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Structural, Elastic, and Electronic Properties of a New Phase of Carbon

Communications in Theoretical Physics 2015年第8期64卷 237-243页

作者：邢孟江李彬华余正涛陈颀 Faculty of Information Engineering & Automation Kunming University of Science and Technology

Based on density function theory with the ultrasoft pseudopotential scheme in the frame of the local density approximation and the generalized gradient approximation, the structural, elastic, and electronic properties of carbon with P2221 phase have been systematically studied in this paper. The calculated results show that the P2221 phase of carbon is mechanically stable and dynamically stable at ambient pressure. The anisotropy studies of Young's modulus,Poisson's ratio, shear anisotropic factor, the percentage of elastic anisotropy for bulk modulus, the percentage of elastic anisotropy for shear modulus and the universal anisotropic index show that P2221 phase of carbon exhibits *** addition, P2221 phase is an indirect semiconductor with bandgap of 3.423 e V. But, the band gap of P2221 phase for carbon increase with increasing pressure.

关键词： elastic properties anisotropic properties electron

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MHFS-FORMER: Multiple-Scale Hybrid Features Transformer for Lane Detection

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Sensors 2025年第9期25卷 2876页

作者： Yan, Dongqi Zhang, Tao Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China

Although deep learning has exhibited remarkable performance in lane detection, lane detection remains challenging in complex scenarios, including those with damaged lane markings, obstructions, and insufficient lighting. Furthermore, a significant drawback of most existing lane-detection algorithms lies in their reliance on complex post-processing and strong prior knowledge. Inspired by the DETR architecture, we propose an end-to-end Transformer-based model, MHFS-FORMER, to resolve these issues. To tackle the interference with lane detection in complex scenarios, we have designed MHFNet. It fuses multi-scale features with the Transformer Encoder to obtain enhanced multi-scale features. These enhanced multi-scale features are then fed into the Transformer Decoder. A novel multi-reference deformable attention module is introduced to disperse the attention around the objects to enhance the model’s representation ability during the training process and better capture the elongated structure of lanes and the global environment. We also designed ShuffleLaneNet, which meticulously explores the channel and spatial information of multi-scale lane features, significantly improving the accuracy of target recognition. Our method has achieved an accuracy score of 96.88%, a real-time processing speed of 87 fps on the TuSimple dataset, and an F1 score of 77.38% on the CULane dataset. Compared with the methods based on CNN and those based on Transformer, our method has demonstrated excellent performance. © 2025 by the authors.

关键词： Image coding

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Temperature stability analysis of a Ti:LiNbO3 optical waveguide electric field sensor

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Applied Optics 2025年第13期64卷 3693-3701页

作者： Song, Wei Dong, Manling Kou, Xiaokuo Zhan, Zhenyu Zhang, Jiahong Ding, Qihong He, Shaoqi Chen, Qian Zhang, Jing Li, Yingna State Grid Henan Electric Power Company Henan Zhengzhou450000 China State Grid Henan Electric Power Research Institute Henan Zhengzhou450052 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650504 China Yunnan Key Laboratory of Computer Technology Applications Kunming University of Science and Technology Kunming650504 China

The output static optical power of the developed y-cut z-propagation Ti:LiNbO3 optical waveguide electric field sensor has been tested under the temperature changes from 5◦C to 35◦C. The results show that the static optical power has decreased for 866.6 µW, corresponding to a change of 32.79◦ in the static operating point B and an electric field measurement error exceeding 10%. Based on the finite element method, the light wave modes in the optical waveguide have been simulated. The results reveal that there is a small Ez component in both the quasi-TE and TM modes, causing B to be affected not only by the thermal expansion effect but also by the thermal-optic effect. It has been calculated that the effects of thermal-optic and thermal expansion on B are approximately −0.3301◦/◦C and 0.0087◦/◦C, respectively, resulting within 5–35◦C, the static optical power variation is approximately −267.94 µW, with a rate of change is −8.93 µW/◦C. Furthermore, the influence of temperature on the birefringence of the sensor output polarization-maintaining fiber (PMF) has been considered and analyzed. It has been found that the phase difference of the slow and fast light waves changes by −3.050 rad/K when the length of the PMF is 1488 mm. As a result, considering the effects of thermo-optical and thermal expansion as well as the influence of the PMF, the sensor output static optical power changes −868.86 µW, with temperature changes from 5◦C to 35◦C, which is consistent with the experiment results. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Electric field measurement

