检索结果-内蒙古大学图书馆

IEEE Transactions on Artificial Intelligence 2025年

作者： He, Yifan Ding, Xi Tang, Yateng Guan, Jihong Zhou, Shuigeng Fudan University School of Computer Science Shanghai200433 China Shanghai Key Laboratory of Intelligent Information Processing Shanghai200433 China Tencent Weixin Group Guangzhou510308 China Tongji University Department of Computer Science and Technology Shanghai201804 China Ministry of Education Key Laboratory of Embedded System and Service Computing Tongji University Shanghai200092 China

Anomalies are usually regarded as data errors or novel patterns previously unseen, which are quite different from most observed data. Accurate detection of anomalies is crucial in various application scenarios. This paper focuses on unsupervised anomaly detection from Multivariate Time Series (MTS), as real-world data collected from sources such as wearable devices, medical equipment and industrial machines typically manifest as MTS and are often unlabeled. Anomaly detection in MTS represents a data-driven challenge that traditionally requires substantial centralized data for training models. However, in practice, data are frequently distributed among multiple institutions, with privacy concerns restricting unrestricted access. To address these issues, we introduce Feature Decoupling Federated Learning (FDFL), an approach designed to collaboratively train a representation learning network over multiple clients for unsupervised anomaly detection in MTS. Unlike previous methods that simply integrate MTS anomaly detection algorithms with federated learning strategies, FDFL specifically addresses heterogeneity among clients by decoupling the representation network into shared and private branches through a contrastive learning mechanism. This method aggregates shared parameters during each federated round while maintaining client-specific private parameters locally. Additionally, we develop a self-attention block that integrates the representations derived from both shared and private parameters to reconstruct MTS and identify anomalies based on reconstruction errors. Extensive experiments conducted on three publicly available datasets demonstrate that FDFL outperforms existing algorithms in most cases, highlighting the effectiveness and superiority of our proposed method in MTS anomaly detection. © 2020 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

CPAL: Cross-prompting Adapter with LoRAs for RGB+X Semantic Segmentation

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Liu, Ye Wu, Pengfei Wang, Miaohui Liu, Jun Nanjing University of Posts and Telecommunications School of Automation and Artificial Intelligence Nanjing China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Lancaster University School of Computing and Communications Lancaster United Kingdom

As sensor technology evolves, RGB+X systems combine traditional RGB cameras with another type of auxiliary sensor, which enhances perception capabilities and provides richer information for important tasks such as semantic segmentation. However, acquiring massive RGB+X data is difficult due to the need for specific acquisition equipment. Therefore, traditional RGB+X segmentation methods often perform pretraining on relatively abundant RGB data. However, these methods lack corresponding mechanisms to fully exploit the pretrained model, and the scope of the pretraining RGB dataset remains limited. Recent works have employed prompt learning to tap into the potential of pretrained foundation models, but these methods adopt a unidirectional prompting approach i.e., using X or RGB+X modality to prompt pretrained foundation models in RGB modality, neglecting the potential in non-RGB modalities. In this paper, we are dedicated to developing the potential of pretrained foundation models in both RGB and non-RGB modalities simultaneously, which is non-trivial due to the semantic gap between modalities. Specifically, we present the CPAL (Cross-prompting Adapter with LoRAs), a framework that features a novel bi-directional adapter to simultaneously fully exploit the complementarity and bridging the semantic gap between modalities. Additionally, CPAL introduces low-rank adaption (LoRA) to fine-tune the foundation model of each modal. With the support of these elements, we have successfully unleashed the potential of RGB foundation models in both RGB and non-RGB modalities simultaneously. Our method achieves state-of-the-art (SOTA) performance on five multi-modal benchmarks, including RGB+Depth, RGB+Thermal, RGB+Event, and a multi-modal video object segmentation benchmark, as well as four multi-modal salient object detection benchmarks. The code and results are available at: https://***/abelny56/CPAL. © 1991-2012 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Spiking Neural Network for Intra-cortical Brain Signal Decoding

arXiv

引用

arXiv 2025年

作者： Yang, Song Fu, Haotian Zhang, Herui Zhang, Peng Li, Wei Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Brain-inspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China Department of Biomedical Engineering College of Life Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Decoding brain signals accurately and efficiently is crucial for intra-cortical brain-computer interfaces. Traditional decoding approaches based on neural activity vector features suffer from low accuracy, whereas deep learning based approaches have high computational cost. To improve both the decoding accuracy and efficiency, this paper proposes a spiking neural network (SNN) for effective and energy-efficient intra-cortical brain signal decoding. We also propose a feature fusion approach, which integrates the manually extracted neural activity vector features with those extracted by a deep neural network, to further improve the decoding accuracy. Experiments in decoding motor-related intra-cortical brain signals of two rhesus macaques demonstrated that our SNN model achieved higher accuracy than traditional artificial neural networks;more importantly, it was tens or hundreds of times more efficient. The SNN model is very suitable for high precision and low power applications like intracortical brain-computer interfaces. © 2025, CC BY.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Operation Improvement of Garbage Power Plants - via control Technology 16th

Operation Improvement of Garbage Power Plants - via Contro...

