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IEEE International Symposium on Broadband Multimedia systems and Broadcasting (BMSB)

作者： Xiangyong Chen Ping Han Yanqing Huang Yi Han Yi Zhong Zhuo Li Zhenhui Yuan Gabriel-Miro Muntean School of Information Engineering Wuhan University of Technology Wuhan China Bigdata Operation and Information Technology Department SAIC GM Wuling Automobile Co. Ltd Liu Zhou China Plan and Operation Department SAIC GM Wuling Automobile Co. Ltd Liu Zhou China Department of Computer and Information Science Northumbria University Newcastle Upon Tyne UK Performance Engineering Lab (PEL) School of Electronic Engineering Dublin City University Dublin Ireland

Image enhancement increases the perceived quality and improves the experience of viewers by processing images that are difficult to see, such as due to low light or overexposure. This is specifically important for night monitoring cameras or for the performance of visual-based night autonomous driving algorithms. This paper proposes a multi-adaptive fusion image enhancement algorithm (MAAF) to adaptively select and fuse Histogram Equalization (HE), Multi-scale Retinex (MSR), and Gamma Correction (GC) in the image frequency domain through Discrete Cosine Transform (DCT). Based on a genetic algorithm, the proposed MAAF combines the advantages of the different methods in terms of lighting enhancement (HE), edge enhancement (MSR), and overexposed image enhancement (GC) to achieve an overall performance optimization. A comprehensive evaluation score (CES) is also proposed in this paper as an overall assessment metric. MAAF was evaluated in terms of multiple metrics, including entropy, average gradient, contrast, and PSNR. Experimental results showed that MAAF obtains the highest CES compared with other algorithms.

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Graph Structure Guided Transformer for Semantic Segmentation

Graph Structure Guided Transformer for Semantic Segmentation

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Luyang Qian Canlong Zhang Zhixin Li Zhiwen Wang School of Computer Science and Engineering Guangxi Normal University Guilin China Guangxi Key Lab of Multi-source Information Mining & Security Guilin China School of Computer Science and Technology Guangxi University of Science and Technology Liuzhou China

Segmentation is an essential operation of image processing, and utilizing long-range context information is the key for pixel-wise prediction tasks such as semantic segmentation. Convolutional Neural Networks (CNNs) are good at modeling local relationships through convolutional operations, but they are often inefficient in capturing global relationships between distant regions and require stacking multiple convolutional lay-ers. Utilizing the advantages of transformer in modeling long-range dependency, this paper proposes a novel Graph Structure Guided Transformer (GSGT) to realize semantic segmentation. Different from the previous methods that hard-divide the image in a regular grid manner, our graph projection method maps the two-dimensional feature map into a graph structure according to certain semantic relevance, so as to meet the data structure form required by the transformer. Meanwhile, to fully utilize the graph structure information, we also propose a graph embedding attention module, which utilizes the local topology of the graph structure to complement the global context of transformer. Moreover, GSGT is easy to be incorporated with various CNN backbones and transformer model variants to significantly improve the segmentation accuracy and convergence speed. Experiments on Cityscapes, VOC and ADE20K datasets demonstrate that the proposed method performs well in semantic seamentation task.

关键词： Semantic segmentation Semantics Stacking Network architecture Transformers Data structures Robustness

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DC-MOT: Motion Deblurring and Compensation for Multi-Object Tracking in UAV Videos

DC-MOT: Motion Deblurring and Compensation for Multi-Object ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Song Cheng Meibao Yao Xueming Xiao Intelligent Robotics Lab (IRL) School of Artificial Intelligence Jilin University Changchun China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence Ministry of Education China CVIR lab Changchun University of Science and Technology China Key Lab of Optoelectronic Measurement and Optical Information Transmission Technology Ministry of Education China

In this paper, we propose a multi-object tracking framework for videos captured by UAVs, considering motion imperfection in the following two aspects: 1) motion blurring of objects due to high-speed motion of the UAV and the objects, deteriorating the performance of the detector; 2) motion coupling of the global movement of the UAV camera with the object motion, resulting in the nonlinearity of objects trajectories in adjacent frames and further more difficult to predict. For motion blurring, this paper proposes a hybrid deblurring module that deals with the blurred frames while retaining the clear frames, trading off between video tracking performance and spatio-temporal consistency. For motion coupling, we proposed a motion compensation module to align adjacent frames by feature matching, and the corrected target position is obtained in the next frame to alleviate the interference of camera movement with tracking. We evaluate the proposed methods on VisDrone dataset and validate that our framework achieves new state-of-the-art performance on UAV-based MOT systems.

