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Multi-objective configuration optimization method based on antlion algorithm for customer-oriented modular process

Journal of intelligent and Fuzzy Systems 2024年第3-4期47卷 265-277页

作者： Zhang, Qianqian Fan, Bo Sun, Lifan Information Engineering College Henan University of Science and Technology Luoyang China Henan Key Laboratory of Robot and Intelligent Systems Henan University of Science and Technology Luoyang China

Process parameter configuration needs to respond quickly in the customized manufacturing environment. A multi-objective optimization method based on antlion algorithm for product process configuration design is proposed. The weight mapping mechanism of customer demands is established to clarify the relationship between customer demands and manufacturing process, which also simplifies the process attribute analysis of customized products. The parameters to be optimized in the customization process are analyzed: product performance, process cost and production time. Based on the objective function including these parameters, a multi-objective optimization model of modular process reconstruction is established. The proposed model and algorithm can optimize these parameters in parallel and realize the rapid configuration of product modular process. In the application case of this paper, the proposed method is suitable for the whole process modular configuration of copper plate and strip. The feasibility and effectiveness of the model and algorithm in the proposed configuration scheme are verified. © 2024-IOS Press. All rights reserved.

关键词： Multiobjective optimization

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Single Image Defogging via Recurrent Bilateral Learning 4

Single Image Defogging via Recurrent Bilateral Learning

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4th International Conference on robotics and Computer Vision, ICRCV 2022

作者： Chen, Cheng Liu, Wei Lu, Tao Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Hubei Wuhan China

ISBN: (数字)9781665481700

ISBN: (纸本)9781665481700

Image defogging is an important computer vision topic for a long time. Current end-to-end defogging methods based on convolutional neural networks (CNNs) have achieved significant success. However, the inherent ambiguity between scene albedo and depth in foggy degraded images makes foggy image restoration still challenging. In this paper, we introduce a novel fog removal network based on recurrent bilateral learning (RBL) that generates detailed scene depth features and then fully leverages the scene depth to resolve this ambiguity. This network is composed of multiple sets of bilateral grid modules (BGM). In the BGM, we first extract haze-related features in low-resolution streams. Then, the scene depth information is separated from a trainable bilateral grid. Finally, it performs slicing operations for data-dependent lookup in the bilateral grid to reconstruct scene depth with high-frequency features. Moreover, we embed Conv-LSTM in the BGM to better correlate context-relevant information in recurrent structure. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art algorithms. © 2022 IEEE.

关键词： Image reconstruction

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Oriented object detection based on cross-scale information fusion

Oriented object detection based on cross-scale information f...

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2022 International Conference on Artificial Intelligence and Computer Information technology, AICIT 2022

作者： Li, Chen Zhao, Tongzhou Mao, Chengbo Hu, Wei Wuhan Institute of Technology Hubei Province Key Laboratory of Intelligent Robot Wuhan China

ISBN: (数字)9781665450874

ISBN: (纸本)9781665450874

Due to the enormous size discrepancies between classes and within classes, as well as the high degree of resemblance across classes of oriented objects, traditional remote sensing object detection was made difficult. Although coping with huge scale differences and high inter-class similarity was made possible by multi-scale information fusion, the multiscale weight fusion technique neglected the impact of cross-scale on picture semantic feature extraction, leading to subpar detection performance. The performance of the delayed inference was caused by the rotating region proposal network, which produced high-quality ideas while expanding the network's capacity. In this study, a cross-scale shift oriented object detection method was suggested. First, by creating a feature pyramid network, the multi-layer feature maps were successfully fused. First, the multi-layer feature maps were effectively fused by reconstructing a feature pyramid network. A cross-scale shift module was simultaneously introduced to FPN to enhance the correlation between multi-scale properties. Finally, to raise the quality of the bounding boxes produced, an oriented region proposal network (ORPN) was used. On remote sensing datasets from DOTA-V1.5, the proposed method fared better than the control group in terms of detection accuracy. © 2022 IEEE.

关键词： Remote sensing

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Gradient-based adversarial example generation with root mean square propagation 1

Gradient-based adversarial example generation with root mean...

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1st International Conference on Artificial Intelligence and Human-Computer Interaction, ArtInHCI 2023

作者： Hong, Dian Chen, Deng Hubei Province Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9781643685083

Nowadays, the security of neural networks has attracted more and more attention. Adversarial examples are one of the problems that affect the security of neural networks. The gradient-based attack method is a typical attack method, and the Momentum Iterative Fast Gradient Sign Method (MI-FGSM) is a typical attack algorithm among the gradient-based attack algorithms. However, this method may suffer from the problems of excessive gradient growth and low efficiency. In this paper, we propose a gradient-based attack algorithm RMS-FGSM based on Root Mean Square Propagation (RMSProp). RMS-FGSM algorithm avoids excessive gradient growth by Exponential Weighted Moving Average method and adaptive learning rate when gradient updates. Experiments on MNIST and CIFAR-100 and several models show that the attack success rate of our approach is higher than the baseline methods. Above all, our generated adversarial examples have a smaller perturbation under the same attack success rate. © 2024 The Authors.

关键词： Iterative methods

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An iterative attention context fusion network for semantic segmentation of remote sensing image 12

An iterative attention context fusion network for semantic s...

