• Journal of IKEEE
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• v.24 no.1
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• pp.40-45
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• 2020

In this paper, we consider an efficient design of quantizers at sensor nodes for sequence-based localization (SBL) systems which recently show a competitive performance for in-door positioning, Since SBL systems locate targets by partitioning the sensor field into subregions, each with an unique sequence number, we use the distance samples between sensors and the sequences for quantizer design in order to propose a low weight design process. Furthermore, we present a new cost function devised to assign the number of samples and the number of unique sequences uniformly into each of quantization partitions and design quantizers by searching the quantization partitions and codewords that minimize the cost function. We finally conduct experiments to demonstrate that the proposed algorithm offers an outstanding localization performance over typical designs while maintaining a substantial reduction of design complexity.

• Journal of IKEEE
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• v.22 no.3
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• pp.658-663
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• 2018

In this paper, we consider node deployment algorithms for the sequence-based localization (SBL) which is recently employed for in-door positioning systems, Whereas previous node selection or deployment algorithms seek to place nodes at centrold of the region where more targets are likely to be found, we observe that the boundaries dividing such regions can be good locations for the nodes in SBL systems. Motivated by this observation, we propose an efficient node deployment algorithm that determines the boundaries by using the well-known K-means algorithm and find the potential node locations based on the bi-section method for low-complexity design. We demonstrate through experiments that the proposed algorithm achieves significant localization performance over random node allocation with a substantially reduced complexity as compared with a full search.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.237-242
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• 2005

Predicting the cellular location of an unknown protein gives a valuable information for inferring the possible function of the protein. For more accurate prediction system, we need a good feature extraction method that transforms the raw sequence data into the numerical feature vector, minimizing information loss. In this paper, we propose new methods of extracting underlying features only from the sequence data by computing pairwise sequence alignment scores. In addition, we use composition based features to improve prediction accuracy. To construct an SVM ensemble from separately trained SVM classifiers, we propose specificity based weighted majority voting. The overall prediction accuracy evaluated by the 5-fold cross-validation reached 88.53% for the eukaryotic animal data set. By comparing the prediction accuracy of various feature extraction methods, we could get the biological insight on the location of targeting information. Our numerical experiments confirm that our new feature extraction methods are very useful for predicting subcellular localization of proteins.

• Journal of Korea Game Society
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• v.12 no.3
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• pp.87-96
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• 2012

This paper presents a novel EKF(Extended Kalman Filter) based SLAM(Simultaneous Localization And Mapping) system for stable camera tracking and re-localization. The obtained 3D points by SLAM are triangulated using Delaunay triangulation to establish a reference plane, and features are described by BRISK(Binary Robust Invariant Scalable Keypoints). The proposed method estimates the camera parameters from the homography of the reference plane when the tracking errors of EKF SLAM are much accumulated. Using the robust descriptors over sequence enables us to re-localize the camera position for matching over sequence even though the camera is moved abruptly.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.226-228
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• 2005

Predicting the cellular location of an unknown protein gives valuable information for inferring the possible function of the protein. For more accurate Prediction system, we need a good feature extraction method that transforms the raw sequence data into the numerical feature vector, minimizing information loss. In this paper we propose new methods of extracting underlying features only from the sequence data by computing pairwise sequence alignment scores. In addition, we use composition based features to improve prediction accuracy. To construct an SVM ensemble from separately trained SVM classifiers, we propose specificity based weighted majority voting . The overall prediction accuracy evaluated by the 5-fold cross-validation reached $88.53\%$ for the eukaryotic animal data set. By comparing the prediction accuracy of various feature extraction methods, we could get the biological insight on the location of targeting information. Our numerical experiments confirm that our new feature extraction methods are very useful forpredicting subcellular localization of proteins.

• The Journal of the Acoustical Society of Korea
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• v.21 no.8
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• pp.686-694
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• 2002

This paper proposes an algorithm for estimating positions of array elements placed on a sea floor using acoustic signal in multipath ocean environment. The positions of array elements are estimated by using the travel times of m-sequence signal influenced by the multi-paths environment. The horizontal distance between source and receiver calculated based on the ray model. The proposed paper the algorithm is verified by both simulation data and field experiment in the Bast Sea.

