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Artificial Intelligence and Digital Technology (ICAIDT), International Conference on

作者： Shengnan Yan Yihui Shen School of Computer and Control Engineering Minjiang University Fujian China College of Arts and Sciences Fujian Medical University Fujian China

ISBN: (数字)9798350386905

ISBN: (纸本)9798350386912

To address the issues of lacking datasets and low recognition accuracy for paint film defects, this paper proposes a denoising diffusion implicit model (DDIM) for data augmentation of paint film defects and innovatively suggests a classification method combining Selective Kernel Networks (SKNet) with SqueezeNet model. Initially, the DDIM is used for dataset expansion, followed by an evaluation of the similarity between generated and captured paint film defect images using multiscale structural similarity (MS-SSIM). The training and generation effects of DDIM are then compared with those of DCGAN. In the SqueezeNet model, a Selective Kernel module is added following the fire7 module to enhance the model’s attention mechanism. The results show that all types of paint film defect images generated by DDIM have MS-SSIM indices above 0.64, with most exceeding 0.7. The combined approach of Selective Kernel Networks(SKNet) and SqueezeNet outperforms other attention mechanisms, achieving an accuracy above 96.3%. The method demonstrates promising prospects for paint film defect detection, enhancing identification efficiency and accuracy while reducing detection costs, and is applicable to mobile or embedded devices.

关键词： Training Accuracy Attention mechanisms Noise reduction Data augmentation Data models Object recognition

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Incremental Learning Based on V-I Trajectory Feature for Non-Intrusive Load Monitoring

Incremental Learning Based on V-I Trajectory Feature for Non...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Yuchun Li Keke Li Sheng Wang Zhiao Cao School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Electrical and Information Engineering Tianjin University Tianjin China School of Information Science and Engineering Northeastern University Shenyang China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China

Non-intrusive load monitoring (NILM) implements energy decomposition in a non-intrusive manner and provides a promising path to tap demand response potential for flexible load resources in residential and commercial buildings. Modern deep neural networks (DNNs) have succeeded in NILM fields, especially under offline settings where the task for a network is fixed and all training data are provided simultaneously. However, these algorithms face an inevitable challenge. When new or unknown appliances are added, the learning algorithms mentioned above exhibit a pronounced susceptibility to catas-trophic forgetting as they undertake the incremental acquisition of knowledge associated with these new appliance classes. To tackle this challenge, a novel incremental learning method is proposed in this paper. An effective linear model is introduced to correct the strong deviation when learning unknown appliance class. Numerical results on the PLAID public datasets illustrate the effectiveness of the presented method.

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Obstacle classification by a line-crawling robot: A rough neurocomputing approach 3rd

Obstacle classification by a line-crawling robot: A rough ne...

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3rd International Conference on Rough Sets and Current Trends in Computing, RSCTC 2002

作者： Peters, James F. Ahn, T.C. Borkowski, Maciej Department of Electrical and Computer Engineering University of Manitoba WinnipegMBR3T5V6 Canada Intelligent Information Control ann System Lab School of Electrical and Electronic Engineering Won-Kwang University 344-2 Shinyong-Dong Iksan Chon-Buk570-749 Korea Republic of

ISBN: (纸本)9783540442745

This article considers a rough neurocomputing approach to the design of the classify layer of a Brooks architecture for a robot control system. In the case of the line-crawling robot (LCR) described in this article, rough neurocomputing is used to classify sometimes noisy signals from sensors. The LCR is a robot designed to crawl along high-voltage transmission lines where noisy sensor signals are common because of the electromagnetic field surrounding conductors. In rough neurocomputing, training a network of neurons is defined by algorithms for adjusting parameters in the approximation space of each neuron. Learning in a rough neural network is defined relative to local parameter adjustments. Input to a sensor signal classifier is in the form of clusters of similar sensor signal values. This article gives a very brief description of a LCR that has been developed over the past three years as part of a Manitoba Hydro research project. This robot is useful in solving maintenance problems in power systems. A description of the basic features of the LCR control system and basic architecture of a rough neurocomputing system for robot navigation are given. A sample LCR sensor signal classification experiment is also given. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Electromagnetic fields

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Data-driven-based Bed Temperature control of Circulating Fluidized Bed Units

Data-driven-based Bed Temperature Control of Circulating Flu...