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SPIE 12th International Symposium on Multispectral Image Processing and Pattern Recognition, MIPPR 2023

作者： Chen, Yanpu Wang, Haihui Zhang, Shuiping Hu, Po Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

In recent years, high-resolution remote sensing image segmentation has become a key task involving technology in many fields, mainly used to accurately extract target information from remote sensing images, and widely used in land detection, coverage classification, etc. These images are characterized by high resolution and large scale, and the overall segmentation requires high performance of hardware devices, the downsampling method usually used is easy to affect the segmentation quality, cropping and slicing the image will lead to the lack of edge information, and the problems of category homogenization, changes in complex scenes, and noise and occlusion make the segmentation challenging. Therefore, in this paper, we propose An iterative attention context fusion network (IACFNet) based on the attention mechanism as well as information fusion at different scales, which iteratively calculates the attention module weights connecting low and high features and refines the problem of spatial information loss, utilizing multiscale segmentation for information complementation, and utilizing an improved boundary loss function to precisely define boundary instances. Our proposed method obtains a performance improvement of about 2.8% and 1.5% in the mean intersection over union (mIoU) metric compared to the current state-of-the-art methods, respectively. © 2024 SPIE.

关键词： Semantics

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Epilepsy Detection Using Random Forest Classification Based on Locally Linear Embedding Algorithm 5

Epilepsy Detection Using Random Forest Classification Based ...

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5th International Conference on Control, robotics and Cybernetics, CRC 2020

作者： Hou, Qing Liu, Yang Liu, Jun Sun, Siqi Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan430073 China

ISBN: (数字)9781728186245

ISBN: (纸本)9781728186245

Epilepsy is a common disease of the brain nervous system. The key to epilepsy surgery is to locate the epileptic foci. Research shows that they can be detected by magnetoencephalographic (MEG) data. The Random Forest Classification model based on Locally Linear Embedding (LLE) is used to clean the obtained brain nerve data, and then the dimensionality reduction operation is performed through the LLE algorithm. Finally, the optimized Random Forest algorithm is used to construct a classification model that can obtain a higher average accuracy rate. This experiment shows that the average accuracy of the optimized model is improved to 0.95, which is more effective than the decision tree algorithm, support vector machine algorithm, k-neighbor algorithm and Random Forest algorithm without any optimization. Therefore, it can classify the epilepsy lesion more accurately with a strong generalization ability and robustness. © 2020 IEEE.

关键词： Neurology

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MRLS-TPS-Based Nonrigid Image/Surface Deformation under Manifold Regularization Constraint 5

MRLS-TPS-Based Nonrigid Image/Surface Deformation under Mani...

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5th International Conference on Control, robotics and Cybernetics, CRC 2020

作者： Tian, Yulu Zhou, Huabing Wuhan Institute of Technology Hubei Provincial Key Laboratory of Intelligent Robot Wuhan China

ISBN: (纸本)9781728186245

This paper solves the problem of nonrigid deformation based on labeled and unlabeled feature points between two images or surfaces. The principle is to obtain the labeled deformation control point pairs on the correspondence images and the unlabeled feature control points in the source image, and estimate the deformation function between the deformation control point pairs for each pixel or voxel. To achieve a deformation function with more structure information preserved, we consider the nonrigid deformation function estimating as a semi-supervised learning problem, which exploits the manifold regularization to preserve the intrinsic geometry information of the points contained in the object. Moreover, we choose to define the nonrigid deformation function in a reproducing kernel Hilbert space and a closed-form solution can be derived. To reduce the computation complexity, we also adopt a sparse approximation to realize a fast implementation. The method can be applied to 2D image deformation and 3D surface deformation, where extensive experiments on the data show that our methods are superior to state-of-the-art methods. © 2020 IEEE.

关键词： Deformation

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Single Image Deraining via Two Stages: Bilateral Learning and Feature Decoupling 4

Single Image Deraining via Two Stages: Bilateral Learning an...

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4th International Conference on robotics and Computer Vision, ICRCV 2022

作者： Jiang, Rui Liu, Wei Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Hubei Wuhan China

ISBN: (数字)9781665481700

ISBN: (纸本)9781665481700

Single image deraining is an important technique in the field of computer vision. Existing deraining methods remove rain streaks from an image by recovering a clear image directly from a rainy image or by learning the residual components. However, there is a mixed correlation between rain streaks and background information, and it is difficult to remove rain streaks directly from rain images. In this work, to improve the performance and efficiency of image deraining, we propose a novel two-stage rain removal network. In the first stage, we first extract rain-related features in low-resolution paths and map them into bilateral space. Then, affine curves are fitted in the bilateral space. Finally, a slicing operation is performed on the data-dependent lookups in the bilateral grid to separate the rain traces from the background information with less computational effort. Then, in the second stage we design a progressive separation module (PSM) to further decouple the mixing correlation between the background and rain-scar layers in a progressive coupled representation learning manner. Tested on several synthetic rainfall datasets as well as real natural scene images, qualitative and quantitative results show that our proposed method achieves better performance with lower computational resources. © 2022 IEEE.

关键词： Rain

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Pose estimation for six-axis industrial robots based on pose distillation 5

Pose estimation for six-axis industrial robots based on pose...

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5th International Conference on Advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2022

作者： Gao, Xin Chen, Deng Wuhan Institute of Technology Hubei Province Key Laboratory of Intelligent Robot Wuhan China

ISBN: (数字)9781665484749

ISBN: (纸本)9781665484749

Industrial robot posture monitoring can be used to detect abnormal movements of industrial robots and thus avoid safety accidents. Most of the current research on industrial robot pose estimation has focused on improving the accuracy aspect, and there is a lack of research on its recognition efficiency. In this work, we build a model for recognizing the pose of industrial robots based on the human pose estimation model DensePose, combined with a strategy of pose distillation. Individual industrial robot images are fed into a highly accurate pose estimation network model and the model is compressed by using distillation with a fitted loss function to achieve accurate and efficient industrial robot pose recognition, laying the foundation for safe monitoring of industrial robots. The evaluation shows that compared to the original model without the FDPD method compression, the model compressed by the FDPD method is 6.5% more efficient while maintaining high accuracy. © 2022 IEEE.

关键词： Industrial robots

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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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