• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.41-47
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• 2023

Financial instruments valuation continues to evolve due to various technological changes. Recently, there has been increased interest in valuation using machine learning and artificial intelligence, enabling the financial market to swiftly adapt to changes. This technological advancement caters to the demand for real-time data processing and facilitates accurate and effective valuation, considering the diverse nature of the financial market. Numerical analysis techniques serve as crucial decision-making tools among financial institutions and investors, acknowledged as essential for performance prediction and risk management in investments. This paper analyzes Korean patent trends of numerical analysis-based financial systems, considering the diverse shifts in the financial market and asset data to provide accurate predictions. This study could shed light on the advancement of financial technology and serves as a gauge for technological standards within the financial market.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.135-146
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• 2023

An effective educational program warrants the inclusion of an innovative construction which enhances the higher education efficacy in such a way that accelerates the achievement of desired results and reduces the risk of failures. Educational Decision Support System (EDSS) has currently been a hot topic in educational systems, facilitating the pupil result monitoring and evaluation to be performed during their development. Insufficient information systems encounter trouble and hurdles in making the sufficient advantage from EDSS owing to the deficit of accuracy, incorrect analysis study of the characteristic, and inadequate database. DMTs (Data Mining Techniques) provide helpful tools in finding the models or forms of data and are extremely useful in the decision-making process. Several researchers have participated in the research involving distributed data mining with multi-agent technology. The rapid growth of network technology and IT use has led to the widespread use of distributed databases. This article explains the available data mining technology and the distributed data mining system framework. Distributed Data Mining approach is utilized for this work so that a classifier capable of predicting the success of students in the economic domain can be constructed. This research also discusses the Intelligent Knowledge Base Distributed Data Mining framework to assess the performance of the students through a mid-term exam and final-term exam employing Multi-agent system-based educational mining techniques. Using single and ensemble-based classifiers, this study intends to investigate the factors that influence student performance in higher education and construct a classification model that can predict academic achievement. We also discussed the importance of multi-agent systems and comparative machine learning approaches in EDSS development.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.3
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• pp.563-572
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• 2024

Image processing and computer vision technologies are becoming increasingly important in a variety of application fields that require techniques and tools for sophisticated image analysis. In particular, image segmentation is a technology that plays an important role in image analysis. In this study, in order to identify recent research trends on image segmentation techniques, we used the Web of Science(WoS) database to analyze the R&D topography based on the network structure of the author's keyword co-occurrence matrix. As a result, from 2015 to 2023, as a result of the analysis of the R&D map of research articles on image segmentation, R&D in this field is largely focused on four areas of research and development: (1) researches on collecting and preprocessing image data to build higher-performance image segmentation models, (2) the researches on image segmentation using statistics-based models or machine learning algorithms, (3) the researches on image segmentation for medical image analysis, and (4) deep learning-based image segmentation-related R&D. The scientometrics-based analysis performed in this study can not only map the trajectory of R&D related to image segmentation, but can also serve as a marker for future exploration in this dynamic field.

• Journal of the Korean earth science society
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• v.45 no.2
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• pp.147-156
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• 2024

Science practice is a process of establishing new relationships with 'foreign things' such as learning objects or tools for observation and measurement. Since the practice of science in major subjects has been increasingly emphasized, we sought to understand the meaning co-created by students and numerous materials who have experienced astronomical observation as a Deleuzian experience of "becoming". We collected activity logs and photographic data written by 17 students participating in astronomical observation activities at "A" High School, and conducted in-depth interviews with the students. We assessed the collected data by reconstructing a situation analysis. The main research results include the students' existential-epistemological 'becoming' process: 1) discovering newness through repetition, 2) becoming an 'explanation machine' to convey the affect of astronomical observation activities, 3) breaking out of a stabilized territory, and crossing a threshold. Based on the results, we suggested the need for follow-up research on the practices and new experimental approaches of teachers in earth science education.

• Korean Journal of Cognitive Science
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• v.35 no.1
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• pp.49-95
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• 2024

Polygraph examination, statement validity analysis and P300-based concealed information test are major three examination tools, which are use to determine a person's truthfulness and credibility in criminal procedure. Although polygraph examination is most common in criminal procedure, but it has little admissibility of evidence due to the weakness of scientific basis. In 1990s to support the weakness of scientific basis about polygraph, Farwell and Donchin proposed the P300-based concealed information test technique. The P300-based concealed information test has two strong points. First, the P300-based concealed information test is easy to conduct with polygraph. Second, the P300-based concealed information test has plentiful scientific basis. Nevertheless, the utilization of P300-based concealed information test is infrequent, because of the quantity of probe stimulus. The probe stimulus contains closed information that is relevant to the crime or other investigated situation. In tradition P300-based concealed information test protocol, three or more probe stimuli are necessarily needed. But it is hard to acquire three or more probe stimuli, because most of the crime relevant information is opened in investigative situation. In addition, P300-based concealed information test uses oddball paradigm, and oddball paradigm makes imbalance between the number of probe and irrelevant stimulus. Thus, there is a possibility that the unbalanced number of probe and irrelevant stimulus caused systematic underestimation of P300 amplitude of irrelevant stimuli. To overcome the these two limitation of P300-based concealed information test, one-probe P300-based concealed information test protocol is explored with various machine learning algorithms. According to this study, parameters of the modified one-probe protocol are as follows. In the condition of female and male face stimuli, the duration of stimuli are encouraged 400ms, the repetition of stimuli are encouraged 60 times, the analysis method of P300 amplitude is encouraged peak to peak method, the cut-off of guilty condition is encouraged 90% and the cut-off of innocent condition is encouraged 30%. In the condition of two-syllable word stimulus, the duration of stimulus is encouraged 300ms, the repetition of stimulus is encouraged 60 times, the analysis method of P300 amplitude is encouraged peak to peak method, the cut-off of guilty condition is encouraged 90% and the cut-off of innocent condition is encouraged 30%. It was also conformed that the logistic regression (LR), linear discriminant analysis (LDA), K Neighbors (KNN) algorithms were probable methods for analysis of P300 amplitude. The one-probe P300-based concealed information test with machine learning protocol is helpful to increase utilization of P300-based concealed information test, and supports to determine a person's truthfulness and credibility with the polygraph examination in criminal procedure.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Journal of Service Research and Studies
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• v.6 no.2
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• pp.23-35
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• 2016

