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Surface-PMSM Sensorless Control Strategy Based on Reduced-Order Linear Kalman Filter Cooperating with Prediction Error Rolling Compensation

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IEEE Transactions on Power Electronics 2025年第8期40卷 10804-10815页

作者： Xu, Sang Shen, Anwen Tang, Qipeng Wang, Miaomiao Luo, Pan Luo, Xin Xu, Jinbang National Key Laboratory of Science and Technology on Multispectral Information Processing Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Wuhan430074 China Science and Technology on Reactor System Design Technology Laboratory Chengdu China

Kalman filter (KF) is increasingly attracted for sensorless control of surface permanent magnet synchronous motors (SPMSMs) due to its strong robustness against measurement and system noise. However, conventional method suffers from poor position and speed estimation accuracy under dynamic conditions, and high computational cost. In order to solve these problems, an enhanced sensorless control strategy based on reduced-order linear Kalman filter (RLKF) cooperating with prediction error rolling compensation is introduced in this paper. Differing from conventional method, a reduced-order linear state equation encompassing rotor position, speed and torque disturbance is constructed. Then, by obtaining the prediction error of system output using the motor current equation in the predictive reference frame, the optimal estimation of rotor position and speed in KF algorithm is achieved based on the reduced-order model. This not only simplifies the calculation process, but also enhances the position and speed estimation accuracy under dynamic conditions. Meanwhile, a prediction error rolling compensation algorithm is developed to minimize the estimation deviations caused by motor parameters variations. Additionally, the system's dynamic performance is further improved with an adopted speed controller based on torque disturbance feedforward and acceleration feedback. Finally, experimental results verify the effectiveness and feasibility of the proposed strategy.1 © 1986-2012 IEEE.

关键词： Permanent magnets

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A method of embedding a high-resolution image into a large field-of-view image

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Multimedia Tools and Applications 2025年第14期84卷 13037-13056页

作者： Dong, Yanmei Pei, Mingtao Jia, Yunde Key Laboratory of Intelligent Information Processing and Graph Processing Tus College of Digit Guangxi University of Science and Technology Liuzhou545006 China Beijing Laboratory of Intelligent Information Technology School of Computer Science Beijing Institute of Technology Beijing100081 China

images captured by wide-angle cameras or fisheye cameras are with large Field-of-View (FOV) but low resolution, while images captured by conventional cameras are with high-resolution but limited FOV. To handle this contradiction, in this paper, we propose to improve the resolution of a large FOV image by embedding a high-resolution image into it. We present an image embedding method based on non-rigid transformations and mesh optimization. Without any radial distortion correction on large FOV images, we utilize non-rigid transformations to model the mathematical transformation relationship between the high-resolution image and the large FOV image. Meanwhile, we exploit mesh optimization to generate evenly distributed control points for maintaining the shape and content of the large FOV image. To verify the effectiveness of the proposed method, we capture some image pairs from a conventional camera and a large FOV camera, and exploit several classic image alignment methods to implement four baseline methods. The experimental results show that our method preserves the shape and content of the original FOV image from 58% to 82%, which significantly outperforms all baseline methods. The results indicate that the proposed method can effectively embed a high-resolution image into a large FOV image as well as preserve the shape and content of the large FOV image. In other words, the resolution of a large FOV image can be improved via high-resolution image embedding. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Embeddings

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Integrating the dynamic optimization strategies of Adam and AdaGrad 13

Integrating the dynamic optimization strategies of Adam and ...

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13th International Conference on intelligent Control and information processing, ICICIP 2025

作者： Huang, Zhixuan Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

Artificial intelligence (AI) has been a key research area since the 1950s, initially focused on using logic and reasoning to create systems that understand language, control robots, and offer expert advice. With the rise of big data and deep learning, AI has advanced in applications like recommendation systems, image recognition, and machine translation, primarily through optimizing loss functions in deep neural networks to improve accuracy and reduce training *** descent is the core optimization method but faces challenges like slow convergence and local minima. To overcome these, algorithms like Momentum, AdaGrad, RMSProp, Adadelta, Adam, and Nadam have been developed, introducing momentum and adaptive learning rates to accelerate convergence. This paper presents a new optimization algorithm that combines the strengths of Adam and AdaGrad, offering better adaptability to different learning rates. © 2025 IEEE.

关键词： Contrastive Learning

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Attitude Detection Model Based on Improved High Resolution Network 13

Attitude Detection Model Based on Improved High Resolution N...

