16,575 research outputs found
A fuzzy set theory-based fast fault diagnosis approach for rotators of induction motors
Induction motors have been widely used in industry, agriculture, transportation, national defense engineering, etc. Defects of the motors will not only cause the abnormal operation of production equipment but also cause the motor to run in a state of low energy efficiency before evolving into a fault shutdown. The former may lead to the suspension of the production process, while the latter may lead to additional energy loss. This paper studies a fuzzy rule-based expert system for this purpose and focuses on the analysis of many knowledge representation methods and reasoning techniques. The rotator fault of induction motors is analyzed and diagnosed by using this knowledge, and the diagnosis result is displayed. The simulation model can effectively simulate the broken rotator fault by changing the resistance value of the equivalent rotor winding. And the influence of the broken rotor bar fault on the motors is described, which provides a basis for the fault characteristics analysis. The simulation results show that the proposed method can realize fast fault diagnosis for rotators of induction motors
Green Carbon Footprint for Model Inference Serving via Exploiting Mixed-Quality Models and GPU Partitioning
This paper presents a solution to the challenge of mitigating carbon
emissions from large-scale high performance computing (HPC) systems and
datacenters that host machine learning (ML) inference services. ML inference is
critical to modern technology products, but it is also a significant
contributor to datacenter compute cycles and carbon emissions. We introduce
Clover, a carbon-friendly ML inference service runtime system that balances
performance, accuracy, and carbon emissions through mixed-quality models and
GPU resource partitioning. Our experimental results demonstrate that Clover is
effective in substantially reducing carbon emissions while maintaining high
accuracy and meeting service level agreement (SLA) targets. Therefore, it is a
promising solution toward achieving carbon neutrality in HPC systems and
datacenters
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Effects of Particle Swarm Optimisation on a Hybrid Load Balancing Approach for Resource Optimisation in Internet of Things
This article belongs to the Special Issue Emerging Machine Learning Techniques in Industrial Internet of ThingsCopyright © 2023 by the authors. The internet of things, a collection of diversified distributed nodes, implies a varying choice of activities ranging from sleep monitoring and tracking of activities, to more complex activities such as data analytics and management. With an increase in scale comes even greater complexities, leading to significant challenges such as excess energy dissipation, which can lead to a decrease in IoT devicesâ lifespan. Internet of thingsâ (IoT) multiple variable activities and ample data management greatly influence devicesâ lifespan, making resource optimisation a necessity. Existing methods with respect to aspects of resource management and optimisation are limited in their concern of devices energy dissipation. This paper therefore proposes a decentralised approach, which contains an amalgamation of efficient clustering techniques, edge computing paradigms, and a hybrid algorithm, targeted at curbing resource optimisation problems and life span issues associated with IoT devices. The decentralised topology aimed at the resource optimisation of IoT places equal importance on resource allocation and resource scheduling, as opposed to existing methods, by incorporating aspects of the static (round robin), dynamic (resource-based), and clustering (particle swarm optimisation) algorithms, to provide a solid foundation for an optimised and secure IoT. The simulation constructs five test-case scenarios and uses performance indicators to evaluate the effects the proposed model has on resource optimisation in IoT. The simulation results indicate the superiority of the PSOR2B to the ant colony, the current centralised optimisation approach, LEACH, and C-LBCA.This research received no external funding
Improving Energy Saving of One-sided Matrix Decompositions on CPU-GPU Heterogeneous Systems
One-sided dense matrix decompositions (e.g., Cholesky, LU, and QR) are the
key components in scientific computing in many different fields. Although their
design has been highly optimized for modern processors, they still consume a
considerable amount of energy. As CPU-GPU heterogeneous systems are commonly
used for matrix decompositions, in this work, we aim to further improve the
energy saving of one-sided matrix decompositions on CPU-GPU heterogeneous
systems. We first build an Algorithm-Based Fault Tolerance protected
overclocking technique (ABFT-OC) to enable us to exploit reliable overclocking
for key matrix decomposition operations. Then, we design an energy-saving
matrix decomposition framework, Bi-directional Slack Reclamation(BSR), that can
intelligently combine the capability provided by ABFT-OC and DVFS to maximize
energy saving and maintain performance and reliability. Experiments show that
BSR is able to save up to 11.7% more energy compared with the current best
energy saving optimization approach with no performance degradation and up to
14.1% Energy * Delay^2 reduction. Also, BSR enables the Pareto efficient
performance-energy trade-off, which is able to provide up to 1.43x performance
improvement without costing extra energy
A High-Performance Implementation of Atomistic Spin Dynamics Simulations on x86 CPUs
Atomistic spin dynamics simulations provide valuable information about the
energy spectrum of magnetic materials in different phases, allowing one to
identify instabilities and the nature of their excitations. However, the time
cost of evaluating the dynamical correlation function
increases quadratically as the number of spins , leading to significant
computational effort, making the simulation of large spin systems very
challenging. In this work, we propose to use a highly optimized general matrix
multiply (GEMM) subroutine to calculate the dynamical spin-spin correlation
function that can achieve near-optimal hardware utilization. Furthermore, we
fuse the element-wise operations in the calculation of into
the in-house GEMM kernel, which results in further performance improvements of
44\% - 71\% on several relatively large lattice sizes when compared to the
implementation that uses the GEMM subroutine in OpenBLAS, which is the
state-of-the-art open source library for Basic Linear Algebra Subroutine
(BLAS).Comment: 18 (short) pages, 6 figure
AI-assisted Automated Workflow for Real-time X-ray Ptychography Data Analysis via Federated Resources
We present an end-to-end automated workflow that uses large-scale remote
compute resources and an embedded GPU platform at the edge to enable
AI/ML-accelerated real-time analysis of data collected for x-ray ptychography.
