• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.396-401
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• 2006

It is well-known that a neural network with cyclic connections generates plural limit cycles, thus, being used as a memory system for storing large number of dynamic information. In this paper, a continuous-time cyclic connection neural network was built so that each neuron is connected only to its nearest neurons with binary synaptic weights of ${\pm}1$. The type and the number of limit cycles generated by such network has also been demonstrated through simulation. In particular, the effect of chaos signal for transition between limit cycles has been tested. Furthermore, it is evaluated whether the chaotic noise is more effective than random noise in the process of the dynamical neural networks.

• Geotechnical Engineering
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• v.10 no.3
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• pp.79-96
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• 1994

A tunnel design expert system entitled NESTED is developed using the artificial neural network. The expert system includes three neural network computer models designed for the stability assessment of underground openings and the estimation of correlation between the RMR and Q systems. The expert system consists of the three models and the computerized rock mass classification programs that could be driven under the same user interface. As the structure of the neural network, a multi -layer neural network which adopts an or ror back-propagation learning algorithm is used. To set up its knowledge base from the prior case histories, an engineering database which can control the incomplete and erroneous information by learning process is developed. A series of experiments comparing the results of the neural network with the actual field observations have demonstrated the inferring capabilities of the neural network to identify the possible failure modes and the support timing. The neural network expert system thus complements the incomplete geological data and provides suitable support recommendations for preliminary design of tunnels in rock masses.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.4
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• pp.399-405
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• 2010

The grasping motion of a person's hand for a simplified degree of freedom was modeled by using the photographic motion measured by a high-speed camera. The mathematical expression of distal interphalangeal (DIP) motion was developed by using relation models of the metacarpophalangeal (MCP) and proximal interphalangeal (PIP) motions to reduce the degree of freedom. The mathematical expression for humanoid-hand operation obtained using a learning algorithm with a neural network and using a kernel regression model were compared. A feasible model of hand operation was obtained on the basis of comparative data analysis by using the kernel regression model.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.4
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• pp.354-358
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• 2001

This paper presents a fuzzy neural network model which solves the underutilization problem. This fuzzy neural network has both stability and flexibility because it uses the control structure similar to AHT(Adaptive Resonance Theory)-l neural network. And this fuzzy nenral network does not need to initialize weights and is less sensitive to noise than ART-l neural network is. The learning rule of this fuzzy neural network is the modified and fuzzified version of Kohonen learning rule and is based on the fuzzification of leaky competitive leaming and the fuzzification of conditional probability. The similarity measure of vigilance test, which is performed after selecting a winner among output neurons, is the relative distance. This relative distance considers Euclidean distance and the relative location between a datum and the prototypes of clusters. To compare the performance of the proposed fuzzy neural network with that of Kohonen Self-Organizing Feature Map the IRIS data and Gaussian-distributed data are used.

• Journal of IKEEE
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• v.18 no.4
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• pp.593-602
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• 2014

One of the purposes of an artificial neural netowrk(ANNet) is to implement the largest number of functions as possible with the smallest number of nodes and layers. This paper presents a CoreNet which has a multi-leveled input value and a multi-leveled output value with a 2-layered ANNet, which is the basic structure of an ANNet. I have suggested an equation for calculating the capacity of the CoreNet, which has a p-leveled input and a q-leveled output, as $a_{p,q}={\frac{1}{2}}p(p-1)q^2-{\frac{1}{2}}(p-2)(3p-1)q+(p-1)(p-2)$. I've applied this CoreNet into the simulation model 1(5)-1(6), which has 5 levels of an input and 6 levels of an output with no hidden layers. The simulation result of this model gives, the maximum 219 convergences for the number of implementable functions using the cot(${\sqrt{x}}$) input leveling method. I have also shown that, the 27 functions are implementable by the calculation of weight values(w, ${\theta}$) with the multi-threshold lines in the weight space, which are diverged in the simulation results. Therefore the 246 functions are implementable in the 1(5)-1(6) model, and this coincides with the value from the above eqution $a_{5,6}(=246)$. I also show the implementable function numbering method in the weight space.

