• Journal of Korea Trade
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• v.25 no.2
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• pp.58-74
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• 2021

Purpose - This paper research on the embodied carbon emission in Sino-Korea trade. It calculates and analyzes the carbon emission coefficient and specific carbon emissions in Sino-Korea trade from 2005 to 2014. Design/methodology - This paper conducted an empirical analysis for embodied carbon emission in Sino-Korea trade during the years 2005-2014, using a multi-region input-output model. First, direct and complete CO2 emission coefficient of the two countries were calculated and compared. On this basis, combined with the world input-output table, the annual import and export volume and sector volume of embodied carbon emission are determined. Then through the comparative analysis of the empirical results, the reasons for the carbon imbalance in Sino-Korea trade are clarified, and the corresponding suggestions are put forward according to the environmental protection policies being implemented by the two countries. Findings - The results show that South Korea is in the state of net trade export and net embodied carbon import. The carbon emission coefficient of most sectors in South Korea is lower than that of China. However, the reduction of carbon emission coefficient in China is significantly faster than that in South Korea in this decade. The change of Korea's complete CO2 emission coefficient shows that policy factors have a great impact on environmental protection. The proportion of intra industry trade between China and South Korea is relatively large and concentrated in mechanical and electrical products, chemical products, etc. These sectors generally have large carbon emissions, which need to be noticed by both countries. Originality/value - To the best knowledge of the authors, this study is the first attempt to research the embodied carbon emission of ten consecutive years in Sino-Korea Trade. In addition, In this paper, some mathematical methods are used to overcome the error problem caused by different statistical caliber in different databases. Finally, the accurate measurement of carbon level in bilateral trade will provide some reference for trade development and environmental protection.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1088-1097
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• 2021

Vibration data of mechanical equipment inevitably have noise. This noise adversely af ects the maintenance of mechanical equipment. Accordingly, the performance of a learning model depends on how effectively the noise of the data is removed. In this study, the noise of the data was removed using the Denoising Auto Encoder (DAE) technique which does not include the characteristic extraction process in preprocessing time series data. In addition, the performance was compared with that of the Wavelet Transform, which is widely used for machine signal processing. The performance comparison was conducted by calculating the failure detection rate. For a more accurate comparison, a classification performance evaluation criterion, the F-1 Score, was calculated. Failure data were detected using the One-Class SVM technique. The performance comparison, revealed that the DAE technique performed better than the Wavelet Transform technique in terms of failure diagnosis and error rate.

• Smart Structures and Systems
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• v.30 no.6
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• pp.673-686
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• 2022

Deep learning algorithms for Structural Health Monitoring (SHM) have been extracting the interest of researchers and engineers. These algorithms commonly used loss functions and evaluation indices like the mean square error (MSE) which were not originally designed for SHM problems. An updated loss function which was specifically constructed for deep-learning-based structural damage detection problems has been proposed in this study. By tuning the coefficients of the loss function, the weights for damage localization and quantification can be adapted to the real situation and the deep learning network can avoid unnecessary iterations on damage localization and focus on the damage severity identification. To prove efficiency of the proposed method, structural damage detection using convolutional neural networks (CNNs) was conducted on a truss bridge model. Results showed that the validation curve with the updated loss function converged faster than the traditional MSE. Data augmentation was conducted to improve the anti-noise ability of the proposed method. For reducing the training time, the normalized modal strain energy change (NMSEC) was extracted, and the principal component analysis (PCA) was adopted for dimension reduction. The results showed that the training time was reduced by 90% and the damage identification accuracy could also have a slight increase. Furthermore, the effect of different modes and elements on the training dataset was also analyzed. The proposed method could greatly improve the performance for structural damage detection on both the training time and detection accuracy.

• Journal of Biomedical Engineering Research
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• v.43 no.6
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• pp.382-389
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• 2022

In this study, the accuracy of the orthognathic surgical guides with single-stage split path was upgraded to realize orthognathic surgical guides with two-stage split path and simulated surgery was performed to verify its accuracy. As a result, the average error distance between the simulation model and the scan model was + 0.289 / - 0.468 mm (standard deviation 0.128), which was confirmed to be within ± 0.5 mm, which is a clinically acceptable level. Also, there was no significant difference compared with the average value of + 0.313 / - 0.456 mm (average standard deviation 0.106) of the conventional single-stage split path type device. It is judged that the use of this device can contribute to the reduction of surgical time and increase in accuracy since a separate finishing operation for bone preparation is unnecessary.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.133-141
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• 2010

This research suggests a unified scheme for using stratified sampling and control variates method to improve the efficiency of estimation for parameters in simulation experiments. We utilize standardized concomitant variables defined during the course of simulation runs. We first use these concomitant variables to counteract the unknown error of response by the method of control variates, then use a concomitant variable not used in the controlled response and stratify the response into appropriate strata to reduce the variation of controlled response additionally. In case that the covariance between the response and a set of control variates is known, we identify the simulation efficiency of suggested method using control variates and stratified sampling. We conjecture the simulation efficiency of this method is better than that achieved by separated application of either control variates or stratified sampling in a simulation experiments. We investigate such an efficiency gain through simulation on a selected model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.239-260
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• 2023

