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2020 International Conference on Computer Engineering and intelligent control, ICCEIC 2020

作者： Shi, Siqi Li, Nanting Ma, Yanjun Zheng, Liping Xi'An University of Technology Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing Xian China Xi'An University of Technology School of Electrical Engineering Xian China Xi'An University of Technology School of Automatic and Information Engineering Xian China

ISBN: (数字)9781728185736

ISBN: (纸本)9781728185736

To improve the decreased recognition performance for truck license plate in complex mine environment, which is caused by such factors as polluted, damaged, a robust license plate recognition method is proposed. Firstly, several candidate locations of license plate in the truck image are obtained by utilizing features of both edge and color. Furthermore, the parameter of license plate area intersection ratio is defined to find the precise localization among those above candidates. Then, a character segmentation scheme based on the gray projection method and morphological operator is used to remove those interference factors, such as frame, rivet and stain and uneven illumination. Finally, to increase the robustness of characters recognition by CNN, a self-built license plate character dataset is constructed, which contains various polluted samples obtained by manual simulation. As shown by the experimental results, the proposed method has higher accuracy in license plate location, and obtains an improvement on character recognition accuracy by 4% compared to other existed methods. © 2020 IEEE.

关键词： License plates (automobile)

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The research of the expansion property and chain mixing property of topological group 12

The research of the expansion property and chain mixing prop...

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12th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2020

作者： Zhan-Jiang, Ji Wei, Shi School of Information and Electronic Engineering Wuzhou University China Guangxi Colleges and Universities Key Lab. of Image Processing and Intelligent Information System Wuzhou University China Guangxi Colleges and Universities Key Laboratory of Professional Software Technology Wuzhou University China Party Committee Office Wuzhou Vocational College China

ISBN: (纸本)9781728170817

The expansion property and chain mixing property have an important significance in terms of theory and application. The concept of the expansion property and chain mixing property are introduced in this paper. We study their dynamical properties. The following result are obtained: (1)Letbe the hyperspace of. Ifis expansive, thenis expansive;(2)Letbe the hyperspace of. Ifis chain mixing, thenis chain mixing;(3)Letbe the hyperspace of. Ifis chain transitive, thenis chain transitive;These results enriched the theory of the expansion property and chain mixing property of topological group. © 2020 IEEE.

关键词： Topology

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The research of g-mixing and g-equicontinuity on the hyperspace of topological group action 12

The research of g-mixing and g-equicontinuity on the hypersp...

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12th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2020

ISBN: (纸本)9781728170817

G-mixing and G-equicontinuity have an important significance in terms of theory and application. According to the definition of mixing and equicontinuity, we give the concept of G-mixing and G-equicontinuity in this paper. By inference, some conclusions of the hyperspace were extended to the hyperspace of topological group action. We have the following result: (1)Letbe the hyperspace ofandbe a pseudo equivariant map. All non-empty open set are invariable for. Ifis a G-mixing map, thenis a G-mixing map;(2)Letbe the hyperspace of andbe a equivariant map. Thenis a G-mild mixing map if and only ifis a G-mild mixing map;(3)Letbe the hyperspace ofandbe a equivariant map. Thenis a G-equicontinuity map if and only ifis a G-equicontinuity map. These results enriched the theory of the G-mixing and G-equicontinuity on the hyperspace of topological group action. © 2020 IEEE.

关键词： Mixing

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The research of G-chain transitive and G-chain mixing on the hyperspace of topological group action 12

The research of G-chain transitive and G-chain mixing on the...

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12th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2020

ISBN: (纸本)9781728170817

G-chain transitive and G-chain mixing have an important significance in terms of theory and application. According to the definition of chain transitive and chain mixing, we give the concept of G-chain transitiveand G-chain mixing in this paper. By inference, some conclusions of the hyperspace were extended to the hyperspace of topological group action. We have the following result: (1)Letbe the hyperspace of. Ifis a G-chain transitive map, thenis a G-chain transitive map;(2)Letbe the hyperspace of. Ifis a G-chain mixing map, thenis a G-chain mixing map;(3)Letbe the hyperspace ofandbe a equivariant map. All non-empty open set are invariable for. Thenis a G-exact map if and only ifis a G-exact map. These results enriched the theory of the G-chain transitive and G-chain mixing on the hyperspace of topological group action. © 2020 IEEE.

