• Cross-Cultural Studies
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• v.52
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• pp.393-416
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• 2018

Negative Stripping Construction in English involves the disjunction but, the adverb not, and a constituent NP. This construction is an incomplete sentence although it delivers a complete sentential meaning. Interpretation of this construction may be ambiguous in that the constituent NP can either be construed as the subject, or as the complements including the object. To generate such sentences and resolve the issue of ambiguity, we propose a construction-based analysis under direct interpretation approach, rejecting previous analyses based on deletion approaches. In so doing, we suggest a negative stripping construction rule that can account for ambiguous meaning. This rule further can enable us to explain syntactic structures and readings of Negative Stripping Construction.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.599-604
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• 2005

This paper presents fuzzy rules to predict diagnosis of Wisconsin breast cancer with minimized number of feature in put using the neural network with weighted fuzzy membership functions (NEWFM) and the non-overlap area distribution measurement method. NEWFM is capable of self-adapting weighted membership functions from the given the Wisconsin breast cancer clinical training data. n set of small, medium, and large weighted triangular membership functions in a hyperbox are used for representing n set of featured input. The membership functions are randomly distributed and weighted initially, and then their positions and weights are adjusted during learning. After learning, prediction rules are extracted directly from n set of enhanced bounded sums of n set of small, medium, and large weighted fuzzy membership functions. Then, the non-overlap area distribution measurement method is applied to select important features by deleting less important features. Two sets of prediction rules extracted from NEWFM using the selected 4 input features out of 9 features outperform to the current published results in number of set of rules, number of input features, and accuracy with 99.71%.

• Journal of Navigation and Port Research
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• v.35 no.10
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• pp.823-827
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• 2011

The present Common Structural Rules for double hull oil tanker is not included the residual strength, which is one of the functional requirements in design part of Goal-based new ship construction standards (GBS). The GBS will be enforced after July 1, 2016. The requirement related residual strength has the goal to build safe ship even if she has the specified damages due to marine accidents including collision and grounding. In order to assess the residual strength based on risk for structural damages according to GBS, tons of nonlinear FE analysis work taking into account various types of damage will be needed. The Smith's method, a kind of simplified method for the strength analysis is very useful for this purpose. In this paper, the residual strength assessments based on ultimate strength using Smith's method were carried out. The objected ship is VLCC with stranding damage in bottom structures. Also, the results were compared with that of nonlinear FE analysis using three cargo hold model.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.161-165
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• 2014

In this paper, we proposed the fuzzy prototype pattern classifier. In the proposed classifier, each prototype is defined to describe the related sub-space and the weight value is assigned to the prototype. The weight value assigned to the prototype leads to the change of the boundary surface. In order to define the prototypes, we use Fuzzy C-Means Clustering which is the one of fuzzy clustering methods. In order to optimize the weight values assigned to the prototypes, we use the Differential Evolutionary Algorithm. We use Linear Discriminant Analysis to estimate the coefficients of the polynomial which is the structure of the consequent part of a fuzzy rule. Finally, in order to evaluate the classification ability of the proposed pattern classifier, the machine learning data sets are used.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.343-349
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• 2009

In this paper, it would be shown that individuals can have different responses to the same email based on their preferences through computing the distributions of user clusters' email responses from clustering results based on email users' preference information. This paper presents an approach that incorporates user preferences to construct an anti-spam mail system, which is different from the conventional content-based ones. We consider email category information derived from the email content as well as user preference information. We also build a user preference ontology to formally represent the important concepts and rules derived from a data mining process and then apply a rule optimization procedure to exclude unnecessary rules. Experimental results show that our user preference based system achieves good performance in terms of accuracy, the rules derived from the system and human comprehensibility.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.443-448
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• 2013

Ant Colony System(ACS) is a meta heuristic approach based on biology in order to solve combinatorial optimization problem. It is based on the tracing action of real ants which accumulate pheromone on the passed path and uses as communication medium. In order to search the optimal path, ACS requires to explore various edges. In existing ACS, the local updating rule assigns the same pheromone to visited edge. In this paper, our local updating rule gives the pheromone according to the total frequency of visits of the currently selected node in the previous iteration. I used the ACS algoritm using subpath for search. Our approach can have less local optima than existing ACS and find better solution by taking advantage of more informations during searching.