Ptychography is a lensless method that is being used to image samples through a
simultaneous numerical inversion of a large number of diffraction patterns from
adjacent overlapping scan positions. This acquisition method can enable
nanoscale imaging with x-rays and electrons, but this often requires very large
experimental datasets and commensurately high turnaround times, which can limit
experimental capabilities such as real-time experimental steering and
low-latency monitoring. In this work, we introduce a software system that can
automate ptychography data analysis tasks. We accelerate the data analysis
pipeline by using a modified version of PtychoNN -- an ML-based approach to
solve phase retrieval problem that shows two orders of magnitude speedup
compared to traditional iterative methods. Further, our system coordinates and
overlaps different data analysis tasks to minimize synchronization overhead
between different stages of the workflow. We evaluate our workflow system with
real-world experimental workloads from the 26ID beamline at Advanced Photon
Source and ThetaGPU cluster at Argonne Leadership Computing Resources.Comment: 7 pages, 1 figure, to be published in High Performance Computing for
Imaging Conference, Electronic Imaging (HPCI 2023
Prototype Foamy Virus Capsid â Nucleic Acid Interactions: Mechanistic Insights & Application for Efficient RNA Transfer
Foamy viruses (FV) represent a distinct genus in the retrovirus family and separate themselves from the large group of orthoretroviruses by various distinct features in their replication cycle (reviewed in Lindemann & Rethwilm, 2011). In gene therapy retroviruses are commonly used as vectors to deliver genetic information into target cells and also FV has been successfully used for example in a canine genetic disease model (Trobridge et al., 2009). Here we investigated the interactions between the FV capsid-forming protein âGagâ and nucleic acids. We found that prototype FV (PFV) Gag binds various cellular mRNAs, incorporates them into the nascent particle and thereby enables their transfer into the cytosol of target cells. There these mRNAs can serve as template for protein translation. This feature seems uniquely efficient for PFV and we developed it further into a âRNA transfer vector systemâ allowing efficient transgene mRNA transfer into target cells, as showed in proof-of-principle experiments in vitro and in vivo (Hamann et al., 2014a).
In parallel we started investigating the specificity in viral RNA genome packaging (Hamann et al., 2014b). To date little is known how PFV selects its RNA genome over the vast excess of cellular RNAs present in the cytosol. Elevated fundamental knowledge of this mechanism could help to make the âRNA transfer vector systemâ even more efficient since it would allow enrichment of certain specific âdesigner-RNAsâ in virus particles
Towards Advantages of Parameterized Quantum Pulses
The advantages of quantum pulses over quantum gates have attracted increasing
attention from researchers. Quantum pulses offer benefits such as flexibility,
high fidelity, scalability, and real-time tuning. However, while there are
established workflows and processes to evaluate the performance of quantum
gates, there has been limited research on profiling parameterized pulses and
providing guidance for pulse circuit design. To address this gap, our study
proposes a set of design spaces for parameterized pulses, evaluating these
pulses based on metrics such as expressivity, entanglement capability, and
effective parameter dimension. Using these design spaces, we demonstrate the
advantages of parameterized pulses over gate circuits in the aspect of duration
and performance at the same time thus enabling high-performance quantum
computing. Our proposed design space for parameterized pulse circuits has shown
promising results in quantum chemistry benchmarks.Comment: 11 Figures, 4 Table
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
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