Yiwen Wen

GIS double-fracture disconnect switchgears have high requirements on insulation and stress resistance due to their miniaturized and compact design. How to improve the insulation capacity and stress resistance by optimizing the design of key dimensional parameters within a limited volume is the key point of optimization research. In this paper, the finite element method is used to build simulation models of the electrostatic and stress fields of a double-fracture disconnect switchgear. The key dimensional parameters that have a significant impact on the static electric field and stress field are initially analyzed, and these parameters are parametrically processed. In order to solve the optimization problem of large-scale field models using optimization algorithms, the Box-Behnken experimental design method is used to collect samples and establish a BP neural network model. The global Pareto-optimal solution set under multiple objectives is found by combining the BP neural network model and NSGA-II algorithm, and the global optimal solution is obtained according to different objective weights. Under the optimized dimensional parameters, the maximum electric field strength and the maximum principal stress in critical areas are significantly reduced, achieving an effective improvement in the performance of the double-fracture disconnect switchgear.

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A bstract A search for new top quark interactions is performed within the framework of an effective field theory using the associated production of either one or two top quarks with a Z boson in multilepton final states. The data sample corresponds to an integrated luminosity of 138 fb − 1 of proton-proton collisions at $$ \sqrt{s} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msqrt> <mml:mi>s</mml:mi> </mml:msqrt> </mml:math> = 13 TeV collected by the CMS experiment at the LHC. Five dimension-six operators modifying the electroweak interactions of the top quark are considered. Novel machine-learning techniques are used to enhance the sensitivity to effects arising from these operators. Distributions used for the signal extraction are parameterized in terms of Wilson coefficients describing the interaction strengths of the operators. All five Wilson coefficients are simultaneously fit to data and 95% confidence level intervals are computed. All results are consistent with the SM expectations.

Triple gauge boson production at the LHC can be used to test the robustness of the Standard Model and provide useful information for VBF di-boson scattering measurement. Especially, any derivations from SM prediction will indicate possible new physics. In this paper we present a detailed Monte Carlo study on measuring WWW production in pure leptonic and semileptonic decays, and probing anomalous quartic gauge WWWW couplings at the CERN LHC and future hadron collider, with parton shower and detector simulation effects taken into account. Apart from cut-based method, multivariate boosted decision tree method has been exploited for possible improvement. For the leptonic decay channel, our results show that at the sqrt{s}=8(14)[100] TeV pp collider with integrated luminosity of 20(100)[3000] fb-1, one can reach a significance of 0.4(1.2)[10]sigma to observe the SM WWW production. For the semileptonic decay channel, one can have 0.5(2)[14]sigma to observe the SM WWW production. We also give constraints on relevant Dim-8 anomalous WWWW coupling parameters.

Bond-slip failure modes between reinforcement and concrete can seriously affect the load-bearing performance and safety of reinforced concrete (RC) structures. The pull-out test is widely used to analyze RC bond-slip and failure modes because of its simplicity. Therefore, a prediction model based on Sparrow Search Algorithm (SSA) Optimized Extreme Learning Machine (SSA-ELM) is proposed in this study to quickly and effectively evaluate the failure forms of RC structures. The bond-slip test samples containing 399 sets of deformed bar and concrete deformation pull-out tests were first collected as a database, and the various characteristic parameters were preprocessed. Then, the features significantly influencing the failure pattern were screened out using random forest, and the plausibility tests were performed using the Pearson correlation coefficient and mutual information. Subsequently, a classification prediction model of the pull-out failure pattern was developed based on standard machine learning (ML) algorithms. In addition, nine well-known ML algorithms (LR, K-NN, DT, SVC, BPNN, NB, RF, AdaBoost, and XGBoost) are considered. This model's accuracy and generalization ability are compared based on the Precision and Recall confusion matrix. Finally, the optimized ELM was trained by SSA using the training set data with optimized ELM weights and thresholds. The results showed that the concrete protective layer thickness ratio to reinforcement diameter had the highest sensitivity to the failure pattern of RC-drawn specimens. The accuracy of the prediction results of the SSA-ELM model was better than other ML algorithms (Accuracy = 95.8%), such as BP Neural Network. In addition, SSA has better stability and higher accuracy than Gray Wolf Optimization (GWO) Algorithm, Particle Swarm Optimization (PSO) Algorithm, Genetic Algorithm (GA), Ant Colony Optimization (ACO), and other ML classification algorithms.

Previous studies have shown that split-ring resonators (SRRs) can be utilized to achieve finely tuned nearest-neighbor coupling strengths in various one-dimensional hopping models. In our study, we present a systematic investigation of resonator coupling, providing a comprehensive quantitative description of the interaction between SRRs and complementary split-ring resonators (CSRRs) for any orientation combination. Our method includes an estimation of the coupling strength through a linear combination of periodic functions based on two orientation angles, with a sinusoidal expansion of up to the 3rd order, allowing for efficient and streamlined microwave structure design. Through our approach, we offer a satisfactory explanation of the band structure of SRR chains using a microwave-hopping model, which facilitates the exploration of exotic photonic band structures based on tight-binding theory.

