• Journal of Plant Biotechnology
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• v.33 no.1
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• pp.33-37
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• 2006

Bottle gourd (Lagenaria siceraria Standl.) has been used as a rootstock for the watermelon cultivation because of better growth ability at low temperature and avoidance from contamination of the soil disease. Since the genetic source for the elite rootstock is limited in nature, the genetic engineering method is inevitable to develop new lines especially to obtain the functionally important or multi-disease resistant bottle gourd. Recently, our lab has set up a successful system to transform the bottle gourd. in order to monitor the transformation process, GFP gene is used. Cotyledons of the inbred line 9005, 9006 and G5 were used to induce the shoot under the selection media with MS + 30 g/L sucrose + 3.0 mg/L BAP + 100 mg/L kanamycin + 500 mg/L cefotaxime + 0.5 mg/L $AgNO_3$, pH 5.8. The shoot was developed from the cut side of the explants after 3 weeks on the selection media. The shoot was incubated in the rooting media with 1/2 MS + 30 g/L sucrose + 0.1 mg/L IAA + 50 mg/L kanamycin + 500 mg/L cefotaxime, pH 5.8 and moved to pot for acclimation. Although the shoot development rate was depended on the genotype, the G5 was the best line to be transformed. Monitoring GFP expression from the young shoot under microscope could make the selection much easier to distinguish the transformed shoot from the non-transformed shoots.

• Journal of Bio-Environment Control
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• v.19 no.4
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• pp.266-274
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• 2010

This study aim to investigate fundamentally the growth and physiological responses of tomato plants in responses to two different levels of water deficit, a weak drought stress (-25 kPa) and a severe drought stress (-100 kPa) in soil. The two levels of water deficit were maintained using a micro-irrigation system consisted of soil sensors for the real-time monitoring of soil water content and irrigation modules in a greenhouse experiment. Soil water contents were fluctuated throughout the 30 days treatment period but differed between the two treatments with the average -47 kPa in -25 kPa set treatment and the -119 kPa in -100 kPa set treatment. There were significant differences in plant height between the two different soil water statuses in plant height without differences of the number of nodes. The plants grown in the severe water-deficit treatment had greater accumulation of biomass than the plants in the weak water-deficit treatment. The severe water-deficit treatment (-119 kPa) also induced greater leaf area and leaf dry weight of the plants than the weak water-deficit treatment did, even though there was no difference in leaf area per unit dry weight. These results of growth parameters tested in this study indicate that the severe drought could cause an adaptation of tomato plants to the drought stress with the enhancement of biomass and leaf expansion without changes of leaf thickness. Greater relative water content of leaves and lower osmotic potential of sap expressed from turgid leaves were recorded in the severe water deficit treatment than in the weak water deficit treatment. This finding also postulated physiological adaptation to be better water status under drought stress. The drought imposition affected significantly on photosynthesis, water use efficiency and stomatal conductance of tomato plants. The severe water-deficit treatment increased PSII activities and water use efficiency, but decreased stomatal conductance than the weak water-deficit treatment. However, there were no differences between the two treatments in total photosynthetic capacity. Finally, there were no differences in the number and biomass of fruits. These results suggested that tomato plants have an ability to make adaptation to water deficit conditions through changes in leaf morphology, osmotic potentials, and water use efficiency as well as PSII activity. These adaptation responses should be considered in the screening of drought tolerance of tomato plants.

• Journal of Bio-Environment Control
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• v.29 no.2
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• pp.141-152
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• 2020

The rapid on-site measurement of hydroponic nutrients allows for the more efficient use of crop fertilizers. This paper reports on the development of an embedded on-site system consisting of multiple ion-selective electrodes (ISEs) for the real-time measurement of the concentrations of macronutrients in hydroponic solutions. The system included a combination of PVC ISEs for the detection of NO₃, K, and Ca ions, a cobalt-electrode for the detection of H₂PO₄, a double-junction reference electrode, a solution container, and a sampling system consisting of pumps and valves. An Arduino Due board was used to collect data and to control the volume of the sample. Prior to the measurement of each sample, a two-point normalization method was employed to adjust the sensitivity followed by an offset to minimize potential drift that might occur during continuous measurement. The predictive capabilities of the NO₃ and K ISEs based on PVC membranes were satisfactory, producing results that were in close agreement with the results of standard analyzers (R² = 0.99). Though the Ca ISE fabricated with Ca ionophore II underestimated the Ca concentration by an average of 55%, the strong linear relationship (R² > 0.84) makes it possible for the embedded system to be used in hydroponic NO₃, K, and Ca sensing. The cobalt-rod-based phosphate electrodes exhibited a relatively high error of 24.7±9.26% in the phosphate concentration range of 45 to 155 mg/L compared to standard methods due to inconsistent signal readings between replicates, illustrating the need for further research on the signal conditioning of cobalt electrodes to improve their predictive ability in hydroponic P sensing.

