• Journal of the Korean Society for Precision Engineering
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• v.15 no.12
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• pp.48-54
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• 1998

In sewing, fabrics is fed by an elliptic motion of the feed dog. The feeding control mechanism controls an elliptic motion of the feed dog, finally, controls stitch spacings and feeding directions of fabrics. This study discusses the feeding control mechanism of an industrial lock stitch sewing machine, which is a good example to study a machine kinematics. This study makes mathematical expressions of machine's motion in the feeding control mechanism. Thus, the motions of this mechanism are characterized, which will be used for kinematic analysis of the feed dog later. Also, the above mathematical expressions may be a basis for the new design of the feeding control mechanism and may be applied to development of the similar feeding control mechanism of other type sewing machine.

• East Asian mathematical journal
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• v.37 no.3
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• pp.333-354
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• 2021

A support vector machine (SVM) is a state-of-the-art machine learning model rooted in structural risk minimization. SVM is underestimated with regards to its application to real world problems because of the difficulties associated with its use. We aim at showing that the performance of SVM highly depends on which kernel function to use. To achieve these, after providing a summary of support vector machines and kernel function, we constructed experiments with various benchmark datasets to compare the performance of various kernel functions. For evaluating the performance of SVM, the F1-score and its Standard Deviation with 10-cross validation was used. Furthermore, we used taylor diagrams to reveal the difference between kernels. Finally, we provided Python codes for all our experiments to enable re-implementation of the experiments.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.121-124
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• 1996

In general, cellular manufacturing system can be constructed by the following two steps. The first step forms machine cells and part families, and the second step determines cell layout based on the result of first step. Cell layout has to be considered when cell is formed becauese the result of cell formation affects it. This paper presents a cell formation algorithm and proposes an integrated mathematical model for cell formation and cell layout. The cell formation algorithm minimizes the number of exceptional element in cellular manufacturing system. New concept for similarity and incapability is introduced, based on machine-operation incidence matrix and part-operation incidence matrix. One is similarity between the machines, the other is similarity between preliminary machine cells and machines. The incapability identifies relations between machine cells and parts. In this procedure, only parts without an exceptional element are assigned to machine cell. Bottleneck parts are considered with cell layout design in an integrated mathematical model. The integrated mathematical model determines cell layout and assigns bottleneck parts to minimize the number of exceptional element and intercell moving distance, based on linearixed 0-1 integer programming. The proposed algorithm is illustrated by using numerical examples.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.49-56
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• 2023

Computer architecture serves as a link between application requirements and underlying technology capabilities such as technical, mathematical, medical, and business applications' computational and storage demands are constantly increasing. Machine learning these days grown and used in many fields and it performed better than traditional computing in applications that need to be implemented by using mathematical algorithms. A mathematical algorithm requires more extensive and quicker calculations, higher computer architecture specification, and takes longer execution time. Therefore, there is a need to improve the use of computer hardware such as CPU, memory, etc. optimization has a main role to reduce the execution time and improve the utilization of computer recourses. And for the importance of execution time in implementing machine learning supervised module linear regression, in this paper we focus on optimizing machine learning algorithms, for this purpose we write a (Diabetes prediction program) and applying on it a Practical Swarm Optimization (PSO) to reduce the execution time and improve the utilization of computer resources. Finally, a massive improvement in execution time were observed.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.46-52
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• 2023

With billions of IoT (Internet of Things) devices populating various emerging applications across the world, detecting anomalies on these devices has become incredibly important. Advanced Intrusion Detection Systems (IDS) are trained to detect abnormal network traffic, and Machine Learning (ML) algorithms are used to create detection models. In this paper, the NSL-KDD dataset was adopted to comparatively study the performance and efficiency of IoT anomaly detection models. The dataset was developed for various research purposes and is especially useful for anomaly detection. This data was used with typical machine learning algorithms including eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Deep Convolutional Neural Networks (DCNN) to identify and classify any anomalies present within the IoT applications. Our research results show that the XGBoost algorithm outperformed both the SVM and DCNN algorithms achieving the highest accuracy. In our research, each algorithm was assessed based on accuracy, precision, recall, and F1 score. Furthermore, we obtained interesting results on the execution time taken for each algorithm when running the anomaly detection. Precisely, the XGBoost algorithm was 425.53% faster when compared to the SVM algorithm and 2,075.49% faster than the DCNN algorithm. According to our experimental testing, XGBoost is the most accurate and efficient method.

• Korean Management Science Review
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• v.12 no.1
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• pp.1-17
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• 1995

The success of cell manufacturing applications in FMS rests on the effective cell formation to maintain the independent relations both between machine cells and between part families. This paper presents an integrated method for concurrent formation of cells and families with no E.E (Exceptional Element) in FMS with alternative routings. To determine the maximum number of cell and family with no E.E, mathematical conditions and properties are derived. New concept of nonsimilarity is introduced for each machine and part based on machine-operation incidence matrix and part-operation incidence matrix. To concurrently form the cells and families, integer programming based mathematical models are developed. For the predetermined number of cell or family, model I is used to identify whether E.E exists or not. Model II forms cells and families considering only nonsimilarity. But model III can consider nonsimilarity and processing times. The proposed method is tested and proved by using numerical examples.

• Bulletin of the Korean Mathematical Society
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• v.15 no.1
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• pp.35-41
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• 1978

• Communications of the Korean Mathematical Society
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• v.6 no.2
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• pp.203-208
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• 1991

• The Pure and Applied Mathematics
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• v.24 no.4
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• pp.211-226
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• 2017

In this paper, machine learning models employed in various fields are discussed and applied to KOSPI200 stock index return forecasting. The results of hyperparameter analysis of the machine learning models are also reported and practical methods for each model are presented. As a result of the analysis, Support Vector Machine and Artificial Neural Network showed a better performance than k-Nearest Neighbor and Random Forest.

• Journal of Information Technology Services
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• v.18 no.1
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• pp.173-186
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• 2019

Recently, as the amount of fine dust has risen rapidly, our interest is increasing day by day. It is virtually impossible to remove fine dust. However, it is best to predict the concentration of fine dust and minimize exposure to it. In this study, we developed a mathematical model that can predict the concentration of fine dust using various information related to the weather and air quality, which is provided in real time in 'Air Korea (http://www.airkorea.or.kr/)' and 'Weather Data Open Portal (https://data.kma.go.kr/).' In the mathematical model, various domestic seasonal variables and atmospheric state variables are extracted by multiple regression analysis. The parameters that have significant influence on the fine dust concentration are extracted, and using ANN (Artificial Neural Network) and SVM (Support Vector Machine), which are machine learning techniques, we proposed a prediction model. The proposed model can verify its effectiveness by using past dust and weather big data.