• Journal of The Korean Society of Clinical Toxicology
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• v.19 no.2
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• pp.133-138
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• 2021

Glufosinate-containing herbicides is a non-selective herbicide commonly used worldwide. As the use of them increased gradually since paraquat was banned in 2012, the number of suicides by their ingestion is also increasing continuously. Complications of glufosinate-containing herbicide poisoning include various central nervous system (CNS) toxicities such as convulsions, loss of consciousness, memory impairment, and respiratory depression, which may be accompanied by hemodynamic changes such as bradycardia and hypotension. However, it is very rare that arrhythmias other than bradycardia occurred and Takotsubo cardiomyopathy was combined due to cardiotoxicity. A 71-year-old female patient was transferred to our hospital after ingesting 500 mL of glufosinate-containing herbicide and receiving 5 L of gastric lavage at a local hospital. A few hours later, she presented stuporous mentality, respiratory depression, and convulsions, and was accompanied by hypotension and bradycardia. On the second day of admission, electrocardiogram (ECG) showed bradycardia and QTc prolongation with hemodynamic Instability. Accordingly, we conducted the early treatment with continuous renal replacement therapy (CRRT) and the application of temporary cardiac pacemaker. An echocardiogram demonstrated decreased ejection fraction (EF) and Takotsubo cardiomyopathy on the third day of admission. Then, she was discharged safely with conservative treatment. At the follow-up after 1 year, Takotsubo cardiomyopathy, EF and QTc prolongation were recovered on echocardiogram and ECG. Because cardiac toxicity after glufosinate-containing herbicide poisoning may cause life-threatening consequences, caution is required while treating the patient. Therefore, if electrocardiogram changes are seen in the elderly with a large amount of glufosinate herbicide ingestion, additional cardiac function test through echocardiography should be concerned, and early treatment through CRRT or artificial cardiac pacing should be considered.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1133-1144
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• 2021

Zero-copy is a technology that is also called no-memory copy, and through its use, context switching between the user space and the kernel space can be reduced to minimize the load on the CPU. However, this technology is only used to transmit small random files, and has not yet been widely used for large file transfers. This paper intends to discuss the practical application of zero-copy in processing large files via a network. To this end, we first developed a small test bed and program that can transmit and store data based on zero-copy. Afterwards, we intend to verify the usefulness of the applied technology in detail through detailed performance evaluation

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1432-1440
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• 2022

Recently, as Virtual Desktop Infrastructure(VDI) is widely used not only in office work environments but also in workloads that use high-spec multi-core-based applications, the requirements for real-time and stability of VDI are increasing. Accordingly, the display protocol used for remote access in VDI and performance optimization of virtual machines have also become more important. In this paper, we propose two ways to configure desktop virtualization for multi-core-based application operation. First, we propose a codec configuration of a display protocol with optimal performance in a high load situation due to multi-processing. Second, we propose a virtual CPU scheduling optimization method to reduce scheduling delay in case of CPU contention between virtual machines. As a result of the test, it was confirmed that the H.264 codec of Blast Extreme showed the best and stable frame, and the scheduling performance of the virtual CPU was improved through scheduling optimization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.1-15
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• 2022

The MobileNetV3 is specially designed for mobile devices with limited memory and computing power. To reduce the network parameters and improve the network inference speed, a new lightweight network is proposed based on MobileNetV3. Firstly, to reduce the computation of residual blocks, a partial residual structure is designed by dividing the input feature maps into two parts. The designed partial residual structure is used to replace the residual block in MobileNetV3. Secondly, a dual-path feature extraction structure is designed to further reduce the computation of MobileNetV3. Different convolution kernel sizes are used in the two paths to extract feature maps with different sizes. Besides, a transition layer is also designed for fusing features to reduce the influence of the new structure on accuracy. The CIFAR-100 dataset and Image Net dataset are used to test the performance of the proposed partial residual structure. The ResNet based on the proposed partial residual structure has smaller parameters and FLOPs than the original ResNet. The performance of improved MobileNetV3 is tested on CIFAR-10, CIFAR-100 and ImageNet image classification task dataset. Comparing MobileNetV3, GhostNet and MobileNetV2, the improved MobileNetV3 has smaller parameters and FLOPs. Besides, the improved MobileNetV3 is also tested on CPU and Raspberry Pi. It is faster than other networks

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2991-3007
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• 2022

Two dimensional locality preserving projections (2D-LPP) is an improved algorithm of 2D image to solve the small sample size (SSS) problems which locality preserving projections (LPP) meets. It's able to find the low dimension manifold mapping that not only preserves local information but also detects manifold embedded in original data spaces. However, 2D-LPP is simple and elegant. So, inspired by the comparison experiments between two dimensional linear discriminant analysis (2D-LDA) and linear discriminant analysis (LDA) which indicated that matrix based methods don't always perform better even when training samples are limited, we surmise 2D-LPP may meet the same limitation as 2D-LDA and propose a novel matrix exponential method to enhance the performance of 2D-LPP. 2D-MELPP is equivalent to employing distance diffusion mapping to transform original images into a new space, and margins between labels are broadened, which is beneficial for solving classification problems. Nonetheless, the computational time complexity of 2D-MELPP is extremely high. In this paper, we replace some of matrix multiplications with multiple multiplications to save the memory cost and provide an efficient way for solving 2D-MELPP. We test it on public databases: random 3D data set, ORL, AR face database and Polyu Palmprint database and compare it with other 2D methods like 2D-LDA, 2D-LPP and 1D methods like LPP and exponential locality preserving projections (ELPP), finding it outperforms than others in recognition accuracy. We also compare different dimensions of projection vector and record the cost time on the ORL, AR face database and Polyu Palmprint database. The experiment results above proves that our advanced algorithm has a better performance on 3 independent public databases.

