• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Journal of Korean Academy of Nursing
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• v.12 no.2
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• pp.1-26
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• 1982

The main purpose of this study was to test the effect of the structured information on the sleep amount of the patients undergoing open heart surgery. This study has specifically addressed to the Following two basic research questions: (1) Would the structed in formation influence in the reduction of sleep disturbance related to anxiety and Physical stress before and after the operation? and (2) that would be the effects of the structured information on the level of preoperative state anxiety, the hormonal change, and the degree of behavioral change in the patients undergoing an open heart surgery? A Quasi-experimental research was designed to answer these questions with one experimental group and one control group. Subjects in both groups were matched as closely as possible to avoid the effect of the differences inherent to the group characteristics, Baseline data were also. collected on both groups for 7 days prior to the experiment and found that subjects in both groups had comparable sleep patterns, trait anxiety, hormonal levels and behavioral level. A structured information as an experimental input was given to the subjects in the experimental group only. Data were collected and compared between the experimental group and the control group on the sleep amount of the consecutive pre and post operative days, on preoperative state anxiety level, and on hormonal and behavioral changes. To test the effectiveness of the structured information, two main hypotheses and three sub-hypotheses were formulated as follows; Main hypothesis 1: Experimental group which received structured information will have more sleep amount than control group without structured information in the night before the open heart surgery. Main hypothesis 2: Experimental group with structured information will have more sleep, amount than control group without structured information during the week following the open heart surgery Sub-hypothesis 1: Experimental group with structured information will be lower in the level of State anxiety than control group without structured information in the night before the open heart surgery. Sub-hypothesis 2 : Experimental group with structured information will have lower hormonal level than control group without stuctured information on the 5th day after the open heart surgery Sub-hypothesis 3: Experimental group with structured information will be lower in the behavioral change level than control group without structured information during the week after the open heart surgery. The research was conducted in a national university hospital in Seoul, Korea. The 53 Subjects who participated in the study were systematically divided into experimental group and control group which was decided by random sampling method. Among 53 subjects, 26 were placed in the experimental group and 27 in the control group. Instruments; (1) Structed information: Structured information as an independent variable was constructed by the researcher on the basis of Roy's adaptation model consisting of physiologic needs, self-concept, role function and interdependence needs as related to the sleep and of operational procedures. (2) Sleep amount measure: Sleep amount as main dependent variable was measured by trained nurses through observation on the basis of the established criteria, such as closed or open eyes, regular or irregular respiration, body movement, posture, responses to the light and question, facial expressions and self report after sleep. (3) State anxiety measure: State Anxiety as a sub-dependent variable was measured by Spi-elberger's STAI Anxiety scale, (4) Hormornal change measure: Hormone as a sub-dependent variable was measured by the cortisol level in plasma. (5) Behavior change measure: Behavior as a sub-dependent variable was measured by the Behavior and Mood Rating Scale by Wyatt. The data were collected over a period of four months, from June to October 1981, after the pretest period of two months. For the analysis of the data and test for the hypotheses, the t-test with mean differences and analysis of covariance was used. The result of the test for instruments show as follows: (1) STAI measurement for trait and state anxiety as analyzed by Cronbachs alpha coefficient analysis for item analysis and reliability showed the reliability level at r= .90 r= .91 respectively. (2) Behavior and Mood Rating Scale measurement was analyzed by means of Principal Component Analysis technique. Seven factors retained were anger, anxiety, hyperactivity, depression, bizarre behavior, suspicious behavior and emotional withdrawal. Cumulative percentage of each factor was 71.3%. The result of the test for hypotheses show as follows; (1) Main hypothesis, was not supported. The experimental group has 282 minutes of sleep as compared to the 255 minutes of sleep by the control group. Thus the sleep amount was higher in experimental group than in control group, however, the difference was not statistically significant at .05 level. (2) Main hypothesis 2 was not supported. The mean sleep amount of the experimental group and control group were 297 minutes and 278 minutes respectively Therefore, the experimental group had more sleep amount as compared to the control group, however, the difference was not statistically significant at .05 level. Thus, the main hypothesis 2 was not supported. (3) Sub-hypothesis 1 was not supported. The mean state anxiety of the experimental group and control group were 42.3, 43.9 in scores. Thus, the experimental group had slightly lower state anxiety level than control group, howe-ver, the difference was not statistically significant at .05 level. (4) Sub-hypothesis 2 was not supported. . The mean hormonal level of the experimental group and control group were 338 ㎍ and 440 ㎍ respectively. Thus, the experimental group showed decreased hormonal level than the control group, however, the difference was not statistically significant at .05 level. (5) Sub-hypothesis 3 was supported. The mean behavioral level of the experimental group and control group were 29.60 and 32.00 respectively in score. Thus, the experimental group showed lower behavioral change level than the control group. The difference was statistically significant at .05 level. In summary, the structured information did not influence the sleep amount, state anxiety or hormonal level of the subjects undergoing an open heart surgery at a statistically significant level, however, it showed a definite trends in their relationships, not least to mention its significant effect shown on behavioral change level. It can further be speculated that a great degree of individual differences in the variables such as sleep amount, state anxiety and fluctuation in hormonal level may partly be responsible for the statistical insensitivity to the experimentation.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.171-183
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• 2012

