• Korean Chemical Engineering Research
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• v.58 no.4
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• pp.565-572
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• 2020

This study developed a software platform using machine learning of artificial intelligence to optimize the distillation column system. The distillation column is representative and core process in the petrochemical industry. Process stabilization is difficult due to various operating conditions and continuous process characteristics, and differences in process efficiency occur depending on operator skill. The process control based on the theoretical simulation was used to overcome this problem, but it has a limitation which it can't apply to complex processes and real-time systems. This study aims to develop an empirical simulation model based on machine learning and to suggest an optimal process operation method. The development of empirical simulations involves collecting big data from the actual process, feature extraction through data mining, and representative algorithm for the chemical process. Finally, the platform for the distillation column was developed with verification through a developed model and field tests. Through the developed platform, it is possible to predict the operating parameters and provided optimal operating conditions to achieve efficient process control. This study is the basic study applying the artificial intelligence machine learning technique for the chemical process. After application on a wide variety of processes and it can be utilized to the cornerstone of the smart factory of the industry 4.0.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.5
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• pp.799-810
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• 2022

Welding work is a production work method widely used throughout the industry, and various types of welding technologies exist. In addition, many methods are being studied to automate these welding operations using robots, but in the ship manufacturing field, welding such as painting, cutting, and grinding is also the most common operation, but the manual operation ratio is higher than in other industries. Such a high manual labor ratio in the field of ship manufacturing not only causes quality problems and production delays according to the skill of workers, but also causes problems in the supply and demand of manpower. Therefore, this paper analyzed the reason why the automation rate is low in welding work at ship manufacturing sites compared to other industries, and analyzed the production process and field environment for small and medium-sized ship manufacturing companies that repeatedly manufactured with a small quantity production method. Based on the analysis results, it is intended to propose a robot system that can easily move between workplaces and secure uniform welding quality and productivity by collaborating simple welding tasks with humans. Finally, the simulation environment is constructed and analyzed to secure the suitability of robot system application to current production site environment, work process, and productivity, rather than to develop and apply the proposed robot system. Through such pre-simulation and robot system suitability analysis, it is expected to reduce trial and error that may occur in actual field installation and operation, and to improve the possibility of robot application and positive perception of robot system at ship manufacturing sites.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.124-127
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• 2005

As computer capability and test skill become more and more advanced, finite element method and modal test are being widely applied in engineering design. In order to correlate and reconcile the inevitable discrepancies between the analytical and experimental models, many techniques have been developed. Among these methods, multiple-system methods are known as the effective tools in that they can supply the rich modal data available which are experimentally obtained. These abundant modal data can help structural system parameters estimated well. Multiple-system methods can be classified into the structural modification methods and feedback controller methods. The structural modification methods need the physical attachment of structures and their concept may limit the application of them. To overcome this drawback, the feedback controller methods are addressed which enable us to get more modal data without the structural change. Mode decoupling controller(MDC), one of them, is to use acceleration out)ut feedback to perturb an open-loop system. The output feedback controller generally cannot guarantee the stability of a closed-loop system. However, MDC can solve this problem under the certain constraints. So far, MDC utilizes accelerations as the sensor signals. In this research, strain sensors are going to be picked up to apply to the MDC. Strain output is recently used for structural system identification due to the drastically improved and miniaturized strain sensor. In this paper, we show that the MDC using strain output has differences compared with acceleration output in estimating the structural system parameters. The associated simulation is performed to demonstrate the above mentioned characteristics.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.4
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• pp.345-350
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• 2002

In recent years, as changing the habit of eating, the pathology in the colon grows up annually. The colonoscopy is generalized, but if requires much time to acquire a dexterous skill to perform an operation and the procedure is painful to the patient. biomedical and robotic researchers are developing a locomotive colonoscope that can travel safe1y in colon. In this paper, we propose a new actuator and concept of semi-autonomous colonoscope. The micro robot comprises camera and LED for diagnosis, steer- ing system to pass through the loop, pneumatic actuator and bow-shaped flexible supporters to control a contact force and to pass over haustral folds in colon. For locomotion of semi-autonomous colonoscope, we suggest an actuator that is based on impact force between a cylinder and a piston. In order to validate the concept and the performance of the actuator, we carried out the simulation of moving characteristics and the preliminary experiments in rigid pipes and on the colon of pig.

• Atmosphere
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• v.22 no.1
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• pp.117-128
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• 2012

Does snow depth initialization have a quantitative impact on sub-seasonal to seasonal prediction skill? To answer this question, a snow depth initialization technique for seasonal forecast system has been implemented and the impact of the initialization on the seasonal forecast of surface air temperature during the wintertime is examined. Since the snow depth observation can not be directly used in the model simulation due to the large systematic bias and much smaller model variability, an anomaly rescaling method to the snow depth initialization is applied. Snow depth in the model is initialized by adding a rescaled snow depth observation anomaly to the model snow depth climatology. A suite of seasonal forecast is performed for each year in recent 12 years (1999-2010) with and without the snow depth initialization to evaluate the performance of the developed technique. The results show that the seasonal forecast of surface air temperature over East Asian region sensitively depends on the initial snow depth anomaly over the region. However, the sensitivity shows large differences for different timing of the initialization and forecast lead time. Especially, the snow depth anomaly initialized in the late winter (Mar. 1) is the most effective in modulating the surface air temperature anomaly after one month. The real predictability gained by the snow depth initialization is also examined from the comparison with observation. The gain of the real predictability is generally small except for the forecasting experiment in the early winter (Nov. 1), which shows some skillful forecasts. Implications of these results and future directions for further development are discussed.

