• Journal of Soil and Groundwater Environment
• /
• v.21 no.5
• /
• pp.8-15
• /
• 2016

Bioremediation, which uses microbes to degrade most organic pollutants in soil and groundwater, can be used in solving environmental issues in various polluted sites. In this research, a wind-driven bioventing system is built to degrade about 20,000 mg/kg of high concentration diesel pollutants in soil-pollution mode. The wind-driven bioventing test was proceeded by the bioaugmentation method, and the indigenous microbes used were Bacillus cereus, Achromobacter xylosoxidans, and Pseudomonas putida. The phenomenon of two-stage diesel degradation of different rates was noted in the test. In order to interpret the results of the mode test, three microbes were used to degrade diesel pollutants of same high concentration in separated aerated batch-mixing vessels. The data derived thereof was input into the Haldane equation and calculated by non-linear regression analysis and trial-and-error methods to establish the kinetic parameters of these three microbes in bioventing diesel degradation. The results show that in the derivation of μ_m (maximum specific growth rate) in biodegradation kinetics parameters, K_s (half-saturation constant) for diesel substance affinity, and K_i (inhibition coefficient) for the adaptability of high concentration diesel degradation. The K_s is the lowest in the trend of the first stage degradation of Bacillus cereus in a high diesel concentration, whereas K_i is the highest, denoting that Bacillus cereus has the best adaptability in a high diesel concentration and is the most efficient in diesel substance affinity. All three microbes have a degradation rate of over 50% with regards to Pristane and Phytane, which are branched alkanes and the most important biological markers.

• Smart Structures and Systems
• /
• v.26 no.4
• /
• pp.495-506
• /
• 2020

Monitoring of complex processes faces several challenges mainly due to the lack of relevant sensory information or insufficient elaborated decision-making strategies. These challenges motivate researchers to adopt complex data processing and analysis in order to improve the process representation. This paper presents the development and implementation of quality monitoring framework based on a model-driven approach using embedded artificial intelligence strategies. In this work, the strategies are applied to the supervision of a microfabrication process aiming at showing the great performance of the framework in a very complex system in the manufacturing sector. The procedure involves two methods for modelling a representative quality variable, such as surface roughness. Firstly, the hybrid incremental modelling strategy is applied. Secondly, a generalized fuzzy clustering c-means method is developed. Finally, a comparative study of the behavior of the two models for predicting a quality indicator, represented by surface roughness of manufactured components, is presented for specific manufacturing process. The manufactured part used in this study is a critical structural aerospace component. In addition, the validation and testing are performed at laboratory and industrial levels, demonstrating proper real-time operation for non-linear processes with relatively fast dynamics. The results of this study are very promising in terms of computational efficiency and transfer of knowledge to manufacturing industry.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.6
• /
• pp.1058-1062
• /
• 2008

In clinical decision support system(CDSS), unlike rule-based expert method, appropriate data-driven machine learning method can easily provide the information of individual feature(clinical test) for disease classification. However, currently developed methods focus on the improvement of the classification accuracy for diagnosis. With the analysis of feature importance in classification, one may infer the novel clinical test sets which highly differentiate the specific diseases or disease states. In this background, we introduce a novel CDSS that integrate a classifier and feature selection module together. Random forest algorithm is applied for the classifier and the feature importance measure. The system selects the significant clinical tests discriminating the diseases by examining the classification error during backward elimination of the features. The superior performance of random forest algorithm in clinical classification was assessed against artificial neural network and decision tree algorithm by using breast cancer, diabetes and heart disease data in UCI Machine Learning Repository. The test with the same data sets shows that the proposed system can successfully select the significant clinical test set for each disease.

• Journal of the Korea Computer Graphics Society
• /
• v.23 no.2
• /
• pp.41-47
• /
• 2017

In this paper, we present a method for data-driven correction of the noisy motion data captured from a low-end RGB-D camera such as the Kinect device. For this purpose, our key idea is to construct a synchronized motion database captured with Kinect and additional specialized motion capture device simultaneously, so that the database contains a set of erroneous poses from Kinect and their corresponding correct poses from the mocap device together. In runtime, given motion captured data from Kinect, we search the similar K candidate Kinect poses from the database, and synthesize a new motion only by using their corresponding poses from the mocap device. We present how to build such motion database effectively, and provide a method for querying and searching a desired motion from the database. We also adapt the laze learning framework to synthesize the corrected poses from the querying results.

