• 한국어교육
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• 제28권1호
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• pp.251-268
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• 2017

This study is based on differences of syllable type between Korean and Chinese language pronunciation. For example, Nasal consonant ending 【n】 and 【${\eta}$】 reside in both Korean and Chinese phonetics simultaneously. However, in experiential training, Chinese learners will make errors in pronunciation of the Korean syllable nasal consonant ending like 【n】 and 【${\eta}$】. In the previous research, analysis of pronunciation errors were often based on the perspective of phonological system and combination of the phoneme rules. However, in this study, the analysis is based on the differences between Korean and Chinese syllables category to indicate the cause of pronunciation errors. The main findings of this study indicated that in the process of pronunciation of Chinese, nasal consonant syllable rime and its 【back】 tongue vowel are combined with each other. However, this rule does not apply in Korean pronunciation. Therefore, the Korean syllabic types like "앤, 응, 옹, 앵, 은, 온, 언" also exist in the Chinese language. When theChinese learners pronounce these types of syllables, the combination of the voweland nasal syllable rime rule will be taken, which will result in pronunciationerrors.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.655-657
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• 2021

인공지능 알고리즘을 이용한 유방암의 조기진단에 관련된 연구는 최근들어 활발하게 진행되고 있으나, 사용자의 목적에 맞는 처리속도 및 정확도 등에 다양한 한계점을 보인다. 이러한 문제를 해결하기 위해, 본 논문에서는 ImageNet에서 학습된 ResNet 모델을 현미경 기반 암세포 이미지에서 활용이 가능한 다단계 전이 학습을 제안하고, 이를 다시 전이 학습하여 초음파 유방암 영상을 양성 및 악성으로 분류하는 실험을 진행하였다. 제안된 다단계 전이 학습 알고리즘은 초음파 유방암 영상을 분류하였을 때 96% 이상의 정확도를 보였으며, 향후 암 세포주 및 실시간 영상처리 등의 추가를 통해 보다 높은 활용도와 정확도를 보일 것으로 기대한다.

• Asian-Australasian Journal of Animal Sciences
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• 제7권4호
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• pp.487-491
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• 1994

The experiment was carried out to study the digestibility of nutrients in the neutralized urea-treated rice straw when it was fed singly or in combination with concentrates. A total of 8 crossbred Holstein steers were randomly allocated in a $4{\times}4$ Latin square design consisted of 4 treatments, in which the neutralized straw/concentrates ratio on DM basis varied as 100/0, 90/10, 80/20 and 70/30. The results indicated that the digestibility of DM, ether extract and NFE, and TDN and DE of the diets tended to increase with an increase in the level of concentrates. Regression analysis showed that the values of intercepts should be used for estimating DM digestibility, TDN and DE of neutralized straw, when it was fed with concentrates. Digestibilities of crude fiber, NDF and ADF tended to be higher when it was fed singly than when fed with concentrates. Digestibilities of organic matter and CP were not so much changed with the increasing level of concentrates. Although the animals singly fed the neutralized straw showed positive body weight gain and N-balance, it should be necessary to supplement the concentrates to get more body weight gain and N-balance in the crossbred Holstein steers.

• 한국멀티미디어학회논문지
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• 제23권11호
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• pp.1361-1371
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• 2020

Fonts have developed as a visual element, and their influence has rapidly increased around the world. Research on font automation is actively being conducted mainly in English because Hangul is a combination character and the structure is complicated. In the previous study to solve this problem, the stroke element of the character was automatically extracted by applying the object detection by component. However, the previous research was only for similarity, so it was tested on various print style fonts, but it has not been tested on other characters. In order to extract the stroke elements of all characters and fonts, we performed a performance analysis experiment according to the expansion character in the Hangul stroke element extraction training. The results were all high overall. In particular, in the font expansion type, the extraction success rate was high regardless of having done the training or not. In the character expansion type, the extraction success rate of trained characters was slightly higher than that of untrained characters. In conclusion, for the perfect Hangul stroke element extraction model, we will introduce Semi-Supervised Learning to increase the number of data and strengthen it.

