• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.306-308
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• 2005

The vehicle detection method using pulse radar has the advantage of maintenance in comparison with loop detection method. We have the information about the vehicle being and position by dividing the signals into sectors in accordance with SSC method, and by applying the discriminant function based on stochastical data. We also reduce the signal processing time.

• Proceedings of the KIPE Conference
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• 2016.11a
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• pp.176-177
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• 2016

본 논문에서는 효율적인 배터리 팩 설계를 위해 300개의 18650 리튬이온 셀의 전기적 특성을 비교분석하였고 통계적 분석을 기반으로 스크리닝 기법을 적용하였다. 300개의 고출력 원통형 18650 리튬이온 배터리 셀을 사용하여 전류적산법 기반 방전 용량(discharged capacity)과 HPPC(hybrid pulse power Characterization) test 기반 충전저항과 방전저항을 추출하였다. 추출한 파라미터를 바탕으로 통계적 분석을 수행하고 스크리닝 기법을 적용하였다. 스크리닝 기법을 적용한 셀과 랜덤으로 추출된 셀을 비교 및 분석하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.12C
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• pp.1200-1208
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• 2003

Affective computing plays an important role in intelligent Human Computer Interactions(HCI). To detect emotional events, it is desirable to construct a computing model for extracting emotion related features from video. In this paper, we propose a statistical model based on the probabilistic distribution of low level features in video shots. The proposed method extracts low level features from video shots and then from a GMM(Gaussian Mixture Model) for them to detect emotional shots. As low level features, we use color, camera motion and sequence of shot lengths. The features can be modeled as a GMM by using EM(Expectation Maximization) algorithm and the relations between time and emotions are estimated by MLE(Maximum Likelihood Estimation). Finally, the two statistical models are combined together using Bayesian framework to detect emotional events in video.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.08a
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• pp.73-73
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• 2010

Various organic- and bio-conjugated nanoparticles have been studied extensively for biological applications in medical diagnoses and drug delivery systems. Gold nanoparticles (AuNP) and poly(ethylene glycol) (PEG) are known biocompatible materials to be used in vivo and are becoming increasingly important in biomedical applications. In this work, we investigated the stability of PEG-conjugated AuNPs, dialysis and centrifuge effects after synthesis or pegylation of AuNPs as a function of elapsed time by using ToF-SIMS imaging technique along with dynamic light scattering (DLS), UV-visible absorption spectroscopic and statistical analyses. Roughly 15-nm-sized AuNPs were synthesized in a citrate-conjugated form, and then converted into the thiol-terminated PEG (O-[2-(3-Mercaptopropionylamino)ethyl]-O'-methylpolyethyleneglycol, M.W.=5 kDa) form. Based on our data, we will show that ToF-SIMS imaging analysis along with DLS, UV-visible absorption and statistical analyses would be a useful method to evaluate stability of PEG-conjugated AuNPs in various environmental conditions.

• Advances in Energy Research
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• v.4 no.1
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• pp.69-86
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• 2016

The quest to reduce the level of overdependence on fossil fuel product and to provide all required information on proven existing alternatives for renewable energy has resulted into rapid growth of research globally to identify efficient alternative renewable energy sources and the process technologies that are sustainable and environmentally friendly. The present study is aimed at production and characterization of bioethanol produced from sugarcane juice using a $2^4$ factorial design investigating the effect of four parameters (reaction temperature, time, concentration of bacteria used and amount of substrate). The optimum bioethanol yield of 19.3% was achieved at a reaction temperature of $30^{\circ}C$, time of 72 hours, yeast concentration of 2 g and 300 g concentration of substrate (sugarcane juice). The result of statistical analysis of variance shows that the concentration of yeast had the highest effect of 7.325 and % contribution of 82.72% while the substrate concentration had the lowest effect and % contribution of -0.25 and 0.096% respectively. The bioethanol produced was then characterized for some fuel properties such as flash point, specific gravity, cloud point, pour point, sulphur content, acidity, density and kinematic viscosity. The results of bioethanol characterization conform to American society for testing and materials (ASTM) standard. Hence, sugarcane juice is a good and sustainable feedstock for bioethanol production in Nigeria owing relative abundance, cheap source of supply and available land for large scale production.

