• Proceedings of the KIEE Conference
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• 2001.11a
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• pp.208-212
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• 2001

In this paper, characteristics of the ac and do flashover voltages in the vertical air gap of a needle-plane, sphere-plane and rod-plane electrode system were investigated when the combustion flames were present near the high-voltage electrode. As the results of an experimental we found that the reduction of flashover voltages characteristics with the vertical distance caused, in comparison with the no-flame case were about 1/3 times when a short sap d=1[cm] and 2[cm] of the ac voltages were applied. Also, flashover voltages characteristics of the do negative polarity under sphere-plane and rod-plane electrode system with the combustion flame were increased about two times than those of the do positive polarity.

• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1391-1399
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• 2015

In recent times, studies sentiment analysis are being actively conducted by implementing natural language processing technologies for analyzing subjective data such as opinions and attitudes of users expressed on the Web, blogs, and social networking services (SNSs). Conventionally, to classify the sentiments in texts, most studies determine positive/negative/neutral sentiments by assigning polarity values for sentiment vocabulary using sentiment lexicons. However, in this study, sentiments are classified based on Thayer's model, which is psychologically defined, unlike the polarity classification used in opinion mining. In this paper, as a method for classifying the sentiments, sentiment categories are proposed by extracting sentiment keywords for major sentiments by using hashtags, which are essential elements of Instagram. By applying sentiment categories to user posts, sentiments can be determined through the similarity measurement between the sentiment adjective candidates and the sentiment keywords. The test results of the proposed method show that the average accuracy rate for all the sentiment categories was 90.7%, which indicates good performance. If a sentiment classification system with a large capacity is prepared using the proposed method, then it is expected that sentiment analysis in various fields will be possible, such as for determining social phenomena through SNS.

• 전기의세계
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• v.26 no.6
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• pp.78-85
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• 1977

This paper studies flashover voltage and surface corona loss of A.C and D.C in the mixed gas of air and SF$_{6}$ for solid insulators P.V.C, arcylic, glass and bakelite in two cases. In one case, those solids are covered with transformer oil and the other case, those solids are not covered with it. 1) The flashover voltage for each solids in SF$_{6}$ is more than three times compared with that in the air. The flashover voltage for P.V.C is the highest and then arcylic, glass, bakelite in a decreasing order. 2) The more the amount of SF$_{6}$ in the mixing ratio, the less corona loss. The P.V.C shows the least amount of corona loss and the bakelite the largest. 3) Compared with the corona loss of positive polarity and the negative polarity, the former has less corona loss than the latter. 4) The more the number of flashover discharge, the less insulation of each solids, but in case of bakelite, insulation almost vanishes after a couple of discharge. 5) When each insulator is covered with transformer oil, the flashover voltage generally increases and the corona loss decreases.eases.

• Bulletin of the Korean Chemical Society
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• v.29 no.2
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• pp.431-437
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• 2008

The solvent effects on the relative free energies of binding of K+ and Na+ ions to 12-crown-4 and Dlog Ks (the difference of stability constant of binding) have been investigated by a Monte Carlo simulation of statistical perturbation theory (SPT) in several solvents. Comparing the relative free energies of binding of K+ and Na+ ions to 12-crown-4, in CH3OH of this study with experimental works, there is a good agreement among the studies. We have reported here the quantitative solvent-polarity relationships (QSPR) studied on the solvent effects the relative free energies of binding of K+ and Na+ ions to 12-crown-4. We noted that DN(donor number) dominates the differences in relative solvation Gibbs free energies of K+ and Na+ ions and DN dominates the negative values in differences in the stability constant (Dlog Ks) as well as the relative free energies of binding of K+ and Na+ ions to 12-crown-4 and p* (Kamlet-Tafts solvatochromic parameters) dominates the positive values in differences in the stability constant (Dlog Ks) as well as the relative free energies of binding of K+ and Na+ ions to 12-crown-4.

