In order to improve the energy efficiency of laptop use and design, it is important to gain knowledge of how laptops are being used in real-world settings. However, the current workload studies have focused on computer-usage patterns of business users, but not those of consumer users. As part of the u! Green project at ABC University, a pilot study was undertaken to develop and test software tools and methods for monitoring the laptop computer usage of 13 college students for a period of 1 month. This paper describes such tools and methods for monitoring and collecting laptop usage data. It also illustrates how the collected data can be analyzed to obtain high-impact energy usage patterns, which include long idle periods, excessive use of the AC power source, and high backlight levels. These identified high-impact usage patterns, if confirmed by our subsequent study, offer valuable user behavioral insights to advance more energy-efficient laptop use and design.
This paper presents two types of recurrent neural networks, continuous-time and discrete-time ones, for solving linear inequality and equality systems. In addition to the basic continuous-time and discrete-time neural-network models, two improved discrete-time neural networks with faster convergence rate are proposed by use of scaling techniques. The proposed neural networks can solve a linear inequality and equality system, can solve a linear program and its dual simultaneously, and thus extend and modify existing neural networks for solving linear equations or inequalities. Rigorous proofs on the global convergence of the proposed neural networks are given. Digital realization of the proposed recurrent neural networks are also discussed.
Index Terms-Linear equalities and equations, recurrent neural networks.Youshen Xia received the B.S. and M.S. degrees in computational mathematics from Nanjing University, China, in 1982 and 1989, respectively. . His primary research interests are in applicationdriven algorithms and architectures, reconfigurable computing, and design of high-performance digital/computing systems for applications in areas such as image/signal processing, pattern classification, real-time control, optimization, and computational intelligence.Dr. Hung is a member of Eta Kappa Nu.
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