• 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.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.227-241
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• 2022

Full waveform inversion (FWI) in the field of seismic data processing is an inversion technique that is used to estimate the velocity model of the subsurface for oil and gas exploration. Recently, deep learning (DL) technology has been increasingly used for seismic data processing, and its combination with FWI has attracted remarkable research efforts. For example, DL-based data processing techniques have been utilized for preprocessing input data for FWI, enabling the direct implementation of FWI through DL technology. DL-based FWI can be divided into the following methods: pure data-based, physics-based neural network, encoder-decoder, reparameterized FWI, and physics-informed neural network. In this review, we describe the theory and characteristics of the methods by systematizing them in the order of advancements. In the early days of DL-based FWI, the DL model predicted the velocity model by preparing a large training data set to adopt faithfully the basic principles of data science and apply a pure data-based prediction model. The current research trend is to supplement the shortcomings of the pure data-based approach using the loss function consisting of seismic data or physical information from the wave equation itself in deep neural networks. Based on these developments, DL-based FWI has evolved to not require a large amount of learning data, alleviating the cycle-skipping problem, which is an intrinsic limitation of FWI, and reducing computation times dramatically. The value of DL-based FWI is expected to increase continually in the processing of seismic data.

• KIISE Transactions on Computing Practices
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• v.24 no.3
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• pp.138-144
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• 2018

A video is a vivid medium similar to human's visual-linguistic experiences, since it can inculcate a sequence of situations, actions or dialogues that can be told as a story. In this study, we propose story learning/regeneration frameworks from videos with successive event order supervision for contextual coherence. The supervision induces each episode to have a form of trajectory in the latent space, which constructs a composite representation of ordering and semantics. In this study, we incorporated the use of kids videos as a training data. Some of the advantages associated with the kids videos include omnibus style, simple/explicit storyline in short, chronological narrative order, and relatively limited number of characters and spatial environments. We build the encoder-decoder structure with successive event order embedding, and train bi-directional LSTMs as sequence models considering multi-step sequence prediction. Using a series of approximately 200 episodes of kids videos named 'Pororo the Little Penguin', we give empirical results for story regeneration tasks and SEOE. In addition, each episode shows a trajectory-like shape on the latent space of the model, which gives the geometric information for the sequence models.

• Journal of Broadcast Engineering
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• v.25 no.3
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• pp.305-312
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• 2020

As a demand for a new video coding standard having higher coding efficiency than the existing standards is growing, recently, MPEG and VCEG has been developing and standardizing the next-generation video coding project, named Versatile Video Coding (VVC). Many inter prediction techniques have been introduced to increase the coding efficiency, and among them, an adaptive motion vector resolution (AMVR) technique has contributed on increasing the efficiency of VVC. However, the best motion vector can only be determined by computing many rate-distortion costs, thereby increasing encoding complexity. It is necessary to reduce the complexity for real-time video broadcasting and streaming services, but it is yet an open research topic to reduce the complexity of AMVR. Therefore, in this paper, an efficient technique is proposed, which reduces the encoding complexity of AMVR. For that, the proposed method exploits a special VVC tree structure (i.e., multi-type tree structure) to accelerate the decision process of AMVR. Experiment results show that the proposed decision method reduces the encoding complexity of VVC test model by 10% with a negligible loss of coding efficiency.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.6
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• pp.255-260
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• 2011

The security issue in urban transit system has been widely considered as the common matters. The safe urban transit system is highly demanded because of the vast number of daily passengers, and providing safety is one of the most challenging projects. We introduced a test model for integrated security system for urban transit system and built it at a subway station to demonstrate its performance. This system consists of cameras, sensor network and central monitoring software. We described the smart camera functionality in more detail. The proposed smart camera includes the moving objects recognition module, video analytics, video encoder and server module that transmits video and audio information. We demonstrated the system's excellent performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1195-1211
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• 2016

