• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.1-13
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• 2015

As opinion mining in big data applications has been highlighted, a lot of research on unstructured data has made. Lots of social media on the Internet generate unstructured or semi-structured data every second and they are often made by natural or human languages we use in daily life. Many words in human languages have multiple meanings or senses. In this result, it is very difficult for computers to extract useful information from these datasets. Traditional web search engines are usually based on keyword search, resulting in incorrect search results which are far from users' intentions. Even though a lot of progress in enhancing the performance of search engines has made over the last years in order to provide users with appropriate results, there is still so much to improve it. Word sense disambiguation can play a very important role in dealing with natural language processing and is considered as one of the most difficult problems in this area. Major approaches to word sense disambiguation can be classified as knowledge-base, supervised corpus-based, and unsupervised corpus-based approaches. This paper presents a method which automatically generates a corpus for word sense disambiguation by taking advantage of examples in existing dictionaries and avoids expensive sense tagging processes. It experiments the effectiveness of the method based on Naïve Bayes Model, which is one of supervised learning algorithms, by using Korean standard unabridged dictionary and Sejong Corpus. Korean standard unabridged dictionary has approximately 57,000 sentences. Sejong Corpus has about 790,000 sentences tagged with part-of-speech and senses all together. For the experiment of this study, Korean standard unabridged dictionary and Sejong Corpus were experimented as a combination and separate entities using cross validation. Only nouns, target subjects in word sense disambiguation, were selected. 93,522 word senses among 265,655 nouns and 56,914 sentences from related proverbs and examples were additionally combined in the corpus. Sejong Corpus was easily merged with Korean standard unabridged dictionary because Sejong Corpus was tagged based on sense indices defined by Korean standard unabridged dictionary. Sense vectors were formed after the merged corpus was created. Terms used in creating sense vectors were added in the named entity dictionary of Korean morphological analyzer. By using the extended named entity dictionary, term vectors were extracted from the input sentences and then term vectors for the sentences were created. Given the extracted term vector and the sense vector model made during the pre-processing stage, the sense-tagged terms were determined by the vector space model based word sense disambiguation. In addition, this study shows the effectiveness of merged corpus from examples in Korean standard unabridged dictionary and Sejong Corpus. The experiment shows the better results in precision and recall are found with the merged corpus. This study suggests it can practically enhance the performance of internet search engines and help us to understand more accurate meaning of a sentence in natural language processing pertinent to search engines, opinion mining, and text mining. Naïve Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naïve Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naïve Bayes classifier is not realistic and ignores the correlation between attributes, Naïve Bayes classifier is widely used because of its simplicity and in practice it is known to be very effective in many applications such as text classification and medical diagnosis. However, further research need to be carried out to consider all possible combinations and/or partial combinations of all senses in a sentence. Also, the effectiveness of word sense disambiguation may be improved if rhetorical structures or morphological dependencies between words are analyzed through syntactic analysis.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.55-75
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• 2010

Sequential pattern mining aims to discover interesting sequential patterns in a sequence database, and it is one of the essential data mining tasks widely used in various application fields such as Web access pattern analysis, customer purchase pattern analysis, and DNA sequence analysis. In general sequential pattern mining, only the generation order of data element in a sequence is considered, so that it can easily find simple sequential patterns, but has a limit to find more interesting sequential patterns being widely used in real world applications. One of the essential research topics to compensate the limit is a topic of weighted sequential pattern mining. In weighted sequential pattern mining, not only the generation order of data element but also its weight is considered to get more interesting sequential patterns. In recent, data has been increasingly taking the form of continuous data streams rather than finite stored data sets in various application fields, the database research community has begun focusing its attention on processing over data streams. The data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. In data stream processing, each data element should be examined at most once to analyze the data stream, and the memory usage for data stream analysis should be restricted finitely although new data elements are continuously generated in a data stream. Moreover, newly generated data elements should be processed as fast as possible to produce the up-to-date analysis result of a data stream, so that it can be instantly utilized upon request. To satisfy these requirements, data stream processing sacrifices the correctness of its analysis result by allowing some error. Considering the changes in the form of data generated in real world application fields, many researches have been actively performed to find various kinds of knowledge embedded in data streams. They mainly focus on efficient mining of frequent itemsets and sequential patterns over data streams, which have been proven to be useful in conventional data mining for a finite data set. In addition, mining algorithms have also been proposed to efficiently reflect the changes of data streams over time into their mining results. However, they have been targeting on finding naively interesting patterns such as frequent patterns and simple sequential patterns, which are found intuitively, taking no interest in mining novel interesting patterns that express the characteristics of target data streams better. Therefore, it can be a valuable research topic in the field of mining data streams to define novel interesting patterns and develop a mining method finding the novel patterns, which will be effectively used to analyze recent data streams. This paper proposes a gap-based weighting approach for a sequential pattern and amining method of weighted sequential patterns over sequence data streams via the weighting approach. A gap-based weight of a sequential pattern can be computed from the gaps of data elements in the sequential pattern without any pre-defined weight information. That is, in the approach, the gaps of data elements in each sequential pattern as well as their generation orders are used to get the weight of the sequential pattern, therefore it can help to get more interesting and useful sequential patterns. Recently most of computer application fields generate data as a form of data streams rather than a finite data set. Considering the change of data, the proposed method is mainly focus on sequence data streams.

