• Korean Journal of Remote Sensing
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• v.33 no.6_2
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• pp.1151-1157
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• 2017

Street Trees is an important object for urban environment improvement. Especially the height of the trees needs to be precisely measured as a factor that greatly influences the removal of air pollutants in the Urban Street Canyons. In this study, we extracted the height of the tree based on the stereo image using the precisely adjusted UAV Images of the target area. The adjustment of UAV image was applied photogrammetric SfM (Structure from motion) based on the collinear condition. We measured the height of the trees on the Street Canyon using stereoscopic vision on stereo plotting system. We also acquired the height of the building adjacent to the street trees and the average height of the road surface was calculated for accurate measurement of the height of each object. Through the visual analysis with the plotting operation system, it was possible to measure height of the tree and to calculate the relative height difference value with building quickly. This means that the height of buildings and trees can be calculated without making a 3D point cloud of UAV and it has the advantage of being able to utilize non-experts. In the future, further studies for semiautomatic/automation of this technique should be performed. The development and research of these technologies is expected to help to understand the current status of environmental policies and roadside trees in urban areas.

• Journal of the Korea Computer Graphics Society
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• v.23 no.3
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• pp.31-38
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• 2017

In this paper, we propose a system which estimates Manhattan coordinate systems for urban scene images using a convolutional neural network (CNN). Estimating the Manhattan coordinate system from an image under the Manhattan world assumption is the basis for solving computer graphics and vision problems such as image adjustment and 3D scene reconstruction. We construct a CNN that estimates Manhattan coordinate systems based on GoogLeNet [1]. To train the CNN, we collect about 155,000 images under the Manhattan world assumption by using the Google Street View APIs and calculate Manhattan coordinate systems using existing calibration methods to generate dataset. In contrast to PoseNet [2] that trains per-scene CNNs, our method learns from images under the Manhattan world assumption and thus estimates Manhattan coordinate systems for new images that have not been learned. Experimental results show that our method estimates Manhattan coordinate systems with the median error of $3.157^{\circ}$ for the Google Street View images of non-trained scenes, as test set. In addition, compared to an existing calibration method [3], the proposed method shows lower intermediate errors for the test set.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.667-674
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• 2023

In the event of a disaster occurring within a building, the prompt and efficient evacuation and rescue of occupants within the building becomes the foremost priority to minimize casualties. For the purpose of such rescue operations, it is essential to ascertain the distribution of individuals within the building. Nevertheless, there is a primary dependence on accounts provided by pertinent individuals like building proprietors or security staff, alongside fundamental data encompassing floor dimensions and maximum capacity. Consequently, accurate determination of the number of occupants within the building holds paramount significance in reducing uncertainties at the site and facilitating effective rescue activities during the golden hour. This research introduces a methodology employing computer vision algorithms to count the number of occupants within distinct building locations based on images captured by installed multiple CCTV cameras. The counting methodology consists of three stages: (1) establishing virtual Lines of Interest (LOI) for each camera to construct a multi-camera network environment, (2) detecting and tracking people within the monitoring area using deep learning, and (3) aggregating counts across the multi-camera network. The proposed methodology was validated through experiments conducted in a five-story building with the average accurary of 89.9% and the average MAE of 0.178 and RMSE of 0.339, and the advantages of using multiple cameras for occupant counting were explained. This paper showed the potential of the proposed methodology for more effective and timely disaster management through common surveillance systems by providing prompt occupancy information.

• Tunnel and Underground Space
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• v.29 no.4
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• pp.230-242
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• 2019

While various automatic rock fracture survey methods have been researched, the evaluation of the accuracy of these methods raises issues due to the absence of a metric which fully expresses the similarity between automatic and manual fracture maps. Therefore, this paper proposes a geometry similarity metric which is especially designed to determine the overall similarity of fracture maps and to evaluate the accuracy of rock fracture survey methods by a single number. The proposed metric, Scanline Intersection Similarity (SIS), is derived by conducting a large number of scanline surveys upon two fracture maps using Python code. By comparing the frequency of intersections over a large number of scanlines, SIS is able to express the overall similarity between two fracture maps. The proposed metric was compared with Intersection Over Union (IoU) which is a widely used evaluation metric in computer vision. Results showed that IoU is inappropriate for evaluating the geometry similarity of fracture maps because it is overly sensitive to minor geometry differences of thin elongated objects. The proposed metric, on the other hand, reflected macro-geometry differences rather than micro-geometry differences, showing good agreement with human perception. The metric was further applied to evaluate the accuracy of a deep learning-based automatic fracture surveying method which resulted as 0.674 (SIS). However, the proposed metric is currently limited to 2D fracture maps and requires comparison with rock joint parameters such as RQD.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.363-373
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• 2020

