• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.331-331
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• 2022

This research aims to developing new commercialization project of convergence agricultural industrial model. First, we established an inventory for the planning of convergence agricultural industrial model categorize the relevant factors identified, and then suggested three models which are the business profit model for convergence agriculture industrialization, the resource recycling complex and agricultural tourism model, and the smart agricultural model. Second, in order to investigate the feasibility of each industrial model, we investigated the willingness to participate in the project according to the pilot models such as related organizations and management agencies, and proposed the result of business feasibility analysis. Finally, we suggested the establishment of a demonstration complex through the systemization of element technologies at two models. The related systems and technologies was reviewed as a new commercialization plan through the modeling of convergence agricultural industrial types in main crop production complex presented, and set up mid- to long-term development direction. The results of this study can be applied to the design of convergence agricultural industrial model in main crop production complex.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.4
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• pp.59-68
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• 2009

This paper addresses a hybrid multi-focus image fusion scheme using the recent curvelet transform constructions. Hybridization is obtained by combining the MS fusion rule with a novel "copy" method. The proposed scheme use MS rule to fuse the m most significant terms in spectrum of an image at each decomposition level. The scheme is dubbed in this work as m-term fusion in adherence to its use of the MSC (most significant coefficients) in the transform set at any given scale, orientation, and translation. We applied the edge-sensitive objective quality measure proposed by Xydeas and Petrovic to evaluate the method. Experimental results show that the proposed scheme is a potential alternative to the redundant, shift-invariant Dual-Tree Complex Wavelet transforms. In particular, it was confirmed that a 50% m-term fusion produces outputs with no visible quality degradation.

• Nuclear Engineering and Technology
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• v.53 no.6
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• pp.1736-1746
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• 2021

The results of full-scale numerical experiments of a hybrid thorium-containing fuel cell facility operating in a close-to-critical state due to a controlled source of fusion neutrons are discussed in this work. The facility under study was a complex consisting of two blocks. The first block was based on the concept of a high-temperature gas-cooled thorium reactor core. The second block was an axially symmetrical extended plasma generator of additional neutrons that was placed in the near-axial zone of the facility blanket. The calculated models of the blanket and the plasma generator of D-T neutrons created within the work allowed for research of the neutronic parameters of the facility in stationary and pulse-periodic operation modes. This research will make it possible to construct a safe facility and investigate the properties of thorium fuel, which can be continuously used in the epithermal spectrum of the considered hybrid fusion-fission reactor.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.7-13
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• 2021

This paper presents an vulnerable road user (VRU) classification and tracking algorithm using vision and LiDAR sensor fusion method for urban autonomous driving. The classification and tracking for vulnerable road users such as pedestrian, bicycle, and motorcycle are essential for autonomous driving in complex urban environments. In this paper, a real-time object image detection algorithm called Yolo and object tracking algorithm from LiDAR point cloud are fused in the high level. The proposed algorithm consists of four parts. First, the object bounding boxes on the pixel coordinate, which is obtained from YOLO, are transformed into the local coordinate of subject vehicle using the homography matrix. Second, a LiDAR point cloud is clustered based on Euclidean distance and the clusters are associated using GNN. In addition, the states of clusters including position, heading angle, velocity and acceleration information are estimated using geometric model free approach (GMFA) in real-time. Finally, the each LiDAR track is matched with a vision track using angle information of transformed vision track and assigned a classification id. The proposed fusion algorithm is evaluated via real vehicle test in the urban environment.

• Restorative Dentistry and Endodontics
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• v.44 no.2
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• pp.16.1-16.12
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• 2019

Objectives: The upper molars generally have three roots; therefore, different combinations of fusion can occur, increasing the possibility of finding more complex root canal systems. The purpose of this study was to evaluate the prevalence and characterization of fused roots in first and second maxillary molars using cone-beam computed tomography (CBCT) in a Colombian population. Materials and Methods: A total of 1274 teeth were evaluated, of which 534 were maxillary first molars and 740 were maxillary second molars. Axial sections were made at the cervical, middle, and apical levels to determine the prevalence of root fusion and the types of fusion. Results: Overall, 43% of the molars (n = 551) presented some type of fused root. Root fusion was present in 23.4% of the maxillary first molars. The most frequent type of fused root was type 3 (distobuccal-palatal; DB-P) (58.9%). Root fusion was observed in 57.6% of the maxillary second molars, and the most prevalent type of fused root was type 6 (cone-shaped) (45.2%). Of the maxillary molars, 12.5% were classified as C-shaped. Conclusion: Within the limitations of this study, there was a high prevalence of fused roots in maxillary molars in the Colombian population, mainly in the maxillary second molars. In first molars, the most common type of fused root was type 3 (DB-P) and in second molars, the most common type was type 6 (cone-shaped). Additionally, molars with root fusion presented variation at different levels of the radicular portion, with implications for treatment quality.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.4
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• pp.75-82
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• 2018

Efforts to reduce damages from micro dust and harmful gases in life have been led by national or local governments, and information on air quality has been provided along with real-time weather forecast through TV and internet. It is not enough to provide information on the individual indoor space consumed. So in this paper, we propose a IoT-based Real-Time Air Quality Sensing Board Corresponding Fine Particle for Air Quality Management in Buildings. Proposed board is easy to install and can be placed in the right place. In the proposed board, the air quality (level of pollution level) in the indoor space (inside the building) is easy and it is possible to recognize the changed indoor air pollution situation and provide countermeasures. According to the advantages of proposed system, it is possible to provide useful information by linking information about the overall indoor space where at least one representative point is located. In this paper, we compare the performance of the proposed board with the existing air quality measurement equipment.

