• Journal of Biomedical Engineering Research
• /
• v.30 no.2
• /
• pp.153-161
• /
• 2009

Mammogram is one of the important techniques for mass detection, which is the early diagnosis stage of a breast cancer. Especially, the CAD(Computer Aided Diagnosis) using mammogram improves the working performance of radiologists as it offers an effective mass detection. There are two types of CAD systems using mammogram; automatic and semi-automatic CAD systems. However, the automatic segmentation is limited in performance due to the difficulty of obtaining an accurate segmentation since mass occurs in the dense areas of the breast tissue and has smoother boundaries. Semi-automatic CAD systems overcome these limitations, however, they also have problems including high FP (False Positive) rate and a large amount of training data required for training a classifier. The proposed system which overcomes the aforementioned problems to detect mass is composed of the suspected area selection, the level set segmentation and SVM (Support Vector Machine) classification. To assess the efficacy of the system, 60 test images from the FFDM (Full-Field Digital Mammography) are analyzed and compared with the previous semi-automatic system, which uses the ANN classifier. The experimental results of the proposed system indicate higher accuracy of detecting mass in comparison to the previous systems.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.6
• /
• pp.3-8
• /
• 2014

Recently, a spectrum sensing technique employing the maximum value of a received power spectrum as a test statistic has been presented in the literature for the purpose of detecting a wireless microphone signal in TV bands This detects the presence of a primary user by comparing the test statistic with some threshold, which depends on the background noise power level as well as a target false alarm rate. Therefore its performance may deteriorate when the noise power uncertainty occurs. As a means to mitigate this difficulty, we present a spectrum sensing strategy adopting the ratio of the maximum and the minimum value of the power spectrum as a test statistic and analyze its performance of spectrum sensing.

• The Korean Journal of Emergency Medical Services
• /
• v.19 no.2
• /
• pp.99-115
• /
• 2015

Purpose: This study aimed to provide basic data for clinical training program development by analyzing the operating conditions and satisfaction in a clinical training program for 119 emergency medical technicians (EMTs) in South Korea. Methods: Data from 84 EMTs were collected on June 19, 2014. We administered a 64-item questionnaire about operating conditions and satisfaction in the clinical training program, and analyzed data (SPSS v 21.0). Results: The degree of performance in the field, importance of the item in the field, and level of difficulty were 3.36, 4.23, and 3.21, respectively. In the number of times that an item was directly performed according to the subjects' general characteristics a statistically difference in sex (p = .000), duty (p =.021), and total working time of trainees (p = .002). The subjects' total satisfaction score was 3.77. The difference in satisfaction according to the subjects' characteristics was a statistically significant in terms of sex (p = .016) and clinical training area (p = .005). Conclusion: A more efficient training system for hospital clinical training courses should be developed. The operation condition analyzed in this research may contribute to the improvement of the performance of EMTs.

• Ceramist
• /
• v.22 no.4
• /
• pp.402-416
• /
• 2019

The development of next-generation secondary batteries, including lithium-ion batteries (LIB), requires performance enhancements such as high energy/high power density, low cost, long life, and excellent safety. The discovery of new materials with such requirements is a challenging and time-consuming process with great difficulty. To pursue this challenging endeavor, it is pivotal to understand the structure and interface of electrode materials in a multiscale level at the atomic, molecular, macro-scale during charging / discharging. In this regard, various advanced material characterization tools, including the first-principle calculation, high-resolution electron microscopy, and synchrotron-based X-ray techniques, have been actively employed to understand the charge storage- and degradation-mechanisms of various electrode materials. In this article, we introduce and review recent advances in in-situ synchrotron-based x-ray techniques to study electrode materials for LIBs during thermal degradation and charging/discharging. We show that the fundamental understanding of the structure and interface of the battery materials gained through these advanced in-situ investigations provides valuable insight into designing next-generation electrode materials with significantly improved performance in terms of high energy/high power density, low cost, long life, and excellent safety.

• Phonetics and Speech Sciences
• /
• v.10 no.2
• /
• pp.61-68
• /
• 2018

Phonological awareness, or consciousness of speech sounds and operational skill with them, develops in the order word > syllable > phoneme, over the ages of four to seven. Among the various types of phonological awareness tasks, the deletion task has a higher level of difficulty because it requires operation and deletion of sounds within words. This task also has a high correlation with reading proficiency. This study utilized a deletion task with 20 questions to see how operational development depended on age and gender. The deletion task, with 20 questions, was tested on four- to six-year old children developing normally (N = 90). The results showed that phonological awareness performance improved with age. This age effect was not accompanied by a gender effect; age and gender interacted. The study confirmed the development of phonological awareness in four- to six-year-old children who were developing normally. The deletion task can be used to effectively detect the risk of difficulties with phonological awareness in preschoolers with speech, language, and reading problems.

