We present the electrical characteristics of the first 90nm SiGe BiCMOS technology developed for production in IBM's large volume 200mm fabrication line. The technology features 300 GHz f T and 360 GHz f MAX high performance SiGe HBTs, 135 GHz f T and 2.5V BV CEO medium breakdown SiGe HBTs, 90nm Low Power RF CMOS, and a full suite of passive devices. A design kit supports custom and analog designs and a library of digital functions aids logic and memory design. The technology supports mm-wave and high-performance RF/Analog applications.
The magnetoelastic material possesses several resonance modes, but the magnetoelastic sensors operate in basically the fundamental resonant frequency. This paper investigates the resonance modes of a freestanding ME resonator, tests these modes in liquids with different viscosity and measures the viscosity of human blood. Five resonance modes with different amplitudes appear in the frequency range of 30–280 kHz. The resonance modes are affected by the direction and strength of the DC bias magnetic field. Compared with the width of the resonance peak, the quality factor cannot correctly reflect the sharpness of the resonance peak of different resonance modes. The five modes are affected by liquid. The first mode has better resolution in determining the resonant frequency, but the higher mode is more sensitive to viscosity. The ME resonator can be used to quickly distinguish the viscosity of blood.
Wildfires could pose a significant danger to electrical transmission lines and cause considerable losses to the power grids and residents nearby. Previous studies of preventing wildfire damages to electrical transmission lines mostly analyze wildfire and power system security independently due to their differences in disciplines and cannot satisfy the requirement of the power grid for active and timely responses. In this paper, we have designed an integrated wildfire early warning system framework for power grids, taking prediction of wildfires and early warning of line outage probability together. First, the proposed model simulates the spatiotemporal process of wildfires via a geography cellular automata model and predicts when and where wildfires initially get into the security buffer of an electrical transmission line. It is developed in the context of electrical transmission line operating with various situations of topography, vegetation, wind and, especially, multiple ignition points. Second, we have proposed a line outage model (LOM), based on wildfire prediction and breakdown mechanisms of the air gap, to predict the breakdown probability varying with time and the most vulnerable poles at the holistic line scale. Finally, to illustrate the validation and rationality of our proposed system, a case study for a 500-kV transmission line near Miyi county, China, is presented, and the results under various wildfire situations are studied and compared. By integrating wildfire prediction into the LOM and alarming the holistic line breakdown probability along time, this paper makes a significant contribution in the early warning system to prevent transmission lines to be damaged by wildfires, illustrating the related breakdown mechanisms at the line operation level rather than laboratory experiments only. Meanwhile, the implementation of cellular automata model under comprehensive environmental conditions and simulation of the breakdown probability for the 500-kV transmission line could serve as references for other studies in the community. INDEX TERMS Cellular automata, electrical transmission lines, earning warning, wildfire.
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