Digital Twin Concept, Method and Technical Framework for Smart Meters

Smart meters connect smart grid electricity suppliers and users. Smart meters have become a research hotspot as smart grid applications like demand response, power theft prevention, power quality monitoring, peak valley time of use prices, and peer-to-peer (P2P) energy trading have grown. But, as the carriers of these functions, smart meters have technical problems such as limited computing resources, difficulty in upgrading, and high costs, which to some extent restrict the further development of smart grid applications. To address these issues, this study offers a container-based digital twin (CDT) approach for smart meters, which not only increases the user-facing computing resources of smart meters but also simplifies and lowers the overall cost and technical complexity of meter changes. In order to further validate the effectiveness of this method in real-time applications on the smart grid user side, this article tested and analyzed the communication performance of the digital twin system in three areas: remote application services, peer-to-peer transactions, and real-time user request services. The experimental results show that the CDT method proposed in this paper meets the basic requirements of smart grid user-side applications for real-time communication. The container is deployed in the cloud, and the average time required to complete 100 P2P communications using our smart meter structure is less than 2.4 seconds, while the average time required for existing smart meter structures to complete the same number of P2P communications is 208 seconds. Finally, applications, the future development direction of the digital twin method, and technology architecture are projected

A Novel Features-Based Multivariate Gaussian Distribution Method for the Fraudulent Consumers Detection in the Power Utilities of Developing Countries

According to statistics, developing countries all over the world have suffered significant non-technical losses (NTLs) both in natural gas and electricity distribution. NTLs are thought of as energy that is consumed but not billed e.g., theft, meter tampering, meter reversing, etc. The adaptation of smart metering technology has enabled much of the developed world to significantly reduce their NTLs. Also, the recent advancements in machine learning and data analytics have enabled a further reduction in these losses. However, these solutions are not directly applicable to developing countries because of their infrastructure and manual data collection. This paper proposes a tailored solution based on machine learning to mitigate NTLs in developing countries. The proposed method is based on a multivariate Gaussian distribution framework to identify fraudulent consumers. It integrates novel features like social class stratification and the weather profile of an area. Thus, achieving a significant improvement in fraudulent consumer detection. This study has been done on a real dataset of consumers provided by the local power distribution companies that have been cross-validated by onsite inspection. The obtained results successfully identify fraudulent consumers with a maximum success rate of 75%. 2013 IEEE.This work was supported by the Qatar National Library.Scopus2-s2.0-8510734936

Edge AI for Internet of Energy: Challenges and Perspectives

The digital landscape of the Internet of Energy (IoE) is on the brink of a revolutionary transformation with the integration of edge Artificial Intelligence (AI). This comprehensive review elucidates the promise and potential that edge AI holds for reshaping the IoE ecosystem. Commencing with a meticulously curated research methodology, the article delves into the myriad of edge AI techniques specifically tailored for IoE. The myriad benefits, spanning from reduced latency and real-time analytics to the pivotal aspects of information security, scalability, and cost-efficiency, underscore the indispensability of edge AI in modern IoE frameworks. As the narrative progresses, readers are acquainted with pragmatic applications and techniques, highlighting on-device computation, secure private inference methods, and the avant-garde paradigms of AI training on the edge. A critical analysis follows, offering a deep dive into the present challenges including security concerns, computational hurdles, and standardization issues. However, as the horizon of technology ever expands, the review culminates in a forward-looking perspective, envisaging the future symbiosis of 5G networks, federated edge AI, deep reinforcement learning, and more, painting a vibrant panorama of what the future beholds. For anyone vested in the domains of IoE and AI, this review offers both a foundation and a visionary lens, bridging the present realities with future possibilities

Smart Rocks and Wireless Communication System for Real-Time Monitoring and Mitigation of Bridge Scour -- A Proof-of-Concept Study

This study aims to integrate commercial measurement and communication components into a scour monitoring system with magnets or electronics embedded in smart rocks, and evaluate and improve its performance in laboratory and field conditions for the movement of smart rocks. Properly-designed smart rocks were found to be automatically rolled into the very bottom of a scour hole and can give critical information about the maximum scour depth and effectiveness of rip-rap mitigation strategies. Four types of smart rock technologies were investigated in this proof-of-concept phase of study, including passive with embedded magnets, active with magneto-inductive communication, active with controllable magnet rotation, and active with acoustic communication. Their performances were evaluated against three criteria: 1) movement accuracy within 0.5 m, 2) transmission distance between 5 and 30 m, and 3) at least one measurement every 15 minutes. Test results demonstrated that the proposed smart rocks are cost-effective, viable technologies for bridge scour monitoring

Novel resource provisioning and lightweight security protocols for IoT edge networks

