• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.23-28
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• 2021

The article discusses the features of adaptation of the parameters of the physical layer of data transmission in self-organizing networks based on unmanned aerial vehicles operating in the conditions of "smart cities". The concept of cities of this type is defined, the historical path of formation, the current state and prospects for further development in the aspect of transition to "smart cities" of the third generation are shown. Cities of this type are aimed at providing more comfortable and safe living conditions for citizens and autonomous automated work of all components of the urban economy. The perspective of the development of urban mobile automated technical means of infocommunications is shown, one of the leading directions of which is the creation and active use of wireless self-organizing networks based on unmanned aerial vehicles. The advantages of using small-sized unmanned aerial vehicles for organizing networks of this type are considered, as well as the range of tasks to be solved in the conditions of modern "smart cities". It is shown that for the transition to self-organizing networks in the conditions of "smart cities" of the third generation, it is necessary to ensure the adaptation of various levels of OSI network models to dynamically changing operating conditions, which is especially important for the physical layer. To maintain an acceptable level of the value of the bit error probability when transmitting command and telemetry data, it is proposed to adaptively change the coding rate depending on the signal-to-noise ratio at the receiver input (or on the number of channel decoder errors), and when transmitting payload data, it is also proposed to adaptively change the coding rate together with the choice of modulation methods that differ in energy and spectral efficiency. As options for the practical implementation of these solutions, it is proposed to use an approach based on the principles of neuro-fuzzy control, for which examples of determining the boundaries of theoretically achievable efficiency are given.

• Journal of Power Electronics
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• 제18권3호
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• pp.766-777
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• 2018

In this paper, a sliding mode and proportional plus integral (SM-PI) control combined with self-sustained phase shift modulation (SSPSM) for LLC resonant converters is presented. The proposed control scheme improves the transient response while preserving good steady-state performance. An averaged large signal model of an LLC converter with the ZVS modulation technique is developed for the SM control design. The sliding surface is obtained based on the input-output linearization concept. A system identification method is adopted to obtain the transform function of the LLC resonant converter, which is used to design the PI control. In order to reduce the inherent chattering problem in the steady state, the combined SM-PI control strategy is derived with fuzzy control, where the SM control is responsive during the transient state while the PI control prevails in the steady state. The combination of SSPSM and the SM-PI control provides ZVS operation, robustness and a fast transient response against step load variations. Simulation and experimental results validate the theoretical analysis and the attractive features of the proposed scheme.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.307-310
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• 2003

In the maritime container terminal, LMTT(Linear Motor-based Transfer Technology) is horizontal transfer system for the yard automation, which has been proposed to take the place of AGV(Automated Guided Vehicle). The system is based on PMLSM (Permanent Magnetic Linear Synchronous Motor) that is consists of stator modules on the rail and shuttle car (mover). Because of large variant of mover's weight by loading and unloading containers, the difference of each characteristic of stator modules, and a stator module's trouble etc., LMCPS (Linear Motor Conveyance Positioning System) is considered as that the system is changed its model suddenly and variously. In this paper, we will introduce the soft-computing method of a multi-step prediction control for LMCPS using DR-FNN (Dynamically-constructed Recurrent Fuzzy Neural Network). The proposed control system is used two networks for multi-step prediction. Consequently, the system has an ability to adapt for external disturbance, cogging force, force ripple, and sudden changes of itself.

• 디지털콘텐츠학회 논문지
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• 제16권6호
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• pp.917-925
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• 2015

WebRTC는 내부 및 외부 플러그인 없이 브라우저간의 음성 전화, 비디오 채팅, 및 p2p파일 공유를 지원하는 최신 기술 중 하나이다. 그러나 아직 다뤄져야 할 많은 문제가 있다. 이 논문에서는 그 중 대역폭 이라는 작은 필드에 초점을 맞췄다. 다운로드 및 업로드를 위한 대역폭은 서비스 제공자에 의해 고정되어있지만, 특정 지역에 있는 사용자의 수는 시간에 따라 크게 증가되고 있다. 본 논문에서는 대여폭의 한계를 극복하기 위하여 퍼지 컨트롤을 기반으로 클라이언트 자체에서 프레임 비율 및 스트리밍 비디오의 해상도 변경하는 모델을 제안한다.

• Structural Engineering and Mechanics
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• 제30권6호
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• pp.679-698
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• 2008

On marine vessels, delicate instruments such as navigation radars are normally mounted on ship masts. However the vibrations at the top of mast where the radar is mounted often cause serious deterioration in radar-tracking resolution. The most serious problem is caused by the rotational vibrations at the top of mast that may be due to wind loading, inertial loading from ship rolling and base excitations induced by the running propeller. This paper presents a method of semi-active vibration control using magneto-rheological (MR) dampers to reduce the rotational vibration of the mast. In the study, the classical optimal control algorithm, the independent modal space control algorithm and the double input - single output fuzzy control algorithm are employed for the vibration control. As the phenomenological model of an MR damper is highly nonlinear, which is difficult to analyse, a back- propagation neural network is trained to emulate the inverse dynamic characteristics of the MR damper in the analysis. The trained neural network gives the required voltage for each MR damper based on the displacement, velocity and control force of the MR damper quickly. Numerical simulations show that the proposed control methods can effectively suppress the rotational vibrations at the top of mast.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1287-1292
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• 2005

