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Supportive robotic solutions take over mundane, but essential tasks from human workforce in biomedical research and development laboratories. The newest technologies in collaborative and mobile robotics enable the utilization in the human-centered and low-structured environment. Their adaptability, however, is hindered by the additional complexity that they introduce. In our paper we aim to entangle the convoluted laboratory robot integration architectures. We begin by hierarchically decomposing the laboratory workflows, and mapping the activity representations to layers and components of the automation control architecture. We elaborate the framework in detail on the example of pick-and-place labware transportation - a crucial supportive step, which we identified as the number one area of interest among experts of the field. Our concept proposal serves as a reference architecture model, the key principles of which were used in reference implementations, and are in line with international standardization efforts.

The Laboratory Automation Plug & Play (LAPP) framework is an over-arching reference architecture concept for the integration of robots in life science laboratories. The plug & play nature lies in the fact that manual configuration is not required, including the teaching of the robots. In this paper a digital twin (DT) based concept is proposed that outlines the types of information that must be provided for each relevant component of the system. In particular, for the devices interfacing with the robot, the robot positions must be defined beforehand in a device-attached coordinate system (CS) by the vendor. This CS must be detectable by the vision system of the robot by means of optical markers placed on the front side of the device. With that, the robot is capable of tending the machine by performing the pick-and-place type transportation of standard sample carriers. This basic use case is the primary scope of the LAPP-DT framework. The hardware scope is limited to simple benchtop and mobile manipulators with parallel grippers at this stage. This paper first provides an overview of relevant literature and state-of-the-art solutions, after which it outlines the framework on the conceptual level, followed by the specification of the relevant DT parameters for the robot, for the devices and for the facility. Finally, appropriate technologies and strategies are identified for the implementation.

The rapid advancement of renewable energy technologies necessitates innovative solutions for the efficient deployment and management of microgrid systems. This paper presents a detailed study on the implementation of edge–cloud collaboration-based plug and play (PnP) and topology identification for microgrids, focusing on the Jingshan AC/DC Microgrid Cluster System (JS-MCS) in Hubei, China. Firstly, the paper elucidates the structure of JS-MCS's physical system and integral information system, comprising the cloud platform, communication network, edge unit, and terminal unit. The following sections introduce the methods of PnP, describing the overall procedure and specific implementations between cloud and edge and cloud and terminal. Then, a novel approach for topology identification is further explored based on correlation analysis. An exemplary analysis of the studied system, JS-MCS, accentuates practical insights into the application of PnP and topology identification methods. [ABSTRACT FROM AUTHOR]

Existing one-stage detectors usually use two decoupled branches to optimize two subtasks, i.e., object localization and classification. However, this design paradigm will lead to misalignment of spatial features due to inconsistency in localization and classification. To mitigate this problem, we propose a plug-in and simple AF-Head (Aligned Features) that can generate aligned features for each task. Our proposed AF-Head contains Focus-Guided Feature Enhancement Module (FGM) and Auxiliary Positioning Module (APM). Specifically, in our FGM, we propose a focus branch representing the joint representation of localization confidence and classification scores. Then, we combine the focus and classification branches to alleviate the gap between training and inference. In addition, APM generates more accurate offsets for the localization branch to align with the classification branch. Moreover, we propose AF-Net based on the AF-Head. Extensive experiments on the MS-COCO demonstrate that our AF-Head can boost 0.7 ∼ 1.7 AP on different state-of-the-art one-stage detectors. Notably, AF-Net with a standard ResNeXt-101-32x4d-DCN backbone achieves 49.2 AP on the COCO test-dev. [ABSTRACT FROM AUTHOR]

