• Journal of Applied Reliability
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• v.15 no.1
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• pp.19-26
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• 2015

The amended law of railway safety in Korea has recently come into effect in order to strengthen the railway safety management system. The new law, which took effect March 1, 2014, will implement stricter oversight of railway companies. As a result, a company that manufactures railroad system or components for domestic use must obtain an approval in accordance with the technical specifications of manufacturer approval. Although Korea had established the legal system in enforcing railway safety, the government wants the companies continue to develop the more improved safety systems until they gain competitive edge on the world class railway manufacturers. This paper presents an in-depth analysis of the technical specifications for manufacturer approval in International Railway Industry Standard (IRIS), which is the global standard. This paper also proposes measures and guidelines that would help Korean manufacturers those who want further develop their safety management systems, as a prerequisite for them to obtain the manufacturer approval.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.1
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• pp.47-56
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• 1993

In this paper, pyramidal VQ image coding by DCT classifier based on HVS is studied. The proposed DCT classifier based on HVS is that the transform subblocks of the image are mlultiplied by MTF which is a sort of band pass filter and sorted by the magnitude of their ac energy levels and classifeid into three classes such as low, middle and high variance class by the threshold and then edges are detected in comparison of the energy sum of ac transform coefficients corresponding to the different edge directions.

• Journal of KIISE:Information Networking
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• v.29 no.6
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• pp.706-711
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• 2002

In this paper, we implement a diffserv-capable router on the linux system and evaluate its performance. The router supports the packet marking for the input finks that is different from the previous implementation. The edge diffserv-capable router can guarantee the performance of each class, even in a congested condition. We compare the performance of the diffserv-capable router with that of the normal router in terms of PDBs (per domain behaviors), which are defined with traffic conditioning rules and PHBs (per hop behaviors).

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.9
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• pp.37-49
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• 1992

The standard definition of computational vision is a set of inverse problems of recovering surfaces from images. Thus the common characteristics of the most early vision problems are ill-posed. The main idea for solving ill-posed problems is to restrict the class of admissible solutions by introducing suitable a priori knowledge. Standard regurarization methods lead to satisfactory solutions of early vision problems but cannot deal effectively and directly with a few general problems, such as discontinuity and fusion of information from multiple modules. In this paper, we discuss limitations of standard regularization theory and present new stochastic method. We will outline a rigorous approach to overcome part of ill-posedness of image restoration, edge detection, and stereo vision problems, based on Bayes estimation and MRF(Markov random field) model, that effectively deals with the problems. This result makes one hope that this framework could be useful in the solution of other vision problems.

• Journal of Sensor Science and Technology
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• v.24 no.6
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• pp.368-372
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• 2015

In this paper, we proposed a post-processing technique for improving classification performance of electronic nose (E-Nose) system which may be occurred drift signals from sensor array. An adaptive radial basis function network using stochastic gradient (SG) and singular value decomposition (SVD) is applied to process signals from sensor array. Due to drift from sensor's aging and poisoning problems, the final classification results may be showed bias and fluctuations. The predicted classification results with drift are quantized to determine which identification level each class is on. To mitigate sharp fluctuations moving-averaging (MA) technique is applied to quantized identification results. Finally, quantization and some edge correction process are used to decide levels of the fluctuation-smoothed identification results. The proposed technique has been indicated that E-Nose system was shown correct odor identification results even if drift occurred in sensor array. It has been confirmed throughout the experimental works. The enhancements have produced a very robust odor identification capability which can compensate for decision errors induced from drift effects with sensor array in electronic nose system.

