• Journal of Plant Biotechnology
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• v.50
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• pp.127-136
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• 2023

Water scarcity decreases the rate of photosynthesis and, consequently, the yield of banana plants (Musa spp). In this study, transcriptome analysis was performed to identify photosynthesis-related genes in banana plants and determine their expression profiles under water stress conditions. Banana plantlets were in vitro cultured on Murashige and Skoog agar medium with and without 10% polyethylene glycol and marked as BP10 and BK. Chlorophyll contents in the plant shoots were determined spectrophotometrically. Two cDNA libraries generated from BK and BP10 plantlets, respectively, were used as the reference for transcriptome data. Gene ontology (GO) enrichment analysis was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) and visualized using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway prediction. Morphological observations indicated that water deficiency caused chlorosis and reduced the shoot chlorophyll content of banana plantlets. GO enrichment identified 52 photosynthesis-related genes that were affected by water stress. KEGG visualization revealed the pathways related to the 52 photosynthesisr-elated genes and their allocations in four GO terms. Four, 12, 15, and 21 genes were related to chlorophyll biosynthesis, the Calvin cycle, the photosynthetic electron transfer chain, and the light-harvesting complex, respectively. Differentially expressed gene (DEG) analysis using DESeq revealed that 45 genes were down-regulated, whereas seven genes were up-regulated. Four of the down-regulated genes were responsible for chlorophyll biosynthesis and appeared to cause the decrease in the banana leaf chlorophyll content. Among the annotated DEGs, MaPNDO, MaPSAL, and MaFEDA were selected and validated using quantitative real-time PCR.

• Korean Journal of Radiology
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• v.25 no.4
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• pp.384-394
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• 2024

Objective: To evaluate the image quality of novel dark-blood computed tomography angiography (CTA) imaging combined with deep learning reconstruction (DLR) compared to delayed-phase CTA images with hybrid iterative reconstruction (HIR), to visualize the cervical artery wall in patients with Takayasu arteritis (TAK). Materials and Methods: This prospective study continuously recruited 53 patients with TAK (mean age: 33.8 ± 10.2 years; 49 females) between January and July 2022 who underwent head-neck CTA scans. The arterial- and delayed-phase images were reconstructed using HIR and DLR. Subtracted images of the arterial-phase from the delayed-phase were then added to the original delayed-phase using a denoising filter to generate the final-dark-blood images. Qualitative image quality scores and quantitative parameters were obtained and compared among the three groups of images: Delayed-HIR, Dark-blood-HIR, and Dark-blood-DLR. Results: Compared to Delayed-HIR, Dark-blood-HIR images demonstrated higher qualitative scores in terms of vascular wall visualization and diagnostic confidence index (all P < 0.001). These qualitative scores further improved after applying DLR (Dark-blood-DLR compared to Dark-blood-HIR, all P < 0.001). Dark-blood DLR also showed higher scores for overall image noise than Dark-blood-HIR (P < 0.001). In the quantitative analysis, the contrast-to-noise ratio (CNR) values between the vessel wall and lumen for the bilateral common carotid arteries and brachiocephalic trunk were significantly higher on Dark-blood-HIR images than on Delayed-HIR images (all P < 0.05). The CNR values were significantly higher for Dark-blood-DLR than for Dark-blood-HIR in all cervical arteries (all P < 0.001). Conclusion: Compared with Delayed-HIR CTA, the dark-blood method combined with DLR improved CTA image quality and enhanced visualization of the cervical artery wall in patients with TAK.

• Journal of the Korean Geographical Society
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• v.38 no.5
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• pp.667-685
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• 2003

To address the requirements of gap analysis for species protection, as well as the needs of state and federal agencies for detailed digital land cover, a 43-class map at the vegetation alliance level was created for the state of Kansas using multi-temporal Thematic Mapper imagery. The mapping approach included the use of three-date multi-seasonal imagery, a two-stage classification approach that first masked out cropland areas using unsupervised classification and then mapped natural vegetation with supervised classification, visualization techniques utilizing a map of small multiples and field experts, and extensive use of ancillary data in post-hoc processing. Accuracy assessment was conducted at three levels of generalization (Anderson Level I, vegetation formation, and vegetation alliance) and three cross-tabulation approaches. Overall accuracy ranged from 51.7% to 89.4%, depending on level of generalization, while accuracy figures for individual alliance classes varied by area covered and level of sampling.