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Pregnane X receptor attenuates gold nanoparticles’ toxicity through accelerating zebrafish embryo hatching

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Aquatic Toxicology 2025年 284卷 107399页

作者： Hu, Jia Tian, Jingjing Liu, Pai Zhang, Qian Li, Ningyao Yin, Jian School of Basic Medical Sciences Medical College Soochow University Jiangsu Suzhou 215123 China CAS Key Lab of Bio-Medical Diagnostics Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences Jiangsu Suzhou 215163 China University of Science and Technology of China Hefei 230026 China Hangzhou Science and Technology Information Institute Hangzhou 310006 China

It is well known that fish embryos are vulnerable to waterborne nanoparticles (NPs), with delayed hatching being the most common and sensitive endpoint. Up-regulation of hatching enzymes has been believed to be an important detoxification mechanism for NPs, but the inner mechanism for such phenomena has been seldom investigated. This study aimed to investigate the role of pregnane X receptor (Pxr) in maintaining the robustness of embryo hatching after treatment with gold nanoparticles (AuNPs, 4 and 82 nm). For this purpose, embryos from mating of 6-month-old wild-type (WT) AB strain zebrafish (Danio rerio, 3∼4-cm-length) were treated with AuNPs since 4 h post-fertilization (hpf). It was found that both AuNPs significantly inhibited embryo hatching after 52-h treatment, with Au-4 being more toxic at the same mass concentrations. At non-toxic concentrations and median effective concentrations (EC₅₀) of delayed hatching, both AuNPs induced the mRNA expression of HEs and Pxr at 48 hpf, and Au-4 seemed to be more effective. The induction extents of HEs by AuNPs decreased when Pxr was knocked out or inhibited, indicating the role of Pxr in such process. Additionally, knockout/inhibition of Pxr significantly delayed the hatching of embryos at 56 hpf, and activation of Pxr accelerated the process at moderate concentrations. Such phenomena correlated well with the alterations in the mRNA expression and activities of HEs, indicating a fact that AuNPs activated Pxr and up-regulated HEs, which helped the detoxification of AuNPs. RNA-sequencing analysis of WT and pxr-deficient embryos at 24 hpf confirmed the alteration of he1.1&1.2. In addition, Pxr influenced mRNA encoding muscle development (muscle system process and striated muscle tissue development) and energy metabolism (carbohydrate metabolic process and ATP metabolic process), which were related to the motility of embryos and determined the hatching speed. Such function was confirmed by the reduced locomotor a

关键词： Gold nanoparticles Hatching enzymes Pregnane X receptor Zebrafish embryo

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Motion-Guided Spatial Perception for Enhanced Multi-Object Tracking: A Hierarchical Framework

SSRN

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SSRN 2023年

作者： Zheng, Yujin Qi, Hang Li, Lei Li, Shan Huang, Yan He, Chu Wang, Dingwen School of Computer Science Wuhan University Hubei Wuhan430000 China The Beijing Electro-Mechanical Engineering Institute Beijing100074 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China Zmvision Technology Hubei Wuhan430000 China School of Electronic Information Wuhan University Hubei Wuhan430000 China

Multi-object tracking (MOT) aims to associate objects of interest across sequences, which has rapidly progressed with the development of object detection and optimal matching techniques. However, spatio-temporal modeling complex variations, such as camera motion and occlusion in crowded scenes, have been relatively underexplored in recent years. In this work, a temporal concern through motion-guided feature aggregation module is proposed to capture multi-frame variations for instances. Additionally,an occlusion-aware head and hierarchical spatial association are proposed to handle the challenges of occlusion through spatial perception. The above approach, namely MSPNet addresses the challenges posed by occlusion effectively, resulting in improved tracking performance. Evaluation on benchmark datasets shows that MSPNet achieves the state-of-the-art performance. Specifically, MSPNet achieves 75.4% MOTA, and 72.4% IDF1 on the test set of MOT16, 74.6% MOTA, and 71.9% IDF1 on MOT17, 75.9% MOTA, and 74.4% IDF1 on MOT20, respectively. © 2023, The Authors. All rights reserved.