• Journal of the Korean Mathematical Society
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• v.56 no.4
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• pp.857-867
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• 2019

Suppose that G = (V, E) is a connected locally finite graph with the vertex set V and the edge set E. Let ${\Omega}{\subset}V$ be a bounded domain. Consider the following quasilinear elliptic equation on graph G $$\{-{\Delta}_{pu}={\lambda}K(x){\mid}u{\mid}^{p-2}u+f(x,u),\;x{\in}{\Omega}^{\circ},\\u=0,\;x{\in}{\partial}{\Omega},$$ where ${\Omega}^{\circ}$ and ${\partial}{\Omega}$ denote the interior and the boundary of ${\Omega}$, respectively, ${\Delta}_p$ is the discrete p-Laplacian, K(x) is a given function which may change sign, ${\lambda}$ is the eigenvalue parameter and f(x, u) has exponential growth. We prove the existence and monotonicity of the principal eigenvalue of the corresponding eigenvalue problem. Furthermore, we also obtain the existence of a positive solution by using variational methods.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.101-110
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• 2022

Histopathological analysis of biopsy specimens is still used for diagnosis and classifying the brain tumors today. The available procedures are intrusive, time consuming, and inclined to human error. To overcome these disadvantages, need of implementing a fully automated deep learning-based model to classify brain tumor into multiple classes. The proposed CNN model with an accuracy of 92.98 % for categorizing tumors into five classes such as normal tumor, glioma tumor, meningioma tumor, pituitary tumor, and metastatic tumor. Using the grid search optimization approach, all of the critical hyper parameters of suggested CNN framework were instantly assigned. Alex Net, Inception v3, Res Net -50, VGG -16, and Google - Net are all examples of cutting-edge CNN models that are compared to the suggested CNN model. Using huge, publicly available clinical datasets, satisfactory classification results were produced. Physicians and radiologists can use the suggested CNN model to confirm their first screening for brain tumor Multi-classification.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.135-138
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• 2008

본 논문은 의료 영상에 대한 효과적인 분류와 검색을 위한 알고리즘을 제안한다. 영상 분류와 검색을 위해서 MPEG-7 표준 기술자인 색 구조 기술자와 경계선 히스토그램 기술자를 사용해 영상들에 대한 특징 값을 추출한다. 이렇게 구해진 특징 값들을 의료 영상의 분류와 검색에 적용해 본 결과 비교적 낮은 성능을 보여줌을 확인하고 앞서 구해진 특징 값들을 교사 학습 방법인 SVM(Support Vector Machine)과 비교사 학습 방법인 FCM(Fuzzy C-means Clustering)에 적용시켰다. 기존 연구에서는 SVM과 FCM의 통합으로 의료 영상에 대한 분류와 검색을 시행하였지만 본 논문에서 실험한 결과 SVM과 MPEG-7 시각 기술자 중에 하나인 EHD(Edge Histogram Descriptor)를 가중치 선형 결합하여 실험한 결과가 더 정확한 분류와 높은 검색 성능을 나타냄을 확인하였다.

• 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.

• Korean Journal of Environment and Ecology
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• v.6 no.2
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• pp.168-179
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• 1993

Use impacts on environmental deteriorations of trail were studied on the three major trails of Sobaeksan National Park in 1992. The entire width and bare width of trail as the trail condition were significantly greater on the more heavily used trail. Maximum depth of trail was not so great in spite of steeper grade of trail in comparison with the other National Parks. Percentages of deepening. rock-exposed. diverged points as the deterioration types of trail which were surveyed at the total of 105 points were high and trail conditions were significantly different from those of non-deteriorated points. On the Ridge trail. the damaged area more severe than Class 4 reaches about 10,335$m^2$ and the deterioration is accelerated. The dominant trees of the the upper layer in trail edge vegetation are changed from Q. mongolica. Aar mono to Q. mongolica for Huibang trail. and from Pinus densiflora. Q. mongolica to P. densiflora and to Q. mongolica for Biro trail as altitude increases. Rhododendron schlippenbachii. Weigelu subsessilis. Salix hulteni. Rubus crataeglfolius were classified for tolerant species and R. coreanus. Vaccinium koreanum for intolerant species to use impacts. Highly competetive species on the Ridge trail were grouped R. schlippenbachii. W. subsessilis. Rubus crataegifolius and Symplocos chinensis for. pilosa.