Establishment of super sonic inlet flow pattern monitoring system: A workflow

ABSTRACT: Disclosure and monitoring policy are studied, where disclosure relates to information about the monitoring system. A moral hazard model is presented where employee monitoring occurs with some exogenous probability and the owner privately learns whether he will be monitoring before the employee chooses his productive action. Disclosure policy is an owner choice between revealing to the employee whether he will be monitoring before the action (Disclosure) or remaining silent (Secrecy). The results rely on the joint presence of risk aversion and limited liability. Risk aversion creates an efficiency/risk tradeoff where secrecy obtains risk-sharing benefits. Limited liability reduces these benefits, allowing preference for disclosure. Lower monitoring probabilities increase the risk premium required to obtain effort with secrecy. For small monitoring probabilities, disclosure is preferred even though less efficient production is achieved, because disclosure provides a greater risk-sharing benefit. For high monitoring probabilities, secrecy is preferred because it leads to greater efficiency despite a greater risk premium.

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Creating a Business Process Monitoring System“A-IOMS” for Software Development

Chinese Business Review ◽

10.17265/1537-1506/2012.06.009 ◽

2012 ◽

Vol 11 (06) ◽

Keyword(s):

Software Development ◽

Monitoring System ◽

Process Monitoring ◽

Business Process ◽

System A ◽

Business Process Monitoring

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Cloud Monitoring for Solar Plants with Support Vector Machine Based Fault Detection System

Mathematical Problems in Engineering ◽

10.1155/2014/564517 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Hong-Chan Chang ◽

Shang-Chih Lin ◽

Cheng-Chien Kuo ◽

Hao-Ping Yu

Keyword(s):

Support Vector Machine ◽

Information System ◽

Monitoring System ◽

Solar Power ◽

Video Data ◽

Failure Diagnosis ◽

Support Vector ◽

Management Platform ◽

System A ◽

Cloud Monitoring

This study endeavors to develop a cloud monitoring system for solar plants. This system incorporates numerous subsystems, such as a geographic information system, an instantaneous power-consumption information system, a reporting system, and a failure diagnosis system. Visual C# was integrated with ASP.NET and SQL technologies for the proposed monitoring system. A user interface for database management system was developed to enable users to access solar power information and management systems. In addition, by using peer-to-peer (P2P) streaming technology and audio/video encoding/decoding technology, real-time video data can be transmitted to the client end, providing instantaneous and direct information. Regarding smart failure diagnosis, the proposed system employs the support vector machine (SVM) theory to train failure mathematical models. The solar power data are provided to the SVM for analysis in order to determine the failure types and subsequently eliminate failures at an early stage. The cloud energy-management platform developed in this study not only enhances the management and maintenance efficiency of solar power plants but also increases the market competitiveness of solar power generation and renewable energy.

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