Modeling of complex physical systems with Modelica usually leads to the high-index differential algebraic equation system (DAE), index reduction is an important part of solving the high-index DAE. The structure index ...
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ISBN:
(纸本)9780769548180
Modeling of complex physical systems with Modelica usually leads to the high-index differential algebraic equation system (DAE), index reduction is an important part of solving the high-index DAE. The structure index reduction algorithm is one of the popular methods, but in special cases, it fails. Combinatorial relaxation algorithm can detect and correct the breakdown situation. And the maximum weight matching of bipartite graph is an important part of the combinatorial relaxation algorithm. In order to choose the proper method for the large-scale, dense bipartite graph, this paper provides three implementations of the Hungarian algorithm. The experiment results and the theory show that the BFS single-augmented method is better than others.
The Ising superconductor, due to its unique upper critical magnetic field, has become one of the most prominent subjects in the field of two-dimensional materials in recent years. Currently, the researches on Type I I...
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The Ising superconductor, due to its unique upper critical magnetic field, has become one of the most prominent subjects in the field of two-dimensional materials in recent years. Currently, the researches on Type I Ising superconductors mainly focus on transition metal chalcogenides (TMDs), however, the limited superconducting transition temperature (Tc) restricts their practical application. In addition to TMDs, Janus materials with central inversion symmetry breaking are also promising candidates for possessing strong Ising spin orbital couple (SOC). Here, through high-throughput calculations of T-phase and H-phase Janus materials transition metal nitrogen oxides (TMNOs), we obtain a potential high-Tc superconductor H-WNO. By solving the anisotropic Eliasberg equation, its Tcis predicted to be 46 K with strong electron phonon coupling of 1.71. Further calculations show that its Ising SOC is much stronger than Rashba SOC, and its average spin-orbit splitting energy at the Fermi level is comparable to that of the well-known Ising superconductor NbSe2, indicating that WNO is a potential excellent Ising superconductor. This is the first time that a Janus superconductor with high-Tc and strong Ising SOC was predicted. Our work opens up a new field to searching for high-Tc Ising superconductors.
Alloying strategies provide a high degree of freedom for reducing lead toxicity,improving thermodynamic stability, tuning the optoelectronic properties of ABX3 halide perovskites by varying the alloying element specie...
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Alloying strategies provide a high degree of freedom for reducing lead toxicity,improving thermodynamic stability, tuning the optoelectronic properties of ABX3 halide perovskites by varying the alloying element species and their *** the key role of B-site cations in contributing band edge states and modulating structure factors in halide perovskites,the partial replacement of Pb2+with different B-site metal ions has been *** several experimental attempts have been made to date,the effect of B-site alloying on the stability and electronic properties of halide perovskites has not been fully ***,we take cubic CsPbBr3 perovskite as the prototype material and systematically explore the effects of B-site alloying on Pb-containing *** to the presence or absence of the corresponding perovskite phase,the ten alloying elements investigated are classified into three types(i.e.,Type Ⅰ:Sn Ge,Ca,Sr;Type Ⅱ:Cd,Mg,Mn;Type Ⅲ:Ba,Zn,Cu).Based on the first-principles calculations,we obtain the following ***,these B-site alloys will exist as disordered solid solutions rather than ordered structures at room temperature throughout the composition ***,the alloying of Sn and Ge enhances the thermodynamic stability of the cubic perovskite host,whereas the alloying of the other elements has no remarkable effect on the thermodynamic stability of the cubic perovskite ***,the underlying physical mechanism for bandgap tuning can be attributed to the atomic orbital energy mismatch or quantum confinement ***,the alloying of different elements demonstrates the diversity in the regulation of crystal structure and electronic properties,indicating potential applications in photovoltaic s and self-trapped exciton-based light-emitting *** work provides theoretical guidance for using alloying strategies to reduce lead toxicity,enhance stability,and optimize the electronic properties of halide perovsk
Pipeline welding is essential in oil and gas transport, where weld quality determines pipeline safety and reliability. Although deep neural networks excel at automated weld defect detection, many models are heavy and ...
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Continual Learning aims to empower a single model to continually adapt to novel environments and perform new tasks while retaining previous knowledge without catastrophic forgetting. Compared with rehearsal-based meth...
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Continual Learning aims to empower a single model to continually adapt to novel environments and perform new tasks while retaining previous knowledge without catastrophic forgetting. Compared with rehearsal-based methods, which need expensive buffers, prompt-based methods become popular paradigms recently. However, we observe that sometimes the learned task prompts are difficult to distinguish, which results in the wrong task prompt selection during inference. Therefore, we design a Knowledge-Enhanced Module to learn better task prompts with inter-task and intra-task class semantic information. We also propose to select hard negative samples to learn more distinguishable task prompts by Contrastive Learning. Experiments on two popular benchmark datasets achieve state-of-the-art performance with remarkable advantages. The accuracy reached 88.20% and 71.64% on CIFAR-100 and ImageNet-R, which outperforms the SOTA model 1.12% and 1.14% respectively. At the same time, through top-N hard negative samples optimization, the standard deviation of the experiment is reduced to 0.13% and 0.02%, making the model performance more stable.
