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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Hu, Yanzhe Feng, Chenyuan Jiang, Wei Shi, Wenzhe Feng, Daquan Shenzhen University Shenzhen Key Laboratory of Digital Creative Technology Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen China College of Information Engineering Zhejiang University of Technology Zhejiang Hangzhou China Zte Corporation State Key Laboratory of Mobile Network and Mobile Multimedia Technology China

ISBN: (纸本)9798350325959

The service time of Road-Side Units (RSUs) for vehicle users will directly affect the Quality of Service (QoS) in the Internet of Vehicles (IoV), and it is related to the traffic flow and service decisions. Consequently, a sophisticated service decision mechanism based on the traffic flow prediction is necessary beforehand in order to optimize QoS. In this work, we propose a service decision based on traffic flow prediction to reduce the service waiting time of vehicles. Firstly, we propose a traffic flow prediction model based on deep learning. Convolutional Neural Network (CNN) is used to capture spatial data in order to handle the complicated road network layout in metropolitan roads. First, to reveal people's travel patterns, temporal features are captured by two different models. While short-term features are captured from historical data by Auto-Encoder, daily period feature as well as weekly period feature are captured from bi-directional data by Bidirectional Long Short-Term Memory (Bi-LSTM). Second, by simulating the process of RSUs serving vehicles using a queuing theory model, we propose a service decision mechanism based on the anticipated traffic flow. The proposed service decision mechanism aims to reduce the average waiting time of vehicles for service. Particularly, the RSUs will ask the Macro Base Station (MBS) for assistance when there are too many vehicles traveling through the RSUs. Finally, we conduct comprehensive experiments using real data to evaluate the model performance, and it is demonstrated that our proposed scheme outperforms other benchmarks. © 2023 IEEE.

关键词： Internet of Vehicles queuing theory service decision mechanism traffic flow prediction

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Dynamic Locomotion of Quadruped Robot in Sloping Terrain 6

Dynamic Locomotion of Quadruped Robot in Sloping Terrain

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6th International Symposium on Autonomous Systems, ISAS 2023

作者： Chen, Donglin Cong, Dehong Song, Xiangman Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Northeastern University Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China

ISBN: (纸本)9798350316155

In order to improve the locomotion capability of quadruped robot in the sloping terrain and enhance whose adaptability to the environment, this paper proposes a method to realize dynamic locomotion of quadruped robot in sloping terrain completely based on model predictive control (MPC) and proprioception. According to the foot contact signal and the robot body posture information measured by inertial measurement unit (IMU), quadruped robot actively changes whose body posture to adapt to different sloping terrains. This paper builds quadruped robot virtual prototype and test terrain in Webots simulation environment. The experimental results show that with this method, the roll angle fluctuation range of the quadruped robot is less than 0.05rad and the yaw angle fluctuation range is not more than 0.003rad. At the same time, when the angle of slope changes, the terrain adaptation time of the quadruped robot is less than 1.8s. The above results show that quadruped robot has excellent movement ability in sloping terrain and environmental adaptability under the condition of ensuring stability with the method proposed in this paper. © 2023 IEEE.

关键词： Model predictive control

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A Fault Diagnostic Method for Rolling Bearings Based on CAR-SNN Model

A Fault Diagnostic Method for Rolling Bearings Based on CAR-...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Li, Qiuting Wang, Xiuqing Du, Wenxia Hebei Normal University College of Computer and Cyber Security Shijiazhuang050024 China Hebei Provincial Key Laboratory of Network & Information Security Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang China Hebei Normal University College of China Combustion Engineering Shijiazhuang050024 China

