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Motion Direction Awareness: A Biomimetic Dynamic Capture Mechanism for Video Prediction

IEEE Transactions on Multimedia 2025年

作者： Gan, Lianqiang Lai, Junyu Zhu, Junhong Liu, Huashuo Gao, Lianli University of Electronic Science and Technology of China School of Aeronautics and Astronautics Chengdu611731 China University of Electronic Science and Technology of China School of Aeronautics and Astronautics China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China

Video prediction is an important yet challenging task that generates future frames based on previous observations. Despite recent progress, existing methods still suffer from motion blur, due to weak motion perception capabilities leading to uncertainty in motion direction. To address this, we propose a Motion Direction Awareness (MDA) mechanism inspired by the direction-selective mechanism in animal visual systems. Specifically, MDA can decompose complex motions into horizontal and vertical components, allowing dimension reduction and independent processing, thereby effectively enhancing motion perception and reducing uncertainty in predicted motion directions. Based on MDA, we design a multi-scale feature fusion network named MDANet for video prediction, which incorporates different scales of spatially encoded features in conjunction with MDA mechanism to extract the temporal evolution information of global and local spatial features. Extensive experiments on representative datasets demonstrate that MDANet can alleviate motion blurring, improving prediction accuracy and temporal consistency over state-of-the-art models. Furthermore, we validate the generalizability and effectiveness of our MDA mechanism by integrating it into other advanced models. The code is available at supplementary. © 1999-2012 IEEE.

关键词： Deep learning

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Community structure detection algorithm based on link prediction

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International Journal of Information and Communication technology 2021年第4期19卷 432-448页

作者： Dai, Gang Wang, Quanxin Xu, Baomin Sun, Lijun School of Computer and Information Technology Beijing Jiaotong University Beijing China China University of Petroleum-Beijing at Karamay No. 355 Anding Road Karamay District Xinjiang Karamay City China School of Computer Science Beijing Jiaotong University Haibin College Huanghua China Key Laboratory of Grain Information Processing and Control Ministry of Education Henan University of Technology Zhengzhou China College of Information Science and Engineering Henan University of Technology Zhengzhou China

Community structure identification has received a great effort among computer scientists who are focusing on the properties of complex networks. The label propagation algorithm is a near linear time algorithm to find a good community structure. Despite various subsequent advances, an important issue of this algorithm is the efficiency and accuracy of the identified community structure. In this paper, we propose a novel community detection algorithm by using link prediction algorithm based on label propagation. The method is the first to introduce the idea of link prediction into community detection. The experimental results show that the proposed method is less resolution limited than modularity optimising methods, and it can be more effective in detecting communities. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Complex networks

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TS-CGNet: Temporal-Spatial Fusion Meets Centerline-Guided Diffusion for BEV Mapping

arXiv

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arXiv 2025年

作者： Hong, Xinying Li, Siyu Zeng, Kang Shi, Hao Peng, Bomin Yang, Kailun Li, Zhiyong The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China

Bird’s Eye View (BEV) perception technology is crucial for autonomous driving, as it generates top-down 2D maps for environment perception, navigation, and decision-making. Nevertheless, the majority of current BEV map generation studies focusing on visual map generation lack depth-aware reasoning capabilities. They exhibit limited efficacy in managing occlusions and handling complex environments, with a notable decline in perceptual performance under adverse weather conditions or low-light scenarios. Therefore, this paper proposes TS-CGNet, which leverages Temporal-Spatial fusion with Centerline-Guided diffusion. This visual framework, grounded in prior knowledge, is designed for integration into any existing network for building BEV maps. Specifically, this framework is decoupled into three parts: Local mapping system involves the initial generation of semantic maps using purely visual information;The Temporal-Spatial Aligner Module (TSAM) integrates historical information into mapping generation by applying transformation matrices;The Centerline-Guided Diffusion Model (CGDM) is a prediction module based on the diffusion model. CGDM incorporates centerline information through spatial-attention mechanisms to enhance semantic segmentation reconstruction. We construct BEV semantic segmentation maps by our methods on the public nuScenes and the robustness benchmarks under various corruptions. Our method improves 1.90%, 1.73%, and 2.87% for perceived ranges of 60×30m, 120×60m, and 240×60m in the task of BEV HD mapping. TS-CGNet attains an improvement of 1.92% for perceived ranges of 100×100m in the task of BEV semantic mapping. Moreover, TS-CGNet achieves an average improvement of 2.92% in detection accuracy under varying weather conditions and sensor interferences in the perception range of 240×60m. The source code will be publicly available at https://***/krabs-H/TS-CGNet. Copyright © 2025, The Authors. All rights reserved.

关键词： Decision making

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Wi-Wheat+: Contact-free wheat moisture sensing with commodity WiFi based on entropy

Wi-Wheat+: Contact-free wheat moisture sensing with commodit...

