• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.4
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• pp.78-86
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• 1999

In this paper, a distributed arithmetic digital neural network with learning and testing phase implemented in a body has been studied. The proposed technique is based on the two facts; one is that the weighting coefficients adjusted will be stored in registers without shift, because input values or input patterns are not changed while learning and the other is that the input patterns stored in registers are not changed while testing. The proposed digital neural network is simulated by hardware description language such as VHDL and verified the performance that the neural network was applied to the recognition of seven-segment. To verify proposed neural networks, we compared the learning process of modified perceptron learning algorithm simulated by software with VHDL for 7-segment number recognizer. The results are as follows: There was a little difference in learning time and iteration numbers according to the input pattern, but generally the iteration numbers are 1000 to 10000 and the learning time is 4 to 200$\mu\textrm{s}$. So we knew that the operation of the neural network is learned in the same way with the learning of software simulation, and the proposed neural networks are properly operated. And also the implemented neural network can be built with less amounts of components compared with board system neural network.

• International Journal of Oral Biology
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• v.33 no.4
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• pp.163-177
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• 2008

Periodontal disease, a form of chronic inflammatory bacterial infectious disease, is known to be a risk factor for cardiovascular disease (CVD). Porphyromonas gingivalis has been implicated in periodontal disease and widely studied for its role in the pathogenesis of CVD. A previous study demonstrating that periodontopathic P. gingivalis is involved in CVD showed that invasion of endothelial cells by the bacterium is accompanied by an increase in cytokine production, which may result in vascular atherosclerotic changes. The present study was performed in order to further elucidate the role of P. gingivalis in the process of atherosclerosis and CVD. For this purpose, invasion of human aortic smooth muscle cells (HASMC) by P. gingivalis 381 and its isogenic mutants of KDP150 ($fimA^-$), CW120 ($ppk^-$) and KS7 ($relA^-$) was assessed using a metronidazole protection assay. Wild type P. gingivalis invaded HASMCs with an efficiency of 0.12%. In contrast, KDP150 failed to demonstrate any invasive ability. CW120 and KS7 showed relatively higher invasion efficiencies, but results for these variants were still negligible when compared to the wild type invasiveness. These results suggest that fimbriae are required for invasion and that energy metabolism in association with regulatory genes involved in stress and stringent response may also be important for this process. ELISA assays revealed that the invasive P. gingivalis 381 increased production of the proinflammatory cytokine interleukin (IL)-$1{\beta}$ and the chemotactic cytokines (chemokine) IL (interleukin)-8 and monocyte chemotactic (MCP) protein-1 during the 30-90 min incubation periods (P<0.05). Expression of RANTES (regulation upon activation, normal T cell expressed and secreted) and Toll-like receptor (TLR)-4, a pattern recognition receptor (PRR), was increased in HASMCs infected with P. gingivalis 381 by RT-PCR analysis. P. gingivalis infection did not alter interferon-$\gamma$-inducible protein-10 expression in HASMCs. HASMC nonspecific necrosis and apoptotic cell death were measured by lactate dehydrogenase (LDH) and caspase activity assays, respectively. LDH release from HASMCs and HAMC caspase activity were significantly higher after a 90 min incubation with P. gingivalis 381. Taken together, P. gingivalis invasion of HASMCs induces inflammatory cytokine production, apoptotic cell death, and expression of TLR-4, a PRR which may react with the bacterial molecules and induce the expression of the chemokines IL-8, MCP-1 and RANTES. Overall, these results suggest that invasive P. gingivalis may participate in the pathogenesis of atherosclerosis, leading to CVD.

• Progress in Medical Physics
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• v.26 no.1
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• pp.52-58
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• 2015

The automated dental cavity detection program for a new concept intra-oral dental x-ray imaging device, an auxiliary diagnosis system, which is able to assist a dentist to identify dental caries in an early stage and to make an accurate diagnosis, was to be developed. The primary theory of the automatic dental cavity detection program is divided into two algorithms; one is an image segmentation skill to discriminate between a dental cavity and a normal tooth and the other is a computational method to analyze feature of an tooth image and take an advantage of it for detection of dental cavities. In the present study, it is, first, evaluated how accurately the DRLSE (Direct Regularized Level Set Evolution) method extracts demarcation surrounding the dental cavity. In order to evaluate the ability of the developed algorithm to automatically detect dental cavities, 7 tooth phantoms from incisor to molar were fabricated which contained a various form of cavities. Then, dental cavities in the tooth phantom images were analyzed with the developed algorithm. Except for two cavities whose contours were identified partially, the contours of 12 cavities were correctly discriminated by the automated dental caries detection program, which, consequently, proved the practical feasibility of the automatic dental lesion detection algorithm. However, an efficient and enhanced algorithm is required for its application to the actual dental diagnosis since shapes or conditions of the dental caries are different between individuals and complicated. In the future, the automatic dental cavity detection system will be improved adding pattern recognition or machine learning based algorithm which can deal with information of tooth status.

