79 research outputs found
Power System Fault Diagnosis with Quantum Computing and Efficient Gate Decomposition
Power system fault diagnosis is crucial for identifying the location and
causes of faults and providing decision-making support for power dispatchers.
However, most classical methods suffer from significant time-consuming, memory
overhead, and computational complexity issues as the scale of the power system
concerned increases. With rapid development of quantum computing technology,
the combinatorial optimization method based on quantum computing has shown
certain advantages in computational time over existing methods. Given this
background, this paper proposes a quantum computing based power system fault
diagnosis method with the Quantum Approximate Optimization Algorithm (QAOA).
The proposed method reformulates the fault diagnosis problem as a Hamiltonian
by using Ising model, which completely preserves the coupling relationship
between faulty components and various operations of protective relays and
circuit breakers. Additionally, to enhance problem-solving efficiency under
current equipment limitations, the symmetric equivalent decomposition method of
multi-z-rotation gate is proposed. Furthermore, the small probability
characteristics of power system events is utilized to reduce the number of
qubits. Simulation results based on the test system show that the proposed
methods can achieve the same optimal results with a faster speed compared with
the classical higher-order solver provided by D-Wave
Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks
Federated Learning (FL) has gained increasing interest in recent years as a
distributed on-device learning paradigm. However, multiple challenges remain to
be addressed for deploying FL in real-world Internet-of-Things (IoT) networks
with hierarchies. Although existing works have proposed various approaches to
account data heterogeneity, system heterogeneity, unexpected stragglers and
scalibility, none of them provides a systematic solution to address all of the
challenges in a hierarchical and unreliable IoT network. In this paper, we
propose an asynchronous and hierarchical framework (Async-HFL) for performing
FL in a common three-tier IoT network architecture. In response to the largely
varied delays, Async-HFL employs asynchronous aggregations at both the gateway
and the cloud levels thus avoids long waiting time. To fully unleash the
potential of Async-HFL in converging speed under system heterogeneities and
stragglers, we design device selection at the gateway level and device-gateway
association at the cloud level. Device selection chooses edge devices to
trigger local training in real-time while device-gateway association determines
the network topology periodically after several cloud epochs, both satisfying
bandwidth limitation. We evaluate Async-HFL's convergence speedup using
large-scale simulations based on ns-3 and a network topology from NYCMesh. Our
results show that Async-HFL converges 1.08-1.31x faster in wall-clock time and
saves up to 21.6% total communication cost compared to state-of-the-art
asynchronous FL algorithms (with client selection). We further validate
Async-HFL on a physical deployment and observe robust convergence under
unexpected stragglers.Comment: Accepted by IoTDI'2
PromotionLens: Inspecting Promotion Strategies of Online E-commerce via Visual Analytics
Promotions are commonly used by e-commerce merchants to boost sales. The
efficacy of different promotion strategies can help sellers adapt their
offering to customer demand in order to survive and thrive. Current approaches
to designing promotion strategies are either based on econometrics, which may
not scale to large amounts of sales data, or are spontaneous and provide little
explanation of sales volume. Moreover, accurately measuring the effects of
promotion designs and making bootstrappable adjustments accordingly remains a
challenge due to the incompleteness and complexity of the information
describing promotion strategies and their market environments. We present
PromotionLens, a visual analytics system for exploring, comparing, and modeling
the impact of various promotion strategies. Our approach combines
representative multivariant time-series forecasting models and well-designed
visualizations to demonstrate and explain the impact of sales and promotional
factors, and to support "what-if" analysis of promotions. Two case studies,
expert feedback, and a qualitative user study demonstrate the efficacy of
PromotionLens.Comment: IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE
VIS 2022
Biomarker study of symptomatic intracranial atherosclerotic stenosis in patients with acute ischemic stroke
ObjectiveAcute ischemic stroke (AIS) is characterized by high rates of morbidity, disability, mortality, and recurrence, often leaving patients with varying degrees of sequelae. Symptomatic intracranial atherosclerotic stenosis (sICAS) is a significant contributor to AIS pathogenesis and recurrence. The formation and progression of sICAS are influenced by pathways such as lipid metabolism and inflammatory response. Given its high risk of clinical recurrence, timely assessment of intracranial vascular stenosis in AIS is crucial for diagnosing sICAS, treating stroke, and preventing stroke recurrence.MethodsFourteen AIS patients were divided into stenosis and control groups based on the presence or absence of intracranial vessel stenosis. Initially, 4D Label-free proteome quantification technology was employed for mass spectrometry analysis to identify differential proteins between the groups. Subsequently, functional enrichment analysis, including GO classification, KEGG pathway, and Domain, revealed trends related to differential proteins. The STRING (v.11.5) protein interaction network database was used to identify differential protein interactions and target proteins. Finally, parallel reaction monitoring (PRM) validated the selected target proteins.ResultsMass spectrometry identified 1,096 proteins, with 991 being quantitatively comparable. Using a p-value <0.05 and differential expression change thresholds of >1.3 for significant up-regulation and < 1/1.3 for significant down-regulation, 46 differential proteins were identified: 24 significantly up-regulated and 22 significantly down-regulated. PRM experiments validated five proteins related to lipid metabolism and inflammatory response: namely alpha-2-macroglobulin (A2M), lipopolysaccharide-binding protein (LBP), cathepsin G (CTSG), cystatin (CST)3, and fatty acid-binding protein (FABP)1.ConclusionThe detection of changes in these five proteins in AIS patients can aid in the diagnosis of sICAS, inform stroke treatment, and assist in preventing stroke recurrence. Moreover, it can contribute to the development of drugs for preventing AIS recurrence by integrating traditional Chinese and Western medicine