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作者： Yang, Weidong Shen, Erbo Wang, Xuyu Mao, Shiwen Gong, Yuehong Hu, Pengming College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Zhengzhou450001 China College of International Education Kaifeng University Henan Kaifeng475004 China Knight Foundation School of Computing & Information Sciences Florida International University MiamiFL33199 United States Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

In this paper, we propose a contact-free wheat moisture monitoring system, termed Wi-Wheat+, to address the several limitations of the existing grain moisture detection technologies, such as time-consuming process, expensive equipment, low accuracy, and difficulty in real-time monitoring. The proposed system is based on Commodity WiFi and is easy to deploy. Leveraging WiFi CSI data, this paper proposes a feature extraction method based on multi-scale and multi-channel entropy. The feasibility and stability of the system are validated through experiments in both Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) scenarios, where ten types of wheat moisture content are tested using multi-class Support Vector Machine (SVM). Compared with the Wi-Wheat system proposed in our prior work, Wi-Wheat+ has higher efficiency, requiring only a simple training process, and can sense more wheat moisture content levels. © 2022 Chongqing University of Posts and Telecommunications

关键词： Channel state information (CSI) Multi-class support vector machine (SVM) Multi-scale entropy Radio frequency (RF) sensing WiFi

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Service Function Chain Dynamic Scheduling in Space-Air-Ground Integrated Networks

arXiv

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arXiv 2025年

作者： Jia, Ziye Cao, Yilu He, Lijun Wu, Qihui Zhu, Qiuming Niyato, Dusit Han, Zhu College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China State Key Laboratory of ISN Xidian University Xian710071 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Department of Electrical and Computer Engineering the University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

As an important component of the sixth generation communication technologies, the space-air-ground integrated network (SAGIN) attracts increasing attentions in recent years. However, due to the mobility and heterogeneity of the components such as satellites and unmanned aerial vehicles in multilayer SAGIN, the challenges of inefficient resource allocation and management complexity are aggregated. To this end, the network function virtualization technology is introduced and can be implemented via service function chains (SFCs) deployment. However, urgent unexpected tasks may bring conflicts and resource competition during SFC deployment, and how to schedule the SFCs of multiple tasks in SAGIN is a key issue. In this paper, we address the dynamic and complexity of SAGIN by presenting a reconfigurable time extension graph and further propose the dynamic SFC scheduling model. Then, we formulate the SFC scheduling problem to maximize the number of successful deployed SFCs within limited resources and time horizons. Since the problem is in the form of integer linear programming and intractable to solve, we propose the algorithm by incorporating deep reinforcement learning. Finally, simulation results show that the proposed algorithm has better convergence and performance compared to other benchmark algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Network function virtualization

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General Impedance Modeling for Modular Multilevel Converter with Grid-forming and Grid-following control

arXiv

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arXiv 2024年

作者： Sun, Chu Zhang, Fei Xiao, Huafeng Wang, Na Chen, Jikai The School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden The School of Electrical Engineering Southeast University Nanjing210096 China The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China

Modular multilevel converter (MMC) has complex topology, control architecture and broadband harmonic spectrum. For this, linear-time-periodic (LTP) theory, covering multi-harmonic coupling relations, has been adopted for MMC impedance modeling recently. However, the existing MMC impedance models usually lack explicit expressions and general modeling procedure for different control strategies. To this end, this paper proposes a general impedance modeling procedure applicable to various power converters with grid-forming and grid-following control strategies. The modeling is based on a unified representation of MMC circuit as the input and output relation between the voltage or current on the AC side and the exerted modulation index, while the control part vice versa, thereby interconnected as closed-loop feedback. With each part expressed as transfer functions, the final impedance model keeps the explicit form of harmonic transfer function matrix, making it convenient to directly observe and analyze the influence of each part individually. Thereby the submodule capacitance is found as the main cause of difference between MMC impedance compared to two-level converter, which will get closer as the capacitance increases. Effectiveness and generality of the impedance modeling method is demonstrated through comprehensive comparison with impedance scanning using electromagnetic transient simulation. Copyright © 2024, The Authors. All rights reserved.

关键词： Harmonic analysis

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Prediction Method of Truck Travel Time in Open Pit Mines Based on LSTM Model 42

Prediction Method of Truck Travel Time in Open Pit Mines Bas...

引用

42nd Chinese control Conference, CCC 2023

作者： Ao, Mengting Li, Changhe Yang, Shengxiang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

ISBN: (纸本)9789887581543

Aiming at the prediction of truck travel time in open pit mines, we established a prediction model based on long short-term memory(LSTM). This model fully accounts for 11 factors, including the nature of trucks, weather, road conditions, and driver's behaviors, as well as the influence of neighbor road segments in the route on the current predicted road segment. The experiment shows that the error of the LSTM prediction model is significantly reduced compared with SVR and BP models. In addition, the maximum absolute mean error under different conditions is less than 12 seconds. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Long short-term memory

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Corrosion Detection in Transformers Based on Hierarchical Annotation 7th

Corrosion Detection in Transformers Based on Hierarchical An...

