• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제12권1호
• /
• pp.1-5
• /
• 2012

Data reduction has been used widely in data mining for convenient analysis. Principal component analysis (PCA) and factor analysis (FA) methods are popular techniques. The PCA and FA reduce the number of variables to avoid the curse of dimensionality. The curse of dimensionality is to increase the computing time exponentially in proportion to the number of variables. So, many methods have been published for dimension reduction. Also, data augmentation is another approach to analyze data efficiently. Support vector machine (SVM) algorithm is a representative technique for dimension augmentation. The SVM maps original data to a feature space with high dimension to get the optimal decision plane. Both data reduction and augmentation have been used to solve diverse problems in data analysis. In this paper, we compare the strengths and weaknesses of dimension reduction and augmentation for classification and propose a classification method using data reduction for classification. We will carry out experiments for comparative studies to verify the performance of this research.

• 한국멀티미디어학회논문지
• /
• 제22권7호
• /
• pp.749-758
• /
• 2019

This paper proposes a data augmentation algorithm to improve the performance of DNN(Deep Neural Network) based speech enhancement. Many deep learning models are exploring algorithms to maximize the performance in limited amount of data. The most commonly used algorithm is the data augmentation which is the technique artificially increases the amount of data. For the effective data augmentation algorithm, we used a formant enhancement method that assign the different weights to the formant frequencies. The DNN model which is trained using the proposed data augmentation algorithm was evaluated in various noise environments. The speech enhancement performance of the DNN model with the proposed data augmentation algorithm was compared with the algorithms which are the DNN model with the conventional data augmentation and without the data augmentation. As a result, the proposed data augmentation algorithm showed the higher speech enhancement performance than the other algorithms.

• 대한두개안면성형외과학회지
• /
• 제11권2호
• /
• pp.77-84
• /
• 2010

Purpose: The nasal bones are the most common fracture sites of the facial bones, and a careful reduction may still result in secondary deformities, such as saddle nose, deviated nose, hump nose etc, requiring secondary cosmetic rhinoplasty. Therefore, this study examined the clinical characteristics of nasal bone fractures to propose guidelines for patient selection and surgical procedures to achieve more satisfactory results and to prevent secondary deformities with simultaneous augmentation rhinoplasty and bony reduction. Methods: The study was based on 26 out of 149 nasal bone fracture patients who underwent simultaneous augmentation rhinoplasty with bony reduction between May 2008 and April 2009. Retrospective analysis was performed according to the clinical data, surgical techniques and postoperative results. Results: Of the 26 patients, there were 15 males and 11 females. The incidence according to the Stranc's classification revealed that 62% of patients were injured by a frontal impact and 38% by a lateral impact. Frontal impact plane I (50%) was the most frequent type. At the follow up, 18 (81.2%) out of 22 patients were satisfied with their postoperative outcome, and the remaining 4 patients were fair. No one was dissatisfied. However, 5 cases in 3 patients (23%) had some complications; minimal implant deviation in 2 cases, minor irregularity on the nasal dorsum in 2 cases and palpable implant movement under palpation in 1 case. None of these cases required surgical correction. Conclusion: With the proper guidance, simultaneous augmentation rhinoplasty with bony reduction can prevent secondary deformities and satisfy the cosmetic outcomes.

• 한국가시화정보학회지
• /
• 제19권1호
• /
• pp.68-73
• /
• 2021

In this study, the detection of rubber o-ring defects was carried out using k-fold cross validation and Support Vector Machine (SVM) algorithm. The data process was carried out in 3 steps. First, we proceeded with a frame alignment to eliminate unnecessary regions in the learning and secondly, we applied gray-scale changes for computational reduction. Finally, data processing was carried out using image augmentation to prevent data overfitting. After processing data, SVM algorithm was used to obtain normal and defect detection accuracy. In addition, we applied the SVM algorithm through the k-fold cross validation method to compare the classification accuracy. As a result, we obtain results that show better performance by applying the k-fold cross validation method.

• 대한의용생체공학회:의공학회지
• /
• 제44권4호
• /
• pp.293-301
• /
• 2023

Scoliosis is a three-dimensional deformation of the spine that is a deformity induced by physical or disease-related causes as the spine is rotated abnormally. Early detection has a significant influence on the possibility of nonsurgical treatment. To train a deep learning model with preprocessed images and to evaluate the results with and without data augmentation to enable the diagnosis of scoliosis based only on a chest X-ray image. The preprocessed images in which only the spine, rib contours, and some hard tissues were left from the original chest image, were used for learning along with the original images, and three CNN(Convolutional Neural Networks) models (VGG16, ResNet152, and EfficientNet) were selected to proceed with training. The results obtained by training with the preprocessed images showed a superior accuracy to those obtained by training with the original image. When the scoliosis image was added through data augmentation, the accuracy was further improved, ultimately achieving a classification accuracy of 93.56% with the ResNet152 model using test data. Through supplementation with future research, the method proposed herein is expected to allow the early diagnosis of scoliosis as well as cost reduction by reducing the burden of additional radiographic imaging for disease detection.

