• 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 Intelligence and Information Systems
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• v.25 no.1
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• pp.21-41
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• 2019

Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.

• Asia pacific journal of information systems
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• v.23 no.1
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• pp.87-106
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• 2013

In the service-oriented economy, knowledge and skills are considered core resources to secure competitive advantages and service innovation. Knowledge management capability, which facilitates to produce, share, accumulate and reuse knowledge, becomes as important as knowledge itself to create service value. Along with knowledge management capability, dynamic capability and operational capability are the key capabilities related to managing service delivery processes. Previous studies indicated that these three capabilities are related to service innovation. Although separately investigate the relationship between the three capabilities. The purpose of this study is 1) to define variables that have effects on service innovation including knowledge management capability, dynamic capability and operational capability, and 2) to empirically test to identify relationship among variables. In this study, knowledge management capability is defined as the capability to manage knowledge process. Dynamic capability is regarded as the firm's ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments. Operational capability refers to a high-level routine that, together with its implementing input flows, confers upon an organization's management a set of decision options for producing significant outputs of a particular type. The proposed research model was tested against the data collected through the survey method. The survey questionnaire was distributed to the managers who participated in an educational program for management consulting. Each individual who answered the questionnaire represented a knowledge based service firm. About 212 surveys questionnaires were sent via e-mail or directly delivered to respondents. The number of useable responses was 93. Measurement items were adapted from previous studies to reflect the characteristics of the industry each informant worked in. All measurement items were in, 5 point Likert scale with anchors ranging from strongly disagree (1) to strongly agree (5). Out of 93 respondents, about 81% were male, 82% of respondents were in their 30s. In terms of jobs, managers were 39.78%, professions/technicians were 24.73%, researchers were 12.90%, and sales people were 10.75%. Most of respondents worked for medium size enterprises (47,31%) in their, less than 30 employees (46.24%) in their number of employees, and less than 10 million USD (65.59%) in terms of sales volume. To test the proposed research model, structural equation modeling (SEM) technique (SPSS 16.0 and AMOS version 5) was used. We found that the three organizational capabilities have influence on service innovation directly or indirectly. Knowledge management capability directly affects dynamic capability and service innovation but indirectly affect operational capability through dynamic capability. Dynamic capability has no direct impact on service innovation, but influence service innovation indirectly through operational capability. Operational capability was found to positively affect service innovation. In sum, three organizational capabilities (knowledge management capability, dynamic capability and operational capability) need to be strategically managed at firm level, because organizational capabilities are significantly related to service innovation. An interesting result is that dynamic capability has a positive effect on service innovation only indirectly through operational capability. This result indicates that service innovation might have a characteristics similar to process innovation rather than product orientation. The results also show that organizational capabilities are inter-correlated to influence each other. Dynamic capability enables effective resource management, arrangement, and integration. Through these dynamic capability affected activities, strategic agility and responsibility get strength. Knowledge management capability intensify dynamic capability and service innovation. Knowledge management capability is the basis of dynamic capability as well. The theoretical and practical implications are discussed further in the conclusion section.

• Journal of Mushroom
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• v.9 no.4
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• pp.161-165
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• 2011

Small scale mushroom growers take a lot of the costs in the bottle cultivation than the large scale growers. Therefore, they are not competitive in the market. Mushroom cultivation in small scale mushroom farm is labor-intensive and this experiment was carried out to establish the method for the cultivation of various kinds of mushrooms together under the similar conditions in the production system. As a result, the common medium were selected based on the growing conditions of Pleurotus eryngii and Agrocybe cylindracea, and the level of medium moisture content were studied. The results were shown below. When the medium in the input stage for bottle cultivation was filled by using automatic machine, the range of the three state rates in the bottle is different depending on ingredients and the mixing ratio. The optimum moisture content of the medium for some mushroom production was a different trend based on the medium cluster of the raw materials. The optimum moisture content for P. eryngii in the medium was 70% (douglas fir sawdust, rice bran or sawdust, dried bean curd refuse). In the medium containing douglas fir sawdust, wheat bran was 65%, and the medium including douglas fir sawdust, wheat bran, dried bean curd refuse was 67%. The optimum moisture content of the above three media for A. cylindracea was 70%. The suitable medium for the bottle cultivation of P. eryngii was selected as douglas fir sawdust 75%, wheat bran 20%, dried bean curd refuse 5%, and moisture content 67%. The medium of A. cylindracea was selected as douglas fir sawdust 75%, wheat bran 25%, and moisture content 70%. The suitable medium for bottle cultivation of P. eryngii and A. cylindracea was shown as douglas fir sawdust 75%, rice bran20%, dried bean curd refuse 5%, and 70% moisture content to be used as a common medium of the growing. The incubation period, primordial formation, and growth environment conditions of P. eryngii and A. cylindracea were a similar trend. Therefore, the small scale farmers of P. eryngii can cultivate A. cylindracea together with P. eryngii.

