This paper presents distributed and asynchronous active fault management (DA-AFM) to manage renewable energy upon faults. Addressed here are two challenges in fault management for photovoltaic (PV) farms and wind farms. The first one is the activation of crowbars in doubly-fed induction generator (DFIG) wind turbine systems during fault ride-though. The activation undesirably makes DFIG-based wind farms lose control and absorb reactive power. The second challenge is implementation of distributed fault management for distinct PV farms with different objectives and constraints. Coordination for large number of PV farms facilitates integration of themselves and other renewable energy. To prevent crowbars from being activated, DA-AFM controls nearby PV farms’ interface converters to smooth voltage drops so that DFIGs experience voltages with a lower dropping speed. To enable distributed computation of DA-AFM’s optimization formulation, a distributed and asynchronous surrogate Lagrangian relaxation (DA-SLR) method is devised to coordinate a cluster of PV farms. Simulation results have demonstrated DA-AFM’s effectiveness in preventing crowbars’ activation in wind farms and in coordinating diverse PV farms.

A novel power system transient simulation scheme based on frequency response optimized integrators considering second order derivative is applied to a single-phase grid-feeding converter system in this paper. The converter system under study is introduced in detail as well as how the different numerical integrators are utilized for each constituting component. Numerical case studies show that the novel transient simulation scheme is able to achieve computational efficiency without sacrificing accuracy. This paper has been accepted by the 2021 IEEE PES Innovative Smart Grid Technologies Conference.

We describe in the paper a ring voltage-controlled oscillator (VCO) indicating an improved phase noise over a wide range of frequency offsets and an extended frequency/voltage tuning range. The phase noise is improved by leveraging a better linearity approach, while reducing the VCO gain and maintaining wide tuning range. The proposed VCO is a block of a time-domain comparator embedded in a monitoring and readout circuit of an industrial sensor interface. An analytical model is extracted resulting in closed-form expressions for both input-referred noise and phase noise of the VCO. Employing the analytical expressions, the contributed noise and phase noise limitations are fully addressed, and all the effective factors are investigated. The prototype of the proposed VCO was implemented and fabricated in a 0.35 µm CMOS process. The integrated VCO consumes 0.903 mW from a 3.3 V supply, when running at its maximum frequency of 9.37 MHz. The measured phase noise of the proposed VCO is -147.57 dBc/Hz at 1 MHz offset from the 9.37 MHz oscillation frequency, and the occupied silicon area of circuit is 0.005 mm2.

Large-scale testing involving more than a trillion comparisons between the iris patterns of different persons has confirmed that the entropy of IrisCodes is large enough to deliver collision avoidance (absence of False Matches) in population sizes of national, continental, and even planetary scale. This short paper explains how the entropy of this biometric achieves it.

The work physical model was designed through a physical model, and then the polymer adsorption which causing blockage resulting in permeability decrease computed by mathematical model.

The study ameliorates the feasibility of IR thermometer, and to introduce a novel design with upgraded applications & functions. The custom compact device (shape illustrates a “Badge”) measures the surface temperature of a body and received data processed through a microcontroller (AtMega328P). The device functions in a way that if there is any thermal change from ambient temperature (D-1 m), the mode is activated and triggers a pre-defined alert. For distance measuring, it measures the intensity of IR radiation emitted by a body from a particular direction. It also reads the temperature when it comes across a body. It will provide an optimum way in the primary healthcare instrumentation and helpful for noncontact, quick, and accurate measurement of moving and high temperature body.

This brief presents a novel level-shifter circuit for high-frequency high-voltage (HV) gate-drives. The proposed level shifter (LS) is designed based on a capacitive-coupler/current mirror/ latch structure which helps to extend operation voltage of a floating supply into the negative range, achieves sub-ns and constant delay, and consumes very low power from the floating supply. Additionally, common-mode noise cancellers based on a cross-current mirror and transmission gates are also presented to enhance the dV/dt immunity of the LS against slewing of the floating ground. Implemented in 0.18 µm HV BCD-on-SOI (bipolar-CMOS-DMOS on silicon-on-isolator) process, the post-layout simulation of the proposed design shows a delay of 680 ps, 200 V/ns of dVSSF/dt slew rate immunity, It dissipates no static power and only 8.1 pJ/transition from the floating supply, improving FoM1 and FoM2 of the proposed LS by 3 times and 11.7 times compared to respective state-of-the-art works.