• Journal of the Korea Convergence Society
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• v.13 no.5
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• pp.267-278
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• 2022

This paper was intended to examine the function of emotion that affects the accuracy of statements in the context of eyewitness testimony. The main emotion theories and concepts introduced in previous studies examining the relation between testimony accuracy and negative emotions were examined based on the characteristics of the research method. The results were presented in the order of emotion definition, emotion inducing method, and emotion measurement method. Specifically, the definition of emotion was described based on studies on negative emotions, arousal, stress, and mood. The emotion inducing method was mainly described based on images, virtual reality, and staged events designed by researchers, which have been mainly used in laboratories. Emotion measurement methods were described with respect to the self-report, behavioral checklist, and psychophysiology. In addition, the emotional approach for objective and scientific repeated verification, the importance of effective experimental design and appropriate scientific memory test, and the need for individual difference control were discussed. This paper reinterprets the contradictions shown by previous research by systematically structuring the function of emotion that affects the accuracy of testimony. It was meaningful to provide a frame for comparative analysis of related studies. Ultimately, it is expected that such knowledge will be used as basic documents for judging the reliability of eyewitness testimony in a legal context.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.69-75
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• 2023

Small communication devices used in the Internet of Things are vulnerable to various hacking because they do not apply advanced encryption techniques due to their low memory capacity or slow computation speed. In order to increase the authentication reliability of small-sized transmitters operating in 433MHz band, we introduce an RF fingerprint and adopt a convolutional neural network (CNN) as a classification algorithm. The preamble signal transmitted by each transmitter are extracted and collected using software-defined-radio to constitute a training data set, which is used for training the CNN. We tested identification of 20 transmitters in four different scenarios and obtained high identification accuracy. In particular, the accuracy of 95.8% and 92.6% was obtained, respectively in the scenario where the test was performed at a location different from the transmitter's location at the time of collecting training data, and in the scenario where the transmitter moves at walking speed.

• Journal of the Korean Geosynthetics Society
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• v.21 no.2
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• pp.11-19
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• 2022

In this paper, we propose a GoogleNet transfer learning and CNN-LSTM combination method to improve the time-series prediction performance for crack detection using crack data captured inside the sewer pipes. LSTM can solve the long-term dependency problem of CNN, so spatial and temporal characteristics can be considered at the same time. The predictive performance of the proposed method is excellent in all test variables as a result of comparing the RMSE(Root Mean Square Error) for time series sections using the crack data inside the sewer pipe. In addition, as a result of examining the prediction performance at the time of data generation, the proposed method was verified that it is effective in predicting crack detection by comparing with the existing CNN-only model. If the proposed method and experimental results obtained through this study are utilized, it can be applied in various fields such as the environment and humanities where time series data occurs frequently as well as crack data of concrete structures.

• Geomechanics and Engineering
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• v.31 no.6
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• pp.545-556
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• 2022

Evaluation and optimization of tunnel wall convergence (TWC) plays a vital role in preventing potential problems during tunnel construction and utilization stage. When convergence occurs at a high rate, it can lead to significant problems such as reducing the advance rate and safety, which in turn increases operating costs. In order to design an effective solution, it is important to accurately predict the degree of TWC; this can reduce the level of concern and have a positive effect on the design. With the development of soft computing methods, the use of deep learning algorithms and neural networks in tunnel construction has expanded in recent years. The current study aims to employ the long-short-term memory (LSTM) deep neural network predictor model to predict the TWC, based on 550 data points of observed parameters developed by collecting required data from different tunnelling projects. Among the data collected during the pre-construction and construction phases of the project, 80% is randomly used to train the model and the rest is used to test the model. Several loss functions including root mean square error (RMSE) and coefficient of determination (R²) were used to assess the performance and precision of the applied method. The results of the proposed models indicate an acceptable and reliable accuracy. In fact, the results show that the predicted values are in good agreement with the observed actual data. The proposed model can be considered for use in similar ground and tunneling conditions. It is important to note that this work has the potential to reduce the tunneling uncertainties significantly and make deep learning a valuable tool for planning tunnels.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.11-18
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• 2023

In this paper, we evaluate deep learning time series forecasting models. Recent studies show that those models perform better than the traditional prediction model such as ARIMA. Among them, recurrent neural networks to store previous information in the hidden layer are one of the prediction models. In order to solve the gradient vanishing problem in the network, LSTM is used with small memory inside the recurrent neural network along with BI-LSTM in which the hidden layer is added in the reverse direction of the data flow. In this paper, we compared the performance of Informer by comparing with other models (LSTM, BI-LSTM, and Transformer) for real Nitrogen dioxide (NO2) data. In order to evaluate the accuracy of each method, mean square root error and mean absolute error between the real value and the predicted value were obtained. Consequently, Informer has improved prediction accuracy compared with other methods.