• Journal of Positioning, Navigation, and Timing
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• v.13 no.1
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• pp.1-13
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• 2024

In this paper, we investigate the signal design of six (USA, EU, Russia, China, Japan, and India) countries for Global Navigation Satellite Systems (GNSS). Recently, a navigation satellite system that is capable of high-precision and reliable Positioning, Navigation, Timing (PNT) services has been developed. Prior to system design, a survey of the signal design for other GNSS systems should precede to ensure compatibility and interoperability with other GNSS. The signal design includes carrier frequency, Pseudorandom Noise (PRN) code, modulation, navigation service, etc. Specifically, GNSS is allocated L1, L2, and L5 bands, with recent additions of the L6 and S bands. GNSS uses PRN code (such as Gold, Weil, etc) to distinguish satellites that transmit signals simultaneously on the same frequency band. For modulation, both Binary Phase Shift Keying (BPSK) and Binary Offset Carrier (BOC) have been widely used to avoid collision in the frequency spectrum, and alternating BOCs are adopted to distinguish pilot and data components. Through the survey of other GNSS' signal designs, we provide insights for guiding the design of new satellite navigation systems.

• Journal of the Korean Institute of Gas
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• v.14 no.5
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• pp.32-37
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• 2010

In this study, vapor-liquid equilibrium (VLE) data at several isothermal temperatures for carbon dioxide and ethane binary systems were estimated using binary interaction parameters (BIP's) in Peng-Robinson (PR) equation of state built-in PRO/II with PROVISION (PRO/II) process simulator. Moreover, BIP's in PR equation of state were newly determined by regressing the experimental VLE data for carbon dioxide and ethane systems for each different isothermal temperatures using the summation of squares of the bubble point deviations as an objective function. Comparative works have been performed for absolute average deviation % (AAD(%)) between experimental and predicted bubble pressures using built-in BIP's in PRO/II and newly regressed one, respectively. Our calculation results gave a better estimation result than the simulation result using an existing parameter built-in PRO/II.

• Journal of Astronomy and Space Sciences
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• v.16 no.2
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• pp.217-266
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• 1999

The light curves of the representative 6 contact binary stars observed by OGLE Project of searching for dark matter in our Galaxy have been analyzed by the method of the Wilson and Devinney Differential Correction to find photometric solutions. The orbital inclinations of these Devinney Differential Correction to find photometric solutions. The orbital inclinations of these binaries are in the range of $52^{circ}-69^{\circ}$ which is lower than that of the solar neighborhood binaries. The Roche lobe filling factor of these binaries are distributed in large range of 0.12 - 0.90. Since absence of spectroscopic observations for these binaries we have found masses of the 6 binary systems based on the intersection between Kepler locus and locus derived from Vandenberg isochrones in the mass - luminosity plane. Then absolute dimensions and distances have been found by combining the masses and the photometric solutions. The distances of the 6 binary systems are distributed in the range of 1 kpc- 6 kpc. This distance range is the limiting range where the contact binaries which have period shorter than a day are visible. Most contact binaries discovered in the Baade window do not belong to the Galactic bulge.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.95-95
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• 2014

The mass measurement of neutron stars or black holes is of fundamental importance in our understanding of the evolution of massive stars and core-collapse supernova explosions as well as some exotic physics of the extreme conditions. Despite the importance, however, it's very difficult to measure mass of these objects directly. One way to do this, if they are in binary systems, to measure their binary motions (i.e., Doppler shifts) which can give us direct information on their mass. Recently many new highly-obscured massive X-ray binaries have been discovered by new hard X-ray satellites such as INTEGRAL and NuSTAR. The new highly-obscured massive X-ray binaries are faint in the optical, but bright in the infrared with many emission lines. Based on the near-infrared spectroscopy, one can first understand the nature of stellar companions to the compact objects, determining its spectral types and luminosity classes as well as mass losses and conditions of (potential) circumstellar material. Next, spectroscopic monitoring of these objects can be used to estimate the mass of compact objects via measuring the Doppler shifts of the lines. For the former, broad-band spectroscopy is essential; for the latter, high-resolution spectroscopy is critical. Therefore, IGRINS appears to be an ideal instrument to study them. An IGRINS survey of these new highly-obscured massive X-ray binaries can give us a rare opportunity to carry out population analyses for understanding the evolution of massive binary systems and formation of compact objects and their mass ranges. In this talk, we will present a sample near-infrared high resolution spectra of HMXB, IGR J19140+0951 and discuss about its spectral feature. These spectra are obtained on 13th July, 2014 from IGRINS commissioning run at McDonald 2.7m telescope. And at final, we will introduce the upgrade plan of IGRINS Operation Software (IGOS), to gather the input from IGRINS observer.

