• Journal of Intelligence and Information Systems
• /
• v.20 no.4
• /
• pp.1-23
• /
• 2014

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.

• Proceedings of the KSME Conference
• /
• 2004.04a
• /
• pp.1029-1034
• /
• 2004

Recent engineering process requires fast development and manufacturing of the products. This paper mainly discusses the process of rapid product development (RPD) from the reverse engineering to the optimal design. A laser scanning system scans a product and the efficient data processing method reduces the scanned point data. The reduced (scanned) points model is transformed to a finite element model without the construction of a CAD model. Since CAD modeling is a time-consuming work, skipping this step can save much time. This FE model is updated from the result based on the structural characteristics from modal test of the real model. For FE model updating, Response Surface Method is adopted. Finally, the updated FE model is optimized using the reliability-based topology optimization, which is developed recently. All these processes are applied to the design of an upper part model of a cellular phone.

• Journal of Intelligence and Information Systems
• /
• v.21 no.1
• /
• pp.161-177
• /
• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Proceedings of the Korean Institute Of Construction Engineering and Management
• /
• autumn
• /
• pp.99-106
• /
• 2002

Researches on integration of design and construction information from earlier era focused on the conceptual data models. Development and prevalent use of commercial database management system led many researchers to design database schemas for enlightening of relationship between non-graphic data items. Although these researches became the foundation fur the proceeding researches. they did not utilize the graphic data providable from CAD system which is already widely used. 4D CAD concept suggests a way of integrating graphic data with schedule data. Although this integration provided a new possibility for integration, there exists a limitation in data dependency on a specific application. This research suggests a new approach on integration for design and construction information, 'Product Model Centered Integration Methodology'. This methodology achieves integration by preliminary research on existing methodology using 4D CAD concept. and by development and application of new integration methodology, 'Product Model Centered Integration Methodology'. 'Design Component' can be converted into digital format by object based CAD system. 'Unified Object-based Graphic Modeling' shows how to model graphic product model using CAD system. Possibility of reusing design information in latter stage depends on the ways of creating CAD model, so modeling guidelines and specifications are suggested. Then prototype system for integration management, and exchange are presented, using 'Product Frameworker', and 'Product Database' which also supports multiple-viewpoints. 'Product Data Model' is designed, and main data workflows are represented using 'Activity Diagram', one of UML diagrams. These can be used for writing programming codes and developing prototype in order to automatically create activity items in actual schedule management system. Through validation processes, 'Product Model Centered Integration Methodology' is suggested as the new approach for integration of design and construction information.

• Journal of the Korean Data and Information Science Society
• /
• v.25 no.1
• /
• pp.19-26
• /
• 2014

In this paper, we propose a polychotomous regression model when independent variables include both categorical and numerical variables. For categorical independent variables, we use direct sums, and tensor product splines are used for continuous independent variables. We use BIC for varible selections criterior. We implemented the algorithm and apply the algorithm to real data. The use of direct sums and tensor products outperformed the usual multinomial logistic regression model.

• Proceedings of the Korean Nuclear Society Conference
• /
• 1996.05b
• /
• pp.637-642
• /
• 1996

This paper presents the analysis results for the core degradation processes and the fission product release of the PHEBUS FPT0 experiment using MELCOR1.8.3. The objective of this study is to assess models associated with the core damage and fission product behavior in MELCOR. The calculation results were much improved through sensitivity studies. Thermal/hydraulic behavior in the core and the circuit was well predicted under the intact core geometry. In non-eutectic model case. the UO$_2$ dissolution model in the MELCOR always showed such a tendency that the resulting dissolved UO$_2$ mass was small at the highly oxidized condition due to the model logic. Total H$_2$ generation mass was underpredicted because the stiffner was not modeled and the liner in the shroud was not allowed to be oxidized in MELCOR. Some difficulties were found in modeling the activation product were solved by manipulating the RN input associated with the initial fission product inventory. These problem were occurred because there are no control rod model in MELCOR. Generally the fission product release ratio showed a similar trend compared with the measured data except the activation product. which have no model to simulate in MELCOR.

