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7th International Conference on Mechatronics, robotics and Automation

作者： Yang, Yi Yi, Guohong Wuhan Inst Technol Hubei Key Lab Intelligent Robot Wuhan Peoples R China

ISBN: (纸本)9798350352481;9798350352474

With the rapid development of the Internet, explosive access requests pose significant challenges to server performance and capacity. Considering the inefficiencies of current load balancing algorithms, such as decreased system efficiency at lower loads and suboptimal allocation efficiency at higher loads, this paper proposes a dynamic load balancing algorithm based on the Nginx server and the artificial bee colony algorithm. Through the artificial bee colony algorithm, the threshold for the conversion between static and dynamic load balancing is calculated. If the server load is below this threshold, a static algorithm is employed;otherwise, a dynamic algorithm is used. This algorithm utilizes server performance parameters such as CPU performance, memory performance, disk I/O, and network bandwidth as evaluation metrics. Through comparative experiments, the algorithm's effectiveness is demonstrated in experimental environments, showing superior load balancing effects compared to weighted round-robin and least connection algorithms, effectively improving average server response time and actual concurrent connection numbers.

关键词： Server Performance Threshold Load Balancing Artificial Bee Colony Algorithm

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Reconstructing visible-near-infrared hyperspectral images via liquid crystal based optoelectronic hybrid neural network

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OPTICS AND LASER TECHNOLOGY 2025年 184卷

作者： Li, Shiqi Li, Hui Li, Tian Song, Xiaoyue Wu, Yuntao Wuhan Inst Technol Sch Comp Sci & Engn Wuhan 430205 Peoples R China Hubei Key Lab Intelligent Robot Wuhan 430205 Peoples R China

We introduce an Optoelectronic Hybrid Neural Network (OHNN) that integrates software and hardware optimizations to enable the simultaneous reconstruction of visible-near-infrared (VIS-NIR) hyperspectral images. The network employs a linear polarizer (LP) and liquid crystal (LC) microlens array (MLA). We harness the polarization and electrically controlled tunable properties of LC-MLA in conjunction with digital optics technology to construct a software-defined dispersion element digital prism (DP). The OHNN can reconstruct VIS-NIR hyperspectral images, ranging from 400 nm to 900 nm, from limited datasets with a spectral accuracy of 10 nm. The reconstructed hyperspectral images boast a Peak signal-to-noise ratio of approximately 31 dB, marking a 3.2 % improvement over existing state-of-the-art methods.

关键词： Visible-near-infrared hyperspectral reconstruction Optoelectronic Hybrid Neural Network Digital prism Liquid crystal microlens array

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TripleA: An Unsupervised Domain Adaptation Framework for Nighttime VRU Detection

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION SYSTEMS 2025年第6期26卷 8320-8336页

作者： Wang, Yuankun Shao, Zhenfeng Wang, Jiaming Wang, Yu Ding, Yulin Cheng, Gui Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430079 Peoples R China Wuhan Inst Technol Hubei Key Lab Intelligent Robot Wuhan 430200 Peoples R China

Detecting vulnerable road users (VRUs) at night presents significant challenges. Numerous methods rely heavily on annotations, yet the low visibility of nighttime images poses difficulties for labeling. To obviate the need for nighttime annotations, unsupervised domain adaptation manifests as a viable solution. However, existing approaches primarily focus on semantic-level domain gaps, often overlooking pixel-level discrepancies caused by inherent degradations in the nighttime domain. These degradations can impair machine vision and limit detection performance. In this paper, we propose TripleA, an unsupervised domain adaptation framework tailored for nighttime VRU detection. TripleA includes triple alignment. First, it aligns daytime and nighttime images to generate synthetic nighttime images, which are then enhanced for illumination and noise. To remove noise, we introduce an illumination difference-aware denoising network, incorporating a novel pseudo-supervised attention to achieve pixel-wise noise distribution alignment. This alignment is driven by pseudo-ground truth generated through a carefully designed exchange-recombination strategy, facilitating self-supervised training of the denoising network. Additionally, we introduce degradation alignment to ensure domain-invariant degradation encoding, which enhances the network's robustness for real-world nighttime images. Extensive experiments demonstrate the effectiveness of our framework for nighttime VRU detection, all without the need for annotated nighttime data.

