检索结果-内蒙古大学图书馆

Joint Sensing and Computation Incentive Mechanism for Mobile Crowdsensing Networks: A Multiagent Reinforcement Learning Approach

引用

IEEE Internet of Things Journal 2025年第9期12卷 13033-13046页

作者： Zhao, Nan Sun, Yiling Pei, Yiyang Niyato, Dusit Hubei University of Technology Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Wuhan430061 China Singapore Institute of Technology Information and Communications Technology Cluster Tampines Singapore Nanyang Technological University School of Computer Science and Engineering Jurong West Singapore

Mobile crowdsensing (MCS) is a novel sensing paradigm by utilizing mobile users (MUs) to collect data from environment. Considering the finite sensing and computing resources of MUs, it is crucial to inspire MUs to take part in crowdsensing willingly. In this study, a multiagent-deep-reinforcement-learning (DRL)-based incentive mechanism is investigated to tackle the joint data sensing and computing issues. Specifically, due to the heterogeneity of sensing tasks, multiple MCS platforms (MCPs) motivate MUs to participate in different tasks. The interaction between MCPs and MUs is modeled as a multileader-multifollower Stackelberg game with Stackelberg equilibrium proved by derivation. Moreover, the Stackelberg game is transformed as a Markov decision process (MDP) to deal with a multiagent DRL method without any prior knowledge. Due to the continuous high-dimensional action space of multiple MCPs and MUs, a multiagent double actors deep deterministic policy gradient (MA-DADDPG) algorithm is proposed to obtain the optimal sensing data size, computing resource, and incentive payment policies. Extensive simulation results illustrate the effectiveness of the proposed crowdsensing incentive mechanism. © 2014 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Integrating clinical knowledge and imaging for medical report generation

引用

Pattern Recognition Letters 2025年 195卷 59-65页

作者： Zhao, Meng Liu, Juncai Shen, Hongyu Yan, Bin Pei, Mingtao Wang, Yi College of Electrical Engineering and Automation Shandong University of Science and Technology China Beijing Laboratory of Intelligent Information Technology Beijing Institute of Technology China

Medical report generation is an important cross-modal task in the field of medicine, aiming to automatically generate professional and accurate reports for given medical images. Integrating clinical knowledge into the task of medical report generation can enhance the semantic accuracy of medical image feature descriptions and improves the interpretability and robustness of the model. In this paper, we propose to integrate clinical knowledge and image content to generate medical report. The clinical knowledge is presented by a clinical knowledge graph, where anatomical structures and observations are represented as nodes, and their relationships are represented as edges. We design a graph generation module to dynamically generate relevant knowledge graphs specific to each image, which can diversify the knowledge graph structures and expand the coverage of clinical knowledge to provide a more extensive range of clinical knowledge during report generation. Furthermore, we design a graph attention module to facilitate optimized feature representation within the clinical knowledge graph by incorporating message passing between nodes and edges. This fosters a more comprehensive understanding of the relationships and significance of clinical information. Experimental evaluations conducted on the IU X-Ray and MIMIC CXR datasets demonstrate the superiority of the proposed method in generating medical reports. The results highlight the potential of leveraging clinical knowledge for enhancing the precision and clinical relevance of the generated reports. © 2025 Elsevier B.V.

关键词： Clinical knowledge graph Graph attention Radiology reports generation

来源：评论

学校读者我要写书评

暂无评论

Regional Sea Surface Skin Temperature Retrieval From HY-1C and HY-1D COCTS

引用

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 11502-11511页

作者： Li, Zhuomin Chen, Rui Liu, Mingkun Guan, Lei Ocean University of China Sanya Oceanographic Laboratory Key Laboratory of Ocean Observation and Information of Hainan Province Sanya Oceanographic Institution Sanya572024 China Ocean University of China Laboratory for Regional Oceanography and Numerical Modeling Qingdao Marine Science and Technology Center College of Marine Technology Faculty of Information Science and Engineering Qingdao266100 China

Haiyang-1C (HY-1C) and Haiyang-1D (HY-1D) are Chinese satellites for marine observation. They operationally work to form the networking of morning and afternoon satellites, improving the capacity of coverage in the temporal and spatial dimensions. These satellites carry Chinese ocean color and temperature scanners (COCTS), containing two thermal bands with central wavelengths of 10.8 and 12.0 μm and enabling sea surface temperature (SST) observations. Herein, regional algorithms for SST retrieval are developed for the South China Sea (SCS) by applying radiative transfer modeling. The SCS has unique atmospheric conditions characterized by high temperature and humidity, particularly in the central and southern regions. Atmospheric profiles sufficient to represent the atmospheric conditions of the SCS are selected. The relationship between the top-of-the-atmosphere simulated brightness temperature and the reanalysis skin SST of the selected profiles is determined. The HY-1C and HY-1D COCTS SSTs in the SCS are retrieved utilizing the obtained algorithm. The HY-1C/COCTS SSTs are evaluated using the sea and land surface temperature radiometer SST, and the HY-1D/COCTS SSTs are evaluated using the Visible Infrared Imaging Radiometer Suite SST. For the HY-1C/1D COCTS SSTs, the biases are around 0, and the standard deviations are around 0.5°C. The differences between the HY-1C and HY-1D COCTS SSTs are analyzed to further validate the accuracy of the retrieval method. The difference is 0.38°C during the day and 0.03°C at night, which indicates a diurnal warming phenomenon in the SCS. © 2008-2012 IEEE.

