• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.282-282
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• 2022

Arsenic (As) is a toxic heavy metal that affects the major rice-growing regions of the world and can cause cancer in humans. Rice paddy fields in South Asia are mostly dependent on arsenic-contaminated water sources due to which rice takes up the arsenic from the soil through roots and accumulates it in plant different parts. Here, we present a quantitative trait locus (QTL) mapping study to find out candidate genes conferring As toxicity tolerance in rice (Oryza sativa L.) at the seedling stage. Three weeks old, 120 double haploid CNDH lines derived from a cross between the Indica variety Cheongcheong and the Japonica variety Nagdong and their parental lines were used by treating with 25 μM As. After 2 weeks ofAs stress, 5 traits such as; shoot length (SL), root length (RL), shoot fresh weight (SFW), root fresh weight (RFW), and chlorophyll contents (CHC) were measured. A linkage map of 12 rice chromosomes was constructed from genotypic data DH lines using 778 SSR markers. The linkage map covered a total genetic distance of 2121.7 cM of the rice genome with an average interval of 10.6 cM between markers. A total of seventeen QTLs (LOD>2) were mapped on chromosomes 1, 2, 3, 6, 7, 8, 9, 11, and 12 using composite interval mapping with trait-increasing alleles coming from both parents. Five QTLs for SL, Two QTLs for RL, Five QTLs for SHL, Three QTLs for RFW, and Two QTLs for CHC were detected. The QTLs related to CHC were selected for forther study.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1081-1086
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• 2023

The rapid growth and increasing complexity of modern networks have highlighted the limitations of traditional network architectures. The emergence of SDN (Software-Defined Network) in response to these challenges has changed the existing network environment. The SDN separates the control unit and the data unit, and adjusts the network operation using a centralized controller. However, this structure has also recently caused a huge amount of traffic due to the rapid spread of numerous Internet of Things (IoT) devices, which has not only slowed the transmission speed of the network but also made it difficult to ensure quality of service (QoS). Therefore, this paper proposes a method of load distribution by switching the IP and any server (processor) from the existing data processing scheduling technique, RR (Round-Robin), to mapping when a large amount of data flows in from a specific IP, that is, server overload and data loss.

• Bulletin of the Korean Chemical Society
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• v.32 no.11
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• pp.4005-4010
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• 2011

We investigated thermal behavior of spin-coated films of P(HB-co-HHx)/PEG blends by using infraredreflection absorption (IRRAS) spectroscopy and 2D correlation spectroscopy. Based on 2D IRRAS correlation spectra, we could determine the sequence of spectral intensity changes with increasing temperature that PEG band changes first and then a band for crystalline component of P(HB-co-HHx) changes before a band for amorphous component. The intensities of bands for PEG and amorphous P(HB-co-HHx) were changed greatly as PEG weigh % of P(HB-co-HHx)/PEG blends increased. Transition temperatures of P(HB-co-HHx)/PEG blends were successfully determined by 2D gradient mapping method. The transition temperature of spincoated films of 98/2 and 90/10 P(HB-co-HHx)/PEG blends and 80/20 P(HB-co-HHx)/PEG blend determined by 2D gradient map are, respectively, about 137.5 and $132.5^{\circ}C$. Furthermore, P(HB-co-HHx)/PEG blends show an additional transition temperature that have been interpreted in terms of different lamellar thicknesses in spin coated films.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3701-3727
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• 2016

Recently Service Function Chaining (SFC) is promising to innovate the network service mode in modern networks. However, a feasible implementation of SFC is still difficult due to the need to achieve functional equivalence with traditional modes without sacrificing performance or increasing network complexity. In this paper, we present a configurable network service chaining (CNSC) mechanism to provide services for network traffics in a flexible and optimal way. Firstly, we formulate the problem of network service chaining and design an effective service chain construction framework based on integrating software-defined networking (SDN) with network functions virtualization (NFV). Then, we model the service path computation problem as an integer liner optimization problem and propose an algorithm named SPCM to cooperatively combine service function instances with a network utility maximum policy. In the procedure of SPCM, we achieve the service node mapping by defining a service capacity matrix for substrate nodes, and work out the optimal link mapping policies with segment routing. Finally, the simulation results indicate that the average request acceptance ratio and resources utilization ratio can reach above 85% and 75% by our SPCM algorithm, respectively. Upon the prototype system, it is demonstrated that CNSC outperforms other approaches and can provide flexible and scalable network services.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.293-298
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• 2001