• Molecules and Cells
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• v.19 no.3
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• pp.342-349
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• 2005

Cytokinins are adenine derivatives that regulate numerous plant growth and developmental processes, including apical and floral meristem development, stem growth, leaf senescence, apical dominance, and stress tolerance. However, not much is known about how cytokinin biosynthesis and metabolism is regulated. We identified a novel Arabidopsis gene, ALL, encoding an aldolase-like enzyme that regulates cytokinin signaling. An Arabidopsis mutant, all-1D, in which ALL is activated by the nearby insertion of the 35S enhancer, exhibited extreme dwarfism with rolled, dark-green leaves and reduced apical dominance, symptomatic of cytokinin-overproducing mutants. Consistent with this, ARR4 and ARR5, two representative primary cytokinin-responsive genes, were significantly induced in all-1D. Whereas SHOOT MERISTEMLESS (STM) and KNAT1, which regulate meristem development, were also greatly induced, expression of REV and PHV that regulate lateral organ polarity was inhibited. ALL encodes an aldolase-like enzyme that belongs to the HpcH/HpaI aldolase family in prokaryotes and is down-regulated by exogenous cytokinin, possibly through a negative feedback pathway. We propose that ALL is involved in cytokinin biosynthesis or metabolism and acts as a positive regulator of cytokinin signaling during shoot apical meristem development and determination of lateral organ polarity.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.11
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• pp.796-805
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• 1987

The flashover voltages have been investigated for spacer and unbridged-gap in SF6-N2 gas mixtures up to the value of 760(torr. cm), The gap was stressed by DC source The results obtained are as follows` 1) The flashover voltages for an unbridged gap and for a spacer in SF6, N2 and SF6-N2 gas mixtures follow the Paschen's curve. 2) The polarity effects was not observed in both unbridged gap and a spacer which had per ect contact with an electrode. The flashover voltages for negative polatity are lower than those for positive polarity in case of imperfect contact. 3) 3%flashover voltage is decreased by putting a spacer which had perfect contact with an electrode. The spacer which has a gap void shows the lowest flashover voltage. 4) The lowest spacer efficiency was obtained with higher gas pressure & large amount of N2 content. The flashover voltages depend on the gas pressure rather than the spacer efficienty at low value of pd. 5) The flashover voltages of gas mixtures of N2 with SF6 are relatively high, even though the amount of SF6 gas content is small.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.9
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• pp.1307-1312
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• 2013

In this paper, alkaline or acidic solution, in particular the principle of electrolysis to oxidize the metal surface to form a device isolation film is developed. In the past, mainly in the form of pulse voltage is applied to the anode only a unipolar method, but in this paper by using the H-bridge to the amount of the positive (+) voltage and the negative supply voltage, alternating voltage polarity devices were fabricated according to the characteristics of metal specimens with different electrical conditions to form an oxide film on the device was developed. Supply current variable was used for the PWM modulation, (+) and (-) polarity change of the H-bridge bipolar pulse voltage to supply the was that. As a result, a more uniform pores with unipolar film was formed.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.206-211
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• 2021

Internet users are increasingly invited to express their opinions on various subjects in social networks, e-commerce sites, news sites, forums, etc. Much of this information, which describes feelings, becomes the subject of study in several areas of research such as: "Sensing opinions and analyzing feelings". It is the process of identifying the polarity of the feelings held in the opinions found in the interactions of Internet users on the web and classifying them as positive, negative, or neutral. In this article, we suggest the implementation of a sentiment analysis tool that has the role of detecting the polarity of opinions from people about COVID-19 extracted from social media (tweeter) in the Arabic language and to know the impact of the pre-processing phase on the opinions classification. The results show gaps in this area of research, first of all, the lack of resources when collecting data. Second, Arabic language is more complexes in pre-processing step, especially the dialects in the pre-treatment phase. But ultimately the results obtained are promising.

• Annual Conference of KIPS
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• 2020.05a
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• pp.389-392
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• 2020

The growth in social media, blogs and restaurant listing directories have led to increasing customer reviews about restaurants, their quality of food items and services available on the internet. These user reviews offer a massive amount of valuable information that can be used for various decision-making purposes. Currently, most food recommendation sites provide recommendation scores about restaurants rather than food items of the restaurant and the provided recommendation scores may be biased since they are calculated only from user reviews listed only in their sites. Usually, people wants a reliable recommendation about foods, not restaurant. In this paper, we present a reliable Korean food items rating method; we first extract food items by applying NER technique to restaurant reviews collected from many Korean restaurant recommendation web sites, blogs and web data. Then, we apply lexicon-based sentiment analysis on collected user reviews and predict people's opinions as sentiment polarity scores (+1 for positive; -1 for negative; 0 for neutral). Finally, by taking average of all calculated polarity scores about a food item, we obtain a rating to individual menu items of the restaurant. The proposed food item rating is more reliable since it does not depend on reviews of only one site.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.39-70
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• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.