Firms should seek greater profits and corporate growth through new businesses. New businesses contribute realizing creative economy that creates good jobs, and expanding the company by securing new markets and creating new profits and growth. However, new business is risky management decision-making to have a high failure rate because it involves the adaptation of new business environment and the burden of new investments, including the uncertainty of success in business. Therefore, innovation strategies play important roles for the new business entry, using product innovation, process innovation, business model innovation, disruptive innovation, and strategic innovation, etc. and company will get huge economic results by pushing them into successful business. It is essential that innovation strategy and IT development strategy along with business strategy of a firm are linked, and their strategic alignment is considered to be a critical success factor for new business success. Hyundai Heavy Industries(HHI) pursued marine engine business for the development of precision machinery industry and shipbuilding industry of Korea, and the company recognized the importance of new business strategy, innovation strategy, and IT strategy inter-linked, and pushed strategic alignment boldly. As a result, HHI won the competition in European and Japanese engine manufacturers and climbed into the world's largest engine manufacturer. This study suggests investigating and analyzing a case that HHI succeeded in marine engine business expansion using strategic innovation strategy as a way of the introduction of CNC machine tools and the development of HEMAPT system.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.1
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• pp.9-18
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• 2015

In this paper, we verify the optimal topology design for heat conduction problems in steady stated which is obtained numerically using the adjoint design sensitivity analysis(DSA) method. In adjoint variable method(AVM), the already factorized system matrix is utilized to obtain the adjoint solution so that its computation cost is trivial for the sensitivity. For the topology optimization, the design variables are parameterized into normalized bulk material densities. The objective function and constraint are the thermal compliance of the structure and the allowable volume, respectively. For the experimental validation of the optimal topology design, we compare the results with those that have identical volume but designed intuitively using a thermal imaging camera. To manufacture the optimal design, we apply a simple numerical method to convert it into point cloud data and perform CAD modeling using commercial reverse engineering software. Based on the CAD model, we manufacture the optimal topology design by CNC.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.9
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• pp.61-67
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• 2017

Research into the selection of suitable materials and the development of fast processing methods for press die manufacturing is absolutely necessary to reduce the production time and cost. In particular, knowledge of its heat properties must be considered whendeveloping a long press die. Generally, as the main component materials of press dies, Cr, W low alloy tool steel, high carbon-high chrome steel, high speed steel, etc., are used as thetooling steel for the cold die. Machine tools and wire-cut electric discharge machining are mainly used for processing the press die parts. There are many differences in the machining time and life cycle of die parts depending on the machining process. The parts produced by milling and grinding have a high manufacturing time and cost with a long life cycle, while thosemade by milling and wire-cut discharge machining have areduced manufacturing time and cost,whereastheir die life cycle is reduced. Therefore, in this study, we will discuss amethod of improving the life cycle of the die parts by using heat treatment as a processing method that reduces the manufacturing time and cost. SEM, EDS analysis and the surface roughness analysis of the surface and center of the workpiece are used for analyzing the specimens produced by three machining methods, viz. milling - grinding, milling - wire cut discharge, and milling - wire cut discharge - heat treatment. A method of making die parts having the same life cycle as those produced by milling - grinding is developed with the milling - wire cut discharge - high temperature tempering method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.5
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• pp.635-643
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• 2018

This paper propose a method to identify the motor parameters and improve input voltage error which affect the low speed position error of the back-emf(back electromotive force) based sensorless algorithm and to secure the operation reliability and stability even in the case where the load fluctuation is severe and the start and low speed operation frequently occurs. In the model-based observer used in this paper, stator resistance, inductance, and input voltage are particularly influential factors on low speed performance. Stator resistance can cause resistance value fluctuation which may occur in mass production process, and fluctuation of resistance value due to heat generated during operation. The inductance is influenced by the fluctuation due to the manufacturing dispersion and at a low speed where the change of the current is severe. In order to find stator resistance and inductance which have different initial values and fluctuate during operation and have a large influence on sensorless performance at low speed, they are commonly measured through 2-point calculation method by 2-step align current injection. The effect of voltage error is minimized by offsetting the voltage error. In addition, when the command voltage is used, it is difficult to estimate the back-emf due to the relatively large distortion voltage due to the dead time and the voltage drop of the power device. In this paper, we propose a simple circuit and method to detect the voltage by measuring the PWM(Pulse Width Modulation) pulse width and compensate the voltage drop of the power device with the table, thereby minimizing the position error due to the exact estimation of the back-emf at low speed. The suitability of the proposed algorithm is verified through experiment.