Fault Estimation for a Class of Markov Jump Piecewise-Affine Systems: Current Feedback Based Iterative Learning Approach

In this paper, the issues of stochastic stability analysis and fault estimation are investigated for a class of continuoustime Markov jump piecewise-affine(PWA) systems against actuator and sensor faults. Firstly, a novel mode-dependent PWA iterative learning observer with current feedback is designed to estimate the system states and faults, simultaneously, which contains both the previous iteration information and the current feedback mechanism. The auxiliary feedback channel optimizes the response speed of the observer, therefore the estimation error would converge to zero rapidly. Then, sufficient conditions for stochastic stability with guaranteed performance are demonstrated for the estimation error system, and the equivalence relations between the system information and the estimated information can be established via iterative accumulating representation.Finally, two illustrative examples containing a class of tunnel diode circuit systems are presented to fully demonstrate the effectiveness and superiority of the proposed iterative learning observer with current feedback.

Adaptive Pseudo Inverse Control for a Class of Nonlinear Asymmetric and Saturated Nonlinear Hysteretic Systems

This paper aims at eliminating the asymmetric and saturated hysteresis nonlinearities by designing hysteresis pseudo inverse compensator and robust adaptive dynamic surface control(DSC)scheme.The"pseudo inverse"means that an on-line calculation mechanism of approximate control signal is developed by applying a searching method to the designed temporary control signal where the true control signal is included.The main contributions are summarized as:1)to our best knowledge,it is the first time to compensate the asymmetric and saturated hysteresis by using hysteresis pseudo inverse compensator because the construction of the true saturated-type hysteresis inverse model is very difficult;2)by designing the saturated-type hysteresis pseudo inverse compensator,the construction of true explicit hysteresis inverse and the identifications of its corresponding unknown parameters are not required when dealing with the saturated-type hysteresis;3)by combining DSC technique with the tracking error transformed function,the"explosion of complexity"problem in backstepping method is overcome and the prespecified tracking performance is achieved.Analysis of stability and experimental results on the hardware-inloop platform illustrate the effectiveness of the proposed adaptive pseudo inverse control scheme.

Energy inefficiency diagnosis for Android applications: a literature review

Android applications are becoming increasingly powerful in recent years. While their functionality is still of paramount importance to users, the energy efficiency of these applications is also gaining more and more attention. Researchers have discovered various types of energy defects in Android applications, which could quickly drain the battery power of mobile devices. Such defects not only cause inconvenience to users, but also frustrate Android developers as diagnosing the energy inefficiency of a software product is a non-trivial task. In this work, we perform a literature review to understand the state of the art of energy inefficiency diagnosis for Android applications. We identified 55 research papers published in recent years and classified existing studies from four different perspectives, including power estimation method, hardware component, types of energy defects, and program analysis approach. We also did a cross-perspective analysis to summarize and compare our studied techniques. We hope that our review can help structure and unify the literature and shed light on future research, as well as drawing developers' attention to build energy-efficient Android applications.

AI-aided on-chip nucleic acid assay for smart diagnosis of infectious disease

Global pandemics such as COVID-19 have resulted in significant global social and economic disruption.Although polymerase chain reaction(PCR)is recommended as the standard test for identifying the SARS-CoV-2,conventional assays are time-consuming.In parallel,although artificial intelligence(AI)has been employed to contain the disease,the implementation of AI in PCR analytics,which may enhance the cognition of diagnostics,is quite rare.The information that the amplification curve reveals can reflect the dynamics of reactions.Here,we present a novel AI-aided on-chip approach by integrating deep learning with microfluidic paper-based analytical devices(μPADs)to detect synthetic RNA templates of the SARS-CoV-2 ORF1ab gene.TheμPADs feature a multilayer structure by which the devices are compatible with conventional PCR instruments.During analysis,real-time PCR data were synchronously fed to three unsupervised learning models with deep neural networks,including RNN,LSTM,and GRU.Of these,the GRU is found to be most effective and accurate.Based on the experimentally obtained datasets,qualitative forecasting can be made as early as 13 cycles,which significantly enhances the efficiency of the PCR tests by 67.5%(∼40 min).Also,an accurate prediction of the end-point value of PCR curves can be obtained by GRU around 20 cycles.To further improve PCR testing efficiency,we also propose AI-aided dynamic evaluation criteria for determining critical cycle numbers,which enables real-time quantitative analysis of PCR tests.The presented approach is the first to integrate AI for on-chip PCR data analysis.It is capable of forecasting the final output and the trend of qPCR in addition to the conventional end-point Cq calculation.It is also capable of fully exploring the dynamics and intrinsic features of each reaction.This work leverages methodologies from diverse disciplines to provide perspectives and insights beyond the scope of a single scientific field.It is universally applicable and can be extended to multiple areas of fundamental research.