• Communications for Statistical Applications and Methods
• /
• 제29권5호
• /
• pp.603-614
• /
• 2022

The naive Bayes classifier is one of the most straightforward classification tools and directly estimates the class probability. However, because it relies on the independent assumption of the predictor, which is rarely satisfied in real-world problems, its application is limited in practice. In this article, we propose employing sufficient dimension reduction (SDR) to substantially improve the performance of the naive Bayes classifier, which is often deteriorated when the number of predictors is not restrictively small. This is not surprising as SDR reduces the predictor dimension without sacrificing classification information, and predictors in the reduced space are constructed to be uncorrelated. Therefore, SDR leads the naive Bayes to no longer be naive. We applied the proposed naive Bayes classifier after SDR to build a recommendation system for the eyewear-frames based on customers' face shape, demonstrating its utility in the top-k classification problem.

• 한국측량학회지
• /
• 제31권6_2호
• /
• pp.559-565
• /
• 2013

Spatial resolution of land covers from remotely sensed images can be increased using super-resolution mapping techniques for soft-classified land cover proportions. A further development of super-resolution mapping technique is downscaling the original remotely sensed image using super-resolution mapping techniques with a forward model. In this paper, the model for increasing spatial resolution of remote sensing multispectral image is tested with real SPOT 5 imagery at 10m spatial resolution for an area in Bac Giang Province, Vietnam in order to evaluate the feasibility of application of this model to the real imagery. The soft-classified land cover proportions obtained using a fuzzy c-means classification are then used as input data for a Hopfield neural network (HNN) to predict the multispectral images at sub-pixel spatial resolution. The 10m SPOT multispectral image was improved to 5m, 3,3m and 2.5m and compared with SPOT Panchromatic image at 2.5m resolution for assessment.Visually, the resulted image is compared with a SPOT 5 panchromatic image acquired at the same time with the multispectral data. The predicted image is apparently sharper than the original coarse spatial resolution image.

• International Journal of Computer Science & Network Security
• /
• 제22권5호
• /
• pp.348-358
• /
• 2022

Software fault prediction is a method to compute fault in the software sections using software properties which helps to evaluate the quality of software in terms of cost and effort. Recently, several software fault detection techniques have been proposed to classifying faulty or non-faulty. However, for such a person, and most studies have shown the power of predictive errors in their own databases, the performance of the software is not consistent. In this paper, we propose a hybrid soft computing technique for SFP based on optimal feature extraction and classification (HST-SFP). First, we introduce the bat induced butterfly optimization (BBO) algorithm for optimal feature selection among multiple features which compute the most optimal features and remove unnecessary features. Second, we develop a layered recurrent neural network (L-RNN) based classifier for predict the software faults based on their features which enhance the detection accuracy. Finally, the proposed HST-SFP technique has the more effectiveness in some sophisticated technical terms that outperform databases of probability of detection, accuracy, probability of false alarms, precision, ROC, F measure and AUC.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
• /
• pp.521-524
• /
• 2003

In this paper, we propose a design process of 'personalized' classification with soft computing techniques. Based on human's thinking way, a construction methodology for personalized classifier is mentioned. Here, two fuzzy similarity measures and ensemble of classifiers are effectively used. As one of the possible applications, facial expression recognition problem is discussed. The numerical result shows that the proposed method is very useful for on-line learning, reusability of previous knowledge and so on.

• Archives of Reconstructive Microsurgery
• /
• 제18권1호
• /
• pp.16-22
• /
• 2009

The purpose of this study was to present the clinical result of anterolateral thigh free flap for pretibial soft tissue lesion after chronic tibia osteomyelitis. From December 2006 to September 2008, Five patients were included in our study. 4 of 5 were superficial or localized types of chronic tibia osteomyelitis, based on the classification of Cierny and Mader. Average age at the surgery was 45 years, three were males and two were females. All had a history of chronic tibia osteomyelitis and subsequent pretbial soft tissue lesions coming from previous operations or pus drainage. Pretibial soft tissue defects included small ulcers, fibrotic, bruisable soft tissue and small bony exposures, but not large-sized bony exposures nor active pus discharge. After complete debridement of large sized pretibial soft tissue lesions and decortication of anterior tibial cortical dead bone, anterolateral thigh free flap was applied to cover remained large pretibial soft tissue defect and to prevent the recurrence of infection. All flaps survived and provided satisfactory coverage of soft tissue defect on pretibial region for 16 months' mean follow up period. No patients has had recurrence of osteomyelitis. Anterolateral thigh free flap could be recommend for large sized pretibial soft tissue defect of supreficial or localized types of chronic tibia osteomyelitis after through debridement.

