• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.607-613
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• 2018

The aim of this study is to predict the exact time of parturition from analysis and classification of preliminary behavior based on parturition signals in sows. This study was conducted with 12 crossbred sows (with an average of 3.5 parities). Behavioral characteristics were analyzed for duration and the frequency of different behaviors on a checklist, which includes the duration of the basic behaviors (feeding, standing, lying down, and sitting). The frequency of specific behaviors (investigatory behavior, shame-chewing, scratching, and bar-biting) was also recorded. Image information was collected every two minutes for 24 hours before the first piglets were born. As a result, the basic behavior of a sows' standing time (22.6% of the time after 24 h, 24.9% after 12 h) and time lying down (55.9% after 24 h, 66.3% after 12 h) increased over the 12 h period before parturition, compared with the 24 h period before parturition (p<0.01). Feeding (13.42% after 24 h, 4.38% after 12 h) and sitting (8.2% after 24 h, 4.5% after 12 h) tended to decrease during the 12 h before parturition (p>0.05). The sows' investigatory behavior ($11.44{\pm}1.80$ after 24 h, $55.97{\pm}6.13$ after 12 h), scratching ($3.75{\pm}1.92$ after 24 h, $20.99{\pm}5.81$ after 12 h), and bar-biting ($0.69{\pm}0.15$ after 24 h, $3.71{\pm}1.53$ after 12 h) increased in the 12-hour period before parturition, compared with the 24-hour period before parturition (p<0.01). On the other hand, shame-chewing ($2.20{\pm}1.67$ after 24 h, $0.07{\pm}0.01$ after 12 h) decreased compared to the 12-hour period before parturition (p>0.05). Thus, standing, investigatory behavior, scratching, and bar-biting could be used as behaviors indicative of parturition in sows.

• Korea Science and Art Forum
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• v.37 no.5
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• pp.119-133
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• 2019

This study is as skyscrapers are becoming increasingly taller, more constructors have decided the height alone cannot be a sufficient differentiator. As a result, atypical architecture is emerging as a new competitive factor. Also, it can be used for symbolizing the economic competitiveness of a country, city, or business through its form. Before the introduction of digital media, there was a discrepancy between the structure and form of a building and correcting this discrepancy required a separate structural medium. Since the late 1980s, however, digitally-based atypical form development began to be used experimentally, and, until the 2000s, it was used mostly for super-tall skyscrapers for offices or for industrial chimneys and communication towers. Since the 2000s, many global brand hotels and commercial and residential buildings have been built as super-tall skyscrapers, which shows the recent trend in architecture that is moving beyond the traditional limits. Complex atypical structure is formed and the formative characteristics of diagonal lines and curved surfaces, which are characteristics of atypical architecture, are created digitally. Therefore, it's goal is necessary to identify a new relationship between the structure and forms. According to the data of Council on Tall Buildings and Urban Habitat (CTBUH), 100-story and taller buildings were classified into typical, diagonal, curved, and segment types in order to define formative shapes of super-tall skyscrapers and provide a ground of the design process related to the initial formation of the concept. The purpose of this study was to identify the correlation between different forms for building atypical architectural shapes that are complex and diverse. The study results are presented as follows: Firstly, complex function follows convergence form characteristics. Secondly, fold has inside of architecture with repeat. Thirdly, as curve style which has pure twist, helix twist, and spiral twist. The findings in this study can be used as basic data for classifying and predicting trends of the future super-tall skyscrapers.

• Journal of Korean Society of Forest Science
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• v.110 no.1
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• pp.53-63
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• 2021

Sophora koreensis Nakai is an indigenous plant in Koreawith a restricted natural range, part of which is in Gangwon province. The species is known to contain phytochemicals that have beneficial effects on human health, and it is economically important in bioindustry. Because of the limited number of plants in a small range of habitats, the mass-propagation method should be developed for use and conservation. In vitro tissue culture is a reliable method in terms of mass propagation from selected clones of the species. We investigated the optimal conditions of the medium in this process, especially focusing on the concentrations of plant growth regulators(PGRs) in the culture of stem-containing axillary buds. Three statistical methods, i.e., ANOVA, response surface method(RSM), and fuzzy clustering were used to analyze the plant growth, number of shoots induced, and shoot length with various combinations of PGRs. Results from the RSM differed from those of the other two methods; thus, the method was not suitable. ANOVA and fuzzy clustering showed similar results. However, more accurate results were obtained using fuzzy clustering because it provided a probability for each treatment. On the basis of the fuzzy clustering analysis, stem tissue produced the greatest number of shoots(11.03 per explant; 63.33%) on a medium supplemented with 5-��M 6-benzylaminopurine and 2.5-��M thidiazuron(TDZ). Proliferation of shoots(2.18 ± 0.21 cm, 63.33%) was attained on a medium supplemented with 2.5-��M BA, 2.5-��M TDZ, and 2.5-��M gibberellic acid.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1827-1836
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• 2022

