• Corrosion Science and Technology
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• v.18 no.5
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• pp.155-167
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• 2019

In this study, two novel Schiff base compounds including (3-(4-Chlorophenyl isoxazole-5-yl) methanol and (3-(2,4 dichlorophenol isoxazole-5-yl) methanol as corrosion inhibitors for mild steel in 1 M hydrochloric acid solution were investigated by potentiodynamic polarization, electrochemical impedance spectroscopy (EIS), and density functional theory (DFT) computations. The results showed that the corrosion inhibition efficiency (IE) is remarkably enhanced with the growing concentration of the Schiff base inhibitors. The results from Tafel polarization and EIS methods showed that IE decreases with gradual increments of temperature. This process can be attributed to the displacement of the adsorption/desorption balance and hence to the diminution of the level of a surface coating. Also, the adsorption of two inhibitors over mild steel followed the Langmuir adsorption isotherm. Too, the results of the scanning electron microscope (SEM) images showed that the Schiff base inhibitors form an excellent protective film over mild steel and verified the results by electrochemical techniques. Additionally, the results from the experimental and those from DFT computations are in excellent accordance.

• Advances in materials Research
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• v.13 no.1
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• pp.1-18
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• 2024

The present experimental investigation focuses on finding optimal parametric data-set of laser micro-drilling operation with minimum taper and Heat-affected zone during laser micro-drilling of Carbon Nanotube/Epoxy-based composite materials. Experiments have been conducted as per Box-Behnken design (BBD) techniques considering cutting speed, lamp current, pulse frequency and air pressure as input process parameters. Then, the relationship between control parameters and output responses is developed using second-order nonlinear regression models. The analysis of variance test has also been performed to check the adequacy of the developed mathematical model. Using the Response Surface Methodology (RSM) and an Accelerated particle swarm optimization (APSO) technique, optimum process parameters are evaluated and compared. Moreover, confirmation tests are conducted with the optimal parameter settings obtained from RSM and APSO and improvement in performance parameter is noticed in each case. The optimal process parameter setting obtained from predictive RSM based APSO techniques are speed=150 (m/s), current=22 (amp), pulse frequency (3 kHz), Air pressure (1 kg/cm²) for Taper and speed=150 (m/s), current=22 (amp), pulse frequency (3 kHz), air pressure (3 kg/cm²) for HAZ. From the confirmatory experimental result, it is observed that the APSO metaheuristic algorithm performs efficiently for optimizing the responses during laser micro-drilling process of nanocomposites both in individual and multi-objective optimization.

• Archives of design research
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• v.14 no.2
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• pp.27-36
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• 2001

This study examines consumer\` and manufacturers\` perceptions and preference on the bamboo industry and bamboo craft products made in Korea. Also, this study investigates the possibilities of modem bamboo blind production by applying various new materials, techniques, and design patterns. The results of the survey to consumers and bamboo product manufacturers were that ordinary Korean consumers evaluated negatively the quality of bamboo products, particularly the design of them and the manufactures themselves evaluated negatively the design of their products and eagerly desired to team new design techniques for their product improvements. The result of experimental study was that the combinations of fabric materials, natural dyeing, and design techniques in the development of bamboo blinds yielded modernized bamboo blind image that could be appealed by consumers. This study could be judged as a good example for the modem recreation of traditional art. This study proposes the product devolvement and commercialization of traditional culture through the collaborations between bamboo craft industry and Universities.

• Membrane and Water Treatment
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• v.7 no.3
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• pp.223-240
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• 2016

In this study, poly(vinyl-alcohol) and water insoluble ${\beta}$-cyclodextrin polymer (${\beta}$-CDP) cross-linked with citric acid, have been used as macrocyclic carrier in the preparation of polymer inclusion membranes (PIMs) for aniline (as molecule model) extraction from aqueous media. The obtained membranes were firstly characterized by X-ray diffraction, Fourier transform infrared and water swelling test. The transport of aniline was studied in a two-compartment transport cell under various experimental conditions, such as carrier content in the membranes, stirring rate and initial aniline concentration. The kinetic study was performed and the kinetic parameters were calculated as rate constant (k), permeability coefficient (P) and flux (J). These first results demonstrated the utility of such polymeric membranes for environmental decontamination of toxic organic molecules like aniline. Predictive modeling of transport flux through these materials was then studied using design of experiments; the design chosen was a two level full factorial design $2^k$. An empirical correlation between aniline transport flux and independent variables (Poly ${\beta}$-CD membrane content, agitation speed and initial aniline concentration) was successfully obtained. Statistical analysis showed that initial aniline concentration of the solution was the most important parameter in the study domain. The model revealed the existence of a strong interaction between the Poly ${\beta}$-CD membrane content and the stirring speed of the source solution. The good agreement between the model and the experimental transport data confirms the model's validity.

• Journal of IKEEE
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• v.25 no.3
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• pp.540-543
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• 2021

In this paper, we present an experimental comparative study of recurrent neural network based techniques for trajectory prediction of flight vehicles. We defined and investigated various relationships between input and output under the same experimental setup. In particular, we proposed a relationship based on the relative positions of flight vehicles. Furthermore, we conducted an ablation study on the network architectures and hyperparameters. We believe that this comprehensive comparative study serves as a reference point and guide for developers in choosing an appropriate recurrent neural network based techniques for building (flight) vehicle trajectory prediction systems.

