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Multi-UAV Collaborative Mission Planning Method for Self-Organized Sensor Data Acquisition

Computers, Materials & Continua 2024年第10期81卷 1529-1563页

作者： Shijie Yang Jiateng Yuan Zhipeng Zhang Zhibo Chen Hanchao Zhang Xiaohui Cui School of Information Science and Technology Beijing Forestry UniversityBeijing100083China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing100083China State Key Laboratory of Efficient Production of Forest Resources Beijing100083China Division of Biostatistics Department of Population HeathGrossman School of MedicineNew York UniversityNew YorkNY 10016USA

In recent years,sensor technology has been widely used in the defense and control of sensitive areas in cities,or in various scenarios such as early warning of forest fires,monitoring of forest pests and diseases,and protection of endangered *** sensors to collect data and then utilizing unmanned aerial vehicle(UAV)to collect the data stored in the sensors has replaced traditional manual data collection as the dominant *** current strategies for efficient data collection in above scenarios are still imperfect,and the low quality of the collected data and the excessive energy consumed by UAV flights are still the main problems faced in data *** regards this,this paper proposes a multi-UAV mission planning method for self-organized sensor data acquisition by comprehensively utilizing the techniques of self-organized sensor clustering,multi-UAV mission area allocation,and sub-area data acquisition scheme *** improvedα-hop clustering method utilizes the average transmission distance to reduce the size of the collection sensors,and the K-Dimensional method is used to form a multi-UAV cooperative workspace,and then,the genetic algorithm is used to trade-off the speed with the age of information(AoI)of the collected information and the energy consumption to form the multi-UAV data collection operation *** combined optimization scheme in paper improves the performance by 95.56%and 58.21%,respectively,compared to the traditional baseline *** order to verify the excellent generalization and applicability of the proposed method in real scenarios,the simulation test is conducted by introducing the digital elevation model data of the real terrain,and the results show that the relative error values of the proposed method and the performance test of the actual flight of the UAV are within the error interval of±10%.Then,the advantages and disadvantages of the present method with the existing mainstream schemes are tested,and

关键词： Unmanned aerial vehicle sensor self-organization path planning multi-UAV task assignment

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Scalable and Fast Electrophoresis-Accelerated Assembly of Chiroptical Carbon Fibers With Multi Functions

SSRN

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SSRN 2025年

作者： Jiao, Haixing Song, Baiqi Li, Fa Tang, Yuanhan Zhang, Na Zhang, Xiaofang Xu, Weilin State Key Laboratory of New Textile Materials and Advanced Processing Wuhan Textile University Wuhan430200 China School of Transportation Science and Engineering Harbin Institute of Technology Harbin150090 China

Colorful carbon fibers (CFs) with combinations of structural robustness, environmental stability, and advanced functions are very promising in the cutting-edge fields. However, the efficient coloration simultaneously with the functionalization for CFs in a fast, scalable, and sustainable manner remains a grand challenge. In this work, we develop large-area chiroptical CFs with long-ranged chirality decoration via the electrophoresis-accelerated self-assembly of renewable cellulose nanocrystals (CNCs).That leads a cholesteric photonic "cast" conformally and tightly coated on CFs surface in a seamlessly tessellated way to bring chiroptical functionalities and mechanical adaption. The resulting chiroptical CFs not only comprise strong optical properties with controllable and brilliant colors, but also demonstrate impressive opt-mechanical combination against external mechanical forces, hydration effect, and extreme environments. More importantly, chiroptical CFs with customized patterns on the surface can be used as programmable infrared display for encryption and anti-counterfeiting. Those exciting findings extend the potentials of CF materials towards optical encryption, wearable device display, defense, and aerospace special applications. © 2025, The Authors. All rights reserved.

