• International Journal of Railway
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• v.3 no.4
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• pp.123-125
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• 2010

That Russia is potentially great transport power becomes obvious if look to map of any route. The geographical position of the Russian Federation unequivocally specifies intended by nature the role of geobridge between the countries of Asia-Pacific Region and Europe. However, in construction engineering practice and feasibility study the construction of difficult and strategically important bridges is generally joins in wider concept of bridge crossing. The last includes not only actually the bridge(through the river, gulf, etc.), but also approaches to it, which construction in view of features of a relief and a configuration of new transport communications which have already developed and subject to construction not less difficult technically and not only economically expended, than building of the basic artificial construction.

• Journal of Ecology and Environment
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• v.44 no.4
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• pp.207-211
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• 2020

Adult land crabs generally live on land while their larvae live in the sea. In the case of Sesarma haematoche, female crabs migrate from land to sea to release the larvae at the high tide of syzygy night. Artificial structures along coastal areas are being obstacles for the migration of land crabs and causing synchronized roadkills on coastal roads during breeding migration. In this research, we compared the sex ratios of crab populations in coastal areas with coastal roads and uninhabited island areas with no road. The proportion of females in inland habitats with coastal roads was significantly smaller than island habitats. In particular, females are exposed to the risk of annually repeated roadkills, and the proportion of females decreases rapidly with their growth. If this tendency is general for land crab populations in the coastal areas with roads, significant road mortality of female land crabs during breeding migration can lead to severe population decline in coastal areas. Therefore, it is necessary to take an action to save land crabs crossing coastal roads.

• Journal of Veterinary Science
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• v.23 no.1
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• pp.17.1-17.10
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• 2022

Background: Inspection of livestock farms using surveillance cameras is emerging as a means of early detection of transboundary animal disease such as African swine fever (ASF). Object tracking, a developing technology derived from object detection aims to the consistent identification of individual objects in farms. Objectives: This study was conducted as a preliminary investigation for practical application to livestock farms. With the use of a high-performance artificial intelligence (AI)-based 3D depth camera, the aim is to establish a pathway for utilizing AI models to perform advanced object tracking. Methods: Multiple crossovers by two humans will be simulated to investigate the potential of object tracking. Inspection of consistent identification will be the evidence of object tracking after crossing over. Two AI models, a fast model and an accurate model, were tested and compared with regard to their object tracking performance in 3D. Finally, the recording of pig pen was also processed with aforementioned AI model to test the possibility of 3D object detection. Results: Both AI successfully processed and provided a 3D bounding box, identification number, and distance away from camera for each individual human. The accurate detection model had better evidence than the fast detection model on 3D object tracking and showed the potential application onto pigs as a livestock. Conclusions: Preparing a custom dataset to train AI models in an appropriate farm is required for proper 3D object detection to operate object tracking for pigs at an ideal level. This will allow the farm to smoothly transit traditional methods to ASF-preventing precision livestock farming.

• Korean Journal of Plant Tissue Culture
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• v.22 no.5
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• pp.261-266
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• 1995

This study was carried out to develop new varieties which are dwarf and tolerant to winter cold in the Aurantioideae by intergeneric crossing. to do that, the reciprocal crosses of Hwanggeumyooza and trifoliate orange, yooza and trifoliate orange were done and in vitro immature ovule culture of their hybrid was carried out .The callus formation from immature ovule was good in order of Hwanggeumyooza, Hwanggeumyooza $\times$ tifoliate orange, yooza, and trifoliate orange and best at 1 to 3 mg/L NAA+0.5mg/L zeatin on MT medium. In vitro germination percentage of 20week old hybrid of Hwanggeumyooza $\times$ tifoliate orange and trifoliate orange $\times$ Hwanggeumyooza were 41.3% and 37.7, respectively. The phenotype of hybrid (95%) of Hwanggeumyooza $\times$ trifoliate orange and that (100%) of trifoliate orange $\times$ Hwanggeumyooza were similar to that of trifoliate orange. After Hwanggeumyooza was pollinated by pollens of trifoliate orange, the pollen tubes grew on stigma after 3h of pollination and entered into micropyle after about 24~28 h. One gamete in pollen was fused with polar nuclei after 2 days and other one fused with egg nucleus at 3days after pollination. The fruit set percentage by intergeneric crossing was 14.0% in Hwanggeumyooza $\times$ trtfoliate orange and 17.5% in trifoliate orange $\times$ Hwanggeumyooza. The fruit set percentages of Hwanggeumyooza. and trifoliate orange were 34.2% and 39.5% by artificial self-fertilization, 34.2% and 39.5% by artificial cross fertilization, 3.1% and 1.4% by parthenocarpy and 13.0% and 3.0% by natural fertilization, respectively. The somatic and gametic chromosome numbers of Hwanggeumyooza, yooza, and trifoliate orange were 2n=18 and n=9.

• Weed & Turfgrass Science
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• v.6 no.4
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• pp.306-312
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• 2017

This study was carried out to develop new hybrid zoysiagrass cultivar 'Seah' (The application no. for cultivar protection : 2014-22). Native zoysiagrasses were collected from south-west seaside of Korea from 2010 to 2011. Artificial crossing was conducted to develop F1 hybrid between Z2011 (Z. sinica) and NM1 (Z. matrella) at plastic house in 2011. Among the progenies, 'Seah' showed fine leaf texture and high shoot density from the space planting plots at field. 'Seah' showed genetically light green color, with fine leaf with 1.8mm and height to the lowest leaf blade was 1.94 cm. Ground coverage rate was slower than medium leaf zoysiagrass (Jung-gi), but plant height of 7.1 cm was the lowest among the compared zoysiagrasses and height to lowest leaf of 1.94 cm was lower than most zoysiagrass, which may allow low mowing height.