关键词： Object detection

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Dynamic Power Distribution Controlling for Directional Chargers

Dynamic Power Distribution Controlling for Directional Charg...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Yuzhuo Ma Dié Wu Jing Gao Wen Sun Jilin Yang Tang Liu College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Lab Sichuan Normal University Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Cyberspace Security Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Recently, deploying static chargers to construct timely and robust Wireless Rechargeable Sensor Networks (WRSNs) has become an important research issue for solving the limited energy problem of wireless sensor networks. However, the established fixed power distribution lacks flexibility in response to dynamic charging requests from sensors and may render some sensors to be continuously impacted by destructive wave interference. This results in a gap between energy supply and practical demand, making the charging process less efficient. In this paper, we focus on the real-time sensor charging requests and formulate a dynamic power disTributIon controlling for Directional chargErs (TIDE) problem to maximize the overall charging utility. To solve the problem, we first build a charging model for directional chargers while considering wave interference and extract the candidate charging orientations from the continuous search space. Then we propose the neighbor set division method to narrow the scope of calculation. Finally, we design a dynamic power distribution controlling algorithm to update the neighbor sets timely and select optimal orientations for chargers. Our experimental results demonstrate the effectiveness and efficiency of the proposed scheme, it outperforms the comparison algorithms by 142.62% on average.

关键词： Wireless communication Wireless sensor networks Heuristic algorithms Computational modeling Power distribution Interference Search problems

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SCSQ-MDD: A Detection Approach for Multi-Style Strokes of Chinese Characters

SSRN

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SSRN 2023年

作者： Zhou, Tian Xie, Wu Zhang, Huimin Fan, Yong Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China School of Mechanical and Electrical Engineering Guilin University of Electronic Science and Technology Guilin China

In the Chinese character writing task of the robotic arms, the stroke category and position information should be extracted by object detection. The detection algorithms based on predefined anchor frames have difficulty in resolving the differences among many different styles of Chinese character strokes. While the deformable detection transformer (deformable DETR) algorithms without predefined anchor frames result in some invalid sampling points having no contribution to the feature update of the current reference point due to the random sampling of sampling points in the deformable attention module. These processes cause the effectiveness of correlation calculations between reference points in Chinese strokes and their surrounding sampled points is limited. So that the speed of vector learning stroke features in the detection head is reduced. In view of this problem, a new detection method of multi-style strokes of Chinese characters via SCSQ-MDD (Simple Conditional Spatial Query Mask Deformable DETR) is proposed in this paper. Firstly, a mask prediction layer is jointly determined using the shallow feature map of the Chinese character image and the query vector of the transformer encoder, which is used to filter the points with actual contribution and resample the points without contribution, so that the randomness of correlation calculation among reference points is solved. Secondly, by separating the content query and spatial query of the transformer deocder, the content embedding and spatial embedding can be separately focused on when cross-attention computations are performed. Thus the dependence of the prediction task on the content embedding is relaxed and the training process is simplified. Finally, the detection model without predefined anchor frames based on deformable DETR called SCSQ-MDD is constructed using the mask mechanism and the simple conditional spatial query mechanism, and trained and validated on a multi-style Chinese character stroke dataset

关键词： Object detection

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Research and test of 10kv zno thermal stability based on finite element analysis 9

Research and test of 10kv zno thermal stability based on fin...