This paper presents a hierarchical cyber-physical multi-agent model for an AC microgrid (MG). A new distributed finite-time secondary controller is designed in the provision of ancillary services, such as voltage and frequency synchronization and active and reactive power regulation. The control algorithm developed is fully distributed and intended to restore the system voltage and frequency to their nominal value finite time. The existing distributed controllers achieve a consensus in an infinite-time horizon, whereas the proposed control provides a quick convergence to a consensus even in the face of disturbances and restores the voltage and frequency in less than 0.25 s. For accurate active and reactive power regulation, a distributed control algorithm is proposed that corrects the power mismatch among neighboring distributed generator units and eventually for the entire MG network. A Lyapunov analysis is used to establish the upper limit on the convergence time. The proposed control scheme is evaluated and compared with the previously reported asymptotic control technique based on a neighborhood tracking error using time-domain simulations under load variation and communication constraints. The controller withstands a communication link delay up to 2 s with a small deviation and settles down to the nominal values within 0.4 s. Further, the convergence analysis shows that the performance of the proposed algorithm depends exclusively on the controller parameters and communication network connectivity, not on the line and load parameters of the AC MG. The proposed controller enables the plug-and-play capability of the AC MG and effectively reduces the load change-induced disturbance. [ABSTRACT FROM AUTHOR]

Bayesian methods to solve imaging inverse problems usually combine an explicit data likelihood function with a prior distribution that explicitly models expected properties of the solution. Many kinds of priors have been explored in the literature, from simple ones expressing local properties to more involved ones exploiting image redundancy at a non-local scale. In a departure from explicit modelling, several recent works have proposed and studied the use of implicit priors defined by an image denoising algorithm. This approach, commonly known as Plug & Play (PnP) regularization, can deliver remarkably accurate results, particularly when combined with state-of-the-art denoisers based on convolutional neural networks. However, the theoretical analysis of PnP Bayesian models and algorithms is difficult and works on the topic often rely on unrealistic assumptions on the properties of the image denoiser. This papers studies maximum a posteriori (MAP) estimation for Bayesian models with PnP priors. We first consider questions related to existence, stability and well-posedness and then present a convergence proof for MAP computation by PnP stochastic gradient descent (PnP-SGD) under realistic assumptions on the denoiser used. We report a range of imaging experiments demonstrating PnP-SGD as well as comparisons with other PnP schemes. [ABSTRACT FROM AUTHOR]

Increasing the level of automation in pharmaceutical laboratories and production facilities plays a crucial role in delivering medicine to patients. However, the particular requirements of this field make it challenging to adapt cutting-edge technologies present in other industries. This article provides an overview of relevant approaches and how they can be utilized in the pharmaceutical industry, especially in development laboratories. Recent advancements include the application of flexible mobile manipulators capable of handling complex tasks. However, integrating devices from many different vendors into an end-to-end automation system is complicated due to the diversity of interfaces. Therefore, various approaches for standardization are considered in this article, and a concept is proposed for taking them a step further. This concept enables a mobile manipulator with a vision system to “learn” the pose of each device and - utilizing a barcode - fetch interface information from a universal cloud database. This information includes control and communication protocol definitions and a representation of robot actions needed to operate the device. In order to define the movements in relation to the device, devices have to feature - besides the barcode - a fiducial marker as standard. The concept will be elaborated following appropriate research activities in follow-up papers.

In a cyberphysical production system, the connectivity between the physical entities of a production system with the digital component that controls and monitors that system takes fundamental importance. This connectivity has been increasing from the transducers' side, through gathering new functionalities and operating increasingly independently, taking the role of smart transducers, and from the applications' side, by being developed in a distributed and decentralized paradigm. This work presents a plug-and-play solution capable of integrating smart transducers compliant with the IEEE 1451 standard in industrial applications based on the IEC 61499 standard. For this, we implemented the NCAP module of the smart transducer defined in IEEE 1451, which, when integrated with 4diac IDE and DINASORE (development and execution tools compliant with IEC 61499), enabled a solution that presented automatically the smart sensors and actuators in the IDE application and embedded their functionalities (access to data and processing functions) in the runtime environment. In this way, a complete plug-and-play solution was presented from the connection of the transducer to the network until its integration into the application. [ABSTRACT FROM AUTHOR]