• Agricultural and Biosystems Engineering
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• v.4 no.1
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• pp.8-15
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• 2003

In Korea, quality evaluation of dried oak mushrooms are done first by classifying them into more than 10 different categories based on the state of opening of the cap, surface pattern, and colors. And mushrooms of each category are further classified into 3 or 4 groups based on its shape and size, resulting into total 30 to 40 different grades. Quality evaluation and sorting based on the external visual features are usually done manually. Since visual features of mushroom affecting quality grades are distributed over the entire surface of the mushroom, both front (cap) and back (stem and gill) surfaces should be inspected thoroughly. In fact, it is almost impossible for human to inspect every mushroom, especially when they are fed continuously via conveyor. In this paper, considering real time on-line system implementation, image processing algorithms utilizing artificial neural network have been developed for the quality grading of a mushroom. The neural network based image processing utilized the raw gray value image of fed mushrooms captured by the camera without any complex image processing such as feature enhancement and extraction to identify the feeding state and to grade the quality of a mushroom. Developed algorithms were implemented to the prototype on-line grading and sorting system. The prototype was developed to simplify the system requirement and the overall mechanism. The system was composed of automatic devices for mushroom feeding and handling, a set of computer vision system with lighting chamber, one chip microprocessor based controller, and pneumatic actuators. The proposed grading scheme was tested using the prototype. Network training for the feeding state recognition and grading was done using static images. 200 samples (20 grade levels and 10 per each grade) were used for training. 300 samples (20 grade levels and 15 per each grade) were used to validate the trained network. By changing orientation of each sample, 600 data sets were made for the test and the trained network showed around 91 % of the grading accuracy. Though image processing itself required approximately less than 0.3 second depending on a mushroom, because of the actuating device and control response, average 0.6 to 0.7 second was required for grading and sorting of a mushroom resulting into the processing capability of 5,000/hr to 6,000/hr.

• Journal of Korean Society of Transportation
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• v.27 no.4
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• pp.175-181
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• 2009

Intelligent Transportation Systems(ITS) enables road users to enhance efficiency of their trips in a variety of traffic conditions. As a significant part of ITS, information communication technology among vehicles and between vehicles and infrastructure has been being developed to upgrade current traffic data collection technology through location-based traffic surveillance systems. A wider and detailed range of traffic data can be acquired with ease by the technology. However, its performance level falls with environmental impediments such as large vehicles, buildings, harsh weather, which often bring about wireless communication failure. For imputation of vehicle trajectory data discontinued by the failure, several potential existing methods were reviewed and a new method to complement them was devised. AIMSUN API(Application Programming Interface) software was utilized to simulate vehicle trajectories data and missing vehicle trajectories data was randomly generated for the verification of the method. The method was proven to yield more accurate and reliable traffic data than the existing ones.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.4
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• pp.398-405
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• 2001

The rivet joint has typical structural feature that can be initiation site for the fatigue crack due to the combination of local stress concentration around rivet hole and the moisture trapping. From a viewpoint of structural assurance, it is crucial to evaluate the size of crack around the rivet holes by appropriate nondestructive evaluation techniques. Lamb wave that is one of guided waves, offers a more efficient tool for nondestructive inspection of plates. The neural network that is considered to be the most suitable for pattern recognition has been used by researchers in NDE field to classify different types of flaws and flaw sizes. In this study, clack size evaluation around the rivet hole using the neural network based on the back-propagation algorithm has been tarried out by extracting some features from the ultrasonic Lamb wave for A12024-T3 skin panel of aircraft. Special attention was paid to reduce the coupling effect between the transducer and the specimen by extracting some features related to time md frequency component data in ultrasonic waveform. It was demonstrated clearly that features extracted from the time and frequency domain data of Lamb wave signal were very useful to determine crack size initiated from rivet hole through neural network.

