• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5723-5743
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• 2018

The evolving internet-based services demand high-speed data transmission in conjunction with scalability. The next generation optical network has to exploit artificial intelligence and cognitive techniques to cope with the emerging requirements. This work proposes a novel way to solve the dynamic provisioning problem in optical network. The provisioning in optical network involves the computation of routes and the reservation of wavelenghs (Routing and Wavelength assignment-RWA). This is an extensively studied multi-objective optimization problem and its complexity is known to be NP-Complete. As the exact algorithms incurs more running time, the heuristic based approaches have been widely preferred to solve this problem. Recently the software-defined networking has impacted the way the optical pipes are configured and monitored. This work proposes the dynamic selection of path computation algorithms in response to the changing service requirements and network scenarios. A software-defined controller mechanism with a novel packet matching feature was proposed to dynamically match the traffic demands with the appropriate algorithm. A software-defined controller with Path Computation Element-PCE was created in the ONOS tool. A simulation study was performed with the case study of dynamic path establishment in ONOS-Open Network Operating System based software defined controller environment. A java based NOX controller was configured with a parent path computation element. The child path computation elements were configured with different path computation algorithms under the control of the parent path computation element. The use case of dynamic bulk path creation was considered. The algorithm selection method is compared with the existing single algorithm based method and the results are analyzed.

• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.455-467
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• 2017

When performing target detection using hyperspectral imagery, a feature extraction process is necessary to solve the problem of redundancy of adjacent spectral bands and the problem of a large amount of calculation due to high dimensional data. This study proposes a new band selection method using the $L_{2,1}$-norm regression model to apply the feature selection technique in the machine learning field to the hyperspectral band selection. In order to analyze the performance of the proposed band selection technique, we collected the hyperspectral imagery and these were used to analyze the performance of target detection with band selection. The Adaptive Cosine Estimator (ACE) detection performance is maintained or improved when the number of bands is reduced from 164 to about 30 to 40 bands in the 350 nm to 2500 nm wavelength band. Experimental results show that the proposed band selection technique extracts bands that are effective for detection in hyperspectral images and can reduce the size of the data without reducing the performance, which can help improve the processing speed of real-time target detection system in the future.

• Fibers and Polymers
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• v.6 no.3
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• pp.186-199
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• 2005

A selection of commercially available poly(ethy1ene terephtha1ate) fibers with different degrees of molecular alignment and crystallinity have been investigated utilizing a wide range of techniques including optical microscopy, infrared spectroscopy together with thermal and wide-angle X-ray diffraction techniques. Annealing experiments showed increased molecular alignment and crystallinity as shown by the increased values of birefringence and melting enthalpies. Crystallinity values determined from thermal analysis, density, unpolarized infrared spectroscopy and X-ray diffraction are compared and discussed in terms of the inherent capabilities and limitations of each measurement technique. The birefringence and refractive index values obtained from optical microscopy are found to decrease with increasing wavelength of light used in the experiments. The wide-angle X-ray diffraction analysis shows that the samples with relatively low orientation possess oriented non-crystalline array of chains whereas those with high molecular orientation possess well defined and oriented crystalline array of chains along the fiber axis direction. X-ray analysis showed increasing crystallite size trend with increasing molecular orientation. SEM images showed micro-cracks on low oriented fiber surfaces becoming smooth on highly oriented fiber surfaces. Excellent bending characteristics were observed with knotted fibers implying relatively easy fabric formation.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.2
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• pp.50.2-50.2
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• 2013

Galaxy clusters, the largest gravitationally bound systems, are an important means to place constraints on cosmological models. Moreover, they are excellent places to test galaxy evolution models in connection to the environments. To this day, massive clusters have been found unexpectedly at high redshfit (Kang & Im 2009, Durret et al. 2011, Tashikawa et al. 2012), and evolution of galaxies in cluster has not been fully understood. Finding galaxy cluster candidates at z > 1 in wide, deep imaging survey data will enable us to solve such issues of modern extragalactic astronomy. We report new candidates of galaxy clusters in the wide and deep survey fields, European Large Area ISO Survey North1(ELAIS-N1) and North2(ELAIS-N2) fields, covering sky area of $8.75deg^2$ and $4.85deg^2$ each. We also suggest a new useful colour-colour selection technique to separate 1 < z < 2 galaxies from low-z galaxies by combining multi-wavelength data from the UKIRT Infrared Deep Sky Survey Deep Extragalactic Survey (UKIDSS DXS, JK bands), Spitzer Wise-area InfraRed Extragalactic survey (SWIRE, Optical-Infrared bands), Canada France Hawaii Telescope (CFHT, z band) and Infrared Medium-deep Survey(IMS, J band).

