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31st International Conference on Multimedia Modeling, MMM 2025

作者： Cai, Zeyu Zhang, Can Chen, Yuchong Chen, Xunhao Yang, Jiming Shi, Wubin Da, Feipeng Jin, Chengqian School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of CSE Ministry of Education Nanjing210000 China Agricultural Mechanization Development and Intelligent Information Research Center Nanjing Institute of Argicultural Mechanization Ministry of Agriculture and Rural Affairs Nanjing210000 China

ISBN: (纸本)9789819620609

The coded Aperture Snapshot Spectral Imaging (CASSI) system has great advantages in dynamically acquiring Hyper-Spectral image (HSI) compared to traditional measurement methods, but there are the following problems. 1) Traditional mask relies on random patterns or analytical design, both of which limit the performance improvement of CASSI. 2) Existing high-quality reconstruction algorithms are slow in reconstruction and can only reconstruct scene information offline. To address the above two problems, this paper introduces an RGB camera with CASSI based on Adaptive-Mask as multimodal input to improve the reconstruction quality. The existing SOTA reconstruction schemes are based on the transformer, but the operation of self-attention pulls down the operation efficiency of the network. To improve the inference speed of the reconstruction network, this paper proposes An MLP Architecture for Adaptive-Mask-based Dual-Camera (MLP-AMDC) to replace the transformer structure of the network. Numerous experiments have shown that MLP performs no less well than transformer-based structures for HSI reconstruction, while MLP greatly improves the network inference speed and has less number of parameters and operations, our method has an 8 dB improvement over SOTA and at least a 5-fold improvement in reconstruction speed. (https://***/caizeyu1992/MLP-AMDC.) © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hyperspectral imaging

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IF-Font: Ideographic Description Sequence-Following Font Generation 38

IF-Font: Ideographic Description Sequence-Following Font Gen...

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38th Conference on Neural Information processing Systems, NeurIPS 2024

作者： Chen, Xinping Ke, Xiao Guo, Wenzhong Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350116 China

Few-shot font generation (FFG) aims to learn the target style from a limited number of reference glyphs and generate the remaining glyphs in the target font. Previous works focus on disentangling the content and style features of glyphs, combining the content features of the source glyph with the style features of the reference glyph to generate new glyphs. However, the disentanglement is challenging due to the complexity of glyphs, often resulting in glyphs that are influenced by the style of the source glyph and prone to artifacts. We propose IF-Font, a novel paradigm which incorporates Ideographic Description Sequence (IDS) instead of the source glyph to control the semantics of generated glyphs. To achieve this, we quantize the reference glyphs into tokens, and model the token distribution of target glyphs using corresponding IDS and reference tokens. The proposed method excels in synthesizing glyphs with neat and correct strokes, and enables the creation of new glyphs based on provided IDS. Extensive experiments demonstrate that our method greatly outperforms state-of-the-art methods in both one-shot and few-shot settings, particularly when the target styles differ significantly from the training font styles. The code is available at https://***/Stareven233/IF-Font. © 2024 Neural information processing systems foundation. All rights reserved.

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Demand-Side Frequency control in Power Network Based on Multi-Agent Deep Reinforcement Learning

Demand-Side Frequency Control in Power Network Based on Mult...

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International Conference on Industrial Artificial Intelligence (IAI)

作者： Xianggang Liu Zhi-Wei Liu Ming Chi Yifan Zhang Key Laboratory of Image Processing and Intelligent Control Ministry of Education College of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

The integration of renewable energy sources into the power grid has led to new challenges in maintaining the stability of the system frequency. This paper proposes a novel approach to address the Optimal Demand Side Frequency control (ODFC) problem using Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method. The proposed method models the ODFC problem as a Markov game, with centralized training based on multi-agent cooperative self-learning and associative storage service. In the decentralized execution stage, each agent independently outputs control actions to the controlled plant using local observations. Numerical simulations show that the proposed method effectively addresses the ODFC problem with superior performance compared to traditional methods.

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Resource Allocation for Coupled Slices in Edge-Assisted Industrial IoT Systems

Resource Allocation for Coupled Slices in Edge-Assisted Indu...