In this paper, film thickness and temperature distribution are measured in EHL point contact at high roll/slip ratios. Infrared temperature mapping with two band pass filters, proposed by Ausherman (1976), is used to measure temperature distribution. And the optical interferometric method with two filters (red and green filters) is used to measure film thickness. Result of experiment showed that temperature rising at film and ball surface occurred very dramatically in Dimple zone. As slip velocity, roll/slip ratio and load increased, size of Dimple and temperature rising became more large. In addition, position and shape of Dimple were changed by roll/slip ratios, and increasing of Dimple size decreased traction coefficient. In short, it is appointed that the Dimple phenomenon be developed by the effect of viscosity wedge.

• Journal of the Korean Home Economics Association
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• v.40 no.6
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• pp.159-171
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• 2002

Today, many people seek for their own personal character which is distinguished from another people and they utilize fashion coordination as the was of expression their own image. In addition, interest in electronic commerce and cuber shopping mall on the internet is increasing. For this reason, visual and interesting virtual fashion coordination system is needed. The purpose of this study is to propose possibility of fashion coordination by virtual 3D model. For this study, 1. We make a 3D standard body model by automatic generation. 2. We make 3D fashion item (sleeveless top and flare skirt) by automatic generation. 3. We combine 3D body model with fashion item by special point, grouping and gap being between body and clothes. 4. We make textile palettes and textile DB for texture mapping and rendering. As a effect of this study, 1. It can give the chance to coordinate clothes suitable for their own character and bodyshape on the cuber space more speedily and variously. 2. It can help fashion internet shopping mall company can save a time, expenses and tries to advertise their new products, offer service for customers and lead customers to purchasing. 3. It can accumulate a database of design and textile for using by fashion and textile industry.

• Journal of Environmental Impact Assessment
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• v.19 no.6
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• pp.573-581
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• 2010

Natural ecosystem is an essential part to connect with the plan for biodiversity conservation in response strategy against climate change. For connecting biodiversity conservation with climate change strategy, Europe, America, Japan, and China are making an effort to discuss protection necessity through national biodiversity valuation but precedent studies lack in Korea. In this study, we made biodiversity maps representing biodiversity distribution range using species richness in National Forest Inventory (NFI) and Forest Description data. Using regression tree algorithm, we divided various classes by decision rule and constructed biodiversity maps, which has accuracy level of over 70%. Therefore, the biodiversity maps produced in this study can be used as base information for decision makers and plan for conservation of biodiversity & continuous management. Furthermore, this study can suggest a strategy for increasing efficiency of forest information in national level.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.2
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• pp.387-393
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• 2014

In recent years, the spread of computers is increasing, and efficient processing effort for unstructured Big Data is required. In this paper, we are proposed a system to extract the data typed in a word processor quickly by user creating and XML mapping file after converting XML data that has been entered in the office file(HWP, MS-office). In addition, we proposed a system is able to lookup the necessary data from a database by entered form in advance and convert word processor document to office files by the application program. The unstructured big data will be available to be used.

• Wind and Structures
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• v.38 no.4
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• pp.231-244
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• 2024

As wind farms expand into low wind speed areas, an increasing number are being established in mountainous regions. To fully utilize wind energy resources, it is essential to understand the details of mountain flow fields. Reconstructing the wind speed field in complex terrain is crucial for planning, designing, operation of wind farms, which impacts the wind farm's profits throughout its life cycle. Currently, wind speed reconstruction is primarily achieved through physical and machine learning methods. However, physical methods often require significant computational costs. Therefore, we propose a Full Convolutional Neural Network (FCNN)-based reconstruction method for mountain wind velocity fields to evaluate wind resources more accurately and efficiently. This method establishes the mapping relation between terrain, wind angle, height, and corresponding velocity fields of three velocity components within a specific terrain range. Guided by this mapping relation, wind velocity fields of three components at different terrains, wind angles, and heights can be generated. The effectiveness of this method was demonstrated by reconstructing the wind speed field of complex terrain in Beijing.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.