• Journal of Plant Biotechnology
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• v.34 no.4
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• pp.347-354
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• 2007

Commercialization of genetically modified (GM) plants will be required the assessment of risks associated with the release of GM plants that should include a detailed risk assessment of their impacts in human health and the environment. Prior to GM plant release, applicants should provide the information on GM crops for approval. We carried out this study to provide the molecular data for risk assessment of the GM Chinese cabbage plants with insect-resistance gene, modified CryIAc, which we obtained by Agrobacterium-transformation. From the molecular analysis with GM Chinese cabbage, we confirmed the transgene copy number and stability, the expression of the transgene, and integration region sequences between the transgene and the Chinese cabbage genome. Based on the unique integration DNA sequences, we designed specific primer set to detect GM Chinese cabbage and set up the GM cabbage detection method by qualitative PCR analysis. Qualitative analysis with GM Chinese cabbage progenies analysis was revealed the same as the result of herbicide treatment. Our results provided the molecular data for risk assessment analysis of GM Chinese cabbage and demonstrated that the primer set proposed could be useful to detect GM Chinese cabbage.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.2
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• pp.146-155
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• 2004

Currently most of commercial operating systems contain a high-level audit feature to increase their own security level. Linux does not fall behind the other commercial operating systems in performance and stability, but Linux does not have a good audit feature. Linux is required to support a higher security feature than C2 level of the TCSEC in order to be used as a server operating system, which requires the kernel-level audit feature that provides the system call auditing feature and audit event. In this paper, we present LxBSM, which is a kernel module to provide the kernel-level audit features. The audit record format of LxBSM is compatible with that of Sunshield BSM. The LxBSM is implemented as a loadable kernel module, so it has the enhanced usability. It provides the rich audit records including the user-level audit events such as login/logout. It supports both the pipe and file interface for increasing the connectivity between LxBSM and intrusion detection systems (IDS). The performance of LxBSM is compared and evaluated with that of Linux kernel without the audit features. The response time was increased when the system calls were called to create the audit data, such as fork, execve, open, and close. However any other performance degradation was not observed.

• Journal of Biomedical Engineering Research
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• v.20 no.1
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• pp.81-90
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• 1999

This paper describes an EEG(electroencephalogram) software for two-channel cerebral function monitoring system to detect the cerebral ischemia. In the software, two-channel bipolar analog EEG signals are digitized and from the signals various EEG parameters are extracted and displayed on a monitor in real-time. Digitized EEG signal is transformed by FFT(Fast Fourier transform) and represented as CSA(compressed spectral array) and DSA(density spectral array). Additional 5 parameters, such as alpha ratio, percent delta, spectral edge frequency, total power, and difference in total power, are estimated using the FFT spectra. All of these are effectively merged in a monitor and displayed in real-time. Through animal experiments and clinical trials on men, the software is modified and enhanced. Since the software provides raw EEG, CSA, DSA, simultaneously with additional 5 parameters in a monitor, it is possible to observe patients multilaterally. For easy comparison of patient's status, reference patterns of CSA, DSA can be captured and displayed on top of the monitor. And user can mark events of surgical operation and patient's conditions on the software, this allow him jump to the points of events directly, when reviewing the recorded EEG file afterwards. Other functions, such as forward/backward jump, gain control, file management are equipped and these are operated by simple mouse click. Clinical tests in a university hospital show that the software responds accurately according to the conditions of patients and medical doctors can use the software easily.

• Atmosphere
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• v.29 no.3
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• pp.257-267
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• 2019