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2023 IEEE/CIC International Conference on Communications in china, ICCC 2023

作者： Wen, Xiaojing Chen, Cailian Ren, Cheng Lyu, Ling Guan, Xinping Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China Dalian Maritime University School of Information Science and Technology Dalian116026 China

ISBN: (纸本)9798350345384

Multiple types of sensors are utilized for remote monitoring of single industrial process to compensate for the limited accuracy of a single type of sensor. Network slicing technology is considered promising for collecting data from heterogeneous sensors and meeting diverse Quality of Service (QoS) requirements. However, existing research on network slicing optimization mainly focuses on inter-slice resource constraints based on slice heterogeneity, neglecting slice coupling feature. In this regard, this paper proposes a novel network slicing framework for edge-assisted industrial IoT systems. The framework includes a slice coupling model and network resource allocation (transmission and computing) to address the problem of improving estimation performance while ensuring system convergence. The slice coupling model is primarily composed of the estimation error covariance of two slices, calculated using the Age of Task (AoT) as a bridge, which reflects the accuracy and freshness of the estimation. To reduce the complexity of the direct solution, the optimization problem is scaled down and decoupled. The iterative Particle Swarm Optimization (PSO) algorithm is proposed to determine the allocation rates of inter-slice transmission and computing resources. Simulation results demonstrate that the proposed algorithm achieves a flexible balance under heterogeneous estimation weights and exhibits near-optimal performance. © 2023 IEEE.

关键词： Resource allocation

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Semi-Supervised Generalized Source-Free Domain Adaptation (SSG-SFDA)

Semi-Supervised Generalized Source-Free Domain Adaptation (S...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jiayu An Changming Zhao Dongrui Wu Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Continual learning aims to learn on a sequence of new tasks while maintaining the performance on previous tasks. Source-free domain adaptation (SFDA), which adapts a pretrained source model to a target domain, is useful in protecting the source domain data privacy. Generalized SFDA (G-SFDA) combines continual learning and SFDA to achieve outstanding performance on both the source and the target domains. This paper proposes semi-supervised G-SFDA (SSG-SFDA) for domain incremental learning, where a pre-trained source model (instead of the source data), few labeled target data, and plenty of unlabeled target data, are available. The goal is to achieve good performance on all domains. To cope with domain-ID agnostic, SSG-SFDA trains a conditional variational auto-encoder (CVAE) for each domain to learn its feature distribution, and a domain discriminator using virtual shallow features generated by CVAE to estimate the domain ID. To cope with catastrophic forgetting, SSG-SFDA uses soft domain attention to improve the sparse domain attention in G-SFDA. To cope with insufficient labeled target data, SSG-SFDA uses MixMatch to augment the unlabeled target data and better exploit the few labeled target data. Experiments on three datasets demonstrated the effectiveness of SSG-SFDA.

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Research on Grain Yield Prediction Model Based on Wavelet transform and LSTM 3

Research on Grain Yield Prediction Model Based on Wavelet tr...

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3rd International Seminar on Artificial Intelligence, Networking, and Information Technology, AINIT 2022

作者： Zhu, Chunhua Li, Pengle Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education China College of Information Science and Engineering Henan University of Technology China Henan Key Laboratory of grain photoelectric detection and control Henan University of Technology Zhengzhou Henan China

ISBN: (数字)9781510662971

ISBN: (纸本)9781510662964

To improve the accuracy of grain yield prediction, a grain yield prediction model based on wavelet transform and long short-term memory (LSTM) is proposed. Firstly, the original data is decomposed by wavelet transform algorithm to obtain a series of sub-sequences of different scales, and then LSTM prediction models are built for the sub-sequences, finally wavelet reconstruction is used to obtain the predicted yield and analyze the model performance. The article uses china's 1999-2018 grain yield as experimental data. The experiment shows that the method proposed in this article has excellent performance in both short-term and medium-term predictions compared to the existing methods. © 2023 SPIE.

关键词： Forecasting

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Large depth range dithered binary focusing fringe projection technique 1

Large depth range dithered binary focusing fringe projection...

