• Journal of Korean Institute of Industrial Engineers
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• v.26 no.4
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• pp.363-375
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• 2000

In this paper we propose a content-based image retrieval method that can search large image databases efficiently by color, texture, and shape content. Quantized RGB histograms and the dominant triple (hue, saturation, and value), which are extracted from quantized HSV joint histogram in the local image region, are used for representing global/local color information in the image. Entropy and maximum entry from co-occurrence matrices are used for texture information and edge angle histogram is used for representing shape information. Relevance feedback approach, which has coupled proposed features, is used for obtaining better retrieval accuracy. Simulation results illustrate the above method provides 77.5 percent precision rate without relevance feedback and increased precision rate using relevance feedback for overall queries. We also present a new indexing method that supports fast retrieval in large image databases. Tree structures constructed by k-means algorithm, along with the idea of triangle inequality, eliminate candidate images for similarity calculation between query image and each database image. We find that the proposed method reduces calculation up to average 92.9 percent of the images from direct comparison.

• Journal of Information Science Theory and Practice
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• v.2 no.1
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• pp.6-21
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• 2014

This paper proposes a novel knowledge extraction system, TAKES (Two-step Approach for Knowledge Extraction System), which integrates advanced techniques from Information Retrieval (IR), Information Extraction (IE), and Natural Language Processing (NLP). In particular, TAKES adopts a novel keyphrase extraction-based query expansion technique to collect promising documents. It also uses a Conditional Random Field-based machine learning technique to extract important biological entities and relations. TAKES is applied to biological knowledge extraction, particularly retrieving promising documents that contain Protein-Protein Interaction (PPI) and extracting PPI pairs. TAKES consists of two major components: DocSpotter, which is used to query and retrieve promising documents for extraction, and a Conditional Random Field (CRF)-based entity extraction component known as FCRF. The present paper investigated research problems addressing the issues with a knowledge extraction system and conducted a series of experiments to test our hypotheses. The findings from the experiments are as follows: First, the author verified, using three different test collections to measure the performance of our query expansion technique, that DocSpotter is robust and highly accurate when compared to Okapi BM25 and SLIPPER. Second, the author verified that our relation extraction algorithm, FCRF, is highly accurate in terms of F-Measure compared to four other competitive extraction algorithms: Support Vector Machine, Maximum Entropy, Single POS HMM, and Rapier.

• Journal of Biomedical Engineering Research
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• v.21 no.5
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• pp.527-535
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• 2000

For a reliable patient set-up verification, better portal films are needed to track relevant features. Simulator films are compared with portal films as a reference image in radiotherapy planning. This shows some possibilities of the use of image information of simulator images for enhancement and restorations of portal images which are very poor in quality compared with the simulator images. This paper present an approach that combine an associative memory, a kind of artificial neural networks with fuzzy image enhancement technique using genetic algorithm which determines the fuzzy region of membership function by the use of maximum entropy principles. A higher portal image quality than conventional technique is achieved.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1103-1125
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• 2017

This paper aims to extract an ObjectProperty-UsageMethod relation, in particular the HerbalMedicinalProperty-UsageMethod relation of the herb-plant object, as a semantic relation between two related sets, a herbal-medicinal-property concept set and a usage-method concept set from several web documents. This HerbalMedicinalProperty-UsageMethod relation benefits people by providing an alternative treatment/solution knowledge to health problems. The research includes three main problems: how to determine EDU (where EDU is an elementary discourse unit or a simple sentence/clause) with a medicinal-property/usage-method concept; how to determine the usage-method boundary; and how to determine the HerbalMedicinalProperty-UsageMethod relation between the two related sets. We propose using N-Word-Co on the verb phrase with the medicinal-property/usage-method concept to solve the first and second problems where the N-Word-Co size is determined by the learning of maximum entropy, support vector machine, and naïve Bayes. We also apply naïve Bayes to solve the third problem of determining the HerbalMedicinalProperty-UsageMethod relation with N-Word-Co elements as features. The research results can provide high precision in the HerbalMedicinalProperty-UsageMethod relation extraction.

• Journal of The Geomorphological Association of Korea
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• v.24 no.2
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• pp.91-101
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• 2017

Quantitative forecasting methods based on spatial data and geographic information system have been used in predicting the landslide location. This study compared the simulated results of logistic, Bayesian, and maximum entropy models to understand the uncertainties of each model and identify the main factors that influence landslide. The study area is Boeun gun where 388 landslides occurred in the year of 1998. The verification results showed that the AUC of the three models was 0.84. However, the landslide susceptibility distribution of Maxent model was different from those of the other two models. With the same landslide occurrence data, the result of high susceptible area in Maxent model is smaller than Logistic or Bayesian. Maxent model, however, proved to be more efficient in predicting landslide than the other two models. In Maxent's simulations, the responsible factors for landslide susceptibility are timber age class, land cover, timber diameter, crown closure, and soil drainage. The results suggest that it is necessary to consider the possibility of overestimation when using Logistic or Bayesian model, and forest management around the study area can be an effective way to minimize landslide possibility.