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13th International Conference on intelligent Control and information processing, ICICIP 2025

作者： Shi, Dongliang Li, Yuhan Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

2D human posture detection, a challenging task in computer vision, aims to identify human body key points and is widely used. The HRNet model performs well in human key point detection. To enhance its effect in human posture detection and address suboptimal prediction accuracy in interactive occlusion, this paper combines HRNet and Transformer. The Transformer's attention mechanism handles complex image dependencies, and HRNet obtains image features. By embedding the Transformer's attention mechanism module into HRNet's backbone network and integrating a self-designed double-branch structure and convolutional attention module, an improved HRNet model is constructed. The DConvAttention module processes extracted features, and the convolutional attention mechanism balances image frequencies, enhancing processing and discrimination of complex backgrounds and occlusions and improving detection performance. In tests of complex and occluded images, the improved HRNet Local model has a 4.2M parameter increase, 0.5G FLOPs increase, and 1% mAP increase compared to HRNet, with the same recall rate. The improved HRNet Global model has 11M more parameters, 1.1G more FLOPs, a similar or slightly higher recall rate, and a 2% higher mAP value. © 2025 IEEE.

关键词： Face recognition

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Improved LYT-Net image Enhancement Model for Underwater Robotics Vision 13

Improved LYT-Net Image Enhancement Model for Underwater Robo...

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13th International Conference on intelligent Control and information processing, ICICIP 2025

作者： Li, Wenjie Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Güttin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

—Although underwater robots can replace humans to explore the ocean which is rich in resources but fraught with unknown risks, there are phenomena such as monotonous colors, complex backgrounds and uneven illumination in the underwater environment, which makes it difficult for underwater robots relying on the optical vision system to obtain clear images and is not conducive to subsequent recognition and grasping tasks. In this paper, relevant preparatory work was carried out for underwater image processing applicable to underwater robots, and the underwater imaging model as well as other underwater image processing models were analyzed. Subsequently, a method combining the underwater physical model and the dark channel image was proposed to improve LYT-Net, enabling it to be applicable to mobile embedded devices such as underwater robots. After training and testing on the real datasets LSUI and UIEB, our model achieved good results on important indicators such as PSNR and SSIM. Meanwhile, it met the theoretical requirements of mobile devices in terms of FLOPs, Params and single-frame processing time, which verified its robustness and real-time processing ability in various underwater environments. © 2025 IEEE.

关键词： Underwater imaging

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Underwater image Restoration Based on Improved SwinIR Model 13

Underwater Image Restoration Based on Improved SwinIR Model

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13th International Conference on intelligent Control and information processing, ICICIP 2025

作者： Lu, Yanke Li, Fang Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798331516147

image restoration is a classic foundational visual task, aimed at recovering damaged images, such as those affected by compression, blurring, or noise, to high-definition clarity. Although current image enhancement techniques largely rely on convolutional neural networks, the efficient capabilities of Transformers demonstrated in complex visual tasks have not yet been extensively applied in the field of image restoration. This paper presents an optimized image restoration strategy based on the SwinIR model, which is applied to the task of underwater image restoration. In the shallow feature extraction phase, deep separable convolution technology is employed;while in the deep feature extraction phase, a Spatial Gated Feedforward Network (SGFN) is utilized. The experimental data suggests that the novel approach surpasses conventional techniques in effectiveness and has effectively minimized both the model's parameter quantity and computational demands. © 2025 IEEE.

关键词： Convolution

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Identification of Non-Restorative Sleep Associated with Depression Using Ambulatory Electrocardiogram and Triaxial Acceleration

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IEEE Transactions on Biomedical Engineering 2025年第6期72卷 1909-1919页

作者： Liu, Li Wang, Chongyang Liu, Guangyuan Wen, Wanhui Southwest University Chongqing Key Laboratory of Generic Technology and System of Service Robots Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China Sichuan University Key Laboratory of Rehabilitation Medicine in Sichuan Province Department of Rehabilitation Medicine Institute of Rehabilitation Medicine West China Hospital Chengdu610041 China

Objective: Non-restorative sleep is prevalent among individuals with depression and is strongly associated with the severity of the condition. Therefore, identifying non-restorative sleep can aid in the early screening of depression. Investigating non-restorative sleep in depression necessitates long-term monitoring under naturalistic conditions. Methods: In this study, we recruited 149 participants and collected electrocardiogram and triaxial acceleration from them, resulting in a total of 761 nights of data. The period from midnight to 6:30 AM was segmented into 78 five-minute intervals, from which 40 physiological features were extracted for each interval. To deal with variations in sleep patterns across participants and dates, we reordered the sleep data based on levels of parasympathetic nervous system (PNS) activation to explore the underlying neural mechanisms of non-restorative sleep in individuals with depressive symptoms. Results: We developed a model that integrated convolutional neural networks with an attention mechanism to identify nonrestorative sleep in individuals with depressive symptoms. The model demonstrated impressive performance on an independent test set, achieving an accuracy of 81.25% and an F1 score of 77.85%. Additionally, Bayes' theorem was used to compute the posterior probability indicating nonrestorative sleep in this population, assessing abnormal PNS activation. Conclusion: Finally, we designed a system capable of automatically evaluating nighttime sleep status and quantifying changes in non-restorative sleep associated with depression. Significance: This system offers a novel tool and method for the early identification of individuals at risk of depression. © 1964-2012 IEEE.

