• IMMUNE NETWORK
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• v.9 no.3
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• pp.90-97
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• 2009

Background: CD147, as a cellular receptor for cyclophilin A (CypA), is a multifunctional protein involved in tumor invasion, inflammation, tissue remodeling, neural function, and reproduction. Recent observations showing the expression of CD147 in leukocytes indicate that this molecule may have roles in inflammation. Methods: In order to investigate the role of CD147 and its ligand in the pathogenesis of atherosclerosis, human atherosclerotic plaques were analyzed for the expression pattern of CD147 and CypA. The cellular responses and signaling molecules activated by the stimulation of CD147 were then investigated in the human macrophage cell line, THP-1, which expresses high basal level of CD147 on the cell surface. Results: Staining of both CD147 and CypA was detected in endothelial cell layers facing the lumen and macrophage-rich areas. Stimulation of CD147 with its specific monoclonal antibody induced the expression of matrix metalloproteinase (MMP)-9 in THP-1 cells and it was suppressed by inhibitors of both ERK and NF-${\kappa}B$. Accordingly, the stimulation of CD147 was observed to induce phosphorylation of ERK, phosphorylation-associated degradation of $I{\kappa}B$, and nuclear translocation of NF-${\kappa}B$ p65 and p50 subunits. Conclusion: These results suggest that CD147 mediates the inflammatory activation of macrophages that leads to the induction of MMP-9 expression, which could play a role in the pathogenesis of inflammatory diseases such as atherosclerosis.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.1
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• pp.8-17
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• 2014

In this research, we employed various data mining techniques to build predictive models for win-loss prediction in Korean professional baseball games. The historical data containing information about players and teams was obtained from the official materials that are provided by the KBO website. Using the collected raw data, we additionally prepared two more types of dataset, which are in ratio and binary format respectively. Dividing away-team's records by the records of the corresponding home-team generated the ratio dataset, while the binary dataset was obtained by comparing the record values. We applied seven classification techniques to three (raw, ratio, and binary) datasets. The employed data mining techniques are decision tree, random forest, logistic regression, neural network, support vector machine, linear discriminant analysis, and quadratic discriminant analysis. Among 21(= 3 datasets${\times}$7 techniques) prediction scenarios, the most accurate model was obtained from the random forest technique based on the binary dataset, which prediction accuracy was 84.14%. It was also observed that using the ratio and the binary dataset helped to build better prediction models than using the raw data. From the capability of variable selection in decision tree, random forest, and stepwise logistic regression, we found that annual salary, earned run, strikeout, pitcher's winning percentage, and four balls are important winning factors of a game. This research is distinct from existing studies in that we used three different types of data and various data mining techniques for win-loss prediction in Korean professional baseball games.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.10
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• pp.69-75
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• 2008

This paper presents an algorithm robust to error for estimating partial discharge (PD) location using acoustic emission sensors. In operating transformers, the velocity computing of the acoustic signal is difficult because the temperature of the Insulation oil is not homogeneous. So, some error occurs in the process. Therefore, the algorithm estimating PD location must consider this error to provide maintenance person with useful information. The conventional algorithm shows the PD position as a point, while the new algorithm using LookUp-Table(LUT) shows PD position as error-map visually. The error-map is more useful than the conventional result because of robustness to error. Also, we compared performance of them, by adding error to data on purpose.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.1
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• pp.185-196
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• 1999

In this dissertation, a new off-line recognition system is proposed using a subgraph pattern, neural network. After thinning is applied to input characters, balance having a noise elimination function on location is performed. Then as the first step for recognition procedure, circular elements are extracted and recognized. From the subblock HT, space feature points such as endpoint, flex point, bridge point are extracted and a subgraph pattern is formed observing the relations among them. A region where vowel can exist is allocated and a candidate point of the vowel is extracted. Then, using the subgraph pattern dictionary, a vowel is recognized. A same method is applied to extract horizontal vowels and the vowel is recognized through a simple structural analysis. For verification of recognition subgraph in this paper, experiments are done with the most frequently used Myngjo font, Gothic font for printed characters and handwritten characters. In case of Gothic font, character recognition rate was 98.9%. For Myngjo font characters, the recognition rate was 98.2%. For handwritten characters, the recognition rate was 92.5%. The total recognition rate was 94.8% with mixed handwriting and printing characters for multi-font recognition.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.6
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• pp.758-766
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• 2017

In this paper, we propose a robust duality of CNN(Du-CNN) method which can classify the target and clutter in coastal environment for IR Imaging Sensor. In coastal environment, there are various clutter that have many similarities with real target due to diverse change of air temperature, water temperature, weather and season. Also, real target have various feature due to the same reason. Thus, the proposed Du-CNN method adopts human's multiple personality utilization and CNN technique to learn and classify target and clutter. This method has an advantage of the real time operation. Experimental results on sampled dataset of real infrared target and clutter demonstrate that the proposed method have better success rate to classify the target and clutter than general CNN method.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1759-1769
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• 1997

