• KIPS Transactions on Software and Data Engineering
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• v.4 no.3
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• pp.143-150
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• 2015

As smart devices get popular, research on gesture recognition using their embedded-accelerometer draw attention. As Dynamic Time Warping(DTW), recently, has been used to perform gesture recognition on data sequence from accelerometer, in this paper we propose Feature-Strengthened Gesture Recognition(FsGr) Model which can improve the recognition success rate when DTW is used. FsGr model defines feature-strengthened parts of data sequences to similar gestures which might produce unsuccessful recognition, and performs additional DTW on them to improve the recognition rate. In training phase, FsGr model identifies sets of similar gestures, and analyze features of gestures per each set. During recognition phase, it makes additional recognition attempt based on the result of feature analysis to improve the recognition success rate, when the result of first recognition attempt belongs to a set of similar gestures. We present the performance result of FsGr model, by experimenting the recognition of lower case alphabets.

• Journal of IKEEE
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• v.23 no.4
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• pp.1314-1320
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• 2019

In this paper, we propose a new method for optimizing bit operations and applying them to DNN(Deep Neural Network) in software environment. As a method for this, we propose a packing function for bitwise optimization and a masking matrix multiplication operation for application to DNN. The packing function converts 32-bit real value to 2-bit quantization value through threshold comparison operation. When this sequence is over, four 32-bit real values are changed to one 8-bit value. The masking matrix multiplication operation consists of a special operation for multiplying the packed weight value with the normal input value. And each operation was then processed in parallel using a GPU accelerator. As a result of this experiment, memory saved about 16 times than 32-bit DNN Model. Nevertheless, the accuracy was within 1%, similar to the 32-bit model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.57-62
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• 2014

This paper proposes the improving the equalization performance using the adaptive modulus concept to the MCMA blind equalizer in order to the reduction of intersymbol interference which occurs in the band limited and time dispersive communication channel. In MCMA blind algorithm, it is possible to reducing the amplitude and phase rotation of intersymbol interference without training sequence, the fixed constant modulus of transmission signal is used. But in proposed algorithm, the modulus are adaptively varies according to the equalizer output signal, then the improved equalization performance were obtained by the computer simulation. For this, the recovered signal constellation that is the output of the equalizer, the convergence performance by MSE, MD (maximum distortion) and residual isi characteristic learning curve were used. The propose algorithm has fairly good performance compared to the traditional MCMA algorithm in the same adaptive equalization algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.5
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• pp.592-601
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• 2016

The recurrent neural network (RNN) is a deep learning model which is suitable to sequential or length-variable data. The Long Short-Term Memory (LSTM) mitigates the vanishing gradient problem of RNNs so that LSTM can maintain the long-term dependency among the constituents of the given input sequence. In this paper, we propose a LSTM based language model which can predict following words of a given incomplete sentence to generate a complete sentence. To evaluate our method, we trained our model using multiple Korean corpora then generated the incomplete part of Korean sentences. The result shows that our language model was able to generate the fluent Korean sentences. We also show that the word based model generated better sentences compared to the other settings.

• Proceedings of the Korea Crystallographic Association Conference
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• 2002.11a
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• pp.4-4
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• 2002

PDZ domains bind to short segments within target proteins in a sequence-specific fashion. GRIP/ABP family proteins contain six to seven PDZ domains and interact via its sixth PDZ domain (class Ⅱ) with the C-termini of various proteins, including liprin-α. In addition the PDZ456 domain mediates the formation of homo- and heteromultimers of GRIP proteins. To better understand the structural basis of peptide recognition by a class Ⅱ PDZ domain and DZ-mediated multimerization, we determined the crystal structures of the GRIPI PDZ6 domain, alone and in complex with a synthetic C-terminal octapeptide of human liprin-α, at resolutions of 1.5 Å and 1.8 Å, respectively. Remarkably, unlike other class Ⅱ PDZ domains, Ile736 at αB5 rather than conserved Leu732 at αB1 makes a direct hydrophobic contact with the side chain of the Tyr at the -2 position of the ligand. Moreover, the peptide-bound structure of PDZ6 shows a slight reorientation of helix αB, indicating that the second hydrophobic pocket undergoes a conformational adaptation to accommodate the bulkiness of the Tyr's side chain, and forms an antiparallel dimer through an interface located at a site distal to the peptide-binding groove. This configuration may enable formation of GRIP multimers and efficient clustering of GRIP-binding proteins.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.2
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• pp.318-328
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• 2014

