• Information Systems Review
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• v.21 no.1
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• pp.23-38
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• 2019

The social network service (SNS) is one of the important marketing channels, so many companies actively exploit SNSs by posting SNS messages with appropriate content and style for their customers. In this paper, we focused on the psychological distances embedded in the SNS messages and developed a method to measure the psychological distance in SNS message by mixing a traditional content analysis, natural language processing (NLP), and machine learning. Through a traditional content analysis by human coding, the psychological distance was extracted from the SNS message, and these coding results were used for input data for NLP and machine learning. With NLP, word embedding was executed and Bag of Word was created. The Support Vector Machine, one of machine learning techniques was performed to train and test the psychological distance in SNS message. As a result, sensitivity and precision of SVM prediction were significantly low because of the extreme skewness of dataset. We improved the performance of SVM by balancing the ratio of data by upsampling technique and using data coded with the same value in first content analysis. All performance index was more than 70%, which showed that psychological distance can be measured well.

• Journal of Environmental Policy
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• v.5 no.1
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• pp.45-70
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• 2006

China has achieved great economic growth above 9% annual since it changed to more of a market economy system by its reform and open-door policy. At the same time, China has experienced severe ecological deterioration, such as air and water pollutions caused by its rapid urbanization and industrialization. China is now confronted with environmental pollution and ecological deterioration at a critical point, at which economic development in China is limited. Moreover, environmental problems in China have become a lit fuse for social fluctuation beyond pollution problems. The root and background of environmental problems in China, firstly, are its government's lack of understanding of these problems and incorrect economic policies affected by political and ideological prejudice. Secondly, the plundering of resources, 'the principle of development first' which didn't consider environmental sustainability is another source of environmental deterioration in China. In addition, a huge population and poverty in China have increased the difficulty in solving its environmental problems, and in fact have accelerated them. The Chinese government has established many environmental laws and institutions, increased environmental investments, and is enlarging the participation of NGOs and the general public in some limited scale to solve its environmental problems. However, it has not obtained effective results because of the lack of environmental investments owing to the government's limit of the development phase, a structural limit of law enforcement and local protectionism, and the limit of political independency in NGOs and the lack of public participation in China. It seems that China remains in the stage of 'economic development first, environmental protection second', contrary to its catch-phrase of 'the harmony between economic development and environmental protection'. China is now confronted with dual pressure both domestically and abroad because of deepening environmental problems. There are growing public's protests and demonstrations in China in response to the spread of damage owing to environmental pollution and ecological deterioration. On the other hand, international society, in particular neighboring countries, regard China as a principal cause of ecological disaster. In the face of this dual pressure, China is presently contemplating a 'recycling economy' that helps sustainable development through the structural reform of industries using too much energy and through more severe law enforcement than now. Therefore, it is desirable to promote regional cooperation more progressively and practically in the direction of building China's ability to solve environmental problems.

• The Korean Journal of Pesticide Science
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• v.6 no.4
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• pp.250-256
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• 2002

The objective was to know if chinese cabbage growers in Gangwon alpine farmland control agricultural pests including weeds effectively and use pesticide properly. Examiners visited 185 farmers at Taebaek, Pyongchang, and Jeongseon and surveyed 33 questions on pest control methods pesticide use. Chinese cabbage farmers have noxious plant diseases such as clubroot, bacterial soft rot, downy mildew, anthracnose, and mosaic disease, and also noxious insects such as diamondback moth, aphid, beet armyworm, common cabbage worm, and Japanese native slug. In addition, farmers have noxious weeds such as common chickweed, marsh pepper, hairy crabgrass, common purslane, and horseweed. To control diseases and insects, 51.3% of farmers used many chemical agents, while 20.7% of farmers used chemical agents with too much emphasis on paraquat and glyphosate to control weeds: 87.2% of the answered farmers have a preference of the both non-selective herbicides. Farmers in the survey area selected pesticides on the basis of their own experience and sales managers' recommendation (84.2%) which results in the use of inappropriate pesticides such diniconazole. Many farmers have experienced phytotoxicities (46.7%) and pesticide poisoning (51.2%). We conclude that a systematic educational program for the proper selection and use of pesticides should be conducted for chinese cabbage growers in Gangwon alpine farmland.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.351-360
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• 2018

