• Journal of the Korean Institute of Landscape Architecture
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• v.38 no.5
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• pp.64-79
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• 2010

Students with intellectual disabilities typically have great difficulties communicating their needs and wants and may get frustrated, anxious or show signs of aggressive behaviour. They are often unable to understand the concept of relationships with other people as well. This lack of social communication and interaction leads to poor motivation which increases other areas of difficulty in their lives. Therefore, to teach students with intellectual disabilities, it is essential to find special teaching methods to support their positive behavior. This paper proposes that special educational curriculum for students with intellectual impairments utilize natural environments(forests) and materials. The purpose of this study was to investigate the effects of Educational Programs Utilizing Forests on maladjusted behavior of students with mental retardation and to examine the positive effects of such planning practices. The subjects were middle school students who attended a special school for mentally-retarded students. They were divided into two separate groups- an experimental group and a control group. All subjects received the pre and post test using the same method. This program was applied to the formal educational process of middle school from March 1st to October 30th, 2009. In this experiment the results indicate that the effect of using a natural environment(forest) and the materials within a formal educational curriculum was positive as a type of horticultural therapy and that it supports positive behavior strategies in students with intellectual impairments. The usage of various natural materials including plants and flowers within the natural environments provide students with increased opportunities to participate. Teaching methods including natural materials help teachers engage with their students more easily during routine activities as their interest is already stimulated. This project will help students with intellectual impairments to build skills which enable effective participation and increase independence throughout their lives. This should be embedded into both routine and planned activities of the classroom Also, it offers a needed interior plan for the treatment space through an analysis of psychological factors of how the environment affects students.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Trans-
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• v.9
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• pp.37-66
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• 2020

Entering the 21st century, the flow of society and culture is emerging as a cultural phenomenon in which one experiences, enjoys, and experiences on one's own. This trend has emerged as community dance, which has been active since 2010. Community dances can be targeted by anyone and can be divided into children's, adult and senior citizens' dances depending on the characteristics and age of the group, allowing them to work in various age groups. It also refers to all kinds of dances for the happiness and self-achievement of everyone who can promote gender, race and religion health or meet the needs of expression and improve their physical strength at meetings by age group, from preschoolers to senior citizens. Community dance is a dance activity in which everyone takes advantage of their leisure time and voluntarily participates in joyous activities, making it expandable to lifelong education and social learning. It is a voluntary community gathering conducted by experts for the general public. The definition of community dance can be said to be the aggregate of physical activities that enrich an individual's daily life and enhance their social sense to create a bright society, while individuals achieve the goals of health promotion and aesthetic education. In the contemporary community dance, the dance experience in body and creativity as self-expression reflects the happiness perspective by exploring the positive psychological experience and influence of the participants in the process of participation, and participants have continued networking through online offline to enjoy the dance culture. Although research has been conducted in various fields for 10 years since the boom in community dance began, the actual methodology of the program has been insufficient to present the Feldenkrais Method, hoping that it will be used as a methodology necessary for local community dance, and will be used as part of the educational effects and choreography creation methods of artists that can improve the physical functional aspects of dance and give a sense of psychological stability.

• Korean Journal of Heritage: History & Science
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• v.52 no.2
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• pp.172-195
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• 2019

Among the paintings of Goam Lee Ungno (1904-1989), his works of the 1960s in Paris have been evaluated as his most avant-garde works of experimenting with and innovating objects as an artist. At that time, his works, such as Papier Colle and Abstract Letter, were influenced by abstract expressionism and Western Art Informel, illustrating his transformation from a traditional artist into a contemporary artist. An exhibition, which was held prior to his going to Paris in March 1958, has received attention because it exhibited the painting style of his early Informel art. Taking this into consideration, this study was conducted by interpreting his work from two perspectives; first, that his works of 1958 were influenced by abstract expressionism and Art Informel, and, second, that he expressed Xieyi (寫意) as literati painting, focusing on the fact that Lee Ungno first started his career adopting this style. In this paper, I aimed to confirm Lee Ungno's recognition of Art Informel and abstract painting, which can be called abstract expressionism. To achieve this, it was necessary to study Lee's painting theory at that time, so I first considered Hae-gang Kim Gyu-jin whom Lee Ungno began studying painting under, and his paintings during his time in Japan. It was confirmed that in order to escape from stereotypical paintings, deep contemplation of nature while painting was his first important principle. This principle, also known as Xieyi (寫意), lasted until the 1950s. In addition, it is highly probable that he understood the dictionary definition of abstract painting, i.e., the meaning of extracting shapes from nature according to the ideas which became important to him after studying in Japan, rather than the theory of abstract painting realized in Western paintings. Lee Ungno himself also stated that the shape of nature was the basis of abstract painting. In other words, abstractive painting and abstract painting are different concepts and based on this, it is necessary to analyze the paintings of Lee Ungno. Finally, I questioned the view that Lee Ungno's abstract paintings of the 1950s were painted as representative of the Xieyi (寫意) mind of literary art painting. Linking traditional literary art painting theory directly to Lee Ungno, who had been active in other worlds in space and time, may minimize Lee Ungno's individuality and make the distinction between traditional paintings and contemporary paintings obscure. Lee Ungno emphasized Xieyi (寫意) in his paintings; however, this might have been an emphasis signifying a great proposition. This is actually because his works produced in the 1950s, such as Self-Portrait (1956), featured painting styles with boldly distorted forms achieved by strong ink brushwork, a style which Lee Ungno defined as 'North Painting.' This is based on the view that it is necessary to distinguish between Xieyi (寫意) and 'the way of Xieyi (寫意) painting' as an important aspect of literary art painting. Therefore, his paintings need a new interpretation in consideration of the viewpoint that he represented abstract paintings according to his own Xieyi (寫意) way, rather than the view that his paintings were representations of Xieyi (寫意), or rather a succession of traditional paintings in the literary artist's style.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.17-32
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• 2021

