• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.6
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• pp.27-36
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• 2021

At a time when the equitable use of urban parks is gradually emerging as a social issue, this study was initiated to expand the influence of urban parks by improving the quality of park services, thereby resolving areas not covered by urban park services. This study targeted the Hangang Park in Seoul, where the qualitative service of parks shows the greatest difference. The influence relationship between the qualitative services of the park and the user's sphere of influence, which indicates the distribution of park users, was proposed to assess the influence of improvements in the quality of service. As a research method, the top three districts and the bottom three districts were selected through the Han River Park user satisfaction survey conducted from 2017 to 2019, and a qualitative service evaluation was carried out. It was derived using the data acquired in September. Afterward, by performing a spatial autocorrelation analysis on the user's sphere of influence, additional verification of the user's sphere of influence was performed numerically and visually. As a result of the study, the user influence in the top three districts, with high-quality service, was stronger and wider than that of the lower three districts. It was confirmed that the quality of service of the park affects the user influence. This shows that to realize park equity, it is necessary to improve the quality of services through continuous management and improvement of individual parks and the creation of new parks. This study has significance in that it recognizes the limitations of research on park services from a supplier's point of view and evaluates the qualitative services of parks from the perspective of actual park users. We propose an alternative to deal with the lower the park deprivation index.

• Journal of Korean Home Economics Education Association
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• v.35 no.2
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• pp.145-161
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• 2023

The purpose of this study is to develop a content system for the home economics curriculum that integrates Education for Sustainable Development(ESD) and provides basic material for ESD implementation in schools. In view of this, the content elements of the revised home economics curriculum for 2022 were analyzed, and a content system for the home economics curriculum, linked to ESD, was proposed based on the implications drawn from the analysis. The results are as follows. First, the three components of competencies, namely knowledge, values, and skills, were organized equally as a whole. However, the association between the content elements and key competencies in sustainability was found to be insufficient. Consequently, it is proposed that key competencies in sustainability should be cultivated integrally. Second, no content element was identified that can promote social participation. Therefore, it is proposed that solutions should be dealt with at the level of social participation. Third, the connection with Sustainable Development Goals(SDGs) was observed in only six of the 28 content elements. Wherever relevant, it is proposed to incorporate key issues related to SDGs. Fourth, the analysis confirmed that only the environmental dimension of sustainable development was considered. Therefore, it is proposed to pursue coexistence based on temporal and spatial relationship and consider the dimensions of environment, society, and economy in an integrated manner.

• Journal of the Korean Society of Earth Science Education
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• v.16 no.3
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• pp.340-350
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• 2023

This study analyzed the difficulty level of class on the seasonal change unit for 84 students at a university of education. The conclusions of this study are as follows. First, if we first present the four topics that make up the seasonal changes in elementary science, the subjects that have the greatest difficulty in teaching for prospective elementary school teachers are 'Why do seasonal changes occur?' (Teaching difficulty level 4.05), 'The sun changes depending on the season' What is the difference between the southern altitude and the length of day and night?' (difficulty level of class, 3.12), 'What is the relationship between the altitude of the sun, length of shadow, and temperature during the day?' (difficulty level of class, 2.85), 'How does the temperature change depending on the season?' (class difficulty level 2.80). As a result, in the elementary science season change unit, the class on the four topics 'Why do seasons change?', which is classified as a class topic that requires the concept of spatial perception, showed a higher level of class difficulty than other units. Second, in the seasonal change unit, various factors of class difficulty appeared depending on the class topic. When pre-service elementary school teachers look at the factors that make class difficult when teaching a lesson on seasonal changes in order of frequency, 42 (50%) said 'Experimental instruction for comparing the altitude of solar masculine according to the tilt of the axis of rotation', followed by 'Solar masculine'. 38 people (45%) answered 'Difficulty in explaining mid-high altitude and the length of day and night', 27 people (32%) answered 'Difficulty in explaining the concept of mid-high altitude', and 24 people (32%) answered 'Difficulty in explaining seasonal changes in the sun's position.' 29%), 20 people (24%) said 'Explain the reasonable reason why the height of the light should be adjusted when measuring the solar altitude', and 16 people (19%) said 'It is difficult to explain the reason for the discrepancy between the solar altitude and the maximum temperature'. ), 'difficulties in measuring sand (ground) temperature' were mentioned by 12 people (14%). Third, when analyzing the factors of class difficulty, there were more curriculum factors than teacher factors. In this context, the exploratory activities on 'Why do seasonal changes occur?', the fourth topic of the seasonal change unit in which elementary school pre-service teachers showed the greatest difficulty in teaching, need improvement in terms of the curriculum.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.427-435
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• 2023