关键词： Lighting Noise Degradation Noise reduction Training Image enhancement Annotations Detectors Atmospheric modeling Roads Vulnerable road user detection unsupervised domain adaptation image enhancement low-light denoising

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Image Enhancement via Associated Perturbation Removal and Texture Reconstruction Learning

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2253-2269页

作者： Kui Jiang Ruoxi Wang Yi Xiao Junjun Jiang Xin Xu Tao Lu IEEE the School of Computer Science and Technology Harbin Institute of Technology Zhengzhou Research Institute Harbin Institute of Technology the School of Artificial Intelligence Jianghan University the School of Geodesy and Geomatics Wuhan University the Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology the School of Computer Science and Engineering and also with the Hubei Province Key Laboratory of Intelligent Robot Wuhan Institute of Technology

Degradation under challenging conditions such as rain, haze, and low light not only diminishes content visibility, but also results in additional degradation side effects, including detail occlusion and color distortion. However, current technologies have barely explored the correlation between perturbation removal and background restoration, consequently struggling to generate high-naturalness content in challenging scenarios. In this paper, we rethink the image enhancement task from the perspective of joint optimization: Perturbation removal and texture reconstruction. To this end, we advise an efficient yet effective image enhancement model, termed the perturbation-guided texture reconstruction network(PerTeRNet). It contains two subnetworks designed for the perturbation elimination and texture reconstruction tasks, respectively. To facilitate texture recovery,we develop a novel perturbation-guided texture enhancement module(PerTEM) to connect these two tasks, where informative background features are extracted from the input with the guidance of predicted perturbation priors. To alleviate the learning burden and computational cost, we suggest performing perturbation removal in a sub-space and exploiting super-resolution to infer high-frequency background details. Our PerTeRNet has demonstrated significant superiority over typical methods in both quantitative and qualitative measures, as evidenced by extensive experimental results on popular image enhancement and joint detection tasks. The source code is available at https://***/kuijiang94/PerTeRNet.

关键词： Association learning attention mechanism image enhancement perturbation modeling

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A TWO-STAGE DEHAZING FRAMEWORK BASED ON INVERTED IMAGE CURVE-ENHANCEMENT 49

A TWO-STAGE DEHAZING FRAMEWORK BASED ON INVERTED IMAGE CURVE...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Luo, Hongwei Liu, Wei Chen, Cheng Wuhan Inst Technol Hubei Key Lab Intelligent Robot Wuhan 430205 Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Due to relying on accurate estimates of the atmospheric scattering parameters for atmospheric scattering models(ASM), existing haze removal methods suffer from some drawbacks, such as imbalanced contrast and missing details. To address the above issues, this paper proposes a two-stage dehazing network called CEDhazeNet. It consists of a contrast enhancement module (CE) and a texture detail restoration module (TDR). In CE, we introduce a novel curve enhancement dehazing model to tackle the problem of contrast imbalance. Specifically, we observe that inverting the hazy image can accentuate all the regions obscured by haze, and by enhancing the exposure of the inverted image, we can significantly reduce the impact of haze on image contrast. In TDR, we construct a multi-scale information distillation network, which use more effective information, such as edges and spots to recover the texture details of hazy image. Extensive experiments on both synthetic and real world hazy image datasets demonstrate that CEDhazeNet outperforms state-of-the-art haze removal methods in terms of quantitative accuracy and subjective visual quality.

关键词： Image Dehazing Curve-enhanced Model Inverted Image

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Flood Peak Prediction of Three Gorges Reservoir in China: Evidence from Artificial Intelligence and Observation Data

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JOURNAL OF HYDROLOGIC ENGINEERING 2025年第3期30卷

作者： Wang, Xiaopeng Hua, Hongpeng Meng, Fanwei Wu, Biqiong Cao, Hui Tang, Zhengyang China Three Gorges Univ Yichang Key Lab Robot & Intelligent Syst Yichang 443002 Hubei Peoples R China China Univ Min & Technol Sch Resources & Geosci Xuzhou 221116 Peoples R China China Yangtze Power Co Ltd Hubei Key Lab Intelligent Yangtze & Hydroelect Sci Yichang 443000 Hubei Peoples R China

The Three Gorges Project in China is the world's largest water conservancy hub, located in the middle reaches of the Yangtze River in central China. After the storage of water in the Three Gorges Reservoir, the artificial lakes formed between the Three Gorges Reservoir area change frequently with the fluctuation of the reservoir water level. The size of the flood peak seriously affects the safety and regulation of the Three Gorges Dam. In this paper, based on the Three Gorges Reservoir interval observation data, a method of adaptive segmentation of rainfall-runoff events in time periods is proposed, and an artificial intelligence prediction model for the Three Gorges Reservoir interarea flooding is established on the basis of hydrological principles using data mining and machine learning methods. Nonlinear partial least squares regression and random forest are two models used and supported by later observations. Comparing the simulation results of the model with the hydrological observation records of the Three Gorges Reservoir, it is found that the model has its own advantages for different prediction objects. The results show that the nonlinear partial least squares regression model is suitable for flood prediction with high rainfall, while the random forest model can better predict the flood prediction process with low rainfall and a complex flood process.

