• 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.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.36 no.3
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• pp.175-185
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• 2000

This paper described on a basic study to organize fishing vessel control system in order to control efficiently fishing vessel in Korean offshore. It was digitalized ARPA image on the fishing processing of a fleet of purse seiner in conducting fishing operation at Cheju offshore in Korea as a digital camera and then simulated by used VTMS. Futhermore, it was investigated on the application of FVTMS which can control efficiently fishing vessels in fishing ground. The results obtained were as follows ; (1) It was taken 16 minutes and 35 minutes to casting and hauling net in fishing processing respectively. The length of rope pulled by scout boat was 200m, tactical diameter in casting net was 340.8m, turning speed was 6kts as well. (2) The processing of casting and hauling net was moved to SW, NE as results of simulation when the current direction and speed set into NE, 2kts and SW, 2kts respectively. Such as these results suggest that can predict to control the fishing vessel previously with information of fishing ground, fishery and ship's maneuvering, etc. (3) The control range of VTMS radar used in simulation was about 16 miles. Although converting from a radar of the control vessel to another one, it was continuously acquired for the vector and the target data. The optimum control position could be determined by measuring and analyzing to distance and direction between the control vessel and the fleet of fishing vessel. (4) The FVTMS(fishing vessel traffic management services) model was suggested that fishing vessels received fishing conditions and safety navigation information can operate safely and efficiently.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.16 no.2
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• pp.106-115
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• 2011

The aim of this study is to assess the importance of ballast water discharge as a vector for the introduction of exotic species into Pusan and Daesan Ports, Korea. We also examined to understand the impacts of environmental factors on the survival success of introduced species by ship's ballast water in laboratory experiments. Seven ship's ballast water originated from the coastal water of China (Taicang, Ningbo and Jinshan), Japan (Tokuyama, Moji and Akita), and Singapore. According to PCA (principal components analysis) analysis, environmental factor in the each ballast and shipside waters were different by bioregion. Based on cluster analysis, the phytoplankton community structures were distinguished for ballast water origin. Most of the major taxonomic groups were diatoms and, the others were dinoflagellate, silcoflagellate and several fresh-waters species. In particular, species number and standing crops of phytoplankton in the ballast tanks decreased with the increasing age ofballast water(r = -0.35 for standing crop; r = -0.63 for species number). In the laboratory study, although phytoplankton in ballast water treatment did not survive even in optimal temperature, the in vivo fluorescence of phytoplankton viability increased under the nutrient typical of shipside water and F/2 medium at $15^{\circ}C$ and $20^{\circ}C$. The diatoms species such as Skeletonema costatum and Thalassiosira pseudonana in ballast water were successfully regrown. On the salinity gradient experiments for Shui Shan (2) vessel, several freshwater species, brackish and marine species were successfully adapted. Of these, S.costatum was able to tolerate a wide range of salinities (10 to 30 psu) and its species-specific viability was suitable for colonization.

• Journal of the Korean Ceramic Society
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• v.34 no.2
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• pp.145-156
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• 1997

ZrO2 phase transformations depending on the type and amount of dopants and the sintering temperatures were studied for the 2 components (CaO-, Y2O3-, MgO-ZrO2) and the 3 components(MgO-ZrO2-Al2O3)ZrO2 powder by X-ray diffraction and Raman spectroscopy. In the CaO- and Y2O3-ZrO2 systems, as the CaO and Y2O3 contents increased to 6~15mol% and 3~15mol% respectively, we were not able to identify between tetragonal and cubic in the X-ray diffraction patterns. On the other hand, all Raman modes shifted to lower wavenumbers, decreasing in intensity and the number of bands, markedly. These phenomena were caused by tetragonallongrightarrowcubic phase transformation and interpreted by the breakdown of the wave vector selection rule(k=0) and the structural disorder associated with the formation of oxygen sublattice which was caused by the substitution between Zr4+ ion and Ca2+ or Y3+ ion in ZrO2 matrix. The monoclinic to cubic phase transformation occurred in 10mol% MgO-ZrO2 system. As the Al2O3 content increased from 0 to 20mol% in the MgO-ZrO2-Al2O3 systems, cubic phase transformed to monoclinic phase, this is because the MgO didn't play a role in a stabilizer because of the formation of the spinel(MgAl2O4) by the reaction between MgO and Al2O3, Also, the ZrO2 phase transformation was explained by the change of it's lattice parameters depending on the type and amount of dopants. Namely, as the amount of dopant increased to 10~13mol%, the axial ra-tio c/a came close to unity with increasing the lattice parameter a and decreasing the lattice parameter c. At that time, the tetragonallongrightarrowcubic phase transformation occurred.