• 한국지반공학회지:지반
• /
• 제14권6호
• /
• pp.45-56
• /
• 1998

대상 연약지반의 적절한 개량기술 선택과 개량 효과들을 평가, 관리하기 위해서는 현장 연약 점토지반 특성을 정확히 평가할 수 있는 적절한 현장 시험기법의 적용이 필수적이다. 본 논문에서는 여러 현장시험 방법 중에 경제적이면서도 효과적인 것으로 알려져 국내에서 그 수요가 증가하고 있는 피에조 콘(piezocone)과 딜라토메터(dilatometer)를 이용하여 연약지반의 현장 물성을 평가하고 비교하였다. 연구결과 두 장비 모두 유사한 흙 분류 결과를 제공하였으나. 특히 간극수압으로부터 도출한 흙 분류 결과가 연약점토층 사이에 실트나 모래층 들이 산재한 우리나라 실정에 보다 적절하며 일관성 있는 결과를 주는 것으로 평가되었다. 점토층의 비배수 전단강도는 피에조 콘의 경우 간극수압과 선단저항력으로부터 도출된 간들이 유사하였고 딜라토메터로부터 추정된 비배수 전단강도는 피에조 콘의 두 관측 값으로 유추된 결과들의 평균간에 근접한 것으로 평가되었다. 그리고 실트 또는 모래층이 산재하는 경우 연약지반의 압밀특성을 평가하기 위해서는 관입과정에서 유발되는 소산효과를 고려한 이론적 시간계수가 보다 적절한 것으로 평가된다.

• 터널과지하공간
• /
• 제19권5호
• /
• pp.407-420
• /
• 2009

이 연구에서는 시추 조사와 물리탐사 자료와 같은 다양한 지반 정보를 통합하여 최적의 암반 분류 기법을 도출하는데 지시크리깅을 적용하였다. 최적의 지시크리깅 결과를 얻기 위해서는 효과적으로 hard data(시추조사)와 soft data(물리탐사 자료)를 통합하기 위한 알맞은 방법을 모색할 필요가 있다. 이론적인 베리오그램 모델변수를 결정하기 위해 반복적 비선형 역산 방법을 적용하였고 이 알고리즘의 타당성 검증을 위해 목적함수의 분포양상을 검토한 결과 상관거리에 따른 구배는 대단히 작은 특성을 보였다. 현장 적용지역은 지표에서 터널 계획고까지의 심도가 최대 500 m인 대규모 산악터널 예정지이다. 지시크리깅을 이용하여 soft data인 AMT (Audio frequency Magneto-Telluric) 탐사 자료와 hard data인 RMR자료를 하나로 통합하고자 하였다. 결론적으로 터널계획고와 터널 상부 1D 구간에 대한 암반등급도를 작성하여 도시하였다.

• Communications for Statistical Applications and Methods
• /
• 제26권6호
• /
• pp.635-644
• /
• 2019

Class probability is a fundamental target in classification that contains complete classification information. In this article, we propose a class probability estimation method when the predictor is functional. Motivated by Wang et al. (Biometrika, 95, 149-167, 2007), our estimator is obtained by training a sequence of functional weighted support vector machines (FWSVM) with different weights, which can be justified by the Fisher consistency of the hinge loss. The proposed method can be extended to multiclass classification via pairwise coupling proposed by Wu et al. (Journal of Machine Learning Research, 5, 975-1005, 2004). The use of FWSVM makes our method model-free as well as computationally efficient due to the piecewise linearity of the FWSVM solutions as functions of the weight. Numerical investigation to both synthetic and real data show the advantageous performance of the proposed method.

• 한국지반공학회:학술대회논문집
• /
• 한국지반공학회 2010년도 추계 학술발표회
• /
• pp.658-663
• /
• 2010

Recently the most of deep foundation were socketed into weathered rock or soft rock to carry large foundation loads. The end bearing behavior of piles socketed in rock is generally dependent on the rock mass conditions with discontinuities and rock strength. Therefore, it is very important that the estimating rock classification with relation of TCR, RQD and unpredicted rock condition. In this study, the construction failure example of drilled shaft due to mistaking to estimate the rock classification on penetration were analyzed in site, so we hope to discuss problems of determining the rock socketed length of drilled shaft on construction.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권6호
• /
• pp.1743-1758
• /
• 2023

Automatic modulation classification is a critical algorithm for non-cooperative communication systems. This paper addresses the challenging problem of closed-set and open-set signal modulation classification in complex channels. We propose a novel approach that incorporates a self-learning filter and center-loss in Deep Residual Shrinking Networks (DRSN) for closed-set modulation classification, and the Opendistance method for open-set modulation classification. Our approach achieves better performance than existing methods in both closed-set and open-set recognition. In closed-set recognition, the self-learning filter and center-loss combination improves recognition performance, with a maximum accuracy of over 92.18%. In open-set recognition, the use of a self-learning filter and center-loss provide an effective feature vector for open-set recognition, and the Opendistance method outperforms SoftMax and OpenMax in F1 scores and mean average accuracy under high openness. Overall, our proposed approach demonstrates promising results for automatic modulation classification, providing better performance in non-cooperative communication systems.