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Improved Evolutionary Multitasking Optimization Algorithm With Similarity Evaluation of Search Behavior

IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Wu, Xiaolong Wang, Wei Zhang, Tengfei Han, Honggui Qiao, Junfei Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing Artificial Intelligence Institute and Beijing Laboratory for Intelligent Environmental Protection Faculty of Information Technology Beijing University of Technology Beijing China

Task similarity is a major requisite to trigger knowledge sharing in evolutionary multitasking optimization (EMTO). Unfortunately, most of the existing EMTO algorithms only focus on the similarity between population distributions of tasks, but ignore the search behavior of populations, which may degrade the performance of cross-task knowledge sharing. Motivated by this, an improved EMTO algorithm with similarity evaluation of search behavior (SESB-IEMTO), employing the particle swarm optimization (PSO) algorithm as a task solver for each task, is proposed. It comprises three key elements: 1) a dynamic similarity-based evaluation strategy, 2) a cross-task knowledge adaptation method, and 3) a search direction sharing mechanism. Primarily, the source tasks with similar search behavior are discriminated with the dynamic similarity-based evaluation strategy, where individuals can be fully exploited for cross-task evolution. Then, the knowledge derived from these source tasks is regulated by the cross-task knowledge adaption method for alleviating the risk of negative transfer caused by the heterogeneity between tasks. Moreover, to further promote knowledge sharing between tasks, the search direction sharing mechanism is developed to navigate tasks efficiently searching for promising regions. Finally, the convergence of SESB-IEMTO is analyzed, and the effectiveness and superiority are also verified with the experiments on several benchmark tests and a real-world application study. IEEE

关键词： Multitasking

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Multi-stage Image Deraining based on Pre-trained Diffusion Model 6

Multi-stage Image Deraining based on Pre-trained Diffusion M...

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6th ACM International Conference on Multimedia in Asia, MMAsia 2024

作者： Zeng, Xiong Jiang, Min Huang, RongHua School of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9798400712739

Image deraining typically involves synthesizing low-quality degraded data for training using a predefined degraded model of a single weather condition. While in real world scenarios, varying rain intensities result in different sizes and densities of raindrops and rain streaks, increasing the complexity of image degradation. In this paper, we proposed a multi-stage deraining framework based on pre-trained diffusion model, it can efficiently perform the rain removal task under a variety of weather situations. We diffuse degraded images into a noisy state where various types of degradation are transformed into Gaussian noise. Then, during the denoising process, the low-frequency information of the image is replaced through iterative refinement, guiding the pre-trained diffusion model for image reconstruction. Our method effectively utilizes the generative priors in diffusion models and avoid the computational burden of retraining conditional diffusion models. Experimental results on four rainy degradation image datasets show its robustness to different types and severities of degradation (such as raindrops and rain streaks). Compared to recent deraining algorithms, our method achieves a maximum improvement of 0.96 dB (3.5%) in PSNR and 0.021 dB (2.7%) in SSIM for the restored images. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Rain

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Sparse convolutional model with semantic expression for waste electrical appliances recognition

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Science China(Technological Sciences) 2024年第9期67卷 2881-2893页

作者： HAN HongGui LIU YiMing LI FangYu DU YongPing Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China Engineering Research Center of Digital Community Ministry of EducationBeijing 100124China Beijing Artificial Intelligence Institute and Beijing Laboratory for Intelligent Environmental Protection Beijing 100124China

Deep neural networks play an important role in the recognition of waste electrical appliances. However, deep neural network components still lack reliability in decision-making features. To address this problem, a sparse convolutional model with semantic expression(SCMSE) is proposed. First, a low-rank sparse semantic expression component, combining the benefits of residual networks and sparse representation, is adapted to enhance sparse feature extraction and semantic expression. Second, a reliable network architecture is obtained by iterating the optimal sparse solution, enhancing semantic expression. Finally, the results of visualization experiments on the waste electrical appliances dataset demonstrate that the proposed SCMSE can obtain excellent semantic performance.

关键词： sparse convolutional model deep neural network semantic expression visualization computer vision

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NOMA-Based Collaborative Beam Hopping Frequency Allocation Mechanism for Future LEO Satellite Systems

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China Communications 2023年第6期20卷 321-338页

作者： Fei Zheng Zhao Pi Zou Zhou Miao Ye Hongbing Qiu Ministry of Education Key Laboratory of Cognitive Radio and Information Processing Guilin University of Electronic TechnologyGuilin 541004China State key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China

