• Korean journal of food and cookery science
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• v.15 no.5
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• pp.500-506
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• 1999

Scanning electron microscopy was used to study changes in granule shape, dough and cooked noodle structure of pea starch-wheat composite flour with 20% and 30% pea starch substitution. The granule shape of pea starch with low swelling power and solubility was oval, irregular and smooth, which had more a deep groove than corn starch and wheat flour. During gelatinization, pea starch after swelling was partially collapsed but it still held its main shape. The dough microstructure of 20% pea starch substitution showed compact structure distributed with more small starch granules than wheat dough and was held in discontinuous network. When cooked, more open filamentous network where starch gelatinization was complete were noticed. Swollen but partially collapsed large starch granules maintaining their shape were appeared in noodle structure after 30 min soaking in soup. In farinograph studies, 20% pea starch substitution to wheat flour showed that MTI value was as same as wheat flour even though stability was slightly decreased so that it was considered that it has proper property of noodle making.

• Journal of Biomedical Engineering Research
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• v.34 no.1
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• pp.8-13
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• 2013

Persons with sensorineural hearing impairment have troubles in hearing at noisy environments because of their deteriorated hearing levels and low-spectral resolution of the auditory system and therefore, they use hearing aids to compensate weakened hearing abilities. Various algorithms for hearing loss compensation and environmental noise reduction have been implemented in the hearing aid; however, the performance of these algorithms vary in accordance with external sound situations and therefore, it is important to tune the operation of the hearing aid appropriately in accordance with a wide variety of sound situations. In this study, a sound classification algorithm that can be applied to the hearing aid was suggested. The proposed algorithm can classify the different types of speech situations into four categories: 1) speech-only, 2) noise-only, 3) speech-in-noise, and 4) music-only. The proposed classification algorithm consists of two sub-parts: a feature extractor and a speech situation classifier. The former extracts seven characteristic features - short time energy and zero crossing rate in the time domain; spectral centroid, spectral flux and spectral roll-off in the frequency domain; mel frequency cepstral coefficients and power values of mel bands - from the recent input signals of two microphones, and the latter classifies the current speech situation. The experimental results showed that the proposed algorithm could classify the kinds of speech situations with an accuracy of over 94.4%. Based on these results, we believe that the proposed algorithm can be applied to the hearing aid to improve speech intelligibility in noisy environments.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.7 s.349
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• pp.60-71
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• 2006

Group key agreement protocols are designed to allow a group of parties communicating over a public network to securely and efficiently establish a common secret key, Over the years, a number of solutions to the group key agreement protocol have been proposed with varying degrees of complexity, and the research relating to group key agreement to securely communicate among a group of members in integrated wired and wireless networks has been recently proceeded. Both features of wired computing machines with the high-performance and those of wireless devices with the low-power are considered to design a group key agreement protocol suited for integrated wired and wireless networks. Especially, it is important to reduce computational costs of mobile devices which have the limited system resources. In this paper, we present an efficient group key agreement scheme which minimizes the computational costs of mobile devices and is well suited for this network environment and prove its security.

• Korean Journal of Optics and Photonics
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• v.16 no.1
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• pp.13-20
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• 2005

Fiber-Bragg-grating external cavity laser(FGL) modules were fabricated and experimentally analyzed. Proposed as a cost-effective solution for optical sources in the WDM-PON access network, FGL modules were packaged to TO-CAN type. We obtained a low threshold current of 13 mA, and an optical output power of 3.6 mW with a bias current of 60 mA at $25^{\circ}C$. The lasing wavelength dependencies on current and temperature were as small as 5.2 pm/mA and 30 pm/$^{\circ}C$, respectively. These change rates of the wavelength with the temperature and current are smaller than those of the DFB laser. Single-mode oscillations with the side-mode suppression ratio(SMSR) over 30 dB are maintained above the threshold current level. The FGL modules can be directly modulated at 155 Mbps, PRBS(2$^{23}$ -1) NRZ signal. Through the BER plots, we did not see the significant degradations before and after the transmission over 20km of the SMF at 155 Mb/s.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.4
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• pp.341-353
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• 2022

Pipelines are buried in urban area, and the position (depth and orientation) of buried pipeline should be clearly identified before ground excavation. Although various geophysical methods can be used to detect the buried pipeline, it is not easy to identify the exact information of pipeline due to heterogeneous ground condition. Among various non-destructive geo-exploration methods, ground penetration radar (GPR) can explore the ground subsurface rapidly with relatively low cost compared to other exploration methods. However, the exploration data obtained from GPR requires considerable experiences because interpretation is not intuitive. Recently, researches on automated detection technology for GPR data using deep learning have been conducted. However, the lack of GPR data which is essential for training makes it difficult to build up the reliable detection model. To overcome this problem, we conducted a preliminary study to improve the performance of the detection model using finite difference time domain (FDTD)-based numerical analysis. Firstly, numerical analysis was performed with homogeneous soil media having single permittivity. In case of heterogeneous ground, numerical analysis was performed considering the ground heterogeneity using fractal technique. Secondly, deep learning was carried out using convolutional neural network. Detection Model-A is trained with data set obtained from homogeneous ground. And, detection Model-B is trained with data set obtained from homogeneous ground and heterogeneous ground. As a result, it is found that the detection Model-B which is trained including heterogeneous ground shows better performance than detection Model-A. It indicates the ground heterogeneity should be considered to increase the performance of automated detection model for GPR exploration.

