• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.282-286
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• 2000

In this paper, magneto-rheological(MR) damper is studied for vibration control of large infra structures under earthquake. Generally, active control devices need a large control force and a high power supply system to reduce the vibration effectively. Large and miss tuned control force may induce the dangerous situation such that the generated large control force acts to amplify the structural vibration. Recently, to overcome the weaknesses of the active control, the semi-active control method is suggested by many researchers. Semi-active control uses the passive control device of which the characteristics can be modified. Control force of the semi-active device is not generated from the actuator with power supply. It is generated as a dynamic reaction force of the device same as in the passive control case, so the control system is inherently stable and robust. Unlike the case of passive control, control force of semi-active control is adjusted depending on the measured response of the structure, so the vibration can be reduced more effectively against various unknown environmental loads. Magneto-rheological(MR) damper is one of the semi-active devices. Dynamic characteristics of the MR material can be changed by applying the magnetic fields. So the control of MR damper needs only small power. Response time of MR to the input voltage is very short, so the high performance control is possible. MR damper has a high force capacity so it is adequate to the vibration control of large infra structure. Because MR damper has a nonlinear property, normal control method used in active control may not be effective. Clipped optimal control, modified bang-bang control etc. have been suggested to MR damper by many researchers. In this study, sliding mode fuzzy control(SMFC) is applied to MR damper. Genetic algorithm is used for the controller tuning. To verify the applicability of MR damper and suggested algorithm, numerical simulation on the aseismic control is carried out. Simulation model is three-story building structure, which was used in the paper of Dyke, et al. The control performance is compared with clipped optimal control. The present results indicate that the SMFC algorithm can reduce the earthquake-induced vibration very effectively.

• Journal of the Korea Convergence Society
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• v.10 no.6
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• pp.41-47
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• 2019

It is very important to search for and obtain an example of a similar judgment in case of court judgment. The existing judge's document search uses a method of searching through key-words entered by the user. However, if it is necessary to input an accurate keyword and the keyword is unknown, it is impossible to search for the necessary document. In addition, the detected document may have different contents. In this paper, we want to improve the effectiveness of the method of vectorizing a document into a three-dimensional space, calculating cosine similarity, and searching close documents in order to search an accurate judge's example. Therefore, after analyzing the similarity of words used in the judge's example, a method is provided for extracting the mode and inserting it into the text of the text, thereby providing a method for improving the cosine similarity of the document to be retrieved. It is hoped that users will be able to provide a fast, accurate search trying to find an example of a tax-related judge through the proposed model.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.163-172
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• 2022

Typical anti-jamming technologies based on array antennas, Space Time Adaptive Process (STAP) & Space Frequency Adaptive Process (SFAP), are very effective algorithms to perform nulling and beamforming. However, it does not perform equally well for all types of jamming signals. If the anti-jamming algorithm is not optimized for each signal type, anti-jamming performance deteriorates and the operation stability of the system become worse by unnecessary computation. Therefore, jamming classification technique is required to obtain optimal anti-jamming performance. Machine learning, which has recently been in the spotlight, can be considered to classify jamming signal. In general, performing supervised learning for classification requires a huge amount of data and new learning for unfamiliar signal. In the case of jamming signal classification, it is difficult to obtain large amount of data because outdoor jamming signal reception environment is difficult to configure and the signal type of attacker is unknown. Therefore, this paper proposes few-shot jamming signal classification technique using meta-learning and transfer-learning to train the model using a small amount of data. A training dataset is constructed by anti-jamming algorithm input data within the GNSS receiver when jamming signals are applied. For meta-learning, Model-Agnostic Meta-Learning (MAML) algorithm with a general Convolution Neural Networks (CNN) model is used, and the same CNN model is used for transfer-learning. They are trained through episodic training using training datasets on developed our Python-based simulator. The results show both algorithms can be trained with less data and immediately respond to new signal types. Also, the performances of two algorithms are compared to determine which algorithm is more suitable for classifying jamming signals.

• Tuberculosis and Respiratory Diseases
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• v.51 no.6
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• pp.517-529
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• 2001

Background : Surfactant protein C(SP-C) is a hydrophobic 5,000 dalton molecule. SP-C has the primary roles in accelerating surface spreading of a surfactant phospholipid. The filter hybridization and solution hybridization assays are both rapid and sensitive and can be used to measure the RNAs complementary to any cloned DNA sequence. Methods : The authors measured the SP-C mRNA levels quantitatively using solution hybridization and filter hybridization assays to obtain a standard curve equation to quantify the mRNA of unknown samples comparatively. Results : 1. The minimum level of the specimens by solution hybridization was 3 pg for SP-C mRNA. 2. The standard curve equation of the solution hybridization assay between the counts per minute(Y) and the SP-C mRNA transcript input(X) was Y=6.46 X+244. The correlation coefficient was 0.99. 3. The minimum detection level of specimens by filter hybridization was 0.1 ng for SP-C mRNA. 4. The standard curve equation of the filter hybridization assay between the counts per minute(Y) and SP-C mRNA transcript input(X) is Y=2541.6 X+252.7. The correlation coefficient was 0.99. Conclusions : A comparison of CPM/filter in the linear range allowed an accurate and reproducible estimation of the SP-C mRNA copy number. Filter hybridization and solution hybridization assays are both rapid and sensitive and can be used to measure the RNAs complementary to any cloned DNA sequence. It is ideally suited to situations where accurate quantitation of multiple samples is required.

