• Journal of Multimedia Information System
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• v.9 no.2
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• pp.113-120
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• 2022

Mongolian folk songs are inspired by Mongolian labor songs and are classified into long and short songs. Mongolian long songs have ancient origins, are rich in legends, and are a great source of folklore. So it was inscribed by UNESCO in 2008. Mongolian written literature is formed under the direct influence of oral literature. Mongolian long song has 3 classes: ayzam, suman, and besreg by their lyrics and structure. In ayzam long song, the world perfectly embodies the philosophical nature of world phenomena and the nature of human life. Suman long song has a wide range of topics such as the common way of life, respect for ancestors, respect for fathers, respect for mountains and water, livestock and animal husbandry, as well as the history of Mongolia. Besreg long songs are dominated by commanded and trained characters. In this paper, we proposed a method to classify their 3 types of long songs using machine learning, based on their lyrics structures without semantic information. We collected lyrics of over 80 long songs and extracted 11 features from every single song. The features are the name of a song, number of the verse, number of lines, number of words, general value, double value, elapsed time of verse, elapsed time of 5 words, and the longest elapsed time of 1 word, full text, and type label. In experimental results, our proposed features show on average 78% recognition rates in function type machine learning methods, to classify the ayzam, suman, and besreg classes.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.4
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• pp.40-47
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• 2022

The present study aimed to examine the effectiveness of different techniques for controlling the diagonal cracks at the corners of openings on the strength, deformation, and crack propagation in reinforced concrete walls. The crack control strip proposed in this study, the conventional diagonal steel reinforcing bars, and stress-dispersion curved plates were investigated for controlling the diagonal cracks at the opening corners. An additional crack self-healing function was also considered for the crack control strip. To evaluate the volume change ratio and crack width propagation around the opening, downsized wall specimens with a opening were tested under the diagonal shear force at the opening corner. Test result showed that the proposed crack control strip was more effective in reducing the volume change and controlling the crack width around the opening when compared to the conventional previous methods. The crack control strip with crack healing feature displayed the superior performance in improving the strength of the wall and reducing the crack width while healing cracks occurred in the previous tests.

• Journal of Software Assessment and Valuation
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• v.17 no.1
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• pp.67-73
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• 2021

In this paper, a system configuration method that can practice Linux-based programming language online is presented. The proposed system utilizes the web-server function, which is the biggest feature of the Linux operating system, and simulates the telnet and FTP functions without firewalls or other security restrictions, so that it is possible to practice similar to the actual Linux console. To do this, we analyzed the functional elements that a programming tool should have on the web and established an algorithm to implement it. In particular, a method was implemented in which an error message caused by a user's mistake can appear in the same form as the actual telnet screen. As a result of using the implemented learning system in the class for students, it is possible to practice the Linux programming language online, as well as the instructor can directly check and guide all the learners, so the learner's satisfaction is similar to that of the offline class was confirmed.

• Korean Journal of Optics and Photonics
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• v.32 no.6
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• pp.306-313
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• 2021

We study the amplification properties of an optical amplifier based on a cesium-vapor cell. An optical amplification system including cesium vapor mixed with a buffer gas is built, and its amplification feature is investigated as a function of the size of the incident beam and the temperature of the cesium-vapor cell. We observe that the optical amplification properties, such as amplification factor and extraction efficiency, change significantly depending on the temperature and beam diameter of the pump and seed light. A maximum extraction efficiency of 56% is obtained when the temperature of the cesium cell is 90 ℃, with a 200-㎛ diameter of the pump (500 mW) and seed light (10 mW). The numerical simulation of the amplification properties agrees reasonably with the results obtained from the experiment.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Korean Journal of Materials Research
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• v.32 no.8
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• pp.345-353
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• 2022

In this study, using deep learning, super-resolution images of transmission electron microscope (TEM) images were generated for nanomaterial analysis. 1169 paired images with 256 × 256 pixels (high resolution: HR) from TEM measurements and 32 × 32 pixels (low resolution: LR) produced using the python module openCV were trained with deep learning models. The TEM images were related to DyVO₄ nanomaterials synthesized by hydrothermal methods. Mean-absolute-error (MAE), peak-signal-to-noise-ratio (PSNR), and structural similarity (SSIM) were used as metrics to evaluate the performance of the models. First, a super-resolution image (SR) was obtained using the traditional interpolation method used in computer vision. In the SR image at low magnification, the shape of the nanomaterial improved. However, the SR images at medium and high magnification failed to show the characteristics of the lattice of the nanomaterials. Second, to obtain a SR image, the deep learning model includes a residual network which reduces the loss of spatial information in the convolutional process of obtaining a feature map. In the process of optimizing the deep learning model, it was confirmed that the performance of the model improved as the number of data increased. In addition, by optimizing the deep learning model using the loss function, including MAE and SSIM at the same time, improved results of the nanomaterial lattice in SR images were achieved at medium and high magnifications. The final proposed deep learning model used four residual blocks to obtain the characteristic map of the low-resolution image, and the super-resolution image was completed using Upsampling2D and the residual block three times.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2188-2208
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• 2023