关键词： Cellulose nanocrystals

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REPAIR: Rank Correlation and Noisy Pair Half-replacing with Memory for Noisy Correspondence

arXiv

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arXiv 2024年

作者： Zheng, Ruochen Hong, Jiahao Gao, Changxin Sang, Nong National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology China

The presence of noise in acquired data invariably leads to performance degradation in cross-modal matching. Unfortunately, obtaining precise annotations in the multimodal field is expensive, which has prompted some methods to tackle the mismatched data pair issue in cross-modal matching contexts, termed as noisy correspondence. However, most of these existing noisy correspondence methods exhibit the following limitations: a) the problem of self-reinforcing error accumulation, and b) improper handling of noisy data pair. To tackle the two problems, we propose a generalized framework termed as Rank corrElation and noisy Pair hAlf-replacing wIth memoRy (REPAIR), which benefits from maintaining a memory bank for features of matched pairs. Specifically, we calculate the distances between the features in the memory bank and those of the target pair for each respective modality, and use the rank correlation of these two sets of distances to estimate the soft correspondence label of the target pair. Estimating soft correspondence based on memory bank features rather than using a similarity network can avoid the accumulation of errors due to incorrect network identifications. For pairs that are completely mismatched, REPAIR searches the memory bank for the most matching feature to replace one feature of one modality, instead of using the original pair directly or merely discarding the mismatched pair. We conduct experiments on three cross-modal datasets, i.e., Flickr30K, MS-COCO, and CC152K, proving the effectiveness and robustness of our REPAIR on synthetic and real-world noise. © 2024, CC BY.

关键词： Data handling

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Study on the Influence of Titanium -Coated Diamond Content on Microstructure and Wear Resistance of Ni60a Laser Cladding Coating

SSRN

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SSRN 2023年

作者： Yu, Jinyu Huang, Xu Jiang, Jibin Huang, WenTai Fujian Provincial Key Laboratory of Intelligent Processing Technology and Equipment China Fujian University of Technology China

In this paper, the single factor variable method is used to analyze the influence of Titanium diamond-plated on nickel based coatings on titanium alloys. The thermal stress change, phase composition, element distribution, microhardness and wear resistance mechanism of the coating were discussed;The cladding layer is mainly composed of diamond, TiC, Cr23C6, NiTi2 and other phases;When the content of Ti coated diamond is 4%, The grain size is significantly reduced;The microhardness of cladding layer reaches 68 HRC. The cladding shows little wear,the wear coefficient is 0.21, and the wear volume of friction pair(ZrO2 ball) is 1.29*10-4cm3;The results show that adding 4% Ti coated diamond to the coating can reduce the crack sensitivity between the coating and the substrate, and the nickel based diamond coating with good performance can be prepared. © 2023, The Authors. All rights reserved.

关键词： Wear resistance

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Multi-Task Affinity Propagation Based Natural Image Matting

Multi-Task Affinity Propagation Based Natural Image Matting

引用

IEEE International Conference on Image processing

作者： Renkai Zhang Nong Sang School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Image matting, aiming to accurately extract foreground objects by estimating their opacity against the background, has made remarkable progress through deep-learning approaches. Nevertheless, the majority of these methods require a user-defined auxiliary input, such as a trimap, which limits their applications in real-world scenarios. There are many auxiliary input-free methods that have been proposed by now, and some of them adopt a multi-task learning framework that includes a shared encoder and two separate decoders. However, these methods lack interactions between the two decoders, or interactions are implemented through simple summation or concatenation. Unfortunately, the integration of different features may cause negative transfer and limit the model performance due to the invisible information transmission process. To address the issue, we introduce the Pattern-Affinitive Propagation Module (PAP) to explicitly model cross-task propagation and task-specific propagation. Furthermore, image matting not only requires high-resolution detail features, but also semantic features. However, current CNN-based methods have limited receptive fields, making it challenging to capture global semantic features. Therefore, we design a module that integrates Dilated Convolution and Spectral Transformer (DSM), which can effectively capture global features and enhance global-local feature fusion. Extensive experiments on AM-2k and P3M-10k datasets demonstrate the superiority of our method.

关键词： Fuses Computational modeling Semantics Urban areas Information processing Multitasking Feature extraction

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M3GPT: an advanced multimodal, multitask framework for motion comprehension and generation 24

M3GPT: an advanced multimodal, multitask framework for motio...

