• International journal of advanced smart convergence
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• v.8 no.1
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• pp.9-17
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• 2019

In this article, we present a performance enhancement scheme for mobile applications by adopting persistent memory. The proposed scheme supports the deadline guarantee of real-time applications like a video player, and also provides reasonable performances for non-real-time applications. To do so, we analyze the program execution path of mobile software platforms and find two sources of unpredictable time delays that make the deadline-guarantee of real-time applications difficult. The first is the irregular activation of garbage collection in flash storage and the second is the blocking and time-slice based scheduling used in mobile platforms. We resolve these two issues by adopting high performance persistent memory as the storage of real-time applications. By maintaining real-time applications and their data in persistent memory, I/O latency can become predictable because persistent memory does not need garbage collection. Also, we present a new scheduler that exclusively allocates a processor core to a real-time application. Although processor cycles can be wasted while a real-time application performs I/O, we depict that the processor utilization is not degraded significantly due to the acceleration of I/O by adopting persistent memory. Simulation experiments show that the proposed scheme improves the deadline misses of real-time applications by 90% in comparison with the legacy I/O scheme used in mobile systems.

• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.51-58
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• 2020

The DDS discovery is a behind-the-scenes way in which DDS objects on different nodes find out each other in a same domain. If the DDS discovery takes a long time, the preparation time for DDS communication is also delayed. And if the DDS discovery between several nodes fails, DDS communication between nodes related to them would be also failed. This problems can be a big cause of overall system performance degradation. Therefore, the improvement of performance for the DDS discovery gives the effect that improves the performance of the entire system. In this paper, I propose an efficient new method which improves the performance and reduces the time of DDS discovery. I simulate both the origin and the new proposed method for DDS discovery, and I compare the result of performance. This result will help for improving a DDS discovery in a large-scale system.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.1 s.173
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• pp.134-143
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• 2000

A train performance simulation (TPS) software is a computer program that simulates the operation of a train system over a specified railway route and it is widely used in railroad operation and research applications. Numerical and graphical results from the simulation software, which is developed in this study, provide information on such performance variables as travel time. running speed, energy consumption at a specific time interval and in overall service time as the train moves along the route. Three types of input data are required for a computer simulation: track information, train information, and running conditions. The simulation of train performance starts with several simple mathematical models including train configuration. traction efforts, running resistance. and braking requirements. Based on the basic specifications of Korean High Speed Railway, System. this study, puts a focus on the estimation and assessment of train performance comparing. the specific train configurations of KEST20/11. CPLE20/10. PROP20/10, which are proposed from the previous G7 projects.

• Korea-Australia Rheology Journal
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• v.18 no.3
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• pp.153-160
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• 2006

We analyzed the non-Newtonian and non-isothermal flow with Carreau-Yasuda viscosity model in co-rotating and counter-rotating twin screw extruder systems. The mixing performances with respect to the screw speed, the screw pitch, and the rotating direction have been investigated. The dynamics of mixing was studied numerically by tracking the motions of particles. The extent of mixing was characterized in terms of the deformation rate, the residence time distribution, and the average strain. The results showed that the high screw speed decreases the residence time but increases the deformation rate. Small screw pitch increases the residence time. It is concluded that the high screw speed increases the dispersive mixing performance, while the small screw pitch increases the distributive mixing performance. Co-rotating screw extruder has the better conveying performance and the distributive mixing performance than counter-rotating screw extruder with the same screw speed and pitch. Co-rotating screw extruder developed faster transport velocity and it is advantageous the flow characteristics to the mixing that transfers polymer melt from one barrel to the other barrel.

• Journal of Drive and Control
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• v.10 no.3
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• pp.27-33
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• 2013

Solenoid valve has used in various industrial field extensively. A solenoid valve has different size, shape and method of operation accordantly to industrial field. Many researchers study on kinds of solenoid valve such as flow rate, dynamic, magnetic field, valve shape and operating method. But the flow rate characteristic and dynamic response time performance on the diaphragm valve are not studied. This paper describes the flow rate characteristic and dynamic response time performance on the diaphragm valve. At first, the diaphragm valve is simulated in AMESim simulation tool. AMESim model found that an effect of valve performance depends on parameter. The parameter is the diaphragm orifice area. And the performance test bench confirms the effect in this parameter. Finally, it finds out the flow rate characteristic and dynamic response time performance on the diaphragm valve.