• The KIPS Transactions:PartB
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• v.17B no.6
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• pp.437-444
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• 2010

In this paper, we propose a method of analysing the relation between the patient's face color and his(her) kidney disease using image processing technology. This method is based on the ocular inspection which is one of the most famous diagnosis methods used in the oriental medical system. The way of processing and analysing the face image, which is for visualization and objectification of the color difference, is included. The objects are selected from the patients who suffer the kidney disease and use the hemodialyzer. Their facial images and clinical data are collected. From these data, we propose a hypothesis that the color of the patient's face is changed according to the patient's kidney state. At the same time, we present two algorithms of extracting the specific part of face which can identify the state of the patient's kidney and tracing the history of the color's change. This proposed method is evaluated through the practical experiments and their analysis.

• Bulletin of the Korean Space Science Society
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• 2008.10a
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• pp.26.3-26.3
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• 2008

This research develops a GPS-based formation-flying testbed (FFTB) for formation navigation and control. The FFTB is a simulator in which spacecraft simulation and modeling software and loop test capabilities are integrated for test and evaluation of spacecraft navigation and formation control technologies. The FFTB is composed of a GPS measurement simulation computer, flight computer, environmental computer for providing true environment data and 3D visualization computer. The testbed can be simulated with one to two spacecraft, thus enabling a variety of navigation and control algorithms to be evaluated. In a formation flying simulation, GPS measurement are generated by a GPS measurement simulator to produce pseudorange, carrier phase measurements, which are collected and exchanged by the flight processors and subsequently processed in a navigation filter to generate relative and/or absolute state estimates. These state estimates are the fed into control algorithm, which are used to generate maneuvers required to maintain the formation. In this manner, the flight processor also serves as a test platform for candidate formation control algorithm. Such maneuvers are fed back through the controller and applied to the modeled truth trajectories to close simulation loop. Currently, The FFTB has a closed-loop capability of simulating a satellite navigation solution using software based GPS measurement, we move forward to improve using SPIRENT GPS RF signal simulator and space-based GPS receiver

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.2
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• pp.213-221
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• 2009

An aircraft collision accident is a disaster that causes great losses of inventories and lives. Though a collision avoidance warning function is provided automatically to pilots in the aircrafts by the enhancement of the aircraft capability, achieving fast decision-making to escape a collision situation is a complex and dangerous work for pilots. If an in-flight collision situation is controlled by the air traffic control system which monitors all airplanes in the air, it would be more efficient to prevent in-flight collisions because it can handle the emergency before the pilot's action. In this paper, we develop the collision avoidance warning function in the air traffic control system. Specifically, we design and implement the five stages of the collision avoidance function, and propose a visualization method which could effectively provide the operators with the trajectories and altitudes of the aircrafts in a collision situation. By developing an in-flight collision warning function in the air traffic control system that visualizes flight patterns through the state transition data of in-flight aircrafts on the flight path lines, it can effectively prevent in-flight collisions with traffic alerts. The developed function allows operators to effectively select and control the aircraft in a collision situation by providing the operators with the expected collision time, the relative distance, and the relative altitude while assessing the level of alert, and visualizing the alert information which includes the Attention-Warning-Alert phase via embodying the TCAS standard. With the developed function the air traffic control system could sense an in-flight collision situation before the pilot's decision-making moment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5842-5861
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• 2019