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9th China International Conference on Electricity Distribution, CICED 2021

作者： Cheng, Jinhao Jiang, Zhenlong Zou, Hao Deng, Guiliang School of Electrical Information Engineering Changsha University of Science Technology Changsha China State Key Lab. of Disaster Prev. Reduction for Power Grid Transmission and Distribution Equipment Changsha China

ISBN: (纸本)9781728192291

The zinc oxide surge arrester is an important barrier to protect the power system from lightning, and maintaining its good thermal stability is of great significance for its safe operation. In this paper, the model of lightning arrester is established by finite element, the simulation calculation of the electrical-thermal characteristics under the impact of lightning current and large current is studied, the heating and heat dissipation characteristics of the arrester under different conditions are obtained, and the arrester is found in different environments Maximum operating temperature at temperature. And through the joint pressurization test, the temperature of the resistor sheet when the arrester thermally collapses under actual conditions is found, and the test results are in agreement with the simulation results. The research results show that if the zinc oxide resistor has a large amount of heat, poor heat dissipation from the resistor, and uneven electric field distribution, the arrester will reach the limit operating temperature. When the limit temperature is exceeded, the arrester will soon fall into thermal collapse and thermal fusion will occur. Wear, crack, and explode. Therefore, effective measures must be taken to improve the thermal stability of zinc oxide surge arresters. © 2021 IEEE.

关键词： Finite element method

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Accurate landing system of electrical power plant inspection UAV based on vision guidance 4

Accurate landing system of electrical power plant inspection...

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4th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2021

作者： Huang, Qimeng Shao, Yanhua Mei, Yanying Chang, Zhiyuan Zhong, Liangtao Zhou, Zhiyang Chu, Hongyu Zhan, Huayi Huang, Mingqi Southwest University of Science and Technology School of Information Engineering Mianyang China Changhong AI Lab Mianyang China China Aerodynamics Research and Development Center Rotor Aerodynamics Key Laboratory Mianyang China

ISBN: (纸本)9781665413220

Due to the complex terrain of the electrical power plant, it is difficult for the inspection UAV to distinguish the landing site from the environment and achieve accurate landing in the process of inspection. Aiming at the above problems, this paper designs an accurate landing system based on vision guidance for the inspection UAV. Firstly, the black circular block is designed as the landing sign. Then, vision sensors and embedded processing units on the UAV are utilized to acquire images of the ground in real time, and the image filtering and the adaptive threshold segmentation are used to separate the landing sign from the background. Next, the position of the landing sign is detected by the ellipse detection algorithm, and the UAV is guided to land according to the position of landing signs. Finally, the autonomous landing experiments under different scenarios are carried out, the experimental results show that the system proposed in this paper has stable and reliable performance. Among them, the maximum landing deviation of the system is 11.5 cm, and the autonomous landing time is stable within one minute, which meets the requirements of specific projects. © 2021 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Mixture of Experts for Network Optimization: A Large Language Model-enabled Approach

arXiv

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arXiv 2024年

作者： Du, Hongyang Liu, Guangyuan Lin, Yijing Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Optimizing various wireless user tasks poses a significant challenge for networking systems because of the expanding range of user requirements. Despite advancements in Deep Reinforcement Learning (DRL), the need for customized optimization tasks for individual users complicates developing and applying numerous DRL models, leading to substantial computation resource and energy consumption and can lead to inconsistent outcomes. To address this issue, we propose a novel approach utilizing a Mixture of Experts (MoE) framework, augmented with Large Language Models (LLMs), to analyze user objectives and constraints effectively, select specialized DRL experts, and weigh each decision from the participating experts. Specifically, we develop a gate network to oversee the expert models, allowing a collective of experts to tackle a wide array of new tasks. Furthermore, we innovatively substitute the traditional gate network with an LLM, leveraging its advanced reasoning capabilities to manage expert model selection for joint decisions. Our proposed method reduces the need to train new DRL models for each unique optimization problem, decreasing energy consumption and AI model implementation costs. The LLM-enabled MoE approach is validated through a general maze navigation task and a specific network service provider utility maximization task, demonstrating its effectiveness and practical applicability in optimizing complex networking systems. © 2024, CC BY.

关键词： Reinforcement learning

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