• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.127-135
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• 2020

The purpose of this study is to develop a human activity recognition (HAR) system, which distinguishes 13 activities, including five activities commonly dealt with in conventional HAR researches and eight activities from the Eastern-Asian culture. The eight special activities include floor-sitting/standing, chair-sitting/standing, floor-lying/up, and bed-lying/up. We used a 3-axis accelerometer sensor on the wrist for data collection and designed a machine learning model for the activity classification. Data clustering through preprocessing and feature extraction/reduction is performed. We then tested six machine learning algorithms for recognition accuracy comparison. As a result, we have achieved an average accuracy of 99.7% for the 13 activities. This result is far better than the average accuracy of current HAR researches based on a smartwatch (89.4%). The superiority of the HAR system developed in this study is proven because we have achieved 98.7% accuracy with publically available 'pamap2' dataset of 12 activities, whose conventionally met the best accuracy is 96.6%.

• ETRI Journal
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• v.33 no.4
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• pp.569-579
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• 2011

This paper presents a novel approach for human activity recognition (HAR) using the joint angles from a 3D model of a human body. Unlike conventional approaches in which the joint angles are computed from inverse kinematic analysis of the optical marker positions captured with multiple cameras, our approach utilizes the body joint angles estimated directly from time-series activity images acquired with a single stereo camera by co-registering a 3D body model to the stereo information. The estimated joint-angle features are then mapped into codewords to generate discrete symbols for a hidden Markov model (HMM) of each activity. With these symbols, each activity is trained through the HMM, and later, all the trained HMMs are used for activity recognition. The performance of our joint-angle-based HAR has been compared to that of a conventional binary and depth silhouette-based HAR, producing significantly better results in the recognition rate, especially for the activities that are not discernible with the conventional approaches.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.49-62
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• 2022

Realized volatility is well known to have long memory, strong association with other global financial markets and interdependences among macroeconomic indices such as exchange rate, oil price and interest rates. This paper proposes autoencoder factor-augmented heterogeneous autoregressive (AE-FAHAR) model for realized volatility forecasting. AE-FAHAR incorporates long memory using HAR structure, and exogenous variables into few factors summarized by autoencoder. Autoencoder requires intensive calculation due to its nonlinear structure, however, it is more suitable to summarize complex, possibly nonstationary high-dimensional time series. Our AE-FAHAR model is shown to have smaller out-of-sample forecasting error in empirical analysis. We also discuss pre-training, ensemble in autoencoder to reduce computational cost and estimation errors.

• Journal of Advanced Navigation Technology
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• v.28 no.4
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• pp.428-435
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• 2024

In this paper, a human activity regeneration (HAR) system based on multiple input multiple output frequency modulation continuous wave (MIMO FMCW) radar was designed and implemented. Using point cloud data from MIMO radar sensors has advantages in terms of privacy, safety, and accuracy. For the implementation of the HAR system, a customized neural network based on PointPillars and depthwise separate convolutional neural network (DS-CNN) was developed. By processing high-resolution point cloud data through a lightweight network, high accuracy and efficiency were achieved. As a result, the accuracy of 98.27% and the computational complexity of 11.27M multiply-accumulates (Macs) were achieved. In addition, the developed neural network model was implemented on Raspberry-Pi embedded system and it was confirmed that point cloud data can be processed at a speed of up to 8 fps.

• Communications for Statistical Applications and Methods
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• v.24 no.4
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• pp.367-382
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• 2017

We consider an infinite-order long-memory heterogeneous autoregressive (HAR) model, which is motivated by a long-memory property of realized volatilities (RVs), as an extension of the finite order HAR-RV model. We develop bootstrap tests for structural mean or variance changes in the infinite-order HAR model via stationary bootstrapping. A functional central limit theorem is proved for stationary bootstrap sample, which enables us to develop stationary bootstrap cumulative sum (CUSUM) tests: a bootstrap test for mean break and a bootstrap test for variance break. Consistencies of the bootstrap null distributions of the CUSUM tests are proved. Consistencies of the bootstrap CUSUM tests are also proved under alternative hypotheses of mean or variance changes. A Monte-Carlo simulation shows that stationary bootstrapping improves the sizes of existing tests.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.07a
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• pp.233-236
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• 2003