关键词： Sleep research

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Distributed Fixed-Time Dynamic Event-Triggered Algorithm for Economic Dispatch in Smart Grids Over Directed Networks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 173-180页

作者： Ji, Lianghao Liu, Qi Zhang, Cuijuan Yang, Shasha Li, Huaqing Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Image Cognition Chongqing400065 China Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China

A fresh algorithm is presented for tackling the economic dispatch problem (EDP) in smart grids with directed network topology. This algorithm is based on distributed consensus and aims to minimize the total cost of power generation while maintaining the balance between power supply and demand and complying with generation capacity restrictions. Even with unknown and bounded disturbances, this algorithm can achieve optimal dispatch within a fixed time. In addition, an expected settling time can be calculated, which is a more accurate upper bound estimation that relies solely on the generators' initial conditions. Meanwhile, the dynamic event-triggered strategy is employed for reducing sampling frequency and network resource consumption. Finally, numerical simulations validate the effectiveness of the designed algorithm. © 2024 IEEE.

关键词： Consensus algorithm

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AES-AUDIO: An Encryption Scheme for Audio Supporting Differentiated Decryption

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IEEE Transactions on Multimedia 2025年 27卷 2268-2280页

作者： Zhao, Ruoyu Zhang, Yushu Ji, Junhao Yi, Shuang Wen, Wenying Lan, Rushi Jiangxi University of Finance and Economics School of Computer and Artificial Intelligence Nanchang330013 China Jiangxi Provincial Key Laboratory of Multimedia Intelligent Processing Nanchang330013 China Southwest University of Political Science and Law Criminal Investigation School Chongqing401120 China Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin541004 China

In this paper, we propose an audio encryption scheme that supports differentiated decryption, called AES-AUDIO, in which an audio only needs to be encrypted once and can be decrypted into different resolutions as needed. First, we design four security levels, confidential, harsh, noisy, and clear, based on the audio resolution perceived by human auditory perception. Second, the audio data in decimal floating-point numbers (D-FPNs) are unfolded to 32 bits (B-FPNs). Third, we design a region of interest (RoI) encryption algorithm for the audio with the B-FPN format, where the result preserves some perceptual information as needed. Fourth, we construct the AES-AUDIO scheme based on the RoI encryption algorithm, which allows the audio to be encrypted once and then decrypted into different security levels. It supports changing parameters to alter the perception effect corresponding to the security level. Overall, it achieves a balance between the security and usability of the protected audio. User experiments verify that the audios produced by differential decryption can achieve the expected security levels. Some security tests also yielded excellent results, such as an NSCR value of 1. © 1999-2012 IEEE.

关键词： Encryption algorithms

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Research on Collaborative Filtering Algorithm of E-commerce Based on Flink 13

Research on Collaborative Filtering Algorithm of E-commerce ...

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13th International Conference on intelligent Control and information processing, ICICIP 2025

作者： Zheng, Haohao Luo, Xiaonan Zhu, Xiaoshu School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

In online shopping, a person's interest in a product is closely related to whether they will purchase it Analyzing people's interest in various products on time, along with product recommendations, can increase purchase rates. However, in many cases, We only know user implicit feedback, such as browsing or favoriting items, and cannot directly access user explicit feedback, such as product ratings. Moreover, users have limited exposure to products, resulting in a sparse user- item matrix. The traditional Alternating Least Squares (ALS) algorithm can partially address the issue of matrix sparsity, but its accuracy in recommending products based on implicit feedback alone is insufficient This paper proposes an improved ALS algorithm that converts implicit feedback into explicit ratings. We calculate the browsing frequency of a product based on user browsing counts. For a given product, we represent user preference by summing the browsing frequencies of all products with browsing frequencies less than or equal to that of the product. Then, by multiplying the preference level by the product's maximum rating, we derive a product rating, achieving a transformation from implicit feedback to explicit scores. Additionally, traditional collaborative filtering algorithms often use offline data, which may not align with user current preferences. To improve the algorithm's accuracy, we also utilize Flink for real-time data processing, allowing us to capture the most recent feedback data. This study tests the approach using the UserBehavior dataset from the publicly available Tianchi dataset. Experimental results indicate improvements in both Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). © 2025 IEEE.

关键词： Collaborative filtering

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