• Korean Journal of Environmental Agriculture
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• v.22 no.4
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• pp.278-283
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• 2003

This study was carried out to examine the nitrogen removal rate of a subsurface-flow treatment wetland system which was constructed on floodplain of the Kwangju River from May to June 2001. Its dimensions were 29m in length, 9m in width and 0.65m in depth. A bottom layer of 45cm in depth was filled with crushed granite with about $15{\sim}30\;mm$ in diameter and a middle layer of 10cm in depth had pea pebbles with about 10 mm in diameter. An upper layer of 5 cm in depth contained course sand. Reeds (Phragmites australis) were transplanted on the surface of the system. They were dug out of natural wetlands and stems were cut at about 40 cm height from their bottom ends. Water of the Kwangju River flowed into it via a pipe by gravity flow and its effluent was funneled back into the river. The height of reed stems was 44.2 cm in July 2001 and 75.3cm in September 2001. The number of stems was increased from $80\;stems/m^2$ in July 2001 to $136\;stems/m^2$ in September 2001. Volume and water quality of inflow and outflow were analyzed from July 2001 through December 2001. Inflow and outflow averaged 40.0 and $39.2\;m^3/day$, respectively. Hydraulic detention time was about 1.5 days. Average nitrogen uptake by reeds was $69.31\;N\;mg/m^2/day$. Removal rate of $NO_3-N$, $NH_3-N$, T-N averaged 195.58, 53.65, and $628.44\;mg/m^2/day$, respectively. Changes of $NO_3-N$ and $NH_3-N$ abatement rates were closely related to those of wetland temperatures. The lower removal rate of nitrogen species compared with that of subsurface-flow wetlands operating in North America could be attributed to the initial stage of the system and inclusion of two cold months into the six-month monitoring period. Increase of standing density of reeds within a few years will develop both root zones suitable for the nitrification of ammonia and surface layer substrates beneficial to the denitrification of nitrates into nitrogen gases, which may lead to increment in the nitrogen retention rate.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.57-77
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• 2013

Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Journal of the Korean Geographical Society
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• v.30 no.1
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• pp.57-67
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• 1995

The study on the factorial ecology of the residential patterns is to provide one of the yardsticks for description and comparison of urban structures. Many Korean geographers have adopted this method to analyse the urban structure of Korean cities. According to these studies, one of the main factors in Korean cities in large and middle sizes is family status. The spatial pattern of family status is zonal, similar to the cases of Japanese and Western cities. The age is one of the principal indices of familyf status, hence the author chose the age composition to analyse the residential patten. This Paper is to describe the residential segreagation pattern due to age composition and recent pattern changes in the Cheongju city, and than to explain the reason for these changes. All data are derived from the Population Censuses of Korea for 1970, 1980 and 1990. Eighteen groups of age with five-year interval (0-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, 85- and- over) are adopted here. Unit area for this analysis is administrative district(Dong) within Cheonaju city. District are classified into smaller groups based on the similarity of age composition, using the method of cluster analysis. The main findings are summarized as follows: 1. Population have increased remarkably in the eastern reaion neighboring CBD of Cheongju city in 1970's. And in western region from CBD new residential area have developed in 1980's. 2. Spatial pattarns showed a concentric circle type in central district and its neighbor regions and a sector type in periphery regions in 1970; a cirele type in central district and a sector typesin neighbor regions and periphery regions in 1980 and 1990. Thess residential pattern play an important role in the population composition ratio of younger aged group (l5-34) and older aged group (65-and-over). 3. Spatial change of types by age composition showed the higher ratio of groups of 0-9 and 35-49, and lower ratio of group 20-24 in 1970's. Dominent groups are ratio of 0-14, 40-49, 55-64, 7O-79, and 85- and- over in 1980's. These changes mainly appeared in central district and periphery regions. 4. The reasons for the change of age composition was the development of msnufacturing industries with the increase of population and new construction of residential areas both in the neighbor regions of cnetral district and periphery regions. These phenomena were caused by immigration of younger aged groups and increasing of residents of aged groups in these regions.