In the development of technologies, the exhibition industry has received much attention from governments and companies as an important way of marketing activities. Also, the exhibitors have considered the exhibition as new channels of marketing activities. However, the growing size of exhibitions for net square feet and the number of visitors naturally creates the competitive environment for them. Therefore, to make use of the effective marketing tools in these environments, they have planned and implemented many promotion technics. Especially, through smart environment which makes them provide real-time information for visitors, they can implement various kinds of promotion. However, promotions ignoring visitors' various needs and preferences can lose the original purposes and functions of them. That is, as indiscriminate promotions make visitors feel like spam, they can't achieve their purposes. Therefore, they need an approach using STP strategy which segments visitors through right evidences (Segmentation), selects the target visitors (Targeting), and give proper services to them (Positioning). For using STP Strategy in the smart exhibition environment, we consider these characteristics of it. First, an exhibition is defined as market events of a specific duration, which are held at intervals. According to this, exhibitors who plan some promotions should different events and promotions in each exhibition. Therefore, when they adopt traditional STP strategies, a system can provide services using insufficient information and of existing visitors, and should guarantee the performance of it. Second, to segment automatically, cluster analysis which is generally used as data mining technology can be adopted. In the smart exhibition environment, information of visitors can be acquired in real-time. At the same time, services using this information should be also provided in real-time. However, many clustering algorithms have scalability problem which they hardly work on a large database and require for domain knowledge to determine input parameters. Therefore, through selecting a suitable methodology and fitting, it should provide real-time services. Finally, it is needed to make use of data in the smart exhibition environment. As there are useful data such as booth visit records and participation records for events, the STP strategy for the smart exhibition is based on not only demographical segmentation but also behavioral segmentation. Therefore, in this study, we analyze a case of the promotion methodology which exhibitors can provide a differentiated service to segmented visitors in the smart exhibition environment. First, considering characteristics of the smart exhibition environment, we draw evidences of segmentation and fit the clustering methodology for providing real-time services. There are many studies for classify visitors, but we adopt a segmentation methodology based on visitors' behavioral traits. Through the direct observation, Veron and Levasseur classify visitors into four groups to liken visitors' traits to animals (Butterfly, fish, grasshopper, and ant). Especially, because variables of their classification like the number of visits and the average time of a visit can estimate in the smart exhibition environment, it can provide theoretical and practical background for our system. Next, we construct a pilot system which automatically selects suitable visitors along the objectives of promotions and instantly provide promotion messages to them. That is, based on the segmentation of our methodology, our system automatically selects suitable visitors along the characteristics of promotions. We adopt this system to real exhibition environment, and analyze data from results of adaptation. As a result, as we classify visitors into four types through their behavioral pattern in the exhibition, we provide some insights for researchers who build the smart exhibition environment and can gain promotion strategies fitting each cluster. First, visitors of ANT type show high response rate for promotion messages except experience promotion. So they are fascinated by actual profits in exhibition area, and dislike promotions requiring a long time. Contrastively, visitors of GRASSHOPPER type show high response rate only for experience promotion. Second, visitors of FISH type appear favors to coupon and contents promotions. That is, although they don't look in detail, they prefer to obtain further information such as brochure. Especially, exhibitors that want to give much information for limited time should give attention to visitors of this type. Consequently, these promotion strategies are expected to give exhibitors some insights when they plan and organize their activities, and grow the performance of them.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

• Journal of KIISE:Databases
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• v.31 no.5
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• pp.468-478
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• 2004

