• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.48-57
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• 2019

The purpose of this study is to understand the intention of the inquirer from the single text type question in Goal-oriented dialogue. Goal-Oriented Dialogue system means a dialogue system that satisfies the user's specific needs via text or voice. The intention analysis process is a step of analysing the user's intention of inquiry prior to the answer generation, and has a great influence on the performance of the entire Goal-Oriented Dialogue system. The proposed model was used for a daily chemical products domain and Korean text data related to the domain was used. The analysis is divided into a speech-act which means independent on a specific field concept-sequence and which means depend on a specific field. We propose a classification method using the word embedding model and the CNN as a method for analyzing speech-act and concept-sequence. The semantic information of the word is abstracted through the word embedding model, and concept-sequence and speech-act classification are performed through the CNN based on the semantic information of the abstract word.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.197-216
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• 2022

With the development of Internet and mobile technology and the spread of social media, a large amount of information is being generated and distributed online. Some of them are useful information for the public, but others are misleading information. The misleading information, so-called 'fake news', has been causing great harm to our society in recent years. Since the global spread of COVID-19 in 2020, much of fake news has been distributed online. Unlike other fake news, fake news related to COVID-19 can threaten people's health and even their lives. Therefore, intelligent technology that automatically detects and prevents fake news related to COVID-19 is a meaningful research topic to improve social health. Fake news related to COVID-19 has spread rapidly through social media, however, there have been few studies in Korea that proposed intelligent fake news detection using the information about how the fake news spreads through social media. Under this background, we propose a novel model that uses Graph2vec, one of the graph embedding methods, to effectively detect fake news related to COVID-19. The mainstream approaches of fake news detection have focused on news content, i.e., characteristics of the text, but the proposed model in this study can exploit information transmission relationships in social engagement networks when detecting fake news related to COVID-19. Experiments using a real-world data set have shown that our proposed model outperforms traditional models from the perspectives of prediction accuracy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.277-283
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• 2019

Advances in technology have allowed the development and commercialization of various parts; however this has shortened the discontinuation cycle of the components. This means that repair and logistic support of weapon system which is applied to thousands of part components and operated over the long-term is difficult, which is the one of main causes of the decrease in the availability of weapon system. To improve this problem, the United States has created a special organization for this problem, whereas in Korea, commercial tools are used to predict and manage DMSMS. However, there is rarely a method to predict life cycle of parts that are not presented DMSMS information at the commercial tools. In this study, the structured and unstructured data of parts of a commercial tool were gathered, preprocessed, and embedded using neural network algorithm. Then, a method is suggested to predict the life cycle risk (LC Risk) and year to end of life (YTEOL). In addition, to validate the prediction performance of LC Risk and YTEOL, the prediction value is compared with descriptive statistics.

• Smart Media Journal
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• v.8 no.2
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• pp.58-65
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• 2019

Automatic text summarization is a process of shortening a text document by either extraction or abstraction. The abstraction approach inspired by deep learning methods scaling to a large amount of document is applied in recent work. Abstractive text summarization involves utilizing pre-generated word embedding information. Low-frequent but salient words such as terminologies are seldom included to dictionaries, that are so called, out-of-vocabulary(OOV) problems. OOV deteriorates the performance of Encoder-Decoder model in neural network. In order to address OOV words in abstractive text summarization, we propose a copy mechanism to facilitate copying new words in the target document and generating summary sentences. Different from the previous studies, the proposed approach combines accurate pointing information and selective copy mechanism based on bidirectional RNN and bidirectional LSTM. In addition, neural network gate model to estimate the generation probability and the loss function to optimize the entire abstraction model has been applied. The dataset has been constructed from the collection of abstractions and titles of journal articles. Experimental results demonstrate that both ROUGE-1 (based on word recall) and ROUGE-L (employed longest common subsequence) of the proposed Encoding-Decoding model have been improved to 47.01 and 29.55, respectively.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.3
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• pp.46-55
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• 2012

Recently, the concept of PES(Personal Environment Service) is being widely discussed on various standardization organizations such as ITU-R, ETSI, 3GPP, TTA and etc. The purpose of PES is to introduce the services which can dynamically, automatically and intelligently reconfigures the electronic, electrical, and mechanical equipment surrounding the user according to the user preferences included in a user's profile by using a smartphone embedding WPAN radio technologies such as bluetooth and WiFi. This research introduces an Android Platform-based PES system which consists of a PES app, PES devices and a PES server. A smartphone platform is Android 2.2(Froyo) version and 4 simulated PES devices were implemented by using Galaxy Tab. It has shown that the PES would be a killer application of M2M(Machine-to-Machine) or D2D(Device-to-Device) in the future and it would need to study how to update a user's profile based on analyzing user's behaviour for enhancing the PES user's satisfaction.