To improve the robustness and reliability of multi-sensor navigation and reduce the uncertainties and complexity of sensor management in challenging environment, a resilient interactive sensor-independent-update (ISIU) method is proposed. Inspired by the interactive cooperation theory, the contributions can be divided into two aspects. Firstly the priority of trust of navigation sensors is introduced into the information fusion in the form of transition probability matrix defined by Markov chain. Secondly every observable sensor is integrated with the propagated system in an elemental filter with sensor-independent-update structure. The multi-sensor integration is implemented in state estimation domain enhanced by interactive information fusion rather than in measurement domain implemented in traditional filter method. The overall estimation is determined by the weighted sum of average from every filter estimate. This weight of every model is dynamic updated by the prior transition information and posterior model likelihood. The same independent structure is also applied to adopt new available sensor to realize plug-and-play navigation. The kinematic vehicle experiment in sub-urban and urban canyon environment verified the superiority of the proposed method. The ISIU method shows better accuracy and reliability compared to classical Kalman filters. The introduction of priority of sensors and decoupled measurement update process make it robust and insensitive to sensor measurement noise and outliers. The interactive sensor-independent-update structure has the natural function of fault detection and exclusion without additional operations. The effect of dynamic sensor selection is achieved in this processing. The proposed ISIU method is pretty suitable for resilient navigation in challenging environments. [ABSTRACT FROM AUTHOR]

Large pre-trained models based on Vision Transformers (ViTs) contain nearly billions of parameters, demanding substantial computational resources and storage space. This restricts their transferability across different tasks. Recent approaches try to use adapter fine-tuning to address this drawback. However, there is still potential to improve the number of tunable parameters and the accuracy in these methods. To address this challenge, we propose an adapter fine-tuning module called Lv-Adapter, which consists of a linear layer and vector. This module enables targeted parameter fine-tuning of pretrained models by learning both the prior knowledge of pre-trained task and the information from downstream specific task, to adapt to various downstream tasks in image and video tasks while transfer learning. Compared to full fine-tuning methods, Lv-Adapter has several appealing advantages. Firstly, by adding only about 3% extra parameters to ViT, Lv-Adapter achieves comparable accuracy to full fine-tuning methods and even significantly surpasses them on action recognition benchmarks. Secondly, Lv-Adapter is a lightweight module that can be plug-and-play in different transformer models due to its simplicity. Finally, to validate these claims, extensive experiments were conducted on five image and video datasets in this study, providing evidence for the effectiveness of Lv-Adapter. When only 3.5% of the extra parameters are updated, it respectively achieves a relative boost of about 13% and 24% compared to the fully fine-tuned model on SSv2 and HMDB51. • Proposed a Lv-Adapter fine-tuning module composed of Linear-layers and Vectors. • Lv-Adapter can be plug-and-play in transformer-based models. • When pretrained models are transferred to downstream tasks, Lv-Adapter can improve model adaptability while balancing advanced accuracy and low parameter count. • Lv-Adapter is effective in both image classification and video classification tasks. [ABSTRACT FROM AUTHOR]

The current navigation systems used in many autonomous mobile robotic applications, like unmanned vehicles, are always equipped with various sensors to get accurate navigation results. The key point is to fuse the information from different sensors efficiently. However, different sensors provide asynchronous measurements, some of which even appear to be nonlinear. Moreover, some sensors are vulnerable in specific environments, e.g., GPS signal is likely to work poorly in interior space, underground, and tall buildings. We propose a multi-sensor information fusion method based on a factor graph to fuse all available asynchronous sensor information and efficiently and accurately calculate a navigation solution. Assuming the sensor measurements and navigation states in a navigation system as factor nodes and variable nodes in a factor graph, respectively, the update of the states can be implemented in the framework of the factor graph. The proposed method is experimentally validated using two different datasets. A comparison with Federated Filter, which has been widely used in integrated navigation systems, demonstrates the proposed method's effectiveness. Additionally, analyzing the navigation results with data loss verifies that the proposed method could achieve sensor plug and play in software.