This study is to assess the applicability of the Extreme Forecast Index (EFI) algorithm of the ECMWF seasonal forecast system to the Global Seasonal Forecasting System version 5 (GloSea5), operational seasonal forecast system of the Korea Meteorological Administration (KMA). The EFI is based on the difference between Cumulative Distribution Function (CDF) curves of the model's climate data and the current ensemble forecast distribution, which is essential to diagnose the predictability in the extreme cases. To investigate its applicability, the experiment was conducted during the heat-wave cases (the year of 1994 and 2003) and compared GloSea5 hindcast data based EFI with anomaly data of ERA-Interim. The data also used to determine quantitative estimates of Probability Of Detection (POD), False Alarm Ratio (FAR), and spatial pattern correlation. The results showed that the area of ERA-Interim indicating above 4-degree temperature corresponded to the area of EFI 0.8 and above. POD showed high ratio (0.7 and 0.9, respectively), when ERA-Interim anomaly data were the highest (on Jul. 11, 1994 (> $5^{\circ}C$) and Aug. 8, 2003 (> $7^{\circ}C$), respectively). The spatial pattern showed a high correlation in the range of 0.5~0.9. However, the correlation decreased as the lead time increased. Furthermore, the case of Korea heat wave in 2018 was conducted using GloSea5 forecast data to validate EFI showed successful prediction for two to three weeks lead time. As a result, the EFI forecasts can be used to predict the probability that an extreme weather event of interest might occur. Overall, we expected these results to be available for extreme weather forecasting.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.4
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• pp.69-81
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• 2006

The Computer Forensics is a research area that finds the malicious users by collecting and analyzing the intrusion or infringement evidence of computer crimes such as hacking. Many researches about Computer Forensics have been done so far. But those researches have focussed on how to collect the forensic evidence for both analysis and poofs after receiving the intrusion or infringement reports of hosts from computer users or network administrators. In this paper, we describe how to collect the forensic evidence of good quality from observable and protective hosts at the time of infringement occurrence by malicious users. By correlating the event logs of Intrusion Detection Systems(IDSes) and hosts with the configuration information of hosts periodically, we calculate the value of infringement severity that implies the real infringement possibility of the hosts. Based on this severity value, we selectively collect the evidence for proofs at the time of infringement occurrence. As a result, we show that we can minimize the information damage of the evidence for both analysis and proofs, and reduce the amount of data which are used to analyze the degree of infringement severity.

• Korean Journal of Construction Engineering and Management
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• v.23 no.4
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• pp.3-14
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• 2022

Autonomous equipment requires a large amount of data from various environments. However, it takes a lot of time and cost for an experiment in a real construction sites, which are difficulties in data collection and processing. Therefore, this study aims to develop a virtual environment for autonomous tower cranes technology development and validation. The authors defined automation functions and operation conditions of tower cranes with three performance criteria: operational design domain, object and event detection and response, and minimum functional conditions. Afterward, this study developed a virtual environment for learning and validation for autonomous functions such as recognition, decision making, and control using the Unity game engine. Validation was conducted by construction industry experts with a fidelity which is the representative matrix for virtual environment assessment. Through the virtual environment platform developed in this study, it will be possible to reduce the cost and time for data collection and technology development. Also, it is also expected to contribute to autonomous driving for not only tower cranes but also other construction equipment.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1069-1080
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• 2022

The tropospheric ozone is a pollutant that causes a great deal of damage to humans and ecosystems worldwide. In the event that ozone moves downwind from its source, a localized problem becomes a regional and global problem. To enhance ozone monitoring efficiency, geostationary satellites with continuous diurnal observations have been developed. The objective of this study is to derive the Tropospheric Ozone Movement Vector (TOMV) by employing continuous observations of tropospheric ozone from geostationary satellites for the first time in the world. In the absence of Geostationary Environmental Monitoring Satellite (GEMS) tropospheric ozone observation data, the GEOS-Chem model calculated values were used as synthetic data. Comparing TOMV with GEOS-Chem, the TOMV algorithm overestimated wind speed, but it correctly calculated wind direction represented by pollution movement. The ozone influx can also be calculated using the calculated ozone movement speed and direction multiplied by the observed ozone concentration. As an alternative to a backward trajectory method, this approach will provide better forecasting and analysis by monitoring tropospheric ozone inflow characteristics on a continuous basis. However, if the boundary of the ozone distribution is unclear, motion detection may not be accurate. In spite of this, the TOMV method may prove useful for monitoring and forecasting pollution based on geostationary environmental satellites in the future.

