• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.25-52
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• 2018

This study performs social network analysis based on consumer sentiment related to a location in Seoul using data reflecting consumers' web search activities and emotional evaluations associated with commerce. The study focuses on large commercial districts in Seoul. In addition, to consider their various aspects, social network indexes were combined with the trading area's public data to verify factors affecting the area's sales. According to R square's change, We can see that the model has a little high R square value even though it includes only the district's public data represented by static data. However, the present study confirmed that the R square of the model combined with the network index derived from the social network analysis was even improved much more. A regression analysis of the trading area's public data showed that the five factors of 'number of market district,' 'residential area per person,' 'satisfaction of residential environment,' 'rate of change of trade,' and 'survival rate over 3 years' among twenty two variables. The study confirmed a significant influence on the sales of the trading area. According to the results, 'residential area per person' has the highest standardized beta value. Therefore, 'residential area per person' has the strongest influence on commercial sales. In addition, 'residential area per person,' 'number of market district,' and 'survival rate over 3 years' were found to have positive effects on the sales of all trading area. Thus, as the number of market districts in the trading area increases, residential area per person increases, and as the survival rate over 3 years of each store in the trading area increases, sales increase. On the other hand, 'satisfaction of residential environment' and 'rate of change of trade' were found to have a negative effect on sales. In the case of 'satisfaction of residential environment,' sales increase when the satisfaction level is low. Therefore, as consumer dissatisfaction with the residential environment increases, sales increase. The 'rate of change of trade' shows that sales increase with the decreasing acceleration of transaction frequency. According to the social network analysis, of the 25 regional trading areas in Seoul, Yangcheon-gu has the highest degree of connection. In other words, it has common sentiments with many other trading areas. On the other hand, Nowon-gu and Jungrang-gu have the lowest degree of connection. In other words, they have relatively distinct sentiments from other trading areas. The social network indexes used in the combination model are 'density of ego network,' 'degree centrality,' 'closeness centrality,' 'betweenness centrality,' and 'eigenvector centrality.' The combined model analysis confirmed that the degree centrality and eigenvector centrality of the social network index have a significant influence on sales and the highest influence in the model. 'Degree centrality' has a negative effect on the sales of the districts. This implies that sales decrease when holding various sentiments of other trading area, which conflicts with general social myths. However, this result can be interpreted to mean that if a trading area has low 'degree centrality,' it delivers unique and special sentiments to consumers. The findings of this study can also be interpreted to mean that sales can be increased if the trading area increases consumer recognition by forming a unique sentiment and city atmosphere that distinguish it from other trading areas. On the other hand, 'eigenvector centrality' has the greatest effect on sales in the combined model. In addition, the results confirmed a positive effect on sales. This finding shows that sales increase when a trading area is connected to others with stronger centrality than when it has common sentiments with others. This study can be used as an empirical basis for establishing and implementing a city and trading area strategy plan considering consumers' desired sentiments. In addition, we expect to provide entrepreneurs and potential entrepreneurs entering the trading area with sentiments possessed by those in the trading area and directions into the trading area considering the district-sentiment structure.

• Journal of Yeungnam Medical Science
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• v.8 no.2
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• pp.185-201
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• 1991

A household survey was conducted to compare the patterns of morbidity and medical care utilization between medical aid beneficiaries and medical insurance beneficiaries. The study population included 285 medical aid beneficiaries that were completely surveyed and 386 medical insurance benficiaries selected by simple random sampling from a Dong(Township) in Taegu. Well-trained surveyers mainly interviewed housewives with a structured questionnaire. The morbidity rates of acute illness during the 15-day period, were 63 per 1,000 medical aid beneficiaries and 62 per 1,000 medical insurance beneficiaries. The rates for chronic illness were 123 per 1,000 medical aid beneficiaries and 73 per 1,000 medical insurance beneficiaries. The most common type of acute illness in medical aid and medical insurance beneficiaries was respiratory disease. In medical aid beneficiaries, musculoskeletal disease was most common, but in medical insurance beneficiaries, gastrointestinal disease was most common. The mean duration of acute illness of medical aid beneficiaries was 3.8 days and that of medical insurance beneficiaries was 6.8 days. During the one year period, mean duration of medical aid beneficiaries chronic illnesses was 11.5 months which was almost twice as long compared to medical insurance beneficiaries. Pharmacy was most preferrable facility among the acute illness patient in medical aid beneficiaries, but acute cases of medical insurance beneficiaries visited the clinic most commonly. Chronic cases of both groups visited the clinic most frequently. There were some findings suggesting that much unmet need existed among the medical aid beneficiaries. In acute cases, the average number of days of medical aid users utilized medical facilities was less than medical insurance users. On the other hand, the length of medical care utilization of chronic cases was reversed. Geographical accessibility was the most important factors in utilization of medical facilities. Almost half of the study population answered the questions about source of funds on medical security correctly. Most respondents considered that the objective of medical security was afford ability. The chief complaint on hospital utilization was the complicated administrative procedures. These findings suggest that there were some problems in the medical aid system, especially in the referral system.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.95-110
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• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.23 no.4
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• pp.153-178
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• 2018

