• Journal of Plant Biotechnology
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• v.49 no.3
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• pp.213-221
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• 2022

Wild garlic (Allium victorialis var. platyphyllum, AVVP) is a nontimber forest product used as an edible and medicinal vegetable. AVVP is usually propagated form offspring bulbs but it takes a long time to harvest. Using tissue culture technology could overcome this problem. This study investigated the optimal conditions for shoot multiplication, root growth, and plant growth by scooping AVVP bulbs. AVVP bulbs harvested from Ulleung Island, Korea, the main producer of AVVP, were surface-sterilized and used for in vitro propagation. Shoot multiplication was performed by the scooping method. More than five multiple shoots were induced from scooped tissue in Quoirin and Lepoivre (QL) medium containing plant growth regulators (PGRs); the maximum number of multiple shoots were induced from scooped tissue in QL medium containing 0.45 μM thidiazuron (TDZ) after 16 weeks of culture. Roots were induced directly at the base of the shoots in all treatments. In vitro rooting depended on the type of PGRs, and the best root-inducing treatment was QL medium containing 9.84 μM indole-3-butyric acid (IBA). Plants with in vitro roots were transferred to pots containing artificial soil and successfully acclimatized for 4 weeks. The acclimatized plants showed a survival rate of 80% after 20 weeks and gradually promoted growth depending on the acclimatization period. The results of this study will be of great help to AVVP dissemination through sustainable mass propagation.

• Journal of Ecology and Environment
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• v.46 no.3
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• pp.161-171
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• 2022

Background: Epilobium hirsutum L. is designated as an endangered plant in South Korea located in Asia, due to the destruction of its habitats through the development of wetlands. Therefore, in this study, in order to find a light condition suitable for the growth and ecophysiological responses of Epilobium hirsutum L., those of this plant under treatment with various light qualities in a smart farm were measured. Results: In order to examine the changes in the physiological and growth responses of Epilobium hirsutum L. according to the light qualities, the treatment with light qualities of the smart farm was carried out using the red light: blue light irradiation time ratios of 1:1, 1:1/2, and 1:1/5 and a red light: blue light: white light irradiation time ratio of 1:1:1. As a result, the ecophysiological responses (difference between leaf temperature and atmospheric temperature, transpiration rate, net photosynthetic rate, intercellular CO₂ partial pressure, photosynthetic quantum efficiency) to light qualities appeared differently according to the treatments with light qualities. The increase in the blue light ratio increased the difference between the leaf temperature and the atmospheric temperature and the photosynthetic quantum efficiency and decreased the transpiration rate and the intercellular CO₂ partial pressure. On the other hand, the white light treatment increased the transpiration rate and intercellular CO₂ partial pressure and decreased the temperature difference between the leaf temperature and the ambient temperature and photosynthetic quantum efficiency. Conclusions: The light condition suitable for the propagation by the stolons, which are the propagules of Epilobium hirsutum L., in the smart farm, is red, blue and white mixed light with high net photosynthetic rates and low difference between leaf temperature and atmospheric temperature.

• Korean Journal of Plant Resources
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• v.35 no.4
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• pp.508-514
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• 2022

Bulbophyllum auricomum Lindl. is very popular among orchid growers due to the attractive fragrance of its flowers and has become an endangered orchid in Myanmar. In this study, we carried out an aseptic technique that can be used to conserve this endangered orchid species. The seeds of B. auricomum Lindl. were obtained from artificial pollination and cultured in MS basal medium for seed germination. The effect of coconut water and BAP in MS basal medium on callus induction was investigated. The highest callus induction was found at 2.0 mg/L BAP. The maximum growth of protocorm-like bodies (PLBs) was evaluated, and the best response was observed on MS medium supplemented with 150 mL/L coconut water at pH 5.6. MS basal medium supplemented with 150 mL/L coconut water along with 2.0 mg/L BAP and 1.0 mg/L NAA (MCBN) showed the highest number of plantlets at 15℃ at the second week of culture. At the second and third week of culture, MS medium supplemented with 2.0 mg/L BAP and 1.0 mg/L NAA (MBN) showed the best result in terms of the number of leaves and the longest leaves at 15℃ and 25℃, respectively. The present study showed evidence of successful in vitro propagation of B. auricomum Lindl.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2C
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• pp.81-88
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• 2006

This study performed field and laboratory tests for poor subsoils taken in six regions of the country and determined undrain shear strength. Su values and preconsolidation pressure are predicted using Back Propagation neural network (BPNN) and the application of BPNN is verified. The result of BPNN shows that correlation coefficient between test and neural network result is over 0.9, which means high correlativity. Especially the neural network uses only 6 parameters such as natural water content, void ratio, specific gravity, rate of passing 200th sieve, liquid limits and plasticity index among various affecting factors to estimate value and the correlation coefficent is 0.93. The conclusions obtained in this paper are from the tests performed for poor subsoils taken in the several regions of the country. If there were more test results, the prediction and influence of various soil properties could be effectively performed by neural network.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.570-573
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• 2022

This paper presents the identification of void diameters for a cast-resin transformer using an artificial neural network (ANN) model. A PD signal was measured by the Rogowski coil sensor which has the planar and thin structures fabricated on a printed circuit board (PCB), and the PD electrode system was fabricated to simulate a PD defect by a void. In addition, void samples with different diameters were fabricated by injecting air in a cylindrical aluminum frame using a syringe during the epoxy curing process. To identify the diameter of void defects, PD characteristics such as the discharge magnitude, pulse count, and phase angle were extracted and back propagation algorithm (BPA) was designed using virtual instrument (VI) based on the Labview program. From the experimental results, the BPA algorithm proposed in this paper has over 90% accurate rate to identify the diameter of void defects and is expected to use reference data of maintenance and replacement of insulation for cast-resin transformers in the on-site PD measurement.