• Journal of Service Research and Studies
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• v.13 no.1
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• pp.127-143
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• 2023

Currently, the domestic traditional market has not escaped the swamp of stagnation that began in the early 2000s despite various projects promoted by many related players such as the central government and local governments. In order to overcome the crisis faced by the traditional market, various R&Ds have recently been conducted on how to build a smart traditional market that combines information and communication technologies such as big data analysis, artificial intelligence, and the Internet of Things. This study analyzes various previous studies, users of traditional markets, and application cases of ICT technology in foreign traditional markets since 2012 and proposes a model to build a smart traditional market using ICT technology based on the analysis. The model proposed in this study includes building a traditional market metaverse that can interact with visitors, certifying visits to traditional markets through digital signage with NFC technology, improving accuracy of fire detection functions using IoT and AI technology, developing smartphone apps for market launch information and event notification, and an e-commerce system. If a smart traditional market platform is implemented and operated based on the smart traditional market platform model presented in this study, it will not only draw interest in the traditional market to MZ generation and foreigners, but also contribute to revitalizing the traditional market in the future.

• Journal of Environmental Health Sciences
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• v.48 no.1
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• pp.1-8
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• 2022

Objectives: The purpose of this study is to reveal the circumstances under which the cases of harm to health caused by humidifier disinfectant were neglected and show the points where the number of victims and the degree of damage could have been reduced. In addition, it attempts to describe how damage management proceeded immediately after the incident and actually exacerbated the damage. Finally, it explores the unfortunate aspects of the recent trial. By doing so, it attempts to take this as an opportunity to consider whether a tragic event such as the humidifier disinfectant incident could occur in the future. Methods: This study collected and analyzed data on chemical material characteristics related to humidifier disinfectants, data on health effect characteristics, data on related laws and regulations from the Ministry of Environment, data related to the damage investigation by the Korea Environmental Industry and Technology Institute, and current contents. Results: The lack of related systems and laws is the area where the greatest responsibility for the cause of the humidifier disinfectant disaster falls, so it is difficult for the government to escape this responsibility. Establishing a dedicated department to identify the prevalence of certain diseases within the functions of the Health Insurance Review and Assessment Service to monitor health can greatly contribute to the prevention and management of diseases through early detection and management of group outbreaks caused by harmful factors. Humidifier disinfectant damage relief should have been expanded earlier beyond HDLI (humidifier disinfectant lung injury) to include non-specific diseases such as asthma, pneumonia, and interstitial pneumonia. The scope of relief benefits should have also been expanded earlier to include the payment of disability benefits. Fortunately, with the 2020 revision of the Special Act, the conditions for estimating causal relations were eased and individual screening systems such as health impact assessment were reorganized along with the introduction of a rapid screening system. Conclusions: The management system for chemical substances in a country is clearly of paramount importance, and the ministry in charge must have a response system in case of damage to health effects. Administration that looks at the victims' situation from their point of view is needed, and technical countermeasures are required to quickly recognize the prevalence of certain diseases.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5B
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• pp.485-493
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• 2008

The Artificial Neural Network (ANN) model was suggested for predicting probability of precipitation (PoP) using RDAPS NWP model, observation at AWS and upper-air sounding station. The prediction work was implemented for flood season and the data period is the July, August of 2001 and June of 2002. Neural network input variables (predictors) were composed of geopotential height 500/750/1000 hPa, atmospheric thickness 500-1000 hPa, X & Y-component of wind at 500 hPa, X & Y-component of wind at 750 hPa, wind speed at surface, temperature at 500/750 hPa/surface, mean sea level pressure, 3-hr accumulated precipitation, occurrence of observed precipitation, precipitation accumulated in 6 & 12 hrs previous to RDAPS run, precipitation occurrence in 6 & 12 hrs previous to RDAPS run, relative humidity measured 0 & 12 hrs before RDAPS run, precipitable water measured 0 & 12 hrs before RDAPS run, precipitable water difference in 12 hrs previous to RDAPS run. The suggested ANN has a 3-layer perceptron (multi layer perceptron; MLP) and back-propagation learning algorithm. The result shows that there were 6.8% increase in Hit rate (H), especially 99.2% and 148.1% increase in Threat Score (TS) and Probability of Detection (POD). It illustrates that the suggested ANN model can be a useful tool for predicting rainfall event prediction. The Kuipers Skill Score (KSS) was increased 92.8%, which the ANN model improves the rainfall occurrence prediction over RDAPS.