• International Journal of Contents
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• v.9 no.4
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• pp.1-10
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• 2013

The electroencephalogram (EEG) time series is a measure of electrical activity received from multiple electrodes placed on the scalp of a human brain. It provides a direct measurement for characterizing the dynamic aspects of brain activities. These EEG signals are formed from a series of spatial and temporal data with multiple dimensions. Missing data could occur due to fault electrodes. These missing data can cause distortion, repudiation, and further, reduce the effectiveness of analyzing algorithms. Current methodologies for EEG analysis require a complete set of EEG data matrix as input. Therefore, an accurate and reliable imputation approach for missing values is necessary to avoid incomplete data sets for analyses and further improve the usage of performance techniques. This research proposes a new method to automatically recover random consecutive missing data from real world EEG data based on Linear Dynamical System. The proposed method aims to capture the optimal patterns based on two main characteristics in the coevolving EEG time series: namely, (i) dynamics via discovering temporal evolving behaviors, and (ii) correlations by identifying the relationships between multiple brain signals. From these exploits, the proposed method successfully identifies a few hidden variables and discovers their dynamics to impute missing values. The proposed method offers a robust and scalable approach with linear computation time over the size of sequences. A comparative study has been performed to assess the effectiveness of the proposed method against interpolation and missing values via Singular Value Decomposition (MSVD). The experimental simulations demonstrate that the proposed method provides better reconstruction performance up to 49% and 67% improvements over MSVD and interpolation approaches, respectively.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2010-2014
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• 2005

Object detection and parameter estimation in point cloud data is a relevant subject to robotics, reverse engineering, computer vision, and sport mechanics. In this paper a software is presented for fully-automatic object detection and parameter estimation in unordered, incomplete and error-contaminated point cloud with a large number of data points. The software consists of three algorithmic modules each for object identification, point segmentation, and model fitting. The newly developed algorithms for orthogonal distance fitting (ODF) play a fundamental role in each of the three modules. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model feature and the measurement points. Curvature analysis of the local quadric surfaces fitted to small patches of point cloud provides the necessary seed information for automatic model selection, point segmentation, and model fitting. The performance of the software on a variety of point cloud data will be demonstrated live.

• Journal of IKEEE
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• v.25 no.1
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• pp.76-82
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• 2021

In this paper, the compressed sensing basic pursuit denoise algorithm adopted to synthetic aperture radar imaging is investigated to improve the object recognition. From the incomplete data sets for image processing, the compressed sensing algorithm had been integrated to recover the data before the conventional back- projection algorithm was involved to obtain the synthetic aperture radar images. This method can lead to the reduction of measurement events while scanning the objects. An ultra-wideband radar scheme using a stripmap synthetic aperture radar algorithm was utilized to detect objects hidden behind the box. The Ultra-Wideband radar system with 3.1~4.8 GHz broadband and UWB antenna were implemented to transmit and receive signal data of two conductive cylinders located inside the paper box. The results confirmed that the images can be reconstructed by using a 30% randomly selected dataset without noticeable distortion compared to the images generated by full data using the conventional back-projection algorithm.

• Journal of the Earthquake Engineering Society of Korea
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• v.8 no.6 s.40
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• pp.73-80
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• 2004

A nonlinear time-domain system identification algorithm using measured acceleration data is developed for structural damage assessment. To take account of nonlinear behavior of structural systems, an output error between measured and computed acceleration increments has been defined and a constrained nonlinear optimization problem is solved for optimal structural parameters. The algorithm estimates time-varying properties of stiffness and damping parameters. Nonlinear response of restoring force of a structural system is recovered by using the estimated time-varying structural properties and computed displacement by Newmark-$\beta$ method. In the recovery, no pre-defined model for inelastic behavior has been assumed. In developing the algorithm, noise and incomplete measurement in space and state have been considered. To examine the developed algorithm, numerical simulation and laboratory experimental studies on a three-story shear building have been carried out.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.396-399
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• 2008

Kalman Filter is an efficient recursive filter that estimates the state of a dynamic system from a series of incomplete and noisy measurement. The filter is very powerful in the field of autonomous and assisted navigation. In this paper, we carry out comparative stduy to validate the performance of the application of Kalman Filter. We will build personal localization system based on Cricket mote, our system can present the real-time position of person when the man with PDA moves around. The proposed system is composed of cricket sensor networks, PDA and host computer. There is one listener attached to the PDA. The PDA will get the distance data from the listener synchronously. It will calculate the position of the person in the coordinate of the Cricket system with the trilateration method. Furthermore, it sends the real-time position information to the host computer by Bluetooth. The host computer will use Kalman Filter to process data and get the final estimated track of the person.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.23 no.9
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• pp.691-695
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• 2010

