• Journal of Advanced Marine Engineering and Technology
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• 제23권2호
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• pp.168-175
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• 1999

Unlike a general inverted pendulum system which is moved on the cart the proposed inverted pendulum system in this paper has an inverted pendulum which is moved on the two-degree-of-freedom parallelogram link. The dynamic equation of the pendulum system activated by the DD(Direct Drive)motor includes many nonlinear terms and has the high degree of freedoms. The problem is followed hat the exact mathmatical equations can not be analized by a general linear theory However the neural network trained by a simple learning method can control the dynamic system with hard nonlinearities. Learning procedure is the backpropagation algorithm with super-visory signal. The plant inputs obtained by the designed neural network in this paper can stabilize the pendu-lem and get the servo control. Experiment results have proce the effectiveness of the designed neural network controller.

• 제어로봇시스템학회논문지
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• 제3권6호
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• pp.594-601
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• 1997

A new algorithm is proposed for training the multilayer perceptrion(MLP) in pattern classification problems to accelerate the learning speed. It is shown that the sigmoid activation function of the output node can have deterimental effect on the performance of learning. To overcome this detrimental effect and to use the information fully in supervised learning, an objective function for binary modes is proposed. This objective function is composed with two new output activation functions which are selectively used depending on desired values of training patterns. The effect of the objective function is analyzed and a training algorithm is proposed based on this. Its performance is tested in several examples. Simulation results show that the performance of the proposed method is better than that of the conventional error back propagation (EBP) method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2588-2590
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• 2003

The part of manipulators is normally studied with regularized environmental conditions. however, it is the most difficult that the part of AMR must be studied with uncertainty in the environmental conditions. The part of AMR has skelton, sensor fusion, path planning etc. This paper is the research of the local pass planning that gathers information about external environment using neural network from each sensors and designs the algorithm which can determine which correct direction the robot can find. As the result of the research, AMR has been able to drive similarly as if the expert does and has been able to observe it acting without any control.

• Journal of Information Processing Systems
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• 제13권1호
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• pp.141-151
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• 2017

Effective identification of wireless channel in different scenarios or regions can solve the problems of multipath interference in process of wireless communication. In this paper, different characteristics of wireless channel are extracted based on the arrival time and received signal strength, such as the number of multipath, time delay and delay spread, to establish the feature vector set of wireless channel which is used to train backpropagation (BP) neural network to identify different wireless channels. Experimental results show that the proposed algorithm can accurately identify different wireless channels, and the accuracy can reach 97.59%.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2007년도 추계학술발표논문집
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• pp.81-84
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• 2007

In this thesis, we have designed the indirect adaptive controller using Dynamic Neural Units(DNU) for unknown nonlinear systems. Proposed indirect adaptive controller using Dynamic Neural Unit based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our method is different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its training.

• 디지털산업정보학회논문지
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• 제19권4호
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• pp.127-136
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• 2023

Many research results on the traffic enforcement of illegal driving of twowheeled motor vehicles using license plate recognition are introduced. Deep learning convolutional neural networks can be used for character and word recognition of license plates because of better generalization capability compared to traditional Backpropagation neural networks. In the plates of twowheeled motor vehicles, the interdependent government and city words are included. If we implement the mutually independent word recognizers using error correction rules for two word recognition results, efficient license plate recognition results can be derived. The CPU based convolutional neural network without library under real time processing has an advantage of low cost real application compared to GPU based convolutional neural network with library. In this paper twowheeled motor vehicle license plate recognition algorithm is introduced using CPU based deep-learning convolutional neural network. The experimental results show that the proposed plate recognizer has 96.2% success rate for outdoor twowheeled motor vehicle images in real time.

• 한국정보처리학회논문지
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• 제6권5호
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• pp.1393-1402
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• 1999

본 논문에서는 새로운 학습알고리즘의 3층 전향 신경망을 이용한 입력데이터의 주요 특징추출에 대해서 제안하였다. 제안된 학습알고리즘에서에서는 빠른 수렴속도의 최적화가 가능하도록 하기 위하여 기울기하강의 역전파 알고리즘을 이용하고, 국소최적해를 만났을 때 이를 벗어난 새로운 연결가중치의 설정을 위하여 동적터널링의 역전파 알고리즘을 이용함으로써 빠른 수렴속도로 전역최적해로에 수렴되도록 학습시킬 수 있다. 제안된 학습 알고리즘을 이용한 다층신경망을 $12{\times}12$ 픽셀의 영상 데이터들과 $128{\times}128$ 픽셀의 Lenna 영상데이터를 대상으로 시뮬레이션한 결과, 단층신경망을 이용하는 Sanger 방법이나 측면연결을 가지는 단충신경망을 이용하는 Foldiak 방법 및 기울기하강에 기초를 둔 기존의 역전파 알고리즘을 이용한 다층신경망에 의한 결과와 비교할 때 더욱 우수한 수렴성능과 추출성능이 있음을 확인할 수 있었다.

• 한국항행학회논문지
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• 제3권1호
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• pp.32-43
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• 1999

본 논문에서는 터보제트 엔진 제어기의 신뢰성을 향상시키기 위한 지능형 고장진단 알고리즘을 제안하였다. 제안된 기법은 다층 신경망을 이용한 고장진단 기법으로 서로 다른 종류의 센서를 사이의 기능적 종속관계를 추정하여 고장 부위를 규명하고 처치한다. 고장센서 신호는 역전파 알고리즘을 이용한 훈련된 신경망을 통하여 추정한다. 실험데이터에 기초한 터보제트 엔진의 선형공간 모델에 적용한 시뮬레이션을 통하여 제안된 알고리즘의 구현 가능성을 검증한다.

• 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.

• Ocean Systems Engineering
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• 제7권2호
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• pp.157-177
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• 2017

This research is based on the concept of safety airbag to design a self-rescue system for the autonomous underwater vehicle (AUV) using micro inertial sensing module. To reduce the possibility of losing the underwater vehicle and the difficulty of searching and rescuing, when the AUV self-rescue system (ASRS) detects that the AUV is crashing or encountering a serious collision, it can pump carbon dioxide into the airbag immediately to make the vehicle surface. ASRS consists of 10-DOF sensing module, sensing attitude algorithm and air-pumping mechanism. The attitude sensing modules are a nine-axis micro-inertial sensor and a barometer. The sensing attitude algorithm is designed to estimate failure attitude of AUV properly using sensor calibration and extended Kalman filter (SCEKF), feature extraction and backpropagation network (BPN) classify. SCEKF is proposed to be used subsequently to calibrate and fuse the data from the micro-inertial sensors. Feature extraction and BPN training algorithms for classification are used to determine the activity malfunction of AUV. When the accident of AUV occurred, the ASRS will immediately be initiated; the airbag is soon filled, and the AUV will surface due to the buoyancy. In the future, ASRS will be developed successfully to solve the problems such as the high losing rate and the high difficulty of the rescuing mission of AUV.