• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.290-294
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• 1997

Chromosomes, as the genetic vehicles, provide the basic material for a large proportion of genetic investigations. The human chromosome analysis is widely used to diagnose genetic disease and various congenital anomalies. Many researches on automated chromosome karyotype analysis has been carried out, some of which produced commercial systems. However, there still remains much room for improving the accuracy of chromosome classification. In this paper, we propose an optimal pattern classifier by neural network to improve the accuracy of chromosome classification. The proposed pattern classifier was built up of two-step multi-layer neural network(TMANN). We are employed three morphological feature parameters ; centromeric index(C.I.), relative length ratio(R.L.), and relative area ratio(R.A.), as input in neural network by preprocessing twenty human chromosome images. The results of our experiments show that our TMANN classifier is much more useful in neural network learning and successful in chromosome classification than the other classification methods.

• Proceedings of the KIEE Conference
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• 1998.11c
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• pp.910-912
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• 1998

In this paper, we purpose automatic diagnosis in online, as the fundamental study to diagnose the partial discharge mechanism and to predict the lifetime, by introduction a neural network. In the proposed method, Ire use acoustic emission sensing system and calculate a fixed quantity statistic operator by pulse number and amplitude. Using statically operators such as the center of gravity(G) and the gradient of the discharge distribute(C), we analyzed the early stage and the middle stage. the fixed quantity statistic operators are learned by a neural network. The diagnosis of insulation degradation and a lifetime prediction by the early stage time are achieved. On the basis of revealed excellent diagnosis ability through the neural network learning for the patterns during degradation, it was proved that the neural network is appropriate for degradation diagnosis and lifetime prediction in partial discharge.

• The Journal of Korean Association of Computer Education
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• v.16 no.5
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• pp.49-58
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• 2013

The purpose of this review is to identify factors that affect the quality of cyber universities. While the previous studies focused on the university-level factors, the review revealed that the country-level, social-level, and individual-level variables are also important factors influencing the quality of cyber universities organically and systematically. Therefore, the current study asserts that additional predictors should be added with regard to its systemic structure. These variables would include basic educational environment (e.g., teachers, staff, educational system), related regulations, management system, student willingness to participate in learning and their motivation, the government's awareness of cyber universities and polity support, and societal recognition/assessment of cyber universities.

• Journal of information and communication convergence engineering
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• v.13 no.3
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• pp.205-214
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• 2015

In this paper, we propose a new approach to learning a discriminative model for figure/ground segmentation by incorporating the bag-of-features and conditional random field (CRF) techniques. We advocate the use of image patches instead of superpixels as the basic processing unit. The latter has a homogeneous appearance and adheres to object boundaries, while an image patch often contains more discriminative information (e.g., local image structure) to distinguish its categories. We use pixel-level sparse coding to represent an image patch. With the proposed feature representation, the unary classifier achieves a considerable binary segmentation performance. Further, we integrate unary and pairwise potentials into the CRF model to refine the segmentation results. The pairwise potentials include color and texture potentials with neighborhood interactions, and an edge potential. High segmentation accuracy is demonstrated on three benchmark datasets: the Weizmann horse dataset, the VOC2006 cow dataset, and the MSRC multiclass dataset. Extensive experiments show that the proposed approach performs favorably against the state-of-the-art approaches.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.5
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• pp.828-834
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• 1997

The knowledge of material stress-strain behavior is an essential requirement for design and analysis of deformation processes. Empirical stress-strain relationship and constitutive equations describing material behavior during deformation are being widely used, despite suffering some drawbacks in terms of ease of development, accuracy and speed. In the present study, back-propagation neural networks are used to model and predict the flow stresses of a HSLA steel under conditions of constant strain, strain rate and temperature. The performance of the network model is comparedto those of statistical models on rate equations. Well-trained network model provides fast and accurate results, making it superior to statistical models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.6116-6132
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• 2017

The proxy re-encryption allows an intermediate proxy to convert a ciphertext for Alice into a ciphertext for Bob without seeing the original message and leaking out relevant information. Unlike many prior identity based proxy re-encryption schemes which are based on the number theoretic assumptions such as large integer factorization and discrete logarithm problem. In this paper, we first propose a novel identity based proxy re-encryption scheme which is based on the hardness of standard Learning With Error(LWE) problem and is CPA secure in the standard model. This scheme can be reduced to the worst-case lattice hard problem that is able to resist attacks from quantum algorithm. The key step in our construction is that the challenger how to answer the private query under a known trapdoor matrix. Our scheme enjoys properties of the non-interactivity, unidirectionality, anonymous and so on. In this paper, we utilize primitives include G-trapdoor for lattice and sample algorithms to realize simple and efficient re-encryption.

