• Journal of Information Processing Systems
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• v.9 no.4
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• pp.621-632
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• 2013

The most important things for a forest fire detection system are the exact extraction of the smoke from image and being able to clearly distinguish the smoke from those with similar qualities, such as clouds and fog. This research presents an intelligent forest fire detection algorithm via image processing by using the Gaussian Mixture model (GMM), which can be applied to detect smoke at the earliest time possible in a forest. GMMs are usually addressed by making the model adaptive so that its parameters can track changing illuminations and by making the model more complex so that it can represent multimodal backgrounds more accurately for smoke plume segmentation in the forest. Also, in this paper, we suggest a way to classify the smoke plumes via a feature extraction using HSL(Hue, Saturation and Lightness or Luminanace) color space analysis.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.534-537
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• 1999

This paper describes a design of pipelined adaptive decision-feedback equalizer (PADFE) for high bit-rate wireless digital communication systems. To enhance the throughput rate of ADFE, two pipeline stages are inserted into the critical path of ADFE by using delayed least-mean-square (DLMS) algorithm. Redundant binary (RB) arithmetic is applied to all the data processing of ADFE including filter laps and coefficient update blocks. When compared with conventional methods based on two's complement arithmetic, the proposed approach reduces arithmetic complexity, as well as results in a very simple complex-valued filter structure, thus suitable for VLSI implementation. The design parameters (filter tap, coefficient and internal bit-width, etc.) and equalization performance (bit error rate, convergence speed, etc.) are analyzed by algorithm-level simulation using COSSAP. The PADFE was designed using VHDL and Synopsys, and mapped into two ALTERA FLEX10k100 FPGAs.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2929-2931
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• 1999

In this paper, an adaptive fuzzy inference and HCM(Hard C-Means) clustering method are used for on-line fuzzy modeling of nonlinear and complex system. Here HCM clustering method is utilized for determining the initial parameter of membership function of fuzzy premise rules and also avoiding overflow phenomenon during the identification of consequence parameters. To obtain the on-line model structure of fuzzy systems. we use the recursive least square method for the consequent parameter identification. And the proposed on-line identification algorithm is carried out and is evaluated for sewage treatment process system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2942-2960
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• 2022

The core of cognitive radio is the problem concerning intelligent decision-making for communication parameters, the objective of which is to find the most appropriate parameter configuration to optimize transmission performance. The current algorithms have the disadvantages of high dependence on prior knowledge, large amount of calculation, and high complexity. We propose a new decision-making model by making full use of the interactivity of reinforcement learning (RL) and applying the Q-learning algorithm. By simplifying the decision-making process, we avoid large-scale RL, reduce complexity and improve timeliness. The proposed model is able to find the optimal waveform parameter configuration for the communication system in complex channels without prior knowledge. Moreover, this model is more flexible than previous decision-making models. The simulation results demonstrate the effectiveness of our model. The model not only exhibits better decision-making performance in the AWGN channels than the traditional method, but also make reasonable decisions in the fading channels.

• Earthquakes and Structures
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• v.22 no.4
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• pp.421-430
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• 2022

This paper addresses the stochastic control problem of robots within the framework of parameter uncertainty and uncertain noise covariance. First of all, an open circle deterministic trajectory optimization issue is explained without knowing the unequivocal type of the dynamical framework. Then, a Linear Quadratic Gaussian (LQG) controller is intended for the ostensible trajectory-dependent linearized framework, to such an extent that robust hereditary NN robotic controller made out of the Kalman filter and the fuzzy controller is blended to ensure the asymptotic stability of the non-continuous controlled frameworks. Applicability and performance of the proposed algorithm shown through simulation results in the complex systems which are demonstrate the feasible to improve the performance by the proposed approach.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.109.2-109
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• 2001

In the PCB assembly lines, as components become more complex and smaller, the conventional inspection method using traditional ICT and function test show their limitations in application. The automatic optical inspection(AOI) gradually becomes the alternative in the PCB assembly line. In Particular, the PCB inspection machines need more reliable and flexible object recognition algorithms for high inspection accuracy. The conventional AOI machines use the algorithmic approaches such as template matching, Fourier analysis, edge analysis, geometric feature recognition or optical character recognition (OCR), which mostly require much of teaching time and expertise of human operators. To solve this problem, in this paper, a statistical learning based part recognition method is proposed. The performance of the ...

