• Korea Science and Art Forum
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• v.23
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• pp.111-122
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• 2016

The purpose of this multi-disciplinary convergent study is to establish Image Copyright Archive Model for Museums to protect image copyright and vitalize the use of images out of necessity of research and development on copyright services over the life cycle of art contents created by the museums and out of the necessity to vitalize distribution market of image copyright contents in creative industry and to formulate management system of copyright services. This study made various suggestions for enhancement of transparency and efficiency of art contents ecosystem through vitalization of use and recycling of image copyright materials by proposing standard system for calculation, distribution, settlement and monitoring of copyright royalty of 1,000 domestic museums, galleries and exhibit halls. First, this study proposed contents and structure design of image copyright archive model and, by proposing art contents distribution service platform for prototype simulation, execution simulation and model operation simulation, established art contents copyright royalty process model. As billing system and technological development for image contents are still in incipient stage, this study used the existing contents billing framework as basic model for the development of billing technology for distribution of museum collections and artworks and automatic division and calculation engine for copyright royalty. Ultimately, study suggested image copyright archive model which can be used by artists, curators and distributors. In business strategy, study suggested niche market penetration of museum image copyright archive model. In sales expansion strategy, study established a business model in which effective process of image transaction can be conducted in the form of B2B, B2G, B2C and C2B through flexible connection of museum archive system and controllable management of image copyright materials can be possible. This study is expected to minimize disputes between copyright holder of artwork images and their owners and enhance manageability of copyrighted artworks through prevention of such disputes and provision of information on distribution and utilization of art contents (of collections and new creations) owned by the museums. In addition, by providing a guideline for archives of collections of museums and new creations, this study is expected to increase registration of image copyright and to make various convergent businesses possible such as billing, division and settlement of copyright royalty for image copyright distribution service.

• The Korean Society of Law and Medicine
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• v.24 no.3
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• pp.155-206
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• 2023

When a person with diminished mental capacity refuses necessary medical care, normative judgments about when paternalistic intervention can be justified come into question. A typical example is involuntary hospitalization for people with mental disabilities, traditionally governed by mental health law. However, Korean civil law reform in 2011 introduced a new form of involuntary hospitalization through guardianship legislation, leading to a dualized system to involuntary hospitalization. Consequently, a conflict has arisen between the 'best interest and surrogate decision-making' paradigm of civil law and the 'social defense and preventive detention' paradigm of mental health law. Many countries have criticized this dualized system as not only inefficient but also unfair. Moreover, the requirement for the presence of 'mental illness' for involuntary hospitalization under mental health law has faced criticism for unfairly discriminating against people with mental disabilities. In response, attempts have been made to integrate guardianship legislation and mental health law based on mental capacity. This study examines the legislative process and framework of the Mental Capacity Act (Northern Ireland) 2016, which reorganized the mental health care system by fusing guardianship legislation with mental health law based on mental capacity. By analyzing the case of Northern Ireland, which has grappled with conflicts between guardianship legislation and mental health law since the 1990s and recently proposed mental capacity as a single, non-discriminatory standard, we aimed to offer insights for the Korean guardianship and mental health systems.

• Sijohaknonchong
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• v.23
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• pp.161-188
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• 2005

The purpose of this study was to delve into the morphological types of Sijo in an effort to determine the morphological structure of Sasul-sijo, and it's also attempted to present standard about how to discriminate Pyong-si, Eos-sijo and Sasul-sijo from one another from a morphological standpoint. It's suggested that Si with tee Jangs, six verses and 12 stanzas or more, with three Jangs, seven verses and 14 stanzas or more, and with three Jangs, eight verses and 16 stanzas or more should respectively be called Pyong-sijo, Eos-sijo and Sasul-sijo. After what Sijo was and what's not were discussed, how to distinguish Eos-sijo from Sasul-sijo was described, and finally, the structure of Sasul-sijo was presented. As for Sijo and non-Sijo, the types of works that consisted of tee Jangs, like Sijo, yet didn't suit its framework and Yuljo and were written in Chinese characters were regarded as non-Sijo. Concerning discrimination between Eos-si and Sasul-sijo, the type of Sijo that included one more or higher number of verse(s) and two more or higher number of stanzas in one of three Jangs was defined as Eos-sijo, and the type of Sijo that involved two more or higher number of verses and four more or higher number of stanzas in one of three Jangs was called Sasul-sijo. In other words, Eos-sijo contained one more verse in one of tee Jangs, and Sasul-sijo included one more Jang in one tee Jangs. The sort of Sijo that contained one more Jang in one of three Jangs could be viewed as Sasul-sijo. Regarding the structure of Sasul-si, there should be three Jangs, eight verses and 16 stanzas in one piece of Sasul-sijo. Any type of Sijo that contained two more or higher number of verses and four more or higher number of stanzas could be called Sasul-sijo. Such an addition of verse and stanza could done in various ways. The examples were (1) adding stanzas the first Jang, 2) adding stanzas to the second Jang, (3) adding stanzas to the final Jang, (4) adding stanzas to both the first and Second Jangs, (5) adding stanzas to th the second and final Jangs, and (6) adding stanzas to all the first, second and third Jangs at the same time. Besides, there was an extremely broad gap between the numbers of verse and stanza in Sasul-sijo, which ranged from a low of eight stanzas to a high of 87 ones in one of three Jangs.

• 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.