• 한국국방경영분석학회지
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• 제8권1호
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• pp.71-76
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• 1982

A procedure is established for combining a regression model and a time series model to fit to a set of autocorrelated data. This procedure is based on an iterative method to compute regression parameter estimates and time series parameter estimates simultaneously. The time series model which is discussed is basically AR(p) model, since MA(q) model or ARMA(p,q) model can be inverted to AR({$\infty$) model which can be approximated by AR(p) model. The procedure discussed in this articled is applied in general to any combination of regression model and time series model.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.433-436
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• 2008

A demand on the spatial data management has been rapidly increased with the introduction and diffusion process of ITS, Telematics, and Wireless Sensor Network, and many different people use the digital map that offers various thematic spatial data. Spatial data for digital map can manage to tile-based and feature-based data. The existing tile-based digital map management systems have difficult problems of data construction, history management, and updating based on a spatial object. In order to solve these problems, this paper proposed the data model for the feature-based digital map management system that is designed for feature-based seamless map, history management, real-time updating of spatial data, and analyzed the validity and utility of the proposed model.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.670-673
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• 2002

This paper proposes a digital algorithm for gray-scale/color image segmentation of real-time video signals and a cell-network-based implementation architecture in state-of-the-art CMOS technology. Through extrapolation of design and simulation results we predict that about 300$\times$300 pixels can be integrated on a chip at 100nm CMOS technology, realizing very high-speed segmentation at about 1600sec per color image. Consequently real-time color-video segmentation will become possible in near future.

• ETRI Journal
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• 제35권6호
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• pp.949-959
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• 2013

Since the recent launch of Microsoft Xbox Kinect, research on 3D human pose estimation has attracted a lot of attention in the computer vision community. Kinect shows impressive estimation accuracy and real-time performance on massive graphics processing unit hardware. In this paper, we focus on further reducing the computation complexity of the existing state-of-the-art method to make the real-time 3D human pose estimation functionality applicable to devices with lower computing power. As a result, we propose two simple approaches to speed up the random-forest-based human pose estimation method. In the original algorithm, the random forest classifier is applied to all pixels of the segmented human depth image. We first use a multi-scale approach to reduce the number of such calculations. Second, the complexity of the random forest classification itself is decreased by the proposed cascade approach. Experiment results for real data show that our method is effective and works in real time (30 fps) without any parallelization efforts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.1-14
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• 2024

Time-series forecasting is extensively used in the actual world. Recent research has shown that Transformers with a self-attention mechanism at their core exhibit better performance when dealing with such problems. However, most of the existing Transformer models used for time series prediction use the traditional encoder-decoder architecture, which is complex and leads to low model processing efficiency, thus limiting the ability to mine deep time dependencies by increasing model depth. Secondly, the secondary computational complexity of the self-attention mechanism also increases computational overhead and reduces processing efficiency. To address these issues, the paper designs an efficient multi-layer attention-based time-series forecasting model. This model has the following characteristics: (i) It abandons the traditional encoder-decoder based Transformer architecture and constructs a time series prediction model based on multi-layer attention mechanism, improving the model's ability to mine deep time dependencies. (ii) A cross attention module based on cross attention mechanism was designed to enhance information exchange between historical and predictive sequences. (iii) Applying a recently proposed sparse attention mechanism to our model reduces computational overhead and improves processing efficiency. Experiments on multiple datasets have shown that our model can significantly increase the performance of current advanced Transformer methods in time series forecasting, including LogTrans, Reformer, and Informer.

• Journal of Mechanical Science and Technology
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• 제20권12호
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• pp.2147-2158
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• 2006

The continuous wavelet transform (CWT) has a frequency-adaptive time-frequency tiling property, which makes it popular for the analysis of dispersive elastic wave signals. However, because the time-frequency tiling of CWT is not signal-dependent, it still has some limitations in the analysis of elastic waves with spectral components that are dispersed rapidly in time. The objective of this paper is to introduce an advanced time-frequency analysis method, called the dispersion-based continuous wavelet transform (D-CWT) whose time-frequency tiling is adaptively varied according to the dispersion relation of the waves to be analyzed. In the D-CWT method, time-frequency tiling can have frequency-adaptive characteristics like CWT and adaptively rotate in the time-frequency plane depending on the local wave dispersion. Therefore, D-CWT provides higher time-frequency localization than the conventional CWT. In this work, D-CWT method is applied to the analysis of dispersive elastic waves measured in waveguide experiments and an efficient procedure to extract information on the dispersion relation hidden in a wave signal is presented. In addition, the ridge property of the present transform is investigated theoretically to show its effectiveness in analyzing highly time-varying signals. Numerical simulations and experimental results are presented to show the effectiveness of the present method.

