• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.621-623
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• 2023

전자금융거래 시장이 활발해지며 이에 따라 신용 카드 이상 거래가 증가하고 있다. 따라서 많은 금융 기관은 신용 카드 이상 거래 탐지 시스템을 사용하여 신용 카드 이상 거래를 탐지하고 개인 피해를 줄이는 등 소비자를 보호하기 위해 큰 노력을 하고 있으며, 이에 따라 높은 정확도로 신용 카드 이상 거래를 탐지할 수 있는 실시간 자동화 시스템에 대한 개발이 요구되었다. 이에 본 논문에서는 머신러닝 기법 중 부스팅 알고리즘을 사용하여 더욱 정확한 신용 카드 이상 거래 탐지 시스템을 제안하고자 한다. XGBoost, LightGBM, CatBoost 부스팅 알고리즘을 사용하여 보다 정확한 신용 카드 이상 거래 탐지 시스템을 개발하였으며, 실험 결과 평균적으로 정밀도 99.95%, 재현율 99.99%, F1-스코어 99.97%를 취득하여 높은 신용 카드 이상 거래 탐지 성능을 보여주는 것을 확인하였다.

• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.157-165
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• 2023

Ultrasound imaging uses sound waves of frequencies to cause physical actions such as reflection, absorption, refraction, and transmission at the edge between different tissues. Improvement is needed because there is a lot of noise due to the characteristics of the data generated from the ultrasound equipment, and it is difficult to grasp the shape of the tissue to be actually observed because the edge is vague. The edge enhancement method is used as a method to solve the case where the edge surface looks clumped due to a decrease in image quality. In this paper, as a method to strengthen the interface, the quality improvement was confirmed by strengthening the interface, which is the high-frequency part, in each image using an unsharpening mask and high boost. The mask filtering used for each image was evaluated by measuring PSNR and SNR. Abdominal, head, heart, liver, kidney, breast, and fetal images were obtained from Philips epiq5g and affiniti70g and Alpinion E-cube 15 ultrasound equipment. The program used to implement the algorithm was implemented with MATLAB R2022a of MathWorks. The unsharpening and high-boost mask array size was set to 3*3, and the laplacian filter, a spatial filter used to create outline-enhanced images, was applied equally to both masks. ImageJ program was used for quantitative evaluation of image quality. As a result of applying the mask filter to various ultrasound images, the subjective image quality showed that the overall contour lines of the image were clearly visible when unsharpening and high-boost mask were applied to the original image. When comparing the quantitative image quality, the image quality of the image to which the unsharpening mask and the high boost mask were applied was evaluated higher than that of the original image. In the portal vein, head, gallbladder, and kidney images, the SNR, PSNR, RMSE and MAE of the image to which the high-boost mask was applied were measured to be high. Conversely, for images of the heart, breast, and fetus, SNR, PSNR, RMSE and MAE values were measured as images with the unsharpening mask applied. It is thought that using the optimal mask according to the image will help to improve the image quality, and the contour information was provided to improve the image quality.

• Journal of Korea Multimedia Society
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• v.14 no.4
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• pp.494-503
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• 2011

In this paper, we propose a recognition application of facial expression for laughter theraphy on smartphone. It detects face region by using AdaBoost face detection algorithm from the front camera image of a smartphone. After detecting the face image, it detects the lip region from the detected face image. From the next frame, it doesn't detect the face image but tracks the lip region which were detected in the previous frame by using the three step block matching algorithm. The size of the detected lip image varies according to the distance between camera and user. So, it scales the detected lip image with a fixed size. After that, it minimizes the effect of illumination variation by applying the bilateral symmetry and histogram matching illumination normalization. After that, it computes lip eigen vector by using PCA(Principal Component Analysis) and recognizes laughter expression by using a multilayer perceptron artificial network. The experiment results show that the proposed method could deal with 16.7 frame/s and the proposed illumination normalization method could reduce the variations of illumination better than the existing methods for better recognition performance.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.1
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• pp.23-29
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• 2018

A stand-alone Photovoltaic (PV) system is one of the most important energy system for Mongolian nomadic herders. Basically, a stand-alone PV system uses two DC-DC converters. This makes the system costly, size bigger and difficult to move from one place to another place for the nomadic herders. A combined control algorithm for charging the battery using Stage of Charge (SOC) and Maximum Power Point Tracking (MPPT) is proposed in this paper. The batteries are charged by the three stage method; bulk, absorption and float charge. In the bulk stage used the MPPT function in this study. The performance of the proposed control algorithm is evaluated in both steady and changing weather conditions. The results are obtained using PSIM software. The results obtained in this paper are useful in designing a stand-alone PV system in the rural life like Mongolian nomadic herders.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.330-339
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• 2008

