• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.503-513
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• 2020

This paper presents a method to evaluate denoising filters based on edge locations in their denoised images. Image quality assessment has often been performed by using structural similarity (SSIM). However, SSIM does not provide clearly the geometric accuracy of features in denoised images. Thus, in this paper, a method to localize edge locations with subpixel accuracy based on adaptive weighting of gradients is used for obtaining the subpixel locations of edges in ground truth image, noisy images, and denoised images. Then, this paper proposes a method to evaluate the geometric accuracy of edge locations based on root mean squares error (RMSE) and jaggedness with reference to ground truth locations. Jaggedness is a measure proposed in this study to measure the stability of the distribution of edge locations. Tested denoising filters are anisotropic diffusion (AF), bilateral filter, guided filter, weighted guided filter, weighted mean of patches filter, and smoothing filter (SF). SF is a simple filter that smooths images by applying a Gaussian blurring to a noisy image. Experiments were performed with a set of simulated images and natural images. The experimental results show that AF and SF recovered edge locations more accurately than the other tested filters in terms of SSIM, RMSE, and jaggedness and that SF produced better results than AF in terms of jaggedness.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.252-257
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• 2001

In conventional boolean retrieval systems, document ranking is not supported and similarity coefficients cannot be computed between queries and documents. The MMM, Paice and P-norm models have been proposed in the past to support the ranking facility for boolean retrieval systems. They have common properties of interpreting boolean operators softly. In this paper we propose a new soft evaluation method for Information retrieval using query splitting relevance feedback model. We also show through performance comparison that query splitting relevance feedback(QSRF) is more efficient and effective than MMM, Paice and P-norm.

• Animal cells and systems
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• v.6 no.2
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• pp.167-170
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• 2002

The long terminal repeats (LTRs) of human endogenous retrovirus (HERV) have been found to be coexpressed with sequences of closely located genes. It has been suggested that the LTR elements have contributed to the structural change or genetic variation of human genome connected to various diseases and evolution. We examined the HERV-W LTR elements in various cancer cells (2F7, A43l , A549, HepG2, MIA-PaCa-2, PC-3, RT4, SiHa, U-937, and UO-31). Using genomic DNA from the cancer cells, we performed PCR amplification and identified twelve new HERV-W LTR elements. Those LTR elements showed a high degree of sequence similarity (88-99％) with HERV-W LTR (AF072500). A phylogenetic tree obtained by the neighbor-joining method revealed that HERV-W LTR elements could be mainly divided into two groups through evolutionary divergence. Three HERV-W LTR elements (RT4-2, A43l-1, and UO3l-2) belonged to Group 1, whereas nine LTR elements (2F7-2, A549-1, A549-3, HepG2-3, MP2-2, PC3-1, SiHa-8, SiHa-10, and U937-1) belonged to Group 11. Taken together, our new sequence data of the HERV-W LTR elements may contribute to an understanding of tissue-specific cancer by genomic instability of LTR integration.

• The Journal of the Korea Contents Association
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• v.6 no.5
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• pp.20-28
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• 2006

This paper proposes visual-feature extraction for each band in wavelet domain with both spatial frequency features and multi resolution features, and the combination of visual features using fuzzy integral. In addition, it uses color feature expression method taking advantage of the frequency of the same color after color quantization for reducing quantization error, a disadvantage of the existing color histogram intersection method. Also, it is found that the final similarity can be represented in a linear combination of the respective factors(Homogram, color, energy) when each factor is independent one another. With respect to the combination patterns the fuzzy measurement is defined and the fuzzy integral is taken. Experiments are peformed on a database containing 1,000 color images. The proposed method gives better performance than the conventional method in both objective and subjective performance evaluation.

• ETRI Journal
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• v.27 no.6
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• pp.747-758
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• 2005

The problem of selection among competing models has been a fundamental issue in statistical data analysis. Good fits to data can be misleading since they can result from properties of the model that have nothing to do with it being a close approximation to the source distribution of interest (for example, overfitting). In this study we focus on the preference among models from a family of polynomial regressors. Three decades of research has spawned a number of plausible techniques for the selection of models, namely, Akaike's Finite Prediction Error (FPE) and Information Criterion (AIC), Schwartz's criterion (SCH), Generalized Cross Validation (GCV), Wallace's Minimum Message Length (MML), Minimum Description Length (MDL), and Vapnik's Structural Risk Minimization (SRM). The fundamental similarity between all these principles is their attempt to define an appropriate balance between the complexity of models and their ability to explain the data. This paper presents an empirical study of the above principles in the context of model selection, where the models under consideration are univariate polynomials. The paper includes a detailed empirical evaluation of the model selection methods on six target functions, with varying sample sizes and added Gaussian noise. The results from the study appear to provide strong evidence in support of the MML- and SRM- based methods over the other standard approaches (FPE, AIC, SCH and GCV).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6C
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• pp.490-496
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• 2005