In this paper, as a way of performance improvement of business card recognition in the mobile environment, we suggested a hybrid OCR agent which combines data using a parallel processing sequence between various algorithms and different kinds of business card recognition engines which have learning data. We also suggested an Image Processing Method on mobile cameras which adapts to the changes of the lighting, exposing axis and the backgrounds of the cards which occur depending on the photographic conditions. In case a hybrid OCR agent is composed by the method suggested above, the average recognition rate of Korean business cards has improved from 90.69% to 95.5% compared to the cases where a single engine is used. By using the Image Processing Method, the image capacity has decreased to the average of 50%, and the recognition has improved from 83% to 92.48% showing 9.4% improvement.

• Journal of Korean Library and Information Science Society
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• v.52 no.1
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• pp.229-254
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• 2021

The purpose of this study is to construct contents of elementary and secondary education which will be included in the information literacy education and to obtain basic information and implications necessary for developing new textbooks. For this study, three types of previously developed textbooks for information literacy education were analyzed, and curriculum content structure, and textbook structure of the draft version of the textbook 'Media and Information Life' developed in 2019-2020 were analyzed. The analysis results are as follows. the information literacy education textbook applied the information problem solving process model and contained the contents of print, and digital media and the media literacy necessary for democratic citizens, but it was necessary to add the types of libraries and media in sequence. Second, library, media, information, and reading literacy were major learning elements that made up the contents of the information literacy curriculum. Third, the "media and information life" textbook needed to present subject competencies, generalized knowledge, content system, and achievement standards in accordance with the system of the 2015 revised curriculum. In addition, social discussion was needed to derive the name of the information literacy curriculum.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.1
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• pp.22-33
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• 2005

In this paper, an efficient signal interference control technique to improve the high convergence speed of LMS algorithms is introduced in the adaptive transversal filter of DS/SS. The convergence characteristics of the proposed algorithm, whose coefficients are multiply adapted in a symbol time period by recycling the received data, is analyzed to prove theoretically the improvement of high convergence speed. According as the step-size parameter ${\mu}$ is increased, the rate of convergence of the algorithm is controlled. Also, an increase in the stop-size parameter ${\mu}$ has the effect of reducing the variation in the experimentally computed learning curve. Increasing the eigenvalue spread has the effect of controlling which is downed the rate of convergence of the adaptive equalizer. Increasing the steady-state value of the average squared error, proposed algorithm also demonstrate the superiority of signal interference control to the filter algorithm increasing convergence speed by (B+1) times due to the data-recycling LMS technique.

• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.279-287
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• 2006

This paper proposes a method to detect fall action by using stereo images to recognize emergency situation. It uses 3D information to extract the visual information for learning and testing. It uses HMM(Hidden Markov Model) as a recognition algorithm. The proposed system extracts background images from two camera images. It extracts a moving object from input video sequence by using the difference between input image and background image. After that, it finds the bounding rectangle of the moving object and extracts 3D information by using calibration data of the two cameras. We experimented to the recognition rate of fall action with the variation of rectangle width and height and that of 3D location of the rectangle center point. Experimental results show that the variation of 3D location of the center point achieves the higher recognition rate than the variation of width and height.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.295-302
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• 2013

Many reported methods assume that the people in an image or an image sequence have been identified and localization. People detection is one of very important variable to affect for the system's performance as the basis technology about the detection of other objects and interacting with people and computers, motion recognition. In this paper, we present an efficient linear discriminant for multi-view people detection. Our approaches are based on linear discriminant. We define training data with fisher Linear discriminant to efficient learning method. People detection is considerably difficult because it will be influenced by poses of people and changes in illumination. This idea can solve the multi-view scale and people detection problem quickly and efficiently, which fits for detecting people automatically. In this paper, we extract people using fisher linear discriminant that is hierarchical models invariant pose and background. We estimation the pose in detected people. The purpose of this paper is to classify people and non-people using fisher linear discriminant.