• Journal of the Institute of Convergence Signal Processing
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• v.15 no.1
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• pp.1-8
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• 2014

Digital watermarking techniques are attracting attention as a proper solution to protect copyright for multimedia data. This paper proposes a new audio watermarking method based on Discrete Cosine Transformation (DCT) and Discrete Wavelet Transformation (DWT) for copyright protection. In our proposed watermarking method, the original audio is transformed into DCT domain and divided into two parts. Synchronization code is applied on the signal in first part and 2 levels DWT domain is applied on the signal in second part. The absolute value of DWT coefficient is divided into arbitrary number of segments and calculates the energy of each segment and middle peak. Watermarks are then embedded into each middle peak. Watermarks are extracted by performing the inverse operation of watermark embedding process. Experimental results show that the hidden watermark data is robust to re-sampling, low-pass filtering, re-quantization, MP3 compression, cropping, echo addition, delay, and pitch shifting, amplitude change. Performance analysis of the proposed scheme shows low error probability rates.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.83-88
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• 1999

In this paper, a novel gray-scale lossless image coder combining context-based minimum mean squared error (MMSE) prediction and entropy coding is proposed. To obtain context of prediction, this paper first defines directional difference according to sharpness of edge and gradients of localities of image data. Classification of 4 directional differences forms“geometry context”model which characterizes two-dimensional general image behaviors such as directional edge region, smooth region or texture. Based on this context model, adaptive DPCM prediction coefficients are calculated in MMSE sense and the prediction is performed. The MMSE method on context-by-context basis is more in accord with minimum entropy condition, which is one of the major objectives of the predictive coding. In entropy coding stage, context modeling method also gives useful performance. To reduce the statistical redundancy of the residual image, many contexts are preset to take full advantage of conditional probability in entropy coding and merged into small number of context in efficient way for complexity reduction. The proposed lossless coding scheme slightly outperforms the CALIC, which is the state-of-the-art, in compression ratio.

• Journal of the Korean Solar Energy Society
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• v.34 no.3
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• pp.42-48
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• 2014

An experimental study was performed estimating COP(Coefficient of Performance) of air-conditioning cycle with inverter scroll compressor. All experiments were done for various compressor speeds from 1000~4000 rpm and used the inverter controller called CANDy to change the compressor rpm. The air-conditioning cycle components in the apparatus were used as same with components of YF hybrid car. To estimate the COP, this study measured the temperature and pressure at inlets and outlets of compressor, condenser, and evaporator. And also measured the compressor input power using Powermeter. Through the experiments, the maximum error to estimate COP was shown about ${\pm}6.09%$ at 3500rpm. This study revealed that the condenser temperature and pressure were increased and the evaporator temperature and pressure were decreased with the increased compressor speed. And also, the COP was decreased with increased compressor speed. Those results can be used the basic and fundamental data to design the air-conditioning cycle with inverter scroll compressor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.452-472
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• 2019

Most existing wavelet-based multiplicative watermarking methods are affected by geometric attacks to a certain extent. A serious limitation of wavelet-based multiplicative watermarking is its sensitivity to rotation, scaling, and translation. In this study, we propose an image watermarking method by using dual-tree complex wavelet transform with a multi-objective optimization approach. We embed the watermark information into an image region with a high entropy value via a multiplicative strategy. The major contribution of this work is that the trade-off between imperceptibility and robustness is simply solved by using the multi-objective optimization approach, which applies the watermark error probability and an image quality metric to establish a multi-objective optimization function. In this manner, the optimal embedding factor obtained by solving the multi-objective function effectively controls watermark strength. For watermark decoding, we adopt a maximum likelihood decision criterion. Finally, we evaluate the performance of the proposed method by conducting simulations on benchmark test images. Experiment results demonstrate the imperceptibility of the proposed method and its robustness against various attacks, including additive white Gaussian noise, JPEG compression, scaling, rotation, and combined attacks.

• Journal of Korea Multimedia Society
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• v.22 no.7
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• pp.790-803
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• 2019

The effects of different heat treatments on the sensory characteristics of abalones are studied in this study. In this paper, the sensory evaluation of abalone samples under different heat treatment conditions is carried out, and the evaluation results are analyzed. The three-dimensional (3D) scanning and reverse engineering are used in tooth modeling of the sensory evaluation of abalone samples under different heat treatment conditions. Besides, the chewing movement models are simplified into three modes, including the cutting mode, compressing mode and grinding mode, which are simulated using finite element simulation. The elastic modulus of the abalone samples is obtained through the compression testing using a texture analyzer to distinguish their material properties under different heat treatments and to obtain simulated mechanical parameters. Finally, taking the mechanical parameters of the finite element simulation of abalone chewing as input and sensory evaluation parameters as the output, BP neural network is established in which the sensory texture evaluation model of abalone samples is obtained. Through verification, the neural network prediction model can meet the requirements of food texture evaluation, with an average error of 9.12%.

