• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.41 no.3
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• pp.1-12
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• 2023

After 60 years since the enactment of the cultural property protection act, Korea's national system of protecting what has been valued has been transformed into a 'National Heritage System'. To meet the new system, the classification of national heritage has been re-classified into three categories as cultural heritage, natural heritage, and intangible heritage. In accordance with the sub-classification, acts for cultural heritage act and intangible heritage has been amended and act for natural heritage has been enacted. Act for natural heritage defines natural heritage as natural objects or cultural heritage formed through the interaction between human beings and natural environment. The sub-classification are categorized as follows; animal, plant, geological and natural reserves, natural landscapes, historical-cultural landscapes, and mixed landscapes. It also allows creating traditional landscapes so that traditional way of landscaping can be encouraged and integrated with modern life within historic environment. In line with the new concept of traditional landscaping, existing practices will be also needed to be changed. Traditional landscaping will play a significant role in setting out the new administrative paradigm which focuses on more value preservation. This paper recommends that effective collaboration between government, experts of traditional landscaping, and owners should be established to integrate the new policy in practice.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of the HCI Society of Korea
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• v.13 no.2
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• pp.31-44
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• 2018

In the IoT environment, various things could be connected. Those connected things learn and operate themselves, by acquiring data. As human being, they have self-learning and self-operating systems. In the field of IoT study, therefore, the key issue is to design communication system connecting both of the two different types of subjects, human being(user) and the things. With the advent of the IoT environment, much research has been done in the field of UI design. It can be seen that research has been conducted to take complex factors into account through keywords such as multi-modality and interusability. However, the existing UI design method has limitations in structuring or testing interaction between things and users of IoT environment. Therefore, this paper suggests a new UI prototyping method. In this paper, the major analysis and studies are as follows: (1) defined what is the behavior process of the things (2) analyzed the existing IoT product (3) built a new framework driving personality types (4) extracted three representative personality models (5) applied the three models to the smart home service and tested UI prototyping. It is meaningful with that this study can confirm user experience (UX) about IoT service in a more comprehensive way. Moreover, the concept of the personality of things will be utilized as a tool for establishing the identity of artificial intelligence (AI) services in the future.

• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.103-110
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• 2010

TPR(Total Physical Response) is a new representative learning method for children's education. Today's approach to TPR has focused on signals from a user which becomes input data in a human-computer interaction, but the accuracy of sensing from body signals(e. g. motion and voice) isn't so perfect that it seems difficult to apply on an education system. To overcome these limits, we suggest a mediating interface device which can detect the user's motion using correct numerical values such as acceleration and angular speed. In addition, we suggest new design requirements for the mediating device through analyzing children's behavior as human factors by ethnography research and protocol analysis. As a result, we found that; children are unskilled in physical control when they use objects; tend to lean on an object unconsciously with touch. Also their behaviors are restricted, when they use objects. Therefore a mediating device should satisfy new design requirements which are make up for unskilled handling, support familiar and natural physical activity.

• Journal of Educational Research in Mathematics
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• v.9 no.1
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• pp.81-109
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• 1999

This study aims to reflect the basic principles and teaching-teaming principles of Realistic Mathematics Education in order to suppose an way in which mathematics as an activity is carried out in primary school. The development of what is known as RME started almost thirty years ago. It is founded by Freudenthal and his colleagues at the former IOWO. Freudenthal stressed the idea of matheamatics as a human activity. According to him, the key principles of RME are as follows: guided reinvention and progressive mathematisation, level theory, and didactical phenomenology. This means that children have guided opportunities to reinvent mathematics by doing it and so the focal point should not be on mathematics as a closed system but on the process of mathematisation. There are different levels in learning process. One should let children make the transition from one level to the next level in the progress of mathematisation in realistic contexts. Here, contexts means that domain of reality, which in some particular learning process is disclosed to the learner in order to be mathematised. And the word of 'realistic' is related not just with the real world, but is related to the emphasis that RME puts on offering the students problem situations which they can imagine. Under the background of these principles, RME supposes the following five instruction principles: phenomenological exploration, bridging by vertical instruments, pupils' own constructions and productions, interactivity, and interwining of learning strands. In order to reflect how to realize these principles in practice, the teaming process of algorithms is illustrated. In this process, children follow a learning route that takes its inspiration from the history of mathematics or from their own informal knowledge and strategies. Considering long division, the first levee is associated with real-life activities such as sharing sweets among children. Here, children use their own strategies to solve context problems. The second level is entered when the same sweet problems is presented and a model of the situation is created. Then it is focused on finding shortcomings. Finally, the schema of division becomes a subject of investigation. Comparing realistic mathematics education with constructivistic mathematics education, there interaction, reflective thinking, conflict situation are many similarities but there are alsodifferences. They share the characteristics such as mathematics as a human activity, active learner, etc. But in RME, it is focused on the delicate balance between the spontaneity of children and the authority of teachers, and the development of long-term loaming process which is structured but flexible. In this respect two forms of mathematics education are different. Here, we learn how to develop mathematics curriculum that respects the theory of children on reality and at the same time the theory of mathematics experts. In order to connect the informal mathematics of children and formal mathematics, we need more teachers as researchers and more researchers as observers who try to find the mathematical informal notions of children and anticipate routes of children's learning through thought-experiment continuously.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.175-201
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• 2023

