• Language and Information
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• v.14 no.2
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• pp.159-181
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• 2010

This paper presents a hybrid approach to the sentiment analysis of online texts. The sentiment of a text refers to the feelings that the author of a text has towards a certain topic. Many existing approaches employ either a pattern-based approach or a machine learning based approach. The former shows relatively high precision in classifying the sentiments, but suffers from the data sparseness problem, i.e. the lack of patterns. The latter approach shows relatively lower precision, but 100% recall. The approach presented in the current work adopts the merits of both approaches. It combines the pattern-based approach with the machine learning based approach, so that the relatively high precision and high recall can be maintained. Our experiment shows that the hybrid approach improves the F-measure score for more than 50% in comparison with the pattern-based approach and for around 1% comparing with the machine learning based approach. The numerical improvement from the machine learning based approach might not seem to be quite encouraging, but the fact that in the current approach not only the sentiment or the polarity information of sentences but also the additional information such as target of sentiments can be classified makes the current approach promising.

• Journal of the Korean institute of surface engineering
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• v.47 no.4
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• pp.174-180
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• 2014

In this study, TiAlSiN coatings have been successfully synthesized on stainless steel and tungsten carbide substrate by a hybrid coating method employing a cathodic arc and a magnetron sputtering source. TiAl and Si target were vaporized with the cathodic arc source and the magnetron sputtering source, respectively. Process gas was the mixture of nitrogen and argon gas. With the increase of Si content, the crystallinity and the grain size of TiAlSiN film was decreased. At the Si content of more than 8 at.%, grain size of TiAlSiN was saturated at around 2 nm. The hardness value of the TiAlSiN film increased with incorporation of Si, and had the maximum value of ~ 3,233 Hv at the Si content of 9.2 at.%. The oxidation resistance of TiAlSiN film was enhanced with the increase of Si content.

• Animal cells and systems
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• v.12 no.3
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• pp.143-148
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• 2008

Heterogeneous nuclear ribonucleoprotein E1(hnRNP E1) is one of the primary pre-mRNA binding proteins in human cells. It consists of 356 amino acid residues and harbors three hnRNP K homology(KH) domains that mediate RNA-binding. The hnRNP E1 protein was shown to play important roles in mRNA stabilization and translational control. In order to enhance our understanding of the cellular functions of hnRNP E1, we searched for interacting proteins through a yeast two-hybrid screening while using HeLa cDNA library as target. One of the cDNA clones was found to be human ribosomal protein L18a cDNA(GenBank accession number BC071920). We demonstrated in this study that human ribosomal protein L18a, a constituent of ribosomal protein large subunit, interacts specifically with hnRNP E1 in the yeast two-hybrid system. Such an interaction was observed for the first time in this study, and was also verified by biochemical assay.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2004.03a
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• pp.271-277
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• 2004

As one of the concepts of the laser/electric hybrid propulsion system, a feasibility study on possibilities of electrostatic acceleration of a laser ablation plasma induced from a solid target was conducted. Energy distributions of accelerated ions were measured by a Faraday cup. A time-of-flight measurement was also conducted for ion velocity measurement. It was found that an average speed of ions from a pure laser ablation in this case was about 20 km/sec for pulse energy of 40 $\mu$J/pulse with pulse width of 250 psec. On the other hand, through an electrostatic field with a + I ,000 V electrode, the speed could be accelerated up to 40 km/sec. It was shown that the electrode with positive potential was more effective than that with negative potential for positive-ion acceleration in laser induced plasma, or pulsed plasma, in which ions were induced with the Coulomb explosion following electrons. In addition, the ion-acceleration or deceleration strongly depended on conditions of pairs of inner diameter and electrodes gap.

• Journal of Power System Engineering
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• v.21 no.4
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• pp.5-17
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• 2017

This study focuses on the application of the PEM Fuel Cell(PEMFC) hybrid system, which includes a regenerative braking system with supercapacitor(SC) and battery. The purpose of this study is to evaluate the characteristics of regenerative energy and to propose solutions to increase regenerative energy via vehicle simulation. To achieve this target, we set the rated motor speed to 3,000/2,500/2,000 rpm. Because the flywheel is directly connected to the motor, the generator activates regenerative braking by using the rotational momentum of the flywheel when the flywheel reaches the set speed after the motor stops. We could then measure the characteristics of regenerative braking of voltage, current, power, energy change, etc. Meanwhile, we calculate the storage efficiency of the SC or the battery. Our results show that the SC stores 18% of the regenerative energy, while battery stores 15% of the energy. Since the regenerative energy decreases with the decrease of the motor rotating speed that 5,027 J and 2,915 J are restored at 3,000 and 2,500 rpm, respectively. The experimental results also prove that regenerative braking energy is able to be obtained if and only if the speed of flywheel is over 2,500 PRM, and the efficiency of the system can be further improved.

