• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.51-66
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• 2018

As AI (Artificial Intelligence) technologies have been swiftly evolved, a lot of products and services are under development in various fields for better users' experience. On this technology advance, negative effects of AI technologies also have been discussed actively while there exists positive expectation on them at the same time. For instance, many social issues such as trolley dilemma and system security issues are being debated, whereas autonomous vehicles based on artificial intelligence have had attention in terms of stability increase. Therefore, it needs to check and analyse major social issues on artificial intelligence for their development and societal acceptance. In this paper, multi-categorical sentiment analysis is conducted over online public opinion on artificial intelligence after identifying the trending topics related to artificial intelligence for two years from January 2016 to December 2017, which include the event, match between Lee Sedol and AlphaGo. Using the largest web portal in South Korea, online news, news headlines and news comments were crawled. Considering the importance of trending topics, online public opinion was analysed into seven multiple sentimental categories comprised of anger, dislike, fear, happiness, neutrality, sadness, and surprise by topics, not only two simple positive or negative sentiment. As a result, it was found that the top sentiment is "happiness" in most events and yet sentiments on each keyword are different. In addition, when the research period was divided into four periods, the first half of 2016, the second half of the year, the first half of 2017, and the second half of the year, it is confirmed that the sentiment of 'anger' decreases as goes by time. Based on the results of this analysis, it is possible to grasp various topics and trends currently discussed on artificial intelligence, and it can be used to prepare countermeasures. We hope that we can improve to measure public opinion more precisely in the future by integrating empathy level of news comments.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.101-107
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• 2014

With the development of online service, recent forms of databases have been changed from static database structures to dynamic stream database structures. Previous data mining techniques have been used as tools of decision making such as establishment of marketing strategies and DNA analyses. However, the capability to analyze real-time data more quickly is necessary in the recent interesting areas such as sensor network, robotics, and artificial intelligence. Landmark window-based frequent pattern mining, one of the stream mining approaches, performs mining operations with respect to parts of databases or each transaction of them, instead of all the data. In this paper, we analyze and evaluate the techniques of the well-known landmark window-based frequent pattern mining algorithms, called Lossy counting and hMiner. When Lossy counting mines frequent patterns from a set of new transactions, it performs union operations between the previous and current mining results. hMiner, which is a state-of-the-art algorithm based on the landmark window model, conducts mining operations whenever a new transaction occurs. Since hMiner extracts frequent patterns as soon as a new transaction is entered, we can obtain the latest mining results reflecting real-time information. For this reason, such algorithms are also called online mining approaches. We evaluate and compare the performance of the primitive algorithm, Lossy counting and the latest one, hMiner. As the criteria of our performance analysis, we first consider algorithms' total runtime and average processing time per transaction. In addition, to compare the efficiency of storage structures between them, their maximum memory usage is also evaluated. Lastly, we show how stably the two algorithms conduct their mining works with respect to the databases that feature gradually increasing items. With respect to the evaluation results of mining time and transaction processing, hMiner has higher speed than that of Lossy counting. Since hMiner stores candidate frequent patterns in a hash method, it can directly access candidate frequent patterns. Meanwhile, Lossy counting stores them in a lattice manner; thus, it has to search for multiple nodes in order to access the candidate frequent patterns. On the other hand, hMiner shows worse performance than that of Lossy counting in terms of maximum memory usage. hMiner should have all of the information for candidate frequent patterns to store them to hash's buckets, while Lossy counting stores them, reducing their information by using the lattice method. Since the storage of Lossy counting can share items concurrently included in multiple patterns, its memory usage is more efficient than that of hMiner. However, hMiner presents better efficiency than that of Lossy counting with respect to scalability evaluation due to the following reasons. If the number of items is increased, shared items are decreased in contrast; thereby, Lossy counting's memory efficiency is weakened. Furthermore, if the number of transactions becomes higher, its pruning effect becomes worse. From the experimental results, we can determine that the landmark window-based frequent pattern mining algorithms are suitable for real-time systems although they require a significant amount of memory. Hence, we need to improve their data structures more efficiently in order to utilize them additionally in resource-constrained environments such as WSN(Wireless sensor network).

