• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.747-763
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• 2022

Ocean salinity affects ocean circulation on a global scale and low salinity water around coastal areas often has an impact on aquaculture and fisheries. Microwave satellite sensors (e.g., Soil Moisture Active Passive [SMAP]) have provided sea surface salinity (SSS) based on the dielectric characteristics of water associated with SSS and sea surface temperature (SST). In this study, a Light Gradient Boosting Machine (LGBM)-based model for generating high resolution SSS from Geostationary Ocean Color Imager (GOCI) data was proposed, having machine learning-based improved SMAP SSS by Jang et al. (2022) as reference data (SMAP SSS (Jang)). Three schemes with different input variables were tested, and scheme 3 with all variables including Multi-scale Ultra-high Resolution SST yielded the best performance (coefficient of determination = 0.60, root mean square error = 0.91 psu). The proposed LGBM-based GOCI SSS had a similar spatiotemporal pattern with SMAP SSS (Jang), with much higher spatial resolution even in coastal areas, where SMAP SSS (Jang) was not available. In addition, when tested for the great flood occurred in Southern China in August 2020, GOCI SSS well simulated the spatial and temporal change of Changjiang Diluted Water. This research provided a potential that optical satellite data can be used to generate high resolution SSS associated with the improved microwave-based SSS especially in coastal areas.

• Journal of Korean Society of Forest Science
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• v.48 no.1
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• pp.1-24
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• 1980

One of the most important basic problems for developing the new techniques in the field of road landscape planting practices in Korea, is to clarify, analyse, and evaluate the existing technical level through actual field survey on the various kinds of planting techniques. This study is, therefore, aimed at the good grasp of detail essences of the existing level of road landscape planting techniques through field investigations of the executed sites. In this study, emphasized efforts are made to the detail analysis and systematic rearrangements of such main subjects as; 1) principles and functions of the road landscape planting techniques; 2) essential elements in planning of it; 3) advanced practices in execution of planting of it; 4) and improved methods in maintenance of plants and lands as an entire system of road landscape planting techniques. The road landscape planting techniques could be explained as the planting and landscaping practices to improve the road function through introduction of plants (green-environment) on and around the roads. The importances of these techniques have been recognized by the landscape architects and road engineers, and they also emphasize not on]y the establishment of road landscape features but also conservation of human's life environment by planting of suitable trees, shrubs, and other vegetations around the roads. It is essentially required to improve the present p]anting practices for establishment of the beautiful road landscape features, specially in planning, design, execution, establishment, and maintenance of plantings of the environmental conservation belts, roadside trees, footpathes, median strips, traffic islands, interchanges, rest areas, and including the adjoining route roads.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

• Journal of the Korean Wood Science and Technology
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• v.14 no.1
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• pp.3-44
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• 1986

In order to obtain the basic physical and mechanical properties of steel wire reinforced particleboard, particleboards were formed with large particles through 2.11 mm (12 meshes) and retained on 1.27mm (20 meshes) sieves and small particles through 1.27mm (20 meshes) and retained on 0.42mm (60 meshes) sieves from the plywood mill wastes of meranti (Shorea spp.) in the form of pallmanchips, applying urea-formaldehyde resin as an adhesive on the particle surface in 10 percent on the oven dried weight of particles, and arranging steel wires of 1mm in diameter 5,10,15,20, and 25mm in longitudinal and transverse direction with crossing in the mid of the board depth in single layer boards, 10mm in longitudinal or transverse direction without crossing in two layers and 10mm in longitudinal and transverse directions with and without crossing in three steel wire layers boards. The stepwise 9-minutes-multi-pressing schedule in 5 minutes at 35 kgf/$cm^2$, 2.5 minutes at 25 kgf/$cm^2$. and 1.5 minutes at 15 kgf/$cm^2$ was applied for $300{\times}200{\times}13$mm board at the temperature of 160$^{\circ}C$ in a hot press. Specific gravity, thickness swelling, bending properties of modulus of rupture (MOR), modulus of elasticity(MOE), work to proportional limit, and work to ultimate load, internal bond (IB), and screw holding power(SHP) of the reinforced boards were analyzed on the wire openings and wire layers. The results obtained are summarized as follows; 1) In specific gravity, particleboards with large particles and small particles had higher value with more steel wire placements and more steel layers composition, 2) Particleboards with large particles in accordance with more steel wire liners composition gave very poor thickness swelling. 3) The mechanical properties of particleboards formed with large or small particles were reinforced with more steel wire layers. Therefore, bending strength was improved in modulus of rupture, modulus of elasticity, and work to ultimate load. Especiallv, particleboards with two or three steel wire layers showed the tension lamination effect when the steels in lower steel wire layer were oriented parallel to the board length. 4) The modulus of rupture, modulus of elasticity, and work to ultimate load in bending varied with opening area, distance of lengthwise wires multipled by distance of transverse wires. Particleboards formed with large particles resulted in higher value in modulus of rupture with 1.5-3 $cm^2$ opening area, 1-2cm distance between transverse wires, and 1.5-2.5cm distance between lengthwise wires. Particle boards formed with small particles showed higher value with 0.5-1.5$cm^2$ or 3.75-6.25 $cm^2$ opening area, 0.5 or 2.5cm distance between transverse wires. 5) In modulus of elasticity, particleboards formed with large particles with one steel wire layer suggested higher value with 5-3$cm^2$ opening area, 1-2.5cm distance between transverse wires and also 1-2.5 cm distance between lengthwise wires. Particleboards formed with small particles showed higher value with 0.75-1.25$cm^2$ or 3-6.25$cm^2$ opening area and 0.5 or 2.5cm distance between transverse wires. 6) Particleboards formed with large particles gaved higher value in work to ultimate load with 1-3$cm^2$ opening area. Particleboards formed with small particles showed increasing tendancy with decreasing opening area. 7) In internal bond and screw holding power, particleboards formed with large particles had increasing value in two and three steel wire layers compositions, but particleboards formed with small particles showed no difference. Particleboards formed with large particles containing one steel wire layer showed no difference in internal bond and screw holding power, and particleboards formed with small panicles containing one steel wire layer resulted in increasing value in internal bond and decreasing value in screw holding power in accordance with increase in opening area.