• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_1
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• pp.455-461
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• 2013

Kompsat-3 is an optical high-resolution earth observation satellite launched in May 2012. The AEISS sensor of the Korean satellite provides 0.7m panchromatic and 2.8m multi-spectral images with 16.8km swath width from the sun-synchronous near-circular orbit of 685km altitude. Kompsat-3 is more advanced than Kompsat-2 and the improvements include better agility such as in-track stereo acquisition capability. This study investigated the characteristic of the epipolar curves of in-track Kompsat-3 stereo images. To this end we used the RPCs(Rational Polynomial Coefficients) to derive the epipolar curves over the entire image area and found out that the third order polynomial equation is required to model the curves. In addition, we could observe two different groups of curve patterns due to the dual CCDs of AEISS sensor. From the experiment we concluded that the third order polynomial-based RPCs update is required to minimize the sample direction image distortion. Finally we carried out the experiment on the epipolar resampling and the result showed the third order polynomial image transformation produced less than 0.7 pixels level of y-parallax.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.117-127
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• 2001

The Main object of this research is directed to LG Oil company harbor in kwangyang-hang, where various ships ranging from 300 ton to 48000ton DWT use seven berths in the harbor. This harbor suffered from inefficient and unsafe management procedures since it is difficult to set guidelines for arrival and departure of ships according to the weather conditions and the current guidelines dose not offer clear basis of its implications. Therefore, it has long been suggested that these guidelines should be improved. This paper proposes a decision-support system, which can quantitatively decide the possibility of entry or departure on a harbor by analyzing the weather conditions (wind, current, and wave) and taking account of factors such as harbor characteristics, ship characteristics, weight condition, and operator characteristics. This system has been verified using 5$_{th}$ and 7$_{th}$ berth in Kwangyang-hang harbor. Machine learning technique using genetic programming(GP) is introduced to the system to quantitatively decide and produce results about the possibility of entry or arrival, and weighted linear associative memory (WLAM) method is also used to reduce the amount of calculation the GP has to perform. Group of additive genetic programming trees (GAGPT) is also used to improve learning performance by making it easy to find global optimum.mum.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.629-635
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• 2019

In Deep Learning models derivative is implemented by error back-propagation which enables the model to learn the error and update parameters. It can find the global (or local) optimal points of parameters even in the complex models taking advantage of a huge improvement in computing power. However, deliberately generated data points can 'fool' models and degrade the performance such as prediction accuracy. Not only these adversarial examples reduce the performance but also these examples are not easily detectable with human's eyes. In this work, we propose the method to detect adversarial datasets with random noise addition. We exploit the fact that when random noise is added, prediction accuracy of non-adversarial dataset remains almost unchanged, but that of adversarial dataset changes. We set attack methods (FGSM, Saliency Map) and noise level (0-19 with max pixel value 255) as independent variables and difference of prediction accuracy when noise was added as dependent variable in a simulation experiment. We have succeeded in extracting the threshold that separates non-adversarial and adversarial dataset. We detected the adversarial dataset using this threshold.

• Journal of KIISE:Software and Applications
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• v.37 no.12
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• pp.882-891
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• 2010

Many automated dynamic test data generation technique have been proposed. The techniques evaluate fitness of test data through executing instrumented Software Under Test (SUT) and then generate new test data based on evaluated fitness values and optimization algorithms. Previous researches and experiments have been showed that these techniques generate effective test data. However, optimization algorithms in these techniques incur much time to generate test data, which results in huge test case generation cost. In this paper, we propose a technique for reducing the time of evaluating a fitness of test data among steps of dynamic test data generation methods. We introduce the concept of Fitness Evaluation Program (FEP), derived from a path constraint of SUT. We suggest a test data generation method based on FEP and implement a test generation tool, named ConGA. We also apply ConGA to generate test cases for C programs, and evaluate efficiency of the FEP-based test case generation technique. The experiments show that the proposed technique reduces 20% of test data generation time on average.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.34 no.3
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• pp.261-269
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• 1989

Agricultural activities in Chejudo require more specialized weather services in this region. The meteorological information available from the Korea Meteorological Service (KMS) is limited in its areal coverage because the KMS stations are located along the narrow band of coastal area. topoclimatological technique which makes use of empirical relationships between the topography and the weather can be applied to produce reasonable estimates of the climatic variables such as air temperature and precipitation over remote land area where routine observations are rare. Presentation of these estimates in a from of fine-mesh grid map can also be helpful to upgrade the quality of weather services in this region. Altitude values of the 250 m grid points were read from a 1: 25000 topographic map and the mean altitude, the mean slope, and the aspect of the slope were determined for each 1 km$^2$ land area from these altitude data. Daily minimum air temperature data were collected from 18 points in Chejudo during the winter period from November 1987 to February 1988. The data were grouped into 3 sets based on synoptic pressure pattern. Departures from the KMS observations were regressed to the topographical variables to delineate empirical relationships between the local minimum temperature under specific pressure patterns and the site topography. The selected regression equations were used to calculate the daily minimum temperature for each 1 km$^2$ land area under the specific pressure patterns. The outputs were presented in a fine-mesh grid map with a 6-level contour capability.

