• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.123-128
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• 1992

This paper presents an autotuning algorithm of scaling factor in order to improve system performance. We define the scaling factor of fuzzy controller as a function of error and error change. This function is tuned by the output of performance evaluation level utilizing the error of overshoot and rising time. Simulation results show that the proposed algorithm has good tuning performance for a system with parameter change.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.12
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• pp.2401-2409
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• 2016

Android CPU governors exploit the DVFS (Dynamic Voltage Frequency Scaling) technique. The DVFS is a power management technique where the CPU operating frequency is decreased to allow a corresponding reduction in the CPU supply voltage. The power consumed by a CPU is approximately proportional to the square of the CPU supply voltage. Therefore, lower CPU operating frequency allows the CPU supply voltage to be lowered. This helps to reduce the CPU power consumption. However, lower CPU operating frequency increases a task's execution time. Such an increase in the task's execution time makes the task's response time longer and makes the task's deadline miss occur. This finally leads to degrading the quality of service provided by the task. In this paper, we evaluated the performance of Android CPU governors in terms of the power consumption, tasks's response time and deadline miss ratio.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.11
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• pp.601-610
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• 2002

Intra-task voltage scheduling (IntraVS), which adjusts the supply voltage within an individual task boundary, is an effective technique for developing low-power applications. In this paper, we propose a novel intra-task voltage scheduling algorithm for hard real-time applications based on average-case execution time. Unlike the conventional IntraVS algorithm where voltage scaling decisions are based on the worst-case execution cycles, tile proposed algorithm improves the energy efficiency by controlling the execution speed based on average-case execution cycles while meeting the real-time constraints. The experimental results using an MPEG-4 decoder program show that the proposed algorithm reduces the energy consumption by up to 34% over conventional IntraVS algorithm.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.3
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• pp.73-82
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• 2022

Auto-scaling is one of the most important functions for cloud computing technology. Even if the number of users or service requests is explosively increased or decreased, system resources and service instances can be appropriately expanded or reduced to provide services suitable for the situation and it can improves stability and cost-effectiveness. However, since the policy is performed based on a single metric data at the time of monitoring a specific system resource, there is a problem that the service is already affected or the service instance that is actually needed cannot be managed in detail. To solve this problem, in this paper, we propose a method to predict system resource and service response time using a multivariate time series analysis model and establish an auto-scaling policy based on this. To verify this, implement it as a custom scheduler in the Kubernetes environment and compare it with the Kubernetes default auto-scaling method through experiments. The proposed method utilizes predictive data based on the impact between system resources and response time to preemptively execute auto-scaling for expected situations, thereby securing system stability and providing as much as necessary within the scope of not degrading service quality. It shows results that allow you to manage instances in detail.

• Journal of dental hygiene science
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• v.15 no.3
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• pp.318-324
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• 2015

The purpose of this study is to provide the basic date which is increase the number of revisits for scaling after analyzing factors that influence trend of dental revisits for 7 years. Research data was about 1,471 patients who visited S dental clinic in Seoul at 2007 for scaling. Data from January 2007 to December 2013 was collected. The subjects were divided into 3 groups by their trend in number of dental revisits for scaling: once, 2 times, 3~7 times. The data were analyzed using the chi-squire, independent-samples t-test and one-way ANOVA, binary logistic regression analysis. As a result, the trend of dental revisit for scaling is significantly decreased from first time to second time, and after 3rd time dental revisits were steadily continued. Factors affecting dental revisits for scaling are distance, family hospital, systemic disease, presence or absence of periodontal therapy. According to the results of the study, providing dental service in accordance with the patients' characteristics and increasing the dental revisits for scaling could give a positive influence to improvement of oral health.

• International Journal of Contents
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• v.13 no.2
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• pp.29-34
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• 2017

Cloud computing is becoming an effective and efficient way of computing resources and computing service integration. Through centralized management of resources and services, cloud computing delivers hosted services over the internet, such that access to shared hardware, software, applications, information, and all resources is elastically provided to the consumer on-demand. The main enabling technology for cloud computing is virtualization. Virtualization software creates a temporarily simulated or extended version of computing and network resources. The objectives of virtualization are as follows: first, to fully utilize the shared resources by applying partitioning and time-sharing; second, to centralize resource management; third, to enhance cloud data center agility and provide the required scalability and elasticity for on-demand capabilities; fourth, to improve testing and running software diagnostics on different operating platforms; and fifth, to improve the portability of applications and workload migration capabilities. One of the key features of cloud computing is elasticity. It enables users to create and remove virtual computing resources dynamically according to the changing demand, but it is not easy to make a decision regarding the right amount of resources. Indeed, proper provisioning of the resources to applications is an important issue in IaaS cloud computing. Most web applications encounter large and fluctuating task requests. In predictable situations, the resources can be provisioned in advance through capacity planning techniques. But in case of unplanned and spike requests, it would be desirable to automatically scale the resources, called auto-scaling, which adjusts the resources allocated to applications based on its need at any given time. This would free the user from the burden of deciding how many resources are necessary each time. In this work, we propose an analytical and efficient VM-level scaling scheme by modeling each VM in a data center as an M/M/1 processor sharing queue. Our proposed VM-level scaling scheme is validated via a numerical experiment.

