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Dichotomy: Trajectory Planning Algorithm Based on Point Group Distribution 1

6th China High Resolution Earth Observation Conference, CHREOC 2019

作者： Xu, Naiting Yang, Fan Lian, HaiMing Wang, Yi Institute of Electronics Chinese Academy of Sciences SuzhouJiangsu215123 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Beijing University of Science and Technology Beijing China

ISBN: (数字)9789811539473

ISBN: (纸本)9789811539466

The historical navigation trajectory carries rich information about the spatiotemporal patterns of a moving target, as well as its transboundary potentials. To obtain the moving track of a ship or a plane, a large amount of point-to-point data is required via the measurements of satellites or aviation equipments. On the basis of time, we can easily draw a track. However, the distribution of the point-to-point data information provided by each detection unit is irregular due to the inconsistency of the standards of the data units. Some of the regional point groups are densely distributed, while the others may be very sparse. The point density can mess up the trajectory detection and make it not ideal to observe the behavior habits of a target unit. In this study, a dichotomy trajectory planning algorithm was proposed to fix the problem mentioned above. The target area was set as a grid with the points in the grid dichotomized. In that case, the distribution of the targeted point group after dichotomization can perform as similar as possible, and an approximate trajectory line was formed. Such a method considers the change of point density. Results were tested against the performance of the clustering, the random, and the greedy methods, showing that the former had a better adaptability in dealing with different data distribution. In this paper, a novel approximate estimation algorithm based on the density change rate is proposed. Finally, we will compare the performance of our dichotomous method with clustering method, random method, and greedy algorithm through experiments and show the trajectory fitting effect through different distribution data. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Trajectories

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Frequency-Guided Spatial Adaptation for Camouflaged Object Detection

arXiv

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arXiv 2024年

作者： Zhang, Shizhou Kong, Dexuan Xing, Yinghui Lu, Yue Ran, Lingyan Liang, Guoqiang Wang, Hexu Zhang, Yanning National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi’an China The Research & Development Institute Northwestern Polytechnical University Shenzhen China The School of Information and Technology Northwest University Xi’an710127 China Xi’an Key Laboratory of Human-Machine Integration and Control Technology for Intelligent Rehabilitation Xijing University Shaanxi Xi’an710123 China

Camouflaged object detection (COD) aims to segment camouflaged objects which exhibit very similar patterns with the surrounding environment. Recent research works have shown that enhancing the feature representation via the frequency information can greatly alleviate the ambiguity problem between the foreground objects and the background. With the emergence of vision foundation models, like InternImage, Segment Anything Model etc, adapting the pretrained model on COD tasks with a lightweight adapter module shows a novel and promising research direction. Existing adapter modules mainly care about the feature adaptation in the spatial domain. In this paper, we propose a novel frequency-guided spatial adaptation method for COD task. Specifically, we transform the input features of the adapter into frequency domain. By grouping and interacting with frequency components located within non overlapping circles in the spectrogram, different frequency components are dynamically enhanced or weakened, making the intensity of image details and contour features adaptively adjusted. At the same time, the features that are conducive to distinguishing object and background are highlighted, indirectly implying the position and shape of camouflaged object. We conduct extensive experiments on four widely adopted benchmark datasets and the proposed method outperforms 26 state-of-the-art methods with large margins. Code will be released. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

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SDGFormer: An Efficient Convolution Network Structurally Similar to Transformer

SDGFormer: An Efficient Convolution Network Structurally Sim...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Chaohao Wen Xun Gong School of Computing and Artificial Intelligence SWJTU Chengdu China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education China National Engineering Laboratory of Integrated Transportation Big Data Application Technology SWJTU Chengdu China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province SWJTU Chengdu China

Deep Neural Networks (DNN) have achieved extraordinary success in many visual recognition tasks. Visual Transformer (ViT), which is derived from Natural Language Processing (NLP), has achieved state-of-the-art (SOTA) results on many tasks due to its capability of capturing long-range dependencies in visual data. However, Existing ViT models are challenging to deploy on devices due to their massive computational consumption, huge memory overhead, and reliance on large datasets. In this work, we address these issues by replacing some computationally expensive and memory-intensive modules in ViT with standard Convolutional Neural Network (CNN) modules. Firstly, we propose an efficient Self-Attention module called SDG-Attention (SDGA) with linear space and time complexity, and an economical FeedForward Network (FFN) composed of group convolution and shuffle channel (SFFN). Then, we develop a lightweight CNN model with SDGA and SFFN, SDGFormer, which embraces several priors of ViT and is LayerNorm-Free. We evaluate SDGFormer on ImageNet-1K and Mini-ImageNet, and the SDGFormer-S achieves a comparable top-1 accuracy of 77.6% on ImageNet-1K with 9.1M parameters and 1.6 GFlops regimes. Moreover, our SDGFormer-T achieves SOTA performance on Mini-ImageNet with 83.3% accuracy, demonstrating good generalization on small datasets without extra data.