• Journal of Korea Water Resources Association
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• v.53 no.9
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• pp.681-690
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• 2020

Generally, the design capacity of the rainwater harvesting unit is determined by trial and error method that is repeatedly calculating various analysis scenarios with capacity, reliability, and rainwater utilization ratio, etc. This method not only takes a lot of time to analyze but also involves a lot of calculations, so analysis errors may occur. In order to solve the problem, this study suggested a way to directly determine the minimum capacity to meet arbitrary target reliabilities using the global optimization method. The method was implemented by simulation model with particle swarm optimization (PSO) algorithms using Python language. The pyswarm that is provided as an open-source of python was used as optimization method, that can explore global optimum, and consider constraints. In this study, the developed program was applied to the design data for the rainwater harvesting constructed in Cheongna district 1 in Incheon to verify the efficiency, stability, and accuracy of the analysis. The method of determining the capacity of the rainwater harvesting presented in this study is considered to be of practical value because it can improve the current level of analytical technology.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.1
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• pp.52-64
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• 1995

Robot manipulator is a highly nonlinear-time varying system. Therefore, a lot of control theory has been applied to the system. Robot manipulator has two types of control; one is path planning, another is path tracking. In this paper, we select the path tracking, and for this purpose, propose the intelligent control¬ler which is combined with fuzzy logic and neural network. The fuzzy logic provides an inference morphorlogy that enables approximate human reasoning to apply to knowledge-based systems, and also provides a mathematical strength to capture the uncertainties associated with human cognitive processes like thinking and reasoning. Based on this fuzzy logic, the fuzzy logic controller(FLC) provides a means of converhng a linguistic control strategy based on expert knowledge into automahc control strategy. But the construction of rule-base for a nonlinear hme-varying system such as robot, becomes much more com¬plicated because of model uncertainty and parameter variations. To cope with these problems, a auto-tuning method of the fuzzy rule-base is required. In this paper, the GA-based Fuzzy-Neural control system combining Fuzzy-Neural control theory with the genetic algorithm(GA), that is known to be very effective in the optimization problem, will be proposed. The effectiveness of the proposed control system will be demonstrated by computer simulations using a two degree of freedom robot manipulator.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.1
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• pp.64-77
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• 2013

In this paper, we propose a joint bandwidth allocation and path selection scheme for IEEE 802.16j networks in uplink with cooperative relaying, and we evaluate the performance of the proposed scheme by using OPNET based simulation in hilly terrain with heavy tree density. The proposed scheme maximizes the system throughput in uplink with cooperative relaying in IEEE 802.16j networks. Then, we transform the proposed scheme into multi-dimensional multiple choice knapsack problem (MMKP) based scheme. We also propose uplink throughput maximization scheme and MMKP based scheme without cooperative relaying. We show that the system throughput of the proposed MMKP based scheme is higher than that of link quality based scheme, and cooperative relaying provides higher system throughput than the conventional case without cooperative relaying in uplink.

• Journal of Broadcast Engineering
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• v.10 no.3
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• pp.270-285
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• 2005

A joint design scheme is proposed to optimize the up/down scaler and the motion vector estimation module in the transcoder system. The proposed scheme first optimizes the resolution scaler for a fixed motion vector, and then a new motion vector is estimated for the fixed scaler. These two steps are iteratively repeated until they reach a local optimum solution. In the optimization of the scaler, we derive an adaptive version of a cubic convolution interpolator to enlarge or reduce digital images by arbitrary scaling factors. The adaptation is performed at each macroblock of an image. In order to estimate the optimal motion vector, a temporary motion vector is composed from the given motion vectors. Then the motion vector is refined over a narrow search range. It is well-known that this refinement scheme provides the comparable performance compared to the full search method. Simulation results show that a jointly optimized system based on the proposed algorithms outperforms the conventional systems. We can also see that the algorithms exhibit significant improvement in the minimization of information loss compared with other techniques.

• Journal of Korea Water Resources Association
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• v.52 no.10
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• pp.681-695
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• 2019

A Transient Storage Model (TSM) is one of the most widely used model accounting for complex solute transport in natural river to understanding natural river properties with four TSM key parameters. The TSM parameters are estimated via inverse modeling. Parameter estimation of the TSM is carried out by solving optimization problem about finding best fitted simulation curve with measured curve obtained from tracer test. Several studies have reported uncertainty in parameter estimation from non-convexity of the problem. In this study, we assessed best combination of optimization method and objective function for TSM parameter estimation using Cheong-mi Creek tracer test data. In order to find best optimization setting guaranteeing convergence and speed, Evolutionary Algorithm (EA) based global optimization methods, such as CCE of SCE-UA and MCCE of SP-UCI, and error based objective functions were compared, using Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL). Overall results showed that multi-EA SC-SAHEL with Percent Mean Squared Error (PMSE) objective function is the best optimization setting which is fastest and stable method in convergence.