Most operational uses of wind speed data require measurements at, or estimates generated for, the reference height of 10 m above mean sea level (AMSL). On the Ieodo Ocean Research Station (IORS), wind speed is measured by instruments installed on the lighthouse tower of the roof deck at 42.3 m AMSL. This preliminary study indicates how these data can best be converted into synthetic 10 m wind speed data for operational uses via the Korea Hydrographic and Oceanographic Agency (KHOA) website. We tested three well-known conventional empirical neutral wind profile formulas (a power law (PL); a drag coefficient based logarithmic law (DCLL); and a roughness height based logarithmic law (RHLL)), and compared their results to those generated using a well-known, highly tested and validated logarithmic model (LMS) with a stability function (${\psi}_{\nu}$), to assess the potential use of each method for accurately synthesizing reference level wind speeds. From these experiments, we conclude that the reliable LMS technique and the RHLL technique are both useful for generating reference wind speed data from IORS observations, since these methods produced very similar results: comparisons between the RHLL and the LMS results showed relatively small bias values ($-0.001m\;s^{-1}$) and Root Mean Square Deviations (RMSD, $0.122m\;s^{-1}$). We also compared the synthetic wind speed data generated using each of the four neutral wind profile formulas under examination with Advanced SCATterometer (ASCAT) data. Comparisons revealed that the 'LMS without ${\psi}_{\nu}^{\prime}$ produced the best results, with only $0.191m\;s^{-1}$ of bias and $1.111m\;s^{-1}$ of RMSD. As well as comparing these four different approaches, we also explored potential refinements that could be applied within or through each approach. Firstly, we tested the effect of tidal variations in sea level height on wind speed calculations, through comparison of results generated with and without the adjustment of sea level heights for tidal effects. Tidal adjustment of the sea levels used in reference wind speed calculations resulted in remarkably small bias (<$0.0001m\;s^{-1}$) and RMSD (<$0.012m\;s^{-1}$) values when compared to calculations performed without adjustment, indicating that this tidal effect can be ignored for the purposes of IORS reference wind speed estimates. We also estimated surface roughness heights ($z_0$) based on RHLL and LMS calculations in order to explore the best parameterization of this factor, with results leading to our recommendation of a new $z_0$ parameterization derived from observed wind speed data. Lastly, we suggest the necessity of including a suitable, experimentally derived, surface drag coefficient and $z_0$ formulas within conventional wind profile formulas for situations characterized by strong wind (${\geq}33m\;s^{-1}$) conditions, since without this inclusion the wind adjustment approaches used in this study are only optimal for wind speeds ${\leq}25m\;s^{-1}$.

• Journal of Food Hygiene and Safety
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• v.23 no.3
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• pp.239-247
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• 2008

