• Journal of KIISE
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• v.43 no.1
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• pp.34-39
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• 2016

JavaScript is widely used in web pages with HTML. Many JavaScript engines adopt Just-in-time compilers to accelerate the execution of JavaScript programs. Recently, many newly introduced devices are adopting 64-bit CPUs instead of 32-bit and Just-in-time compilers for 64-bit CPU are slowly being introduced in JavaScript engines. However, there are many inefficiencies in the currently available Just-in-time compilers for 64-bit devices. Especially, the size of code is significantly increased compared to 32-bit devices, mainly due to 64-bit wide addresses in 64-bit devices. In this paper, we are going to address the inefficiencies introduced by 64-bit wide addresses and values in the Just-in-time compiler for the V8 JavaScript engine and propose more efficient ways of generating constant values and addresses to reduce the size of code. We implemented the proposed optimization in the V8 JavaScript engine and measured the size of code as well as performance improvements with Octane and SunSpider benchmarks. We observed a 3.6% performance gain and 0.7% code size reduction in Octane and a 0.32% performance gain and 2.8% code size reduction in SunSpider.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.4
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• pp.153-161
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• 2018

This paper describes the performance improvement of Software (SW) digital filter using GPU (Graphical Processing Unit). The previous developed SW digital filter has a problem that it operates on a CPU (Central Processing Unit) basis and has a slow speed. The GPU was introduced to filter the data of the EAVN (East Asian VLBI Network) observation to improve the operation speed and to process data with other stations through filtering, respectively. In order to enhance the computational speed of the SW digital filter, NVIDIA Titan V GPU board with built-in Tensor Core is used. The processing speed of about 0.78 (1Gbps, 16MHz BW, 16-IF) and 1.1 (2Gbps, 32MHz BW, 16-IF) times for the observing time was achieved by filtering the 95 second observation data of 2 Gbps (512 MHz BW, 1-IF), respectively. In addition, 2Gbps data is digitally filtered for the 1 and 2Gbps simultaneously observed with KVN (Korean VLBI Network), and compared with the 1Gbps, we obtained similar values such as cross power spectrum, phase, and SNR (Signal to Noise Ratio). As a result, the effectiveness of developed SW digital filter using GPU in this research was confirmed for utilizing the data processing and analysis. In the future, it is expected that the observation data will be able to be filtered in real time when the distributed processing optimization of source code for using multiple GPU boards.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.202-202
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• 2018

본 논문에서는 분포형 강우-유출 모형인 GRM(Grid based Rainfall-runoff Model)(최윤석, 김경탁, 2017)을 이용해서 낙동강 유역을 대상으로 대유역 홍수해석시스템을 구축하고, 유출해석을 위한 실행시간을 평가하였다. 유출모형은 낙동강의 주요 지류와 본류를 소유역으로 구분하여 모형을 구축하고, 각 소유역의 유출해석 결과를 실시간으로 연계할 수 있도록 하여 낙동강 전체 유역의 유출모형을 구축하였다. 이와 같이 하나의 대유역을 다수의 소유역시스템으로 분할하여 모형을 구축할 경우, 유출해석시스템 구성이 복잡해지는 단점이 있으나, 소유역별로 각기 다른 자료를 이용하여 다양한 해상도로 유출해석을 할 수 있으므로, 소유역별 특성에 맞는 유출모형 구축이 가능한 장점이 있다. 또한 각 소유역시스템은 별도의 프로세스로 계산이 진행되므로, 대유역을 고해상도로 해석하는 경우에도 계산시간을 단축할 수 있다. 본 연구에서는 낙동강 유역을 20개(본류 구간 3개, 1차 지류 13개, 댐상류 4개)의 소유역으로 분할하여 계산 시간을 검토하였으며, 최종적으로 21개(본류 구간 3개, 1차 지류 13개, 댐상류 5개)의 소유역으로 분할하여 유출해석시스템을 구축하였다. 댐 상류 유역은 댐하류와 유량전달이 없이 독립적으로 모의되고, 댐과 연결된 하류 유역은 관측 방류량을 상류단 하천의 경계조건으로 적용한다. 지류 유역은 본류 구간과 연결되고, 지류의 계산 유량은 본류와의 연결지점에 유량조건으로 실시간으로 입력된다. 이때 본류와 지류의 유량 연계는 데이터베이스를 매개로 하였다. 유출해석시스템의 성능을 평가하기 위해서 Microsoft 클라우드 서비스인 Azure를 이용하였다. 낙동강 유역을 20개 소유역으로 구성한 경우에서의 유출해석시스템의 속도 평가 결과 Azure virtual machine instance DS15 v2(OS : Windows Server 2012 R2, CPU : 2.4 GHz Intel $Xeon^{(R)}$ E5-2673 v3 20 cores)에서 1.5분이 소요 되었다. 계산시간 평가시 GRM은 'IsParallel=false' 옵션을 적용하였으며, 모의 기간은 24시간을 기준으로 하였다. 연구결과 분포형 모형을 이용한 대유역 유출해석시스템 구축이 가능했으며, 계산시간도 충분히 단축할 수 있었다. 또한 추가적인 CPU와 병렬계산을 적용할 경우, 계산시간은 더 단축될 수 있으며, 이러한 기법들은 분포형 모형을 이용한 대유역 유출해석시스템 구축시 유용하게 활용될 수 있을 것으로 판단된다.

