• East Asian mathematical journal
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• v.39 no.2
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• pp.93-117
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• 2023

The aim of this study is to explore the pre-service math teachers' characteristics of education to develop their AI literacy and SW competency, and to derive some implications. We conducted a 14-hours AI and SW education program for pre-service teachers with theory and practice, and an analysis on class observation data, video frames of classes and interview, Python programming assignments and papers. The results of this case study for 3 pre-service teachers are as follows. First, two students understood artificial neural network and deep learning system accurately, furthermore, all students conducted a couple of explorations related with performance improvement of deep learning system with interest. Second, coding and exploration activities using Python improved students' computational thinking as well as SW competency, which help them give convergence education in the future. Third, they responded positively to the necessity of AI literacy and SW competency development, and to applying coding to math class. Lastly, it's necessary to endeavor to give a coding education to the student's eye level according to his or her prerequisite and to ease the burden of student's studying AI technology.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.299-304
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• 2023

Among artificial intelligence techniques, deep learning is a model that has been used in many places and has proven its effectiveness. However, deep learning models are not used effectively in everywhere. In this paper, we will show the limitations of deep learning models through comparison of regression analysis and deep learning models, and present a guide for effective use of deep learning models. In addition, among various techniques used for optimization of deep learning models, data normalization and data shuffling techniques, which are widely used, are compared and evaluated based on actual data to provide guidelines for increasing the accuracy and value of deep learning models.

• Advances in nano research
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• v.14 no.2
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• pp.141-154
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• 2023

Effects of viscoelastic foundation on vibration of curved-beam structure with clamped and simply-supported boundary conditions is investigated in this study. In doing so, a micro-scale laminate composite beam with two piezoelectric face layer with a carbon nanotube reinforces composite core is considered. The whole beam structure is laid on a viscoelastic substrate which normally occurred in actual conditions. Due to small scale of the structure non-classical elasticity theory provided more accurate results. Therefore, nonlocal strain gradient theory is employed here to capture both nano-scale effects on carbon nanotubes and microscale effects because of overall scale of the structure. Equivalent homogenous properties of the composite core is obtained using Halpin-Tsai equation. The equations of motion is derived considering energy terms of the beam and variational principle in minimizing total energy. The boundary condition is assumed to be clamped at one end and simply supported at the other end. Due to nonlinear terms in the equations of motion, semi-analytical method of general differential quadrature method is engaged to solve the equations. In addition, due to complexity in developing and solving equations of motion of arches, an artificial neural network is design and implemented to capture effects of different parameters on the inplane vibration of sandwich arches. At the end, effects of several parameters including nonlocal and gradient parameters, geometrical aspect ratios and substrate constants of the structure on the natural frequency and amplitude is derived. It is observed that increasing nonlocal and gradient parameters have contradictory effects of the amplitude and frequency of vibration of the laminate beam.

• Advances in nano research
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• v.14 no.2
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• pp.155-164
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• 2023

Grain size in sheet metals in one of the main parameters in determining formability. Grain size control in industry requires delicate process control and equipment. In the present study, effects of grain size on the formability of steel sheets is investigated. Experimental investigation of effect of grain size is a cumbersome method which due to existence of many other effective parameters are not conclusive in some cases. On the other hand, since the average grain size of a crystalline material is a statistical parameter, using traditional methods are not sufficient for find the optimum grain size to maximize formability. Therefore, design of experiment (DoE) and artificial intelligence (AI) methods are coupled together in this study to find the optimum conditions for formability in terms of grain size and to predict forming limits of sheet metals under bi-stretch loading conditions. In this regard, a set of experiment is conducted to provide initial data for training and testing DoE and AI. Afterwards, the using response surface method (RSM) optimum grain size is calculated. Moreover, trained neural network is used to predict formability in the calculated optimum condition and the results compared to the experimental results. The findings of the present study show that DoE and AI could be a great aid in the design, determination and prediction of optimum grain size for maximizing sheet formability.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.320-324
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• 2023

As urbanization accelerates and facilities that use energy increase, human life and property damage due to fire is increasing. Therefore, a fire monitoring system capable of quickly detecting a fire is required to reduce economic loss and human damage caused by a fire. In this study, we aim to develop an improved artificial intelligence model that can increase the accuracy of low fire alarms by mixing DeepSORT, which has strengths in object tracking, with the YOLOv5s model. In order to develop a fire detection model that is faster and more accurate than the existing artificial intelligence model, DeepSORT, a technology that complements and extends SORT as one of the most widely used frameworks for object tracking and YOLOv5s model, was selected and a mixed model was used and compared with the YOLOv5s model. As the final research result of this paper, the accuracy of YOLOv5s model was 96.3% and the number of frames per second was 30, and the YOLOv5s_DeepSORT mixed model was 0.9% higher in accuracy than YOLOv5s with an accuracy of 97.2% and number of frames per second: 30.