• Journal of Mushroom
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• v.9 no.4
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• pp.161-165
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• 2011

Small scale mushroom growers take a lot of the costs in the bottle cultivation than the large scale growers. Therefore, they are not competitive in the market. Mushroom cultivation in small scale mushroom farm is labor-intensive and this experiment was carried out to establish the method for the cultivation of various kinds of mushrooms together under the similar conditions in the production system. As a result, the common medium were selected based on the growing conditions of Pleurotus eryngii and Agrocybe cylindracea, and the level of medium moisture content were studied. The results were shown below. When the medium in the input stage for bottle cultivation was filled by using automatic machine, the range of the three state rates in the bottle is different depending on ingredients and the mixing ratio. The optimum moisture content of the medium for some mushroom production was a different trend based on the medium cluster of the raw materials. The optimum moisture content for P. eryngii in the medium was 70% (douglas fir sawdust, rice bran or sawdust, dried bean curd refuse). In the medium containing douglas fir sawdust, wheat bran was 65%, and the medium including douglas fir sawdust, wheat bran, dried bean curd refuse was 67%. The optimum moisture content of the above three media for A. cylindracea was 70%. The suitable medium for the bottle cultivation of P. eryngii was selected as douglas fir sawdust 75%, wheat bran 20%, dried bean curd refuse 5%, and moisture content 67%. The medium of A. cylindracea was selected as douglas fir sawdust 75%, wheat bran 25%, and moisture content 70%. The suitable medium for bottle cultivation of P. eryngii and A. cylindracea was shown as douglas fir sawdust 75%, rice bran20%, dried bean curd refuse 5%, and 70% moisture content to be used as a common medium of the growing. The incubation period, primordial formation, and growth environment conditions of P. eryngii and A. cylindracea were a similar trend. Therefore, the small scale farmers of P. eryngii can cultivate A. cylindracea together with P. eryngii.

• Resources Recycling
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• v.15 no.3 s.71
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• pp.38-45
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• 2006

The thermal filter press dewatering(TFPD) technology to improve the dewaterability through increasing the inner vapor pressure, lowering the filtration viscosity and forming the porosity easily within cake as applying the heat at the sludge layer was developed in this study. The hot water with temperature of $95^{\circ}C$ and pressure of $1.2kg_f/cm^2$ was supplied to the heating plate equipped between filter plates with plate size of $470{\times}470mm$ and material of polypropylene. Sludge was dewaterd by supplying pressure of $5kg_f/cm^2$ and then by squeezing pressure of $15kg_f/cm^2$. As a results of estimating the characteristics of thermal dewatering to consider the initial water content and organic content to be influenced by a period of water shortage and rainwater, the dewatered cake water content was about 35 wt% and dewatering velocity was $4DSkg/m^2{\cdot}hr$ under the rainwater period, and the dewatered cake water content was about 50 wt% and dewatering velocity was $1.5DSkg/m^2{\cdot}hr$ in the case of sludge of water shortage season. These results was superior to the mechanical dewatering performance with water content of 70wt% and dewatering velocity of $0.9DSkg/m^2{\cdot}hr$. On the base of the results of TFPD, energy consumpted to deal with DS(Dry Solid) of 1kg was estimated by 300 kJ. It was analyzed that the energy consumption of TFPD was decreased about one third with comparison to the dryer system. Dewatering velocity of this technology was faster than the one of mechanical dewatering equipment and it was easier to product low water content cake. Therefore, this technology was recognized that dewaterability was predominant because of the fast of dewatering velocity and production of low water content cake, and also this known as economical efficiency was excellent because of low energy consumption in comparison with dryer.