• Korean Journal of Soil Science and Fertilizer
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• v.41 no.2
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• pp.126-136
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• 2008

Tobacco plants grown in pots by sand culture for 70 days after transplanting were used to evaluate the sensing distance and measurement efficiency of ground-based remote sensors. The leaf distribution of tobacco plant and sensing distance from the sensors to the target leaves were controlled by two removal methods of leaves, top-down and bottom-up removal. In the case of top-down removal, the canopy reflectance was measured by the sensor located at a fixed position having an optimum distance from the detector to the uppermost leaf of tobacco every time that the higher leaves were one at a time. The measurement of bottom-up removal, a the other hand, was conducted in the same manner as that of the top-down removal except that the lower leaves were removed one by one. Canopy reflectance measurements were made with hand held spectral sensors including the active sensors such as $GreenSeeker^{TM}$ red and green, $Crop\;Circle\;ACS-210^{TM}$ red and amber, the passive sensors of $Crop\:Circle^{TM}$, and spectroradiometer $SD2000^{TM}$. The reflectance indices by all sensors were generally affected by the upper canopy condition rather than lower canopy condition of tobacco regardless of sensor type, passive or active. The reflectance measurement by $GreenSeeker^{TM}$ was affected sensitively at measurement distance longer than 120 cm, the upper limit of effective sensing distance, beyond which measurement errors are appreciable. In case of the passive sensors that has no upper limit of effective distance and $Crop\;Circle^{TM}(ACS210)$ that has the upper limit of effective sensing distance specified with 213 cm, longer than that of estimated distance, the measurement efficiency affected by the sensing distance showed no difference. This result suggests that it is necessary to use the sensor specified optimum distance. The result revealed that active sensors are more superior than their passive counterparts in establishing between the relative ratio of reflectance index and the dry weight of tobacco treated by top-down removal, and in the evaluation of biomass. $The\;Crop\;Circle\;ACS-210^{TM}$ red was proved to have the highest efficiency of measurement, followed by $Crop\;Circle^{TM}(ACS210)$ amber and $GreenSeeker^{TM}$ red, $Crop\;Circle^{TM}$ passive, $GreenSeeker^{TM}$ green, and spectroradiometer, in descending order.

• Journal of Distribution Science
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• v.10 no.6
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• pp.39-53
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• 2012

It is widely known that knowledge management plays a facilitating role that contributes to upgrading organizational performance. Knowledge management systems (KMS), especially, support the knowledge management process including the sharing, creating, and using of knowledge within a company, and maximize the value of knowledge resources within an organization. Despite this widely held belief, there are few studies that describe how companies actually develop, share, and practice their knowledge. Companies in the domestic small franchise sector, which are in the early stages in terms of knowledge management, need to improve their KMS to manage their franchisees effectively. From this perspective, this study uses a qualitative approach to explore the actual process of knowledge management implementation. This article presents a case study of PB (Paris Baguette) company, which is the first to build a KMS in the franchise industry. The study was able to confirm the following facts through the analysis of target companies. First, the chief executive's support is a critical success factor and this support can increase the participation of organization members. Second, it is important to build a process and culture that actively creates and leverages information in knowledge management activities. The organizational learning culture should be one where the creation, learning, and sharing of new knowledge is developed continuously. Third, a horizontal network organization is needed in order to make relationships within the organization more close-knit. Fourth, in order to connect the diverse processes such as knowledge acquisition, storage, and utilization of knowledge management activities, information technology (IT) capabilities are essential. Indeed, IT can be a powerful tool for improving the quality of work and maximizing the spread and use of knowledge. However, during the construction of an intranet based KMS, research is required to ensure that the most efficient system is implemented. Finally, proper evaluation and compensation are important success factors. In order to develop knowledge workers, an appropriate program of promotion and compensation should be established. Also, building members' confidence in the benefits of knowledge management should be an ongoing activity. The company developed its original KMS to achieve a flexible and proactive organization, and a new KMS to improve organizational and personal capabilities. The PB case shows that there are differences between participants perceptions and actual performance in managing knowledge; that knowledge management is not a matter of formality but a paradigm that assures the sharing of knowledge; and that IT boosts communication skills, thus creating a mutual relationship to enhance the flow of knowledge and information between people. Knowledge management for building organizational capabilities can be successful when considering its focus and ways to increase its acceptance. This study suggests guidelines for major factors that corporate executives of domestic franchises should consider to improve knowledge management and the higher operating activities that can be used.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.6
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• pp.719-730
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• 2017

