By Ib M. Skovgaard
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Additional info for Analytic statistical models
The plot "number of inventions vs. time" (Fig. 2) has two inflection points. Point A is associated with efforts to promote the technology from a concept to production; point C reflects the drop of interest after unsuccessful attempts to improve the stagnating technology. After pioneering inventions gave birth to a new breakthrough, the levels of invention drop and then rise again (point A in Fig. 2). This dynamic is due to the resolution of significant problems that were keeping the concept from becoming a viable product.
Various existing product planning tools process these opinions in different ways, but do not change their subjective nature. Various methods are used to answer these questions: market research, competitive benchmarking, customer clinics, QFD, just to name a few. These methods work reasonably well for incremental improvements, but they are rarely effective when used to predict radically new innovations. There is a tangible need in a systematic approach to evaluate future technological opportunities.
Finally, an upgrade to a more powerful controller will be necessary. 5 Million. CONTACT Thierry Rolina is Business Development Manager for the Software Components product line of ETAS in North America. ). 56 Million. Dr. Nigel Tracey is Director of Product Management at LiveDevices (ETAS group). com 26 2004-01-0757 Constraint-Driven Simulation-Based Automatic Task Allocation on ECU Networks Paolo Giusto Automotive Team - Cadence Design Systems, Inc. Gary Rushton E/E Vehicle Systems - Visteon Corporation Copyright © 2004 SAE International ABSTRACT Engine Control With the increasing number of ECUs in modern automotive applications (70 in high end cars), designers are facing the challenge of managing complex design tasks, such as the allocation of software tasks over a network of ECUs.