• International Journal of Control, Automation, and Systems
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• 제1권1호
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• pp.134-141
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• 2003

A major research issue associated with service robots is the creation of an environment recognition system for mobile robot navigation that is robust and efficient on various environment situations. In recent years, intelligent autonomous mobile robots have received much attention as the types of service robots for serving people and industrial robots for replacing human. To help people, robots must be able to sense and recognize three dimensional space where they live or work. In this paper, we propose a three dimensional environmental modeling method based on an edge enhancement technique using a planar fitting method and a neural network technique called "Growing Neural Gas Network." Input data pre-processing provides probabilistic density to the input data of the neural network, and the neural network generates a graphical structure that reflects the topology of the input space. Using these methods, robot's surroundings are autonomously clustered into isolated objects and modeled as polygon patches with the user-selected resolution. Through a series of simulations and experiments, the proposed method is tested to recognize the environments surrounding the robot. From the experimental results, the usefulness and robustness of the proposed method are investigated and discussed in detail.in detail.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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• pp.202-205
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• 2005

Cell phenotypes are determined by the concerted activity of thousands of genes and their products. This activity is coordinated by a complex network that regulates the expression of genes. Understanding this organization is crucial to elucidate cellular activities, and many researches have tried to construct gene regulatory networks from mRNA expression data which are nowadays the most available and have a lot of information for cellular processes. Several computational tools, such as Boolean network, Qualitative network, Bayesian network, and so on, have been applied to infer these networks. Among them, Bayesian networks that we chose as the inference tool have been often used in this field recently due to their well-established theoretical foundation and statistical robustness. However, the relative insufficiency of experiments with respect to the number of genes leads to many false positive inferences. To alleviate this problem, we had developed the algorithm of MONET(MOdularized NETwork learning), which is a new method for inferring modularized gene networks by utilizing two complementary sources of information: biological annotations and gene expression. Afterward, we have packaged and improved MONET by combining dispersed functional blocks, extending species which can be inputted in this system, reducing the time complexities by improving algorithms, and simplifying input/output formats and parameters so that it can be utilized in actual fields. In this paper, we present the architecture of MONET system that we have improved.

• 조명전기설비학회논문지
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• 제18권1호
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• pp.85-89
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• 2004

산업자동화의 고정밀도에 따라 궤환 제어시스템은 강인한 제어가 요구되고 있다. 하지만 신경망 궤환 제어시스템이 외란의 영향을 받았을 때, 시스템의 강인한 제어는 어렵게 된다. 본 논문에서는 이러한 문제를 해결하기 위한 한 방법으로 신경회로망제어기와 PR제어기의 복합형 제어방법을 제시하였다. 신경회로망 제어기는 주 제어기로서 동작하고, PID제어기는 허용오차가 경계영역을 벗어날 때 동작하는 보조제어기로 사용된다. 신경회로망-PID복합형제어기의 강인성은 전동기의 속도제어에 의해서 확인하였다.

• International Journal of Aeronautical and Space Sciences
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• 제17권2호
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• pp.195-203
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• 2016

In order to overcome the influence of system stability and accuracy caused by uncertainty, estimation errors and external disturbances in Eight-Rotor MAV, L2 gain control method was proposed based on interval type II fuzzy neural network identification here. In this control strategy, interval type II fuzzy neural network is used to estimate the uncertainty and non-linearity factor of the dynamic system, the adaptive variable structure controller is applied to compensate the estimation errors of interval type II fuzzy neural network, and at last, L2 gain control method is employed to suppress the effect produced by external disturbance on system, which is expected to possess robustness for the uncertainty and non-linearity. Finally, the validity of the L2 gain control method based on interval type II fuzzy neural network identifier applied to the Eight-Rotor MAV attitude system has been verified by three prototy experiments.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제49권8호
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• pp.557-564
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• 2000

A very simple control approach using neural network for the robust position control of a Permanent Magnet Synchronous Motor(PMSM) is presented. The linear quadratic controller plus feedforward neural network is employed to obtain the robust PMSM system approximately linearized using field-orientation method for an AC servo. The neural network is trained in on-line phases and this neural network is composed by a feedforward recall and error back-propagation training. Since the total number of nodes are only eight, this system can be easily realized by the general microprocessor. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. In addition, the robustness is also obtained without affecting overall system response. This method is realized by a floating-point Digital Signal Processor DS1102 Board (TMS320C31).