• Journal of Navigation and Port Research
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• v.40 no.4
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• pp.189-196
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• 2016

We propose a virtual sailor training platform which supports emergency drills for ship's fire in virtual environment. Proposed platform not only enhances training efficiency by providing immersiveness, but also enables a consolidated virtual training due to the network-based multiplayer capabilities. Based on the offline fire simulation results using FDS and CFAST the platform visualizes a realistic fire spread in real-time. The training platform on the basis of the fire training material of the maritime safety education institute induces equipment proficiency and environment adaptation throughout immersive virtual environment in addition to procedure proficiency as well. In the implementation we showed that the equipment and environment controls and telepresence improve the training proficiency and enable collaborative virtual training that participates multiple trainees and induces cooperation for a common goal. Implementation of the platform demonstrated the skill mastery capability of the drill such as efficient fire apparatus controls and passenger controls.

• Journal of Korean Academy of Nursing Administration
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• v.14 no.3
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• pp.344-351
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• 2008

Purpose: The aim of this study was to testify whether clinical performance examination (CPX) was useful to evaluate comprehensive performance for nursing students just prior to graduation. Method: A cross-sectional descriptive study was designed to examine the usefulness. A total of 61 nursing students whose requirement credits were completed for graduation from a C University in G-city, at December, 5, 2007. Data were analyzed by Pearson's Correlation Coefficient and Spearman's rank Correlation Coefficient. Results: This study showed that both of the finals scores with paper and pens and the clinical practicum scores were not correlated with the CPX scores (r=-.031, p=.811; r=.028, p=.831). Consistency of scores between faculties and standardized patients was moderate (r=.752, p=.000). Conclusion: CPX was considered as a different and innovative evaluation from previous testing systems to test the various aspects of performance including knowledge, skill and attitude. Therefore, CPX under high raters' consistency was useful to test nursing students's final performance. Further study would be needed to develop the standard of CPX system.

• The Journal of the Korea Contents Association
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• v.12 no.5
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• pp.293-302
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• 2012

The purpose of this study was to identify the effect of human patient simulator(HPS)-based education on self-directed learning(SDL) and collective efficacy(CE) for nursing students. This study design was one group pre-posttest. The subjects were 2nd grade 92 students enrolling in the integrated practice. They have no previous experience of HPS-based education. HPS-based education included team based learning, skill training, taking a high-fidelity simulation with Medical Education Technologies, Inc (METI) simulator and being debriefed during 12 weeks. The pretest and posttest were conducted to understand the improvement in SDL and CE. After the subjects had participated in the HPS-based education, they showed statistically significant higher SDL(t=4.24, p=0.000) than before. However, there was no significant change in CE. Based on the results, this study suggests that SDL for nursing students were significantly improved by HPS-base education.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.2
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• pp.28-37
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• 1999

Differentially Responsible Adaptive Critic Learning technique is proposed for learning the control technique with multiple control inputs as in robot system using reinforcement learning. The reinforcement learning is a self-learning technique which learns the control skill based on the critic information Learning is a after a long series of control actions. The Adaptive Critic Learning (ACL) is the representative reinforcement learning structure. The ACL maximizes the learning performance using the two learning modules called the action and the critic modules which exploit the external critic value obtained seldomly. Drawback of the ACL is the fact that application of the ACL is limited to the single input system. In the proposed Differentially Responsible Action Dependant Adaptive Critic learning structure, the critic function is constructed as a function of control input elements. The responsibility of the individual control action element is computed based on the partial derivative of the critic function in terms of each control action element. The proposed learning structure has been constructed with the CMAC neural networks and some simulations have been done upon the two dimensional Cart-Role system and robot squatting problem. The simulation results are included.

• Atmosphere
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• v.25 no.4
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• pp.669-683
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• 2015

In this study, we investigated the prediction skills of four multiple linear regression methods for monthly air temperature over South Korea. We used simulation results from four regional climate models (RegCM4, SNURCM, WRF, and YSURSM) driven by two boundary conditions (NCEP/DOE Reanalysis 2 and ERA-Interim). We selected 15 years (1989~2003) as the training period and the last 5 years (2004~2008) as validation period. The four regression methods used in this study are as follows: 1) Homogeneous Multiple linear Regression (HMR), 2) Homogeneous Multiple linear Regression constraining the regression coefficients to be nonnegative (HMR+), 3) non-homogeneous multiple linear regression (EMOS; Ensemble Model Output Statistics), 4) EMOS with positive coefficients (EMOS+). It is same method as the third method except for constraining the coefficients to be nonnegative. The four regression methods showed similar prediction skills for the monthly air temperature over South Korea. However, the prediction skills of regression methods which don't constrain regression coefficients to be nonnegative are clearly impacted by the existence of outliers. Among the four multiple linear regression methods, HMR+ and EMOS+ methods showed the best skill during the validation period. HMR+ and EMOS+ methods showed a very similar performance in terms of the MAE and RMSE. Therefore, we recommend the HMR+ as the best method because of ease of development and applications.