• Journal of Environmental Impact Assessment
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• v.20 no.5
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• pp.705-716
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• 2011

For the efficient discovery of knowledge and information from the observed systems, data mining techniques can be an useful tool for the prediction of water quality at intake station in rivers. Deterioration of water quality can be caused at intake station in dry season due to insufficient flow. This demands additional outflow from dam since some extent of deterioration can be attenuated by dam reservoir operation to control outflow considering predicted water quality. A seasonal occurrence of high ammonia nitrogen ($NH_3$-N) concentrations has hampered chemical treatment processes of a water plant in Geum river. Monthly flow allocation from upstream dam is important for downstream $NH_3$-N control. In this study, prediction models of water quality based on multiple regression (MR), artificial neural network and data mining methods were developed to understand water quality variation and to support dam operations through providing predicted $NH_3$-N concentrations at intake station. The models were calibrated with eight years of monthly data and verified with another two years of independent data. In those models, the $NH_3$-N concentration for next time step is dependent on dam outflow, river water quality such as alkalinity, temperature, and $NH_3$-N of previous time step. The model performances are compared and evaluated by error analysis and statistical characteristics like correlation and determination coefficients between the observed and the predicted water quality. It is expected that these data mining techniques can present more efficient data-driven tools in modelling stage and it is found that those models can be applied well to predict water quality in stream river systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.286-289
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• 2016

In this paper, we propose an architecture that affords mobile app based on nomadic smartphone using not only mobile cloud computing- architecture but also a dedicated web platform called Node.js built-in with the asynchronous, Nonblocking, Event-Driven programming paradigm. Furthermore, the design of the proposed architecture takes document oriented database known as MongoDB to deal with the large amount of data transmit by users of mobile web access application. The Node.js aims to give the programmers the tools needed to solves the large number of concurrent connections problem. We demonstrate the effectiveness of the proposed architecture by implementing an android application responsible of real time analysis by using a vehicle to applications smart phones interface approach that considers the smartphones to acts as a remote users which passes driver inputs and delivers output from external applications.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.6
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• pp.746-755
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• 2002

EXPRESS is product information description language. It is interpretable by human and software. Product data written in EXPRESS make it possible to exchange between heterogeneous systems. However, the number of software that can use EXPRESS is limited and it is expensive to use the software. XML makes it possible to update and manage data on the Web. Because the Web is easier to use and access than other tools comparatively, data represented by XML need not depend on specific applications or systems and it can be used for exchange of data. Therefore, if we represent EXPRESS-driven data in XML, there will be more active data exchange widely and easily In this work, a method of translation EXPRESS document to XML DTD and XML Schema is proposed. By classification all of EXPRESS syntax element and consideration complex cases caused by this syntax element, a translation rule that represent XML DTD and XML Schema is suggested. Also, a translator which is corresponding to this rule is implemented.

• Language and Information
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• v.13 no.1
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• pp.77-92
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• 2009

Geis (1973) has provided various properties of the subjects and by + Gerund Phrase (GerP) in English causative constructions. Among them, the two main issues of Geis's analysis are as follows: unlike Lakoff (1965; 1966), the subject of English causative constructions, including causative-inchoative verbs such as liquefy, first of all, should be acts or events, not persons, and the by + GerP in the construction is a complement of the causative verbs. In addition to these issues, Geis has provided various data exhibiting other idiosyncratic properties and proposed some transformational rules such as the Agent Creation Rule and rule orderings to explain them. Against Geis's claim, I propose that English causative verbs require either Proper nouns or GerP subjects and that the by + GerP in the constructions as a Verbal Modifier needs Gerunds, whose understood Affective-agent subject is identical to the subject of causative verbs with respect to the semantic index value. This enables us to solve the two main issues. At the same time, the other properties Geis mentioned also can be easily accounted for in Head-driven Phrase Structure Grammar (HPSG) by positing a few lexical constraints. On this basis, it is shown that given the few lexical constraints and existing grammatical tools in HPSG, the constraint-based analysis proposed here gives a simpler explanation of the properties of English causative constructions provided by Geis without transformational rules and rule orderings.

