• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.340-340
• /
• 2021

가뭄의 특성상 시점과 종점을 명확하게 정의하기 어렵기 때문에 기준수문량을 설정하고 부족량과 지속기간을 정의하는 것이 일반적이다. 대상 수문량은 강우나 유출량을 사용할 수 있지만, 두 성분간 지체와 감쇄효과로 인하여 빈도해석의 결과는 차이를 보일 수 밖에 없어, 사용 목적에 따라 선별적으로 적용해야 한다. 가뭄빈도해석은 강우를 기반으로 지속기간과 심도를 정의하여 빈도를 해석하는 연구가 선행되어왔지만, 기본적으로 강우의 간헐적 발생특성과 체감도의 한계가 문제로 지적되고 있다. 본 연구에서는 댐 유입량의 Run 시계열 특성을 이용하여 다양한 유황을 기준유량으로 활용하여 가뭄의 시점과 종점에 대한 가뭄사상을 추출하고 지속기간과 누적부족량을 계산하여 가뭄빈도해석의 변수로 설정하였다. 두 변수간의 복잡한 상호 관계를 해석하기 위해 Copula 함수를 이용한 이변량 가뭄빈도해석을 진행하였다. 먼저 소양강댐('74-'19) 유입량, 충주댐('86-'19) 유입량을 연구대상지역으로 설정하여, 두 유역의 유입량의 추세분석을 통해 시간의존성을 파악하였다. 유황분석에 사용되는 분위량중 평수량을 기준값으로 사용하여 각 년별 최대 지속기간과 누적부족량을 추출하였다. Copula 가뭄빈도해석을 수행하기 전에 지속기간에는 GEV, 누적 부족량에는 Log-normal 분포를 적용해 단변량 누적확률분포를 계산하여 재현기간을 도출하였다. 이변량 빈도해석에 Clayton Copula 함수를 적용하여 가뭄빈도해석을 진행하였고, Copula 이변량 재현기간과 SDF곡선을 도출하였다. Clayton Copula를 이용한 이변량 가뭄빈도해석의 결과로 소양강댐의 가장 극심한 가뭄은 1996년으로 단변량 재현기간은 지속기간 기준 9.11년, 누적부족량 기준 17.26년, Copula 재현기간은 141.19년 이며 충주댐의 가장 극심한 가뭄은 2014년으로 단변량 재현기간은 지속기간 기준 17.76년, 누적부족량 기준 18.72년, Copula 재현기간은 184.19년으로 단변량 가뭄빈도해석을 통한 재현기간보다 Copula 재현기간이 높은 결과가 도출되었다. Run 시계열을 바탕으로 한 기준유량의 임계값 기준 Event 산정과 Copula를 이용한 빈도해석은 가뭄분석에 이용되는 자료의 상관관계와 분포특성을 재현하는데 효과적인 특징이 있다. 이를 미루어 보아 Copula 함수를 이용한 가뭄빈도해석의 재현기간은 보다 현실적인 재현기간을 도출할 수 있는 것으로 판단된다. 임계값의 조정을 통해 가뭄빈도해석의 변수의 양이 늘어나면, 보다 정확도 높은 재현기간을 도출하여 수문학적 가뭄을 정의할 수 있을 것이라고 사료된다.

• Journal of Intelligence and Information Systems
• /
• v.23 no.2
• /
• pp.71-88
• /
• 2017

