Using artificial intelligence to optimize intermodal networking of organizational agents within the digital economy

The problems of forming a digital economy supersystem have been analyzed by the authors using a key business model of sharing digital assets by organizational agents based on the convergence of telematic, computing and information services with the final output of a complex of managed digital objects to a new quality control based on the M2M principle integration. The integration of standardized network infrastructures allows the transformation of traditional linear-hierarchical chains of management transactions in a network of transactions of digital objects that perform intelligent interaction without human intervention through IP-like connections. The introduction of universal shells of any business process from the processing of primary data to processing in the deep region with the use of advanced multi-agent optimization algorithms of the next [after 3G] generation [based on 4G, 5G, etc.], implemented within the electronic micro-, meso-and macrocontent allows us to ensure the stability of the super system within the optimal values of the activities of controlled digital objects due to the increased possibilities of observation and control

New Hampshire University Research and Industry Plan: A Roadmap for Collaboration and Innovation

This University Research and Industry plan for New Hampshire is focused on accelerating innovation-led development in the state by partnering academia’s strengths with the state’s substantial base of existing and emerging advanced industries. These advanced industries are defined by their deep investment and connections to research and development and the high-quality jobs they generate across production, new product development and administrative positions involving skills in science, technology, engineering and math (STEM)

A selected glossary of electronic data interchange and related terms

School of Managemen

Meter Scoping Study

This report presents a summary of metering technology and cost information from past studies in an attempt to identify key barriers to more widespread implementation

Computer-Aided System for Wind Turbine Data Analysis

Context: The current work on wind turbine failure detection focuses on researching suitable signal processing algorithms and developing efficient diagnosis algorithms. The laboratory research would involve large and complex data, and it can be a daunting task. Aims: To develop a Computer-Aided system for assisting experts to conduct an efficient laboratory research on wind turbine data analysis. System is expected to provide data visualization, data manipulation, massive data processing and wind turbine failure detection. Method: 50G off-line SCADA data and 4 confident diagnosis algorithms were used in this project. Apart from the instructions from supervisor, this project also gained help from two experts from Engineering Department. Java and Microsoft SQL database were used to develop the system. Results: Data visualization provided 6 different charting solutions and together with robust user interactions. 4 failure diagnosis solutions and data manipulations were provided in the system. In addition, dedicated database server and Matlab API with Java RMI were used to resolve the massive data processing problem. Conclusions: Almost all of the deliverables were completed. Friendly GUI and useful functionalities make user feel more comfortable. The final product does enable experts to conduct an efficient laboratory research. The end of this project also gave some potential extensions of the system