Disasters that occur unexpectedly are difficult to predict. In addition, the scale and damage are increasing compared to the past. Sometimes one disaster can develop into another disaster. Among the four stages of disaster management, search and rescue are carried out in the response stage when an emergency occurs. Therefore, personnel such as firefighters who are put into the scene are put in at a lot of risk. In this respect, in the initial response process at the disaster site, robots are a technology with high potential to reduce damage to human life and property. In addition, Light Detection And Ranging (LiDAR) can acquire a relatively wide range of 3D information using a laser. Due to its high accuracy and precision, it is a very useful sensor when considering the characteristics of a disaster site. Therefore, in this study, development and experiments were conducted so that the robot could perform real-time monitoring at the disaster site. Multi-sensor module was developed by combining LiDAR, Inertial Measurement Unit (IMU) sensor, and computing board. Then, this module was mounted on the robot, and a customized Simultaneous Localization and Mapping (SLAM) algorithm was developed. A method for stably mounting a multi-sensor module to a robot to maintain optimal accuracy at disaster sites was studied. And to check the performance of the module, SLAM was tested inside the disaster building, and various SLAM algorithms and distance comparisons were performed. As a result, PackSLAM developed in this study showed lower error compared to other algorithms, showing the possibility of application in disaster sites. In the future, in order to further enhance usability at disaster sites, various experiments will be conducted by establishing a rough terrain environment with many obstacles.

• Journal of Bio-Environment Control
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• v.32 no.1
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• pp.48-56
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• 2023

High-pressure sodium (HPS) lamps have been widely used as a useful supplemental light source to emit sufficient photosynthetically active radiation and provide a radiant heat, which contribute the heat requirement in greenhouses. The objective of this study to analyze the thermal characteristics of HPS lamp and thermal behavior in supplemented greenhouse, and evaluate the performance of a horizontal leaf temperature of sweet pepper plants using computational fluid dynamics (CFD) simulation. We simulated horizontal leaf temperature on upper canopy according to three growth stage scenarios, which represented 1.0, 1.6, and 2.2 plant height, respectively. We also measured vertical leaf and air temperature accompanied by heat generation of HPS lamps. There was large leaf to air temperature differential due to non-uniformity in temperature. In our numerical calculation, thermal energy of HPS lamps contributed of 50.1% the total heat requirement on Dec. 2022. The CFD model was validated by comparing measured and simulated data at the same operating condition. Mean absolute error and root mean square error were below 0.5, which means the CFD simulation values were highly accurate. Our result about vertical leaf and air temperature can be used in decision making for efficient thermal energy management and crop growth.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.

• Journal of Applied Biological Chemistry
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• v.65 no.4
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• pp.421-427
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• 2022

In this study, Jeju Tatary buckwheat tea's chemical composition and physiological activities were compared according to the leaching temperature (60, 80, 100 ℃). As the leaching temperature is increased, the degree of browning is induced. However, there was no significant change in pH. The total polyphenol content was higher at 80 ℃ than at 60 ℃ leaching temperature, but significantly decreased at 100 ℃ leaching temperature (60 ℃: 17.06 mg GA/g, 80 ℃: 20.09 mg GA/g, 100 ℃ :18.45 mg GA/g). There were high content of flavonoid and rutin as the leaching temperature increased. Consistently, 2,2-diphenyl1-picrylhydrazyl (DPPH) radical scavenging activity and tyrosinase inhibitory activity were significantly higher with increasing temperature (DPPH % inhibition: 60 ℃: 41.88%, 80 ℃: 46.01%, 100 ℃: 46.80%/tyrosinase inhibitory activity: 60 ℃: 9.38%, 80 ℃: 22.94%, 100 ℃: 28.17%). However, there was no significant difference in DPPH radical scavenging activity between 80 and 100 ℃. A cytotoxicity test was performed by treating with Jeju Tatary buckwheat extract into mouse macrophage cells (Raw264.7). 100 and 200 ㎍/mL treatment (100 ℃ extract) were significantly upregulated the survival rate, but there was no significant difference in other concentrations. Collectively, most of the bioactive components, antioxidant activity, and tyrosinase inhibitory activity were induced as the leaching temperature increased. However, the content of polyphenols which are known to have antioxidant activity, was significantly reduced at 100 ℃ leaching temperature. Several reports have demonstrated that leaching at too high temperature lowered the overall acceptability, so the optimal leaching condition of Tatary Buckwheat is 80 ℃, 5 min in this study.

