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Artificial Intelligence and Computer Engineering (ICAICE), International Conference on

作者： Yanping Zhang Shuang Li Xi Chen Fulan Qian Shu Zhao Shuwei Zhu Yulu Wang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China School of Computer Science and Technology Anhui University Hefei China

ISBN: (数字)9781728191461

ISBN: (纸本)9781728191478

With the increasing number of patents, the task of patent citation recommendation becomes more and more important and meaningful. The core of a patent is textual content, which describes technical ideas and scope of the protection. Therefore, most of the existing patent citation recommendation methods focus on textual content. Recently, some scholars introduce bibliographic (structural) data to construct heterogeneous information network (HIN) on the basis of textual content to further improve the effect of recommendation. Most of these methods build HIN directly based on candidate patents selected in advance according to text similarity, which simplifies the complexity of the problem. However, the latent semantic relationships between patents formed by the textual content are often ignored, which can be used to mine more semantic and structural relationships in HIN. In this paper, in order to capture deep semantic and structural information, we introduce Semantic based Heterogeneous Information Network Embedding (SHINE) which obtains the latent semantic relationships between patents (referred to as semantic links in this paper) by textual content similarity and topic similarity, and links the semantic relationships with network structure to build a novel HIN. First, to obtain semantic links, we not only consider the textual content similarity between patents but also the topic similarity. We use the linear fusion method to combine these two similarities. Secondly, we build a HIN with semantic links and bibliographic information to integrate semantic and structural information, and we use network embedding to map the integration of two kinds of information into a common vector space. Finally, we recommend patent citations by linear combination of multi-modal similarities. The experimental results on two U.S. patent datasets show the effectiveness of SHINE. Compared to state-of-the art of patent citation recommendation, the proposed method SHINE achieves significantl

关键词： Patents Art Computational modeling Semantics Complexity theory Task analysis Artificial intelligence

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Local Smoothing Optimization Penalized Least Squares: Application to Spectrum Baseline Correction

Local Smoothing Optimization Penalized Least Squares: Applic...

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第31届中国过程控制会议（CPCC 2020）

作者： Yuqiang Li Tianhong Pan Haoran Li Shan Chen Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui University School of Electrical and Information Engineering Jiangsu University

Baseline shift, as an undesirable component of processing signal, seriously contaminates the essential information of the analysis object and deteriorates the performance of the developed model. The baseline correction methods based on penalized least squares have been widely applied in various industries and obtained better results in the single interval signal. However, the real analyzed signal is composed of multiple intervals with different smoothness, and the current baseline correction methods using constant smoothing parameters throughout the process cannot eliminate the baseline of the multi-interval signal. To address this issue, a local smoothing optimization penalized least squares(lsoPLS) method is presented in this work. The local smoothing optimization strategy using the derivative difference between the previously fitted baseline and the original signal is used to identify the interval location, while smoothing parameter is optimized by the interval characteristics. Two simulated cases and one real experiment were used to evaluate the effectiveness of the proposed lsoPLS method. Experimental results confirm that the proposed method has better performance in the multi-interval baseline correction and can be served as an effective pre-processing method for the complex spectral signal.

关键词： Baseline correction local smoothing optimization penalized least square signal pre-processing spectral analysis

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Iterated Problem Reformulation for Evolutionary Large-Scale Multiobjective Optimization

Iterated Problem Reformulation for Evolutionary Large-Scale ...

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Congress on Evolutionary Computation

作者： Cheng He Ran Cheng Ye Tian Xingyi Zhang Department of Computer Science and Engineering University Key Laboratory of Evolving Intelligent Systems of Guangdong Province Southern University of Science and Technology Shenzhen China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

Due to the curse of dimensionality, two main issues remain challenging for applying evolutionary algorithms (EAs) to large-scale multiobjective optimization. The first issue is how to improve the efficiency of EAs for reducing computation cost. The second one is how to improve the diversity maintenance of EAs to avoid local optima. Nevertheless, these two issues are somehow conflicting with each other, and thus it is crucial to strike a balance between them in practice. Thereby, we propose an iterated problem reformulation based EA for large-scale multiobjective optimization, where the problem reformulation based method and the decomposition based method are used iteratively to address the aforementioned issues. The proposed method is compared with several state-of-the-art EAs on a variety of large-scale multiobjective optimization problems. Experimental results demonstrate the effectiveness of our proposed iterated method in large-scale multiobjective optimization.