Ground-based interceptors(GBI) comprise a major element of the strategic defense against hostile targets like Intercontinental Ballistic Missiles(ICBM) and reentry vehicles(RV) dispersed from them. An optimum design of the subsystems is required to increase the performance and reliability of these GBI. Propulsion subsystem design and optimization is the motivation for this effort. This paper describes an effort in which an entire GBI missile system, including a multi-stage solid rocket booster, is considered simultaneously in a Genetic Algorithm(GA) performance optimization process. Single goal, constrained optimization is performed. For specified payload and miss distance, time of flight, the most important component in the optimization process is the booster, for its takeoff weight, time of flight, or a combination of the two. The GBI is assumed to be a multistage missile that uses target location data provided by two ground based RF radar sensors and two low earth orbit(LEO) IR sensors. 3Dimensional model is developed for a multistage target with a boost phase acceleration profile that depends on total mass, propellant mass and the specific impulse in the gravity field. The monostatic radar cross section (RCS) data of a three stage ICBM is used. For preliminary design, GBI is assumed to have a fixed initial position from the target launch point and zero launch delay. GBI carries the Kill Vehicle(KV) to an optimal position in space to allow it to complete the intercept. The objective is to design and optimize the propulsion system for the GBI that will fulfill mission requirements and objectives. The KV weight and volume requirements are specified in the problem definition before the optimization is computed. We have considered only continuous design variables, while considering discrete variables as input. Though the number of stages should also be one of the design variables, however, in this paper it is fixed as three. The elite solution from GA is passed on to(Sequential Quadratic Programming) SQP as near optimal guess. The SQP then performs local convergence to identify the minimum mass of the GBI. The performance of the three staged GBI is validated using a ballistic missile intercept scenario modeled in Matlab/SIMULINK.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1530-1544
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• 2023

Data monitoring is an important foundation of modern science. In most cases, the monitoring data is time-series data, which has high application value. The deep learning algorithm has a strong nonlinear fitting capability, which enables the recognition of time series by capturing anomalous information in time series. At present, the research of time series recognition based on deep learning is especially important for data monitoring. Deep learning algorithms require a large amount of data for training. However, abnormal sample is a small sample in time series, which means the number of abnormal time series can seriously affect the accuracy of recognition algorithm because of class imbalance. In order to increase the number of abnormal sample, a data augmentation method called GANBATS (GAN-based Bi-LSTM and Attention for Time Series) is proposed. In GANBATS, Bi-LSTM is introduced to extract the timing features and then transfer features to the generator network of GANBATS.GANBATS also modifies the discriminator network by adding an attention mechanism to achieve global attention for time series. At the end of discriminator, GANBATS is adding averagepooling layer, which merges temporal features to boost the operational efficiency. In this paper, four time series datasets and five data augmentation algorithms are used for comparison experiments. The generated data are measured by PRD(Percent Root Mean Square Difference) and DTW(Dynamic Time Warping). The experimental results show that GANBATS reduces up to 26.22 in PRD metric and 9.45 in DTW metric. In addition, this paper uses different algorithms to reconstruct the datasets and compare them by classification accuracy. The classification accuracy is improved by 6.44%-12.96% on four time series datasets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1951-1975
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• 2023

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.3
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• pp.286-293
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• 2004

This paper presents the result of a study on the starter control for a turbo generator. Because a starter in gear box type turbo-generator system is composed of gearbox and brush DC motor, it should be replaced with High Speed Generator(HSG)) in HSG type Turbo-generator. There-ore, it is necessary to design a new starting algorithm and starter. In gearbox type system, brush DC motor is rotated to the designed speed using low voltage-high current battery power. After brush DC motor speed is increased to several times by gearbox, gas turbine engine can be rotated to designed starting speed. If we implement a starter with High Speed Generator(HSG), it is necessary to drive high-speed generator to high-speed motor. High-speed generator with permanent magnet on rotor has a low leakage inductance fur driving high-speed rotation, and it is necessary high DC link voltage for inverter when High-speed generator is driven to high speed. This paper presents result of development of the boost converter for converting high voltage DC from low battery voltage and design of the inverter for controlling a high frequency current to be injected to motor winding. Also, we show performance of the designed starter by driving the turbo generator.

• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.3
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• pp.234-243
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• 2015

It is required to have the capability to predict the impact point of the ballistic target in order to assign the firing unit with high engagement possibility for the interception in the ballistic target defense systems. In this paper, a novel method is proposed to predict the impact point of the ballistic target using a flight phase discrimination algorithm given the insufficient measurements on the partial trajectory. The flight of a ballistic target is composed of a boost phase and a ballistic phase with different dynamics. The flight phase is discriminated by using the normalized innovation distance between measurements and a priori estimated measurements. The threshold and tolerance in the flight phase discrimination are determined from the probabilistic characteristics of the estimation error. Monte Carlo simulations are performed to verify the proposed method.

• Computers and Concrete
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• v.16 no.6
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• pp.919-932
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• 2015

This paper presents a new method for assessing the three-point-bending (3PB) strength of mortar beams in a non-destructive manner, based on neural network (NN) models. The models are based on the radial basis function (RBF) architecture and the fuzzy means algorithm is employed for training, in order to boost the prediction accuracy. Data for training the models were collected based on a series of experiments, where the cement mortar beams were subjected to various bending mechanical loads and the resulting pressure stimulated currents (PSCs) were recorded. The input variables to the NN models were then calculated by describing the PSC relaxation process through a generalization of Boltzmannn-Gibbs statistical physics, known as non-extensive statistical physics (NESP). The NN predictions were evaluated using k-fold cross-validation and new data that were kept independent from training; it can be seen that the proposed method can successfully form the basis of a non-destructive tool for assessing the bending strength. A comparison with a different NN architecture confirms the superiority of the proposed approach.