• Journal of Korean Society of Forest Science
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• v.90 no.4
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• pp.420-430
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• 2001

The object of this study is to develop a computer program for the arrangement of the forest-road network maximizing the investment effect in forest-road construction with factors such as terrains, forest physiognomy, management plan, logging system, cost of forest-road construction, capacity of inputted labour, capacity of timber production and so on. The operating system developed by this study is Korean Windows 95/98 and Microsoft Visual Basic ver. 5.0. User interface was designed as systematic structure, it is presented as a kind of GUI(graphic user interface). The developed program has result of the most suitable forest-road arrangement, has suitable forest-road density calculated with cost of logging, cost of forest-road construction, diversion ratio of forest-road, cost of walking in forest. And the most suitable forest-road arrangement was designed for forest-road arrangement network which maximized investment effect through minimizing the sum of cost of logging and cost of forest-road construction. Input data were divided into map data and control data. Digital terrain model, division of forest-road layout plan, division of forest function and the existing road network are obtained from map data. on the other hand, cost of logging related terrain division, diversion ratio of forest-road and working road, cost of forest-road construction, cost of walking, cost of labor, walking speed, capacity of inputted labor, capacity of timber production and total distance of forest-road are inputted from control data. And map data was designed to be inputted by mesh method for common matrix. This program can be used to construct a new forest-road or vice forest-road which compensate already existing forest-road for the functional forestry.

• Journal of the Korean earth science society
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• v.38 no.4
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• pp.283-292
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• 2017

In this study, the empirical models were established to estimate the concentrations of surface-level $PM_{2.5}$ over Seoul, Korea from 1 January 2012 to 31 December 2013. We used six different multiple linear regression models with aerosol optical thickness (AOT), ${\AA}ngstr{\ddot{o}}m$ exponents (AE) data from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua satellites, meteorological data, and planetary boundary layer depth (PBLD) data. The results showed that $M_6$ was the best empirical model and AOT, AE, relative humidity (RH), wind speed, wind direction, PBLD, and air temperature data were used as input data. Statistical analysis showed that the result between the observed $PM_{2.5}$ and the estimated $PM_{2.5}$ concentrations using $M_6$ model were correlations (R=0.62) and root square mean error ($RMSE=10.70{\mu}gm^{-3}$). In addition, our study show that the relation strongly depends on the seasons due to seasonal observation characteristics of AOT, with a relatively better correlation in spring (R=0.66) and autumntime (R=0.75) than summer and wintertime (R was about 0.38 and 0.56). These results were due to cloud contamination of summertime and the influence of snow/ice surface of wintertime, compared with those of other seasons. Therefore, the empirical multiple linear regression model used in this study showed that the AOT data retrieved from the satellite was important a dominant variable and we will need to use additional weather variables to improve the results of $PM_{2.5}$. Also, the result calculated for $PM_{2.5}$ using empirical multi linear regression model will be useful as a method to enable monitoring of atmospheric environment from satellite and ground meteorological data.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.292-309
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• 2016