关键词： Sociology Convergence Pareto optimization Indexes Computer science

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The Application of the ADI-FDTD method in the 3D hybrid subgrid technique

The Application of the ADI-FDTD method in the 3D hybrid subg...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Ziheng Song Guoda Xie Zhixiang Huang Guilin Hou Pan Pan School of Electronic and Information Engineering Anhui university Hefei China Education of Anhui Province. Anhui Province Key Laboratory of Target Recognition and Feature Extraction (Luan Anhui 237000 China) Ministry of Education. the Key Laboratory of Intelligent Computing and Signal Processing Hefei China Anhui Province Anhui University. Key Laboratory of Electromagnetic Environmental Sensing (Hefei 230601 China) Anhui university Hefei China

A hybrid subgrid scheme based on the conventional finite-difference time-domain (FDTD) schemes are proposed. The alternating-direction-implicit FDTD (ADI-FDTD) is used to calculate electromagnetic fields in fine grid regions subgrids and the FDTD method is applied to the coarse grid regions. Numerical results demonstrate that the drastically reduced computation time and memory can be achieved by using the proposed 3D hybrid algorithm.

关键词： Three-dimensional displays Costs Computational modeling Computational electromagnetics Electromagnetic fields Time-domain analysis Finite difference methods

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3D LOD-FDTD for the Multi-Pole Debye Dispersive Media Using Z-Transform

3D LOD-FDTD for the Multi-Pole Debye Dispersive Media Using ...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Guilin Hou Ziheng Song Guoda Xie Pan Pan Zhixiang Huang School of Electronic and Information Engineering Anhui university Hefei China Education of Anhui Province. Anhui Province Key Laboratory of Target Recognition and Feature Extraction (Luan Anhui 237000 China) Ministry of Education. the Key Laboratory of Intelligent Computing and Signal Processing Hefei China Anhui Province Anhui University. Key Laboratory of Electromagnetic Environmental Sensing (Hefei 230601 China) Anhui university Hefei China

This paper introduces the application of unconditionally stable locally one-dimensional finite-difference time-domain(LOD-FDTD) method to 3D multi-pole Debye dispersive media model. Compared with other methods for dispersive media, the Z-transform method does not need to retain the electromagnetic values of the previous moments, has less memory footprint, and its numerical implementation is more straightforward. Through numerical examples, ZT-LOD-FDTD shows the advantages of accuracy and high computational efficiency.

关键词： Solid modeling Three-dimensional displays Memory management Media Electromagnetic modeling Numerical models Time-domain analysis

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An Octagonal Iterative Fractal Antenna with Notch Band and UWB Characteristics

An Octagonal Iterative Fractal Antenna with Notch Band and U...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Yong Cai Shuping He Xingang Ren Zhixiang Huang Anhui Province Key Laboratory of Target Recognition and Feature Extraction Luan China Key Laboratory of Electromagnetic Environmental Sensing Anhui University Hefei China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Institute of Physical Science and Information Technology Anhui University Hefei China

ISBN: (纸本)9781665409889

In this paper, based on the rectangular iterative technique, we propose an omnidirectional UWB fractal antenna by using microstrip line as feed. The antenna takes regular octagonal shape as patch element. Meanwhile, the iterative fractal and notch technique has been adopted to optimize the UWB antenna performance. After multiple iterations of the configured octagonal patch element, the optimized UWB performance can be obtained. The simulation results reveal that the operating bandwidth of the antenna meets Federal Communications Commission (FCC) broadband standard. Moreover, in order to avoid the electromagnetic interference in the X-band identified by the IEEE 521-2002 standard, we further adopt notch techniques and add the U-shaped gap to the microstrip line to achieve electromagnetic shielding. The impedance matching has been achieved for the antenna by imposing the slot on the ground. After optimizing the slot structure, the reflection of the antenna has reduced in the whole operating frequency band. The overall size of the ultra-wideband fractal antenna was designed to $25 \times 35 mm^{2}$ and was composed of FR-4 material with the relative dielectric constant $\varepsilon_{r} = 4.4$ and loss tangent angle $\tan \theta = 0.02$. After optimization, the antenna has good impedance matching and meets the UWB antenna omnidirectional standard. In 3.1–15.4GHz (excluding X-band), and the reflection coefficient S 11 is lower than -10 dB, voltage standing wave ratio (VSWR) is less than 2, maximum gain reaches 6dB. The results indicated that our proposed fractal antenna is promising for UWB antenna applications.

关键词： Slot antennas Impedance matching Voltage FCC Ultra wideband antennas Fractals Microstrip

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Simulating Maxwell-Schrodinger Equations by High-Order Symplectic FDTD Algorithm

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IEEE JOURNAL ON MULTISCALE AND MULTIPHYSICS COMPUTATIONAL TECHNIQUES 2019年 4卷 143-151页

作者： Xie, Guoda Huang, Zhixiang Fang, Ming Sha, Wei E., I Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China Key Laboratory of Micro-Nano Electronic Devices and Smart Systems of Zhejiang Province College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

A novel symplectic algorithm is proposed to solve the Maxwell Schrodinger (M-S) system for investigating light-matter interaction. Using the fourth-order symplectic integration and fourth-order collocated differences, M-S equations are discretized in temporal and spatial domains, respectively. The symplectic finite-difference time-domain (SFDTD) algorithm is developed for accurate and efficient study of coherent interaction between electromagnetic fields and artificial atoms. Particularly, the Dirichlet boundary condition is adopted for modeling the Rabi oscillation problems under the semiclassical framework. To implement the Dirichlet boundary condition, image theory is introduced, tailored to the high-order collocated differences. For validating the proposed SFDTD algorithm, three-dimensional numerical studies of the population inversion in the Rabi oscillation are presented. Numerical results show that the proposed high-order SFDTD(4, 4) algorithm exhibits better numerical performance than the conventional FDTD(2, 2) approach at the aspects of accuracy and efficiency for the long-term simulation. The proposed algorithm opens up a promising way toward a high-accurate energy-conservation modeling and simulation of complex dynamics in nanoscale light-matter interaction.

