• The Journal of Korean Academy of Prosthodontics
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• v.61 no.3
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• pp.189-197
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• 2023

Purpose. This study aimed to compare and evaluate the shear bond strength between various temporary prostheses resin blocks fabricated by subtractive and additive manufacturing methods bonded to self-curing reline resin. Materials and methods. The experimental groups were divided into 4 groups according to the manufacturing methods of the resin block specimens and each specimen was fabricated by subtractive manufacturing (SM), additive manufacturing stereolithography apparatus manufacturing (AMS), additive manufacturing digital light processing manufacturing (AMD) and conventional self-curing (CON). To bond the resin block specimens and self-curing resin, the reline resin was injected and polymerized into the same location of each resin block using a silicone mold. The shear bond strength was measured using a universal testing machine, and the surface of the adhesive interface was examined by scanning electron microscopy. To compare between groups, one-way ANOVA was done followed by Tukey post hoc test (α = 0.05). Results. The shear bond strength showed higher values in the order of CON, SM, AMS, and AMD group. There were significant differences between CON and AMS groups, as well as between CON and AMD groups. but there were no significant differences between CON and SM groups (P > .05). There were significant differences between SM and AMD groups, but there were no significant differences between SM and AMS groups. The AMS group was significantly different from the AMD group (P < .001). The most frequent failure mode was mixed failures in CON and AMS groups, and adhesive failures in SM and AMD groups. Conclusion. The shear bond strength of SM group showed lower but not significant bond strength compared to the CON group. The additive manufacturing method groups (AMS and AMD) showed significantly lower bond strength than the CON group, with the AMD group the lowest. There was also a significant difference between the AMD and SM group.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Journal of the Korean Applied Science and Technology
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• v.40 no.4
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• pp.674-684
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• 2023

The purpose of this study was to investigate the effect of acute listening to music on the cardiovascular reactivity to sympathoexcitation. In this crossover design study, 15 healthy adults(23.1±1.94(yrs) were randomized to either (1)acute listen to the subject's preferred music for 30 minutes and (2)sat as a time control by an experiment coordinator. After completing each trial, the cold pressor test(CPT) was conducted. Heart rate(HR) and blood pressure(BP) were measured for 4 times at baseline, during and after the CPT. Heart rate variability(HRV) were measured for 3 times at baseline, prior and after the CPT. HR and BP increased during the CPT in both trial and returned to baseline after CPT(time effect, p < .001). After CPT, brachial systolic BP reactivity to the CPT was attenuated in listening to music trial compared to control trial(p = . 008). As a result of heart rate variability(HRV), the difference values between the baseline and prior to the CPT showed a significant increase in standard deviation of the NN intervals(SDNN), total power(TP) and high frequency(HF) only in the music trial (p = .001, p = .002, p = .011). The difference value between prior to and after the CPT did not show significance. But compared with the control trial, the music trial was confirmed that SDNN, TP and HF were more activated. Therefore, listening to music alleviated anxiety and tension before the CPT, and it is estimated that it had a favorable effect on stability after the CPT. This findings showed that listening to music may have a positive effect on brachial systolic BP and HRV to sympathoexcitation.

• Journal of Distribution Research
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• v.17 no.2
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• pp.63-92
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• 2012

