• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.241-247
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• 2021

In the article, an analysis of the factors was carried out, which injected into the efficiency of the function of navigation systems in the minds of unimportance. Carrying out analyzes allowing for the visibility of relevant direct adjustments to the effectiveness of the function Inertial navigation systems in the minds of non-value. The designation of the navigation system was assigned to a complex of navigation systems on ships processing of the vector of navigation parameters, so that they can be victorious in the control systems of the ship's collapse, and the safety of floating is safe.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.07a
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• pp.310-311
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• 2015

Ships' tracking data are being monitored and collected by vessel traffic service center in real time. In this paper, we intend to contribute to vessel traffic service operators' decision making through extracting ships' tracking patterns and models based on these data. Support Vector Machine algorithm was used for vessel track modeling to handle and process the data sets and k-fold cross validation was used to select the proper parameters. Proposed data processing methods could support vessel traffic service operators' decision making on case of anomaly detection, calculation ships' dead reckoning positions and etc.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.6
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• pp.435-439
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• 2009

Software used in safety-critical system must have high dependability. Software testing and V&V (Verification and Validation) activities are very important for assuring high software quality. If we can predict the risky modules of safety-critical software, we can focus testing activities and regulation activities more efficiently such as resource distribution. In this paper, we classified the estimated risk class which can be used for deep testing and V&V. We predicted the risk class for each module using support vector machines. We can consider that the modules classified to risk class 5 and 4 are more risky than others relatively. For all classification error rates, we expect that the results can be useful and practical for software testing, V&V, and activities for regulatory reviews.

• Journal of Korean Society of Transportation
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• v.4 no.1
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• pp.12-27
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• 1986

This study examined user preferences toward transportation modes in Seoul. Two multidimensional scaling models, the ideal point and vector models, were applied to data on mode preferences of 114 adults in the metropolitan area. While both models produced fairly similar results, the vector model performed slightly better than the other in terms of interpretability of the results. The transport attributes elicited are comfort, flexibility, travel cost, travel time, privacy, and safety; among which comfort is salient most. The comfort variable is a multi-faceted attribute in nature. The variations of attribute preferences are most significant between the gender groups as well as worker/nonworker groups. In particular, male workers, female workers and female nonworkers form three distinctive market segments. An unidimensional scaling of the preference data reveals that subway, auto-driver, and subscription bus modes are preferred most, whereas motorcycle and bicycle least. The other modes of express bus, taxt, auto-passenger, bus and walk rank intermediately. An examination of how preference orders vary among modal groups hints that users align their stated attitudes to their choice in order to reduce cognitive dissonance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.8
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• pp.1089-1098
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• 2018

In this paper, we propose an autonomous feeding robot and its obstacle classification system using ultrasonic sensors to secure the driving safety of the robot and efficient feeding operation. The developed feeding robot is verified by operation experiments in a cattle shed. In the proposed classification algorithm, not only the maximum amplitude of the ultrasonic echo signal but also two gradients of the signal and the variation of amplitude are considered as the feature parameters for object classification. The experimental results show the efficiency of the proposed classification method based on the Support Vector Machine, which is able to classify objects or obstacles such as a human, a cow, a fence and a wall.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.2
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• pp.135-140
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• 2021

This study introduces a new pulse width modulation (PWM) method to reduce common-mode voltages (CMVs) and then compares its performance with other reduced CMV-PWM (RCMV-PWM) methods. CMVs should be reduced to ensure the electromagnetic compatibility and safety of grid-connected inverters. RCMV-PWM methods attempt to synthesize voltage references without zero vectors, which cause high CMV peaks. In these methods, the peak-to-peak magnitude of CMVs can be reduced by one-third of the conventional space-vector PWM. The introduced method splits every reference vector into two vectors to avoid the use of zero vectors. The performances of the RCMV-PWM methods are analyzed in accordance with the modulation index through simulation and experiment.

