• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2365-2377
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• 2017

This paper addresses two interrelated problems concerning the tracking control of pod propulsion unmanned surface vessel (USV), namely, the modeling of pod propulsion USV, and tracking controller design. First, based on MMG modeling theory, the model of pod propulsion USV is derived. Furthermore, a practical adaptive neural tracking controller is proposed by backstepping technique, neural network approximation and adaptive method. Meanwhile, unlike some existing tracking methods for surface vessel whose control algorithms suffer from "explosion of complexity", a novel neural shunting model is introduced to solve the problem. Using a Lyapunov functional, it is proven that all error signals in the system are uniformly ultimately bounded. The advantages of the paper are that first, the underactuated characteristic of pod propulsion USV is proved; second, the neural shunting model is used to solve the problem of "explosion of complexity", and this is a combination of knowledge in the field of biology and engineering; third, the developed controller is able to capture the uncertainties without the exact information of hydrodynamic damping structure and the sea disturbances. Numerical examples have been given to illustrate the performance and effectiveness of the proposed scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5594-5615
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• 2019

Malicious social robots, which are disseminators of malicious information on social networks, seriously affect information security and network environments. The detection of malicious social robots is a hot topic and a significant concern for researchers. A method based on classification has been widely used for social robot detection. However, this method of classification is limited by an unbalanced data set in which legitimate, negative samples outnumber malicious robots (positive samples), which leads to unsatisfactory detection results. This paper proposes the use of generative adversarial networks (GANs) to extend the unbalanced data sets before training classifiers to improve the detection of social robots. Five popular oversampling algorithms were compared in the experiments, and the effects of imbalance degree and the expansion ratio of the original data on oversampling were studied. The experimental results showed that the proposed method achieved better detection performance compared with other algorithms in terms of the F1 measure. The GAN method also performed well when the imbalance degree was smaller than 15%.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.225-230
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• 1992

In this paper, a new learning fuzzy logic controller(LFLC) is presented. The proposed controller is composed of the main control part and the learning part. The main control part is a fuzzy logic controller(FLC) based on linguistic rules and fuzzy inference. For the learning part, artificial neural network(ANN) is added to FLC so that the controller may adapt to unknown plant and environment. According to the output values of the ANN part, which is learned using error back-propagation algorithm, scale factors of the FLC part are determined. These scale factors transfer the range of values of input variables into corresponding universe of discourse in the FLC part in order to achieve good performance. The effectiveness of the proposed control strategy has been demonstrated through simulations involving the control of an unknown robot manipulator with load disturbance.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.45-50
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• 2003

Recently, robots technique has been developed remarkably. Emotion recognition is necessary to make an intimate robot. This paper shows the simulator and simulation result which recognize or classify emotions by learning pitch pattern. Also, because the pitch is not sufficient for recognizing emotion, we added acoustic elements. For that reason, we analyze the relation between emotion and acoustic elements. The simulator is composed of the DRNN(Dynamic Recurrent Neural Network), Feature extraction. DRNN is a learning algorithm for pitch pattern.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.9-15
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• 1998

This paper presents an adaptive learning control method for unknown nonlinear systems by combining neuro control and iterative learning control techniques. In the present control system, an iterative learning controller (ILC) is used for a process of short term memory involved in a temporary adaptive and learning manipulation and a short term storage of a specific temporary action. The learning gain of the iterative learning law is estimated by using a neural network for an unknown system except relative degrees. The control informations obtained by ILC are transferred to a long term memory-based feedforward neuro controller (FNC) and accumulated in it in addition to the previously stored infonnations. This scheme is applied to a two link robot manipulator through simulations.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.2
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• pp.127-134
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• 2004