Based on the energy storage cloud platform architecture, this study considers the extensive configuration of energy storage devices and the future large-scale application of electric vehicles at the customer side to build a new mode of smart power consumption with a flexible interaction, smooth the peak/valley difference of the load side power, and improve energy efficiency. A plug and play device for customer-side energy storage and an internet-based energy storage cloud platform are developed herein to build a new intelligent power consumption mode with a flexible interaction suitable for ordinary customers. Based on the load perception of the power grid, this study aims to investigate the operating state and service life of distributed energy storage devices. By selecting an integrated optimal control scheme, this study designs a kind of energy optimization and deployment strategy for stratified partition to reduce the operating cost of the energy storage device on the client side. The effectiveness of the system and the control strategy is verified through the Suzhou client-side distributed energy storage demonstration project.

Supportive robotic solutions take over mundane, but essential tasks from human workforce in biomedical research and development laboratories. The newest technologies in collaborative and mobile robotics enable the utilization in the human-centered and low-structured environment. Their adaptability, however, is hindered by the additional complexity that they introduce. In our paper we aim to entangle the convoluted laboratory robot integration architectures. We begin by hierarchically decomposing the laboratory workflows, and mapping the activity representations to layers and components of the automation control architecture. We elaborate the framework in detail on the example of pick-and-place labware transportation - a crucial supportive step, which we identified as the number one area of interest among experts of the field. Our concept proposal serves as a reference architecture model, the key principles of which were used in reference implementations, and are in line with international standardization efforts., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Adam Wolf reports financial support was provided by Baxalta Innovations GmbH. Adam Wolf reports financial support was provided by Obuda University Doctoral School of Applied Informatics and Applied Mathematics. Peter Galambos reports financial support was provided by National Research, Development and Innovation Fund of Hungary. Karoly Szell reports financial support was provided by National Research, Development and Innovation Fund of Hungary. Adam Wolf reports a relationship with Association Consortium Standardization in Lab Automation (SiLA) that includes: board membership. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

In a cyberphysical production system, the connectivity between the physical entities of a production system with the digital component that controls and monitors that system takes fundamental importance. This connectivity has been increasing from the transducers’ side, through gathering new functionalities and operating increasingly independently, taking the role of smart transducers, and from the applications’ side, by being developed in a distributed and decentralized paradigm. This work presents a plug-and-play solution capable of integrating smart transducers compliant with the IEEE 1451 standard in industrial applications based on the IEC 61499 standard. For this, we implemented the NCAP module of the smart transducer defined in IEEE 1451, which, when integrated with 4diac IDE and DINASORE (development and execution tools compliant with IEC 61499), enabled a solution that presented automatically the smart sensors and actuators in the IDE application and embedded their functionalities (access to data and processing functions) in the runtime environment. In this way, a complete plug-and-play solution was presented from the connection of the transducer to the network until its integration into the application.

The industrial cyber-physical system enables collaboration between distributed nodes across industrial clouds and edge devices. Flexibility and interoperability could be enhanced significantly by introducing the service-oriented architecture to industrial edge devices. From the industrial edge computing perspective, software components shall be dynamically composed across heterogeneous edge devices to perform various functionalities. In this paper, a knowledge-driven Microservice-based architecture to enable plug-and-play software components is proposed for industrial edges. These software components can be dynamically configured based on the orchestration of microservices with the support of the knowledge base and the reasoning process. These semantically enhanced plug-and-play microservices could provide rapid online reconfiguration without any programming efforts. The use of the plug-and-play software components is demonstrated by an assembly line example.

• Virtual refrigerant charge sensor for refrigeration systems with a receiver unit. • Online system identification of receiver dynamics. • Leakage investigation of a supermarket refrigeration system. • Relation between receiver dynamic and receiver liquid charge. In this paper a virtual refrigerant charge sensor (VRCS) for a booster refrigeration system with a receiver unit is developed and proposed. The VRCS estimates the liquid charge in the receiver unit by utilizing a combined mass and energy balance equation for mapping the change in the receiver unit's time constant to the liquid volume in the receiver unit. The time constant for the receiver unit is found by utilizing the commonly available data in an online subspace identification method. The parameters estimated by the online subspace identification method are recast in a linear fractional transformation structure to identify parameter changes due to liquid volume change. Hence, the VRCS functions in a plug and play framework where only the initial refrigerant charge in percentage is required to be known. Results from utilizing the VRCS on experimental data from a charge test conducted on a test setup located at Danfoss A/S are shown, where the VRCS shows an accurate estimation of the liquid charge in the receiver. The results from the VRCS can be employed for: early prediction of low charge in the system, early detection of a too large leakage rate, estimation of the current charge on a daily basis, and estimation of the amount of charge lost. [ABSTRACT FROM AUTHOR]