The emerging video coding standard High Efficiency Video Coding (HEVC) has shown almost 40% bit-rate reduction over the state-of-the-art Advanced Video Coding (AVC) standard but at about 40% computational complexity overhead. The main reason for HEVC computational complexity is the inter prediction that accounts for 60%-70% of the whole encoding time. In this paper, we propose several fast coding unit (CU) encoding schemes based on the Merge mode and motion estimation information to reduce the computational complexity caused by the HEVC inter prediction. Firstly, an early Merge mode decision method based on motion estimation (EMD) is proposed for each CU size. Then, a Merge mode based early termination method (MET) is developed to determine the CU size at an early stage. To provide a better balance between computational complexity and coding efficiency, several fast CU encoding schemes are surveyed according to the rate-distortion-complexity characteristics of EMD and MET methods as a function of CU sizes. These fast CU encoding schemes can be seamlessly incorporated in the existing control structures of the HEVC encoder without limiting its potential parallelization and hardware acceleration. Experimental results demonstrate that the proposed schemes achieve 19%-46% computational complexity reduction over the HEVC test model reference software, HM 16.4, at a cost of 0.2%-2.4% bit-rate increases under the random access coding configuration. The respective values under the low-delay B coding configuration are 17%-43% and 0.1%-1.2%.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.6
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• pp.692-702
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• 2001

In this paper, a new block-matching algorithm for standard video encoder is proposed. The algorithm finds a motion vector using the increasing SAD transition curve for each predefined candidates, not a coarse-to-fine approach as a conventional method. To remove low-probability candidates at the early stage of accumulation, a dispersed accumulation matrix is also proposed. This matrix guarantees high-linearity to the SAD transition curve. Therefore, base on this method, we present a new fast block-matching algorithm with the slice competition technique. The Candidate Selection Step and the Candidate Competition Step makes an out-performance model that considerably reduces computational power and not to be trapped into local minima. The computational power is reduced by 10%~70% than that of the conventional BMAs. Regarding computational time, an 18%~35% reduction was achieved by the proposed algorithm. Finally, the average MAD is always low in various bit-streams. The results were also very similar to the MAD of the full search block-matching algorithm.

• The Journal of the Acoustical Society of Korea
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• v.22 no.6
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• pp.443-451
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• 2003

Since a codebook-based CELP coder models its excitation signal according to one of several bit rates pre-assigned to codebooks and synthesizes speech signal using codebooks, it can not support encoding of speech signal at an arbitrary bit rate in one encoder. The proposed variable bit rate speech coder encodes the excitation signal based on the bit rate assigned to a present frame of speech using one-dimensional SPIHT and wavelet transform. Also it does't need to model excitation signal (or codebook) to some types as CELP coder, and can encode excitation signal at various bit rates without exact pitch information according to user requirement. As a result, since the coder doesn't have a codebook structure, it has relatively low coder complexity and provides equal or better speech quality compared to G.729 and G.723.1 coder.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.12
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• pp.573-580
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• 2017

Deep Neural Network (DNN) is a large layered neural network which is consisted of a number of layers of non-linear units. Deep Learning which represented as DNN has been applied very successfully in various applications. However, many issues in DNN have been identified through past researches. Among these issues, generalization is the most well-known problem. A Recent study, Dropout, successfully addressed this problem. Also, Dropout plays a role as noise, and so it helps to learn robust feature during learning in DNN such as Denoising AutoEncoder. However, because of a large computations required in Dropout, training takes a lot of time. Since Dropout keeps changing an inter-layer representation during the training session, the learning rates should be small, which makes training time longer. In this paper, using mutation operation, we reduce computation and improve generalization performance compared with Dropout. Also, we experimented proposed method to compare with Dropout method and showed that our method is superior to the Dropout one.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.2
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• pp.385-393
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• 2016

This paper presents the new position estimation method which contains the optical displacement sensor and the dead reckoning based position estimation algorithm for automation of hull cleaning robot. To evaluate feasibility of the proposed position estimation method on the hull cleaning robot, it was applied on the small scale robot model which has an identical drive method with the hull cleaning robot and then a set of the position estimation experiments were performed. The experimental results of the position estimation demonstrate that the estimated results with the optical displacement sensors is more accurate than used rotary encoder method. In addition, it continuously calculated the robot position quite close to the real robot driving path. In a follow-up study, the proposed position estimation method will be complemented and exploited on the actual hull cleaning robot by adding additional sensor modules that correct measurement errors.