• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.810-819
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• 2013

FCM (Fuzzy C-Means) clustering algorithm, a typical split-based clustering algorithm, has been successfully applied to the various fields. Nonetheless, the FCM clustering algorithm has some problems, such as high sensitivity to noise and local data, the different clustering result from the intuitive grasp, and the setting of initial round and the number of clusters. To address these problems, in this paper, we determine fuzzy numbers which project the FCM clustering result on the axis with the specific attribute. And we propose a model that the fuzzy numbers apply to FDT (Fuzzy Decision Tree). This model improves the two problems of FCM clustering algorithm such as elevated sensitivity to data, and the difference of the clustering result from the intuitional decision. And also, this paper compares the effect of the proposed model and the result of FCM clustering algorithm through the experiment using real traffic and rainfall data. The experimental results indicate that the proposed model provides more reliable results by the sensitivity relief for data. And we can see that it has improved on the concordance of FCM clustering result with the intuitive expectation.

• Microbiology and Biotechnology Letters
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• v.51 no.4
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• pp.403-415
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• 2023

This study aimed to isolate and identify L-(+)-lactic acid-producing bacteria from tree barks collected in Thailand and evaluate the potential strain as probiotics. Twelve strains were isolated and characterized phenotypically and genotypically. The strains exhibited a rod-shaped morphology, high-temperature tolerance, and the ability to ferment different sugars into lactic acid. Based on 16S rRNA gene analysis, all strains were identified as belonging to Weizmannia coagulans. Among the isolated strains, BKMTCR2-2 demonstrated exceptional lactic acid production, with 96.41% optical purity, 2.33 g/l of lactic acid production, 1.44 g/g of lactic acid yield (per gram of glucose consumption), and 0.0049 g/l/h of lactic acid productivity. This strain also displayed a wide range of pH tolerance, suggesting suitability for the human gastrointestinal tract and potential probiotic applications. The whole-genome sequence of BKMTCR2-2 was assembled using a hybridization approach that combined long and short reads. The genomic analysis confirmed its identification as W. coagulans and safety assessments revealed its non-pathogenic attribute compared to type strains and commercial probiotic strains. Furthermore, this strain exhibited resilience to acidic and bile conditions, along with the presence of potential probiotic-related genes and metabolic capabilities. These findings suggest that BKMTCR2-2 holds promise as a safe and effective probiotic strain with significant lactic acid production capabilities.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.332-335
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• 2008

Newly developing SAR (Synthetic Aperture Radar) sensors commonly include high resolution X-band those data are expected to contribute various applications. Recent few studies are presenting potential of X-band SAR data in forest related application. This study tried to investigate the relationship between forest stand parameters and multi-band SAR normalized backscattering. Multi-band SAR data was radiometric corrected to compare signal from different forest stand condition. Then correlation coefficients were estimated between attribute of forest stand map and normalized backscattering coefficients. Although overall correlation coefficients are not high, only X-band shows strong relationship with DBH class than other bands. The signal of C- and L-band is composed of a large number of discrete tree components such as leaves, stems, even background soil. In forest, strength of radar backscattering is affected by complex parameters. Further study might be considered more various forest stand parameters such as canopy density, stand height, volume, and biomass.

• Journal of Intelligence and Information Systems
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• v.2 no.1
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• pp.45-58
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• 1996

This paper describes an a, pp.oach to recognizing composite features of prismatic parts. AAG (Attribute Adjacency Graph) is adopted as the basis of describing basic feature, but it is extended to enhance the expressive power of AAG by adding face type, angles between faces and normal vectors. Our a, pp.oach is called Extended AAG (EAAG). To simplify the recognition procedure, feature classification tree is built using the graph types of EEA and the number of EAD's. Algorithms to find open faces and dimensions of features are exemplified and used in decomposing composite feature. The processing sequence of recognized features is automatically determined during the decomposition process of composite features.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.892-894
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• 2005

의미 분석이란 프로그램의 각 구성요소의 결합이 의미적으로 타당한가를 분석하는 과정으로 최근 컴파일러의 제작에서 필수 불가결한 요소이며, 속성문법(attribute grammar)이나 경험적인 방법(manual method)으로 해결한다. 그러나 이러한 방법론들은 효율성이나 자동화 측면에서 제약성을 가진다. 본 연구에서는 이러한 단점을 보완하기 위해 의미 분석정보가 포함된 시멘틱 트리를 정의하고, 대부분의 컴파일러에서 사용되는 구문분석 결과물인 추상 구문 트리를 시멘틱 트리로 변환하는 기법을 제안한다. 시멘틱 트리 변환기법은 의미 분석과정을 시멘틱 노드 단위로 처리하므로, 의미 분석 과정이 일관적으로 수행되며 효율적이고, 타 자료구조로의 변환이 용이하며 자동화가 용이하다.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.2
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• pp.21-25
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• 2023

