• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.83-93
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• 2007

In this paper, we address a power-aware scheduling algorithm for a mixed real-time system which consists of periodic and sporadic tasks, each of which is characterized by its minimum period, worst-case execution requirement and deadline. We propose a dynamic voltage scaling algorithm called DVSMT(DVS for mixed tasks), which dynamically scales down the supplying voltage(and thus the frequency) using on-line distribution of the borrowed resources when jobs complete while still meeting their deadlines. With this scheme, we could reduce more energy consumption. As the proposed algorithm can be easily incorporated with RTOS(Real-Time Operating System), it is applicable for handhold devices and sensor network nodes that use a limited battery power. Simulation results show that DVSMT saves up 60% more than the existing algorithms both in the periodic-task and mixed-task systems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.2
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• pp.106-112
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• 2012

Real-life time series characteristic data has significant amount of non-stationary components, especially periodic components in nature. Extracting such components has required many ad-hoc techniques with external parameters set by users in case-by-case manner. In our study, we evaluate whether Hilbert-Huang Transform, a new tool of time-series analysis can be used for effective analysis of such data. It is divided into two points : 1) how effective it is in finding periodic components, 2) whether we can use its results directly in detecting values outside control limits, for which a traditional method such as ARIMA had been used. We use glass furnace temperature data to illustrate the method.

• IE interfaces
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• v.18 no.4
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• pp.494-503
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• 2005

This paper considers the problem of performance analysis for the multiplexer, when homogeneous periodic on-off sources are statistically multiplexed. Periodic on-off source model is defined that on-period and off-period are repeated by once in a deterministic periodic time and in on-period, cell arrives with deterministic time interval. In order to reflect periodicity of source model, we consider two multiplexing situation, such as random multiplexing and synchronized multiplexing. In both case, we obtain the overload-period distribution in the multiplexer, and an approximate method using the overload period distribution is suggested for obtaining the CLP(Cell Loss Probability) in the buffer of the multiplexer. A numerical example using MPEG-I real traffic samples and the results are also presented.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.955-969
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• 2003

The classical Fourier analysis, which is the typical frequency domain approach, is used to detect periodic trends that are of the sinusoidal shape in time series data. In this article, using a sequence of periodic step functions, describes an adaptive Fourier series where the patterns may take general periodic shapes that include sinusoidal as a special case. The results, which extend both Fourier analysis and Walsh-Fourier analysis, are applies to investigate the shape of the periodic component. Through the real data, compare the goodness-of-fit of the model using two methods, the adaptive Fourier method which is proposed method in this paper and classical Fourier method.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.8
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• pp.898-906
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• 1999

A control structure is introduced for the purpose of rejecting periodic (or repetitive) disturbances on a tracking system. The objective of the proposed structure is to drive the output of the system to the reference input that will result in perfect following without any changing the inner configuration of the system. The structure includes an adaptation block which learns the dynamics of the periodic disturbance and forces the interferences, caused by disturbances, on the output of the system to be reduced. Since the control structure acquires the dynamics of the disturbance by on-line adaptation, it is possible to generate control signals that reject any slowly varying time-periodic disturbance provided that its amplitude is bounded. The artificial neural network is adopted as the adaptation block. The adaptation is done at an on-line process. For this , the real-time recurrent learning (RTRL) algoritnm is applied to the training of the artificial neural network.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06a
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• pp.170-172
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• 2012

실시간 시스템(real-time system)은 논리적 연산을 일정한 시간적 제약 하에서 수행하는 시스템이다. 시간적 제약을 충족하도록 주기적 태스크(periodic task)를 스케줄(schedule)할 때 일반적으로 태스크 오프셋(initial offset)이 0 이거나 고정된 것으로 가정한다. 그러나 오프셋에 약간의 유연성을 허용함으로써 태스크들의 평균 응답 시간을 줄일 수도 있다. 이 논문에서는 주기적 태스크의 오프셋을 주어진 허용 범위 안에서 선택하여 평균 응답 시간(response time)을 최적화할 수 있음을 보이고, 임의의 태스크 집합에 대하여 최적 오프셋이 존재하는 좁은 범위를 제시한다.