Cohesion plays a crucial role in achieving collective goals, promoting cooperation and trust, and improving efficiency within social groups. To gain deeper insights into the dynamics of group cohesion, we have extended our previous model of noisy group formation by incorporating asymmetric voting behaviors. Through a combination of theoretical analysis and numerical simulations, we have explored the impact of asymmetric voting noise, the attention decay rate, voter selection methods, and group sizes on group cohesion. For a single voter, we discovered that as the group size approaches infinity, group cohesion converges to $1/(R+1)$, where $R$ represents the ratio of asymmetric voting noise. Remarkably, even in scenarios with extreme voting asymmetry ($R \to \infty$), a significant level of group cohesion can be maintained. Furthermore, when the positive or negative voter's voting noise surpasses or falls below the phase transition point of $R_c=1$, a higher rate of attention decay can lead to increased group cohesion. In the case of multiple voters, a similar phenomenon arises when the attention decay rate reaches a critical point. These insights provide practical implications for fostering effective collaboration and teamwork within growing groups striving to achieve shared objectives.

Cohesion plays a crucial role in the achievement of collective goals, the promotion of cooperation and trust, and the enhancement of efficiency within social groups. In this study, we introduce a simple model of group formation by incorporating asymmetric voting behaviors to gain a deeper understanding of group cohesion dynamics. Through a combination of theoretical analysis and numerical simulations, we systematically investigate the influence of asymmetric voting noise, attention decay, voter selection methods, and group sizes on group cohesion. Our findings reveal that for a single voter, as the group size tends towards infinity, group cohesion converges to a value determined by the ratio of asymmetric voting noise. Interestingly, even in scenarios with significant voting asymmetry, a substantial level of group cohesion can be maintained. Moreover, we observe that when the voting noise of positive or negative voters surpasses or falls below a critical threshold, a higher rate of attention decay leads to an increase in group cohesion. A similar phenomenon occurs in the case of multiple voters when the attention decay rate reaches a critical point. By understanding the factors that influence group cohesion, organizations and communities can adopt strategies to promote cohesive interactions, thereby enhancing their ability to accomplish common goals.

Grid-based structures are commonly used to encode explicit features for graphics primitives such as images, signed distance functions (SDF), and neural radiance fields (NeRF) due to their simple implementation. However, in $n$-dimensional space, calculating the value of a sampled point requires interpolating the values of its $2^n$ neighboring vertices. The exponential scaling with dimension leads to significant computational overheads. To address this issue, we propose a simplex-based approach for encoding graphics primitives. The number of vertices in a simplex-based structure increases linearly with dimension, making it a more efficient and generalizable alternative to grid-based representations. Using the non-axis-aligned simplicial structure property, we derive and prove a coordinate transformation, simplicial subdivision, and barycentric interpolation scheme for efficient sampling, which resembles transformation procedures in the simplex noise algorithm. Finally, we use hash tables to store multiresolution features of all interest points in the simplicial grid, which are passed into a tiny fully connected neural network to parameterize graphics primitives. We implemented a detailed simplex-based structure encoding algorithm in C++ and CUDA using the methods outlined in our approach. In the 2D image fitting task, the proposed method is capable of fitting a giga-pixel image with 9.4% less time compared to the baseline method proposed by instant-ngp, while maintaining the same quality and compression rate. In the volumetric rendering setup, we observe a maximum 41.2% speedup when the samples are dense enough.

Abstract In this paper, an improved YOLOv5 multiscale marine organism target detection algorithm (YOLOv5‐Mult) is proposed to address the insufficient feature extraction ability of small targets, low detection accuracy, and high catching error of existing models in complex environments. First, real frame clustering is performed using the Kmeans++ method. Second, the BiFPN network module is adopted in lieu of the PANet network module to enhance the feature fusion ability. Next, the multilayer semantic fusion module RBC (RepBlock CSP) replaces the C3 module before the SPP layer of the Backbone network and the C3 module in the Neck layer to enrich the image semantic information. Finally, the multiscale feature fusion module MC (Mult Conv) replaces the last C3 module in the Backbone network to mitigate the semantic gap between different feature channel layers. Experimental results demonstrate that the improved algorithm attains a mAP value of 71.18%, which is 5.22% higher than that of the original YOLOv5 algorithm, providing accurate identification and fishing for underwater robots.

In July, 2012, the CMS and ATLAS experiments at the CERN LHC claimed they have found a new particle with a mass near 125GeV. After that, with more accumulating data analyzed and sophisticated technique exploited, CERN made a further statement on Mar. 14, 2013, that New results indicate that particle discovered at CERN is a Higgs In this paper, after a brief introduction of the Standard Model Higgs theory and the search results pre-LHC, we present a detailed review on the experimental analyzes leading to the discovery of the Higgs boson at the LHC, based on our own Higgs search experiences inside the CMS collaborations during these recent years, we also mention the status of measuring the mass, coupling strength, spin and parity of the Higgs boson.

The latest results of the search for MSSM Higgs boson decaying into two tau leptons with the full 2016 data will be presented. The analysis is performed using the dataset recorded by the CMS experiment at the LHC from pp collisions at centre-of-mass energies of 13 TeV corresponding to an integrated luminosity of 12.9/fb.