• Korean Journal of Ecology and Environment
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• v.47 no.1
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• pp.13-23
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• 2014

To obtain optimum filter media, configuration method and greenhouse wastewater loading in small-scale constructed wetlands (CWs) for treating greenhouse wastewater, the apparatuses were constructed with 4 kinds of combined systems such as vertical flow (VH)-Horizontal flow (HF), HF-VH, HF-VF-HF, VF-HF-HF CWs. The efficiencies of pollutants in greenhouse wastewater were investigated in various CWs under different filter media, configuration methods and agricultural water loading. Removal rates of pollutants under different filter media were in the other of coarse sand>broken stone${\fallingdotseq}$calcite${\fallingdotseq}$mixed filter media for COD, broken stone>mixed filter media>coarse sand>calcite for T-N, and calcite>mixed filter media>broken stone>coarse sand for T-P. The removal rates of pollutants in HF-VH-HF CWs at different configuration methods were higher than those in other configuration methods. The removal rates of pollutants were higher in the order of $150L\;m^{-2}day^{-1}{\fallingdotseq}300L\;m^{-2}day^{-1}$ > $600L\;m^{-2}day^{-1}$ under different greenhouse wastewater loading. Therefore, optimum configuration method was HF-VH-HF CWs, the optimum filter media was mixed filter media (coarse sand : broken stone : calcite=1 : 1 : 1), and the optimum greenhouse wastewater loading was $300L\;m^{-2}day^{-1}$ in HF-VH-HF CWs.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.99-120
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• 2010

Due to the wide spread of customers' frequent access of non face-to-face services, there have been many attempts to improve customer satisfaction using huge amounts of data accumulated throughnon face-to-face channels. Usually, a call center is regarded to be one of the most representative non-faced channels. Therefore, it is important that a call center has enough agents to offer high level customer satisfaction. However, managing too many agents would increase the operational costs of a call center by increasing labor costs. Therefore, predicting and calculating the appropriate size of human resources of a call center is one of the most critical success factors of call center management. For this reason, most call centers are currently establishing a department of WFM(Work Force Management) to estimate the appropriate number of agents and to direct much effort to predict the volume of inbound calls. In real world applications, inbound call prediction is usually performed based on the intuition and experience of a domain expert. In other words, a domain expert usually predicts the volume of calls by calculating the average call of some periods and adjusting the average according tohis/her subjective estimation. However, this kind of approach has radical limitations in that the result of prediction might be strongly affected by the expert's personal experience and competence. It is often the case that a domain expert may predict inbound calls quite differently from anotherif the two experts have mutually different opinions on selecting influential variables and priorities among the variables. Moreover, it is almost impossible to logically clarify the process of expert's subjective prediction. Currently, to overcome the limitations of subjective call prediction, most call centers are adopting a WFMS(Workforce Management System) package in which expert's best practices are systemized. With WFMS, a user can predict the volume of calls by calculating the average call of each day of the week, excluding some eventful days. However, WFMS costs too much capital during the early stage of system establishment. Moreover, it is hard to reflect new information ontothe system when some factors affecting the amount of calls have been changed. In this paper, we attempt to devise a new model for predicting inbound calls that is not only based on theoretical background but also easily applicable to real world applications. Our model was mainly developed by the interactive decision tree technique, one of the most popular techniques in data mining. Therefore, we expect that our model can predict inbound calls automatically based on historical data, and it can utilize expert's domain knowledge during the process of tree construction. To analyze the accuracy of our model, we performed intensive experiments on a real case of one of the largest car insurance companies in Korea. In the case study, the prediction accuracy of the devised two models and traditional WFMS are analyzed with respect to the various error rates allowable. The experiments reveal that our data mining-based two models outperform WFMS in terms of predicting the amount of accident calls and fault calls in most experimental situations examined.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.261-268
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• 2021

Cloud computing is a computing paradigm in which users can utilize computing resources in a pay-as-you-go manner. In a cloud system, resources can be dynamically scaled up and down to the user's on-demand so that the total cost of ownership can be reduced. The Modeling and Simulation (M&S) technology is a renowned simulation-based method to obtain engineering analysis and results through CAE software without actual experimental action. In general, M&S technology is utilized in Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), Multibody dynamics (MBD), and optimization fields. The work procedure through M&S is divided into pre-processing, analysis, and post-processing steps. The pre/post-processing are GPU-intensive job that consists of 3D modeling jobs via CAE software, whereas analysis is CPU or GPU intensive. Because a general-purpose desktop needs plenty of time to analyze complicated 3D models, CAE software requires a high-end CPU and GPU-based workstation that can work fluently. In other words, for executing M&S, it is absolutely required to utilize high-performance computing resources. To mitigate the cost issue from equipping such tremendous computing resources, we propose HEMOS-Cloud service, an integrated cloud and cluster computing environment. The HEMOS-Cloud service provides CAE software and computing resources to users who want to experience M&S in business sectors or academics. In this paper, the economic ripple effect of HEMOS-Cloud service was analyzed by using industry-related analysis. The estimated results of using the experts-guided coefficients are the production inducement effect of KRW 7.4 billion, the value-added effect of KRW 4.1 billion, and the employment-inducing effect of 50 persons per KRW 1 billion.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.13-26
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• 2024

