• ETRI Journal
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• v.33 no.4
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• pp.537-546
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• 2011

This paper introduces an automated method for building height recovery through the integration of high-resolution satellite images and digital vector maps. A cross-correlation matching method along the vertical line locus on the Ikonos images was deployed to recover building heights. The rational function models composed of rational polynomial coefficients were utilized to create a stereopair of the epipolar resampled Ikonos images. Building footprints from the digital maps were used for locating the vertical guideline along the building edges. The digital terrain model (DTM) was generated from the contour layer in the digital maps. The terrain height derived from the DTM at each foot of the buildings was used as the starting location for image matching. At a preset incremental value of height along the vertical guidelines derived from vertical line loci, an evaluation process that is based on the cross-correlation matching of the images was carried out to test if the top of the building has reached where maximum correlation occurs. The accuracy of the reconstructed buildings was evaluated by the comparison with manually digitized 3D building data derived from aerial photographs.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.6
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• pp.468-476
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• 2002

The confidence of information from image processing depends on the original image quality. Enhancing the confidence by an algorithm has an essential limitation. Especially, road images are exposed to lots of noisy sources, which makes image processing difficult. We, in this paper, propose a FNN (fuzzy-neural network) capable oi deciding the quality of a road image prior to extracting lane-related information. According to the decision by the FNN, road images are classified into good or bad to extract lane-related information. A CDF (cumulative distribution function), a function of edge histogram, is utilized to construct input parameters of the FNN, it is based on the fact that the shape of the CDF and the image quality has large correlation. Input pattern vector to the FNN consists of ten parameters in which nine parameters are from the CDF and the other one is from intensity distribution of raw image. Correlation analysis shows that each parameter represents the image quality well. According to the experimental results, the proposed FNN system was quite successful. We carried out simulations with real images taken by various lighting and weather conditions and achieved about 99% successful decision-making.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.8
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• pp.1091-1098
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• 1995

This paper proposes an efficient noiseless encoding method of vector quantization(VQ) index using a dynamic index mapping. Using high interblock correlation, the proposed index mapper transforms an index into a new one with lower entropy. In order to achieve good performance with low computational complexity, we adopt 'the sum of differences in pixel values on the block boundaries' as the cost function for index mapping. Simulation results show that the proposed scheme reduces the average bitrate by 40 - 50 % in ordinary VQ system for image compression. In addition, it is shown that the proposed index mapping method can be also applied to mean-residual VQ system, which allows the reduction of bitrate for VQ index by 20 - 30 %(10 - 20 % reduction in total bitrate). Since the proposed scheme is one for noiseless encoding of VQ index, it provides the same quality of the reconstructed image as the conventional VQ system.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.12
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• pp.58-65
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• 2008

This paper presents a new geographic distributed hash table (GDHT) for MANETs or Mesh networks, where virtual geographic protocol is used. In previous wort GDHT is applied to a network scenario based on two dimensional Cartesian coordinate system. Further, logical data space is supposed to be uniformly distributed. However, mobile node distribution in a network using virtual geographic routing is not matched to data distribution in GDHT. Therefore, if we apply previous GDHT to a virtual geographic routing network, lots of DHT data are probably located at boundary nodes of the network or specific nodes, resulting in long average-delay to discover resource (or service). Additionally, in BVR(Beacon Vector Routing) or LCR(Logical Coordinate Routing), because there is correlation between coordinate elements, we cannot use normal hash function. For this reason, we propose to use "geographic hash function" for GDHT that matches data distribution to node distribution and considers correlation between coordinate elements. We also show that the proposed scheme improves resource discovery efficiently.

• Nuclear Engineering and Technology
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• v.49 no.7
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• pp.1423-1430
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• 2017

Detonation cell width is an important parameter in hydrogen explosion assessments. The experimental data on gas detonation are statistically analyzed to establish a universal method to numerically predict detonation cell widths. It is commonly understood that detonation cell width, ${\lambda}$, is highly correlated with the characteristic reaction zone width, ${\delta}$. Classical parametric regression methods were widely applied in earlier research to build an explicit semiempirical correlation for the ratio of ${\lambda}/{\delta}$. The obtained correlations formulate the dependency of the ratio ${\lambda}/{\delta}$ on a dimensionless effective chemical activation energy and a dimensionless temperature of the gas mixture. In this paper, support vector regression (SVR), which is based on nonparametric machine learning, is applied to achieve functions with better fitness to experimental data and more accurate predictions. Furthermore, a third parameter, dimensionless pressure, is considered as an additional independent variable. It is found that three-parameter SVR can significantly improve the performance of the fitting function. Meanwhile, SVR also provides better adaptability and the model functions can be easily renewed when experimental database is updated or new regression parameters are considered.