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1st Advanced Imaging and Information processing Conference, AIIP 2023

作者： Tan, Ji Wang, Xu Su, Wenqing He, Zhaoshui School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong University of Technology Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou510006 China

ISBN: (纸本)9781510671904

The binary defocusing technique is sensitive to the defocusing degree. The defocusing projection mechanism will introduce high-frequency harmonics at the inappropriate defocused level, leading to limitations in measurement accuracy and depth range. In this paper, a binary-focusing projection technique combining generative adversarial networks is proposed. First, the focusing binary patterns based on error diffusion are projected on the measured surface, and then the captured fringe patterns are input to generative adversarial networks, which achieves sinusoidal correction and optimization for both the focused region and the low-quality defocused region due to its strong image translation ability. Finally, 3D measurement is realized by a phase-shifting algorithm. Compared with the traditional binary defocusing technique, the proposed method is not limited by the defocusing degree and maintains the advantages of high-speed projection, so it can achieve a larger measured depth range and improve measurement accuracy. Simulation and experiments verify the performance of the proposed method. © 2023 SPIE.

关键词： Focusing

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Research and Implementation of Chinese Resume Named Entity Recognition Method Based on BLDC 4

Research and Implementation of Chinese Resume Named Entity R...

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4th International Conference on image processing and intelligent control, IPIC 2024

作者： Chen, Min Wei, Bin Yu, Hongzhi Wan, Fucheng Feng, Yulin Lu, Baoqing Key Laboratory of Linguistic and Cultural Computing Ministry of Education Northwest Minzu University Gansu Lanzhou730000 China Key Laboratory of Minzu Languages and Cultures Intelligent Information Processing Northwest Minzu University Gansu Province Gansu Lanzhou730000 China Lanzhou Technology and Business College Lanzhou China

ISBN: (纸本)9781510682313

Aiming at the semantic complexity and the difficulty of capturing long-distance dependencies in the task of named entity recognition in Chinese resume text, a BLDC (BERT-BiLSTM-Dense-CRF)-based named entity recognition method for Chinese resumes is investigated. The model first uses the pre-trained BERT-Base-Chinese model to obtain the semantic information of the text, then learns the contextual information of the sequence through the BiLSTM layer, followed by mapping the output of BiLSTM to the label space through the Dense layer, and finally uses the CRF layer to consider the dependencies between the labels for sequence annotation. Experimental results on public datasets show that the method outperforms other models in terms of precision, recall, and F1 value, and effectively improves the performance of named entity recognition in Chinese resume text. © 2024 SPIE.

关键词： Semantics

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Uniform Solution to QSAT by P Systems with Proteins

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Chinese Journal of Electronics 2023年第4期21卷 667-672页

作者： Chun LU Xiaolong SHI Department of Control Science and Engineering Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

P systems are distributed parallel computing models in the area of membrane computing, which are inspired by the structure and the functioning of living cells, as well as the organization of cells in tissues, organs, and other higher order structures. P systems with proteins on membranes are a class of P systems, which have proved to be very efficient computing devices. Specifically, it was known that the Quantified satisfiability problem (QSAT) of a Boolean formula can be solved by a semi-uniform family of P systems with proteins on membranes and with membrane division. However, it remains open whether a uniform families of P systems with proteins on membranes can solve in polynomial time exactly the class of problems PSPACE. In this work, we present a uniform solution to QSAT problem by P systems with proteins on membranes in a linear time with respect to both the number $n$ of Boolean variables and the number $m$ of clauses of the instance, which answers the above open problem.

关键词： Proteins image processing Computational modeling Organizations Parallel processing intelligent control

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3-D temperature field reconstruction of lithium-ion battery pack using real-time distributed moving horizon estimation

3-D temperature field reconstruction of lithium-ion battery ...

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第35届中国控制与决策会议

作者： Jiayu Yan Yiming Wan School of Artificial Intelligence and Automation Huazhong University of Science and Technologyand Key Laboratory of Image Processing and Intelligent ControlMinistry of Education

As lithium-ion batteries are widely used in different fields,the thermal effect is of serious *** to achieve accurate temperature estimation in real time is the main challenge of current *** address this problem,we propose a realtime distributed moving horizon estimation(RT-DMHE) based on partial differential equations describing thermal dynamics of a lithium-ion battery *** decomposes the real-time centralized moving horizon estimation(RT-CMHE) into multiple local estimators that run in parallel with information exchange from adjacent *** validation shows that the root mean square error of temperature estimates from the proposed RT-DMHE is smaller than that of an existing distributed Kalman filter in ***,compared to the RT-CMHE,the proposed RT-DMHE achieves comparable estimation accuracy while vastly reducing the average computation time per sample.

关键词： lithium-ion battery pack distributed moving horizon estimation real-time computation state-of-temperature estimation

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