• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.39-57
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• 2012

Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.

• Journal of Wetlands Research
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• v.8 no.2
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• pp.53-64
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• 2006

A study on the restoration process of a stream ecosystem and the water quality renovation technique by removing algae, vegetation and fish monitoring as evaluating the removal of the algae by dietetic characteristics of fishes were performed on Sangdong stream in the B city after stream restoration it to the artificial stream as the cases, restoring urban stream into close-to-nature stream are being increased domestically with the aim of ecological city. As a result, restoration and rehabilitation of the fundamental stream ecosystem was well maintained 4 years later the reclamation at the moment and total 93 diagnosis which were all vascular plant phylum including 44 families, 73 genuses, 79 species and 14 varieties in flora and vegetation community were observed. 3 families, 8 species and 354 populations in total among Fishes were found and Pseudorasbora Parva, Cyprinus Carpic and Carassius Auratus strongly resistant to water pollution were dominantly appeared in order of 50.5% of Pseudorasbora Parva 21.2% of Cyprinus Carpic, 20.9% of Carassius Auratus, 7.1% of Macropodus chinensis and 0.3% of Misqurnus anguillicaudatus according to relative richness index. It turned out to be that Cyprinus Carpic ingests algae over 90% and Carassius Auratus takes it over 30% according to the analysis about the alimentary object of the fishes as a consequences of algae's excrescent from characteristics of the tested experimental stream. It is reported that a Cyprinus Carpic, about 34 cm in length, ingested wet-weight 43.2g algae on the rough analysis toward the sample which makes us recognize how effective a macro community Cyprinus Carpic is for removing algae.As a consequence of this research, the effect of stream ecosystem characteristics and water quality purification could not be expected by aquatic plants and trees which were eliminated at experimental stream. From now on, a close-to-nature stream should be formed of ecological hydraulic and hydrologic engineered modeling from the beginning so that it can perform the water quality purifying function. It is determined that the structure of food chain will be abundantly influenced by the induction of oversized macro community like Cyprinus Carpic because a biomass of a consumer of higher order is increased. It is estimated that the removal algae by fishes is not effective despite in some cases of dietetic characteristics so much more studies should be executed in the future.

• Asia pacific journal of information systems
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• v.23 no.4
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• pp.103-128
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• 2013

When a consumer needs a product or service and multiple sellers are available online, the process of selecting a seller to buy online from is complex since the process involves many behavioral dimensions that have to be taken into account. As a part of this selection process, consumers may set minimum trust expectation that can be used to screen out less trustworthy sellers. In the previous research, the level of consumers' trust expectation has been anchored on two important factors: product involvement and perceived risk. Product involvement refers to the extent to which a consumer perceives a specific product important. Thus, the higher product involvement may result in the higher trust expectation in sellers. On the other hand, other related studies found that when consumers perceived a higher level of risk (e.g., credit card fraud risk), they set higher trust expectation as well. While abundant research exists addressing the relationship between product involvement and perceived risk, little attention has been paid to the integrative view of the link between the two constructs and their impacts on the trust expectation. The present paper is a step toward filling this research gap. The purpose of this paper is to understand the process by which a consumer chooses an online merchant by examining the relationships among product involvement, perceived risk, trust expectation, and intention to buy from an e-tailer. We specifically focus on the mediating role of perceived risk in the relationships between enduring product involvement and the trust expectation. That is, we question whether product involvement affects the trust expectation directly without mediation or indirectly mediated by perceived risk. The research model with four hypotheses was initially tested using data gathered from 635 respondents through an online survey method. The structural equation modeling technique with partial least square was used to validate the instrument and the proposed model. The results showed that three out of the four hypotheses formulated were supported. First, we found that the intention to buy from a digital storefront is positively and significantly influenced by the trust expectation, providing support for H4 (trust expectation ${\rightarrow}$ purchase intention). Second, perceived risk was found to be a strong predictor of trust expectation, supporting H2 as well (perceived risk ${\rightarrow}$ trust expectation). Third, we did not find any evidence of direct influence of product involvement, which caused H3 to be rejected (product involvement ${\rightarrow}$ trust expectation). Finally, we found significant positive relationship between product involvement and perceived risk (H1: product involvement ${\rightarrow}$ perceived risk), which suggests that the possibility of complete mediation of perceived risk in the relationship between enduring product involvement and the trust expectation. As a result, we conducted an additional test for the mediation effect by comparing the original model with the revised model without the mediator variable of perceived risk. Indeed, we found that there exists a strong influence of product involvement on the trust expectation (by intentionally eliminating the variable of perceived risk) that was suppressed (i.e., mediated) by the perceived risk in the original model. The Sobel test statistically confirmed the complete mediation effect. Results of this study offer the following key findings. First, enduring product involvement is positively related to perceived risk, implying that the higher a consumer is enduringly involved with a given product, the greater risk he or she is likely to perceive with regards to the online purchase of the product. Second, perceived risk is positively related to trust expectation. A consumer with great risk perceptions concerning the online purchase is likely to buy from a highly trustworthy online merchant, thereby mitigating potential risks. Finally, product involvement was found to have no direct influence on trust expectation, but the relationship between the two constructs was indirect and mediated by the perceived risk. This is perhaps an important theoretical integration of two separate streams of literature on product involvement and perceived risk. The present research also provides useful implications for practitioners as well as academicians. First, one implication for practicing managers in online retail stores is that they should invest in reducing the perceived risk of consumers in order to lower down the trust expectation and thus increasing the consumer's intention to purchase products or services. Second, an academic implication is that perceived risk mediates the relationship between enduring product involvement and trust expectation. Further research is needed to elaborate the theoretical relationships among the constructs under consideration.