In this paper, we propose a self-constructed clustering algorithm based on inference information of the fuzzy model. This method makes it possible to automatically detect and optimize the number of cluster and parameters by using input-output data. The propose method improves the performance of clustering by extended supervised learning technique. This technique uses the output information as well as input characteristics. For effect the similarity measure in clustering, we use the TSK fuzzy model to sent the information of output. In the conceptually, we design a learning method that use to feedback the information of output to the clustering since proposed algorithm perform to separate each classes in input data space. We show effectiveness of proposed method using simulation than previous ones

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.1
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• pp.59-67
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• 2009

The purpose of this study is to provide a decision support to select an appropriate layered manufacturing(LM) machine that suits the application of a part. Selection factors include concept model, form/fit/functional model, pattern model far molding, material property, build time and part cost that greatly affect the performance of LM machines. However, the selection of a LM is not an easy decision because they are uncertain and vague. For this reason, the aim of this research is to propose hybrid multiple attribute decision making approaches to effectively evaluate LM machines. In addition, because subjective considerations are relevant to selection decision, a fuzzy logic approach is adopted. The proposed selection procedure consists of several steps. First, we identify LM machines that the users consider After constructing the evaluation criteria, we calculate the weights of the criteria by applying the fuzzy Analytic Hierarchy Process(AHP) method. Finally, we construct the fuzzy Technique of Order Preference by Similarity to Ideal Solution(TOPSIS) method to achieve the ranking order of all machines providing the decision information for the selection of LM machines.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1241-1251
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• 2014

Based on the 4 principal research-performance criteria in 28 national universities in Korea, both cluster analysis and multidimensional scaling are performed in this paper. We can classify and/or specialize the initially unknown groups into a group of relatively homogeneous universities and then create new groupings without any preconceived notion of what clusters may arise. Furthermore, the level of similarity of individual universities can be visualized on the multidimensional space so that each university is then assigned coordinates in each of the 2 dimensions. Both types and characteristics of each university can be relatively evaluated and be practically exploited for the policy of the university authority through these results.

• Journal of Microbiology and Biotechnology
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• v.21 no.11
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• pp.1101-1108
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• 2011

The rise in global energy demand has prompted researches on developing strategies for transforming coal into a cleaner fuel. This requires isolation of microbes with the capability to degrade complex coal into simpler substrates to support methanogenesis in the coal beds. In this study, aerobic bacteria were isolated from an Indian coal bed that can solubilize and utilize coal as the sole source of carbon. The six bacterial isolates capable of growing on coal agar medium were identified on the basis of their 16S rRNA gene sequences, which clustered into two groups; Group I isolates belonged to the genus Rhizobium, whereas Group II isolates were identified as Chelatococcus species. Out of the 4 methods of whole genome fingerprinting (ERIC-PCR, REP-PCR, BOX-PCR, and RAPD), REP-PCR showed maximum differentiation among strains within each group. Only Chelatococcus strains showed the ability to solubilize and utilize coal as the sole source of carbon. On the basis of 16S rRNA gene sequence and the ability to utilize different carbon sources, the Chelatococcus strains showed maximum similarity to C. daeguensis. This is the first report showing occurrence of Rhizobium and Chelatococcus strains in an Indian coal bed, and the ability of Chelatococcus isolates to solubilize and utilize coal as a sole source of carbon for their growth.

• Journal of the Korean Institute of Landscape Architecture
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• v.34 no.1 s.114
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• pp.10-20
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• 2006

This study is to introduce the improvement and management of Ecoduct located in Uiwang's Mt. Obong, Gyeonggi province. The inner planting of Ecoduct is not very related to the flora of mountain areas nearby. The difference in the index of plant crown volume(GVZ), the similarity index(S.I.), and monitored animals is significant between Ecoduct and mountain areas. Putting together all of the results, the initial selection of movement and target species is excessive in number. Target species must be selected again. They will be species that are currently found on the site. The Ecoduct space must be divided into two parts: buffer planting space and wildlife corridor. Reforestation is needed since the GVZ is remarkably lower in Ecoduct than in the nearby mountain areas. Besides the vegetation of Ecoduct is densely planted in guidance planting space to connect the southern Quercus mongolica community and the northern Quercus acutissima community. Proper upkeep after construction should be continuously conducted to maintain the procedure of natural change.