ISBN: (纸本)9798350337754

In the era of big data, high-performance rolling bearing fault diagnostic methods are of great significance to ensure manufacturing smoothly and avoid mass economic losses. A rolling bearing fault diagnostic CAR-SNN model based on Convolutional Block Attention Module (CBAM), Residual Network (ResNet), and Spiking Neural Networks (SNNs) is proposed based on deep learning (DL). The CAR-SNN model combines CBAM, ResNet, and SNNs to extract critical features while avoiding gradient vanishing or explosion phenomena. The ability of the CAR-SNN model to process spatio-temporal information is improved by using SNNs that incorporate spatio-temporal information simultaneously. The proposed model is used for the rolling bearing fault diagnosis, which datasets are from the experimental platforms of CWRU and Jiangnan University, with the highest diagnostic accuracy 99.995% and 99.18%, respectively. The experimental results validate the effectiveness and stability of the proposed model. The results of comparison experiments show that the CAR-SNN model has better fault diagnostic accuracy and stability than that of the CAR model and other comparison methods. The proposed data-driven rolling bearing fault diagnostic method based on the CAR-SNN model is easy to be implemented, and meeting the needs of high fault diagnostic accuracy in practical applications, and provides valuable input for fault diagnosis of rolling bearings. © 2023 IEEE.

关键词： Failure analysis

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Variable lanes traffic optimization control method combined with colorful asphalt materials 9

Variable lanes traffic optimization control method combined ...

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9th International Conference on Mechanical engineering, Materials, and Automation Technology, MMEAT 2023

作者： Wang, Ruihan Zhan, Xuejuan Liang, Zijun Li, Yuqi Xiao, Yun Peng, Min School of Urban Construction and Transportation Hefei University China Anhui Province Transportation Big Data Analysis and Application Engineering Laboratory China Key Laboratory of Traffic Information and Safety of Anhui Higher Education Institutes Anhui Sanlian University China

ISBN: (纸本)9781510668584

Along with our country's continuous growth, rising rush hours traffic congestion, many scholars proposed setting variable lanes solution, but due to the variable lanes different times the direction of the lane change, which made the driver can not be correctly into the right lane. So we propose a traffic optimization control method for variable lanes combined with colorful asphalt materials. Firstly, enumeration method is used to list all the direction schemes and phase schemes of the variable lanes, and the optimal scheme is obtained according to the constraints. In order to better distinguish the variable lanes from the conventional lanes, the variable lanes are paved with colorful asphalt environmental materials. Then the phase timing is calculated according to the determined variable lanes, phase scheme and traffic flow. Finally, through VISSIM traffic simulation software to evaluate the results, the results show that combining with colorful asphalt materials clearly calibrate the position of the variable lanes, effective rule on the variable lanes traffic space, and on this basis, through phase timing optimization technology to further improve the intersection traffic efficiency, effectively alleviate the intersection rush hours traffic congestion. It provides a basis for the research of variable lanes optimization control combined with colorful asphalt materials. © 2023 SPIE.

关键词： Traffic congestion

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knowledge Representation and Reasoning for Complex Time Expression in Clinical Text

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data Intelligence 2022年第3期4卷 573-598页

作者： Danyang Hu Meng Wang Feng Gao Fangfang Xu Jinguang Gu School of Computer Science and Technology Wuhan University of Science and TechnologyWuhan 430065China Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration of the People’s Republic of ChinaBeijing 10038China Institute of Big Data Science and Engineering Wuhan University of Science and TechnologyWuhan 430065China

Temporal information is pervasive and crucial in medical records and other clinical text,as it formulates the development process of medical conditions and is vital for clinical decision ***,providing a holistic knowledge representation and reasoning framework for various time expressions in the clinical text is *** order to capture complex temporal semantics in clinical text,we propose a novel Clinical Time Ontology(CTO)as an extension from OWL *** specifically,we identified eight timerelated problems in clinical text and created 11 core temporal classes to conceptualize the fuzzy time,cyclic time,irregular time,negations and other complex aspects of clinical ***,we extended Allen’s and TEO’s temporal relations and defined the relation concept description between complex and simple ***,we provided a formulaic and graphical presentation of complex time and complex time *** carried out empirical study on the expressiveness and usability of CTO using real-world healthcare ***,experiment results demonstrate that CTO could faithfully represent and reason over 93%of the temporal expressions,and it can cover a wider range of time-related classes in clinical domain.