Language models were originally developed for speech recognition and language processing. Using a set of example sentences, a language model predicts the next word or character based on sequential input data. N-gram models have been widely used but this model cannot model the correlation between the input units efficiently since it is a probabilistic model which are based on the frequency of each unit in the training set. Recently, as the deep learning algorithm has been developed, a recurrent neural network (RNN) model and a long short-term memory (LSTM) model have been widely used for the neural language model (Ahn, 2016; Kim et al., 2016; Lee et al., 2016). These models can reflect dependency between the objects that are entered sequentially into the model (Gers and Schmidhuber, 2001; Mikolov et al., 2010; Sundermeyer et al., 2012). In order to learning the neural language model, texts need to be decomposed into words or morphemes. Since, however, a training set of sentences includes a huge number of words or morphemes in general, the size of dictionary is very large and so it increases model complexity. In addition, word-level or morpheme-level models are able to generate vocabularies only which are contained in the training set. Furthermore, with highly morphological languages such as Turkish, Hungarian, Russian, Finnish or Korean, morpheme analyzers have more chance to cause errors in decomposition process (Lankinen et al., 2016). Therefore, this paper proposes a phoneme-level language model for Korean language based on LSTM models. A phoneme such as a vowel or a consonant is the smallest unit that comprises Korean texts. We construct the language model using three or four LSTM layers. Each model was trained using Stochastic Gradient Algorithm and more advanced optimization algorithms such as Adagrad, RMSprop, Adadelta, Adam, Adamax, and Nadam. Simulation study was done with Old Testament texts using a deep learning package Keras based the Theano. After pre-processing the texts, the dataset included 74 of unique characters including vowels, consonants, and punctuation marks. Then we constructed an input vector with 20 consecutive characters and an output with a following 21st character. Finally, total 1,023,411 sets of input-output vectors were included in the dataset and we divided them into training, validation, testsets with proportion 70:15:15. All the simulation were conducted on a system equipped with an Intel Xeon CPU (16 cores) and a NVIDIA GeForce GTX 1080 GPU. We compared the loss function evaluated for the validation set, the perplexity evaluated for the test set, and the time to be taken for training each model. As a result, all the optimization algorithms but the stochastic gradient algorithm showed similar validation loss and perplexity, which are clearly superior to those of the stochastic gradient algorithm. The stochastic gradient algorithm took the longest time to be trained for both 3- and 4-LSTM models. On average, the 4-LSTM layer model took 69% longer training time than the 3-LSTM layer model. However, the validation loss and perplexity were not improved significantly or became even worse for specific conditions. On the other hand, when comparing the automatically generated sentences, the 4-LSTM layer model tended to generate the sentences which are closer to the natural language than the 3-LSTM model. Although there were slight differences in the completeness of the generated sentences between the models, the sentence generation performance was quite satisfactory in any simulation conditions: they generated only legitimate Korean letters and the use of postposition and the conjugation of verbs were almost perfect in the sense of grammar. The results of this study are expected to be widely used for the processing of Korean language in the field of language processing and speech recognition, which are the basis of artificial intelligence systems.