• Journal of the Korean Society of School Health
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• v.12 no.1
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• pp.109-129
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• 1999

AIDS is worldwide problem. It has threatens societies and is potentially a big problem among youth. UNAIDS has warned that collective global responses are necessary; with half of the newly infected being children and young people in 1998, educating youth below age 24 is essential. Because of both their unexpected, strong sexual activities and an easjer tendency to change their behavior than adults, UNAIDS emphasizes the importance of prevention education for youth. In Korea, 4 cases of HIV infection have officially been reported among high school students. Considering the potential seriousness of HIV infection among youth, the Korean AntiAIDS Federation (KAAF) began an education programme for Korean youth (middle & high school pupils) as one intervention method in 1993. The purpose of this study is to evaluate the impact of the education programs performed and to develop a better programme. The intervention method used in this study was mainly a lecture in a big or small auditorium or through broadcasting systems using audio-visual teaching aids. The period of this survey was from Nov. 10 to Dec. 26. in 1998. The subjects surveyed were 792 pupils who were sampled from 12 different middle and high schools in Seoul by a two-stage cluster sampling. Self-recording the structured questionnaire was used for data collection. Data were analyzed by using a paired t-test to compare the knowledge difference before and after the interrention method. A chi-square test to compare the consciousness difference between pre-post-education results was used, and an ANOVA was ased to compare the knowledge difference among the selected groups. Major results of this study are summarized as follows: (1) AIDS knowledge significantly changed after AIDS prevention education. (2) The direct lecture in a big or small auditorium is more effective than an indirect lecture through a broadcasting system or through audio-visual teaching aids. (3) Those who had not seen obscene materials (e.g. pornography films, magazines etc.) acguired much more knowledge than others after AIDS prevention education. (4) Those who had no girlfriend/or boyfriend acquired much more knowledge than the others after AIDS prevention education. (5) As to the attitudes toward chastity, more girls than boys and students who had not seen obscene materials rather than those who already had were liable to support the state ment: "all should remain chase" and their consciousness on "being chaste" changed much more than that of others after intervention. (6) As to the individual evaluation of lectures, 23.1% shows "very good", 44.1% "good", 26.9% "normar" '-' thus, the positive evaluation was to 94.2%. The negative response was 5.8%. (7) As to the desire for AIDS education sponsored by KAAF in the future, more than 90.5% showed a positive reaction. (8) As to the educators in future education methods, pupils wanted lecturers from outside their schood. (9) Students from vocational high schools have seen obscene materials much more than other groups and the percentage of those who had a girlfriend/or boyfriend was higher than that of the others as wello. (10) 90% of those who watched obscene materials responded that they experienced these materials while at middle school and 100% before reaching the second grade of high school. (11) The number of boys who have experienced obscene materials is twice as many as that of girls. (12) The percentage of boys who have a girlfriend is much higher than the percentage of girls who have a boyfriend. (13) Among those who have friends of the opposite sex, 11.1% say that they hare had sexual contact and 20.8% hare experienced kissing and caressing.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.13 no.4
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• pp.179-183
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• 1980

The efficiency of energy transfer by a population of the farmed pacific oyster, Crassostrea gigas was studied during culture period of 10 months July 1979-April 1980, in Geoje-Hansan Bay near Chungmu City. Energy use by the farmed oyster population was calculated from estimates of half-a-month unit age specific natural mortality rate and data on growth, gonad output, shell organic matter production and respiration. Total mortality during the culture period was estimated approximate $36\%$ from data on survivor individual number per cluster. Growth may be dual consisted of a curved line during the first half culture period (July-November) and a linear line in the later half period (December-April). The first half growth was approximated by the von Bertalanffy growth model; shell height, $SH=6.33\;(1-e^{0.2421(t+0.54)})$, where t is age in half-a-month unit. In the later half growth period shell height was related to t by SH=4.44+0.14t. Dry meat weight (DW) was related to shell height by log $DW=-2.2907+2.589{\cdot}log\;SH,\;(2, and/or log $DW=-5.8153+7.208{\cdot}log\;SH,\;(5. Size specific gonad output (G) as calculated by condition index of before and after the spawning season, was related to shell height by $G=0.0145+(3.95\times10^{-3}{\times}SH^{2.9861})$. Shell organic matter production (SO) was related to shell height by log $SO=-3.1884+2.527{\cdot}1og\;SH$. Size and temperature specific respiration rate (R) as determined in biotron system with controlled temperature, was related to dry meat weight and temperature (T) by log $R=(0.386T-0.5381)+(0.6409-0.0083T){\cdot}log\;DW$. The energy used in metabolism was calculated from size, temperature specific respiration and data on body composition. The calorie contents of oyster meat were estimated by bomb calorimetry based on nitrogen correction. The assimilation efficiency of the oyster estimated directly by a insoluble crude silicate method gave $55.5\%$. From the information presently available by other workers, the assimilation efficiency ranges between $40\%\;and\;70\%$. Twenty seven point four percent of the filtered food material expressed by energy value for oyster population was estimated to have been rejected as pseudofaeces : $17.2\%$ was passed as faeces; $35.04\%$ was respired and lost as heat; $0.38\%$ was bounded up in shell organics; $2.74\%$ was released as gonad output, $2.06\%$ was fell as meat reducing by mortality. The remaining $15.28\%$ was used as meat production. The net efficiency of energy transfer from assimilation to meat production (yield/assimilation) of a farm population of the oyster was estimated to be $28\%$ during culture period July 1979-April 1980. The gross efficiency of energy transfer from ingestion to meat production (yield/food filtered) is probably between $11\%\;and\;20\%$.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.227-240
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• 2011

