• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.37 no.4
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• pp.382-393
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• 2024

The Microplotter system with a fluid dispensing method, sprays fluid based on ultrasonic pumping through piezoelectric devices. This technique can possible for various materials with a wide range of viscosities to be printed in microscale. In this paper, we introduces dispenser printing technology as well as aim to understand and apply various processes using the equipment. In addition, we will explain how to optimize the equipment by adjusting parameters such as spray intensity, tip height during printing, and patterning speed. By utilizing Microplotter's advantage of being compatible with a wide range of fluids, including metal nanoparticles, carbon nanotubes, DNA, and proteins, it is expected to be used in various fields such as printed electronics, biotechnology, and chemical engineering.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.763-770
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• 2024

Currently, land-based fish farms utilizing seawater have introduced and are utilizing various equipment such as real-time water quality monitoring systems, facility automation systems, and automated dissolved oxygen supply devices. Furthermore, data collected from various equipment in these fish farms produce structured and unstructured big data related to water quality environment, facility operations, and workplace visual information. The big data generated in the operational environment of fish farms aims to improve operational and production efficiency through the development and application of various methods. This study aims to develop a system for effectively analyzing and visualizing big data produced from land-based fish farms. It proposes a data visualization process suitable for use in a fish farm big data analysis system, develops big data visualization tools, and compares the results. Additionally, it presents intuitive visualization models for exploring and comparing big data with time-series characteristics.

• Journal of the Korean Society for information Management
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• v.41 no.3
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• pp.249-268
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• 2024

Information literacy is a necessary competency for all people living in the information society, but undergraduate students are especially in need of information literacy in the process of academic performance and career preparation. In this study, we conducted frequency analysis, network analysis, and topic modeling on the English abstracts of information literacy-related research on undergraduate students listed in KCI to identify trends in information literacy research on undergraduate students. The main keywords and subsequent research topics were derived by analyzing the frequency analysis and keyword network and comparing the results, and eight subtopics were derived from the topic modeling to observe the main research areas. Information literacy for college students was mainly studied for educational purposes, and nursing information and analysis model development were the main subtopics.

• Geomechanics and Engineering
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• v.39 no.1
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• pp.1-11
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• 2024

Geotechnical parameter estimation is critical to the design, performance, safety, and cost and schedule management in Tunnel Boring Machine projects. Since these parameters vary within a certain range, relying on mean values for evaluation introduces significant risks to the project. Due to the non-homogeneous characteristics of geological formation, data may not exhibit a normal distribution and the presence of outliers might be deceptive. Therefore, the use of reliable analyses and simulation models is inevitable in the course of the data evaluation process. Advanced modeling techniques enable comprehensive analysis of the project data and allowing to model the uncertainty in geotechnical parameters. This study involves using Monte Carlo Simulation method to predict probabilistic distributions of field data, and therefore, establish a basis for designs and in turn to minimize project risks. In the study, 166 sets of geotechnical data Obtained from 35 boreholes including Standard Penetration Test, Limit Pressure, Liquid Limit, and Plastic Limit values, which are mostly utilized parameters in estimating project requirements, were used to estimate the geotechnical data distribution of the study field. In this context, firstly, the data was subjected to multi-parameter linear regression and variance analysis. Then, the obtained equations were implemented into a Monte Carlo Simulation, and probabilistic distributions of the geotechnical data of the field were simulated and corresponding to the 90% probability range, along with the minimum and maximum values at the 5% probability levels presented. Accordingly, while the average SPT N30 value is 42.86, but the highest occurrence rate is 50.81. For Net Limit Pressure, the average field data is 17.07 kg/cm², with the maximum occurrence between 9.6 kg/cm² and 13.7 kg/cm². Similarly, the average Plastic Limit value is 22.32, while the most probable value is 20.6. The average Liquid Limit value is 56.73, with the highest probability at 54.48, as indicated in the statistical data distribution. Understanding the percentage distribution of data likely to be encountered in the project allows for accurate forecasting of both high and low probability scenarios, offering a significant advantage, particularly in ordering TBM requirements.