It is essential in various application areas of data mining and bioinformatics to effectively retrieve the occurrences of interesting patterns from sequence databases. For example, let's consider a network event management system that records the types and timestamp values of events occurred in a specific network component(ex. router). The typical query to find out the temporal casual relationships among the network events is as fellows: 'Find all occurrences of CiscoDCDLinkUp that are fellowed by MLMStatusUP that are subsequently followed by TCPConnectionClose, under the constraint that the interval between the first two events is not larger than 20 seconds, and the interval between the first and third events is not larger than 40 secondsTCPConnectionClose. This paper proposes an indexing method that enables to efficiently answer such a query. Unlike the previous methods that rely on inefficient sequential scan methods or data structures not easily supported by DBMSs, the proposed method uses a multi-dimensional spatial index, which is proven to be efficient both in storage and search, to find the answers quickly without false dismissals. Given a sliding window W, the input to a multi-dimensional spatial index is a n-dimensional vector whose i-th element is the interval between the first event of W and the first occurrence of the event type Ei in W. Here, n is the number of event types that can be occurred in the system of interest. The problem of‘dimensionality curse’may happen when n is large. Therefore, we use the dimension selection or event type grouping to avoid this problem. The experimental results reveal that our proposed technique can be a few orders of magnitude faster than the sequential scan and ISO-Depth index methods.hods.

• Radiation Oncology Journal
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• v.21 no.3
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• pp.238-244
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• 2003

Purpose: The Planning of High-Dose-Rate (HDR) brachytherapy treatments are becoming individualized and more dependent on the treatment planning system. Therefore, computer software has been developed to perform independent point dose calculations with the integration of an isodose distribution curve display into the patient anatomy images. Meterials and Methods: As primary input data, the program takes patients'planning data including the source dwell positions, dwell times and the doses at reference points, computed by an HDR treatment planning system (TPS). Dosimetric calculations were peformed in a $10\times12\times10\;Cm^3$ grid space using the Interstitial Collaborative Working Group (ICWG) formalism and an anisotropy table for the HDR Iridium-192 source. The computed doses at the reference points were automatically compared with the relevant results of the TPS. The MR and simulation film images were then imported and the isodose distributions on the axial, sagittal and coronal planes intersecting the point selected by a user were superimposed on the imported images and then displayed. The accuracy of the software was tested in three benchmark plans peformed by Gamma-Med 12i TPS (MDS Nordion, Germany). Nine patients'plans generated by Plato (Nucletron Corporation, The Netherlands) were verified by the developed software. Results: The absolute doses computed by the developed software agreed with the commercial TPS results within an accuracy of $2.8\%$ in the benchmark plans. The isodose distribution plots showed excellent agreements with the exception of the tip legion of the source's longitudinal axis where a slight deviation was observed. In clinical plans, the secondary dose calculations had, on average, about a $3.4\%$ deviation from the TPS plans. Conclusion: The accurate validation of complicate treatment plans is possible with the developed software and the qualify of the HDR treatment plan can be improved with the isodose display integrated into the patient anatomy information.

• The Journal of Engineering Geology
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• v.13 no.2
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• pp.239-256
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• 2003

The purpose of this study is to investigate the hydrochemical characteristics of emergency-use groundwater in the Daejeon area, and to elucidate the contamination source of $NO_3-N$ and the origin of sulfate in the groundwater. The groundwater shows weak acidic pH, the electrical conductivity ranging from 142 to $903{\;}\mu\textrm{S}/cm$, and the hydrochemical types of $Ca-HCo_3$ and $Ca-Cl(SO_4,{\;}NO_3)$. The Box-Whisker analysis and the Krigging analysis of chemical data of groundwater were made to demonstrate the concentration distribution of hydrochemical composition, and to compare the trend of hydrochemical data. The groundwater in the area of Dong-gu, Jung-gu and Daeduk-gu, where are old town, shows higher electrical conductivity, nitrate content, sulfate and $EpCO_2$ levels than groundwater in new town area of Seo-gu and Yusung-gu. ${\delta}^{15}N$ of groundwater in the area of Seo-gu and Yusung-gu ranges from +7.4 to $+9.6{\textperthousand}$, indicating that major contamination source of $NO_3-N$ is the leakage from municipal sewage pipe lines. ${\delta}^{15}N$ of groundwater in the old town area of Tong-gu, Jung-gu and Daeduk-gu shows the range between +10.2 and $+23.5{\textperthousand}$, meaning that major contamination source is leakage of septic tank. ${\delta}^{34}S$ of groundwater shows the range of $+3~13.4{\;}{\textperthousand}$. Sulfur isotope indicates the possibility of a sulfate reduction and the input of anthrophogenic source.