The underwater acoustic wireless communication networks are generally formed by the different autonomous underwater acoustic vehicles, and transceivers interconnected to the bottom of the ocean with battery deployed modems. Orthogonal frequency division multiplexing (OFDM) has become the most popular modulation technique in underwater acoustic communication due to its high data transmission and robustness over other symmetrical modulation techniques. To maintain the operability of underwater acoustic communication networks, the power consumption of battery-operated transceivers becomes a vital necessity to be minimized. The OFDM technology has a major lack of peak to average power ratio (PAPR) which results in the consumption of more power, creating non-linear distortion and increasing the bit error rate (BER). To overcome this situation, we have contributed our symmetry research into three dimensions. Firstly, we propose a machine learning-based underwater acoustic communication system through long short-term memory neural network (LSTM-NN). Secondly, the proposed LSTM-NN reduces the PAPR and makes the system reliable and efficient, which turns into a better performance of BER. Finally, the simulation and water tank experimental data results are executed which proves that the LSTM-NN is the best solution for mitigating the PAPR with non-linear distortion and complexity in the overall communication system.

• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.182-189
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• 2023

Depth estimation is a key technology in 3D map generation for autonomous driving of vehicles, robots, and drones. The existing sensor-based method has high accuracy but is expensive and has low resolution, while the camera-based method is more affordable with higher resolution. In this study, we propose self-attention-based unsupervised monocular depth estimation for UAV camera system. Self-Attention operation is applied to the network to improve the global feature extraction performance. In addition, we reduce the weight size of the self-attention operation for a low computational amount. The estimated depth and camera pose are transformed into point cloud. The point cloud is mapped into 3D map using the occupancy grid of Octree structure. The proposed network is evaluated using synthesized images and depth sequences from the Mid-Air dataset. Our network demonstrates a 7.69% reduction in error compared to prior studies.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.1-8
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• 2023

An Orthogonal Frequency Division Multiplexing (OFDM) based wireless communication system has drawn wide attention for its high transmission rate and high spectrum efficiency in not only radio but also Underwater Acoustic (UWA) applications. Because of the narrow sub-carrier spacing of OFDM, orthogonality between sub-carriers is easily affected by Doppler effect caused by the movement of transmitter or receiver. Previously, Doppler compensation signal processing algorithm for Desired propagation path was proposed. However, other Doppler shifts caused by delayed Undesired signal arriving from different directions cannot be perfectly compensated. Then Receiver Bit Error Rate (BER) is degraded by Inter-Carrier-Interference (ICI) caused in the case of Multi-path Doppler channel. To mitigate the ICI effect, a modified Delay and Doppler Profiler (mDDP), which estimates not only attenuation, relative delay and Doppler shift but also sampling clock shift of each multi-path component, is proposed. Based on the outputs of mDDP, an ICI canceling multi-tap equalizer is also proposed. Computer simulated performances of one-tap equalizer with the conventional Time domain linear interpolated Channel Transfer Function (CTF) estimator, multi-tap equalizer based on mDDP are compared. According to the simulation results, BER improvement has been observed. Especially, in the condition of 16QAM modulation, transmitting vessel speed of 6m/s, two-path multipath channel with direct path and ocean surface reflection path; more than one order of magnitude BER reduction has been observed at CNR=30dB.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.87-95
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• 2020

Generally, occupational safety and particularly construction safety is an intricate phenomenon. Industry professionals have devoted vital attention to enforcing Occupational Safety and Health (OHS) from the last three decades to enhance safety management in construction. Despite the efforts of the safety professionals and government agencies, current safety management still relies on manual inspections which are infrequent, time-consuming and prone to error. Extensive research has been carried out to deal with high fatality rates confronting by the construction industry. Sensor systems, visualization-based technologies, and tracking techniques have been deployed by researchers in the last decade. Recently in the construction industry, computer vision has attracted significant attention worldwide. However, the literature revealed the narrow scope of the computer vision technology for safety management, hence, broad scope research for safety monitoring is desired to attain a complete automatic job site monitoring. With this regard, the development of a broader scope computer vision-based risk recognition system for correlation detection between the construction entities is inevitable. For this purpose, a detailed analysis has been conducted and related rules which depict the correlations (positive and negative) between the construction entities were extracted. Deep learning supported Mask R-CNN algorithm is applied to train the model. As proof of concept, a prototype is developed based on real scenarios. The proposed approach is expected to enhance the effectiveness of safety inspection and reduce the encountered burden on safety managers. It is anticipated that this approach may enable a reduction in injuries and fatalities by implementing the exact relevant safety rules and will contribute to enhance the overall safety management and monitoring performance.

• The Journal of the Acoustical Society of Korea
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• v.28 no.6
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• pp.566-571
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• 2009

This paper proposes noise-robust fast speaker adaptation method based on the eigenvoice framework in various noisy environments. The proposed method is focused on de-noising and environment clustering. Since the de-noised adaptation DB still has residual noise in itself, environment clustering divides the noisy adaptation data into similar environments by a clustering method using the cepstral mean of non-speech segments as a feature vector. Then each adaptation data in the same cluster is used to build an environment-clustered speaker adapted (SA) model. After selecting multiple environmentally clustered SA models which are similar to test environment, the speaker adaptation based on an appropriate linear combination of clustered SA models is conducted. According to our experiments, we observe that the proposed method provides error rate reduction of $40{\sim}59%$ over baseline with speaker independent model.