• Food Science and Preservation
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• v.5 no.3
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• pp.286-291
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• 1998

The sensitivity of various pathogenic bacteria(Escherichia coli O157:H7, Listeria monocytogenes, Staphylococcus aureus 196E, Salmonella typhimurium) to the water extract of green tea was tested. Tryptic soy broth was inoculated with 10$\^$5/CPU/ml of pathogenic bacteria and incubated at 35$^{\circ}C$ for 30 hours. The extract was added at a final concentration of 0-2%(w/v) into culture broth at the mid or late exponential phase of bacteria. The growth of pathogenic bacteria was inhibited with increasing concentrations of the extract in culture broth and the late exponential phase cells were more resistant than the mid exponential phase cells. Cram positive bacteria(L. monocytogenes and S. aureus 196E) were more sensitive than Cram negative bacteria(E. coli O157:H7 and S. typhimurium). S. aureus had the highest sensitivity, followed by L monocytogenes, E. coli O157:H7. S. typhimurium was the most resistant to the water extract of green tea.

• KNOM Review
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• v.24 no.1
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• pp.1-12
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• 2021

VM (Virtual Machine) live migration is a server virtualization technique for deploying a running VM to another server node while minimizing downtime of a service the VM provides. Currently, in cloud data centers, VM live migration is widely used to apply load balancing on CPU workload and network traffic, to reduce electricity consumption by consolidating active VMs into specific location groups of servers, and to provide uninterrupted service during the maintenance of hardware and software update on servers. It is critical to use VMlive migration as a prevention or mitigation measure for possible failure when its indications are detected or predicted. In this paper, we propose two VNF live migration methods; one for predictive load balancing and the other for a proactive measure in failure. Both need machine learning models that learn periodic monitoring data of resource usage and logs from servers and VMs/VNFs. We apply the second method to a vEPC (Virtual Evolved Pakcet Core) failure scenario to provide a detailed case study.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.279-286
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• 2013

This paper introduces a new method for identification of building energy performance problems. The presented method is based on automated analysis and visualization of deviations between actual and expected energy performance of the building using EPAR (Energy Performance Augmented Reality) models. For generating EPAR models, during building inspections, energy auditors collect a large number of digital and thermal imagery using a consumer-level single thermal camera that has a built-in digital lens. Based on a pipeline of image-based 3D reconstruction algorithms built on GPU and multi-core CPU architecture, 3D geometrical and thermal point cloud models of the building under inspection are automatically generated and integrated. Then, the resulting actual 3D spatio-thermal model and the expected energy performance model simulated using computational fluid dynamics (CFD) analysis are superimposed within an augmented reality environment. Based on the resulting EPAR models which jointly visualize the actual and expected energy performance of the building under inspection, two new algorithms are introduced for quick and reliable identification of potential performance problems: 1) 3D thermal mesh modeling using k-d trees and nearest neighbor searching to automate calculation of temperature deviations; and 2) automated visualization of performance deviations using a metaphor based on traffic light colors. The proposed EPAR v2.0 modeling method is validated on several interior locations of a residential building and an instructional facility. Our empirical observations show that the automated energy performance analysis using EPAR models enables performance deviations to be rapidly and accurately identified. The visualization of performance deviations in 3D enables auditors to easily identify potential building performance problems. Rather than manually analyzing thermal imagery, auditors can focus on other important tasks such as evaluating possible remedial alternatives.

