• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5542-5550
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• 2012

Third-order shear deformation theory is reformulated using the nonlocal elasticity of Eringen. The equation of equilibrium of the nonlocal elasticity are derived. This theory has ability to capture the both small scale effects and quadratic variation of shear strain through the plate thickness. Navier's method has been used to solve the governing equations for all edges simply supported boundary conditions. Analytical solutions of buckling of nano-scale plates are presented using this theory to illustrate the effect of nonlocal theory on buckling load of the nano-scale plates. The relations between nonlocal third-order and local theories are discussed by numerical results. Further, effects of (i) length (ii) nonlocal parameter, (iii) aspect ratio and (iv) mode number on nondimensional buckling load are studied. In order to validate the present solutions, the reference solutions are used and discussed. The present results of nano-scale plates using the nonlocal theory can provide a useful benchmark to check the accuracy of related numerical solutions.

• Advances in nano research
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• v.16 no.4
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• pp.375-394
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• 2024

The extensive utilization of concrete has given rise to environmental concerns, specifically concerning the depletion of river sand. To address this issue, waste deposits can provide manufactured-sand (MS) as a substitute for river sand. The objective of this study is to explore the application of machine learning techniques to facilitate the production of manufactured-sand concrete (MSC) containing stone nano-powder through estimating the splitting tensile strength (STS) containing compressive strength of cement (CSC), tensile strength of cement (TSC), curing age (CA), maximum size of the crushed stone (Dmax), stone nano-powder content (SNC), fineness modulus of sand (FMS), water to cement ratio (W/C), sand ratio (SR), and slump (S). To achieve this goal, a total of 310 data points, encompassing nine influential factors affecting the mechanical properties of MSC, are collected through laboratory tests. Subsequently, the gathered dataset is divided into two subsets, one for training and the other for testing; comprising 90% (280 samples) and 10% (30 samples) of the total data, respectively. By employing the generated dataset, novel models were developed for evaluating the STS of MSC in relation to the nine input features. The analysis results revealed significant correlations between the CSC and the curing age CA with STS. Moreover, when delving into sensitivity analysis using an empirical model, it becomes apparent that parameters such as the FMS and the W/C exert minimal influence on the STS. We employed various loss functions to gauge the effectiveness and precision of our methodologies. Impressively, the outcomes of our devised models exhibited commendable accuracy and reliability, with all models displaying an R-squared value surpassing 0.75 and loss function values approaching insignificance. To further refine the estimation of STS for engineering endeavors, we also developed a user-friendly graphical interface for our machine learning models. These proposed models present a practical alternative to laborious, expensive, and complex laboratory techniques, thereby simplifying the production of mortar specimens.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2003.10a
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• pp.97-100
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• 2003

Globally, the various machine components, as in electronics and communications, are demanded to being high-performance and micro-scale with abrupt development of the fields of computers, mobile communications. As this current tendency, production of the parts that must have high accuracy, so called milli-structure, are accomplished by the method of top-down, differently as in the techniques of MEMS, NANO. But, in the case of milli-structure, production procedure is highly costs, difficult and demands more accurate dimension than the conservative forming, processing technique. In this paper, forming analysis of the micro-former as the milli-structure are performed and then calculate the punch force etc. This information calculated is applied to decide the forming capacity of micro-former and design the process of forming stage, dimension of dies in another forming bodies. And, for the better precise forming analysis, elasto-plastic analysis is to be performed, then the consideration about effect of elastic recovery when punch and die are unloaded, have to be discussed in change of dimensions.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.4
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• pp.735-739
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• 2013

The error motion of a machine tool spindle directly affects the surface errors of machined parts. Spindle motion errors such as three translational motions and two rotational motions are undesirable. These are usually due to the imperfectness of bearings, stiffness of spindle, assembly errors, and external force or unbalance of rotors. The error motions of the spindle need to be reduced for achieving the desired performance. Therefore, the level of error motion needs to be estimated during the design and assembly process of the spindle. In this study, an estimation method for five degree-of-freedom (5 DOF) error motions for a spindle with an angular contact ball bearing is suggested. To estimate the error motions of the spindle, the waviness of the inner-race of bearings and an external force model were used as input data. The estimation model considers the geometric relationship and force equilibrium of the five DOFs. To calculate the error motions of the spindle, not only the imperfections of the shaft and bearings but also driving elements such as belt pulley and direct driving motor systems are considered.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.4
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• pp.51-58
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• 2008

This paper presents a new design of the precision stage for the reticle in lithography process and a low hunting control method for the stage. The stage has three axes for X, Y, ${\theta}_z$ those actuated by three voice coil motors individually. The designed reticle stage system has three gap sensors and voice coil motors, and supported by four air bearings and the forward/inverse kinematics of the stage were solved to get an accurate reference position. When a stage is in regulating control mode, there always exist small fluctuations(stage hunting) in the stage movement. Because the low stage hunting characteristic is very important in recent lithography and nano-level applications, a special regulating controller for ultra low hunting is proposed in this paper. Also this research proposed the 2-step transmission system for preventing the noise infection from environmental devices. The experimental results showed the proposed regulating control system reduced hunting noise as 35nm(rms) when a conventional PID generates 77nm(rms) in the same mechanical system. Besides the reticle stage has 100nm linear accuracy and $1{\mu}rad$ rotation accuracy at the control frequency of 8kHz.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.39-51
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• 2022

Image-based gender classification and age estimation of human are classic problems in computer vision. Most of researches in this field focus just only one task of either gender classification or age estimation and most of the reported methods for each task focus on accuracy performance and are not computationally light. Thus, running both tasks together simultaneously on low cost mobile or embedded systems with limited cpu processing speed and memory capacity are practically prohibited. In this paper, we propose a novel light-weight gender classification and age estimation method based on ensemble multitasking deep learning with light-weight processing neural network architecture, which processes both gender classification and age estimation simultaneously and in real-time even for embedded systems. Through experiments over various well-known datasets, it is shown that the proposed method performs comparably to the state-of-the-art gender classification and/or age estimation methods with respect to accuracy and runs fast enough (average 14fps) on a Jestson Nano embedded board.

