• The KIPS Transactions:PartC
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• v.12C no.4 s.100
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• pp.473-480
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• 2005

We can obtain useful information by deploying large scale sensor networks in various situations. Security is also a major concern in sensor networks, and we need to establish pairwise keys between sensor nodes for secure communication. In this paper, we propose new pairwise key establishment mechanism based on clustering and polynomial sharing. In the mechanism, we divide the network field into clusters, and based on the polynomial-based key distribution mechanism we create bivariate Polynomials and assign unique polynomial to each cluster. Each pair of sensor nodes located in the same cluster can compute their own pairwise keys through assigned polynomial shares from the same polynomial. Also, in our proposed scheme, sensors, which are in each other's transmission range and located in different clusters, can establish path key through their clusterheads. However, path key establishment can increase the network overhead. The number of the path keys and tine for path key establishment of our scheme depend on the number of sensors, cluster size, sensor density and sensor transmission range. The simulation result indicates that these schemes can achieve better performance if suitable conditions are met.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.5
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• pp.543-550
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• 2011

The gravity anomaly is a basic geophysical data applied in various fields such as geophysics, geodesy and national defense. In general, the gravity anomaly is used through a interpolation process based on the constructed database. The gravity variation, however, is appeared in various shapes depending on the topography and the density of the underground structures. Therefore, the interpolation could lead to a large differences if the gravity fields do not satisfy the assumptions on the signal behavior like linear or a certain degree polynomials. Furthermore, the interpolation does not reflect the physical characteristics of the gravity such as the harmonic condition. In this study, the gravity modeling using the plane Fourier series and radial basis functions are performed to overcome the problems in the usual interpolation. The results of the modeling is analyzed for the case of the gravity referenced navigation focused on the signal characteristics. Based on the study, it was found that the results from modeling are not much different to that from the interpolation in a smoothly varied area. In case of the highly varied area, however, a large differences are appeared among the three methods. Especially, the Fourier series shows the most smooth variations in the modeled gravity values while the highest variations appeared in the interpolation. Applying to the gravity referenced navigation, it was found that the modeling is more effective in calculation cost. It is considered that the results from this study provides a basis on effective modeling of the gravity fields in terms of the signal characteristics and resolution for various application fields.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.5
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• pp.447-454
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• 2017

Using two large coast guard ships at sea, we created four encounter situations ($000^{\circ}$, $045^{\circ}$, $090^{\circ}$, $135^{\circ}$) with high possibility of collision, from 3 NM up to 0.25 NM. As relative distance was gradually decreased, the subjects were measured at 0.25 NM intervals and perceived ship collision risk (PSCR) was determined by looking at the opponent ship. Characteristics were statistically analyzed using the obtained data. The purpose of this study was to analyze the characteristics of collision risk values obtained from twelve intervals, from 3 NM to 0.25 NM relative to encounter situations by curve fitting with appropriate polynomials, to determine the distance from which the change in perceived collision risk is greatest. As a result, an optimal regression equation for each distance interval was derived from each analysis direction. The greatest variation in average collision risk value was over the range 1.25 ~ 1 NM, and the collision risk value was largest at 1 NM. The maximum change in perceived collision risk was at 1 NM. These results can contribute to preventive guidelines to minimize human error in close proximity situations with a high probability of ship collision.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1150-1158
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• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• Journal of the Korea Concrete Institute
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• v.21 no.1
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• pp.105-116
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• 2009

Reinforced concrete (RC) structures have been most widely used because of their good economic efficiency. However, it is very weak in tensile stresses and difficult to control deflection due to the heavy self-weight of concrete. On the other hand, it is generally known that prestressed concrete structures can be the most effective to overcome the demerits of RC structures by using various tendon lay-out and its amount. In the prestressed concrete members, the inflection points of tendons should be placed effectively for the deflection control and the moment reduction. Therefore, in this study, the equations of tendon profiles are derived in terms of polynomials that satisfy essential conditions of tendon geometries such as inflection points and natural curved shapes of tendons placed in continuous members, from which vertical components of prestressing forces can be also calculated. The derived high order polynomial expression for the distributed shape of the upward and downward forces was transformed to an simplified equivalent uniform vertical force in order to improve the applicability in the calculation of member deflection. The influences of vertical forces by tendons to deflection and moment in a continuous slab were also considered depending on the distance from column face to the location of tendons. The applicability of the proposed method was examined by an example of deflection calculation for the cases of slabs with and without tendons, and the efficiency of deflection control by tendons was also quantitatively estimated.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.3
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• pp.59-67
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• 2013

Reed-Solomon (RS) codes are powerful error-correcting codes used in diverse applications. Recently, algebraic soft-decision decoding algorithm for RS codes that can correct the errors beyond the error correcting bound has been proposed. The algorithm requires very intensive computations for interpolation, therefore an efficient VLSI architecture, which is realizable in hardware with a moderate hardware complexity, is mandatory for various applications. In this paper, we propose an efficient architecture with low hardware complexity for interpolation in soft-decision list decoding of Reed-Solomon codes. The proposed architecture processes the candidate polynomial in such a way that the terms of X degrees are processed in serial and the terms of Y degrees are processed in parallel. The processing order of candidate polynomials adaptively changes to increase the efficiency of memory access for coefficients; this minimizes the internal registers and the number of memory accesses and simplifies the memory structure by combining and storing data in memory. Also, the proposed architecture shows high hardware efficiency, since each module is balanced in terms of latency and the modules are maximally overlapped in schedule. The proposed interpolation architecture for the (255, 239) RS list decoder is designed and synthesized using the DongbuHitek $0.18{\mu}m$ standard cell library, the number of gate counts is 25.1K and the maximum operating frequency is 200 MHz.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.4 s.22
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• pp.41-50
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• 2002

