To reduce the impact on microgrid stability due to the high penetration of distributed energy sources, a novel method based on the virtual synchronous machine (VSM) is used to enhance the dynamic characteristics. The ...
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The flow systems in nuclear power plants and aircraft engine fuel pipelines are often subjected to ex-treme high-pressure conditions, which can induce cavitation and severely affect essential system com-ponents. Orifi...
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The flow systems in nuclear power plants and aircraft engine fuel pipelines are often subjected to ex-treme high-pressure conditions, which can induce cavitation and severely affect essential system com-ponents. Orifice plates are the most typical infrastructures representing the flow principle of the aboved flow systems. In this paper, a modified cavitation model combining local flow characteristics, supervised learning, and genetic algorithms is proposed to investigate the cavitation flow characteristics of orifice plates under high-pressure conditions. The modified cavitation model eliminates the influence of the bub-ble diameter and nucleation site volume fraction on mass flow rate and achieves dimensionality reduc-tion at the physical level. The relationship between evaporation/condensation coefficients and mass flow rate is constructed by supervised learning, and the two coefficients are determined using the genetic al-gorithm. The mass flow rates are calculated by the modified cavitation model within a 5% experiment error, proving the accuracy of the modified cavitation model. The effect of the diameter ratio (the di-ameter of the pipe to the orifice plate) and pressure drop on the mass flow rate are obtained based on the validated cavitation model. Finally, an empirical formula for calculating the mass flow rate based on the diameter ratio and pressure drop is derived. The modified cavitation model shows great potential for cavitation prediction applications for throttling devices such as nuclear power safety valves and aircraft engine nozzles.(c) 2022 Elsevier Ltd. All rights reserved.
Accurate traffic flow prediction is critical for traffic management and route guidance, enabling urban traffic to be free-flowing conditions and maximizing transport efficiency. In current prediction methods, the simp...
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Accurate traffic flow prediction is critical for traffic management and route guidance, enabling urban traffic to be free-flowing conditions and maximizing transport efficiency. In current prediction methods, the simple and fixed spatial graph only uses the prior knowledge of the traffic network, resulting in weak prediction performance. This paper proposes an Improved Graph Convolution Res-Recurrent Network (IGCRRN), which relies on uncertain spatio-temporal information for traffic flow prediction. In particular, a spatial dependence matrix that combines the origin graph matrix and the data-generated embedding node matrix is created. In this way, the spatial connection relationship can be obtained from the static graph information and changing traffic flow series, making the improved graph convolution block infer and quantify the different contributions in both spatial dependence and temporal dependence in a data-driven manner. In addition, the residual structure is employed to model the multi-level spatial dependence, and the IGCRRN-cell units based on the residual connection block and LSTM are designed to make the model automatically capture the spatio-temporal dependence in the traffic flow sequence. Experiments are conducted on two real traffic datasets, and the experiment results show that our proposed spatial dependence matrix can investigate the valuable information and consider the heterogeneity in the traffic flow. The IGCRRN model outperforms the baseline and state-of-the-art methods in prediction performance.
Substation grounding network is an important measure to maintain the safe and reliable operation of power system and ensure the safety of operators. The condition of grounding device is directly related to the safe op...
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Recent developments in Multimodal Large Language Models (MLLMs) have shown rapid progress, moving towards the goal of creating versatile MLLMs that understand inputs from various modalities. However, existing methods ...
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ISBN:
(纸本)9798891760943
Recent developments in Multimodal Large Language Models (MLLMs) have shown rapid progress, moving towards the goal of creating versatile MLLMs that understand inputs from various modalities. However, existing methods typically rely on joint training with paired multimodal instruction data, which is resource-intensive and challenging to extend to new modalities. In this paper, we propose a new paradigm through the model composition of existing MLLMs to create a new model that retains the modal understanding capabilities of each original model. Our basic implementation, NaiveMC, demonstrates the effectiveness of this paradigm by reusing modality encoders and merging LLM parameters. Furthermore, we introduce DAMC to address parameter interference and mismatch issues during the merging process, thereby enhancing the model performance. To facilitate research in this area, we propose MCUB, a benchmark for assessing ability of MLLMs to understand inputs from diverse modalities. Experiments on this benchmark and four other multimodal understanding tasks show significant improvements over baselines, proving that model composition can create a versatile model capable of processing inputs from multiple modalities.
Cutting force is a significant indicator for in-process monitoring of cutting status. Although dynamometers are widely used in cutting force measurement, they are not suitable for nano-cutting due to insufficient sens...
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Cutting force is a significant indicator for in-process monitoring of cutting status. Although dynamometers are widely used in cutting force measurement, they are not suitable for nano-cutting due to insufficient sensitivities and low integrating possibilities. This paper presents a smart tool holder composed by piezoelectric ceramics and a flexible hinge, which could be integrated with a fast tool servo (FTS), for three-axial cutting force measurements in micro/nano-cutting. An algorithm was developed for enabling its quasi-static force measurement. Experiments were performed to verify the capabilities of cutting force measurement, demonstrating a high measurement sensitivity for indicating the micro/nano-cutting status. (c) 2021 CIRP. Published by Elsevier Ltd. All rights reserved.
Silver (Ag) flakes are widely used as conductive fillers in the electrically conductive adhesives (ECAs). However, there are organic lubricants on the Ag surface such as oleic acid and stearic acid, a kind of surfacta...
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Silver (Ag) flakes are widely used as conductive fillers in the electrically conductive adhesives (ECAs). However, there are organic lubricants on the Ag surface such as oleic acid and stearic acid, a kind of surfactant commonly used in the production of Ag flakes, which will deteriorate the conductivity and mechanical properties of the ECAs. In this study, glutaric acid was used to remove the organic lubricants, which can increase the electrical conductivity and mechanical properties of the ECAs. FTIR, Raman, and XPS tests revealed that the glutaric acid was chemically adsorbed on the Ag surface by single-dentate coordination and then reacted with organic lubricants, explaining the action mechanism by which the glutaric acid enhanced the electrical and mechanical properties of the ECAs. In addition, the effects of glutaric acid on the thermal stability, bulk resistivity, shear strength, and viscosity of the ECAs were investigated. When the glutaric acid reached 0.12 wt %, the minimum bulk resistivity of the ECAs was 1.52 x 10-4 S center dot cm, the shear strength was 24.57 MPa, and the viscosity was 149 Pa center dot s. This provides a method for fabricating high-performance ECAs.
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