The LWR (Locally Weighted Regression) model, which is traditionally a lazy learning model, is designed to obtain the solution of the prediction according to the input variable, the query point, and it is a kind of the regression equation in the short interval obtained as a result of the learning that gives a higher weight value closer to the query point. We study on an incremental ensemble learning approach for LWR, a form of lazy learning and memory-based learning. The proposed incremental ensemble learning method of LWR is to sequentially generate and integrate LWR models over time using a genetic algorithm to obtain a solution of a specific query point. The weaknesses of existing LWR models are that multiple LWR models can be generated based on the indicator function and data sample selection, and the quality of the predictions can also vary depending on this model. However, no research has been conducted to solve the problem of selection or combination of multiple LWR models. In this study, after generating the initial LWR model according to the indicator function and the sample data set, we iterate evolution learning process to obtain the proper indicator function and assess the LWR models applied to the other sample data sets to overcome the data set bias. We adopt Eager learning method to generate and store LWR model gradually when data is generated for all sections. In order to obtain a prediction solution at a specific point in time, an LWR model is generated based on newly generated data within a predetermined interval and then combined with existing LWR models in a section using a genetic algorithm. The proposed method shows better results than the method of selecting multiple LWR models using the simple average method. The results of this study are compared with the predicted results using multiple regression analysis by applying the real data such as the amount of traffic per hour in a specific area and hourly sales of a resting place of the highway, etc.

• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.3
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• pp.54-65
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• 2017

The purpose of this study is to investigate the perception of landscape law and institutions and to provide basic data for improvement of landscape systems. Specifically, we analyzed the importance and achievement of various landscape systems, and examined the understanding and perception of government officers in landscape plan, landscape project, landscape agreement, landscape reviews and landscape committees, landscape ordinance, and landscape administration. The main results of the study are summarized as follows. 1. Overall, the landscape administration system was highly interested, and it was also positive about the utility of the landscape law and the landscape charter. As a result of analysis of the IPA, the landscape plan and the landscape policy plan need to be intensively improved. 2. The landscape plan is mostly used for the purpose of responding to the scenery review or complaint request, but about 10.8% of respondents said that they did not refer it at all, so it is urgent to make the contents of the landscape plan real and improve the performance. Although many officers thought that less than 18 months would be quite enough for landscape plans, but it is necessary to change this duration issue. 3. In order to improve landscape projects and landscape agreements, it seems that budget securing, experts, and promotional organizations should be improved first. 4. It is urgently necessary to enhance the understanding about overall landscape law and systems of landscape review committee in order to supplement the landscape review and the landscape committee. 5. Administrative support such as personnel recruitment is required for landscape ordinance and landscape administration, and it is also found that many officers also have a great burden in making subjective judgment as the person in charge. There could be a positive bias in the results of the study, because the survey was conducted only for public officials who participated in the education. But the result will be helpful to look at the overall tendency of the landscape system. I hope that it will help improve the landscape system in the future much more realistic.

• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.2
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• pp.109-123
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• 2017

