Chuo-Yean Chang

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This paper presents an indoor positioning technique based on neural networks (NN). The received signal strengths (RSS) sensed by Zigbee wireless sensor network were used to estimate the position of object. From the simulation results shown, the NN technique proposed still has the high accuracy even the signal strengths sensed are unstable. Besides, from the(More)
In this paper, the transmittance estimation of touch panel decoration film by using quantum neural network (QNN) is proposed. This model is able to catch the complex relationship between the film’s transmittance and its possible influencing factors. An artificial intelligent (AI) mechanism for the decision of control parameters of film evaporation(More)
In this paper, a new logic circuit design technique by using neural network and particle swarm optimization (PSO) method is proposed. The neural network was used to substitute the logic unit and PSO algorithm was used to determine the possibility of connections among the logic units. By off-line gate-level samples, the simulation results clearly demonstrate(More)
This paper presents the power load forecasting by using neural models for Toronto area, Canada. Different neural models were used to carry out the forecasting works. One-day-ahead daily total load and peak load forecasts were implemented by using different neural models in order to find the more accurate forecasting results. The load data and temperatures(More)
This paper presents the chromatic aberration estimations of touch panel (TP) film by using quasi-Newton neural networks. The data of TP film with one layer coating was studied and simulated. Through the training of neural network, the complex relationship between the chromatic aberration, i.e., L.A.B. values, and the relative parameters of TP decoration(More)
In this paper, a hybrid supervised learning algorithm for neural network was proposed. The problem of local minimum learning usually occurred in the real application of neural network is tried to be solved or reduced. In order to improve the efficiency and stability of conventional error back-propagation learning algorithm, a hybrid learning method(More)
In this paper, the neural network estimator for mechanical property of rolled steel bar was proposed. Based on the learning capability of neural network, the nonlinear, complex relationships among the steel bar, the billet materials and the control parameters of production are expected to be automatically developed. Such a neural network estimator can help(More)
In this paper, an AI estimator of electric contract capacity for community antenna television system (CATV) based on quantum neural network (QNN) is proposed. This intelligent estimator not only can make CATV company have a good planning on the development of TV network system and power demand, but also can greatly reduce the company's running cost. In this(More)
In this paper, the signal processing by using polynomial neural network (NN) and its equivalent polynomial function is studied and simulated. To demonstrate the superiority of the equivalent polynomial function proposed, the signal recognition in a two-dimension (NC2) non-convex system and system identification were simulated and discussed. All simulations(More)