• Proceedings of the KSPS conference
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• 1996.10a
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• pp.430-431
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• 1996

Among the scholars of second language (L2) acquisition who have used prosodic considerations in syntactic analyses, pausing and intonation contours have been used to define utterances in the speech of second language learners (e.g., Sato, 1990). In recent research on conversational analysis, it has been found that lexically marked causal clause combining in the discourse of native speakers can be distinguished as "intonational subordination" and "intonational coordination(Couper-Kuhlen, Elizabeth, forthcoming.)". This study uses Pienemann's Processability Theory (1995) for an analysis of the speech of native speakers of Japanese (L1) learning English. In order to accurately assess the psycholinguistic stages of syntactic development, it is shown that pitch, loudness, and timing must all be considered together with the syntactic analysis of interlanguage speech production. Twelve Japanese subjects participated in eight fifteen minute interviews, ninety-six dyads. The speech analyzed in this report is limited to the twelve subjects interacting with two different non-native speaker interviews for a total of twenty-four dyads. Within each of the interviews, four different tasks are analyzed to determine the stage of acquisition of English for each subject. Initially the speech is segmented according to intonation contour arid pauses. It is then classified accoding to specific syntactic units and further analysed for pitch, loudness and timing. Results indicate that the speech must be first claasified prosodic ally and lexically, prior to beginning syntactic analysis. This analysis stinguishes three interlanguage lexical categories: discourse markers, coordinator $s_ordinators, and transfer from Japanese. After these lexical categories have been determined, the psycholinguistic stages of syntactic development can be more accurately assessed.d.

• Journal of Engineering Education Research
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• v.18 no.3
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• pp.59-68
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• 2015

This study was implemented for the purpose of analyzing the effects of Engineering Education Accreditation. Now, 15 years has passed adopting the engineering education system. We need to analyze the effect that this system has changed on the ground of engineering education, and it cultivated the human resource. In order to achieve the purpose of this study, the survey were done on the professors, graduates, and workers. The results and conclusions of this study are summarized as follows: First, it is urgent need to change the system of accreditation, and to get the public trust on assessment. Second, it is necessary to make circumstance that engineering education accreditation is advertised to the industries, and the industry can join the development, consulting, evaluation of curriculum. Third, government needs to make the policy that gives the incentive to the industries, if they give some merits to the accreditation graduates. Fourth, certificate of program graduate is desired to spread the accreditation proliferation. Fifth, government should systemize that accreditation program can get advantage to be selected for the public finance business(e.g. BK, LINC).. It will impact the quality Improvement and accountability of engineering programs.

• Proceeding of EDISON Challenge
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• 2014.03a
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• pp.1-13
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• 2014

Potential Energy Surface(PES)를 양자 계산을 통해 알아내는 것은 화학 반응을 이해하는 데에 큰 도움이 된다. 이를테면 Transition State(TS)의 configuration을 알 수 있고, 따라서 reaction path와 활성화 에너지 값을 예측하여, 진행시키고자 하는 화학반응의 이해를 도울 수 있다. 하지만 PES를 그리기 위해서는 해당 분자의 다양한 configuration에 대한 singlet point energy 계산이 필요하기 때문에, 계산적인 측면에서 많은 비용을 요구한다. 따라서 product와 reactant의 구조와 같은 critical point의 정보를 이용하여 최소한의 configuration을 sampling하여 전체 PES를 재구성하는 기계학습 알고리즘을 개발하여 다차원 PES 상에서의 화학반응의 예측을 가능하게 하고자 한다. 본 연구에서는 Barbaralane의 두 안정화 된 구조의 critical point로 하여 이 주변을 random normal distribution하여, B3LYP/6-31G(d) level의 DFT 계산을 통해 relaxed scanning하여 구조와 에너지를 구하였으며, 이 정보를 Support Vector Regression(SVR) 알고리즘을 적용하여 PES를 재구현하였으며, 반응경로와 TS의 구조 그리고 활성화 에너지를 구하였다. 또한 본 기계학습 알고리즘을 바닥상태에서 일어나는 반응이 아닌, 들뜬 상태와 전자 구조가 변하는 화학반응, avoid crossing, conical intersection과 같은 Non-adiabatic frame에서 일어나는 현상에 적용 가능성을 논하고자 한다.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.1057-1070
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• 2020

This paper investigates the determinants of students' success in Vietnam through conducting online interviews and surveys with 2,500 Vietnamese students at eight famous universities in Vietnam. By applying both SPSS 22 and STATA software, the study is to evaluate the impact of four driver factors, which affect GPA, language efficacy, and personal achievement. These factors are psychological perspective, home environmental, student demographic, and school environmental. The research results emphasize a positive effect of psychological and home-environmental determinants, but the negative effect of school environmental factors on the students' success. Besides, the relationship between demographics and student success was tested and indicated that male students have a better language learning ability, but a low level of academic achievement than female students. The results also point out the impact of religious affiliation and ethnicity on personal achievement. Non-religious students are better achievers than those having a religion or those in minority ethnicity. Moreover, accumulated schooling years are negatively associated with students' success. The more working experience students accrued, the higher possibility they are successful. Finally, the finding provides an insight into students' success that might be useful to government authorities and other universities in designing policies for enhancing the quality of education.