• Journal of Distribution Science
• /
• v.14 no.10
• /
• pp.147-152
• /
• 2016

Purpose - High quality films are affected by both the production stage and various variables such as the size of the movie investment and marketing that changes consumers' perceptions. Consumer preferences should be recognized first to ensure that the movie is successful. If a film is produced without pre-investigation and analysis of consumer demand and taste, the probability of success will be low. This study investigates the balance of production costs, marketing costs, and profits using game theory, suggesting an optimization strategy using the simplex method of linear programming. Research design, data, and methodology - Before the release of the movie, initial demand is assumed to be driven largely by marketing costs. In the next phase, demand is assumed to be driven purely by a movie's production cost and quality, which might also further determine consumer demand. Thus, it is essential to determine how to distribute pure production costs and other costs (marketing) in a limited movie production budget. Moreover, it should be taken into account how to optimally distribute under the assumption that the audience and production company's input resources are limited. This research simplifies the assumptions for large-scale and relatively small-scale movie investments and examines how movie distribution participant profits differ when each cost is invested differently. Results - When first movers or market leaders have to choose both quality and marketing, it has been proven that pursuing a strategy choosing only one is more likely than choosing both. In this situation, market leaders should maximize marketing costs under the premise that market leaders will not lag their quality behind the quality of second movers. Additionally, focusing on movie marketing that produces a quick effect while ceding creative activity to increase movie quality is a natural outcome in the movie distribution environment since a cooperative strategy between market competitors is not feasible. Conclusions - Government film development policy should ignore quality competition between movie production companies and focus on preventing marketing competition. If movie production companies focus on movie production quality improvement then a creative competition would ensue.

• The Journal of the Korea Contents Association
• /
• v.21 no.3
• /
• pp.410-418
• /
• 2021

In a program that runs an engineering education certification, program outcomes refer to the knowledge, skills, and attitudes a student must have until graduation. In general, capstone design is used as a tool for evaluating program outcomes. This paper applies the intraclass correlation coefficient (ICC) to measure the raters' reliability in assessing program outcomes. Several raters evaluate program outcomes, and the result is used to obtain the raters' ICC. ICC measures the reliability of ratings or measurements for clusters - data that has been collected as groups or sorted into groups. If the ICC is close to 1, it means that the reliability among the raters is high. We evaluated the proposed method's usefulness through case analysis. As a method for assessing an evaluation tool's objectivity, multiple raters measure the same evaluation tool. As a result, we measured the ICC values for all POs, and analyzed the cause for the low measured POs. We applied this method to evaluate program outcomes of the Department of Computer Engineering in the past two years. As a result, we derived guidelines for improvement and program outcomes.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.65 no.2
• /
• pp.108-113
• /
• 2016

The real-world problems usually show nonlinear and multi-variate characteristics, so it is difficult to establish concrete mathematical models for them. Thus, it is common to practice data-driven modeling techniques in these cases. Among them, most widely adopted techniques are regression model and intelligent model such as neural networks. Regression model has drawback showing lower performance when much non-linearity exists between input and output data. Intelligent model has been shown its superiority to the linear model due to ability capable of effectively estimate desired output in cases of both linear and nonlinear problem. This paper proposes modeling method of daily photovoltaic power systems using ELM(Extreme Learning Machine) based modular networks. The proposed method uses sub-model by fuzzy clustering rather than using a single model. Each sub-model is implemented by ELM. To show the effectiveness of the proposed method, we performed various experiments by dataset acquired during 2014 in real-plant.

• The Journal of Korea Robotics Society
• /
• v.17 no.3
• /
• pp.308-313
• /
• 2022

This paper proposes a velocity control method for a permanent magnet synchronous motor (PMSM) based on the Koopman operator that does not require model parameter information except for pole-pair of the motor and external load. First, the Koopman operator is derived using observable functions and observation data. Then, the desired q-axis current corresponding to the desired velocity is generated using the relationship between the continuous-time Koopman operator and the dynamics of PMSM. Also, the dynamic equation of PMSM is expressed as a linear form in observable space using the discrete-time Koopman operator. Finally, it is applied to the linear quadratic regulator (LQR) to derive the final form of control input. To verify the proposed method, the conventional cascade PI controller and the LQR controller configured with the existing technique are compared with the proposed method in the viewpoint of q-axis current generation and velocity tracking performance in an environment with noise and external load.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.175-175
• /
• 2023

Detection and monitoring of wildfires are essential for limiting their harmful effects on ecosystems, human lives, and property. In this research, we propose a novel method running in the Google Earth Engine platform for identifying and characterizing burnt regions using a hybrid of Sentinel-1 (C-band synthetic aperture radar) and Sentinel-2 (multispectral photography) images. The 2022 Uljin wildfire, the severest event in South Korean history, is the primary area of our investigation. Given its documented success in remote sensing and land cover categorization applications, we select the Random Forest (RF) method as our primary classifier. Next, we evaluate the performance of our model using multiple accuracy measures, including overall accuracy (OA), Kappa coefficient, and area under the curve (AUC). The proposed method shows the accuracy and resilience of wildfire identification compared to traditional methods that depend on survey data. These results have significant implications for the development of efficient and dependable wildfire monitoring systems and add to our knowledge of how machine learning and remote sensing-based approaches may be combined to improve environmental monitoring and management applications.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.2016-2021
• /
• 2003

This paper deals with a control technique of eliminating the transient vibration with respect to a waist axis of an articulated robot. This control technique is based on a model-based control in order to establish the damping effect on the driven mechanical part. The control model is composed of reduced-order electrical and mechanical parts related to the velocity control loop. The parameters of the control model can be obtained from design data or experimental data. This model estimates a load speed converted to the motor shaft. The difference between the estimated load speed and the motor speed is calculated dynamically, and it is added to the velocity command to suppress the transient vibration. This control method is applied to an articulated robot regarded as a time-invariant system. The effectiveness of the model-based control integrated into the position control loop is verified by simulations. Simulations show satisfactory control results to reduce the transient vibration at the end-effector.