关键词： Group theory

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Non-negative Matrix Factorization for Binary Space Learning

Non-negative Matrix Factorization for Binary Space Learning

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Meng Zhang Xiangguang Dai Xiangqin Dai Nian Zhang Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University Chongqing China University of the District of Columbia Washington D.C. USA

Non-Negative matrix factorization (NMF) is a popular research problem in data dimensional reduction. Conventional NMF approaches cannot achieve a subspace made up of binary codes from the high-dimensional data space. To address the above-mentioned problem, we propose a method based on nonnegative matrix factorization to generate a low-dimensional subspace made up of binary codes from the high-dimensional data. The problem can be mathematically expressed as a 0-1 integer mixed optimization problem. For this purpose, We put forward a method based on discrete cyclic coordination descent to obtain a local optimal solution. Experiments show that our means can obtain the better clustering ability than conventional non-negative matrix factorization and its variant approaches.

关键词： Binary codes Optimization Computational intelligence

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NccFlow: Unsupervised learning of optical flow with non-occlusion from geometry

arXiv

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arXiv 2021年

作者： Wang, Guangming Ren, Shuaiqi Wang, Hesheng The Key Laboratory of System Control and Information Processing of Ministry of Education Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

Optical flow estimation is a fundamental problem of computer vision and has many applications in the fields of robot learning and autonomous driving. This paper reveals novel geometric laws of optical flow based on the insight and detailed definition of non-occlusion. Then, two novel loss functions are proposed for the unsupervised learning of optical flow based on the geometric laws of non-occlusion. Specifically, after the occlusion part of the images are masked, the flowing process of pixels is carefully considered and geometric constraints are conducted based on the geometric laws of optical flow. First, neighboring pixels in the first frame will not intersect during the pixel displacement to the second frame. Secondly, when the cluster containing adjacent four pixels in the first frame moves to the second frame, no other pixels will flow into the quadrilateral formed by them. According to the two geometrical constraints, the optical flow non-intersection loss and the optical flow non-blocking loss in the non-occlusion regions are proposed. Two loss functions punish the irregular and inexact optical flows in the non-occlusion regions. The experiments on datasets demonstrated that the proposed unsupervised losses of optical flow based on the geometric laws in non-occlusion regions make the estimated optical flow more refined in detail, and improve the performance of unsupervised learning of optical flow. In addition, the experiments training on synthetic data and evaluating on real data show that the generalization ability of optical flow network is improved by our proposed unsupervised approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Computer vision

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Truncated Cauchy for Robust fast supervised discrete hashing

Truncated Cauchy for Robust fast supervised discrete hashing

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International Conference on information Science and Technology (ICIST)

作者： Yao Xiao Xiangguang Dai Xiangqin Dai Nian Zhang Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University Chongqing China University of the District of Columbia Washington D.C. USA

We propose a novel data-dependent hashing algorithm named Truncated Cauchy for Robust fast supervised discrete hashing (RFSDH) for robust subspace learning. In this paper, a Truncated Cauchy loss is proposed to measure the factorization error, which can handle outliers by truncating large errors. The proposed method can inhibit the unreliable binary codes, which generate the optimal binary codes. Our method can be expressed as a mixed-integer optimization problem, which can be to solve by iterative discrete cyclic coordinate descent. RFSDH method accomplishes outperform other hashing learning methods in image retrieval effect on two large scale datasets.

关键词： Learning systems information science Measurement uncertainty image retrieval Binary codes Loss measurement Iterative methods

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Optimized Dispatching of Seawater Pumping-Storing/Wind/Solar Hybrid Power Generating System

Optimized Dispatching of Seawater Pumping-Storing/Wind/Solar...