A measurement method of the particle-based reflective display is proposed, estimated, and compared with reported method. The reflectivity measurement by previous studies is simply obtained by integrating sphere, but it has a limitation for the estimation of real moving particles because its data include surface reflection and incomplete attachment on electrodes. To get the number of real moving particles, the area by attached particles on the electrodes is calculated at microscopic signals. The moving particles on subthreshold voltage are observed and this fluctuational variation of surface on subthreshold voltage gives a tip to understand the driving mechanism. By this measurement we ascertained the relationship of a particle layer and real driving particles, and the feasibility of observation and estimation for moving color particles, which were measured by the reflectivity and CIE (Commission Internationale de I'Eclairage) system of color specification at previous studies.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.39 no.5
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• pp.203-206
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• 2013

Statistics is the science of data. As the foundation of scientific knowledge, data refers to evidentiary facts from the nature of reality by human action, observation, or experiment. Clinicians should be aware of the conditions of good data to support the validity of clinical modalities in reading scientific articles, one of the resources to revise or update their clinical knowledge and skills. The cause-effect link between clinical modality and outcome is ascertained as pattern statistic. The uniformity of nature guarantees the recurrence of data as the basic scientific evidence. Variation statistics are examined for patterns of recurrence. This provides information on the probability of recurrence of the cause-effect phenomenon. Multiple causal factors of natural phenomenon need a counterproof of absence in terms of the control group. A pattern of relation between a causal factor and an effect becomes recognizable, and thus, should be estimated as relation statistic. The type and meaning of each relation statistic should be well-understood. A study regarding a sample from the population of wide variations require clinicians to be aware of error statistics due to random chance. Incomplete human sense, coarse measurement instrument, and preconceived idea as a hypothesis that tends to bias the research, which gives rise to the necessity of keen critical independent mind with regard to the reported data.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.6
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• pp.940-953
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• 2006

The major source of carbon monoxide (CO) at the Earth's surface is the incomplete combustion of biomass and fossil fuels. Because the global lifetime of CO is about two months, it can be used as a tracer for pollution from anthropogenic activities and biomass hurtling. In this paper, we introduced the principle and algorithm of the Measurement of Pollution in the Troposphere (MOPITT) instrument for global CO monitoring. The MOPITT instrument, which was launched on the Satellite Terra in 1999, measures CO column and mixing ratio based on gas correlation radiometry. CO levels can be determined by a retrieval algorithm based on the maximum likelihood method minimizing the difference between observed and modeled radiances. MOPITT level 2 data (HDF format) can be downloaded through the Earth Observing System (EOS) data gateway of NASA. ASCII files of CO parameters can be extracted from HDF files, and then temporal and spatial distributions can be obtained. Finally, we showed an example of CO monitoring in April 2000. The locations of forest fires and distribution of MOPITT CO clearly indicated that not only anthropogenic emissions but also forest fires play an important role in CO levels and global CO distribution. Our introduction to MOPITT and the example of MOPITT data interpretation would be helpful for scientists who want to use the EOS data.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.5
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• pp.583-597
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• 2001

Tracking the targets of interest has been one of the major research areas in radar surveillance system. We formulate the tracking problem as an incomplete data problem and apply the EM algorithm to obtain the MAP estimate. The resulting filter has a recursive structure analogous to the Kalman filter. The difference is that the measurement-update deals with multiple measurements and the parameter-update can estimate the system parameters. Through extensive experiments, it turns out that the proposed system is better than PDAF and NNF in tracking the targets. Also, the performance degrades gracefully as the disturbances become stronger.

• Korean Journal of Computational Design and Engineering
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• v.14 no.1
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• pp.10-17
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• 2009

Surface detection and parameter estimation in point cloud is a relevant subject in CAD/CAM, reverse engineering, computer vision, coordinate metrology and digital factory. In this paper we present a software for a fully automatic surface detection and parameter estimation in unordered, incomplete and error-contaminated point cloud with a large number of data points. The software consists of three algorithmic modules each for object identification, point segmentation, and model fitting, which work interactively. Our newly developed algorithms for orthogonal distance fitting(ODF) play a fundamental role in each of the three modules. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model feature and the measurement points. We demonstrate the performance of the software on a variety of point clouds generated by laser radar, computer tomography, and stripe-projection method.