关键词： Atmospheric temperature

来源：评论

学校读者我要写书评

暂无评论

Unveiling the confined dispersion mechanism of nanomaterials by stereocomplex cross-linked networks in polylactic acid

引用

Journal of Materials science and technology 2025年 235卷 293-301页

作者： Su, Xiaolong Pan, Qingchun Zhai, Yaling Jia, Chao Wang, Jinqi Xu, Zhe Li, Jiaxin Zhao, Jian Xiang, Hengxue Wei, Xiaoding Zhu, Meifang State Key Laboratory of Advanced Fiber Materials College of Material Science and Engineering Donghua University Shanghai201620 China State Key Laboratory for Turbulence and Complex System Department of Mechanics and Engineering Science College of Engineering Beijing Innovation Center for Engineering Science and Advanced Technology Peking University Beijing100871 China College of Material Science and Engineering Xinjiang University Urumqi830046 China

Nanomaterials are extensively utilized in a multitude of sectors, but their propensity to aggregate can considerably diminish the efficacy of functional materials. A pivotal challenge in this domain is achieving a homogenous distribution of nanomaterials, which is essential for enhancing their performance while also reducing production costs. In this work, we achieve uniform and stable dispersion of various nanomaterials through the confinement effect generated by the stereocomplex cross-linked network formed by the combination of poly (L-lactic) acid and poly (D-lactic) acid. The unique confinement effect of polylactic acid (PLA) isomers is universal and significantly enhances the dispersion of nanomaterials in both PLA solutions and films. To demonstrate the efficacy of our approach, we disperse aggregation-induced emission (AIE) molecules within PLA, which leads to the production of PLA films exhibiting improved fluorescence property. This work provides an effective solution for the preparation of nanocomposite materials that are both high-performing and cost-efficient. © 2025

关键词： Nanoclay

来源：评论

学校读者我要写书评

暂无评论

Real-Time Event Recognition of Long-Distance Distributed Vibration Sensing With Knowledge Distillation and Hardware Acceleration

引用

IEEE Internet of Things Journal 2025年第11期12卷 17896-17905页

作者： Luo, Zhongyao Wu, Hao Ge, Zhao Tang, Ming Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Next Generation Internet Access National Engineering Laboratory Hubei Optics Valley Laboratory School of Optical and Electronic Information Wuhan430074 China

Fiber-optic sensing, especially distributed optical fiber vibration (DVS) sensing, is gaining importance in internet of things (IoT) applications, such as industrial safety monitoring and intrusion detection. Despite their wide application, existing post-processing methods that rely on deep learning models for event recognition in DVS systems face challenges with real-time processing of large sample data volumes, particularly in long-distance applications. To address this issue, we propose to use a four-layer convolutional neural network (CNN) as the student model with ResNet as the teacher model for knowledge distillation. Compared to the baseline CNN model, which achieves 83.41% accuracy on data from previously untrained environments, the distilled CNN improves accuracy significantly to 95.39%, demonstrating its superior generalizability and robustness. Additionally, we propose a novel hardware design based on field-programmable gate arrays (FPGA) to further accelerate model inference. This design replaces multiplication with binary shift operations and quantizes model weights, enabling high parallelism and low latency. Our implementation achieves an inference time of 0.083 ms for a spatial-temporal sample covering a 12.5 m fiber length and 0.256 s time frame. This performance enables real-time signal processing over approximately 38.55 km of fiber, about 2.14× the capability of an Nvidia GTX 4090 GPU. The proposed method greatly enhances the efficiency of vibration pattern recognition, promoting the use of DVS as a smart IoT system. The data and code are available at https://***/HUST-IOF/Efficient-DVS. © 2014 IEEE.

