arXiv subjects

Yishi Zhang

Publications and source records attributed to Yishi Zhang.

5 recordsLinked to original sources

arXiv2026

Energy Efficiency Maximization for Discrete Activation based NOMA-assisted Pinching-Antenna Systems

Pinching-antenna systems is a promising architecture for flexible wireless communications, but energy efficiency (EE) maximization remains largely unexplored, as limited existing studies mainly focus on transmit power minimization. This paper investigates EE maximization in a downlink non-orthogonal multiple access (NOMA)-assisted PASS by explicitly modeling the pinching antenna (PA) activation power and jointly optimizing discrete PA activation and power allocation under both quality-of-service and transmit power constraints. To tackle the resulting mixed-integer nonlinear programming problem, a two-layer iterative algorithm is proposed with an EE-oriented matching-based PA activation and a low-complexity Dinkelbach-based power allocation with closed-form updates. Numerical results demonstrate that the proposed solution achieves substantial EE gains over the considered benchmark schemes, while exhibiting fast convergence. The impact of activation power has been analyzed and the significance of accounting it in EE maximization problem is also demonstrated.

eess.SP↗

arXiv2026

Generalized Phase Diagrams for Graphene CVD growth on Copper

Understanding the competition between first-layer lateral expansion and second-layer nucleation is essential for layer-controlled graphene growth via chemical vapor deposition (CVD). Building on our previous phase diagram framework based on the dimensionless parameters $α$ and $Γ$, we develop an enhanced model incorporating two previously neglected effects: thermal-expansion-induced substrate strain and chemical desorption of carbon monomers via reverse dehydrogenation. First-principles calculations are employed to determine the strain-dependent diffusion and attachment barriers on both exposed and graphene-covered Cu(111) surfaces. By mapping the multi-step CVD process into an effective quasi-physical vapor deposition, we construct a generalized phase diagram characterized by the coupled effects of $α$, $Γ$, and a newly introduced desorption parameter $Z$. Our results show that tensile strain expands the bilayer graphene (BLG) growth window for critical nucleus sizes $i^*>1$. In contrast, chemical desorption suppresses BLG formation in the high-$Γ$ regime via $Z$-dependent monomer depletion. This unified framework provides a predictive guide for the rational synthesis of high-quality bilayer graphene by linking macroscopic growth parameters to microscopic layer-selection mechanisms.

cond-mat.mtrl-sci↗

arXiv2022

A spatial-temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism

Short-term traffic flow prediction is a vital branch of the Intelligent Traffic System (ITS) and plays an important role in traffic management. Graph convolution network (GCN) is widely used in traffic prediction models to better deal with the graphical structure data of road networks. However, the influence weights among different road sections are usually distinct in real life, and hard to be manually analyzed. Traditional GCN mechanism, relying on manually-set adjacency matrix, is unable to dynamically learn such spatial pattern during the training. To deal with this drawback, this paper proposes a novel location graph convolutional network (Location-GCN). Location-GCN solves this problem by adding a new learnable matrix into the GCN mechanism, using the absolute value of this matrix to represent the distinct influence levels among different nodes. Then, long short-term memory (LSTM) is employed in the proposed traffic prediction model. Moreover, Trigonometric function encoding is used in this study to enable the short-term input sequence to convey the long-term periodical information. Ultimately, the proposed model is compared with the baseline models and evaluated on two real word traffic flow datasets. The results show our model is more accurate and robust on both datasets than other representative traffic prediction models.

cs.LG↗

arXiv2015

Feature selection for classification with class-separability strategy and data envelopment analysis

In this paper, a novel feature selection method is presented, which is based on Class-Separability (CS) strategy and Data Envelopment Analysis (DEA). To better capture the relationship between features and the class, class labels are separated into individual variables and relevance and redundancy are explicitly handled on each class label. Super-efficiency DEA is employed to evaluate and rank features via their conditional dependence scores on all class labels, and the feature with maximum super-efficiency score is then added in the conditioning set for conditional dependence estimation in the next iteration, in such a way as to iteratively select features and get the final selected features. Eventually, experiments are conducted to evaluate the effectiveness of proposed method comparing with four state-of-the-art methods from the viewpoint of classification accuracy. Empirical results verify the feasibility and the superiority of proposed feature selection method.

cs.LG↗

arXiv2015

Feature Selection with Redundancy-complementariness Dispersion

Feature selection has attracted significant attention in data mining and machine learning in the past decades. Many existing feature selection methods eliminate redundancy by measuring pairwise inter-correlation of features, whereas the complementariness of features and higher inter-correlation among more than two features are ignored. In this study, a modification item concerning the complementariness of features is introduced in the evaluation criterion of features. Additionally, in order to identify the interference effect of already-selected False Positives (FPs), the redundancy-complementariness dispersion is also taken into account to adjust the measurement of pairwise inter-correlation of features. To illustrate the effectiveness of proposed method, classification experiments are applied with four frequently used classifiers on ten datasets. Classification results verify the superiority of proposed method compared with five representative feature selection methods.

cs.LG↗