• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.6
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• pp.808-816
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• 2016

As the number of CPU/GPU cores and IPs in SOC increases and applications require explosive memory bandwidth, simultaneously achieving good throughput and fairness in the memory system among interfering applications is very challenging. Recent works proposed priority-based thread scheduling and channel partitioning to improve throughput and fairness. However, combining these different approaches leads to performance and fairness degradation. In this paper, we analyze the problems incurred when combining priority-based scheduling and channel partitioning and propose dynamic priority thread scheduling and adaptive channel partitioning method. In addition, we propose dynamic address mapping to further optimize the proposed scheme. Combining proposed methods could enhance weighted speedup and fairness for memory intensive applications by 4.2% and 10.2% over TCM or by 19.7% and 19.9% over FR-FCFS on average whereas the proposed scheme requires space less than TCM by 8%.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.12
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• pp.18-24
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• 2007

As VLSI technology scales to nano-meter order, relatively increasing global wire-delay has added complexity to system design. Global wire-delay could be reduced by inserting pipeline-elements onto wire but it should be coupled with LIP(Latency Intensive Protocol) to have correct system timing. This combination however, drops the throughput although it ensures system functionality. In this paper, we propose a computation method useful for minimizing throughput deterioration when pipeline-elements are inserted to reduce global wire-delay. We apply this method while placing blocks in the floorplanning stage. When the necessary for this computation is reflected on the floorplanning cost function, the throughput increases by 16.97% on the average when compared with the floorplanning that uses the conventional heuristic throughput-evaluation-method.

• International Journal of Contents
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• v.10 no.1
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• pp.62-67
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• 2014

Usually, cloud storage systems are developed based on DFS (Distributed File System) for scalability and reliability reasons. DFSs are designed to improve throughput than IO response time, and therefore, they are appropriate for batch processing jobs. Recently, cloud storage systems have been used for update intensive applications such as OLTP and so on. However, in DFSs, in-place update operations are not carefully considered. Therefore, when updates are frequent, I/O performance of DFSs are degraded significantly. DFSs with RAID techniques have been proposed to improve their performance and reliability. Their performance degradation caused by frequent update operations can be more significant. In this paper, we propose an in-place update method for DFS RAID exploiting a differential logging technique. The proposed method reduces the I/O costs, network traffic and XOR operation costs for RAID. We demonstrate the efficiency of our proposed in-place update method through various experiments.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.12
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• pp.1916-1924
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• 1990

In this paper attenuation effect on the measurement and the tomography of nonlinear parameter is discussed. We perform computer simulation with the method using harmonic components and the method using secondary wave components, and then estimate attenuation effect through the results and compare two measurement techniques. According to simulation result the attenuation effect is more intensive as large n and \ulcorner, and the degree of the attenuation effect is represented as error functions. In the aspect of measuremnet techniques, the method using secondary wave components is more insensitive to attenuation effect than the method using harmonic compnents. We obtain the same result in the nonlinear tomography, and show that the attenuation compensive filter is required because the whole tomogram is affected by frequency dependent attenuation(or nonlinear attenuation)

• International conference on construction engineering and project management
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• 2009.05a
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• pp.907-913
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• 2009

Pipeline construction is a highly repetitive and resource-intensive process that is exposed to various constraints and uncertainties in the working environment. Effective look-ahead scheduling based on the most recent project performance data can greatly improve project execution and control. This study enhances the traditional linear scheduling method with stochastic simulation to incorporate activity performance uncertainty in look-ahead scheduling. To facilitate the use of this stochastic method, a computer program, Stochastic Linear Scheduling Method (SLSM), was designed and implemented. Accurate look-ahead scheduling can help schedulers to better anticipate problem areas and formulate new plans to improve overall project performance.

• Journal of information and communication convergence engineering
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• v.12 no.4
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• pp.263-270
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• 2014

Corner detection and feature extraction are essential aspects of computer vision problems such as object recognition and tracking. Feature detectors such as Scale Invariant Feature Transform (SIFT) yields high quality features but computationally intensive for use in real-time applications. The Features from Accelerated Segment Test (FAST) detector provides faster feature computation by extracting only corner information in recognising an object. In this paper we have analyzed the efficient object detection algorithms with respect to efficiency, quality and robustness by comparing characteristics of image detectors for corner detector and feature extractors. The simulated result shows that compared to conventional SIFT algorithm, the object recognition system based on the FAST corner detector yields increased speed and low performance degradation. The average time to find keypoints in SIFT method is about 0.116 seconds for extracting 2169 keypoints. Similarly the average time to find corner points was 0.651 seconds for detecting 1714 keypoints in FAST methods at threshold 30. Thus the FAST method detects corner points faster with better quality images for object recognition.

• Journal of Internet Technology
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• v.20 no.3
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• pp.827-838
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• 2019

The recent evolution of mobile devices and services have resulted in such plethora of mobile applications (apps) that users have difficulty finding the ones they wish to use in a given moment. We design an app recommendation system which predicts the app to be executed with high accuracy so that users are able to access their next app conveniently and quickly. We introduce the App-Usage Tracking Feature (ATF), a simple but powerful feature for predicting next app launches, which characterizes each app use from the sequence of previously used apps. In addition, our method can be implemented without compromising the user privacy since it is solely trained on the target user's mobile usage data and it can be conveniently implemented in the individual mobile device because of its less computation-intensive behavior. We provide a comprehensive empirical analysis of the performance and characteristics of our proposed method on real-world mobile usage data. We also demonstrate that our system can accurately predict the next app launches and outperforms the baseline methods such as the most frequently used apps (MFU) and the most recently used apps (MRU).

• Journal of the Korea Computer Graphics Society
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• v.13 no.3
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• pp.11-18
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• 2007

Creating stylistic facial animation is one of the most important problems in character animation. Traditionally facial animation is created manually by animators of captured using motion capture systems. But this process is very difficult and labor-intensive. In this work, we present an intuitive, easy-to-use, sketch-based user interface system that facilitates the process of creating facial animation and key-frame interpolation method using facial capture data. The user of our system is allowed to create expressive speech facial animation easily and rapidly.

• Journal of the Korea Society of Computer and Information
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• v.22 no.3
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• pp.69-79
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• 2017

In this paper, we propose our four-phase life cycle of P2P botnet with corresponding detection methods and the future direction for more effective P2P botnet detection. Our proposals are based on the intensive analysis that compares existing P2P botnet detection schemes in different points of view such as life cycle of P2P botnet, machine learning methods for data mining based detection, composition of data sets, and performance matrix. Our proposed life cycle model composed of linear sequence stages suggests to utilize features in the vulnerable phase rather than the entire life cycle. In addition, we suggest the hybrid detection scheme with data mining based method and our proposed life cycle, and present the improved composition of experimental data sets through analysing the limitations of previous works.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.10-15
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• 2003

In computer graphics, most animations of characters have been created using the traditional and often highly labor intensive key-framing technique. Recently, character animation is demanded increasingly automated techniques for animation according to interaction with the user or environment of the user. In this paper, we will propose a new method which can animate characters automatical/y with user interactions. The character's behavior is determined as a result of understanding the emotional condition of the user. Psychology and cognitive AI provide some ideas about how to approach this problem. Our study is based mostly on the theories of Ortony, Clore and Collins, which were designed to be implemented computationally. In our system, we can make 22 emotion types and some more behavior features and we apply to some characters.