• Korean Journal of Microbiology
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• v.43 no.3
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• pp.151-158
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• 2007

The possibility of inadvertent introduction of therapeutic gene expressing viral vectors has raised safety concerns about germ-line infection. Particularly, for indications such as prostate cancer and ovarian cancer, the proximity of the point of viral administration to organs of the reproductive system raises concerns regarding inadvertent germ-line transmission of genes carried by the virus vector. To evaluate the safety of in vivo adenovirus mediated gene transfer, we explored the biodistribution, persistance and potential germ-line transmission of p53-expressing adenovirus (Ad-CMV-p53). Both male and female Balb/c mice were injected with $1{\times}10^9$ PFU of Ad-CMV-p53. The PCR analysis showed that there were detectable vector sequences in liver, kidney, spleen, seminal vesicle, epididymis, prostate, ovary, and uterus. The RT-PCR analysis for detecting inserted gene, p53 showed that Ad-CMV-p53 viral RNA were present in spleen, prostate and ovary. Direct injected male and female mice of adenovirus vector into testis and ovary were mated and their of offspring were evaluated for germ-line transmission of the adenoviral vector. The PCR and RT-PCR analysis showed no evidence of germline transmission, although vector sequences were detected in DNA extracted from gonadal tissues. Real-time PCR result confirmed a significant decrease of adenovirus in gonad tissues 1 week after injection. We have also analysed the cell specific localization of viral DNA in gonad tissues by using in-situ PCR. Positive signals were detected in interstitial tissue but not in seminiferous tubule in sperm. In the case of ovary, adenovirus signal were localized to the stromal tissue, but no follicular signals were observed. Together, these data provide strong evidence that the risk of the Inadvertent germ-line transmission of vector sequences following intraperitoneal or direct injection into genito-urinary system of adenovirus is extremely low.

• Journal of the Korean Society of Safety
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• v.31 no.4
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• pp.35-41
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• 2016

In order to guarantee the process safety and prevent accidents, the deviations from normal operating conditions should be monitored and their root causes have to be identified as soon as possible. The statistical theories-based method among various fault diagnosis methods has been gaining popularity, due to simplicity and quickness. However, according to fault magnitudes, the scalar value generated by statistical methods can be changed and this point can lead to produce wrong information. To solve this difficulty, this work employs PCA (Principal Component Analysis) based method with qualitative information. In the case study of our previous study, the number of assumed faults is much smaller than that of process variables. In the case study of this study, the number of predefined faults is 19, while that of process variables is 6. It means that a fault diagnosis becomes more difficult and it is really hard to isolate a single fault with a small number of variables. The PCA model is constructed under normal operation data in order to get a loading vector and the data set of assumed faulty conditions is applied with PCA model. The significant changes on PC (Principal Components) axes are monitored with CUSUM (Cumulative Sum Control Chart) and recorded to make the information, which can be used to identify the types of fault.

• Korean Chemical Engineering Research
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• v.52 no.5
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• pp.564-573
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• 2014

Economic optimization of cumene manufacturing process to produce cumene from benzene and propylene was studied. The chosen objective function was the operational profit per year that subtracted capital cost, utility cost, and reactants cost from product revenue and other benefit. The number of design variables of the optimization are 6. Matlab connected to and controlled Unisim Design to calculate operational profit with the given design variables. As the first step of the optimization, design variable points was sampled and operational profit was calculated by using Unisim Design. By using the sampled data, the estimation model to calculate the operational profit was constructed, and the optimization was performed on the estimation model. This study compared second order polynomial and support vector regression as the estimation method. As the sampling method, central composite design was compared with Hammersley sequence sampling. The optimization results showed that support vector regression and Hammersley sequence sampling were superior than second order polynomial and central composite design, respectively. The optimized operational profit was 17.96 MM$ per year, which was 12% higher than 16.04 MM$ of base case.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.33-42
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• 2016

Recently, the investment and study competition regarding machine running is accelerating mainly with Google, Amazon, Microsoft and other leading companies in the field of artificial intelligence. The security weakness of virtualization technology security structure have been a serious issue continuously. Also, in most cases, the internal data security depend on the virtualization security technology of platform provider. This is because the existing software, hardware security technology is hard to access to the field of virtualization and the efficiency of data analysis and processing in security function is relatively low. This thesis have applied user significant information to machine learning algorithm, created security authentication vector able to learn to provide with a method which the security authentication can be conducted in the field of virtualization. As the result of performance analysis, the interior transmission efficiency of authentication vector in virtualization environment, high efficiency of operation method, and safety regarding the major formation parameter were demonstrated.