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Technique for Recovering Wavefront Phase Bad Points by Deep Learning

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Chinese Journal of Electronics 2023年第2期32卷 303-312页

作者： WU Jiali LIANG Jingyuan FEI Shaolong ZHONG Xirui Faculty of Automation & Information Engineering Xi'an University of Technology

In adaptive optics systems, the bad spot detected by the wavefront detector affects the wavefront reconstruction accuracy. A convolutional neural network(CNN) model is established to estimate the missing information on bad points, reduce the reconstruction error of the distorted wavefront. By training 10,000 groups of spot array images and the corresponding 30th order Zernike coefficient samples, learns the relationship between the light intensity image and the Zernike coefficient, and predicts the Zernike mode coefficient based on the spot array image to restore the wavefront. Following the wavefront restoration of 1,000 groups of test set samples, the root mean square(RMS) error between the predicted value and the real value was maintained at approximately 0.2 μm. Field wavefront correction experiments were carried out on three links of 600 m, 1.3 km,and 10 km. The wavefront peak-to-valley values corrected by the CNN decreased from 12.964 μm, 13.958 μm,and 31.310 μm to 0.425 μm, 3.061 μm, and 11.156 μm, respectively, and the RMS values decreased from 2.156 μm,9.158 μm, and 12.949 μm to approximately 0.166 μm, 0.852μm, and 6.963 μm, respectively. The results show that the CNN method predicts the missing wavefront information of the sub-aperture from the bad spot image, reduces the wavefront restoration error, and improves the wavefront correction performance.

关键词： Training Atmospheric modeling Optical distortion Feature extraction Adaptive optics Image restoration Convolutional neural networks

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Term-sparse polynomial optimization for the design of frame structures

arXiv

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arXiv 2025年

作者： Handa, Marouan Tyburec, Marek Kočvara, Michal Institute of Information Theory and Automation Prague18208 Czech Republic Faculty of Civil Engineering Czech Technical University in Prague Thakurova 7 Prague 616629 Czech Republic School of Mathematics University of Birmingham BirminghamB15 2TT United Kingdom

This work investigates an efficient solution to two fundamental problems in topology optimization of frame structures. The first one involves minimizing structural compliance under linear-elastic equilibrium and weight constraint, while the second one minimizes the weight under compliance constraints. These problems are non-convex and generally challenging to solve globally, with the non-convexity concentrated in a polynomial matrix inequality. In [32, 31], the authors tackled the problems using the moment-sum-of-squares hierarchy (mSOS), but were only able to solve smaller instances globally. Here, we aim to improve the scalability of solution to these problems by using the mSOS hierarchy supplemented with the Term Sparsity Pattern technique (TSP), which was introduced by Magron and Wang [22]. Due to the unique polynomial structure of our problems in which the objective and constraint functions are separable polynomials, we further improve scalability by adopting a reduced monomial basis containing non-mixed terms only. From extensive numerical testing, we conclude that these techniques allow for a global solution to two times larger instances when compared to [32, 31], and accelerate the solution of the problems from [32, 31] significantly. © 2025, CC BY.

关键词： Shape optimization

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An efficient binary programming method for black-box optimization and its application in processor design

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Science China(information Sciences) 2024年第12期67卷 140-156页

作者： Xiaoliang LV Qiaozhu ZHAI Jianchen HU Yuhang ZHU Jinhui LIU Xiaohong GUAN Faculty of Electronic and Information Engineering Xi'an Jiaotong University Department of Automation Tsinghua University

Optimizing the parameter settings in a large design space for the processor with limited simulation resources is a challenging task. The current black-box optimization algorithms for processor design space exploration(DSE) problems usually require a large amount of simulation resources for high-dimensional and discrete problems. Besides, the constraints handling techniques in these algorithms need to be improved. To address the issues, we propose an efficient binary integer programming(BIP) approach for the DSE of the processor with strictly guaranteed constraints. Our approach involves adopting the separability assumption to establish a surrogate objective function that is ordinal consistent, thus avoiding the complex non-linearity of the real objective function. Moreover, the design rules can be taken simply as constraints in BIP model to further reduce the design space. Thus, the efforts spent in the infeasible exploration space can be *** experimental results show that the proposed algorithm outperforms the state-of-the-art Bayesian optimization and evolutionary algorithms in terms of exploration efficiency, required simulation points and performance of the recommended points.

