• Journal of Information Technology Applications and Management
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• v.23 no.4
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• pp.25-39
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• 2016

Asset prices decline sharply and stock markets collapse when financial crisis happens. Recently we have encountered more frequent financial crises than ever. 1998 currency crisis and 2008 global financial crisis triggered academic researches on early warning systems that aim to detect the symptom of financial crisis in advance. This study proposes a risk recovery index for detection of good opportunities from financial market instability. We use SVM classifier algorithms to separate recovery period from unstable financial market data. Input variables are KOSPI index and V-KOSPI200 index. Our SVM algorithms show highly accurate forecasting results on testing data as well as training data. Risk recovery index is derived from our SVM-trained outputs. We develop a trading system that utilizes the suggested risk recovery index. The trading result records very high profit, that is, its annual return runs to 121%.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.10 no.5
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• pp.26-30
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• 2011

Currently, many researches are carried out about tilting index table, which is one of the main component of 5-axis machine tool. The performance of the tilting index table is associated the rotational accuracy which is very important factor for high precision machining because it have an effect on machining error. In this paper, a tilting index table is developed, and the rotational accuracy of the tilting index table using a laser measurement equipment is measured. In addition, a correction value is obtained from the measured value through compensation, and the correction value is used to improve the accuracy of the table. Comparative analysis is carried out for the accuracy of the table before and after compensation. This paper can be used by a reference for performance and reliability advancement of tilting index table.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.36 no.2
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• pp.37-49
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• 2024

To date, numerous empirical formulas have been proposed through hydraulic model experiments to predict the wave breaker index, including wave height and depth of wave breaking, due to the inherent complexity of generation mechanisms. Unfortunately, research on the characteristics of wave breaking and the prediction of the wave breaker index for gravel beaches has been limited. This study aims to forecast the wave breaker index for gravel beaches using representative linear-based machine learning techniques known for their high predictive performance in regression or classification problems across various research fields. Initially, the applicability of existing empirical formulas for wave breaker indices to gravel seabeds was assessed. Various linear-based machine learning algorithms were then employed to build prediction models, aiming to overcome the limitations of existing empirical formulas in predicting wave breaker indices for gravel seabeds. Among the developed machine learning models, a new calculation formula for easily computable wave breaker indices based on the model was proposed, demonstrating high predictive performance for wave height and depth of wave breaking on gravel beaches. The study validated the predictive capabilities of the proposed wave breaker indices through hydraulic model experiments and compared them with existing empirical formulas. Despite its simplicity as a polynomial, the newly proposed empirical formula for wave breaking indices in this study exhibited exceptional predictive performance for gravel beaches.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.2
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• pp.19-33
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• 2024

CCTV for crime prevention is expanding; however, due to the absence of guidelines for determining installation locations, CCTV is being installed in locations unrelated to areas with frequent crime occurrences. In this study, we developed a CCTV Priority Installation Index and applied it in a case study area. The index consists of crime vulnerability and surveillance vulnerability indexes, calculated using machine learning algorithms to predict crime incident counts per grid and the proportion of unmonitored area per grid. We tested the index in a pilot area and found that utilizing the Viewshed function in CCTV visibility analysis resolved the problem of overestimating surveillance area. Furthermore, applying the index to determine CCTV installation locations effectively improved surveillance coverage. Therefore, the CCTV Priority Installation Index can be utilized as an effective decision-making tool for establishing smart and safe cities.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.4
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• pp.570-575
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• 2010

This study is regarding development of an index table for antomation of welding process in impeller fabrication. A PLC, which is widely used for automation in industry, was also adopted as a controller for the index table because of it effectiveness, easy maintenance and repair. The index table consists of centering jig, blade jig, workbench, driving system and a controller. A touch screen was also prepaired as a man-machine interface to provide convenience for workers. Water jacket was installed inside the workbench to reduce thermal stress come from the welding. Temperature of the water jacket was kept constant to cool an impeller main plate effectively. The index table developed in this study convinces that it reduces the total welding time by 50% compared with the conventional process without the table.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.10
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• pp.138-148
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• 1995

While Model-based Monitoring systems based on state observer theory have shown much promise in the laboratory, they have not been widely accepted by industry because, inpractice, these systems often have poor performance with respect to accuracy, band-width, reliability(false alarms), and robustness. In this paper, the linitations of the deterministic discrete-time state observer are investigated quantitatively from the machine monitoring viewpoint. The limitations in the transient and steady-state observer performance are quantified as estimation error bounds from which performance indices are selected. Each index represents the conditioning of the corresponding performance. By utilizing matrix norm theory, an unified main index is determined, that dominates all the indices. This index could from the basis for an observer design methodology that should improve the performance of model-based monitoring systems.

