• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.383-403
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• 2021

With the development of mobile edge computing (MEC), some late-model application technologies, such as self-driving, augmented reality (AR) and traffic perception, emerge as the times require. Nevertheless, the high-latency and low-reliability of the traditional cloud computing solutions are difficult to meet the requirement of growing smart cars (SCs) with computing-intensive applications. Hence, this paper studies an efficient offloading decision and resource allocation scheme in collaborative vehicular edge computing networks with multiple SCs and multiple MEC servers to reduce latency. To solve this problem with effect, we propose a context-aware offloading strategy based on differential evolution algorithm (DE) by considering vehicle mobility, roadside units (RSUs) coverage, vehicle priority. On this basis, an autoregressive integrated moving average (ARIMA) model is employed to predict idle computing resources according to the base station traffic in different periods. Simulation results demonstrate that the practical performance of the context-aware vehicular task offloading (CAVTO) optimization scheme could reduce the system delay significantly.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.6
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• pp.87-96
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• 2014

Since markup is defined as price over marginal cost by Hall(1988), the New Keynesians have intensively applied its definition in elucidating the relationship between market structure and business cycle. In lots of literatures markups proved to be counter cyclical empirically and theoretically. At the same time many studies analysed the determinants for markup in relation with business cycles. This paper establishes the markup equation based on the constant returns to scale production function including intermediate goods with technology being assumed to be AR(1) process and estimates the industrial markups in the Korean 5 service industries over the period 1975:1-2010:4. The paper also analyzed the markup determinants using the autoregressive distributed lag scheme ADL(1,1) in which the dependent variable and the single explanatory variable are each lagged once.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1752-1758
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• 2010

A FM-CW radar is used for the various purposes as a remote sensing device since it has the advantages of the relatively simple implementation and the low probability of signal interception. A FM-CW radar uses the same frequency modulated continuous wave for both transmission and demodulation. Therefore, the received beat frequency represents the range and Doppler information of targets. However, using the conventional FFT method, the degree of accuracy and resolution in the spectrum estimation can be seriously degraded in the detection and tracking of fast moving targets because of the short dwell time. Therefore, in this paper, the model parameter estimation methods called as an autoregressive method is applied to overcome these problems and showed that the improved accuracy and resolution can be obtained for the target range and velocity estimation.

• Journal of Korea Water Resources Association
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• v.35 no.6
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• pp.763-777
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• 2002

The objective of this study is to develop computer simulation model that produces precipitation patterns from stochastic model. The hourly precipitation process consists of the precipitation occurrence and precipitation amounts. In this study, an event cluster model developed by Lee and Lee(2002) is used to describe the occurrence process of events, and the hourly precipitation amounts within each event is described by a nonstationary form of a first-order autoregressive process. The complete stochastic model for hourly precipitation is fitted to historical precipitation data by estimating the model parameters. An analysis of historical and simulated hourly precipitation data for Seoul indicates that the stochastic model preserves many of the features of historical precipitation. The autocorrelation coefficients of the historical and simulated data are nearly identical except for lags more than about 3 hours. The precipitation intensity, duration, marginal distributions, and conditional distributions for event characteristics for the historical and simulated data showed in general good agreement with each other.

• Water for future
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• v.29 no.6
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• pp.167-178
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• 1996

The simulation techniques of hydrologic data series have been developed for the purposes of the design of water resources system, the optimization of reservoir operation, and the design of flood control of reservoir, etx. While the stochastic models are usually used in most analysis of water resources fields for the generation of data sequences, the indexed sequential modeling (ISM) method based on generation of a series of overlapping short-term flow sequences directly from the historical record has been used for the data generation in western USA since the early of 1980's. It was reported that the reliable results by ISM were obtained in practical applications. In this study, we generate annual inflow series at a location of Hong Cheon Dam site by using ISM method and first order autoregressive model (AR(1)), and estimate the drought characteristics for the comparison aim between ISM and AR(1).

• Journal of the Korean Society of Clothing and Textiles
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• v.24 no.4
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• pp.605-615
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• 2000

This study was carried out to investigate sound characteristics including sound parameters and subjective sensation, and primary hand values related with sound of fabrics for blouse, and furthermore to predict subjective sound sensation with mechanical properties and sound parameters. Sound of specimens was analyzed by FFT. Level pressure of total sound(LPT), loudness(Z), coefficients of autoregressive(AR) functions for fitting the spectra, and sound color factors(ΔL and Δf) were obtained as sound parameters. Primary hand values for women's thin dress were calculated by using KES-FB. Subjective sensation for sound including softness, loudness, sharpness, clearness, roughness, highness, and pleasantness was evaluated by free modulus magnitude estimation. The results were as follows; 1. Fabrics for blouse showed similar spectral shapes to one another in that amplitude values were lower in most ranges of frequencies than fabrics for other uses. 2. It was found that fabrics for blouse were less louder because LPT, loudness(Z), and ARC values were lower than other fabrics. 3. Primary hand values indicated that specimens were soft-touched, flexible, and less crisp. Among primary hands related with sound, Shari values were higher for silk fabrics than for synthetic ones, while the values for Kishimi were similar, 4. Fabrics for blouse were rated more highly for softness, clearness, and pleasantness than for loudness, sharpness. roughness, and highness. Silk fabrics were evaluated more pleasant than synthetic fabrics. 5. Subjective sensation for sound of blouse fabrics were predicted with mechanical properties and physical sound parameters.