This paper presents an effective pattern classification model by designing an artificial neural network based pattern classifiers for face recognition. First, a RGB image inputted from a frame grabber is converted into a HSV image which is similar to the human beings' vision system. Then, the coarse facial region is extracted using the hue(H) and saturation(S) components except intensity(V) component which is sensitive to the environmental illumination. Next, the fine facial region extraction process is performed by matching with the edge and gray based templates. To make a light-invariant and qualified facial image, histogram equalization and intensity compensation processing using illumination plane are performed. The finally extracted and enhanced facial images are used for training the pattern classification models. The proposed H-ART2 model which has the hierarchical ART2 layers and F-LVQ model which is optimized by fuzzy membership make it possible to classify facial patterns by optimizing relations of clusters and searching clustered reference patterns effectively. Experimental results show that the proposed face recognition system is as good as the SVM model which is famous for face recognition field in recognition rate and even better in classification speed. Moreover high recognition rate could be acquired by combining the proposed neural classification models.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2821-2823
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• 2002

In this paper we propose the Multi-FNN (Fuzzy-Neural Networks) for optimal identification modeling of complex system. The proposed Multi-FNNs is based on a concept of FNNs and exploit linear inference being treated as generic inference mechanisms. In the networks learning, backpropagation(BP) algorithm of neural networks is used to updata the parameters of the network in order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM(Hard C-Means)clustering algorithm which carry out the input-output dat a preprocessing function and Genetic Algorithm which carry out optimization of model The HCM clustering method is utilized to determine the structure of Multi-FNNs. The parameters of Multi-FNN model such as apexes of membership function, learning rates, and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization abilities of the model. NOx emission process data of gas turbine power plant is simulated in order to confirm the efficiency and feasibility of the proposed approach in this paper.

• 정보교육학회논문지
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• 제14권2호
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• pp.273-283
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• 2010

복잡계 관점에서 트래픽 패턴, 도시 및 다세포 생물학적 유기체 등이 하나의 창발적인 현상인 것처럼, '그린 IT'의 개념 또한 지구 온난화 문제로 인해 인간 세계에서 태동할 수밖에 없는 불가피한 창발적인 현상이다. 복잡계 이론의 관점에서 그린 IT는 무작위한 것으로부터 대단히 복잡한 방식으로 상호작용을 하는 상당히 뒤얽힌 시스템으로 발전할 가능성이 높다. 그렇지만, 그린 IT 시스템 또한 하나의 복잡계라 할 때, 그러한 시스템을 구동시키고 억제시키는 미지의 끌개들이 존재한다. 이러한 맥락에서 본 논문은 그린 IT 시스템의 잠정적인 끌개들을 식별하고, 평가할 수 있는 하나의 새로운 모델을 제안하고, 이를 교육적으로 활용하는데 있다. 구체적으로 그린 IT의 끌개라 할 수 있는 그린 IT 너비-깊이 행렬을 기반으로 그린 IT 진화 및 자기조직화 되어가는 과정을 측정할 수 있는 유형-2 퍼지 시스템을 구축한다.

• 한국수자원학회논문집
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• 제31권6호
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• pp.821-832
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• 1998

본 논문에서는 상수도시설을 효율적으로 운영하는 데 필요한 1일 급수량 수요를 예측하는 방식에 대하여 인공지능(Artificial Inteligence)이라 불리는 퍼지 뉴론(fuzzy neuron)을 이용하여 연구하였다. 퍼지뉴론이란 퍼지정보(fuzzy information)를 입력으로 받아들이고 처리하는 퍼지 신경망을 일컫는 말이다. 본 연구에서는 소속함수와 퍼지규칙을 신경망으로 학습하는 기능인 적응식 학습방법을 통하여 1일 급수량을 예측하였으며 연구대상 지역으로는 광주광역시를 선정하였다. 또한 1일 급수량 예측에 있어서 필요한 변수 선택을 위해 입력자료를 상관분석, 자기상관, 부분자기상관, 교차상관 분석 등을 하였으며 동정된 입력변수는 급수량, 평균기온, 급수인구이다. 먼저 급수량, 평균기온, 급수인구로 모델을 구성하였고, 한편으론 기상청의 기후예보자료를 신뢰할 수 없는 경우에는 급수량을 예측할 수 있도록 급수량 자료만으로 모델을 구성하여 그 유효성을 검증하였다. 제안된 모형식은 사고 등의 인위적인 조작(단수 등)이 가해지는 시기를 포함하고도 실측치와 모형의 예측치와의 오차율이 최대 18.46%, 평균2.36% 이내로 나타나, 모형의 결과는 상수도 시설의 운용 및 급·배수관망의 실시간 제어에 많은 도움을 주리라 생각된다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권3호
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• pp.185-191
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• 2008

In this paper, experimental studies of a neural network (NN) control technique for non-model based position control of the x-y table robot are presented. Decentralized neural networks are used to control each axis of the x-y table robot separately. For an each neural network compensator, an inverse control technique is used. The neural network control technique called the reference compensation technique (RCT) is conceptually different from the existing neural controllers in that the NN controller compensates for uncertainties in the dynamical system by modifying desired trajectories. The back-propagation learning algorithm is developed in a real time DSP board for on-line learning. Practical real time position control experiments are conducted on the x-y table robot. Experimental results of using neural networks show more excellent position tracking than that of when PD controllers are used only.