• Electronics and Telecommunications Trends
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• v.34 no.4
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• pp.55-64
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• 2019

In this study, we introduce trends in neural-network-based deep learning research applied to dialogue systems. Recently, end-to-end trainable goal-oriented dialogue systems using long short-term memory, sequence-to-sequence models, among others, have been studied to overcome the difficulties of domain adaptation and error recognition and recovery in traditional pipeline goal-oriented dialogue systems. In addition, some research has been conducted on applying reinforcement learning to end-to-end trainable goal-oriented dialogue systems to learn dialogue strategies that do not appear in training corpora. Recent neural network models for end-to-end trainable chit-chat systems have been improved using dialogue context as well as personal and topic information to produce a more natural human conversation. Unlike previous studies that have applied different approaches to goal-oriented dialogue systems and chit-chat systems respectively, recent studies have attempted to apply end-to-end trainable approaches based on deep neural networks in common to them. Acquiring dialogue corpora for training is now necessary. Therefore, future research will focus on easily and cheaply acquiring dialogue corpora and training with small annotated dialogue corpora and/or large raw dialogues.

• Cross-Cultural Studies
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• v.33
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• pp.263-286
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• 2013

Is this a right way in politics that attitude of Japanese scholars to separate Natsume Soseki from the expansionism of pre-war Japan to protect 'sanctity'? Nowadays, most Japanese scholars are regarded to share the desire that minimize the memory of the behavior of Japanese Imperialism in East Asia, such as Korea, China, etc. Furthermore, 'the desire to minimize' inescapably concluded in avoidance, concealment, at last the temptation of deliberate misleading. Until now, the controversy about the Natsume Soseki's travel to Korea and Manchuria has repeated in defence and criticism surrounding the self-awareness and recognition of others of Natsume Soseki, making the expression in a record of Natsume's travel as the subject of study, for example, the degrading expression about Chosun people and scorn for Chinese and Russian. This paper will investigate that Natsume's travel is the political practice which is combined with the desire for the empire, focusing on the political context in the action of journey of Natsume and its contents other than the expression itself.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.73-78
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• 2022

The aim of this paper is to investigate the prevalence of Deep Learning in the literature on Fire & Rescue Service. It is found that deep learning techniques are only beginning to benefit the firefighters. The popular areas where deep learning techniques are making an impact are situational awareness, decision making, mental stress, injuries, well-being of the firefighter such as his sudden fall, inability to move and breathlessness, path planning by the firefighters while getting to an fire scene, wayfinding, tracking firefighters, firefighter physical fitness, employment, prediction of firefighter intervention, firefighter operations such as object recognition in smoky areas, firefighter efficacy, smart firefighting using edge computing, firefighting in teams, and firefighter clothing and safety. The techniques that were found applied in firefighting were Deep learning, Traditional K-Means clustering with engineered time and frequency domain features, Convolutional autoencoders, Long Short-Term Memory (LSTM), Deep Neural Networks, Simulation, VR, ANN, Deep Q Learning, Deep learning based on conditional generative adversarial networks, Decision Trees, Kalman Filters, Computational models, Partial Least Squares, Logistic Regression, Random Forest, Edge computing, C5 Decision Tree, Restricted Boltzmann Machine, Reinforcement Learning, and Recurrent LSTM. The literature review is centered on Firefighters/firemen not involved in wildland fires. The focus was also not on the fire itself. It must also be noted that several deep learning techniques such as CNN were mostly used in fire behavior, fire imaging and identification as well. Those papers that deal with fire behavior were also not part of this literature review.

• Smart Structures and Systems
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• v.29 no.1
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• pp.105-116
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• 2022

Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.

• The Journal of the Acoustical Society of Korea
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• v.42 no.3
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• pp.221-225
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• 2023

In this paper, we proposed an efficient method for rapid diagnosis of COVID-19 by voice. A novel Strided Convolution Separable Transformer (SC-SepTr) is proposed by modifying the conventional Separable Transformer (SepTr) for audio signal recognition. The proposed method reduces the memory and computational requirements to enable rapid diagnosis of COVID-19. As a result of experiments on Coswara, it was shown that the proposed method perform rapid diagnosis with guaranteeing Area Under the Curve (AUC) performance even for a relatively small amount of learning data.

• The KIPS Transactions:PartD
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• v.11D no.2
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• pp.443-452
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• 2004

The paper presents an intelligent space management system for ubiquitous computing. The system is basically a home/office automation system that could control light, electronic key, and home appliances such as TV and audio. On top of these basic capabilities, there are four elegant features in the system. First, we can access the system using either a cellular Phone or using a browser on the PC connected to the Internet, so that we control the system at any time and any place. Second, to provide more human-oriented interface, we integrate voice recognition functionalities into the system. Third, the system supports not only reactive services but also proactive services, based on the regularities of user behavior. Finally, by exploiting embedded technologies, the system could be run on the hardware that has less-processing power and storage. We have implemented the system on the embedded board consisting of StrongARM CPU with 205MHz, 32MB SDRAM, 16MB NOR-type flash memory, and Relay box. Under these hardware platforms, software components such as embedded Linux, HTK voice recognition tools, GoAhead Web Server, and GPIO driver are cooperated to support user-friendly intelligent space.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.11-19
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• 2009