This study investigated consumer intention to use a location-based mobile shopping service (LBMSS) that integrates cognitive and affective responses. Information relevancy was integrated into pleasure-arousal-dominance (PAD) emotional state model in the present study as a conceptual framework. The results of an online survey of 335 mobile phone users in the U.S. indicated the positive effects of arousal and information relevancy on pleasure. In addition, there was a significant relationship between pleasure and intention to use a LBMSS. However, the relationship between dominance and pleasure was not statistically significant. The results of the present study provides insight to retailers and marketers as to what factors they need to consider to implement location-based mobile shopping services to improve their business performance. Extended Abstract : Location aware technology has expanded the marketer's reach by reducing space and time between a consumer's receipt of advertising and purchase, offering real-time information and coupons to consumers in purchasing situations (Dickenger and Kleijnen, 2008; Malhotra and Malhotra, 2009). LBMSS increases the relevancy of SMS marketing by linking advertisements to a user's location (Bamba and Barnes, 2007; Malhotra and Malhotra, 2009). This study investigated consumer intention to use a location-based mobile shopping service (LBMSS) that integrates cognitive and affective response. The purpose of the study was to examine the relationship among information relevancy and affective variables and their effects on intention to use LBMSS. Thus, information relevancy was integrated into pleasure-arousal-dominance (PAD) model and generated the following hypotheses. Hypothesis 1. There will be a positive influence of arousal concerning LBMSS on pleasure in regard to LBMSS. Hypothesis 2. There will be a positive influence of dominance in LBMSS on pleasure in regard to LBMSS. Hypothesis 3. There will be a positive influence of information relevancy on pleasure in regard to LBMSS. Hypothesis 4. There will be a positive influence of pleasure about LBMSS on intention to use LBMSS. E-mail invitations were sent out to a randomly selected sample of three thousand consumers who are older than 18 years old and mobile phone owners, acquired from an independent marketing research company. An online survey technique was employed utilizing Dillman's (2000) online survey method and follow-ups. A total of 335 valid responses were used for the data analysis in the present study. Before the respondents answer any of the questions, they were told to read a document describing LBMSS. The document included definitions and examples of LBMSS provided by various service providers. After that, they were exposed to a scenario describing the participant as taking a saturday shopping trip to a mall and then receiving a short message from the mall. The short message included new product information and coupons for same day use at participating stores. They then completed a questionnaire containing various questions. To assess arousal, dominance, and pleasure, we adapted and modified scales used in the previous studies in the context of location-based mobile shopping service, each of the five items from Mehrabian and Russell (1974). A total of 15 items were measured on a seven-point bipolar scale. To measure information relevancy, four items were borrowed from Mason et al. (1995). Intention to use LBMSS was captured using two items developed by Blackwell, and Miniard (1995) and one items developed by the authors. Data analyses were conducted using SPSS 19.0 and LISREL 8.72. A total of usable 335 data were obtained after deleting the incomplete responses, which results in a response rate of 11.20%. A little over half of the respondents were male (53.9%) and approximately 60% of respondents were married (57.4%). The mean age of the sample was 29.44 years with a range from 19 to 60 years. In terms of the ethnicity there were European Americans (54.5%), Hispanic American (5.3%), African-American (3.6%), and Asian American (2.9%), respectively. The respondents were highly educated; close to 62.5% of participants in the study reported holding a college degree or its equivalent and 14.5% of the participants had graduate degree. The sample represents all income categories: less than $24,999 (10.8%), $25,000-$49,999 (28.34%), $50,000-$74,999 (13.8%), and $75,000 or more (10.23%). The respondents of the study indicated that they were employed in many occupations. Responses came from all 42 states in the U.S. To identify the dimensions of research constructs, Exploratory Factor Analysis (EFA) using a varimax rotation was conducted. As indicated in table 1, these dimensions: arousal, dominance, relevancy, pleasure, and intention to use, suggested by the EFA, explained 82.29% of the total variance with factor loadings ranged from .74 to .89. As a next step, CFA was conducted to validate the dimensions that were identified from the exploratory factor analysis and to further refine the scale. Table 1 exhibits the results of measurement model analysis and revealed a chi-square of 202.13 with degree-of-freedom of 89 (p =.002), GFI of .93, AGFI = .89, CFI of .99, NFI of .98, which indicates of the evidence of a good model fit to the data (Bagozzi and Yi, 1998; Hair et al., 1998). As table 1 shows, reliability was estimated with Cronbach's alpha and composite reliability (CR) for all multi-item scales. All the values met evidence of satisfactory reliability in multi-item measure for alpha (>.91) and CR (>.80). In addition, we tested the convergent validity of the measure using average variance extracted (AVE) by following recommendations from Fornell and Larcker (1981). The AVE values for the model constructs ranged from .74 through .85, which are higher than the threshold suggested by Fornell and Larcker (1981). To examine discriminant validity of the measure, we again followed the recommendations from Fornell and Larcker (1981). The shared variances between constructs were smaller than the AVE of the research constructs and confirm discriminant validity of the measure. The causal model testing was conducted using LISREL 8.72 with a maximum-likelihood estimation method. Table 2 shows the results of the hypotheses testing. The results for the conceptual model revealed good overall fit for the proposed model. Chi-square was 342.00 (df = 92, p =.000), NFI was .97, NNFI was .97, GFI was .89, AGFI was .83, and RMSEA was .08. All paths in the proposed model received significant statistical support except H2. The paths from arousal to pleasure (H1: ${\ss}$=.70; t = 11.44), from information relevancy to intention to use (H3 ${\ss}$ =.12; t = 2.36), from information relevancy to pleasure (H4 ${\ss}$ =.15; t = 2.86), and pleasure to intention to use (H5: ${\ss}$=.54; t = 9.05) were significant. However, the path from dominance to pleasure was not supported. This study investigated consumer intention to use a location-based mobile shopping service (LBMSS) that integrates cognitive and affective responses. Information relevancy was integrated into pleasure-arousal-dominance (PAD) emotional state model as a conceptual framework. The results of the present study support previous studies indicating that emotional responses as well as cognitive responses have a strong impact on accepting new technology. The findings of this study suggest potential marketing strategies to mobile service developers and retailers who are considering the implementation of LBMSS. It would be rewarding to develop location-based mobile services that integrate information relevancy and which cause positive emotional responses.

