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Policy iteration-based adaptive optimal control for Markov jump systems: a transition-probability-free asynchronous approach

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Science China(Information Sciences) 2024年第11期67卷 363-364页

作者： Weidi CHENG Chengcheng REN Shuping HE Changyin SUN Key Laboratory of Intelligent Computing and Signal Processing (Ministry of Education) School of Electrical Engineering and Automation Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Engineering Research Center of Autonomous Unmanned System Technology (Ministry of Education) School of Artificial Intelligence Anhui University

The controller and filter design problems of Markov jump systems(MJSs) have gained significant attention over the past few decades. These studies include various aspects,including stochastic stabilization [1], optimal tracking control [2], and dissipative filter design [3]. Although numerous publications address the optimal controller design for MJSs, the issue of hidden MJSs, particularly those with mismatched jumping modes between the system and the controller, has rarely been explored.

关键词： Markov processes

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Remote Sensing Few-shot Object Detection Using Adaptive Feature Modification and Gaussian Dynamic Dilated Convolution 16

Remote Sensing Few-shot Object Detection Using Adaptive Feat...

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Zhiwei, Zhang Jiangtao, Wang Penglei, Chen Cheng, He School of Physics and Electronic Information Huaibei Normal University Anhui Province Huaibei City China Anhui Province Key Laboratory of Intelligent Computing and Applications Anhui Province Huaibei City China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Object detection in remote sensing images relies on a large amount of annotated data for training, but adequately annotating novel classes is difficult. Few-shot object detection(FSOD) address this problem by fine-tuning on finitely annotated novel classes, this results in its within-class variation being limited, making it difficult to adequately fit the within-class distribution. In order to alleviate this problem, this paper proposed a few-shot object detection method in remote sensing based on adaptive feature modification and guided by Gaussian dynamic dilated convolution. Firstly, in view of the difficulty of novel classes recognition, an adaptive feature modification algorithm is proposed. By using the characteristics of the two-branch network architecture of meta-learning itself, the support features are integrated into the query feature extraction network as convolutional bias to adaptively modify the query features. Secondly, in order to simulate intra-class variation of noise (feature perturbation caused by noise in the foreground), the backbone network weights were reparameterized to make them fit Gaussian distribution variables during fine-tuning, and the feature perturbation was achieved with parameter variation. Finally, in order to simulate intra-class scale variation (different foreground sizes), the convolution is changed into a dynamic dilation convolution, and different dilation rates are used to replace feature scale variation. Extensive experiments on two widely used remote sensing datasets (NWPU.V2, dior) demonstrate the effectiveness of the proposed method. © 2025 SPIE.

关键词： Gaussian distribution

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Temporal Attention Convolution Network for Energy Time Series Forecasting 7

Temporal Attention Convolution Network for Energy Time Serie...

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7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering

作者： Hu, Min Xu, Haijun Wang, Xiaohua Li, Liang Li, Hongbo Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei230009 China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei230009 China MediaTek Hefei230009 China

ISBN: (纸本)9781510656437

Energy time series are characterized by stochasticity, short-term periodicity, and long-term periodicity. Many traditional models such as the autoregressive model, Gaussian process regression are unable to capture the complex time relationships of energy time series. Recurrent neural networks (RNNs), which are widely used for energy time series forecasting, are prone to gradient disappearance or gradient explosion when processing a long sequence data. Thus, RNNs are failed to capture the complete information of the whole long sequence data. To extract complex irregular patterns of energy time series and selectively learn spatio-temporal features, a Temporal Attention Convolution Network (TACN) is proposed in this paper. This structure can extract spatial and temporal features of energy time series, which full filled with our demand. Firstly, to solve the problem that RNNs are prone to information loss when processing the long sequence data, a temporal convolutional network (TCN) is introduced. TCN enables the output to capture the complete information of the whole long sequence data. Then, to reinforce the TCN for extracting effective spatio-temporal features, Squeeze-and-Excitation Network (SENet), a module for computing channel attention, was introduced to this TCN structure. By applying this module, the ability of the acquisition of important channel information has been enhanced. Finally, to further improve the robustness of the model, an autoregressive linear module is constructed to capture the linear relationship among the variables. Experiments on the publicly available datasets Electricity and Solar-Energy show that compare with existing deep learning models, TACN achieves more ideal performance. © 2022 SPIE. All rights reserved.

