• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.49-55
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• 2021

Recently, with the development of database, it is possible to store a lot of data generated in finance, security, and networks. These data are being analyzed through classifiers based on machine learning. The main problem at this time is data imbalance. When we train imbalanced data, it may happen that classification accuracy is degraded due to over-fitting with majority class data. To overcome the problem of data imbalance, oversampling strategy that increases the quantity of data of minority class data is widely used. It requires to tuning process about suitable method and parameters for data distribution. To improve the process, In this study, we propose a strategy to explore and optimize oversampling combinations and ratio based on various methods such as synthetic minority oversampling technique and generative adversarial networks through genetic algorithms. After sampling credit card fraud detection which is a representative case of data imbalance, with the proposed strategy and single oversampling strategies, we compare the performance of trained classifiers with each data. As a result, a strategy that is optimized by exploring for ratio of each method with genetic algorithms was superior to previous strategies.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.5
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• pp.380-386
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• 2022

This paper proposes a method for structuring the preprocessing process of a training image when color is applied using Pix2Pix, one of the adversarial generative neural network techniques. This paper concentrate on the prediction result can be damaged according to the degree of light reflection of the training image. Therefore, image preprocesisng and parameters for model optimization were configured before model application. In order to increase the image resolution of training and prediction results, it is necessary to modify the of the model so this part is designed to be tuned with parameters. In addition, in this paper, the logic that processes only the part where the prediction result is damaged by light reflection is configured together, and the pre-processing logic that does not distort the prediction result is also configured.Therefore, in order to improve the usability, the accuracy was improved through experiments on the part that applies the light reflection tuning filter to the training image of the Pix2Pix model and the parameter configuration.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.4
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• pp.691-708
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• 2022

As the technology of machine learning and deep learning became common, it began to be applied to research on anomaly(abnormal) detection of industrial control systems. In Korea, the HAI dataset was developed and published to activate artificial intelligence research for abnormal detection of industrial control systems, and an AI contest for detecting industrial control system security threats is being conducted. Most of the anomaly detection studies have been to create a learning model with improved performance through the ensemble model method, which is applied either by modifying the existing deep learning algorithm or by applying it together with other algorithms. In this study, a study was conducted to improve the performance of anomaly detection with a post-processing method that detects abnormal data and corrects the labeling results, rather than the learning algorithm and data pre-processing process. Results It was confirmed that the results were improved by about 10% or more compared to the anomaly detection performance of the existing model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3904-3922
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• 2022

As a research hotspot, pedestrian detection has a wide range of applications in the field of computer vision in recent years. However, current pedestrian detection methods have problems such as insufficient detection accuracy and large models that are not suitable for large-scale deployment. In view of these problems mentioned above, a lightweight pedestrian detection and early warning method using a new model called you only look once (Yolov5) is proposed in this paper, which utilizing advantages of Yolov5s model to achieve accurate and fast pedestrian recognition. In addition, this paper also optimizes the loss function of the batch normalization (BN) layer. After sparsification, pruning and fine-tuning, got a lot of optimization, the size of the model on the edge of the computing power is lower equipment can be deployed. Finally, from the experimental data presented in this paper, under the training of the road pedestrian dataset that we collected and processed independently, the Yolov5s model has certain advantages in terms of precision and other indicators compared with traditional single shot multiBox detector (SSD) model and fast region-convolutional neural network (Fast R-CNN) model. After pruning and lightweight, the size of training model is greatly reduced without a significant reduction in accuracy, and the final precision reaches 87%, while the model size is reduced to 7,723 KB.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.4
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• pp.233-242
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• 2023

The unscented Kalman filter (UKF), which is widely used to estimate the states of nonlinear dynamic systems, can be improved to realize robust system identification by using multiple sigma-point sets. When using Kalman filter methods for system identification, artificial noises must be appropriately selected to achieve optimal estimation performance. Additionally, an appropriate scaling factor for the sigma-points must be selected to capture the nonlinearity of the state-space model. This study entailed the use of Bouc-Wen hysteresis model to examine the nonlinear behavior of a single-degree-of-freedom oscillator. On the basis of the effects of the selected artificial noises and scaling factor, a new UKF method using multiple sigma-point sets was devised for improved robustness of the estimation over various signal-to-noise-ratio values. The results demonstrate that the proposed method can accurately track nonlinear system states even when the measurement noise levels are high, while being robust to the selection of artificial noise levels.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.49-56
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• 2023

Machine learning methods diversely applied to the Internet of Things (IoT) field have been successful due to the enhancement of computer processing power. They offer an effective way of detecting malicious intrusions in IoT because of their high-level feature extraction capabilities. In this paper, we proposed a novel feature selection method for malicious intrusion detection in IoT by using an evolutionary technique - Genetic Algorithm (GA) and Machine Learning (ML) algorithms. The proposed model is performing the classification of BoT-IoT dataset to evaluate its quality through the training and testing with classifiers. The data is reduced and several preprocessing steps are applied such as: unnecessary information removal, null value checking, label encoding, standard scaling and data balancing. GA has applied over the preprocessed data, to select the most relevant features and maintain model optimization. The selected features from GA are given to ML classifiers such as Logistic Regression (LR) and Support Vector Machine (SVM) and the results are evaluated using performance evaluation measures including recall, precision and f1-score. Two sets of experiments are conducted, and it is concluded that hyperparameter tuning has a significant consequence on the performance of both ML classifiers. Overall, SVM still remained the best model in both cases and overall results increased.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.3
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• pp.483-494
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• 2023