• Clean Technology
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• v.7 no.2
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• pp.133-140
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• 2001

The adsorption characteristics of binary-component Volatile Organic Compounds(VOCs) with benzene and toluene were studied in a fixed bed backed with activated carbon. The adsorption intensites of benzene and toluene resulted from equilibrium adsorption capacity led to roll up phenomenon in a fixed bed and behaved benzene as non-key component which meant the smaller affinity to the activated carbon of the two. From comparion with breakthrough curves between binary-component and single component systems at the same concentration conditions, the stoichiomertic breakthrough time of toluene in both systems had no difference, but that of benzene as non-key component had a tendency to shorten 130min than 200min of single component. In the breakthrough characteristics of binary-component adsorbates, the magnitude of roll-up of the non-key component increased with the increasing of non-key component ratio and aspect ratio(L/D) of fixed bed, while decreased with the increasing of interstitial velocity. Especially, the roll-up phenomenon was more conspicuous with the increasing of mole fraction of key component.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.6
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• pp.527-533
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• 2013

BCI (Brain-Computer Interface) is a system that transforms a subject's brain signal related to their intention into a control signal by classifying EEG (electroencephalograph) signals obtained during the imagination of movement of a subject's limbs. The BCI system allows us to control machines such as robot arms or wheelchairs only by imaging limbs. With the exact same experiment environment, activated brain regions of each subjects are totally different. In that case, a simple approach is to use as many channels as possible when measuring brain signals. However the problem is that using many channels also causes other problems. When applying a CSP (Common Spatial Pattern), which is an EEG extraction method, many channels cause an overfitting problem, and in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest an optimal channel selection method using a BPSO (Binary Particle Swarm Optimization), BPSO with channel impact factor, and GA. This paper examined optimal selected channels among all channels using three optimization methods and compared the classification accuracy and the number of selected channels between BPSO, BPSO with channel impact factor, and GA by SVM (Support Vector Machine). The result showed that BPSO with channel impact factor selected 2 fewer channels and even improved accuracy by 10.17~11.34% compared with BPSO and GA.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.483-488
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• 2015

This paper presents a local binary pattern(LBP) for effectively classifying textures, which is based on the multidimensional intensity difference between the adjacent pixels in the block image. The intensity difference by considering the a extent of 4 directional changes(verticality, horizontality, diagonality, inverse diagonality) in brightness between the adjacent pixels is applied to reduce the computation load as a results of decreasing the levels of histogram for classifying textures of image. And the binary patterns that is represented by the relevant intensities within a block image, is also used to effectively classify the textures by accurately reflecting the local attributes. The proposed method has been applied to classify 24 block images from USC Texture Mosaic #2 of 128*128 pixels gray image. The block images are different in size and texture. The experimental results show that the proposed method has a speedy classification and makes a free size block images classify possible. In particular, the proposed method gives better results than the conventional LBP by increasing the range of histogram level reduction as the block size becomes larger.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.191-196
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• 2009

In this paper, we proposed a feature selection method using Binary Particle Swarm Optimization(BPSO) and Mutual information. This proposed method consists of the feature selection part for selecting candidate feature subset by mutual information and the optimal feature selection part for choosing optimal feature subset by BPSO in the candidate feature subsets. In the candidate feature selection part, we computed the mutual information of all features, respectively and selected a candidate feature subset by the ranking of mutual information. In the optimal feature selection part, optimal feature subset can be found by BPSO in the candidate feature subset. In the BPSO process, we used multi-object function to optimize both accuracy of classifier and selected feature subset size. DNA expression dataset are used for estimating the performance of the proposed method. Experimental results show that this method can achieve better performance for pattern recognition problems than conventional ones.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.3
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• pp.110-116
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• 2008

In this paper, we proposed an improvement technique of image encryption using binary phase computer generated hologram(BPCGH) and multi exclusive-OR(XOR) operations. For the encryption process, a BPCGH that reconstructs the original image is designed, using an iterative algorithm, and the resulting hologram is regarded as the image to be encrypted. The BPCGH is encrypted through the exclusive-OR operation with the random generated phase key image. Then the encrypted image is divided into several slide images using XOR operations. So, the performance of encryption for the image is improved. For the decryption process, we cascade the encrypted slide images and phase key image and interfere with reference wave. Then decrypted hologram image is transformed into phase information. Finally, the original image is recovered by an inverse Fourier transformation of the phase information. If the slide images are changed, we can get various decrypted BPCGH images. In the proposed security system, without a random generated key image, the original image can not be recovered. And we recover another hologram pattern according to the slide images, so it can be used in the differentiated authorization system.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.4
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• pp.427-436
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• 1999

This paper describes an ECG signal labeling algorithm based on fuzzy clustering, which is very useful to the automated ECG diagnosis. The existing labeling methods compares the crosscorrelations of each wave form using IF-THEN binary logic, which tends to recognize the same wave forms such as different things when the wave forms have a little morphological variation. To prevent this error, we have proposed as ECG signal labeling algorithm using fuzzy clustering. The center and the membership function of a cluster is calculated by a cluster validity function. The dominant cluster type is determined by RR interval, and the representative beat of each cluster is determined by MF (Membership Function). The problem of IF-THEN binary logic is solved by FCM (Fuzzy C-Means). The MF and the result of FCM can be effectively used in the automated fuzzy inference -ECG diagnosis.