• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.6
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• pp.15-21
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• 2010

In the recent years, various researches about the signal processing have been presented to improve the performance of power analysis. Among these signal processing techniques, the research about the signal compression is not enough than a signal alignment and a noise reduction; even though that can reduce considerably the computation time for the power analysis. But, the existing compression method can sometimes reduce the performance of the power analysis because those are the unsophisticated method not considering the characteristic of the signal. In this paper, we propose the new PCA (principal component analysis)-based signal compression method, which can block the loss of the meaningful factor of the original signal as much as possible, considering the characteristic of the signal. Also, we prove the performance of our method by carrying out the experiment.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.29-36
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• 2021

In this paper, we classify ECG signal data for mobile devices using deep learning models. To classify abnormal heartbeats with high accuracy, three factors of the deep learning model are selected, and the classification accuracy is compared according to the changes in the conditions of the factors. We apply a CNN model that can self-extract features of ECG data and compare the performance of a total of 48 combinations by combining conditions of the depth of model, optimization method, and activation functions that compose the model. Deriving the combination of conditions with the highest accuracy, we obtained the highest classification accuracy of 97.88% when we applied 19 convolutional layers, an optimization method SGD, and an activation function Mish. In this experiment, we confirmed the suitability of feature extraction and abnormal beat detection of 1-channel ECG signals using CNN.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.2
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• pp.73-78
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• 2020

Musculoskeletal disease is often caused by sitting down for long period's time or by bad posture habits. In order to prevent musculoskeletal disease in daily life, it is the most important to correct the bad sitting posture to the right one through real-time monitoring. In this study, to detect the sitting information of user's without any constraints, we propose posture measurement system based on multi-channel pressure sensor and CNN model for classifying sitting posture types. The proposed CNN model can analyze 5 types of sitting postures based on sitting posture information. For the performance assessment of posture classification CNN model through field test, the accuracy, recall, precision, and F1 of the classification results were checked with 10 subjects. As the experiment results, 99.84% of accuracy, 99.6% of recall, 99.6% of precision, and 99.6% of F1 were verified.

• The Journal of Information Systems
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• v.31 no.1
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• pp.147-173
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• 2022

Purpose As the use of social network services has become common, it has become possible to freely communicate and establish relationships with other people anytime, anywhere for communication and information sharing. Influencers who have a strong influence on consumers' perceptions and attitudes through their own opinions and stories have appeared on various social media channels such as YouTube. Recently, companies utilize influencers with a large number of followers to check interactions with customers to understand customer attitudes and opinions about products in real time. Start-ups with insufficient resources need to quickly examine customer responses to reduce the probability of failure after product planning. The Lean process of creating an MVP and quickly confirming and learning the market response should be repeated over and over again. Findings In this paper, we try to suggest that the YouTube platform can play a sufficient role as a customer experiment space through examples. The case company is a company that has successfully commercialized products by continuously interacting with customers through the YouTube platform for the first four months of its founding. This paper is expected to be helpful in the experimental process for prospective founders and early founders to examine customer responses to reduce the probability of market failure before commercialization. Design/methodology/approach This paper analyzed the YouTube channel data of case companies based on the netnography methodology and presented the contents of the lean process management carried out in the experimental stage and the post-production stage through interview research.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.230-230
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• 2022