• Steel and Composite Structures
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• v.39 no.2
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• pp.189-200
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• 2021

The compressive strength of circular concrete filled steel tubular (C-CFST) stubs strengthened with carbon fiber reinforced polymer (CFRP) is studied theoretically. According to previous experimental results, the failure process and mechanism of circular CFRP-concrete filled steel tubular (C-CFRP-CFST) stubs is analyzed, and the loading process is divided into 3 stages, i.e., elastic stage, elasto-plastic stage and failure stage. Based on continuum mechanics, the theoretical model of C-CFRP-CFST stubs under axial compression is established based on the assumptions that steel tube and concrete are both in three-dimensional stress state and CFRP is in uniaxial tensile stress state. Equations for calculating the yield strength and the ultimate strength of C-CFRP-CFST stubs are deduced. Theoretical predictions from the presented equations are compared with existing experimental results. There are a total of 49 tested specimens, including 15 ones for comparison of yield strength and 44 ones for comparison of ultimate strength. It is found that the predicted results of most specimens are within an error limit of 10%. Finally, simplified equations for calculating both yield strength and ultimate strength of C-CFRP-CFST stubs are proposed.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.19 no.1
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• pp.75-85
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• 2021

Evaluating the quantitative damage to rocks through acoustic emission (AE) has become a research focus. Most studies mainly used one or two AE parameters to evaluate the degree of damage, but several AE parameters have been rarely used. In this study, several data-driven models were employed to reflect the combined features of AE parameters. Through uniaxial compression tests, we obtained mechanical and AE-signal data for five granite specimens. The maximum amplitude, hits, counts, rise time, absolute energy, and initiation frequency expressed as the cumulative value were selected as input parameters. The result showed that gradient boosting (GB) was the best model among the support vector regression methods. When GB was applied to the testing data, the root-mean-square error and R between the predicted and actual values were 0.96 and 0.077, respectively. A parameter analysis was performed to capture the parameter significance. The result showed that cumulative absolute energy was the main parameter for damage prediction. Thus, AE has practical applicability in predicting rock damage without conducting mechanical tests. Based on the results, this study will be useful for monitoring the near-field rock mass of nuclear waste repository.

• Earthquakes and Structures
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• v.19 no.6
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• pp.411-425
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• 2020

Unreinforced masonry (URM) walls present low shear strength and are prone to brittle failure when subjected to inplane seismic overloads. This paper discusses the shear strengthening of URM walls with Textile Reinforced Mortar (TRM) jackets. The available literature is thoroughly reviewed and an extended database is developed including available brick, concrete and stone URM walls retrofitted and subjected to shear tests to assess their strength. Further, the experimental results of the database are compared against the available shear strength design models from ACI 549.4R-13, CNR DT 215 2018, CNR DT 200 R1/2013, Eurocode 6 and Eurocode 8 guidelines as well as Triantafillou and Antonopoulos 2000, Triantafillou 1998, Triantafillou 2016. The performance of the available models is investigated and the prediction average absolute error (AAE) is as high as 40%. A new model is proposed that takes into account the additional contribution of the reinforcing mortar layer of the TRM jacket that is usually neglected. Further, the approach identifies the plethora of different block materials, joint mortars and TRM mortars and grids and introduces rational calibration of their variable contributions on the shear strength. The proposed model provides more accurate shear strength predictions than the existing models for all different types of the URM substrates, with a low AAE equal to 22.95%.

• Steel and Composite Structures
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• v.43 no.6
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• pp.809-819
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• 2022

This paper proposes a method to predict the deflection of prestressed concrete (PC) beams with corrugated steel web (CSW) under constant load concerning time-dependent variation in concrete material. Over time, the top and bottom concrete slabs subjected to asymmetric compression experience shrinkage and creep deformations. Here, the classical Euler-Bernoulli beam theory assumption that the plane sections remain plane is not valid due to shear deformation of CSW. Therefore, this study presents a method based on the first-order shear deformation to find the long-term deflection of the composite beams under bending by considering time effects. Two experimental prestressed beams of this type were monitored under their self-weight over time, and the theoretical results were compared with those data. Additionally, 3D analytical models of the experimental beams were used according to material properties, and the results were compared with two previous cases. There was good consistency between the analytical and numerical results with low error, which increased by wave radius. It is concluded that the proposed method could reliably be used for design purposes.

• 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.