Real-time Correction Research Articles

Correction of microwave pulse reflection by digital filters in superconducting quantum circuits

Abstract Reducing the control error is vital for high-fidelity digital and analog quantum operations. In superconducting circuits, one disagreeable error arises from the reflection of microwave signals due to impedance mismatch in the control chain. Here, we demonstrate a reflection cancelation method when considering that there are two reflection nodes on the control line. We propose to generate the pre-distortion pulse by passing the envelopes of the microwave signal through digital filters, which enables real-time reflection correction when integrated into the field-programmable gate array (FPGA). We achieve a reduction of single-qubit gate infidelity from 0.67% to 0.11% after eliminating microwave reflection. Real-time correction of microwave reflection paves the way for precise control and manipulation of the qubit state and would ultimately enhance the performance of algorithms and simulations executed on quantum processors.

Forecast Uncertainties Real-Time Data-Driven Compensation Scheme for Optimal Storage Control

This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts, which are integral to an optimal energy storage control system. By expanding on an existing algorithm, this study resolves issues discovered during implementation and addresses previously overlooked concerns, resulting in significant enhancements in both performance and reliability. The refined real-time control scheme is integrated with a day-ahead optimization engine and forecast model, which is utilized for illustrative simulations to highlight its potential efficacy on a real site. Furthermore, a comprehensive comparison with the original formulation was conducted to cover all possible scenarios. This analysis validated the operational effectiveness of the scheme and provided a detailed evaluation of the improvements and expected behavior of the control system. Incorrect or improper adjustments to mitigate forecast uncertainties can result in suboptimal energy management, significant financial losses and penalties, and potential contract violations. The revised algorithm optimizes the operation of the battery system in real time and safeguards its state of health by limiting the charging/discharging cycles and enforcing adherence to contractual agreements. These advancements yield a reliable and efficient real-time correction algorithm for optimal site management, designed as an independent white box that can be integrated with any day-ahead optimization control system.

Study on the Identification of Execution Errors, Causes, and Specific Corrective Kinetic and Prophylactic Means in Free Throws in Basketball

The identification of execution errors in real-time correction through an innovative specific device to free throw refinement, and through corrective kinetic and prophylactic exercises adapted to basketball game. The study included 92 subjects structured into sports categories: U14, U16, U18, and by gender: female (46 subjects) and male (46 subjects). Subjects were observed to identify the incidence of execution errors in free throws, their causes and correction methods during the study's testing. The study results highlight the decrease in the number of execution errors between the final and initial testing due to the implementation of innovative technology specific to free throw refinement and the application of specific corrective kinetic and prophylactic exercises. For all junior and gender categories of the study, the number of errors decreased in the final testing compared to the initial one, highlighting the importance of identifying errors and causes in real-time and correcting them through specific means.

CRASH2p: Closed-loop Two Photon Imaging in Freely Moving Animals.

Direct measurement of neural activity in freely moving animals is essential for understanding how the brain controls and represents behaviors. Genetically encoded calcium indicators report neural activity as changes in fluorescence intensity, but brain motion confounds quantitative measurement of fluorescence. Translation, rotation, and deformation of the brain and the movements of intervening scattering or auto-fluorescent tissue all alter the amount of fluorescent light captured by a microscope. Compared to single-photon approaches, two photon microscopy is less sensitive to scattering and off-target fluorescence, but more sensitive to motion, and two photon imaging has always required anchoring the microscope to the brain. We developed a closed-loop resonant axial-scanning high-speed two photon (CRASH2p) microscope for real-time 3D motion correction in unrestrained animals, without implantation of reference markers. We complemented CRASH2p with a novel scanning strategy and a multi-stage registration pipeline. We performed volumetric ratiometrically corrected functional imaging in the CNS of freely moving Drosophila larvae and discovered previously unknown neural correlates of behavior.

Entertainment robots based on digital new media application in real-time error correction mode for Chinese English translation

With the assistance of digital new media technology, virtual entertainment robots, as a new learning experience mode, can effectively enhance the interactive process of e-learning learning. This article studies the application of entertainment robots based on digital new media in real-time error correction mode for Chinese English translation. Through experiments, it has been verified that the flexible use of deep learning technology can significantly improve user satisfaction and translation accuracy, and has already improved the level of error correction and positioning. This article first introduces the existing mainstream machine learning models, including supervised neural network models and attention mechanisms. On this basis, the system was optimized to further improve its performance. At the same time, this article proposes new improvement plans to address the shortcomings of current mainstream translation systems. We conducted comparative experiments on the error correction model of the proposed adaptive algorithm for specific error types, and also tested it using real datasets. Research has shown that using adaptive algorithms based on reinforcement deep learning can not only significantly optimize the error correction efficiency of our system, but also flexibly adapt to the needs of various optimization strategies.