• Advances in nano research
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• v.12 no.2
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• pp.223-239
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• 2022

Object detection has always been to pursue objects with particular properties or representations and to predict details on objects including the positions, sizes and angle of rotation in the current picture. This was a very important subject of computer vision science. While vision-based object tracking strategies for the analysis of competitive videos have been developed, it is still difficult to accurately identify and position a speedy small ball. In this study, deep learning (DP) network was developed to face these obstacles in the study of tennis motion tracking from a complex perspective to understand the performance of athletes. This research has used CNN-VGG 16 to tracking the tennis ball from broadcasting videos while their images are distorted, thin and often invisible not only to identify the image of the ball from a single frame, but also to learn patterns from consecutive frames, then VGG 16 takes images with 640 to 360 sizes to locate the ball and obtain high accuracy in public videos. VGG 16 tests 99.6%, 96.63%, and 99.5%, respectively, of accuracy. In order to avoid overfitting, 9 additional videos and a subset of the previous dataset are partly labelled for the 10-fold cross-validation. The results show that CNN-VGG 16 outperforms the standard approach by a wide margin and provides excellent ball tracking performance.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.2
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• pp.51-58
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• 2023

This paper proposes a deep learning method for estimating the heart rate from facial videos. Our proposed method estimates remote photoplethysmography (rPPG) signals to predict the heart rate. Although there have been proposed several methods for estimating rPPG signals, most previous methods can not be utilized in low-power single board computers due to their computational complexity. To address this problem, we construct a lightweight student model and employ a knowledge distillation technique to reduce the performance degradation of a deeper network model. The teacher model consists of 795k parameters, whereas the student model only contains 24k parameters, and therefore, the inference time was reduced with the factor of 10. By distilling the knowledge of the intermediate feature maps of the teacher model, we improved the accuracy of the student model for estimating the heart rate. Experiments were conducted on the UBFC-rPPG dataset to demonstrate the effectiveness of the proposed method. Moreover, we collected our own dataset to verify the accuracy and processing time of the proposed method on a real-world dataset. Experimental results on a NVIDIA Jetson Nano board demonstrate that our proposed method can infer the heart rate in real time with the mean absolute error of 2.5183 bpm.

• Bulletin of the Korean Chemical Society
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• v.32 no.4
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• pp.1170-1178
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• 2011

In the present study, the approach of high accuracy mass measurements for phospholipid identifications was evaluated using a 7 T ESI-FTMS/linear ion trap MS/MS. Experiments were carried out for porcine brain, bovine liver, and soybean total lipid extracts in both positive and negative ion modes. In total, 59, 55, and 18 phospholipid species were characterized in the positive ion mode for porcine brain, bovine liver, and soybean lipid extracts, respectively. Assigned lipid classes were PC, PE, PEt, PS, and SM. In the negative ion mode, PG, PS, PA, PE, and PI classes were observed. In the negative ion mode, for porcine brain, bovine liver, and soybean lipid extracts, 28, 34, and 29 species were characterized, respectively. Comparison of our results with those obtained by other groups using derivatization-LC-APCI MS and nano-RP-LC-MS/MS showed that our approach can characterize PC species as effectively as those methods could. In conclusion, we demonstrated that high accuracy mass measurements of total lipid extracts using a high resolution FTMS, particularly, 7T FTMS, plus ion-trap MS/MS are very useful in profiling lipid compositions in biological samples.

• Advances in nano research
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• v.12 no.5
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• pp.515-527
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• 2022

The application of fibers in concrete obviously enhances the properties of concrete, also the application of natural fibers in concrete is raising due to the availability, low cost and environmentally friendly. Besides, predicting the mechanical properties of concrete in general and shear strength in particular is highly significant in concrete mixture with fiber nanocomposite reinforced concrete (FRC) in construction projects. Despite numerous studies in shear strength, determining this strength still needs more investigations. In this research, Adaptive Neuro-Fuzzy Inference System (ANFIS) have been employed to determine the strength of reinforced concrete with fiber. 180 empirical data were gathered from reliable literature to develop the methods. Models were developed, validated and their statistical results were compared through the root mean squared error (RMSE), determination coefficient (R²), mean absolute error (MAE) and Pearson correlation coefficient (r). Comparing the RMSE of PSO (0.8859) and ANFIS (0.6047) have emphasized the significant role of structural parameters on the shear strength of concrete, also effective depth, web width, and a clear depth rate are essential parameters in modeling the shear capacity of FRC. Considering the accuracy of our models in determining the shear strength of FRC, the outcomes have shown that the R² values of PSO (0.7487) was better than ANFIS (2.4048). Thus, in this research, PSO has demonstrated better performance than ANFIS in predicting the shear strength of FRC in case of accuracy and the least error ratio. Thus, PSO could be applied as a proper tool to maximum accuracy predict the shear strength of FRC.