Leakage current simulations of Low Gain Avalanche Diode with improved Radiation Damage Modeling
We report precise TCAD simulations of IHEP-IME-v1 Low Gain Avalanche Diode
(LGAD) calibrated by secondary ion mass spectroscopy (SIMS). Our setup allows
us to evaluate the leakage current, capacitance, and breakdown voltage of LGAD,
which agree with measurements' results before irradiation. And we propose an
improved LGAD Radiation Damage Model (LRDM) which combines local acceptor
removal with global deep energy levels. The LRDM is applied to the IHEP-IME-v1
LGAD and able to predict the leakage current well at -30 C after an
irradiation fluence of . The
charge collection efficiency (CCE) is under development
Study on GPS/INS System Using Novel Filtering Methods for Vessel Attitude Determination
Any vehicle such as vessel has three attitude parameters, which are mostly defined as pitch, roll, and heading from true north. In hydrographic surveying, determination of these parameters by using GPS or INS technologies is essential for the requirements of vehicle measurements. Recently, integration of GPS/INS by using data fusion algorithm became more and more popular. Therefore, the data fusion algorithm plays an important role in vehicle attitude determination. To improve attitude determination accuracy and efficiency, two improved data fusion algorithms are presented, which are extended Kalman particle filter (EKPF) and genetic particle filter (GPF). EKPF algorithm combines particle filter (PF) with the extended Kalman filter (EKF) to avoid sample impoverishment during the resampling process. GPF is based on genetic algorithm and PF; several genetic operators such as selection, crossover, and mutation are adopted to optimize the resampling process of PF, which can not only reduce the particle impoverishment but also improve the computation efficiency. The performances of the system based on the two proposed algorithms are analyzed and compared with traditional KF. Simulation results show that, comprehensively considering the determination accuracy and consumption cost, the performance of the proposed GPF is better than EKPF and traditional KF
Parameter Identification of a Governing System in a Pumped Storage Unit Based on an Improved Artificial Hummingbird Algorithm
Parameter identification is an important method to establish the governing system of a pumped storage unit. Appropriate parameters will make the governing system obtain better control performance. Therefore, in this study, an improved artificial hummingbird algorithm (IAHA) is proposed for the parameter identification of the governing system in a pumped storage unit. The algorithm integrates two key strategies to improve the optimization ability of the algorithm. First, the Chebyshev chaotic map is employed to initialize the artificial hummingbirds, which in turn increases and enhances the global search capability of the initial population. Second, the Levy flight is introduced in the guided foraging phase to expand the search space and avoid premature convergence. The performance of the proposed IAHA algorithm is compared with that of four other algorithms on 23 standard test functions, and the results show that IAHA has higher accuracy and faster convergence than the other four algorithms. Finally, IAHA was applied to the parameter identification of the governing system of a pumped storage unit to verify the effectiveness of the algorithm in tracking real-world problems
Parameter Identification of a Governing System in a Pumped Storage Unit Based on an Improved Artificial Hummingbird Algorithm
Parameter identification is an important method to establish the governing system of a pumped storage unit. Appropriate parameters will make the governing system obtain better control performance. Therefore, in this study, an improved artificial hummingbird algorithm (IAHA) is proposed for the parameter identification of the governing system in a pumped storage unit. The algorithm integrates two key strategies to improve the optimization ability of the algorithm. First, the Chebyshev chaotic map is employed to initialize the artificial hummingbirds, which in turn increases and enhances the global search capability of the initial population. Second, the Levy flight is introduced in the guided foraging phase to expand the search space and avoid premature convergence. The performance of the proposed IAHA algorithm is compared with that of four other algorithms on 23 standard test functions, and the results show that IAHA has higher accuracy and faster convergence than the other four algorithms. Finally, IAHA was applied to the parameter identification of the governing system of a pumped storage unit to verify the effectiveness of the algorithm in tracking real-world problems
The interaction of guanine nucleobase with B
The adsorption of guanine nucleobase on borospherene (B40) is investigated with density functional theory (DFT). We obtained that guanine is adsorbed on the surface of B40 cluster in five different stable configurations. The considerable adsorption energies (−1.023 ~ −1.586 eV) and charge transfers (0.292 ~ 0.345|e|) indicate that the guanine can be adsorbed on the surface of B40 in chemisorption state. The direct orbital overlaps between B40 and guanine account for the chemisorption. Depending on the significant variation of electrical conductivity, it is discovered that B40 cluster is feasible as a promising sensor for guanine nucleobase
Influence of tower anticorrosion coating as contaminant on operation characteristics of composite insulator
Anticorrosion coatings are commonly applied to prevent rust-based corrosion in transmission towers. Unfortunately, anticorrosion coatings may drip onto the surface of composite insulators during the construction process, which is likely to do harm to operation characteristics of composite insulators. This Letter was conducted to determine the influence of anticorrosion coatings on composite insulator performance. The mechanical properties, hydrophobicity, hydrophobic transfer, and flashover voltage of high-temperature vulcanised (HTV) silicone rubber specimens with and without the coating were investigated in detail. Appropriate solutions for avoiding unwanted effects on insulator performance are proposed accordingly. The influence of coating on the pollution flashover voltage of the composite insulator was also determined by changing the type, area, and shape of coating attached to the insulator surface. Finite element software was also used to analyse the effects of coatings on the electric field distribution of the composite insulator. The coating slightly affects the mechanical properties of HTV silicone rubber, but significantly impacts its hydrophobicity and hydrophobicity transfer. The pollution flashover characteristics of test composite insulators covered with the anticorrosion coating are also worst than those without coating
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