Plug-and-play Research Articles

Service-oriented industrial internet of things gateway for cloud manufacturing

• Proposed a generic system architecture of IIoT-supported cloud manufacturing as a strategic guideline for integrating IIoT technologies in a cloud manufacturing environment. • Proposed a service-oriented PnP IIoT gateway for efficient data acquisition, communication, query, analysis, and visualisation of manufacturing equipment. • Developed two generic service-oriented data schemas for machine tools and 3D printers that allow efficient data management and query for decision-making tasks in a cloud manufacturing platform. • Developed two practical PnP IIoT gateways for a 3D printer and a CNC machine tool that validate the feasibility of the proposed approach. Cloud manufacturing represents a service-oriented manufacturing paradigm that allows ubiquitous and on-demand access to various customisable manufacturing services in the cloud. While a vast amount of research in cloud manufacturing has focused on high-level decision-making tasks, such as service composition and scheduling, the link between field-level manufacturing data and the cloud manufacturing platform has not been well established. Efficient data acquisition, communication, storage, query, and analysis of field-level manufacturing equipment remain a significant challenge that hinders the development of cloud manufacturing systems. Therefore, this paper investigates the implementation of the emerging Industrial Internet of Things (IIoT) technologies in a cloud manufacturing system to address this challenge. We propose a service-oriented plug-and-play (PnP) IIoT gateway solution based on a generic system architecture of IIoT-supported cloud manufacturing system. Service-oriented data schemas for manufacturing equipment are developed to capture just-enough information about field-level manufacturing equipment and allow efficient data storage and query in a cloud time-series database (TSDB). We tested the feasibility and advantages of the proposed approach via the practical implementation of the IIoT gateways on a 3D printer and a machine tool. Our research suggests that purposely developed service-oriented data schemas that capture the essential information for high-level cloud manufacturing decision-making via PnP IIoT technologies are a good solution for connecting field-level manufacturing equipment to a cloud manufacturing platform.

A Plug-and-Play, Scalable Control Method for AC Island Microgrid

In this paper, a scalable, plug-and-play (PnP) and system-stable synthesis control method is proposed for the AC island microgrid consisting of a distributed generator units (DGUs) and loads connected by power lines. The proposed method only requires a limited global parameter design controller, so the design process of the controller is decentralized, so that the addition and removal of DGU only need to adjust the controller of the DGU with its physical connection, without affecting the overall stability of the microgrid. In addition, we used the Lyapunov method to proved the stability of the close-loop system. Finally, the simulation verification was carried out with the MATLAB/Simulink and specific cases.

Seismic Intelligent Deblending via Plug and Play Method With Blended CSGs Trained Deep CNN Gaussian Denoiser

Deblending can extract good quality seismic data from blended seismic data. Generally, the deblending methods can be categorized as model- and data-driven methods. The model-driven deblending methods usually suffer from a massive computational burden, while the data-driven ones need to implement a forward blending process to construct training samples for network training. To solve these issues, we develop a plug and play (PnP) method that integrates a trained convolutional neural network (CNN) Gaussian denoiser as the prior for seismic intelligent deblending. Specifically, we propose to use acquired blended common shot gathers (CSGs) as training dataset for the CNN Gaussian denoiser training to avoid constructing or collecting any extra data. According to the theory and dedicated designed experiments, this training mode can greatly improve network performance. Then, the trained CNN Gaussian denoiser is plugged into the alternating direction method of multiplier (ADMM) algorithm to solve the deblending problem. Furthermore, based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${l}_{{\mathrm{2}}} $ </tex-math></inline-formula> norm data fidelity term and the special structure of the network architecture, the PnP-ADMM method converges to a fixed point. Experiments on synthetic and field data demonstrate that the presented PnP method with blended CSGs trained deep CNN Gaussian denoiser has superior deblending performances over the dictionary learning and discriminative deblending CNN methods.