Speaker recognition is generally divided into speaker identification and speaker verification. Speaker recognition plays an important function in the automatic voice system, and the importance of speaker recognition technology is becoming more prominent as the recent development of portable devices, voice technology, and audio content fields continue to expand. Previous speaker recognition studies have been conducted with the goal of automatically determining who the speaker is based on voice files and improving accuracy. Speech is an important sociolinguistic subject, and it contains very useful information that reveals the speaker's attitude, conversation intention, and personality, and this can be an important clue to speaker recognition. The final ending used in the speaker's speech determines the type of sentence or has functions and information such as the speaker's intention, psychological attitude, or relationship to the listener. The use of the terminating ending has various probabilities depending on the characteristics of the speaker, so the type and distribution of the terminating ending of a specific unidentified speaker will be helpful in recognizing the speaker. However, there have been few studies that considered speech in the existing text-based speaker recognition, and if speech information is added to the speech signal-based speaker recognition technique, the accuracy of speaker recognition can be further improved. Hence, the purpose of this paper is to propose a novel method using speech style expressed as a sentence-final ending to improve the accuracy of Korean speaker recognition. To this end, a method called sentence sequencing that generates vector values by using the type and frequency of the sentence-final ending appearing in the utterance of a specific person is proposed. To evaluate the performance of the proposed method, learning and performance evaluation were conducted with a actual drama script. The method proposed in this study can be used as a means to improve the performance of Korean speech recognition service.

• Journal of Nutrition and Health
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• v.55 no.1
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• pp.21-35
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• 2022

The recommended meal composition allows the general people to organize meals using the number of intakes of foods from each of six food groups (grains, meat·fish·eggs·beans, vegetables, fruits, milk·dairy products and oils·sugars) to meet Dietary Reference Intakes for Koreans (KDRIs) without calculating complex nutritional values. Through an integrated analysis of data from the 6th to 7th Korean National Health and Nutrition Examination Surveys (2013-2018), representative foods for each food group were selected, and the amounts of representative foods per person were derived based on energy. Based on the EER by age and gender from the KDRIs, a total of 12 kinds of diets were suggested by differentiating meal compositions by age (aged 1-2, 3-5, 6-11, 12-18, 19-64, 65-74 and ≥ 75 years) and gender. The 2020 Food Balance Wheel included the 6th food group of oils and sugars to raise public awareness and avoid confusion in the practical utilization of the model by industries or individuals in reducing the consistent increasing intakes of oils and sugars. To promote the everyday use of the Food Balance Wheel and recommended meal compositions among the general public, the poster of the Food Balance Wheel was created in five languages (Korean, English, Japanese, Vietnamese and Chinese) along with card news. A survey was conducted to provide a basis for categorizing nutritional problems by life cycles and developing customized web-based messages to the public. Based on survey results two types of card news were produced for the general public and youth. Additionally, the educational program was developed through a series of processes, such as prioritization of educational topics, setting educational goals for each stage, creation of a detailed educational system chart and teaching-learning plans for the development of educational materials and media.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.461-472
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• 2022

Natural and anthropogenic factors cause forest types to continuously change. Since the ratio of forest area by forest type is important information for identifying the characteristics of national forest resources, an accurate understanding of the prospect of forest type change is required. The study aim was to use National Forest Inventory (NFI) time-series data to understand the characteristics of forest type change and to estimate future prospects of nationwide forest type change. We used forest type change information from the fifth and seventh NFI datasets, climate, topography, forest stand, and disturbance variables related to forest type change to analyze trends and characteristics of forest type change. The results showed that the forests in Korea are changing in the direction of decreasing coniferous forests and increasing mixed and broadleaf forests. The forest sites that were changing from coniferous to mixed forests or from mixed to broadleaf forests were mainly located in wet topographic environments and climatic conditions. The forest type changes occurred more frequently in sites with high disturbance potential (high temperature, young or sparse forest stands, and non-forest areas). We used a climate change scenario (RCP 8.5) to establish a forest type change model (SVM) to predict future changes. During the 40-year period from 2015 to 2055, the SVM predicted that coniferous forests will decrease from 38.1% to 28.5%, broadleaf forests will increase from 34.2% to 38.8%, and mixed forests will increase from 27.7% to 32.7%. These results can be used as basic data for establishing future forest management strategies.

