• Journal of KIISE:Computing Practices and Letters
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• v.14 no.5
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• pp.542-546
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• 2008

Recently dual-port SDRAM (DPSDRAM) architecture tailored for dual-processor based mobile embedded systems has been announced where a single memory chip plays the role of the local memories and the shared memory for both processors. In order to maintain memory consistency from simultaneous accesses of both ports, every access to the shared memory should be protected by a synchronization mechanism, which can result in substantial access latency. We propose two optimization techniques by exploiting the communication patterns of target applications: lock-priority scheme and static-copy scheme. Further, by dividing the shared bank into multiple blocks, we allow simultaneous accesses to different blocks thus achieve considerable performance gain. Experiments on a virtual prototyping system show a promising result - we could achieve about 20-50% performance gain compared to the base DPSDRAM architecture.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.105-111
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• 2023

Recently, research on prediction algorithms using deep learning has been actively conducted. In addition, algorithmic trading (auto-trading) based on predictive power of artificial intelligence is also becoming one of the main investment methods in stock trading field, building its own history. Since the possibility of human error is blocked at source and traded mechanically according to the conditions, it is likely to be more profitable than humans in the long run. In particular, for the virtual currency market at least for now, unlike stocks, it is not possible to evaluate the intrinsic value of each cryptocurrencies. So it is far effective to approach them with technical analysis and cryptocurrency market might be the field that the performance of algorithmic trading can be maximized. Currently, the most commonly used artificial intelligence method for financial time series data analysis and forecasting is Long short-term memory(LSTM). However, even t4he LSTM also has deficiencies which constrain its widespread use. Therefore, many improvements are needed in the design of forecasting and investment algorithms in order to increase its utilization in actual investment situations. Meanwhile, Prophet, an artificial intelligence algorithm developed by Facebook (META) in 2017, is used to predict stock and cryptocurrency prices with high prediction accuracy. In particular, it is evaluated that Prophet predicts the price of virtual currencies better than that of stocks. In this study, we aim to show Prophet's virtual currency price prediction accuracy is higher than existing deep learning-based time series prediction method. In addition, we execute mock investment with Prophet predicted value. Evaluating the final value at the end of the investment, most of tested coins exceeded the initial investment recording a positive profit. In future research, we continue to test other coins to determine whether there is a significant difference in the predictive power by coin and therefore can establish investment strategies.

• The KIPS Transactions:PartA
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• v.9A no.4
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• pp.441-450
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• 2002

As the size of embedded system software increase bigger and bigger, and it's complexity is grower and grower, the usage of dynamic memory management scheme such collector also has been increased. Using the garbage collector, however, inherently lead us performance degradation. In order to resolve this kind of performance problem in the Java based embedded system. we introduce an explicit dynamic memory free method to the automated dynamic memory management environment. which can be performed by a programmer. In the worst case, the prosed scheme shows the same performance as the case of that only garbage collector is working, since the unclaimed garbage objects will eventually be collected later by the garbage collector. In the best case. our method is free from any runtime overhead because the applications can be implemented without any intervention of the garbage collector. Although the proposed method can be facilitated with all the existing garbage collection algorithms, it shows an outperform in the case of mark-and-sweep algorithm.

• The KIPS Transactions:PartA
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• v.8A no.4
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• pp.299-310
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• 2001

In this paper, we study the methodology of distributing the requests of clients based on the state of real server in the LVS(Linux virtual server) clustering system. The WLC(weighted least connection) algorithm in the LVS cluster system is studied. The load distributing algorithm which assigns a weight into each real server is devised by considering the load of real servers. The load test is executed to estimate the load of real servers using a load generating tool. Throughout the result of the experiment, we suggest new load distributing algorithm based on the usage of physical memory of each real server. It is shown that the correction potentiality of new algorithm is somewhat better than that of the WLC algorithm.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.6
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• pp.377-384
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• 2017

The scope of the matrix application is so broad that it can not be limited. A typical matrix application area in computer science is image processing. Particularly, radar scanning equipment implemented on a small embedded system requires real-time matrix transposition for image processing, and since its memory size is small, a general matrix transposition algorithm can not be applied. In this case, matrix transposition must be done in disk space, such as flash disk, using a limited memory buffer. In this paper, we analyze and improve a recently published flash disk-based matrix transposition algorithm named as asymmetric sub-matrix transposition algorithm. The performance analysis shows that the asymmetric sub-matrix transposition algorithm has lower performance than the conventional sub-matrix transposition algorithm, but the improved asymmetric sub-matrix transposition algorithm is superior to the sub-matrix transposition algorithm in 13 of the 16 experimental data.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.187-190
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• 2005