Decentralized secondary control for frequency regulation based on fuzzy logic control in islanded microgrid

This paper presents a fuzzy-based decentralized secondary control for frequency restoration and active power-sharing in an islanded microgrid, this controller uses the local frequency error to generate an extra term for compensating the deviation and maintaining accurate active power-sharing. No communication infrastructures are needed, event detection, time dependent-protocols, and state estimation are not required. Its design and implementation are straightforward, also, it offers quick dynamic frequency recovery with accurate active power-sharing. To verify the validity of the proposed controller, several tests have been carried out: frequency restoration and active power-sharing during load disturbances, synchronization with plug and play (PnP) ability, as well the communication latency impact. Moreover, the effect of data drop-out and interferences were analyzed in real-time simulation using Truetime. The results have shown the high capability and the fast response of frequency restoration while maintaining the active power-sharing, no oscillations and ripples were presented in steady-state response, likewise, a smooth dynamic response during PnP test is observed. The communication latency and interferences have no impact on the proposed controller that showing a significant improvement in settling time 50% without frequency packet losses and 81%, 90% in the presence of 30% and 70% of frequency packet losses respectively.

Tensor Completion via Complementary Global, Local, and Nonlocal Priors.

Completing missing entries in multidimensional visual data is a typical ill-posed problem that requires appropriate exploitation of prior information of the underlying data. Commonly used priors can be roughly categorized into three classes: global tensor low-rankness, local properties, and nonlocal self-similarity (NSS); most existing works utilize one or two of them to implement completion. Naturally, there arises an interesting question: can one concurrently make use of multiple priors in a unified way, such that they can collaborate with each other to achieve better performance? This work gives a positive answer by formulating a novel tensor completion framework which can simultaneously take advantage of the global-local-nonlocal priors. In the proposed framework, the tensor train (TT) rank is adopted to characterize the global correlation; meanwhile, two Plug-and-Play (PnP) denoisers, including a convolutional neural network (CNN) denoiser and the color block-matching and 3 D filtering (CBM3D) denoiser, are incorporated to preserve local details and exploit NSS, respectively. Then, we design a proximal alternating minimization algorithm to efficiently solve this model under the PnP framework. Under mild conditions, we establish the convergence guarantee of the proposed algorithm. Extensive experiments show that these priors organically benefit from each other to achieve state-of-the-art performance both quantitatively and qualitatively.

Decentralized Robust LMI-based Voltage Control Strategy for Autonomous Inverter-Interfaced Multi-DG Microgrids

This article's objective is to propose a decentralized one Degree of Freedom (DoF) robust control system with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> performance criteria for optimal voltage control of an uncertain inverter-interfaced AC microgrid in the islanding mode of operation. The islanded microgrid system comprises multiple Distributed Generation (DG) units connected with general topology. The proposed one DoF voltage control system provides robust performance alongside robust stability against various sources of uncertainty, such as local load variations, Plug-and-Play (PnP) operation of the DG units, topology variations, and different subsequent changes. To that end, first, each DG unit is modeled as a Linear Time-Invariant (LTI) system with a new polytopic type uncertainty. Next, a Linear Matrix Inequality (LMI) based state-feedback control system with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> performance criteria is implemented on the LTI polytopic islanded microgrid system. The controller's decentralized structure excludes the need for communication channels, and the controller's robust performance characteristic eliminates the necessity of pre-filter design. Simulating multiple scenarios in MATLAB/SimPowerSystems Toolbox confirms the developed controller's performance in different scenarios, including voltage tracking, local load changes, PnP operation, and topology changes.

