usability of the proposed equivalent magnetic circuit.

• Structural Engineering and Mechanics
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• v.78 no.2
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• pp.145-161
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• 2021

Dynamic compaction of Aluminum powder using gas detonation forming technique was investigated. The experiments were carried out on four different conditions of total pre-detonation pressure. The effects of the initial powder mass and grain particle size on the green density and strength of compacted specimens were investigated. The relationships between the mentioned powder design parameters and the final features of specimens were characterized using Response Surface Methodology (RSM). Artificial Neural Network (ANN) models using the Group Method of Data Handling (GMDH) algorithm were also developed to predict the green density and green strength of compacted specimens. Furthermore, the desirability function was employed for multi-objective optimization purposes. The obtained optimal solutions were verified with three new experiments and ANN models. The obtained experimental results corresponding to the best optimal setting with the desirability of 1 are 2714 kg·m-3 and 21.5 MPa for the green density and green strength, respectively, which are very close to the predicted values.

• Steel and Composite Structures
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• v.33 no.2
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• pp.277-298
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• 2019

Concrete filled steel tubular (CFST) column joints with composite beams have been widely used as lateral loading resisting elements in civil infrastructure. To better utilize these innovative joints for the application of structural seismic design and analysis, it is of great importance to investigate the dynamic behavior of the joint under cyclic loading. With this aim in mind, a novel phenomenal model has been put forward in this paper, in which a Bouc-Wen hysteresis component is employed to portray the strength and stiffness deterioration phenomenon caused by increment of loading cycle. Then, a modified chicken swarm optimization algorithm was used to estimate the optimal model parameters via solving a global minimum optimization problem. Finally, the experimental data tested from five specimens subjected to cyclic loadings were used to validate the performance of the proposed model. The results effectively demonstrate that the proposed model is an easy and more realistic tool that can be used for the pre-design of CFST column joints with reduced beam section (RBS) composite beams.

• Structural Engineering and Mechanics
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• v.72 no.4
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• pp.503-512
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• 2019

The construction of segmental precast concrete bridge is an increase due to its superior performance and economic advantages. This type of bridge is appropriate for spans within 30 to 150 m (100 to 500 ft), known as mega-projects and the design optimization would lead to considerable economic benefits. A box-girder cross section superstructure of balanced cantilever construction method is assessed here. The depth of cross section, (variable along the span linearly), bottom flange thickness, and the count of strands are considered as design variables. The optimum design is characterized by geometry, serviceability, ductility, and ultimate limit states specified by AASHTO. Genetic algorithm (GA) is applied in two fronts: as to the saving in construction cost 8% and as to concrete volume 6%. The sensitivity analysis is run by considering different parameters like span/depth ratio, relation between superstructure cost, span length and concrete compressive strength.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.131-138
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• 2022

Soft sensors are used to anticipate complicated model parameters using data from classifiers that are comparatively easy to gather. The goal of this study is to use artificial intelligence techniques to design and build soft sensors. The combination of a Long Short-Term Memory (LSTM) network and Grey Wolf Optimization (GWO) is used to create a unique soft sensor. LSTM is developed to tackle linear model with strong nonlinearity and unpredictability of manufacturing applications in the learning approach. GWO is used to accomplish input optimization technique for LSTM in order to reduce the model's inappropriate complication. The newly designed soft sensor originally brought LSTM's superior dynamic modeling with GWO's exact variable selection. The performance of our proposal is demonstrated using simulations on real-world datasets.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.101-110
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• 2022

Histopathological analysis of biopsy specimens is still used for diagnosis and classifying the brain tumors today. The available procedures are intrusive, time consuming, and inclined to human error. To overcome these disadvantages, need of implementing a fully automated deep learning-based model to classify brain tumor into multiple classes. The proposed CNN model with an accuracy of 92.98 % for categorizing tumors into five classes such as normal tumor, glioma tumor, meningioma tumor, pituitary tumor, and metastatic tumor. Using the grid search optimization approach, all of the critical hyper parameters of suggested CNN framework were instantly assigned. Alex Net, Inception v3, Res Net -50, VGG -16, and Google - Net are all examples of cutting-edge CNN models that are compared to the suggested CNN model. Using huge, publicly available clinical datasets, satisfactory classification results were produced. Physicians and radiologists can use the suggested CNN model to confirm their first screening for brain tumor Multi-classification.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.163-167
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• 2022

In this paper, an optimization design method of a diffuser using Bernoulli's theorem was reviewed. The aspect ratio of the cylindrical diffuser chamber and the diameter ratio of the air inlet and outlet were used as design parameters. For the optimal design of the cylindrical diffuser chamber, the air flow inside the chamber was simulated using ANSYS while changing the aspect ratio of the chamber. In order to confirm the simulation results, the diffuser manufactured using the laser processing machine was measured. Through ANSYS simulation and measurement, it was found that the optimal design condition was when the aspect ratio (chamber height/radius) of the diffuser chamber was 1/2 and the diameter ratio of the air inlet and outlet was also 1/2.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.2877-2882
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• 2022

The paper presents the main results of computational and experimental optimization of the automatic power control system (AC) of the IBR-2M pulsed reactor in the presence of a high level of oscillatory instability. Optimization of the parameters of the AC made it possible to significantly reduce the influence of random and deterministic oscillations of reactivity on the noise of the pulse energy, as well as to sharply reduce the manifestation of the oscillatory instability of the reactor. As a result, the safety and reliability of operation of the reactor has increased substantially.

• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.130-138
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• 2023

The selection of the correct examination variables for diagnosing heart disease provides many benefits, including faster diagnosis and lower cost of examination. The selection of inspection variables can be performed by referring to the data of previous examination results so that future investigations can be carried out by referring to these selected variables. This paper proposes a model for selecting examination variables using an Artificial Bee Swarm Optimization method by considering the variables of accuracy and cost of inspection. The proposed feature selection model was evaluated using the performance parameters of accuracy, area under curve (AUC), number of variables, and inspection cost. The test results show that the proposed model can produce 24 examination variables and provide 95.16% accuracy and 97.61% AUC. These results indicate a significant decrease in the number of inspection variables and inspection costs while maintaining performance in the excellent category.

• Advances in Computational Design
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• v.8 no.2
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• pp.179-190
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• 2023

In this paper, building information modelling (BIM) associated to the principle of significant items emerged at quantities and costs in the optimization of productivity related to the rehabilitation of the building where proposed and discussed. A quantitative and qualitative study related to the field of application based on some parameters such as pathology diagnosis, projects documents and bills of quantities were used for model development at the preliminary stage of this work. The study identified 14 quantities significant items specified to cost value based on the use of the 80/20 Pareto rule, through the integration of building information modelling (BIM) in the optimisation of labour productivity for rehabilitation of buildings. The results of this study reveal the reliability and the improvement of labour productivity using building information modelling process integrating quantities and cost significant items.