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In the paper, design and Implementation of cloud-dust based intelligent system is proposed. For achieving applications of intelligent system, such as records, surveillance, assessments, predictions, diagnosis, prescription, scheduling and fool-proofing checks, an architecture named Cloud-Dust is developed. The intelligent system is separated into the cloud system and the dust system. The dust system contains (1) Wireless sensors network (2) Features extraction circuits (3) Intelligent computing circuits (4) Embedded system. It can play a role as real-time preprocessor very well, just like an intelligent agent. However, the cloud system contains (1) Cloud database (2) Intelligent computing engine (3) Ubiquitous human-machine-interface. It can flexibly use computing resources and integrate information from many different dust systems. By the experiments, we can find the advantages of the cloud-dust based intelligent system. It meets the both needs of real-time and integration for intelligent systems. So it is necessary to develop the cloud-dust based system for design and implementation of the intelligent system.

In the paper, a cloud-dust based intelligent maximum power analysis system for photovoltaic is proposed. In order to resolve NP problem for photovoltaic, factors of photovoltaic are integrated to cloud-dust based intelligent maximum power analysis system for computing. This study is the development of the maximum power analysis system for photovoltaic, to improve the solar panels effects of the different region and enable them to get maximum efficiency of the power generation. The design methodology of this study includes: (1) The monitoring and control Module (2) The prediction and evaluation module (3) The performance diagnosis module (4) The maintenance prescription module. At last, we can find the advantages of the cloud-dust based intelligent maximum power analysis system for photovoltaic. It increases overall competitive performance of products, reduces cost of products and consummation rates of human resources.

The main purpose of this research is applying an intelligent strategy to improve the customer satisfaction prediction system of airlines service. Historically, the measurement of customer satisfaction collects by questionnaire. We collect and computing the customer satisfaction of each flight by the cloud computing based prediction system. It compares the customer questionnaire, then continuous modifying the accuracy of prediction system by neural network methodology. It is more efficiency and precisely to improve the customer satisfaction for the long-term perspective. We are proposing a parameter of evaluation module that selected 12 influence factors from MEPH and 12 satisfaction evaluation factors of airlines service (by customer perception of service quality). In this study, we are using back-propagation neural network method to build the module. The module could be used by calculating the airlines service satisfaction from the quality factors such as material, machine, product, and staff. However, we could get the prediction of service satisfaction through the way of data fusion by the 12 satisfaction indicators. The results could be used to make the service quality strategy, in order to lead a higher customer satisfaction. The findings help airlines managers to predict their customer satisfaction more efficiently, making changes to service quality strategy easily to meet the customers’ satisfaction level. Even if the managers pre-set a customers’ satisfaction level, a real-time cloud computing could help managers deploy the resources to achieve the goal. After all, in order to prove the feasibility of the parameters and the intelligent evaluation methodology, this study will collect data and test the evaluation of quality from an experimentation of airlines service system in Kaohsiung city in Taiwan.

This paper proposes a method using non-linear voltage-current characteristics for multiple harmonic sources classification using wavelet hybrid neural network (WHNN). Typical voltage-current characteristics of harmonic sources are non-linear closed curves in the time-domain, referring to the converters, reactors, and non-linear loads. The hybrid neural network is a two-subnetwork architecture, consisting of wavelet layer and a self-organizing feature map (SOFM) network connected in cascade. The effectiveness of the proposed method is demonstrated by numerical tests. The results of multiple harmonic sources show the computational efficiency and accurate classification.

In this paper, an intelligent solar panel cleaning system that monitors the output of solar panels is designed. The output voltage of the solar panel is used to decide if the solar panel needs to clean or not. The control system is developed using Lab-VIEW. The direction and position of the system is set by the light sensor, which is parallel to the direction of sunlight. The data from the light sensors, along with the fuzzy logic control software developed using Lab-View determines the control commands for the stepper motors controlling the cleaning process. The commands are stop, forward or reverse and the cleaning process is repeated until the generated power output of the solar panels is sufficient. The cleaning process is performed in real-time to maintain the power generating capacity of the solar cells.

In the paper, a cloud computing based intelligent scheduling system for manufacturing quality is proposed. Most of scheduling system is only considered producing time but manufacturing quality. It is very important to focus on manufacturing quality for the industry upgrade. The factors of manufacturing quality are product, equipment, material and human. It makes higher cost for scheduling the flexible manufacturing system. So we develop the cloud computing-based intelligent scheduling system by using artificial neural network and optimized layout method for manufacturing quality in the research. The architecture of the intelligent scheduling system contains: (1) Cloud database structure. (2) Intelligent scheduling engine. (3) Real-time human-machine interface. SQL Azure is used as the cloud database for scattering and storing data. And the intelligent scheduling engine contains the intelligent sequence score system of the products, the optimized layout system and the monitoring system of available resources. By using Visual C#, we can program a human-machine interface with real-time data updating. So that we can see real-time scheduling states of manufacturing by the human-machine interface at any time everywhere. We get good performance from the results of the experiment in the intelligent scheduling system considering manufacturing quality. It is important to develop a cloud computing-based intelligent scheduling system for manufacturing quality because of the advantages of production fluency, low cost, just good quality and flexible management.