There have been many studies to improve the performance of a database system focused on modifying data structure, data partitioning, and materializing strategy. The main contribution of this study is to propose a new alternative towards improving database performance by designing single table schema or processing queries virtually in main memory space. Material Requirement Planning(MRP) part explosion process has shown almost 2 times shorter under DB schema we suggested, and even more than 10 times shorter when separating and filtering policy of DB archiving process are assumed. Several experimental results are shown to illustrate the excellence of our solution.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.210-215
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• 2009

In this study event-related optical signals were extracted from the prefrontal cortexes using functional near infrared spectroscopy while subjects were carrying out 2-back working memory tasks. Four events such as start, yes, no, and error were considered based on the onsets of the stimulus, positive true responses, positive false responses, and negative responses in the 2-back working memory task, respectively. The optical signals recorded were analyzed by peri-event histograms and power spectrum distributions. The results showed specific characteristics of the event-related optical neuronal signals and an opened possibility of an application to control a non-invasive brain-computer interface system or an object of a virtual reality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2895-2921
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• 2018

Security has become one of the major concerns in mobile adhoc networks (MANETs). Data and voice communication amongst roaming battlefield entities (such as platoon of soldiers, inter-battlefield tanks and military aircrafts) served by MANETs throw several challenges. It requires complex securing strategy to address threats such as unauthorized network access, man in the middle attacks, denial of service etc., to provide highly reliable communication amongst the nodes. Intrusion Detection and Prevention System (IDPS) undoubtedly is a crucial ingredient to address these threats. IDPS in MANET is managed by Command Control Communication and Intelligence (C3I) system. It consists of networked computers in the tactical battle area that facilitates comprehensive situation awareness by the commanders for timely and optimum decision-making. Key issue in such IDPS mechanism is lack of Smart Learning Engine. We propose a novel behavioral based "Smart Multi-Instance Multi-Label Intrusion Detection and Prevention System (MIML-IDPS)" that follows a distributed and centralized architecture to support a Robust C3I System. This protocol is deployed in a virtually clustered non-uniform network topology with dynamic election of several virtual head nodes acting as a client Intrusion Detection agent connected to a centralized server IDPS located at Command and Control Center. Distributed virtual client nodes serve as the intelligent decision processing unit and centralized IDPS server act as a Smart MIML decision making unit. Simulation and experimental analysis shows the proposed protocol exhibits computational intelligence with counter attacks, efficient memory utilization, classification accuracy and decision convergence in securing C3I System in a Tactical Battlefield environment.

• Journal of Computing Science and Engineering
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• v.6 no.1
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• pp.26-38
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• 2012

Java has been increasingly used in programming for real-time systems. However, some of Java's features such as automatic memory management and dynamic compilation are harmful to time predictability. If these problems are not solved properly then it can fundamentally limit the usage of Java for real-time systems, especially for hard real-time systems that require very high time predictability. In this paper, we propose to exploit multicore computing in order to reduce the timing unpredictability that is caused by dynamic compilation and adaptive optimization. Our goal is to retain high performance comparable to that of traditional dynamic compilation, while at the same time, obtain better time predictability for Java virtual machine (JVM). We have studied pre-compilation techniques to utilize another core more efficiently, preoptimization on another core (PoAC) scheme to replace the adaptive optimization system (AOS) in Jikes JVM and the counter based optimization (CBO). Our evaluation reveals that the proposed approaches are able to attain high performance while greatly reducing the variation of the execution time for Java applications.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.816-819
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• 2005

In Java virtual machine which is the running environment of Java programs, almost every bytecode execution requires data transfers in memory. Data transfer incurs energy consumption and hence minimizing the transfer operation is very important for improving the energy efficiency of JVM. As the number of data transfers for a Java iterative statement is directly proportional to the iteration count, improving the energy efficiency of iterative statement is crucial to keep the energy efficiency of JVM high. This paper analyzes Java iterative statement at bytecode level and proposes some points how to improve the energy efficiency.