• Journal of Auto-vehicle Safety Association
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• v.15 no.1
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• pp.55-62
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• 2023

In this study, a chest deflection is predicted by introducing a deep learning technique with the results of the frontal impact of the USNCAP conducted for 110 car models from MY2018 to MY2020. The 120 data are divided into training data and test data, and the training data is divided into training data and validation data to determine the hyperparameters. In this process, the deceleration data of each vehicle is averaged in units of 10 ms from crash pulses measured up to 100 ms. The performance of the deep learning model is measured by the indices of the mean squared error and the mean absolute error on the test data. A DNN (Deep Neural Network) model can give different predictions for the same hyperparameter values at every run. Considering this, the mean and standard deviation of the MSE (Mean Squared Error) and the MAE (Mean Absolute Error) are calculated. In addition, the deep learning model performance according to the inclusion of CVW (Curb Vehicle Weight) is also reviewed.

• Structural Monitoring and Maintenance
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• v.10 no.1
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• pp.43-62
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• 2023

Damage monitoring is a prerequisite step to ensure the safety and performance of concrete structures. Smart aggregate (SA) technique has been proven for its advantage to detect early-stage internal cracks in concrete. In this study, a 1-D CNN-based method is developed for autonomously classifying the damage feature in a concrete anchorage zone using the raw impedance signatures of the embedded SA sensor. Firstly, an overview of the developed method is presented. The fundamental theory of the SA technique is outlined. Also, a 1-D CNN classification model using the impedance signals is constructed. Secondly, the experiment on the SA-embedded concrete anchorage zone is carried out, and the impedance signals of the SA sensor are recorded under different applied force levels. Finally, the feasibility of the developed 1-D CNN model is examined to classify concrete damage features via noise-contaminated signals. The results show that the developed method can accurately classify the damaged features in the concrete anchorage zone.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.8
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• pp.102-108
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• 1998

본 논문에서는 Nd-YAG 레이저 용접 프로세스를 이용하여 두께가 다른 STS304스테인레스 박강판을 대상으로한 점용접에 관한 연구로서, 레이저 용접은 미소부위에 효율적인 접합가공이 가능한 공정으로 비접촉식 가열원을 이용하기 때문에 접합공정 중 기계적 변형이 없고, 레이저 빔을 국부가열원으로 하여 매우 좁은 부분에 제한적으로 열을 가할 수 있어서 강한 금속적 결합이 요구되는 소형부품의 접합에 이용될 수 있다. 뿐만 아니라 공정 변수들을 변화시켜 실제 접합부에 들어 가는 입열량을 쉽게 제어할 수 있다는 등 많은 장점을 가지고 있다. 본 연구에서는 1mm이하의 스테인레스 박판에 대한 레이저 점용접을 FDM과 신경회로망을 이용하여 해석하고 용접부의 너겟 크기, 용접부 깊이 등의 형상을 예측하였다. 또한 레이저 점용접에 있어서의 주요 변수인 펄스 에너지, 펄스 타임, 박판의 두께, 두 판사이의 간극크기 등득 변화시켜 실험하고 수치해석을 검증하기 위하여 여러 가지 강에 대한 레이저 점용접 실험을 수행하였다. 또한 수치해석 시뮬레이션을 위하여 윈도우 프로그래밍을 개발하였다.

• Membrane and Water Treatment
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• v.13 no.1
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• pp.51-62
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• 2022

Vacuum-assisted air gap membrane distillation (V-AGMD) has the potential to achieve higher flux and productivity than conventional air gap membrane distillation (AGMD). Nevertheless, there is not much information on technical aspects of V-AGMD operation. Accordingly, this study aims to analyze the effect of membrane deformation on flux in V-AGMD operation. Experiments were carried out using a bench-scale V-AGMD system. Statistical models were applied to understand the flux behaviors. Statistical models based on MLR, GNN, and MLFNN techniques were developed to describe the experimental data. Results showed that the flux increased by up to 4 times with the application of vacuum in V-AGMD compared with conventional AGMD. The flux in both AGMD and V-AGMD is affected by the difference between the air gap pressure and the saturation pressure of water vapor, but their dependences were different. In V-AGMD, the membranes were found to be deformed due to the vacuum pressure because they were not fully supported by the spacer. As a result, the deformation reduced the effective air gap width. Nevertheless, the rejection and LEP were not changed even if the deformation occurred. The flux behaviors in V-AGMD were successfully interpreted by the GNN and MLFNN models. According to the model calculations, the relative impact of the membrane deformation ranges from 10.3% to 16.1%.

• Annual Conference of KIPS
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• 2021.05a
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• pp.216-218
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• 2021

최근 침입 탐지 시스템은 기존 시그니처 기반이 아닌 AI 기반 연구로 많이 진행되고 있다. 이는 시그니처 기반의 한계인 이전에 보지 못한 악성 행위의 탐지가 가능하기 때문이다. 또한 로그 정보는 시스템의 중요 이벤트를 기록하여 시스템의 상태를 반영하고 있기 때문에 로그 정보를 사용한 침입 탐지 시스템에 대한 연구가 활발히 이루어지고 있다. 하지만 로그 정보는 시스템 상태의 일부분만 반영하고 있기 때문에, 회피하기 쉬우며, 이를 보완하기 위해 system call 정보를 사용한 멀티 모달 기반 침입 시스템을 제안한다.