• Korean Journal of Food Science and Technology
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• v.41 no.2
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• pp.220-224
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• 2009

Toll-like receptors(TLRs) are pattern recognition receptors(PRRs) that recognize pathogen-associated molecular patterns(PAMPs) and regulate the activation of innate immunity. All TLR signaling pathways culminate in the activation of NF-${\kappa}$B, leading to the induction of inflammatory gene products such as COX-2. Licorice (Glycyrrhiza uralensis) has been used for centuries as an herbal medicine. Isoliquiritigenin(ILG), a simple chalcone-type flavonoid, is an active component present in licorice and has been used to treat many chronic diseases. However, the mechanism as to how ILG mediates health effects is still largely unknown. In the present report, we present biochemical evidence that ILG inhibits the NF-${\kappa}$B activation induced by TLR agonists and the overexpression of downstream signaling components of TLRs, MyD88, IKK${\beta}$, and p65. ILG also inhibits TLR agonists-induced COX-2 expression. These results suggest that anti-inflammatory effects of ILG are caused by modulation of the immune responses regulated by TLR signaling pathways.

• Journal of Elementary Mathematics Education in Korea
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• v.14 no.3
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• pp.937-957
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• 2010

The purpose of this study is to discover the features of symbol sense. This study tries to sum up the meaning and elements of symbol sense and the measures to improve them through documents. Also based on this, it analyzes the learning conditions about symbol sense for 6th grade mathematically able students and suggests the method that activates symbol sense in the math of elementary schools. Considering various studies on symbol sense, symbol sense means the exact knowledge and essential understanding in a comprehensive way. Symbol sense is an intuition about symbols that grasps the meaning of symbols, understands the situation of question, and realizes the usefulness of symbols in resolving a process. Considering all other scholars' opinions, this study sums up 5 elements of the symbol sense. (The recognition of needs to introduce symbol, ability to read the meaning of symbols, choice of suitable symbols according to the context, pattern guess through visualization, recognize the role of symbols in other context) This study draws the following conclusions after applying the symbol questionnaires targeting 6th grade mathematically able students : First, although they are math talents, there are some differences in terms of the symbol sense level. Second, 5 elements of the symbol sense are not completely separated. They are rather closely related in terms of mainly the symbol understanding, thereby several elements are combined.

• Journal of Sensor Science and Technology
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• v.6 no.6
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• pp.476-482
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• 1997

A portable electronic nose system has been fabricated and characterized using an oxide semiconductor gas sensor array and pattern recognition techniques such as principal component analysis and back-propagation artificial neural network. The sensor array consists of six thick-film gas sensors whose sensing layers are Pd-doped $WO_{3}$, Pt-doped $SnO_{2}$, $TiO_{2}-Sb_{2}O_{5}-Pd$-doped $SnO_{2}$, $TiO_{2}-Sb_{2}O_{5}-Pd$-doped $SnO_{2}$ + Pd coated layer, $Al_{2}O_{3}$-doped ZnO and $PdCl_{2}$-doped $SnO_{2}$. The portable electronic nose system consists of an 16bit Intel 80c196kc as CPU, an EPROM for storing system main program, an EEPROM for containing optimized connection weights of artificial neural network, an LCD for displaying gas concentrations. As an application the system has been used to identify 26 carbon monoxide/hydrocarbon (CO/HC) car exhausting gases in the concentration range of CO 0%/HC 0 ppm to CO 7.6%/HC 400 ppm and the identification has been successfully demonstrated.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.183-190
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• 2016

This paper proposes algorithm to reduce the computing time in a neural network that reduces transmission of data for tracking mobile objects in surveillance networks in terms of detection and communication load. Object Detection can be defined as follows : Given image sequence, which can forom a digitalized image, the goal of object detection is to determine whether or not there is any object in the image, and if present, returns its location, direction, size, and so on. But object in an given image is considerably difficult because location, size, light conditions, obstacle and so on change the overall appearance of objects, thereby making it difficult to detect them rapidly and exactly. Therefore, this paper proposes fast and exact object detection which overcomes some restrictions by using neural network. Proposed system can be object detection irrelevant to obstacle, background and pose rapidly. And neural network calculation time is decreased by reducing input vector size of neural network. Principle Component Analysis can reduce the dimension of data. In the video input in real time from a CCTV was experimented and in case of color segment, the result shows different success rate depending on camera settings. Experimental results show proposed method attains 30% higher recognition performance than the conventional method.