• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.3 s.333
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• pp.25-34
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• 2005

In this paper, we analyzed and measured the electrical crosstalk characteristics of a triplex transceiver module for ethernet Passive optical networks(EPONS). And we improved the electrical crosstalk levels using Dummy ground lines with signal lines. The triplex transceiver module consists of a laser diode as a transmitter, a digital photodetector as a digital data receiver, and a analog photodetector as a community antenna television signal receiver. And there are integrated on silicon substrate. The digital receiver and analog receiver sensitivity have to meet -24 dBm at $BER=10^{-l2}$ and -7.7 dBm at 44 dB SNR. And the electrical crosstalk levels have to maintain less than -86 dB from DC to 3 GHz. From analysis and measurement results, the proposed silicon substrate structure that contains the Dummy ground line with $100\;{\mu}m$ space from signal lines and separates 4 mm among devices respectively, is satisfied the electrical crosstalk level compared to simple structure. This proposed structure can be easily implemented with design convenience and greatly reduced the silicon substrate size about $50\%$.

• Applied Chemistry for Engineering
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• v.34 no.3
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• pp.207-212
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• 2023

Near-infrared (NIR) absorbing dyes have been applied to various applications such as optical filters, biotechnology, energy storage and conversion, coating additive, and traditionally information-storage materials. Because image sensors used in cellphones and digital cameras have sensitivity in the NIR region, the NIR cut-off filter is essential to achieving more clear images. As energy storage and conversion have been important, diverse NIR absorbing materials have been developed to extend the absorption region to the NIR region, and NIR absorbing materials-based research has proceeded to improve device performances. Adding NIR-absorbing dye with a photo-thermal effect to a self-healable coating system has been attractive for future mobility technology, and more effective self-healing properties have been reported. In this report, the chemical structures of representative NIR-absorbing dyes and state of the art research based on NIR-absorbing dyes are introduced.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.2
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• pp.64-70
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• 2011

Portable artificial electronic nose (E-nose) system suffers from noisy fluctuation in surroundings such as temperature, vapor concentration, and gas flow, because its measuring condition is not controled precisely as in the laboratory. It is important to develop a simple and robust vapor recognition technique applicable to this uncontrolled measurement, especially for the portable measuring and diagnostic system which are expanding its area with the improvements in micro bio sensor technology. This study used a PDA-based portable E-nose to collect the uncontrolled vapor measurement signals, and applied the image matching algorithm developed in the previous study on the measured signal to verify its robustness and improved accuracy in portable vapor recognition. The results showed not only its consistent performance under noisy fluctuation in the portable measurement signal, but also an advanced recognition accuracy for 2 similar vapor species which have been hard to discriminate with the conventional maximum sensitivity feature extraction method. The proposed method can be easily applied to the data processing of the ubiquitous sensor network (USN) which are usually exposed to various operating conditions. Furthermore, it will greatly help to realize portable medical diagnostic and environment monitoring system with its robust performance and high accuracy.

• 전자공학회논문지 IE
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• v.48 no.1
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• pp.30-35
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• 2011

In this paper, we propose a method to improve the performance of initial channel estimation (CE) for the multiband-OFDM (MB-OFDM) UWB. The performance of the initial CE can be generally improved as increasing the number of the used preamble symbols. The MB-OFDM specification presents two CE symbols per band in preamble format. The performance of CE estimation with two CE symbols may be satisfied in relatively high sensitivity -77.5 and -72.5 dBm for 200 Mbps and 480 Mbps data rate, respectively, but can not be enough in the degraded 55 Mbps and 110 Mbps sensitivities such as -83.5 and -80.5 dBm, respectively. A method proposed in this paper achieves the performance improvement by extending CE estimation region to packet synchronization (PS) symbols and frame synchronization (FS) symbols including two CE symbols. This can improve the CE performance in the degraded SNR and increase the link-margin by reducing the error rate in physical-layer header. The link-margin improvement obtained by the proposed CE preamble can induce the decrease of error-rate in physical-layer header and increase of communication throughput. Simulation results for the proposed initial method show that the performance is improved by about 0.7 dB at 10-4 bit-error-rate using '4' symbols than initial method using only two CE symbols.

• Journal of Korea Water Resources Association
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• v.39 no.2 s.163
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• pp.151-160
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• 2006

In this study, threshold runoff which is a hydrologic component of flash flood guidance(FFG) is estimated by using Manning's bankfull flow and Geomorphoclimatic Instantaneous Unit Hydrograph(GcIUH) methods on Han River watershed. Geographic Information System(GIS) and 3' Digital Elevation Model database have been used to prepare the basin parameters of a very fine drainage area($1.02\~56.41km^2$), stream length and stream slope for threshold runoff computation. Also, cross-sectional data of basin and stream channel are collected for a statistical analysis of regional regression relationships and then those are used to estimate the stream parameters. The estimated threshold runoff values are ranged from 2 mm/h to 14 mm/6hr on Han River headwater basin with the 1-hour duration values are$97\%$ up to 8mm and the 6-hour values are $98\%$ up to 14mm. The sensitivity analysis shows that threshold runoff is more variative to the stream channel cross-sectional factors such as a stream slope, top width and friction slope than the drainage area. In comparisons between the computed threshold runoffs on this study area and the three other regions in the United States, the computed results on Han River watershed are reasonable.

