West Pacific Earthquake Forecasting Using NOAA Electron Bursts With Independent L-Shells and Ground-Based Magnetic Correlations

Recent advances in statistical correlations between strong earthquakes and several non-seismic phenomena have opened the possibility of formulating warnings within days or even hours. The retrieved correlations have been discovered for those ionospheric physical observations which lasted a long time and realized using the same instruments, including multi-satellite recordings. One of those regarded the electron burst phenomena detected by NOAA, for which the conditional probability of a seismic event was calculated. Then an earthquake probability greater than its frequency was assigned when a satellite realized such a phenomenological observation. This approach refers to the correlations obtained between high-energy electrons detected using the NOAA POES and strong Indonesian and Philippine earthquakes. It is reformulated here to realize a test of earthquake forecasting. The fundamental step is obtained by using a unique electron L-shell interval of 1.21 ≤ L ≤ 1.31, which decouples the electron parameters from the earthquake parameters. Then, the optimized correlation was recalculated to be 1.5–3.5 h early, between electron bursts and an increased number of seismic events with M ≥ 6, therein improving the significance too. Moreover, this methodology is reconnected to the frequency theory, and to Molchan’s error diagram, by the probability gain, where a comparison among the significances of various methods is given. The previously proposed physical link between the crust and the ionosphere through magnetic interaction, presumably operating 4–6 h before strong earthquakes, is examined quantitatively on the basis of recent magnetic pulse measurements. Consequently, the probability gain of earthquake forecasting is hypothetically calculated for both the dependent measurements of electron bursts using NOAA satellites and possible ground-based magnetic pulse detection. This method of combining probability gains for earthquake forecasting is general enough that it can be applied to any pair of observables from space and the ground.

A Target-Oriented Multiple-Attribute Decision-Making Approach Based on Probabilistic Linguistic Preference Relations

While the approach to multiple-attribute decision-making (MADM) is widely used in a variety of fields, including models with fuzzy sets and corresponding extensions, it cannot solve target-oriented decision problems with both selective and targeted alternatives. Therefore, this study provides the first description of a target-oriented MADM problem and proposes a novel decision framework. An attribute value function for target orientation is defined by integrating range and frequency values derived under cumulative prospect theory and range-frequency theory. A Choquet integral with discrete fuzzy measures is then used to integrate attribute values and determine comprehensive values for selective alternatives. In this determination of comprehensive values, a parameter estimation model is also established, with its input assumed to be the pairwise comparison judgment matrix with probabilistic linguistic preference relation. This model as well as its transformation aims at determining the parameters of both the attribute value function and fuzzy measures. Finally, the process of target-oriented MADM is summarized, and an illustrative example is provided to demonstrate the applicability of the proposed techniques.

H∞ fault estimation of robot joint in finite frequency domain

The fault estimation of robot joint is of great significance to improve the reliability and stability of robot joint. Based on the design of fault diagnosis observer and the finite frequency theory, a method of H infinite fault estimation with frequency domain is proposed. The design method combines H infinite filter wave with finite frequency technology effectively, and has strong anti-interference performance, Compared with other design methods, the method proposed in this paper can improve the accuracy of fault estimation

Novel resonant pressure sensor based on piezoresistive detection and symmetrical in-plane mode vibration

In this paper, a novel resonant pressure sensor is developed based on electrostatic excitation and piezoresistive detection. The measured pressure applied to the diaphragm will cause the resonant frequency shift of the resonator. The working mode stress–frequency theory of a double-ended tuning fork with an enhanced coupling beam is proposed, which is compatible with the simulation and experiment. A unique piezoresistive detection method based on small axially deformed beams with a resonant status is proposed, and other adjacent mode outputs are easily shielded. According to the structure design, high-vacuum wafer-level packaging with different doping in the anodic bonding interface is fabricated to ensure the high quality of the resonator. The pressure sensor chip is fabricated by dry/wet etching, high-temperature silicon bonding, ion implantation, and wafer-level anodic bonding. The results show that the fabricated sensor has a measuring sensitivity of ~19 Hz/kPa and a nonlinearity of 0.02% full scale in the pressure range of 0–200 kPa at a full temperature range of −40 to 80 °C. The sensor also shows a good quality factor >25,000, which demonstrates the good vacuum performance. Thus, the feasibility of the design is a commendable solution for high-accuracy pressure measurements.