A RAM-based Neural Net(RBNN) which has multi-discriminators is more effective than RBNN with a discriminator. Experience Sensitive Cumulative Neural Network and 3-D Neuro System(3DNS) that accumulate the features point improved the performance of BNN, which were enabled to train additional and repeated patterns and extract a generalized pattern. In recognition process of Neural Net with multi-discriminator, the selection of class was decided by the value of MRD which calculates the accumulated sum of each class. But they had a saturation problem of its memory cells caused by learning volume increment. Therefore, the decision of MRD has a low performance because recognition rate is decreased by saturation. In this paper, we propose the method which improve the MRD ability. The method consists of the optimum MRD and the matching ratio prototype to generalized image, the cumulative filter ratio, the gap of prototype response MRD. We experimented the performance using NIST database of NIST without preprocessor, and compared this model with 3DNS. The proposed MRD method has more performance of recognition rate and more stable system for distortion of input pattern than 3DNS.

• Journal of Popular Narrative
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• v.26 no.1
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• pp.175-209
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• 2020

This paper examines how the cinematic representation of the Japanese military "comfort women" stimulates 'imagination' in the realm of everyday life and in the memory of the masses, creating a common awareness and affect. The history of the Japanese military "comfort women" was hidden for a long time, and it was not until the 1990s that it entered the field of public recognition. Such a transition can be attributed to the external and internal chronopolitics that made possible the testimony of the victims and the discourse of the "comfort women" issue. It shows the peculiar status of the comfort women history as 'politics of time'. In the same vein, the cinematic representations of the Japanese military "comfort women" can be found in similar chronopolitics. The 'comfort women' films have shown the dual time frame of the continuity and discontinuity of the 'silence'. In Korean film history, the chronotope of the reproduction of "comfort women" can be divided into four phases: 1) the fictional representations of "comfort women" before the 1990s 2) documentaries in the late 1990s as the work of testimony and history writing, 3) melodramatic transformation in the feature films in the 2000s, and 4) the diffusion of media and categories. The purpose of this article is to focus on the first phase and the third phase in which the issue of 'comfort women' is represented in the category of popular fiction films. While the "comfort women" representations before 1990 were strictly adhering to the framework of commercial movies and pursued the sexual exploitation of "comfort women" history, the recent films since the 2000s are experimenting with various attempts in the style of popular imagination. Especially, the emergence of 'comfort women' feature films in the 2000s, such as Spirit's Homecoming, I Can Speak, and Herstory, raise various questions as to whether we are "properly" aware of issues and how to remember and present the "cultural memory" of comfort women. Also, focusing on the cinematic representation strategies of the 2000s "comfort women", this article discusses the popular politics of melodrama, the representation of victims and violence, and the feature of 'comfort women' as meta-memory. As a melodramatic imagination and meta-memory for the historical trauma, the "comfort women" drama shows the historical, political, and aesthetic gateways to which the "comfort women" problem must pass. As we have seen in recent fiction films, the issue of "comfort women" goes beyond transnational relations between Korea and Japan; it demands a postcolonial task to dismantle the old colonial structure and explores a transnational project in which women's movements and human rights movements are linked internationally.

• Korean Journal of Heritage: History & Science
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• v.57 no.3
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• pp.162-182
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• 2024

Since the adoption of the World Heritage Convention, sites associated with dark histories have been inscribed as World Heritage sites over the past fifty years. However, in 2018, the review of nomination dossiers for these sites was temporarily suspended to prevent additional discomfort or the conflicts these inscriptions might cause. Despite concerns raised by experts about nominations of these sites, the increasing demands from State Parties led to the adoption of the Guiding Principles for the Preparation of Nominations Concerning Sites of Memory Associated with Recent Conflicts. These Guiding Principles have made it possible to inscribe such sites as World Heritage sites. The Guiding Principles play a crucial role in outlining the nature and criteria for inscription, the components required in the nomination dossier, and mechanisms for notifying a contestation in cases of differing interpretations of the site. Their primary aim is to minimize further conflicts that may arise from the inscription of sites of memory. They affirm that such sites can contribute to achieving the objectives of the World Heritage Convention and represent a significant step in addressing heritage interpretation in the World Heritage system. The amendment of the Operational Guidelines to incorporate a contestation mechanism has arguably established a more transparent and open inscription process. However, the Guiding Principles also have limitations. Among the ten criteria set by the World Heritage Convention, sites related to conflicts or dark histories can use Criterion (vi). This criterion focuses on the site's outstanding universal value linked to historical events or associations, regardless of physical evidence. If a State Party chooses not to use Criterion (vi), the application of the Guiding Principles cannot be expected. Furthermore, while the Guiding Principles require a heritage interpretation strategy in the nomination dossier, the lack of detailed guidance may confuse nominating countries. Sites of memory associated with recent conflicts are not just places that need protection and remembrance due to their association with dark histories. They have also evolved to become spaces for reconciliation and healing. The inscription of these sites as World Heritage sites is not just a recognition of their historical significance, but also a platform for discussing the impact of past conflicts on modern society. It opens up a dialogue on how current generations can address these issues. With the adoption of the Guiding Principles, we hope that inscribed sites will not only promote reconciliation and healing but also serve as a starting point for addressing present and future challenges.

• 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.