• Journal of Biosystems Engineering
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• 제25권1호
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• pp.33-38
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• 2000

This study was performed to investigate airtight storage characteristics of rough rice using airtight flexible pve container. A storage test of rough rice of 4 tonnes was carried out to determine the changes of gas composition of air in the container, grain moisture content, air temperature and relative humidity , the presence of insects ,germination rate, crack ratio , fat acidity , 1000-kernel weight, and brown rice recovery over storage period of 5 months in Suwon. Concentration of oxygen was decreased from 20% to 16% and carbon dioxide was increased of from 0.03% to 1.6%. The grain moisture content was decreased from 14.4%(w.b) to 14.1%(w.b) for 5 months storage period . Insect population levels were low but these increased after 5 months storage. Most of insects were dead, Fat acidity increased from 7.5(mg KOH/100g) to 10.2(mg KOH/100g). Other storage factors such as germination rate, brown rice recovery and 1000-kernel , and 1000-kernel weight slightly decreased and crack ratio was slightly increased. Qualities of rough rice during 5 months storage period under hemetic air conditions were maintained fairly good considering the above changes of quality factors during storage.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.211-213
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• 2006

In this study, plasma etching process was modeled by using support vector machine (SVM). The data used in modeling were collected from the etching of silica thin films in inductively coupled plasma. For training and testing neural network, 9 and 6 experiments were used respectively. The performance of SVM was evaluated as a function of kernel type and function type. For the kernel type, Epsilon-SVR and Nu-SVR were included. For the function type, linear, polynomial, and radial basis function (RBF) were included. The performance of SVM was optimized first in terms of kernel type, then as a function of function type. Five film characteristics were modeled by using SVM and the optimized models were compared to statistical regression models. The comparison revealed that statistical regression models yielded better predictions than SVM.

• 한국컴퓨터산업교육학회:학술대회논문집
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• 한국컴퓨터산업교육학회 2003년도 제4회 종합학술대회 논문집
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• pp.17-22
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• 2003

The surge of Internet traffic makes the bottleneck nowadays. This problem can be reduced by substituting the media of network, routers and switches with more high-performance goods. However, we focused radically the server performance of processing the service requests. We prepose the method improving performance of server in the Linux kernel stack. This accelerator accepts the requests from many clients, and processes them using not user threads but kernel thread. To do so, we can reduce the overhead caused by frequent calling of system calls and the overhead of context switching between threads. Furthermore, we implement CPN(Coloured Petri Net) model. By using the CPN model criteria, we can analyze the characteristics of operation times in addition to the reachability of system. Benchmark of the system proves the model is valid.

• 한국자동차공학회논문집
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• 제3권6호
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• pp.274-284
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• 1995

The effect of turbulence and ignition energy on flame kernel growth in mathanol-air mixtures has been studied in a constant volume vessel. Experiments were made under different turbulent intensity conditions, ignition energy and over a range of equivalence ratio. Characteristics of turbulent flow were grasped by measurments of gas pressure and visualization of flame propagation. Flow velocity was measured by use of hot wire anemometer. A comparison of the effect of turbulence on ignition probability and flame kernel volume variation ratio is also presented.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.499-500
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• 2007

The various sensor nodes operating in Ubiquitous Sensor Network environment require the tiny Operating System different from the existing pc-type operating system because of their characteristics. Also Sensor Network operating system needs to support the rapid event handling which sensor node must implement. In this paper, we overcome the drawbacks of the existing sensor network operating system and propose the new kernel which is designed to assist developer to construct event-central operating system entirely. We also evaluate the performance of the super tiny sensor network operating system based on proposed kernel, comparing with that of the existing sensor network operating system.

