• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.5
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• pp.59-69
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• 2011

Since power analysis attack which uses a characteristic that power consumed by crypto device depends on processed data has been proposed, many logics that can block these correlation originally have been developed. DRP logic has been adopted by most of logics maintains power consumption balanced and reduces correlation between processed data and power consumption. However, semi-custom design is necessary because recently design circuits become more complex than before. This design method causes unbalanced design pattern that makes DRP logic consumes unbalanced power consumption which is vulnerable to power analysis attack. In this paper, we have developed new logic style which adds another discharge phase to discharge two output nodes at the same time based on DyCML to remove this unbalanced power consumption. Also, we simulated 1bit fulladder to compare proposed logic with other logics to prove improved performance. As a result, proposed logic is improved NED and NSD to 60% and power consumption reduces about 55% than any other logics.

• Journal of Embryo Transfer
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• v.19 no.2
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• pp.101-112
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• 2004

Chloride($Cl^-$) channels play critical roles in cell homeostasis and its specific functions such as volume regulation, differentiation, secretion, and membrane stabilization. The presence of these channels have been reported in all kinds of cells and even in frog oocytes. These essential role of $Cl^-$­ channels in cell homeostasis possibly play any role in egg homeostasis and in the early stage of development, however, there has been no report about the presence of $Cl^-$­ channel in the mammalian oocyte. This study was performed to elucidate the presence of $Cl^-$­ channels in hamster eggs. When allowing only $Cl^-$­ to pass through the channel of the egg membrane by using impermeant cation such as N-methyl-D-glucamine(NMDG), single channel currents were recorded. These channel currents showed typical long-lasted openings interrupted by rapid flickering. Mean open $time({\tau}o)$ was 43${\pm}$10.14 ms(n=9, at 50 mV). The open probability(Po) was decrease with depolarization. The current-voltage relation showed outward rectification. Outward slop conductance(32${\pm}$5.4 pS, n=22) was steeper than the inward slop conductance(10${\pm}$1.3 pS). Under the condition of symmetrical 140 mM NaCl, single channel currents were reversed at 0 mV(n=4). This reversal potential(Erev) was shifted from 0 mV at 140 mM concentration of internal NaCl(140 mM [Na+]i) to ­9.8${\pm}$0.5 mV(n=4) at 70 mM [Na+]i and 11.5${\pm}$1.9 mV at 280 mM [Na+]i(n=4) respectively, strongly suggesting that these are single $Cl^-$­ channel currents. To examine further whether this channel has pharmacological property of the $Cl^-$­ channel, specific Cl­ channel blockers, IAA-94(Indanyloxyacetic acid-94) and DIDS(4, 4'-diisothiocyan ostillben- 2-2'disulfonic acid) were applied. IAA-94 inhibited the channel current in a dose-dependent manner and revealed a rapid and flickering block. From these electrophysiological and pharmacological resluts, we found the novel $Cl^-$­ channel present in the hamster oocyte membrane. The first identification of $Cl^-$­ channel in the hamster oocyte may give a clue for the further study on the function of $Cl^-$­ channel in the fertilization and cell differentiation.

• Journal of Oral Medicine and Pain
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• v.30 no.4
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• pp.457-464
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• 2005

The purpose of our study was to investigate whether the intrapulpal temperature during cavity preparation of enamel or dentin with Er:YAG laser still remained in range of safety for dental pulp protection when combined with appropriate water flow rate. The effect of different pulse repetition rates at the same pulse energy during ablation was evaluated as well. Caries-free, restoration-free extracted human molar teeth were prepared for the specimen and divided two experimental groups of enamel and dentin. Each group comprised 5 specimens and each of tooth specimens were embedded into a resin block each and measuring probe was placed on the irradiated pulpal walls. For experiments of dentin ablation, enamel layers were prepared to produce dentin specimen with a same dentin thickness of 2 mm. A pulse energy of Er:YAG laser was set to 300 mJ and three different pulse repetition rates of 20 Hz, 15 Hz and 10 Hz were employed. Laser beam was delivered with 3 seconds and less per application over enamel and dentin surfaces constant sized by $3\;mm{\times}2\;mm$ and water spray added during irradiation was a rate of 1.6 ml/min. Temperature change induced by Er:YAG laser irradiation was monitored and recorded While enamel was ablated, there was no significant difference of temperature related to pulse repetition rates(p=0.358) and temperature change at any pulse repetition rate was negligible. Significant statistical difference in temperature changes during cavity preparation in dentin existed among three different pulse groups(p=0.001). While temperature rise was noticeable when the dentinal wall was perforated, actual change of temperature due to Er:YAG laser irradiation was not enough to compromise safety of dental pulp when irradiation was conjugated with appropriate water spray. Conclusively, it can be said that cavity preparation on enamel or dentin with an Er:YAG laser is performed safely without pulp damage if appropriate volume of water is sprayed properly over the irradiated site.

• Journal of Animal Science and Technology
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• v.47 no.4
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• pp.565-572
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• 2005

An experiment was conducted to determine the effect of supplementation of insoluble dietary fiber (Vitacel®) on growth performance and nutrient digestibility in weanling pigs. A total of 96 pigs that averaged 6.49$\pm$0.52kg BW and 23$\pm$2.1d age were allocated in a randomized block design with two pigs per pen and 12 pens per treatment. Pigs and feeders were weighed 10-days interval for the 40-d trials to determine ADG, ADFI and feed:gain ratio(F:G). Pigs were fed one of four diets:1) Control diet (C) 2) C+0.3% insoluble dietary fiber(IDF) 3) C+0.6% IDF and 4) c+0.9% IDF. For the determination of fecal nutrients digestibility, pigs were fed diets(diet 2) with 1% Celite-545(Fluka) as a marker and feces were collected on $9^{th}$ day and $18^{th}$ day after feeding diet 2. During the whole experimental period, pigs fed diet with 0.3% IDF have significantly higher ADG than other dietary treatment groups(P<0.05). ADG of pigs fed diet with 0.6% IDF was higher than that of pigs fed control diet(P<0.05). However, there was no significant difference in ADG between control group and 0.9% IDF group(P>0.05). ADFI of pigs fed diet with 0.3% IDF was significantly higher than any other dietary treatment groups(P<0.05). There was no significant difference in ADFI among control group, 0.6% and 0.9% IDF supplementation groups (P>0.05). Digestibilities of organic matter, crude protein, crude fiber and energy were significantly higher in 0.3% IDF supplementation group than any other dietary groups. However, there was no difference in over all nutrient digestibilities between 0.6% and 0.9% IDF group. Feeding diets more than 0.6% IDF did not affect the rest of the nutrients digestibilities except for ADF digestibility compared to control diet. Dietary supplementation level of IDF showed a significant quadratic effect on performance improvement of piglets. This response of growth performance to IDF supplementation is, as expected, in agreement with that of nutrient digestibility. Our results showed that IDF supplementation to diet for weaned piglets might be beneficial in terms of growth and nutrient digestibility. However, there should be more study on the relationship between level of IDF supplementation and piglet response as well as the exact mode of action of IDF in weaned piglets.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.