• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.6
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• pp.691-699
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• 2012

In this paper, we present a Vt-DRO with a low phase noise, which is achieved by improving the coupling structure between the dielectric resonator and microstrip line. The Vt-DRO is a closed-loop type and is composed of 3 blocks; dielectric resonator, phase shifter, and amplifier. We propose a mathematical estimation method of phase noise, using the group delay of the resonator. By modifying the coupling structure between the dielectric resonator and microstrip line, we achieved a group delay of 53 nsec. For convenience of measurement, wafer probes were inserted at each stage to measure the S-parameters of each block. The measured S-parameter of the Vt-DRO satisfies the open-loop oscillation condition. The Vt-DRO was implemented by connecting the input and output of the designed open-loop to form a closed-loop. As a result, the phase noise of the Vt-DRO was measured as -132.7 dBc/Hz(@ 100 kHz offset frequency), which approximates the predicted result at the center frequency of 5.3 GHz. The tuning-range of the Vt-DRO is about 5 MHz for tuning voltage of 0~10 V and the power is 4.5 dBm. PFTN-FOM is -31 dBm.

• Journal of the Korean Society of Radiology
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• v.3 no.1
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• pp.29-39
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• 2009

In this paper has proposed to the recognition of the disease on medical images using neural network. The neural network is constructed as three-layers of the input-layer, the hidden-layer and the output-layer. The training method applied for the recognition of disease region is adaptive error back-propagation. The low-frequency region analyzed by DWT are expressed by matrix. The coefficient-values of the characteristic polynomial applied are n+1. The normalized maximum value +1 and minimum value -1 in the range of tangent-sigmoid transfer function are applied to be use as the input vector of the neural network. To prove the validity of the proposed methods used in the experiment with a simulation experiment, the input medical image recognition rate the evaluation of areas of disease. As a result of the experiment, the characteristic polynomial coefficient of low-frequency area matrix, conversed to 4 level DWT, was proved to be optimum to be applied to the feature parameter. As for the number of training, it was marked fewest in 0.01 of learning coefficient and 0.95 of momentum, when the adaptive error back-propagation was learned by inputting standardized feature parameter into organized neural network. As to the training result when the learning coefficient was 0.01, and momentum was 0.95, it was 100% recognized in fifty-five times of the stomach image, fifty-five times of the chest image, forty-six times of the CT image, fifty-five times of ultrasonogram, and one hundred fifty-seven times of angiogram.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.6C
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• pp.502-511
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• 2012

As the importance of GNSS system increases, the necessity of independent system is increased also. When the independent GNSS system is required, GNSS signal design is necessary with requirement definition. This paper suggests the design method of GNSS signal using the analysis result of receiver performance. First, the candidates are defined based on the design elements. Then the receiver performance of the candidates is analyzed based on the performance evaluation parameters. The weights of performance evaluation parameter are defined in order to consider the receiver performance in a various aspects. Finally, the calculation of normalized performance evaluation parameters and weights are derived to obtain the compared value for signal selection. Spreading code, modulation method and carrier frequency are considered as design parameters. Also, correlation width, DLL PLL thermal noise jitter, frequency bandwidth and side lobe peak ratio are considered as performance evaluation parameters. And positioning performance, robustness to noise, bandwidth efficiency are considered as the performance aspects. This paper analyzes the performance of each candidate using software based simulator and suggest the method to compare objectively the performance of each candidates.

• Journal of the Korean Geotechnical Society
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• v.32 no.9
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• pp.17-27
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• 2016