Prediction of Errors in the Field of Additive Manufacturing Technology

Additive manufacturing, also known as 3D printing, allows the formation of complex geometric structures layer by layer. Predicting errors in this process may help identify potential problems in a timely manner and minimise waste. A human may detect an additive manufacturing error, but cannot provide continuous monitoring or real-time correction. The article is focused on the design of a camera system design for online monitoring of the 3D printing process with the task of detecting process errors arising during 3D printing of objects. The article describes the methodology for tracking the occurrence of process errors in 3D printing, which are identified in the OctoPrint Nexus AI plug-in environment for the subsequent application of a suitable solution to minimize the occurrence of defects. The application of a real-time process monitoring system including the ability to correctly predict anomalous behaviour in the context of artificial intelligence has proven to be an appropriate solution to that particular problem.

Additive Manufacturing Provides Infinite Possibilities for Self-Sensing Technology.

Integrating sensors and other functional parts in one device can enable a new generation of integrated intelligent devices that can perform self-sensing and monitoring autonomously. Applications include buildings that detect and repair damage, robots that monitor conditions and perform real-time correction and reconstruction, aircraft capable of real-time perception of the internal and external environment, and medical devices and prosthetics with a realistic sense of touch. Although integrating sensors and other functional parts into self-sensing intelligent devices has become increasingly common, additive manufacturing has only been marginally explored. This review focuses on additive manufacturing integrated design, printing equipment, and printable materials and stuctures. The importance of the material, structure, and function of integrated manufacturing are highlighted. The study summarizes current challenges to be addressed and provides suggestions for future development directions.

Simulation and experimental research on energy management control strategy for fuel cell heavy-duty truck

Energy Management Control Strategies (EMCS) play a significant role in improving the economy of Fuel Cell Heavy-duty Truck (FCHT). In this paper, a Single Fuzzy (S-Fuzzy) control strategy is introduced for the FCHT by considering the output characteristics of the fuel cell as well as the charging and discharging power characteristics of the power battery at the current state of charge (SOC). On this basis, the real-time difference between the current SOC and the target SOC of the power battery during the actual operation of the vehicle was further considered, the real-time correction coefficient of the fuel cell output power was introduced, and a Compound Fuzzy (C-Fuzzy) control strategy suitable for FCHT was developed. Meanwhile, the established vehicle model was verified dynamically and economically to ensure its accuracy, with the maximum speed error lower than 2%, the SOC error within 4.9%, and the relative dynamic simulation error of 1.3%. Utilizing co-simulation platforms Cruise and Matlab, the research demonstrates that the C-Fuzzy control strategy significantly outperforms the S-Fuzzy strategy, reducing Equivalent Hydrogen Consumption (EHC) by 1.63% under C-WTVC conditions and by 3.12% under CHTC-TT conditions. To counteract efficiency loss from fuel cell load fluctuations, a real-time EMCS based on C-Fuzzy control and multi-level power output was developed. Validated over a 122-km route, the real-time EMCS reduced the equivalent hydrogen consumption per 100 km of the FCHT to 10.14 kg, maintained the average fuel cell efficiency at 51.02%, and ensured stable SOC of the power battery within the predetermined target range.

Two-layer energy management strategy for grid-integrated multi-stack power-to-hydrogen station

Large-scale power-to‑hydrogen (P2H) stations with multi-stack configurations, are emerging as valuable flexible resources for the power grid. The energy management strategy (EMS) determines multi-stack operation statuses. Nonetheless, existing EMS focus on production without adequately addressing the implications for grid-side power factor (PF) and potential security concerns. This paper addresses this limitation by presenting a model that characterizes the PF of a multi-stack P2H system across varying operation statuses defined by current and temperature. Through this model, we highlight a clear trade-off between the PF constraint and production targets in multi-stack scheduling. Subsequently, we introduce an improved EMS framework for multi-stack P2H that seeks a balance between PF, production, and security. This EMS is organized as a two-layer execution structure to guarantee control accuracy and tractability, which includes a model-based robust multi-stack scheduling programming and a rule-based real-time increment correction algorithm in series. Case studies compare multi-stack scheduling strategies under the proposed EMS with the traditional production-oriented strategy. The effectiveness of the extended PF and security dimensions is verified to comprehensively improve the responsiveness to power instructions. Furthermore, we outline five representative cluster-level scheduling strategies aligned with different load scenarios, offering insights for practical industrial implementations.