Due to the positive effect of forest space for child development, the creation and operation of forest activity space of various organizations is increasing in quantity; however, the research on practical space design and management program is insufficient. Therefore, the purpose of this study is to evaluate the space and management programs of the forest experience centers through the post-occupancy evaluation of teachers and preschoolers participating in forest activities. To do this, we analyzed the selected twelve sites through field survey, class observation, and interviews with forest education specialists, and then surveyed 115 forest education experts and childcare teachers for importance, performance, overall satisfaction, and space preference. In addition, we accessed overall satisfaction and space preference of twenty-nine preschoolers through interviews, photo-simulation, and questionnaires. As a result, the importance and performance of management program area was rated higher than the spatial characteristics area. In terms of group comparison, the group with active structured program rated two areas higher than the groups with free play. Preschoolers with structured programs preferred facility space, but preschoolers with free play preferred nature. Two preschooler groups rated forest activity as satisfactory. Based on the analysis results: 1) The composition of the forest activity space should ensure accessibility, safety, diversity of diversity, water space, connect to the forest road, and secure various terrains, trees, and natural materials; 2) The management program should ensure that forest activity programs have the proportional balance of structural programs and free play; also. management programs should plan for sufficient free playtime and a high share of play in the forest; and 3) Ensure the role and expertise of forestry specialists and run a program to increase the autonomy of preschoolers.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.366-373
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• 2021

This study attempted to establish a field data-based write analysis standard by analyzing field observation data, which is non-linear data of southern garlic. Five regions, including Goheung, Namhae, Sinan, Changnyeong, and Haenam, were selected for analysis. Observation values for each observation station were extracted from the temperature data of farmland in the region through inverse distance weighted. Southern-type garlic production and temperature data were collected for 10 years, from 2010 to 2019. Local regression analysis (Kernel) of the obtained data was performed, and growth temperatures were analyzed, such as 0.8 (18.781℃), 0.9 (18.930℃), 1.0 (19.542℃), 1.1 (20.165℃), and 1.2 (21.042℃) depending on the bandwidth. The analyzed optimum temperature and the grown temperature (4℃/25℃) were applied to extract the growth temperature for each temperature by using the temperature response model analysis. Regression analysis and correlation analysis were performed between the analyzed growth temperature and production data. The coefficient of determination(R²) was analyzed as 0.325 to 0.438, and in the correlation analysis, the correlation coefficient of 0.57 to 0.66 was analyzed at the significance probability 0.001 level. Overall, as the bandwidth increased, the coefficient of determination was higher. However, in all analyses except bandwidth 1.0, it was analyzed that all variables were not used due to bias. The purpose of this study is to accommodate all data through non-linear data. It was analyzed that bandwidth 1.0 with a high coefficient of determination while accepting modeling as a whole is the most suitable.

• The Korean Society of Law and Medicine
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• v.24 no.4
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• pp.67-101
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• 2023

The utilization of generative AI in the medical field is also being rapidly researched. Access to vast data sets reduces the time and energy spent in selecting information. However, as the effort put into content creation decreases, there is a greater likelihood of associated issues arising. For example, with generative AI, users must discern the accuracy of results themselves, as these AIs learn from data within a set period and generate outcomes. While the answers may appear plausible, their sources are often unclear, making it challenging to determine their veracity. Additionally, the possibility of presenting results from a biased or distorted perspective cannot be discounted at present on ethical grounds. Despite these concerns, the field of generative AI is continually advancing, with an increasing number of users leveraging it in various sectors, including biomedical and life sciences. This raises important legal considerations regarding who bears responsibility and to what extent for any damages caused by these high-performance AI algorithms. A general overview of issues with generative AI includes those discussed above, but another perspective arises from its fundamental nature as a large-scale language model ('LLM') AI. There is a civil law concern regarding "the memorization of training data within artificial neural networks and its subsequent reproduction". Medical data, by nature, often reflects personal characteristics of patients, potentially leading to issues such as the regeneration of personal information. The extensive application of generative AI in scenarios beyond traditional AI brings forth the possibility of legal challenges that cannot be ignored. Upon examining the technical characteristics of generative AI and focusing on legal issues, especially concerning the protection of personal information, it's evident that current laws regarding personal information protection, particularly in the context of health and medical data utilization, are inadequate. These laws provide processes for anonymizing and de-identification, specific personal information but fall short when generative AI is applied as software in medical devices. To address the functionalities of generative AI in clinical software, a reevaluation and adjustment of existing laws for the protection of personal information are imperative.