Low Earth orbit(LEO) satellite systems provide terrestrial users with services that are not limited by geographical location. However, the conflict between existing allocation schemes and the business variability between beams is becoming increasingly prominent. Beam hopping technology allows for a more flexible and versatile approach to satellite resource allocation. This paper proposes a beam hopping pattern optimization scheme that jointly considers the interference threshold distance and beam service priority, reducing the inter-beam co-channel interference(CCI). In the cluster area, a non-orthogonal multiple access(NOMA)-based collaborative beam hopping(NCBH) scheme is proposed to minimize the cell-edge user(CEU) interference. Since there is a difference in channel gain between the CEU and cellcenter user(CCU), this scheme forms a NOMA cluster to perform power domain multiplexing and formulates a NOMA cluster pairing strategy according to the user location to reduce the CCI of the CEU. After NOMA cluster pairing, the optimal carrier frequency of the NOMA cluster is selected by a reinforcement learning algorithm. The simulation results verify the excellent performance of the proposed NCBH scheme regarding the user’s received power, transmission rate, and outage probability.

关键词： LEO satellite beam hopping NOMA resource allocation

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Centroid-Based Security Monitoring for Indoor Elderly Care with Millimeter Wave Radar 43

Centroid-Based Security Monitoring for Indoor Elderly Care w...

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43rd Chinese Control Conference, CCC 2024

作者： Huang, Chicheng Li, Chuandong Gao, Zelin Wu, Zhenyu Shi, Yawei Li, Huaqing Southwest University College of Electronic and Information Engineering Chongqing400715 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing China

ISBN: (纸本)9789887581581

The increasing use of millimeter wave radar for smart elderly care is due to its precision, privacy, and cost-effectiveness. This technology enables real-time monitoring of unexpected incidents, particularly falls among the elderly indoors. By examining the relationship between cloud points and human centroids, this paper exploits the unique features of millimeter wave radar for personnel detection and human activity recognition. An improved Kalman filter algorithm for centroid tracking has been introduced. It effectively monitors the three-dimensional trajectory and velocity changes associated with various human behaviors. Subsequently, machine learning algorithms and neural networks are employed to classify these behaviors, including walking, sitting, and falling. Compared to conventional Kalman filtering, this approach effectively handles dispersion characteristics within point clouds, resulting in a smoother trajectory and more consistent centroid. This method enables the recognition of abnormal activities of people indoors with a maximum classification accuracy of 93.02%. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Kalman filters

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L2GC: Lorentzian Linear Graph Convolutional Networks For Node Classification 30

L2GC: Lorentzian Linear Graph Convolutional Networks For Nod...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liang, Qiuyu Wang, Weihua Bao, Feilong Gao, Guanglai College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot China

ISBN: (纸本)9782493814104

Linear Graph Convolutional Networks (GCNs) are used to classify the node in the graph data. However, we note that most existing linear GCN models perform neural network operations in Euclidean space, which do not explicitly capture the tree-like hierarchical structure exhibited in real-world datasets that modeled as graphs. In this paper, we attempt to introduce hyperbolic space into linear GCN and propose a novel framework for Lorentzian linear GCN. Specifically, we map the learned features of graph nodes into hyperbolic space, and then perform a Lorentzian linear feature transformation to capture the underlying tree-like structure of data. Experimental results on standard citation networks datasets with semi-supervised learning show that our approach yields new state-of-the-art results of accuracy 74.7% on Citeseer and 81.3% on PubMed datasets. Furthermore, we observe that our approach can be trained up to two orders of magnitude faster than other nonlinear GCN models on PubMed dataset. Our code is publicly available at https://***/llqy123/LLGC-master. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Convolution

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A Multiband Frequency Reconfigurable Antenna for Wireless Communication 43

A Multiband Frequency Reconfigurable Antenna for Wireless Co...

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43rd Chinese Control Conference, CCC 2024

作者： Liang, Rongkai Li, Chuandong Su, Youxun Liang, Weiwei Chen, Menggang Luo, Zhen Southwest University College of Electronic and Information Engineering Chongqing400715 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing China

ISBN: (纸本)9789887581581

In order to enhance the adaptability of wireless devices across diverse applications, it becomes imperative for an antenna to embody a multitude of functionalities, including the capacity to dynamically reconfigure the operating frequency, polarization mode, and radiation pattern. This paper introduces a frequency-reconfigurable antenna that incorporates an L-U shaped slot band with a Split-Ring Resonator (SRR). Fabricated on a 1.2 mm low-cost FR4 substrate using a partially grounded design approach, which measures 40 × 35 mm2. By manipulating the bias states of the two PIN diodes, the radiation element current and field distributions can be altered, resulting in reconfigurable bands spanning 2.45 GHz, 3.3 GHz, 4.5 GHz, 7.4 GHz, and 9.4 GHz, with an expansive Ultra-Wide Bandwidth (UWB) of 4230 MHz. This antenna design caters to a range of wireless applications, including WLAN (Wireless Local Area Network), WiMAX (Worldwide Interoperability for Microwave Access), Remote Radio, and X-band Satellite Communication. Through meticulous simulation optimization, four reconfiguration scenarios are thoroughly examined by presenting and analyzing the simulation results. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Interoperability

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Leveraging Knowledge Distillation for Improved Event Extraction in QA Models 6

Leveraging Knowledge Distillation for Improved Event Extract...

