• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.1-36
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• 2020

This study is to reveal the acceptance factors of the Market Sentiment Index (MSI) created by reflecting the investor sentiment extracted by processing unstructured big data. The research model was established by exploring exogenous variables based on the rational behavior theory and applying the Technology Acceptance Model (TAM). The acceptance of MSI provided to investors in the stock market was found to be influenced by the exogenous variables presented in this study. The results of causal analysis are as follows. First, self-efficacy, investment opportunities, Innovativeness, and perceived cost significantly affect perceived ease of use. Second, Diversity of services and perceived benefits have a statistically significant impact on perceived usefulness. Third, Perceived ease of use and perceived usefulness have a statistically significant effect on attitude to use. Fourth, Attitude to use statistically significantly influences the intention to use, and the investment opportunities as an independent variable affects the intention to use. Fifth, the intention to use statistically significantly affects the final dependent variable, the intention to use continuously. The mediating effect between the independent and dependent variables of the research model is as follows. First, The indirect effect on the causal route from diversity of services to continuous use intention was 0.1491, which was statistically significant at the significance level of 1%. Second, The indirect effect on the causal route from perceived benefit to continuous use intention was 0.1281, which was statistically significant at the significance level of 1%. The results of the multi-group analysis are as follows. First, for groups with and without stock investment experience, multi-group analysis was not possible because the measurement uniformity between the two groups was not secured. Second, the analysis result of the difference in the effect of independent variables of male and female groups on the intention to use continuously, where measurement uniformity was secured between the two groups, In the causal route from usage attitude to usage intention, women are higher than men. And in the causal route from use intention to continuous use intention, males were very high and showed statistically significant difference at significance level 5%.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3D
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• pp.399-408
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• 2009

The major changes in construction environment are that construction project is bigger and more complicated and the power of construction market changes from the supplier to the client or the user. Especially public construction enterprises have advanced to introduce the value engineering (VE) which is one of the cost management based on the owner's leading at the design phase for economical efficiency and quality improvement. According to the these efforts, the implementation of VE was legislated in the revised Construction Technology Management Act in 2000, governmental agencies, local autonomies, and construction public enterprises universally has taken the VE into consideration. In this circumstance, the scope that VE construction applied at 50 billion won projects from 2003 has been extended to 10 billion won projects in 2006. Therefore, the VE construction will be activated in the future. The cost savings and function improvement, which are the purpose of VE are not only construction public enterprises, but also every public client supported from government's budget or owned by the government. Therefore, the purpose of this study is to propose the improved process and performance index of VE for governmental agencies, local autonomies, and construction public enterprises which want to introduce or improve the VE process. This research also suggested the To-be design-phased VE process model. In addition, it suggested the To-be model of design management reflected the To-be design-phased VE process model, which is eliminated two problems reflected for the performance improvement of the As-is model of design management.

• Journal of Korean Society of Transportation
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• v.25 no.2 s.95
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• pp.121-132
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• 2007

This paper addressed the issue of a generalized cost model for transit assignment. The model composed of walk time, waiting time (including transfer waiting time), line-haul time, transfer walk time, and fare. The weights of each component were supposed to be calculated using the stated preference (SP) data, which were collected prudently in order to reflect reality. The marginal rate of substitution and wage rate were applied to calculate the weights. The results showed that the weight of walking time per in-vehicle travel time (IVTT) was 1.507, the weight of waiting time (per IVTT) was 1.749, that of transfer time (per IVTT) was 1.474, and that of fare (per IVTT) was 1.476 for trips between inner-city areas in Seoul. Weights for each component were identified as 1.871, 1.967, 1.015, and 0.857, respectively, for trips between Seoul and Gyeonggi. Statistical significance existed between two cases and each variable was also statistically significant. Transit assignment using the relative weights estimated in this study was implemented to analyze the travel index in a macroscopic and quantitative basis. The results showed that average total travel times were 30.23 minutes and 63.29 minutes and average generalized costs were 2,510 won and 3,880 won for trips between inner-city areas in Seoul and between Seoul and Gyeonggi, respectively.

• 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 the Korea Society of Computer and Information
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• v.12 no.4
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• pp.169-177
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• 2007

Competitive international business environment has forced many firms to focus on supply chain management to cope with highly increasing competition. Hence the supplier selection is the most important decision of a company. Because it has a direct effect on cost reduction and quality, profitability and flexibility improvement of a company, so the right supplier selection significantly affect on the organization's efficiency and effectiveness and competitiveness. The primary research objects of this study is to evaluate an importance of supplier selection factors as an index and to present the evaluation model for supplier selection. For this purpose, this study adopts the AHP method to calculate the importance of supplier selection factors. In this study, 16 factors which affect on the supplier selection decision making are classified into three factors-product supply related factor, product related factor, management ability related factor.