Abstract—While deep learning-based methods have gained popularity and have made remarkable progress in remote sensing (RS) image change detection (CD), the limited amount of available data hinders the performance of most supervised methods. The CD networks transferred or derived from other fields can be fronted with a weak generalization capability. Developing a universal benchmark for performance evaluations based on the available datasets is urgent. To address these problems, we proposed a lightweight network, termed 3M-CDNet, which only requires about 3.12 M parameters. The lighter the network, the easier it is to train and alleviate overfitting the limited amount of data, resulting in a better generalization capability. 3M-CDNet has a flexible modular design that achieves performance improvements by incorporating plug-and-play modules. 3M-CDNet gains accuracy improvements in two ways: (1) the application of deformable convolutions (DConv) in the backbone network to gain a good geometric transformation modeling capacity for CD and (2) the application of an effective two-level feature fusion strategy to enhance the feature representation capacity. 3M-CDNet gains a good generalization capacity by incorporating effective “tricks” to alleviate overfitting, in which online data augmentation (Online DA) is applied to increase the diversity of the training samples, and Dropout regularization is applied in the classifier. Extensive ablation studies have proved the effectiveness of the core components. Experiment results suggest that 3M-CDNet outperforms state-of-the-art methods on several optical RS datasets and serves as a new universal benchmark. Specifically, 3M-CDNet achieves the best F1-score, i.e., LEVIR-CD (0.9161), Season-Varying (0.9473), and DSIFN (0.7031).

As a novel layered noble metal dichalcogenide material, palladium diselenide (PdSe2) has attracted wide interest due to its excellent optical and electronic properties. In this work, a strong third-order nonlinear optical response of 2D PdSe2 films is reported. We conduct both open-aperture (OA) and closed-aperture (CA) Z-scan measurements with a femtosecond pulsed laser at 800 nm to investigate the nonlinear absorption and nonlinear refraction, respectively. In the OA experiment, we observe optical limiting behaviour originating from large two photo absorption (TPA) in the PdSe2 film of β = 3.26 ×10-8 m/W. In the CA experiment, we measure a peak-valley response corresponding to a large and negative Kerr nonlinearity of n2 = -1.33×10-15 m2/W – two orders of magnitude larger than bulk silicon. In addition, the variation of n2 as a function of laser intensity is also characterized, with n2 decreasing in magnitude when increasing incident laser intensity, becoming saturated at n2 = -9.96×10-16 m2/W at high intensities. Our results show that the extraordinary third-order nonlinear optical properties of PdSe2 have strong potential for high-performance nonlinear photonic devices.

Recent research has focused on assessing either event- or time-based prospective memory (PM) using laboratory tasks. Yet, the findings pertaining to PM performance on laboratory tasks are often inconsistent with the findings on corresponding naturalistic experiments. Ecologically valid neuropsychological tasks resemble the complexity and cognitive demands of everyday tasks, offer an adequate level of experimental control, and allow a generalisation of the findings to everyday performance. The Virtual Reality Everyday Assessment Lab (VR-EAL), an immersive virtual reality neuropsychological battery with enhanced ecological validity, was implemented to comprehensively assess everyday PM (i.e., focal and non-focal event-based, and time-based). The effects of the length of delay between encoding and initiating the PM intention and the type of PM task on everyday PM performance were examined. The results revealed that everyday PM performance was affected by the length of delay rather than the type of PM task. The effect of the length of delay differentially affected performance on the focal, non-focal, and time-based tasks and was proportional to the PM cue focality (i.e., semantic relationship with the intended action). This study also highlighted methodological considerations such as the differentiation between functioning and ability, distinction of cue attributes, and the necessity of ecological validity.

Studies on prospective memory (PM) predominantly assess either event- or time-based PM by implementing non-ecological laboratory-based tasks. The results deriving from these paradigms have provided findings that are discrepant with ecologically valid research paradigms that converge on the complexity and cognitive demands of everyday tasks. The Virtual Reality Everyday Assessment Lab (VR-EAL), an immersive virtual reality (VR) neuropsychological battery with enhanced ecological validity, was implemented to assess everyday event- and time-based PM, as well as the influence of other cognitive functions on everyday PM functioning. The results demonstrated the importance of delayed recognition, planning, and visuospatial attention on everyday PM. Delayed recognition and planning ability were found to be central in event- and time-based PM respectively. In order of importance, delayed recognition, visuospatial attention speed, and planning ability were found to be involved in event-based PM functioning. Comparably, planning, visuospatial attention accuracy, delayed recognition, and multitasking/task-shifting ability were found to be involved in time-based PM functioning. These findings further suggest the importance of ecological validity in the study of PM, which may be achieved using immersive VR paradigms.