• Restorative Dentistry and Endodontics
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• v.34 no.4
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• pp.324-332
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• 2009

The purpose of this study was to investigate the effect of rigidity of post core systems on stress distribution by the theoretical technique, finite element stress-analysis method. Three-dimensional finite element models simulating an endodontically treated maxillary central incisor restored with a zirconia ceramic crown were prepared and 1.5 mm ferrule height was provided. Each model contained cortical bone, trabecular bone, periodontal ligament, 4 mm apical root canal filling, and post-and-core. Six combinations of three parallel type post (zirconia ceramic, glass fiber, and stainless steel) and two core (Paracore and Tetric ceram) materials were evaluated, respectively. A 50 N static occlusal load was applied to the palatal surface of the crown with a $60^{\circ}$angle to the long axis of the tooth. The differences in stress transfer characteristics of the models were analyzed. von Mises stresses were chosen for presentation of results and maximum displacement and hydrostatic pressure were also calculated. An increase of the elastic modulus of the post material increased the stress, but shifted the maximum stress location from the dentin surface to the post material. Buccal side of cervical region (junction of core and crown) of the glass fiber post restored tooth was subjected to the highest stress concentration. Maximum von Mises stress in the remaining radicular tooth structure for low elastic modulus resin core (29.21 MPa) was slightly higher than that for high elastic modulus resin core (29.14 MPa) in case of glass fiber post. Maximum displacement of glass fiber post restored tooth was higher than that of zirconia ceramic or stainless steel post restored tooth.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.1-28
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• 2019

This study analyzed the density and mortality rate of Korean fir at 9 sites where individuals of Korean firs were marked into the live and dead trees with coordinates on orthorectified aerial images by digital photogrammetric system. As a result of the analysis, Korean fir in each site showed considerable heterogeneity in density and mortality rate depending on the location within site. This make it possible to assume that death of Korean fir can occur by specific factors that vary depending on the location. Based on the analyzed densities and mortality rates of Korea fir, we investigated the correlation between topographic factors such as altitude, terrain slope, drainage network, solar radiation, aspect and the death of Korean fir. The density of Korean fir increases with altitude, and the mortality rate also increases. A negative correlation is found between the terrain slope and the mortality rate, and the mortality rate is higher in the gentle slope where the drainage network is less developed. In addition, it is recognized that depending on the aspect, the mortality rate varies greatly, and the mean solar radiation is higher in live Korean fir-dominant area than in dead Korean fir-dominant area. Overall, the mortality rate of Korean fir in Mt. Halla area is relatively higher in areas with relatively low terrain slope and low solar radiation. Considering the results of previous studies that the terrain slope has a strong negative correlation with soil moisture and the relationship between solar radiation and evaporation, these results lead us to infer that excess soil moisture is the cause of Korean fir mortality. These inferences are supported by a series of climate change phenomena such as precipitation increase, evaporation decrease, and reduced sunshine duration in the Korean peninsula including Jeju Island, increase in mortality rate along with increased precipitation according to the elevation of Mt. Halla and the vegetation change in the mountain. It is expected that the spatial patterns in the density and mortality rate of Korean fir, which are controlled by topography such as altitude, slope, aspect, solar radiation, drainage network, can be used as spatial variables in future numerical modeling studies on the death or decline of Korean fir. In addition, the method of forest distribution survey using the orthorectified aerial images can be widely used as a numerical monitoring technique in long - term vegetation change research.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.198-221
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• 2021

