• 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 Korean Institute of Traditional Landscape Architecture
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• v.41 no.3
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• pp.59-66
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• 2023

This study attempts to examine the process of establishing a Japanese-style garden in the modern period through the perspectives of garden designers, spatial composition, spatial components, and materials used in their works, and to use it as data for embodying the identity of Korean garden. The results are as follows: First, by incorporating elements associated with Koreanness into the modern garden culture, there are differences in location, presence, and subjectivity when compared to Japan. This reflects Japan's relatively seamless cultural continuity compared to Korea's cultural disconnection during the modern period. Second, prior to the modern period, Japan's garden culture spread and continued to develop throughout the country without significant interruptions. However, during the modern period, the Meiji government promoted the policy of 'civilization and enlightenment (Bunmei-kaika, 文明開化)' and introduced advanced European and American civilizations, leading to the popularity of Western-style architectural techniques. Unfortunately, the rapid introduction of Western culture caused the traditional Japanese culture to be overshadowed. In 1879, British architect Josiah Condor guided Japanese architects and introduced atelier and traditional designs of Japanese gardens into the design. The garden style of Ogawa Jihei VII, a garden designer in Kyoto during the Meiji and Taisho periods, was accepted by influential political and business leaders who sought to preserve Japan's traditional culture. And a protection system of garden was established through the preparation of various laws and regulations. Third, as a comprehensive analysis of Japanese modern gardens, the examination of garden designers, Japanese components, materials, elements, and the Japanese-style showed that Yamagata Aritomo, Ogawa Jihei VII, and Mirei Shigemori were representative garden designers who preserved the Japanese-style in their gardens. They introduced features such as the creation of a Daejicheon(大池泉) garden, which involves a large pond on a spacious land, as well as the naturalistic borrowed scenery method and water flow. Key components of Japanese-style gardens include the use of turf, winding garden paths, and the variation of plant species. Fourth, an analysis of the Japanese-style elements in the target sites revealed that the use of flowing water had the highest occurrence at 47.06% among the individual elements of spatial composition. Daejicheon and naturalistic borrowed scenery were also shown. The use of turf and winding paths were at 65.88% and 78.82%, respectively. The alteration of tree species was relatively less common at 28.24% compared to the application of turf or winding paths. Fifth, it is essential to discover more gardens from the modern period and meticulously document the creators or owners of the gardens, the spatial composition, spatial components, and materials used. This information will be invaluable in uncovering the identity of our own gardens. This study was conducted based on the analysis of the process of establishing the Japanese-style during Japan's modern period, utilizing examples of garden designers and gardens. While this study has limitations, such as the absence of in-depth research and more case studies or specific techniques, it sets the stage for future exploration.