• Journal of Information Processing Systems
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• v.20 no.2
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• pp.263-272
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• 2024

A model based on genetic algorithm optimization, GA-SVM, is proposed to warn university students of their status. This model improves the predictive effect of support vector machines. The genetic optimization algorithm is used to train the hyperparameters and adjust the kernel parameters, kernel penalty factor C, and gamma to optimize the support vector machine model, which can rapidly achieve convergence to obtain the optimal solution. The experimental model was trained on open-source datasets and validated through comparisons with random forest, backpropagation neural network, and GA-SVM models. The test results show that the genetic algorithm-optimized radial basis kernel support vector machine model GA-SVM can obtain higher accuracy rates when used for early warning in university learning.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.765-774
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• 2002

Suppose one is given a vector X of a finite set of quantities $X_i$ which are independent Poisson random variables. A null hypothesis $H_0$ about E(X) is to be tested against an alternative hypothesis $H_1$. A quantity $\sum\limits_{i}w_ix_i$ is to be computed and used for the test. The optimal values of $W_i$ are calculated for three cases: (1) signal to noise ratio is used in the test, (2) normal approximations with unequal variances to the Poisson distributions are used in the test, and (3) the Poisson distribution itself is used. The above three cases are considered to the situations that are without background noise and with background noise. A comparison is made of the optimal values of $W_i$ in the three cases for both situations.

• Proceedings of the KIEE Conference
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• 2003.04a
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• pp.217-220
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• 2003

In this paper, Inverter drives adopting 2 phase space vector SRP-PWM (Separately Randomized Pulse Position PWM) with fixed switching frequency is proposed. The proposed 2 phase space vector SRP-PWM scheme is based on the 3 phase SRP-PWM. In the proposed SRP-PWM scheme, each of two phase pulses is located randomly in each switching interval. The experimental results show that the voltage and acoustic noise harmonics are spread to a wide band area. Also, the performance of the proposed 2 phase SRP-PWM and the conventional center aligned SVM are compared to each other. In resuit, the speed response is nearly simitar to each other from the viewpoint of the v/f constant control.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.979-982
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• 2002

Compressing image data by using Vector Quantization (VQ)［1］-［3］will compare Training Vectors with Codebook. The result is an index of position with minimum distortion. The implementing Random Codebook will reduce the image quality. This research presents the Splitting solution ［4］,［5］to implement the Codebook, which improves the image quality［6］by the average Training Vectors, then splits the average result to Codebook that has minimum distortion. The result from this presentation will give the better quality of the image than using Random Codebook.

• Proceedings of the KIPE Conference
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• 2003.11a
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• pp.21-30
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• 2003

본 연구에서는 변조지수 M이 큰 영역에서는 고조파 스펙트럼의 광대역화 효과가 저하되는 2상 랜덤 중앙 정렬 변조기법 (Random Centered Distribution PWM : RCD)기법의 문제점을 해결하고자 멀티 영벡터 2상 RCD (Multi Zero Vector RCD : MZRCD) 변조기법을 제안하였다. 제안된 2상 MZRCD기법은 변조지수 M=0.7을 기준으로 하여 M이 0.7보다 큰 영역에서는 영벡터를 V(111)로 선택하고, 0.7보다 작은 영역에서는 V(000)을 선택한다. 제안된 방법을 유도모터 구동시스템에 적용해 본 결과, M이 0.7이상인 영역에서도 모터의 전압/전류 및 소음 스펙트럼의 광대역화 특성이 종전의 방법에 비하여 우수함을 확인할 수 있었다.

• Proceedings of the KIEE Conference
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• 2002.04a
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• pp.82-89
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• 2002

In this paper, a new two-phase space vector RPWM(Random PWM) is proposed. In the proposed RPWM, each of two-phase PWM pulses is located randomly in each switching interval. Based on the space vector modulation technique, the duty ratio of the pulses is calculated. Along with the randomization of the PWM pulses, we can obtain the effects of spread spectra of inverter output voltage, d.c link current and audible switching acoustic noise as in the case of randomly changed switching frequency. To verify the validity of the proposed two-phase RPWM scheme, the experiment based on the C167 micro-controller was executed. The performance of the proposed scheme was compared with traditional PWM schemes experimentally.

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.185-193
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• 2009

Machine learning methods such as support vector machines and random forests yield nonparametric prediction functions of the form y = $f(x_1,{\ldots},x_p)$. As a sequel to the previous article (Huh and Lee, 2008) for visualizing nonparametric functions, I propose more sensible graphs for visualizing y = $f(x_1,{\ldots},x_p)$ herein which has two clear advantages over the previous simple graphs. New graphs will show a small number of prototype curves of $f(x_1,{\ldots},x_{j-1},x_j,x_{j+1}{\ldots},x_p)$, revealing statistically plausible portion over the interval of $x_j$ which changes with ($x_1,{\ldots},x_{j-1},x_{j+1},{\ldots},x_p$). To complement the visual display, matching importance measures for each of p predictor variables are produced. The proposed graphs and importance measures are validated in simulated settings and demonstrated for an environmental study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2086-2097
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• 2012

A terminal-based dynamic clustering algorithm is proposed in a multi-cell scenario, where the user could select the cooperative BSs from the predetermined static base stations (BSs) set based on dynamic channel condition. First, the user transmission rate is derived based on linear precoding and per-cell feedback scheme. Then, the dynamic clustering algorithm can be implemented based on two criteria: (a) the transmission rate should meet the user requirement for quality of service (QoS); (b) the rate increment exceeds the predetermined constant threshold. By adopting random vector quantization (RVQ), the optimized number of cooperative BSs and the corresponding channel conditions are presented respectively. Numerical results are given and show that the performance of the proposed method can improve the system resources utilization effectively.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.1
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• pp.17-27
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• 2021

In this paper, we develop a galaxy image classification method based on hand-crafted features and machine learning techniques. Additionally, we provide an empirical analysis to reveal which combination of the techniques is effective for galaxy image classification. To achieve this, we developed a framework which consists of four modules such as preprocessing, feature extraction, feature post-processing, and classification. Finally, we found that the best technique for galaxy image classification is a method to use a median filter, ORB vector features and a voting classifier based on RBF SVM, random forest and logistic regression. The final method is efficient so we believe that it is applicable to embedded environments.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.258-270
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• 2022

Cancer has become a common disease for the past two decades throughout the globe and there is significant increase of cancer among women. Breast cancer and ovarian cancers are more prevalent among women. Majority of the patients approach the physicians only during their final stage of the disease. Early diagnosis of cancer remains a great challenge for the researchers. Although several drugs are being synthesized very often, their multi-benefits are less investigated. With millions of drugs synthesized and their data are accessible through open repositories. Drug repurposing can be done using machine learning techniques. We propose a feature selection technique in this paper, which is novel that generates multiple populations for the grey wolf algorithm and classifies breast cancer drugs efficiently. Leukemia drug dataset is also investigated and Multilayer perceptron achieved 96% prediction accuracy. Three supervised machine learning algorithms namely Random Forest classifier, Multilayer Perceptron and Support Vector Machine models were applied and Multilayer perceptron had higher accuracy rate of 97.7% for breast cancer drug classification.