• BMB Reports
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• v.46 no.7
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• pp.358-363
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• 2013

In this paper, we firstly reported a C-type lectin cDNA clone of 1029 bps from the larvae of A. Pernyi (Ap-CTL) using PCR and RACE techniques. The full-length cDNA contains an open reading frame encoding 308 amino acid residues which has two different carbohydrate-recognition domains (CRDs) arranged in tandem. To investigate the biological activities in the innate immunity, recombinant Ap-CTL was expressed in E. coli with a 6-histidine at the amino-terminus (Ap-rCTL). Besides acted as a broad-spectrum recognition protein binding to a wide range of PAMPs and microorganisms, Ap-rCTL also had the ability to recognize and trigger the agglutination of bacteria and fungi. In the proPO activation assay, Ap-rCTL specifically restored the PO activity of hemolymph blocked by anti-Ap-rCTL antibody in the presence of different PAMPs or microorganisms. In summary, Ap-rCTL plays an important role in insect innate immunity as an pattern recognition protein.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.10
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• pp.928-933
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• 2012

In this paper, the IR and compass sensors for the underwater system were used. The walls of the water tank have been recognized and avoided treating the walls as obstacles by the bio-mimetic underwater robot. This paper is consists of two parts: 1.The hardware part for the IR and compass sensors and 2.The software part for obstacle avoidance algorithm while the bio-mimetic robot is swimming with the obstacle recognition. Firstly, the hardware part controls through the RS-485 communications between a microcontroller and the bio-mimetic underwater robot. The software part is simulated for obstacle recognition and collision avoidance based upon the data from IR and compass sensors. Actually, the bio-mimetic underwater robot recognizes where is the obstacle as well as where is the bio-mimetic robot itself while it is moving in the water. While the underwater robot is moving at a constant speed recognizing the wall of water tank as an obstacle, an obstacle avoidance algorithm is applied for the wall following swimming based upon the IR and compass sensor data. As the results of this research, it is concluded that the bio-mimetic underwater robot can follow the wall of the water tank efficiently, while it is avoiding collision to the wall.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.935-939
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• 2003

The emotion recognition is one of the most significant interface technologies which make the high level of human-machine communication possible. The central nervous system stimulated by emotional stimuli affects the autonomous nervous system like a heart, blood vessel, endocrine organs, and so on. Therefore bio-signals like HRV, ECG and EEG can reflect one' emotional state. This study investigates the correlation between emotional states and bio-signals to realize the emotion recognition. This study also covers classification of human emotional states, selection of the effective bio-signal and signal processing. The experimental results presented in this paper show possibility of the emotion recognition.

• Bulletin of the Korean Chemical Society
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• v.30 no.6
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• pp.1247-1251
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• 2009

The recognition of neutral aromatic substrates by different neutral calix[4]arene receptors 1, 2, and 3 was studied by NMR spectroscopy. The stoichiometry is 1:1 in all cases as was confirmed by jobs plot. Owing to the deep cavity, 1 affords stronger binding abilities for substrate 4 and 5, while all receptors remained inert for substrates 6 and 7. The binding constants determined by $^1H$ NMR titration show that the recognition of substrate 4 by 1 gives strongest complexation ($K_a\;of\;9.8\;{\times}\;102\;M^{-1}$).

• BMB Reports
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• v.46 no.5
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• pp.264-269
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• 2013

Pattern recognition receptors are known to participate in the activation of Prophenoloxidase system. In this study, a 1,3-${\beta}$-D-glucan recognition protein was detected for the first time in Antheraea pernyi larvae (Ap-${\beta}GRP$). Ap-${\beta}GRP$ was purified to 99.9% homogeneity from the hemolymph using traditional chromatographic methods. Ap-${\beta}GRP$ specifically bind 1,3-${\beta}$-D-glucan and yeast, but not E. coli or M. luteus. The 1,3-${\beta}$-D-glucan dependent phenoloxidase (PO) activity of the hemolymph inhibited by anti-Ap-${\beta}GRP$ antibody could be recovered by addition of purified Ap-${\beta}GRP$. These results demonstrate that Ap-${\beta}GRP$ acts as a biosensor of 1,3-${\beta}$-Dglucan to trigger the Prophenoloxidase system. A trace mount of 1,3-${\beta}$-D-glucan or Ap-${\beta}GRP$ alone was unable to trigger the proPO system, but they both did. Ap-${\beta}GRP$ was specifically degraded following the activation of proPO with 1,3-${\beta}$-Dglucan. These results indicate the variation in the amount of Ap-${\beta}GRP$ after specific immune challenge in A. pernyi hemolymph is an important regulation mechanism to immune response.