引用

16th International Conference on Modelling, Identification and control, ICMIC 2024

作者： Cao, Guizhou Xia, Dawei Meng, Kai Zhu, Zheran Yuan, Jie Wu, Zhenlong State Grid Henan Electric Power Company Electric Power Research Institute Zhengzhou450052 China State Grid Shangqiu Electric Power Company Shangqiu476002 China State Grid Henan Electric Power Company Zhengzhou450018 China Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing210096 China School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China State Key Laboratory of Intelligent Agricultural Power Equipment Zhengzhou University Luoyang471000 China

ISBN: (纸本)9789819617760

This paper focuses on the operation improvement of garbage power plants, which are a key disposal technology for household waste, construction waste, etc. However, the low of the furnace temperature can cause a sharp increase in dioxins and result in consequences of severe environmental pollution. To enhance the control performance of the combustion system for garbage power plants, proportional-integral-derivative based on predictive (PID-predictive) and proportional-integral based on predictive (PI-predictive) controllers are designed for the combustion system, which is dominated by time delay. By equaling the desired dynamic characteristics and the closed-loop system combined with the combustion system and controller, the PID-predictive and PI-predictive can be designed. The comprehensive comparison of the tracking performance, disturbance rejection ability, measurement noise rejection performance and ability to handle system uncertainties is carried out with PI, active disturbance rejection control, delay active disturbance rejection control and Smith predictor active disturbance rejection control. Simulation results show that PID-predictive and PI-predictive obtain similar control performance, and have a faster tracking speed than other comparative controllers. The disturbance rejection ability of PID-predictive and PI-predictive is weaker than delay active disturbance rejection control and Smith predictor active disturbance rejection control, and is stronger than PI and active disturbance rejection control. When the controlled process has system uncertainties, PID-predictive and PI-predictive still obtain satisfactory control performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Disturbance rejection

来源：评论

学校读者我要写书评

暂无评论

Reduced-Order Consensus Protocols with Pure Relative Output: Design, Existence, and Dual Results

引用

IEEE Transactions on Automatic control 2025年

作者： Ma, Yuwen Li, Xianwei Li, Shaoyuan Lin, Zongli Shanghai Jiao Tong University Department of Automation Shanghai200240 China State Key Laboratory of Submarine Geoscience China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China University of Virginia Charles L. Brown Department of Electrical and Computer Engineering CharlottesvilleVA22904 United States

This paper studies reduced-order dynamic consensus protocols for homogeneous linear multi-agent systems using pure relative output information. By applying H∞ control theory, a separation principle-like method with an additional small-gain constraint is proposed for designing an (nx-nu)th order protocol, where nx and nu are the numbers of states and inputs of each agent, respectively. Existence conditions for the protocol are then systematically discussed from the graph, low gain and high gain perspectives, respectively. It is revealed that the protocol always exists and can be found by the proposed design method for three cases: 1) directed acyclic networks, 2) agents with all eigenvalues in the closed left-half complex plane and 3) agents of minimum phase. Another reduced-order protocol of (nx - ny)th order is further discussed through duality. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method. © 1963-2012 IEEE.

关键词： Robust control

来源：评论

学校读者我要写书评

暂无评论

S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

来源：评论

学校读者我要写书评

暂无评论

A Holistically Point-guided Text Framework for Weakly-Supervised Camouflaged Object Detection

arXiv

引用

arXiv 2025年

作者： Mok, Tsui Qin Gao, Shuyong Xing, Haozhe He, Miaoyang Wang, Yan Zhang, Wenqiang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Academy for Engineering & Technology The Yiwu Research Institute of Fudan University Chengbei Road Zhejiang Yiwu City322000 China

Weakly-Supervised Camouflaged Object Detection (WSCOD) has gained popularity for its promise to train models with weak labels to segment objects that visually blend into their surroundings. Recently, some methods using sparsely-annotated supervision shown promising results through scribbling in WSCOD, while point-text supervision remains under-explored. Hence, this paper introduces a novel holistically point-guided text framework for WSCOD by decomposing into three phases: segment, choose, train. Specifically, we propose Point-guided Candidate Generation (PCG), where the point’s foreground serves as a correction for the text path to explicitly correct and rejuvenate the loss detection object during the mask generation process (SEGMENT). We also introduce a Qualified Candidate Discriminator (QCD) to choose the optimal mask from a given text prompt using CLIP (CHOOSE), and employ the chosen pseudo mask for training with a self-supervised Vision Transformer (TRAIN). Additionally, we developed a new point-supervised dataset (P2C-COD) and a text-supervised dataset (T-COD). Comprehensive experiments on four benchmark datasets demonstrate our method outperforms state-of-the-art methods by a large margin, and also outperforms some existing fully-supervised camouflaged object detection methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Uncertainty-Guided Diffusion Model for Camouflaged Object Detection