Recently, oceanic research has been carried out investigating global scientific interests and the territorial management of national marine jurisdictional waters, including exclusive economic zones (EEZ) and the open seas. To meet the needs of ocean researchers pursuing these - objectives, acquiring advanced research infrastructure, including research vessels, large facilities, and equipment, is a top priority in ocean science. However, ocean science is a similar to space science, and securing resources and state-of-the-art technology can be expensive. Faced with these challenges, our study focused on establishing a strategy for the efficient operation and management of research vessels, attempting to establish benchmarks from foreign examples that can be adapted to suit the target context. The results of this study provide ways to identify operating systems that could increase the efficiency of joint-use research vessels. The different systems examined in this study included a joint-use committee-based management system (JCMS, Type 1), private enterprise entrusted operating system (PEOS, Type 2), institutional investment operating system (IIOS, Type 3), and commissioned executive operating system (CEOS, Type 4). The efficiencies of JCMS, PEOS, IIOS and CEOS were 9.17, 5.82, 11.2 and -1.72 %, respectively. Given the total costs involved, the most affordable operating system was IIOS. JCMS was the most cost-effective system based on a quantitative cost-benefit analysis, but IIOS also had an acceptable cost-benefit balance. An operational committee would be required and regulations and guidelines shoulde be established to employ, JCMS, while a strategy to yield independent revenue would be needed to utilize an IIOS system.

• Food Science and Preservation
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• v.18 no.3
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• pp.366-373
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• 2011

Recent studies suggested that Cheonnyuncho is a significant source of bioactive phenolic compounds, comparable to phytochemicals, including green tea and onion. In this study, the hot-water and 80% ethanolic extracts of Cheonnyuncho were assessed as to their total phenol content, total flavonoids content, antioxidant activity (DPPH radical-scavenging activity and reducing power), and anti-obesity activity. The results showed that the total phenol contents of the hot water extract and the 80% ethanolic extract were $16.52{\pm}3.87$ and $13.44{\pm}0.85$ mg GAE/g, respectively. The total flavonoids content was detected only in the 80% ethanolic extract, however, with a 778.08 ${\mu}g$ catechin equivalents/g content. The DPPH radical-scavenging activity and reducing power of the 80% ethanolic extract from Cheonnyuncho was significantly higher than those of the water extract (p < 0.05). During the adipocyte differentiation, the 80% ethanolic extract of Cheonnyuncho more significantly inhibited lipid accumulation and ROS production than the 3T3-L1 cells that were treated with hot water extract. Furthermore, the 80% ethanolic extract of Cheonnyuncho suppressed the mRNA abundance of the adipogenic transcription factor, $PPAR{\gamma}$ (peroxisome proliferator-activated receptor ${\gamma}$), and its target gene, aP2 (adipocyte protein 2). These results indicate that Cheonnyuncho extracts can inhibit adipogenesis through a mechanism that involves direct down regulation of $PPAR{\gamma}$ gene expression or via modulation of ROS production associated with radical-scavenging activities.

• Korean Journal of Environmental Biology
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• v.39 no.3
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• pp.390-398
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• 2021

In this study, the toxicity of chlorothalonil was investigated using survival rate and population growth rate of a marine rotifer, Brachionus plicatilis, typically used in live food in marine aquaculture systems. The survival rate of B. plicatilis was determined after 24 h of exposure to chlorothalonil (0.010-0.156 mg L^-1). Population growth rate of B. plicatilis was calculated after 72 h of exposure to chlorothalonil (0.078-1.250 mg L^-1). The survival rate and population growth rate of B. plicatilis exposed to chlorothalonil in single-dose toxicity assessment showed concentration-dependent reductions. Survival rates of B. plicatilis exposed to chlorothalonil had the following values: NOEC, 0.020mg L^-1; LOEC, 0.039 mg L^-1; and EC₅₀, 0.057 mg L^-1. Population growth rate of B. plicatilis exposed to chlorothalonil had the following values: NOEC, 0.156 mg L^-1; LOEC, 0.313 mg L^-1; and EC₅₀, 0.506 mg L^-1. When the residual concentration of chlorothalonil in the marine coastal area was more than 0.039 mg L^-1, it had a toxic effect on B. plicatilis, a zooplankton. This paper provides toxicity values that can be used as baseline data for organizing environmental standards of chlorothalonil. It also provides insight into toxic effects of chlorothalonil on other non-target organisms.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.10-20
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• 2019

