• Journal of Mushroom
• /
• v.18 no.4
• /
• pp.380-386
• /
• 2020

The study was conducted for the safety evaluation of 320 pesticide residues in 768 Lentinula edodes fruit body samples and 143 L. edodes media samples, which are distributed nationwide in South Korea. The monitoring method was the second of the multi-residue methods in the Korean Food Code. GC-ECD, GC-NPD, and GC-MSD were used as evaluation equipment for analysis. Single-analysis of the target pesticides was performed for mepiquat chloride. Through the analysis of collected L. edodes samples, pesticide residues were detected in total seven cases, including four L. edodes fruit body samples and three L. edodes media samples. The detected pesticide residues were carbendazim, diflubenzuron, fluopyram, and dinotefuran. In this study, carbendazim was detected in three L. edodes fruit body samples and one L. edodes media sample. The detected amount of carbendazim was 0.056, 0.17, 0.043, and 0.09 mg/kg, respectively. The amount of carbendazim in the collected L. edodes samples was detected below the MRLs (maximum residue level). The detected amounts of fluopyram and dinotefuran were 0.068 mg/kg and 0.06 mg/kg, respectively. Two pesticide residues were detected in the medium in one case. Mepiquat chloride was not detected in this study. These results suggested that residual pesticides were detected in a small number of collected L. edodes. However, the PLS for unregistered pesticides MRL was 0.01 ppm; therefore, we have to conduct research on preparing safety standards for mushrooms, including L. edodes.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.1
• /
• pp.663-668
• /
• 2023

Recently it is very important to collect information such as enemy positions and facilities. To this end, unmanned aerial vehicles such as multicopters have been actively developed, and various mission equipment mounted on unmanned aerial vehicles have also been developed. The coordinate-oriented algorithm refers to an algorithm that calculates a gaze angle so that the mission equipment can fix the gaze at a desired coordinate or position. Flight data and GPS data were collected and simulated using Matlab for coordinate-oriented algorithms. In the simulation using only the coordinate data, the average Pan axis angle was about 0.42°, the Tilt axis was 0.003°~0.43°, and the relatively wide error was about 0.15° on average. As a result of converting this into the distance in the NE direction, the error distance in the N direction was about 2.23m on average, and the error distance in the E direction was about -1.22m on average. The simulation applying the actual flight data showed a result of about 19m@CEP. Therefore, we conducted a study on the self-error of coordinate-oriented algorithms in monitoring and information collection, which is the main task of EOTS, and confirmed that the quantitative target of 500m is satisfied with 30m@CEP, and showed that the desired coordinates can be directed.

• Journal of Internet of Things and Convergence
• /
• v.10 no.2
• /
• pp.1-15
• /
• 2024

The construction of smart communities is a new method and important measure to ensure the security of residential areas. In order to solve the problem of low accuracy in face recognition caused by distorting facial features due to monitoring camera angles and other external factors, this paper proposes the following optimization strategies in designing a face recognition network: firstly, a global graph convolution module is designed to encode facial features as graph nodes, and a multi-scale feature enhancement residual module is designed to extract facial keypoint features in conjunction with the global graph convolution module. Secondly, after obtaining facial keypoints, they are constructed as a directed graph structure, and graph attention mechanisms are used to enhance the representation power of graph features. Finally, tensor computations are performed on the graph features of two faces, and the aggregated features are extracted and discriminated by a fully connected layer to determine whether the individuals' identities are the same. Through various experimental tests, the network designed in this paper achieves an AUC index of 85.65% for facial keypoint localization on the 300W public dataset and 88.92% on a self-built dataset. In terms of face recognition accuracy, the proposed network achieves an accuracy of 83.41% on the IBUG public dataset and 96.74% on a self-built dataset. Experimental results demonstrate that the network designed in this paper exhibits high detection and recognition accuracy for faces in surveillance videos.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.17 no.1
• /
• pp.76-81
• /
• 2024

Recently, agricultural methods are changing from experience-based agriculture to data-based agriculture. Changes in agricultural production due to the 4th Industrial Revolution are largely occurring in three areas: smart sensing and monitoring, smart analysis and planning, and smart control. In order to realize open-field smart agriculture, information on the physical and chemical properties of soil is essential. Conventional physicochemical measurements are conducted in a laboratory after collecting samples, which consumes a lot of cost, labor, and time, so they are quickly measured in the field. Measurement technology that can do this is urgently needed. In addition, a soil analysis system that can be carried and moved by the measurer and used in Korea's rice fields, fields, and facility houses is needed. To solve this problem, our goal is to develop and commercialize software that can collect soil samples and analyze the information. In this study, basic soil composition measurement was conducted using soil composition measurement sensors consisting of hardness measurement and electrode sensors. Through future research, we plan to develop a system that applies soil sampling using a CCD camera, ultrasonic sensor, and sampler. Therefore, we implemented a sensor and soil analysis UI that can measure and analyze the soil condition in real time, such as hardness measurement display using a load cell and moisture, PH, and EC measurement display using conductivity.

