• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.715-723
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• 2015

In this paper, we propose a context-awareness routing algorithm DDV (Dynamic Direction Vector)-hop algorithm in Mobile Ad Hoc Networks. The existing algorithm in MANET, it has a vulnerability that the dynamic network topology and the absence of network expandability of mobility of nodes. The proposed algorithm performs cluster formation using a range of direction and threshold of velocity for the base-station, we calculate the exchange of the cluster head node probability using the direction and velocity for maintaining cluster formation. The DDV algorithm forms a cluster based on the cluster head node. As a result of simulation, our scheme could maintain the proper number of cluster and cluster members regardless of topology changes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2699-2707
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• 2013

We proposed the context-awareness routing algorithm DDV (Dynamic Direction Vector)-hop algorithm at Mobile Ad-hoc Network(MANET). MANET has problem about dynamic topology, the lack of scalability of the network by mobile of node. By mobile of node, energy consumption rate is different. So it is important choosing routing algorithms for the minium of energy consumption rate. DDV-hop algorithms considers of the attribute of mobile node, create a cluster and maintain. And it provides a path by searching a route more energy efficient. We apply mobile of node by direction and time, the alogorighm of routning path and energy efficiency clustering is provided, it is shown the result of enery consumption that is optimized for the network.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.11
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• pp.2285-2291
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• 2011

In this paper, H.264 high profile intra frame encoder, which integrates intra prediction, context-based adaptive variable length coding(CAVLC), and DDR2 memory control module, is proposed. The designed encoder can be operated in 440 cycle for one-macroblock. In order to verify the encoder function, we developed the reference C from JM 13.2 and verified the developed hardware using test vector generated by reference C. The designed encoder is verified in the FPGA (field programmable gate array) with operating frequency of 200 MHz for DMA (direct memory access), operating frequency of 50 MHz of Encoder module, and 25 MHz for VIM(video input module). The number of LUT is 43099, which is about 20 % of Virtex 5 XC5VLX330.

• Journal of the Korea Society of Computer and Information
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• v.22 no.4
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• pp.41-49
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• 2017

Word sense disambiguation(WSD) that determines the exact meaning of homonym which can be used in different meanings even in one form is very important to understand the semantical meaning of text document. Many recent researches on WSD have widely used NNLM(Neural Network Language Model) in which neural network is used to represent a document into vectors and to analyze its semantics. Among the previous WSD researches using NNLM, RNN(Recurrent Neural Network) model has better performance than other models because RNN model can reflect the occurrence order of words in addition to the word appearance information in a document. However, since RNN model uses only the forward order of word occurrences in a document, it is not able to reflect natural language's characteristics that later words can affect the meanings of the preceding words. In this paper, we propose a WSD scheme using Bidirectional RNN that can reflect not only the forward order but also the backward order of word occurrences in a document. From the experiments, the accuracy of the proposed model is higher than that of previous method using RNN. Hence, it is confirmed that bidirectional order information of word occurrences is useful for WSD in Korean language.

• ETRI Journal
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• v.32 no.5
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• pp.729-739
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• 2010

Due to the rapid development of mobile devices equipped with cameras, instant translation of any text seen in any context is possible. Mobile devices can serve as a translation tool by recognizing the texts presented in the captured scenes. Images captured by cameras will embed more external or unwanted effects which need not to be considered in traditional optical character recognition (OCR). In this paper, we segment a text image captured by mobile devices into individual single characters to facilitate OCR kernel processing. Before proceeding with character segmentation, text detection and text line construction need to be performed in advance. A novel character segmentation method which integrates touched character filters is employed on text images captured by cameras. In addition, periphery features are extracted from the segmented images of touched characters and fed as inputs to support vector machines to calculate the confident values. In our experiment, the accuracy rate of the proposed character segmentation system is 94.90%, which demonstrates the effectiveness of the proposed method.

