The proceedings contain 71 papers. The topics discussed include: talking after lights out: an ad hoc network for electric grid recovery;data-driven frequency regulation reserve prediction based on deep learning approa...
ISBN:
(纸本)9781665415026
The proceedings contain 71 papers. The topics discussed include: talking after lights out: an ad hoc network for electric grid recovery;data-driven frequency regulation reserve prediction based on deep learning approach;analysis of moving target defense in unbalanced and multiphase distribution systems considering voltage stability;scalable integration of high sampling rate measurements in deterministic process-level networks;minimizing age of information for distributed control in smart grids;cyber-physical disaster response of power supply using a centralised-to-distributed framework;detecting attacks on synchrophasor protocol using machine learning algorithms;and achieving runtime state verification assurance in critical cyber-physical infrastructures.
In this study, we present the design and implementation of an automated dynamic assembly line to enhance productivity, reduce downtime, and improve the quality of assembly processes by integrating multiple advanced au...
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ISBN:
(数字)9798331532970
ISBN:
(纸本)9798331532987
In this study, we present the design and implementation of an automated dynamic assembly line to enhance productivity, reduce downtime, and improve the quality of assembly processes by integrating multiple advanced automation technologies. This study aims to demonstrate the feasibility of automating industrial assembly lines, significantly improving operational efficiency and product quality. This setup links the PLC, HMI, SCADA system, and Raspberry Pi to the remote view. The chat among these parts occurs using the OPC UA and MQTT rules employing KepServerEX and Node-RED. In this manner, the plan can ensure easy data sharing, which helps in real-time monitoring and management. A test and check were conducted to confirm how well the system works in collecting information, handling it, and providing remote access to mobile devices.
For the biochemical reaction stage of the wastewater treatment process, which involves a complex microbial reaction process, there are many parameters that are difficult to monitor online. Therefore, using a multi-mod...
For the biochemical reaction stage of the wastewater treatment process, which involves a complex microbial reaction process, there are many parameters that are difficult to monitor online. Therefore, using a multi-model online soft measurement method is a key but challenging problem. In this paper, advanced machine learning technology is applied, and a multi-model online soft sensing method is adopted to address the difficulty of online monitoring of effluent quality parameters in the sewage treatment process. Specifically, the method uses an online subtraction clustering algorithm to divide real-time working condition data samples, and according to the distribution of real-time working condition data samples in the space, the fuzzy strategy is adopted to assign the real-time working condition data of the corresponding subspace to different submodels for learning. Finally, the final soft measurement results are obtained by dynamically integrating the outputs of each submodel. The actual operation data of a sewage treatment plant were used to detect the ammonia nitrogen in the effluent water of the sewage treatment process. Experimental results show that the proposed method is able to self-organize multi-model soft sensor model driven by real-time condition data, and the multi-model soft sensor model constructed by this method has improved the modeling accuracy, modeling speed, and the ability to track real-time conditions.
We propose a strategy to optimize energy utilization through battery management in a cooperative environment where households share access to a community-owned energy farm. The households are equipped with lossy recha...
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The study developed a technology adoption model to assess the intent of micro, small, and medium-sized businesses (MSMEs) across several industries to use robotic processautomation (RPA). The model captured the impac...
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The study developed a technology adoption model to assess the intent of micro, small, and medium-sized businesses (MSMEs) across several industries to use robotic processautomation (RPA). The model captured the impact of individual, social, and system characteristics on the ease of use and usability perception of RPA among MSMEs. Technology acceptance model (TAM3), validated primary responses collected from managers and owners of MSMEs operating in the industrial zones of the Pune industrial region (N=279). The results based on a structural equation model analysis indicate that perceived external control did not influence the ease-of-use perception (PEOU), and results demonstrability did not influence the perceived usefulness (PU) of RPA among the MSME respondents. The significant influencing factors for PEOU are perceived motivation, self-eff$\iota$cacy, technophobia, and perceived obstacles. Subjective norms, Job relevance, and output quality significantly determined PU. PEOU and PU significantly impacted the MSME’s intention to adopt RPA. The study will help MSMEs establish plans for implementing RPA and give MSME decisionmakers more confidence.