• Korean Journal of Agricultural Science
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• v.4 no.2
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• pp.254-282
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• 1977

To obtain information on the inheritance of the quantitative characters related with the vegetative and reproductive growth of rice, the $F_1$ seeds were obtained in 1974 from the all possible combinations of the diallel crosses among five leading rice varieties : Nongbaek, Tongil, Palgueng, Mangyeong and Gimmaze. The $F_1$'s including reciprocals and parents were grown under the standard cultivation method at Chungnam Provincial Office of Rural Development in 1975. The arrangement of experimental plots was randomized block design with 3 replications and 12 characters were used for the analysis. Analytical procedure for genetic components was followed the Griffing's and Hayman's methods and the results obtained are summarized as follows. 1. In all $F_1$'s of Tongil crosses, the longer duration to heading was due to dominant effect of Tongil and each $F_1$ showed high heterosis in delaying the heading time. It was assumed that non-allelic gene action besides dominant gene effect might be involed in days to heading character. However, in all $F_1$'s from the crosses among parents excluding Tongil the shorter duration was due to dominant gene action and the degree of dominance was partial, since dominance effects were not greater than the additive effect. The non-allelic gene interaction was not significant. Considering the results mentioned above, it was regarded that there were two kinds of Significantly different genetic systems in the days to heading. 2. The rate of heterosis was significantly different depending upon the parents used in the crosses. For instance, the $F_1$'s from Togil cross showed high rate of heterosis in longer culm. Compared to short culm, longer culm was due to recesive gene action and short culm was due to recesive gene action. The dominant gene effect was greater than the additive gene effect in culm length. The narrow sense of heretability was very low and the maternal effects as well as reciprocal effects were significantly recognized. 3. The lenght of the of the uppermost internode of each $F_1$ plant was a little lorger than these of respective parental means or same as those of parents having long internodes, indicating partial dominance in the direction of lengthening the uppermost internodes. The additive gene effects on the uppermost internode was greater than the dominance gene effect. The narrow as well as broad sense of heritabilities for the character of the uppermost internode were very high. There were significant maternal and reciprocal effect in the uppermost internode. 4. The gene action for the flag leaf angle was rather dominance in a way of getting narrower angle. However, in the Palgueng combinations, heterosis of $F_1$ was observed in both narrow and wide angles of the flag leaf. The dominant effects were greater than the additive effects on the flag leaf angle. There were observed also a great deal of non-allelic gene interacticn on the inheritance of the flag leaf angle. 5. Even though the dominant gene action on the length and width of flag leaf was effective in increasing the length or width of the flag leaf, there were found various degrees of hetercsis depending upon the cross combination. Over-dominant gene effect were observed in the inheritance of length of the flag leaf, while additive gene effects was found in the inheritance of the width of the flag leaf. High degree of heretabilities, either narrow or broad sense, were found in both length and width of the flag leaf. No maternal and reciprocal effect were found in both characters. 6. When Tongil was used as one parent in the cross, the length of panicle of $F_1$'s was remarkedly longer than that of parents. In other cross comination, the length of panicle of $F_1$'s was close to the parental mean values. Rather greater dominent gene effect than additive gene effect was observed in the inheritance of panicle length and the dominant gene was effective in increasing the panicle length. 7. The effect of dominant genes was effective in increasing the number of panicles. The degree of heterosis was largely dependent on the cross combination. The effect of dominant gene in the inheritance of panicle number was a little greater than that of additive genes, and the inheritance of panicle number was assumed to be due to complete dominant gene effects. Significantly high maternal and reciprocal effects were found in the character studied. 8. There were minus and plus values of heterosis in the kernel number per panicle depending upon the cross combination. The mean dominant effect was effective in increasing the kernel number per panicle, the degree of dominant effect varied with cross combination. The dominant gene effect and non-allelic gene interaction were found in the inheritance of the kernel number per panicle. 9. Genetic studies were impossible for the maturing ratio, because of environmental effects such as hazards delaying heads. The dominant gene effect was responsible for improving the maturing ratio in all the cross combinations excluding Tongil 10. The heavier 1000 grain weight was due to dominant gene effects. The additive gene effects were greater than the dominant gene effect in the 1000 grain weight, indicating that partial dominance was responsible for increasing the 1000 grain weight. The heritabilites, either narrow or broad sense of, were high for the grain weight and maternal or reciprocal effects were not recognized. 11. When Tongil was used as parent, the straw weight was showing high heterosis in the direction of increasing the weight. But in other crosses, the straw weight of $F_1$'s was lower than those of parental mean values. The direction of dominant gene effect was plus or minus depending upon the cross combinations. The degree of dominance was also depending on the cross combination, and apparently high nonallelic gene interaction was observed.