关键词： Time series

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Linear Subspace Surrogate Modeling for Large-Scale Expensive Single/Multi-Objective Optimization

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IEEE Transactions on Evolutionary Computation 2023年第3期29卷 1-1页

作者： Si, Langchun Zhang, Xingyi Tian, Ye Yang, Shangshang Zhang, Limiao Jin, Yaochu School of Computer Science and Technology Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China School of Artificial Intelligence Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei China Faculty of Technology Bielefeld University Bielefeld Germany

Despite that the surrogate-assisted evolutionary algorithms have achieved great success in addressing expensive optimization problems, they still suffer from stiff challenges when the number of dimensions of problems becomes large. The primary reason lies in that it is very hard to build an acceptable surrogate model in the high-dimensional search space with small amounts of evaluated historical data in evolution. To tackle this issue, we suggest an effective surrogate modeling method for large-scale expensive optimization in this paper, where the models are built on a number of linear subspaces instead of the original search space. Specifically, a linear subspace is constructed by a pair of points/solutions which are generated based on the set of elite solutions. For each linear subspace, several historical solutions are first associated according to their distance to the linear subspace, and then a surrogate model is trained by the associated solutions and used to evaluate the offspring. To ensure the exploration and exploitation capacity of the proposed method, these linear subspaces and the surrogate models are updated after a few iterations. Experimental results on CEC’2010 and CEC’2013 single-objective optimization problems with up to 1500 decision variables show that the proposed algorithm is superior over six comparison algorithms. Moreover, we also extend the proposed algorithm to multi-objective optimization problems and verified its competitiveness on problems with up to 1500 decision variables. IEEE

关键词： Evolutionary algorithms

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Single-layer crystalline triazine-based organic framework photocatalysts with different linking groups for H_(2)O_(2)production

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Chinese Journal of Structural Chemistry 2023年第12期42卷 33-42页

作者： Jing Wang Zhongliao Wang Jinfeng Zhang Kai Dai Laboratory of Green and Precise Synthetic Chemistry and Applications Ministry of EducationAnhui Province Key Laboratory of Pollutant Sensitive Materials and Environmental RemediationAnhui Province Key Laboratory of Intelligent Computing and ApplicationsAnhui Province Industrial Generic Technology Research Center for Alurnics MaterialsHuaibei Normal UniversityHuaibei235000PR China

Harnessing solar energy for photocatalytic hydrogen peroxide(H_(2)O_(2))synthesis represents a pinnacle of environmentally-sensitive and sustainable *** single-layer crystalline triazine-based organic frameworks(CTFs)are known for their prodigious photocatalytic potential in H_(2)O_(2)generation,ramifications of the connecting group within the triazine ring(TR)on underlying photocatalytic mechanism warrant deeper *** this study,we simulate three distinct CTFs characterized by different TR linkers:CTF-1(benzene group(BG)),CTF-2(horizontally-oriented naphthyl group(NGH)),and CTF-DCN(vertically-oriented naphthyl group(NGV)).These diverse TR linkers profoundly modulate the absorption band edge of CTFs,subsequently dictating the orientation and constitution of the frontier *** modulation plays a decisive role in determining the requisite energy for photoexcitation in CTFs,orchestrating the generation and distribution of photo-induced electrons and ***,the NGV linkage imparts CTF-DCN with unparalleled light ab-sorption,superior charge separation efficiency,and the lowest energy barrier for associated *** this investigation,we illuminate the pivotal influence of TR linkers in sculpting the photocatalytic dynamics of CTFs,providing fresh perspectives for architecting CTFs with amplified photocatalytic prowess in H_(2)O_(2)synthesis.