• The Bulletin of The Korean Astronomical Society
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• v.39 no.1
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• pp.48.1-48.1
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• 2014

Galaxy clusters, the largest gravitationally bound systems, are an important means to place constraints on cosmological models. Moreover, they are excellent places to test galaxy evolution models in connection to the environments. To this day, massive clusters have been found unexpectedly(Kang & Im 2009, Durret et al. 2011, Tashikawa et al. 2012) and evolution of galaxies in cluster have been still controversial (Elbaz et al. 2007, Cooper et al. 2008, Tran et al. 2009). Finding galaxy cluster candidates at z>1 in a wide, deep imaging survey data will enable us to solve the such issues of modern extragalactic astronomy. We report new candidates of galaxy clusters and their physical properties in one of the wide and deep survey fields, European Large Area ISO Survey North1(ELAIS-N1) and North2(ELAIS-N2) fields, covering sky area of and each. We also suggest a new useful color selection technique to separate 1 < z < 2 galaxies from low-z galaxies by combining multi-wavelength data from the UKIRT Infrared Deep Sky Survey Deep Extragalactic Survey (UKIDSS DXS/J and K band), Spitzer Wise-area InfraRed Extragalactic survey (SWIRE/two mid-infrared bands), Canada France Hawaii Telescope (CFHT/z band), Issac Newton Telescope(INT/ u, g, r, i, z band) and Infrared Medium-deep Survey(IMS/J band).

• Journal of IKEEE
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• v.9 no.2 s.17
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• pp.115-122
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• 2005

We report 400 femto-second highly stable, nearly transform-limited, pulse generation at 10 GHz in $1540{\sim}1550$ nm wavelength region by adiabatic soliton pulse compression of an actively mode-locked fiber ring laser output. Without using any supermode selection device, supermode beating noise has been suppressed below -123 dB/Hz, resulting less than 100 femto-second timing jitters at the noise band of $1\;kHz{\sim}100\;MHz$.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.314-319
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• 1998

In this paper, for efficient data classification in multispectral bands environment, a band feature extraction method using the Rough sets theroy is proposed. First, we make a look up table from training data, and analyze the properties of experimental multispectral image data, then select the efficient band usin indiscernibility relation of Rough sets theory from analysis results. Proposed method is applied to LAMDSAT TM data on 2, June, 1992. Among them, normal distributive data were experimented, mainly. From this, we show clustering trends that similar to traditional band selection results by wavelength properties, from this, we verify that can use the proposed method that centered on data properties to select the efficient bands, though data sensing environment change to hyperspectral band environments.

• Publications of The Korean Astronomical Society
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• v.32 no.1
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• pp.275-279
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• 2017

The AKARI Deep Field South (ADF-S) is a large extragalactic survey field that is covered by multiple instruments, from optical to far-IR and radio. I summarise recent results in this and related fields prompted by the release of the Herschel far-IR/submm images, including studies of cold dust in nearby galaxies, the identification of strongly lensed distant galaxies, and the use of colour selection to find candidate very high redshift sources. I conclude that the potential for significant new results from the ADF-S is very great. The addition of new wavelength bands in the future, eg. from Euclid, SKA, ALMA and elsewhere, will boost the importance of this field still further.

• Journal of Biosystems Engineering
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• v.23 no.3
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• pp.253-258
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• 1998

The part I was for developing regression models to predict the moisture content, protein content and viscosity of brown and milled rice using Near Infrared(NIR) Reflectance analyzer. The purpose of this study(part II) is to measure fundamental data required for the prediction of rice quality, and to develop regression models to predict the protein content of brown and milled rice, brown rice yield from undried paddy powder by using Near Infrared(NIR) Reflectance analyzer. The results of this study were summarized as follows : The predicted values of protein contents obtained from the undried paddy powder were well correlated to the measured values from brown and milled rice. The predicted yields of brown rice from undried paddy powder were not well correlated to the lab measured values from dried paddy. Continuous study in wavelength selection and of constituent relationship is necessary for practical application.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1998.06b
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• pp.171-177
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• 1998

The part Ⅰ was for developing regression models to predict the moisture content, protein content and viscosity of brown and milled rice using Near Unfrared (NIR) Reflectance analyzer. The purpose of this study(part Ⅱ) is to measure fundamental data required for the prediction of rice quality , and to develop regression models to predict the protein content of brown and milled rice, brown rice yield from undreid paddy powder by using Near Infrared (NIR) Reflectance analyzer. The results of this study were summarized as follows . The predicted values of protein contents obtained from the undried paddy powder were will correlated to the measured values from brown and milled rice. The predicted yields of brown rice from undried paddy powder were not well correlated to be lab measured values from dried paddy. Continuous study in wavelength selection and of constituent relationship is necessary for practical application.