• Journal of Korean Home Economics Education Association
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• v.29 no.4
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• pp.161-193
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• 2017

The purpose of this study was to develop and evaluate pre-parenthood education program(PPEP) based on Home Economics(HE) subject for high school students. The development and evaluation of PPEP based on HE subject in this study followed ADDIE model except implementation through 4 processes such as analysis, design, development, and evaluation. First, program development directions were set in three aspects such as 'general development', 'contents', and 'teaching and learning methods'. Themes of the program are 11 in total such as '1. Parenting, what is being a parent', '2. Choosing your spouse, happy marital relationship, the best gift to your children', '3. Pregnancy and birth, a moving meeting with a new life', '4. Taking care of a new born infant for 24 hours', '5. Taking care of infants, relationship with my lovely baby, attachment', '6. Taking care of young children, my child from another planet', '7. Parents and children in healthy family', '8. Parent-child relationship, wise parents to make effective interaction with their children', '9. Parents safety manager at home,', '10. Practice to take care of infants', and '11. Practice of community nurturing support service development'. In particular, learning activities of the program have major characteristics such as 1) utilization of cases including practice problems related to parenting, 2) community exchange activities utilizing learned knowledge and techniques, 3) actual life project activities utilizing learning contents related with parenting, 4) activities inducing positive changes in current life of high school students, and 5) practice activities for the necessities of life such as food, clothing and shelter supporting development of children. Second, the program was developed according to the design. Teaching-learning plans and materials for 17 classes were developed according to 11 themes. The developed plans include class flow and teacher's reference. It starts with receiving a class-related message from a virtual child at the introduction stage and ended with replying to the message by summarizing contents of the class and making a promise as a parent-to-be. That is the basic frame of class flow. Learning materials included various plans and reports necessary for learning activities and they are prepared in details so that they can be play the role of textbooks in regular curriculum. Third, evaluation of developed program was executed by a 5 point Likert scale survey on 13 HE experts on two aspects of program development process and program development results. In the evaluation of development process, mean value was 4.61 and index of content validity was 97.4%. For development results, mean value was 4.37 and index of content validity was 86.9%. These values showed that validity in the development process and results in this study was highly secured and confirmed that PPEP based on HE was appropriate and valid to enhance parent qualifications of high school learners.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.32 no.2
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• pp.68-81
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• 2014

This study was conducted to identify the spacialty, to illuminate the existence and values of Myeongwoldae(明月臺) and Forest Myeongwol, and to suggest the sustainable usage, preservation and management plans with the purpose of ecological and cultural landscaping characteristic and value identification. The result of the study is as follows. Castle Myeongwol and Port Myeongwol shows the status of Hallim-eup Myeongwol District which is the administrative center of western Jeju as well as is the fortress. Building Wolgyejeongsa and School Woohakdang, the head temple of education and culture, located in Myeongwol District represents the spaciality of Myeonwol-ri which was the center of education. Stand Myeongwol is one of the most representative Confucian cultural landscapes in Jeju Island and the field of communion with nature where scholars enjoy poetries, nature, changgi(Korean chess), and go in the Joseon Dynasty period. It was found that the current relics of Myeongwoldae was recovered through the maintenance project conducted by Youth Group Myeongwol composed with Hongjong-si(洪鍾時) as the center during the Japanese colonial era in 1931. It seems that the stonework of Myeongwoldae composed of three levels in the order of square, octagon, and circle based on the heaven-man unity theory of Confucianism and the octagon in the middle is the messenger of Cheonwonjibang(天圓地方), in other words, between the square-shaped earth and the circle-shaped sky. It is assumed that both Grand Bridge Myeongwol and Bridge Myeongwol were constructed as arched bridges in early days. Bridge Myeongwol is the only arched bridge remaining in Jeju Island now, which has the modern cultural heritage value. In Forest Myeongwol, 97 taxa of plants were confirmed and in accordance with 'Taxonomic Group and Class Criteria of Floristic Specific Plants', eight taxa were found; Arachniodes aristata of FD IV and Ilex cornuta, Piper kadsura, Litsea japonica, Melia azedarach, Xylosma congestum, Richosanthes kirilowii var. japonica, Dichondra repens, Viburnum odoratissimum var. awabuki of FD III. Otherwise, 14 taxa of naturalized plants including Apium leptophylihum which is imported to Jeju Island only were confirmed. In Forest Myeongwol, 77 trees including 41 Celtis sinensis, 30 Aphananthe aspera, two Wylosma congestum, a Pinus densiflora, a Camellia japonica, a Melia azedarach, and an Ilex cornuta form a colony. Based on the researched data, the preservation and plans of Myeongwoldae and Forest Myeongwol is suggested as follows. Myeongwoldae, Bridge Myeongwol, and Forest Myeongwol should be managed as one integrated division. Bridge Myeongwol, an arched bridge which is hard to be found in Jeju Island is a high-standard stonework requiring long-term preservation plans. Otherwise, Grand Bridge Myeongwol that is exposed to accident risks because of deterioration and needs safety diagnosis requires measures according to the result of precise safety diagnosis. It is desirable to restore it to a two-sluice arched bridge as its initial shape and to preserve and use it as a representative local landmark with Stand Myeongwol. In addition, considering the topophsis based on the analysis result, the current name of Jeju Special Self-Governing Province Monument No. 19 'Myoengwol Hackberry Colony' should change to 'Myeongwol Hackberry-Muku Tree Colony'. In addition, the serial number system which is composed without distinction of hackberry and muku tree should be improved and the regular monitoring of big and old trees, specific plants, and naturalized species is required.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.177-198
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• 2018

