Nonnuclear air-to-surface ordnance for the future: An approach to propulsion technology risk assessment

Hutzler, William P.; Nelson, Jeff; Pei, Richard Y.; Francisco, Corazon M.

doi:10.1016/0040-1625(85)90059-9

Cited by 10 publications

(2 citation statements)

References 14 publications

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“…All less than 10 All less than 10 13 (CBW), 11 (CBR) All less than 10 Therefore, the model for forecasting wireless mobile communication technologies using a composite measure is defined as following with three variables; Using multiple linear regression for forecasting has a long history (Merz et al, 1972;Alexander and Nelson, 1973;Rohatgi and Weiss, 1977;Hutzler et al, 1985) and raises obvious questions about multicollinearity (Farrar and Glauber, 1967;Fujii and Mak, 1981;Askin, 1982;Belsley, 1982;Belsley, 1984;Belsley, 1987). The potential for tradeoffs among independent variables is a violation of the independences assumption among independent variables when regression method is used.…”

Section: Vifmentioning

confidence: 99%

A look into the future of wireless mobile communication technologies

Kim

Daim

Anderson

2010

Technology Analysis & Strategic Management

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The number of wireless mobile communication service subscribers has reached 4.6 billion worldwide in 2009, and mobile revenues are expected to be over $1 trillion around 2012 according to the International Telecommunication Union (ITU) (ITU, 2010). A significant number of studies have been done to forecast the growing market and evaluate the new generation technology, the Beyond 3 Generation (B3G). However, there is no study forecasting when any of these new technologies will be commercialized. This paper presents a technical framework for forecasting the commercialization timeline of B3G technologies and provides insight on technology trajectories from 1G to 4G. The results show that a combination of technical parameters can explain heterogeneous wireless mobile communication technologies.Three parameters selected include channel bandwidth, channel bit rate, and data capacity for technical framework.the exponential trend, but if 4G technologies do not follow this trend, the overall forecasting model will need to be modified with new data.The study focused only on technical factors to forecast the future of wireless mobile communication technologies without detailed consideration of the markets, so the year of first commercialization was used to reflect market adoption indirectly into the model. However, market penetration and carrier's strategy for both current and future wireless mobile communication technologies need to be incorporated into the analysis in order to provide more comprehensive and accurate forecasting. Even though there are some limitations, this study provides a useful forecasting framework and an outlook for future wireless mobile communication technologies through a quantitative analysis. Notes on contributorsTugrul U Daim is an Associate Professor of Engineering and Technology Management at Portland State University. Dr Daim had been with Intel Corporation for over a decade before he joined PSU as a full time faculty. Dr Daim's research involves exploration of technology assessment in industries including automotive, energy, semiconductor manufacturing, communications and health care. He consults with government agencies and companies all around the world. He is also a visiting Professor at Technical University of Hamburg Harburg. Dr. Daim has over 100 papers published in journals and conference proceedings. He is the editor in chief for International Journal of Innovation and Technology Management.

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Section: Vifmentioning

confidence: 99%

A look into the future of wireless mobile communication technologies

Kim

Daim

Anderson

2010

Technology Analysis & Strategic Management

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“…This definition of risk is very general and finds applications in many different fields, such as insurance (Williams and Heins 1976), technology (Hultzer et al 1983), environmental analysis (Simmons, Erdman, and Naft 1973), engineering (Diamond 1981), finance (Arbia 1999, 2002), and many others (for a review, see, e.g., RSS 1999). From now on, we will restrict our attention to the environmental case and consider, without loss of generality, only upward threshold exceedances.…”

Section: Motivationmentioning

confidence: 99%

Exploring Nonlinear Spatial Dependence in the Tails

Arbia

Lafratta

2005

Geographical Analysis

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In many instances it is of interest to measure the degree of similarity between neighboring regions. Spatial autocorrelation measures are the most popular means of doing it. However, such measures only capture a global linear relationship between regions, whereas in many circumstances a more general instrument is required. For instance, in economic poverty analysis or environmental applications (and in other cases where we are interested in extreme events and threshold exceedances) we should be more interested in the spatial pattern in the tails of the joint distributions. In this article we introduce some exploratory tool that focuses on the bivariate joint tails behavior to detect a pattern of spatial regularities. The method will be illustrated with reference to simulated environmental data.

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A comparison of two composite measures of technology

Martino

1993

Technological Forecasting and Social Change

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Nonnuclear air-to-surface ordnance for the future: An approach to propulsion technology risk assessment

Cited by 10 publications

References 14 publications

A look into the future of wireless mobile communication technologies

A look into the future of wireless mobile communication technologies

Exploring Nonlinear Spatial Dependence in the Tails

A comparison of two composite measures of technology

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