• The Journal of the Acoustical Society of Korea
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• v.28 no.1
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• pp.1-9
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• 2009

This paper deals with the realization of multi-channel live karaoke. In this study, 6-channel MP3 decoding and tempo/key scaling was operated in real time by using the TMS320C6713 DSP, which is 32 bit floating-point DSP made by TI Co. The 6 channel consists of front L/R instrument, rear L/R instrument, melody, and woofer. In case of the 4 channel, rear L/R instrument can be replaced with drum L/R channel. And the final output data is generated as adjusted to a 5.1 channel speaker. The SOLA algorithm was applied for tempo scaling, and key scaling was done with interpolation and decimation in the time domain. Drum channel was excluded in key scaling by separating instruments into drums and non-drums, and in processing SOLA, high-quality tempo scaling was made possible by differentiating SOLA frame size, which was optimized for real-time process. The use of 6 channels allows the composition of various channels, and the multi-channel audio system of this study can be effectively applied at any place where live music is needed.

• Journal of Mechanical Science and Technology
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• v.20 no.7
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• pp.950-960
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• 2006

An output time delay always exists in practical systems. Analysis of the delay phenomenon in a continuous-time domain is sophisticated. It is appropriate to obtain its corresponding discrete-time model for implementation via a digital computer. A new method for the discretization of nonlinear systems using Taylor series expansion and the zero-order hold assumption is proposed in this paper. This method is applied to the sampled-data representation of a nonlinear system with a constant output time-delay. In particular, the effect of the time-discretization method on key properties of nonlinear control systems, such as equilibrium properties and asymptotic stability, is examined. In addition, 'hybrid' discretization schemes resulting from a combination of the 'scaling and squaring' technique with the Taylor method are also proposed, especially under conditions of very low sampling rates. A performance of the proposed method is evaluated using two nonlinear systems with time-delay output.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.12
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• pp.736-748
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• 2003

Dynamic voltage scaling(DVS), which adjusts the clock speed and supply voltage dynamically, is an effective technique in reducing the energy consumption of embedded real-time systems. The energy efficiency of a DVS algorithm largely depends on the performance of the slack estimation method used in it. In this paper, we propose novel DVS algorithms for periodic hard real-time tasks based on an improved slack estimation algorithm. Unlike the existing techniques, the proposed method can be applied to most priority-driven scheduling policies. Especially, we apply the proposed slack estimation method to EDF and RM scheduling policies. The experimental results show that the DVS algorithms using the proposed slack estimation method reduce the energy consumption by 20∼40 % over the existing DVS algorithms.

• Structural Engineering and Mechanics
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• v.85 no.4
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• pp.485-500
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• 2023

The non-linear static analysis of reinforced concrete (RC) structures using the three-dimensional (3D) finite element method is a time-consuming and challenging task. Moreover, this type of analysis encounters numerical problems such as the lack of convergence of results in the stages of growth and propagation of cracks in the structure. The time integration analysis along with the mass scaling (MS) technique is usually used to overcome these limitations. Despite the use of this method in the 3D finite element analysis of RC structures, a comprehensive study has not been conducted so far to assess the effects of the MS method on the accuracy of results. This study aims to evaluate the accuracy of the MS method in the non-linear quasi-static finite element analysis of RC structures. To this aim, different types of RC structures were simulated using the finite element approach based on the implicit time integration method and the mass scaling technique. The influences of effective parameters of the MS method (i.e., the allowable values of increase in the mass of the RC structure, the relationship between the duration of the applied load and fundamental vibration period of the RC structure, and the pattern of applied loads) on the accuracy of the simulated results were investigated. The accuracy of numerical simulation results has been evaluated through comparison with existing experimental data. The results of this study show that the achievement of accurate structural responses in the implicit time integration analyses using the MS method involves the appropriate selection of the effective parameters of the MS method.