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6th International Conference on Frontier Technologies of information and Computer, ICFTIC 2024

作者： Zhou, Peiyao Hu, Zhikui Zi, Kangli Zhang, Dawei School of Computer Science Jiangsu University of Science and Technology Zhenjiang China Institute of Computing Technology Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Beijing China

ISBN: (纸本)9798331541750

Event extraction is an important task in natural language processing, and it is widely utilized in intelligence domains such as business and military for information extraction. Recently, many works have successfully transformed document-level event extraction into Question-answering (QA) tasks with remarkable results. This approach embeds event argument information within the questions, introducing prior knowledge into the process. Jin et al. highlighted the importance of high-quality QA pairs for practical QA tasks, emphasizing that constructing these pairs remains a key challenge[1]. In this study, we propose a Prompt-based question generation method to automatically generate questions containing event arguments, which converts event extraction into a QA task. We introduce an event ontology-based information retrieval module to enhance answer accuracy and select the most relevant document segments as input text. Additionally, we employ knowledge distillation to build the QA model, transferring knowledge from a large pre-trained model to a more compact and efficient one, improving the student model's performance. Experiments show that our model performs strongly in mainstream benchmarks, with 4.8 improvements on WikiEvents. © 2024 IEEE.

关键词： Question answering

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Unraveling the heterogeneity of solid electrolyte interphase kinetically affecting lithium electrodeposition on lithium metal anode

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Journal of Energy Chemistry 2023年第10期85卷 181-190,I0007页

作者： Mengyuan Zhou Yaqi Liao Longhui Li Ruoyu Xiong Guancheng Shen Yifu Chen Tianlun Huang Maoyuan Li Huamin Zhou Yun Zhang State Key Laboratory of Materials Processing and Die&Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074HubeiChina Guangdong Key Laboratory of Modern Control Technology Institute of Intelligent ManufacturingGuangdong Academy of SciencesGuangzhou 510075GuangdongChina

The stability and uniformity of solid electrolyte interphase(SEI)are critical for clarifying the origin of capacity fade and safety issues for lithium metal anodes(LMA).However,understanding the interplay of SEI heterogeneity and Li electrodeposition is limited by the coupling of complex electrochemistry and mechanics ***,the correlation between the SEI failure behavior and Li deposition morphology is investigated through a quantitative electrochemical-mechanical *** local deformation and stress of SEI during Li electrodeposition identify that the heterogeneous interface between different components first *** with the well-known mechanical strength,component uniformity plays the most important role in the initial SEI failure and uneven Li deposition,and a relative component uniformity(p>0.01)represents a proper balance to ensure the stability of the naturally heterogeneous ***,the component regulation of SEI via the designed electrolyte experimentally demonstrates that improving component uniformity benefits SEI stability and the uniform Li electrodeposition for LMA,thereby increasing the capacity by~20%after 300 *** fundamental understandings and proposed strategy can be not only used to guide the SEI optimization via the electrolyte regulation,but also extended to the rational designs of artificial SEI for high-performance LMA.

关键词： Li metal anode Solid electrolyte interphase Component uniformity Electrochemical-mechanical model Failure mechanism

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Few-Shot Table-to-Text Generation with Structural Bias Attention 20th

Few-Shot Table-to-Text Generation with Structural Bias Atten...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Liu, Di Wang, Weihua Bao, Feilong Gaov, Guanglai College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot China

ISBN: (纸本)9789819970216

Table-to-text generation task refers to converting tabular data into language text to facilitate easier understanding and analysis of the table. Recently, pre-trained models have made significant advancements in this kind of tasks. However, the inherent structural differences between tabular data and text, and the lack of domain-specific knowledge in few-shot datasets, make it challenging for pre-trained models to generate faithful text. To solve these problems, we proposed a framework that encodes tables by obtaining structural bias attention through pruning full self-attention, distinguishing the importance of cells from a structural perspective. We use the pre-trained model with the structural bias framework to the generation component of Prototype-to-Generation. To encourage prototype memory to adhere to the table content and generate more accurate and aligned sentences, we employ Reinforcement Learning. We conducted extensive experiments on three few-shot table datasets. Compared to previous advanced methods, our model achieved superior performance across multiple evaluation metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Reinforcement learning

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