• Earthquakes and Structures
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• 제11권3호
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• pp.461-481
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• 2016

The research described in this paper investigates the seismic behaviour of lightly reinforced concrete (RC) bearing sandwich panels, heavily conditioned by shear deformation. A numerical model has been prepared, within an open source finite element (FE) platform, to simulate the experimental response of this emerging structural system, whose squat-type geometry affects performance and failure mode. Calibration of this equivalent mechanical model, consisting of a group of regularly spaced vertical elements in combination with a layer of nonlinear springs, which represent the cyclic behaviour of concrete and steel, has been conducted by means of a series of pseudo-static cyclic tests performed on single full-scale prototypes with or without openings. Both cantilevered and fixed-end shear walls have been analyzed. After validation, this numerical procedure, including cyclic-related mechanisms, such as buckling and subsequent slippage of reinforcing re-bars, as well as concrete crushing at the base of the wall, has been used to assess the capacity of two- and three-dimensional low- to mid-rise box-type buildings and, hence, to estimate their strength reduction factors, on the basis of conventional pushover analyses.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2001년도 학술발표회 발표논문집
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• pp.156-161
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• 2001

Currently, agricultural facilities are evaluated using either basic inspections or detailed analysis. However, conventional analyses as well as methods based on fuzzy logic and rule of thumb have not been very successful in providing a clear relationship between rating and real state of agricultural facilities, because they can't provide exactly acceptable reliability of degraded structures with manager or supervisor. Therefore, in this stage, we must define probabilistic variables for representing degradation of structures being given damages during a survival time. This paper describes the application of neural network system in developing the relation between subjective ratings and parameters of agricultural reservoir as well as that between subjective and analytical ratings. It is shown that neural networks can be trained and used successfully in estimating a rating based on several parameters. The specific application problem for agricultural reservoir in the rural area of Korea is presented and database is constructed to maintain training data set, the information of inspection and facilities. This study showed that a successful training of a neural network could be useful, especially if the input data set for target problem contains parameters with a diverse combination of inter-correlation coefficients. And the networks had a prediction rating of about $^{\ast}^{\ast}^{\ast}%$. The neural network system is expected to show high performance fairly in estimate than statistical method to use equation that is consisted of very lowly interrelated variables.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.6-10
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• 2007

Classification of hyperspectral images is challenging. A very high dimensional input space requires an exponentially large amount of data to adequately and reliably represent the classes in that space. In other words in order to obtain statistically reliable classification results, the number of necessary training samples increases exponentially as the number of spectral bands increases. However, in many situations, acquisition of the large number of training samples for these high-dimensional datasets may not be so easy. This problem can be overcome by using multiple classifiers. In this paper we compared the effectiveness of two approaches for creating multiple classifiers, feature selection and feature extraction. The methods are based on generating multiple feature subsets by running feature selection or feature extraction algorithm several times, each time for discrimination of one of the classes from the rest. A maximum likelihood classifier is applied on each of the obtained feature subsets and finally a combination scheme was used to combine the outputs of individual classifiers. Experimental results show the effectiveness of feature extraction algorithm for generating multiple classifiers.

• Journal of Information Processing Systems
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• 제13권4호
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• pp.703-715
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• 2017

Mammogram images are sensitive in nature and even a minor change in the environment affects the quality of the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In this paper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet based adaptive sigmoid function. The cascade feed-forward back propagation technique has been used for training and testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process the images directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function and then the suspicious regions were selected to extract the features. A combination of texture features and gray-level co-occurrence matrix features were extracted and used for training and testing purposes. The system was trained with 150 images, while a total 100 mammogram images were used for testing. A classification accuracy of more than 95% was obtained with our proposed method.

• 한국기계가공학회지
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• 제18권11호
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• pp.47-56
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• 2019

A deep neural network (DNN) model was proposed to predict the upset in the inertia friction welding process using a database comprising results from a series of FEM analyses. For the database, the upset length, upset beginning time, and upset completion time were extracted from the results of the FEM analyses obtained with various of axial pressure and initial rotational speed. A total of 35 training sets were constructed to train the proposed DNN with 4 hidden layers and 512 neurons in each layer, which can relate the input parameters to the welding results. The mean of the summation of squared error between the predicted results and the true results can be constrained to within 1.0e-4 after the training. Further, the network model was tested with another 10 sets of welding input parameters and results for comparison with FEM. The test showed that the relative error of DNN was within 2.8% for the prediction of upset. The results of DNN application revealed that the model could effectively provide welding results with respect to the exactness and cost for each combination of the welding input parameters.

• International Journal of Computer Science & Network Security
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• 제21권8호
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• pp.276-280
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• 2021

The article presents the concept of ICT - competence, which is considered as the most important characteristic of professional competence, which includes a combination of the following components: motivational-value (orientation of the individual to the development of his ITC-competence in future professional activities); technological (complex of skills and abilities of ICT activities); cognitive (a system of knowledge of modern technologies of future professional activity); it is determined that the pedagogical support of the formation of ICT competence of future specialists is the individualization of the process training, due to their personal and professional needs and the specifics of a regional university, providing the necessary conditions for the implementation of this process.