• Journal of KIISE:Information Networking
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• v.37 no.1
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• pp.8-15
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• 2010

In this study, we present an extended complex event model by considering both of the complex event and the aspect-oriented programming. We propose an advanced scheme for the event specification suited for the event correlation based network management systems by merging these two models. Specifically, we extend the model to support hierarchical event structures and let the model recognize point-cuts of aspect-oriented programming as events. We provide the event operators designed to specify the events on instances and handle temporal relations of the instances. Lastly, we compare the proposed model with other event models and present the benefits of it.

• Journal of Contemporary Eastern Asia
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• v.18 no.2
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• pp.118-141
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• 2019

In South Korea, as awareness of gender equality increased since the 1990s, policies for gender equality and social awareness of equality have been established. Until recently, however, the gap between men and women in social and economic activities has not reached the globally desired level and led to social conflict throughout the country. In this study, we analyze the content of online news comments to understand the public perception of gender equality and the details of gender conflict and to grasp the emergence and diffusion process of emerging issues on gender equality. We collected text data from the online news that included the word 'gender equality' posted from January 2012 to June 2017 and also collected comments on each selected news item. Through text mining and the temporal semantic network analysis, we tracked the changes in discourse on gender equality and conflict. Results revealed that gender conflicts are increasing in the online media, and the focus of conflict is shifting from 'position and role inequality' to 'opportunity inequality'.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.228-228
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• 2023

국지적 지역에 내리는 강한 강도의 강우는 많은 인명 및 재산 피해를 발생시킨다. 이러한 피해를 예방하기 위해 도시 침수 예측에 관한 연구가 오랜 기간 수행되어 왔으며, 최근에는 다양한 신경망(neural network) 모델이 활발히 이용되고 있다. 강우 지속 기간이나 강도는 일정하지 않고, 공간적 특징 또한 도시마다 다르므로 안정적인 침수 예측을 위한 신경망 모델은 강건성(robustness)을 지녀야 한다. 강건한 신경망 모델이란 적대적 공격(adversarial attack)을 방어할 수 있는 능력을 갖춘 모델을 일컫는다. 따라서 본 연구에서는, 도시 침수 예측을 위한 시공간 신경망(spatio-temporal neural network) 모델의 강건성 제고를 위한 방법론을 제안한다. 먼저 적대적 공격의 유형과 방어 방법을 분류하고, 시공간 신경망 모델의 학습 데이터 특성 및 모델 구조구성 조건 등을 활용하여 최적의 강건성 제고 방안을 도출하였다. 해당 모델은 집중호우로 인해 나타날 다양한 관망에서의 침수 피해를 각각 예측하고 피해를 예방하기 위해 활용될 수 있다.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.120-120
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• 2020

Spatial precipitation data is one of the essential components in modeling hydrological problems. The estimation of these data has achieved significant achievements own to the recent advances in remote sensing technology. However, there are still gaps between the satellite-derived rainfall data and observed data due to the significant dependence of rainfall on spatial and temporal characteristics. An effective approach based on the Convolutional Neural Network (CNN) model to correct the satellite-derived rainfall data is proposed in this study. The Mekong River basin, one of the largest river system in the world, was selected as a case study. The two gridded precipitation data sets with a spatial resolution of 0.25 degrees used in the CNN model are APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). In particular, PERSIANN-CDR data is exploited as satellite-based precipitation data and APHRODITE data is considered as observed rainfall data. In addition to developing a CNN model to correct the satellite-based rain data, another statistical method based on standard deviations for precipitation bias correction was also mentioned in this study. Estimated results indicate that the CNN model illustrates better performance both in spatial and temporal correlation when compared to the standard deviation method. The finding of this study indicated that the CNN model could produce reliable estimates for the gridded precipitation bias correction problem.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.89-95
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• 2023

In recent decades, human action recognition (HAR) has demonstrated potential applications in sports analysis, human-robot interaction, and large-scale signage content. In this paper, spatial temporal attention graph convolutional network (STAGCN)-based HAR system is proposed. Spatioal-temmporal features of skeleton sequences are assigned different weights by STAGCN, enabling the consideration of key joints and viewpoints. From simulation results, it has been shown that the performance of the proposed model can be improved in terms of classification accuracy in the NTU RGB+D dataset.