Targeted drug delivery systems based on metal-organic frameworks (MOFs) have progressed tremendously since inception and are now widely applicable in diverse scientific fields. However, translating MOF agents directly to targeted drug delivery systems remains a challenge due to the biomolecular corona phenomenon. Here, we observed that supramolecular conjugation of antibodies to the surface of MOF particles (MOF-808) via electrostatic interactions and coordination bonding can reduce protein adhesion in biological environments and show stealth shields. Once antibodies are stably conjugated to particles, they were neither easily exchanged with nor covered by biomolecule proteins, which is indicative of the stealth effect. Moreover, upon conjugation of the MOF particle with specific targeted antibodies, namely, anti-CD44, human epidermal growth factor receptor 2 (HER2), and epidermal growth factor receptor (EGFR), the resulting hybrid exhibits an augmented targeting efficacy toward cancer cells overexpressing these receptors, such as HeLa, SK-BR-3, and 4T1, as evidenced by flow cytometry. The therapeutic effectiveness of the antibody-conjugated MOF (anti-M808) was further evaluated through in vivo imaging and the assessment of tumor inhibition effects using IR-780-loaded EGFR-M808 in a 4T1 tumor xenograft model employing nude mice. This study therefore provides insight into the use of supramolecular antibody conjugation as a promising method for developing MOF-based drug delivery systems.

In this article, a distributed switched control system is proposed, which aims at frequency regulation and total generation cost minimization within a droop-based microgrid considering generator and line power constraints. The minimization is solved with the Lagrange method and is achieved by realizing the equal incremental cost criterion via a consensus algorithm. An online network reconfiguration is also proposed, which bypasses a distributed generator when it violates the generation limits or gets disconnected, or when its corresponding line power flow exceeds its upper limit. It is mathematically probed that the proposed reconfiguration is a distributed online Kron reduction, which leads to a new reduced data network; it preserves the existing spanning trees from cutting when a distributed generator is bypassed. Equilibrium analyses are conducted to show that the proposed switched system converges to the desired steady state. The stability of the system based on common quadratic Lyapunov function is discussed. The effectiveness of the proposed controller for different case studies is verified by adapting it to a test microgrid system. [ABSTRACT FROM AUTHOR]

This paper considers a reliable cooperative control and plug-and-play operation problem in networked heterogeneous systems (NHSs), and focuses on the scenario subjected to physical attack-induced actuator failures/uncertainties and Denial-of-Service (DoS) attacks on communication channels between physical nodes. To solve it, a new self-feedback control with an adaptive integral sliding-mode compensator is developed. The new one configures each heterogeneous system under the malicious actuator failures/uncertainties to be passivity-short (PS). The PS index quantifies the impact of each attacked heterogeneous dynamics on their networked operations. Furthermore, based on cloud networks, a novel network-level distributed control is proposed. It improves robustness of the systems against DoS with no frequency and duration constraints. And then, a technical condition is presented with the aid of the impact equivalence principle. Under the condition, the self-feedback and network-level controls designed separately but performed together ensure the leader–follower consensus and plug-and-play operation of the attacked NHSs. Finally, the effectiveness of the proposed method is verified by flight control systems. • A PnP cooperative control scheme is given for NHS with cyber–physical attack. • PS indices quantify effect of attacked physical dynamics on networked operations. • A cloud network-based high-level control improves robustness of NHS against DoS. [ABSTRACT FROM AUTHOR]

Main problems about application of existing coal mine safety monitoring and control system were pointed out, such as unstable sensor performance and power supply, poor anti-interference performance of transmission lines, different system technology level, incomplete standards and so on. Technology requirements of Technology schemes of upgrading of coal mine safety monitoring and control system(exposure draft) were analyzed about system functions, performances and communication interfaces. A design scheme of new coal mine safety monitoring and control system was proposed according to requirements of Technology schemes of upgrading of coal mine safety monitoring and control system(exposure draft). Key technologies of the new system were introduced in details including new digital sensing, multi-system data fusion, distributed/local event/remote control, electromagnetic compatibility, fault diagnosis, plug and play, resuming from broken point, etc.

