• 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.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• The Journal of Korean Society for School & Community Health Education
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• v.1 no.1
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• pp.11-43
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• 2000

The purpose of this study was, through analyzing the previous researches, to grasp the present status of environment of school building(ESB), research the sundry records of each element and, through comparative analysis of the standard of ESB in Korea, the United States, and Japan, select the normative standard of ESB, to clarify the point at issue presented in Regulation of Construction & facility Management for Elementary and and Secondary School in Korea, and to suggest an alternative preliminary standard of ESB. To carry out a research for this purpose, these were required: 1. to investigate the existing present status of ESB, 2. to make a comparative analysis of the standard of ESB in each country, 3. to suggest the normative standard of preliminary standard of ESB, 4. to analyze the controversial points of the standard of ESB in Korea, 5. to suggest an alternative preliminary standard of ESB. The conclusions were as follows: 1. Putting, through analyzing the previous researches, the existing present status of ESB together, it seemed that lighting environment, indoor air environment and noise environment were all in poor conditions. 2. In the result of a comparative analysis of the standard of ESB in Korea, Japan and the United States, in Korea the factors of each lighting and indoor air environment were not presented properly, in Japan, in lighting environment aspect, the standard on natural lighting and the factors on brightness were not presented., and in the USA the essential factors of each environment were throughly presented. In the comparison of the standards on each factor, Korea showed that the standard level presented was less properly prescribed than those of the USA and Japan but it also showed that the standard levels prescribed in the USA and in Japan were mostly similar to the standard levels in records investigated. 3. With the result of the normative standard selection on School Builiding environment factor of prescribed in this study, the controversial points of the standard of ESB in Korea were analyzed and the result was utilized to suggest new preliminary standard of ESB. 4. As the result of the analysis of the controversial points of the standard of ESB in Korea, it was found that the standard of ESB in Korea should be established on a basis of School Health Act and be concretely presented in School Health Regulation and School Health Rule. The factors of each environment was improperly presented in the existing standard of ESB in Korea. Moreover the standard of them was inferior to that of the records investigated and those of in the USA and in Japan and it also showed that the standard of it in Korea was improper to maintain Comfortable Learning Environment. 5. A suggested preliminary standard of ESB acquired through above study as follows: 1) In this study a new kind of preliminary standard of ESB is divided into lighting environment, indoor air environment, noise environment, odor environment and for above classification, reasonable factor and standard should be established and the controling way on each standard and countermeasures against it should be considered. 2) In lighting environment, the factors of natural lighting are divided into daylight rate, brightness, glare. In the standard on each factor, daylight rate should secure 5% of a mean daylight rate and 2% of a minimum daylight rate, brightness ratio of maximum illumination to minimum illumination should be under 10:1, and in glare there should not be an occurrence factor from a reflector outside of the classroom. And the factors of unnatural lighting are illumination, brightness, and glare. In the standard on each factor, illumination should be 750 lux or more, brightness ratio should be under 3 to 1, and glare should not occur. And Optimal reflection rate(%) of Colors and Facilities of Classroom which influences lighting environment should be considered. 3) In indoor air environment factors, thermal factors are divided into (1) room temperature, (2) relative humidity, (3) room air movement, (4) radiation heat, and harmful gases (5) CO, (6) $CO_2$ that are proceeded from using the heating fuel such as oval briquettes, firewood, charcoal being used in most of the classroom, and finally (7) dust. In the standard on each factor, the next are necessary; room temperature: $16^{\circ}C{\sim}26^{\circ}C$(summer : $E.T18.9{\sim}23.8^{\circ}C$, winter: $E.T16.7{\sim}21.7^{\circ}C$), relative humidity: $30{\sim}80%$, room air movement: under 0.5m/sec, radiation heat: under $5^{\circ}C$ gap between dry-bulb temperature and wet-bulb temperature, below 1000 ppm of ca and below 10ppm of $CO_2$, dust: below 0.10 $mg/m^3$ of Volume of dust in indoor air, and ventilation standard($CO_2$) for purification of indoor air : once/6 min.(about 7 times/40 min.) in an airtight classroom. 4) In the standard on noise environment, noise level should be under 40 dB(A) and the noise measuring way and the countermeasures against it should be considered. 5) In the standard on odor environment, odor level under Physical Method should be under 2 degrees, and the inspecting way and the countermeasures against it should be considered.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.57-76
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• 2017