• 한국가시화정보학회:학술대회논문집
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• 2002.11a
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• pp.35-38
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• 2002

Variations of temperature and velocity fields in a Hele-Shaw Convection Cell (HSC) were measured using a holographic interferometry and PIV technique with varying Rayleigh number. Experimental results show a steady flow pattern at low Rayleigh numbers and a time-dependent periodic flow at high Rayleigh numbers. Two different measurement methods of holographic interferometry, double-exposure method and real-time method, were employed to measure the temperature field variations of HSC convective flow. In the double-exposure method, unwanted waves can be eliminated and reconstruction images are clear, but transient flow structure cannot be observed clearly. On the other hand, transient flow can be observed and reconstructed well using the real-time method. PIV results show that flow inside the HSC is periodic and the oscillating state is well matched with the temperature field results. The holographic interferometry and PIV techniques employed in this study are useful for analyzing the unsteady convective thermal fluid flows.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.1
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• pp.61-69
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• 2020

Systems such as database and socal network systems have been broadly used, and their unexpected failure, with great losses and sometimes a social confusion, has received attention in recent years. Therefore, it is an important issue to find optimal maintenance plans for such kind of systems from the points of system reliability and maintaining cost. However, it is difficult to maintain a system during its working cycle, since stopping works might incur users some troubles. From the above viewpoint, this paper discusses minimal repair maintenance policy with periodic replacement, while considering the random working cycles. The random working cycle and periodic replacement policies with minimal repair has been discussed in traditional literatures by usually analyzing cases for the nonstopping works. However, maintenance can be more conveniently done at discrete time and even during the working cycle in real applications. So, we propose that periodic replacement is planned at discrete times while considering the random working cycle, and moreover provide a model in which system, with a minimal repair at failures between replacements, is replaced at the minimum of discrete times KT and random cycles Y. The average cost rate model is used to determine the optimal number of periodic replacement.

• Journal of Information Technology Services
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• v.16 no.1
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• pp.73-82
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• 2017

Recently in-memory data stream processing has been actively applied to various subjects such as query processing, OLAP, data mining, i.e., frequent item sets, association rules, clustering. However, finding regular periodic patterns of events in an infinite data stream gets less attention. Most researches about finding periods use autocorrelation functions to find certain changes in periodic patterns, not period itself. And they usually find periodic patterns in time-series databases, not in data streams. Literally a period means the length or era of time that some phenomenon recur in a certain time interval. However in real applications a data set indeed evolves with tiny differences as time elapses. This kind of a period is called as a pseudo-period. This paper proposes a new scheme called FPMH (Finding Periods using Multiple Hash functions) algorithm to find such a set of pseudo-periods over a data stream based on multiple hash functions. According to the type of pseudo period, this paper categorizes FPMH into three, FPMH-E, FPMH-PC, FPMH-PP. To maximize the performance of the algorithm in the data stream environment and to keep most recent periodic patterns in memory, we applied decay mechanism to FPMH algorithms. FPMH algorithm minimizes the usage of memory as well as processing time with acceptable accuracy.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.134-142
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• 2021

This paper proposes a probabilistic method in analyzing timing measurements to determine the periodicity of real-time tasks. The proposed method fills a gap in existing techniques, which either concentrate on the estimation of worst-case execution times, or do not consider the stochastic behavior of the real-time scheduler. Our method is based on the Z-test statistical analysis which calculates the probability of the measured period to fall within a user-defined standard deviation limit. The distribution of the measured period should satisfy two conditions: its center (statistical mean) should be equal to the scheduled period of the real-time task, and that it should be symmetrical with most of the samples focused on the center. To ensure that these requirements are met, a data adjustment process, which omits any outliers in the expense of accuracy, is presented. Then, the Z-score of the distribution according to the user-defined deviation limit provides a probability which determines the periodicity of the real-time task. Experiments are conducted to analyze the timing measurements of real-time tasks based on real-time Linux extensions of Xenomai and RT-Preempt. The results indicate that the proposed method is able to provide easier interpretation of the periodicity of real-time tasks which are valuable especially in comparing the performance of various real-time systems.