• The Journal of Korean Medicine
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• v.37 no.2
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• pp.93-103
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• 2016

Objectives: The purpose of this study was to report Four cases of Failed Back Surgery Syndrome (FBSS) patients after spinal fusion surgery who showed significant improvement in pain and function with Complex Korean medical treatment. Methods: This study was a retrospective observational study. We reviewed medical records of Four patients with lumbar pain or radiating leg pain, who have received spinal fusion surgery in the past. All Four patients took complex treatments of Mokhuri Neck and Back hospital which involes Acupuncture, Pharmaco-acupuncture, Gangchuk herbal medicine, Chuna and Physical therapy during about four-week of admission treatment. Visual Analogue Scale (VAS), Oswestry disability index (ODI), Pain Free Walking Distance (PFWD) scores were assessed before and after treatments. Results: The average of hospitalization period was 28.5 days. Mean VAS scores decreased from 6.5 to 2.3, Oswestry Disability Index (ODI) scores decreased from 56.25 to 38.25 and Pain Free Walking Distance (PFWD) also improved from 10m to 166.6m. Conclusion: This study implies that a combination of Korean medical treatments might be effective in relieving pain, and improving the functional status of FBSS patients. Further studies are needed to fully understand the mechanisms underlying the effects.

• International Journal of Advanced Culture Technology
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• v.5 no.1
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• pp.90-97
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• 2017

Nowadays, the most of commercial buildings are build-out with complex architecture and decorated with more complicated interiors of buildings so establishing intelligible escape routes becomes an important case of fire or other emergency in a limited time. The commercial buildings are already equipped with multiple exit signs and these exit signs may create confusion and leads the people into different directions under emergency. This can jeopardize the emergency situation into a chaotic state, especially in a complex layout buildings. There are many research focused on implementing different approached to improve the exit sign system with better visual navigating effects, such as the use of laser beams, the combination of audio and video cues, etc. However the digital signage system based emergency exit sign management is one of the best solution to guide people under emergency situations to escape. This research paper, propose an intelligent evacuation route GUIDE that uses the combination centralized Wireless Sensor Networks (WSN) and digital signage for people safety and avoids dangers from emergency conditions. This proposed system applies WSN to detect the environment condition in the building and uses an evacuation algorithm to estimate the safe route to escape using the sensor information and then activates the signage system to display the safe evacuation route instruction GUIDE according to the location the signage system is installed. This paper presented the prototype of the proposed signage system and execution time to find the route with future research directions. The proposed system provides a natural intelligent evacuation route interface for self or remote operation in facility management to efficiently GUIDE people to the safe exit under emergency conditions.

• Molecules and Cells
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• v.38 no.2
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• pp.180-186
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• 2015

Escherichia coli AcrAB-TolC is a multidrug efflux pump that expels a wide range of toxic substrates. The dynamic nature of the binding or low affinity between the components has impeded elucidation of how the three components assemble in the functional state. Here, we created fusion proteins composed of AcrB, a transmembrane linker, and two copies of AcrA. The fusion protein exhibited acridine pumping activity, suggesting that the protein reflects the functional structure in vivo. To discern the assembling mode with TolC, the AcrBA fusion protein was incubated with TolC or a chimeric protein containing the TolC aperture tip region. Three-dimensional structures of the complex proteins were determined through transmission electron microscopy. The overall structure exemplifies the adaptor bridging model, wherein the funnel-like AcrA hexamer forms an intermeshing cogwheel interaction with the ${\alpha}$-barrel tip region of TolC, and a direct interaction between AcrB and TolC is not allowed. These observations provide a structural blueprint for understanding multidrug resistance in pathogenic Gram-negative bacteria.

• Neurospine
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• v.15 no.4
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• pp.329-337
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• 2018

Objective: Machine learning algorithms excel at leveraging big data to identify complex patterns that can be used to aid in clinical decision-making. The objective of this study is to demonstrate the performance of machine learning models in predicting postoperative complications following anterior cervical discectomy and fusion (ACDF). Methods: Artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), and random forest decision tree (RF) models were trained on a multicenter data set of patients undergoing ACDF to predict surgical complications based on readily available patient data. Following training, these models were compared to the predictive capability of American Society of Anesthesiologists (ASA) physical status classification. Results: A total of 20,879 patients were identified as having undergone ACDF. Following exclusion criteria, patients were divided into 14,615 patients for training and 6,264 for testing data sets. ANN and LR consistently outperformed ASA physical status classification in predicting every complication (p < 0.05). The ANN outperformed LR in predicting venous thromboembolism, wound complication, and mortality (p < 0.05). The SVM and RF models were no better than random chance at predicting any of the postoperative complications (p < 0.05). Conclusion: ANN and LR algorithms outperform ASA physical status classification for predicting individual postoperative complications. Additionally, neural networks have greater sensitivity than LR when predicting mortality and wound complications. With the growing size of medical data, the training of machine learning on these large datasets promises to improve risk prognostication, with the ability of continuously learning making them excellent tools in complex clinical scenarios.