• Journal of Electrical Engineering and Technology
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• v.7 no.5
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• pp.789-796
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• 2012

Fingerprinting is a widely used positioning technology for received signal strength (RSS) based wireless local area network (WLAN) positioning system. Though spatial RSS variation is the key factor of the positioning technology, temporal RSS variation needs to be considered for more accuracy. To deal with the spatial and temporal RSS characteristics within a unified framework, this paper proposes an extended signal propagation mode (ESPM) and a fingerprint generation method. The proposed spatiotemporal fingerprint generation method consists of two algorithms running in parallel; Kalman filtering at several measurement-sampling locations and Kriging to generate location fingerprints at dense reference locations. The two different algorithms are connected by the extended signal propagation model which describes the spatial and temporal measurement characteristics in one frame. An experiment demonstrates that the proposed method provides an improved positioning accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2226-2231
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• 2008

As the mobile devices and the wireless networks have high-performance capabilities, it is possible to synchronize the spatio-temporal data of a server with the spatio-temporal data of a mobile device which are collected at a field. However, since the server process the synchronization which the model device requests, the whole synchronizations of mass mobile devices take long time. In this paper, we propose the scheme to process concurrently the synchronizations of mobile devices to use multi-queue which does not conflict.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4026-4040
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• 2019

In this paper, we present an approach for detecting slide transitions in lecture videos by introducing the spatio-temporal residual networks. Given a lecture video which records the digital slides, the speaker, and the audience by multiple cameras, our goal is to find keyframes where slide content changes. Since temporal dependency among video frames is important for detecting slide changes, 3D Convolutional Networks has been regarded as an efficient approach to learn the spatio-temporal features in videos. However, 3D ConvNet will cost much training time and need lots of memory. Hence, we utilize ResNet to ease the training of network, which is easy to optimize. Consequently, we present a novel ConvNet architecture based on 3D ConvNet and ResNet for slide transition detection in lecture videos. Experimental results show that the proposed novel ConvNet architecture achieves the better accuracy than other slide progression detection approaches.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.830-841
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• 2023

Tokenization is the process of segmenting the input text into smaller units of text, and it is a preprocessing task that is mainly performed to improve the efficiency of the machine learning process. Various tokenization methods have been proposed for application in the field of natural language processing, but studies have primarily focused on efficiently segmenting text. Few studies have been conducted on the Korean language to explore what tokenization methods are suitable for document classification task. In this paper, an exploratory study was performed to find the most suitable tokenization method to improve the performance of a representative spatio-temporal document classifier in Korean. For the experiment, a convolutional neural network model was used, and for the final performance comparison, tasks were selected for document classification where performance largely depends on the tokenization method. As a tokenization method for comparative experiments, commonly used Jamo, Character, and Word units were adopted. As a result of the experiment, it was confirmed that the tokenization of word units showed excellent performance in the case of representative spatio-temporal document classification task where the semantic embedding ability of the token itself is important.

• The Journal of the Acoustical Society of Korea
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• v.29 no.2
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• pp.133-140
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• 2010

We improve the performance of cardiac disorder classification by adding new temporal features extracted from continuous heart sound signals. We add three kinds of novel temporal features to a conventional feature based on mel-frequency cepstral coefficients (MFCC): Heart sound envelope, murmur probabilities, and murmur amplitude variation. In cardiac disorder classification and detection experiments, we evaluate the contribution of the proposed features to classification accuracy and select proper temporal features using the sequential feature selection method. The selected features are shown to improve classification accuracy significantly and consistently for neural network-based pattern classifiers such as multi-layer perceptron (MLP), support vector machine (SVM), and extreme learning machine (ELM).