This Ph.D. thesis introduces a novel dynamic resource allocation framework tailored for Edge Computing (EC) in Internet of Things (IoT) networks, addressing the pressing challenges posed by resource limitations and escalating user demands. Edge-driven IoT networks, characterized by their reliance on locally available computational resources from a heterogeneous ensemble of devices such as sensors, vehicles, and mobile phones, present unique challenges. These resources, in contrast to their cloud counterparts, exhibit inherent variability in terms of processing power, distribution, and operating system diversity. Moreover, their connectivity is subject to fluctuations, including failures, intermittent connections, and unpredictable network entry and exit events, rendering the EC network inherently dynamic. The inadequacy of existing solutions to effectively manage the dynamic nature of resource availability at the edge underscores the necessity for a resource allocation framework capable of adapting to these dynamic conditions. To this end, we propose a dynamic resource allocation framework that dynamically assigns computational and network resources. This framework aims to minimize average service delays and achieve resource utilization balance at the edge. To realize this objective, two resource allocation models are developed using TensorFlow: a classification-based approach and a regression-based approach. Experimental results in dynamic environments demonstrate remarkable performance improvements, with the regression model achieving an 87% task completion rate within specified time constraints and the classification model achieving 56%. To underscore the practicality and efficiency of our proposed framework, two real-world use cases are explored. The first use case deals with the detection of spoofing attacks in autonomous vehicles (AVs) using Shadow Analyzer, a technique that identifies ghost object attacks with reduced 2D data derived from 3D point cloud information. The second use case focuses on the implementation of homomorphic encryption for secure communication, presenting a novel distributed approach to Fully Homomorphic Encryption (FHE)-based data processing. To validate the applicability and efficiency of our framework, extensive simulation experiments are conducted across various scenarios and operational conditions on a hardware testbed. These experiments yield promising results, establishing the viability of our dynamic resource allocation framework in addressing the dynamic challenges posed by resource availability at the edge in IoT networks

Rehabilitation Engineering

Population ageing has major consequences and implications in all areas of our daily life as well as other important aspects, such as economic growth, savings, investment and consumption, labour markets, pensions, property and care from one generation to another. Additionally, health and related care, family composition and life-style, housing and migration are also affected. Given the rapid increase in the aging of the population and the further increase that is expected in the coming years, an important problem that has to be faced is the corresponding increase in chronic illness, disabilities, and loss of functional independence endemic to the elderly (WHO 2008). For this reason, novel methods of rehabilitation and care management are urgently needed. This book covers many rehabilitation support systems and robots developed for upper limbs, lower limbs as well as visually impaired condition. Other than upper limbs, the lower limb research works are also discussed like motorized foot rest for electric powered wheelchair and standing assistance device

Collected Papers in Structural Mechanics Honoring Dr. James H. Starnes, Jr.

This special publication contains a collection of structural mechanics papers honoring Dr. James H. Starnes, Jr. presented at the 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference held in Austin, Texas, April 18-21, 2005. Contributors to this publication represent a small number of those influenced by Dr. Starnes' technical leadership, his technical prowess and diversity, and his technical breath and depth in engineering mechanics. These papers cover some of the research areas Dr. Starnes investigated, which included buckling, postbuckling, and collapse of structures; composite structural mechanics, residual strength and damage tolerance of metallic and composite structures; and aircraft structural design, certification and verification. He actively pursued technical understanding and clarity, championed technical excellence, and modeled humility and perseverance

A quantitative FRET approach to characterize protein-protein interactions in living cells