• Journal of the Society of Naval Architects of Korea
• /
• v.31 no.3
• /
• pp.19-30
• /
• 1994

The modeling of a product data is becomming more and more important in engineering environment, especially for the development of CAD/CAM system as a basis of computer integrated manufacturing system. Model is a formalized representation of the real world, and modeling is the task to identify, abstract, and formalize the product information into an unambiguous representation. In this study, the piping system, one of typical product of ship outfitting system, is modeled. The STEP idea is followed to provide a common mechanism to represent the product information throughout the life-cycle, and the object oriented paradigm is used in the analysis and design of the model. The definitions given within this model are independent of the specific application domain so that the same methodology can be used for other purpose.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.4
• /
• pp.517-527
• /
• 2021

While the two principles of lean manufacturing and time-driven activity-based costing (TDABC) have been established out of multiple incentives and do not follow the same particular targets, there is substantial commonality between them. In these conditions, the supply management of a multi-product system needs a rigorous production model to minimize costs. In this sense, this paper proposes an interactive model with the consideration of optimizing product-mix decisions using both lean development tools and TDABC. This paper proposes a qualitative approach using the case study of the Iraqi state company for battery production. The suggested model decreased manufacturing time and costs, along with some substantial reduction in idle production capacity by 26 percent in 2019, based on the findings of the case study. On the other hand, the proposed model gives two side advantages: an efficient division of costs on goods due to the use of time spent as a cost factor for products and cost savings due to the introduction of the lean manufacturing approach that reduces all additional costs and increases product-mix decisions. Furthermore, the analytical data gathered here suggests that the incorporation of lean management concepts and TDABC has a strong and important influence on product-mix decisions.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1999.10a
• /
• pp.444-451
• /
• 1999

The purpose of this study is to Propose data models for central database in integrated system for structural design building. In order to efficiently express data related to structure, I analyzed the structure design process and classified data considering design step. 1 used an object-oriented modeling methodology for logical data model and relational modeling for physical data model. Based on this model, we will develop an integration system with several applications for structure design. Each application will communicate through the central database.

• The Korean Journal of Franchise Management
• /
• v.8 no.4
• /
• pp.7-19
• /
• 2017

Purpose - The purpose of this study is to investigate the effect of model attractiveness on brand attitude and purchase intention, and examine whether product-model match-up plays a moderating role in the relationship between model attractiveness and brand attitude and purchase intention. The model attractiveness is consist of psychological and physical attractiveness of the model. The authors investigate how product-model match-up influence the strength of the relationship between model attractiveness - brand attitude and purchase intention. The purpose of this is to test whether product-model match-up influence the form and effectiveness of a model attractiveness on brand attitude and purchase intention and suggest the effective and efficient methods in the model selection strategies to increase advertising effectiveness based on the results of this study. Research design, data, and methodology - The experimental design for this study was the between subject design based on 2 group of the psychological attractiveness(high vs. low) × product-model match-up(high vs low) and 2 group of the physical attractiveness(high vs. low) × product-model match-up(high vs low). And a preliminary investigation was conducted to develop experimental stimuli through manipulation check to enhance the external validity of experimental research. The attractiveness of the model and product-model match-up are independent variables and manipulative variables in presentation of experimental stimuli. The self-administered methode experiment was conducted on 300 subjects in four groups constructed according to the independent variables. Result - The findings provide partial support for a moderator for product-model match-up on the model attractiveness - brand attitude and purchase intention. First, the influence of psychological attractiveness and physical attractiveness on brand attitude and purchase intention was shown significant. Also, it was found that the average value of brand attitude and purchase intention according to psychological attractiveness was significantly higher than the average value of brand attitude and purchase intention according to physical attractiveness in additional analysis. Second, the average value of brand attitude and purchase intention were higher when product - model match-up was high in both high and low psychological attractiveness and physical attractiveness of the model. However, in the case of psychological attractiveness, the correlation effect with product - model match-up was significant, but in the case of physical attractiveness, it was not significant. Conclusions - The results of this study suggest that the attractiveness factor should be considered in selecting the ad model by verifying the effect of the attractiveness of the model on the advertising effect. In particular, this study has great significance both academically and practically in terms of suggesting such implications that the advertising effect of psychological attractiveness and physical attractiveness may be different depending on the product type by additional analysis.