The hierarchical sequential three-way decision model is a method for addressing complex problem- solving. The existing hierarchical sequential three-way decision models mostly employ multi-view and/or multi-level appr...
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The hierarchical sequential three-way decision model is a method for addressing complex problem- solving. The existing hierarchical sequential three-way decision models mostly employ multi-view and/or multi-level approaches. However, as the number of views increases and the levels deepen, the model becomes too large to solve problems efficiently. In order to solve this problem, this paper proposes a generalized multiview hierarchical sequential three-way decisions based on local partition order product space model. Specifically, we first use a nested partition sequence to represent a view. Next, the linear order relations between levels within the views are split according to the number of levels to obtain local linear order relations. Then, in the multiple views, the local linear order relations between levels close to each other from different views are combined using Cartesian product operations to construct a generalized local partition order product space. Finally, by integrating the hierarchical sequential three-way decisions, the generalized local partition order product space is transformed into a multiview hierarchical sequential three-way decisions model. Experimental results on multiple datasets demonstrate that the proposed multiview hierarchical sequential three-way decision model achieves better performance compared to the existing models.
The cooperative output regulation problem of heterogeneous nonlinear multiagent systems is investigated by using the distributed event-triggered control. One of the main challenges in solving this problem is removing ...
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The cooperative output regulation problem of heterogeneous nonlinear multiagent systems is investigated by using the distributed event-triggered control. One of the main challenges in solving this problem is removing the restrictions imposed on the exosystem matrix while avoiding using global information. To address this challenge, a distributed event-triggered observer is proposed for each agent to estimate the upper bound of the norm of the exosystem matrix, based on which all agents can obtain the same upper bound in finite time. In particular, two types of distributed event-triggered observers are designed to estimate the exosystem matrix and the exosystem state. Meanwhile, with a clock-like variable being incorporated into the event-triggered mechanism, the existence of a positive minimum interevent time can be guaranteed for each agent. Furthermore, external disturbances with known dynamics can be compensated effectively by the proposed adaptive controllers. A numerical example is finally given to illustrate the effectiveness of the proposed control technique.
With the development of autonomous driving technologies, Vehicular Edge Computing (VEC) has become a powerful computing paradigm that improves vehicle performance and edge resource efficiency by introducing Computatio...
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In recent years, graph neural networks (GNNs) have gained significant attention due to their outstanding performance on graph-related tasks by utilizing neighborhood aggregation. However, traditional GNNs are primaril...
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In recent years, graph neural networks (GNNs) have gained significant attention due to their outstanding performance on graph-related tasks by utilizing neighborhood aggregation. However, traditional GNNs are primarily designed based on the homophily assumption, which means that they show poor performance on heterophilic networks where dissimilar nodes prefer to connect. To address this issue, several heterophilic GNNs have been proposed that employ techniques, such as extending local neighbors and improving GNN architectures. From another perspective, recent studies have shown that, unlike homophilic GNNs, heterophilic GNNs exhibit higher robustness against graph adversarial attacks. In these attacks, the attackers try to inject small perturbations into the graph. Therefore, in this study, we delve into the core designs of heterophilic GNNs, including high-order neighbor, potential neighbor, ego-neighbor separation, and interlayer combination designs. We further analyze the influence of these key designs on the robustness of GNNs. We conducted comprehensive experiments to compare the impact of different designs on baseline and real-world GNN models. The findings of this work can serve as a reference for future studies aiming to design more robust and universal GNNs.
In the academic research field, identifying suitable collaboration partners and selecting appropriate journals for publication remain significant challenges for researchers. Existing academic recommendation systems of...
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In the academic research field, identifying suitable collaboration partners and selecting appropriate journals for publication remain significant challenges for researchers. Existing academic recommendation systems often fail to provide personalized, accurate, and efficient recommendations. To address these issues, this article proposes an innovative academic recommendation system that incorporates multisimilarity features. By constructing an academic collaboration network and optimizing the transfer probability matrix to reflect scholars' relationships, the system captures scholars' collaborative tendencies and potential connections. A key innovation of this work is the proposed Muls-IRWR algorithm, which improves traditional random walk with restart (RWR) by integrating various similarity measures. Using a subset of the DBLP citation data, we develop our academic collaboration network to calculate precise scholar similarities. Experimental results demonstrate that our system significantly outperforms existing models in terms of recommendation accuracy and efficiency, highlighting its practical value and potential for use in real-world academic applications.
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