Multi-modal generation is the process of generating results based on a variety of information, such as text, images, and audio. With the rapid development of AI technology, there is a growing number of multi-modal based systems that synthesize different types of data to produce results. In this paper, we present an AI system that uses speech and text recognition to describe a person and generate a montage image. While the existing montage generation technology is based on the appearance of Westerners, the montage generation system developed in this paper learns a model based on Korean facial features. Therefore, it is possible to create more accurate and effective Korean montage images based on multi-modal voice and text specific to Korean. Since the developed montage generation app can be utilized as a draft montage, it can dramatically reduce the manual labor of existing montage production personnel. For this purpose, we utilized persona-based virtual person montage data provided by the AI-Hub of the National Information Society Agency. AI-Hub is an AI integration platform aimed at providing a one-stop service by building artificial intelligence learning data necessary for the development of AI technology and services. The image generation system was implemented using VQGAN, a deep learning model used to generate high-resolution images, and the KoDALLE model, a Korean-based image generation model. It can be confirmed that the learned AI model creates a montage image of a face that is very similar to what was described using voice and text. To verify the practicality of the developed montage generation app, 10 testers used it and more than 70% responded that they were satisfied. The montage generator can be used in various fields, such as criminal detection, to describe and image facial features.

• Horticultural Science & Technology
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• v.33 no.2
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• pp.155-165
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• 2015

Well-feathered (over 10 feathers) 'Fuji'/M.9 apple trees were planted at $3.0{\times}1.0m$ and trained to slender spindle with 2.5 m height or to tall spindle with 3.5 m height, and the vegetative growth, productivity, and fruit quality of two training systems were compared for 8 years. The canopy volume of the tall spindle trees surpassed that of the slender spindle trees 4 years after planting and was 25% larger than that of the slender spindle trees 5 years after planting. The accumulated yield over 8 years for the tall spindle system was 14% higher than that of the slender spindle system. Alternate bearing and incidence of marssonina blotch were observed in both treatments after 5 years of planting. There was often vegetative imbalance in the trees however, the degree of yield loss and vegetative imbalance of the tall spindle trees was lower than those of the slender spindle trees. Soluble solid content and fruit red color of the tall spindle trees were higher than that of the slender spindle trees in 5 year after planting, resulting from increased light penetration in the canopy due to even distribution of lateral branches and from fruit bearing in different height locations of the trees. In conclusion, increasing the tree height to about 3.5 m using slender spindle 'Fuji'/M.9 apple trees planted with over 333 trees per 10a led to better light penetration, yield and fruit quality compared to a conventional wide training system with the slender spindle.

• Journal of Hospice and Palliative Care
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• v.13 no.3
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• pp.181-189
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• 2010

Purpose: Recently, health policy making is increasingly based on evidence. Therefore, Korean Terminal Cancer Patient Information System (KTCPIS) was developed to meet such need. We aimed to report its developmental process and statistics from 6 months data. Methods: Items for KTCPIS were developed through the consultation with practitioners. E-Velos web-based clinical trial management system was used as a technical platform. Data were collected for patients who were registered to 34 inpatient palliative care services, designated by Ministry of Health, Welfare, and Family Affairs, from $1^{st}$ of January to $30^{th}$ of June in 2009. Descriptive statistics were used for the analysis. Results: From the nationally representative set of 2,940 patients, we obtained the following results. Mean age was $64.8{\pm}12.9$ years, and 56.6% were male. Lung cancer (18.0%) was most common diagnosis. Only 50.3% of patients received the confirmation of terminal diagnosis by two or more physicians, and 69.7% had an insight of terminal diagnosis at the time of admission. About half of patients were admitted to the units on their own without any formal referral. Average and worst pain scores were significantly reduced after 1 week when compared to those at the time of admission. 73.4% faced death in the units, and home-discharge comprised only 13.3%. Mean length of stay per admission was $20.2{\pm}21.2$ days, with median value of 13. Conclusion: Nationally representative data on the characteristics of patients and their caregiver, and current practice of service delivery in palliative care units were obtained through the operation of KTCPIS.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.147-161
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• 2010

