Kalman filter (KF) is increasingly attracted for sensorless control of surface permanent magnet synchronous motors due to its strong robustness against measurement and system noise. However, the conventional method su...
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Kalman filter (KF) is increasingly attracted for sensorless control of surface permanent magnet synchronous motors due to its strong robustness against measurement and system noise. However, the 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 article. Differing from conventional methods, 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 the 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 parameter variations. In addition, 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.
This article investigates the multirate disturbance rejection control problem for linear control systems with mismatched disturbance and measurement delay using predictor-based extended state observer. A new extended ...
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This article investigates the multirate disturbance rejection control problem for linear control systems with mismatched disturbance and measurement delay using predictor-based extended state observer. A new extended state observer together with output predictor is first designed to obtain the estimation values of system state and mismatched disturbance, where output predictor is used to compensate the influences of measurement delay and sampling of output. To attenuate the undesirable influence of mismatched disturbance, we then design a new sampled-data robust controller with disturbance compensation, and the updating rate of the proposed controller is allowed to be different from that of the sensor. Thanks to prediction and disturbance/uncertainty estimation and attenuation techniques, the disturbance rejection property of the resultant closed-loop control systems is enhanced despite the multirate and measurement delay. Some sufficient conditions are presented to ensure the stability property of the resultant control systems. We finally consider the application of the visual servoing control system for an inertially stabilized platform, and the experiment results verify superiorities of the predictor-based disturbance rejection control method proposed in the article.
Unsupervised image translation aims to learn the translation between two domains without paired data. Although impressive progress has been made in recent years, existing methods are difficult to build mapping between...
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ISBN:
(纸本)9783031189067;9783031189074
Unsupervised image translation aims to learn the translation between two domains without paired data. Although impressive progress has been made in recent years, existing methods are difficult to build mapping between domains with drastic visual discrepancies. In this paper, we propose a novel framework for unsupervised image translation with large discrepancies. The key insight is to leverage the generative prior from pretrained GANs for the target domain (e.g., StyleGAN), to learn rich prior information of the target domain. We propose a two-stage framework, the GAN of the target domain is pretrained to obtain the prior information in the first stage, the pretrained GAN for the target domain is embedded as the decoder of the translation network and the translation network is trained with the guidance of the prior information in the second stage. Experimental results show the superiority of our method compared to the existing state-of-the-art methods for the translation between challenging and distant domains.
Bilinear features arise in fine-grained visual recognition. They are advantageous to encode detailed representations and attributes to differentiate visually similar objects. The apparent similarity is challenging in ...
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ISBN:
(数字)9783031024443
ISBN:
(纸本)9783031024443;9783031024436
Bilinear features arise in fine-grained visual recognition. They are advantageous to encode detailed representations and attributes to differentiate visually similar objects. The apparent similarity is challenging in visual tracking where background distractors interfere siamese trackers to localize the target object. Especially when distractors and the target belong to the same object category. To increase the discrimination between similar appearance objects, we propose an efficient bilinear encoding method for siamese tracking. The proposed method consists of a self-bilinear encoder and an cross-bilinear encoder. The bilinear features generated via the self-bilinear encoder and the cross-bilinear encoder represent target variations itself and target distractor difference, respectively. To this end, the proposed bilinear encoders advance siamese trackers to capture target appearance variations while differentiating the target and background distractors. Experiments on the benchmark datasets show the effectiveness of bilinear features. Our tracker performs favorably against state-of-the-art approaches.
A brain-computer interface (BCI) establishes a direct communication pathway between the brain and an external device. Electroencephalogram (EEG) is the most popular input signal in BCIs, due to its convenience and low...
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A brain-computer interface (BCI) establishes a direct communication pathway between the brain and an external device. Electroencephalogram (EEG) is the most popular input signal in BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the accurate decoding of EEG signals;however, EEG signals also contain rich private information, e.g., user identity, emotion, and so on, which should be protected. This paper first exposes a serious privacy problem in EEG-based BCIs, i.e., the user identity in EEG data can be easily learned so that different sessions of EEG data from the same user can be associated together to more reliably mine private information. To address this issue, we further propose two approaches to convert the original EEG data into identity-unlearnable EEG data, i.e., removing the user identity information while maintaining the good performance on the primary BCI task. Experiments on seven EEG datasets from five different BCI paradigms showed that on average the generated identity-unlearnable EEG data can reduce the user identification accuracy from 70.01% to at most 21.36%, greatly facilitating user privacy protection in EEG-based BCIs.
