• 한국지능시스템학회논문지
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• 제14권2호
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• pp.247-252
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• 2004

In this paper are proposed 2nd-order PD-type iterative learning control algorithms for linear continuous-time system and linear discrete-time system. In contrast to conventional methods, the proposed learning algorithms are constructed based on both time-domain performance and iteration-domain performance. The convergence of the proposed learning algorithms is proved. Also, it is shown that the proposed method has robustness in the presence of external disturbances and the convergence accuracy can be improved. A numerical example is provided to show the effectiveness of the proposed algorithms.

• Journal of Electrical Engineering and Technology
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• 제13권4호
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• pp.1705-1714
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• 2018

This paper develops a novel controller called iterative learning sliding mode (ILSM) to control linear and nonlinear fractional-order systems. This control applies a combination structures of continuous and discontinuous controller, conducts the system output to the desired output and achieve better control performance. This controller is designed in the way to be robust against the external disturbance. It also estimates unknown parameters of fractional-order systems. The proposed controller unlike the conventional iterative learning control for fractional systems does not need to apply direct control input to output of the system. It is shown that the controller perform well in partial and complete observable conditions. Simulation results demonstrate very good performance of the iterative learning sliding mode controller for achieving the desired control objective by increasing the number of iterations in the control loop.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 A
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• pp.369-372
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• 1993

In this thesis, we consider an iterative learning controller to control the continuous trajectory of 2 links direct drive robot manipulator and process computer simulation and real-time experiment. To improve control performance, we adapt an iterative learning control algorithm, drive a sufficient condition for convergence from which is drived extended conventional control algorithm and get better performance by extended learning control algorithm than that by conventional algorithm from simulation results. Also, experimental results show that better performance is taken by extended learning algorithm.

• Journal of Positioning, Navigation, and Timing
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• 제10권2호
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• pp.113-120
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• 2021

The Distance Measuring Equipment (DME) is a ground-based aircraft navigation system and is considered as an infrastructure that ensures resilient aircraft navigation capability during the event of a Global Navigation Satellite System (GNSS) outage. The main problem of DME as a GNSS back up is a poor positioning accuracy that often reaches over 100 m. In this paper, a novel approach of applying deep reinforcement learning to a DME pulse design is introduced to improve the DME distance measuring accuracy. This method is designed to develop multipath-resistant DME pulses that comply with current DME specifications. In the research, a Markov Decision Process (MDP) for DME pulse design is set using pulse shape requirements and a timing error. Based on the designed MDP, we created an Environment called PulseEnv, which allows the agent representing a DME pulse shape to explore continuous space using the Soft Actor Critical (SAC) reinforcement learning algorithm.

• 대한임베디드공학회논문지
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• 제15권6호
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• pp.307-316
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• 2020

Machine learning model data is highly affected by performance. preprocessing is needed to enable analysis of various types of data, such as letters, numbers, and special characters. This paper proposes a development environment that aims to process categorical and continuous data according to the type of missing values in stage 1, implementing the function of selecting the best performing algorithm in stage 2 and automating the process of checking model performance in stage 3. Using this model, machine learning models can be created without prior knowledge of data preprocessing.

• Journal of information and communication convergence engineering
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• 제21권3호
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• pp.225-232
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• 2023

Owing to advancements in intelligent transportation systems (ITS) and artificial-intelligence technologies, various machine-learning models can be employed to simulate and predict the number of traffic accidents under different weather conditions. Furthermore, we can analyze the relationship between weather and traffic accidents, allowing us to assess whether the current weather conditions are suitable for travel, which can significantly reduce the risk of traffic accidents. In this study, we analyzed 30000 traffic flow data points collected by traffic cameras at nearby intersections in Washington, D.C., USA from October 2012 to May 2017, using Pearson's heat map. We then predicted, analyzed, and compared the performance of the correlation between continuous features by applying several machine-learning algorithms commonly used in ITS, including random forest, decision tree, gradient-boosting regression, and support vector regression. The experimental results indicated that the gradient-boosting regression machine-learning model had the best performance.

• 센서학회지
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• 제32권6호
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• pp.378-385
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• 2023

Human biosignals provide essential information for diagnosing diseases such as dementia and Parkinson's disease. Owing to the shortcomings of current clinical assessments, noninvasive solutions are required. Machine learning (ML) on wearable sensor data is a promising method for the real-time monitoring and early detection of abnormalities. ML facilitates disease identification, severity measurement, and remote rehabilitation by providing continuous feedback. In the context of wearable sensor technology, ML involves training on observed data for tasks such as classification and regression with applications in clinical metrics. Although supervised ML presents challenges in clinical settings, unsupervised learning, which focuses on tasks such as cluster identification and anomaly detection, has emerged as a useful alternative. This review examines and discusses a variety of ML algorithms such as Support Vector Machines (SVM), Random Forests (RF), Decision Trees (DT), Neural Networks (NN), and Deep Learning for the analysis of complex clinical data.

• 반도체디스플레이기술학회지
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• 제22권4호
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• pp.136-141
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• 2023

The continuous shrinking of transistors in integrated circuits leads to difficulties in improving performance, resulting in the emerging transistors such as nanosheet field-effect transistors. In this paper, we propose a TCAD-machine learning framework of nanosheet FETs to model the current-voltage characteristics. Sentaurus TCAD simulations of nanosheet FETs are performed to obtain a large amount of device data. A machine learning model of I-V characteristics is trained using the multi-layer perceptron from these TCAD data. The weights and biases obtained from multi-layer perceptron are implemented in a PSPICE netlist to verify the accuracy of I-V and the DC transfer characteristics of a CMOS inverter. It is found that the proposed machine learning model is applicable to the prediction of nanosheet field-effect transistors device and circuit performance.

• 대한안전경영과학회지
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• 제12권1호
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• pp.83-96
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• 2010

Learning Organization is a learning based community to make the most important value in the era of Knowledge Economy, Creation. That's why people share, facilitate personal, individual's knowledge & experience systems each other and make good thoughts & ideas in the organization. This study measures the building practices having conducted the F team in Samsung electronics using DLOQ that indicates the activate degree of Learning Organization and the quantitative degrees of Learning Organization through comparing the cross-evaluation between the already measured companies in addition to analyzing the F team's success factors. Learning Organization requires sustainable and continuous activity, not completes by changing many factors with human resources. The study will have the achievement if we measure the successful activity through global companies built a Learning Organization and facilitate the improvement activity sustainably.

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2010년도 춘계학술대회
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• pp.218-225
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• 2010

Learning Organization is a learning based community to make the most important value in the era of Knowledge Economy, Creation. That's why people share, facilitate personal, individual's knowledge & experience systems each other and make good thoughts & ideas in the organization. This study measures the building practices having conducted the F team in Samsung electronics using DLOQ that indicates the activate degree of Learning Organization and the quantitative degrees of Learning Organization through comparing the cross-evaluation between the already measured companies in addition to analyzing the F team's success factors. Learning Organization requires sustainable and continuous activity, nor completes by changing many factors with human resources. The study will have the achievement if we measure the successful activity through global companies built a Learning Organization and facilitate the improvement activity sustainably.