• Journal of Christian Education in Korea
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• v.68
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• pp.345-374
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• 2021

This study is a paper which studies the fact that autobiographical writing in Christian educational books is an important channel for studying the identity of Christian educators. The identity of Christian educators is the background and foundation of the study of Christian education theory. It was found through research that the scholar's identity is more evident in autobiographical writing from a first-person perspective experienced by the author than in argumentative writing with objective and cognitive limitations. This study examined the concept and characteristics of autobiographical writing research, the relationship of autobiographical writing research and self-identity, the relationship between autobiographical writing and Christian education, and discovered autobiographical writing in Christian educational scholars' books. Through the autobiographical writing of Maria Harris' Teaching and Religious Imagination, Parker Palmer's The Courage to Teach, and Elizabeth Moore's Teaching as a Sacrament Act, we studied that the identity of Christian educators can meet, transform, and expand learners' identity as well. Through research, it has been confirmed that autobiographical writing takes the form of a story, but as a story distinct from the story, it becomes a place where the authors' identity and readers' identity can meet, wrestle and expand. Autobiographical writing has a relationship with story and self-identity. These characteristics are also linked to Christian educational goals that focus on the formation and transformation of self-identity. The autobiographical writing in Harris, Palmer, and Moore's writings shows the identity of a teacher, including scholars' theological perspectives and views on education. As the writing of Christian education books so far has become argumentative and objective writing, readers has felt a sense of disparity and disconnection. If autobiographical writing becomes educational books' style, it can invite readers to empathize with who the author is. Christian education will experience more fundamental changes with autobiographical writing.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.2
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• pp.80-98
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• 2023

This study explores the effectiveness of various data preprocessing algorithms for improving subseasonal to seasonal (S2S) climate predictions from six climate forecast models and their Multi-Model Ensemble (MME) using a deep learning-based postprocessing model. A pipeline of data transformation algorithms was constructed to convert raw S2S prediction data into the training data processed with several statistical distribution. A dimensionality reduction algorithm for selecting features through rankings of correlation coefficients between the observed and the input data. The training model in the study was designed with TimeDistributed wrapper applied to all convolutional layers of U-Net: The TimeDistributed wrapper allows a U-Net convolutional layer to be directly applied to 5-dimensional time series data while maintaining the time axis of data, but every input should be at least 3D in U-Net. We found that Robust and Standard transformation algorithms are most suitable for improving S2S predictions. The dimensionality reduction based on feature selections did not significantly improve predictions of daily precipitation for six climate models and even worsened predictions of daily maximum and minimum temperatures. While deep learning-based postprocessing was also improved MME S2S precipitation predictions, it did not have a significant effect on temperature predictions, particularly for the lead time of weeks 1 and 2. Further research is needed to develop an optimal deep learning model for improving S2S temperature predictions by testing various models and parameters.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.189-198
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• 2024

Lipreading is one of the important parts of speech recognition, and several studies have been conducted to improve the performance of lipreading in lipreading systems for speech recognition. Recent studies have used method to modify the model architecture of lipreading system to improve recognition performance. Unlike previous research that improve recognition performance by modifying model architecture, we aim to improve recognition performance without any change in model architecture. In order to improve the recognition performance without modifying the model architecture, we refer to the cues used in human lipreading and set other regions such as chin and cheeks as regions of interest along with the lip region, which is the existing region of interest of lipreading systems, and compare the recognition rate of each region of interest to propose the highest performing region of interest In addition, assuming that the difference in normalization results caused by the difference in interpolation method during the process of normalizing the size of the region of interest affects the recognition performance, we interpolate the same region of interest using nearest neighbor interpolation, bilinear interpolation, and bicubic interpolation, and compare the recognition rate of each interpolation method to propose the best performing interpolation method. Each region of interest was detected by training an object detection neural network, and dynamic time warping templates were generated by normalizing each region of interest, extracting and combining features, and mapping the dimensionality reduction of the combined features into a low-dimensional space. The recognition rate was evaluated by comparing the distance between the generated dynamic time warping templates and the data mapped to the low-dimensional space. In the comparison of regions of interest, the result of the region of interest containing only the lip region showed an average recognition rate of 97.36%, which is 3.44% higher than the average recognition rate of 93.92% in the previous study, and in the comparison of interpolation methods, the bilinear interpolation method performed 97.36%, which is 14.65% higher than the nearest neighbor interpolation method and 5.55% higher than the bicubic interpolation method. The code used in this study can be found a https://github.com/haraisi2/Lipreading-Systems.