关键词： Single-layer CTFs H_(2)O_(2)photoreduction DFT calculation Structural design

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Degradation-Trend-Aware Deep Neural Network with Attention Mechanism for Bearing Remaining Useful Life Prediction

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2997-3011页

作者： Liu, Yongkang Pan, Donghui Zhang, Haifeng Zhong, Kai Anhui University Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education School of Mathematical Sciences Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China

Remaining useful life (RUL) prediction of bearings has extraordinary significance for prognostics and health management (PHM) of rotating machinery. RUL prediction approaches based on deep learning have been dedicated to finding a nonlinear mapping relationship between nonstationary monitoring data and RUL. However, most existing approaches pay little attention to the degradation trend of diverse health stages of bearing and lack the discriminative power of crucial degradation features, resulting in the loss of some important information associated with RUL. To address this challenge, this article proposes a novel RUL prediction framework based on degradation-trend-aware deep neural network with attention mechanism (DTADAN). First the multidirection features with evident degradation trend are extracted via the analysis of bearing vibration signal from both time-domain and time-frequency domain. Next, the deep neural network architecture with attention mechanism is utilized to adaptively learn the critical degradation features beneficial for RUL prediction. Distinct from the existing approaches, the proposed framework is able to dynamically extract key degradation features of the bearing including degradation trend information and effectively fuse multidirection information to improve RUL prediction accuracy. The performance of the proposed approach is evaluated via case studies on XJTU-SY bearing dataset and PRONOSTIA bearing dataset. Compared with other state-of-the-art approaches, the proposed framework has better predictive accuracy and robustness. Additionally, interpretable analysis is provided to reveal the process of model learning and data characteristics, and the analysis results are helpful in guiding model learning. © 2020 IEEE.

关键词： Deep neural networks

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Visualizing extended defects at the atomic level in a Bi_(2)Sr_(2)CaCu_(2)O8_(+σ) superconducting wire

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Chinese Physics B 2024年第9期33卷 43-47页

作者： Kejun Hu Shuai Wang Boyu Li Ying Liu Binghui Ge Dongsheng Song Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of EducationInstitutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China

The microstructure significantly influences the superconducting ***,the defect structures and atomic arrangements in high-temperature Bi_(2)Sr_(2)CaCu_(2)O8_(+σ) superconducting wire are directly characterized via stateof-the-art scanning transmission electron *** oxygen atoms are observed in both the charge reservoir layers and grain boundaries in the doped *** phases with varied numbers of CuO_(2) layers are found,and twist interfaces with different angles are *** study provides insights into the structures of Bi-2212 wire and lays the groundwork for guiding the design of microstructures and optimizing the production methods to enhance superconducting performance.

关键词： superconductor microstructure defect scanning transmission electron microscopy

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Blockchain sharding scheme based on generative AI and DRL: Applied to building internet of things

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 333-349页

作者： Wang, Jinlong Li, Yixin Wu, Yunting Zheng, Wenhu Zhou, Shangzhuo Xiong, Xiaoyun Qingdao University of Technology Shandong Qingdao China Anhui Province Key Laboratory of Intelligent Building & Building Energy Saving Anhui Jianzhu University Anhui Hefei China

When applying blockchain sharding technology in the building Internet of Things (IoT) domain to enhance the throughput performance of the blockchain, cross-shard transactions triggered by device collaborative tasks have increasingly become a prominent issue. Existing solutions base their shard division on historical transaction moments, using the outcomes for future transaction processing. However, since the historical interaction characteristics do not accurately reflect the interaction details within specific fine-grained time periods, this leads to poor system performance. Additionally, the parameter configuration in blockchain sharding systems is mostly based on arbitrary or default settings, which also results in unstable system performance. To address these two challenges, this paper proposes a blockchain sharding scheme called AI-Shard. Firstly, the system includes a module, G-AI, that utilizes generative AI to predict future node interaction relationships, enabling more proactive and adaptive shard division based on the predicted interaction matrix. Secondly, the system integrates a reinforcement learning module, DL-AI, specifically tailored for configuring parameters of the blockchain sharding system, such as the number of shards, block size, and block interval, to automatically optimize them, aiming to further enhance the system's throughput. Experimental results show that AI-Shard can reduce the proportion of cross-shard transactions and improve the system's throughput. © 2024 The Authors