• Journal of The Korean Astronomical Society
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• v.36 no.3
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• pp.177-187
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• 2003

Active galactic nuclei (AGNs) are distant, powerful sources of radiation over the entire electromagnetic spectrum, from radio waves to gamma-rays. There is much evidence that they are driven by gravitational accretion of stars, dust, and gas, onto central massive black holes (MBHs) imprisoning anywhere from $\~$1 to $\~$10,000 million solar masses; such objects may naturally form in the centers of galaxies during their normal dynamical evolution. A small fraction of AGNs, of the radio-loud type (RLAGNs), are somehow able to generate powerful synchrotron-emitting structures (cores, jets, lobes) with sizes ranging from pc to Mpc. A brief summary of AGN observations and theories is given, with an emphasis on RLAGNs. Preliminary results from the imaging of 10000 extragalactic radio sources observed in the MITVLA snapshot survey, and from a new analytic theory of the time-variable power output from Kerr black hole magnetospheres, are presented. To better understand the complex physical processes within the central engines of AGNs, it is important to confront the observations with theories, from the viewpoint of analyzing the time-variable behaviours of AGNs - which have been recorded over both 'short' human ($10^0-10^9\;s$) and 'long' cosmic ($10^{13} - 10^{17}\;s$) timescales. Some key ingredients of a basic mathematical formalism are outlined, which may help in building detailed Monte-Carlo models of evolving AGN populations; such numerical calculations should be potentially important tools for useful interpretation of the large amounts of statistical data now publicly available for both AGNs and RLAGNs.

• Journal of Korean Biological Nursing Science
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• v.23 no.3
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• pp.170-179
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• 2021

Purpose: The aim of this study was to identify core keywords and topic groups in the 'Diabetes mellitus and mobile applications' field of research for better understanding research trends in the past 20 years. Methods: This study was a text-mining and topic modeling study including four steps such as 'collecting abstracts', 'extracting and cleaning semantic morphemes', 'building a co-occurrence matrix', and 'analyzing network features and clustering topic groups'. Results: A total of 789 papers published between 2002 and 2021 were found in databases (Springer). Among them, 435 words were extracted from 118 articles selected according to the conditions: 'analyzed by text network analysis and topic modeling'. The core keywords were 'self-management', 'intervention', 'health', 'support', 'technique' and 'system'. Through the topic modeling analysis, four themes were derived: 'intervention', 'blood glucose level control', 'self-management' and 'mobile health'. The main topic of this study was 'self-management'. Conclusion: While more recent work has investigated mobile applications, the highest feature was related to self-management in the diabetes care and prevention. Nursing interventions utilizing mobile application are expected to not only effective and powerful glycemic control and self-management tools, but can be also used for patient-driven lifestyle modification.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.33-42
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• 2022

One of the most pressing socioeconomic problems confronting humanity on a worldwide scale is food security, particularly in light of the expanding population and declining land productivity. These causes have increased the number of people in the world who are at risk of starving and have caused the natural ecosystems to degrade at previously unheard-of speeds. Happily, the Internet of Things (IoT) development provides a glimmer of light for those worried about food security through smart agriculture-a development that is particularly relevant to automating food production operations in order to reduce labor expenses. When compared to conventional farming techniques, smart agriculture has the benefit of maximizing resource use through precise chemical input application and regulation of environmental factors like temperature and humidity. Farmers may make data-driven choices about the possibility of insect invasion, natural disasters, anticipated yields, and even prospective market shifts with the use of smart farming tools. The technical foundation of smart agriculture serves as a potential response to worries about food security. It is made up of wireless sensor networks and integrated cloud computing modules inside IoT.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.175-185
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• 2023