As broadcasting and communication are converged recently, communication is jointed to TV. TV viewing has brought about many changes. The IPTV (Internet Protocol Television) provides information service, movie contents, broadcast, etc. through internet with live programs + VOD (Video on demand) jointed. Using communication network, it becomes an issue of new business. In addition, new technical issues have been created by imaging technology for the service, networking technology without video cuts, security technologies to protect copyright, etc. Through this IPTV network, users can watch their desired programs when they want. However, IPTV has difficulties in search approach, menu approach, or finding programs. Menu approach spends a lot of time in approaching programs desired. Search approach can't be found when title, genre, name of actors, etc. are not known. In addition, inserting letters through remote control have problems. However, the bigger problem is that many times users are not usually ware of the services they use. Thus, to resolve difficulties when selecting VOD service in IPTV, a personalized service is recommended, which enhance users' satisfaction and use your time, efficiently. This paper provides appropriate programs which are fit to individuals not to save time in order to solve IPTV's shortcomings through filtering and recommendation-related system. The proposed recommendation system collects TV program information, the user's preferred program genres and detailed genre, channel, watching program, and information on viewing time based on individual records of watching IPTV. To look for these kinds of similarities, similarities can be compared by using ontology for TV programs. The reason to use these is because the distance of program can be measured by the similarity comparison. TV program ontology we are using is one extracted from TV-Anytime metadata which represents semantic nature. Also, ontology expresses the contents and features in figures. Through world net, vocabulary similarity is determined. All the words described on the programs are expanded into upper and lower classes for word similarity decision. The average of described key words was measured. The criterion of distance calculated ties similar programs through K-medoids dividing method. K-medoids dividing method is a dividing way to divide classified groups into ones with similar characteristics. This K-medoids method sets K-unit representative objects. Here, distance from representative object sets temporary distance and colonize it. Through algorithm, when the initial n-unit objects are tried to be divided into K-units. The optimal object must be found through repeated trials after selecting representative object temporarily. Through this course, similar programs must be colonized. Selecting programs through group analysis, weight should be given to the recommendation. The way to provide weight with recommendation is as the follows. When each group recommends programs, similar programs near representative objects will be recommended to users. The formula to calculate the distance is same as measure similar distance. It will be a basic figure which determines the rankings of recommended programs. Weight is used to calculate the number of watching lists. As the more programs are, the higher weight will be loaded. This is defined as cluster weight. Through this, sub-TV programs which are representative of the groups must be selected. The final TV programs ranks must be determined. However, the group-representative TV programs include errors. Therefore, weights must be added to TV program viewing preference. They must determine the finalranks.Based on this, our customers prefer proposed to recommend contents. So, based on the proposed method this paper suggested, experiment was carried out in controlled environment. Through experiment, the superiority of the proposed method is shown, compared to existing ways.

• The Korean Journal of Air & Space Law and Policy
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• v.19 no.2
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• pp.97-136
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• 2004

The Northeast Asian air cargo market has expanded tremendously as a result of the opening up of the Chinese market. The importance of the Asia-Pacific region in the global air transport has also increased. The exchange of human and material resources, services, and information in Northeast Asia, which is expected to increase in the near future, requires that the airlines operating within this region adopt a more liberalized approach. This paper introduced alternatives which can be applied to the Northeast Asian airlines industry so as to bring about the integration of regional air transport: First, this paper found a need for individual Northeast Asian nations to alter their policies towards the airlines industry. Second, each country should further liberalize their respective domestic air transport. Third, there is a need for freer air service agreements to be signed between the nations of Northeast Asia. Fourth, the strategic alliances between the airlines operating in Northeast Asia should be further strengthened. Fifth, this liberalization process should be carried out in an incremental manner, beginning with more competitive airports and routes, or with less-in-demand routes. Sixth, there is a need for a shuttle system to be put into place between the main airports in China, Korea, and Japan. Seventh, these three nations jointly develop aviation safety and security systems that are in accordance with international standards. Eighth, the liberalization process of the aviation industry should be undertaken in conjunction with other related fields. Ninth, organizations linking together civil aviation organization in the Asia-Pacific area should be formed, as should each government linking together. By doing so, these countries will be able to establish regular venues through which to exchange opinions on the integration and liberalization of the air cargo market so as to induce the gradual liberalization of the actual market. The liberalization of the air transport in Northeast Asia will prove to be a daunting task in the short term. However, if the Chinese airlines continue to exhibit continuous growth and Japanese airlines are able to complete their move towards a low-cost structure, this process could be completed earlier than expected. Over the last twenty five years the air transport has undergone tremendous changes. The most important factor behind these changes has been the increased liberalization of the market. As a result, rates have decreased while demand has increased. This has resulted in turning the air transport industry, which was long perceived as an industry in decline, into a high-growth industry. The only method of increasing regional exchanges in the air transport is to pursue further liberalization. The country which implements this liberalization process at the earliest date may very well emerge as a leading force within the air transport industry.