In the midst of going through a non-face-to-face society, most of human activities narrowed down to the platform, restrictions on external activities are bringing the internal scalability of digital technology. Metaverse is virtually shifting reality and increasing the possibility of utilization in various areas. However, researches linked to the educational effects of metaverse, especially students with disabilities, are still an unknown area that lacks exploration. This paper focuses on the fact that metaverse-education is widening educational fields that meets the various needs of disabled students to realize social good and inclusive education, and communication effects such as resolving barriers to interaction are prominent. As a research method, examining literature research papers linked to AR/VR, metaverse with communication skills, interviews, articles, and columns by experts, and policy suggestions and implications for the special education was conducted. Although the limitations of research are confirmed, significant results are found on inclusive education, which provides educational maximizing effects and realizing human rights through direct immersive experience reflecting the Cone of Experience Theory. Hopefully follow-up studies on meta-edu for disabled students will be carried out in the future, and various interdisciplinary discussions are needed to carefully observe inclusive policies and benefits so that the socially vulnerable are not excluded from technologies in ICT society.

• Journal of Life Science
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• v.26 no.8
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• pp.904-910
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• 2016

Complex traits are determined by the combined effects of many loci and are affected by gene networks or biological pathways. Systems biology approaches have an important role in the identification of candidate genes related to complex diseases or traits at the system level. The gene network analysis has been performed by diverse types of methods such as gene co-expression, gene regulatory relationships, protein-protein interaction (PPI) and genetic networks. Moreover, the network-based methods were described for predicting gene functions such as graph theoretic method, neighborhood counting based methods and weighted function. However, there are a limited number of researches in livestock. The present study systemically analyzed genes associated with 102 types of economic traits based on the Animal Trait Ontology (ATO) and identified their relationships based on the gene co-expression network and PPI network in cattle. Then, we constructed the two types of gene network databases and network visualization system (http://www.nabc.go.kr/cg). We used a gene co-expression network analysis from the bovine expression value of bovine genes to generate gene co-expression network. PPI network was constructed from Human protein reference database based on the orthologous relationship between human and cattle. Finally, candidate genes and their network relationships were identified in each trait. They were typologically centered with large degree and betweenness centrality (BC) value in the gene network. The ontle program was applied to generate the database and to visualize the gene network results. This information would serve as valuable resources for exploiting genomic functions that influence economically and agriculturally important traits in cattle.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.1
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• pp.56-63
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• 2007

In this paper, an eye tracking method is presented using a neural network (NN) and mean-shift algorithm that can accurately detect and track user's eyes under the cluttered background. In the proposed method, to deal with the rigid head motion, the facial region is first obtained using skin-color model and con-nected-component analysis. Thereafter the eye regions are localized using neural network (NN)-based tex-ture classifier that discriminates the facial region into eye class and non-eye class, which enables our method to accurately detect users' eyes even if they put on glasses. Once the eye region is localized, they are continuously and correctly tracking by mean-shift algorithm. To assess the validity of the proposed method, it is applied to the interface system using eye movement and is tested with a group of 25 users through playing a 'aligns games.' The results show that the system process more than 30 frames/sec on PC for the $320{\times}240$ size input image and supply a user-friendly and convenient access to a computer in real-time operation.

• Journal of the HCI Society of Korea
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• v.6 no.1
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• pp.1-8
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• 2011

As virtual realty has become an issue of providing immersive services, in the area of virtual realty, it has been actively investigated to develop user interfaces for immersive interaction. In this paper, we propose a gesture recognition based immersive user interface by using an IR LED embedded helmet and data gloves in order to reflect the user's movements to the virtual reality environments effectively. The system recognizes the user's head movements by using the IR LED embedded helmet and IR signal transmitter, and the hand gestures with the data gathered from data gloves. In case of hand gestures recognition, it is difficult to recognize accurately with the general recognition model because there are various hand gestures since human hands consist of many articulations and users have different hand sizes and hand movements. In this paper, we applied the Mixture-of-Experts based gesture recognition for various hand gestures of multiple users accurately. The movement of the user's head is used to change the perspection in the virtual environment matching to the movement in the real world, and the gesture of the user's hand can be used as inputs in the virtual environment. A head mounted display (HMD) can be used with the proposed system to make the user absorbed in the virtual environment. In order to evaluate the usefulness of the proposed interface, we developed an interface for the virtual orchestra environment. The experiment verified that the user can use the system easily and intuituvely with being entertained.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.4
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• pp.227-236
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• 2007

Ubiquitous systems have grown explosively over the few years. Nowadays users' needs for high qualify service lead a various type of user terminals. One of various type of user interface, various types of effective human computer interface methods have been developed. In many researches, researchers have focused on using brain-wave interface, that is to say, BCI. Nowadays, researches which are related to BCI are under way to find out effective methods. But, most researches which are related to BCI are not centralized and not systematic. These problems brought about ineffective results of researches. In most researches related in HCI, that is to say - pattern recognition, the most important foundation of the research is to build correct and sufficient DB. But there is no effective and reliable standard research conditions when researchers are gathering brain-wave in BCI. Subjects as well as researchers do not know effective methods for gathering DB. Researchers do not know how to instruct subjects and subjects also do not know how to follow researchers' instruction. To solve these kinds of problems, we propose effective brain-wave DB building system using the five senses stimulation. Researcher instructs the subject to use the five senses. Subjects imagine the instructed senses. It is also possible for researchers to distinguish whether brain-wave is right or not. In real time, researches verify gathered brain-wane data using spectrogram. To verify effectiveness of our proposed system, we analyze the spectrogram of gathered brain-wave DB and pattern. On the basis of spectrogram and pattern analysis, we propose an effective brain-wave DB building method using the five senses stimulation.