• Journal of Environmental Impact Assessment
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• v.27 no.6
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• pp.635-646
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• 2018

This study sought to identify the characteristics of seasonal concentration differences of particulate matter influenced by land cover types associated with particulate matter emission and reductions, namely forest and urbanized regions. PM10 and PM2.5 was measured with quantitative concentration in 2016 on 23 urban air monitoring stations in Seoul, classified the stations into 3 groups based on the ratio of urbanized and forest land covers within a range of 3km around station, and analysed the differences in particulate matter concentration by season. The center values for the urbanized and forest land covers by group were 53.4% and 34.6% in Group A, 61.8% and 16.5% in Group B, and 76.3% and 6.7% in Group C. The group-specific concentration of PM10 and PM2.5 by season indicated that the concentration of Group A, with high ratio of forests, was the lowest in all seasons, and the concentration of Group C, with high ratio of urbanized regions, had the highest concentration from spring to autumn. These inter-group differences were statistically significant. The concentration of Group C was lower than Group B in the winter; however, the differences between Groups B to C in the winter were not statistically significant. Group A concentration compared to the high-concentration groups by season was lower by 8.5%, 11.2%, 8.0%, 6.8% for PM10 in the order of spring, summer, autumn and winter, and 3.5%, 10.0%, 4.1% and 3.3% for PM2.5. The inter-group concentration differences for both PM10 and PM2.5 were the highest in the summer and grew smaller in the winter, this was thought to be because the forests' ability to reduce particulate matter emissions was the most pronounced during the summer and the least pronounced during the winter. The influence of urbanized areas on particulate matter concentration was lower compared to the influence of forests. This study provided evidence that the particulate matter concentration was lower for regions with higher ratios of forests, and subsequent studies are required to identify the role of green space to manage particulate matter concentration in cities.

• Journal of Environmental Impact Assessment
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• v.27 no.6
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• pp.604-617
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• 2018

As the depletion of forests became more widespread due to the increase in the number of roads, the research was conducted on the relationship between temperature and humidity in the forests, assuming that the forests around the roads were affected. Through the forest monitoring, the temperature and humidity of coniferous forests and broadleaf forests in Sedong and Gongju areas were observed at three point of 10m, 20m and 30m from the road boundary to the inside of the forest, respectively. In Yeongdong area, for more reliable results, it was observed from the point of 0m, 10m, and 20m. During the study period, so it was expected the change in tree growth was small, the change of temperature and humidity inside the forest by the road was compared with the temperature and humidity from the road to the inside of the forest from September 2017 to January 2018, the changes of temperature and humidity inside the forest due to linear development such as roads were quantitatively analyzed. Using the HOBO data logger (MX2301, Onset Corp.), the temperature and humidity changes of each site were measured, and the average of the changes have been analyzed monthly. In the case of Gongju coniferous forests in September 2017, the average weekly temperature is $0.57^{\circ}C$ higher than the forest outside from the forest boundary and $1.23^{\circ}C$ higher than the inside of the forest, at night in November 2017, in Sedong broadleaf forests. That is, the ability to control the temperature and humidity of the forests along the road was larger and less variable as the distance from the road boundary to the inside of the forest increased. In this study, it is considered that the high degree of change in temperature and humidity of the forest and the surrounding area due to artificial linear development such as roads will affect the growth of trees. This results could serve as a basis for studying the quantitative scope of linear development affecting forest growth and for managing forest change caused by linear development.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1283-1297
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• 2023

