• Journal of the Korea Society of Computer and Information
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• v.16 no.5
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• pp.23-31
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• 2011

Improving system boot time has become one of the most important issues in the system software arena. As Linux is widely used in the embedded system environment, extensive research has been conducted in order to mitigate Linux boot time delay. In this respect, this paper mainly focuses on the Hibernation-based boot mechanism, which is the boot mechanism based on Hibernation, as an alternative to the conventional boot sequence. The contributions of this work are as follows. First, we implement the Hibernation-based boot mechanism on a real embedded Linux system and describe the implementation details. Second, we observe the Hibernation-based boot procedures so that we can investigate the possibility whether the boot mechanism has room for improvement in terms of the boot time. Through the in-depth observation and analysis based on the real implementation, we anticipate that the Hibernation-based boot mechanism which adopts various optimization methods can provide maximum of 3.1 times faster booting performance compared to the conventional way.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.137-140
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• 2013

Conventionally embedded devices used simple operating system (OS); however, the number of embedded devices using Linux as OS is increasing to keep up with hardware's performance improvement and customer's various needs. While embedded devices using Linux can take advantage of expandability, generality, portability, Linux's flexibility nature may cause undesirable overheads because of its increased complexity. One such overhead makes boot stage optimization essential in most embedded systems, where many features are redundant and possible to be removed or reconfigured. This paper applies well-known software optimization technique for Linux's boot stage to an CLM9722 DTK, measures the results, and studies about limitation of such techniques from hardware dependancy on the standard framework of Linux. The booting time from power-on until completion were decreased by 33% approximately.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.426-439
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• 2021

To alleviate the cold-start problem and data sparsity in web service recommendation and meet the personalized needs of users, this paper proposes a personalized web service recommendation method based on a hybrid social network and multi-objective immune optimization. The network adds the element of the service provider, which can provide more real information and help alleviate the cold-start problem. Then, according to the proposed service recommendation framework, multi-objective immune optimization is used to fuse multiple attributes and provide personalized web services for users without adjusting any weight coefficients. Experiments were conducted on real data sets, and the results show that the proposed method has high accuracy and a low recall rate, which is helpful to improving personalized recommendation.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.147-150
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• 2017

지능형 로봇의 사용자 품질을 결정하는 주요 요소들 중 하나는 짧은 부트 시간이다. 로봇 시스템에서는 부팅 과정 중에 침입자인지, 자택 순찰, 개인 비서, 엔터테인먼트와 같은 다수의 응용들이 동시에 초기화되는데, 고품질의 사용자 경험을 제공하기 위해서는 사용자 응답성이 중요한 응용들이 우선적으로 초기화되어야한다. 이를 위해 리눅스 기반 로봇 시스템에서 부트 시간을 단축하기 위한 다양한 연구들이 진행되어 왔다. 하지만 이들은 단일 응용 각각에 대한 초기화 시간을 단축하는 연구들이며, 응용들 간에 CPU, 메모리, I/O와 같은 자원 경쟁에 의한 지연 요소를 고려하지 않고 있다. 본 논문에서는 응용들 간의 각종 자원경쟁들을 고려하여 사용자 응답성이 중요한 응용을 우선적으로 초기화하기 위한 외부 컨텍스트 기반 선별적 자원 사용률 조정기법을 제안한다. 이를 리눅스 기반 시스템 상에 구현하여 검증한 결과 응용의 부트 시간이 기존 대비 33.02% 단축됨을 확인했다.

• International Journal of Fluid Machinery and Systems
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• v.10 no.3
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• pp.240-253
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• 2017

Latin hypercube sampling is widely used design-of-experiment technique to select design points for simulation which are then used to construct a surrogate model. The exploration/exploitation properties of surrogate models depend on the size and distribution of design points in the chosen design space. The present study aimed at evaluating the performance characteristics of various surrogate models depending on the Latin hypercube sampling (LHS) procedure (sample size and spatial distribution) for a diverse set of optimization problems. The analysis was carried out for two types of problems: (1) thermal-fluid design problems (optimizations of convergent-divergent micromixer coupled with pulsatile flow and boot-shaped ribs), and (2) analytical test functions (six-hump camel back, Branin-Hoo, Hartman 3, and Hartman 6 functions). The three surrogate models, namely, response surface approximation, Kriging, and radial basis neural networks were tested. The important findings are illustrated using Box-plots. The surrogate models were analyzed in terms of global exploration (accuracy over the domain space) and local exploitation (ease of finding the global optimum point). Radial basis neural networks showed the best overall performance in global exploration characteristics as well as tendency to find the approximate optimal solution for the majority of tested problems. To build a surrogate model, it is recommended to use an initial sample size equal to 15 times the number of design variables. The study will provide useful guidelines on the effect of initial sample size and distribution on surrogate construction and subsequent optimization using LHS sampling plan.