Vehicular Social Networks (VSNs) rely on data shared by users to provide convenient services. Data is outsourced to the cloud server and the distributed roadside unit in VSNs. However, roadside unit has limited resour...
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Electroaerodynamic (EAD) propulsion, where thrust is produced by collisions between electrostatically-accelerated ions and neutral air, is a potentially transformative method for indoor flight owing to its silent and ...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
Electroaerodynamic (EAD) propulsion, where thrust is produced by collisions between electrostatically-accelerated ions and neutral air, is a potentially transformative method for indoor flight owing to its silent and solid-state nature. Like rotors, EAD thrusters exhibit changes in performance based on proximity to surfaces. Unlike rotors, they have no fragile and quickly spinning parts that have to avoid those surfaces; taking advantage of the efficiency benefits from proximity effects may be a route towards longer-duration indoor operation of ion-propelled fliers. This work presents the first empirical study of ground proximity effects for EAD propulsors, both individually and as quad-thruster arrays. It focuses on multi-stage ducted centimeter-scale actuators suitable for use on small robots envisioned for deployment in human-proximal and indoor environments. Three specific effects (ground, suckdown, and fountain lift), each occurring with a different magnitude at a different spacing from the ground plane, are investigated and shown to have strong dependencies on geometric parameters including thruster-to-thruster spacing, thruster protrusion from the fuselage, and inclusion of flanges or strakes. Peak thrust enhancement ranging from 300 to 600% is found for certain configurations operated in close proximity (0.2 mm) to the ground plane and as much as a 20% increase is measured even when operated centimeters away.
A source localization experiment with a group of ground robots is presented in this paper. The process implemented on the robots is shown together with its building blocks which include methods from robotics and contr...
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ISBN:
(纸本)9781665472616
A source localization experiment with a group of ground robots is presented in this paper. The process implemented on the robots is shown together with its building blocks which include methods from robotics and control. The results of the experiments show that the source localization is successful in the presented environment. The workings of the process are explained and an implication about the performance in dependence on the number of robots used is given. A way to use measurements from the mapping phase for source localization is presented which speeds up the localization process and the effect of the tuning parameters is investigated.
In the recent years, the automatous operation of the shipboard power system (SPS) has gained a lot of attention from both academia and industrial researchers as one of the most promising prospects for the transformati...
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ISBN:
(纸本)9781728162072
In the recent years, the automatous operation of the shipboard power system (SPS) has gained a lot of attention from both academia and industrial researchers as one of the most promising prospects for the transformational control development of the new generation electric ships. In this paper, a distributed hybrid control framework based on the concept of Model Integrated computing (MIC) is proposed to solve the reconfigurable problem of SPS. The hybrid automaton model for SPS is developed under this framework and extended to an automaton which consists of normal system operation configuration and fault configuration. The reconfigurability of SPS is then defined as an indicator if the SPS can 1) remain in the safe operation region under fault configuration, or 2) have the chance to return to the normal operation configuration after fault occurs. A complete set of criteria to evaluate the proposed reconfigurability is derived. Finally a practical example of SPS operating in battle mode after a fault is used to illustrate the process to analysis the reconfigurability during system operation under the proposed framework.
Image denoising is needed if there is a need to remove noise while preserving details or structures in images in computing vision and image processing. Recent advancements in generative adversarial networks (GANs) and...
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ISBN:
(数字)9798331518097
ISBN:
(纸本)9798331518103
Image denoising is needed if there is a need to remove noise while preserving details or structures in images in computing vision and image processing. Recent advancements in generative adversarial networks (GANs) and convolutional neural networks (CNNs) have only improved the application of denoising techniques in varying ways. These methods give clearer and more accurate images since these approaches are able to separate noise from image substance. One of the main use of application of the image denoising is that they enhance the images in media, movies and also in medical field where high quality of images are required for diagnosing the diseases. This paper examines the significance of image denoising and introduces a novel GAN-based retinal scan denoising technique that outperforms both CNN-based denoising and conventional Gaussian filtering, achieving higher performance (PSNR: 27). It outperforms other approaches with SCD of 10 dB, SSIM of 0.866 and MSE of 18.99. In this paper, we propose a GAN-based solution for denoising images and leveraging deep learning-based techniques for evaluating the quality of retinal scans and we demonstrate the potential of our approach for enhancing the quality of the images.
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