Recently, consumer needs are diversifying as information technologies are evolving rapidly. A lot of IT devices such as smart phones and tablet PCs are launching following the trend of information technology. While IT devices focused on the technical advance and improvement a few years ago, the situation is changed now. There is no difference in functional aspects, so companies are trying to differentiate IT devices in terms of appearance design. Consumers also consider design as being a more important factor in the decision-making of smart phones. Smart phones have become a fashion items, revealing consumers' own characteristics and personality. As the design and appearance of the smartphone become important things, it is necessary to examine consumer values from the design and appearance of IT devices. Furthermore, it is crucial to clarify the mechanisms of consumers' design evaluation and develop the design evaluation model based on the mechanism. Since the influence of design gets continuously strong, various and many studies related to design were carried out. These studies can classify three main streams. The first stream focuses on the role of design from the perspective of marketing and communication. The second one is the studies to find out an effective and appealing design from the perspective of industrial design. The last one is to examine the consumer values created by a product design, which means consumers' perception or feeling when they look and feel it. These numerous studies somewhat have dealt with consumer values, but they do not include product attributes, or do not cover the whole process and mechanism from product attributes to consumer values. In this study, we try to develop the holistic design evaluation model based on consumer values based on three-step approach from product attributes, perceived attributes, to consumer values. Product attributes means the real and physical characteristics each smart phone has. They consist of bezel, length, width, thickness, weight and curvature. Perceived attributes are derived from consumers' perception on product attributes. We consider perceived size of device, perceived size of display, perceived thickness, perceived weight, perceived bezel (top - bottom / left - right side), perceived curvature of edge, perceived curvature of back side, gap of each part, perceived gloss and perceived screen ratio. They are factorized into six clusters named as 'Size,' 'Slimness,' 'No-Frame,' 'Roundness,' 'Screen Ratio,' and 'Looseness.' We conducted qualitative research to find out consumer values, which are categorized into two: look and feel values. We identified the values named as 'Silhouette,' 'Neatness,' 'Attractiveness,' 'Polishing,' 'Innovativeness,' 'Professionalism,' 'Intellectualness,' 'Individuality,' and 'Distinctiveness' in terms of look values. Also, we identifies 'Stability,' 'Comfortableness,' 'Grip,' 'Solidity,' 'Non-fragility,' and 'Smoothness' in terms of feel values. They are factorized into five key values: 'Sleek Value,' 'Professional Value,' 'Unique Value,' 'Comfortable Value,' and 'Solid Value.' Finally, we developed the holistic design evaluation model by analyzing each relationship from product attributes, perceived attributes, to consumer values. This study has several theoretical and practical contributions. First, we found consumer values in terms of design evaluation and implicit chain relationship from the objective and physical characteristics to the subjective and mental evaluation. That is, the model explains the mechanism of design evaluation in consumer minds. Second, we suggest a general design evaluation process from product attributes, perceived attributes to consumer values. It is an adaptable methodology not only smart phone but also other IT products. Practically, this model can support the decision-making when companies initiative new product development. It can help product designers focus on their capacities with limited resources. Moreover, if its model combined with machine learning collecting consumers' purchasing data, most preferred values, sales data, etc., it will be able to evolve intelligent design decision support system.

• Journal of Korean Home Economics Education Association
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• v.19 no.3
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• pp.37-60
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• 2007

This study has been historically reviewed the clothing and textiles education through references. The clothing and textiles education had been informally offered as a part of household skill, skill of daily life and home skill before the law of school education was established. Therefore, we have reviewed the changes in the clothing and textiles education by separating the prior period between the end of the old Korea and the liberation in 1945, from the later period from the liberation up to now. And also contents of clothing and textiles education between before and after the revision of the 7th curriculum were compared and analyzed. The clothing and textiles education had put emphasis on sewing, handicraft and household during the prior period from the end of the old Korea to the liberation, but after liberation, it became involved in Home Economics Education. The number of classes and the contents of clothing and textiles education had been increased and deepened from the 1st term to the 5th term of curriculum. In particular, the 4th and 5th term of curriculum became a turning point where home life section was intensified. Even though the number of classes was reduced during the 6th term of curriculum, with increase in target school grades for this education, and Home Economics in the middle school remained an independent subject matter. However, during the 7th term of curriculum, Technology-Home Economics Education in the middle school courses led to losing independent subject name and shrinking its contents. Through the revision in the 7th curriculum, the clothing and textiles education achieved great improvement and progress by gaining balance in distribution of contents among school grades and balance between theory and practice. However it still remains as an issue to be more studied and resolved how well the contents are matched with the interests and attention of students. We need to consider and reflect request of students under learner-centered curriculum. Considering enormous changes in the environment surrounding the clothing and textiles education, it is crystal clear that the gap between existing contents of education and teaching methods and our real life will be widened. Because students' perceptions of value have been diversified, it is our task to develop better contents and learning system in order to help students have interests, attention and desire in clothing and textiles life in line with social needs for desirable clothing and textiles education.

• Journal of Ginseng Research
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• v.1 no.1
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• pp.33-50
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• 1976

As a continuation of series of works on the pharmacological actions of Panax ginseng. three kinds of behavioral experiments were carried out using rats and mice. The occurrence of component Posterns of general behavioral activity in rat was examined by visual scanning using the ting sample method in the ad lib. And he hunger deprivated situation. In normal ad lib. situation, the eating behavior of rat treated with 100mg/kg of ginseng saponin was significantly more frequent than that of saline control at the night and throughout the 24 hr period. But grooming was less frequent than the control at the same period. In the hunger situation followed by 90~120 hrs of feed deprivation, the locomotive activity and rearing awe significantly more often and sleeping was less frequent in the two dosage g roups of ginseng saponin (10 and 100 mg/kg) than in the saline group though out the observation period. Training of avoidance conditioning in rats was done in a two-way shuttle box. The number of conditioned response (CR) in which the animal avoided sucessfully an electric shock by running in to the other compartment of the hex was regarded as an index of learning performance. Ginseng saponin in doses of 2.5 mg/kg Produced a significantly increased CR in total avoidance tria1s compared with the control. Although other dosage groups of ginseng saponin (5.0, 50mg and 100 mg/kg) showed no significant statistical difference from the normal control, it tended to increase in CR in the ginseng groups than in the control. An aggressive behavior in mice was observed in n shock-generating fighting box. The occurrence of reflexive fighting between two animals induced by an electric shock applied to the feet war checked as an index of aggression. The occurrence of reciprocal fighting episode immediately after the onset. Of the shock was significantly decreased in the dosage group of 400 mg/kg ginseng saponin, but it did net differ in the 100 mg/kg group of ginseng saponin from the control group. The dose, 400 mg/kg of ginseng saponin, inhibited fighting behavior in more than 80% of the Pairs. but 100 mg/kg of ginseng did inhibit it in less than 20% of the pairs.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.