• PNF and Movement
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• v.19 no.1
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• pp.105-113
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• 2021

Purpose: The purpose of this study was to investigate the effect of trunk-stabilization training using stabilizing reversal and rhythmic stabilization techniques of PNF on trunk muscle strength and respiratory function in elderly stroke patients. Methods: There were 26 stroke patients included in the study. Patients were divided into two groups, and all patients performed exercise 30 min five times per week for six weeks. The experimental group performed trunk stability exercise using stabilizing reversal and rhythmic stabilization techniques of PNF, and the control group performed flexibility and strength training. Trunk muscle strength, forced vital capacity, maximum inspiratory pressure, and maximum expiration pressure were measured to determine the changes after the intervention. For statistical processing, a paired t-test was performed within the group, and the value after intervention was performed as an independent t-test to find out the difference between the two groups. Results: In the experimental group, all of the trunk muscle strength, forced vital capacity, maximum inspiratory pressure, and maximum expiration pressure showed significant differences according to the intervention. In the control group, there were statistically significant differences in trunk muscle strength and forced vital capacity, but the maximum inspiratory pressure and the maximum expiration pressure did not show any statistical change. Conclusion: From these results, it can be seen that the trunk stability exercises that use the proprioceptive neuromuscular promotion method of stable reversal and rhythm stabilization can be a good intervention for the respiratory function of stroke patients.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.399-406
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• 2023

Cloud computing has been evolved to support edge computing architecture that combines fog management layer with edge servers. The main reason why it is received much attention is low communication latency for real-time IoT applications. At the same time, various cloud task scheduling techniques based on artificial intelligence have been proposed. Artificial intelligence-based cloud task scheduling techniques show better performance in comparison to existing methods, but it has relatively high scheduling time. In this paper, we propose a deep learning-based dynamic scheduling with multi-agents supporting scalability in edge computing environments. The proposed method shows low scheduling time than previous artificial intelligence-based scheduling techniques. To show the effectiveness of the proposed method, we compare the performance between previous and proposed methods in a scalable experimental environment. The results show that our method supports real-time IoT applications with low scheduling time, and shows better performance in terms of the number of completed cloud tasks in a scalable experimental environment.

• Convergence Security Journal
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• v.23 no.4
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• pp.61-70
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• 2023

Clustering is an unsupervised learning method that involves grouping data based on features such as distance metrics, using data without known labels or ground truth values. This method has the advantage of being applicable to various types of data, including images, text, and audio, without the need for labeling. Traditional clustering techniques involve applying dimensionality reduction methods or extracting specific features to perform clustering. However, with the advancement of deep learning models, research on deep clustering techniques using techniques such as autoencoders and generative adversarial networks, which represent input data as latent vectors, has emerged. In this study, we propose a deep clustering technique based on deep learning. In this approach, we use an autoencoder to transform the input data into latent vectors, and then construct a vector space according to the cluster structure and perform k-means clustering. We conducted experiments using the MNIST and Fashion-MNIST datasets in the PyTorch machine learning library as the experimental environment. The model used is a convolutional neural network-based autoencoder model. The experimental results show an accuracy of 89.42% for MNIST and 56.64% for Fashion-MNIST when k is set to 10.

• Journal of Korean Society for Quality Management
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• v.41 no.1
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• pp.119-134
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• 2013

Purpose: The purpose of this paper is to show whether the results are changed with respect to the variance of the data, by analysis of data obtained from the Taguchi experimental techniques and general experiment. Because which cannot be prove by mathematical Formula, through experimental examples will show. Methods: Taguchi experiments were carried out with paper Helicopter experiment. Experimental Data are obtained by special designed Drop Test Equipment. While Experimental value arbitrarily changed, we looked at how Significant control Factor of Taguchi Methods and Factor experiments are changed. This process cannot be expressed as a Mathematical formula, but showed as a numerical example. Results: Saw significant changes in the factors when data is outside a certain range of the experimental data. By Test of Equivalence Variance, Experiment data is verified reliability. To find the Control Factor, Taguchi Method is better than the general experiment. Conclusion: We know that a Significant Factor is changed with the range of Variance of Experiment Data. The value of this paper is verified change process with Numerical Data obtained Experiment.

• Computers and Concrete
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• v.18 no.2
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• pp.235-266
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• 2016

A rock mass containing non-persistent joints can only fail if the joints propagate and coalesce through an intact rock bridge. Shear strength of rock mass containing non-persistent joints is highly affected by the both, mechanical behavior and geometrical configuration of non-persistent joints located in a rock mass. Existence of rock joints and rock bridges are the most important factors complicating mechanical responses of a rock mass to stress loading. The joint-bridge interaction and bridge failure dominates mechanical behavior of jointed rock masses and the stability of rock excavations. The purpose of this review paper is to present techniques, progresses and the likely future development directions in experimental and numerical modelling of a non-persistent joint failure behaviour. Such investigation is essential to study the fundamental failures occurring in a rock bridge, for assessing anticipated and actual performances of the structures built on or in rock masses. This paper is divided into two sections. In the first part, experimental investigations have been represented followed by a summarized numerical modelling. Experimental results showed failure mechanism of a rock bridge under different loading conditions. Also effects of the number of non-persistent joints, angle between joint and a rock bridge, lengths of the rock bridge and the joint were investigated on the rock bridge failure behaviour. Numerical simulation results are used to validate experimental outputs.