Irregular rainfall caused by climate change, in combination with non-point pollution, can cause water systems worldwide to suffer from frequent eutrophication and algal blooms. This type of water pollution is more common in agricultural prone to water system inflow of non-point pollution. Therefore, in this study, the correlation between Unmanned Aerial Vehicle(UAV) multi-spectral images and total phosphorus, total nitrogen, and chlorophyll-a with indirect association of algal blooms, was analyzed to identify the usability of UAV image to identify water quality characteristics in agricultural streams. The analysis the vegetation index Normalized Differences Index (NDVI), the Normalized Differences Red Edge(NDRE), and the Chlorophyll Index Red Edge(CIRE) for the detection of multi-spectral images and algal blooms collected from the target regions Yang cheon and Hamyang Wicheon. The analysis of the correlation between image values and water quality analysis values for the water sampling points, total phosphorus at a significance level of 0.05 was correlated with the CIRE(0.66), and chlorophyll-a showed correlation with Blue(-0.67), Green(-0.66), NDVI(0.75), NDRE (0.67), CIRE(0.74). Total nitrogen was correlated with the Red(-0.64), Red edge (-0.64) and Near-Infrared Ray(NIR)(-0.72) wavelength at the significance level of 0.05. The results of this study confirmed a significant correlations between multi-spectral images collected through UAV and the factors responsible for water pollution, In the case of the vegetation index used for the detection of algal bloom, the possibility of identification of not only chlorophyll-a but also total phosphorus was confirmed. This data will be used as a meaningful data for counterplan such as selecting non-point pollution apprehensive area in agricultural area.

• Science of Emotion and Sensibility
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• v.22 no.1
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• pp.23-34
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• 2019

The U-Healthcare era has evolved with the development of the Internet of things (IoT) in the early stages of being connected as a society. Already, many changes such as increased well-being and the extension of human life are becoming evident across cultures. Korea entered the growing group of aging societies in 2017, and its silver industry is expected to grow rapidly by adopting the IoT of a super-connected society. In particular, the senior shift phenomenon has resulted in increased interest in the promotion of the health and well-being of the emergent silver generation which, unlike the existing silver generation, is highly active and wields great economic power. This study conducted in-depth interviews to investigate the characteristics of the new silver generation, and to develop the design for a wearable serious game that intends to boost the interest of the elderly in exercise and fitness activities according to their personalized physical training regimes as prescribed by the U-Hospital service. The usage scenario of this wearable serious game for the 'U-silver generation' is derived from social necessity. Medical professionals can utilize this technology to conduct health examinations and to monitor the rehabilitation of senior patients. The elderly can also use this tool to request checkups or to interface with their healthcare providers. The wearable serious game is further aimed at mitigating concerns about the deterioration of the physical functions of the silver generation by applying personalized exercise prescriptions. The present investigation revealed that it is necessary to merge the on / off line community activities to meet the silver generation's daily needs for connection and friendship. Further, the sustainability of the serious game must be enhanced through the inculcation of a sense of accomplishment as a player rises through the levels of the game. The proposed wearable serious game is designed specifically for the silver generation that is inexperienced in using digital devices: simple game rules are applied to a familiar interface grounded on the gourmet travels preferred by the target players to increase usability.

• Korean Journal of Clinical Laboratory Science
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• v.51 no.3
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• pp.309-315
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• 2019

Cervical cancer is the fourth most common malignant neoplasm in women worldwide. Most cases of cervical cancer are caused by an infection by the human papillomavirus. Molecular diagnostic methods have emerged to detect the HPV for sensitivity, specificity, and objectivity. In particular, real-time PCR has been introduced to acquire a more sensitive target DNA or RNA. RNA extraction and complementary DNA synthesis are proceeded before performing real-time PCR targeting RNA. To identify an adequate and sensitive cDNA synthesis kit, this study evaluated the two commonly used kits for cDNA synthesis. The results show that the $R^2$ and efficiency (%) of the two cDNA synthesis kits were similar in the cervical cancer cell lines. On the other hand, the Takara kit compared to Invitrogen kit showed P<0.001 in the $10^2$ and $10^3$ SiHa cell count. The Takara kit compared to the Invitrogen kit showed P<0.001 in the $10^1$ and $10^2$ HeLa cell count. Furthermore, 8, 4, 2, 1, and 0.5 ml of forty exfoliated cell samples were used to compare the cDNA synthesis kits. The Takara kit compared to the Invitrogen kit showed P<0.01 in 8, 4, and 1 ml and P<0.05 in 0.5 mL. The study was performed to identify the most appropriate cDNA synthesis kit and suggests that a cDNA synthesis kit could affect the real-time PCR results.