The ability of proteins to specifically interact with each other is a key feature in the regulation of biological processes. Knowledge about interaction partners and characterization of protein-protein interactions contribute to the understanding of proper protein function and cell physiology. In particular, Förster resonance energy transfer (FRET) is a suitable method to analyze interactions between proteins in living cells. During the progress of this thesis, a quantitative FRET approach was established that aims to evaluate binding curves for interaction partners. Moreover, the quantitative FRET approach was applied to study biological questions, including the investigation of putative interaction partners of the endolysosomal ion channel two-pore channel 2 (TPC2), the Kv7.2 potassium channel and the photoreceptor-specific transmembrane protein peripherin 2. The FRET approach described in manuscript I computes calibrated FRET efficiencies from fluorescent measurements using three-filter cubes and correlates the FRET efficiencies to the concentration of donor and acceptor molecules to determine binding curves, which bear information about maximal FRET efficiencies and relative binding constants for individual FRET pairings. Calibration factors that represent the optical properties of the imaging setup and the fluorophores are crucial for quantitative measurements. A detailed description how to assess these factors is provided. The quantitative FRET approach is very robust as both donor-centric (E-FRET) and acceptor-centric (SE-FRET) efficiencies are obtained simultaneously from multiple cells. The method was further applied to investigate protein-protein interactions of membrane proteins. First of all, in manuscript II, an epilepsy-causing mutation in the Kv7.2 potassium channel was shown to be implicated in a reduced calmodulin binding affinity to the channel, which affects channel regulation. A second study identified SNARE proteins, such as syntaxin 7 and syntaxin 6, as novel interaction partners of the intracellular ion channel TPC2 (manuscript III), revealing TPC2 as a putative member of the late endosome-lysosome fusion machinery. In manuscript IV, the impact of polymorphic variants of TPC2 on channel dimerization and mTOR binding was investigated. Furthermore, in a study covered by manuscripts V and VI, rhodopsin as well as S- and M-opsins were identified as novel interaction partners of the retinal protein peripherin 2 in rods and cones, respectively. The binding domain underlying the interaction between peripherin 2 and rhodopsin, could be assigned to the fourth transmembrane domain of peripherin 2. Moreover, it could be demonstrated that disease-associated mutations in peripherin 2 attenuated this particular binding, suggesting differential pathophysiological consequences of disrupted interactions in rods and cones. In manuscript VIII, peripherin 2 and its homolog Rom-1 were shown to have opposing effects on rod outer segment targeting of disease-linked peripherin 2 mutants by evaluating their binding affinities. Peripherin 2 is a scaffold protein exclusively expressed in outer segments of rods and cones. As photoreceptors are polarized cells, FRET measurements were not only performed on transfected HEK293 cells but also on acutely isolated outer segments of virally transduced murine photoreceptors (manuscript VII). The results gained in this thesis demonstrate that protein interactions play a crucial role in the regulation of proper protein function. Loss of binding partners or a reduced binding affinity to particular proteins may result in pathophysiological conditions. A deeper knowledge about molecular interactions will contribute to the understanding of cellular mechanisms, etiology of diseases and may further evaluate putative targets of pharmacological interest

A quantitative FRET approach to characterize protein-protein interactions in living cells

The ability of proteins to specifically interact with each other is a key feature in the regulation of biological processes. Knowledge about interaction partners and characterization of protein-protein interactions contribute to the understanding of proper protein function and cell physiology. In particular, Förster resonance energy transfer (FRET) is a suitable method to analyze interactions between proteins in living cells. During the progress of this thesis, a quantitative FRET approach was established that aims to evaluate binding curves for interaction partners. Moreover, the quantitative FRET approach was applied to study biological questions, including the investigation of putative interaction partners of the endolysosomal ion channel two-pore channel 2 (TPC2), the Kv7.2 potassium channel and the photoreceptor-specific transmembrane protein peripherin 2. The FRET approach described in manuscript I computes calibrated FRET efficiencies from fluorescent measurements using three-filter cubes and correlates the FRET efficiencies to the concentration of donor and acceptor molecules to determine binding curves, which bear information about maximal FRET efficiencies and relative binding constants for individual FRET pairings. Calibration factors that represent the optical properties of the imaging setup and the fluorophores are crucial for quantitative measurements. A detailed description how to assess these factors is provided. The quantitative FRET approach is very robust as both donor-centric (E-FRET) and acceptor-centric (SE-FRET) efficiencies are obtained simultaneously from multiple cells. The method was further applied to investigate protein-protein interactions of membrane proteins. First of all, in manuscript II, an epilepsy-causing mutation in the Kv7.2 potassium channel was shown to be implicated in a reduced calmodulin binding affinity to the channel, which affects channel regulation. A second study identified SNARE proteins, such as syntaxin 7 and syntaxin 6, as novel interaction partners of the intracellular ion channel TPC2 (manuscript III), revealing TPC2 as a putative member of the late endosome-lysosome fusion machinery. In manuscript IV, the impact of polymorphic variants of TPC2 on channel dimerization and mTOR binding was investigated. Furthermore, in a study covered by manuscripts V and VI, rhodopsin as well as S- and M-opsins were identified as novel interaction partners of the retinal protein peripherin 2 in rods and cones, respectively. The binding domain underlying the interaction between peripherin 2 and rhodopsin, could be assigned to the fourth transmembrane domain of peripherin 2. Moreover, it could be demonstrated that disease-associated mutations in peripherin 2 attenuated this particular binding, suggesting differential pathophysiological consequences of disrupted interactions in rods and cones. In manuscript VIII, peripherin 2 and its homolog Rom-1 were shown to have opposing effects on rod outer segment targeting of disease-linked peripherin 2 mutants by evaluating their binding affinities. Peripherin 2 is a scaffold protein exclusively expressed in outer segments of rods and cones. As photoreceptors are polarized cells, FRET measurements were not only performed on transfected HEK293 cells but also on acutely isolated outer segments of virally transduced murine photoreceptors (manuscript VII). The results gained in this thesis demonstrate that protein interactions play a crucial role in the regulation of proper protein function. Loss of binding partners or a reduced binding affinity to particular proteins may result in pathophysiological conditions. A deeper knowledge about molecular interactions will contribute to the understanding of cellular mechanisms, etiology of diseases and may further evaluate putative targets of pharmacological interest