After large-scale reforestation in the 1960s and 1970s, forests in Korea have gradually been aging. Net ecosystem CO₂ exchange of old-growth forests is theoretically near zero; however, it can be a CO₂ sink or source depending on the intervention of disturbance or management. In this study, we report the CO₂ budget dynamics of the Gwangneung deciduous old-growth forest (GDK) in Korea and examined the following two questions: (1) is the preserved GDK indeed CO₂ neutral as theoretically known? and (2) can we explain the dynamics of CO₂ budget by the common mechanisms reported in the literature? To answer, we analyzed the 15-year long CO₂ flux data measured by eddy covariance technique along with other biometeorological data at the KoFlux GDK site from 2006 to 2020. The results showed that (1) GDK switched back-and-forth between sink and source of CO₂ but averaged to be a week CO₂ source (and turning to a moderate CO₂ source for the recent five years) and (2) the interannual variability of solar radiation, growing season length, and leaf area index showed a positive correlation with that of gross primary production (GPP) (R²=0.32~0.45); whereas the interannual variability of both air and surface temperature was not significantly correlated with that of ecosystem respiration (RE). Furthermore, the machine learning-based model trained using the dataset of early monitoring period (first 10 years) failed to reproduce the observed interannual variations of GPP and RE for the recent five years. Biomass data analysis suggests that carbon emissions from coarse woody debris may have contributed partly to the conversion to a moderate CO₂ source. To properly understand and interpret the long-term CO₂ budget dynamics of GDK, new framework of analysis and modeling based on complex systems science is needed. Also, it is important to maintain the flux monitoring and data quality along with the monitoring of coarse woody debris and disturbances.

• Journal of Korean Society of Disaster and Security
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• v.15 no.1
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• pp.1-12
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• 2022

An automatic optimization technique is used to estimate the optimal parameters of the hydrologic model, and different hydrologic response results can be provided depending on objective functions. In this study, the parameters of the event-based rainfall-runoff model were estimated using various objective functions, the reproducibility of the hydrograph according to the objective functions was evaluated, and appropriate objective functions were proposed. As the rainfall-runoff model, the storage function model(SFM), which is a lumped hydrologic model used for runoff simulation in the current Korean flood forecasting system, was selected. In order to evaluate the reproducibility of the hydrograph for each objective function, 9 rainfall events were selected for the Cheoncheon basin, which is the upstream basin of Yongdam Dam, and widely-used 7 objective functions were selected for parameter estimation of the SFM for each rainfall event. Then, the reproducibility of the simulated hydrograph using the optimal parameter sets based on the different objective functions was analyzed. As a result, RMSE, NSE, and RSR, which include the error square term in the objective function, showed the highest accuracy for all rainfall events except for Event 7. In addition, in the case of PBIAS and VE, which include an error term compared to the observed flow, it also showed relatively stable reproducibility of the hydrograph. However, in the case of MIA, which adjusts parameters sensitive to high flow and low flow simultaneously, the hydrograph reproducibility performance was found to be very low.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.101-114
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• 2023

In the wartime, aircraft carrying out a mission to strike the enemy deep in the depth are exposed to the risk of being shoot down. As a key combat force in mordern warfare, it takes a lot of time, effot and national budget to train military flight personnel who operate high-tech weapon systems. Therefore, this study studied the path problem of predicting the route of emergency escape from enemy territory to the target point to avoid obstacles, and through this, the possibility of safe recovery of emergency escape military flight personnel was increased. based problem, transforming the problem into a TSP, VRP, and Dijkstra algorithm, and approaching it with an optimization technique. However, if this problem is approached in a network problem, it is difficult to reflect the dynamic factors and uncertainties of the battlefield environment that military flight personnel in distress will face. So, MDP suitable for modeling dynamic environments was applied and studied. In addition, GIS was used to obtain topographic information data, and in the process of designing the reward structure of MDP, topographic information was reflected in more detail so that the model could be more realistic than previous studies. In this study, value iteration algorithms and deterministic methods were used to derive a path that allows the military flight personnel in distress to move to the shortest distance while making the most of the topographical advantages. In addition, it was intended to add the reality of the model by adding actual topographic information and obstacles that the military flight personnel in distress can meet in the process of escape and escape. Through this, it was possible to predict through which route the military flight personnel would escape and escape in the actual situation. The model presented in this study can be applied to various operational situations through redesign of the reward structure. In actual situations, decision support based on scientific techniques that reflect various factors in predicting the escape route of the military flight personnel in distress and conducting combat search and rescue operations will be possible.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Asia pacific journal of information systems
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• v.22 no.1
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• pp.29-52
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• 2012

