• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.17-25
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• 2023

In this paper, we propose a lightweight deep learning architecture tailored for efficient text detection in fashion design sketch images. Given the increasing prominence of Digital Transformation in the fashion industry, there is a growing emphasis on harnessing digital tools for creating fashion design sketches. As digitization becomes more pervasive in the fashion design process, the initial stages of text detection and recognition take on pivotal roles. In this study, a lightweight network was designed by building upon existing text detection deep learning models, taking into consideration the unique characteristics of apparel design drawings. Additionally, a separately collected dataset of apparel design drawings was added to train the deep learning model. Experimental results underscore the superior performance of our proposed deep learning model, outperforming existing text detection models by approximately 20% when applied to fashion design sketch images. As a result, this paper is expected to contribute to the Digital Transformation in the field of clothing design by means of research on optimizing deep learning models and detecting specialized text information.

• Structural Engineering and Mechanics
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• v.57 no.2
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• pp.265-280
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• 2016

A NLFE model was proposed to investigate the mechanical behavior of short columns, cast using plain or fibrous lightweight aggregate concrete (LWAC), and subjected to elevated temperatures of up to $700^{\circ}C$. The model was validated, before its predictions were extended to study the effect of other variables, not studied experimentally. The three-dimensional NLFE model was developed using ANSYS software and involved rational simulation of thermal mechanical behavior of plain and fibrous LWAC as well as longitudinal and lateral steel reinforcement. The prediction from the NLFE model of columns' mechanical behavior, as represented by the stress-strain diagram and its characteristics, compared well with the experimental results. The predictions of the proposed models, considering wide range of lateral reinforcement ratios, confirmed the behaviors observed experimentally and stipulated the importance of steel confinement in preserving post-heating mechanical properties of plain and fibrous LWAC columns, being subjected to high temperature.

• Advances in aircraft and spacecraft science
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• v.3 no.3
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• pp.331-349
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• 2016

Stiff and light fibre reinforced composites as used in air- and space-craft applications tend to high sound emission. Therefore, the damping properties are essential for the entire structural and acoustic engineering. Viscous damping is an established and reasonably linear model of the dissipation behaviour. Commonly, it is assumed to be isotropic and constant over all modes. For anisotropic materials it depends on the fibre orientation as well as the elastic and thermal material properties. To portray the orthogonal anisotropic behaviour, a model for unidirectional fibre reinforced plastics (frp) has been developed based on the classical laminate theory by ADAMS and BACON starting in 1973. Their approach includes three damping coefficients - for longitudinal damping in fibre direction, damping transversal to the fibres and shear based dissipation. The damping of a laminate is then accumulated layer wise including the anisotropic stiffness. So far, the model has been applied mainly to thermoset matrix materials. In this study, an experimental parameter estimation for different thermoplastic frp with angle ply and cross ply layups was carried out by measuring free vibrations of cantilever beams. The results show potential and limits of the ADAMS/BACON damping criterion. In addition, a possibility of modelling the anisotropic damping is shown. The implementation in standard FEA software is used to study the influence of boundary conditions on the damping properties and numerically estimate the radiated sound power of thin-walled frp parts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.840-860
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• 2023

Aiming at the serious occlusion and slow tracking speed in pedestrian target tracking and recognition in complex scenes, a target tracking method based on improved YOLO v5 combined with Deep SORT is proposed. By merging the attention mechanism ECA-Net with the Neck part of the YOLO v5 network, using the CIoU loss function and the method of CIoU non-maximum value suppression, connecting the Deep SORT model using Shuffle Net V2 as the appearance feature extraction network to achieve lightweight and fast speed tracking and the purpose of improving tracking under occlusion. A large number of experiments show that the improved YOLO v5 increases the average precision by 1.3% compared with other algorithms. The improved tracking model, MOTA reaches 54.3% on the MOT17 pedestrian tracking data, and the tracking accuracy is 3.7% higher than the related algorithms and The model presented in this paper improves the FPS by nearly 5 on the fps indicator.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.793-810
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• 2015