• Journal of Korea Technology Innovation Society
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• v.20 no.2
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• pp.334-367
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• 2017

The ICT industry has been the main driver of Korea's economy with international competitiveness and is expected to be the growth engine that will revitalize the currently depressed economy. A broad range of different perspectives and opinions on the industry exist in Korea and overseas. Some of these are stereotypes, not all of which are based on objective evidence. Stereotypes refer to widely-held fixed opinions on a specific group and do not necessarily have negative connotations. However, they should not be viewed lightly because they can substantially affect decision-making process. In this regard, this study sought to review the stereotypes of ICT industry and identify objective and relative stereotypes. In the study, a decision-tree analysis was conducted on a survey result of 3,300 small and medium-sized enterprises (SMEs) in order to identify Korean ICT companies' characteristics that distinguish them from other technology companies. The decision-tree analysis, a data mining process based on machine learning, took a total of 291 variables into account in 10 subjects such as: corporate business in general, technology development activities as well as organization and people in technology development. Identifying the variables that distinguish ICT companies from other technology companies with the decision-tree analysis, the study then came up with a list of objective stereotypes of ICT companies. The findings from the stereotypes of Korean ICT companies are as follows. First, the companies are in need of technology policies that help R&D planning and market penetration. Second, policies must better support the companies working to sell new products or explore new business. Third, the companies need policies that support secure protection of development outcomes and proper management of IP rights. Fourth, the administrative procedures related to governmental support for ICT companies' R&D projects must be simplified. It is hoped that the outcome of this study will provide meaningful guidance in establishment, implementation and evaluation of technology policies for ICT SMEs, particularly to policymakers or researchers in relevant government agencies who determine R&D policies for ICT SMEs.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.1-23
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• 2019

Big data refers to data that is difficult to store, manage, and analyze by existing software. As the lifestyle changes of consumers increase the size and types of needs that consumers desire, they are investing a lot of time and money to understand the needs of consumers. Companies in various industries utilize Big Data to improve their products and services to meet their needs, analyze unstructured data, and respond to real-time responses to products and services. The financial industry operates a decision support system that uses financial data to develop financial products and manage customer risks. The use of big data by financial institutions can effectively create added value of the value chain, and it is possible to develop a more advanced customer relationship management strategy. Financial institutions can utilize the purchase data and unstructured data generated by the credit card, and it becomes possible to confirm and satisfy the customer's desire. CRM has a granular process that can be measured in real time as it grows with information knowledge systems. With the development of information service and CRM, the platform has change and it has become possible to meet consumer needs in various environments. Recently, as the needs of consumers have diversified, more companies are providing systematic marketing services using data mining and advanced CRM (Customer Relationship Management) techniques. KB Kookmin Card, which started as a credit card business in 1980, introduced early stabilization of processes and computer systems, and actively participated in introducing new technologies and systems. In 2011, the bank and credit card companies separated, leading the 'Hye-dam Card' and 'One Card' markets, which were deviated from the existing concept. In 2017, the total use of domestic credit cards and check cards grew by 5.6% year-on-year to 886 trillion won. In 2018, we received a long-term rating of AA + as a result of our credit card evaluation. We confirmed that our credit rating was at the top of the list through effective marketing strategies and services. At present, Kookmin Card emphasizes strategies to meet the individual needs of customers and to maximize the lifetime value of consumers by utilizing payment data of customers. KB Kookmin Card combines internal and external big data and conducts marketing in real time or builds a system for monitoring. KB Kookmin Card has built a marketing system that detects realtime behavior using big data such as visiting the homepage and purchasing history by using the customer card information. It is designed to enable customers to capture action events in real time and execute marketing by utilizing the stores, locations, amounts, usage pattern, etc. of the card transactions. We have created more than 280 different scenarios based on the customer's life cycle and are conducting marketing plans to accommodate various customer groups in real time. We operate a smart offering system, which is a highly efficient marketing management system that detects customers' card usage, customer behavior, and location information in real time, and provides further refinement services by combining with various apps. This study aims to identify the traditional CRM to the current CRM strategy through the process of changing the CRM strategy. Finally, I will confirm the current CRM strategy through KB Kookmin card's big data utilization strategy and marketing activities and propose a marketing plan for KB Kookmin card's future CRM strategy. KB Kookmin Card should invest in securing ICT technology and human resources, which are becoming more sophisticated for the success and continuous growth of smart offering system. It is necessary to establish a strategy for securing profit from a long-term perspective and systematically proceed. Especially, in the current situation where privacy violation and personal information leakage issues are being addressed, efforts should be made to induce customers' recognition of marketing using customer information and to form corporate image emphasizing security.

