• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.27 no.7
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• pp.452-457
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• 2014

Copper manganite thin films were fabricated on $SiN_x/Si$ substrate by metal organic decomposition (MOD) process. They were burned-out at $400^{\circ}C$ and annealed at various temperatures ($400{\sim}800^{\circ}C$) for 1h in ambient atmosphere. Their micro-structure and negative temperature coefficient of resistance (NTCR) characteristics were analyzed for micro-bolometer application. The copper manganite film with a cubic spinel structure was well developed at $500^{\circ}C$ which confirmed by XRD and HRTEM analysis. It showed a low resistivity ($47.5{\Omega}{\cdot}cm$) at room temperature and high NTCR characteristics of $-4.12%/^{\circ}C$ and $-2.15%/^{\circ}C$ at room temperature and $85^{\circ}C$, implying a good thin film for micro-bolometer application. Furthermore, its crystallinity was enhanced with increasing temperature to $600^{\circ}C$. However, the appearance of secondary phase at temperatures higher than $600^{\circ}C$ lead to deteriorate the NTCR characteristics.

• ETRI Journal
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• v.25 no.6
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• pp.517-522
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• 2003

This investigation confirms that silver stearate consists of an infinite-sheet, two-dimensional, nonmolecular layered structure. Scanning electron microscopy, X-ray diffraction, and infrared spectroscopy reveal the following: plate-like morphology is identified from the SEM image, XRD peaks can be indexed to the (0k0) reflections of a layered structure, and infrared peaks show that alkyl chains are present in an all-trans conformational state with little or no significant gauche population. Based on these structural characteristics, we demonstrate that silver stearate, a prototype of layered organic-inorganic hybrid material, can be grown not only in a designed two-dimensional pattern but also in three-dimensionally ordered ways by using carboxyl-group terminated nanoparticles as a template.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.6
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• pp.456-465
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• 2003

It is well-known that bio-systems does not calculate inverse dynamics for trajectory planning, but they move by proper modulation of system impedances. Inspired by bio-systems, a biomimetic trajectory planning method is proposed in this work. This scheme is based on employment of redundant actuation which prevails in bio-systems. We discuss that for the generation of the biomimetic trajectory, intelligent structure of bio-systems plays an important role. Redundant actuation and kinematic redundancy fall into such a category of intelligent structure. The proposed biomimetic trajectory planning modulates the complete dynamic behavior such as natural frequencies and damping ratios by using the intelligent structure. Experimental work is illustrated to show the effectiveness of the proposed biomimetic trajectory planning for a five-bar mechanism with redundant actuators.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.195-198
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• 2000

We propose a new wavelet neural network structure, for which we apply new recurrent nodes to the network, in this paper for the dynamic system identification and control. We will construct the wavelet neural network by using wavelet frame function. The function does not have the best approximation property, but it may be possible to apply some modification to the structure of the network because the constriction of orthogonality is loosened a little. This wavelet neural network we propose can obtain previous state information by its structure of the network without any addition of input, though the conventional wavelet network needs additional previous state input for the improvement of the dynamic performance. In numerical experience, the performance of the new wavelet neural network we propose in the nonlinear system with uncertainity of parameter Is equal to that of the wavelet network which used the additional previous information input, superior to that of the conventional wavelet network.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2319-2324
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• 2005

This paper proposes a intelligent gain and boundary layer based sliding mode control (SMC) method for robotic systems with unknown model uncertainties. For intelligent gain and boundary layer, we employ the self recurrent wavelet neural network (SRWNN) which has the properties such as a simple structure and fast convergence. In our control structure, the SRWNNs are used for estimating the width of boundary layer, uncertainty bound, and nonlinear terms of robotic systems. The adaptation laws for all parameters of SRWNNs and reconstruction error bounds are derived from the Lyapunov stability theorem, which are used for an on-line control of robotic systems with unknown uncertainties. Accordingly, the proposed method can overcome the chattering phenomena in the control effort and has the robustness regardless of unknown uncertainties. Finally, simulation results for the three-link manipulator, one of the robotic systems, are included to illustrate the effectiveness of the proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.27-34
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• 1996

