• The Journal of Korean Medicine
• /
• v.32 no.4
• /
• pp.111-127
• /
• 2011

Objectives: The aim of this study was to establish a fundamental base for hGMP operational systems implementation. Method: The survey was done with a questionnaire developed through consulting specialists, in order to investigate the present state of manpower, facilities and capitalization of private enterprises, and opinions on what the road map for hGMP implementation must include. Results: The results showed that the business scales of related enterprises were relatively small. Education and health monitoring of employees has been done in fair amounts, but a standard must be established. Essential facilities required for herbal product production were present in most cases. Recognition and understanding of hGMP was low. Various opinions on the implementation of hGMP were gathered. Conclusion: Standardized hGMP education programs, plans to modify existing facilities, public announcements and advertisement of the system, and public assistance funds and tax privileges are needed for the successful implementation of the hGMP operational system.

• Smart Structures and Systems
• /
• v.33 no.2
• /
• pp.145-163
• /
• 2024

The evaluation of dynamic characteristics of bridges under operational traffic loads is a crucial aspect of bridge structural health monitoring. In the vehicle-bridge interaction (VBI) system, the vibration responses of bridge exhibit time-varying characteristics. To address this issue, an accurate time-frequency analysis method that combines the autoregressive power spectrum based empirical wavelet transform (AR-EWT) and local maximum synchrosqueezing transform (LMSST) is proposed to identify the time-varying instantaneous frequencies (IFs) of the bridge in the VBI system. The AR-EWT method decomposes the vibration response of the bridge into mono-component signals. Then, LMSST is employed to identify the IFs of each mono-component signal. The AR-EWT combined with the LMSST method (AR-EWT+LMSST) can resolve the problem that LMSST cannot effectively identify the multi-component signals with weak amplitude components. The proposed AR-EWT+LMSST method is compared with some advanced time-frequency analysis techniques such as synchrosqueezing transform (SST), synchroextracting transform (SET), and LMSST. The results demonstrate that the proposed AR-EWT+LMSST method can improve the accuracy of identified IFs. The effectiveness and applicability of the proposed method are validated through a multi-component signal, a VBI numerical model with a four-degree-of-freedom half-car, and a VBI model experiment. The effect of vehicle characteristics, vehicle speed, and road surface roughness on the identified IFs of bridge are investigated.

• Journal of Korean Society for Atmospheric Environment
• /
• v.28 no.5
• /
• pp.571-580
• /
• 2012

Epidemiologic studies of air pollution need accurate exposure assessments at unmonitored locations. A land use regression (LUR) model has been used successfully for predicting traffic-related pollutants, although its application has been limited to Europe, North America, and a few Asian region. Therefore, we modeled traffic-related pollutants by LUR then examined whether LUR models could be constructed using a regulatory monitoring network in Metropolitan area in Korea. We used the annual-mean nitrogen dioxide ($NO_2$) in 2010 in the study area. Geographic variables that are considered to predict traffic-related pollutants were classified into four groups: road type, traffic intensity, land use, and elevation. Using geographical variables, we then constructed a model to predict the monitored levels of $NO_2$. The mean concentration of $NO_2$ was 30.71 ppb (standard deviation of 5.95) respectively. The final regression model for the $NO_2$ concentration included five independent variables. The LUR models resulted in $R^2$ of 0.59. The mean concentration of $NO_2$ of elementary schools was 34.04 ppb (standard deviation of 5.22) respectively. The present study showed that even if we used regulatory monitoring air quality data, we could estimate $NO_2$ moderately well. These analyses confirm the validity of land use regression modeling to assign exposures in epidemiological studies, and these models may be useful tools for assessing health effects of long-term exposure to traffic related pollution.

• Korean Journal of Remote Sensing
• /
• v.35 no.5_1
• /
• pp.751-761
• /
• 2019

Mt. Baekdu, Mt. Aso, Mt. Sakurajima, Mt. Kikai and etc are distributed around the Korean Peninsula. Recently signs of eruption of Mt. Baekdu are increasing, raising concerns over possible damage to volcanic ash from seasonal winds during the winter eruption. Therefore, detailed procedures for investigation and countermeasures for volcanic ash spread and damage are required. But the standards for the warning and alarm signal of volcanic ash presented by Korea Ministry of Government Legislation are vague, with "when damage is expected" and "when serious damage is expected". In this study, to analyze the damage threshold and to apply the cases of overseas damage to the country, a survey was conducted on the establishment of domestic spatial information by public institutions with public confidence. As a result of the investigation of damage from volcanic ash overseas, the details of the damage cases were different depending on the type of life or income sources of each country. Therefore, instead of applying the volcanic ash damage cases abroad in Korea, spatial information analysis was performed to reflect domestic social and natural characteristics. In addition, we selected the areas to be considered in the event of volcanic ash damage in Korea. Finally, domestic volcanic ash damages should be classified as health, residential, road, railroad, aviation, power, water, agriculture, livestock, forest, and soil. When establishing the volcanic ash alarm optimized for Korea in the future, overseas volcanic ash damage cases and domestic spatial information construction in this study will be helpful in policy establishment.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.20 no.2
• /
• pp.60-74
• /
• 2017