Closing the loop in minimally supervised human–robot interaction: formative and summative feedback

Human instructors fluidly communicate with hand gestures, head and body movements, and facial expressions, but robots rarely leverage these complementary cues. A minimally supervised social robot with such skills could help people exercise and learn new activities. Thus, we investigated how nonverbal feedback from a humanoid robot affects human behavior. Inspired by the education literature, we evaluated formative feedback (real-time corrections) and summative feedback (post-task scores) for three distinct tasks: positioning in the room, mimicking the robot’s arm pose, and contacting the robot’s hands. Twenty-eight adults completed seventy-five 30-s-long trials with no explicit instructions or experimenter help. Motion-capture data analysis shows that both formative and summative feedback from the robot significantly aided user performance. Additionally, formative feedback improved task understanding. These results show the power of nonverbal cues based on human movement and the utility of viewing feedback through formative and summative lenses.

Improved Multiple Feature-Electrochemical Thermal Coupling Modeling of Lithium-Ion Batteries at Low-Temperature with Real-Time Coefficient Correction

Improved Multiple Feature-Electrochemical Thermal Coupling Modeling of Lithium-Ion Batteries at Low-Temperature with Real-Time Coefficient Correction

Exploring the capacities of ChatGPT: A comprehensive evaluation of its accuracy and repeatability in addressing helicobacter pylori-related queries.

Educational initiatives on Helicobacter pylori (H. pylori) constitute a highly effective approach for preventing its infection and establishing standardized protocols for its eradication. ChatGPT, a large language model, is a potentially patient-friendly online tool capable of providing health-related knowledge. This study aims to assess the accuracy and repeatability of ChatGPT in responding to questions related to H. pylori. Twenty-one common questions about H. pylori were collected and categorized into four domains: basic knowledge, diagnosis, treatment, and prevention. ChatGPT was utilized to individually answer the aforementioned 21 questions. Its responses were independently assessed by two experts on H. pylori. Questions with divergent ratings were resolved by a third reviewer. Cohen's kappa coefficient was calculated to assess the consistency between the scores of the two reviewers. The responses of ChatGPT on H. pylori-related questions were generally satisfactory, with 61.9% marked as "completely correct" and 33.33% as "correct but inadequate." The repeatability of the responses of ChatGPT to H. pylori-related questions was 95.23%. Among the responses, those related to prevention (comprehensive: 75%) had the best response, followed by those on treatment (comprehensive: 66.7%), basic knowledge (comprehensive: 60%), and diagnosis (comprehensive: 50%). In the "treatment" domain, 16.6% of the ChatGPT responses were categorized as "mixed with correct or incorrect/outdated data." However, ChatGPT still lacks relevant knowledge regarding H. pylori resistance and the use of sensitive antibiotics. ChatGPT can provide correct answers to the majority of H. pylori-related queries. It exhibited good reproducibility and delivered responses that were easily comprehensible to patients. Further enhancement of real-time information updates and correction of inaccurate information will make ChatGPT an essential auxiliary tool for providing accurate H. pylori-related health information to patients.

Long-latency and Volitional Responses Modulate Upper Extremity Movements During a Novel Visuomotor Tracking Task

Precise upper limb movements are essential for daily tasks and motor function. These movements are governed by feedforward and feedback mechanisms. Feedforward responses enable anticipatory movement planning, while feedback responses utilize sensory information for real-time corrections. Long-latency reflexes (LLRs) represent crucial feedback responses during unexpected perturbations. LLRs are integral in maintaining motor control and can adapt based on various factors. However, the mechanisms governing LLRs, especially in the upper extremity, remain unclear. Methods: Forty healthy participants performed a visuomotor elbow flexion task with one arm while the opposite limb remained static. Muscle activity of the biceps and triceps muscles of both upper extremities was recorded during perturbations. Participants completed a task with varying speeds and resistance levels. Electromyography data was analyzed across pre-perturbation, short-latency reflex (SLR), long-latency reflex (LLR), and voluntary response (VR) phases. Results: In response to unexpected upper extremity perturbations, participants predominantly relied on two core strategies. Inhibitory long-latency reflexes within the biceps played a central role, with participants emphasizing inhibition to maintain movement stability in a window of 50-120 ms after the disturbance. Furthermore, volitional control, primarily executed through the triceps muscles, emerged as another dominant approach, allowing participants to actively regain precision and control after the perturbation in a volitional manner starting from 120 ms. Participants’ response to perturbations reflected the task context and perceived urgency. Over repeated trials, participants refined their response strategies, becoming more precise and effcient. Variations in movement speed and resistance influenced muscle activity. Preliminary evidence suggested the involvement of cross-transcortical communication in response to perturbations, with delayed responses observed in the non-participating upper extremity. Conclusion: This study reveals that participants use both long-latency and volitional responses to counteract perturbations during an upper extremity visuomotor task, adapting their response strategies over time. Biceps inhibitory LLRs and triceps voluntary activation characterize feedback control in this context. These findings shed light on the dynamic nature of upper extremity feedback responses during unpredictable perturbations and suggest the involvement of cross-transcortical communication. Understanding these mechanisms can inform rehabilitation strategies for individuals with neurological conditions affecting motor control. Research Support: This study was funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development: R01HD084645, R01HD082109. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Airborne Platform Three-Dimensional Positioning Method Based on Interferometric Synthetic Aperture Radar Interferogram Matching