The TWINE is a new Generalized Feistel Structure (GFS) lightweight cryptosystem in the Internet of Things. It has 36 rounds and the key lengths support 80 bits and 128 bits, which are flexible to provide security for the RFID, smart cards and other highly-constrained devices. Due to the strong attacking ability, fast speed, simple implementation and other characteristics, the differential fault analysis has become an important method to evaluate the security of lightweight cryptosystems. On the basis of the 4-bit fault model and the differential analysis, we propose an effective differential fault attack on the TWINE cryptosystem. Mathematical analysis and simulating experiments show that the attack could recover its 80-bit and 128-bit secret keys by introducing 8 faulty ciphertexts and 18 faulty ciphertexts on average, respectively. The result in this study describes that the TWINE is vulnerable to differential fault analysis. It will be beneficial to the analysis of the same type of other iterated lightweight cryptosystems in the Internet of Things.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3421-3437
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• 2018

With the enlargement of wireless technology, vehicular ad-hoc networks (VANETs) are rising as a hopeful way to realize smart cities and address a lot of vital transportation problems such as road security, convenience, and efficiency. To achieve data confidentiality, integrity and authentication applying lightweight cryptosystems is widely recognized as a rather efficient approach for the VANETs. The Khudra cipher is such a lightweight cryptosystem with a typical Generalized Feistel Network, and supports 80-bit secret key. Up to now, little research of fault analysis has been devoted to attacking Khudra. On the basis of the single nibble-oriented fault model, we propose a differential fault analysis on Khudra. The attack can recover its 80-bit secret key by introducing only 2 faults. The results in this study will provides vital references for the security evaluations of other lightweight ciphers in the VANETs.

• Journal of the Korea Concrete Institute
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• v.22 no.2
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• pp.159-169
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• 2010

A multiphysics model analysis including moisture transport, heat transfer and solid mechanics and experiments on the normal and light weight concrete were carried out in order to study the effect of preabsorbed water in the light weight aggregates on the drying and shrinkage characteristics of concrete. Consequently, with fixed water-cement ratio, loss of water content of normal and light weight concrete were compared and the results showed that the lightweight concrete lost less moist than the normal concrete in early age and long term which was by moist supply effect. Accordingly, shrinkage strain size and distribution of lightweight concrete were decreased, and shrinkage reducing effect was efficient in early age with water cement ratio 0.3 and in both early age, and long term with water cement ratio 0.5. The comparison of analysis results and exaperimental results indicate that characteristic values of moisture transport and the relation humidity and shrinkage strain from this study are resonable for application for other differential shrinkage analysis in lightweight concrete.

• Journal of Convergence for Information Technology
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• v.11 no.9
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• pp.28-34
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• 2021

In the case of object detection using deep learning, both accuracy and real-time are required. However, it is difficult to use a deep learning model that processes a large amount of data in a limited resource environment. To solve this problem, this paper proposes an object detection model for small embedded devices. Unlike the general detection model, the model size was minimized by using a structure in which the pre-trained feature extractor was removed. The structure of the model was designed by repeatedly stacking lightweight convolution blocks. In addition, the number of region proposals is greatly reduced to reduce detection overhead. The proposed model was trained and evaluated using the public dataset PASCAL VOC. For quantitative evaluation of the model, detection performance was measured with average precision used in the detection field. And the detection speed was measured in a Raspberry Pi similar to an actual embedded device. Through the experiment, we achieved improved accuracy and faster reasoning speed compared to the existing detection method.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.969-976
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• 2019

A lightweight artificial intelligence hardware has made great strides in many application areas. In general, a lightweight artificial intelligence system consist of lightweight artificial intelligence engine and preprocessor including feature selection, generation, extraction, and normalization. In order to achieve optimal performance in broad range of applications, lightweight artificial intelligence system needs to choose a good preprocessing function and set their respective hyper-parameters. This paper proposes a unified framework for a lightweight artificial intelligence system and utilization method for finding models with optimal performance to use on a given dataset. The proposed unified framework can easily generate a model combined with preprocessing functions and lightweight artificial intelligence engine. In performance evaluation using handwritten image dataset and fall detection dataset measured with inertial sensor, the proposed unified framework showed building optimal artificial intelligence models with over 90% test accuracy.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.7
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• pp.177-184
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• 2003

In this study we performed optimal design for the vehicle mechanical component which satisfies both a sufficient stiffness and a lightweight using topology optimization technique. The FEA for the initial model before optimal design is performed by ABAQUS/Standard. And, we suggest optimization model using the topology optimal design program Altair Optisturuct 3.6. The FEA of optimal design is performed under the same condition as the initial model. We performed the FEA fur the topology optimal design model and verified the validity of the present method.