• Journal of Wind Energy
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• v.14 no.3
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• pp.83-90
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• 2023

The purpose of this study is to evaluate the inter-laminar fracture toughness characteristics of CFRP pultrusion spar cap materials reinforced with non-woven glass fabric. Test specimens were fabricated by the infusion technique. A non-woven glass fabric and artificial defects were embedded on the middle surface between two pultruded CFRP panels. Double cantilever beam (DCB) and End Notched Flexure (ENF) tests were performed according to ASTM standards. Fracture toughness and crack propagation characteristics were evaluated with load-displacement curves and delamination resistance curves (R-Curve). The fracture toughness results were calculated by compliance calibration (CC) method. The initiation and propagation values of Mode-I critical strain energy release rate value G_Ic were 1.357 kJ/m2 and 1.397 kJ/m2, respectively, and Mode-II critical strain energy release rate values G_IIc were 4.053 kJ/m2 for non-precracked test and 4.547 kJ/m2 for precracked test. It was found that the fracture toughness properties of the CFRP pultrusion spar-cap are influenced by the interface between the layers of CFRP and glass fiber non-woven.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.3
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• pp.679-690
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• 2015

The need to remove the cause of traffic accidents by improving the engineering system for a vehicle and the road in order to minimize the accident hazard. This is likely to cause traffic accident continue to take a large and significant social cost and time to improve the reliability and efficiency of this generally poor road, thereby generating a lot of damage to the national traffic accident caused by improper environmental factors. In order to minimize damage from traffic accidents, the cause of accidents must be eliminated through technological improvements of vehicles and road systems. Generally, it is highly probable that traffic accident occurs more often on roads that lack safety measures, and can only be improved with tremendous time and costs. In particular, traffic accidents at intersections are on the rise due to inappropriate environmental factors, and are causing great losses for the nation as a whole. This study aims to present safety countermeasures against the cause of accidents by developing an intersection Traffic safety evaluation model. It will also diagnose vulnerable traffic points through BPA (Back -propagation algorithm) among artificial neural networks recently investigated in the area of artificial intelligence. Furthermore, it aims to pursue a more efficient traffic safety improvement project in terms of operating signalized intersections and establishing traffic safety policies. As a result of conducting this study, the mean square error approximate between the predicted values and actual measured values of traffic accidents derived from the BPA is estimated to be 3.89. It appeared that the BPA appeared to have excellent traffic safety evaluating abilities compared to the multiple regression model. In other words, The BPA can be effectively utilized in diagnosing and practical establishing transportation policy in the safety of actual signalized intersections.

• Economic and Environmental Geology
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• v.46 no.4
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• pp.301-312
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• 2013

Landslides are caused by complex interaction among a large number of interrelated factors such as topography, geology, forest and soils. In this study, a comparative study was carried out using fuzzy relationship method and artificial neural network to evaluate landslide susceptibility. For landslide susceptibility mapping, maps of the landslide occurrence locations, slope angle, aspect, curvature, lithology, soil drainage, soil depth, soil texture, forest type, forest age, forest diameter and forest density were constructed from the spatial data sets. In fuzzy relation analysis, the membership values for each category of thematic layers have been determined using the cosine amplitude method. Then the integration of different thematic layers to produce landslide susceptibility map was performed by Cartesian product operation. In artificial neural network analysis, the relative weight values for causative factors were determined by back propagation algorithm. Landslide susceptibility maps prepared by two approaches were validated by ROC(Receiver Operating Characteristic) curve and AUC(Area Under the Curve). Based on the validation results, both approaches show excellent performance to predict the landslide susceptibility but the performance of the artificial neural network was superior in this study area.

• Journal of Korea Water Resources Association
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• v.46 no.7
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• pp.697-706
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• 2013

In recent, Korea has faced on water quality management problems in reservoir and river because of increasing water temperature and rainfall frequency caused by climate change. This study is effectively to manage water quality for establishment of algal bloom forecasting models with artificial neural network. Daecheong reservoir located in Geum river has suitable environment for algal bloom because it has lots of contaminants that are flowed by rainfall. By using back propagation algorithm of artificial neural networks (ANNs), a model has been built to forecast the algal bloom over short-term (1, 3, and 7 days). In the model, input factors considered the hydrologic and water quality factors in Daecheong reservoir were analyzed by cross correlation method. Through carrying out the analysis, input factors were selected for algal bloom forecasting model. As a result of this research, the short term algal bloom forecasting models showed minor errors in the prediction of the 1 day and the 3 days. Therefore, the models will be very useful and promising to control the water quality in various rivers.

• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.45-52
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• 2008

As the recent development of the IT services, there is a urgent need of effective diagnosis system to present appropriate solution for the complicated problems of breakdown control, a cause analysis of breakdown and others. So we propose an intelligent diagnosis system that integrates the case-based reasoning and the artificial neural network to improve the system performance and to achieve optimal diagnosis. The case-based reasoning is a reasoning method that resolves the problems presented in current time through the past cases (experience). And it enables to make efficient reasoning by means of less complicated knowledge acquisition process, especially in the domain where it is difficult to extract formal rules. However, reasoning by using the case-based reasoning alone in diagnosis problem domain causes a problem of suggesting multiple causes on a given symptom. Since the suggested multiple causes of given symptom has the same weight, the unnecessary causes are also examined as well. In order to resolve such problems, the back-propagation learning algorithm of the artificial neural network is used to train the pairs of the causes and associated symptoms and find out the cause with the highest weight for occurrence to make more clarified and reliable diagnosis.