Quality of Service (QoS) is a critical feature of Wireless Sensor Networks (WSNs) with routing algorithms. Data packets are moved between cluster heads with QoS using a number of energy-efficient routing techniques. However, sustaining high scalability while increasing the life of a WSN's networks scenario remains a challenging task. Thus, this research aims to develop an energy-balancing component that ensures equal energy consumption for all network sensors while offering flexible routing without congestion, even at peak hours. This research work proposes a Gravitational Blackhole Search Optimised splay tree routing framework. Based on the splay tree topology, the routing procedure is carried out by the suggested method using three distinct steps. Initially, the proposed GBSO decides the optimal route at initiation phases by choosing the root node with optimum energy in the splay tree. In the selection stage, the steps for energy update and trust update are completed by evaluating a novel reliance function utilising the Parent Reliance (PR) and Grand Parent Reliance (GPR). Finally, in the routing phase, using the fitness measure and the minimal distance, the GBSO algorithm determines the best route for data broadcast. The model results demonstrated the efficacy of the suggested technique with 99.52% packet delivery ratio, a minimum delay of 0.19 s, and a network lifetime of 1750 rounds with 200 nodes. Also, the comparative analysis ensured that the suggested algorithm surpasses the effectiveness of the existing algorithm in all aspects and guaranteed end-to-end delivery of packets.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.4
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• pp.621-631
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• 2023

Adversarial training improves the robustness of deep neural networks for adversarial examples. However, the previous adversarial training method focuses only on the adversarial loss function, ignoring that even a small perturbation of the input layer causes a significant change in the hidden layer features. Consequently, the accuracy of a defended model is reduced for various untrained situations such as clean samples or other attack techniques. Therefore, an architectural perspective is necessary to improve feature representation power to solve this problem. In this paper, we apply an attention module that generates an attention map of an input image to a general model and performs PGD adversarial training upon the augmented model. In our experiments on the CIFAR-10 dataset, the attention augmented model showed higher accuracy than the general model regardless of the network structure. In particular, the robust accuracy of our approach was consistently higher for various attacks such as PGD, FGSM, and BIM and more powerful adversaries. By visualizing the attention map, we further confirmed that the attention module extracts features of the correct class even for adversarial examples.

• Nuclear Engineering and Technology
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• v.25 no.1
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• pp.154-165
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• 1993

A stochastic method using continuous time Markov process is presented to model the one-dimensional convective nuclide transport in geologic media, which have usually heterogeneous feature in physical/geochemical parameters such as velocity, dispersion coefficient, and retardation factor resulting poor description by conventional deterministic advection-dispersion model. The primary desired quantities from a stochastic model are the mean values and variance of the state variables as a function of time. The time-dependent probability distributions of nuclides are presented for each discretized compartment given the volumetric groundwater flux and the intensity of transition. Since this model is discrete in medium space, physical/geochemical parameters which affect nuclide transport can be easily incorporated for the heterogeneous media as well as remarkably layered media having spatially varied parameters. Even though the Markov process model developed in this study was shown to be sensitive to the number of discretized compartments showing numerical dispersion as the number of compartments are increased, this could be easily calibrated by comparing with the analytical deterministic model.

• Fashion & Textile Research Journal
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• v.25 no.2
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• pp.199-209
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• 2023

This study identified design elements of the functions required for children's ski pants. Data for this study were collected through questionnaire surveys conducted among children's ski instructors and children's sportswear developers. Five functionalities of children's skiwear were evaluated: mobility, stability, comfort, protection, and convenience. A total of 25 functional design elements related to the patterns, design details, and physical characteristics of fabrics for ski garments, were evaluated. The results of this study are as follows. First, children's sportswear developers evaluated that the pattern elements were important. Most of the pattern design elements highly related to mobility. Children's ski instructors' appraisal was that the height of the back waist was the important feature. Second, regarding the design details, children's ski instructors evaluated the size adjustment function and ventilation system as important elements. Many design detail elements were highly related in respect of stability, comfort, protection, and convenience. Third, the physical characteristics of fabric were strongly associated with mobility, comfort, and protection. As regards the physical characteristics of fabric, children's ski instructors valued anti-fouling highly, but children's sportswear developers attached more importance to the weight of the fabric. The results of this study will be useful in designing functional ski pants for children of elementary and intermediate ski levels. Since there may be limitations related to the ski level and age of children wearing ski pants, it is suggested that follow-up studies according to various groups of the ski pant wearers should be done.