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9th International Conference on Computer and Communications, ICCC 2023

作者： Wang, Suzhen Zhi, Hui Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Hefei China Anhui University School of Electronic and Information Engineering Hefei China

ISBN: (纸本)9798350317251

Subarray partition of reconfigurable intelligent surface (RIS) can significantly reduce the computational complexity of solving optimal reflection coefficients. However, there is no research about the RIS subarray partition of RIS-aided multiple-input multiple-output (MIMO) communication for the eavesdropper scenario. In this paper, we intend to solve the optimization subarray partition problem for the eavesdropper scenario, design a RIS-aided MIMO secure communication scheme based on subarray partition (RSC-SP). We consider minimizing the number of subarrays while satisfying secrecy rate requirements. First, this problem is described as a nonconvex combinatorial optimization problem, then we solve it by combining alternating optimization and bisection. In this scheme, we derive the closed expressions of the optimal transmit covariance matrix and the optimal reflection coefficients of RIS. The alternating optimization algorithm is used to jointly optimize the transmit covariance matrix and the reflection coefficients of RIS, the bisection method is used to calculate the minimum number of subarrays. Simulation results show that compared with the traditional scheme without subarray partition, RSC-SP can significantly reduce the computational complexity while meeting secrecy rate requirements. © 2023 IEEE.

关键词： Covariance matrix

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A Gradient-Guided Evolutionary Approach to Training Deep Neural Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第9期33卷 4861-4875页

作者： Yang, Shangshang Tian, Ye He, Cheng Zhang, Xingyi Tan, Kay Chen Jin, Yaochu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong SAR Department of Computer Science University of Surrey Guildford U.K

It has been widely recognized that the efficient training of neural networks (NNs) is crucial to classification performance. While a series of gradient-based approaches have been extensively developed, they are criticized for the ease of trapping into local optima and sensitivity to hyperparameters. Due to the high robustness and wide applicability, evolutionary algorithms (EAs) have been regarded as a promising alternative for training NNs in recent years. However, EAs suffer from the curse of dimensionality and are inefficient in training deep NNs (DNNs). By inheriting the advantages of both the gradient-based approaches and EAs, this article proposes a gradient-guided evolutionary approach to train DNNs. The proposed approach suggests a novel genetic operator to optimize the weights in the search space, where the search direction is determined by the gradient of weights. Moreover, the network sparsity is considered in the proposed approach, which highly reduces the network complexity and alleviates overfitting. Experimental results on single-layer NNs, deep-layer NNs, recurrent NNs, and convolutional NNs (CNNs) demonstrate the effectiveness of the proposed approach. In short, this work not only introduces a novel approach for training DNNs but also enhances the performance of EAs in solving large-scale optimization problems.

关键词： Artificial neural networks Training Optimization Search problems Scalability Complexity theory Genetic algorithms Deep neural networks (DNNs) evolutionary algorithm (EA) genetic operator gradient descent sparsity

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A Unified Framework Integrating Knowledge and Data for Collaborative Root Cause Identification 5

A Unified Framework Integrating Knowledge and Data for Colla...

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5th International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Yu, Jiefei Cao, Zicheng He, Siyi Gu, Zuyi Xu, Yingcheng Zhong, Kai Institutes of Physical Science and Information Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui China School of Big Data and Statistics Anhui University Anhui China

ISBN: (纸本)9798331529192

Capturing the root cause and propagation path of the fault is critical to ensuring the safety and efficiency of industrial processes, especially those that inadequately utilize process knowledge and data. To address this issue, a unified framework integrating knowledge and data for collaborative root cause identification is proposed. First, the knowledge causal graph (KCG) is constructed using expert knowledge and industrial flow charts, providing a preliminary reference for subsequent causality analysis. Next, by replacing the traditional vector autoregression (VAR) model in Granger Causality (GC) with the gated recurrent unit (GRU), a more reliable causal relationship between variables is obtained. Additionally, a causality fusion propagation path identification method (CF-PPI) is designed to identify the root cause and propagation path of the fault, so that the obtained fault propagation path has less redundancy and higher accuracy. Finally, the method is validated using data from the ASHRAE RP-1043 centrifugal chiller. © 2024 IEEE.