수소 가스발생을 위한 마이크로 수소 발생기 개발에서 MEMS 공정을 이용하여 기판에 반응 유로를 위해 HAR(High Aspect Ratio) 구조물을 형성하고 Ru(ruthenium) 박막을 증착하여 수소 발생량을 측정하였다. Pyrex glass 기판상에 sand blast 방법으로 반응 구조물을 만들었으며, 그 위에 sputter system을 이용하여 Ru 박막을 $5500{\AA}$었다. 수소 발생량은 촉매 박막이 증착된 기판 재질과 기판의 표면 상태 그리고 마이크로 수소 발생기에 두께로 증착하였다. 반응 구조물의 전체 크기가 가로 2.0 cm, 세로 2.0cm의 면적에서 약 12.3 ml/min의 수소가 측정되 형성한 구조물의 형상에 의존하였다. Pyrex glass 기판을 사용하여 HAR로 반응 구조물을 형성한 경우에 단위 면적당 Ru 박반응 막의 반응 표면적이 증가되어 기존에 구조물을 형성하지 않은 평면 기판에 비교하여 약 5.5배 이상의 수소 발생이 증가하였다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.463-464
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• 2006

This paper present experimental results by non-contact mode Atomic Force Microscopy using high aspect ratio tips (HAR-T). We fabricated the carbon nanotube tip based on dielectrophoresis and the carbon nano probe by focused ion beam after dielectrophoretic assembling. In this paper, we measure AAO sample and trench structure to estimate HAR-T's performance and compared with conventional Si tip. We confirmed that results of HAR-T's performance in non contact mode was very superior than conventional tip.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.120-124
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• 2021

Recently, human attribute recognition (HAR) attracts a lot of attention due to its wide application in video surveillance systems. Recent deep-learning-based solutions for HAR require time-consuming training processes. In this paper, we propose a post-processing technique that utilizes the historical video frames to improve prediction results without invoking re-training or modifying existing deep-learning-based classifiers. Experiment results on a large-scale benchmark dataset show the effectiveness of our proposed method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.8
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• pp.731-736
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• 2016

Three-dimensional (3D) high-aspect-ratio (HAR) microstructures for biomedical applications (e.g., microneedle, microadhesive, etc.) are microfabricated using the hybrid ultraviolet (UV) lithography in which inclined, rotational, and reverse-side UV exposure processes are combined together. The inclined and rotational UV exposure processes are intended to fabricate tapered axisymmetric HAR microstructures; the reverse-side UV exposure process is designed to sharpen the end tip of the microstructures by suppressing the UV reflection on a bottom substrate which is inevitable in conventional UV lithography. Hybrid UV lithography involves fabricating 3D HAR microstructures with an epoxy-based negative photoresist, SU-8, using our customized UV exposure system. The effects of hybrid UV lithography parameters on the geometry of the 3D HAR microstructures (aspect ratio, radius of curvature of the end tip, etc.) are measured. The dependence of the end-tip shape on SU-8 soft-baking condition is also discussed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.177-183
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• 2022

Recently, interesting of home training is getting bigger due to COVID-19. Accordingly, research on applying HAR(human activity recognition) technology to home training has been conducted. However, existing paper of HAR proposed static activity instead of dynamic activity. In this paper, the deep learning model where dynamic exercise posture can be analyzed and the accuracy of the user's exercise posture can be shown is proposed. Fitness images of AI-hub are analyzed by blaze pose. The experiment is compared with three types of deep learning model: RNN(recurrent neural network), LSTM(long short-term memory), CNN(convolution neural network). In simulation results, it was shown that the f1-score of RNN, LSTM and CNN is 0.49, 0.87 and 0.98, respectively. It was confirmed that CNN is more suitable for human activity recognition than other models from simulation results. More exercise postures can be analyzed using a variety learning data.