Objective of this study was to investigate residual the levels of heavy metals in rice grain and soils of "Top-Rice" and common rice cultivation areas from 2005 to 2007. Soil and rice grain samples were taken from 33 "Top-rice" areas and neighboring paddies, and analyzed for the elements using ICP-OES and ICP-TOF-MS after acid digestion. A concentration of arsenic in paddy soil was 1.33 mg/kg which was below 1/5-1/11 fold of the threshold levels(concern: 4 mg/kg, action: 10 mg/kg), and paddy soil was 0.06 mg/kg of Cd(cadmium) being below 1/25-1/67 fold of the limits(concern: 1.5 mg/kg, action: 4 mg/kg). A level of Cu(copper) in paddy soil was 4.57 mg/kg which was below 1/11-1/27 fold of the threshold levels(concern: 50 mg/kg, action: 125 mg/kg), and Pb(lead) concentration in paddy soil was found to be a 4.68 mg/kg. In addition, Hg(mercury) concentration in paddy soil was to be a 0.03 mg/kg, which was below 1/131-1/328 fold of the threshold levels(concern: 4 mg/kg, action: 10 mg/kg). The average concentrations of As, Cd, Cu, Pb and Hg in the polished rice samples were 0.037, 0.043, 0.280, 0.048 and 0.002 mg/kg, respectively. These levels are lower than those of other countries in rice grains. Assuming the rice consumption of 205.7 g/day by total dietary supplements in Korea, the amount of total weekly metal intake of As, Cd, Cu, Pb and Hg by polished rice were estimated to be 0.0892, 1.035, 6.712, 1.161 and 0.054 ${\mu}g/kg$ body weigh/week, respectively. The PTWI(%) of As, Cd, Cu, Pb and Hg were 5.95(inorganic arsenic), 0.26(total arsenic), 14.79, 0.19, 4.65 and 1.07% estimated to be 0.0892, 1.035, 6.712, 1.161 and 0.054 ${\mu}g/kg$ body weigh/week, respectively. In conclusion, it was appeared that the heavy metals contamination in the brown and polished rice should not be worried in Korea.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.34 no.1
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• pp.83-96
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• 2016

Through the research and analysis on the vegetation environment, flora of habitats through documentary and field studies over 14 habitats of Euryale ferox Salisbury within Jeollabukdo, with the objective of acquiring the basic data for forming an environment based on plantation of reservoirs that are composed with Euryale ferox, the following results were obtained. 1. The entire flora of the 14 habitats appeared to be 79 families, 211 genus, 298 species, two subspecies, 30 varieties and six forma, thus, a total of 336 taxa was confirmed. Among these, emergent water plants appeared to compose 17 taxa, floating-leaved plants to compose seven taxa including Euryale ferox floating plants to compose five taxa and submerged water plants to compose two taxa. As a result of analyzing the similarity only over the water plants. The lowest similarity rate appeared between Gamdong Reservoir and Aedang Reservoir, as the similarity rate between the two regions appeared to be 0% as a result of the analysis. Floating-leaved plants, lotuses and caltrops, appeared to be equally inhabiting in Hanseongji at Jeongeup and Seoknam Reservoir at Gochang, which showed the highest similarity rate, in addition to Euryale ferox. 2. When examining the appearance frequency of aquatic plants per growth type, Actinostemma lobatum and Phragmites communis, in addition to Euryale ferox each appeared 11 times, showing a high frequency of 78.6% and Trapa japonica, which is a floating-leaved water plant, appeared ten times(71.4%) and Zizania latifolia appeared eight times(57.1%). In addition, the appearance rate appeared to be high in the order of Persicaria thunbergii, Leersia sayanuka, Ceratophyllum demersum, Echinochloa crusgalli var. oryzicola, Scirpus maritimus, and Nelumbo nucifera. 3. The rare plants discovered in the Euryale ferox habitats pursuant to the IUCN evaluation standards was confirmed to be composed of five taxa, with three taxa including the least concerned species(LC), Melothria japonica at Yanggok Reservoir, Hydrocharis dubia at Myeongdeokji and Ottelia alismoides at Daewi Reservoir, in addition to vulnerable species(VU), Utricularia vulgaris at Sangpyeong Reservoir, along with Euryale ferox. 4. Most of the group or community types of the natural habitats of Euryale ferox appeared to be the Euryale ferix community' and the Daewi Reservoir of Gunsan was defined as caltrop + Euryale ferox + Nymphoides indica community. The green coverage ratio of Euryale ferox per natural habitats showed a considerably huge deviation from 0.03 to 36.50 and as the average green coverage ratio was appropriated as 9.8, it can be considered that maintaining the green coverage ratio of Euryale ferox in a 10% level would be advisable when forming a reservoir with Euryale ferox as the key composition species. 5. The vegetation community nearby the natural habitats of Euryale ferox per research subject area appeared to be composed of three Leersia japonica communities, two communities each for Zizania latifolia community and Trapa japonica community and one community each for Nelumbo nucifera community, Nymphoides peltata + Typha orientalis community, Trapa japonica + Nelumbo nucifera community, Hydrocharis dubia community, Leersia japnica + Paspalum distichum var. indutum community and Euryale ferox + Trapa japonica community, showing a slight difference depending on the location conditions of each reservoir. Thus, this result may be suggested as a guideline to apply when allocating the vegetation ratio and the types of floating-leaved plants upon planting plants in reservoirs with Euryale ferox as the main companion species.