• The KIPS Transactions:PartA
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• v.16A no.6
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• pp.453-462
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• 2009

Studies on software plagiarism detection, prevention and judgement have become widespread due to the growing of interest and importance for the protection and authentication of software intellectual property. Many previous studies focused on comparing all pairs of submitted codes by using attribute counting, token pattern, program parse tree, and similarity measuring algorithm. It is important to provide a clear-cut model for distinguishing plagiarism and collaboration. This paper proposes a source code clustering algorithm using a probability model on extreme value distribution. First, we propose an asymmetric distance measure pdist($P_a$, $P_b$) to measure the similarity of $P_a$ and $P_b$ Then, we construct the Plagiarism Direction Graph (PDG) for a given program set using pdist($P_a$, $P_b$) as edge weights. And, we transform the PDG into a Gumbel Distance Graph (GDG) model, since we found that the pdist($P_a$, $P_b$) score distribution is similar to a well-known Gumbel distribution. Second, we newly define pseudo-plagiarism which is a sort of virtual plagiarism forced by a very strong functional requirement in the specification. We conducted experiments with 18 groups of programs (more than 700 source codes) collected from the ICPC (International Collegiate Programming Contest) and KOI (Korean Olympiad for Informatics) programming contests. The experiments showed that most plagiarized codes could be detected with high sensitivity and that our algorithm successfully separated real plagiarism from pseudo plagiarism.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.35D no.10
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• pp.39-50
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• 1998

Wide-band sharp-transition filters are widely used in applications such as wireless CODEC design or medical systems. Since these filters suffer from large sensitivity and roundoff noise, large word-length is required for the VLSI implementation, which increases the hardware size and the power consumption of the chip. In this paper, a low-power implementation technique for digital filters with wide-band sharp-transition characteristics is proposed using CPL (Complementary Pass-Transistor Logic), LWDF (Lattice Wave Digital Filter) and a modified DIFIR (Decomposed & Interpolated FIR) algorithm. To reduce the short-circuit current component in CPL circuits due to threshold voltage reduction through the pass transistor, three different approaches can be used: cross-coupled PMOS latch, PMOS body biasing and weak PMOS latch. Of the three, the cross-coupled PMOS latch approach is the most realistic solution when the noise margin as well as the energy-delay product is considered. To optimize CPL transistor size with insight, the empirical formulas for the delay and energy consumption in the basic structure of CPL circuits were derived from the simulation results. In addition, the filter coefficients are encoded using CSD (Canonic Signed Digit) format and optimized by a coefficient quantization program. The hardware cost is minimized further by a modified DIFIR algorithm. Simulation result shows that the proposed method can achieve about 38% reductions in power consumption compared with the conventional method.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.11
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• pp.589-600
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• 2002

Deformable models offer an attractive approach for extracting three-dimensional boundary structures from volumetric images. However, conventional deformable models have three major limitations - sensitive to initial condition, difficult to represent complex boundaries with severe object concavities and protrusions, and self-intersective between model elements. This paper proposes a deformable model that is effective to extract geometrically complex boundary surfaces by improving away the limitations of conventional deformable models. First, the proposed deformable model resamples its elements hierarchically based on volume image pyramid. The hierarchical resampling overcomes sensitivity to initialization by extracting the boundaries of objects in a multiscale scheme and enhances geometric flexibility to be well adapted to complex image features by refining and regularizing the size of model elements based on voxel size. Second, the physics-based formulation of our model integrates conventional internal and external forces, as well as a non-self-intersecting force. The non-self-intersecting force effectively prevents collision or crossing over between non-neighboring model elements by pushing each other apart if they are closer than a limited distance. We show that the proposed model successively extracts the complex boundaries including severe concavities and protrusions, neither depending on initial position nor causing self-intersection, through the experiments on several computer-generated volume images and brain MR volume images.

• Korean Chemical Engineering Research
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• v.60 no.1
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• pp.1-6
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• 2022

As the demand for wearable devices increases, many studies have been studied on the development of flexible electrode materials recently. In particular, the development of high-performance flexible electrode materials is very important for wearable sensors for healthcare because it is necessary to continuously monitor and accurately detect body information such as body temperature, heart rate, blood glucose, and oxygen concentration in real time. In this study, we fabricated the nonenzymatic glucose sensor based on polyaniline/carbon nanotube fiber (PANI/CNT fiber) electrode. PANI layer was synthesized on the flexible CNT fiber electrode through electrochemical polymerization process in order to improve the performance of a flexible CNT fiber based electrode material. Surface morphology of the PANI/CNT fiber electrode was observed by scanning electron microscopy. And its electrochemical characteristics were investigated by chronoamperometry, cyclic voltammetry, electrochemical impedance spectroscopy. Compared to bare CNT fiber electrode, this PANI/CNT fiber electrode exhibited small electron transfer resistance, low peak separation potential and large surface area, resulting in enhanced sensing properties for glucose such as wide linear range (0.024~0.39 and 1.56~50 mM), high sensitivity (52.91 and 2.24 ㎂/mM·cm²), low detection limit (2 μM) and good selectivity. Therefore, it is expected that it will be possible to develop high performance CNT fiber based flexible electrode materials using various nanomaterials.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.