引用

Proceedings of the 38th International Conference on Neural Information processing Systems

作者： Mingshuang Luo Ruibing Hou Zhuo Li Hong Chang Zimo Liu Yaowei Wang Shiguang Shan Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS China and Peng Cheng Laboratory China and University of Chinese Academy of Sciences China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS China WeChat Tencent Inc Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS China and University of Chinese Academy of Sciences China Peng Cheng Laboratory China Peng Cheng Laboratory China and Harbin Institute of Technology Shenzhen

ISBN: (纸本)9798331314385

This paper presents M3 GPT, an advanced Multimodal, Multitask framework for Motion comprehension and generation. M3 GPT operates on three fundamental principles. The first focuses on creating a unified representation space for various motion-relevant modalities. We employ discrete vector quantization for multimodal conditional signals, such as text, music and motion/dance, enabling seamless integration into a large language model (LLM) with a single vocabulary. The second involves modeling motion generation directly in the raw motion space. This strategy circumvents the information loss associated with a discrete tokenizer, resulting in more detailed and comprehensive motion generation. Third, M3 GPT learns to model the connections and synergies among various motion-relevant tasks. Text, the most familiar and well-understood modality for LLMs, is utilized as a bridge to establish connections between different motion tasks, facilitating mutual reinforcement. To our knowledge, M3 GPT is the first model capable of comprehending and generating motions based on multiple signals. Extensive experiments highlight M3GPT's superior performance across various motion-relevant tasks and its powerful zero-shot generalization capabilities for extremely challenging tasks. Project page: https://***/M3GPT/.

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion 38

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent D...

引用

38th Conference on Neural Information processing Systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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Research on Image Description Generation Method Based on G-AoANet 5

Research on Image Description Generation Method Based on G-A...

引用

5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Qiao, Pingan Shen, Ruixue Li, Yuan Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781450396899

Most of the image description generation methods in the attention-based encoder-decoder framework extract local features from images. Despite the relatively high semantic level of local features, it still has two problems to be solved, one is object loss, where some important objects may be lost when generating image descriptions, and the other is prediction error, as an object may be identified in the wrong class. In this paper, a G-AoANet model is proposed to solve the above problems. The model uses an attention mechanism to combine global features with local features. In this way, our model can selectively focus on both object and contextual information, improving the quality of the generated descriptions. Experimental results show that the model improves the initially reported best CIDEr-D and SPICE scores on the MS COCO dataset by 9.3% and 5.1% respectively. © 2022 ACM.

关键词： Semantics

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SCAPE: A Simple and Strong Category-Agnostic Pose Estimator

arXiv

引用

arXiv 2024年

作者： Liang, Yujia Ye, Zixuan Liu, Wenze Lu, Hao National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology China

Category-Agnostic Pose Estimation (CAPE) aims to localize keypoints on an object of any category given few exemplars in an in-context manner. Prior arts involve sophisticated designs, e.g., sundry modules for similarity calculation and a two-stage framework, or takes in extra heatmap generation and supervision. We notice that CAPE is essentially a task about feature matching, which can be solved within the attention process. Therefore we first streamline the architecture into a simple baseline consisting of several pure self-attention layers and an MLP regression head-this simplification means that one only needs to consider the attention quality to boost the performance of CAPE. Towards an effective attention process for CAPE, we further introduce two key modules: i) a global keypoint feature perceptor to inject global semantic information into support keypoints, and ii) a keypoint attention refiner to enhance inter-node correlation between keypoints. They jointly form a Simple and strong Category-Agnostic Pose Estimator (SCAPE). Experimental results show that SCAPE outperforms prior arts by 2.2 and 1.3 PCK under 1-shot and 5-shot settings with faster inference speed and lighter model capacity, excelling in both accuracy and efficiency. Code and models are available at ***/tiny-smart/SCAPE. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Prediction of GDP Carbon Emission Based on Grey Model and Neural Network 4th

Prediction of GDP Carbon Emission Based on Grey Model and Ne...

引用

4th EAI International Conference on Robotic Sensor Networks, 2020

作者： Zhang, Feng Zhao, Huihuang Ramasamy, Manimaran College of Computer Science and Technology Hengyang Normal University Hengyang China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang China

ISBN: (纸本)9783030704506

In order to improve the prediction accuracy of Gross Domestic Product (GDP) carbon emission, combining the advantages of grey prediction and neural network, a discrete grey neural network prediction model named DGMBP is established. This model not only has the advantages of less data sample and larger development coefficient range of GM 1,1 model but also combines the advantages of neural network to deal with nonlinear data sample. Taking the GDP carbon emission data of China, India, the United States and the European Union as an example, the prediction results obtained are stable, and the accuracy of the prediction data is over 95%, which is much higher than that obtained by the linear regression equation and the logical regression equation. Finally, the carbon emission of GDP in 2020–2030 is predicted by using this model. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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