Projects can be seen as the crucial building blocks whereby organizations execute and implement their short, and long-term strategic vision. Projects are thought to solve problems, drive change, satisfy unique needs, add value, or exploit opportunities, just to name a few. In order to successful deliver projects, project management tools and techniques are applied throughout a project´s lifecycle, essentially to efficiently and in a timely manner, identify and manage project risks. However, according to latest reviewed literature, projects keep failing at an impressive rate. Although research in the project management field argues that such failure rate is due to a huge variety of reasons, it highlights particular importance to a still underexplored and not quite well understood (regarding how it emerges and evolves) risk type, that may lead projects to failure. This risk type, called as corporate behavioral risks, usually emerge, and evolve as organizations work together across a finite period of time (for example, across a project lifecycle) to deliver projects, and is characterized by the mix of countless formal and informal dynamic interactions between the different elements that constitute the different organizations. Understanding the extent to which such corporate behavior influences project´s outcomes, is a breakthrough of high importance that positively impacts two dimensions; first, enables organizations that deliver projects (but not only), to increase the chances of project success, which in turn is a driver of sustainable business, because it allows the development and implementation of effective, and timely corrective measures to project´s tasks and activities, and second, it contributes to the scientific community (on the organizations field), to generate valuable and actionable new knowledge regarding the emergence and evolution of such cooperative risks, which can lead to the development of new theories and approaches on how to manage them. In this work, we propose a heuristic model to efficiently identify and analyze how corporate behavioral risks may influence project´s outcomes. The proposed model in this work, lays its foundations on four fundamental fields ((1) project management, (2) risk management, (3) corporate behavior, and (4) social network analysis), and will quantitatively measure four critical project social networks ((1) communication, (2) problem-solving, (3) advice, and (4) trust) that usually emerge as projects are being delivered, by applying the theory of social network analysis (SNA), more concretely, SNA centrality metrics. The proposed model in this work is supported with a case study to illustrate its implementation across a project lifecycle, and how organizations can benefit from its application.

We present theoretical designs of high performance optical filters in integrated silicon photonic nanowire resonators. We use mode interference in formed by zig-zag waveguide coupled Sagnac loop reflectors (ZWC-SLRs), tailored to achieve diverse filtering functions with good performance. These include compact bandpass filters with improved roll-off, optical analogues of Fano resonances with ultrahigh spectral extinction ratios (ERs) and slope rates, and resonance mode splitting with high ERs and low free spectral ranges. The analysis verifies the feasibility of multi-functional integrated photonic filters based on ZWC-SLR resonators for flexible spectral engineering in diverse applications.

This paper theoretically investigates the asymptotic properties of maximum likelihood estimates of GaGLM, and discusses some properties about Gamma distribution. It can provide theoretical foundation for expanding the application scope of gamma distribution based regression model, and benefit the further interval estimates, hypothesis tests and stochastic control design.The existence of the Gamma function in Gamma distribution makes correlation analysis certain specificity, and few researchers do relevant theoretical research. To complement this part, we established the asymptotic properties and the application condition of maximum likelihood estimates of GaGLM. In addition to this, we also discussed the propertis of the Fisher information matrix ,and n-order moment of Z and log(Z).

This work sheds light on the dynamics of aqueous droplets in hydrophobic environment as well as their distant interactions, reasoned by well-studied Cheerios effect.

Since E-Mobility is on the rise worldwide, large Charging Infrastructure (CI) networks are required to satisfy the upcoming Charging Demand (CD). Understanding this CD with its spatial and temporal uncertainties is important for grid operators to quantify the grid impact of Electric Vehicle integration and for Charging Station (CS) operators to assess long-term CI investments. Hence, we introduce an Agent-based E-Mobility Model assessing regional CI systems with their multi-directional interactions between CSs and vehicles. A Global Sensitivity Analysis (GSA) is applied to quantify the impact of 11 technical levers on 17 relevant charging system outputs. The GSA evaluates the E-Mobility integration in terms of grid impact, economic viability of CSs and Service Quality of the deployed Charging Infrastructure (SQCI). Based on this impact assessment we derive general guidelines for E-Mobility integration into regional systems. We found, inter alia, that CI policies should aim at allocating CSs across different types of locations to utilize cross-locational effects such as CSs at public locations affecting the charging peak in residential areas by up to 18%. Additionally, while improving the highway charging network is an effective lever to increase the SQCI in urban areas, public charging is an even stronger lever in rural areas.

This paper is motivated by the need in certain engineering contexts to construct approximations for irrational functions in the complex variable z. The main mathematical tool that will be used is a special continued fraction expansion that cause the rational approximant to collocate the irrational function at specific important values of z. This paper introduces two theorems that facilitate the construction of the rational approxima- tion.

Analog computing has been recovering its relevance in the recent years. FPAAs are the equivalent to FPGAs but in the analog domain. The main drawback of FPAAs is their reduced integration capacity. In order to increase the amount of analog resources, in this paper a cluster of 40 FPAAs is proposed. As a use case, a 19-8-6-4 feedforward Neural Network has been implemented on such cluster. With the help of a DCT-based software framework, this NN is able to classify 28x28 MNIST images. Results show that the analog network is able to obtain almost the same results as the software baseline network.