• The Journal of Engineering Geology
• /
• v.15 no.2 s.42
• /
• pp.213-227
• /
• 2005

This study aims to assess the problems with investigation method and to suggest the complementary solutions by comparing the predicted data from surface investigation with the outcome data from underground cavern. In the study area, one(NE-1) of 6 fracture zones predicted during the surface investigation was only confirmed in underground caverns. Therefore, it is necessary to improve the confidence level for prediction. In this study, the fracture classification criteria was quantitatively suggested on the basis of the BHTV images of NE-1 fracture zone. The major orientation of background fractures in rock mass was changed at the depth of the storage cavern, the length and intensity were decreased. These characteristics result in the deviation of predieted predicted fracture properties and generate the investigation bias depending on the bore hole directions and investigated scales. The evaluation of hydraulic connectivity in the surface investigation stage needs to be analyze by the groundwater pressures and hydrochemical properties from the monitoring bore hole(s) equipped with a double completion or multi-packer system during the test bore hole is pumping or injecting. The hydraulic conductivities in geometric mean measured in the underground caverns are 2-3 times lower than those from the surface and furthermore the horizontal hydraulic conductivity in geometric mean is six times lower than the vertical one. To improve confidence level of the hydraulic conductivity, the orientation of test hole should be considered during the analysis of the hydraulic conductivity and the methodology of hydro-testing and interpretation should be based on the characteristics of rock mass and investigation purposes.

• Asian Pacific Journal of Cancer Prevention
• /
• v.13 no.4
• /
• pp.1217-1224
• /
• 2012

Background and Aim: Less than 50% of oral cancer cases are diagnosed at early stages of the disease and this is in part due to poor awareness and lack of knowledge on the signs and symptoms of oral cancer. This study sought to measure the baseline awareness of oral cancer in Malaysia and aimed to increase public awareness and knowledge of oral cancer using a mass media campaign. Methods: Baseline awareness and impact of the campaign was measured using self-administered questionnaires sent via email to individuals. The campaign was aired on two national television channels and the reach was monitored through an independent programme monitoring system. Results: 78.2% of respondents had heard of oral cancer, and this increased significantly after the campaign. However, the ability to recognize signs and symptoms remains unchanged. We found that the level of awareness differed between the distinct ethnic subgroups and the reach of the campaign was not uniform across all ethnicities. Conclusion: This substantial study to measure the oral cancer awareness in Malaysia provides important baseline data for the planning of public health policies. Despite encouraging evidence that a mass media campaign could increase the awareness of oral cancer, further research is required to address the acceptability, comprehensiveness and effectiveness. Furthermore, different campaign approaches may be required for specific ethnic groups in a multi-ethnic country such as Malaysia.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2011.05a
• /
• pp.18-18
• /
• 2011

Climate change is impacting and will increasingly impact both the quantity and quality of the world's water resources in a variety of ways. In some areas warming climate results in increased rainfall, surface runoff, and groundwater recharge while in others there may be declines in all of these. Water quality is described by a number of variables. Some are directly impacted by climate change. Temperature is an obvious example. Notably, increased atmospheric concentrations of $CO_2$ triggering climate change increase the $CO_2$ dissolving into water. This has manifold consequences including decreased pH and increased alkalinity, with resultant increases in dissolved concentrations of the minerals in geologic materials contacted by such water. Climate change is also expected to increase the number and intensity of extreme climate events, with related hydrologic changes. A simple framework has been developed in New Zealand for assessing and predicting climate change impacts on water resources. Assessment is largely based on trend analysis of historic data using the non-parametric Mann-Kendall method. Trend analysis requires long-term, regular monitoring data for both climate and hydrologic variables. Data quality is of primary importance and data gaps must be avoided. Quantitative prediction of climate change impacts on the quantity of water resources can be accomplished by computer modelling. This requires the serial coupling of various models. For example, regional downscaling of results from a world-wide general circulation model (GCM) can be used to forecast temperatures and precipitation for various emissions scenarios in specific catchments. Mechanistic or artificial intelligence modelling can then be used with these inputs to simulate climate change impacts over time, such as changes in streamflow, groundwater-surface water interactions, and changes in groundwater levels. The Waimea Plains catchment in New Zealand was selected for a test application of these assessment and prediction methods. This catchment is predicted to undergo relatively minor impacts due to climate change. All available climate and hydrologic databases were obtained and analyzed. These included climate (temperature, precipitation, solar radiation and sunshine hours, evapotranspiration, humidity, and cloud cover) and hydrologic (streamflow and quality and groundwater levels and quality) records. Results varied but there were indications of atmospheric temperature increasing, rainfall decreasing, streamflow decreasing, and groundwater level decreasing trends. Artificial intelligence modelling was applied to predict water usage, rainfall recharge of groundwater, and upstream flow for two regionally downscaled climate change scenarios (A1B and A2). The AI methods used were multi-layer perceptron (MLP) with extended Kalman filtering (EKF), genetic programming (GP), and a dynamic neuro-fuzzy local modelling system (DNFLMS), respectively. These were then used as inputs to a mechanistic groundwater flow-surface water interaction model (MODFLOW). A DNFLMS was also used to simulate downstream flow and groundwater levels for comparison with MODFLOW outputs. MODFLOW and DNFLMS outputs were consistent. They indicated declines in streamflow on the order of 21 to 23% for MODFLOW and DNFLMS (A1B scenario), respectively, and 27% in both cases for the A2 scenario under severe drought conditions by 2058-2059, with little if any change in groundwater levels.