The purpose of the present paper is to investigate the effects of stress and depression experienced by the first (1955-1964) and second (1968-1974) generations of baby boomers on their psychological well-being as well as the moderation effects of social supports for them. For this purpose, 369 who come under the category of the first- and second-generation baby boomers were selected by convenient sampling from among the participants in the programs of the life-long educational institutes in Seoul metropolitan area and questionnaires of self-administered type were distributed to them. Frequency analysis, t-test, ANOVA, correlation analysis and hierarchial regression analysis which used input of mean-centered variables and interaction term were conducted to determine the moderating effects of social supports based on the replies to the questionnaires. The major outcomes of the analyses could be summed up as follows: first, the stress and depression suffered by the first and second generations of baby boomers turned out to have negative (-) influence upon their psychological well-being; second, the social supports for those two generations were found to exercise positive (+) effects upon their psychological well-being; and third, a survey of difference in the moderating effects of social supports between the first- and second-generation baby boomers showed that, in case of the first generation, the worse their economic status and the higher their stress and depression were, the lower their psychological well-being tended to be and that social supports functioned to hike their psychological well-being but had moderating effects only in connection with stress. In case of the second generation, however, it was shown that the higher their stress and depression got, the lower their psychological well-being developed and that social supports might increase their psychological well-being but without any moderating effects on the part of relationship with their stress and depression. A practical and political method was discussed to improve baby boomers mental health.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.19-29
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• 2006

As the security of personal information is becoming more important in mobile phones, we are starting to apply iris recognition technology to these devices. In conventional iris recognition, magnified iris images are required. For that, it has been necessary to use large magnified zoom & focus lens camera to capture images, but due to the requirement about low size and cost of mobile phones, the zoom & focus lens are difficult to be used. However, with rapid developments and multimedia convergence trends in mobile phones, more and more companies have built mega-pixel cameras into their mobile phones. These devices make it possible to capture a magnified iris image without zoom & focus lens. Although facial images are captured far away from the user using a mega-pixel camera, the captured iris region possesses sufficient pixel information for iris recognition. However, in this case, the eye region should be detected for accurate iris recognition in facial images. So, we propose a new fast iris detection method, which is appropriate for mobile phones based on corneal specular reflection. To detect specular reflection robustly, we propose the theoretical background of estimating the size and brightness of specular reflection based on eye, camera and illuminator models. In addition, we use the successive On/Off scheme of the illuminator to detect the optical/motion blurring and sunlight effect on input image. Experimental results show that total processing time(detecting iris region) is on average 65ms on a Samsung SCH-S2300 (with 150MHz ARM 9 CPU) mobile phone. The rate of correct iris detection is 99% (about indoor images) and 98.5% (about outdoor images).

• 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.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.4 s.316
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• pp.60-69
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• 2007

The human eye usually experiences a loss of color sensitivity when it is subjected to high levels of luminance, and perceives a discrepancy in color between high and normal-luminance displays, generally known as a hue shift. Accordingly, this paper models the hue-shift phenomenon and proposes a hue-correction method to provide perceptual matching between high and normal-luminance displays. The value of hue-shift is determined by perceived hue matching experiments. At first the phenomenon is observed at three lightness levels, that is, the ratio of luminance is the same between high and normal-luminance display when the perceived hue matching experiments we performed. To quantify the hue-shift phenomenon for the whole hue angle, color patches with the same lightness are first created and equally spaced inside the hue angle. These patches are then displayed one-by-one on both displays with the ratio of luminance between two displays. Next, the hue value for each patch appearing on the high-luminance display is adjusted by observers until the perceived hue for the patches on both displays appears the same visually. After obtaining the hue-shift values, these values are fit piecewise to allow shifted-hue amounts to be approximately determined for arbitrary hue values of pixels in a high-luminance display and then used for correction. Essentially, input RGB values of an image is converted to CIELAB values, and then, LCh (lightness, chroma, and hue) values are calculated to obtain the hue values for all the pixels. These hue values are shifted according to the amount calculated by the functions of the hue-shift model. Finally, the corrected CIELAB values are calculated from corrected hue values, after that, output RGB values for all pixels are estimated. For evaluation, an observer's preference test was performed with hue-shift results and Almost observers conclude that the images from hue-shift model were visually matched with images on normal luminance display.