A current autonomous vehicle determines its driving strategy by considering only external factors (Pedestrians, road conditions, etc.) without considering the interior condition of the vehicle. To solve the problem, this paper proposes "An Optimal Driving Support Strategy(ODSS) based on an Genetic Algorithm for Autonomous Vehicles" which determines the optimal strategy of an autonomous vehicle by analyzing not only the external factors, but also the internal factors of the vehicle(consumable conditions, RPM levels etc.). The proposed ODSS consists of 4 modules. The first module is a Data Communication Module (DCM) which converts CAN, FlexRay, and HSCAN messages of vehicles into WAVE messages and sends the converted messages to the Cloud and receives the analyzed result from the Cloud using V2X. The second module is a Data Management Module (DMM) that classifies the converted WAVE messages and stores the classified messages in a road state table, a sensor message table, and a vehicle state table. The third module is a Data Analysis Module (DAM) which learns a genetic algorithm using sensor data from vehicles stored in the cloud and determines the optimal driving strategy of an autonomous vehicle. The fourth module is a Data Visualization Module (DVM) which displays the optimal driving strategy and the current driving conditions on a vehicle monitor. This paper compared the DCM with existing vehicle gateways and the DAM with the MLP and RF neural network models to validate the ODSS. In the experiment, the DCM improved a loss rate approximately by 5%, compared with existing vehicle gateways. In addition, because the DAM improved computation time by 40% and 20% separately, compared with the MLP and RF, it determined RPM, speed, steering angle and lane changes faster than them.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.6
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• pp.38-45
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• 2004

This paper studies the effects of intake port configuration on the swirl that is key parameter in the flow field of direct injection diesel engines. In-cylinder flow characteristics is known to have significant effects on fuel air mixing, combustion and emissions. To investigate the swirl flow generated by various intake ports, steady state flow tests were conducted to evaluate the swirl. Helical port geometry, SCV shape and bypass were selected as the design parameters to increase the swirl flow and parametric study was performed to choose the optimal port shape that would generate a high swirl ratio efficiently. The results revealed that a key factor in generating a high swirl ratio was to suitably control the direction of the intake air flow passing through the valve seat. For these purposes, we changed the distance of helical and tangential port as well as installed bypass near the valve seat and the effects of intake port geometry on in-cylinder flow field were visualized by a laser sheet visualization method. From the experimental results, we found that the swirl ratio and mass flow rate had a trade off relation. In addition, the result indicates that the bypass is a effective method to increase the swirl ratio without sacrificing mass flow rate.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.4
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• pp.115-121
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• 2020

As the participation of blockchain increases, governance to maintain the ecosystem is being activated. Governance must be managed/supervised to maintain and improve the blockchain. However, as the usage of blockchain increases, there is a limit for people to manage it individually. In addition, smart contracts applied from Ethereum need to be verified for trade that are determined according to status values, as new trade types are possible. This research proposes a surveillance system, Blockchain Watchdog, based on the state monitoring of the blockchain smart contract as a tool for maintaining and developing the blockchain ecosystem of governance, and implemented and tested the surveillance system. As a case study, we conducted experiments on Ropsten and Ganache, and were able to see real-time information collection and visualization using dashboards, graphs, and charts. We expect to contribute to the maintenance and development of the ecosystem of blockchain governance through Blockchain Watchdog.

• Interdisciplinary Bio Central
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• v.5 no.1
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• pp.1.1-1.8
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• 2013

High quality publicly-available transcriptomic data representing relationships in gene expression across a diverse set of biological conditions is used as a context network to explore transcriptomics of the CNS. The context network, 18367Hu-matrix, contains pairwise Pearson correlations for 22,215 human genes across18,637 human tissue samples1. To do this, we compute a network derived from biological samples from CNS cells and tissues, calculate clusters of co-expressed genes from this network, and compare the significance of these to clusters derived from the larger 18367Hu-matrix network. Sorting and visualization uses the publicly available software, MetaOmGraph (http://www.metnetdb.org/MetNet_MetaOm-Graph.htm). This identifies genes that characterize particular disease conditions. Specifically, differences in gene expression within and between two designations of glial cancer, astrocytoma and glioblastoma, are evaluated in the context of the broader network. Such gene groups, which we term outlier-networks, tease out abnormally expressed genes and the samples in which this expression occurs. This approach distinguishes 48 subnetworks of outlier genes associated with astrocytoma and glioblastoma. As a case study, we investigate the relationships among the genes of a small astrocytoma-only subnetwork. This astrocytoma-only subnetwork consists of SVEP1, IGF1, CHRNA3, and SPAG6. All of these genes are highly coexpressed in a single sample of anaplastic astrocytoma tumor (grade III) and a sample of juvenile pilocytic astrocytoma. Three of these genes are also associated with nicotine. This data lead us to formulate a testable hypothesis that this astrocytoma outlier-network provides a link between some gliomas/astrocytomas and nicotine.