• Journal of Wetlands Research
• /
• v.20 no.3
• /
• pp.256-262
• /
• 2018

Multiple temporal scale can be a useful method to understand population dynamics in ecosystem. The multi-temporal scale approach for population dynamics has rarely been researched till lately. This study was carried out to identify the factors in affecting the population dynamics of herons, including Eastern Cattle Egret (Bubulcus coromadus), Grey Heron (Ardea cinerea), Great Egret (A. alba), Intermediate Egret (Egretta intermedia) and Little Egret (E. garzetta), at rice paddy fields of Seokmun-myeon in the city of Dangjin, South Chungcheong Province during the main breeding periods from 2014 to 2017. We identified the population dynamics of herons at different time interval (day and month) using the unmanned monitoring system. As a result, monthly population dynamics was mostly affected by time, mean temperature and mean precipitation, whereas daily population dynamics was affected by mean temperature and habitat types. The results suggest that there are differences in the factors affecting the population dynamics of herons according to the time scale.

• Korean Journal of Remote Sensing
• /
• v.37 no.6_3
• /
• pp.2027-2034
• /
• 2021

Using multi-temporal KOMPSAT-3/3A high-resolution satellite images, the Normalized Difference Vegetation Index (NDVI) for the area around the Fukushima daiichi nuclear power plant was determined, and the pattern of vegetation changes was analyzed. To calculate the NDVI, surface reflectance from the KOMPSAT-3/3A satellite image was used. Satellite images from four years were used, and the zones where the images overlap was designated as the area of interest (AOI) for the study, and by setting a profile passing through highly vegetated area as a data analysis method, the changes by year were examined. In addition, random points were extracted within the AOI and displayed as a box plot to quantitatively indicate change of NDVI distribution pattern. The main results of this study showed that the NDVI in 2014 was low within AOI in the vicinity of the nuclear power plant, but vegetated area continued to expand until 2021. These results were also confirmed in the change monitoring results shown in a profile or box plot. In disaster areas where access is restricted, such as the Fukushima nuclear power plant area, where it is difficult to collect field data, obtaining land cover classification products with high accuracy using satellite images is challenging, so it is appropriate to analyze them using primary outputs such as vegetation indices obtained from high-resolution satellite imagery. It is necessary to establish an international cooperation system for jointly utilizing satellite images. Meanwhile, to periodically monitor environmental changes in neighboring countries that may affect the Korean peninsula, it is necessary to establish utilization models and systems using high-resolution satellite images.

• Laboraroty Animal Research
• /
• v.34 no.4
• /
• pp.302-310
• /
• 2018

CD47 (integrin-associated protein), a multi-spanning transmembrane protein expressed in all cells including red blood cells (RBCs) and leukocytes, interacts with signal regulatory protein ${\alpha}$ ($SIRP{\alpha}$) on macrophages and thereby inhibits phagocytosis of RBCs. Recently, we generated a novel C57BL/6J CD47 knockout ($CD47^{-/-}$ hereafter) mouse line by employing a CRISPR/Cas9 system at Center for Mouse Models of Human Disease, and here report their hematological phenotypes. On monitoring their birth and development, $CD47^{-/-}$ mice were born viable with a natural male-to-female sex ratio and normally developed from birth through puberty to adulthood without noticeable changes in growth, food/water intake compared to their age and sex-matched wild-type littermates up to 26 weeks. Hematological analysis revealed a mild but significant reduction of RBC counts and hemoglobin in 16 week-old male $CD47^{-/-}$ mice which were aggravated at the age of 26 weeks with increased reticulocyte counts and mean corpuscular volume (MCV), suggesting hemolytic anemia. Interestingly, anemia in female $CD47^{-/-}$ mice became evident at 26 weeks, but splenomegaly was identified in both genders of $CD47^{-/-}$ mice from the age of 16 weeks, consistent with development of hemolytic anemia. Additionally, helper and cytotoxic T cell populations were considerably reduced in the spleen, but not in thymus, of $CD47^{-/-}$ mice, suggesting a crucial role of CD47 in proliferation of T cells. Collectively, these findings indicate that our $CD47^{-/-}$ mice have progressive hemolytic anemia and splenic depletion of mature T cell populations and therefore may be useful as an in vivo model to study the function of CD47.