Parial drainage characteristics of clayey silt with low plasticity from the west coast (Incheon and Hwaseong) was analyzed using CPTU based existing correlation equations and compulsory replacement method. Generally, the estimated $OCRs={\kappa}{\cdot}((q_t-{\sigma}_{vo})/{\sigma}^{\prime}_{vo})$ using Powell and Quartman(1988) were higher than those obtained by the oeodometer tests. These trends were noticeable for the layers containing a lot of silty and sand soils. The assessment of partial drainage conditions was performed through Schnaid et al. (2004)'s equation; it is based on plotting the normalized cone resistance, $Q_t$ versus the pore pressure parameter, $B_q$ in combination with the strength incremental ratio, $s_u/{\sigma}^{\prime}_{vo}$ to the CPTU data. It is evident that more than half of the data fall in the range where $B_q$ < 0.3, corresponding to the domain in which the partial drainage prevails when testing normally consolidated soils at a standard rate of penetration (2 cm/s). To estimate the replacement depth of clayey silt with low plasticity, back analysis was carried out to evaluate the internal friction angle based on where the design depths are equal to the checked depths using bearing capacity equation. The internal friction angels obtained from the back analysis tended to increase as the plasticity index decreases, which is ranged approximately from ${\varphi}^{\prime}=2^{\circ}$ to ${\varphi}^{\prime}=7^{\circ}$.

• Journal of The Korean Society For Urban Railway
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• v.6 no.4
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• pp.339-350
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• 2018

The rail wear measurements in urban underground railway have been analyzed to predict the particulate matter emission caused by rail wear which is one of the major sources of particulate matter emission for underground railway systems. From the rail profile variations measured in the interval of one and half year by dedicated rail wear measuring instrument over the commercial urban underground railway line, 'line-s' which is about 45km long, the characteristics as well as the amount of rail wear have been analyzed after dividing the whole line into about 170 section with radius of curve(R). It has been concluded that the vertical wear parameter V0 and corner wear parameter C0 have been selected to represent the wear amount of straight and curved rail respectively. The amount of rail wear as well as the particulate matter emission by rail wear over the whole line normalized by the rail length as well as the number of train has also been deduced from the relationship between the rail wear parameters and the amount of rail cross-section area.

• Investigative Magnetic Resonance Imaging
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• v.18 no.2
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• pp.120-132
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• 2014

Purpose : To compare dynamic susceptibility contrast imaging, diffusion-weighted imaging, and susceptibility-weighted imaging (SWI) for the differentiation of tumor recurrence and delayed radiation therapy (RT)-related changes in patients treated with RT for primary brain tumors. Materials and Methods: We enrolled 24 patients treated with RT for various primary brain tumors, who showed newly appearing enhancing lesions more than one year after completion of RT on follow-up MRI. The enhancing-lesions were confirmed as recurrences (n=14) or RT-changes (n=10). We calculated the mean values of normalized cerebral blood volume (nCBV), apparent diffusion coefficient (ADC), and proportion of dark signal intensity on SWI (proSWI) for the enhancing-lesions. All the values between the two groups were compared using t-test. A multivariable logistic regression model was used to determine the best predictor of differential diagnosis. The cutoff value of the best predictor obtained from receiver-operating characteristic curve analysis was applied to calculate the sensitivity, specificity, and accuracy for the diagnosis. Results: The mean nCBV value was significantly higher in the recurrence group than in the RT-change group (P=.004), and the mean proSWI was significantly lower in the recurrence group (P<.001). However, no significant difference was observed in the mean ADC values between the two groups. A multivariable logistic regression analysis showed that proSWI was the only independent variable for the differentiation; the sensitivity, specificity, and accuracy were 78.6% (11 of 14), 100% (10 of 10), and 87.5% (21 of 24), respectively. Conclusion: The proSWI was the most promising parameter for the differentiation of newly developed enhancing-lesions more than one year after RT completion in brain tumor patients.

• Journal of Cadastre & Land InformatiX
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• v.47 no.2
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• pp.107-120
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• 2017