As the demand for precise navigation of aircraft increases in modern society, researching high-precision, high-autonomy navigation systems is both theoretically valuable and practically significant. Because the inertial navigation system (INS) has systematic and random errors, its output information diverges. Therefore, it is necessary to combine them with other navigation systems for real-time compensation and correction of these errors. The SAR matching positioning and navigation system uses synthetic aperture radar (SAR) image matching for platform positioning and compensates for the drift caused by errors in the inertial measurement unit (IMU). Images obtained by SAR are matched with digital landmark data, and the platform’s position is calculated based on the SAR imaging geometry. However, SAR matching positioning faces challenges due to seasonal variations in SAR images, the need for typical landmarks for matching, and the lack of elevation information in two-dimensional SAR image matching. This paper proposes an airborne platform positioning method based on interferometric SAR (InSAR) interferogram matching. InSAR interferograms contain terrain elevation information, are less affected by seasonal changes, and provide higher positioning accuracy and robustness. By matching real-time InSAR-processed interferograms with simulated interferograms using a digital elevation model (DEM), three-dimensional position information about the matching points has been obtained. Subsequently, a three-dimensional positioning model for the platform has bene established using the unit line-of-sight vector decomposition method. In actual flight experiments using an FMCW Ku-band Interferometric SAR system, the proposed platform positioning framework demonstrated its ability to achieve precise positioning in the absence of signals from the global navigation satellite system (GNSS).

Real-time model correction using Kalman filter for Raman-controlled cell culture processes

Raman spectroscopy has found extensive use in monitoring and controlling cell culture processes. In this context, the prediction accuracy of Raman-based models is of paramount importance. However, models established with data from manually fed-batch cultures often exhibit poor performance in Raman-controlled cultures. Thus, there is a need for effective methods to rectify these models. The objective of this paper is to investigate the efficacy of Kalman filter (KF) algorithm in correcting Raman-based models during cell culture. Initially, partial least squares (PLS) models for different components were constructed using data from manually fed-batch cultures, and the predictive performance of these models was compared. Subsequently, various correction methods including the PLS-KF-KF method proposed in this study were employed to refine the PLS models. Finally, a case study involving the auto-control of glucose concentration demonstrated the application of optimal model correction method. The results indicated that the original PLS models exhibited differential performance between manually fed-batch cultures and Raman controlled cultures. For glucose, the RMSEP of manually fed-batch culture and Raman-controlled culture was 0.23 and 0.40 g·L-1. With the implementation of model correction methods, there was a significant improvement in model performance within Raman-controlled cultures. The RMSEP for glucose from updating-PLS, KF-PLS, and PLS-KF-KF was 0.38, 0.36, and 0.17 g·L-1, respectively. Notably, the proposed PLS-KF-KF model correction method was found to be more effective and stable, playing a vital role in the automated nutrient feeding of cell cultures.

Lidar AOD inversion and aerosol extinction profile correction method based on GA-BP neural network

Lidar is an effective remote sensing method to obtain the vertical distribution of aerosols, and how to select the aerosol extinction-backscattering ratio (AE-BR) during the inversion process is a key step to guarantee the accuracy of the lidar inversion of aerosol optical thickness (AOD) and aerosol extinction coefficient profile (AECP). In this paper, an inversion algorithm for AOD and AECP based on a genetic BP (GA-BP) neural network is proposed. Simultaneous measurements are carried out using CE318 sun photometer and lidar, and the mapping relationship between the lidar echo signal and AOD is established based on the genetic BP (GA-BP) neural network method, which achieves the accurate inversion of AOD with an absolute error mean value of 0.0156. Based on the AOD output from the GA-BP neural network, the real-time best AE- BR to improve the inversion accuracy of AECP. Finally, practical tests show that the method achieves accurate inversion of AOD, determines the range of AE-BR from 20-50sr, realizes real-time dynamic correction of AECP, and has strong generalization ability and applicability in practical situations.