• The KIPS Transactions:PartB
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• v.11B no.7 s.96
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• pp.759-768
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• 2004

Recently the biometric recognition technology is intensively studied and the standardization of the technology has been highly demanded for its commercialization. Currently many blometric recognition products are being developed based on the BioAPl(Biometric Application Program-ming Interface) specification. However, the reliable testing tools (or scenarios) to evaluate performance and conformance of the products are not shown yet. In this paper, a conformance testing method is presented, which verifies a biometric recognition system to meet the requirements of the BioAPl standard. Two different testing procedures are used in the proposed method. The first procedure evaluates that each functions offered in the BioAPl specification are correctly implemented and that the functions are actually used in the system. Through the Procedure, a BSP(Biometric Service Provider) system is executed on the framework of the BioAPl functions. It requires selection of parameters and prece-dent functions that should be executed first. The second procedure evaluates the abilities of module management, handling operations and ver-ification process by the analysis of the test cases. It tests the correctness of the system operation when a testing scenario is given. The proposed testing method is applied on a fingerprint verification BSP using the sample BSP provided by the BioAPl consortium. The experimental results shows the benefits of the proposed testing method.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.8
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• pp.299-308
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• 2014

Various literatures related computing of information processing have been recently shown the researches inspired from the remarkably excellent human capabilities which recognize and categorize very complex visual patterns such as body motions and facial expressions. Applied from human's outstanding ability of perception, the classification function of visual sequences without context information is specially crucial task for computer vision to understand both the coding and the retrieval of spatio-temporal patterns. This paper presents a biological process based action recognition model of computer vision, which is inspired from visual information processing of human brain for action recognition of visual sequences. Proposed model employs the structure of neural fields of bio-inspired visual perception on detecting motion sequences and discriminating visual patterns in human brain. Experimental results show that proposed recognition model takes not only into account several biological properties of visual information processing, but also is tolerant of time-warping. Furthermore, the model allows robust temporal evolution of classification compared to researches of action recognition. Presented model contributes to implement bio-inspired visual processing system such as intelligent robot agent, etc.

• Korean Journal of Metals and Materials
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• v.46 no.1
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• pp.6-12
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• 2008

Peptides with specific sequences against $LaPO_4$ and $TiO_2$ nanoparticles were discovered through peptide phage display technique as an application to biomolecular recognition of inorganic materials. Sequencing results showed that a motif consisting of serine and proline was commonly expressed in specific sequences. It was postulated that serine directly bound to nanoparticles using its terminal hydroxyl (OH) group. In this sense, oxygen atom seemed to work as a ligand to metal ions and hydrogen atom as a H-bond donor, was thought to bind to the oxygen atoms or the hydroxyl groups on particle surface. Also, it was expected that proline assists serine to make an ideal van der Waals contact between serine and nanoparticles, which optimizes the binding of peptide onto surface.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.4
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• pp.819-833
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• 2013

Computing global features based on local features using a bio-inspired framework has shown promising performance. However, for some tough applications with large intra-class variances, a single local feature is inadequate to represent all the attributes of the images. To integrate the complementary abilities of multiple local features, in this paper we have extended the efficacy of the bio-inspired framework, HMAX, to adapt heterogeneous features for global feature extraction. Given multiple global features, we propose an approach, designated as parameterized metric learning, for high dimensional feature fusion. The fusion parameters are solved by maximizing the canonical correlation with respect to the parameters. Experimental results show that our method achieves significant improvements over the benchmark bio-inspired framework, HMAX, and other related methods on the Caltech dataset, under varying numbers of training samples and feature elements.

• Nutrition Research and Practice
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• v.13 no.6
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• pp.521-528
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• 2019

BACKGROUND/OBJECTIVES: The aim of this study was to develop Korean food image detection and recognition model for use in mobile devices for accurate estimation of dietary intake. MATERIALS/METHODS: We collected food images by taking pictures or by searching web images and built an image dataset for use in training a complex recognition model for Korean food. Augmentation techniques were performed in order to increase the dataset size. The dataset for training contained more than 92,000 images categorized into 23 groups of Korean food. All images were down-sampled to a fixed resolution of $150{\times}150$ and then randomly divided into training and testing groups at a ratio of 3:1, resulting in 69,000 training images and 23,000 test images. We used a Deep Convolutional Neural Network (DCNN) for the complex recognition model and compared the results with those of other networks: AlexNet, GoogLeNet, Very Deep Convolutional Neural Network, VGG and ResNet, for large-scale image recognition. RESULTS: Our complex food recognition model, K-foodNet, had higher test accuracy (91.3%) and faster recognition time (0.4 ms) than those of the other networks. CONCLUSION: The results showed that K-foodNet achieved better performance in detecting and recognizing Korean food compared to other state-of-the-art models.