Mid-wave infrared (MWIR) imagery, due to its ability to capture the temperature of land cover and objects, serves as a crucial data source in various fields including environmental monitoring and defense. The KOMPSAT-3A satellite acquires MWIR imagery with high spatial resolution compared to other satellites. However, the limited spatial resolution of MWIR imagery, in comparison to electro-optical (EO) imagery, constrains the optimal utilization of the KOMPSAT-3A data. This study aims to create a highly visible MWIR fusion image by leveraging the edge information from the KOMPSAT-3A panchromatic (PAN) image. Preprocessing is implemented to mitigate the relative geometric errors between the PAN and MWIR images. Subsequently, we employ a pre-trained pixel difference network (PiDiNet), a deep learning-based edge information extraction technique, to extract the boundaries of objects from the preprocessed PAN images. The MWIR fusion imagery is then generated by emphasizing the brightness value corresponding to the edge information of the PAN image. To evaluate the proposed method, the MWIR fusion images were generated in three different sites. As a result, the boundaries of terrain and objects in the MWIR fusion images were emphasized to provide detailed thermal information of the interest area. Especially, the MWIR fusion image provided the thermal information of objects such as airplanes and ships which are hard to detect in the original MWIR images. This study demonstrated that the proposed method could generate a single image that combines visible details from an EO image and thermal information from an MWIR image, which contributes to increasing the usage of MWIR imagery.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.1-33
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• 2019

The small and medium sized enterprises (hereinafter SMEs) are already at a competitive disadvantaged when compared to large companies with more abundant resources. Manufacturing SMEs not only need a lot of information needed for new product development for sustainable growth and survival, but also seek networking to overcome the limitations of resources, but they are faced with limitations due to their size limitations. In a new era in which connectivity increases the complexity and uncertainty of the business environment, SMEs are increasingly urged to find information and solve networking problems. In order to solve these problems, the government funded research institutes plays an important role and duty to solve the information asymmetry problem of SMEs. The purpose of this study is to identify the differentiating characteristics of SMEs that utilize the public information support infrastructure provided by SMEs to enhance the innovation capacity of SMEs, and how they contribute to corporate performance. We argue that we need an infrastructure for providing information support to SMEs as part of this effort to strengthen of the role of government funded institutions; in this study, we specifically identify the target of such a policy and furthermore empirically demonstrate the effects of such policy-based efforts. Our goal is to help establish the strategies for building the information supporting infrastructure. To achieve this purpose, we first classified the characteristics of SMEs that have been found to utilize the information supporting infrastructure provided by government funded institutions. This allows us to verify whether selection bias appears in the analyzed group, which helps us clarify the interpretative limits of our study results. Next, we performed mediator and moderator effect analysis for multiple variables to analyze the process through which the use of information supporting infrastructure led to an improvement in external networking capabilities and resulted in enhancing product competitiveness. This analysis helps identify the key factors we should focus on when offering indirect support to SMEs through the information supporting infrastructure, which in turn helps us more efficiently manage research related to SME supporting policies implemented by government funded institutions. The results of this study showed the following. First, SMEs that used the information supporting infrastructure were found to have a significant difference in size in comparison to domestic R&D SMEs, but on the other hand, there was no significant difference in the cluster analysis that considered various variables. Based on these findings, we confirmed that SMEs that use the information supporting infrastructure are superior in size, and had a relatively higher distribution of companies that transact to a greater degree with large companies, when compared to the SMEs composing the general group of SMEs. Also, we found that companies that already receive support from the information infrastructure have a high concentration of companies that need collaboration with government funded institution. Secondly, among the SMEs that use the information supporting infrastructure, we found that increasing external networking capabilities contributed to enhancing product competitiveness, and while this was no the effect of direct assistance, we also found that indirect contributions were made by increasing the open marketing capabilities: in other words, this was the result of an indirect-only mediator effect. Also, the number of times the company received additional support in this process through mentoring related to information utilization was found to have a mediated moderator effect on improving external networking capabilities and in turn strengthening product competitiveness. The results of this study provide several insights that will help establish policies. KISTI's information support infrastructure may lead to the conclusion that marketing is already well underway, but it intentionally supports groups that enable to achieve good performance. As a result, the government should provide clear priorities whether to support the companies in the underdevelopment or to aid better performance. Through our research, we have identified how public information infrastructure contributes to product competitiveness. Here, we can draw some policy implications. First, the public information support infrastructure should have the capability to enhance the ability to interact with or to find the expert that provides required information. Second, if the utilization of public information support (online) infrastructure is effective, it is not necessary to continuously provide informational mentoring, which is a parallel offline support. Rather, offline support such as mentoring should be used as an appropriate device for abnormal symptom monitoring. Third, it is required that SMEs should improve their ability to utilize, because the effect of enhancing networking capacity through public information support infrastructure and enhancing product competitiveness through such infrastructure appears in most types of companies rather than in specific SMEs.