During last decades numerous studies generating orthoimage have been carried out. Traditional methods require exterior orientation parameters of aerial images and precise 3D object modeling data and DTM (Digital Terrain Model) to detect and recover occlusion areas. Furthermore, it is challenging task to automate the complicated process. In this paper, we proposed a new concept of true orthoimage generation using DL (Deep Learning). DL is rapidly used in wide range of fields. In particular, GAN (Generative Adversarial Network) is one of the DL models for various tasks in imaging processing and computer vision. The generator tries to produce results similar to the real images, while discriminator judges fake and real images until the results are satisfied. Such mutually adversarial mechanism improves quality of the results. Experiments were performed using GAN-based Pix2Pix model by utilizing IR (Infrared) orthoimages, intensity from LiDAR data provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) through the ISPRS (International Society for Photogrammetry and Remote Sensing). Two approaches were implemented: (1) One-step training with intensity data and high resolution orthoimages, (2) Recursive training with intensity data and color-coded low resolution intensity images for progressive enhancement of the results. Two methods provided similar quality based on FID (Fréchet Inception Distance) measures. However, if quality of the input data is close to the target image, better results could be obtained by increasing epoch. This paper is an early experimental study for feasibility of DL-based true orthoimage generation and further improvement would be necessary.

• The Korean Journal of Pharmacology
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• v.30 no.3
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• pp.321-330
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• 1994

The protective effect of 'ischemic preconditioning (PC)' on ischemia-reperfusion injury of heart has been reported in various animal species, but without known mechanisms in detail. In an attempt to investigate the cardioprotective mechanism of PC, we examined the effects of PC on the myocardial oxidative injuries and the oxygen free radical production in the ischemia-reperfusion model of isolated Langendorff preparations of rat hearts. PC was performed with three episodes of 5 min ischemia and 5 min reperfusion before the induction of prolonged ischemia (30 min)-reperfusion(20 min). PC prevented the depression of cardiac function (left ventricular pressure x heart rate) observed in the ischemic-reperfused heart, and reduced the release of lactate dehydrogenase during the reperfusion period. On electron microscopic pictures, myocardial ultrastructures were relatively well preserved in PC hearts as compared with non-PC ischemic-reperfused hearts. In PC hearts, lipid peroxidation of myocardial tissue as estimated from malondialdehyde production was markedly reduced. PC did not affect the activity of xanthine oxidase which is a major source of oxygen radicals in the ischemic rat hearts, but the myocardial content of hypoxanthine (a substrate for xanthine oxidase) was much lower in PC hearts. It is suggested from these results that PC brings about significant myocardial protection in ischemic-reperfused heart and this effect may be related to the suppression of oxygen free radical reactions.

• Journal of the Korean Society of Food Science and Nutrition
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• v.37 no.3
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• pp.379-389
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• 2008

This study was conducted to assess the microbiological quality of raw and cooked foods served in the elementary school food service. Raw and cooked food samples were collected from 11 selected elementary schools in both June to July and September to October of 2005. Petrifilm plates were used to determine (in duplicate) total aerobic colony counts (PAC), Enterobacteriaceae (PE), coliform counts (PCC), and E. coli counts (PEC). Heavy contamination of Enterobacteriaceae (from 0.08 to 7.40 log CFU/g) and total coliform (0.50 to 6.52 log CFU/g) were observed in raw materials and cooked foods. Escherichia coli (E. coli) were detected in the sample of currant tomato (3.70 log CFU/g), sesame leaf (3.59 log CFU/g), dropwort (0.20 log CFU/g), crown daisy (3.15 log CFU/g), parsley (3.00 log CFU/g), peeled green onion (1.74 log CFU/g), frozen pork (0.65 log CFU/g), frozen beef (0.20 or 1.50 log CFU/g), chicken (1.78 log CFU/g), and young radish leaf seasoned with soybean paste (1.24 log CFU/g). Multiplex PCR system was used to determine the food-borne pathogens: Salmonella spp., Bacillus cereus (B. cereus), E. coli O157:H7, Staphylococcus aureus, Listeria monocytogenes (L. monocytogenes), Vibrio parahaemolyticus, Campylobacter jejuni (C. jejuni), Shigella spp., B. cereus was detected in 19 samples of raw materials and 8 samples of cooked foods. With regard to quantitative analysis, B. cereus counts exceeded 5.46, 3.48 and 1.79 log CFU/g in sesame leaf, peeled green onion and seasoned mungbean jelly, respectively. E. coli O157:H7 was detected on 2 samples of frozen beefs, and its biochemical characteristics of one beef sample was confirmed with API 20E kit (93.7%). L. monocytogenes was detected in fried rice paper dumpling, but the presumptive colonies were not detected onto the conventional plate. C. jejuni was detected in peeled & washed onion.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.9 no.1
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• pp.98-104
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• 1998

