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3rd International Conference on Artificial Intelligence and computer engineering, ICAICE 2022

作者： Ji, Xiu Yang, Huamin Han, Cheng Wang, Hexin Lang, Chaoyue School of Computer Science and Technology Changchun University of Science and Technology Jilin Changchun130022 China School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin132022 China

ISBN: (纸本)9781510663473

The fisheye camera is widely used in computer vision because of its large field of view. However, in optical theory, the large field of view is at the cost of distortion. It is impossible to obtain effective information directly from the fisheye image, so the original image needs to be distorted first to become a linear image without distortion. Aiming at the existence of traditional longitude correction in the image center, edge correction effect is different and edge distortion, introducing the repositioning center algorithm and stretch factor. Firstly, the effective area of fisheye image is obtained by row-by-row column scanning method. And then uses the repositioning center algorithm to obtain the new center and radius, according to the distortion principle to calculate the distortion principle. The mapping relationship between the target image and the original image is obtained, and finally modified by the bilinear interpolation method. Compared with the traditional longitude correction, the proposed algorithm can correct the fisheye image accurately and effectively, and improve the quality of correction. © 2023 SPIE.

关键词： Image enhancement

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Iterative learning algorithm of upper limb rehabilitation robot based on BP neural network 12

Iterative learning algorithm of upper limb rehabilitation ro...

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12th International Conference on Intelligent Control and Information Processing, ICICIP 2024

作者： Pang, Zaixiang Deng, Xiaomeng Gong, Linan Wang, Nan Changchun University of Technology School of Engineering Training Center Changchun China Changchun University of Technology School of Computer Science and Engineering Changchun China Changchun Vocational Institute of Technology School of Mechanical and Electrical Engineering Changchun China Jilin Weimiao Medical Technology Co. Ltd Changchun China

ISBN: (纸本)9798350308020

At present, in the trajectory tracking control of upper limb rehabilitation robot, there are nonlinear and uncertain problems of patients' spasticity disturbance. Therefore, an iterative learning control algorithm based on Back Propagation neural network is proposed in this paper. Based on the repeatability of the rehabilitation robot system, a multi-layer feedforward neural network trained by error reverse propagation algorithm is combined with iterative learning. The algorithm can effectively compensate the effect of spasmodic interference on the system performance and improve the convergence rate of trajectory tracking errors. The structure of the iterative learning control system based on neural network and the iterative learning control algorithm based on Back Propagation neural network are described. The process of network selection and robot trajectory tracking iterative learning control is given. Through simulation experiments, the feasibility and effectiveness of the algorithm are verified, which solves the above problems and meets the control requirements of rehabilitation training. © 2024 IEEE.

关键词： Network layers

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A hybrid physics-informed data-driven neural network for CO_(2) storage in depleted shale reservoirs

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Petroleum science 2024年第1期21卷 286-301页

作者： Yan-Wei Wang Zhen-Xue Dai Gui-Sheng Wang Li Chen Yu-Zhou Xia Yu-Hao Zhou College of Construction Engineering Jilin UniversityChangchun130026JilinChina Institute of Intelligent Simulation and Early Warning for Subsurface Environment Jilin UniversityChangchun130026JilinChina School of Energy Resources China University of GeosciencesBeijing100083China Department of Safety Environmental Protection and Quality ManagementShengli OilfieldSinopecDongying257000ShandongChina Key Laboratory of Thermo-Fluid Science and Engineering of MOE School of Energy and Power EngineeringXi'an Jiaotong UniversityXi'an710049ShaanxiChina College of Computer Science and Technology China University of Petroleum(East China)Qingdao266580ShandongChina Department of Physics University of FribourgFribourg1700Switzerland

To reduce CO_(2) emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport ***,most current CO_(2) sequestration models do not adequately consider multiple transport ***,the evaluation of CO_(2) storage processes usually involves laborious and time-consuming numerical simulations unsuitable for practical prediction and *** this paper,an integrated model involving gas diffusion,adsorption,dissolution,slip flow,and Darcy flow is proposed to accurately characterize CO_(2) storage in depleted shale reservoirs,supporting the establishment of a training *** this basis,a hybrid physics-informed data-driven neural network(HPDNN)is developed as a deep learning surrogate for prediction and *** incorporating multiple sources of scientific knowledge,the HPDNN can be configured with limited simulation resources,significantly accelerating the forward and inversion ***,the HPDNN can more intelligently predict injection performance,precisely perform reservoir parameter inversion,and reasonably evaluate the CO_(2) storage capacity under complicated *** validation and test results demonstrate that the HPDNN can ensure high accuracy and strong robustness across an extensive applicability range when dealing with field data with multiple noise *** study has tremendous potential to replace traditional modeling tools for predicting and making decisions about CO_(2) storage projects in depleted shale reservoirs.