• Clean Technology
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• v.29 no.3
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• pp.193-199
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• 2023

Biomass has been used to secure renewable energy certificates (REC) in domestic and overseas coal-fired power plants. In recent years, biofuel has been diversified from traditional wood pellets to non-woody biomass. Non-woody biomass has a higher content of alkaline metals such as K and Na than wood-based biomass, resulting in a lower melting point and an increase in slagging on boiler tubes, which reduces boiler efficiency. This study analyzed the effect of kaolin, an additive commonly used to increase melting points, on biomass co-firing to coal through thermochemical equilibrium calculations. In a previous experiment on biomass co-firing to coal conducted at 80 kW_th, it was interpreted that the use of kaolin actually increased the amount of fouling. In this study, analysis showed that when kaolin was added, aluminosilicate compounds were generated due to Al₂O₃, which is abundant in coal, and mullite was formed. Thus, it was confirmed that the amount of slag increased when more kaolin was used. Further analysis was conducted by increasing the biomass co-firing rate from 0% to 100% at 10% intervals, and the results showed non-linear liquid slag generation. As a result, it was found that the least amount of liquid slag was generated when the biomass co-firing rate was between 50 and 60%. The phase diagram analysis showed that high melting point compounds such as leucite and feldspar were most abundantly generated under these conditions.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.145-165
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• 2023

Recommendation system plays a significant role on relieving difficulties of selecting information among rapidly increasing amount of information caused by the development of the Internet and on efficiently displaying information that fits individual personal interest. In particular, without the help of recommendation system, E-commerce and OTT companies cannot overcome the long-tail phenomenon, a phenomenon in which only popular products are consumed, as the number of products and contents are rapidly increasing. Therefore, the research on recommendation systems is being actively conducted to overcome the phenomenon and to provide information or contents that are aligned with users' individual interests, in order to induce customers to consume various products or contents. Usually, collaborative filtering which utilizes users' historical behavioral data shows better performance than contents-based filtering which utilizes users' preferred contents. However, collaborative filtering can suffer from cold-start problem which occurs when there is lack of users' historical behavioral data. In this paper, hybrid music recommendation system, which can solve cold-start problem, is proposed based on the playlist data of Melon music streaming service that is given by Kakao Arena for music playlist continuation competition. The goal of this research is to use music tracks, that are included in the playlists, and metadata of music tracks and playlists in order to predict other music tracks when the half or whole of the tracks are masked. Therefore, two different recommendation procedures were conducted depending on the two different situations. When music tracks are included in the playlist, LightFM is used in order to utilize the music track list of the playlists and metadata of each music tracks. Then, the result of Item2Vec model, which uses vector embeddings of music tracks, tags and titles for recommendation, is combined with the result of LightFM model to create final recommendation list. When there are no music tracks available in the playlists but only playlists' tags and titles are available, recommendation was made by finding similar playlists based on playlists vectors which was made by the aggregation of FastText pre-trained embedding vectors of tags and titles of each playlists. As a result, not only cold-start problem can be resolved, but also achieved better performance than ALS, BPR and Item2Vec by using the metadata of both music tracks and playlists. In addition, it was found that the LightFM model, which uses only artist information as an item feature, shows the best performance compared to other LightFM models which use other item features of music tracks.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.1
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• pp.42-55
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• 2023

A methodology to predict the carbon performance of newly created urban greening plans is required as policies based on quantifying carbon performance are rapidly being introduced in the face of the climate crisis caused by global warming. This study developed a tree carbon calculator that can be used for carbon reduction designs in landscaping and attempted to verify its effectiveness in landscape design. For practical operability, MS Excel was selected as a format, and carbon absorption and storage by tree type and size were extracted from 93 representative species to reflect plant design characteristics. The database, including tree unit prices, was established to reflect cost limitations. A plantation experimental design to verify the performance of the tree carbon calculator was conducted by simulating the design of parks in the central region for four landscape design, and the causal relationship was analyzed by conducting semi-structured interviews before and after. As a result, carbon absorption and carbon storage in the design using the tree carbon calculator were about 17-82% and about 14-85% higher, respectively, compared to not using it. It was confirmed that the reason for the increase in carbon performance efficiency was that additional planting was actively carried out within a given budget, along with the replacement of excellent carbon performance species. Pre-interviews revealed that designers distrusted data and the burdens caused by new programs before using the arboreal carbon calculator but tended to change positively because of its usefulness and ease of use. In order to implement carbon reduction design in the landscaping field, it is necessary to develop it into a carbon calculator for trees and landscaping performance. This study is expected to present a useful direction for ntroducing carbon reduction designs based on quantitative data in landscape design.