关键词： Flood forecast Data mining Machine learning Rainfall-runoff analysis Artificial intelligence predictive models Three Gorges Reservoir

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Multi-Level Adaptive Attention Fusion Network for Infrared and Visible Image Fusion

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 366-370页

作者： Hu, Ziming Kong, Quan Liao, Qing Wuhan Inst Technol Hubei Key Lab Opt Informat & Pattern Recognit Wuhan 430205 Peoples R China Wuhan Inst Technol Hubei Key Lab Opt Informat & Pattern Recognit Wuhan 430205 Peoples R China Wuhan Inst Technol Hubei Key Lab Intelligent Robot Wuhan 430205 Peoples R China

Infrared and visible image fusion involves integrating complementary or critical information extracted from different source images into one image. Due to the significant differences between the two modality features and those across different scales, traditional fusion strategies, such as addition or concatenation, often result in information redundancy or the degradation of crucial information. This letter proposes a multi-level adaptive attention fusion network to adaptively fuse features extracted from different sources. Specifically, we introduced an Adaptive Scale Attention Fusion (ASAF) module that uses a soft selection mechanism to assess the relative importance of different modality features at the same scale and assign corresponding fusion weights. Additionally, a guided upsampling layer is utilized to integrate shallow and deep feature information at different scales in the multi-scale structure. Qualitative and quantitative results on public datasets validate the superior performance of our approach in both visual effects and quantitative metrics.

关键词： Feature extraction Convolution Fuses Image fusion Context modeling Adaptation models Adaptive systems Transforms Optical signal processing Optical imaging Adaptive fusion strategy image fusion infrared image upsampling

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Long-term stock price forecast based on PSO-Informer model 5

Long-term stock price forecast based on PSO-Informer model

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5th International Conference on Computer Information Science and Artificial Intelligence, CISAI 2022

作者： Liu, Hailun Chen, Deng Wei, Wei Wei, Ziqiang Hubei Province Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9781510662520

The long-term prediction of stock prices provides an important reference for quantitative investment decisions. Aiming at the problem of insufficient accuracy of long-term series prediction in existing stock forecasting models, this paper proposes a long-term stock price series forecasting method based on PSO-Informer. Methods First, 43 kinds of technical indicator factors and K-line data were selected to construct the input data, and then the PSO-Informer model was used to predict the future 60 time points of the stock closing price. In the model training process, the particle swarm algorithm is used to optimize the parameters of the Informer network. Based on the 5-minute K-line data of the SSE 50 stock index and the CSI 300 stock index, experimental research was conducted respectively. Taking the accuracy of the long-term stock price prediction overall trend as the evaluation index, and the prediction accuracy reaches 68.2% and 67.5% respectively. The comparison experiments with ARIMA, Prophet, PSO-LSTM and Informer prediction models show that the model has the best performance and is practical. © 2023 SPIE.

关键词： Investments

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Progressive Multi-Scale Learning for Remote Sensing Image Super-Resolution with Residual Prior

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2025年第4期E108A卷 649-654页

作者： Xu, Qiuyu Zhao, Kanghui Lu, Tao Wang, Zhongyuan Hu, Ruimin Wuhan Inst Technol Hubei Key Lab Intelligent Robot Wuhan 430073 Peoples R China Wuhan Univ Sch Comp Sci NERCMS Wuhan 430079 Peoples R China

Global contextual information and spatial structural details are pivotal elements in the context of super-resolution (SR) reconstruction for remote sensing images. Therefore how to generate rich contextual semantic information and accurate spatial structure information simultaneously is a key challenge for remote sensing image SR. In this paper, we propose a novel progressive multi-scale learning strategy based on residual prior to solve the remote sensing image SR problem. In particular, we propose a novel progressive up-down mapping unit (PUMU) that asymptotically maps the input low-dimensional vectors into a high-dimensional space to learn global context information, which avoids loss of global information. Subsequently, we suggest introducing a novel method of explicitly mining spatial structure information, called residual prior (RP), which can help the proposed model to achieve spatial-structure-preserving SR. We have conducted extensive experiments on two public datasets including UCMerced and PatternNet, and the experimental results demonstrate the effectiveness of the proposed method.

关键词： remote sensing image super-resolution progressive multi-scale learning residual prior

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Face Hallucination via Multi-Scale Structure Prior Learning

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2023年第1期E106A卷 92-96页

作者： Yao, Yuexi Lu, Tao Zhao, Kanghui Zhang, Yanduo Wang, Yu Wuhan Inst Technol Sch Comp Sci & Engn Hubei Key Lab Intelligent Robot Wuhan 430073 Peoples R China

Recently, the face hallucination method based on deep learning understands the mapping between low-resolution (LR) and high -resolution (HR) facial patterns by exploring the priors of facial structure. However, how to maintain the face structure consistency after the recon-struction of face images at different scales is still a challenging problem. In this letter, we propose a novel multi-scale structure prior learning (MSPL) for face hallucination. First, we propose a multi-scale structure prior block (MSPB). Considering the loss of high-frequency information in the LR space, we mainly process the input image in three different scale ascend-ing dimensional spaces, and map the image to the high dimensional space to extract multi-scale structural prior information. Then the size of feature maps is recovered by downsampling, and finally the multi-scale information is fused to restore the feature channels. On this basis, we propose a local detail attention module (LDAM) to focus on the local texture information of faces. We conduct extensive face hallucination reconstruction experiments on a public face dataset (LFW) to verify the effectiveness of our method.

关键词： convolution neural network multi-scale structure prior learn-ing local detail attention mechanism face hallucination

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