关键词： Deep learning Physics-informed data-driven neural network Depleted shale reservoirs CO_(2)storage Transport mechanisms

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Distributed Voltage Regulation of Distribution Network Based on Partition Control 2

Distributed Voltage Regulation of Distribution Network Based...

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2nd International Conference on Cyber-Energy Systems and Intelligent Energy, ICCSIE 2024

作者： Jine, Wang Hui, Chen Liying, Zhao Guangxin, Zhang School of Electrical Information Changchun University of Architecture and Civil Engineering Changchun City China School of Computer Technology and Engineering Changchun Institute of Technology Changchun City China School of Information and Engineering College of Science & Technology Ningbo University Ningbo City China

ISBN: (纸本)9798350373318

As China's social economy progresses, the amount of equipment in distribution networks continues to grow, leading to increasingly complex system operations. In this environment, traditional centralized control methods consume excessive computing resources and fail to achieve the processing speeds required by modern intelligent distribution networks. To address these challenges, this article introduces the concept of electrical distance to segment the distribution network and proposes voltage regulation algorithms based on the alternating direction multiplier method and dual ascent method for efficient distributed voltage regulation. © 2024 IEEE.

关键词： DC distribution systems

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Research on face video generation algorithm based on speech content 2

Research on face video generation algorithm based on speech ...

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2nd International Conference on Image Processing, computer Vision and Machine Learning, ICICML 2023

作者： Pan, Qunqin Zhang, Chao Wu, Chengqin Chen, Liangyu Sun, Yulin Changchun University of Science and Technology School of Computer Science and Technology Jilin Province Changchun City China Shanghai Ocean University College of Engineering Science and Technology Shanghai China Jilin Jianzhu University School of Architecture and Planning Jilin Province Changchun City China

ISBN: (纸本)9798350331417

Aiming at the problem that the audio and lip motion are not synchronized and the face appears white light in the speech-driven face speech video, the audio-visual synchronization module and the highlight noise reduction module are proposed on the basis of the Wav2Lip network. Firstly, the sound visual alignment module is added to make the model learn the context information of the speech signal in the forward and backward directions, so as to improve the accuracy of lip synchronization. Secondly, in the image encoder, the highlight noise reduction module (HDM) is introduced to solve the problem of noise and overexposure in the highlight area of the generated speech face video. The experimental results on the LRS2 dataset show that the confidence score (LSE-C), the deviation index (LSE-D) and the image structure similarity (SSIM) of the audio-visual quality alignment network (AVQA) in this paper reach 8.565 and 4.605 and 0.872 respectively. © 2023 IEEE.

关键词： Audio-visual synchronization module High light noise reduction module Talking face video

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A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

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Frontiers of computer science 2024年第5期18卷 1-10页

作者： Lingling ZHAO Shitao SONG Pengyan WANG Chunyu WANG Junjie WANG Maozu GUO Faculty of Computing Harbin Institute of TechnologyHarbin 150001China School of Electrical Engineering Liaoning University of TechnologyJinzhou 121004China School of Computer Science Northeast Electric Power UniversityJilin 132000China Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.

关键词： lithium-ion battery remaining useful life deep learning MLP-Mixer mixture-of-experts

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A method of walking trajectory for biped robot based on Newton interpolation

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International Journal of Wireless and Mobile Computing 2022年第2期23卷 108-113页

作者： Shu, Yingli Yuan, Quande Li, Huazhong Ke, Wende School of Electrical Engineering and Information Technology Changchun Institute of Technology Jilin Changchun China School of Computer Technology and Engineering Changchun Institute of Technology Jilin Changchun China Sino-German School Shenzhen Institute of Information Technology Guangdong Shenzhen China Department of Mechanical and Energy Engineering Southern University of Science and Technology Guangdong Shenzhen China

Trajectory design of biped robot is the premise of effective walking. By analysing the kinematics characteristics of biped robot, Newton’s interpolation is used to design the trajectory of insertion point when planning periodic motion and realising Zero Moment Point (ZMP) constraint. With the increase of interpolation points, the curve fitting effect is improved and it will not lead to more expensive calculation cost, which can effectively meet the real-time requirements. Simulation results show the effectiveness of the method. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Interpolation

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Semantic segmentation of ultra-high resolution remote sensing images based on fully convolutional neural networks 4

Semantic segmentation of ultra-high resolution remote sensin...