The purpose of this study was to compare the performance using multiple regression models to predict the energy consumption of steel industry. Specific independent variables were selected in consideration of correlation among various attributes such as CO2 concentration, NSM, Week Status, Day of week, and Load Type, and preprocessing was performed to solve the multicollinearity problem. In data preprocessing, we evaluated linear and nonlinear relationships between each attribute through correlation analysis. In particular, we decided to select variables with high correlation and include appropriate variables in the final model to prevent multicollinearity problems. Among the many regression models learned, Boosted Decision Tree Regression showed the best predictive performance. Ensemble learning in this model was able to effectively learn complex patterns while preventing overfitting by combining multiple decision trees. Consequently, these predictive models are expected to provide important information for improving energy efficiency and management decision-making at steel industry. In the future, we plan to improve the performance of the model by collecting more data and extending variables, and the application of the model considering interactions with external factors will also be considered.

• Bioinformatics and Biosystems
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• v.1 no.2
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• pp.134-138
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• 2006

Depression is the most common and widespread mood disorder. About 20% of the population might suffer a major, incapacitating episode of depression during their lifetime. This disorder can be classified into two types: major depressive disorders and bipolar disorder. Since pharmaceutical treatments are different according to types of depression disorders, correct and fast classification is quite critical for depression patients. Yet, classical statistical method, such as minnesota multiphasic personality inventory (MMPI), have some difficulties in applying to depression patients, because the patients suffer from concentration. We used electroencephalogram (EEG) analysis method fer classification of depression. We extracted nonlinearity of information flows between channels and estimated approximate entropy (ApEn) for the EEG at each channel. Using these attributes, we applied two types of data mining classification methods: decision tree and possibilistic support vector machines (PSVM). We found that decision tree showed 85.19% accuracy and PSVM exhibited 77.78% accuracy for classification of depression, 30 patients with major depressive disorder and 24 patients having bipolar disorder.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.4
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• pp.169-185
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• 2016

Recently mean stand height has been added as new attribute to forest type maps, but it is often too costly and time consuming to manually measure 9,100,000 points from countrywide stereo aerial photos. In addition, tree heights are frequently measured around tombs and forest edges, which are poor representations of the interior tree stand. This work proposes an estimation of mean stand height using an image-based point cloud, which was extracted from stereo aerial photo with FUSION S/W. Then, a digital terrain model was created by filtering the DSM point cloud and subtracting the DTM from DSM, resulting in nDSM, which represents object heights (buildings, trees, etc.). The RMSE was calculated to compare differences in tree heights between those observed and extracted from the nDSM. The resulting RMSE of average total plot height was 0.96 m. Individual tree heights of the whole study site area were extracted using the USDA Forest Service's FUSION S/W. Finally, mean stand height was produced by averaging individual tree heights in a stand polygon of the forest type map. In order to automate the mean stand height extraction using photogrammetric methods, a module was developed as an ArcGIS add-in toolbox.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.9
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• pp.409-418
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• 2015

In order to delivery of the correct information in IoT environment, it is important to deduce collected information according to a user's situation and to create a new information. In this paper, we propose a control access scheme of information through context-aware to protect sensitive information in IoT environment. It focuses on the access rights management to grant access in consideration of the user's situation, and constrains(access control policy) the access of the data stored in network of unauthorized users. To this end, after analysis of the existing research 'CP-ABE-based on context information access control scheme', then include dynamic conditions in the range of status information, finally we propose a access control policy reflecting the extended multi-dimensional context attribute. Proposed in this paper, access control policy considering the dynamic conditions is designed to suit for IoT sensor fusion environment. Therefore, comparing the existing studies, there are advantages it make a possible to ensure the variety and accuracy of data, and to extend the existing context properties.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.391-396
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• 2003

This paper proposes a new approach that converts continuous-valued attributes to categorical-valued ones considering the distribution of target attributes(classes). In this approach, It can be possible to get optimal interval boundaries by considering the distribution of data itself without any requirements of parameters. For each attributes, the distribution of target attributes is projected to one-dimensional space. And this space is clustered according to the criteria like as the density value of each target attributes and the amount of overlapped areas among each density values of target attributes. Clusters which are made in this ways are based on the probabilities that can predict a target attribute of instances. Therefore it has an interval boundaries that minimize a loss of information of original data. An improved performance of proposed discretization method can be validated using C4.5 algorithm and UCI Machine Learning Data Repository data sets.