关键词： Deep reinforcement learning

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FRLI-Net: Feature Reconstruction and Learning Interaction Network for Tiny Object Detection in Remote Sensing Images

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IEEE Signal Processing Letters 2025年 32卷 2159-2163页

作者： Chen, Penglei Wang, Jiangtao Zhang, Zhiwei He, Cheng Huaibei Normal University School of Physics and Electronic Information Huaibei235000 China Huaibei Normal University Anhui Province Key Laboratory of Intelligent Computing and Applications Huaibei235000 China

In the field of computer vision, detecting tiny objects in remote sensing images has long been a challenging task. This difficulty primarily stems from the poor matching of tiny objects with specific feature scales, compounded by susceptibility to interference from complex surroundings. To this end, we propose a Feature Reconstruction and Learning Interaction Network (FRLI-Net) for overcoming the aforementioned difficulties in tiny object detection. Specifically, to mitigate information loss of tiny objects in the feature space, we introduce a Feature Reconstruction Supervision Branch (FRSB) used only during training, which utilizes supervised loss to guide the attention of tiny objects within the reconstructed feature regions. Then, to tackle false detections caused by blurred boundaries of tiny objects, we design a Saliency Learning Interaction Module (SLIM) that employs learnable weights and proposed sub-functions to facilitate feature interaction, thereby reducing interference from complex backgrounds in the foreground. Extensive experiments conducted on the remote sensing image datasets AI-TOD-v2 and TinyPerson demonstrate that our FRLI-Net significantly enhances the detection of tiny objects, outperforming other state-of-the-art detection methods. © 2025 IEEE.

关键词： Image reconstruction

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A Weighting Possibilistic Fuzzy C-Means Algorithm for Interval Granularity 17th

A Weighting Possibilistic Fuzzy C-Means Algorithm for Interv...

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17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： Tang, Yiming Xi, Lei Wu, Wenbin Wu, Xi Li, Shujie Chen, Rui Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Anhui Hefei230601 China Engineering Research Center of Safety Critical Industry Measure and Control Technology Ministry of Education Hefei University of Technology Anhui Hefei230601 China

ISBN: (纸本)9789819923847

Granular clustering is an emerging branch in the field of clustering. However, the existing granular clustering algorithms are still immature in terms of weight setting of granular data and noise resistance. In this study, a weighting possibilistic fuzzy c-means algorithm for interval granularity (WPFCM-IG) is proposed. To begin with, a new weight setting method for interval granular data is given. The principle of justifiable granularity is used as the evaluation criterion of granular data, and a weight is assigned to each granular data from two perspectives of coverage and specificity to measure the quality of the granular data. In addition, the idea of possibilistic clustering is introduced, which is helpful to improve the noise resistance. And, with the proposed weights of interval granular data, the influence of data with smaller weights on the clustering results can be reduced during the clustering process. Based upon these ideas, the WPFCM-IG algorithm is put forward, and its core idea, formula derivation and implementation process are described. Finally, the performance of the proposed algorithm is verified by comparison experiments on the artificial and UCI datasets. The experimental results show that the WPFCM-IG algorithm is better than other advanced algorithms in this field in terms of reconstruction error. Next, the WPFCM-IG algorithm is smoother than other algorithms on the collaborative relationship curve between the fuzzy coefficient and the reconstruction error, so WPFCM-IG can better optimize the fuzzy coefficient. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy clustering

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