Distributed Finite-Time Secondary Frequency and Voltage Restoration Control Scheme of an Islanded AC Microgrid

To solve the problems of frequency and voltage deviation caused by the droop control while meeting the requirements of rapid response, a distributed finite-time secondary control scheme is presented. Unlike the traditional cooperative controllers, this scheme is fully distributed; each unit only needs to communicate with its immediate neighbors. A control protocol for frequency restoration and active power sharing is proposed to synchronize the frequency of each unit to the reference value, and achieve accurate active power distribution in a finite-time manner as well. The mismatch of the line impedance is considered, and a consensus-based adaptive virtual impedance control is proposed. The associated voltage drop is considered to be the compensator for the voltage regulation. Then, a distributed finite-time protocol for voltage restoration is designed. The finite-time convergence property and the upper bound of convergence times are guaranteed with rigorous Lyapunov proofs. Case studies in MATLAB are carried out, and the results demonstrate the effectiveness, the robustness to load changes, plug-and play capacity, and better convergence performance of the proposed control scheme.

Joint ptycho-tomography with deep generative priors

Joint ptycho-tomography is a powerful computational imaging framework to recover the refractive properties of a 3D object while relaxing the requirements for probe overlap that is common in conventional phase retrieval. We use an augmented Lagrangian scheme for formulating the constrained optimization problem and employ an alternating direction method of multipliers (ADMM) for the joint solution. ADMM allows the problem to be split into smaller and computationally more efficient subproblems: ptychographic phase retrieval, tomographic reconstruction, and regularization of the solution. We extend our ADMM framework with plug-and-play (PnP) denoisers by replacing the regularization subproblem with a general denoising operator based on machine learning. While the PnP framework enables integrating such learned priors as denoising operators, tuning of the denoiser prior remains challenging. To overcome this challenge, we propose a denoiser parameter to control the effect of the denoiser and to accelerate the solution. In our simulations, we demonstrate that our proposed framework with parameter tuning and learned priors generates high-quality reconstructions under limited and noisy measurement data.

A Critical Review of IEC 61850 Testing Tools

Smartgrid technologies necessitate the use of information technologies (IT) and communication in power system networks. There are different ways of integrating power system equipment in the communication layer for successful information exchange. IEC 61850 offers standard support object-oriented modeling and standardized parameter declaration. This lends itself to the diverse nature of power systems and supports plug-and-play (PnP) operation in smartgrids. Considering the amount of time that is invested in customizing non-PnP communication networks, this is a huge advantage and the main reason behind the popularity of IEC 61850. In line with this popularity, the body of research regarding this standard is constantly growing. In order to test the developed IEC 61850 models and messages, various tools are required. Researchers operate with a limited budget and have to know the abilities and limitations of such tools before making a procurement decision. This paper provides a critical review of IEC 61850 testing tools available in the market. It compares them in terms of their abilities, technical superiority and customer experience, including delivery time and customer support. Researchers in this field will benefit significantly from this work when making procurement decisions based on their needs.

Voltage Sharing With Series Output Connected Battery Modules in a Plug-and-Play DC Microgrid

This article presents output voltage sharing among series output connected battery power modules (BPMs) in plug-and-play (PnP) dc microgrids with a wide bus voltage range. The system provides active cell balancing and interfaces cells to the bus voltage through series output connected BPMs. A cell current sensor is utilized to achieve both state-of-charge regulation and output voltage sharing. To improve microgrid modularity, the battery pack behaves as a current source. A current-dependent droop between the individual output voltages and input currents is used to facilitate series output connected voltage sharing with bidirectional power flow, simple control, and smooth transitions between charge and discharge regions. Experimental results are reported for a 3 kWh 1C-rate prototype consisting of two parallel 24 V subsystems, with five series output connected BPMs in each subsystem. Each BPM consists of three battery cells and three 100 W dc-dc converters.