关键词： Multilayer neural networks

来源：评论

学校读者我要写书评

暂无评论

DDT-Net:Deep Detail Tracking Network for Image Tampering Detection

引用

Computers, Materials & Continua 2025年第5期83卷 3451-3469页

作者： Jim Wong Zhaoxiang Zang Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang443002China College of Computer and Information Technology ChinaThree Gorges UniversityYichang443002China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650504China

In the field of image forensics,image tampering detection is a critical and challenging *** methods based on manually designed feature extraction typically focus on a specific type of tampering operation,which limits their effectiveness in complex scenarios involving multiple forms of *** deep learningbasedmethods offer the advantage of automatic feature learning,current approaches still require further improvements in terms of detection accuracy and computational *** address these challenges,this study applies the UNet 3+model to image tampering detection and proposes a hybrid framework,referred to as DDT-Net(Deep Detail Tracking Network),which integrates deep learning with traditional detection *** contrast to traditional additive methods,this approach innovatively applies amultiplicative fusion technique during downsampling,effectively combining the deep learning feature maps at each layer with those generated by the Bayar noise *** design enables noise residual features to guide the learning of semantic features more precisely and efficiently,thus facilitating comprehensive feature-level ***,by leveraging the complementary strengths of deep networks in capturing large-scale semantic manipulations and traditional algorithms’proficiency in detecting fine-grained local traces,the method significantly enhances the accuracy and robustness of tampered region *** with other approaches,the proposed method achieves an F1 score improvement exceeding 30% on the DEFACTO and DIS25k *** addition,it has been extensively validated on other datasets,including CASIA and *** results demonstrate that this method achieves outstanding performance across various types of image tampering detection tasks.

关键词： Image forensics image tampering detection image manipulation detection noise flow Bayar

来源：评论

学校读者我要写书评

暂无评论

Fault-induced gear meshing modulation sideband extraction and evaluation for fault diagnosis of planetary gearboxes

引用

science China(Technological sciences) 2025年第1期68卷 335-351页

作者： Peng ZHOU Xiaoluo YU Yang YANG Qingbo HE Zhike PENG State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghai 200240China School of Mechanical Engineering Ningxia UniversityYinchuan 750021China Science and Technology on Vehicle Transmission Laboratory China North Vehicle InstituteBeijing 100072China

Gearboxes,known for their compact size and stable transmission capability,are widely used as power transmission structures in various types of mechanical equipment,such as wind turbines,helicopters,and special ***,due to harsh and non-stationary working conditions,gear surfaces often deteriorate,leading to faults such as wear,pitting,and ***,it is vital to monitor the working status of gearboxes and diagnose gear faults as early as *** faults can induce characteristic modulation effects near the gear meshing frequency(GMF),resulting in the appearance of faultinduced sidebands in a vibration *** of these sidebands allows for the diagnosis of gear faults in a ***,when faced with a planetary gearbox having a complex configuration,strong background noise and additional faultunrelated sidebands can interfere with fault-induced sidebands,making accurate diagnosis *** address these challenging issues,this paper proposes a novel fault-induced gear meshing modulation sideband extraction *** the process,the sidebands directly related to the gear fault near the GMF are first identified and then extracted by a variational harmonic mode decomposition(VHMD)***,a fault-related gear meshing modulation component(GMMC)can be accurately reconstructed by summing up the extracted fault-induced *** the GMMC,the gear fault severity can be assessed by evaluating its amplitude modulation(AM)*** superior performance of the proposed method is finally demonstrated by experimental data.

关键词： planetary gearbox gear meshing frequency modulation sideband signal decomposition fault diagnosis

来源：评论

学校读者我要写书评

暂无评论

Ultra-low power MoS2 optoelectronic synapse with wavelength sensitivity for color target recognition

引用

science China(information sciences) 2025年第4期68卷 187-196页

作者： Bo WEI Yabo CHEN Xiaotong HAN Yan KANG Bujia LIANG Cheng LI Xiaokuo YANG Liang FANG Yuanxi PENG Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology Fundamentals Department Air Force Engineering University College of Advanced Interdisciplinary Studies National University of Defense Technology Institute of Quantum Information Science and Technology College of ScienceNational University of Defense Technology College of Computer National University of Defense Technology

Optoelectronic synapses that integrate visual perception and pre-processing hold significant potential for neuromorphic vision systems（NVSs）. However, due to a lack of wavelength sensitivity, existing NVS mainly focuses on gray-scale image processing, making it challenging to recognize color images. Additionally, the high power consumption of optoelectronic synapses, compared to the 10 fJ energy consumption of biological synapses, limits their broader application. To address these challenges, an energy-efficient NVS capable of color target recognition in a noisy environment was developed,utilizing a MoS2optoelectronic synapse with wavelength sensitivity. Benefiting from the distinct photon capture capabilities of 450, 535, and 650 nm light, the optoelectronic synapse exhibits wavelength-dependent synaptic plasticity, including excitatory postsynaptic current（EPSC）, paired-pulse facilitation（PPF）, and long-term plasticity（LTP）. These properties can effectively mimic the visual memory and color discrimination functions of the human vision system. Results demonstrate that the NVS, based on MoS2optoelectronic synapses, can eliminate the color noise at the sensor level, increasing color image recognition accuracy from 50% to 90%. Importantly, the optoelectronic synapse operates at a low voltage spike of0.0005 V, consuming only 0.075 fJ per spike, surpassing the energy efficiency of both existing optoelectronic and biological synapses. This ultra-low power, color-sensitive device eliminates the need for color filters and offers great promise for future deployment in filter-free NVS.