关键词： Knowledge graph

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Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics

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Chinese Journal of Electronics 2023年第1期32卷 111-129页

作者： TIAN Ye ZHANG Xingyi HE Cheng TAN Kay Chen JIN Yaochu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University School of Artificial Intelligence Anhui University School of Electrical and Electronic Engineering Huazhong University of Science and Technology Department of Computing The Hong Kong Polytechnic University Faculty of Technology Bielefeld University

A large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems. While most variation operators in existing metaheuristics are empirically designed,new operators are automatically designed in this work,which are expected to be search space independent and thus exhibit robust performance on different *** work first investigates the influence of translation invariance, scale invariance, and rotation invariance on the search behavior and performance of some representative operators. This work then deduces the generic form of translation, scale, and rotation invariant operators, and proposes a principled approach for the automated design of operators, which searches for high-performance operators based on the deduced generic form. The experimental results demonstrate that the operators generated by the proposed approach outperform state-of-the-art ones on a variety of problems with complex landscapes and up to 1000decision variables.

关键词： Deep learning Metaheuristics Reinforcement learning Benchmark testing Search problems Robustness Behavioral sciences

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SES: Bridging the Gap between Explainability and Prediction of Graph Neural Networks 40

SES: Bridging the Gap between Explainability and Prediction ...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Huang, Zhenhua Li, Kunhao Wang, Shaojie Jia, Zhaohong Zhu, Wentao Mehrotra, Sharad Anhui University Hefei China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Hefei China Amazon Research Seattle United States University of California Irvine Irvine United States

ISBN: (纸本)9798350317152

Despite the Graph Neural Networks' (GNNs) pro-ficiency in analyzing graph data, achieving high-accuracy and interpretable predictions remains challenging. Existing GNN interpreters typically provide post-hoc explanations disjointed from GNNs' predictions, resulting in misrepresentations. Self-explainable GNNs offer built-in explanations during the training process. However, they cannot exploit the explanatory outcomes to augment prediction performance, and they fail to provide high-quality explanations of node features and require additional processes to generate explainable subgraphs, which is costly. To address the aforementioned limitations, we propose a self-explained and self-supervised graph neural network (SES) to bridge the gap between explainability and prediction. SES comprises two processes: explainable training and enhanced predictive learning. During explainable training, SES employs a global mask generator co-trained with a graph encoder and directly produces crucial structure and feature masks, reducing time consumption and providing node feature and subgraph explanations. In the enhanced predictive learning phase, mask-based positive-negative pairs are constructed utilizing the ex-planations to compute a triplet loss and enhance the node representations by contrastive learning. Extensive experiments demonstrate the superiority of SES on multiple datasets and tasks. SES outperforms baselines on real-world node classification datasets by notable margins of up to 2.59% and achieves state-of-the-art (SOTA) performance in explanation tasks on synthetic datasets with improvements of up to 3.0%. Moreover, SES delivers more coherent explanations on real-world datasets, has a fourfold increase in Fidelity+ score for explanation quality, and demonstrates faster training and expla-nation generating times. To our knowledge, SES is a pioneering GNN to achieve SOTA performance on both explanation and prediction tasks. © 2024 IEEE.

关键词： Classification (of information)

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A Dual-band Low-profile Quadrifilar-Helix Antenna for Satellite Navigation System

A Dual-band Low-profile Quadrifilar-Helix Antenna for Satell...