• Journal of KIISE:Software and Applications
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• v.33 no.5
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• pp.470-480
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• 2006

The longer the input sentences, the worse the parsing results. To improve the parsing performance, many methods about long sentence segmentation have been reserarched. As an isolating language, Chinese sentence has fewer cues for sentence segmentation. However, the average frequency of comma usage in Chinese is higher than that of other languages. The syntactic information that the comma conveys can play an important role in long sentence segmentation of Chinese languages. This paper proposes a method for classifying commas in Chinese sentences according to the context where the comma occurs. Then, sentences are segmented using the classification result. The experimental results show that the accuracy of the comma classification reaches 87.1%, and with our segmentation model, the dependency parsing accuracy of our parser is improved by 5.6%.

• Nuclear Engineering and Technology
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• v.52 no.6
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• pp.1327-1333
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• 2020

The global warming phenomenon emerges from the issue of climate change, which attracts the attention of intellectuals towards clean energy sources from dirty energy sources. Among clean sources, nuclear energy is getting immense attention among policymakers. However, the role of nuclear energy in pollution emissions reduction has remained inconclusive and demand for further investigation. Therefore, the current study contributes to extend knowledge by investigating the nexus between nuclear energy, economic growth, and CO₂ emissions in a developing country context such as Pakistan for the period between 1973 and 2017. The auto-regressive distributive lag model summarizes the nuclear energy has negative effect on environmental pollution as it releases carbon emission in the environment. Moreover, vector error correction Granger causality provides evidence for bidirectional causality between nuclear energy and carbon emissions. These interesting findings provide new insight, and policy guidelines provided based on these results.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.371-377
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• 2003

In this paper, comparison of feature parameters for emotion recognition using speech signal is studied. For this purpose, a corpus of emotional speech data recorded and classified according to the emotion using the subjective evaluation were used to make statical feature vectors such as average, standard deviation and maximum value of pitch and energy and phonetic feature such as MFCC parameters. In order to evaluate the performance of feature parameters speaker and context independent emotion recognition system was constructed to make experiment. In the experiments, pitch, energy parameters and their derivatives were used as a prosodic information and MFCC parameters and its derivative were used as phonetic information. Experimental results using vector quantization based emotion recognition system showed that recognition system using MFCC parameter and its derivative showed better performance than that using the pitch and energy parameters.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.659-663
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• 2010

In recent, flood inundation damages by hydraulic structure failures have increased drastically and thus a variety of countermeasures were needed to minimize such damages. A real-time flood inundation prediction technique is essential to protect and mitigate flood inundation damages. In the context of real time flood inundation modeling, this study aims to develop a grid based two-dimensional numerical method for flood inundation modeling using globally-available DEM data: SRTM with $90m{\times}90m$ spatial resolution. The newly-developed model guarantees computational efficiency in terms of geometric data processing by direct application of DEM for flood inundation modeling and also have good compatibility with various types of raster data when compared to a commercial model such as FLUMEN. The model, which employed the leap-frog algorithm to solve shallow water and continuity equations, can simulate inundating flow from channel to lowland and also returning flow from lowland to channel by comparing water levels between channel and lowland in real time. We applied the model to simulate the BaekSan levee break in the Nam river during a flood period from August 10 to 13, 2002. The simulation results had good agreements with the field-surveyed data in terms of inundated area and also showed physically-acceptable velocity vector maps with respect to inundating and returning flows.

• Journal of Information Processing Systems
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• v.10 no.4
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• pp.503-522
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• 2014

In the cursive handwriting recognition process, script trajectory segmentation and modeling represent an important task for large or open lexicon context that becomes more complicated in multi-writer applications. In this paper, we will present a developed system of Arabic online handwriting modeling based on graphemes segmentation and the extraction of its geometric features. The main contribution consists of adapting the Fourier descriptors to model the open trajectory of the segmented graphemes. To segment the trajectory of the handwriting, the system proceeds by first detecting its baseline by checking combined geometric and logic conditions. Then, the detected baseline is used as a topologic reference for the extraction of particular points that delimit the graphemes' trajectories. Each segmented grapheme is then represented by a set of relevant geometric features that include the vector of the Fourier descriptors for trajectory shape modeling, normalized metric parameters that model the grapheme dimensions, its position in respect to the baseline, and codes for the description of its associated diacritics.