• Journal of Biomedical Engineering Research
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• v.19 no.1
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• pp.59-68
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• 1998

Digital hearing aids offer many advantages over conventional analog hearing aids. With the advent of high speed digital signal processing chips, new digital techniques have been introduced to digital hearing aids. In addition, the evaluation of new ideas in hearing aids is necessarily accompanied by intensive subject-based clinical tests which requires much time and cost. In this paper, we present an objective method to evaluate and predict the performance of hearing aid systems without the help of such subject-based tests. In the hearing impairment simulation(HIS) algorithm, a sensorineural hearing impairment medel is established from auditory test data of the impaired subject being simulated. Also, the nonlinear behavior of the loudness recruitment is defined using hearing loss functions generated from the measurements. To transform the natural input sound into the impaired one, a frequency sampling filter is designed. The filter is continuously refreshed with the level-dependent frequency response function provided by the impairment model. To assess the performance, the HIS algorithm was implemented in real-time using a floating-point DSP. Signals processed with the real-time system were presented to normal subjects and their auditory data modified by the system was measured. The sensorineural hearing impairment was simulated and tested. The threshold of hearing and the speech discrimination tests exhibited the efficiency of the system in its use for the hearing impairment simulation. Using the HIS system we evaluated three typical hearing aid algorithms.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.811-827
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• 2018

The purpose of this study is to compare machine learning algorithm and deep learning algorithm in crop classification using multi-temporal remote sensing data. For this, impacts of machine learning and deep learning algorithms on (a) hyper-parameter and (2) training sample size were compared and analyzed for Haenam-gun, Korea and Illinois State, USA. In the comparison experiment, support vector machine (SVM) was applied as machine learning algorithm and convolutional neural network (CNN) was applied as deep learning algorithm. In particular, 2D-CNN considering 2-dimensional spatial information and 3D-CNN with extended time dimension from 2D-CNN were applied as CNN. As a result of the experiment, it was found that the hyper-parameter values of CNN, considering various hyper-parameter, defined in the two study areas were similar compared with SVM. Based on this result, although it takes much time to optimize the model in CNN, it is considered that it is possible to apply transfer learning that can extend optimized CNN model to other regions. Then, in the experiment results with various training sample size, the impact of that on CNN was larger than SVM. In particular, this impact was exaggerated in Illinois State with heterogeneous spatial patterns. In addition, the lowest classification performance of 3D-CNN was presented in Illinois State, which is considered to be due to over-fitting as complexity of the model. That is, the classification performance was relatively degraded due to heterogeneous patterns and noise effect of input data, although the training accuracy of 3D-CNN model was high. This result simply that a proper classification algorithms should be selected considering spatial characteristics of study areas. Also, a large amount of training samples is necessary to guarantee higher classification performance in CNN, particularly in 3D-CNN.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.5
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• pp.705-715
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• 2010

The adoption of carbon foot print system is being activated mostly in the developed countries as one of the long-term response towards tightened up regulations and standards on carbon emission in the agricultural sector. The Korean Ministry of Environment excluded the primary agricultural products from the carbon foot print system due to lack of LCI (life cycle inventory) database in agriculture. Therefore, the research on and establishment of LCI database in the agriculture for adoption of carbon foot print system is urgent. Development of LCA (life cycle assessment) methodology for application of LCA to agricultural environment in Korea is also very important. Application of LCA methodology to agricultural environment in Korea is an early stage. Therefore, this study was carried out to find out the effect of lettuce cultivation on agricultural environment by establishing LCA methodology. Data collection of agricultural input and output for establishing LCI was carried out by collecting statistical data and documents on income from agro and livestock products prepared by RDA. LCA methodology for agriculture was reviewed by investigating LCA methodology and LCA applications of foreign countries. Results based on 1 kg of lettuce production showed that inputs including N, P, organic fertilizers, compound fertilizers and crop protectants were the main sources of major emission factor during lettuce cropping process. The amount of inputs considering the amount of active ingredients was required to estimate the actual quantity of the inputs used. Major emissions due to agricultural activities were $N_2O$ (emission to air) and ${NO_3}^-$/${PO_4}^-$ (emission to water) from fertilizers, organic compounds from pesticides and air pollutants from fossil fuel combustion in using agricultural machines. The softwares for LCIA (life cycle impact assessment) and LCA used in Korea are 'PASS' and 'TOTAL' which have been developed by the Ministry of Knowledge Economy and the Ministry of Environment. However, the models used for the softwares are the ones developed in foreign countries. In the future, development of models and optimization of factors for characterization, normalization and weighting suitable to Korean agricultural environment need to be done for more precise LCA analysis in the agricultural area.