• The Journal of the Acoustical Society of Korea
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• v.21 no.7
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• pp.656-664
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• 2002

We present a statistical analysis of Korean phonological variations using a Grapheme-to-Phoneme (GPT) system. The GTP system used for experiments generates pronunciation variants by applying rules modeling obligatory and optional phonemic changes and allophonic changes. These rules are derived form morphophonological analysis and government standard pronunciation rules. The GTP system is optimized for continuous speech recognition by generating phonetic transcriptions for training and constructing a pronunciation dictionary for recognition. In this paper, we describe Korean phonological variations by analyzing the statistics of phonemic change rule applications for the 60,000 sentences in the Samsung PBS Speech DB. Our results show that the most frequently happening obligatory phonemic variations are in the order of liaison, tensification, aspirationalization, and nasalization of obstruent, and that the most frequently happening optional phonemic variations are in the order of initial consonant h-deletion, insertion of final consonant with the same place of articulation as the next consonants, and deletion of final consonant with the same place of articulation as the next consonant's, These statistics can be used for improving the performance of speech recognition systems.

• Geophysics and Geophysical Exploration
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• v.25 no.3
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• pp.140-161
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• 2022

Seismic data with missing traces are often obtained regularly or irregularly due to environmental and economic constraints in their acquisition. Accordingly, seismic data interpolation is an essential step in seismic data processing. Recently, research activity on machine learning-based seismic data interpolation has been flourishing. In particular, convolutional neural network (CNN) and generative adversarial network (GAN), which are widely used algorithms for super-resolution problem solving in the image processing field, are also used for seismic data interpolation. In this study, CNN-based algorithm, U-Net and GAN-based algorithm, and conditional Wasserstein GAN (cWGAN) were used as seismic data interpolation methods. The results and performances of the methods were evaluated thoroughly to find an optimal interpolation method, which reconstructs with high accuracy missing seismic data. The work process for model training and performance evaluation was divided into two cases (i.e., Cases I and II). In Case I, we trained the model using only the regularly sampled data with 50% missing traces. We evaluated the model performance by applying the trained model to a total of six different test datasets, which consisted of a combination of regular, irregular, and sampling ratios. In Case II, six different models were generated using the training datasets sampled in the same way as the six test datasets. The models were applied to the same test datasets used in Case I to compare the results. We found that cWGAN showed better prediction performance than U-Net with higher PSNR and SSIM. However, cWGAN generated additional noise to the prediction results; thus, an ensemble technique was performed to remove the noise and improve the accuracy. The cWGAN ensemble model removed successfully the noise and showed improved PSNR and SSIM compared with existing individual models.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.200-208
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• 2003

This paper proposes a separation and feature extraction of image signals using the independent component analysis(ICA) based on neural networks of efficient learning rule. The proposed learning rule is a hybrid fixed-point(FP) algorithm based on secant method and momentum. Secant method is applied to improve the performance by simplifying the 1st-order derivative computation for optimizing the objective function, which is to minimize the mutual informations of the independent components. The momentum is applied for high-speed convergence by restraining the oscillation in the process of converging to the optimal solution. The proposed algorithm has been applied to the composite images generated by random mixing matrix from the 10 images of $512\times512$-pixel. The simulation results show that the proposed algorithm has better performances of the separation speed and rate than those using the FP algorithm based on Newton and secant method. The proposed algorithm has been also applied to extract the features using a 3 set of 10,000 image patches from the 10 fingerprints of $256\times256$-pixel and the front and the rear paper money of $480\times225$-pixel, respectively, The simulation results show that the proposed algorithm has also better extraction speed than those using the another methods. Especially, the 160 basis vectors(features) of $16\times16$-pixel show the local features which have the characteristics of spatial frequency and oriented edges in the images.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11a
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• pp.477-480
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• 2003

문서를 읽고 그 내용을 개념상으로 정리해 보면, 그 문서를 대표할 수 있는 적은 수의 복합단어로 이루어진 키워드를 찾을 수 있다. 그러나, 문서 내에 키워드가 존재할 경우는 별 문제가 없지만, 존재하지 않을 때는 적당한 키워드 추출이 불가능해진다. 따라서, 본 논문에서는 문서 본문의 출현단어의 개념정보를 기초로 복합어 생성 규칙을 구축하고, 나아가 문서의미와 관련 있는 요소만을 정제하는 중요도 결정법을 사용하여 이에 대한 유용성을 확인하였다.