引用

7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Cao, Yong Zhou, Yinian Zhang, Zhao Wu, Wenjun Chen, Xihai Yang, Sha Zhang, Baili School of Computer Science and Engineering Southeast University Nanjing China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China State Key Laboratory of Smart Grid Protection and Control Nanjing China Nari Group Corporation Nanjing China

ISBN: (纸本)9783030786083

Transformers are prone to metal corrosion due to long-term exposure in the external environment. It is necessary to detect corrosion in time to prolong their service life-time. Detecting metal corrosion based on deep learning is becoming a feasible method at present. However, because of the irregularity and detachability of metal corrosion, traditional annotation approach results in such problems as ambiguity and uncertainty in the annotating process. Thus, a novel hierarchical annotation approach is proposed in this paper. The rationale for this initiative is as follows: traditional annotation approach has been used to annotate a large area covering the range of corrosion, as long as the area is visually continuous and adjacent to the corrosion that cannot be clearly divided. The annotation result is recorded as training set A;secondly, in the annotating boxes from the first step, the areas with obvious and relatively independent features are re-annotated to form the second level of nested annotation. The annotation result is recorded as training set B. Finally, Faster R-CNN and YOLOv5 models were trained by A, B training sets respectively in the experiment. It can be concluded that the detection performance with hierarchical annotation approach proved better than traditional annotation approach for Faster R-CNN and YOLOv5 models. © 2021, Springer Nature Switzerland AG.

关键词： Object detection

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Iterative Equalization of CPM With Unitary Approximate Message Passing

arXiv

引用

arXiv 2024年

作者： Liu, Zilong Song, Yi Guo, Qinghua Sun, Peng Gong, Kexian Wang, Zhongyong The School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China The Key Laboratory of Grain Information Processing and Control Ministry of Education The College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China The School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW2522 Australia

Continuous phase modulation (CPM) has extensive applications in wireless communications due to its high spectral and power efficiency. However, its nonlinear characteristics pose significant challenges for detection in frequency selective fading channels. This paper proposes an iterative receiver tailored for the detection of CPM signals over frequency selective fading channels. This design leverages the factor graph framework to integrate equalization, demodulation, and decoding functions. The equalizer employs the unitary approximate message passing (UAMP) algorithm, while the unitary transformation is implemented using the fast Fourier transform (FFT) with the aid of a cyclic prefix (CP), thereby achieving low computational complexity while with high performance. For CPM demodulation and channel decoding, with belief propagation (BP), we design a message passing-based maximum a posteriori (MAP) algorithm, and the message exchange between the demodulator, decoder and equalizer is elaborated. With proper message passing schedules, the receiver can achieve fast convergence. Simulation results show that compared with existing turbo receivers, the proposed receiver delivers significant performance enhancement with low computational complexity. Copyright © 2024, The Authors. All rights reserved.

关键词： Fast Fourier transforms

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Active Wildfires Detection and Dynamic Escape Routes Planning for Humans Through Information Fusion between Drones and Satellites

Active Wildfires Detection and Dynamic Escape Routes Plannin...

引用

International Conference on Intelligent Transportation

作者： Chang Liu Tamas Sziranyi Department of Networked Systems and Services Budapest University of Technology and Economics Müegyetem rkp. 3 H-1111 Budapest Hungary and Machine Perception Research Laboratory of Institute for Computer Science and Control (SZTAKI) Budapest Hungary Faculty of Transportation Engineering and Vehicle Engineering Machine Perception Research Laboratory of Institute for Computer Science and Control (SZTAKI) Budapest University of Technology and Economics (BME-KJK) Müegyetem rkp. 3 H-1111 Budapest Hungary Budapest Hungary

UAVs are playing an increasingly important role in the field of wilderness rescue by virtue of their flexibility. This paper proposes a fusion of UAV vision technology and satellite image analysis technology for active wildfires detection and road networks extraction of wildfire areas and real-time dynamic escape route planning for people in distress. Firstly, the fire source location and the segmentation of smoke and flames are targeted based on Sentinel 2 satellite imagery. Secondly, the road segmentation and the road condition assessment are performed by D-linkNet and NDVI values in the central area of the fire source by UAV. Finally, the dynamic optimal route planning for humans in real time is performed by the weighted A* algorithm in the road network with the dynamic fire spread model. Taking the Chongqing wildfire on August 24, 2022, as a case study, the results demonstrate that the dynamic escape route planning algorithm can provide an optimal real-time navigation path for humans in the presence of fire through the information fusion of UAVs and satellites.

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