• Korean Journal of Animal Reproduction
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• v.25 no.4
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• pp.339-347
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• 2001

Sperm-mediated DNA transfer has a potential to markedly simplify techniques for the generation of transgenic animals. The exogenous DNA transfer by intracytoplasmic sperm injection (ICSI) procedure has been recently introduced in the production of transgenic animals. In this study, the developmental competence and tile expression rates of transgene were investigated after injection of spermatozoon or sperm head with enhanced green fluorescent protein (EGFP) gene into the mature porcine oocytes. The porcine oocytes were injected with intact sperm, membrane-disrupted sperm or sperm head. After injection. embryos were cultured in NCSU23 medium up to the blastocyst stage, and the developmental competence and expression rates were studied. The developmental rate (67.0%) of sperm injection group was higher than that (59.7%) of sperm head injection group, and the rates of EGFP expression were also significantly different between sperm injection and sperm head injection groups (42.1 vs 20.0%) (F＜0.05). In the porcine oocytes injected with sperm treated with different methods of membrane disruption, the removal of sperm membrane did not alter the developmental competence of embryos. The rate of blastocysts at 7 days after injection with intact and membrane disrupted sperm were 15.0 and 14.2%, respectively. The EGFP expression rates, 38.4% in embryos injected with frozen-thawed sperm was higher than that, 22.4% of embryos injected with the Triton X-100 treated sperm. Prior to injection, sperm were cultured in different EGFP gene concentrations from 0.Ol to 1ng/u${mu}ell$. However, no significant difference in developmental rates of embryos among different concentrations of EGFP gene were observed. The highest expression rate of EGFP gene, 37.4% was obtained from the embryos injected with spermatozoa treated with 0.1 ng/${mu}ell$ EGFP gene. These results suggested that exogenous DNA could be attached to the membrane disrupted sperm, and that these sperm could be used as a vector carrying foreign DNA into embryos.

• Journal of Korea Port Economic Association
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• v.37 no.2
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• pp.73-89
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• 2021

The handysize bulk carriers are capable of transporting a variety of cargo that cannot be transported by mid-large size ship, and the spot chartering market is active, and it is a market that is independent of mid-large size market, and is more risky due to market conditions and charterage variability. In this study, Granger causality test, the Impulse Response Function(IRF) and Forecast Error Variance Decomposition(FEVD) were performed using monthly time series data. As a result of Granger causality test, coal price for coke making, Japan steel plate commodity price, hot rolled steel sheet price, fleet volume and bunker price have causality to Baltic Handysize Index(BHSI) and charterage. After confirming the appropriate lag and stability of the Vector Autoregressive model(VAR), IRF and FEVD were analyzed. As a result of IRF, the three variables of coal price for coke making, hot rolled steel sheet price and bunker price were found to have significant at both upper and lower limit of the confidence interval. Among them, the impulse of hot rolled steel sheet price was found to have the most significant effect. As a result of FEVD, the explanatory power that affects BHSI and charterage is the same in the order of hot rolled steel sheet price, coal price for coke making, bunker price, Japan steel plate price, and fleet volume. It was found that it gradually increased, affecting BHSI by 30% and charterage by 26%. In order to differentiate from previous studies and to find out the effect of short term lag, analysis was performed using monthly price data of major cargoes for Handysize bulk carriers, and meaningful results were derived that can predict monthly market conditions. This study can be helpful in predicting the short term market conditions for shipping companies that operate Handysize bulk carriers and concerned parties in the handysize chartering market.

• Journal of Korean Academy of Dental Administration
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• v.5 no.1
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• pp.31-37
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• 2017

Recently interest in infection control is increasing in hospitalsnfection control has become more important in the overall health care practiceental hospital also requires thorough infection control. There are various kinds of vectormedical clothing. Contaminated clothing of a hospital staff can be a vector of nosocomial infecton. actual case of nosocomial infecton caused by contaminated medical clothing, nursing students were measuring contamination levels of uniforms and pathogenic microorganism wdetected in front of the uniform and pocket. There is also a high risk of exposure to contamination in the dental hospital. We conducted a study to enhance awareness about infection and proper clothing management by comparing before and after contamination of clothing caused by aerosols produced during scaling. Subjects were scaling operators' uniforms in the department of dental hygiene, K University located in Daejeon. Before scaling, the uniform was sterilized by autoclavecaling was performed times in the same place (an average of 60 minutes per person, a total of 180 minutes). ive parts of the uniform (sleeves, chest, belly, thigh, edge of pants) contracted Rodak-plate for 15 seconds. After incubating the contacted Rodak-plate at 37℃ incubator, contamination levels by measuring the number of colonies. As a result, all parts increased number of colonies. ontamination order chestedge of pants thigh belly sleeves. Increase rate of colonies was also high in the order chest edge of pants thigh belly sleeves. This study showed seriousness of clothing contaminationcaused by aerol produced during scalingcontamination of clothing can be a path to nosocomial infecton. According to th study, infection control for clothing as well as dental instruments should be implemented and thorough infection control training needed for dental staff. In further researches, practical infection prevention supplementing clothing management method.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.