RPM is the one of the sensor models which is proposed by Open GIS Consortium (OGC) as image transfer standard. And it is the sensor model for end-users using IKONOS, a commercial pushbroom satellite, imagery which provide about 1m ground resolution. Parameters called RPC which is IKONOS RFM coefficients are serviced to end-users. But if some users try to make additional effort to get rigorous geo-spatial information, it is necessary to apply mathematic or abstract sensor models, because vendors don't offer any ancillary data for physical sensor models such as satellite orbit and navigation. Abstract sensor models such as pushbroom Direct Linear Transform (DLT) require many GCPs well distributed in imagery, and mathematic sensor model such as RFM, polynomials need much more GCPs. Therefore RPC modification using additional a few GCPs is the best solution. In this paper, two methods are proposed to modify RPC. One is method to use pseudo GCPs generated in normalized cubic, and another method uses parameters observations and a few GCPs. Through two methods, we get improvement of accuracy 50% and over.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.1
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• pp.98-107
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• 2016

The purpose of this study is for Human error prevention to acquire Collision Risk Perception Index (CRPI) sensed by the Officer Of the Watch (OOW) when the approaching distances are reduced in six types of ship encountering situations (Head on, $045^{\circ}$, $090^{\circ}$, $135^{\circ}$, Overtaking, Overtaken) between own ship and a target ship and then to predict CRPI fitting coefficients with polynomials in the curve-fitting process. CRPI acquisition experiments are carried out on two coast-guard ships and with the total of 30 crew members. Analysing results shows that CRPI data have goodness of fit to the six types of encountering situation. Futhermore, the One-Way ANOVA results show that CRPI has a negative affect to the OOW's age, career and license grade as the approaching distances is reduced. The availability of CRPI curve fitting with 3 degrees of polynomial was testified through the RMSE as 1.19 to Head on, 0.87 to $045^{\circ}$, 0.81 to $090^{\circ}$, 0.71 to $135^{\circ}$, 1.29 to Overtaking and 0.87 to Overtaken.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.8 no.2
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• pp.161-172
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• 1997

In this paper, electromagnetic scattering by a perfectly conducting strip grating on dielectric multilayers is analyzed for the normalized reflected and transmitted power by applying the Fourier-Galeakin moment method. The induced current density is expanded in a series of multiplication of chebyshev polynomials of the first kind and functions with appropriate edge boundary condition, the continuous condition of electromagnetic field is applied in the boundary planes. The confirm the validity of the proposed method, the nor- malized reflected and transmitted power obtained by varying the relative permittivity and thickness of each dielectric layers are evaluated and compared with those of the existing numerical method and a paper, and then the numerical results in this paper are in good agreement with those of the existing numerical method and the paper. The sharp variation position in the geometrically normalized reflected and transmitted power can be moved by the incident angle, grating period, and the relative permittivity and thickness of the dielectric multilayers, these sharp variation points which are called the Wood's anomaly of the Geome- trically normalized reflected power are observed as a main factor when the reflected powers of the higher order mode are transitted between propagating and evanescent modes, and the local minimum positions are slightly moved to the left hand direction in which grating period is getting small according to the increase of the relative permittivity of dielectric layers.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.11
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• pp.1741-1754
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• 2020

Objective: This study was conducted to estimate genetic parameters for milk yield traits using daily milk yield records from parlour data generated in an intensively managed commercial dairy farm with Jersey and Jersey-Friesian cows in Sri Lanka. Methods: Genetic parameters were estimated for first and second lactation predicted and realized 305-day milk yield using univariate animal models. Genetic parameters were also estimated for total milk yield for each 30-day intervals of the first lactation using univariate animal models and for daily milk yield using random regression models fitting second-order Legendre polynomials and assuming heterogeneous residual variances. Breeding values for predicted 305-day milk yield were estimated using an animal model. Results: For the first lactation, the heritability of predicted 305-day milk yield in Jersey cows (0.08±0.03) was higher than that of Jersey-Friesian cows (0.02±0.01). The second lactation heritability estimates were similar to that of first lactation. The repeatability of the daily milk records was 0.28±0.01 and the heritability ranged from 0.002±0.05 to 0.19±0.02 depending on day of milk. Pearson product-moment correlations between the bull estimated breeding values (EBVs) in Australia and bull EBVs in Sri Lanka for 305-day milk yield were 0.39 in Jersey cows and -0.35 in Jersey-Friesian cows. Conclusion: The heritabilities estimated for milk yield in Jersey and Jersey-Friesian cows in Sri Lanka were low, and were associated with low additive genetic variances for the traits. Sire differences in Australia were not expressed in the tropical low-country of Sri Lanka. Therefore, genetic progress achieved by importing genetic material from Australia can be expected to be slow. This emphasizes the need for a within-country evaluation of bulls to produce locally adapted dairy cows.