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第27届中国控制与决策会议

作者： Huifang Guo Fang Fang JiZhen Liu School of Control and Computer Engineering North China Electric Power University Suzhou Institute of North China Electric Power University

ISBN: (纸本)9781479970186

In modern industry,data has become an indispensible resource for the whole process of production and *** the optimization of the control performance,the data-driven-based PID controller is introduced in this *** on the data-driven control law,the parameters of the PID controller will be renewed automatically by using the real and historical input/output data of the *** then,the control action can be update in real *** the perspective of applications,the data-driven-based PID controller is researched as the controller in the bed temperature of circulating fluidized bed *** simulation results show that the data-driven-based PID controller is suitable for the bed temperature control,and its performance is better than that of the conventional controller.

关键词： Data-driven PID control Circulating Fluid Bed Units and Bed Temperature control

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Bimodal Fusion Network for Basic Taste Sensation Recognition from Electroencephalography and Electromyography

Bimodal Fusion Network for Basic Taste Sensation Recognition...

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

作者： Han Gao Shuo Zhao Huiyan Li Li Liu You Wang Ruifen Hu Jin Zhang Guang Li State Key Laboratory of Industrial Control Technology Zhejiang University School of Computer and Communication Engineering Changsha University of Science and Technology

Taste sensation can be objectively measured using electroencephalography (EEG) or electromyography (EMG). How-ever, it is still challenging to effectively utilize the complementary information from EEG and EMG signals in taste sensation recognition. This paper proposes a bimodal fusion network (Bi-FusionNet) for recognizing basic taste sensations (sour, sweet, bitter, salty, umami, and blank). Two convolutional backbones with similar structures are designed to separately extract the single-modal features of EEG and EMG. Then, EEG and EMG features are concatenated for bimodal interaction and complementarity. Finally, three loss functions are adopted: a center loss for aggregating intra-class samples, a mean squared error loss for sequence positions for minimizing the difference between signals during the stimulation, and a softmax loss for minimizing the entropy of prediction and true labels. The results on the taste sensation dataset show that bimodal fusion improves recognition performance, and Bi-FusionNet outperforms single-modal methods and other fusion methods. Bi-FusionNet paves the way for the application of multimodal fusion in taste sensation recognition.

关键词： Convolution Speech recognition Brain modeling Feature extraction Electromyography Electroencephalography Entropy

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Diversified Image Inpainting with Transformers and Denoising Iterative Refinement

SSRN

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SSRN 2023年

作者： Xu, Suzhen Xiang, Wenlong Lv, Cuicui Wang, Shuo School of Computer and Control Engineering Yantai University No.30 Qingquan Road Laishan District Shandong Yantai264005 China

Image inpainting is a long-standing key problem in the field of computer vision, which aims to fill the missing parts of an image with visually realistic and semantically appropriate content. For a long time, in the research work at home and abroad, how to generate diverse and realistic images is a dilemma faced by image inpainting. With the continuous iteration of deep learning technology, transformer feature extraction model and a new generation paradigm, diffusion model, are emerging in vision tasks. Attention-based transformer models can effectively model long-distance dependencies and flexibly design output content. The diffusion model is stable in training, and the quality of its generated images is already better than that of generative adversarial networks. We decompose the inpainting problem into two key steps: diversified pre-generation and high-resolution reconstruction. Firstly, referring to the discretized pixel set, a transformer information association model is designed to sample from the granularity of pixel values, so as to obtain low-resolution results with diverse appearance. Then, a denoising diffusion model is used to reconstruct the high-resolution image, which is a conditional and iterative refinement process. Ultimately, we achieve a set of image restoration methods that produce diverse results and support high-fidelity output. © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

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Input design for nonlinear stochastic dynamic systems - A particle filter approach

Input design for nonlinear stochastic dynamic systems - A pa...

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作者： Gopaluni, R. Bhushan Schön, Thomas B. Wills, Adrian G. Department of Chemical and Biological Engineering University of British Columbia Vancouver V6T 1Z3 Canada Division of Automatic Control Linköping University SE-581 83 Linköping Sweden School of Electrical Engineering and Computer Science University of Newcastle Callaghan NSW 2308 Australia

ISBN: (纸本)9783902661937

We propose an algorithm for optimal input design in nonlinear stochastic dynamic systems. The approach relies on minimizing a function of the covariance of the parameter estimates of the system with respect to the input. The covariance matrix is approximated using a joint likelihood function of hidden states and measurements, and a combination of state filters and smoothers. The input is parametrized using an autoregressive model. The proposed approach is illustrated through a simulation example. © 2011 IFAC.