• Journal of the Korea Computer Industry Society
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• v.2 no.11
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• pp.1465-1476
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• 2001

For the applications like video indexing, text understanding, and automatic captions localization system, real-time localization of captions is an essential task. This paper presents a algorithm for localization of captions in MPEG compression images based on I frame. In this algorithm, caption text regions are segmented from background images using their distinguishing texture characteristics and chrominance information. Unlike previously published algorithms which fully decompress the video sequence before extracting the text regions, this algorithm locates candidate caption text region directly in the DCT compressed domain.

• Parasites, Hosts and Diseases
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• v.58 no.6
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• pp.675-679
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• 2020

MYB2 protein was identified as a transcription factor that showed encystation-induced expression in Giardia lamblia. Although nuclear import is essential for the functioning of a transcription factor, an evident nuclear localization signal (NLS) of G. lamblia MYB2 (GlMYB2) has not been defined. Based on putative GlMYB2 NLSs predicted by 2 programs, a series of plasmids expressing hemagglutinin (HA)-tagged GlMYB2 from the promoter of G. lamblia glutamate dehydrogenase were constructed and transfected into Giardia trophozoites. Immunofluorescence assays using anti-HA antibodies indicated that GlMYB2 amino acid sequence #507-#530 was required for the nuclear localization of GlMYB2, and this sequence was named as NLSGlMYB2. We further verified this finding by demonstrating the nuclear location of a protein obtained by the fusion of NLSGlMYB2 and G. lamblia glyceraldehyde 3-phosphate dehydrogenase, a non-nuclear protein. Our data on GlMYB2 will expand our understanding on NLSs functioning in G. lamblia.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.158-166
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• 2004

Predicting the destination of a protein in a cell gives valuable information for annotating the function of the protein. Recent technological breakthroughs have led us to develop more accurate methods for predicting the subcellular localization of proteins. The most important factor in determining the accuracy of these methods, is a way of extracting useful features from protein sequences. We propose a new method for extracting appropriate features only from the sequence data by computing pairwise sequence alignment scores. As a classifier, support vector machine (SVM) is used. The overall prediction accuracy evaluated by the jackknife validation technique reach 94.70% for the eukaryotic non-plant data set and 92.10% for the eukaryotic plant data set, which show the highest prediction accuracy among methods reported so far with such data sets. Our numerical experimental results confirm that our feature extraction method based on pairwise sequence alignment, is useful for this classification problem.

• Molecules and Cells
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• v.40 no.11
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• pp.828-836
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• 2017

Eukaryotic cells consist of a complex network of thousands of proteins present in different organelles where organelle-specific cellular processes occur. Identification of the subcellular localization of a protein is important for understanding its potential biochemical functions. In the post-genomic era, localization of unknown proteins is achieved using multiple tools including a fluorescent-tagged protein approach. Several fluorescent-tagged protein organelle markers have been introduced into dicot plants, but its use is still limited in monocot plants. Here, we generated a set of multicolored organelle markers (fluorescent-tagged proteins) based on well-established targeting sequences. We used a series of pGWBs binary vectors to ameliorate localization and co-localization experiments using monocot plants. We constructed different fluorescent-tagged markers to visualize rice cell organelles, i.e., nucleus, plastids, mitochondria, peroxisomes, golgi body, endoplasmic reticulum, plasma membrane, and tonoplast, with four different fluorescent proteins (FPs) (G3GFP, mRFP, YFP, and CFP). Visualization of FP-tagged markers in their respective compartments has been reported for dicot and monocot plants. The comparative localization of the nucleus marker with a nucleus localizing sequence, and the similar, characteristic morphology of mCherry-tagged Arabidopsis organelle markers and our generated organelle markers in onion cells, provide further evidence for the correct subcellular localization of the Oryza sativa (rice) organelle marker. The set of eight different rice organelle markers with four different FPs provides a valuable resource for determining the subcellular localization of newly identified proteins, conducting co-localization assays, and generating stable transgenic localization in monocot plants.