关键词： Clinical text Temporal ontology Temporal relations OWL Negation of temporal relation

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Multi-objective Optimization of Multi-video Synopsis Based on NSGA-III 8

Multi-objective Optimization of Multi-video Synopsis Based o...

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8th International Conference on Virtual Reality, ICVR 2022

作者： Chen, Xiaoxiao Xie, Yujia Wang, Xing Nanjing University of Finance and Economics College of Information Engineering Jiangsu Key Laboratory of Grain Big-data Mining Nanjing China Nanjing China

ISBN: (数字)9781665479110

ISBN: (纸本)9781665479110

Multi-video synopsis can display all information of surveillance video and show all sports solution of moving objects in the sight of multi-cameras. However, most of the existing multi-video synopsis can't achieve these functions. This is because these approaches do not comprehensively consider these factors: video target display timing, maximum target display number, target display consistency, maximum moving target display density, and compression ratio. To address the comprehensive optimization problem of multiple elements for multi-video synopsis, a multi-objective optimization algorithm for multi-video synopsis is proposed. The proposed approach differs significantly from the existing methods and has several appealing properties. First, we propose five objective functions, which are consistent in time sequence, coherent in cross-camera pedestrian display, largest in number of moving objects displayed, smallest in density of moving objects, and smallest in compression ratio, then, establish a multi-objective optimization model based on the five objective functions. Second, we build a model for multi-video synopsis. Third, we use the non-dominated sorting genetic algorithm NSGA-III to optimize the multi-object model of the multi-video synopsis. Through such a solution, the poor performance of multi-video synopsis can be avoided by comprehensively considering the main influencing factors of multi-video synopsis. Besides, we present extensive experiments that demonstrate the effectiveness and efficiency of the proposed approach. © 2022 IEEE.

关键词： Multiobjective optimization

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Multi-granularity decision information integration network for hierarchical classification via local and global constraints

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Applied Intelligence 2025年第10期55卷 1-14页

作者： Liu, Haoyang Li, Peipei Hu, Xuegang Bai, Shengxing Lin, Yaojin Ministry of Education Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Hefei P. R. China School of Computer Science and Information Engineering Hefei University of Technology Hefei City P. R. China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei P. R. China School of Computer Science Minnan Normal University Zhangzhou P. R. China Key Laboratory of Data Science and Intelligence Application Minnan Normal University Zhangzhou P. R. China

Hierarchical classification learning is an effective means of solving large-scale classification problems, which models classification problems at different levels of granularity according to a hierarchical structure. In hierarchical classification tasks, classification results at different levels of granularity are mutually beneficial, where coarse-grained results can reduce the number of fine-grained candidate categories, and fine-grained results can provide a basis for coarse-grained classification. However, in the final decision-making process, the contributions of different granularity levels within the hierarchy vary and are not explicitly known. Therefore, it is essential to flexibly integrate feedback from multiple granularity levels to enhance classification performance. In addition, errors in hierarchical classification are not equally significant; some misclassifications can lead to severe consequences in practical applications, highlighting the need for a more refined error-handling strategy. In order to address the above challenges, we propose a new hierarchical network to integrate multi-granularity knowledge for decision making. On the one hand, the network computes the decision scores of instances at each granularity level and learns a set of weights to combine all scores. Based on the learnable weights, the model can effectively utilize the multi-granularity classification results to obtain the final decision. On the other hand, we design local and global classification losses to guide model optimization, which allows the model to gain a hierarchical perspective and the ability to understand the degree of hierarchical misclassification. As a result, the extent to which the method results in misclassification consequences is limited. Experimental results on five benchmark datasets show that our method arises as a state-of-the-art hierarchical classification method.

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Remote Image Acquisition System Based on STM32 for Spodoptera frugiperda 5

Remote Image Acquisition System Based on STM32 for Spodopter...