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Privacy-Preserving Federated Learning Framework with Resisting Source Inference Attacks in Smart Healthcare

SSRN

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SSRN 2024年

作者： Chenghe, Dong Xu, Guangquan Zhang, Jianhong Bai, Hongpeng Hao, Peng College of Intelligence and Computing Tianjin University Tianjin300354 China School of Big Data Qingdao Huanghai University Qingdao China Tianjin300350 China Department of Electrical and Computer Engineering North China University of Technology Beijing100144 China Key Laboratory of Intelligent Education Technology Application of Zhejiang Province School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China

Federated Learning (FL) is a distributed machine learning technology that is extensive applications in smart healthcare, since it allows multiple medical institutions to jointly train medical diagnostic models without sharing their local sensitive dataset with others. Despite its benefits, FL is susceptible to source inference attacks and model poisoning. To avoid these issues, we propose a federated learning framework for smart healthcare that preserves privacy and employs certificateless ring signatures to counteract potential attacks. During the collaborative training process of the FL model, certificateless ring signatures are used to obscure the origin of parameter updates, ensuring the anonymity of the parameter sources. This approach has the advantage of being computationally efficient due to no complicated pairing operations. Furthermore, compared with the previous certificateless ring signature schemes, our proposed scheme is more effective in reducing the success rate of source inference attacks, and is efficient in terms of computation and communication overhead. © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Continual Learning for Smart City: A Survey

arXiv

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arXiv 2024年

作者： Yang, Li Luo, Zhipeng Zhang, Shiming Teng, Fei Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Sichuan Chengdu611756 China

With the digitization of modern cities, large data volumes and powerful computational resources facilitate the rapid update of intelligent models deployed in smart cities. Continual learning (CL) is a novel machine learning paradigm that constantly updates models to adapt to changing environments, where the learning tasks, data, and distributions can vary over time. Our survey provides a comprehensive review of continual learning methods that are widely used in smart city development. The content consists of three parts: 1) Methodology-wise. We categorize a large number of basic CL methods and advanced CL frameworks in combination with other learning paradigms including graph learning, spatial-temporal learning, multi-modal learning, and federated learning. 2) application-wise. We present numerous CL applications covering transportation, environment, public health, safety, networks, and associated datasets related to urban computing. 3) Challenges. We discuss current problems and challenges and envision several promising research directions. We believe this survey can help relevant researchers quickly familiarize themselves with the current state of continual learning research used in smart city development and direct them to future research trends. Copyright © 2024, The Authors. All rights reserved.

关键词： Smart city

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Intra-Person Camera Adversarial for Intra-Camera Supervised Person Re-Identification

Intra-Person Camera Adversarial for Intra-Camera Supervised ...

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International Conference on Automatic Face and Gesture Recognition

作者： Ruochen Tang Xun Gong School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

Re-identification (ReID) of individuals across different cameras is a challenging task due to the high-quality large-scale datasets required for the model. Intra-camera supervised (ICS) person re-identification has been proposed to address the high cost of annotating large-scale datasets, but reducing the gap between camera domains remains a major challenge. Current approaches use intra- and inter-camera learning with contrastive learning performed separately in both phases. However, the effect of features from the same person under different cameras on the model during inter-camera learning, and the fine-grained characteristic where the same person can be classified into multiple classes based on the camera labels, still require further research. To address this issue, we propose a Camera-Based Contrastive Learning (CBCL) method that moves features away from their respective cameras and closer to other cameras to reduce domain gaps. We also introduce an Intra-Person Camera Adversarial (IPCA) loss that effectively utilizes fine-grained characteristics of person re-identification and improve IPCA by introducing camera labels to obtain IPCA_2 which achieves better model recognition performance than IPCA alone. Extensive experiments on multiple datasets demonstrate that our method outperforms existing methods and is comparable to fully-supervised methods.

关键词： Costs Scalability Face recognition Contrastive learning Gesture recognition Cameras Robustness

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DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach

arXiv

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arXiv 2024年

作者： Tang, Xin Chen, Qian Weng, Wenjie Liao, Binhan Wang, Jiacheng Cao, Xianbin Li, Xiaohuan Guangxi University Key Laboratory of Intelligent Networking and Scenario System School of Information and Communication Guilin University of Electronic Technology Guilin541004 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore School of Architecture and Transportation Engineering Guilin University of Electronic Technology Guilin541004 China National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology Guangxi Nanning530001 China College of Computing and Data Science Nanyang Technological University 639798 Singapore School of Electronic and Information Engineering The Key Laboratory of Advanced Technology of Near Space Information System Ministry of Industry and Information Technology of China Beihang University Beijing100191 China