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4th International Conference on computer Vision, Image and Deep Learning, CVIDL 2023

作者： Zhang, Hua Jiang, Zhengang Xu, Jun Yao, Xuekun School of Computer Science and Technology Changchun University of Science and Technology Changchun China Changchun Institute of Technology Jilin Key Laboratory of Changbai History and Culture and VR Technology Reconstruction Changchun China School of Computer Technology and Engineering Changchun Institute of Technology Changchun China

ISBN: (纸本)9798350326444

Semantic segmentation has been widely used in various fields of remote sensing;however, ultra-high resolution remote sensing images, due to their extremely high resolution, exhibit more spatial details and cannot be directly segmented using manual segmentation or traditional semantic segmentation methods. We applied fully convolutional deep learning-based neural networks to the semantic segmentation of ultra-high-resolution remote sensing images and at the same time designed an efficient image segmentation strategy to reduce the difficulty of training while ensuring the integrity of the dataset. We conducted experiments on the ultra-high resolution remote sensing dataset Potsdam and demonstrated that the semantic segmentation method based on fully convolutional neural networks can achieve 93.78% MIoU and 97.39% PA on the training set. © 2023 IEEE.

关键词： Semantic Segmentation

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An Improved Semantic Segmentation Network for Ultra-High Resolution Remote Sensing Images Based on DeepLabV3+ 3

An Improved Semantic Segmentation Network for Ultra-High Res...

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3rd International Conference on Electronic Information engineering and computer science, EIECS 2023

作者： Zhang, Hua Jiang, Zhengang Yao, Xuekun Chen, Shifa Changchun University of Science and Technology School of Computer Science and Technology Changchun China Changchun Institute of Technology School of Computer Technology and Engineering Changchun China Changchun Institute of Technology Jilin Key Laboratory of Changbai History and Culture and Vr Technology Reconstruction Changchun China

ISBN: (纸本)9798350316674

With the continuous development of computer vision, semantic segmentation of remote sensing images has been known as a research hotspot in the field of remote sensing. In this paper, we propose an encoder-decoder structure to improve the DeepLabV3+ network based on ResNet for semantic segmentation of ultra-high resolution remote sensing images. We use ResNet18, ResNet101, and ResNet152 as the backbone of the network, respectively, and combine atrous spatial pyramid pooling (ASPP) to extract the semantic information of the remote sensing images at multiple scales and then reduce the feature maps to the input size by bilinear interpolation to realize the "end-to-end"pixel-level image classification and precise localization. We conducted experiments on the ultra-high resolution remote sensing image dataset ISRPS Vaihingen to verify the effectiveness of the proposed network. The experimental results show that the improved DeepLabV3+ network outperforms the original DeepLabV3+ network by 5.05%, 9.24%, 9.05%, and 7.62% in PA, MPA, MIoU, and FWIoU, respectively. © 2023 IEEE.

关键词： atrous spatial pyramid pooling DeepLabV3+ Remote sensing semantic segmentation ultra-high resolution

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Partially training, isolating and unlearning, mitigating backdoor attack

Partially training, isolating and unlearning, mitigating bac...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Li, Yong Gao, Han Chen, Hao Wang, Yiming Yu, Chun School of Computer Science and Engineering Changchun University of Technology Changchun130012 China Ai Research Institute Changchun University of Technology Changchun130012 China School of Computer Science and Technology Jilin University Changchun130012 China Non-Commissioned Officer School Army Academy of Armored Forces Changchun130051 China

ISBN: (纸本)9798350362480

Neural networks are widely known to be vulnerable to backdoor attacks, witch are full of threats, a method that poisons a portion of the training data to make the target model perform well on normal data sets, while outputting attacker-specified or random categories on the poisoned samples. Nowadays, poisoned samples are becoming more and more similar to corresponding normal samples, even the human eye cannot easily distinguish them and the accuracy of models carrying backdoors on normal samples is no different from that of clean models witch also leads to the inability to determine whether a model carries a backdoor based on its accuracy. In this article, by observing the characteristics of backdoor attacks, We provide a new model training method, Partial Training(PT), that freezes part of the model to train a model that can isolate suspicious samples. Then, on this basis, a clean model is fine-tune to resist backdoor attacks. We used three datasets and four backdoor attack methods to conduct our experiments. The experiments showed that PT performed very well in defending against backdoor attacks mentioned above. © 2024 IEEE.

关键词： Adversary Attack Backdoor Attack

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