关键词： Maxwell Schrodinger (M-S) equations nanoscale light-matter interaction Rabi oscillation symplectic finite-difference time-domain (SFDTD)

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LasHeR: A large-scale high-diversity benchmark for RGBT tracking

arXiv

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arXiv 2021年

作者： Li, Chenglong Xue, Wanlin Jia, Yaqing Qu, Zhichen Luo, Bin Tang, Jin Sun, Dengdi Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

RGBT tracking receives a surge of interest in the computer vision community, but this research field lacks a large-scale and high-diversity benchmark dataset, which is essential for both the training of deep RGBT trackers and the comprehensive evaluation of RGBT tracking methods. To this end, we present a Large-scale High-diversity benchmark for short-term RGBT tracking (LasHeR) in this work. LasHeR consists of 1224 visible and thermal infrared video pairs with more than 730K frame pairs in total. Each frame pair is spatially aligned and manually annotated with a bounding box, making the dataset well and densely annotated. LasHeR is highly diverse capturing from a broad range of object categories, camera viewpoints, scene complexities and environmental factors across seasons, weathers, day and night. We conduct a comprehensive performance evaluation of 12 RGBT tracking algorithms on the LasHeR dataset and present detailed analysis. In addition, we release the unaligned version of LasHeR to attract the research interest for alignment-free RGBT tracking, which is a more practical task in real-world applications. The datasets and evaluation protocols are available at: https://***/mmic-lcl/Datasets-and-benchmark-code. Copyright © 2021, The Authors. All rights reserved.

关键词： Large dataset

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Pre-trained Prompt-driven Community Search

arXiv

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arXiv 2025年

作者： Ni, Li Xu, Hengkai Mu, Lin Zhang, Yiwen Luo, Wenjian Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education School of Computer Science and Technology Anhui University Anhui Hefei230601 China School of Computer Science and Technology Anhui University Anhui Hefei230601 China School of Computer Science and Technology Harbin Institute of Technology Shenzhen518055 China

The "pre-train, prompt" paradigm is widely adopted in various graph-based tasks and has shown promising performance in community detection. Most existing semi-supervised community detection algorithms detect communities based on known ones, and the detected communities typically do not contain the given query node. Therefore, they are not suitable for searching the community of a given node. Motivated by this, we adopt this paradigm into the semi-supervised community search for the first time and propose Pre-trained Prompt-driven Community Search (PPCS), a novel model designed to enhance search accuracy and efficiency. PPCS consists of three main components: node encoding, sample generation, and prompt-driven fine-tuning. Specifically, the node encoding component employs graph neural networks to learn local structural patterns of nodes in a graph, thereby obtaining representations for nodes and communities. Next, the sample generation component identifies an initial community for a given node and selects known communities that are structurally similar to the initial one as training samples. Finally, the prompt-driven fine-tuning component leverages these samples as prompts to guide the final community prediction. Experimental results on five real-world datasets demonstrate that PPCS performs better than baseline algorithms. It also achieves higher community search efficiency than semi-supervised community search baseline methods, with ablation studies verifying the effectiveness of each component of PPCS. Copyright © 2025, The Authors. All rights reserved.

关键词： Semi-supervised learning

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A Diversity Based Competitive Multi-objective PSO for Feature Selection 1

引用

15th International Conference on intelligent computing, ICIC 2019

作者： Qiu, Jianfeng Cheng, Fan Zhang, Lei Xu, Yi School of Computer Science and Technology Anhui University Hefei230039 China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230039 China

ISBN: (数字)9783030269692

ISBN: (纸本)9783030269685

Multi-Objective Particle Swarm Optimization (MOPSO) for feature selection has attracted increasing attention of researchers recently. However, in the existing methods, quick convergence usually degrades the diversity of the population, especially when many irrelevant and redundant features involved in them. To this end, a diversity based competitive multi-objective particle swarm optimization for feature selection problem (named D-CMOPSO) is proposed. In D-CMOPSO, a diversified competition based learning mechanism is proposed to improve the quality of found feature subset, which consists of three parts: exemplar particle construction, pairwise competition, and diversified learning strategy. The proposed competition mechanism utilizes the above three parts to boost the diversity in the following generations. Moreover, in order to guide the initial population to evolve the promising area, a maximal information coefficient based initialization strategy is also suggested. The experimental results demonstrate that the proposed D-CMOPSO is competitive for feature selection problem. © 2019, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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