• Journal of Korean Medical classics
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• v.32 no.3
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• pp.47-57
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• 2019

Objectives : We aim to assist in choosing an appropriate method for word extraction when analyzing East Asian Traditional Medical texts based on unsupervised learning. Methods : In order to assign ranks to substrings, we conducted a test using one method(BE:Branching Entropy) for exterior boundary value, three methods(CS:cohesion score, TS:t-score, SL:simple-ll) for interior boundary value, and six methods(BExSL, BExTS, BExCS, CSxTS, CSxSL, TSxSL) from combining them. Results : When Miss Rate(MR) was used as the criterion, the error was minimal when the TS and SL were used together, while the error was maximum when CS was used alone. When number of segmented texts was applied as weight value, the results were the best in the case of SL, and the worst in the case of BE alone. Conclusions : Unsupervised-Learning-Based Word Extraction is a method that can be used to analyze texts without a prepared set of vocabulary data. When using this method, SL or the combination of SL and TS could be considered primarily.

• Current Applied Physics
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• v.18 no.11
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• pp.1289-1293
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• 2018

The influence of crystallization treatment on the structure, magnetic properties and magnetocaloric effect of $Gd_{71}Ni_{29}$ melt-spun ribbons has been investigated in detail. Annealing of the melt-spun samples at 610 K for 30 min, a majority phase with a $Fe_3C$-type orthorhombic structure (space group, Pnma) and a minority phase with a CrB-type orthorhombic structure (space group, Cmcm) were obtained in the amorphous matrix. The amorphous melt-spun ribbons undergo a second-order ferromagnetic to paramagnetic phase transition at 122 K. For the annealed samples, two magnetic phase transitions caused by amorphous matrix and $Gd_3Ni$ phases occur at 82 and 100 K, respectively. The maximum magnetic entropy change $(-{\Delta}S_M)^{max}$ is $9.0J/(kg{\cdot}K)$ (5T) at 122 K for the melt-spun ribbons. The values of $(-{\Delta}S_M)^{max}$ in annealed ribbons are 1.0 and $5.7J/(kg{\cdot}K)$, corresponding to the two adjacent magnetic transitions.

• Corrosion Science and Technology
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• v.21 no.1
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• pp.9-20
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• 2022

The aim of this paper is to study corrosion inhibition of Aluminum with Capparis decidua extract. The study was performed in a 1.0 M solution of hydrochloric acid (HCl) and was monitored both by measuring mass loss and by using electrochemical and polarization methods. A scanning electron microscopy (SEM) technique was also applied for surface morphology analysis. The results revealed high inhibition efficiency of Capparis decidua extract. Our data also determined that efficiency is governed by temperature and concentration of extract. Optimum (88.2%) inhibitor efficiency was found with maximum extract concentration at 45 o C. The results also showed a slight diminution of aluminum dissolution when the temperature is low. Based on the Langmuir adsorption model, Capparis decidua adsorption on the aluminum surface shows a high regression coefficient value. From the results, the activation enthalpy (∆H#) and activation entropy (∆S#) were estimated and discussed. In conclusion, the study clearly shows that Capparis decidua extract acted against aluminum corrosion in acidic media by forming a protective film on top of the aluminum surface.

• Smart Structures and Systems
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• v.32 no.1
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• pp.61-81
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• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

• Journal of the Korean Chemical Society
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• v.68 no.2
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• pp.82-86
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• 2024

This paper presents the solid-state synthesis of insoluble Prussian blue (Fe₄[Fe(CN)₆]₃·xH₂O, PB) in a ball mill, utilizing the fundamental components of PB. Solid-state synthesis offers several advantages, such as being solvent-free, quantitative, and easily scalable for industrial production. Traditionally, the solid-state synthesis of PB has been limited to the reaction between iron(II/III) ions and hexacyanoferrate(II/III) complex ions, essentially an extension of the solution-based coprecipitation method to solid-state reaction. Taking a bottom-up approach, a reaction is designed where the reactants consist of the basic building blocks of PB: Fe²⁺ ions and CN^- ions. The reaction, with a molar ratio of Fe²⁺ and CN^- corresponding to 1:2.8, yields PB, while a ratio of 1:6.6 results in a mixture of potassium hexacyanoferrate(II) (K₄Fe(CN)₆), potassium chloride (KCl), and potassium cyanide (KCN). This synthetic approach holds promise for environmentally friendly methods to synthesize multimetallic PB with maximum entropy in nearly quantitative yield.