• Journal of Oral Medicine and Pain
• /
• v.32 no.2
• /
• pp.157-175
• /
• 2007

This study was conducted through an interview process in which questionnaires were administered to 293 people. The questionnaires related to the behaviors of oral hygiene care, and disease history related to halitosis, and status of halitosis, halitosis measurement, oral examination, and caries activity tests such as the snyder test, Salivary flow rate test, and Salivary buffering capacity test. Our sample was taken from 293 rural residents within the period from 4th to 21st of January 2006. This was done in order to provide basic data to prepare both policies of halitosis prevention and a device to efficiently measure halitosis status and investigate the factors related therein. The major findings of this study results are as follows: 1. As for frequency of tooth brushing, twice a day occupied the greatest portion at 46.1 % Women exceeded men in frequency of tooth brushing. Tongue brushing everyday produced a 25.6 % result among subjects and The use of auxiliary oral hygiene devices occupied 9.2 %. 2. As for degree of usual self-awareness of halitosis: 62.5 %. This result also demonstrate that the severest time of self-awareness in regards to halitosis is wake up time in the morning. The time period produced the highest portion of 72.7 % in times of self-awareness. In terms of the area in which halitosis was observed, gum resulted in 23.0 %. As for types of halitosis, fetid smell was the most frequent at 37.2 %. 3. As for the result of halitosis measurement, values of OG less than 50 ppm occupied 54.3 % and $50{\sim}100ppm$ occupied 41.6 %. As for $NH_3$ values, $20{\sim}60ppm$ showed the highest value range of 52.6 %. 4. As for OG per disease history related to halitosis, values of OG were significantly high in the ranges of $50{\sim}100ppm$ within family history groups of food impaction by dental caries, diabetes mellitus and halitosis. As for values of $NH_3$, there showed a significant difference in respiratory system disease groups. 5 Value range of OG per ordinary halitosis self-awareness degree: values ranging less than 50 ppm were recorded at 55.9 % from the group realizing not aware of smell. 57.5 % from groups only realizing sometimes, while values range of $50{\sim}100ppm$ were recorded at 52.0 % from groups always aware of smell. 63.6 % from groups always strongly aware of smell. Meanwhile as for the values ranges of $NH_3$, $20{\sim}60ppm$. they occupied high portions for all groups of exams. 6. Values of OG per oral examination: the more pulp-exposed teeth and food impaction and the higher the tongue plaque index, values of OG increased within the range of $50{\sim}100ppm$. As for values of $NH_3$, the more prosthetic teeth and the higher the tongue plaque index, this value increased significantly, and the values increased up to no less than 60 ppm for groups of mandibular partial denture. 7. Within the realm of caries activity test: as for the Snyder test, high activity was highest by 43.0 % wherewith the higher the activity of acidogenic bacteria the higher the OG values. As for the salivary flow rate test, the number of cases below 8.0 ml showed the highest tendency by 62.5 %. The larger the salivary flow rate the more decreased OG values distribution. As for the salivary buffering capacity test, $6{\sim}10$ drops of 0.1N lactic acid showed the overwhelming trend by 58.7 % whereby the higher the salivary buffering capacity the greater distribution occupancy ratio of OG values below 50 ppm which is scentless to on ordinary person. 8. As for the correlation between oral environment and halitosis, OG showed the positive correlation with pulp exposed teeth, filled teeth, present teeth, tongue plaque index, and food impaction, while the negative correlation with salivary flow rate and prosthetic teeth. $NH_3$ showed a positive correlation with prosthetic teeth and frequency of tooth brushing, while decayed teeth was negative correlation. 9. As for the multiple regression analysis result, there have been selected female, pulp exposed teeth, prosthetic teeth, food impaction, salivary flow rate, tongue plaque index and severe activities in the Snyder test as factors affecting OG wherein explanatory power on it was 45.1 %. There have been selected females, pulp exposed teeth, tongue plaque index, and prosthetic teeth as factors affecting on $NH_3$ wherein explanatory power on it was 6.6 %. With the aforementioned results in mind, the status of halitosis among rural residents is considered to bare a close relation with oral environments and other factors related to halitosis such as the Snyder test from caries activity test, and salivary flow rate test. For the prevention of halitosis of residents in rural areas, we have to focus on correct tooth brushing methods and tongue brushing, with using auxiliary oral hygiene devices to remove fur of tongue plaque and food impaction. Also, when the cause and ingredients of halitosis are diverse and complex, in order to analyze exactly the factors of individual halitosis development, we need continuous and systematic study in order to provide rural residents with programs of oral hygiene education and encourage the use of dental hygienists in public health centers.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.4 no.1
• /
• pp.115-124
• /
• 1968

