Jen-Pin Yang

The Estimations of Mechanical Property of Rolled Steel Bar by Using Quantum Neural Network

In this paper, the estimations of mechanical property of rolled steel bar by using quantum neural network (QNN) were 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 could be automatically developed. Such an artificial intelligent (AI) estimator then can help the operation technician to set the related control parameters of rolling process. Not only the quality of steel bars could be improved, but also the cost of bar’s production could be greatly reduced.

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.

The estimations of ammonia concentration by using neural network SH-SAW sensors

Research highlights? This paper presents the estimations of ammonia concentration by using neural network (NN) models. ? The shear horizontal surface acoustic wave (SH-SAW) devices coated with L-glutamic acid hydrochloride and polyaniline (PANI) film, respectively, were applied as the ammonia sensors. ? The signal sensed by SH-SAW sensors were implemented by using different NN models for the estimation of ammonia concentration. ? A reliable and superior neural network SAW identifier is expected to be found for effectively overcoming the interference of humidity in ammonia detection.Display Omitted This paper presents the estimations of ammonia concentration by using neural network (NN) models. The shear horizontal surface acoustic wave (SH-SAW) devices coated with L-glutamic acid hydrochloride and polyaniline (PANI) film, respectively, were applied as the ammonia sensors. The data sensed by SH-SAW sensors were implemented by using different NN models. A reliable and superior neural network SAW identifier is expected to be found for effectively overcoming the interference of humidity in ammonia detection.

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 transmittance estimations of TP film with Cr and Cr 2 O 3 coating

This paper presents the transmittance estimations for touch panel (TP) film with Cr and Cr2O3 coating by using neural network (NN) model. The NN model with quasi-Newton learning method was used to obtain the mapping between TP transmittance and its all possible influencing factors. This study tries to develop an artificial intelligent (AI) evaporation decision mechanism which can help the technician to set the related control parameters before the films evaporation process is taken. The transmittance is one of important determination factors used for checking whether the quality of TP is qualified or not. Thus, a smart decision mechanism not only can help technician to improve the work efficiency, but also can reduce the running cost of the company due to the defective products.

Investigation of Rayleigh surface acoustic wave sensors for NO gas sensing applications

This paper presents the estimation of the interference of NH3 on the detection of NO by using neural network (NN) model. The NO Rayleigh surface acoustic wave (RSAW) sensor coated with polyaniline/WO3 (PANI/WO3) nanocomposite was employed as the detection sensor. The data sensed by RSAW sensor was collected and implemented and the detection property of the RSAW sensor was studied at room temperature. A neural network RSAW identifier is expected to be created in order to estimate the interference of NH3 on the NO detection of the RSAW sensor.

Quality Identification of the Riveting Process by QNN Model

In this paper, an automatic quality inspection system for the riveting process by using quantum neural network (QNN) was proposed. This inspection system not only can monitor the real time riveting process, but also can give the assistance on the riveting quality verification. For demonstrating the superiority of the inspection system we developed, the data provided by the experiment did by Chinese Air Force Institute of Technology was simulated. The method of riveting quality index (RQI) was also performed as a comparison.

An AI Estimator of Electric Contract Capacity for CATV System Based on QNN Model

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 companys running cost. In this AI estimator, the neural model was used to execute the estimation of power demand. Due to the powerful learning capability of neural network, the nonlinear and complex relationships between power demand and its possible influencing factors could be automatically developed. Thus, such a well-trained neural model could be employed into the electricity demand estimation with high accuracy.

An Intelligent Fault Diagnostics for Turbine Generator by Modified Neural Model

In this paper, an intelligent fault diagnostic tool for oil-fired power plant with turbine generator by using the modified neural network was proposed. This tool is able to monitor the running condition of power plant immediately. It also can reveal the fault situation if the power plant had some troubles. Therefore, such a well designed mechanism can be used as the training tool for laboratory course in power turbine studies. To demonstrate the feasibility of the tool we developed, several real case studies were simulated. From the simulation results shown, the tool we developed is very promising in the real applications.

The Identification of Ammonia Concentration by QNN

In this paper, the humidity interference on the ammonia sensors was studied and proposed. The shear horizontal surface acoustic wave (SH-SAW) devices coated with L-glutamic acid hydrochloride were applied as ammonia sensors. The responses to ammonia in the humid environment were positive frequency shifts and the perturbation was primarily from the elastic effect. In order to precisely estimate the ammonia in humid air, the quantum neural network (QNN) is used as the identifier and its identification results are reported.