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

• Journal of Korea Technology Innovation Society
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• v.21 no.1
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• pp.64-91
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• 2018

This study explores the interactive pattern among social issue, academic research, and governmental policy on science and technology during the last 20 years. In particular, we try understand wether the science and technology policy research and governmental policy meets social needs appropriately. In order to do this, we have collected text data from news articles, papers, and governmental documents. Based on these data, social network analysis and cluster analysis has been carried out. According to the results, we have found that science and technology policy researches tend to focus on fragmented technological innovation meeting urgent practical needs at the initial stage. However, recently, the main characteristics of science and technology policy research shows co-evolutionary patterns responding to society. Furthermore, time lag also has been observed in the process of interaction among the three bodies. Based on these results, we put forward some suggestions for upcoming researches in science and technology policy. Firstly, analysis levels are needed to be shifted from micro level to mezo or macro level. Secondly, more research efforts are required to be focused on policy process in science technology and its public management. Finally, we have to enhance the sensitiveness to social issues through studies on agenda setting in science and technology policy.

• Resources Recycling
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• v.20 no.3
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• pp.12-27
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• 2011

DOWA group has been working on metal recycling applying the smelting and refining process of KOSAKA Smelter. DOWA has developed it's metal recycling technologies through the treatment of black ore(complex sulfide ores) that contain many kinds of non-ferrous metals. In addition to these special technologies, DOWA has strengthened its hydrometallurgical process of precious metals and ability to deal with low-grade materials such as used electrical appliances or vehicles. On the other hand, JX Nippon Mining & Metals Corporation(JX-NMMC) carries out its metal recycling and industrial waste treatment businesses employing advanced separation, extraction and refining technologies developed through its extensive experience in the smelting of non-ferrous metals. JX-NMMC collects approximately 100,000t/y of copper and precious metal scraps from waste sources such as electronic parts, mobile phones, catalytic converters, print circuit boards and gold plated parts. These items are recycled through the smelting and refining operations of Saganoseki smelter and Hitachi Metal-recycling complex(HMC). In this like, metal recycling industries combined with environmental business service in Japan have been developed through excellent technologies for mineral processing and non-ferrous smelting. Also, both group, Dowa and JX-NMMC, were contributed to establish Japan's recycling-oriented society as the typical leading company of non-ferrous smelting. Now. it is an important issue to set up the collection system for e-waste.

• The KIPS Transactions:PartD
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• v.8D no.1
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• pp.71-80
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• 2001

Object-Oriented (OO) methodology to use the concept like encapsulation, inheritance, polymorphism, and message passing demands metrics that are different from structured methodology. There are many studies for OO software metrics such as program complexity or design metrics. But the metrics for the analysis class need to decrease the complexity in the analysis phase so that greatly reduce the effort and the cost of system development. In this paper, we propose new metrics to measure the complexity of analysis classes which draw out in the analysis phase based on Unified Process. By the collaboration complexity, is denoted by CC, we mean the maximum number of the collaborations can be achieved with each of the collaborator and detennine the potential complexity. And the interface complexity, is denoted by IC, shows the difficulty related to understand the interface of collaborators each other. We prove mathematically that the suggested metrics satisfy OO characteristics such as class size and inheritance. And we verify it theoretically for Weyuker' s nine properties. Moreover, we show the computation results for analysis classes of the system which automatically respond to questions of the it's user using the text mining technique. As we compared CC and IC to CBO and WMC, the complexity can be represented by CC and IC more than CBO and WMC. We expect to develop the cost-effective OO software by reviewing the complexity of analysis classes in the first stage of SDLC (Software Development Life Cycle).