In this paper, human motor behaving model based sensory motor coordination(SMC) algorithm is implemented on robotic grasping task. Compare to conventional SMC models which connect sensor to motor directly, the proposed method used biologically inspired human behaving system in conjunction with SMC algorithm for fast grasping force control of robot arm. To characterize various grasping objects, pressure sensors on hand gripper were used. Measured sensory data are simultaneously transferred to perceptual mechanism(PM) and long term memory(LTM), and then the sensory information is forwarded to the fastest channel among several information-processing flows in human motor system. In this model, two motor learning routes are proposed. One of the route uses PM and the other uses short term memory(STM) and LTM structure. Through motor learning procedure, successful information is transferred from STM to LTM. Also, LTM data are used for next moor plan as reference information. STM is designed to single layered perception neural network to generate fast motor plan and receive required data which comes from LTM. Experimental results showed that proposed method can control of the grasping force adaptable to various shapes and types of greasing objects, and also it showed quicker grasping-behavior lumining time compare to simple feedback system.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.592-595
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• 2003

In this paper, we have designed a Home Networking System using an embedded linux board. The system based on the World-Wide-Web is composed of three parts - a Server, a Client and a Simulator. The Home Networking Server is built in an embedded board using an embedded linux kernel. A web-server and Home Networking Server Seryice Demon programming with a Jaya-Language is included in the board. Clients can connect to the server board using a web-browser in the desktop computer, PDA or any other machines which include a web-browser. For this purpose, we made the client program using a Java-Applet. So, the clients who connect to the server for the control of the applications, download the class-file and execute the client-program in the web-browser. So, the clients don't need any other programs to control the applications from a remote place. The size of server board is very small (86.3$\times$74mm), which makes it very useful not only for the Home-Networking-System but also in many other fields, e.g., embedded robot control system, etc. Using an embedded board instead of a desktop computer is good for a simple network environment and it occupies only a small space to make the system.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.209-216
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• 2023

This paper aims to develop a model to estimate the paint thickness in a shipyard spray painting according to changes of spraying distance and speed. We acquired the experimental datasets of five different conditions with respect to the spraying distance and speed using a painting robot. In addition, we applied a preprocessing step to handle noises which might be caused by various reasons such as a nozzle damage. Our method is to transform a thickness function of a specified spraying distance and speed into another function of an unknown spraying and speed. We observed that the proposed method shows more stable and more accurate predictions compared with an artificial neural network-based approach.

• Journal of Engineering Education Research
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• v.25 no.3
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• pp.35-44
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• 2022

Artificial intelligence is a core technology in the era of digital transformation, and as the technology level is advanced and used in various industries, its influence is growing in various fields, including social, ethical and legal issues. Therefore, it is time to raise social awareness on ethics of artificial intelligence as a prevention measure as well as improvement of laws and institutional systems related to artificial intelligence development. In this study, we analyzed unstructured data, typically text, such as online news articles and comments to confirm the degree of social awareness on ethics of artificial intelligence development. The analysis showed that the public intended to concentrate on specific issues such as "Human," "Robot," and "President" in 2018 to 2019, while the public has been interested in the use of personal information and gender conflics in 2020 to 2021.

• The Journal of the Korea Contents Association
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• v.19 no.2
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• pp.135-146
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• 2019

This study was designed to analyze trends in the entire convergence research beyond academic research through social media big data analysis at a time when interdisciplinary convergence research is emphasized along with the fourth industrial revolution. For this purpose, about 150,000 cases of texts and titles were acquired for about 10 years from January 2009 to September 2018 in connection with the convergence research in social media, and word cloud and network analysis were conducted. As a results, the research fields that were actively conducted for each period were eco-tech in 2009 and 2010, smart technology in 2011 and 2012, information and communication in 2013 and 2014, robots in 2015 and 2016, and artificial intelligence in 2017 and 2018. Also, the research areas that have been consistently conducted for about 10 years are culture, design, chemistry, nanotechnology, biotechnology, robot, IT, and information and communication. Since this study identifies trends in convergence research over time, it can be helpful to researchers who are planning convergence research direction by understanding the trends of convergence research.