关键词： black-box optimization binary programming design space exploration processor design expensive simulation system

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Enhanced Feature Contrastive Self-Supervised Learning Method Based on Deep Unfolding POCS for Accelerated Parallel MRI Reconstruction

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Ding, Peng Duan, Jizhong Tao, Haibo Liu, Yu Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Kunming Medical University Department of Radiology Yunnan Cancer Hospital The Third Affiliated Hospital Kunming650118 China Tianjin University School of Microelectronics Tianjin300072 China

The application of deep learning has significantly accelerated magnetic resonance imaging (MRI). However, these methods encounter substantial challenges when fully sampled datasets are unavailable. Self-supervised techniques enable network training using only undersampled MRI data. Nevertheless, the absence of fully sampled reference data often limits reconstruction quality. To address this limitation, we propose a parallel framework called the feature contrastive self-supervised learning method based on DUPOCS (FC-DUPOCS). This framework leverages deep unfolding projection onto convex sets (POCSs) and maximizes the similarities between parallel reconstruction results through contrastive learning, with the goal of improving self-supervised reconstruction quality. In FC-DUPOCS, we design deep unfolding POCS (DUPOCS) as the backbone network by unrolling the POCS-based iterative optimization process used to solve the L1SPIRiT model. Moreover, to enhance the model's generalizability across various undersampling masks and acceleration factors (AFs), we introduce an enhanced version, EFC-DUPOCS, which simulates different undersampling masks during re-undersampling. Experimental results show that EFC-DUPOCS can effectively generalize to other unseen undersampling masks and AFs. It significantly outperforms other self-supervised methods and achieves reconstruction quality comparable to supervised methods in in-distribution (ID) settings. Additionally, FC-DUPOCS can be trained unsupervised, yielding superior reconstruction quality compared to classical scan-specific methods. Pretrained parameters on arbitrary datasets further enhance reconstruction performance. The proposed methods effectively address the limitations of self-supervised MRI reconstruction by improving reconstruction quality and model generalizability without requiring fully sampled data. These methods offer a promising alternative to supervised approaches, significantly reducing data acquisition time and cost

关键词： Self-supervised learning

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Variable structure multiple model fixed-interval smoothing

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Chinese Journal of Aeronautics 2023年第2期36卷 139-148页

作者： Bolun ZHANG Yongxin GAO Zhansheng DUAN Faculty of Electronic and Information Engineering School of Automation Science and EngineeringXi’an Jiaotong UniversityXi'an 710049China

This paper focuses on fixed-interval smoothing for stochastic hybrid *** the truth-mode mismatch is encountered,existing smoothing methods based on fixed structure of model-set have significant performance degradation and are *** develop a fixedinterval smoothing method based on forward-and backward-filtering in the Variable Structure Multiple Model(VSMM)framework in this *** propose to use the Simplified Equivalent model Interacting Multiple Model(SEIMM)in the forward and the backward filters to handle the difficulty of different mode-sets used in both filters,and design a re-filtering procedure in the model-switching stage to enhance the estimation *** improve the computational efficiency,we make the basic model-set adaptive by the Likely-Model Set(LMS)*** turns out that the smoothing performance is further improved by the LMS due to less competition among *** results are provided to demonstrate the better performance and the computational efficiency of our proposed smoothing algorithms.

关键词： Fixed-interval smoothing Model-set adaptation Multiple model estimation Smoothing algorithm Variable structure

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Data-Filtered Prediction with Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong Beijing University of Technology School of Software Engineering The Faculty of Information Technology Beijing100124 China Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing China CSSC Systems Engineering Research Institute Beijing China Southern Methodist University Department of Computer Science The Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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A new ant colony-based routing algorithm with unidirectional link in UV mesh communication wireless network

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Optoelectronics Letters 2011年第2期7卷 139-142页

作者：柯熙政何华吴长丽 Faculty of Automation and Information Engineering Xi'an University of Technology

Aiming at the unidirectional links coming from nodes with different transmitting power and the obstacle blocking in UV mesh wireless communication network and the traditional ant colony algorithm only supporting bidirectional links, a new ant colony based routing algorithm with unidirectional link in UV mesh communication wireless network is proposed. The simulation results show that the proposed algorithm can improve the overall network connectivity and the survivability by supporting the combination of unidirectional link and bidirectional link.

关键词：单向链路路由算法蚁群算法无线网络光通信网紫外线无线通信网络发射功率

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