GENIE is a learning-based engine for building intelligent systems. Learning in GENIE proceeds by incrementally modeling its human or technical environment using a neural network and a genetic algorithm. The neural network is used to represent the knowledge for solving a given task and has the ability to grow its structure. The genetic algorithm provides the neural network with training examples by actively exploring the example space of the problem. Integrated into the training examples by actively exploring the example space of the problem. Integrated into the GENIE system architecture, the genetic algorithm and the neural network build a virtually self-teaching autonomous learning system. This paper describes the structure of GENIE and its learning components. The performance is demonstrated on a robot learning problem. We also discuss the lessons learned from experiments with GENIE and point out further possibilities of effectively hybridizing genetic algorithms with neural networks and other softcomputing techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.792-812
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• 2022

Multi-agent systems often need to achieve the goal of learning more effectively for a task through coordination. Although the introduction of deep learning has addressed the state space problems, multi-agent learning remains infeasible because of the joint action spaces. Large-scale joint action spaces can be sparse according to implicit or explicit coordination structure, which can ensure reasonable coordination action through the coordination structure. In general, the multi-agent system is dynamic, which makes the relations among agents and the coordination structure are dynamic. Therefore, the explicit coordination structure can better represent the coordinative relationship among agents and achieve better coordination between agents. Inspired by the maximization of social group utility, we dynamically construct a factor graph as an explicit coordination structure to express the coordinative relationship according to the utility among agents and estimate the joint action values based on the local utility transfer among factor graphs. We present the application of such techniques in the scenario of multiple intelligent vehicle systems, where state space and action space are a problem and have too many interactions among agents. The results on the multiple intelligent vehicle systems demonstrate the efficiency and effectiveness of our proposed methods.

• Bulletin of the Korean Chemical Society
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• v.27 no.9
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• pp.1323-1328
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• 2006

Mesoporous nickel intercalated aluminosilicate nanohybrid has been synthesized through a recombination reaction between the colloidal suspension of exfoliated montmorillonite nanosheets and aqueous nickel acetate solution. According to powder X-ray diffraction and field emission-scanning electron microscopic analyses, the intercalation of nickel species expands significantly the basal spacing of the host montmorillonite clay and the crystallites of the intercalation compound are assembled to form a house-of-card structure. $N_2$ adsorption-desorption isotherm measurements with BJH pore analyses clearly demonstrated that the porosity of the intercalate originates mainly from mesopores (diameter $\sim50\;\AA$) formed by the house-of-card type stacking of clay crystallites. From FT-IR and X-ray absorption spectroscopic analyses, it becomes certain that intercalated nickel ion is stabilized in an isolated $NiO_6$ octahedral unit. The present mesoporous intercalation compound is expected to be applicable as efficient catalysts or absorbents.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.457-467
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• 2004

In this paper, a robot is considered as an agent, a structure of robot is presented which consisted by multi-subagents and they have diverse capacity such as perception, intelligence, action etc., required for robot. Also, subagents are consisted by micro-agent($\mu$agent) charged for elementary action required. The structure of robot control have two sub-agents, the one is behavior based reactive controller and action selection sub agent, and action selection sub-agent select a action based on the high label action and high performance, and which have a learning mechanism based on the reinforcement learning. For presented robot structure, it is easy to give intelligence to each element of action and a new approach of multi robot control. Presented robot is simulated for two goals: chaotic exploration and obstacle avoidance, and fabricated by using 8bit microcontroller, and experimented.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.3
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• pp.135-142
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• 2011

Unlike using the sequence-based representation for a chromosome in previous genetic algorithms for Bayesian structure learning, we proposed a matrix representation-based genetic algorithm. Since a good chromosome representation helps us to develop efficient genetic operators that maintain a functional link between parents and their offspring, we represent a chromosome as a matrix that is a general and intuitive data structure for a directed acyclic graph(DAG), Bayesian network structure. This matrix-based genetic algorithm enables us to develop genetic operators more efficient for structuring Bayesian network: a probability matrix and a transpose-based mutation operator to inherit a structure with the correct edge direction and enhance the diversity of the offspring. To show the outstanding performance of the proposed method, we analyzed the performance between two well-known genetic algorithms and the proposed method using two Bayesian network scoring measures.