• Journal of IKEEE
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• v.15 no.4
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• pp.354-362
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• 2011

One of the unsolved problems in Artificial Neural Networks is related to the capacity of a neural network. This paper presents a CoreNet which has a multi-leveled input and a multi-leveled output as a 2-layered artificial neural network. I have suggested an equation for calculating the capacity of the CoreNet, which has a p-leveled input and a q-leveled output, as $a_{p,q}=\frac{1}{2}p(p-1)q^2-\frac{1}{2}(p-2)(3p-1)q+(p-1)(p-2)$. With an odd value of p and an even value of q, (p-1)(p-2)(q-2)/2 needs to be subtracted further from the above equation. The simulation model 1(3)-1(6) has 3 levels of an input and 6 levels of an output with no hidden layer. The simulation result of this model gives, out of 216 possible functions, 80 convergences for the number of implementable function using the cot(x) input leveling method. I have also shown that, from the simulation result, the two diverged functions become implementable by precalculating the weight values. The simulation result and the precalculation of the weight values give the same result as the above equation in the total number of implementable functions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.3
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• pp.197-205
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• 1992

In this paper object recognition using neural network is studied. The recognition is accomplished by matching linear line segments which are formed by local features extracted from the curvature points. Since there is similarities among segments. The boundary of models is not distinct in feature space. Due to these indistinctness the ambiguity of recognition occurs, and the recognition rate becomes degraded according to the limitation of boundary decision capability of neural network for similar of features. Object recognition and to improve recognition rate. Local features are used to represent the object effectively. The validity of the object recognition system is demonstrated by experiments for the occluded and varied objects.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.441-444
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• 1997

증기 발생기 수위 제어기의 성능 향상은 발전소의 정기 횟수를 줄여 발전소 신뢰도 및 가동률을 향상시키고 또한 기타 여러 부품의 수명에도 영향을 주어 경제적으로 보다 효율적인 발전소 운영에 기여한다. 이러한 수위 제어의 발전을 위해서 본 연구에서는 E. Irvingd의 모델을 사용하였다. E. Irving이 모델이 단순화한 관계로 단점을 가지고는 있으나 프로그램화가 편리하고, 또한 증기 발생기의 특성을 잘 표현하기 때문에 이용하였다. 먼저 시스템의 출력, 즉 증기 발생기의 수위를 안정화시키기 위하여 퍼지 제어기를 Case by Case로 선정하여 제어를 하였으며, 그 다음으로 시스템의 두 입력, 증기량과 퍼지 제어기에서 선택되어진 급수 유량, 그리고 전 단계의 출력인 증기 발생기의 수위를 입력으로 하는 신경 회로망을 이용하여 시스템을 규명하였다.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.1
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• pp.98-105
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• 2008

In this paper, the system for monitoring process of aluminum laser welding was developed using the light signal emitted from the plasma which comes from interaction between material and laser. Photodiode for monitoring system was selected based on the spectrum analysis of light from plasma and keyhole. Behavior of plasma and keyhole was analyzed through the sensor signals. Value of sensor signal represented the light intensity and fluctuation of signal indicated the stability of plasma and keyhole. For the relation between welding condition and sensor signals, the input power and weld geometry greatly effected on the average of each sensor signals. Using the feature values of signals, estimation model for tensile strength of weld was formulated with neural network algorithm. Performance of this model was verified through coefficient of determination and average error rate.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.1946-1948
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• 2001

본 논문에서는 저급 관성 센서를 이용한 개인 항법 장치의 모델 및 오차 보정 기법을 제시하고 성능 평가를 위하여 시뮬레이션을 수행하였다. 걸음 검출에 의한 보행 항법에서 중요한 변수인 보폭은 신경 회로망(Neural Network)을 이용하여 결정하였고, 자이로 바이어스 등에 의하여 누적되는 오차는 GPS와의 결합에 의하여 추정, 보상하였다. 이때 GPS와의 결합은 칼만필터를 이용하였으며 칼말필터를 구성하는데 필요한 오차 모델 및 결합 방법을 제시하였다. WNS/GPS 결합에 의하여 오차의 발산을 막을 수 있으며 GPS신호가 중간에 단절되는 경우에도 오차가 발산하지 않고 좋은 결과를 유지함을 보인다.