• The Korean Journal of Pesticide Science
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• v.5 no.2
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• pp.45-50
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• 2001

In recent, non virus-induced mosaic symptoms(NVMS) on potato leaves were observed in the seed potato fields, and its incidence rate was $5{\sim}20%$ nationwide. It made difficult to rogue out virus-infected plants, and caused much arguments between seed potato production farmers and seed potato inspectors. The objectives of these experiments were to find out the causes of NVMS, and also to induce mosaic symptom(phytotoxicity) on potato plants by treatment of several herbicides. No significant correlations were found between incidence rates of NVMS and values from soil analyses; soil pH, soil EC, organic matter content, and contents of inorganic constituents($P_2O_5,\;NO_3$, Ca, Mg, K) in the soil around the potato planted. The examinations by ELISA, virus indicator plants, and TEM showed that NVMS on potato leaves was not caused by the viruses infection. But, the use of herbicides could induced the NVMS on potato leaves. The incidence rates of potato treated with pendimethalin linuron of 400 mL/10 a, pendimethalin of 200 mL/10 a, pendimethalin.oxadiazon of 300 mL/10 a, and control were 61.1%, 47.2%, 19.4%, and 1.4%, respectively. Based on these results, we confirmed that the treatment of pendimethalin alone and in mixture with other herbicides were the reason of NVMS on potato leaves. The yields among test plots were similar except dicamba treated plot, which decreased by about 23% compared to control plot. When their progenies harvested in 1999 were planted in the following season, no symptoms of mosaic were observed.

• Elastomers and Composites
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• v.45 no.4
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• pp.263-271
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• 2010

Intermittent CSR testing was used to investigate the degradation of an FKM O-ring, also the prediction of its life-time. An intermittent CSR jig was designed taking into consideration the O-ring's environment under use. The testing allowed observation of the effects of friction, heat loss, and stress relaxation by the Mullins effect. Degradation of O-rings by thermal aging was observed between 60 and $160^{\circ}C$. In the high temperature of range ($100-160^{\circ}C$) O-rings showed linear degradation behavior and satisfied the Arrhenius relationship. The activation energy was about 60.2 kJ/mol. From Arrhenius plots, predicted life-times were 43.3 years and 69.9 years for 50% and 40% failure conditions, respectively. Based on TTS (time-temperature superposition) principle, degradation was observed at $60^{\circ}C$, and could save testing time. Between 60 and $100^{\circ}C$ the activation energy decreased to 48.3 kJ/mol. WLF(William-Landel-Ferry) plot confirmed that O-rings show non-linear degradation behavior under $80^{\circ}C$. The life-time of O-rings predicted by TTS principle was 19.1 years and 25.2 years for each failure condition. The life-time predicted by TTS principle is more conservative than that from the Arrhenius relationship.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.23-31
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• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.

• Journal of the Korean Geotechnical Society
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• v.25 no.4
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• pp.91-103
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• 2009

Slope failure depends upon the climatic features related to related rainfall, structural geology and geomorphological features as well as the variation of the mechanical behaviors of soil constituting a slope. In this paper, among many variables, effects of soil layer thickness on the slope failure process, and variations of matric suction and volumetric water content were observed. When the soil layer is relatively thick, the descending wetting front decreases matric suction and the observed matric suction reaches to "0" value. When the wetting front reaches to the impermeable boundary, the bottom surface of steel soil box, ascending wetting front was observed. This observation can be postulated to be the effects of various sizes of pores. When macro size pores exist, the capillary effects can be reduced and infilling of pore will be limited. The partially filled pores would be filled with water during the ascending of the wetting front, which bounces from the impermeable boundary. This assumption has been assured from the observation of variation of the volumetric water contents at different depth. When the soil layer is thick (thickness = 20 cm), for granular material, erosion is a cause triggering the slope failure. It has been found that the initiation of erosion occurs when the top soil is fully saturated. Meanwhile, when the soil layer is shallow (thickness = 10 cm), slope slides as en mass. The slope failure for this condition occurs when the wetting front reaches to the interface between the soil layer and steel soil box. As the wetting front approaches to the bottom of soil layer, reduction of shear resistance along the boundary and increase of the unit weight due to the infiltration occur and these produce complex effects on the slope failure processes.