Faecal Standing Crop with real time correction using scat detection dogs to estimate population density

Abstract Population density is fundamental information for assessing the conservation status of species and support management and conservation actions for in situ populations, but is unknown for many forest species due to their difficulty in detection. The Faecal Standing Crop (FSC) method using detection dogs is an alternative for cryptic or elusive species. An intrinsic difficulty of FSC is the ability to find faecal samples in the field and to estimate the probability of which faeces detection is influenced by degradation due to climatic conditions. Our goal was to propose a concurrent FSC parameter estimation using a scat detection dog under different climatic conditions and apply those parameters in a wild deer population. Ten faecal samples of grey brocket deer (Subulo gouazoubira) were placed weekly in a transect (24 × 1440 m) in both dry and wet seasons (12 weeks each). A scat detection dog was then employed to find experimental faecal samples to determine the FSC parameters that were subsequently used with naturally occurring faecal samples (also dog‐detected) to estimate population density. The oldest dog found samples were 21 (Dry) and seven (Wet) days after placement, resulting in dog efficiency of 22.5% (Dry) and 30% (Wet). Adjusting the model to account for efficiency and scat durability, we estimated similar, seasonal, densities of 4.51 individuals km−2 (SD = 2.21, Dry) and 5.37 individuals km−2 (SD = 3.71, Wet). Synthesis and applications: Our results demonstrate that our concurrent methodology corrected the effects of weather and habitat on FSC parameters thereby allowing for accurate population density estimation. Additionally, this method can provide reasonably precise density estimates with a logistically feasible sample size, as demonstrated by simulation. Following our recommendations, this method allows a reliable estimate of population density because it incorporates any influence of study area, dog ability and climate in faecal sample detection, providing fundamental information for the conservation of many cryptic and elusive species.

A New Real-Time Positioning Correction System Based on an Inherited Sampling Algorithm With an FPGA Accelerator

A New Real-Time Positioning Correction System Based on an Inherited Sampling Algorithm With an FPGA Accelerator

An improved Cauchy robust correction-sage Husa extended Kalman filtering algorithm for high-precision SOC estimation of Lithium-ion batteries in new energy vehicles

The accurate estimation of battery State of Charge (SOC) is a key technology in the research of electric vehicle battery management systems. In order to solve the problem of inaccurate noise estimation in nonlinear systems, an improved Cauchy robust correction-Sage Husa extended Kalman filtering (CRC-SHEKF) algorithm is proposed for high-precision SOC estimation of lithium-ion batteries in new energy vehicles. Considering the polarization effect of the battery, the FFRLS algorithm is used for online parameter identification of the Dual Polarization model. Using robust data correction methods, the Cauchy robust function is simplified for real-time correction of the covariance matrix Q of system state noise and the covariance matrix R of the observed noise in the filtering process and combined with SHEKF for SOC estimation. The experimental results show that under different temperature conditions and complex working conditions, the proposed CRC-SHEKF algorithm has the minimum mean absolute error (MAE), root mean square error (RMSE), and maximum error (MAX). Under the condition of the Beijing bus dynamic stress test (BBDST) at 15 °C, the MAE, RMSE, and MAX of the CRC-SHEKF algorithm are 0.392 %, 0.716 %, and 0.945 %, with the computing time of only 4.839 s. The algorithm proposed in this article has high accuracy and robustness, and has practical application value, providing a reference for the application of lithium battery condition monitoring.

DEDALO: Device for Enhanced Dust Analyses with Light Obscuration sensors

Instruments based on light obscuration sensors are widely used for measuring the size distribution of insoluble sub-visible particles in liquid suspensions, being fast and suitable for in situ and real-time measurements. Such instruments are typically calibrated by means of reference polystyrene spherical particles with a specific refractive index, which unavoidably leads to systematic errors when determining the size of particles of different materials. In this paper, we propose a reliable and consistent method to overcome this limitation by setting the refractive index value according to the sample, thus achieving an improved particle size distribution (PSD) measurement. An ad hoc, ready-to-use, open source code with a graphical interface able to drive an in-line instrument and obtain a real-time correction to the PSD has been developed. The method has been extensively validated with several oil emulsions characterized by different refractive index values and the results have been compared with an independent optical method. As an example of application, we have adopted this approach for the analysis of dust suspended in meltwater of an ice core from a glacier in the Aosta Valley (Italy). We believe that our approach will strongly improve the accuracy in characterizing liquid suspensions and reduce discrepancies between data obtained with different methods. The code has been made publicly available at: https://instrumentaloptics.fisica.unimi.it/dedalo/ and on the GitHub page of the corresponding author (https://github.com/LucaTeruzzi/DEDALO).