• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1910-1911
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• 2007

A new method for estimating and eliminating electrocardiogram (ECG) artifacts from single channel scalp electroencephalogram (EEG) is proposed. The proposed method consists of emphasis of QRS complex from EEG using least squares acceleration (LSA) filter, generation of synchronized pulse with R-peak and ECG artifacts estimation and elimination using adaptive filter. The performance of the proposed method was evaluated using simulated and real EEG recordings, we found that the ECG artifacts were successfully estimated and eliminated in comparison with the conventional multi-channel techniques, which are independent component analysis (ICA) and ensemble average (EA) method. In conclusion, we can conclude that the proposed method is useful for the detecting and eliminating the ECG artifacts from single channel EEG and simple to use for ambulatory/portable EEG monitoring system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3144-3164
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• 2019

Face recognition systems have several potential applications, such as security and biometric access control. Ongoing research is focused to develop a robust face recognition algorithm that can mimic the human vision system. Face pose, non-uniform illuminations, and low-resolution are main factors that influence the performance of face recognition algorithms. This paper proposes a novel method to handle the aforementioned aspects. Proposed face recognition algorithm initially uses 68 points to locate a face in the input image and later partially uses the PCA to extract mean image. Meanwhile, the AdaBoost and the LDA are used to extract face features. In final stage, classic nearest centre classifier is used for face classification. Proposed method outperforms recent state-of-the-art face recognition algorithms by producing high recognition rate and yields much lower error rate for a very challenging situation, such as when only frontal ($0^{\circ}$) face sample is available in gallery and seven poses ($0^{\circ}$, ${\pm}30^{\circ}$, ${\pm}35^{\circ}$, and ${\pm}45^{\circ}$) as a probe on the LFW and the CMU Multi-PIE databases.

• Journal of the Korea Convergence Society
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• v.9 no.12
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• pp.9-15
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• 2018

The main goal of extended data expression is to develop a data structure suitable for common problems in ubiquitous environments. The greatest feature of this method is that the attribute values can be represented with probability. The next feature is that each event in the training data has a weight value that represents its importance. After this data structure has been developed, an algorithm has been devised that can learn it. In the meantime, this algorithm has been applied to various problems in various fields to obtain good results. This paper first introduces the extended data expression technique, UChoo, and rule refinement method, which are the theoretical basis. Next, this paper introduces some examples of application areas such as rule refinement, missing data processing, BEWS problem, and ensemble system.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.227-227
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• 2016

일반적으로 가뭄은 신뢰성 높고 활용이 쉬운 강수량 자료를 활용하여 판단되고 있으나, 복합적인 대응을 하기 위해서는 증발산량, 토양수분 등 다양한 변수를 고려해야 한다. 이러한 수문기상정보들은 관측자료의 자료 확보기간이 통계 분석을 하기에 짧거나, 시공간적 대표성 부족 등의 단점이 있다. 이러한 문제점을 극복하기 위해 지면모델이 대안으로 널리 활용중이나, 이를 실제로 가뭄에 활용한 응용연구는 상대적으로 부족한 실정이다. 본 연구에서는 미국 NASA의 전지구지표자료동화체계 GLDAS (Global Land Data Assimilation System) 산출물을 활용하여 지면모델 기반의 수문기상정보를 국내 가뭄감시 연구에 적용하고자 하였다. 이를 위해, GLDAS 프로젝트를 통해 제공되는 다중모델 기반의 증발산량, 토양수분 결과를 비교 분석하고 이를 직접 활용할 수 있는 가뭄판단 지수에 적용하여 성능을 검토하였다. 이를 통해 GLDAS 산출 정보가 가뭄판단에 있어 발휘하는 성능을 평가함으로써, 향후 본원에서 구축할 지면 모델 앙상블 시스템의 가뭄감시정보 산출의 효과를 간접적으로 검토하고자 한다.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.4
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• pp.361-368
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• 2022

An indoor navigation system that utilizes long-term evolution (LTE) signals has the benefit of no additional infrastructure installation expenses and low base station database management costs. Among the LTE signal measurements, received signal strength (RSS) is particularly appealing because it can be easily obtained with mobile devices. Propagation channel models can be used to estimate the position of mobile devices with RSS. However, conventional channel models have a shortcoming in that they do not discriminate between line-of-sight (LOS) and non-line-of-sight (NLOS) conditions of the received signal. Accordingly, a previous study has suggested separated LOS and NLOS channel models. However, a method for determining LOS and NLOS conditions was not devised. In this study, a machine learning-based LOS/NLOS classification method using RSS measurements is developed. We suggest several machine-learning features and evaluate various machine-learning algorithms. As an indoor experimental result, up to 87.5% classification accuracy was achieved with an ensemble algorithm. Furthermore, the range estimation accuracy with an average error of 13.54 m was demonstrated, which is a 25.3% improvement over the conventional channel model.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.428-439
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• 2020