It was shown that the most desirable characters for kenaf are high-fiber weight and moderately early maturity. Therefore, the objectives of this research on this crop is to find varieties possessing these characteristics. The experiments covered in this report provided new information relative to segregation, mode of inheritance, estimate of the number of genes involved in fiber weight and their response to short day length of 10 hours and the qualitative characters, such as, color of stem, capsule, petiole and shape of leaves. The associations which exist among these characters are also indicated. Fiber weight per plant, days to flowering, Stem color, Petiole color, Capsule color, and shape of leaves were studied in parental, $F_1$.$F_2$and backcross populations of a cross between Dashkent, a low-fiber weight but early maturing kenaf variety, and G 38 F-1, a high-fiber weight but late maturing kenaf variety. Crosses were made using the varieties, Dashkent and G 38 F-1 as parents. The Dashkent parent had the following characteristics: green stems, capsules and petioles and lobed shaped leaves; 105.8234 mean-days to flowering in the field, and 106.9222 mean-days under 10 hours short day treatment. The other parent, G 38 F-1 had red stems yellow capsules and red petioles and unlobed shaped leaves; 149.8921 mean-days to flowering in the field, and 62.3684 mean-days under 10 hours short day treatment. Both of the parents, $F_1$, $F_2$, $BC_1$ ($F_1$ X Dashkent, ) and $BC_2$($F_1$ ${\times}$ G38F-1) of the kenaf cross were grown at the Crops Experiment Station, Suwon, Korea in 1965. Color of stems, petioles and capsules, and shape of leaves were noted to be simply inherited as a single factor. Red stem color was dominant over green stem color, red petiole color was dominant over green petiole, lobed shaped leaves were dominant over unlobed shaped leaves and yellow capsules were dominant over green capsule. It was, also, noted that the factor for color of petiole was linked with the factor for shape of leaf with a 11.9587 percent recombination value, however no interaction or linkage were found among the color of stem and capsule color. Using Powers partitioning method, theoretical means and frequency distributions for each population, the days to flowering were calculated with the assumption that two gene pairs were involved. The values obtained fitted the theoretical values. In general this would indicate that Dashkent and G 38 F -1 were differentiated by two gene pairs. Heritability values were calculated as the percent of additive genetic variance. Heritability value of days to flowering, 89.5% in the broad sense and 79.91% in the narrow sense, indicated that the selection for this character would be effective in relatively early generations. Particularly, high positive correlations were found between days to flowering and the color of petioles and shape of leaves. However, there was no relation between days to flowering and capsule color nor between these and stem color. On the basis of the results of this experiment there is evidence that the hereditary factor for shape of leaves and the color of petioles is linked with an effective factor or factors for the characters of days to flowering. The association was sufficiently close to offer a possible simple and efficient means of selection for moderately early mat. uring plants by leaf shape and petiole color selection. Again using Powers partitioning method the frequency distribution for each population to the fiber weight were calculated with the assumption that two gene pairs, AaBb, were involved. Both phenotypic and genotypic dominance were complete. The obtained value did not agree with the theoretical value for $F_2$ and $BC_1$ ($F_1$ ${\times}$ Dashkent.) It seems that Dashkent and G 38 F-1 were differentiated by two major gene pairs but some the other minor genes are necessary. It is certain that the hereditary factor for shape of leaves and color of petioles is linked with an effective factor or factors for fiber weight. Also, high. yielding plants with moderately early maturity were found in the $F_2$ population. Thus, simultaneous selection for high-fiber yield and moderately early maturing plants should be possible in these populations. Phenotypic and genotypic correlation coefficients between fiber weight per plant and days to flowering, stem height and stem diameter were calculated. In general, genotypic correlations are higher than the phenotypic correlation. The highest correlation is found between stem height and fiber weight per plant (0.7852 in genotypic and 0.4103 in phenotypic) and between days to flowering and fiber weight per plant (0.7398 in genotypic and 0.3983 in phenotypic.) It was also expected that the selection of high stem height and moderately early maturing plants were given the efficient means of selection for high fiber weight.

• Journal of Intelligence and Information Systems
• /
• v.23 no.4
• /
• pp.111-126
• /
• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.101-123
• /
• 2013

Recently due to the advancements of science and information technology, the socio-economic business areas are changing from the industrial economy to a knowledge economy. Furthermore, companies need to do creation of new value through continuous innovation, development of core competencies and technologies, and technological convergence. Therefore, the identification of major trends in technology research and the interdisciplinary knowledge-based prediction of integrated technologies and promising techniques are required for firms to gain and sustain competitive advantage and future growth engines. The aim of this paper is to understand the recent research trend in management of technology (MOT) and to foresee promising technologies with deep knowledge for both technology and business. Furthermore, this study intends to give a clear way to find new technical value for constant innovation and to capture core technology and technology convergence. Bibliometrics is a metrical analysis to understand literature's characteristics. Traditional bibliometrics has its limitation not to understand relationship between trend in technology management and technology itself, since it focuses on quantitative indices such as quotation frequency. To overcome this issue, the network focused bibliometrics has been used instead of traditional one. The network focused bibliometrics mainly uses "Co-citation" and "Co-word" analysis. In this study, a keywords network analysis, one of social network analysis, is performed to analyze recent research trend in MOT. For the analysis, we collected keywords from research papers published in international journals related MOT between 2002 and 2011, constructed a keyword network, and then conducted the keywords network analysis. Over the past 40 years, the studies in social network have attempted to understand the social interactions through the network structure represented by connection patterns. In other words, social network analysis has been used to explain the structures and behaviors of various social formations such as teams, organizations, and industries. In general, the social network analysis uses data as a form of matrix. In our context, the matrix depicts the relations between rows as papers and columns as keywords, where the relations are represented as binary. Even though there are no direct relations between papers who have been published, the relations between papers can be derived artificially as in the paper-keyword matrix, in which each cell has 1 for including or 0 for not including. For example, a keywords network can be configured in a way to connect the papers which have included one or more same keywords. After constructing a keywords network, we analyzed frequency of keywords, structural characteristics of keywords network, preferential attachment and growth of new keywords, component, and centrality. The results of this study are as follows. First, a paper has 4.574 keywords on the average. 90% of keywords were used three or less times for past 10 years and about 75% of keywords appeared only one time. Second, the keyword network in MOT is a small world network and a scale free network in which a small number of keywords have a tendency to become a monopoly. Third, the gap between the rich (with more edges) and the poor (with fewer edges) in the network is getting bigger as time goes on. Fourth, most of newly entering keywords become poor nodes within about 2~3 years. Finally, keywords with high degree centrality, betweenness centrality, and closeness centrality are "Innovation," "R&D," "Patent," "Forecast," "Technology transfer," "Technology," and "SME". The results of analysis will help researchers identify major trends in MOT research and then seek a new research topic. We hope that the result of the analysis will help researchers of MOT identify major trends in technology research, and utilize as useful reference information when they seek consilience with other fields of study and select a new research topic.

