Chuo-Yean Chang

The indoor positioning technique based on neural networks

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 experimental results shown, it is concluded that the positioning accuracy could be improved if the number of wireless sensors is added more. In this research, the polar coordinate system of objects position was also studied. It is found that the accuracy of positioning by polar form is better than by rectangular form.

The operational strategy of cogeneration plants in a competitive market

This paper presented an operational strategy for cogeneration plants under a deregulated market. The objective of this paper includes fuel cost, population cost, and electricity wheeling cost, subjective to the use of mixed fuels, operational limits, emissions constraints, and transmission line flow constraints. Enhanced immune algorithm (EIA) was proposed by an improved crossover and mutation mechanism with a competition and auto-adjust scheme to avoid prematurity. Results verify that EIA can offer an efficient way for cogeneration plants to solve the problem of economic dispatch, environmental protection, and electricity wheeling.

Transmittance Estimation of TP Decoration Film by QNN

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 is expected to be developed. Based on this AI mechanism, the technician could make a good setting work for the real-line evaporation process. Thus, this system not only can help the technician to improve the efficiency of the touch panel, but also can reduce the cost caused by the defective products for the company.

A Hybrid Supervised Neural Network Learning Algorithm

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 combining the linear multi-regression and backpropagation techniques was developed. To demonstrate the superiority of the method we developed, one example was simulated. The conventional BP learning method was also performed as the comparison with the new method proposed. From the results shown, the conventional BP method easily makes neural model plunge into the local minima. On the contrary, the new method we proposed not only has a fast learning, but also has a better learning efficiency.

Power Signal Forecasting by Neural Model with Different Layer Structures

In this paper, the non-stationary power load forecasting by using neural model with different layer structures is presented. In the neural forecasting model we developed, the neuron types used in different layers are different. Each layer is composed of the same kind of neurons. A reliable and accurate neural forecasting model for the non-stationary power loads is trying to be found in this study. To demonstrate the superiority of the model we created, all simulations are executed by using the conventional neural model with same neurons as a comparison. From the results shown, it is clearly found that the neural model we constructed do have better nonlinear mapping and forecasting capabilities in comparison with the conventional neural model

The data mining for TP film's transmittance by using neural network

This paper presents a new computation method based on the weights of the well-trained neural network (NN) for the data mining of touch panel (TP) films transmittance. By using the method developed, the influence degree of each input variable to the transmittance could be obtained and then the useful influencing inputs could also be determined. In this research, the data of the transmittance of TP film with Cr and Cr2O3 coating are studied. The possible influencing factors including the coating target composition, the layers of coating material, the films thickness, the position of panel placed and the rotation speed of evaporators holder are collected and analyzed. The relationship between films transmittance and these possible influencing factors is expected to be obtained.

The Chromatic Aberration Estimation of TP Film with Two Layers Coating for Electronic Appliance

This paper presents the chromatic aberration estimations of touch panel (TP) film with two layers coating for electronic appliance in human’s daily life. The data of TP film with Cr and Cr2O3 coats were studied and simulated. The complex relationship between the chromatic aberration, i.e. L, a, b values, and the relevant evaporation parameters of TP film could be developed through the neural network’s (NN) training. The well-trained NN model then can be used to estimate the chromatic aberration. In other words, an artificial intelligent (AI) mechanism for the estimation of the optical properties of TP film is possibly to be created. Thus, the technician is able to set the control parameters of evaporation process in advance to make the quality of TP film meet the customer’s request.

The Identifications of Ammonia Concentration by Different Neural Models

This paper presents the identifications of ammonia concentration by using several different neural network (NN) models. The shear horizontal surface acoustic wave (SH-SAW) device coated with polyaniline (PANI) film was applied as ammonia sensor. The data sensed by SH-SAW sensor was implemented by these NN models. A reliable and superior intelligent identifier is expected to be found for effectively overcoming the interference of humidity in ammonia detection.

The Neural Network Estimator for Mechanical Property of Rolled Steel Bar

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 the technician to make a precise judgment for setting the related control parameters of rolling process. Not only the quality of steel bars can meet the standard asked for, but also can reduce the running cost caused by failure production.

The Transmittance Estimation of Two Layers Coating TP Film by Neural Network

In this paper, the estimations for the optical property of touch panel (TP) decoration film with two layers coating are presented. The technique of neural network is used to develop an artific ial intelligent (AI) TP transmittance estimator which is able to catch the complicated relationship between TP transmittance and its all possib le influencing factors, such as the compositions of coating material, the thickness of coating, the rotation speed of evaporator ?s holder and so on. This AI estimator then can provide the useful information which could help the engineer to set the relevant control parameters of evaporator properly before the evaporation process is taken. The simulation results show that such an AI system is quite promising to be developed.