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권12호
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• pp.3364-3382
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• 2023

Remote sensing image segmentation plays an important role in realizing intelligent city construction. The current mainstream segmentation networks effectively improve the segmentation effect of remote sensing images by deeply mining the rich texture and semantic features of images. But there are still some problems such as rough results of small target region segmentation and poor edge contour segmentation. To overcome these three challenges, we propose an improved semantic segmentation model, referred to as MRU-Net, which adopts the U-Net architecture as its backbone. Firstly, the convolutional layer is replaced by BasicBlock structure in U-Net network to extract features, then the activation function is replaced to reduce the computational load of model in the network. Secondly, a hybrid multi-scale recognition module is added in the encoder to improve the accuracy of image segmentation of small targets and edge parts. Finally, test on Massachusetts Buildings Dataset and WHU Dataset the experimental results show that compared with the original network the ACC, mIoU and F1 value are improved, and the imposed network shows good robustness and portability in different datasets.

• 한국방재학회 논문집
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• 제10권1호
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• pp.73-80
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• 2010

상수도관망의 최적설계는 단목적함수와 고정된 수리학적 변수로 구성된 비용최소화의 문제로 시작되었다. 하지만, 미래의 불확실한 수요량의 변동과 같이 상수도관망 내에 존재하는 여러 불확실성을 고려하여 설계하는 것이 실제 상수도관망의 거동을 보다 적절히 예측하는 것이다. 따라서 상수도관망 내 존재하는 불확실성을 양적으로 고려하는 다양한 방법이 연구되어 상수도관망의 최적설계에 반영되었고, 다목적함수를 사용한 최적화문제도 다루게 되었다. 본 연구에서는 관망의 절점에서의 수요량과 관의 조도계수를 불확실성을 가진 변수로 두고, 비용 최소화와 관망의 강건성 (Robustness)을 최대화 하는 두 가지 목적함수를 가진 다목적함수 최적화 문제를 다루었다. 최적화 과정은 비용최소화와 불확실성을 고려한 최종 최적화의 두 과정으로 나뉜다. 각 절점에서의 수요량과 관의 조도계수는 베타확률밀도함수 (Beta PDF)를 사용, Latin Hypercube 샘플링 방법으로 불확실성을 고려하였고, 다목적함수의 최적화는 유전자 알고리듬 (Multi-objective Genetic Algorithms, MOGA)을 사용하였다. 제안된 방법은 New York Tunnels이라는 실제 상수도관망에 적용하여 적용성을 검증 하였고 그 결과를 분석하였다. 다목적 최적화 문제에서 최적화가 진행될 수 록 초기 값에 모여 있던 점들이 그 점 주위를 시작으로 해 공간에 최적 해를 찾아 오른쪽 아래 부분으로 탐색해 나가는 것을 확인할 수 있었고 최적설계의 해는 해 공간에서 Pareto Front를 구성하며 파레토 최적해를 구하였다.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권4호
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• pp.154-157
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• 2006

This paper is proposed hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design..of this algorithm based on fuzzy-neural network(FNN) controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• 한국정보과학회논문지:소프트웨어및응용
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• 제36권11호
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• pp.955-959
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• 2009

본 논문에서는 휴먼 객체들의 이동 궤적 정보를 기반으로 휴먼 상호 행동을 인식하기 위한 새로운 모델을 제안한다. 복잡한 휴먼 상호 행동들은 의미있는 작은 단위로 분할될 수 있는데 이를 '부-상호행동'이라 하며, 이들을 표현하는 모델들의 순차적 연결 또는 네트워크로 상호 행동을 모델링한다. 제안하는 모델은 서로 다른 상호 행동들에 공통적으로 나타나는 부-상호 행동들을 공유하도록 함으로써 모델의 복잡도를 낮추어 매우 효율적이다. 상호 행동 네트워크 모델의 동작 분석 및 기존 방법과의 비교 실험을 통해 제안한 방법의 우수성을 확인할 수 있었다.

• Journal of information and communication convergence engineering
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• 제7권4호
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• pp.475-479
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• 2009

In ad hoc network, the scarce energy management of the mobile devices has become a critical issue in order to extend the network lifetime. Current research activity for the Minimum Energy Multicast (MEM) problem has been focused on devising efficient centralized greedy algorithms for static ad hoc networks. In this paper, we consider mobile ad hoc networks(MANETs) that could provide the reliable monitoring and control of a variety of environments for remote place. Mobility of MANET would require the topology change frequently compared with a static network. To improve the routing protocol in MANET, energy efficient routing protocol would be required as well as considering the mobility would be needed. In this paper, we propose a new method, the CACH(Context-aware Clustering Hierarchy) algorithm, a hybrid and clustering-based protocol that could analyze the link cost from a source node to a destination node. The proposed analysis could help in defining the optimum depth of hierarchy architecture CACH utilize. The proposed CACH could use localized condition to enable adaptation and robustness for dynamic network topology protocol and this provide that our hierarchy to be resilient. As a result, our simulation results would show that CACH could find energy efficient depth of hierarchy of a cluster.