• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.235-244
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• 2004

We propose a format of a binary phrase structure grammar with composite labels. The grammar adopts binary rules so that the dependency between two sub-trees can be represented in the label of the tree. The label of a tree is composed of two attributes, each of which is extracted from each sub-tree so that it can represent the compositional information of the tree. The composite label is generated from part-of-speech tags using an automatic labeling algorithm. Since the proposed rule description scheme is binary and uses only part-of-speech information, it can readily be used in dependency grammar and be applied to other languages as well. In the best-1 context-free cross validation on 31,080 tree-tagged corpus, the labeled precision is 79.30%, which outperforms phrase structure grammar and dependency grammar by 5% and by 4%, respectively. It shows that the proposed rule description scheme is effective for parsing Korean.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2895-2912
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• 2014

Content-based image retrieval has been the most important technique for managing huge amount of images. The fundamental yet highly challenging problem in this field is how to measure the content-level similarity based on the low-level image features. The primary difficulties lie in the great variance within images, e.g. background, illumination, viewpoint and pose. Intuitively, an ideal similarity measure should be able to adapt the data distribution, discover and highlight the content-level information, and be robust to those variances. Motivated by these observations, we in this paper propose a probabilistic similarity learning approach. We first model the distribution of low-level image features and derive the free energy kernel (FEK), i.e., similarity measure, based on the distribution. Then, we propose a learning approach for the derived kernel, under the criterion that the kernel outputs high similarity for those images sharing the same class labels and output low similarity for those without the same label. The advantages of the proposed approach, in comparison with previous approaches, are threefold. (1) With the ability inherited from probabilistic models, the similarity measure can well adapt to data distribution. (2) Benefitting from the content-level hidden variables within the probabilistic models, the similarity measure is able to capture content-level cues. (3) It fully exploits class label in the supervised learning procedure. The proposed approach is extensively evaluated on two well-known databases. It achieves highly competitive performance on most experiments, which validates its advantages.

• Journal of Food Hygiene and Safety
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• v.18 no.3
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• pp.101-106
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• 2003

The aim of this study is to develop a label-free immunosensor for microbial detection and to evaluate its applicability to Pseudomonas aeruginosa detection in various food samples. The antibodies used were a polyclonal antiserum from rabbit (polyvalent type) and a monoclonal antibody raised against the flagella of P. aeruginosa. Antibody immobilization was done by a thiolated antibody chemisorption onto one gold electrode of a piezoelectric quartz crystal with a thiol-cleavable, heterobifunctional cross-linker, sulfosuccinimidyl 6-[3-(2-pyridyldithio)propionamido]hexanoate. To the Stomacher-treated samples from various raw and processed foods under cold storage, comprising sirloin, cod and pettitoes, spiking and enrichment culture were done to prepare the model samples, followed by the measurements of the frequency shifts after sample injections. The frequency shifts obtained by the sample matrices themselves were in the range of 52~89 Hz. The injections of the spiked samples caused the frequency shifts of 108~200 Hz, whereas the enriched samples decreased the steady-state resonant frequencies by 162~222 Hz. All sample measurements including baseline stabilization, sample injection and acquisition of the steady-state response were accomplished within 30 min.

• 한국생물공학회:학술대회논문집
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• 2005.10a
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• pp.742-742
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• 2005

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.04a
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• pp.119-120
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• 2009

• Nuclear Engineering and Technology
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• v.29 no.6
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• pp.500-507
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• 1997

The development of detection techniques is needed, in order for regulating authorities to determine whether or not a particular food sample has been irradiated, and label it accordingly so that a consumer's free choice can be exercised. The chemical and physical changes brought about in foods by practical doses of irradiation are very small, and therefore very sensitive methods are required. A number of promising approaches have been developed and evaluated. These include chemical, physical and biological methods ranging from the very simple to highly sophisticated techniques.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.02a
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• pp.113-114
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• 2013

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) imaging is a powerful technique for producing chemical images of small biomolecules (ex. metabolites, lipids, peptides) "as received" because of its high molecular specificity, high surface sensitivity, and submicron spatial resolution. In addition, matrix-assisted laser desorption and ionization time-of-flight (MALDI-TOF) imaging is an essential technique for producing chemical images of large biomolecules (ex. genes and proteins). For this talk, we will show that label-free mass imaging technique can be a platform technology for biomedical studies such as early detection/diagnostics, accurate histologic diagnosis, prediction of clinical outcome, stem cell therapy, biosensors, nanomedicine and drug screening [1-7].

• Journal of Sensor Science and Technology
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• v.20 no.4
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• pp.226-229
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• 2011

Label-free detection of biomolecular interactions was performed using BioFET(Biologically sensitive Field-Effect Transistor) and SPR(Surface Plasmon Resonance). Qualitative information on the immobilization of an anti-IgG and antibody-antigen interaction was gained using the SPR analysis system. The BioFET was used to explore the pI value of the protein and to monitor biomolecular interactions which caused an effective charge change at the gate surface resulting in a drain current change. The results show that the BioFET can be a useful monitoring tool for biomolecular interactions and is complimentary to the SPR system.

• Journal of Sensor Science and Technology
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• v.30 no.6
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• pp.395-407
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• 2021

The effect of the specifications of a silicon-on-insulator vertical slot optical waveguide on the sensitivity of homogeneous and surface sensing configurations for TE and TM polarization, respectively, was systematically analyzed using numerical software. The specifications were optimized based on the confinement factor and transmission power of the TE-guided mode distributed in the slot. The waveguide sensitivities of homogeneous and surface sensing were calculated according to the specifications of the optimized slot optical waveguide.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2168-2187
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• 2021

The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.