• The Journal of the Acoustical Society of Korea
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• v.18 no.7
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• pp.101-111
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• 1999

This paper presents concrete pattern recognition method to identify the strength of concrete by evidence accumulation with multiple parameters based on artificial intelligence techniques. At first, variance(VAR), zero-crossing(ZCR), mean frequency(MEANF), and autoregressive model coefficient(ARC) and linear cepstrum coefficient(LCC) are extracted as feature parameters from ultrasonic signal of concrete. Pattern recognition is carried out through the evidence accumulation procedure using distance measured with reference parameters. A fuzzy mapping function is designed to transform the distances for the application of the evidence accumulation method. Results(92% successful pattern recognition rate) are presented to support the feasibility of the suggested approach for concrete pattern recognition.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1544-1553
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• 1999

Recently, ultrasonic testing techniques have been widely used in the evaluation of the quality of metal. In this experiment, six heat-treated temperature of specimen have been considered : 0, 1200, 1250, 1300, 1350 and 1387$^{\circ}C$. As heat-treated temperature increases, the grain size of stainless steel also increases and then, eventually make it destroy. In this paper, a pattern recognition method is proposed to identify the heat-treated temperature of metals by evidence accumulation based on artificial intelligence with multiple feature parameters; difference absolute mean value(DAMV), variance(VAR), mean frequency(MEANF), auto regressive model coefficient(ARC), linear cepstrum coefficient(LCC) and adaptive cepstrum vector(ACV). The grain signal pattern recognition is carried out through the evidence accumulation procedure using the distances measured with reference parameters. Especially ACV is superior to the other parameters. The results (96% successful pattern classification) are presented to support the feasibility of the suggested approach for ultrasonic grain signal pattern recognition.

• Journal of Korean Elementary Science Education
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• v.36 no.3
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• pp.286-301
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• 2017

Nowadays, the public as well as science educators pays much attention to the fourth industrial revolution and wonders what will happen to the societies in the future. Thus, this study aimed at predicting the education environment which will be brought from the fourth industrial revolution, and suggesting the solutions or tasks to be investigated in science education. Through the literature review, this study categorized the major changes of future society into a wild fluctuation of job market, the shift from possession-based economy to sharing economy, post-urbanized and distributed system, and the crisis of dehumanization. According to the four major changes, this study predicted the future environment that will occur to the educational system. First, the students should the competences necessary for the future and the school curriculum will be changed in terms of width and depth. Second, sharing economy may bring about the open platform similar to MOOC (Massive Open Online Course) or TED. Third, the manifestation of artificial intelligence in education will enable the individual and paced learning, and thanks to the change, the concept of distributed cognition will be more focused in education research. Fourth, the collaborative learning and character education should be more stressed to resist the dehumanization. This study suggests relevant tasks and issues that should be tackled for the successful change in primary and secondary schools.

• Journal of Intelligence and Information Systems
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• v.15 no.1
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• pp.65-85
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• 2009

The main purpose of this paper is to propose a sophistication framework for a global manufacturing network (GMN) driven by a mother company to autonomously propagate and coordinate transaction data that are exchanged among manufacturing partners. The framework is based on conceptual fundamentals of previous research that provide a step toward ultimate successful collaboration in the supply chain and employs mobile agents for the coordination and propagation of transaction data. Maintaining the integrity of transaction data linked to a huge information web is difficult. With the sophistication functionalities of this framework, it becomes easy to effectively control the overall GMN operations and to accomplish the intended goals. The current level of sophistication focuses on the transaction data propagation. The sophistication level may be expanded up to business intelligence in the future.

• Knowledge Management Research
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• v.19 no.1
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• pp.147-174
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• 2018

Recently, firms have been putting forth significant efforts to fulfill various demands and high expectations of customers. The role and importance of customer centers as a direct contact point for customer relationship management are more emphasized than previously. A customer center draws attention as a new alternative to secure corporate competitiveness as it contributes to sales increase, being in a position to satisfy customers' needs by ensuring customers' access to information. A customer center is an aggregation of various information and communication technologies. In particular, a voice recognition/analysis technology based on big data can elaborate customer services further, enhance customer satisfaction, and trigger constant interactions with customers. A customer center can be transformed to a hub of customer knowledge and the embodiment of business intelligence in the front line of business. This article is a case study on how the customer center of the K life insurance company regarding customer center operation collects and analyzes customer information and how it has established its voice recognition/analysis system based on big data to improve customer experience management. Factors affecting the successful introduction and implementation of voice recognition/analysis system to a firm, are examined.

• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.45-61
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• 2017

Business intelligence has been attracting much attention these days. Despite such popularity of BI systems, it is widely known that about a half of BI system projects have failed. To grasp why many BI projects end in failure and what factors would make BI projects less failure-prone, a number of BI studies were made to produce a variety of CSFs. However, there is a paucity of information on whether these CSFs are distinctive from those of typical information systems. By identifying how BI CSFs differ from CSFs of typical information systems, we would be able to explain why most BI projects are more likely to be failure. It is believed that a corrective measure about CSFs will lead to more success in future BI projects. In addition, though there have been a number of similar types of BI systems such as decision support systems and executive information systems in existence, there was no study to determine whether there is ever a discrimination between CSFs of BI systems and the similarly-titled systems. This study is to answer these questions using a literature review analysis. The findings of our study are expected to be helpful in a successful implementation of BI systems.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.9-21
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• 2020

The aim of this paper is to predict the Borsa Istanbul (BIST) 30 index movements to determine the most accurate buy and sell decisions using the methods of Artificial Neural Networks (ANN) and Genetic Algorithm (GA). We combined these two methods to obtain a hybrid intelligence method, which we apply. In the financial markets, over 100 technical indicators can be used. However, several of them are preferred by analysts. In this study, we employed nine of these technical indicators. They are moving average convergence divergence (MACD), relative strength index (RSI), commodity channel index (CCI), momentum, directional movement index (DMI), stochastic oscillator, on-balance volume (OBV), average directional movement index (ADX), and simple moving averages (3-day moving average, 5-day moving average, 10-day moving average, 14-day moving average, 20-day moving average, 22-day moving average, 50-day moving average, 100-day moving average, 200-day moving average). In this regard, we combined these two techniques and obtained a hybrid intelligence method. By applying this hybrid model to each of these indicators, we forecast the movements of the Borsa Istanbul (BIST) 30 index. The experimental result indicates that our best proposed hybrid model has a successful forecast rate of 75%, which is higher than the single ANN or GA forecasting models.

• Journal of Digital Convergence
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• v.9 no.4
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• pp.191-200
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• 2011

Smart work is a new paradigm of the way of working for knowledge workers. We propose a model which determines the levels of smart work. As the determinants, we identify freedom from both space and time. As an extension of the model by adding a new determinant of the mobilization of social intelligence, we show that a new smart work model, smart work 2.0, can be derived. For successful implementation of smart work 2.0, we also provide strategic suggestions, along with the implication of the study and future research direction.

• Knowledge Management Research
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• v.20 no.3
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• pp.17-38
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• 2019

Rrecently, companies have made great efforts to satisfy various needs and heightened expectations of customers, and the importance of customer center as customer contact department for customer relationship management is increasing. In the knowledge ecosystem, corporate customer centers are emerging as a new alternative to acquiring corporate competitiveness by increasing sales and increasing market share by improving marketing support activities and customer relationship management at customer contact points. As a result, the interest in the customer center has increased rapidly because it provides the opportunity to contact with the customer. In addition, in the era of the fourth industrial revolution, the customer center, which is a collection of information and communication technologies, has a big databased voice recognition technology to elaborate customer service, thereby enhancing customer satisfaction and contributing to marketing through continuous interaction with customers. Of course, we have the opportunity to transform into the frontline business intelligence front for customer knowledge. This study is a comparative case study on how the customer center of K Life Insurance that takes the lead in the customer center industry has successfully renewed and established their key information systems to improve customer services and reinforce marketing support competencies. Based on the above, this study will present factors affecting successful implementation and settlement of the customer service information systems of customer centers by independently analyzing two individual cases.

• World Technopolis Review
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• v.8 no.1
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• pp.21-42
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• 2019

The purpose of this research was to investigate the factors influencing global expansion/scalability of Kenyan Small and Medium Enterprises (SMEs). Factor analysis and multiple/multivariate regression analysis to determine the functional relationship between independent variables (factors) and the dependent variable was used. The independent variables were: innovation & technology, fitness/appropriateness of management, global marketing strategy; and support environment and the dependent variable, global expansion/scalability. Data was collected from a survey of randomly selected firms of 205, drawn from a population of 440 firms from Kenya Manufacturers Directory, with 175 firms responding. The key findings from the research in relation to Kenyan SMEs were that: there is a functional relationship between global market strategy and global expansion; there is a functional relationship between innovation and technology orientation and global expansion, there is no significant functional relationship between supportive environment of firms and their global expansion; and there is no significant functional relationship between fitness/appropriateness of management and global expansion/scalability. The implications for practice is that the ranking of the factors in order of priority supports focusing concern on the orientation of business strategy toward global market strategy, market research geared at obtaining foreign market intelligence, innovation and technology, product adaptation, service orientation, collaborative ventures, and long-range vision as key factors in making Kenyan firms successful in the international market. The implication for policy and practice is that there is need for collaboration between industry and government in pursuing policies for global expansion/scalability and among SMEs and large enterprises particularly in areas of rapid technological change.