Recently, as economic property it has become necessary to acquire and utilize the framework for water resource measurement and performance management as the property of water resources changes to hold "public property". To date, the evaluation of water technology has been carried out by feasibility study analysis or technology assessment based on net present value (NPV) or benefit-to-cost (B/C) effect, however it is not yet systemized in terms of valuation models to objectively assess an economic value of technology-based business to receive diffusion and feedback of research outcomes. Therefore, K-water (known as a government-supported public company in Korea) company feels the necessity to establish a technology valuation framework suitable for technical characteristics of water resources fields in charge and verify an exemplified case applied to the technology. The K-water evaluation technology applied to this study, as a public interest goods, can be used as a tool to measure the value and achievement contributed to society and to manage them. Therefore, by calculating the value in which the subject technology contributed to the entire society as a public resource, we make use of it as a basis information for the advertising medium of performance on the influence effect of the benefits or the necessity of cost input, and then secure the legitimacy for large-scale R&D cost input in terms of the characteristics of public technology. Hence, K-water company, one of the public corporation in Korea which deals with public goods of 'water resources', will be able to establish a commercialization strategy for business operation and prepare for a basis for the performance calculation of input R&D cost. In this study, K-water has developed a web-based technology valuation model for public interest type water resources based on the technology evaluation system that is suitable for the characteristics of a technology in water resources fields. In particular, by utilizing the evaluation methodology of the Institute of Advanced Industrial Science and Technology (AIST) in Japan to match the expense items to the expense accounts based on the related benefit items, we proposed the so-called 'K-water's proprietary model' which involves the 'cost-benefit' approach and the FCF (Free Cash Flow), and ultimately led to build a pipeline on the K-water research performance management system and then verify the practical case of a technology related to "desalination". We analyze the embedded design logic and evaluation process of web-based valuation system that reflects characteristics of water resources technology, reference information and database(D/B)-associated logic for each model to calculate public interest-based and profit-based technology values in technology integrated management system. We review the hybrid evaluation module that reflects the quantitative index of the qualitative evaluation indices reflecting the unique characteristics of water resources and the visualized user-interface (UI) of the actual web-based evaluation, which both are appended for calculating the business value based on financial data to the existing web-based technology valuation systems in other fields. K-water's technology valuation model is evaluated by distinguishing between public-interest type and profitable-type water technology. First, evaluation modules in profit-type technology valuation model are designed based on 'profitability of technology'. For example, the technology inventory K-water holds has a number of profit-oriented technologies such as water treatment membranes. On the other hand, the public interest-type technology valuation is designed to evaluate the public-interest oriented technology such as the dam, which reflects the characteristics of public benefits and costs. In order to examine the appropriateness of the cost-benefit based public utility valuation model (i.e. K-water specific technology valuation model) presented in this study, we applied to practical cases from calculation of benefit-to-cost analysis on water resource technology with 20 years of lifetime. In future we will additionally conduct verifying the K-water public utility-based valuation model by each business model which reflects various business environmental characteristics.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.