• Journal of Intelligence and Information Systems
• /
• v.26 no.2
• /
• pp.57-78
• /
• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.

• Journal of Food Hygiene and Safety
• /
• v.34 no.3
• /
• pp.251-262
• /
• 2019

In this study, we analyzed Korean dietary habits with food intake data from the Korea National Health and Nutrition Examination Survey (KNHANES) and the Korea Centers for Disease Control and Prevention and we proposed a set of management guidelines for future Korean dietary habits. A total of 839 food items (1,419 foods) were analyzed according to the food catagories in "Food Code", which is the representative food classification system in Korea. The average total daily food intake was 1,585.77 g/day, with raw and processed foods accounting for 858.96 g/day and 726.81 g/day, respectively. Cereal grains contributed to the highest proportion of the food intake. Over 90% of subjects consumed cereal grains (99.09%) and root and tuber vegetables (95.80%) among the top 15 consumed food groups. According to the analysis by item, rice, Korean cabbage kimchi, apple, radish, egg, chili pepper, onion, wheat, soybean curds, potato, cucumber and pork were major (at least 1% of the average daily intake, 158.6 g/day) and frequently (eaten by more than 25% of subjects, 5,168 persons) consumed food items, and Korean spices were at the top of this list. In the case of kimchi, the proportion of intake of Korean cabbage kimchi (64.89 g/day) was the highest. In the case of alcoholic beverages, intake was highest by order of beer (63.53 g/day), soju (39.11 g/day) and makgeolli (19.70 g/day), and intake frequency was high in order of soju (11.3%), beer (7.2%), and sake (6.6%). Analysis results by seasonal intake trends showed that cereal grains have steadily decreased and beverages have slightly risen. In the case of alcoholic beverage consumption frequency, some kinds of makgeolli, wine, sake, and black raspberry wine have decreased gradually year by year. The consumption trend for kimchi has been gradually decreasing as well.

• Journal of Intelligence and Information Systems
• /
• v.19 no.4
• /
• pp.21-37
• /
• 2013

