Local Hölder exponent analysis of aeroengine dynamics

This paper describes the application of the local Hölder exponent which can measure sensitive features associated with nonstationary and nonlinearity. By investigating in detail the structure and the main properties of the local Hölder function, a fault diagnosis technology is developed on the basis of error function. The study is conducted for both engines of two Boeing 737 commercial aircrafts as a measure the regularity of aeroengine time series. In order to accurately detect the impending aeroengine faults, the Hölder exponent estimation is performed for comparative analysis of the aeroengine records. Using this analysis, the difference between left and right engine is obvious when one of the engines is fracture failure

The Movement Model of Pilots' Visual Attention

This paper describes the application of an important tool which can capture shifting information from pilots' visual attention data. In order to investigate the shifting information, the shifting state space is defined by visual tracking, visual interference and visual dormancy. Using this analysis, the movement of pilots' visual attention can be completely measured. The results that the forecast of the probability shifting model is coincident with the fact suggest the use of the model as a powerful technique for measuring the movement of pilots' visual attention. Furthermore, the link between visual attention and driving experience or sexual distinction are also discussed in the probability shifting model

Experimental Validation of DeeP-LCC for Dissipating Stop-and-Go Waves in Mixed Traffic

We present results on the experimental validation of leading cruise control (LCC) for connected and autonomous vehicles (CAVs). In a mixed traffic situation that is dominated by human-driven vehicles, LCC strategies are promising to smooth undesirable stop-and-go waves. Our experiments are carried out on a mini-scale traffic platform. We first reproduce stop-and-go traffic waves in a miniature scale, and then show that these traffic instabilities can be dissipated by one or a few CAVs that utilize Data-EnablEd Predicted Leading Cruise Control (DeeP-LCC). Rather than identifying a parametric traffic model, DeeP-LCC relies on a data-driven non-parametric behavior representation for traffic prediction and CAV control. DeeP-LCC also incorporates input and output constraints to achieve collision-free guarantees for CAVs. We experimentally demonstrate that DeeP-LCC is able to dissipate traffic waves caused by car-following behavior and significantly improve both driving safety and travel efficiency. CAVs utilizing DeeP-LCC may bring additional societal benefits by mitigating stop-and-go waves in practical traffic.Comment: 8 pages, 6 figure

Supply network science: Emergence of a new perspective on a classical field.

Supply networks emerge as companies procure goods from one another to produce their own products. Due to a chronic lack of data, studies on these emergent structures have long focussed on local neighbourhoods, assuming simple, chain-like structures. However, studies conducted since 2001 have shown that supply chains are indeed complex networks that exhibit similar organisational patterns to other network types. In this paper, we present a critical review of theoretical and model based studies which conceptualise supply chains from a network science perspective, showing that empirical data do not always support theoretical models that were developed, and argue that different industrial settings may present different characteristics. Consequently, a need that arises is the development and reconciliation of interpretation across different supply network layers such as contractual relations, material flow, financial links, and co-patenting, as these different projections tend to remain in disciplinary siloes. Other gaps include a lack of null models that show whether the observed properties are meaningful, a lack of dynamical models that can inform how layers evolve and adopt to changes, and a lack of studies that investigate how local decisions enable emergent outcomes. We conclude by asking the network science community to help bridge these gaps by engaging with this important area of research

Research on the Spatial Correlation and Spatial Lag of COVID-19 Infection Based on Spatial Analysis

COVID-19 has spread throughout the world since the virus was discovered in 2019. Thus, this study aimed to identify the global transmission trend of the COVID-19 from the perspective of the spatial correlation and spatial lag. The research used primary data collected of daily increases in the amount of COVID-19 in 14 countries, confirmed diagnosis, recovered numbers, and deaths. Findings of the Moran index showed that the propagation of infection was aggregated between 9 May and 21 May based on the composite spatial weight matrix. The results from the Lagrange multiplier test indicated the COVID-19 patients can infect others with a lag

Design of High-precision Broadband Closed-loop Driving Power on Dielectrophoresis

With the development and application of Biological chips and sensors, the demand for high-precision broadband driving power is increasingly rigorous. In order to improve the precision and reliability of the driving power, a high precision broadband closed-loop signal generator has been designed. It works by the high-accuracy amplitude detection circuit, the broadband voltage controlled amplifier and a micro-controller. The principles of amplitude detection circuit and amplifier circuit have been elaborated. Within 1 MHz ~ 200 MHz band, within 1 ~ 10 VPP amplitude range, under 50 Ω output impedance, the error of output signal amplitude can be controlled effectively. Precision is up to 0.15%. It meets the index and scientific research’s requirement

Complexity-Entropy Causality Plane Based on Return Intervals: A Useful Approach to Quantify the Aeroengine Gas Path Parameters

The complexity-entropy causality plane, as a powerful tool for discriminating Gaussian from non-Gaussian process, has been recently introduced to describe the complexity among time series. We propose to use this method to distinguish the stage of climb-cruise-decline of aeroengine. Our empirical results demonstrate that this statistical physics approach is useful. Further, the return intervals based complexity-entropy causality plane is introduced to describe the complexity of aeroengine fuel flow time series. The results can infer that the cruise process has lowest complexity and the decline process has highest complexity