Convolutional plug-and-play sparse optimization for impulsive blind deconvolution

Impulsive blind deconvolution (IBD) is a fundamental ill-posed inverse problem in fault diagnosis community. Current IBD methods mainly utilize the intrinsic prior knowledge of impulsive sources to design various regularization terms (e.g., kurtosis, sparsity) to alleviate its ill-posedness. However, the great potentiality of statistical distribution structures embedded in observation data hasn’t been exploited to establish more effective model and algorithm for IBD problem. Leveraging recent plug-and-play (PnP) strategy, a convolutional sparse optimization framework (dubbed COPS) is proposed to address it. Firstly, based on the fact that the absolute envelope of Gaussian noises follows a Rayleigh distribution and sparse impulsive components can be viewed as its outliers, a noise-aware statistical threshold is introduced to design a data-driven sparse penalty. Secondly, from a Bayesian perspective, a mapping relation between residual distribution and model hyper-parameter is unveiled, and then an adaptive parameter penalty is established to dynamically select model hyper-parameters. Lastly, two penalties are plugged into ADMM solver by PnP strategy to guarantee algorithmic convergence. Comprehensive numerical simulations confirm the COPS’s advantages in terms of robustness, convergence, scalability and effectiveness. Diagnostic results of planetary gearbox faults further corroborate the COPS retains better deconvolutional accuracy than the state-of-the-art IBD techniques.

Data Extrapolation From Learned Prior Images for Truncation Correction in Computed Tomography.

Data truncation is a common problem in computed tomography (CT). Truncation causes cupping artifacts inside the field-of-view (FOV) and anatomical structures missing outside the FOV. Deep learning has achieved impressive results in CT reconstruction from limited data. However, its robustness is still a concern for clinical applications. Although the image quality of learning-based compensation schemes may be inadequate for clinical diagnosis, they can provide prior information for more accurate extrapolation than conventional heuristic extrapolation methods. With extrapolated projection, a conventional image reconstruction algorithm can be applied to obtain a final reconstruction. In this work, a general plug-and-play (PnP) method for truncation correction is proposed based on this idea, where various deep learning methods and conventional reconstruction algorithms can be plugged in. Such a PnP method integrates data consistency for measured data and learned prior image information for truncated data. This shows to have better robustness and interpretability than deep learning only. To demonstrate the efficacy of the proposed PnP method, two state-of-the-art deep learning methods, FBPConvNet and Pix2pixGAN, are investigated for truncation correction in cone-beam CT in noise-free and noisy cases. Their robustness is evaluated by showing false negative and false positive lesion cases. With our proposed PnP method, false lesion structures are corrected for both deep learning methods. For FBPConvNet, the root-mean-square error (RMSE) inside the FOV can be improved from 92HU to around 30HU by PnP in the noisy case. Pix2pixGAN solely achieves better image quality than FBPConvNet solely for truncation correction in general. PnP further improves the RMSE inside the FOV from 42HU to around 27HU for Pix2pixGAN. The efficacy of PnP is also demonstrated on real clinical head data.

Global Alliances to Accelerate Innovation at Plug and Play Technology Center

The Plug and Play (PnP) accelerator model is differentiated vs. traditional accelerators in many ways, especially by encouraging cross industry collaboration globally. PnP has developed a global network spanning the value chain from universities to startup companies, to financial partners, to global industry leaders in multiple industries, including life sciences, med tech and digital technologies. Networking activities across the value chain and cross industry encourage associate thinking and collaboration and differentiates PnP vs. other accelerators.

A plug and play microfluidic platform for standardized sensitive low-input chromatin immunoprecipitation.

Epigenetic profiling by chromatin immunoprecipitation followed by sequencing (ChIP-seq) has become a powerful tool for genome-wide identification of regulatory elements, for defining transcriptional regulatory networks, and for screening for biomarkers. However, the ChIP-seq protocol for low-input samples is laborious and time-consuming and suffers from experimental variation, resulting in poor reproducibility and low throughput. Although prototypic microfluidic ChIP-seq platforms have been developed, these are poorly transferable as they require sophisticated custom-made equipment and in-depth microfluidic and ChIP expertise, while lacking parallelization. To enable standardized, automated ChIP-seq profiling of low-input samples, we constructed microfluidic PDMS-based plates capable of performing 24 sensitive ChIP reactions within 30 min of hands-on time and 4.5 h of machine-running time. These disposable plates can be conveniently loaded into a widely available controller for pneumatics and thermocycling. In light of the plug and play (PnP) ChIP plates and workflow, we named our procedure PnP-ChIP-seq. We show high-quality ChIP-seq on hundreds to a few thousand of cells for all six post-translational histone modifications that are included in the International Human Epigenome Consortium set of reference epigenomes. PnP-ChIP-seq robustly detects epigenetic differences on promoters and enhancers between naive and more primed mouse embryonic stem cells (mESCs). Furthermore, we used our platform to generate epigenetic profiles of rare subpopulations of mESCs that resemble the two-cell stage of embryonic development. PnP-ChIP-seq allows nonexpert laboratories worldwide to conveniently run robust, standardized ChIP-seq, whereas its high throughput, consistency, and sensitivity pave the way toward large-scale profiling of precious sample types such as rare subpopulations of cells or biopsies.

