• Korean Journal of Environmental Biology
• /
• v.34 no.4
• /
• pp.257-263
• /
• 2016

Objective of this study was to evaluate the carbon budget on 40 years old pear orchard at Naju. For carbon budget assessment, we measured the soil respiration, net ecosystem productivity of herbs, pear biomass and net ecosystem exchange. In 2015, pear orchard released about $25.6ton\;CO_2\;ha^{-1}$ by soil respiration. And $27.9ton\;CO_2\;ha^{-1}$ was sequestrated by biomass growth. Also about $12.6ton\;CO_2\;ha^{-1}$ was stored at pruning branches and about $5.2ton\;CO_2\;ha^{-1}$ for photosynthesis of herbs. As a result, 25.6 ton of $CO_2$ per ha is annually released to atmosphere. At the same time about 45.7 ton of $CO_2$ was sequestrated from atmosphere. When it sum up the amount of $CO_2$ release and sequestration, approximately $20.1ton\;CO_2\;ha^{-1}$ was sequestrated by pear orchard in 2015, and it showed no significant differences with net ecosystem exchanges ($17.8ton\;CO_2\;ha^{-1}\;yr^{-1}$) by eddy covariance method with the same period. Continuous research using various techniques will help the understanding of $CO_2$ dynamics in agroecosystem and it can be able to present a new methodology for assessment of carbon budget in woody crop field. Futhermore, it is expected that the this study can be used as the basic data to be recognized as a carbon sink.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.45 no.1
• /
• pp.52-62
• /
• 2017

Overseas landscape practices have already benefited from the awareness of BIM while landscape-related organizations are encouraging its use and the number of landscape projects using BIM is increasing. However, since BIM has not yet been introduced in the domestic field, this study investigated and analyzed overseas landscape projects and discussed the positive effects and implementation of BIM. For this purpose, landscape projects were selected to show three effects of BIM: improvement of design work efficiency, building of a platform for cooperation, and performance of topography design. These three projects were analyzed across four aspects of implementation methods: landscape information, 3D modeling, interoperability, and visualization uses of BIM. First, in terms of landscape information, a variety of building information was constructed in the form of 3D libraries or 2D CAD format from detailed landscape elements to infrastructure. Second, for 3D modeling, a landscape space including simple terrain and trees was modeled with Revit while elaborate and complex terrain was modeled with Maya, a professional 3D modeling tool. One integrated model was produced by periodically exchanging, reviewing, and finally combining each model from interdisciplinary fields. Third, interoperability of data from different fields was achieved through the unification of file formats, conversion of differing formats, or compliance with information standards. Lastly, visualized 3D models helped coordination among project partners, approval of design, and promotion through public media. Reviewing of the case studies shows that BIM functions as a process to improve work efficiency and interdisciplinary collaboration, rather than simply as a design tool. It has also verified that landscape architects could play an important role in integrated projects using BIM. Just as the introduction of BIM into the architecture, engineering and construction industries saw great benefits and opportunities, BIM should also be introduced to landscape architecture.

• Archives of design research
• /
• v.19 no.2 s.64
• /
• pp.53-62
• /
• 2006

E-mail has indeed developed from 'a means of instant communication' to an indispensable part of online marketing. Therefore, companies need to implement consistent customer management. Communication with customers and marketing through e-mail is a powerful way of communication and adapting one-to-one marketing strategies to customer trends, habits and taste preferences. Since setting accurate targets is especially important in the fashion industry, e-mail marketing is the most effective way to communicate with customers and one-to-one marketing constitutes a very important strategy. In this study, I will analyze this powerful one-on-one marketing tool, particularly actual e-mail messages sent by an Internet Shopping Mall from June 12 to July 30, 2005, examine the effect of these messages on sales growth and analyze actual feedback received. Regarding e-mail read rates broken down by age and gender, 1 found that females in their late twenties recorded the highest rate at 21.66% and their contribution to sales growth was recorded at 3.5% From actual sales records, found that 28.10% of total sales were attributable to people in their late twenties, showing that the age group that reads e-mails the most also buys the most. Regarding feedback by e-mail title, e-mails from the 'Casual' category seemed to be the most effective, in that most of these e-mails were read. Also, messages sent on Tuesdays were read the most, according to the feedback analysis by weekday. Section e-mails were read more often than regular e-mails. Regarding the view rate according to the time e-mails were sent, messages sent to females in their late twenties at two o'clock in the afternoon were read by 20.93% of recipients, recording the highest read rate. By offering informative content and practical tips, visitors will be attracted to the site and generate site traffic. Therefore, we can conclude that sending e-mail messages can greatly contribute to sales growth and e-mail marketing is very effective. Also, in order to make e-mail campaigns more effective and improve marketing results, we need to analyze actual results and apply our findings in future e-mail campaigns. With this, we get successful marketing results.

• Journal of Intelligence and Information Systems
• /
• v.28 no.2
• /
• pp.127-146
• /
• 2022

Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.

• Journal of Practical Agriculture & Fisheries Research
• /
• v.4 no.1
• /
• pp.119-127
• /
• 2002

This study was conducted to develop the heat exchanger by utilizing the heat energy of underground water(15℃), which might be used for cooling and heating system of the agricultural facilities. We developed the heat exchanger, parallel type plat fin tube made of Aluminum(Al 6063), which was named Aloo-Heat(No. of The registration design : 0247164, by Korean Intellectual property Office). The fin of exchanger was design of the granulated surface for minimizing fouling factor and dew forms, and also placed parallel to the tube in order to minimized the resistance of flows. 1. Aloo-heat was designed to have 0.03m for inside diameter, 0.036m for outside diameter of tube, 0.0012m for thickness of fin and 0.032m for length of plat fin. 2. t was also designed to have 1.5248m²/m for outside area of heat transfer, 0.0942m²/m for inside area contacting hot liquid, and the ratio (Ra) was 16.1869. 3. Efficiency of the fin was 93 percentage when fin length was 0.032m, and the fin thickness satisfied equation $\frac{h{\rho}}{k}$< 0.2 when it was 0.0012m. 4. According to the performance test of Aloo-heat, as the temperature and rate increased, the heating value also increased, heating value was 504kJ/h·m and 6,048kJ/h·m when it was 60℃, 10 𝑙/min and 80℃, 40 𝑙/min respectively. 5. The test of heating value was confident, because correlation value(R²) was 0.9898 for the temperature and 0.9721 for flow rate of hot liquid, respectively.

• Korean Journal of Remote Sensing
• /
• v.39 no.2
• /
• pp.207-221
• /
• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.