As the era of ChatGPT and generative AI technologies unfolds, the marketing industry stands on the precipice of a paradigm shift. Innovations such as GPT-4, DALL-E 2, and Mid-journey Stable Diffusion possess the capacity to dramatically transform the methods by which advertisers reach and engage with customers. The potential applications of these advanced tools herald a new age for the marketing and advertising sectors, offering unprecedented opportunities for growth and optimization. Nevertheless, the rapid adoption of generative AI within these industries presents a unique set of challenges, particularly for organizations that lack the necessary technological infrastructure and human capital to effectively leverage these innovations. As a result, a competitive crisis may emerge, exacerbating existing disparities between well-equipped enterprises and their less technologically adept counterparts. In this article, we undertake a comprehensive exploration of the implications of generative AI for the future of marketing, examining both its potential benefits and drawbacks. We consider the possible impact of these developments on the advertising and marketing industries at large, as well as the ways in which professionals operating within these fields may need to adapt to remain competitive in an increasingly AI-driven landscape. By providing a holistic overview of the challenges and opportunities associated with generative AI, this study aims to elucidate the complex dynamics at play in the ongoing evolution of the marketing and advertising sectors.

• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.117-129
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• 2023

High-utility itemset mining (HIUM) has emerged as a key data-mining paradigm for object-of-interest identification and recommendation systems that serve as frequent itemset identification tools, product or service recommendation systems, etc. Recently, it has gained widespread attention owing to its increasing role in business intelligence, top-N recommendation, and other enterprise solutions. Despite the increasing significance and the inability to provide swift and more accurate predictions, most at-hand solutions, including frequent itemset mining, HUIM, and high average- and fast high-utility itemset mining, are limited to coping with real-time enterprise demands. Moreover, complex computations and high memory exhaustion limit their scalability as enterprise solutions. To address these limitations, this study proposes a model to extract high-utility frequent closed itemsets based on an improved cumulative summary list structure (CSLFC-HUIM) to reduce an optimal set of candidate items in the search space. Moreover, it employs the lift score as the minimum threshold, called the cumulative utility threshold, to prune the search space optimal set of itemsets in a nested-list structure that improves computational time, costs, and memory exhaustion. Simulations over different datasets revealed that the proposed CSLFC-HUIM model outperforms other existing methods, such as closed- and frequent closed-HUIM variants, in terms of execution time and memory consumption, making it suitable for different mined items and allied intelligence of business goals.

• Journal of Physiology & Pathology in Korean Medicine
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• v.32 no.3
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• pp.149-156
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• 2018

Traditional medicines (TM) in Korea, China, and Japan share most of the theories and therapeutic tools, but there are also differences due to their unique histories and cultures. Here, we aim to identify the differences in the utilization of TM theory between three countries by analyzing herb usage data in terms of the related traditional theories. Herb usage data of each country was collected from "Investigation of Korean medicine use and herbal medicine consumption survey" (Korea), "Analytical report on circulation of key Chinese medicinal materials" (China), and "Survey report on raw material crude drug usage" (Japan). Fifty five herbs with sixty features belonging to five theoretical categories (four properties, five tastes, targeting meridians, treatment strategies, and herbal parts) were selected and analyzed. Weight Sum Model (WSM) and Network-Based Group Features (NBGF) were used to compare the theoretical characteristics of TM between three countries. For the statistical evaluation, we developed and applied Herb Set Enrichment Analysis (HSEA) for WSM and NBGF results. HSEA for WSM results revealed the kidney meridian were targeted more in Korea than Japan, while the spleen meridian were targeted more in Japan than Korea. Herbs with sour taste were used more in Japan than China. HSEA for NBGF results found that NBGF including warm, neutral, sweet, and tonifying features were more dominant in Korea and than Japan, while NBGF including cold, bitter, heat-clearing features were more dominant in Japan than the others. These results suggest that TM in Korea, China, and Japan have unique aspects of practice patterns and theoretical utilization.