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2175-2178
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• 2005

Selective Laser Sintering(SLS) method is one of Rapid Prototyping(RP) technologies. It has been used to fabricate desirable part to sinter powder and stack the fabricated layer. Since the sintering process occurs using infrared laser having high thermal energy, shrinkage and curling of the fabricated part occurs according to thermal distribution. Therefore, the fast scanning path generation is necessary to eliminate the factors of quality deterioration. In case of fabricating larger size parts, the unique scanning device and scanning path generation should be considered. In this paper, the development of SLS machines being capable of large size fabrication(800${\times}$1000${\times}$800 mm, W${\times}$D${\times}$H) will be addressed. The dual laser system and the unique scanning device have been designed and built, which employ CO2 lasers and dynamic 3-axis scanners. The developed system allows scanning a larger planar surface with the desired laser spot size. Also, to generate the fast scanning paths, adaptive path generation is needed with respect to the shape of each layer, and not simply x, y scanning, but the scanning of arbitrary direction should be enabled. To evaluate the suggested method, the complex part will be used for the experiment fabrication.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6243-6259
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• 2019

As a foundation of next-generation air transportation systems, automatic dependent surveillance-broadcast (ADS-B) helps pilots and air traffic controllers create a safer and more efficient national airspace system. Owing to the open communication environment, it is easy to insert fake aircraft into the system via spoofing or the insertion of false messages. Efforts have thus been made in academic research and practice in the aviation industry to ensure the security of transmission of messages of the ADS-B system. An identity-based batch verification (IBV) scheme was recently proposed to enhance the security and efficiency of the ADS-B system, but current IBV schemes are often too resource intensive because of the application of complex hash-to-point operations or bilinear pairing operations. In this paper, we propose a lightweight IBV signature scheme for the ADS-B system that is robust against adaptive chosen message attacks in the random oracle model, and ensures the security of batch message verification and against the replaying attack. The proposed IBV scheme needs only a small and constant number of point multiplication and point addition computations instead of hash-to-point or pairing operations. Detailed performance analyses were conducted to show that the proposed IBV scheme has clear advantages over prevalent schemes in terms of computational cost and transmission overhead.

• ETRI Journal
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• v.42 no.5
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• pp.686-699
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• 2020

In this paper, we propose a supervised-learning-based spatial performance prediction (SLPP) framework for next-generation heterogeneous communication networks (HCNs). Adaptive asset placement, dynamic resource allocation, and load balancing are critical network functions in an HCN to ensure seamless network management and enhance service quality. Although many existing systems use measurement data to react to network performance changes, it is highly beneficial to perform accurate performance prediction for different systems to support various network functions. Recent advancements in complex statistical algorithms and computational efficiency have made machine-learning ubiquitous for accurate data-based prediction. A robust network performance prediction framework for optimizing performance and resource utilization through a linear discriminant analysis-based prediction approach has been proposed in this paper. Comparison results with different machine-learning techniques on real-world data demonstrate that SLPP provides superior accuracy and computational efficiency for both stationary and mobile user conditions.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.1
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• pp.43-50
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• 2001

In this paper, we propose the design procedure of advance Polynomial Neural Networks(PNN) architecture for optimal model identification of complex and nonlinear system. The proposed PNN architecture is presented as the generic and advanced type. The essence of the design procedure dwells on the Group Method of Data Handling(GMDH). PNN is a flexible neural architecture whose structure is developed through learning. In particular, the number of layers of the PNN is not fixed in advance but is generated in a dynamic way. In this sense, PNN is a self-organizing network. With the aid of three representative numerical examples, compari-sons show that the proposed advanced PNN algorithm can produce the model with higher accuracy than previous other works. And performance index related to approximation and generalization capabilities of model is evaluated and also discussed.