• Tunnel and Underground Space
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• v.22 no.4
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• pp.284-295
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• 2012

Abrasiveness of rock plays an important role on the wear of rock cutting tools. In this study, Cerchar abrasiveness tests were carried out to assess the abrasiveness of 19 different Korean rocks. Cerchar abrasiveness test is widely used to assess the abrasiveness of rock because of its simplicity and inexpensive cost. This study examines the relationship between Cerchar Abrasiveness Index (CAI) and mechanical properties (uniaxial compressive strength, Brazilian tensile strength, Young's modulus, Poisson's ratio, porosity, shore hardness of rock), and the effect of quartz content, equivalent quartz content, which was obtained from XRD analysis. As a result of test, CAI was more influenced by petrographical properties than by the bonding strength of the matrix material of rock. CAI prediction model which consisted of UCS and EQC was proposed. CAI decreased linearly with the hardness of the steel pin. Numerical analysis was performed using Autodyn-3D for simulating the Cerchar abrasiveness test. In the simulations, most of pin wear occurred during the initial scratching distance, and CAI increased with the increase of normal loading.

• Journal of The Geomorphological Association of Korea
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• v.21 no.4
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• pp.133-147
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• 2014

Reducing the range of target landform, is required to save the time and cost before real field survey in the case of inaccessible landform such as talus. In this study, Weight of Evidence modeling, which is a Target-driven spatial analysis statistics methods, has been applied to reduce the field survey range of target landform. In order to apply the Weight of Evidence analysis, a likelihood ratio was calculated on the basis of the result of correlation analysis between geomorphic factors and GIS information after selection of geomorphic factors regarding talus. A best combination, which has the biggest possibility for Talus Potential Index, was found by using SRC and AUC methods after calculating the number of cases for each thematic maps. This combination which includes aspect, geology, slope, land-cover, soil depth and soil drainage factors, showed quite high accuracy by 74.47% indicating the ratio of real existent talus to potential talus distribution.

• Earthquakes and Structures
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• v.22 no.2
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• pp.185-201
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• 2022

Structural Health Monitoring (SHM) is used to provide reliable information about the structure's integrity in near realtime following extreme incidents such as earthquakes, considering the inevitable aging and degradation that occurs in operating environments. This paper experimentally investigates an integrated wireless sensor network (Wi-SN) based monitoring technique for damage detection in concrete structures. An effective SHM technique can be used to detect potential structural damage based on post-earthquake data. Two novel methods are proposed for damage detection in reinforced concrete (RC) building structures including: (i) Jerk Energy Method (JEM), which is based on time-domain analysis, and (ii) Modal Contributing Parameter (MCP), which is based on frequency-domain analysis. Wireless accelerometer sensors are installed at each story level to monitor the dynamic responses from the building structure. Prior knowledge of the initial state (immediately after construction) of the structure is not required in these methods. Proposed methods only use responses recorded during ambient vibration state (i.e., operational state) to estimate the damage index. Herein, the experimental studies serve as an illustration of the procedures. In particular, (i) a 3-story shear-type steel frame model is analyzed for several damage scenarios and (ii) 2-story RC scaled down (at 1/6th) building models, simulated and verified under experimental tests on a shaking table. As a result, in addition to the usual benefits like system adaptability, and cost-effectiveness, the proposed sensing system does not require a cluster of sensors. The spatial information in the real-time recorded data is used in global damage identification stage of SHM. Whereas in next stage of SHM, the damage is detected at the story level. Experimental results also show the efficiency and superior performance of the proposed measuring techniques.

• Journal of Distribution Research
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• v.11 no.1
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• pp.69-97
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• 2006

This research aimed to establish and test a empirical model for antecedents and consequences of manufacturer's degree of channel concentration in multiple channels from strategic perspective. For this purpose, I suggested new concept of channel concentration based on related literature review and developed the measurement index of channel concentration. Second, I examined and applied the transaction cost theory and market power theory to provide broad understanding of multiple channel structure and to explain it. Finally, I present the theoretical and managerial implications to the firms that build up channel strategy under multiple channel contexts on this research results. For the purpose of these goals, eight hypotheses were drawn from the previous researches. To verify these hypotheses, 248 data were collected as samples, and the data were tested by reliability test, factor analysis, and covariance structure analysis. Empirical findings strongly support that strategic management of distribution channel especially are important in multiple channels. The overall implications to researchers and practitioners are presented, and limits and further study direction were discussed as a final.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.4
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• pp.306-317
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• 2017

Unmanned aerial vehicles (UAVs) became popular platforms for the collection of remotely sensed data in the last years. This study deals with the monitoring of multi-temporal onion growth with very high resolution by means of low-cost equipment. The concept of the monitoring was estimation of multi-temporal onion growth using normalized difference vegetation index (NDVI) and meteorological factors. For this study, UAV imagery was taken on the Changnyeong, Hapcheon and Muan regions eight times from early February to late June during the onion growing season. In precision agriculture frequent remote sensing on such scales during the vegetation period provided important spatial information on the crop status. Meanwhile, four plant growth parameters, plant height (P.H.), leaf number (L.N.), plant diameter (P.D.) and fresh weight (F.W.) were measured for about three hundred plants (twenty plants per plot) for each field campaign. Three meteorological factors included average temperature, rainfall and irradiation over an entire onion growth period. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, $NDVI_{UAV}$ and rainfall in the model explain 88% and 68% of the P.H. and F.W. with a root mean square error (RMSE) of 7.29 cm and 59.47 g, respectively. And $NDVI_{UAV}$ in the model explain 43% of the L.N. with a RMSE of 0.96. These lead to the result that the characteristics of variations in onion growth according to $NDVI_{UAV}$ and other meteorological factors were well reflected in the model.