To facilitate prevention of highly pathogenic avian influenza (HPAI), a GIS is widely used for monitoring, investigating epidemics, managing HPAI-infected farms, and eradicating the disease. After the outbreak of foot-and-mouth disease in 2010 and 2011, the government of the Republic of Korea (ROK) established the GIS-based Korean Animal Health Integrated System (KAHIS) to avert livestock epidemics, including HPAI. However, the KAHIS is not sufficient for controlling HPAI outbreaks due to lack of responsibility in fieldwork, such as sterilization of HPAI-infected poultry farms and regions, control of infected animal movement, and implementation of an eradication strategy. An outbreak prediction model to support efficient HPAI control in the ROK is proposed here, constructed via analysis of HPAI outbreak patterns in the ROK. The results show that 82% of HPAI outbreaks occurred in Jeolla and Chungcheong Provinces. The density of poultry farms in these regions were $2.2{\pm}1.1/km^2$ and $4.2{\pm}5.6/km^2$, respectively. In addition, reared animal numbers ranged between 6,537 and 24,250 individuals in poultry farms located in HPAI outbreak regions. Following identification of poultry farms in HPAI outbreak regions, an HPAI outbreak prediction model was designed using factors such as the habitat range for migratory birds(HMB), freshwater system characteristics, and local road networks. Using these factors, poultry farms which reared 6,500-25,000 individuals were filtered and compared with number of farms actually affected by HPAI outbreaks in the ROK. The HPAI prediction model shows that 90.0% of the number of poultry farms and 54.8% of the locations of poultry farms overlapped between an actual HPAI outbreak poultry farms reported in 2014 and poultry farms estimated by HPAI outbreak prediction model in the present study. These results clearly show that the HPAI outbreak prediction model is applicable for estimating HPAI outbreak regions in ROK.

• Asian Journal of Atmospheric Environment
• /
• v.5 no.4
• /
• pp.237-246
• /
• 2011

To explore the sources of carbonaceous material in the airborne particulate matter (PM), comprehensive PM sampling was performed (3 to 14 January 2010) at a traffic hot spot site (HS), Farm Gate, Dhaka using several samplers: AirMetrics MiniVol (for $PM_{10}$ and $PM_{2.5}$) and MOUDI (for size fractionated submicron PM). Long-term PM data (April 2000 to March 2006 and April 2000 to March 2010 in two size fractions ($PM_{2.2}$ and $PM_{2.2-10}$) obtained from two air quality-monitoring stations, one at Farm Gate (HS) and another at a semi-residential (SR) area (Atomic Energy Centre, Dhaka Campus, (AECD)), respectively were also analyzed. The long-term PM trend shows that fine particulate matter concentrations have decreased over time as a result of government policy interventions even with increasing vehicles on the road. The ratio of $PM_{2.5}/PM_{10}$ showed that the average $PM_{2.5}$ mass was about 78% of the $PM_{10}$ mass. It was also found that about 63% of $PM_{2.5}$ mass is $PM_1$. The total contribution of BC to $PM_{2.5}$ is about 16% and showed a decreasing trend over the years. It was observed that $PM_1$ fractions contained the major amount of carbonaceous materials, which mainly originated from high temperature combustion process in the $PM_{2.5}$. From the IMPROVE TOR protocol carbon fraction analysis, it was observed that emissions from gasoline vehicles contributed to $PM_1$ given the high abundance of EC1 and OC2 and the contribution of diesel to $PM_1$ is minimal as indicated by the low abundance of OC1 and EC2. Source apportionment results also show that vehicular exhaust is the largest contributors to PM in Dhaka. There is also transported $PM_{2.2}$from regional sources. With the increasing economic activities and recent GDP growth, the number of vehicles and brick kilns has significantly increased in and around Dhaka. Further action will be required to further reduce PM-related air pollution in Dhaka.