A 12-year-old girl with a 6 year history of childhood-onset schizophrenia required 2 hospitalizations and long-term clozapine trial due to inadequate responses to combinations of typical neuroleptics and traditional treatments of schizophrenic disorder. On admission, she had continuous auditory and visual hallucinations, persecutory delusion, emotional instability, regression of behaviors including temper tantrums as well as specific developmental delays in learning, language, and motor coordination. The clozapine trial significantly reduced most of the positive symptoms, and facilitated in successful discharge from the hospital. During the 4 year clozapine treatment, no significant adverse reactions were noted, and she returned to a structured school setting with minimal degrees of schizophrenic symptoms. From this clinical experience, we suggest that clozapine might be safe and effective in treating treatment-refractory schizophrenic children.

• Management & Information Systems Review
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• v.31 no.3
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• pp.223-248
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• 2012

This study analyses support policy and structural change of textile industry in Daegu Kyungbuk region, and suggests major issues for textile industry's innovation. In Daegu Kyungbuk, it was 1999 that a policy, so called Milano Project, in order to promote a textile industry was devised. In 2004, the Regional Industrial Promotion Plan was devised. The plan was born from a view point of establishing a regional innovation system and of promoting the innovative clusters under a knowledge based economy. After then, the Regional Industry Promotion Project or Regional Strategic Industry Promotion Project became a core of regional textile industrial policy. Research results indicated that the first stage Milano project (1999-2003) showed both positive and negative effects. There were no long-term development plan, clear vision and strategy. But, core industrial infrastructure for differentiated product development, such as New product Development Support Center and Dyeing Design Practical Application Center, was constructed. The second stage Daegu Textile Industry Promotion Plan (2004-2008) displayed a significant technological performance and new product sales with the assistance of Kyungbuk province. Also, textile industry revealed positive fruits such as financial structure, productivity, and profitability as a result of strong restructuring. In industrial structure, there was a important change from clothe textile material to industry textile material. Most of textile companies did not showed high capability in CEO's technology innovation intention, entrepreneurship, R&D and human resource competency in compare with other industry. We suggested that Daegu Kyungbuk has to select and concentrate on the high-tech textile material and living textile for sustainable development and competitiveness. We also proposed a confidence and cooperation based innovation network and company oriented innovation cluster.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.61-79
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• 2023

This study is a case study of overseas banana farms as a global agricultural startup that has hardly been attempted so far in terms of paradigm shift in the industry, beyond regional limitations. It was researched for the purpose of revealing the success factors of 'global agricultural startup' in terms of business process, entrepreneurship, and management dimensions learned through direct participation and observation at the local level. In order to study global agricultural startups, this study also conducted a comparative analysis of global startups (global startups) and global agricultural startups(global agricultural startups). In fact, the analysis consists of 'definition', 'components', and 'success factors', and we want to confirm the difference between the two concepts that can be distinguished. The case analysis tried to maximize the advantages of 'participatory action research' by directly observing and experiencing banana farms. In the case of banana farm cases, by dividing them into preparation process for farm development and farm development and management process, various variables considered in farm management were explained through the whole process of farm management. Through the process of overcoming and responding to specific failure cases, we tried to secure the reliability and validity of the research, and the case studies related to entrepreneurship, management, and organization analyzed by applying them by subdividing them into theoretical areas belonging to components and management that were theorized in existing preceding studies. This study is almost the first study on the process of creating a local entry business by directly moving the head office overseas rather than entering overseas agriculture as a subsidiary, joint venture or overseas corporation. In particular, it is a unique case that corresponds to agriculture in terms of region(Africa), scale(startup), and industry that have not been introduced so far as a global agricultural startup. In terms of entrepreneurship, it also concretely exemplified how entrepreneurship components such as innovativeness, risk-taking propensity, proactiveness, vision sharing, social contribution, leadership, etc., which have not been attempted so far in agricultural cases, are manifested and effective. The management and cultural aspects also went beyond the argument that only cultural aspects are important in overseas business, and also confirmed individual failure cases and their responses in recruitment, job, wage, retirement, development, organizational structure management, etc. As a result, there is significance and implications of this study in that it provides theoretical confirmation as well as practical and responsive basis for 'entrepreneurship', 'farming management', and 'management' aspects in overseas agricultural startup business operation.