• Journal of the Korean Society of Safety
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• v.34 no.3
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• pp.1-7
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• 2019

A variety of composite materials are applied to industries for the realization of light weight and high strength. Fiber-reinforced composites have different strength and range of application depending on the weaving method. The mechanical performance of CFRP(Carbon Fiber Reinforced Plastic) in many areas has already been demonstrated. Recently, the application of hybridization has been increasing in order to give a compensation for brittleness of CFRP. Target materials are UHMWPE (Ultra High Molecular Weight Polyethylene), which has excellent cutting and chemical resistance, so it is applied not only to industrial safety products but also to places that lining performance is expected for household appliances. In this study, the CFRP and UHMWPE of plain weave, which are highly applicable to curved products, were molded into laminated hybrid composite materials by autoclave method. The mechanical properties and the mode I failure behavior between the layers were evaluated. The energy release rate G has decreased as the initial crack length ratio increased.

• ETRI Journal
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• v.45 no.3
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• pp.448-461
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• 2023

This article performs a detailed data scrutiny on a chronic kidney disease (CKD) dataset to select efficient instances and relevant features. Data relevancy is investigated using feature extraction, hybrid outlier detection, and handling of missing values. Data instances that do not influence the target are removed using data envelopment analysis to enable reduction of rows. Column reduction is achieved by ranking the attributes through feature selection methodologies, namely, extra-trees classifier, recursive feature elimination, chi-squared test, analysis of variance, and mutual information. These methodologies are ranked via Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) using weight optimization to identify the optimal features for model building from the CKD dataset to facilitate better prediction while diagnosing the severity of the disease. An efficient hybrid ensemble and novel similarity-based classifiers are built using the pruned dataset, and the results are thereafter compared with random forest, AdaBoost, naive Bayes, k-nearest neighbors, and support vector machines. The hybrid ensemble classifier yields a better prediction accuracy of 98.31% for the features selected by extra tree classifier (ETC), which is ranked as the best by TOPSIS.

• Geomechanics and Engineering
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• v.34 no.5
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• pp.507-527
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• 2023

Accurately estimation of the geo-mechanical parameters in Artificial Ground Freezing (AGF) is a most important scientific topic in soil improvement and geotechnical engineering. In order for this, one way is using classical and conventional constitutive models based on different theories like critical state theory, Hooke's law, and so on, which are time-consuming, costly, and troublous. The others are the application of artificial intelligence (AI) techniques to predict considered parameters and behaviors accurately. This study presents a comprehensive data-mining-based model for predicting the Young's Modulus of frozen sand under the triaxial test. For this aim, several single and hybrid models were considered including additive regression, bagging, M5-Rules, M5P, random forests (RF), support vector regression (SVR), locally weighted linear (LWL), gaussian process regression (GPR), and multi-layered perceptron neural network (MLP). In the present study, cell pressure, strain rate, temperature, time, and strain were considered as the input variables, where the Young's Modulus was recognized as target. The results showed that all selected single and hybrid predicting models have acceptable agreement with measured experimental results. Especially, hybrid Additive Regression-Gaussian Process Regression and Bagging-Gaussian Process Regression have the best accuracy based on Model performance assessment criteria.

• Tunnel and Underground Space
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• v.14 no.6 s.53
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• pp.399-410
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• 2004

The utilization of AS(Artificial Intelligence) and Database could be considered as an useful access for the application of underground information from the point of a geotechnical methodology. Its detailed usage has been recently studied in many fields of geo-sciences. In this paper, the target of usage is on controlling the injection of grout which more scientific access is needed in the grouting that has been used a major method in many engineering application. As the proposals for this problem it is suggested the methodology consisting of a fuzzy-neural hybrid system and a database. The database was firstly constructed for parameters dynamically varied according to the conditions of rock mass during the injection of grout. And then the conceptional model for the fuzzy-neural hybrid system was investigated fer optimally finding the controlling range of the grout valve. The investigated model applied to four cases, and it is found that the controlling range of the grout valve was reasonably deduced corresponding to the mechanical phenomena occurred by the injection of grout. Consequently, the algorithm organizing the fuzzy-neural hybrid system and the database as a system can be considered as a tool for controlling the injection condition of grout.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.12
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• pp.94-102
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• 2016

Recently, the I/O performance of a single node is rapidly improving due to the advent of high-performance SSD. As a result, the next-generation storage platform based on SSD has received a great deal of attention and such storage platforms are increasingly adopted to commodity servers or data centers that look for the high-bandwidth computation and I/O. However, building all SSD-based storage platform may not be cost-effective because the price per storage capacity is very high as compared to that of HDD. In this paper. we propose a hybrid file management solution, called HyPLVM(Hybrid Priority Logical Volume Manager), which combines the strength of SSD with the desirable aspects of low-price, high-storage capacity HDD. HyPLVM prioritizes the files and directories to be accessed by users, in order to determine the target storage device (SSD/HDD) in which files are allocated, while mitigating the cost of building storage platforms.