• Smart Media Journal
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• v.11 no.3
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• pp.90-101
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• 2022

Buses are a popular means of transportation. As such, thorough preparation is needed for passenger safety management. However, the safety system is insufficient because there are accidents such as a death accident occurred when the bus departed without recognizing the elderly approaching to get on in 2018. There is a safety system that prevents pinching accidents through sensors on the back door stairs, but such a system does not prevent accidents that occur in the process of getting on and off like the above accident. If it is possible to predict the intention of bus passengers to get on and off, it will help to develop a safety system to prevent such accidents. However, studies predicting the intention of passengers to get on and off are insufficient. Therefore, in this paper, we propose a 1×1 CNN3D-based getting on and off intention prediction model using skeleton keypoints of passengers extracted from the camera image attached to the bus through UDP-Pose. The proposed model shows approximately 1~2% higher accuracy than the RNN and LSTM models in predicting passenger's getting on and off intentions.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1105-1114
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• 2021

Due to the recent surge in popularity of cryptocurrency, the threat of cryptojacking, a malicious code for mining cryptocurrencies, is increasing. In particular, web-based cryptojacking is easy to attack because the victim can mine cryptocurrencies using the victim's PC resources just by accessing the website and simply adding mining scripts. The cryptojacking attack causes poor performance and malfunction. It can also cause hardware failure due to overheating and aging caused by mining. Cryptojacking is difficult for victims to recognize the damage, so research is needed to efficiently detect and block cryptojacking. In this work, we take representative distinct symptoms of cryptojacking as an indicator and propose a new architecture. We utilized the K-Nearst Neighbors(KNN) model, which trained computer performance indicators as behavior-based dynamic analysis techniques. In addition, a K-means model, which trained the frequency of malicious script words for script similarity-based static analysis techniques, was utilized. The KNN model had 99.6% accuracy, and the K-means model had a silhouette coefficient of 0.61 for normal clusters.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.351-359
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• 2022

In this paper, this app is an online learning tool web application that helps learners learn efficiently. It discusses how learners can improve their learning efficiency in these three aspects: retrieval practice, systematization, metacognition. Through this web service, learners can proceed with learning with a flash card-based retrieval practice. In this case, a method of managing a flash card in a form similar to a directory-file system using a composite pattern is described. Learners can systematically organize their knowledge by converting flash cards into a mind map. The color of the mind map varies according to the learner's learning progress, and learners can easily recognize what they know and what they do not know through color. In this case, it is proposed to build a deep learning model to improve the accuracy of an algorithm for determining and predicting learning progress.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.51-56
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• 2022

With the development of industrialization, the amount of food and waste is rapidly increasing. Accordingly, the government is aware of the seriousness and is making efforts in various ways to reduce it. As a part of that, the volume-based food system was introduced, and although there were several trials and errors at the beginning of the introduction, it shows a reduction effect of 20 to 30%. These results suggest that the volume-based food system is being established. However, the waste is caused by foreign substances in the process of recycling resources by collecting them from the 1st collection to the 2nd collection process. Therefore, in this study, to solve these problems fundamentally, artificial intelligence is applied to classify foreign substances and improve them. Due to the nature of food waste, there is a limit to obtaining many images, so we compare several models based on CNNs and classify them as abnormal data, that is, CNN-based models are trained on various types of foreign substances, and then models with high accuracy are selected. We intend to prepare improvement measures for maintenance, such as manpower input to protect equipment and classify foreign substances by applying it.

• Journal of IKEEE
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• v.26 no.1
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• pp.62-66
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• 2022

Recently, there have been many research efforts based on data-based deep learning technologies to deal with the interference problem between heterogeneous wireless communication devices in unlicensed frequency bands. However, existing approaches are commonly based on the use of complex neural network models, which require high computational power, limiting their efficiency in resource-constrained network interfaces and Internet of Things (IoT) devices. In this study, we address the problem of classifying heterogeneous wireless technologies including Wi-Fi and ZigBee in unlicensed spectrum bands. We focus on a data-driven approach that employs a supervised-learning method that uses received signal strength indicator (RSSI) data to train Deep Convolutional Neural Networks (CNNs). We propose a simple measurement methodology for collecting RSSI training data which preserves temporal and spectral properties of the target signal. Real experimental results using an open-source 2.4 GHz wireless development platform Ubertooth show that the proposed sampling method maintains the same accuracy with only a 10% level of sampling data for the same neural network architecture.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.391-392
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• 2021

해마다 실내 냉방기 냉매누설 문제가 고질적으로 반복되며 소비자들의 피해도 커져가고 있다. 특히 제조사와 설치 업체가 다른 경우 냉매 누수의 원인이 제품인지, 설치하자인지 책임소재를 두고 갈등을 빚는 경우가 빈번하다. 이에 더 이상 소비자들의 피해를 막기 위해 냉매누설 검출 방안 마련이 필요해 보인다. 본 연구에서는 실내 냉방기 설치 후 냉매누설 검출을 위한 별도의 하드웨어 장치 추가 없이 냉방기의 운영을 위해 설치된 센서들의 값을 이용하여 냉매누설의 유무를 판단할 수 있는 방안을 제안하는 것을 목적으로 한다. 데이터 분석을 위하여 제조사의 제품 출하 전 현장 테스트 단계에서 측정한 온도값, 전류값, 습도값을 취합하여 데이터 셋을 구축하였다. 이때 자동화된 머신러닝(AutoML)을 이용하여 데이터의 80%를 훈련 데이터로 20%를 테스트 데이터로 사용하여 냉매량 80%는 1, 그 이하는 0으로 훈련시켰다. 구축한 데이터 셋을 이용하여 훈련시킨 결과 99% 정확도로 냉매누설 검출을 분별할 수 있었다. 또한 냉매누설과 관련성이 높은 중요 특징 4개를 추출할 수 있었다. 본 연구를 통하여 별도의 하드웨어 장치 추가 없이 소프트웨어적인 접근 방법으로 문제를 해결할 수 있는 feasibility를 확인할 수 있었다.