Recently, as the advent of the knowledge-based society, there are more people getting interested in the intellectual property. Especially, the ICT companies leading the high-tech industry are working hard to strive for systematic management of intellectual property. As we know, the patent information represents the intellectual capital of the company. Also now the quantitative analysis on the continuously accumulated patent information becomes possible. The analysis at various levels becomes also possible by utilizing the patent information, ranging from the patent level to the enterprise level, industrial level and country level. Through the patent information, we can identify the technology status and analyze the impact of the performance. We are also able to find out the flow of the knowledge through the network analysis. By that, we can not only identify the changes in technology, but also predict the direction of the future research. In the field using the network analysis there are two important analyses which utilize the patent citation information; citation indicator analysis utilizing the frequency of the citation and network analysis based on the citation relationships. Furthermore, this study analyzes whether there are any impacts between the size of the company and patent citation relationships. 74 S&P 500 registered companies that provide IT and communication services are selected for this study. In order to determine the relationship of patent citation between the companies, the patent citation in 2009 and 2010 is collected and sociomatrices which show the patent citation relationship between the companies are created. In addition, the companies' total assets are collected as an index of company size. The distance between companies is defined as the absolute value of the difference between the total assets. And simple differences are considered to be described as the hierarchy of the company. The QAP Correlation analysis and MR-QAP analysis is carried out by using the distance and hierarchy between companies, and also the sociomatrices that shows the patent citation in 2009 and 2010. Through the result of QAP Correlation analysis, the patent citation relationship between companies in the 2009's company's patent citation network and the 2010's company's patent citation network shows the highest correlation. In addition, positive correlation is shown in the patent citation relationships between companies and the distance between companies. This is because the patent citation relationship is increased when there is a difference of size between companies. Not only that, negative correlation is found through the analysis using the patent citation relationship between companies and the hierarchy between companies. Relatively it is indicated that there is a high evaluation about the patent of the higher tier companies influenced toward the lower tier companies. MR-QAP analysis is carried out as follow. The sociomatrix that is generated by using the year 2010 patent citation relationship is used as the dependent variable. Additionally the 2009's company's patent citation network and the distance and hierarchy networks between the companies are used as the independent variables. This study performed MR-QAP analysis to find the main factors influencing the patent citation relationship between the companies in 2010. The analysis results show that all independent variables have positively influenced the 2010's patent citation relationship between the companies. In particular, the 2009's patent citation relationship between the companies has the most significant impact on the 2010's, which means that there is consecutiveness regarding the patent citation relationships. Through the result of QAP correlation analysis and MR-QAP analysis, the patent citation relationship between companies is affected by the size of the companies. But the most significant impact is the patent citation relationships that had been done in the past. The reason why we need to maintain the patent citation relationship between companies is it might be important in the use of strategic aspect of the companies to look into relationships to share intellectual property between each other, also seen as an important auxiliary of the partner companies to cooperate with.

• Child Health Nursing Research
• /
• v.9 no.2
• /
• pp.206-220
• /
• 2003

Purpose: this study was intended to search the relationship between perception of the infant temperament in mother of infant at the age of 1～12 months and maternal confidence and satisfaction in performing maternal role, and to submit a basic data to establish a nursing intervention program which is helpful for determination of infant development and performing maternal role promotion by identify variables associated with infant temperament. Method: The subjects of this study were 300 mothers of infant at the age of 1～12 months who visited well baby clinic in 4 hospitals in Busan city and Kyoung-Nam province. Final analysis was performed in 293 cases. Seven cases was excluded in this study because of its inappropriate data collection. The data was collected from 1st July to 15th August 2002. The questionaries which were fill-up by mother were collected. Infant temperament was measured by using the tool of 'what my baby is like'(WBL) which was developed by Priham et. al.(1994) and translated by Bang(1999). The scale of postpartum self evaluation which was developed by Lederman et al(1981) and translated by Lee(1992) was used for the confidence and satisfaction of maternal role. All statistical analyses were performed using SPSS-PC for window, version 10.0: frequency, percentage, minimum, maximum, mean, SD, t-test, ANOVA, Post-hoc test(Scheffe's test), Pearson Correlation Coefficients. Result: The mean score of maternal perception of the infant temperament was 6.17±1.04, and mother recognized her infant as positive. The mean score of confidence of maternal role was 2.89± .41 and this revealed in an average level. The mean score of satisfaction of maternal role was 3.29± .51 and this revealed in a higher level. There was a weak significant positive correlation between the score of maternal perception of infant temperament and confidence of maternal role(r=0.176, P= .003), but there was no significant correlation between satisfaction of maternal role(P> .05). It revealed the more maternal perception of the infant temperament as positive, the higher confidence of maternal role. There was a moderate significant positive correlation between confidence of maternal role and satisfaction of maternal role(r=0.410, P= .000). It revealed the more confidence of maternal role, the higher satisfaction of maternal role. The variables related with the score of maternal perception of infant temperament were the type of delivery (t=-2.600, P= .010), experience of learning baby care(t=2.382, P= .018), maternal perception on baby's health status(F=3.467, P= .033), maternal perception on her health status(F=3.467, P= .027), baby's age(F=3.080, P= .028). Conclusion: Our result showed the confidence of maternal role was increased as the maternal perception of infant temperament was positive, and conformed that the confidence of maternal role was also related with satisfaction of maternal role. Prenatal education, type of delivery, baby's age were also related with the maternal perception of infant temperament. So, nursing intervention program of developmental stage maybe necessary in order to help maternal perception of infant temperament as positive, and it will be increased the confidence of maternal role and satisfaction of performing maternal role which was considered as real indicate of achievement of maternal role.