关键词： optoelectronic synapse neuromorphic vision system color recognition 2D materials image pre-processing

来源：评论

学校读者我要写书评

暂无评论

Intelligent integration of AI and IoT for advancing ecological health, medical services, and community prosperity

引用

Alexandria engineering Journal 2025年 127卷 522-540页

作者： Alzahrani, Abdulrahman Kostkova, Patty Alshammari, Hamoud Habibullah, Safa Alzahrani, Ahmed Department of Information System and Technology College of Computer Science and Engineering University of Jeddah Saudi Arabia University College London United Kingdom Department of Information systems College of Computer and Information Sciences Jouf University Saudi Arabia

Artificial Intelligence (AI) and the Internet of Things (IoT) are developing so fast that they can bring revolutionary changes in ecological sustainability, public health, and community welfare. In contrast, the present waste management system has a set of inefficiencies due to some challenges, such as poor waste stream segregation, limited real-time data analysis, and negligible integration of recent technology. These challenges lead to environmental degradation, public health hazards, and inefficient usage of resources. This research targets these challenges by designing an IWM framework like AI-IoT for smart waste management. The system employs AI models powered by IoT sensors for efficient waste collection, classification, and optimization of recycling schedules. CNN (convolutional neural networks) with transfer learning enabled by Res-Net provides high-accuracy image recognition, which can be used for waste classification. Bidirectional Encoder Representations from Transformers (BERT) allow multilingual users to interact and communicate properly in any linguistic environment. Data collected from IoT-enabled smart bins is transmitted in real-time to a central control system for dynamic decision-making and follow-up analysis. A pilot exercise to verify the system's effectiveness was implemented in metropolitan settings to show the transformation: landfill dependency was decreased by 30 %, recycling efficiency was greatly increased to 90 %, and thus the cost of waste management was optimized. At the same time, environmental health inequity, causing pathogen-related threats, was reduced by 35 %. The model has an accuracy of 96.8 %. The features of the proposed framework not only provide solutions to the existing inefficiencies but also enhance scalability, cost-effectiveness, and global environmental standardization. This dawns the futuristic growth of AI- and IoT-enabled waste management systems, which hinge on sustainability, public health, and resource efficienc

关键词： Artificial Intelligence Internet of Things BERT Medical Services Res-Net Waste Management

来源：评论

学校读者我要写书评

暂无评论

A Novel 3D Paradigm for Target Expansion of Augmented Reality SSVEP

引用

IEEE Transactions on Neural systems and Rehabilitation engineering 2025年 33卷 1562-1573页

作者： Cao, Beining Li-Ting Tsai, Charlie Zhou, Nan Do, Thomas Lin, Chin-Teng University of Technology Sydney Computational Intelligence and Brain-Computer Interfaces Laboratory Australian AI Institute Faculty of Engineering and Information Technology School of Computer Science UltimoNSW2007 Australia

Steady-State Visual Evoked Potentials (SSVEP) have proven to be practical in Brain-Computer Interfaces (BCI), particularly when integrated with augmented reality (AR) for real-world application. However, unlike conventional computer screen-based SSVEP (CS-SSVEP), which benefits from stable experimental environments, AR-based SSVEP (AR-SSVEP) systems are susceptible to the interference of real-world environment and device instability. Particularly, the performance of AR-SSVEP significantly declines as the target frequency increases. Therefore, our study introduced a 3D paradigm that combines flicker frequency with rotation patterns as stimuli, enabling expansion of target sets without additional frequencies. In the proposed design, in addition to the conventional frequency-based SSVEP feature, bio-marker elicited by visual perception of rotation was investigated. An experimental comparison between this novel 3D paradigm and a traditional 2D approach, which increases targets by adding frequencies, reveals significant advantages. The 12-class 3D paradigm achieved an accuracy of 76.5% and an information transfer rate (ITR) of 70.42 bits/min using 1-second EEG segments. In contrast, the 2D paradigm exhibited a lower performance with 72.07% accuracy and 62.28 bits/min ITR. The result underscores the 3D paradigm’s superiority in enhancing the practical applications of SSVEP-based BCIs in AR settings, especially with shorter time windows, by effectively expanding target recognition without compromising system efficiency. © 2001-2011 IEEE.

关键词： Augmented reality

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：