• Korean Journal of Ecology and Environment
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• v.37 no.2 s.107
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• pp.180-192
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• 2004

This study was carried out to assess the seasonal variation of water quality and the effect of pollutant loading from watershed in a shallow eutrophic reservoir (Shingu reservoir) from November 2002 to February 2004, Stable thermocline which was greater than $1^{\circ}C$ per meter of the water depth formed in May, and low DO concentration (< 2 mg $O_2\;L^{-1}$) was observed in the hypolimnion from May to September, 2003. The ratio of euphotic depth to mixing depth ($Z_{eu}/Z_{m}$) ranged 0.2 ${\sim}$ 1.1, and the depth of the mixed layer exceeded that of the photic layer during study period, except for May when $Z_{eu}$ and $Z_{m}$ were 4 and 4.3 m, respectively. Most of total nitrogen, ranged 1.1 ${\sim}$ 4.5 ${\mu}g\;N\;L^{-1}$, accounted for inorganic nitrogen (Avg, 58.7%), and sharp increase of $NH_3$-N Hand $NO_3$-N was evident during the spring season. TP concentration in the water column ranged 43.9 ${\sim}$ 126.5 ${\mu}g\;P\;L^{-1}$, and the most of TP in the water column accounted for POP (Avg. 80%). During the study period, DIP concentration in the water column was &;lt 10 ${\mu}g\;P\;L^{-1}$ except for July and August when DIP concentration in the hypolimnion was 22.3 and 56.7 ${\mu}g\;P\;L^{-1}$, respectively. Increase of Chl. a concentration observed in July (99 ${\mu}g\;L^{-1}$) and November 2003 (109 ${\mu}g\;L^{-1}$) when P loading through two inflows was high, and showed close relationship with TP concentration (r = 0.55, P< 0.008, n = 22). Mean Chl. a concentration ranged from 13.5 to 84.5 mg $L^{-1}$ in the water column, and the lowest and highest concentration was observed in February 2004 (13.5 ${\pm}$ 1.0 ${\mu}g\;L^{-1}$) and November 2003 (84.5 ${\pm}$29.0 ${\mu}g\;L^{-1}$), respectively. TP concentration in inflow water increased with discharge (r = 0.69, P< 0.001), 40.5% of annual total P loading introduced in 25 July when there was heavy rainfall. Annual total P loading from watershed was 159.0 kg P $yr^{-1}$, and that of DIP loading was 126.3 kg P $yr^{-1}$ (77.7% of TP loading. The loading of TN (5.0ton yr-1) was 30 times higher than that of TP loading (159.0 kg P yr-1), and the 78% of TN was in the form of non-organic nitrogen, 3.9 ton $yr^{-1}$ in mass. P loading in Shingu reservoir was 1.6 g ${\cdot}$ $m^{-2}$ ${\cdot}$ $yr^{-1}$, which passed the excessive critical loading of Vollenweider-OECD critical loading model. The results of this study indicated that P loading from watershed was the major factor to cause eutrophication and temporal variation of water quality in Shingu reservoir Decrease by 71% in TP loading (159 kg $yr^{-1}$) is necessary for the improvement of mesotrophic level. The management of sediment where tine anaerobic condition was evident in summer, thus, the possibility of P release that can be utilized by existing algae, may also be considered.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.111-136
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• 2018

