• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.540-542
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• 1999

We develop jumping ring system with sensor and control system using dynamic neural networks. Jumping ring, sensor and control system are controlled by 586 PC using Turbo C program. Sensor system is composed of 20 optical sensors and encoder. The control circuits are consisted of thyristor, FET and phase controller. A/D converter and optical sensor acquire real time motion data of the jumping ring system. The information of acquired jumping ring Position is estimated by using dynamic neural networks. Estimated control signals are sent to control circuits and D/A converter to track desired position of the jumping ring system. Experiment results are given to verify that proposed dynamic controller is useful in real jumping ring system.

• E2M - 전기 전자와 첨단 소재
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• 제10권8호
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• pp.783-793
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• 1997

A VLSI implementation of a self-learning neural network integrated circuits using a linearity synapse transistor is investigated. The thickness dependence of oxide current density stress current transient current and channel current has been measured in oxides with thicknesses between 41 and 112 $\AA$, which have the channel width $\times$ length 10 $\times$1${\mu}{\textrm}{m}$, 10 $\times$ 0.3${\mu}{\textrm}{m}$ respectively. The transient current will affect data retention in synapse transistors and the stress current is used to estimate to fundamental limitations on oxide thicknesses. The synapse transistor has represented the neural states and the manipulation which gaves unipolar weights. The weight value of synapse transistor was caused by the bias conditions. Excitatory state and inhitory state according to weighted values affected the drain source current.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.968-971
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• 1993

Fuzzy inference system which inferences and processes the Fuzzy information is designed using digital voltage mode neural circuits. The digital fuzzification circuit is designed to MIN,MAX circuit using CMOS neural comparator. A new defuzzification method which uses the center of area of the resultant fuzzy set as a defuzzified output is suggested. The method of the center of area(C. O. A) search for a crisp value which is correspond to a half of the area enclosed with inferenced membership function. The center of area defuzzification circuit is proposed. It is a simple circuit without divider and multiflier. The proposed circuits are verified by implementing with conventional digital chips.

• 대한의용생체공학회:의공학회지
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• 제17권3호
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• pp.387-394
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• 1996

This paper presents a Neural Network Identification(NNI) method for modeling of highly complicated nonlinear and time varing human system with a pneumatically driven mock circulatory system of Left Ventricular Assist Device(LVAD). This system consists of electronic circuits and pneumatic driving circuits. The initiation of systole and the pumping duration can be determined by the computer program. The line pressure from a pressure transducer inserted in the pneumatic line was recorded System modeling is completed using the adaptively trained backpropagation learning algorithms with input variables, heart rate(HR), systole-diastole rate(SDR), which can vary state of system. Output parameters are preload, afterload which indicate the systemic dynamic characteristics. Consequently, the neural network shows good approximation of nonlinearity, and characteristics of left Ventricular Assist Device. Our results show that the neural network leads to a significant improvement in the modeling of highly nonlinear Left Ventricular Assist Device.

• 생물정신의학
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• 제4권2호
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• pp.194-197
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• 1997

Neural network models, also known as connectionist models or PDP models, simulate some functions of the brain and may promise to give insight in understanding the cognitive brain functions. The models composed of neuron-like elements that are linked into circuits can learn and adapt to its environment in a trial and error fashion. In this article, the history and principles of the neural network modeling are briefly reviewed, and its applications to psychiatry are discussed.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 추계종합학술대회 논문집(3)
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• pp.149-152
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• 2000

We propose simple analog MOS circuits producing the one-dimensional compact motion-sensing circuits. In the proposed circuit, the optical flow is computed by a number of local motion sensors which are based on biological motion detectors. Mimicking the structure of biological motion detectors made the circuit structure quite simple, compared with conventional velocity sensing circuits. Extensive simulation results by a simulation program of integrated circuit emphasis (SPICE) indicated that the proposed circuits could compute local velocities of a moving light spot and showed direction selectivity for the moving spot

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.69-70
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• 1995

This paper considers the applications of cellular automata in order to design self-organizing artificial neural decision systems such as self-organizing neurocomputer circuit, machines, and artifical life VLSI circuits for controlling mechanical systems. We consider the L-system and show the results of growth of plants in artificial life.

• 전자공학회논문지B
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• 제32B권5호
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• pp.661-670
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• 1995

This paper presents a fast massively parallel automatic test pattern generator for digital combinational logic circuits using neural networks. Automatic test pattern generation neural network(ATPGNN) evolves its state to a stable local minima by exchanging messages among neural network modules. In preprocessing phase, we calculate the essential assignments for the stuck-at faults in fault list by adopting dominator concept. It makes more neurons be fixed and the system speed up. Consequently. fast test pattern generation is achieved. Test patterns for stuck-open faults are generated through getting initialization patterns for the obtained stuck-at faults in the corresponding ATPGNN.

• 대한의용생체공학회:의공학회지
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• 제9권1호
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• pp.17-24
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• 1988

This paper describes a neural network modelling of a vertebrate retina using a discrete-time and discrete-space approach based on neuro-anatomical data, and the computer simulations of the model which approximate the frog/amphibian negro-physiological data. It then compares them and describes how such a model can be beneficially used for confirming the hypothesis of a given neural system and further predict yet unknown experimental data.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권11호
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• pp.617-624
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• 2000

The negative weight can be ofter seen in Hopfield neural network, which is difficult to implement negative conductance in circuits. Usually, the inverted output of amplifier is used to avoid negative resistors for expressing the negative weights in hardware implementation. However, there is some difference between using negative resistor and the inverted output of amplifier for representing the negative weight. This difference is discussed in this paper.