The continuous development of information and communication technologies has resulted in an exponential increase in data. Consequently, technologies related to data analysis are growing in importance. The shipbuilding industry has high production uncertainty and variability, which has created an urgent need for data analysis techniques, such as machine learning. In particular, the industry cannot effectively respond to changes in the production-related standard time information systems, such as the basic cycle time and lead time. Improvement measures are necessary to enable the industry to respond swiftly to changes in the production environment. In this study, the lead times for fabrication, assembly of ship block, spool fabrication and painting were predicted using machine learning technology to propose a new management method for the process lead time using a master data system for the time element in the production data. Data preprocessing was performed in various ways using R and Python, which are open source programming languages, and process variables were selected considering their relationships with the lead time through correlation analysis and analysis of variables. Various machine learning, deep learning, and ensemble learning algorithms were applied to create the lead time prediction models. In addition, the applicability of the proposed machine learning methodology to standard work hour prediction was verified by evaluating the prediction models using the evaluation criteria, such as the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Logarithmic Error (RMSLE).

• Steel and Composite Structures
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• v.43 no.5
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• pp.625-637
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• 2022

Axial compression capacity (P_u) is a significant yet complex parameter of concrete-filled steel tube (CFST) columns. This study offers a novel ensemble tool, adaptive neuro-fuzzy inference system (ANFIS) supervised by equilibrium optimization (EO), for accurately predicting this parameter. Moreover, grey wolf optimization (GWO) and Harris hawk optimizer (HHO) are considered as comparative supervisors. The used data is taken from earlier literature provided by finite element analysis. ANFIS is trained by several population sizes of the EO, GWO, and HHO to detect the best configurations. At a glance, the results showed the competency of such ensembles for learning and reproducing the P_u behavior. In details, respective mean absolute errors along with correlation values of 4.1809% and 0.99564, 10.5947% and 0.98006, and 4.8947% and 0.99462 obtained for the EO-ANFIS, GWO-ANFIS, and HHO-ANFIS, respectively, indicated that the proposed EO-ANFIS can analyze and predict the behavior of CFST columns with the highest accuracy. Considering both time and accuracy, the EO provides the most efficient optimization of ANFIS and can be a nice substitute for experimental approaches.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.728-735
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• 2022

Roofers get exposed to increased risk of knee musculoskeletal disorders (MSDs) at different phases of a sloped shingle installation task. As different phases are associated with different risk levels, this study explored the application of machine learning for automated classification of seven phases in a shingle installation task using knee kinematics and roof slope information. An optical motion capture system was used to collect knee kinematics data from nine subjects who mimicked shingle installation on a slope-adjustable wooden platform. Four features were used in building a phase classification model. They were three knee joint rotation angles (i.e., flexion, abduction-adduction, and internal-external rotation) of the subjects, and the roof slope at which they operated. Three ensemble machine learning algorithms (i.e., random forests, decision trees, and k-nearest neighbors) were used for training and prediction. The simulations indicate that the k-nearest neighbor classifier provided the best performance, with an overall accuracy of 92.62%, demonstrating the considerable potential of machine learning methods in detecting shingle installation phases from workers knee joint rotation and roof slope information. This knowledge, with further investigation, may facilitate knee MSD risk identification among roofers and intervention development.

• Proceedings of KOSOMES biannual meeting
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• 2005.11a
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• pp.153-158
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• 2005

The turbulent shear flaw around a surface-mounted vertical wall was investigated using the two-frame PIV(CACTUS 3.1) system. From this study, it is revealed that at least 500 instantaneous velocity field data are required for ensemble average to get reliable turbulence statistics, but only 200 field data are sufficient for the time-averaged mean velocity information The flow has an unsteady recirculation region post vertical wall with bottom gap, followed by a slow relaxation to the fiat-plate boundary layer flow. The time-averaged reattachment length estimated from the streamline distribution is about x/H=3H. The large eddy structure in the separated shear layer seems to have signification influence on the development of the separated shear layer and the reattachment process.

• Korean Journal of Ecology and Environment
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• v.42 no.4
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• pp.502-509
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• 2009

In this study a total of 27 stream sites, at 1 km intervals, were monitored for simple physicochemical water characteristics, land coverage patterns, and stream environment characteristics using the Habitat-riparian Indexing System (HIS), in the Yangsan Stream. The HIS has been tested in previous research, resulting in some identification of advantages in the application to the stream ecosystems data. Even though reliable stream environment characterization was possible using HIS, there was no information about the application of this tool to present continuity of environmental changes in stream systems. Also the necessity was raised to compare the results of HIS application with land coverage information in order to provide useful information in management strategy development. The monitoring results of this study showed that changes of environmental degradation were well represented by HIS. Especially, stream environment degradation due to construction was relatively well reflected in the HIS monitoring results, and the main causality of Yangsan Stream degradation was expansion of the urbanized area. In addition, there were significant relationships between the HIS scores and land coverage information. Therefore, it is necessary to prepare appropriate options in controlling or managing the expansion of the industrialized areas in this stream basin in order to improve the stream environment. For this purpose, ensemble utilization of HIS results, water quality, and geographical information, resulting in integration with remote sensing processes can be possible.