Recently, gas turbine generators are widely used for frequency control of power systems. Although the inlet temperature of a gas turbine is a key factor related to the performance and lifespan of the device, the inlet temperature is not measured directly for reasons such as the turbine structure and operating environment. In particular, the inlet temperature of the reheating gas turbine is very important for stable operation management, but field workers are experiencing a lot of difficulties because the manufacturer does not provide information on the calculation formula. Therefore, in this study, we propose a method for estimating the inlet temperature of a gas turbine using a machine learning-based linear regression analysis method based on a polytropic process equation. In addition, by proposing an inlet temperature calculation algorithm through the usefulness analysis and verification of the inlet temperature calculation model obtained through linear regression analysis, it is intended to help to improve the level of reheat gas turbine combustion tuning technology.

• Investigative Magnetic Resonance Imaging
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• v.5 no.1
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• pp.57-65
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• 2001

Purpose : $T_2$-weighted half courier Echo Planar Imaging (T2HEPI) method is proposed to reduce measurement time of existing EPI by a factor of 2. In addition, high $T_2$ contrast is obtained for clinical applications. High resolution single-shot EPI images with $T_2$ contrast are obtained with $128{\times}128$ matrix size by the proposed method. Materials and methods : In order to reduce measurement time in EPI, half courier space is measured, and rest of half courier data is obtained by conjugate symmetric filling. Thus high resolution single shot EPI image with $128{\times}128$ matrix size is obtained with 64 echoes. By the arrangement of phase encoding gradients, high $T_2$ weighted images are obtained. The acquired data in k-space are shifted if there exists residual gradient field due to eddy current along phase encoding gradient, which results in a serious problem in the reconstructed image. The residual field is estimated by the correlation coefficient between the echo signal for dc and the corresponding reference data acquired during the pre-scan. Once the residual gradient field is properly estimated, it can be removed by the adjustment of initial phase encoding gradient field between $70^{\circ}$ and $180^{\circ}$ rf pulses. Results : The suggested T2EPl is implemented in a 1.0 Tela whole body MRI system. Experiments are done with the effective echo times of 72ms and 96ms with single shot acquisitions. High resolution($128{\times}128$) volunteer head images with high $T_2$ contrast are obtained in a single scan by the proposed method. Conclusion : Using the half courier technique, higher resolution EPI images are obtained with matrix size of $128{\times}128$ in a single scan. Furthermore $T_2$ contrast is controlled by the effective echo time. Since the suggested method can be implemented by software alone (pulse sequence and corresponding tuning and reconstruction algorithms) without addition of special hardware, it can be widely used in existing MRI systems.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.42 no.1
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• pp.29-34
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• 2005

The wound induction motor can provide high starting torque and reduced starting current simultaneously by inserting large resistor externally when starting. And this technique is one of the well known methods among the induction motor starting methods and generally used for heavy load starting such as crane and cement factories. The conventional PI controller has been widely used in industrial application due to the simple control algorithm and is generally used for control of current torque, position, and speed for the wound induction motor drive system. However, the conventional control system for wound induction motor may result in poor performance because sensors have to be used but are often limited by the environmental condition. Recently, to overcome these problems, many sensorless vector control methods for the wound induction motor have been studied. This paper presents a MRAS method for sensorless vector control of the wound induction motor drive. In the conventional MRAS method, in low frequency, the stator resistance variation may result in poor performance. Therefore, this paper presents a MRAS method with stator and rotor resistance tuning for sensorless vector control of the wound induction motor to overcome several shortages of the conventional MRAS caused by parameter variation and to enhance the robustness of the sensorless vector control. The validity and effectiveness of the proposed method is verified through digital simulation.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.755-768
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• 2002

We investigate the effect of the data skew of join attributes on the performance of a pipelined multi-way hash join method, and propose two new hash join methods with load balancing capabilities. The first proposed method allocates buckets statically by round-robin fashion, and the second one allocates buckets adaptively via a frequency distribution. Using hash-based joins, multiple joins can be pipelined so that the early results from a join, before the whole join is completed, are sent to the next join processing without staying on disks. Unless the pipelining execution of multiple hash joins includes some load balancing mechanisms, the skew effect can severely deteriorate system performance. In this paper, we derive an execution model of the pipeline segment and a cost model, and develop a simulator for the study. As shown by our simulation with a wide range of parameters, join selectivities and sizes of relations deteriorate the system performance as the degree of data skew is larger. But the proposed method using a large number of buckets and a tuning technique can offer substantial robustness against a wide range of skew conditions.