• Women's Studies Review
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• v.29 no.2
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• pp.139-173
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• 2012

This paper examines the delayed situations for gender equality in South Korean, Japanese, and Taiwanese families despite the challenge to the gender division of labor in modern society, and to analyze the contradiction between the notions of gender equality and the experiences women face in East Asia countries. Using EASS data, we analyze the effective difference over the division of household labor according to women's age and length of school time, attitude for gender division of labor, couple's labor time, and family network. In South Korea and Taiwan, men's actual ratio of household division is higher than Japanese men's. On the other hand, Japanese women's ratio of household division is the highest in spite of their progressive attitude for gender equality. It is due to the difference of women's working time among the countries. In South Korea and Taiwan, women tend to work in full time job, so that they seem to inevitably reduce the time for household labor. The family characteristics have an effect on the women's ratio of household division in Taiwan, and the feature of women's employment does in South Korea. The high percentage of three-generation household contributes to the reduction of housework burden in Taiwan. In South Korea, the higher women's education levels, the higher the women's ratio of household division. Women's weakened bargaining power for household labor is due to the relatively low level of high-educated women's economic participation in South Korea. This paper reveals the effective factors on the gender division of household labor. We propose the necessity of the macro-level analysis as well as the analysis of the personal and conjugal feature.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.344-353
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• 2021

Objective: The mitotic count of gastrointestinal stromal tumors (GIST) is closely associated with the risk of planting and metastasis. The purpose of this study was to develop a predictive model for the mitotic index of local primary GIST, based on deep learning algorithm. Materials and Methods: Abdominal contrast-enhanced CT images of 148 pathologically confirmed GIST cases were retrospectively collected for the development of a deep learning classification algorithm. The areas of GIST masses on the CT images were retrospectively labelled by an experienced radiologist. The postoperative pathological mitotic count was considered as the gold standard (high mitotic count, > 5/50 high-power fields [HPFs]; low mitotic count, ≤ 5/50 HPFs). A binary classification model was trained on the basis of the VGG16 convolutional neural network, using the CT images with the training set (n = 108), validation set (n = 20), and the test set (n = 20). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated at both, the image level and the patient level. The receiver operating characteristic curves were generated on the basis of the model prediction results and the area under curves (AUCs) were calculated. The risk categories of the tumors were predicted according to the Armed Forces Institute of Pathology criteria. Results: At the image level, the classification prediction results of the mitotic counts in the test cohort were as follows: sensitivity 85.7% (95% confidence interval [CI]: 0.834-0.877), specificity 67.5% (95% CI: 0.636-0.712), PPV 82.1% (95% CI: 0.797-0.843), NPV 73.0% (95% CI: 0.691-0.766), and AUC 0.771 (95% CI: 0.750-0.791). At the patient level, the classification prediction results in the test cohort were as follows: sensitivity 90.0% (95% CI: 0.541-0.995), specificity 70.0% (95% CI: 0.354-0.919), PPV 75.0% (95% CI: 0.428-0.933), NPV 87.5% (95% CI: 0.467-0.993), and AUC 0.800 (95% CI: 0.563-0.943). Conclusion: We developed and preliminarily verified the GIST mitotic count binary prediction model, based on the VGG convolutional neural network. The model displayed a good predictive performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.12
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• pp.1091-1101
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• 2012

Device-to-device (D2D) communication is expected to offer local area services with low transmit power and short link distance, even not via any infrastructures. These advantages will lead to the deployment of D2D systems in a massive scale, where the order of magnitude of D2D user density is higher than that of cellular user density. Network-assisted D2D systems, where D2D resources are managed by cellular networks, are unable to support the large number of D2D devices, due to the signaling overhead for control signals. In this case, no coordination can be an answer. This paper considers uncoordinated D2D systems, which is implemented with a number of D2D devices in a large scale. By analyzing the transmission capacity of D2D systems, we found a feasibility condition under which the uncoordinated D2D communications possibly coexist within cellular networks, sharing the uplink spectrum. In addition, we provide guidelines for the operational points of massive D2D communications, giving some knowledge about proper transmit power level and link distance of uncoordinated D2D.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.1-8
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• 2016

Current sensors are used widely in the fields of current control, monitoring, and measuring. They have become more popular with the increasing demand for smart grids in a power network, generation of renewable energy, electric cars, and hybrid cars. Although open loop Hall effect current sensors have merits, such as low cost, small size, and weight, they have low accuracy. This paper describes the design and fabrication of a 300A open loop current sensor that has high accuracy and temperature performance. The core of the current sensor was calculated numerically and the signal conditioning circuits were designed using circuit analysis software. The characteristics of the manufactured open loop current sensor of 300 A class was measured at currents up to 300 A. According to the test of the current sensor, the accuracy error and linearity error were 0.75% and 0.19%, respectively. When the temperature compensation was carried out with the relevant circuit, the temperature coefficients were less than $0.012%/^{\circ}C$ at temperatures between $-25^{\circ}C$ and $85^{\circ}C$.