• 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.

• Toxicological Research
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• v.17
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• pp.157-166
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• 2001

Almost all currently available retroviral vectors based on murine leukemia virus (MLV) contain one or more viral coding sequences. Because these sequences are also present in the packaging genome, it has been suggested that homologous recombination may occur between the same nucleotide sequence in the packaging genome and the vector, resulting in the production of replication competent retrovirus (RCR). Up until now, it has been difficult to completely remove viral coding sequences since some were thought to be involved in the optimum function of the retroviral vector. For example, the gag coding sequence present in almost all available retroviral vectors has been believed to be necessary for efficient viral packaging, while the pol coding sequence present in the highly efficient vector MFG has been thought to be involved in achieving the high levels of gene expression. However, we have now developed a series of retroviral vectors that are absent of any retroviral coding sequences but produce even higher levels of gene expression without compromising viral titer. In these vectors, the intron and exon sequences from heterologous cellular or viral genes are present. When compared to the well known MLV-based vectors, some of these newly developed vectors have been shown to produce significantly higher levels of gene expression for a longer period. In an experimental system that can maximize the production of RCR, our newly constructed vectors produced an absence of RCR. These vectors should prove to be safer than other currently available retroviral vectors containing one or more viral coding sequences.

• Journal of Advanced Navigation Technology
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• v.20 no.6
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• pp.604-611
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• 2016

Drone, which refers to unmanned aerial vehicles (UAV), industries are improving rapidly and exceeding existing level of remote controlled aircraft models. Also, they are applying automation and cloud network technology. Recently, the ability of drones can bring serious threats to public safety such as explosives and unmanned aircraft carrying hazardous materials. On the purpose of reducing these kinds of threats, it is necessary to detect these illegal drones, using acoustic feature extraction and classifying technology. In this paper, we introduce sound feature vector extraction method by harmonic feature extraction method (HLA). Feature vector extraction method based on HLA make it possible to distinguish drone sound, extracting features of sound data. In order to assess the performance of distinguishing sounds which exists in outdoor environment, we analyzed various sounds of things and real drones, and classified sounds of drone and others as simulation of each sound source.

• Proceedings of the Korean Society for Applied Microbiology Conference
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• 2000.10a
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• pp.31-50
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• 2000

Almost all currently available retroviral vectors based on murine leukemia virus (MLV) contain one or more viral coding sequences Because these sequences are also present in the packaging genome, it has been suggested that homologous recombination may occur between the same nucleotide sequence in the packaging genome and the vector, resulting in the production of replication competent retrovirus (RCR). Up until now, it has been difficult to completely remove viral coding sequences since some were thought to be involved in the optimum function of the retroviral vector. For example, the gag coding sequence present in almost all available retroviral vectors has been believed to be necessary for efficient viral packaging, while the pol coding sequence present in the highly efficient vector MFG has been thought to be involved in achieving the high levels of gene e(pression. However, we have now developed a series of reroviral vectors that are absent of any retroviral coding sequences but produce even higher levels of gene expression without compromising viral titer. In these vectors the intron and exon sequences from heterologous cellular or viral genes are present, When compared to the well blown MLV-based vectors, some of these newly developed vectors have been shown to produce significantly higher levels of gene expression for a longer period. In an experimental system that can maximize the production of RCR, our newly constructed vectors produced an absence of RCR. These vectors should prove to be safer than other currently available retroviral vectors containing one or more viral coding sequences

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.483-488
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• 2010

An online monitoring based upon sensor system is essential to assure both efficient operation and safety in the power plant. Of great importance is modeling for auto-association (AA) in online monitoring technique. The objective of auto-associative models lies in predicting true values of plant operation parameters from sensor signals transmitted. This paper presents two AA models using Support Vector Regression (SVR) and Partial Least Square Regression (PLSR). The presented models are useful, in particular, when there are many parameters to monitor in the power plant. Illustrative examples are given by using a real-world plant dataset. AA performances of SVR and PLSR are finally summarized in terms of accuracy and sensitivity. According to our results, SVR shows much higher accuracy and, however, its sensitivity is relatively degraded.