Changes in flowering time due to weather fluctuations impact plant growth and ecosystem dynamics. Accurate prediction of flowering timing is crucial for effective forest ecosystem management. This study uses a process-based model to predict flowering timing in 2023 for five major tree species in Korean forests. Models are developed based on nine years (2009-2017) of flowering data for Abeliophyllum distichum, Robinia pseudoacacia, Rhododendron schlippenbachii, Rhododendron yedoense f. poukhanense, and Sorbus commixta, distributed across 28 regions in the country, including mountains. Weather data from the Automatic Mountain Meteorology Observation System (AMOS) and the Korea Meteorological Administration (KMA) are utilized as inputs for the models. The Single Triangle Degree Days (STDD) and Growing Degree Days (GDD) models, known for their superior performance, are employed to predict flowering dates. Daily temperature readings at a 1 km spatial resolution are obtained by merging AMOS and KMA data. To improve prediction accuracy nationwide, random forest machine learning is used to generate region-specific correction coefficients. Applying these coefficients results in minimal prediction errors, particularly for Abeliophyllum distichum, Robinia pseudoacacia, and Rhododendron schlippenbachii, with root mean square errors (RMSEs) of 1.2, 0.6, and 1.2 days, respectively. Model performance is evaluated using ten random sampling tests per species, selecting the model with the highest R². The models with applied correction coefficients achieve R² values ranging from 0.07 to 0.7, except for Sorbus commixta, and exhibit a final explanatory power of 0.75-0.9. This study provides valuable insights into seasonal changes in plant phenology, aiding in identifying honey harvesting seasons affected by abnormal weather conditions, such as those of Robinia pseudoacacia. Detailed information on flowering timing for various plant species and regions enhances understanding of the climate-plant phenology relationship.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.41 no.4
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• pp.21-29
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• 2023

The Northern Song Dynasty, the heyday of cultural and artistic achievements, brought significant changes to the history of gardens in China. The developments and contemplations that had evolved during the previous Tang Dynasty became intertwined with literature, painting, and art, leading to garden being perceived as works of art. In particular, the emergence of Su Shi(蘇軾) that permeated literature and art during the Northern Song Dynasty, had an impact beyond individual garden creation, influencing the development of public gardens and the diversification of garden. His long exile periods served as an opportunity to understand and reflect the local culture and characteristics, influencing the development of the garden. This study focuses on the ideology of Su Shi(蘇軾) that managed various gardens, examining the relationship between his exlie life and ideology. To do so, the study examines the form of the literati's gardens managed by Su Shi(蘇軾), with a particular emphasis on the Baiheju(白鶴居) garden in Huizhou, revealing the following characteristics and values. First, Su Shi(蘇軾), who was proficient in the Three Houses: Confucianism, Buddhism, and Taoism, combined his philosophy and unique perspective techniques with the location and composition elements of Baiheju(白鶴居) to enjoy the landscape. Although the ancient residence has a simple form, it possesses expansiveness through the combination of internal and external views. The interior is designed to be perceived as a single space, but it allows overlapping experiences of space and simultaneous appreciation of different sceneries. On the other hand, the spatial layout incorporates a hierarchical order to establish a sense of order. Second, the garden reflects the local characteristics, featuring numerous tropical plants and presenting vibrant and contrasting colors with structures. The planting forms embrace the concept of "huosei seikou" (活色生香) to enhance the color harmoniously. Additionally, the garden incorporates the poet's spiritual world, projecting it onto the garden as a contemplative place for spiritual nourishment and exploration of the ideal realm. For the pursuit of serenity and profound contemplation, the selected plantings are simple yet distinctive, providing rhythm and depth to the garden space. Third, Baiheju(白鶴居) has undergone changes over the years, but fundamentally, the form and elements of the garden shaped by Su Shi(蘇軾)'s descendants persist, confirming its heritage value.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.1
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• pp.1-9
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• 2006

Regardless of the recent observed warmer winters in Korea, more freeze injuries and associated economic losses are reported in fruit industry than ever before. Existing freeze-frost forecasting systems employ only daily minimum temperature for judging the potential damage on dormant flowering buds but cannot accommodate potential biological responses such as short-term acclimation of plants to severe weather episodes as well as annual variation in climate. We introduce 'dormancy depth', in addition to daily minimum temperature, as a complementary criterion for judging the potential damage of freezing temperatures on dormant flowering buds of grape vines. Dormancy depth can be estimated by a phonology model driven by daily maximum and minimum temperature and is expected to make a reasonable proxy for physiological tolerance of buds to low temperature. Dormancy depth at a selected site was estimated for a climatological normal year by this model, and we found a close similarity in time course change pattern between the estimated dormancy depth and the known cold tolerance of fruit trees. Inter-annual and spatial variation in dormancy depth were identified by this method, showing the feasibility of using dormancy depth as a proxy indicator for tolerance to low temperature during the winter season. The model was applied to 10 vineyards which were recently damaged by a cold spell, and a temperature-dormancy depth-freeze injury relationship was formulated into an exponential-saturation model which can be used for judging freeze risk under a given set of temperature and dormancy depth. Based on this model and the expected lowest temperature with a 10-year recurrence interval, a freeze risk probability map was produced for Hwaseong County, Korea. The results seemed to explain why the vineyards in the warmer part of Hwaseong County have been hit by more freeBe damage than those in the cooler part of the county. A dormancy depth-minimum temperature dual engine freeze warning system was designed for vineyards in major production counties in Korea by combining the site-specific dormancy depth and minimum temperature forecasts with the freeze risk model. In this system, daily accumulation of thermal time since last fall leads to the dormancy state (depth) for today. The regional minimum temperature forecast for tomorrow by the Korea Meteorological Administration is converted to the site specific forecast at a 30m resolution. These data are input to the freeze risk model and the percent damage probability is calculated for each grid cell and mapped for the entire county. Similar approaches may be used to develop freeze warning systems for other deciduous fruit trees.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.