• Journal of the Earthquake Engineering Society of Korea
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• v.1 no.2
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• pp.1-15
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• 1997

This paper presents substructural identification methods for the assessment of local damages in complex and large structural systems. For this purpose, an auto-regressive and moving average with stochastic input (ARMAX) model is derived for a substructure to process the measurement data impaired by noises. Using the substructural methods, the number of unknown parameters for each identification can be significantly reduced, hence the convergence and accuracy of estimation can be improved. Secondly, the damage index is defined as the ratio of the current stiffness to the baseline value at each element for the damage assessment. The indirect estimation method was performed using the estimated results from the identification of the system matrices from the substructural identification. To demonstrate the proposed techniques, several simulation and experimental example analyses are carried out for structural models of a 2-span truss structure, a 3-span continuous beam model and 3-story building model. The results indicate that the present substructural identification method and damage estimation methods are effective and efficient for local damage estimation of complex structures.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.3
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• pp.533-543
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• 2018

In this paper, we propose an MILP-based method for Optimal Probability of Bit-based Differential Characteristic in SP(Substitution-permutation) ciphers based on Automatic Differential Characteristic Searching Method of Sasaki, et al. In [13], they used input/output variables and probability variables seperatably, but we simplify searching procedure by putting them(variables) together into linear inequalities. Also, In order to decrease the more linear inequalities, we choose Espresso algorithm among that used by Sasaki, et al(Quine-McCluskey algorithm & Espresso algorithm). Moreover, we apply our method to GIFT-64, GIFT-128, SKINNY-64, and we obtained results in the GIFT(Active S-boxs : 6, Probabilities : $2^{-11.415}$) compared with the existing one.(Active S-boxs : 5, Probabilities : unknown). In case of SKINNY-64, we can't find better result, but can find same result compared with the existing one.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.571-576
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• 2002

There have been a lot of considerations dealing with improving the performance of neuro-fuzzy system. The studies on the neuro-fuzzy modeling have largely been devoted to two approaches. First is to improve performance index of system. The other is to reduce the structure size. In spite of its satisfactory result, it should be noted that these are difficult to extend to high dimensional input or to increase the membership functions. We propose a novel neuro-fuzzy system based on the efficient clustering method for initializing the parameters of the premise part. It is a very useful method that maintains a few number of rules and improves the performance. It combine the various algorithms to improve the performance. The Expectation-Maximization algorithm of Gaussian mixture model is an efficient estimation method for unknown parameter estimation of mirture model. The obtained parameters are used for fuzzy clustering method. The proposed method satisfies these two requirements using the Gaussian mixture model and neuro-fuzzy modeling. Experimental results indicate that the proposed method is capable of giving reliable performance.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.43-58
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• 2014

As the use of trading systems has increased rapidly, many researchers have become interested in developing effective stock market prediction models using artificial intelligence techniques. Stock market prediction involves multifaceted interactions between market-controlling factors and unknown random processes. A successful stock prediction model achieves the most accurate result from minimum input data with the least complex model. In this research, we develop a combination model of ${\pi}$-fuzzy logic and support vector machine (SVM) models, using a genetic algorithm to optimize the parameters of the SVM and ${\pi}$-fuzzy functions, as well as feature subset selection to improve the performance of stock market prediction. To evaluate the performance of our proposed model, we compare the performance of our model to other comparative models, including the logistic regression, multiple discriminant analysis, classification and regression tree, artificial neural network, SVM, and fuzzy SVM models, with the same data. The results show that our model outperforms all other comparative models in prediction accuracy as well as return on investment.

• Journal of Korean Society of Transportation
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• v.22 no.4 s.75
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• pp.31-42
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• 2004

This study develops Accident Modification Factors (AMF) of countermeasures with Baysian method which are newly proposed for reducing Roadway-Rail grade crossing accidents. This study proposes a new "Bayesian Analytical Framework" for countermeasure assessment which combines "Subjective" Prior Information with "Logical" based Information. The newly proposed "Bayesian Analytical Framework" consists of the following three steps: The 1st step - Countermeasure Selection, Choice of Participants, Selection of Crashes; The 2nd step-Development of Crash History Manual and Countermeasure Evaluation Manual; The 3rd step-Development of AMFs through sound statistical tests. This study used the Komogorov-Smirnov(K-S) Test to determine whether two unknown distribution functions associated with the two populations are identical. The results of the study are that individual responses did not meet the K-S test of identical distributions. while individual vs. group distributions are identical. This indicates that combining the input of several people reduces the impact of individual subjectivity and assumptions and is important for developing a repeatable distribution to develop sound AMFs of countermeasures for reducing Roadway-Rail grade crossing accidents. The procedures of the AMF development conducted in this study can be used to estimate the safety effects of countermeasures for road segments and intersections, in addition to Roadway-Rail grade crossings.

• Structural Monitoring and Maintenance
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• v.2 no.1
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• pp.77-93
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• 2015

The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network (ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.