• Journal of Korean Society for Quality Management
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• v.51 no.3
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• pp.363-379
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• 2023

Purpose: The main objective of this research is to construct an AI-based Composite Supplementary Index (ACSI) model to achieve accurate predictions of the Composite Index of Business Indicators. By incorporating various economic indicators as independent variables, the ACSI model enables the prediction and analysis of both the leading index (CLI) and coincident index (CCI). Methods: This study proposes an AI-based Composite Supplementary Index (ACSI) model that leverages diverse economic indicators as independent variables to forecast leading and coincident economic indicators. To evaluate the model's performance, advanced machine learning techniques including MLP, RNN, LSTM, and GRU were employed. Furthermore, the study explores the potential of employing deep learning models to train the weights associated with the independent variables that constitute the composite supplementary index. Results: The experimental results demonstrate the superior accuracy of the proposed composite supple- mentary index model in predicting leading and coincident economic indicators. Consequently, this model proves to be highly effective in forecasting economic cycles. Conclusion: In conclusion, the developed AI-based Composite Supplementary Index (ACSI) model successfully predicts the Composite Index of Business Indicators. Apart from its utility in management, economics, and investment domains, this model serves as a valuable indicator supporting policy-making and decision-making processes related to the economy.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.6
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• pp.739-745
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• 2010

The roller gear cam can control the rotational follower periodically by attaching several roller on the circumstance of follower shaft and it is widely used in non-backlash and precise actuating mechanism such as index table or ATC of machine tools. For machining the roller gear cam, 5 axis CNC machine tool is used and the geometric principle of CAM mechanism must be adopted to generate the NC-code and to develop the special CAD/CAM software because there is not commercial CAM system to machine the roller gear cam. The maker of the specially developed software in domestic user is generally from Japan or Taiwan. However these softwares do not reflect the post processing technique for finish machining in the module. Also, there is some limitation for further new application of itself and it needs higher costs for further application. In this study, the CAD/CAM software to overcome these problem was developed. And its reliability was verified by applying it in 5-axis CNC machining. Finally, the experimental result conducted in the 5-axis machining show good consistency in the movement of follower along the flute and in its Size.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.7
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• pp.94-100
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• 2010

This paper describes the design and evaluation procedure of an ultra-precision rotary table for freeform generating machined tools. Design of the thrust and journal hydrostatic bearings and experimental evaluation of the table were performed. To get the compact size and less lost motion direct drive servomotor with ultra precision encoder. From the considered design, following performance were confirmed by experiment. The total stiffness of the prototype rotary table was 483.6 $N/{\mu}m$ and 97.6 $N/{\mu}m$ for axial and radial direction, respectively. Rotational accuracy of the table was investigated by capacitive sensor and reversal measurement technique, and 0.10 ${\mu}m$ radial direction and 0.05 ${\mu}m$ axial direction of the rotational accuracy were confirmed. The micro resolution of the table was also investigated with displacement of capacitive sensor, and $0.5/10000^{\circ}$ of micro resolution was confirmed. Index accuracy of the table was evaluated by the autocollimator and polygon mirror, and the $\pm0.39$ arcsec accuracy and $\pm0.16$ arcsec repeatability of the table were confirmed. Those are under the general requirements of ultra precision rotary tables for freeform generating machined tools.

• Advances in Computational Design
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• v.7 no.3
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• pp.253-279
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• 2022

This study investigated and predicted the Marshall stability of glass-fiber asphalt mix, carbon-fiber asphalt mix and glass-carbon-fiber asphalt (hybrid) mix by using machine learning techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest(RF), The data was obtained from the experiments and the research articles. Assessment of results indicated that performance of the Artificial Neural Network (ANN) based model outperformed applied models in training and testing datasets with values of indices as; coefficient of correlation (CC) 0.8492 and 0.8234, mean absolute error (MAE) 2.0999 and 2.5408, root mean squared error (RMSE) 2.8541 and 3.3165, relative absolute error (RAE) 48.16% and 54.05%, relative squared error (RRSE) 53.14% and 57.39%, Willmott's index (WI) 0.7490 and 0.7011, Scattering index (SI) 0.4134 and 0.3702 and BIAS 0.3020 and 0.4300 for both training and testing stages respectively. The Taylor diagram also confirms that the ANN-based model outperforms the other models. Results of sensitivity analysis show that Carbon fiber has a major influence in predicting the Marshall stability. However, the carbon fiber (CF) followed by glass-carbon fiber (50GF:50CF) and the optimal combination CF + (50GF:50CF) are found to be most sensitive in predicting the Marshall stability of fibrous asphalt concrete.