Decentralized Voltage Control of Autonomous DC Microgrids With Robust Performance Approach

In this article, we propose an optimal decentralized robust <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_\infty $ </tex-math></inline-formula> control system for the voltage control problem of autonomous uncertain dc microgrids consisting of multiple distributed generations (DGs) with general topology. The one-degree-of-freedom (DoF) structure of the developed control system guarantees the robust performance of the system as well as its robust stability against various sources of uncertainty, such as plug-and-play (PnP) operation of the DGs, topology changes, large load perturbations, different subsequent system changes, and the presence of constant power loads (CPLs). To that end, the uncertain dc microgrid is modeled as a Linear Time-Invariant (LTI) polytopic state-space system. Then, a state-feedback control technique based on linear matrix inequality (LMI) with linearly parameter-dependent (LPD) Lyapunov matrices is implemented on the uncertain dc microgrids. The proposed controller does not require any sort of communications, and the robust performance property of the controller eliminates the need for prefilter design. The efficiency of the proposed controller is analyzed by simulating different case studies through SimPowerSystems Toolbox in MATLAB. Finally, the performance of the proposed control method is validated via experimental studies.

Deep plug-and-play priors for spectral snapshot compressive imaging

We propose a plug-and-play (PnP) method that uses deep-learning-based denoisers as regularization priors for spectral snapshot compressive imaging (SCI). Our method is efficient in terms of reconstruction quality and speed trade-off, and flexible enough to be ready to use for different compressive coding mechanisms. We demonstrate the efficiency and flexibility in both simulations and five different spectral SCI systems and show that the proposed deep PnP prior could achieve state-of-the-art results with a simple plug-in based on the optimization framework. This paves the way for capturing and recovering multi- or hyperspectral information in one snapshot, which might inspire intriguing applications in remote sensing, biomedical science, and material science. Our code is available at: https://github.com/zsm1211/PnP-CASSI.

A Plug-and-Play Priors Framework for Hyperspectral Unmixing

Spectral unmixing is a widely used technique in hyperspectral image processing and analysis. It aims to separate mixed pixels into the component materials and their corresponding abundances. Early solutions to spectral unmixing are performed independently on each pixel. Nowadays, investigating proper priors into the unmixing problem has been popular as it can significantly enhance the unmixing performance. However, it is nontrivial to handcraft a powerful regularizer, and complex regularizers may introduce extra difficulties in solving optimization problems in which they are involved. To address this issue, we present a plug-and-play (PnP) priors framework for hyperspectral unmixing. More specifically, we use the alternating direction method of multipliers (ADMM) to decompose the optimization problem into two iterative subproblems. One is a regular optimization problem depending on the forward model, and the other is a proximity operator related to the prior model and can be regarded as an image denoising problem. Our framework is flexible and extendable which allows a wide range of denoisers to replace prior models and avoids handcrafting regularizers. Experiments conducted on both synthetic data and real airborne data illustrate the superiority of the proposed strategy compared with other state-of-the-art hyperspectral unmixing methods.