Consumers differ in the way they make a purchase. An audio mania would willingly make a bold, yet serious, decision to buy a top-of-the-line home theater system, while he is not interested in replacing his two-decade-old shabby car. On the contrary, an automobile enthusiast wouldn't mind spending forty thousand dollars to buy a new Jaguar convertible, yet cares little about his junky component system. It is product involvement that helps us explain such differences among individuals in the purchase style. Product involvement refers to the extent to which a product is perceived to be important to a consumer (Zaichkowsky, 2001). Product involvement is an important factor that strongly influences consumer's purchase decision-making process, and thus has been of prime interest to consumer behavior researchers. Furthermore, researchers found that involvement is closely related to perceived risk (Dholakia, 2001). While abundant research exists addressing how product involvement relates to overall perceived risk, little attention has been paid to the relationship between involvement and different types of perceived risk in an electronic commerce setting. Given that perceived risk can be a substantial barrier to the online purchase (Jarvenpaa, 2000), research addressing such an issue will offer useful implications on what specific types of perceived risk an online firm should focus on mitigating if it is to increase sales to a fullest potential. Meanwhile, past research has focused on such consumer responses as information search and dissemination as a consequence of involvement, neglecting other behavioral responses like online merchant selection. For one example, will a consumer seriously considering the purchase of a pricey Guzzi bag perceive a great degree of risk associated with online buying and therefore choose to buy it from a digital storefront rather than from an online marketplace to mitigate risk? Will a consumer require greater trust on the part of the online merchant when the perceived risk of online buying is rather high? We intend to find answers to these research questions through an empirical study. This paper explores the impact of enduring product involvement and perceived risks on required trust level, and further on online merchant choice. For the purpose of the research, five types or components of perceived risk are taken into consideration, including financial, performance, delivery, psychological, and social risks. A research model has been built around the constructs under consideration, and 12 hypotheses have been developed based on the research model to examine the relationships between enduring involvement and five components of perceived risk, between five components of perceived risk and required trust level, between enduring involvement and required trust level, and finally between required trust level and preference toward an e-tailer. To attain our research objectives, we conducted an empirical analysis consisting of two phases of data collection: a pilot test and main survey. The pilot test was conducted using 25 college students to ensure that the questionnaire items are clear and straightforward. Then the main survey was conducted using 295 college students at a major university for nine days between December 13, 2010 and December 21, 2010. The measures employed to test the model included eight constructs: (1) enduring involvement, (2) financial risk, (3) performance risk, (4) delivery risk, (5) psychological risk, (6) social risk, (7) required trust level, (8) preference toward an e-tailer. The statistical package, SPSS 17.0, was used to test the internal consistency among the items within the individual measures. Based on the Cronbach's ${\alpha}$ coefficients of the individual measure, the reliability of all the variables is supported. Meanwhile, the Amos 18.0 package was employed to perform a confirmatory factor analysis designed to assess the unidimensionality of the measures. The goodness of fit for the measurement model was satisfied. Unidimensionality was tested using convergent, discriminant, and nomological validity. The statistical evidences proved that the three types of validity were all satisfied. Now the structured equation modeling technique was used to analyze the individual paths along the relationships among the research constructs. The results indicated that enduring involvement has significant positive relationships with all the five components of perceived risk, while only performance risk is significantly related to trust level required by consumers for purchase. It can be inferred from the findings that product performance problems are mostly likely to occur when a merchant behaves in an opportunistic manner. Positive relationships were also found between involvement and required trust level and between required trust level and online merchant choice. Enduring involvement is concerned with the pleasure a consumer derives from a product class and/or with the desire for knowledge for the product class, and thus is likely to motivate the consumer to look for ways of mitigating perceived risk by requiring a higher level of trust on the part of the online merchant. Likewise, a consumer requiring a high level of trust on the merchant will choose a digital storefront rather than an e-marketplace, since a digital storefront is believed to be trustworthier than an e-marketplace, as it fulfills orders by itself rather than acting as an intermediary. The findings of the present research provide both academic and practical implications. The first academic implication is that enduring product involvement is a strong motivator of consumer responses, especially the selection of a merchant, in the context of electronic shopping. Secondly, academicians are advised to pay attention to the finding that an individual component or type of perceived risk can be used as an important research construct, since it would allow one to pinpoint the specific types of risk that are influenced by antecedents or that influence consequents. Meanwhile, our research provides implications useful for online merchants (both online storefronts and e-marketplaces). Merchants may develop strategies to attract consumers by managing perceived performance risk involved in purchase decisions, since it was found to have significant positive relationship with the level of trust required by a consumer on the part of the merchant. One way to manage performance risk would be to thoroughly examine the product before shipping to ensure that it has no deficiencies or flaws. Secondly, digital storefronts are advised to focus on symbolic goods (e.g., cars, cell phones, fashion outfits, and handbags) in which consumers are relatively more involved than others, whereas e- marketplaces should put their emphasis on non-symbolic goods (e.g., drinks, books, MP3 players, and bike accessories).