It is a positive trend for hemiplegia with wearable robots in rehabilitation training. Recently, wearable Supernumerary Robotic Limb (SRL) is rising to a hot spot. The difficulty in modeling SRL for hemiplegia is how ...
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This article proposes a novel rotational restart strategy based on the dead time adaptive expansion for the sensorless surface-mounted permanent magnet synchronous machines drive system. With the proposed method, the ...
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This article proposes a novel rotational restart strategy based on the dead time adaptive expansion for the sensorless surface-mounted permanent magnet synchronous machines drive system. With the proposed method, the interval time of zero-voltage pulses (ZVPs) is replaced by the dead time and each pulsewidth modulation (PWM) period consists of the dead time and action time of ZVPs. By regulating the dead time based on the stator-current amplitude feedback, the stator current can be controlled within the reasonable setting range, which not only can achieve the adaptive selection of the action time and interval time of ZVPs, but also avoid the potential overcurrent and overcome the current sampling noise. Meanwhile, aiming at two continuous and discontinuous current modes caused by the inserted dead time, the corresponding calculation expressions of the rotor position are deduced in detail. Finally, experimental results indicate the effectiveness and feasibility of the proposed strategy.
作者:
Ma, JianfeiHuazhong Univ Sci & Technol
Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Luoyu Rd 1037 Wuhan 430074 Hubei Peoples R China
BACKGROUND: Immunomodulatory genes play significant roles in the regulation of immunological properties of gastric cancer, but the effect of epigenetic regulation of these genes on the immune properties is unknown. ME...
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BACKGROUND: Immunomodulatory genes play significant roles in the regulation of immunological properties of gastric cancer, but the effect of epigenetic regulation of these genes on the immune properties is unknown. METHOD: I analyzed the methylation-expression correlation among all immunomodulators and compared with the nonimmunomodulators. The association between epigenetically regulated immunomodulators (ERI) and tumor microenvironment is evaluated. A key immunomodulator TIGIT is further selected to investigate the potential value in the regulation of immunologic properties. Furthermore, the prognostic value and the immunotherapeutic potential of TIGIT are also explored. RESULT: Four genes are identified as ERIs based on the negative correlation between expression and methylation. Association analysis shows that three ERIs participate in the regulation of the immune microenvironment of gastric cancer. Among these ERIs, TIGIT is identified as a key immunomodulator. TIGIT is found to be significantly associated with immune properties. The high TIGIT expression group tends to display an active immune landscape. TIGIT expression is also found to be associated with survival and immunotherapeutic sensitivity. High TIGIT expression group has a favorable prognosis and is more likely to respond to immunotherapy than the low expression group. CONCLUSION: TIGIT is an epigenetically regulated immunomodulator of gastric cancer which can modify the immune activity and affect immunotherapeutic sensitivity. These findings can promote the research of epigenetic therapies and improve the survival of cancer patients by sensitizing tumors to immune therapies.
A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is the preferred input signal in non-invasive BCIs, due to its convenience and low cos...
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This article explores the prescribed performance control design for linear two-time-scale systems (TTSSs). Due to ill-conditioning and high dimensionality, existing prescribed performance control methods for single-ti...
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This article explores the prescribed performance control design for linear two-time-scale systems (TTSSs). Due to ill-conditioning and high dimensionality, existing prescribed performance control methods for single-time-scale systems are unsuitable for TTSSs. Moreover, current TTSS methodologies focus on steady-state performance, often neglecting transient dynamics. To address these challenges, we first apply the Chang transformation to decouple the fast and slow states. Next, we use a state transformation to convert the state equation into block form to eliminate the requirement of a full row-rank input matrix. Finally, the backstepping method is utilized to design the prescribed performance control. The effectiveness and advantages of the proposed control strategy are demonstrated through two examples: a numerical simulation and a hardware-in-the-loop experiment involving an electronic circuit system.
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