최근 기후변화로 인해 예측이 어려운 국지성 호우가 빈번하게 발생하고 있다. 국지성 호우는 대량의 홍수를 일으켜 유송잡물을 동반한 흐름을 야기할 수 있다. 대량의 유송잡물이 하상에 퇴적되면 통수능을 저하시키기도 하며 식생효과와 마찬가지로 유목주변으로 유속이 증가하면서 세굴현상이 발생하게 되는데, 이는 하상저하를 일으키며 수공구조물의 안정성에 지속적으로 피해를 줄 수 있다. 기후변화는 또한 강우패턴을 변화시켜 식생의 성장과 활착에도 영향을 미치게 된다. 본 연구지역인 내성천 회룡포의 경우, 2015년 대가뭄 발생 이후 식생활착으로 인해 식생대 면적이 증가하고 잇는 상황이다. 이는 연구지역의 흐름과 사주교란 및 하상변동에도 큰 영향을 미칠 것으로 판단되며, 특히 식생의 증가는 유송잡물의 증가를 야기 할 수 있기 때문에 식생과 유송잡물의 영향이 하천에 미치는 영향을 예측하는 연구가 필요할 것으로 판단된다. 따라서 본 연구에서는 2차원 흐름모형인 Nays2D와 입자법기반의 유목동역학 모형을 활용하여 식생과 유송잡물이 하상변동에 미치는영향에 대한 수치실험을 수행하고 결과를 분석하였다. 여기서, 식생의 경우, 식생성장모형을 적용하여 시간에 따라 식생이 성장하여 항력이 증가하는 것으로 가정하였으며 유송잡물의 경우, 하상에 퇴적되는 유송잡물의 개수와 면적만큼 항력이 증가하는 것으로 가정하였다. 흐름의 경계 조건은 일반화된 부정류 수문곡선을 입력하였으며 초기 하도의 형상은 일정한 경사를 가지는 평지로 가정하여 부정류에 따라 하상변동이 발생하는 것으로 모의하였다. 시뮬레이션 결과, 유송잡물보다 식생대 번성이 하상변동에 상대적으로 큰 영향을 주는 것으로 나타났으며, 유송잡물의 경우 국부적으로 퇴적 분포되어 그 주변으로 침식을 일으키는 것으로 나타났다. 또한 유송잡물은 하안의 얕은수역에서 주로 퇴적되었다. 본 연구는 만곡부 하상변동에 대해 식생과 유송잡물을 고려하여 예측모의를 수행한 사례로서, 식생과 유송잡물이 흐름과 하상변동에 미치는 영향을 이해하는데 도움이 될 것으로 판단된다. 또한 이는 추후에 식생을 고려하는 하천관리방안을 수립 시, 식생과 유송잡물의 영향을 물리적으로 설명할 수 있는 근거자료로 제시할 수 있을 것으로 보인다.

• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.181-190
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• 2009

This paper presents a multimodal emergency recognition system based on visual information, audio information and gravity sensor information. It consists of video processing module, audio processing module, gravity sensor processing module and multimodal integration module. The video processing module and gravity sensor processing module respectively detects actions such as moving, stopping and fainting and transfer them to the multimodal integration module. The multimodal integration module detects emergency by fusing the transferred information and verifies it by asking a question and recognizing the answer via audio channel. The experiment results show that the recognition rate of video processing module only is 91.5% and that of gravity sensor processing module only is 94%, but when both information are combined the recognition result becomes 100%.

• Natural Product Sciences
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• v.29 no.2
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• pp.104-112
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• 2023

COVID-19 caused a catastrophe in human health. People infected with COVID-19 also suffer from various clinical illnesses during and after the infection. The Boerhavia diffusa plant is well known for its antihypertensive activity. ACE-II inhibitors and calcium channel blockers are reported as mechanisms for the antihypertensive activity of B. diffusa phytoconstituents. Various studies have said ACE-II is the virus's binding site to attack host cells. COVID-19 treatment commonly employs a variety of synthetic antiviral and steroidal drugs. As a result, other clinical illnesses, such as hypertension and hyperglycemia, emerge as serious complications. Safe and effective drug delivery is a prime objective of the drug development process. COVID-19 is treated with various herbal treatments; however, they are not widely used due to their low potency. Many herbal plants and formulations are used to treat COVID-19 infection, in which B. diffusa is the most widely used plant. The current study relies on discovering active phytoconstituents with ACE-II inhibitory activity in the B. diffusa plant. As a result, it can be used as a treatment option for patients with COVID-19 and related diseases. Different phytoconstituents of the B. diffusa plant were selected from the reported literature. The activity of phytoconstituents against ACE-II proteins has been studied. Molecular docking and ligand-protein interaction computation tools are used in the in-silico experiment. Physicochemical, drug-likeness, water solubility, lipophilicity, and pharmacokinetic parameters are used to evaluate phytoconstituents. Liriodenine has the best drug-likeness, bioactivity, and binding score characteristics among the selected ligands. The in-silico study aims to find the therapeutic potential of B. diffusa phytoconstituents against ACE-II. Targeting ACE-II also shows an effect against SARS-CoV-2. It can serve as a rationale for designing a drug for patient infected with COVID-19 and associated diseases.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.463-481
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• 2020