• The Journal of the Acoustical Society of Korea
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• v.39 no.6
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• pp.615-621
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• 2020

In this paper, we propose a multi-site based earthquake event classification method using graph convolution networks. In the traditional earthquake event classification methods using deep learning, they used single-site observation to estimate seismic event class. However, to achieve robust and accurate earthquake event classification on the seismic observation network, the method using the information from the multi-site observations is needed, instead of using only single-site data. Firstly, our proposed model employs convolution neural networks to extract informative embedding features from the single-site observation. Secondly, graph convolution networks are used to integrate the features from several stations. To evaluate our model, we explore the model structure and the number of stations for ablation study. Finally, our multi-site based model outperforms up to 10 % accuracy and event recall rate compared to single-site based model.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.4 s.346
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• pp.1-12
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• 2006

In this paper we propose an efficient mobile multimedia communication protocol, mobile terminal software platform and mobile VoIP application for IP-based convergence networks. The Proposed mobile multimedia communication protocol is called as ST-MRSVP (Split tunnel based Mobile Resource reServation Protocol) which integrates split tunnel based Mobile IP and RSVP in order to support hish speed mobility. Since mobile terminal platform supports QoS (Qualify of Service) with keeping seamless mobility, mobile QoS supporting modules are developed and interworked together by means of shared memory mechanism. Testbed is composed of a core-network embedding the proposed protocols and wireless LAN-based access networks. We verify functionality and performance of the proposed techniques by using various mobility test over the testbed. As a result, the proposed architecture can reduce the handover delay time with QoS support under 30% comparing with the standard mechanisms and support voice quality as good as CDMA phone.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.371-374
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• 2004

The task of development and usage in a corporate computer network of the Far Eastern Branch of the Russian Academy of Sciences (FEB RAS) of integrated technology of joint use by the scientists of satellite and sub satellite data on a Northwestern Pacific is considered. This integrated technology is realized by embedding of satellite data in the corporate oceanographic GIS of FEB RAS as a new information layer, and also by support of GIS by program techniques for specialized processing of both kinds of the data. As a result of integration the specialists of FEB RAS have an opportunity to carry out coordinated samples of satellite and various oceanographic data as a function of area, time and other important conditions, visualize them together and carry out analytical processing with the usage of the GIS tools. Application of the realized approach to improve the techniques of detection and description of the oceanic phenomena on ERS-l and ERS-2 SAR images as well as to improve of perspective techniques of the usage the brightness temperatures measured by a microwave radiometers AMSR-E on a board of Aqua (USA) satellites are discussed.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.4
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• pp.205-211
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• 2019

Steganalysis is a technique that aims to detect and recover data hidden by steganography. Steganalytic methods detect hidden data by analyzing visual and statistical distortions caused during data embedding. However, for recovering the hidden data, they need to know which steganographic methods the hidden data has been embedded by. Therefore, we propose a hierarchical convolutional neural network (CNN) structure that identifies a steganographic method applied to an input image through multi-level classification. We trained four base CNNs (each is a binary classifier that determines whether or not a steganographic method has been applied to an input image or which of two different steganographic methods has been applied to an input image) and connected them hierarchically. Experimental results demonstrate that the proposed hierarchical CNN structure can identify four different steganographic methods (LSB, PVD, WOW, and UNIWARD) with an accuracy of 79%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.7A
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• pp.711-723
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• 2002

A finer granular scalable (FGS) version of ISO/IEC MPEG-4 video streaming is investigated in this work with the prioritized stream delivery over loss-rate differentiated networks. Our proposed system is focused on the seamless integration of rate adaptation, prioritized packetization, and simplified differentiation for the MPEG-4 FGS video streaming. The proposed system consists of three key components: 1) rate adaptation with scalable source encoding, 2) content-aware prioritized packetization, and 3) loss-based differential forwarding. More specifically, a constant-quality rate adaptation is first achieved by optimally truncating the over-coded FGS stream based on the embedding rate-distortion (R-D) information (obtained from a piecewise linear R-D model). The rate-controlled video stream is then packetized and prioritized according to the loss impact of each packet. Prioritized packets are transmitted over the underlying network, where packets are subject to differentiated dropping and forwarding. By focusing on the end-to-end quality, we establish an effective working conditions for the proposed video streaming and the superior performance is verified by simulated MPEG-4 FGS video streaming.