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第40届中国控制会议

作者： Yuyu Liu Ding Liu Lu Kang National & Local Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

Different power sources in Seawater Pumping-Storing/Wind/Solar Hybrid Power Generating System supply power to the island *** order to ensure the reliability of the power supplying and improve the economical efficiency of the island micro-grid system at the same time,the output of each power source in the island micro-grid system needs to be optimized and dispatched according to historical wind power and photo-voltaic *** this paper,the mathematical model of Seawater Pumping-Storing/Wind/Solar Hybrid Power Generating System is established,and the one-time cost,*** the joint system are taken as the objective *** immune particle swarm algorithm is used to figure out the optimal output of each power source in the system after considering the restrictions including power balance of the system,the power constraints of each generator set and the online power of wind power and *** example shows that the optimization result of improved PSO algorithm is better than the original one,and it can also achieve the optimized dispatching of the joint system economically and effectively.

关键词： Seawater pumped-storage power station IPSO Optimized Dispatching

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CNN-Transformer fusion for improving maize kernel storage Quality: A new approach to reducing storage risks

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Journal of Stored Products Research 2025年 114卷

作者： Zhao, Like Jia, Weishi Tao, Mengting Jiang, Huawei Yang, Zhen Liu, Xixi Key Laboratory of Grain Information Processing and Control (Henan University of Technology) Ministry of Education Zhengzhou 450001 China Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making Henan University of Technology Zhengzhou 450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou 450001 China

The efficient identification of imperfect maize kernels is of crucial importance for optimizing storage management and ensuring quality preservation. image-based technologies, known for their rapid and non-destructive characteristics, play a pivotal role in this endeavor. However, these technologies encounter significant challenges due to high inter-class similarity and substantial intra-class variability among maize kernels. To address these challenges, we propose CTNet, an advanced model that synergistically integrates the Convolutional Neural Network (CNN) with the Transformer architecture. This integration is further enhanced by incorporating a Feature Attention Module (FAM) and a DW-Swin Transformer, which collectively facilitate the fusion of local and global features. A Fine-Grained Perception Module (FGPM) is employed to augment the model's sensitivity to subtle imperfections. The model, supplemented with a linear classifier, ensures precise discrimination of kernel quality. CTNet was trained on the comprehensive GrainSpace dataset and achieved 1.35 % higher accuracy than the baseline model, SwinTransformer, on the test set. Compared with other methods, our model demonstrates higher accuracy in identifying defective maize grains and achieves more efficient parameter utilization in its structure. Despite moderate parameters and FLOPs, it enhances recognition performance while maintaining low complexity. As imperfect maize grains directly impact quality and storage safety, the model's effective recognition capability is critical for reducing storage losses and ensuring safety, thus providing strong support for smart agriculture. © 2025 Elsevier Ltd

关键词： CNN Fine-grained perception Maize imperfect kernel identification Swin transformer

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Hybrid integrated modeling of thermal field temperature in Cz silicon single crystal growth process based on VMD-CNN-LSTM-ELM

Hybrid integrated modeling of thermal field temperature in C...

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第40届中国控制会议

作者： Yin Wan Ding Liu Junchao Ren National & Local Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

Czochralski（Cz） silicon single crystal growth is a dynamic time-varying process with complex physical changes,multi-phase coupling of multiple fields, model uncertainty and large hysteresis and nonlinearity, while mechanistic models based on a large number of assumptions are difficult to be applied practically. In order to establish a stable and reliable model of crystal growth process, this paper proposes a variational modal decomposition--convolutional neural network-long and short-term memory network-extreme learning machine（VMD-CNN-LSTM-ELM） nonlinear hybrid integrated modeling method based on the idea of data-driven modeling to construct a model between heater power and thermal field temperature. The model consists of feature extraction, deep learning network, and optimization algorithm. First, VMD is used to decompose the original data into multiple Intrinsic Mode Function（IMF） from low to high frequencies, preserving the variation characteristics of the signal at different frequencies. Second, for each eigenmode function sequence, CNN feature extraction and LSTM prediction are performed in turn. Then, ELM is used to nonlinearly integrate the prediction results of multiple sequences to obtain the final prediction output. Finally, the simulation experimental results based on industrial data show that the proposed VMD-LSTMELM based thermal field temperature has excellent prediction performance and generalization ability.

关键词： Cz silicon single crystal growth Data-driven modeling Thermal field temperature Hybrid integrated modeling

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