New concepts and ideas often result from extensive recombination of existing concepts or ideas. Both researchers and developers build on existing concepts and ideas in published papers or registered patents to develop new theories and technologies that in turn serve as a basis for further development. As the importance of patent increases, so does that of patent analysis. Patent analysis is largely divided into network-based and keyword-based analyses. The former lacks its ability to analyze information technology in details while the letter is unable to identify the relationship between such technologies. In order to overcome the limitations of network-based and keyword-based analyses, this study, which blends those two methods, suggests the keyword network based analysis methodology. In this study, we collected significant technology information in each patent that is related to Light Emitting Diode (LED) through text mining, built a keyword network, and then executed a community network analysis on the collected data. The results of analysis are as the following. First, the patent keyword network indicated very low density and exceptionally high clustering coefficient. Technically, density is obtained by dividing the number of ties in a network by the number of all possible ties. The value ranges between 0 and 1, with higher values indicating denser networks and lower values indicating sparser networks. In real-world networks, the density varies depending on the size of a network; increasing the size of a network generally leads to a decrease in the density. The clustering coefficient is a network-level measure that illustrates the tendency of nodes to cluster in densely interconnected modules. This measure is to show the small-world property in which a network can be highly clustered even though it has a small average distance between nodes in spite of the large number of nodes. Therefore, high density in patent keyword network means that nodes in the patent keyword network are connected sporadically, and high clustering coefficient shows that nodes in the network are closely connected one another. Second, the cumulative degree distribution of the patent keyword network, as any other knowledge network like citation network or collaboration network, followed a clear power-law distribution. A well-known mechanism of this pattern is the preferential attachment mechanism, whereby a node with more links is likely to attain further new links in the evolution of the corresponding network. Unlike general normal distributions, the power-law distribution does not have a representative scale. This means that one cannot pick a representative or an average because there is always a considerable probability of finding much larger values. Networks with power-law distributions are therefore often referred to as scale-free networks. The presence of heavy-tailed scale-free distribution represents the fundamental signature of an emergent collective behavior of the actors who contribute to forming the network. In our context, the more frequently a patent keyword is used, the more often it is selected by researchers and is associated with other keywords or concepts to constitute and convey new patents or technologies. The evidence of power-law distribution implies that the preferential attachment mechanism suggests the origin of heavy-tailed distributions in a wide range of growing patent keyword network. Third, we found that among keywords that flew into a particular field, the vast majority of keywords with new links join existing keywords in the associated community in forming the concept of a new patent. This finding resulted in the same outcomes for both the short-term period (4-year) and long-term period (10-year) analyses. Furthermore, using the keyword combination information that was derived from the methodology suggested by our study enables one to forecast which concepts combine to form a new patent dimension and refer to those concepts when developing a new patent.