• Journal of Ocean Engineering and Technology
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• v.11 no.1
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• pp.84-95
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• 1997

This paper presents a new approach to the design speed and azimuth control of a mobile robot with drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the frmework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simple the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.4
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• pp.133-140
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• 2007

This study aims to design and implement a learning evaluation system using .NET which is developed by Microsoft. .NET technology supports higher processing speed than ASP technology. The learning evaluation system is based on the web, consists of administrator module, questioner module and student module. The functions of the system, i.e., providing test questions, performing test, and evaluating result of test are achieving on the web in real time. Even when many users use this system, the system is stable and has a speed response time.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.149-163
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• 2023

Affected by external factors, errors in time series data collected by sensors are common. Using the traditional method of constraining the speed change rate to clean the errors can get good performance. However, they are only limited to the data of stable changing speed because of fixed constraint rules. Actually, data with uneven changing speed is common in practice. To solve this problem, an online cleaning algorithm for time series data based on dynamic speed change rate constraints is proposed in this paper. Since time series data usually changes periodically, we use the extreme learning machine to learn the law of speed changes from past data and predict the speed ranges that change over time to detect the data. In order to realize online data repair, a dual-window mechanism is proposed to transform the global optimal into the local optimal, and the traditional minimum change principle and median theorem are applied in the selection of the repair strategy. Aiming at the problem that the repair method based on the minimum change principle cannot correct consecutive abnormal points, through quantitative analysis, it is believed that the repair strategy should be the boundary of the repair candidate set. The experimental results obtained on the dataset show that the method proposed in this paper can get a better repair effect.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.1097-1112
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• 1989

An iterative learning control scheme is incorporated to the computed torque method as a means to enhance the accuracy and the flexibility. A learning rule is constructed by utilizing a gradient descent algorithm and data compressing techniques are illustrated. Computer simulation results show a good performance of the scheme under a relatively high speed and a heavy payload condition.

• Investigative Magnetic Resonance Imaging
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• v.25 no.1
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• pp.10-22
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• 2021

Purpose: To overcome the difficulty in building a large data set with a high-quality in medical imaging, a concept of 'blended-transfer learning' (BTL) using a combination of both source data and target data is proposed for the target task. Materials and Methods: Source and target tasks were defined as training of the source and target networks to reconstruct cardiac CINE images from undersampled data, respectively. In transfer learning (TL), the entire neural network (NN) or some parts of the NN after conducting a source task using an open data set was adopted in the target network as the initial network to improve the learning speed and the performance of the target task. Using BTL, an NN effectively learned the target data while preserving knowledge from the source data to the maximum extent possible. The ratio of the source data to the target data was reduced stepwise from 1 in the initial stage to 0 in the final stage. Results: NN that performed BTL showed an improved performance compared to those that performed TL or standalone learning (SL). Generalization of NN was also better achieved. The learning curve was evaluated using normalized mean square error (NMSE) of reconstructed images for both target data and source data. BTL reduced the learning time by 1.25 to 100 times and provided better image quality. Its NMSE was 3% to 8% lower than with SL. Conclusion: The NN that performed the proposed BTL showed the best performance in terms of learning speed and learning curve. It also showed the highest reconstructed-image quality with the lowest NMSE for the test data set. Thus, BTL is an effective way of learning for NNs in the medical-imaging domain where both quality and quantity of data are always limited.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.11
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• pp.76-82
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• 1998

In this study, the Ball screw and Servo motor Mechanism is considered as a High Speed Tool Feed System for the machining of a piston of a reciprocating engine. For the machining of a piston, that shapes oval, high speed servo mechanism is needed as a positioning of a cutting tool, and the stroke of tool is 0.1 mm ~ 1 mm. Ball screw and servo motor Mechanism is available very much because this mechanism is used widely in general machine. This Mechanism has been designed with the use of the decrease in mass and partial wear of the ball screw for high speed positioning of tool. Also the periodic learning control method with the inverse transfer function compensation has been applied to the positioning control for the high accuracy positioning of tool. These applications lead the achievement of the machining of a piston with an accuracy of 5${\mu}{\textrm}{m}$ at 2500 rpm in CNC turning.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.157.6-157
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• 2001

We estimated various neuron functions to construct of engineering neurons, which are the combination of sigmoid, linear, sine, quadric, double/single bended, soft max/minimum functions. These combinations are estimated by the property on the potential surface between the learning points, calculation speed, and learning convergence; because the surface depends on the inference ability of a neuron system; and speed and convergence are depend on the efficiency on the points of engineering applications. After the evaluating discussions, we can select more appropriate combination than original sigmoid function´s, which is single bended function and linear one. The combination ...

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.7
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• pp.1705-1720
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• 2013

Human face gender recognition requires fast image processing with high accuracy. Existing face gender recognition methods used traditional local features and machine learning methods have shortcomings of low accuracy or slow speed. In this paper, a new framework for face gender recognition to reach fast face gender recognition is proposed, which is based on Local Ternary Pattern (LTP) and Extreme Learning Machine (ELM). LTP is a generalization of Local Binary Pattern (LBP) that is in the presence of monotonic illumination variations on a face image, and has high discriminative power for texture classification. It is also more discriminate and less sensitive to noise in uniform regions. On the other hand, ELM is a new learning algorithm for generalizing single hidden layer feed forward networks without tuning parameters. The main advantages of ELM are the less stringent optimization constraints, faster operations, easy implementation, and usually improved generalization performance. The experimental results on public databases show that, in comparisons with existing algorithms, the proposed method has higher precision and better generalization performance at extremely fast learning speed.

• The Journal of Korea Robotics Society
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• v.14 no.4
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• pp.357-362
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• 2019

This paper proposes a model and train method that can real-time detect objects and distances estimation based on a monocular camera by applying deep learning. It used YOLOv2 model which is applied to autonomous or robot due to the fast image processing speed. We have changed and learned the loss function so that the YOLOv2 model can detect objects and distances at the same time. The YOLOv2 loss function added a term for learning bounding box values x, y, w, h, and distance values z as 클래스ification losses. In addition, the learning was carried out by multiplying the distance term with parameters for the balance of learning. we trained the model location, recognition by camera and distance data measured by lidar so that we enable the model to estimate distance and objects from a monocular camera, even when the vehicle is going up or down hill. To evaluate the performance of object detection and distance estimation, MAP (Mean Average Precision) and Adjust R square were used and performance was compared with previous research papers. In addition, we compared the original YOLOv2 model FPS (Frame Per Second) for speed measurement with FPS of our model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.195-201
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• 2022

For a efficient distance education environment, the need for correct problem recommendation guides considering the learner's exact learning pattern is increasing. In this paper, we study block chain based smart contract technology to suggest a method for presenting the optimal problem recommendation path for individual learners based on the data given by situational weights to the problem patterns of learners collected in the distance education environment. For the performance evaluation of this study, the learning satisfaction with the existing similar learning environment, the usefulness of the problem recommendation guide, and the learner data processing speed were analyzed. Through this study, it was confirmed that the learning satisfaction improved by more than 15% and the learning data processing speed was improved by more than 20% compared to the existing learning environment.