• KIEE International Transactions on Power Engineering
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• v.4A no.3
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• pp.129-133
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• 2004

This paper deals with the design and implementation of a TCSC (Thyristor Controlled Series Capacitor) simulator, which is a module for an analog type power system simulator. Principally, it presents configuration of controller hardware/software and its experimental results. An analog type power system simulator consists of numerous power system components, such as various types of generator models, scale-downed transmission line modules, transformer models, switches and FACTS (Flexible AC Transmission System) devices. It has been utilized for the verification of the control algorithm and the study of system characteristics analysis. This TCSC simulator is designed for 50% line compensation rate and considered for damping resister characteristic analysis. Its power rate is three phase 380V 20kVA. For hardware extendibility, its controller is designed with VMEBUS and its main CPU is TMS320C32 DSP (Digital Signal Processor). For real time control and communications, its controller is applied to the RTOS (Real Time Operation System) for multi-tasking. This RTOS is uC/OS-II. The experimental results of capacitive mode and inductive mode operations verify the fundamental operations of the TCSC.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3619-3637
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• 2022

Big data analytics offers endless opportunities for operational enhancement by extracting valuable insights from complex voluminous data. Hadoop is a comprehensive technological suite which offers solutions for the large scale storage and computing needs of Big data. The performance of Hadoop is closely tied with its configuration settings which depends on the cluster capacity and the application profile. Since Hadoop has over 190 configuration parameters, tuning them to gain optimal application performance is a daunting challenge. Our approach is to extract a subset of impactful parameters from which the performance enhancing sub-optimal configuration is then narrowed down. This paper presents a statistical model to analyze the significance of the effect of Hadoop parameters on a variety of performance metrics. Our model decomposes the total observed performance variation and ascribes them to the main parameters, their interaction effects and noise factors. The method clearly segregates impactful parameters from the rest. The configuration setting determined by our methodology has reduced the Job completion time by 22%, resource utilization in terms of memory and CPU by 15% and 12% respectively, the number of killed Maps by 50% and Disk spillage by 23%. The proposed technique can be leveraged to ease the configuration tuning task of any Hadoop cluster despite the differences in the underlying infrastructure and the application running on it.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.37 no.3
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• pp.181-189
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• 2001

This paper describes the positioning accuracy of GPS moduls (KGP9800C, KiRyung) which need to record the operating position of fishing boat as realtime processing in order to develope auto recording system of fishing boat's operating information, and then it's positioning accuracy for DGPS receiver (MGP-100D, Shin-A) and DGPS beacon (GP-36, Furuno) were analyzed. Futhermore, it was examined the positioning error of the GPS module with processing algorithm to minimize the effect of the signal fluctuations. The results indicate that the positioning error is able to decrease 5m as 2drms, and such improvement of positioning performance for GPS module (KGP9800C) can be provide the basic design data in the development of the auto recording system of fishing boat's operating information.

• Journal of Korean Society of Forest Science
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• v.89 no.3
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• pp.405-413
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• 2000

This study was conducted to develop an on-line monitoring system for a forest hydrological environment and its meteorological condition, such as temperature, wind direction and speed, rainfall and water level on V-notch, electrical conductivity(EC), potential of hydrogen(PH) by the motor drive sensor unit and measurement with a single-chip microprocessor as controller. These results are summarized as follows ; 1. The monitoring system consists of a signal process unit, motor drive sensor unit, radio modem unit and power supply. 2. The motor drive sensor unit protects the sensor from swift current or freezing and can constantly maintain fixed water level during measurements. 3. This monitoring system can transfer the data by radio modem. Additionally, this system can monitor hydrological conditions in real time. 4. The hardware was made of several modules with an independent CPU. They can be mounted, removed, repaired and added to. Their function can be changed and expanded. 5. These are the result of an accuracy test, the values of temperature, EC and pH measured within an error range of ${\pm}0.2^{\circ}C$, ${\pm}1{\mu}S$ and ${\pm}0.1pH$ respectively. 6. This monitoring system proved to be able to measure various factors for a forest hydrological environment in various experimental stations.