• Journal of the Korean Society of Clothing and Textiles
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• v.25 no.9
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• pp.1593-1602
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• 2001

본 연구는 스포츠 아우터웨어용 나일론 직물의 소리에 대한 주관적 감각과 이에 관련된 객관적 측정치를 규명하기 위하여, 서로 다른 8종의 나일론 직물의 소리의 스펙트럼 파형을 고찰하였으며, 소리 파라미터로 총음압(level pressure of total sound, LPT),세 가지 AR (autoregressive)계수, Zwicker의 심리음향학적 모델에 따른 크기(Z)와 날카로움(Z)를 계산하였고, Kawabata Evaluation System(KES)으로 직물의 물리적 성질을 측정하였다. 주관적 감각 평가를 위하여 피험자에게 녹음된 각 직물소리를 들려주어 7개 소리 감각 (부드러움, 시끄러움, 날카로움, 맑음, 거 침, 높음, 유쾌함)을 의미분별척도로 답하게 한 후, 단계적 선형 회귀식을 이용하여 직물 소리의 주관적 감각에 대한 예측 모델을 제시하였다. 울트라스웨이드를 제외한 태피터 나일론 직물들은 스펙트럼 파형 에서 다른 조성 섬유의 직물들보다 음압 값이 높고, 총음압이 60dB 안팎의 값을 보여, 착용자에게 불쾌감을 줄 것으로 예상되었으며, 주관적 감각 평가에서도 소리의 부드러움과 맑음, 유쾌함에서 음의 점수를, 시끄러움과 날카로움, 거침, 높음에서 양의 점수를 얻었다. 주관적 감각의 예측모델에서 총음압은 시끄러움과 거침에 정적 영향을, 유쾌함에 부적 영향을 미쳐서 나일론 직물 소리의 총음압이 50dB 이하일 때 주관적으로 유쾌하게 느껴지는 것으로 나타났다.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.11
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• pp.1069-1076
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• 2016

Recent development in visual sensor technologies has encouraged various researches on adding imaging capabilities to sensor networks. Video data are bigger than other sensor data, so it is essential to manage the amount of image data efficiently. In this paper, a new method of video traffic estimation is proposed for efficient traffic management of visual sensor networks. In the proposed method, a first order autoregressive model is used for modeling the traffic with the consideration of the characteristics of video traffics acquired from visual sensors, and a Kalman filter algorithm is used to estimate the amount of video traffics. The proposed method is computationally simple, so it is proper to be applied to sensor nodes. It is shown by experimental results that the proposed method is simple but estimate the video traffics exactly by less than 1% of the average.

• ETRI Journal
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• v.39 no.5
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• pp.718-728
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• 2017

Empirical modeling of wireless fading channels using common schemes such as autoregression and the finite state Markov chain (FSMC) is investigated. The conceptual background of both channel structures and the establishment of their mutual dependence in a confined manner are presented. The novel contribution lies in the proposal of a new approach for deriving the state transition probabilities borrowed from economic disciplines, which has not been studied so far with respect to the modeling of FSMC wireless fading channels. The proposed approach is based on equal portioning of the received signal-to-noise ratio, realized by using an alternative probability construction that was initially highlighted by Tauchen. The associated statistical procedure shows that a first-order FSMC with a limited number of channel states can satisfactorily approximate fading. The computational overheads of the proposed technique are analyzed and proven to be less demanding compared to the conventional FSMC approach based on the level crossing rate. Simulations confirm the analytical results and promising performance of the new channel model based on the Tauchen approach without extra complexity costs.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.4
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• pp.23-27
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• 2010

This paper performs emotion classification test to find out the best parameter of electroencyphalogram(EEG) signal. Linear predictor coefficients, band cross-correlation coefficients of fast Fourier transform(FFT) and autoregressive model spectra are used as the parameters of 10-channel EEG signal. A multi-layer neural network is used as the pattern classifier. Four emotions for relaxation, joy, sadness, irritation are induced by four university students of an acting circle. Electrode positions are Fp1, Fp2, F3, F4, T3, T4, P3, P4, O1, O2. As a result, the Linear predictor coefficients showed the best performance.