Cutter suction dredgers (CSDs) are widely used in various dredging constructions such as channel excavation, wharf construction, and reef construction. During a CSD construction, the main operation is to control the swing speed of cutter to keep the slurry concentration in a proper range. However, the slurry concentration cannot be monitored in real-time, i.e., there is a "time-lag effect" in the log of slurry concentration, making it difficult for operators to make the optimal decision on controlling. Concerning this issue, a solution scheme that using real-time monitored indicators to predict current slurry concentration is proposed in this research. The characteristics of the CSD monitoring data are first studied, and a set of preprocessing methods are presented. Then we put forward the concept of "index class" to select the important indices. Finally, an ensemble learning algorithm is set up to fit the relationship between the slurry concentration and the indices of the index classes. In the experiment, log data over seven days of a practical dredging construction is collected. For comparison, the Deep Neural Network (DNN), Long Short Time Memory (LSTM), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and the Bayesian Ridge algorithm are tried. The results show that our method has the best performance with an R² of 0.886 and a mean square error (MSE) of 5.538. This research provides an effective way for real-time predicting the slurry concentration of CSDs and can help to improve the stationarity and production efficiency of dredging construction.

• The Journal of the Acoustical Society of Korea
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• v.29 no.4
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• pp.229-236
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• 2010

Phase coherent modulation techniques in underwater acoustic channel can improve bandwidth efficiency and data reliability, but they are made difficult by time-varying intersymbol interference. This paper proposes an adaptive equalizer combined with phase estimator which compensates distortions caused by time-varying multipath and phase variation. The experiment in the East sea demonstrates phase coherent signals are distorted by time-varying multipath propagation and the proposed scheme equalizes them. Bit error rate of BPSK and QPSK are 0.0078 and 0.0376 at 300 meter horizontal distance and 0.0146 and 0.0293 at 1000 meter respectively.

• The Journal of Korean Physical Therapy
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• v.36 no.1
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• pp.39-44
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• 2024

Purpose: Cybersickness is a type of motion sickness induced by virtual reality (VR) or augmented reality (AR) environments that presents symptoms including nausea, dizziness, and headaches. This study aimed to investigate how cathodal transcranial direct current stimulation (tDCS) alleviates motion sickness symptoms and modulates brain activity in individuals experiencing cybersickness after exposure to a VR environment. Methods: This study was performed on two groups of healthy adults with cybersickness symptoms. Subjects were randomly assigned to receive either cathodal tDCS intervention or sham tDCS intervention. Brain activity during VR stimulation was measured by 38-channel functional near-infrared spectroscopy (fNIRS). tDCS was administered to the right temporoparietal junction (TPJ) for 20 minutes at an intensity of 2mA, and the severity of cybersickness was assessed pre- and post-intervention using a simulator sickness questionnaire (SSQ). Result: Following the experiment, cybersickness symptoms in subjects who received cathodal tDCS intervention were reduced based on SSQ scores, whereas those who received sham tDCS showed no significant change. fNIRS analysis revealed that tDCS significantly diminished cortical activity in subjects with high activity in temporal and parietal lobes, whereas high cortical activity was maintained in these regions after intervention in subjects who received sham tDCS. Conclusion: These findings suggest that cathodal tDCS applied to the right TPJ region in young adults experiencing cybersickness effectively reduces motion sickness induced by VR environments.