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5th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2022

作者： Chang, Jumei Xu, Quanyuan Lu, Ying Zhang, Hanrui College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China Southwest Forestry University Key Laboratory of Forestry and Ecological Big Data State Forestry Administration China

ISBN: (纸本)9781665479677

To enable remote image acquisition and wireless transmission for field sex lure monitoring of Spodoptera frugiperda, the design consists of a CPU processor module, power supply module, image acquisition module, 4G wireless network transmission module, and external SRAM module. The system employs the STM32F407 control core, the OV2640 camera for image acquisition, the JPEG algorithm to compress and decode the image data collected by the camera, and automatically restore the image collected at the monitoring point, the EC20 as a 4G wireless communication network for image data transmission, and the transmission of real-time image data to the server side of the upper computer to achieve human-computer interaction and real-time interaction of system daemons. The experimental results show that the system achieves remote image acquisition of grassland moths and reliably transmits the collected image data to the server side of the upper computer via a 4G wireless network, allowing the monitor to view images of grassland moth monitoring points collected by the system in real-time and save the collected image data locally. This system accomplishes the automation and intelligence of the grassland moth monitoring system, prepares the data for subsequent grassland moth research, and has a broad application promise for grassland moth population monitoring. © 2022 IEEE.

关键词： Cameras

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Self-Q-switching with interferometers

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Physical Review A 2024年第3期109卷 033507-033507页

作者： Gang Wang Chenxi Zhang Hong Jin Lijun Xu Bo Fu School of Instrumentation and Optoelectronic Engineering Beihang University Beijing 100191 China Key Laboratory of Big Data-Based Precision Medicine Ministry of Industry and Information Technology School of Engineering Medicine Beihang University Beijing 100191 China

Self-Q-switching facilitates the generation of pulsed lasers without resorting to conventional Q-switched techniques, while there has been limited research and theoretical development in this area. In this paper, we introduce an interference-based self-Q-switching phenomenon and conduct a comprehensive numerical analysis. Our findings reveal distinct properties when compared to traditional Q-switching, underscoring the role of interferometers. Through a combination of experimental measurements and waveguide simulations, we propose a self-Q-switching mechanism based on jitter-induced filtering. This mechanism aligns well with our experimental observations and provides an explanation from the perspective of the frequency domain. This research contributes to the understanding of self-Q-switching theory and its excitation.

关键词： Laser dynamics Optics & lasers Laser techniques

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M3amba: CLIP-driven Mamba Model for Multi-modal Remote Sensing Classification

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Cao, Mingxiang Xie, Weiying Zhang, Xin Zhang, Jiaqing Jiang, Kai Lei, Jie Li, Yunsong The State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China The School of Electrical and Data Engineering the University of Technology Sydney Australia

Multi-modal fusion holds great promise for integrating information from different modalities. However, due to a lack of consideration for modal consistency, existing multimodal fusion methods in the field of remote sensing still face challenges of incomplete semantic information and low computational efficiency in their fusion designs. Inspired by the observation that the visual language pre-training model CLIP can effectively extract strong semantic information from visual features, we propose M3amba, a novel end-to-end CLIP-driven Mamba model for multi-modal fusion to address these challenges. Specifically, we introduce CLIP-driven modality-specific adapters in the fusion architecture to avoid the bias of understanding specific domains caused by direct inference, making the original CLIP encoder modality-specific perception. This unified framework enables minimal training to achieve a comprehensive semantic understanding of different modalities, thereby guiding cross-modal feature fusion. To further enhance the consistent association between modality mappings, a multi-modal Mamba fusion architecture with linear complexity and a cross-attention module Cross-SS2D are designed, which fully considers effective and efficient information interaction to achieve complete fusion. Extensive experiments have shown that M3amba has an average performance improvement of at least 5.98% compared with the state-of-the-art methods in multi-modal hyperspectral image classification tasks in the remote sensing field, while also demonstrating excellent training efficiency, achieving a double improvement in accuracy and efficiency. © 2025 IEEE. All rights reserved.

关键词： Visual languages

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