• Geophysics and Geophysical Exploration
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• v.1 no.3
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• pp.170-175
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• 1998

A systematic methodology is developed for the prediction of the lithology using electrofacies classification from wireline log data. Multivariate statistical techniques are adopted to segment well log measurements and group the segments into electrofacies types. To consider corresponding contribution of each log and reduce the computational dimension, multivariate logs are transformed into a single variable through principal components analysis. Resultant principal components logs are segmented using the statistical zonation method to enhance the quality and efficiency of the interpreted results. Hierarchical cluster analysis is then used to group the segments into electrofacies. Optimal number of groups is determined on the basis of the ratio of within-group variance to total variance and core data. This technique is applied to the wells in the Korea Continental Shelf. The results of field application demonstrate that the prediction of lithology based on the electrofacies classification works well with reliability to the core and cutting data. This methodology for electrofacies determination can be used to define reservoir characterization which is helpful to the reservoir management.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.9 s.339
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• pp.19-28
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• 2005

We establish a statistical model for the ultra-wide bandwidth (UMB) indoor channel based on over 2000 frequency response measurements campaign in a Practical apartment. The approach is based on the investigation of the statistical properties of the multipath profiles measured in different place with different rooms. Based on the experimental results, a characterization of the propagation channel from theoretic view point is described. Also we describe a method for measurement of the channel impulse response and channel transfer function. Using the measured data, the authors compares channel impulse responses obtained from time-domain and channel transfer functions obtained from frequency-domain with statistical path loss model. The bandwidth of the signal used in this experiment is from 10MHz to 8.01 GHz. The time-domain results such as maximum excess delay, men excess delay and ms delay spread are presented. As well as, omni-directional biconical antenna were used for transmitter and receiver In addition, measurements presented here support m channel model including the antenna characteristics.

• Proceedings of the Korean Fiber Society Conference
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• 2003.10a
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• pp.45-46
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• 2003

A process control system has been developed for measurement and characterization of the nanofiber web qualities. The nano-fiber information system (NAFIS) developed consists of a measurement device and an analysis algorithm, which are a microscope-laser sensor fusion system and a process information system, respectively. It has been found that NAFIS is so successful in detecting irregularities of pore and diameter that the resulting product has been quitely under control even at the high production rate. Pore distribution, fiber diameter and mass uniformity have been readily measured and analyzed by integrating the non-contact measurement technology and the random function-based time domain signal/image processing algorithm. Qualifies of the nano-fiber webs have been revealed in a way that the statistical parameters for the characteristics above are calculated and stored in a certain interval along with the time-specific information. Quality matrix, scale of homogeneity is easily obtained through the easy-to-use GUI information. Finally, ANFIS has been evaluated both for the real-time measurement and analysis, and for the process monitoring.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.23 no.11
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• pp.831-836
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• 2010

In this paper, Plasma Enhanced Chemical Vapor Deposition (PECVD) $SiO_2$ film properties are modeled using statistical analysis and neural networks. For systemic analysis, Box-Behnken's 3 factor design of experiments (DOE) with response surface method are used. For characterization, deposited film thickness and film stress are considered as film properties and three process input factors including plasma RF power, flow rate of $N_2O$ gas, and flow rate of 5% $SiH_4$ gas contained at $N_2$ gas are considered for modeling. For film thickness characterization, regression based model showed only 0.71% of root mean squared (RMS) error. Also, for film stress model case, both regression model and neural prediction model showed acceptable RMS error. For sensitivity analysis, compare to conventional fixed mid point based analysis, proposed sensitivity analysis for entire range of interest support more process information to optimize process recipes to satisfy specific film characteristic requirements.

• Journal of Communications and Networks
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• v.14 no.4
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• pp.396-409
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• 2012

With the proliferation of diverse wireless devices, there is an increasing concern about the security of location information which can be spoofed or disrupted by adversaries. This paper investigates the characterization and detection of location spoofing attacks, specifically those which are attempting to falsify (degrade) the position estimate through signal strength based attacks. Since the physical-layer approach identifies and assesses the security risk of position information based solely on using received signal strength (RSS), it is applicable to nearly any practical wireless network. In this paper, we characterize the impact of signal strength and beamforming attacks on range estimates and the resulting position estimate. It is shown that such attacks can be characterized by a scaling factor that biases the individual range estimators either uniformly or selectively. We then identify the more severe types of attacks, and develop an attack detection approach which does not rely on a priori knowledge (either statistical or environmental). The resulting approach, which exploits the dissimilar behavior of two RSS-based estimators when under attack, is shown to be effective at detecting both types of attacks with the detection rate increasing with the severity of the induced location error.