The proceedings contain 30 papers. The topics discussed include: portable electric tiller and cutter machine;a short survey of truncated multipliers;multiport bidirectional converter with resonant and PWM modes for dr...
ISBN:
(纸本)9798350304329
The proceedings contain 30 papers. The topics discussed include: portable electric tiller and cutter machine;a short survey of truncated multipliers;multiport bidirectional converter with resonant and PWM modes for drives applications;data-driven urban mobility - comprehensive predictive modeling for traffic congestion;demand response based techno-socio-economic improvements with time-varying incentives at DN using cuckoo search algorithm;accomplishing power flow with UPQC in distributed generation system;a deep learning analysis on facial skin using CNN;smart ignition control and accident alert in two-wheelers;design of electric drive-train for switched reluctance motor based fuel cell electric vehicle;and similarity of depressive/anti-depressive tweets with happiness index parameters.
Based on the multi-agent consensus algorithm, a distributed economic dispatch strategy for multi-microgrid considering environmental costs is proposed. Different from the traditional centralized optimization method, t...
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Autonomous driving is a core application that greatly benefits from Internet of Vehicles (IoV). The calculation of the precise positions of Connected Autonomous Vehicles (CAVs) is mainly done using a Deep Neural Netwo...
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ISBN:
(纸本)9781665467490
Autonomous driving is a core application that greatly benefits from Internet of Vehicles (IoV). The calculation of the precise positions of Connected Autonomous Vehicles (CAVs) is mainly done using a Deep Neural Network (DNN) which requires significant computing power. Therefore, reducing the computational overhead and improving the efficiency are urgent problems to be solved. In this paper, we first propose a CAV cooperative learning architecture based on blockchain to improve the positioning accuracy of vehicles. Then, we introduce an error precision sharing model between CAVs. The proposed framework enables CAVs to train vehicle positioning accuracy models locally and exchange them via a blockchain network. Such a distributed training architecture further reduces the computing power required. Extensive simulation results show that the proposed scheme can also significantly improve the accuracy of the trajectory error compared to existing approaches.
The key performance indicators of the electrical distribution system are its efficiency, the line voltage regulation, system's voltage stability and its reliability. For mitigation of the issues related to these p...
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Driven by 6G networking, Edge Intelligence (EI) makes the most of the widespread edge resources to gain Artificial Intelligence (AI) insight. Future time-critical and data-intensive applications need distributed AI (D...
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ISBN:
(纸本)9798350345148;9798350345155
Driven by 6G networking, Edge Intelligence (EI) makes the most of the widespread edge resources to gain Artificial Intelligence (AI) insight. Future time-critical and data-intensive applications need distributed AI (DAI) and analytics solutions on the Edge computing platforms to enable EI from small devices to whole industrial factories. To deal with critical challenges of DAI implementation such as communication reliability, resource constrains and heterogeneity of edge devices, and dynamic nature of edge computing environment, we integrate digital twin (DT) technology to form an efficient framework. With this framework, efficient DT models are developed for edge devices, edge servers, and edge networks to predict accurately the states of physical entities using probabilistic graphical models (PGMs) and machine learning (ML) algorithms. In addition, a DT-empowered edge computing architecture specifying the optimal edge server placement, DT placement, and edge clustering is developed to support the implementation and development of DAI solutions. Specially, the framework is able to support various task partitioning-based DAI models including data parallelism, model parallelism, and pipeline parallelism as well as federated learning.
As a recently proposed network architecture, the computing power network (CPN) combines the ability of end, edge, cloud computing, and transmission network to realize the flexible and efficient scheduling and transact...
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Cooperative embodied AI systems, where multiple agents collaborate to accomplish complex, long-horizon tasks, show significant promise for real-world applications. These systems integrate perception, cognition, and ac...
Because of the rise in popularity of mobile devices and the rising demand for high-quality multimedia content, mobile node traffic and mobile Internet data have increased dramatically in recent years. distributed mobi...
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In this research, we present developments to ARSPI-NET, a pioneering hybrid framework aiming to redefine feature extraction methodologies for affective clinical EEG signal analysis through the integration of neuromorp...
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ISBN:
(纸本)9798331506674;9798331506667
In this research, we present developments to ARSPI-NET, a pioneering hybrid framework aiming to redefine feature extraction methodologies for affective clinical EEG signal analysis through the integration of neuromorphic computing principles. Focusing on the capabilities of Liquid State Machines (LSMs) and Spiking Neural Networks (SNNs), our work addresses the complexities of emotional processing within EEG signals by proposing an energy-efficient and biologically plausible solution. By employing LSMs as a core component, ARSPI-NET interprets spatiotemporal dynamics in EEG data, enhancing the classification of psychophysiological states. Through comprehensive experiments, including the classification of emotional valence from EEG signals induced by the international Affective Picture System (IAPS), we validate ARSPI-NET's effectiveness against traditional methods. Our findings reveal that, besides achieving competitive accuracy, ARSPI-NET stands out for its potential in reducing computational costs and paving the way for the adoption of neuromorphic hardware solutions like Intel's Loihi and IBM's TrueNorth. This study emphasizes the scalability and interpretability of ARSPI-NET, setting a foundation for future explorations harnessing neuromorphic computing for real-world EEG analysis.
As a new computing paradigm, mobile edge computing can meet users' computing demands with low latency. In reality, multiple edge servers with different computing capabilities are usually deployed in a distributed ...
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