• The Journal of the Korea Contents Association
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• v.9 no.3
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• pp.49-57
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• 2009

This paper has proposed Flow Holding Time based Link State Update(LSU) Algorithm that can improve the performance of the existing LSU algorithms. As the existing LSU algorithms are based on the network traffic information, the proposed algorithm is based on flow holding time so we can minimize the LSU message overhead and has not had a strong influence on the depreciation of QoS routing performance. We apply a flow holding time in order to decrease the number of LSU message. We have evaluated the performance of the proposed model and the existing algorithms on MCI simulation network using the performance metric as the QoS routing blocking rate and the mean update rate per link, it thus appears that we have verified the performance of this algorithm.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.10 no.1
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• pp.33-39
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• 2011

This paper considers a performance analysis of set-top box (STB) power saving schemes. STB converts the signal into content which is then displayed on the television (TV) screen, and there are typically two operation modes: on mode and stand-by mode. The total energy consumption (TEC), a typical measure of power consumption of STB, is defined by the sum of power consumption in each mode. Recently there are some works of STB power saving schemes that transit STB operation modes efficiently, and the mode transition time point of those schemes can be different. Thus it is required to develop a performance evaluation method that reflects mode transition time points of each scheme to get TEC correctly. This paper proposes a performance evaluation method for STB power consumption using Poisson process to consider the mode transition time point. By modeling STB mode transitions as events of Poisson process, the average time duration of STB mode is computed and accordingly the effect of power saving is evaluated. The performance evaluation result shows that the proposed method achieves 1 to 19% improvement in power consumption compared with a conventional performance evaluation method.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.459-471
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• 2022

Purpose: The purpose of this study was to increase prediction accuracy for an anomaly interval identified using an artificial intelligence-based time series anomaly detection technique by establishing a pre-processing process. Methods: Significant variables were extracted by applying feature selection techniques, and anomalies were derived using the TadGAN time series anomaly detection algorithm. After applying machine learning and deep learning methodologies using normal section data (excluding anomaly sections), the explanatory power of the anomaly sections was demonstrated through performance comparison. Results: The results of the machine learning methodology, the performance was the best when SHAP and TadGAN were applied, and the results in the deep learning, the performance was excellent when Chi-square Test and TadGAN were applied. Comparing each performance with the papers applied with a Conventional methodology using the same data, it can be seen that the performance of the MLR was significantly improved to 15%, Random Forest to 24%, XGBoost to 30%, Lasso Regression to 73%, LSTM to 17% and GRU to 19%. Conclusion: Based on the proposed process, when detecting unsupervised learning anomalies of data that are not actually labeled in various fields such as cyber security, financial sector, behavior pattern field, SNS. It is expected to prove the accuracy and explanation of the anomaly detection section and improve the performance of the model.

• Korean Journal of Radiology
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• v.22 no.10
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• pp.1697-1707
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• 2021

The recent introduction of various high-dimensional modeling methods, such as radiomics and deep learning, has created a much greater diversity in modeling approaches for survival prediction (or, more generally, time-to-event prediction). The newness of the recent modeling approaches and unfamiliarity with the model outputs may confuse some researchers and practitioners about the evaluation of the performance of such models. Methodological literacy to critically appraise the performance evaluation of the models and, ideally, the ability to conduct such an evaluation would be needed for those who want to develop models or apply them in practice. This article intends to provide intuitive, conceptual, and practical explanations of the statistical methods for evaluating the performance of survival prediction models with minimal usage of mathematical descriptions. It covers from conventional to deep learning methods, and emphasis has been placed on recent modeling approaches. This review article includes straightforward explanations of C indices (Harrell's C index, etc.), time-dependent receiver operating characteristic curve analysis, calibration plot, other methods for evaluating the calibration performance, and Brier score.

• Smart Structures and Systems
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• v.16 no.6
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• pp.1147-1167
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• 2015

Wireless smart sensors, which have become popular for monitoring applications, are an attractive option for implementing structural control systems, due to their onboard sensing, processing, and communication capabilities. However, wireless smart sensors pose inherent challenges for control, including delays from communication, acquisition hardware, and processing time. Previous research in wireless control, which focused on semi-active systems, has found that sampling rate along with time delays can significantly impact control performance. However, because semi-active systems are guaranteed stable, these issues are typically neglected in the control design. This work achieves active control with smart sensors in an experimental setting. Because active systems are not inherently stable, all the elements of the control loop must be addressed, including data acquisition hardware, processing performance, and control design at slow sampling rates. The sensing hardware is shown to have a significant impact on the control design and performance. Ultimately, the smart sensor active control system achieves comparable performance to the traditional tethered system.