• ETRI Journal
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• 제43권2호
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• pp.197-208
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• 2021

Single-carrier frequency division multiple access (SC-FDMA) is currently being used in long-term evolution uplink communications owing to its low peak-to-average power ratio (PAPR). This study proposes a new transceiver design for an SC-FDMA system based on Walsh-Hadamard transform (WHT). The proposed WHT-based SC-FDMA system has low-PAPR and better bit-error rate (BER) performance compared with the conventional SC-FDMA system. The WHT-based SC-FDMA transmitter has the same complexity as that of discrete Fourier transform (DFT)-based transmitter, while the receiver's complexity is higher than that of the DFT-based receiver. The exponential companding technique is used to reduce its PAPR without degrading its BER. Moreover, the performances of different ordered WHT systems have been studied in additive white Gaussian noise and multipath fading environments. The proposed system has been verified experimentally by considering a real-time channel with the help of wireless open-access research platform hardware. The supremacy of the proposed transceiver is demonstrated based on simulated and experimental results.

• 한국항해항만학회지
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• 제28권2호
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• pp.135-140
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• 2004

A purpose of developing a sound source tracking system in this paper is to reduce the noise efficiently from the received signal by microphone array and measure the signal's time delay between the microphones. I have applied the wavelet analysis algorithm to the system and calculated the sound source's relative position For the performance evaluation, I have compared with the results of utilizing the digital filtering methods based on the FIR LPF using Kaiser window function and the inverse Chebyshev IIR LPF. As a result, I have confirmed the fact that 'time-scale' filter using inverse discrete wavelet transform was suitable for this system.

• 기본간호학회지
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• 제18권3호
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• pp.300-309
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• 2011

Purpose: The purpose of this study was to investigate facilitators of evidence-based practice (EBP) in Clinical Nurses. Method: The instruments used in study were the EBP facilitator scale developed by Nagy et al. and a questionnaire on EBP-related characteristics. Data were collected from 230 nurses at a tertiary hospital and analyzed by descriptive statistics. Results: Compared to previous studies, this study showed that nurses had more experience related to research courses and clinical research. However, the proportion of nurses who reviewed relevant articles still remained low. The respondents had positive perceptions of organizational supports for EBP and belief in the value of EBP, whereas they had negative perceptions of skills in locating and evaluating research reports, knowledge of research terms and statistics, and time to devote to EBP. Conclusion: The findings of the study provide important basic data to develop and implement an EBP programs. In future, EBP programs should cover the nurses' skills to search and review research literature as well as their knowledge of research terms and statistics. Furthermore, nurses will require help to ensure that there is adequate time to devote to EBP.

• Architectural research
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• 제8권2호
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• pp.1-10
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• 2006

This research has acquired preliminary information about the existing conditions and understanding of members regarding time management for members in the architectural field. The goal of this research is to construct a basis for the time management education framework in architectural field in the future. This research mainly focuses on following questions based on limited objectives: Do groups of academics and professionals have understanding and knowledge of time management? Can the level of an individual's scheduling techniques be correlated to the patterns of knowledge and understanding of time management principles and practices? Can the time management practice status in individual's working environment be correlated to the patterns of knowledge and understanding of time management principles and practices? Can an individual's self-confidence level be correlated to the patterns of knowledge and understanding of time management principles and practices? Data have been collected through comprehensive questionnaires given to academics and professionals in United States. By means of statistical analysis, the hidden patterns, deficiencies and relationships in attitudes about time management have been revealed. The statistical analysis has produced conclusions that, among several subdivisions, self-discipline and planning have strong relationships and confidence, personal organization, control, and information gathering subdivisions have certain relationships with objectives of time management education in this research.