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Probabilistic approaches for handling count-valued time sequences have attracted amounts of research attentions because their ability to infer explainable latent structures and to estimate uncertainties, and thus are especially suitable for…
We introduce modeling and simulation of the noise properties associated with types of modal oscillations induced by scaling the asymmetric gain suppression (AGS) in multimode semiconductor lasers. The study is based on numerical integration…
Energy dissipation is an unavoidable phenomenon of physical systems that are directly coupled to an external environmental bath. The ability to engineer the processes responsible for dissipation and coupling is fundamental to manipulate the…
The short-term reliability evaluation techniques provide a rational approach for risk-informed decision making during power system operation. The existing reliability assessment techniques involve large computational burden and therefore…
Since forced oscillations are exogenous to dynamic power system models, the models by themselves cannot predict when or where a forced oscillation will occur. Locating the sources of these oscillations, therefore, is a challenging problem…
Timely stress detection is crucial for protecting vulnerable groups from long-term detrimental effects by enabling early intervention. Wearable devices, by collecting real-time physiological signals, offer a solution for accurate stress…
This paper presents a new learning based Stochastic Hybrid System (LSHS) framework designed for the detection and classification of contingencies in modern power systems. Unlike conventional monitoring schemes, the proposed approach is…
Using deep learning methods to classify EEG signals can accurately identify people's emotions. However, existing studies have rarely considered the application of the information in another domain's representations to feature selection in…
Clinical monitoring of functional decline in ALS relies on periodic assessments that may miss critical changes occurring between visits. To address this gap, semi-supervised regression models were developed to estimate rates of decline in a…
The first generation Low-Level radio frequency(LLRF) control system independently developed by IMPCAS, the operating frequency is 162.5MHz for China ADS, which consists of superconducting cavity amplitude stability control, phase stability…
In this study, the Multivariate Empirical Mode Decomposition (MEMD) approach is applied to extract features from multi-channel EEG signals for mental state classification. MEMD is a data-adaptive analysis approach which is suitable…
Modern power systems with high share of renewable generation are at the risk of rapid changes in frequency and inertia resulting from contingencies. The importance of an accurate assessment of system and load relief, as well as frequency…
A procedure is presented to map from the spatial correlation parameters of a turbulent density field (the radial and binormal correlation lengths and wavenumbers, and the fluctuation amplitude) to correlation parameters that would be…
This paper is devoted to the unidimensional analysis of a 2-ports silicon resonator vibrating in thickness--extensional mode. Both excitation and detection ports are capacitive transducers used to control the system of longitudinal elastic…
A theoretical platform is developed for active elastic-wave sensing of (stationary and advancing) fractures along bi-material interfaces in layered composites. Damaged contact surfaces are characterized by a heterogeneous distribution of…
We propose a novel radio-frequency (RF) receiving architecture based on micro-electro-mechanical system (MEMS) and optical coherent detection module. The architecture converts the received electrical signal into mechanical vibration through…
Localized surface plasmon resonances possess very interesting properties for a wide variety of sensing applications. In many of the existing applications only the intensity of the reflected or transmitted signals is taken into account,…
Accurately diagnosing bearing faults is crucial for maintaining the efficient operation of rotating machinery. However, traditional diagnosis methods face challenges due to the diversification of application environments, including…
Least-squares frequency switching (LSFS) is a new method to reconstruct signal and gain function (known as bandpass or baseline) from spectral line observations using the frequency switching method. LSFS utilizes not only two but a set of…
Rolling bearings are critical components in rotating machinery, and their faults can cause severe damage. Early detection of abnormalities is crucial to prevent catastrophic accidents. Traditional and intelligent methods have been used to…