Unmanned Aerial Vehicles (UAVs) possess high mobility and flexible deployment capabilities, prompting the development of UAVs for various application scenarios within the Internet of Things (IoT). The unique capabilities of UAVs give rise to increasingly critical and complex tasks in uncertain and potentially harsh environments. The substantial amount of data generated from these applications necessitates processing and analysis through deep neural networks (DNNs). However, UAVs encounter challenges due to their limited computing resources when managing DNN models. This paper presents a joint approach that combines multiple-agent reinforcement learning (MARL) and generative diffusion models (GDM) for assigning DNN tasks to a UAV swarm, aimed at reducing latency from task capture to result output. To address these challenges, we first consider the task size of the target area to be inspected and the shortest flying path as optimization constraints, employing a greedy algorithm to resolve the subproblem with a focus on minimizing the UAV’s flying path and the overall system cost. In the second stage, we introduce a novel DNN task assignment algorithm, termed GDM-MADDPG, which utilizes the reverse denoising process of GDM to replace the actor network in multi-agent deep deterministic policy gradient (MADDPG). This approach generates specific DNN task assignment actions based on agents’ observations in a dynamic environment. Simulation results indicate that our algorithm performs favorably compared to benchmarks in terms of path planning, Age of Information (AoI), energy consumption, and task load balancing. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

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A Multi-Step Speed Prediction Method for Traffic Networks Based on MAF-GCN-Seq2Seq Model

A Multi-Step Speed Prediction Method for Traffic Networks Ba...

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Electronic Information technology and Smart Agriculture (ICEITSA), International Conference on

作者： Yi Long Hongbo Xiao Jianhua Xiao Xiaoning Peng School of Computer Science and Engineering Huaihua University Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Hunan P. R. China

ISBN: (纸本)9781665464024

Accurately predicting the future trend of traffic flow is of great significance to the planning of urban traffic. The great challenge of predicting traffic flow stems from the complexity and uncertainty of traffic flow. To address the complexity of traffic flow, this paper proposes a MAF-GCN $\text{Seq}2$ Seq model. The model uses a moving average filter to filter the time series data, extracts the spatial features between each node through a graph convolutional neural network, and forms a time series matrix for input into the encoder (GRU), and introduces a self-attention mechanism to mine the spatial features. time-domain features for multi-step traffic speed prediction. In order to verify the advanced nature of the model, this paper uses the traffic flow data set in Shenzhen to predict the traffic flow. Through the comparison experiments, it can be seen that the prediction accuracy of the MAF-GCN $\text{Seq}2$ Seq model reaches 90%, which has obvious advantages compared with other models.

关键词： Training Uncertainty Time series analysis Predictive models Feature extraction data models Spatial databases

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Hierarchical Context Modeling Network for Landmark Recognition

Hierarchical Context Modeling Network for Landmark Recogniti...

引用

IEEE International Conference on data Mining (ICDM)

作者： Xing Bao Huan Zheng Zhao Zhang Zhengjun Zha Meng Wang School of Information Science and Technology University of Science and Technology of China Hefei China Aerospace Information Research Institute Suzhou China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China

Landmark recognition stands as a prominent classification challenge within the domain of vision and perception, involving the identification and localization of landmarks in images. However, existing landmark recognition methods often fall short in delivering satisfactory performance. A critical issue lies in the lack of simultaneous exploration of local, regional, and global modeling. Intuitively, the local details, regional features, and global structure within an image, especially of a building, significantly contribute to landmark recognition. To address this issue, we propose a novel approach, termed Hierarchical Context Modeling Network (HCMNet), for landmark recognition. In our methodology, we introduce a Hierarchical Context Modeling Block (HCMB) designed with a triplet-branch structure to model image context in a hierarchical manner. These branches consist of the local branch, which leverages a convolutional layer to capture local details, the regional branch focused on extracting regional features through large-kernel asymmetrical convolutional layers, and the global branch aims at encompassing the broader context by incorporating channel attention. To enhance the modeling capacity, we assemble multiple HCMBs into the HCMNet, creating a hierarchical structure. Comprehensive experiments validate the efficacy of the proposed HCMNet for landmark recognition, showcasing its superior performance compared to current methods. The proposed hierarchical context modeling approach proves instrumental in capturing the intricate features essential for accurate landmark recognition.

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Research on Fast Adaptive Transmission Models for International Inland Port Based on Edge Intelligence

Research on Fast Adaptive Transmission Models for Internatio...

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IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)

作者： Liu Yiwen Zhu Zhirong Tangyan Wen Wenkan Peng Xiaoning Shi Yuanquan School of Computer and Artificial Intelligence Huaihua University Huaihua Hunan P. R. China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing Application in Hunan Province Universities Huaihua Hunan P. R. China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Hunan P. R. China

In this paper, we propose a logistics International Inland Port resource allocation method based on intelligent edge scheduling, and we do a reasonable pre-allocation of logistics resources by building an edge network in the urban system. By using multiple integrated learning models and regional prevalence classification algorithms, the distribution demand of each sub-distribution point is predicted. The proposed method is able to cope with uncertainties such as high distribution volume, variable distribution situations or long transportation distances. We use the HuaiHua International Inland Port as the simulation object, and the simulation results show that the proposed method has the highest efficiency in logistics distribution and is still highly adaptive in emergency situations.

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