• Convergence Security Journal
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• v.22 no.2
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• pp.105-114
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• 2022

This paper is presents an intelligent control system that visualizes the direction of arrival for hearing impaired using personal mobility, and aims to recognize and prevent dangerous situations caused by sound such as alarm sounds and crack sounds on roads. The position estimation method of sound source uses a machine learning classification model characterized by generalized correlated phase transformation based on time difference of arrival. In the experimental environment reproducing the road situations, four classification models learned after extracting learning data according to wind speeds 0km/h, 5.8km/h, 14.2km/h, and 26.4km/h were compared with grid search cross validation, and the Muti-Layer Perceptron(MLP) model with the best performance was applied as the optimal algorithm. When wind occurred, the proposed algorithm showed an average performance improvement of 7.6-11.5% compared to the previous studies.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.257-257
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• 2022

댐 건설은 홍수 및 가뭄에 대응하기 위한 구조적 방법으로써 우리나라와 같이 지표수에 의존적인 지역에서는 가용 수자원을 확보하기 위한 확실한 수단으로 활용되어왔다. 신규 댐의 건설은 대상 하천의 수리학적 특성에 큰 변화를 야기할 수 있으며, 댐의 안정성 및 하천의 하도보호를 위해 댐 주변의 수리적 특성의 변화에 대해서도 사전에 인지하여 설계 시 반영할 필요가 있다. 수공구조물 신설에 따라 변화되는 수리학적 특성을 분석하는 방법으로는 그 수단에 따라 상사법칙을 적용한 수리모형실험과 수치기법을 활용한 수치모형실험으로 나눌 수 있다. 본 연구에서는 수리모형실험을 통한 실험적 방법을 이용하여 댐 상하류 구간의 수리학적 특성을 분석하였다. 해당 실험은 한국농어촌공사 농어촌연구원의 대형수리모형실험시설에서 수행되었다. 적용대상 댐의 제원은 높이 약 70m, 길이 약 230m이며, 폭 55m의 여수로를 포함하는 구조로 설정하였다. 수리모형은 Froude 상사법칙을 적용하여 1/30 규모로 축소하였으며, 콘크리트 및 아크릴 재료를 이용하여 제작되었다. 댐 모형이 설치되는 하천구간은 댐 구조물을 포함하여 실규모를 기준으로 흐름방향으로 약 800m, 하폭방향으로 약 450m의 범위를 포함하도록 설계되었으며, 하류구간에 사행하천이 존재하는 것이 특징이다. 본 실험에서 유량은 총 12개의 펌프를 이용하여 공급되었으며, 최대 4cms에 해당하는 유량공급이 가능하도록 설계하였다. 공급유량은 정교한 절차에 의해 보정된 전자식 유량계를 통해 통제되었으며, 사용된 유량계의 허용오차는 약 0.5% 수준인 것으로 나타났다. 수위 측정은 오차범위 0.05mm 수준의 초음파 수위측정기를 이용하였으며, 유속측정은 약 0.5cm/s의 정확도를 지닌 3차원 전자기 유속계를 이용한 접촉식 측정과 흐름구조 가시화를 위한 비접촉식 입자추적기법을 병행하였다. 실험조건은 실규모 기준으로 방류량 3,000~5,000cms에 대해 수행하였으며, 각 방류량별 댐 상하류의 흐름패턴에 대해 정량적으로 분석하였다.

• The Journal of the Acoustical Society of Korea
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• v.25 no.3
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• pp.144-150
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• 2006

Clinicians usually use stethoscopic auscultation for the diagnosis of heart diseases. However, the heart sound signal has varying characteristics due to the noise and/or the conditions of the patients. Also, it is not easy for junior clinicians to find the acoustical differences between different kinds or heart sound signals. which may result in errors in the diagnosis. Thus it will be quite useful for the clinicians to make use of an automatic classification system using signal processing techniques. In this paper, we propose to use hidden Markov models in stead of artificial neural networks which have been conventionally used for the automatic classification of heart sounds. In the experiments classifying heart sound signals. we could see that the proposed methods were quite successful in the classification accuracy.

• Smart Media Journal
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• v.12 no.11
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• pp.1-9
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• 2023

Stress is a significant issue in modern society, often triggered by external or internal factors that are difficult to manage. When high stress persists over a long term, it can develop into a chronic condition, negatively impacting health and overall well-being. However, it is challenging for individuals experiencing chronic stress to recognize their condition, making early detection and management crucial. Using biosignals measured from wearable devices to detect stress could lead to more effective management. However, there are two main problems with using biosignals: first, manually extracting features from these signals can introduce bias, and second, the performance of classification models can vary greatly depending on the subject of the experiment. This paper proposes a model that reduces bias using convo utional autoencoders, which can represent the key features of data, and enhances generalizability by employing soft voting, a method of ensemble learning, to minimize performance variability. To verify the generalization performance of the model, we evaluate it using LOSO cross-validation method. The model proposed in this paper has demonstrated superior accuracy compared to previous studies using the WESAD dataset.