• Steel and Composite Structures
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• v.25 no.4
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• pp.485-496
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• 2017

In this paper, damage detection has been introduced as an optimization problem and a two-step method has been proposed that can detect the location and severity of damage in truss structures precisely and reduce the volume of computations considerably. In the first step, using the residual force vector concept, the suspected damaged members are detected which will result in a reduction in the number of variables and hence a decrease in the search space dimensions. In the second step, the precise location and severity of damage in the members are identified using the genetic algorithm and the results of the first step. Considering the reduced search space, the algorithm can find the optimal points (i.e. the solution for the damage detection problem) with less computation cost. In this step, the Efficient Correlation Based Index (ECBI), that considers the structure's first few frequencies in both damaged and healthy states, is used as the objective function and some examples have been provided to check the efficiency of the proposed method; results have shown that the method is innovatively capable of detecting damage in truss structures.

• Korean Journal of Artificial Intelligence
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• v.8 no.2
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• pp.7-10
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• 2020

In this paper, a study was conducted to find main factorsto Pima Indians Diabetes based on machine learning. Diabetes is a type of metabolic disease such as insufficient secretion of insulin or inability to function normally and is characterized by a high blood glucose concentration. According to a situation report from WHO(World Health Organization), Diabetes is a chronic, metabolic disease characterized by elevated levels of blood glucose (or blood sugar), which leads over time to serious damage to the heart, blood vessels, eyes, kidneys and nerves. And also about 422 million people worldwide have diabetes, the majority living in low-and middle-income countries, and 1.6 million deaths are directly attributed to diabetes each year. Both the number of cases and the prevalence of diabetes have been steadily increasing over the past few decades. Therefore, in this study, we used Support Vector Machine (SVM), Decision Tree, and correlation analysisto discover three important factorsthat predict Pima Indians diabetes with 70% accuracy. Applying the results suggested in this paper, doctors can quickly diagnose potential Pima Indians diabetics and prevent Pima Indians diabetes.

• The Journal of Industrial Distribution & Business
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• v.8 no.1
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• pp.5-13
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• 2017

Purpose - In this article, a dynamic model like a VAR is an appropriate choice for estimating the possible interrelationship between ownership structure and firm performance as a dynamic process. Research design, data, and methodology - Data of this work are collected from Chinese stock exchange including 350 Chinese-listed firms during the period of 1999-2012. We hypothesize that this interrelationship dynamically exists between ownership structure and firm performance. To examine the correlation, a panel Vector Auto-regression (PVAR) approach generated by GMM method is utilized to test the possible dynamic relation embedded in corporate governance. Another two dynamic analysis solutions such as orthogonalized impulse-response function and variance decomposition are also used simultaneously. Results - Findings of this study indicate the evidence that dynamically endogenous relationship exists between ownership structure and firm performance. Further, there is a dynamical correlation between investment and performance. Impulse response and variance decomposition illustrate that impact of a shock to variables themselves is the main source for their variability. Conclusions - The conclusion in this study is that there is a bidirectional and inter-temporal effect between proportion of ownership and corporate performance for a long run in accordance with impulse response function. Overall, our results suggest that corporate governance in China is more market oriented.

• Bulletin of the Korean Chemical Society
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• v.15 no.7
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• pp.553-559
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• 1994

Dynamics of $CH_2CI$ group in ${\alpha},2,6$-trichlorotoluene dissolved in $CDCl_3$ was studied by observing various relaxation modes for $^{13}C$ under proton undecoupled condition. Partially relaxed $^{13}C$ spectra were obtained at $34^{\circ}C$ as a function of evolution time after applying various designed pulse sequences to this $AX_2$ spin system. It was found that nonlinear regression analysis of the relaxation data for these magnetization modes could provide the information about dipolar and spin-rotational auto-correlation and cross-correlation spectral densities for fluctuation of the $^{13}C-^1H$ internuclear vector in $CH_2Cl$ group. The results show that the effect of cross-correlation is comparable in magnitude to that of auto-correlation and the relaxation in this spin system is dominated by dipolar mechanism rather than spin-rotational one. From the resulting spectral density data we could calculate the bond angle ${\angle}HCH\;(105.1$^{\circ}$) and elements of the rotational diffusion tensor for $CH_2Cl$ group.

• The Journal of the Korea Contents Association
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• v.10 no.12
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• pp.34-42
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• 2010

The DOA(direction of arrival), which is based on parametric and nonparametric estimation algorithm, and adaptive beamforming algorithm for mobile communication environments are researched and analyzed. In parametric estimation algorithm, eigenvalues of the signal component and the noise component are obtained from correlation matrix of received signal by array antenna and power spectrum of the received signal is discriminated from them. Otherwise, in nonparametric estimation algorithm, we minimize a regularized objective function for finding a estimate of the signal energy as a function of angle, using nonquadratic norm which leads to supper resolution and noise suppression. And then, DOA is estimated by the signal and noise spatial steering vector, and adaptive beam-forming pattern is improved by weight vectors obtained from the spatial vector. Therefore, the improved directional estimation algorithm with regularizing sparsity constraints offers super-resolution and noise suppression compared to other algorithms.