Global low-rank methods have achieved great successes in tensor completion. However, these methods neglected the abundant non-local self-similarities, which exist in a wide range of multi-dimensional imaging data. To integrate the global and non-local property of the underlying tensor, we propose a novel low-rank tensor completion model via combined non-local self-similarity and low-rank regularization, which is named as NLS-LR. We adopt the parallel low-rank matrix factorization to guarantee the global low-rankness while plugging in non-local based denoisers to promote the non-local self-similarity instead of tailoring regularizers. To tackle the proposed model, we develop an efficient block successive upper-bound minimization (BSUM) based algorithm. Numerical experiment results demonstrate that the proposed method outperforms many state-of-the-art tensor completion methods in terms of quality metrics and visual effects. [ABSTRACT FROM AUTHOR]

Kron reduction (KR) is a methodology for analyzing an electrical network by replacing it with a simpler circuit having less nodes but the same terminal behavior of voltages and currents at target vertices. Existing approaches to instantaneous KR, however, can fail in preserving the structure of transfer functions representing power lines. Therefore, even if the original lines are passive $RLC$ circuits, reduced lines might have transfer functions that do not correspond to a physically realizable passive system. To overcome this drawback, in this paper we focus on $RL$ line models and propose two approximate KR algorithms producing reduced lines with the first-order transfer functions and capable of representing exactly the asymptotic behavior of electric signals, even if they are unbalanced. Then, we show how to apply these KR methods to the design of decentralized voltage and frequency controllers for AC islanded microgrids. Notably, we focus on the plug-and-play algorithm previously proposed by some of the authors, which assumes loads connected to the inverter outputs, and generalize it to networks where loads appear in arbitrary positions. Theoretical results are validated with numerical examples and the application of KR for designing PnP controllers is assessed through a simulation on a 21-bus microgrid. [ABSTRACT FROM AUTHOR]

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Computer-based simulation models are frequently used in hydrological research and water environment decision-making. New case studies often require existing models to be adapted. Extensions may be necessary due to the peculiarities of the studied natural system. This paper introduces the object-oriented framework called FSFM designed to cope with abovementioned challenges. The modeling framework provides an easy-to-use and flexible infrastructure for the rapid development of new, reusable simulation tools by introducing dependency injection and reflection mechanism. So, it is very suitable for building new models only by substituting a few components. In addition, the CASC2D-SED-Govers model engine is developed based on the modeling framework, and it was tested on the arsenic area based on two rainfall events. It is concluded that the modeling framework in this paper is efficient for building new models in the level of fine-grained component. This paper focuses on decomposition of the model and design methods of the plug and play of the component in the modeling framework. [ABSTRACT FROM AUTHOR]

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Automatically identifying the new equipment after its integration and adjusting operation strategy to realize “plug and play” functionality are becoming essential for micro-grid operations. In order to improve and perfect the micro-grid “plug and play” function with the increased amount of equipment with different information protocols and more diverse system applications, this paper presents a solution for adaptive micro-grid operation based on IEC 61850, and proposes the design and specific implementation methods of micro-grid “plug and play” function and system operation mode conversion in detail, by using the established IEC 61850 information model of a micro-grid. Actual operation tests based on the developed IED and micro-grid test platform are performed to verify the feasibility and validity of the proposed solution. The tests results show that the solution can automatically identify the IEC 61850 information model of equipment after its integration, intelligently adjust the operation strategies to adapt to new system states and achieves a reliable system operation mode conversion.