The Error BackPropagation (EBP) algorithm for multi-layered neural networks is widely used in various areas such as associative memory, speech recognition, pattern recognition and robotics, etc. Nevertheless, many researchers have continuously published papers about improvements over the original EBP algorithm. The main reason for this research activity is that EBP is exceeding slow when the number of neurons and the size of training set is large. In this study, we developed new learning speed acceleration methods using variable learning rate, variable momentum rate and variable slope for the sigmoid function. During the learning process, these parameters should be adjusted continuously according to the total error of network, and it has been shown that these methods significantly reduced learning time over the original EBP. In order to show the efficiency of the proposed methods, first we have used binary data which are made by random number generator and showed the vast improvements in terms of epoch. Also, we have applied our methods to the binary-valued Monk's data, 4, 5, 6, 7-bit parity checker and real-valued Iris data which are famous benchmark training sets for machine learning.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.4
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• pp.35-41
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• 2002

To solve the large scale optimization problem approximately, various approaches have been introduced. They are mainly based on recent research advancement of simulations for evolutions, flocking, annealing, and interactions among organisms on artificial environments. The typical ones are simulated annealing(SA), artificial neural network(ANN), genetic algorithms(GA), tabu search(TS), etc. Recently the particle swarm optimization(PSO) has been introduced. The PSO simulates the process of birds flocking or fish schooling for food, as with the information of each agent Is share by other agents. The PSO technique has been applied to various optimization problems of which variables are continuous. However, there are seldom trials for visualization of searching process. This paper proposes a new visualization tool for searching process particle swarm optimization(PSO) algorithm. The proposed tool is effective for understanding the searching process of PSO method and educational for students.

• Transactions of the Korean Society of Automotive Engineers
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• v.15 no.1
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• pp.8-15
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• 2007

Freeway corridors consist of urban freeways and parallel arterials that drivers can use alternatively. Ramp metering in freeways and signal control in arterials are contemporary traffic control methods that have been developed and applied in order to improve traffic conditions of freeway corridors. However, most of the existing studies have focused on either optimal ramp metering in freeways, or progression signal strategies between arterial intersections. There have been no traffic control systems in Korea that integrates the freeway ramp metering and arterial signal control. The effective control strategies for freeway operations may cause negative effects on arterial traffic. On the other hand, traffic congestion and bottleneck phenomenon of arterials due to the increasing peak-hour travel demand and ineffective signal operation may generate an accessibility problem to freeway ramps. Thus, the main function of the freeway which is the through-traffic process has not been successful. The purpose of this study is to develop an integrated control model that connects freeway ramp metering systems and signal control systems in arterial intersections. And Optimization of integrated control model which consists of ramp metering and signal control is another purpose. The design of experiment, neural network, and simulated annealing are used for optimization.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1134-1142
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• 2019

Detection of double compression is one of the most efficient ways of remarking the validity of videos. Many methods have been introduced to detect HEVC double compression with different coding parameters. However, HEVC double compression detection under the same coding environments is still a challenging task in video forensic. In this paper, we introduce a novel method based on the frame partitioning information in intra prediction mode for detecting double compression in with the same coding environments. We propose to extract statistical feature and Deep Convolution Neural Network (DCNN) feature from the difference of partitioning picture including Coding Unit (CU) and Transform Unit (TU) information. Finally, a softmax layer is integrated to perform the classification of the videos into single and double compression by combing the statistical and the DCNN features. Experimental results show the effectiveness of the statistical and the DCNN features with an average accuracy of 87.5% for WVGA and 84.1% for HD dataset.

• Journal of Biomedical Engineering Research
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• v.40 no.6
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• pp.242-249
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• 2019

Forearm electromyography (EMG) generated by wrist movements has been widely used to develop an electrical prosthetic hand, but EMG generated by finger movements has been rarely used even though 20% of amputees lose fingers. The goal of this study is to improve the classification performance of different finger movements using a deep learning algorithm, and thereby contributing to the development of a high-performance finger-based prosthetic hand. Ten participants took part in this study, and they performed seven different finger movements forty times each (thumb, index, middle, ring, little, fist and rest) during which EMG was measured from the back of the right hand using four bipolar electrodes. We extracted mean absolute value (MAV), root mean square (RMS), and mean (MEAN) from the measured EMGs for each trial as features, and a 5x5-fold cross-validation was performed to estimate the classification performance of seven different finger movements. A long short-term memory (LSTM) model was used as a classifier, and linear discriminant analysis (LDA) that is a widely used classifier in previous studies was also used for comparison. The best performance of the LSTM model (sensitivity: 91.46 ± 6.72%; specificity: 91.27 ± 4.18%; accuracy: 91.26 ± 4.09%) significantly outperformed that of LDA (sensitivity: 84.55 ± 9.61%; specificity: 84.02 ± 6.00%; accuracy: 84.00 ± 5.87%). Our result demonstrates the feasibility of a deep learning algorithm (LSTM) to improve the performance of classifying different finger movements using EMG.