• Journal of the Earthquake Engineering Society of Korea
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• v.14 no.1
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• pp.25-34
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• 2010

A method is presented for evaluating the seismic failure probability of bridge structures which show a nonlinear hysteretic dynamic behavior. Bridge structures are modeled as a bilinear dynamic system with a single degree of freedom. We regarded that the failure of bridges will occur when the displacement response of a deck level firstly crosses the predefined limit state during a duration of strong motion. For the estimation of the first-crossing probability of a nonlinear structural system excited by earthquake motion, we computed the average frequency of crossings of the limit state. We presented the non-Gaussian closure method for the approximation of the joint probability density function of response and its derivative, which is required for the estimation of the average frequency of crossings. The failure probabilities are estimated according to the various artificial earthquake acceleration sets representing specific seismic characteristics. For the verification of the accuracy and efficiency of presented method, we compared the estimated failure probabilities with the results evaluated from previous methods and the exact values estimated with the crude Monte-Carlo simulation method.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.117-140
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• 2013

Hindi is the most widely spoken language in India, with more than 300 million speakers. As there is no separation between the characters of texts written in Hindi as there is in English, the Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency. One of the major reasons for the poor recognition rate is error in character segmentation. The presence of touching characters in the scanned documents further complicates the segmentation process, creating a major problem when designing an effective character segmentation technique. Preprocessing, character segmentation, feature extraction, and finally, classification and recognition are the major steps which are followed by a general OCR. The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of the document's textual contents into paragraphs, lines, words, and then at the level of basic symbols. The basic symbols, obtained as the fundamental unit from the segmentation process, are recognized by the neural classifier. In this work, three feature extraction techniques-: histogram of projection based on mean distance, histogram of projection based on pixel value, and vertical zero crossing, have been used to improve the rate of recognition. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. For development of the neural classifier, a back-propagation neural network with two hidden layers is used. The classifier is trained and tested for printed Hindi texts. A performance of approximately 90% correct recognition rate is achieved.

• Asian Journal of Turfgrass Science
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• v.20 no.1
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• pp.1-10
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• 2006

This study was carried out to develope new zoysiagrass cultivar 'Millock'(Patent registration No. : 10-2005-0110051). Artificial selfing of collected line of MJ8 was conducted to develope F1 plant (MJ8S). Among the inbred progenies, MJ8S-9 (Millock) showed superior performance in color, density, and rust resistance. 'Millock' showed genetically dark green color, with medium-textured leaf ($4.2{\pm}0.44$ mm), short internode length ($3.5{\pm}0.28$ cm), and wide leaf angle ($52.5{\pm}10.8$ degree). Height to the lowest leaf blade of this cultivar was $1.9{\pm}0.91$ cm, which may allow low mowing height. 'Millock' has a yellowish green stolen. Also, specific bands with primer number OPB 8 by RAPD analysis can be used for a cultivar identification.

• Journal of Korean Neurosurgical Society
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• v.66 no.1
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• pp.53-62
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• 2023

Objective : Deep learning is a machine learning approach based on artificial neural network training, and object detection algorithm using deep learning is used as the most powerful tool in image analysis. We analyzed and evaluated the diagnostic performance of a deep learning algorithm to identify skull fractures in plain radiographic images and investigated its clinical applicability. Methods : A total of 2026 plain radiographic images of the skull (fracture, 991; normal, 1035) were obtained from 741 patients. The RetinaNet architecture was used as a deep learning model. Precision, recall, and average precision were measured to evaluate the deep learning algorithm's diagnostic performance. Results : In ResNet-152, the average precision for intersection over union (IOU) 0.1, 0.3, and 0.5, were 0.7240, 0.6698, and 0.3687, respectively. When the intersection over union (IOU) and confidence threshold were 0.1, the precision was 0.7292, and the recall was 0.7650. When the IOU threshold was 0.1, and the confidence threshold was 0.6, the true and false rates were 82.9% and 17.1%, respectively. There were significant differences in the true/false and false-positive/false-negative ratios between the anterior-posterior, towne, and both lateral views (p=0.032 and p=0.003). Objects detected in false positives had vascular grooves and suture lines. In false negatives, the detection performance of the diastatic fractures, fractures crossing the suture line, and fractures around the vascular grooves and orbit was poor. Conclusion : The object detection algorithm applied with deep learning is expected to be a valuable tool in diagnosing skull fractures.

• Journal of Korean Society of Disaster and Security
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• v.16 no.4
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• pp.129-136
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• 2023

A special offshore bridge with a high pylon has special structural features.Special offshore bridges have inspection blind spots that are difficult to visually inspect. To solve this problem, safety inspection methods using drones are being studied. In this study, image data of the pylon of a special offshore bridge was acquired using a drone. In addition, an artificial intelligence algorithm was developed to detect damage to the pylon surface. The AI algorithm utilized a deep learning network with different structures. The algorithm applied the stacking ensemble learning method to build a model that formed the ensemble and collect the results.