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2023 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2023

作者： Wang, Jiahong Qi, Junwei Li, Yingsong Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350336740

A new dual-band low-profile quadrifilar-helical antenna (QHA) was presented. The spiral radiation arm of QHA is printed to three circular dielectric plates to reduce the antenna height. In this case, the radiation arm of the lower layer is connected to the feed, and the L-shaped branch is connected to the ground through a 50 ohm resistance. The radiation arm in the middle layer adds branches to realize dual frequency. The upper radiation arm rotates inward and folds, further realizing miniaturization. As a result, the height of QHA is only 20mm. Through CST simulation and optimization, The operating frequency bands of QHA are 1GHz - 1.45GHz and 1.54GHz - 1.74GHz. The beam width of QHA is more than 120°, which is to provide service for satellite navigating systems. The AR for the operation bands are less than 3dB. © 2023 IEEE.

关键词： Satellites

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An Evolutionary Multitasking Algorithm for Efficient Multiobjective Recommendations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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Design of Control Circuit for Active Metasurface Based on Varactor Diode

Design of Control Circuit for Active Metasurface Based on Va...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Zhang, Yue Liu, Yuefei Yang, Guohui Zhang, Kuang Li, Yingsong School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing Hefei China

ISBN: (纸本)9781733509657

A control circuit for active metasurface based on varactor diode is designed in this paper. By varying the reverse bias voltage on the varactor diode, the designed circuit can regulate the additional phase of the active meta-atoms. The circuit adopts a form of one master and multiple slaves, which can simultaneously control the bias voltage of 960 meta-atoms. Each slave can control up to 64 channels. The control accuracy is 0.04V. The power consumption of a single slave is 160mW. Though experiment verification, the circuit has a good effect in controlling the beam deflection of the metasurface and generating OAM beams. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Timing circuits

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A Circularly Polarized Antenna with a Loading Metal Strip for GPS L1 band

A Circularly Polarized Antenna with a Loading Metal Strip fo...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023

作者： Meng, Lulu Qian, Sixian Huang, Zhixiang Li, Yingsong Loh, Tian Hong Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China National Physical Laboratory Electromagnetic & Eletrochemical Technologies Department Middlesex United Kingdom

ISBN: (纸本)9781733509633

The paper presents a novel circular polarization(CP) antenna loading with a parasitic ring metal strip, which is designed for global positioning system (GPS) L1 band applications. The antenna consists of a defected ground plane with four arc-slots and a circular slot, a dielectric substrate, a parasitic ring metal strip, and a radiating patch (RP) with a arc-slot. By rotating the parasitic ring metal strip to opening slot on the RP, the antenna can be transformed from left-handed circular polarization (LHCP) to right-handed circular polarization (RHCP). The size of the antenna can be reduced by using slots on the ground. The simulation and optimization results show that impedance bandwidth of the L/RHCP antenna is over 100 MHz, and the axial ratio bandwidth is over 20 MHz. © 2023 ACES.

关键词： Global positioning system

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A Through-hole Antenna Array for Joint Optical and Millimeter Sensing Applications

A Through-hole Antenna Array for Joint Optical and Millimete...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023

作者： Qian, Sixian Meng, Lulu Huang, Zhixiang Li, Yingsong Loh, Tian Hong Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China National Physical Laboratory Electromagnetic & Electrochemical Technologies Department Middlesex United Kingdom

ISBN: (纸本)9781733509633

A novel millimeter wave sensing antenna array is proposed for application in miniature detection and sensing devices suitable for operating in multiple scenarios, e.g., collecting in the face of video and infrared, and other device materials in complex electromagnetic environments to achieve delightful sensing results. The proposed antenna array is designed to work at 30 GHz which is considered to integrate the optical devices and millimeter radar together by using a number of holes in the millimeter antenna array. Two different excitation modes are considered in the constructed design, where many holes are installed in the millimeter array, which aims to integrate the optical imaging cameras. In this design, the presented array serves as a radar to detect and get sensing results. Since the optical components are not metal and have a little effect on the performance of the antenna array, we use the holes to model the optical cameras to investigate the performance of the radiation patterns and impedance bandwidth. © 2023 ACES.

关键词： Antenna arrays

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