• Journal of the Korean Society of Safety
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• v.36 no.3
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• pp.31-39
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• 2021

In Korea, more than half of work-related fatalities have occurred on construction sites. To reduce such occupational accidents, safety inspection by government agencies is essential in construction sites that present a high risk of serious accidents. To address this issue, this study developed risk prediction models of serious accidents in construction sites using five machine learning methods: support vector machine, random forest, XGBoost, LightGBM, and AutoML. To this end, 15 proactive information (e.g., number of stories and period of construction) that are usually available prior to construction were considered and two over-sampling techniques (SMOTE and ADASYN) were used to address the problem of class-imbalanced data. The results showed that all machine learning methods achieved 0.876~0.941 in the F1-score with the adoption of over-sampling techniques. LightGBM with ADASYN yielded the best prediction performance in both the F1-score (0.941) and the area under the ROC curve (0.941). The prediction models revealed four major features: number of stories, period of construction, excavation depth, and height. The prediction models developed in this study can be useful both for government agencies in prioritizing construction sites for safety inspection and for construction companies in establishing pre-construction preventive measures.

• ETRI Journal
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• v.35 no.6
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• pp.1038-1047
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• 2013

Augmented reality (AR) is currently being applied actively to commercial products, and various types of intelligent AR systems combining both the Global Positioning System and computer-vision technologies are being developed and commercialized. This paper suggests an in-vehicle head-up display (HUD) system that is combined with AR technology. The proposed system recognizes driving-safety information and offers it to the driver. Unlike existing HUD systems, the system displays information registered to the driver's view and is developed for the robust recognition of obstacles under bad weather conditions. The system is composed of four modules: a ground obstacle detection module, an object decision module, an object recognition module, and a display module. The recognition ratio of the driving-safety information obtained by the proposed AR-HUD system is about 73%, and the system has a recognition speed of about 15 fps for both vehicles and pedestrians.

• Journal of the Korean Society of Safety
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• v.36 no.5
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• pp.52-60
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• 2021

As the prevention of fatal accidents is considered an essential part of social responsibilities, both government and individual have devoted efforts to mitigate the unsafe conditions and behaviors that facilitate accidents. Several studies have analyzed the factors that cause fatal accidents and compared them to those of non-fatal accidents. However, studies on mathematical and systematic analysis techniques for identifying the features of fatal accidents are rare. Recently, various industrial fields have employed machine learning algorithms. This study aimed to apply machine learning algorithms for the classification of fatal and non-fatal accidents based on the features of each accident. These features were obtained by text mining literature on accidents. The classification was performed using four machine learning algorithms, which are widely used in industrial fields, including logistic regression, decision tree, neural network, and support vector machine algorithms. The results revealed that the machine learning algorithms exhibited a high accuracy for the classification of accidents into the two categories. In addition, the importance of comparing similar cases between fatal and non-fatal accidents was discussed. This study presented a method for classifying accidents using machine learning algorithms based on the reports on previous studies on accidents.

• International conference on construction engineering and project management
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• 2017.10a
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• pp.80-88
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• 2017

Although hazard identification is one of the most important steps of safety management process, numerous hazards remain unidentified in the construction workplace due to the dynamic environment of the construction site and the lack of available resource for visual inspection. To this end, our previous study proposed the collective sensing approach for safety hazard identification and showed the feasibility of identifying hazards by capturing collective abnormalities in workers' walking patterns. However, workers generally performed different activities during the construction task in the workplace. Thereby, an additional process that can identify the worker's walking activity is necessary to utilize the proposed hazard identification approach in real world settings. In this context, this study investigated the feasibility of identifying walking activities during construction task using Wearable Inertial Measurement Units (WIMU) attached to the worker's ankle. This study simulated the indoor masonry work for data collection and investigated the classification performance with three different machine learning algorithms (i.e., Decision Tree, Neural Network, and Support Vector Machine). The analysis results showed the feasibility of identifying worker's activities including walking activity using an ankle-attached WIMU. Moreover, the finding of this study will help to enhance the performance of activity recognition and hazard identification in construction.

• Proceedings of KOSOMES biannual meeting
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• 2018.06a
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• pp.171-171
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• 2018