引用

IEEE Transactions on Multimedia 2025年

作者： Yang, Jinsheng Zhong, Bineng Liang, Qihua Mo, Zhiyi Zhang, Shengping Song, Shuxiang Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining & Security China Wuzhou University Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou543002 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China

Recently, diffusion models have significantly improved the performance of Camouflaged Object Detection (COD) by adding noise to a mask and iteratively denoising it to match the target distributions. Due to the direct extraction of features from noisy masks and the lack of conditional constraints on a prediction area, the diffusion model may deviate from a correct prediction range and produces mispredictions in regions with high uncertainty. To address this issue, we propose an uncertainty-guided diffusion model (UGDNet) for COD, which explicitly quantifies uncertainty and integrates it as an anchor condition into the diffusion models to provide an initialization of the diffusion regions. The core idea is first to utilize a probability representation and transformer to explicitly model uncertainty, aiming to identify areas where a model may generate overconfident mispredictions. Then, we use the uncertainty as an anchor condition to provide a reference prediction range for the diffusion model, guiding each step of the diffusion process. Furthermore, we use uncertainty to guide feature aggregation, prompting the model to pay extra attention to the semantic features of regions with high uncertainty to refine the segmentation results further. The experimental results indicate that our proposed UGDNet achieves higher accuracy than existing state-of-the-art models on five COD benchmarks, including COD10K, NC4K, CAMO, CHAMELEON, and CDS2K. © 1999-2012 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

A Hierarchical Deep Reinforcement Learning Strategy for Collective Pursuit-Evasion Game with Partial Observations

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年

作者： Wang, Xiaoxiao Yi, Peng Hong, Yiguang Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Shanghai200092 China Tongji University School of Electronics and Information Shanghai200070 China Tongji University Control Science and Engineering Department Shanghai200070 China National Key Laboratory of Autonomous Intelligent Unmanned Systems China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China

This paper addresses the problem of Collective Pursuit-Evasion Game (C-PEG) with partial observation, aiming to enhance cooperation among cluster pursuers, reduce the game space, and optimize the overall pursuer strategy efficiency in complex dynamic environments. Firstly, we consider a distributed communication model suitable for cluster collaboration and model the multi-agent cooperative task as a Collective Decentralized Partially Observable Markov Decision Process (C-Dec POMDP). Then we propose a Hierarchical Regularization Game Multi-agent Deep Deterministic Policy Gradient (HRG-MADDPG) algorithm, which includes High-Level Strategy (HLS) and Low-Level Strategy (LLS). The HLS takes the current observation from the pursuers’ sensors and the pursuers’ status as inputs, and outputs target allocation strategies for the coalitions and individuals to guide the LLS in executing specific pursuit tasks. The LLS is further divided into centralized training and decentralized execution phases. In the centralized training phase, the guiding strategy provided by the HLS and the introduced regularization auxiliary term are combined to promote decision-making through the fusion of favorable situations and auxiliary terms. In the decentralized execution phase, a reliable policy improvement method based on partially observable information is designed. All in all, the HRG-MADDPG algorithm provides a reliable method for collective pursuit-evasion. It is trained in a single environment and further validated in multiple different test environments. Simulation results confirm the effectiveness of the proposed method, showing the adaptability, scalability, and rapid responsiveness of the proposed framework in various practical applications. © 2020 IEEE.

关键词： Hierarchical systems

来源：评论

学校读者我要写书评

暂无评论

Driving Behavior Decision-Making for Unmanned Monorail Cranes Based on Multi-Source Layering Perception information

引用

IEEE Transactions on Transportation Electrification 2025年

作者： Zhang, Xin Zhang, Yidong Yang, Chunyu Yan, Wanzi Ma, Lei Yu, Qiu China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China China University of Mining and Technology State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources Artificial Intelligence Research Institute Xuzhou221116 China

Monorail cranes (MCs) are the emerging transportation in the downhole and often work in dangerous deep confined spaces. To improve the safety production efficiency, a driving behavior decision-making method is proposed for unmanned MC. Firstly, a multi-source perception data layering method is presented. Based on the results of sensor performance testing in the downhole, an onboard perceptual platform is designed, and the sensor information is layered and integrated in a structured manner, considering the significance of different sensor information on safe driving. Secondly, a driving behavior decision-making framework for unmanned MCs is proposed. The decision-making objective and constraints are derived from the coal mine safety regulations, the MC driving manual, and the downhole test results. This framework logic is novel in unmanned MC, which can achieve downhole driving by combining rule-based driving behavior decision-making and reactive one. The experiments in the heading area and the upper yard show that the proposed method increases the perception distance by 3.62 times compared to that of remote driving, and reduces the decision-making response time by 96%. MC has enough distance and time to slow down first until it has to brake. By contrast, this MC driving behavior is more stable and safe in the downhole. © 2015 IEEE.

关键词： Coal mines

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：