• 대한임베디드공학회논문지
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• 제6권6호
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• pp.393-400
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• 2011

Recently, demands of information devices are increasing as we can find so many information devices around us such as smartphone, MID(Mobile Internet Device), Tablet. These characteristics of information devices services should support soft real-time based time guaranteed multimedia services and control internet appliances. In this situation, soft real-time supported system should be developed to consider as a total aspect of hardware, kernel, middleware, application. But this paper will describe soft real-time supporting and evaluation methods for information device as an aspect of only kernel.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3762-3781
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• 2020

Infrared pedestrian target recognition is a vital problem of significant interest in computer vision. In this work, a novel infrared pedestrian target recognition method that uses heterogeneous features with multi-kernel transfer learning is proposed. Firstly, to exploit the characteristics of infrared pedestrian targets fully, a novel multi-scale monogenic filtering-based completed local binary pattern descriptor, referred to as MSMF-CLBP, is designed to extract the texture information, and then an improved histogram of oriented gradient-fisher vector descriptor, referred to as HOG-FV, is proposed to extract the shape information. Second, to enrich the semantic content of feature expression, these two heterogeneous features are integrated to get more complete representation for infrared pedestrian targets. Third, to overcome the defects, such as poor generalization, scarcity of tagged infrared samples, distributional and semantic deviations between the training and testing samples, of the state-of-the-art classifiers, an effective multi-kernel transfer learning classifier called MK-TrAdaBoost is designed. Experimental results show that the proposed method outperforms many state-of-the-art recognition approaches for infrared pedestrian targets.

• Journal of Forest and Environmental Science
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• 제31권4호
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• pp.272-279
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• 2015

Xanthoceras sorbifolium is considered as bio-energy crops owing to the high oil content from kernel. This study was performed to analyze calorific value, crude ash content, ultimate ratio, crude lipid and fatty acid composition among seed sources. Calorific values ranged from $4,526.0\;cal\;g^{-1}$ to $7,377.2\;cal\;g^{-1}$ in seeds and kernels showed the highest value. Calorific values and crude ash contents were observed as significant difference among plantations and/or individuals (p>0.05). Kernel from SD-F plantation showed the highest calorific value and lower crude ash content. C content comprised 63.4%, the highest levels was detected from SD-F (64.8%). Crude lipid content in kernel observed as 54.5 g $100\;g^{-1}$ from SD-F. In contrast it was determined the lowest value from LN-JARS as 46.5 g $100\;g^{-1}$. The fatty acid composition of kernel was determined to those of oleic acid (31.3%) and linoleic acid (38.1%) from SD-F and LN-JARS. These results will be offered to useful information for breeding materials selection.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.451-454
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• 1998

This paper proposes a new method of Model Predictive Control (MPC) of nonlinear process by us-ing the measured Volterra kernels as the nonlinear model. A nonlinear dynamical process is usually de-scribed as Volterra kernel representation, In the authors' method, a pseudo-random M-sequence is ar plied to the nonlinear process, and its output is measured. Taking the crosscorrelation between the input and output, we obtain the Volterra kernels up to 3rd order which represent the nonlinear characteristics of the process. By using the measured Volterra kernels, we can construct the nonlinear model for MPC. In applying Model Predictive Control to a nonlinear process, the most important thing is, in general, what kind of nonlinear model should be used. The authors used the measured Volterra kernels of up to 3rd order as the process model. The authors have carried out computer simulations and compared the simulation results for the linear model, the nonlinear model up to 2nd Volterra kernel, and the nonlinear model up to 3rd order Vol-terra kernel. The results of computer simulation show that the use of Valterra kernels of up to 3rd order is most effective for Model Predictive Control of nonlinear dynamical processes.

• 한국전자통신학회논문지
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• 제13권6호
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• pp.1257-1262
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• 2018

본 논문에서는 보안 감시 카메라 영상을 활용하여 화재 검출을 위한 효과적인 심층학습 방안을 제안한다. AlexNet 모델을 기준으로 효과적인 화재 검출을 위한 커널 크기와 커널 이동 간격의 변화에 따른 분류 성능을 비교 분석한다. 학습을 위한 데이터셋은 정상과 화재 2가지 클래스로 분류한다, 정상 영상에는 구름과 안개 낀 영상을 포함하고, 화재 영상에는 연기와 화염을 각각 포함한다. AlexNet 모델의 첫 번째 계층의 커널 크기와 이동 간격에 따른 분류 성능 분석 결과 커널의 크기는 크고, 이동 간격은 작을수록 화재 분류 성능이 우수한 것을 확인할 수 있다.