In this paper, we propose a methodology to extract answer information about queries from various types of unstructured documents collected from multi-sources existing on web in order to expand knowledge base. The proposed methodology is divided into the following steps. 1) Collect relevant documents from Wikipedia, Naver encyclopedia, and Naver news sources for "subject-predicate" separated queries and classify the proper documents. 2) Determine whether the sentence is suitable for extracting information and derive the confidence. 3) Based on the predicate feature, extract the information in the proper sentence and derive the overall confidence of the information extraction result. In order to evaluate the performance of the information extraction system, we selected 400 queries from the artificial intelligence speaker of SK-Telecom. Compared with the baseline model, it is confirmed that it shows higher performance index than the existing model. The contribution of this study is that we develop a sequence tagging model based on bi-directional LSTM-CRF using the predicate feature of the query, with this we developed a robust model that can maintain high recall performance even in various types of unstructured documents collected from multiple sources. The problem of information extraction for knowledge base extension should take into account heterogeneous characteristics of source-specific document types. The proposed methodology proved to extract information effectively from various types of unstructured documents compared to the baseline model. There is a limitation in previous research that the performance is poor when extracting information about the document type that is different from the training data. In addition, this study can prevent unnecessary information extraction attempts from the documents that do not include the answer information through the process for predicting the suitability of information extraction of documents and sentences before the information extraction step. It is meaningful that we provided a method that precision performance can be maintained even in actual web environment. The information extraction problem for the knowledge base expansion has the characteristic that it can not guarantee whether the document includes the correct answer because it is aimed at the unstructured document existing in the real web. When the question answering is performed on a real web, previous machine reading comprehension studies has a limitation that it shows a low level of precision because it frequently attempts to extract an answer even in a document in which there is no correct answer. The policy that predicts the suitability of document and sentence information extraction is meaningful in that it contributes to maintaining the performance of information extraction even in real web environment. The limitations of this study and future research directions are as follows. First, it is a problem related to data preprocessing. In this study, the unit of knowledge extraction is classified through the morphological analysis based on the open source Konlpy python package, and the information extraction result can be improperly performed because morphological analysis is not performed properly. To enhance the performance of information extraction results, it is necessary to develop an advanced morpheme analyzer. Second, it is a problem of entity ambiguity. The information extraction system of this study can not distinguish the same name that has different intention. If several people with the same name appear in the news, the system may not extract information about the intended query. In future research, it is necessary to take measures to identify the person with the same name. Third, it is a problem of evaluation query data. In this study, we selected 400 of user queries collected from SK Telecom 's interactive artificial intelligent speaker to evaluate the performance of the information extraction system. n this study, we developed evaluation data set using 800 documents (400 questions * 7 articles per question (1 Wikipedia, 3 Naver encyclopedia, 3 Naver news) by judging whether a correct answer is included or not. To ensure the external validity of the study, it is desirable to use more queries to determine the performance of the system. This is a costly activity that must be done manually. Future research needs to evaluate the system for more queries. It is also necessary to develop a Korean benchmark data set of information extraction system for queries from multi-source web documents to build an environment that can evaluate the results more objectively.

• Journal of Nutrition and Health
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• v.49 no.3
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• pp.189-200
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• 2016

Purpose: This study analyzed job importance, job performance, and job satisfaction in 38 dietitians working at geriatric hospitals and elderly healthcare facilities in Jeju surveyed from September 15-24, 2014 with the aim of providing basic data for improving the quality of meals and nutrition management for elderly patients. Methods: Data were analyzed using descriptive analysis, ${\chi}^2$-test, t-test, ANOVA, and Pearson's correlation coefficients using the SPSS Win program (version 12.0). Results: Regarding job importance, the average score was 4.29 (out of 5), indicating that hygiene and safety management scored the highest at 4.77 (out of 5), and nutrition management scored the lowest at3.86. In terms of job performance of subjects, the average score was 2.87 (out of 5), indicating that cooking operation management scored the highest at 4.42 (out of 5). Regarding the Importance-Performance Analysis (IPA) of job importance and job performance, hygiene and safety management and cooking operation management scored high for importance and performance (B quadrant) menu management, human resource management, and nutrition management scored low for importance and performance (C quadrant) and purchasing management and financial management were included inD quadrant and A quadrant, respectively. For the level of job satisfaction of subjects, the average score was 3.37 (out of 5), indicating that relationships with colleagues scored the highest at 3.72, and improving professionalism scored the lowest at 2.95. Additionally, job importance and performance of subjects were positively correlated withjob satisfaction (r = 0.395, r = 0.386, both p < 0.05). Conclusion: In conclusion, scores for job importance and job performance of nutrition management were low, and job satisfaction of improving professionalism scored low. Therefore, continuous training and education programs of nutrition management should be provided to improve professionalism of dietitians at geriatric hospitals and elderly healthcare facilities.