There are many wildfire risk indices worldwide, but objective comparisons between such various wildfire risk indices and surface dryness indices have not been conducted for the wildfire cases in Korea. This paper describes a sensitivity analysis on the wildfire risk indices and surface dryness indices for Korea using LDAPS(Local Analysis and Prediction System) meteorological dataset on a 1.5-km grid and MODIS(Moderate-resolution Imaging Spectroradiometer) satellite images on a 1-km grid. We analyzed the meteorology-based wildfire risk indices such as the Australian FFDI(forest fire danger index), the Canadian FFMC(fine fuel moisture code), the American HI(Haines index), and the academically presented MNI(modified Nesterov index). Also we examined the satellite-based surface dryness indices such as NDDI(normalized difference drought index) and TVDI(temperature vegetation dryness index). As a result of the comparisons between the six indices regarding 120 wildfire cases with the area damaged over 1ha during the period between January 2013 and May 2017, we found that the FFDI and FFMC showed a good predictability for most wildfire cases but the MNI and TVDI were not suitable for Korea. The NDDI can be used as a proxy parameter for wildfire risk because its average CDF(cumulative distribution function) scores were stably high irrespective of fire size. The indices tested in this paper should be carefully chosen and used in an integrated way so that they can contribute to wildfire forecasting in Korea.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Korean Journal of Soil Science and Fertilizer
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• v.40 no.6
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• pp.435-441
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• 2007

Ground-based optical sensing over the crop canopy provides information on the mass of plant body which reflects the light, as well as crop nitrogen content which is closely related to the greenness of plant leaves. This method has the merits of being non-destructive real-time based, and thus can be conveniently used for decision making on application of nitrogen fertilizers for crops standing in fields. In the present study relationships among leaf nitrogen content of rice canopy, crop growth status, and Normalized Difference Vegetation Index (NDVI) values were investigated. We measured Green normalized difference vegetation index($gNDVI=({\rho}0.80{\mu}m-{\rho}0.55{\mu}m)/({\rho}0.80{\mu}m+{\rho}0.55{\mu}m)$) and NDVI($({\rho}0.80{\mu}m-{\rho}0.68{\mu}m)/({\rho}0.80{\mu}m+{\rho}0.68{\mu}m)$) were measured by using two different active sensors (Greenseeker, NTech Inc. USA). The study was conducted in the years 2005-06 during the rice growing season at the experimental plots of National Institute of Agricultural Science and Technology located at Suwon, Korea. The experiments carried out with randomized complete block design with the application of four levels of nitrogen fertilizers (0, 70, 100, 130kg N/ha) and same amount of phosphorous and potassium content of the fertilizers. gNDVI and rNDVI increased as growth advanced and reached to maximum values at around early August, G(NDVI) were a decrease in values of observed with the crop maturation. gNDVI values and leaf nitrogen content were highly correlated at early July in 2005 and 2006. On the basis of this finding we attempted to estimate the leaf N contents using gNDVI data obtained in 2005 and 2006. The determination coefficients of the linear model by gNDVI in the years 2005 and 2006 were 0.88 and 0.94, respectively. The measured and estimated leaf N contents using gNDVI values showed good agreement ($R^2=0.86^{***}$). Results from this study show that gNDVI values represent a significant positive correlation with leaf N contents and can be used to estimate leaf N before the panicle formation stage. gNDVI appeared to be a very effective parameter to estimate leaf N content the rice canopy.

• Korean Journal of Soil Science and Fertilizer
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• v.36 no.4
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• pp.200-209
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• 2003

Distribution coefficient ($K_d$) is an universal parameter estimating cadmium partition for a soil-water-crop system in agricultural lands. This study was performed to find some factors affecting soil-water partition coefficients for cadmium in some Korean soils. The distribution coefficients ($K_d$) of cadmium for the 15 series of agricultural soils were measured at quasi-steady state in the pH ranges from 2 to 11. The adsorption data of the selected soils showed a linear relationship between log $K_d$ and pH, which was well agreed with theoretically expected results ; $log\;K_d=0.6339pH+0.5532(r^2=0.70^{**})$. Normalization of the partition coefficients were performed in a range of pH 3.5 ~ 8.5 to minimize adverse effects of Al dissolution, cationic competition, and organic matter dissolution. The $K_d$-om, partition coefficients normalized for organic matter, improved this linearity to the pH of soils. The values of $K_d$-om measured from the field samples were significantly correlated with those of $K_d$ predicted from the sorption-edge experimental data ($r^2=0.68^{**}$).