• Progress in Superconductivity and Cryogenics
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• v.6 no.1
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• pp.18-21
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• 2004

This paper presents the optimization for shape design of a field coil used High Temperature Superconducting Motor (HTSM). In materials of HTSM, critical current Ic is more sensitive to magnetic fields directed along the axis or the unit cell ($B_{\bot}$). Thus, in the shape design of the HTS magnet. the maximum $B_{\bot}$ should be reduced to limit Ic. In order to reduce the maximum $B_{\bot}$, the shape optimization of the magnet, which is used for the field coil of HTSM, is necessary. It can be accomplished by using Response Surface Methodology (RSM). Finally, the result of RSM is verified by comparison with these experimental results.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.265-288
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• 2023

Process mining has generally analyzed the default logs of Information Systems such as SAP ERP, but as the use of automation software called RPA expands, the logs by RPA bots can be utilized. In this study, the actual status of RPA automation in the field was identified by applying RPA bots to the work of three domestic manufacturing companies (cosmetic field) and analyzing them after leaving logs. Using Uipath and Python, we implemented RPA bots and wrote logs. We used Disco, a software dedicated to process mining to analyze the bot logs. As a result of log analysis in two aspects of bot utilization and performance through process mining, improvement requirements were found. In particular, we found that there was a point of improvement in all cases in that the utilization of the bot and errors or exceptions were found in many cases of process. Our approach is very scientific and empirical in that it analyzes the automation status and performance of bots using data rather than existing qualitative methods such as surveys or interviews. Furthermore, our study will be a meaningful basic step for bot behavior optimization, and can be seen as the foundation for ultimately performing process management.

• Journal of the Korean Mathematical Society
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• v.47 no.1
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• pp.17-28
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• 2010

In this paper we deal with linear chance-constrained optimization problems, a class of problems which naturally arise in practical applications in finance, engineering, transportation and scheduling, where decisions are made in presence of uncertainty. After giving the deterministic equivalent formulation of a linear chance-constrained optimization problem we construct a conjugate dual problem to it. Then we provide for this primal-dual pair weak sufficient conditions which ensure strong duality. In this way we generalize some results recently given in the literature. We also apply the general duality scheme to a portfolio optimization problem, a fact that allows us to derive necessary and sufficient optimality conditions for it.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5219-5226
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• 2011

A specialized anthropomorphic robot manipulator which can be attached to the housemaid robot McBot II, is developed In this paper. This built-in type manipulator consists of both arms with 3 DOF (Degree of Freedom) each and both hands with 3 DOF each. The robotic arm is optimally designed to satisfy both the minimum mechanical size and the maximum workspace. Minimum mass and length are required for the built-in cooperated-arms system. But that makes the workspace so small. This paper proposes optimal design method to overcome the problem by using neck joint to move the arms horizontally forward/backward and waist joint to move them vertically up/down. The robotic hand, which has two fingers and a thumb, is also optimally designed in task-based concept. Finally, the good performance of the developed manipulator is confirmed through live test of tasks.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.3
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• pp.125-131
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• 2018

In this paper, we propose a homogeneous multisensor-based navigation algorithm for a mobile robot, which is intelligently searching the goal location in unknown dynamic environments with moving obstacles using multi-ultrasonic sensor. Instead of using "sensor fusion" method which generates the trajectory of a robot based upon the environment model and sensory data, "command fusion" method by fuzzy inference is used to govern the robot motions. The major factors for robot navigation are represented as a cost function. Using the data of the robot states and the environment, the weight value of each factor using fuzzy inference is determined for an optimal trajectory in dynamic environments. For the evaluation of the proposed algorithm, we performed simulations in PC as well as real experiments with mobile robot, AmigoBot. The results show that the proposed algorithm is apt to identify obstacles in unknown environments to guide the robot to the goal location safely.

• The Journal of Korean Society for Radiation Therapy
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• v.34
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• pp.51-60
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• 2022

Objectives: The purpose is to evaluate dosimetric performance and delivery efficiency of VMAT with Halcyon LINAC for double target spine SBRT Materials and Methods: 12 patients with spine oligometastases were retrospectively studied. Single-isocenter spine SBRT plans was established using Halcyon® with Dual Layer MLC and Truebeam® with High Definition MLC. All patients' plans were created in Eclipse TPS through the identical conditions and optimization. C.I, H.I, G.I (Gradient Index), maximal and volumetric doses to spinal cord and low dose area were evaluated for comparison of both plans. Also, total MU and BOT(Beam On Time) were evaluated. Results: Halcyon plans was no Statistical differences in C.I and H.I. However, the average of G.I was 4.64 for Halcyon, which decreased to 5.5% compared to Truebeam (P<0.001). Halcyon plans demonstrated statistically significant reduced G.I. The average of 50% and 25% isodose volume was 487.56 cc (-3.82%, P<0.001), 1859.45 cc (-4.75%, P<0.001) in Halcyon, respectively. Significantly reduced low dose spill were observed in Halcyon plans. In the evaluation of the spinal cord, the average of Dmean and V10 of Halcyon plans in the sample group with an overlap volume of less than 1 cc was 6.802 Gy (-3.504%, P=0.067), 5.766±1.683 cc (-8.199%, P=0.002), respectively. Halcyon plans demonstrated statistically significant reduced D_mean and V10. For delivery efficiency, MU and BOT(maximum dose rate for each machine), on average, increased in Halcyon plans. However, the average of BOT(800MU/min for each machine) was 648.33 sec for Halcyon (-1.74%, P<0.001). Conclusion: Halcyon plan for double-target spine SBRT demonstrated advantages in the low dose area with a steep dose gradient, while having dosimetrically equivalent target dose distribution and spinal cord protective effect. As a result, Halcyon LINAC produced a dosimetrically improved plan for double-target spine SBRT.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.71-86
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• 2022

The water quality index (WQI) has been widely used to evaluate marine water quality. The WQI in Korea is categorized into five classes by marine environmental standards. But, the WQI calculation on huge datasets is a very complex and time-consuming process. In this regard, the current study proposed machine learning (ML) based models to predict WQI class by using water quality datasets. Sihwa Lake, one of specially-managed coastal zone, was selected as a modeling site. In this study, adaptive boosting (AdaBoost) and tree-based pipeline optimization (TPOT) algorithms were used to train models and each model performance was evaluated by metrics (accuracy, precision, F1, and Log loss) on classification. Before training, the feature importance and sensitivity analysis were conducted to find out the best input combination for each algorithm. The results proved that the bottom dissolved oxygen (DO_Bot) was the most important variable affecting model performance. Conversely, surface dissolved inorganic nitrogen (DIN_Sur) and dissolved inorganic phosphorus (DIP_Sur) had weaker effects on the prediction of WQI class. In addition, the performance varied over features including stations, seasons, and WQI classes by comparing spatio-temporal and class sensitivities of each best model. In conclusion, the modeling results showed that the TPOT algorithm has better performance rather than the AdaBoost algorithm without considering feature selection. Moreover, the WQI class for unknown water quality datasets could be surely predicted using the TPOT model trained with satisfactory training datasets.