• Proceedings of the KIEE Conference
• /
• 1999.11b
• /
• pp.375-377
• /
• 1999

It is necessary to obtain the high performance of the inverter system that control the output current of inverters. The dead time causes detrimental effects to the control performance of the inverter system. So we need to compensate the dead time effects. And the dead time minimization switching method can be considered as the best way to avoid the dead time effects fundamentally. In this paper, a new dead time minimization switching strategy is proposed. According to the proposed method, very short dead time is adopted in only once when the current polarity is changing. And the adopted dead time is equal to the applied dead time or shorter than it. As the proposed method can be done with the porlarity information of the reference current. it is easy to avoid some problems in comparison with the case that the real current is used to get the polarity changing time; level detection difficulty, noise problem and so on.

• Journal of Sensor Science and Technology
• /
• v.32 no.2
• /
• pp.124-130
• /
• 2023

Implementing flexible production systems in domestic and foreign automotive design parts assembly has increased demand for automation and power reduction. Consequently, transition to a hybrid production method is observed where multiple vehicles are assembled in a single assembly line. Multiple robots, 3D vision sensors, mounting positions, and correction software have complex configurations in the automotive glass mounting system. Hence, automation is required owing to significant difficulty in the assembly process of automobile parts. This study presents a primer lighting and inspection algorithm that is robust to the assembly environment of real automotive design parts using high power 'ㄷ'-shaped LED inclined lighting. Furthermore, a 2D camera was developed in the primer coating inspection system-the core technology of the glass mounting system. A primer application demo line applicable to the actual automobile production line was established using the proposed high power lighting and algorithm. Furthermore, application inspection performance was verified using this demo system. Experimental results verified that the performance of the proposed system exceeded the level required to satisfy the automobile requirements.

• East Asian Economic Review
• /
• v.26 no.3
• /
• pp.179-204
• /
• 2022

This study examines the extent to which policy-based loans to SME exporters affect their export performance (the intensive margin of exports). We also investigate the heterogeneous export effects of policy-based loans that may depend on firm- and industry-specific characteristics, such as credit ratings, debt-to-assets ratios, firm size and age. To do so, we conduct a survey, of 1,000 Korean SMEs, that collect information on firm-level exports and policy-based loans. The main empirical findings strongly support that SMEs that receive policy-based loans tend to increase their export volumes. However, these loans' positive impact on exports are only valid for SME exporters with credit scores of 12 or greater (that is, SMEs that have difficulty accessing the external financial market). The estimation results with respect to SMEs' dependence on external financing imply that policy-based loans for SMEs in sectors that are heavily dependent on external finance are effective in that they are instrumental in increasing these firms' exports. These empirical findings emphasize the importance of the external financial market to SME exporters who face various up-front investments that are related to their exporting activities.

• Journal of Information Technology Services
• /
• v.18 no.2
• /
• pp.143-159
• /
• 2019

Machine learning (ML) is a method of fitting given data to a mathematical model to derive insights or to predict. In the age of big data, where the amount of available data increases exponentially due to the development of information technology and smart devices, ML shows high prediction performance due to pattern detection without bias. The feature engineering that generates the features that can explain the problem to be solved in the ML process has a great influence on the performance and its importance is continuously emphasized. Despite this importance, however, it is still considered a difficult task as it requires a thorough understanding of the domain characteristics as well as an understanding of source data and the iterative procedure. Therefore, we propose methods to apply deep learning for solving the complexity and difficulty of feature extraction and improving the performance of ML model. Unlike other techniques, the most common reason for the superior performance of deep learning techniques in complex unstructured data processing is that it is possible to extract features from the source data itself. In order to apply these advantages to the business problems, we propose deep learning based methods that can automatically extract features from transaction data or directly predict and classify target variables. In particular, we applied techniques that show high performance in existing text processing based on the structural similarity between transaction data and text data. And we also verified the suitability of each method according to the characteristics of transaction data. Through our study, it is possible not only to search for the possibility of automated feature extraction but also to obtain a benchmark model that shows a certain level of performance before performing the feature extraction task by a human. In addition, it is expected that it will be able to provide guidelines for choosing a suitable deep learning model based on the business problem and the data characteristics.

• Journal of Korean society of Dental Hygiene
• /
• v.9 no.4
• /
• pp.784-794
• /
• 2009

Objectives : This study was to prepare basic data about middle and high school students' school loss due to oral diseasea and to investigate the relevant factors. Methods : The survey is conducted for 575 middle school students in Busan and Gyeongnam. School loss was investigated based on the experiences of absences and leaving school early, which had happened from oral diseasea. The independent variables were oral health behaviors and sociopeconomic factors such as sex, age of their parents, educational level of their parents, family income. Factors related with school loss was analyzed by the multiple logistic regression method. Results : The experience ratio of leaving school early to the dental clinic or having difficulty in studying was higher in the case of high school student than in middle school student case. The parameters related with absence or leaving school early for oral disease were the education level, the distinction of sex, fear about medical examination and the standard of living. The reasons of absence or leaving early for visiting the dental clinic were related with education level, the distinction of sex, fear about medical examination, distrust of oral care and the satisfaction of oral health. The parameters having effect on school performance were education level, sex, fear about medical examination, distrust of oral care, cost burden, interests in oral health by the parents and school record. Conclusions : The oral health promotion should be developed to decrease school loss for students.