关键词： Covariance matrix

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A novel segmentation method for convex lesions based on dynamic programming with local intra-class variance 12

A novel segmentation method for convex lesions based on dyna...

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27th Annual ACM Symposium on Applied Computing, SAC 2012

作者： Yu, Mali Huang, Qiliang Jin, Renchao Song, Enmin Liu, Hong Hung, Chih-Cheng School of Computer Science Huazhong University of Science and Technology Wuhan China Key Laboratory of Education Ministry for Image Processing and Intelligent Control Wuhan Hubei China Radiology Department Second People's Hospital of Nanning Nanning China School of Computing and Software Engineering Southern Polytechnic State University GA United States

ISBN: (纸本)9781450308571

Lesion segmentation plays an important role in medical image processing and analysis. There exist several successful dynamic programming (DP) based segmentation methods for general images. In those methods, the gradient is used as an important factor in the cost function to attract the contours to the boundaries. Since medical images have their characteristics such as low contrast, blurred edges and high noises, the gradient operator cannot work well enough to achieve a satisfactory performance for boundary detection. We define the local intra-class variance and combine it with the dynamic programming method to replace the traditional gradient operation. Experiments on synthetic and X-ray images are carried out and the results are compared with Canny and fast multilevel fuzzy edge detection (FMFED) algorithms. It is demonstrated that the proposed method performs better on medical images with low contrast, blurred edges and high noises. In addition, 483 regions of interest of mammograms randomly extracted from DDSM of the University of South Florida are used to compare our proposed method with the plane-fitting and dynamic programming method (PFDP), and the normalized cut segmentation method (Ncut). The results demonstrate that our method is more accurate and robust than PFDP and Ncut. © 2012 ACM.

关键词： Dynamic programming

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Fault Diagnosis Research of Laser Gyroscope Based on Optimized-Kernel Extreme Learning Machine

SSRN

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SSRN 2023年

作者： Xiaojun, Bai Ma, Zhenxi Chen, Wei Wang, Shenhang Fu, Yanfang Xi’an Technological University School of Computer Science and Engineering Xi’an China Beijing Aerospace Automatic Control Institution Beijing China

Traditional diagnostic models for laser gyroscopes, widely utilized as high-precision angular velocity sensors in aerospace applications, often suffer from limited reliability and accuracy due to the difficulty of feature extraction, a high computational cost, and training time. In light of these challenges, the present study puts forth a new method for diagnosing faults in laser gyroscopes using the Kernel Extreme Learning Machine (KELM). Specifically, the proposed method leverages Wavelet Packet Decomposition (WPD) to effectively extract features from the laser gyroscope signal as input for our diagnostic model. Furthermore, the KELM model is trained for fault diagnosis, after which we employ the Improved Dung Beetle Optimizer (IDBO) algorithm to optimize its parameters for enhanced optimization performance. According to the experimental results, our proposed IDBO-KELM model demonstrates a 3.68% improvement in diagnostic accuracy compared to traditional approaches, while also offering the advantages of a shorter training time and increased precision. © 2023, The Authors. All rights reserved.

关键词： Failure analysis

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A new distributed algorithm of clock synchronization for static sensor networks

A new distributed algorithm of clock synchronization for sta...

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International Conference on control, Automation, Robotics and Vision

作者： Yong Qiao Wenlun Yang Minyue Fu School of Control Science and Engineering Zhejiang University Hangzhou 310027 P. R. China School of Electrical Engineering and Computer Science University of Newcastle NSW 2308 Australia

ISBN: (纸本)9781509035502

In this paper, a new distributed algorithm is presented to achieve the consensus of time variations and that of initial time simultaneously. By combining both controller and estimator design methods, it obtains higher synchronization precision with stronger robustness against noisy inputs resulted from crystal oscillators. Furthermore, the control input is ensured bounded to make our algorithm realizable in practical implementation. The implementation of the algorithm allows the receiving end to be event-triggered, while the transmitting end is executed periodically. The performance of the algorithm is illustrated by the given numerical simulations.

关键词： Synchronization Clocks Peer-to-peer computing Algorithm design and analysis Eigenvalues and eigenfunctions Robot sensing systems Nickel

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