• Journal of Multimedia Information System
• /
• 제8권1호
• /
• pp.31-34
• /
• 2021

The classical HMM is defined by a parameter triple �� = (��, A, B), where each parameter represents a collection of probability distributions: initial state, state transition and output distributions in order. This paper proposes a new stationary parameter e = (e1, e2, …, eN) where N is the number of states and et = P(|xt = i, y) for describing how an input pattern y ends in state xt = i at time t followed by nothing. It is often said that all is well that ends well. We argue here that all should end well. The paper sets the framework for the theory and presents an efficient inference and training algorithms based on dynamic programming and expectation-maximization. The proposed model is applicable to analyzing any sequential data with two or more finite segmental patterns are concatenated, each forming a context to its neighbors. Experiments on online Hangul handwriting characters have proven the effect of the proposed augmentation in terms of highly intuitive segmentation as well as recognition performance and 13.2% error rate reduction.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제16권3호
• /
• pp.208-215
• /
• 2016

The lithography hotspot detection process is crucial for semiconductor design development process. But, the lithography hotspot detection using optical simulation method takes much time and it slowdown the layout design development cycle. Though the geometry based approach is introduced as an alternative, it still revealed low detection performance and sophisticated framework. To solve this problem, we introduce a deep convolutional neural network based hotspot detection method. Our method made better results in ICCCAD 2012 dataset. To reach this score, we used lots of technical effort to improve the result in addition to just utilizing the nature of convolutional neural network. Inspection region reduction, data augmentation, DBSCAN clustering helped our work more stable and faster.

• 한국시뮬레이션학회논문지
• /
• 제31권2호
• /
• pp.11-20
• /
• 2022

병력감축 등 국방 및 안보환경의 변화에 따라 육군의 경계시스템에도 변화가 시급한 상황이다. 또한 경계작전의 특성상 인간의 실수가 번번이 발생하고 있으며 이러한 실수가 전체 경계작전의 실패로 귀결되는 상황은 경계시스템의 인공지능 도입이 필요한 것에 대한 중요한 이유이다. 본 연구의 목적은 합성곱 신경망 방법을 활용하여 군사용 CCTV에 적합한 인공지능 영상인식 시스템을 개발하는 것이다. 본 연구에서 개발한 시스템의 주요 특징은 먼저, 군사용 CCTV의 특징상 상대적으로 작은 객체를 인식해야하는 상황에 적합한 학습데이터를 활용한 것이다. 둘째, 학습용 데이터 셋에 대해 데이터 증강 알고리즘을 활용하여 군사용에 보다 적합하도록 유도한 것이다. 셋째, 군사용 영상의 위장, 악천후 등 상황을 고려하여 영상의 잡음을 개선하는 알고리즘을 적용하였다. 본 연구에서 제안하는 시스템의 성능 평가결과 객체의 인식능력이 기존 방법에 비해 우수함을 확인하였다.

• Archives of Plastic Surgery
• /
• 제41권5호
• /
• pp.535-541
• /
• 2014

Background Patients having unilateral breast reconstruction often require a second stage procedure on the contralateral breast to improve symmetry. In order to provide immediate symmetry and minimize the frequency and extent of secondary procedures, we began performing simultaneous contralateral balancing operations at the time of initial reconstruction. This study examines the indications, safety, and efficacy of this approach. Methods One-hundred and two consecutive breast reconstructions with simultaneous contralateral balancing procedures were identified. Data included patient age, body mass index (BMI), type of reconstruction and balancing procedure, specimen weight, transfusion requirement, complications and additional surgery under anesthesia. Unpaired t-tests were used to compare BMI, specimen weight and need for non-autologous transfusion. Results Average patient age was 48 years. The majority had autologous tissue-only reconstructions (94%) and the rest prosthesis-based reconstructions (6%). Balancing procedures included reduction mammoplasty (50%), mastopexy (49%), and augmentation mammoplasty (1%). Average BMI was 27 and average reduction specimen was 340 grams. Non-autologous blood transfusion rate was 9%. There was no relationship between BMI or reduction specimen weight and need for transfusion. We performed secondary surgery in 24% of the autologous group and 100% of the prosthesis group. Revision rate for symmetry was 13% in the autologous group and 17% in the prosthesis group. Conclusions Performing balancing at the time of breast reconstruction is safe and most effective in autologous reconstructions, where 87% did not require a second operation for symmetry.

• 산경연구논집
• /
• 제14권11호
• /
• pp.19-25
• /
• 2023

Purpose: Prior studies regarding ethical management on worker's performance have primarily focused on specific industries or regions, potentially limiting the generalizability. This gap in knowledge underscores the need for a comprehensive investigation that considers a diverse range of industries and thoroughly examines the multifaceted aspects of ethical management. Research design, data and methodology: The academic search platform used for this study was 'Google Scholar', 'Scopus', and 'Web of Science' indexes various scholarly articles, including peer-reviewed journals and books. By utilizing specific search terms such as "corporate ethical management" and "employee performance," a vast pool of relevant studies was identified. Results: The findings indicated four effects: first, a positive correlation between ethical management practices and heightened employee motivation and engagement; second, an augmentation in organizational commitment and job satisfaction among employees; third, a reduction in turnover rates, indicating enhanced employee retention; and fourth, an elevation in overall productivity and performance outcomes. Conclusions: In sum, this study offers actionable insights, advocating for adopting and reinforcing ethical management strategies as a potent means to foster a high-performance work culture. These encompass fostering a robust ethical framework, cultivating a culture of transparency and open communication, and providing avenues for employees to voice ethical concerns without fear of retribution.