• ETRI Journal
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• v.41 no.6
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• pp.750-759
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• 2019

Presently, the extraction of hand-crafted features is still the dominant method in radar emitter recognition. To solve the complicated problems of selection and updation of empirical features, we present a novel automatic feature extraction structure based on deep learning. In particular, a convolutional neural network (CNN) is adopted to extract high-level abstract representations from the time-frequency images of emitter signals. Thus, the redundant process of designing discriminative features can be avoided. Furthermore, to address the performance degradation of a single platform, we propose the construction of an ensemble learning-based architecture for multi-platform fusion recognition. Experimental results indicate that the proposed algorithms are feasible and effective, and they outperform other typical feature extraction and fusion recognition methods in terms of accuracy. Moreover, the proposed structure could be extended to other prevalent ensemble learning alternatives.

• Proceedings of the KIEE Conference
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• 1993.07a
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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.

• The Journal of Korea Robotics Society
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• v.10 no.2
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• pp.90-95
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• 2015

The main goal of e-learning systems is just-in-time knowledge acquisition. Rule-based e-learning systems, however, suffer from the mesa effect and the cold start problem, which both result in low user acceptance. E-learning systems suffer a further drawback in rendering the implementation of a natural interface in humanoids difficult. To address these concerns, even exceptional questions of the learner must be answerable. This paper aims to propose a method that can understand the learner's verbal cues and then intelligently explore additional domains of knowledge based on crowd data sources such as Wikipedia and social media, ultimately allowing for better answers in real-time. A prototype system was implemented using the NAO platform.

• Transactions of Materials Processing
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• v.30 no.4
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• pp.186-194
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• 2021

Finite element simulation is a widely applied method for practical purpose in various metal forming process. However, in the simulation of elasto-plastic behavior of porous material or in crystal plasticity coupled multi-scale simulation, it requires much calculation time, which is a limitation in its application in practical situations. A machine learning model that directly outputs the constitutive equation without iterative calculations would greatly reduce the calculation time of the simulation. In this study, we examined the possibility of artificial intelligence based constitutive equation with the input of existing state variables and current velocity filed. To introduce the methodology, we described the process of obtaining the training data, machine learning process and the coupling of machine learning model with commercial software DEFROM^TM, as a preliminary study, via rigid plastic finite element simulation.

• International Journal of Advanced Culture Technology
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• v.9 no.1
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• pp.203-209
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• 2021

When carrying out computer programming, the process of checking and correcting errors in the source code is essential work for the completion of the program. Non-computer majors who are learning programming for the first time receive feedback from instructors to correct errors that occur when writing the source code. However, in a learning environment where the time for the learner to practice alone is long, such as an online learning environment, the learner starts to feel many difficulties in solving program errors by himself/herself. Therefore, training on how to check and correct errors after writing the program source code is necessary. In this paper, various types of errors that can occur in a Python program were described, the errors were classified into simple errors and complex errors according to the characteristics of the errors, and the distributions of errors by Python grammar category were analyzed. In addition, a coding learning process to refer error lists was designed to present a coding learning method that enables learners to solve program errors by themselves.

• Journal of Engineering Education Research
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• v.23 no.6
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• pp.40-50
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• 2020

This paper proposes learning modules as a kind of integrated instruction model for soft skills program outcomes to enable CEA. Learning modules consist of course learning objectives(outcomes) described in detail, learning content(elements), learning activities(teaching learning methods), evaluation methods, evaluation rubrics so that they can be evaluated based on the performance criteria of the program learning outcomes. The unit of time for the learning module is 50 minutes. If this learning module is applied, it is expected that the soft skill program outcomes can be evaluated in the technical course. As a result of the expert feasibility study, the positive answers were much higher than the negative answers in most of the questions about the composition of the learning module or the method of managing the class.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.716-719
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• 1999

In this paper, a real-time control system for robot manipulator is implemented using real-time operating system with capabilities of multitasking, intertask communication and synchronization, event-driven, priority-driven scheduling, real-time clock control, etc. The hardware system with VME bus and related devices is developed and applied to implement a dynamic learning control scheme for robot manipulator. Real-time performance of the proposed dynamic learning controller is tested for tasks of tracking moving objects and compared with the conventional servo controller.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.1
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• pp.30-37
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• 2001

The purpose of this study is to present the method to determine the optimal stop time of HVAC using Artificial Neural Network model, one of the learning methods. For this, the performance of determining the stop time of HVAC for unexperienced learning data was evaluated, and time interval for measurement of input data and permissible error needed for practical application of ANN model were presented using the results of daily simulation.

• International Journal of Air-Conditioning and Refrigeration
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• v.13 no.1
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• pp.22-30
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• 2005

The purpose of this study is to present a method to determine the optimal stop time of HVAC using the Artificial Neural Network model, which is one of the learning methods. For this, the performance of determining the stop time of HVAC for unexperienced learning data was evaluated, and time interval for measurement of input data and permissible error needed for practical application of ANN model were presented using the results from daily simulation.

• Journal of Korea Multimedia Society
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• v.9 no.5
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• pp.658-670
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• 2006

As the educational media tends to be more digitalized and individualized, the learning paradigm is dramatically changing into e-learning. Existing on-line courseware gives a learner more chances to learn when they are home with their own PCs. However, it is of little use when they are away from their digital media. Also, it is very labor-intensive to convert the original off-line contents to on-line contents. This paper proposes education mobile contents(EMC) that can supply the learners with dynamic interactions using various multimedia information by recognizing real images of off-line contents using mobile devices. Content-based image retrieval based on object shapes is used to recognize the real image, and shapes are represented by differential chain code with estimated new starting points to obtain rotation-invariant representation, which is fitted to computational resources of mobile devices with low resolution camera. Moreover we use a dynamic time warping method to recognize the object shape, which compensates scale variations of an object. The EMC can provide learners with quick and accurate on-line contents on off-line ones using mobile devices without limitations of space.