Hyperspectral Image Restoration by Tensor Fibered Rank Constrained Optimization and Plug-and-Play Regularization

Hyperspectral images (HSIs) are often contaminated by several types of noise, which significantly limits the accuracy of subsequent applications. Recently, low-rank modeling based on tensor singular value decomposition (T-SVD) has achieved great success in HSI restoration. Most of them use the convex and nonconvex surrogates of the tensor rank, which cannot well approximate the tensor singular values and obtain suboptimal restored results. We suggest a novel HSI restoration model by introducing a fibered rank constrained tensor restoration framework with an embedded plug-and-play (PnP)-based regularization (FRCTR-PnP). More precisely, instead of using the convex and nonconvex surrogates to approximate the fibered rank, the proposed model directly constrains the tensor fibered rank of the solution, leading to a better approximation to the original image. Since exploiting the low-fibered-rankness of HSI is mainly to capture the global structure, we further employ an implicit PnP-based regularization to preserve the image details. Particularly, the above two building blocks are complementary to each other, rather than isolated and uncorrelated. Based on the alternating direction multiplier method (ADMM), we propose an efficient algorithm to tackle the proposed model. For robustness, we develop a three-directional randomized T-SVD (3DRT-SVD), which preserves the intrinsic structure of the clean HSI and removes partial noise by projecting the HSI onto a low-dimensional essential subspace. Extensive experimental results including simulated and real data demonstrate that the proposed method achieves superior performance over compared methods in terms of quantitative evaluation and visual inspection.

On Plug-and-Play Regularization Using Linear Denoisers.

In plug-and-play (PnP) regularization, the knowledge of the forward model is combined with a powerful denoiser to obtain state-of-the-art image reconstructions. This is typically done by taking a proximal algorithm such as FISTA or ADMM, and formally replacing the proximal map associated with a regularizer by nonlocal means, BM3D or a CNN denoiser. Each iterate of the resulting PnP algorithm involves some kind of inversion of the forward model followed by denoiser-induced regularization. A natural question in this regard is that of optimality, namely, do the PnP iterations minimize some f+g , where f is a loss function associated with the forward model and g is a regularizer? This has a straightforward solution if the denoiser can be expressed as a proximal map, as was shown to be the case for a class of linear symmetric denoisers. However, this result excludes kernel denoisers such as nonlocal means that are inherently non-symmetric. In this paper, we prove that a broader class of linear denoisers (including symmetric denoisers and kernel denoisers) can be expressed as a proximal map of some convex regularizer g . An algorithmic implication of this result for non-symmetric denoisers is that it necessitates appropriate modifications in the PnP updates to ensure convergence to a minimum of f+g . Apart from the convergence guarantee, the modified PnP algorithms are shown to produce good restorations.

Learning Factory of 5G-based Plug-and-play Application in Manufacturing

The rise of 5th-Generation (5G) technology has provided great potential for the development of manufacturing industry. How to apply 5G technology in manufacturing industry has become a current hot topic. With the growth of personalized demands and the shortening of product life cycles, the flexibility of production model for Industry 4.0-oriented manufacturing has become an inevitable trend, and manufacturers need to be capable to quickly build or reconfigure new production systems to respond to market changes. Plug-and-play (PnP) of functional units is the basic guarantee to realize the rapid set-up and reconfiguration of production systems, where the construction of the Industrial Internet of Things (IIoT) and Cyber-Physical Systems (CPS) is one of the key problems to be solved. Therefore, the use of 5G technology to realize the agile and real-time transmission of large amounts of data, combined with the standard modular design to achieve the PnP of functional units, and construct IIOT and CPS, will be able to fully stimulate the development potential of future manufacturing industry. The Advanced Manufacturing Technology Center (AMTC) at Tongji University (Shanghai, China) carried out Learning Factory (LF) design and implementation of 5G-based PnP application, in order to provide application scenarios for the rapid establishment and operation of production systems. In addition, the AMTC developed use cases on demand for research, and cultivate students` knowledge and skills related to 5G and PnP in manufacturing industry. This paper introduces the environment and training process design of the LF in detail, as well as the current progress of implementation case in AMTC.