Setup Changes Research Articles

Revealing intracavitary catheter positions: artificial intelligence-enhanced localization with an electrogram-driven variational autoencoder model

Abstract Background The acquisition of electroanatomical (EA) maps in clinical electrophysiology procedures typically depends on locating intracardiac mapping catheters through magnetic triangulation with external patches placed on the patient's skin. However, this configuration is hampered by shifts in the reference system if the patient moves or if the setup changes during the procedure. Leveraging the feature extraction capabilities of artificial intelligence models, it becomes possible to assess catheter positions within the atria solely based on the information encoded in intracavitary electrograms (EGMs). Consequently, a neural network model could serve as an alternative or complementary system for locating EA mapping catheters, eliminating the need for external reference patches. Purpose We aimed to create a deep learning model utilizing a disentangled variational autoencoder (d-VAE) and unipolar EGM signals. The goal was to extract the precise position of a multi-electrode catheter within the left atrium and predict the specific atrial region in which it is situated, relying solely on the information derived from unipolar EGMs. Methods The model was trained using data from 51 patients with persistent atrial fibrillation who underwent pulmonary vein ablation. We exported 3D EA maps obtained with a 20-pole multi-electrode catheter, comprising a total of 40,000 unipolar EGMs with a duration of 30 seconds. Atrial geometries were segmented into 12 different areas (refer to the Figure). Subsequently, the d-VAE model was trained with unipolar EGMs to acquire encoded features, which were then input into a classifier model. The classifier was combined with atrial positions using one-hot encodings as the ground truth. The optimal architecture was selected through a grid search involving different layer numbers, sizes, and fine-tuning. For the encoding part, we assessed the negative evidence lower bound (ELBO) and Pearson’s correlation. Regarding the classifier, we evaluated accuracy, extended accuracy (indicating accuracy in predicting the region or an adjacent one), and the area under the curve (AUC). Results The best-performing autoencoder architecture presented a negative ELBO of -0.087 and a Pearson’s correlation of 0.971; and the classifier obtained an accuracy of 38.1%, an extended accuracy of 75.2%, and an AUC of 0.814. Conclusion We introduced an alternative method for localizing intracardiac catheters within the atrial chambers based on deep-learning techniques. In contrast to traditional hardware technologies, our proposed method eliminates the need for external magnetic or impedance reference patches. This approach is not limited to AF and can be extended to other supra-ventricular procedures. The results of our proposed method are promising, suggesting potential advancements in alternative systems for locating intracardiac catheters to complement existing classical hardware methods in the future.Variational autoencoder architecture

In-Situ Observation of Temperature Distribution on a Planar Type SOEC During Start-Stop Cycle Operation

Introduction It is important to establish various analytical methods for solid oxide electrolysis cells (SOECs) under practical operating conditions in order to improve their performance and to analyze their degradation phenomena. In-situ observation of temperature distribution on a planar type SOEC enables to reveal the distributions of electrochemical reactions and the cell degradation. While there have been several reports on the internal visualization of solid oxide fuel cells (SOFCs)1, 2, there have been only a few reports on that of SOECs. Since the endothermic electrolysis reaction occurs in SOECs, comparing with SOFCs, a different temperature distribution will appear. Therefore, it should be necessary to evaluate the phenomena on the planar SOECs for analyzing the temperature distribution taking gas flow and current distribution into consideration.The purpose of this study is to evaluate the effect of cell degradation caused by the start-stop cycle operation on the temperature distribution using a 5 cm × 5 cm planar cell observed by infrared camera. Experimental In this study, a planar cell was fabricated using a scandia-stabilized zirconia electrolyte (ScSZ, 200 µm thick, 5 cm × 5 cm in area) as a substrate. LSM ((La0.8Sr0.2)0.98MnO3) and LSM-ScSZ composite materials were used for the air electrode, and nickel and ScSZ cermet were used for the fuel electrode. The electrode area was 4 cm × 4 cm (16 cm2). Each electrode material was screen-printed and sintered. A platinum mesh was used as the current collector.In this test, the cell was operated at 800 °C and 50%-humidified hydrogen (200 ml min-1) was supplied to the fuel electrode. The durability test was conducted under the accelerated degradation conditions of a start-stop cycle repeated in every 1 hour with 0.2 A cm-2 for 500 cycles (1000 hours). Electrochemical characteristics were measured by connecting Pt wires, spot-welded every 1 mm on one side of both electrode surfaces, to the voltage terminals of the electrochemical measurement setup and to the current terminals of the external power supply unit.Figure 1 shows a schematic view of the experimental setup and temperature change compared with the initial condition. The temperature distribution was observed by infrared camera (FLIR SC2500 - NIR, uncertainty ±0.02 oC) from the air electrode side open to the electric furnace. The difference between the temperatures before and during current loading was derived as the distribution of temperature changes. Results and discussion First, the electrochemical characteristics of the planar type SOECs during the start-stop cycle test were obtained. The cell voltage at 0.2 A cm-2 increased from 1.19 V to 1.29 V during the test up to 500 cycles. The cell voltage was 1.27 V even after the initial 50 cycles. This suggests that SOEC electrode degradation3,4 occurred in the early stage of the start-stop cycling.The temperature changes after 50 cycles showed that each temperature change was relatively small because the cell was operated near the thermoneutral potential (around 1.29 V at 800 °C). It was confirmed that the endothermic electrolysis reaction was predominant near the fuel inlet. In addition, heat generation due to Joule heating was also appeared near the current collecting Pt wire connected to the power supply. Therefore, in SOEC operation, it is suggested that the temperature distribution is affected by the shape of current collector, gas supply condition, and electrochemical reaction distribution. The detailed analysis on the SOEC electrochemical properties and temperature distribution observed by in-situ measurements during cycling tests is also presented. Acknowledgements A part of this research was supported by New Energy and Industrial Technology Development Organization (NEDO), project No. JPNP14026. References (1) S. Shinichi and H. Iwai, J. Power Sources, 482, 229070 (2021).(2) Y. Shiratori, T. Ogura, H. Nakajima, M. Sakamoto, Y. Takahashi, Y. Wakita, T. Kitaoka, and K. Sasaki, Int. J. Hydrogen Energy, 38 (25), 10542 (2013).(3) M. Keane, M. K. Mahapatra, A. Verma, and P. Singh, Int. J. Hydrogen Energy, 37 (22), 16776 (2012).(4) M. Hubert, J. Laurencin, P. Cloetens, B. Morel, D. Montinaro, and F. Lefebvre, J. Power Sources, 397, 240 (2018). Figure 1

조선소 병렬 기계 공정에서의 납기 지연 및 셋업 변경 최소화를 위한 강화학습 기반의 생산라인 투입순서 결정

The profile shops in shipyards produce section steels required for block production of ships. Due to the limitations of shipyard’s production capacity, a considerable amount of work is already outsourced. In addition, the need to improve the productivity of the profile shops is growing because the production volume is expected to increase due to the recent boom in the shipbuilding industry. In this study, a scheduling optimization was conducted for a parallel welding line of the profile process, with the aim of minimizing tardiness and the number of set-up changes as objective functions to achieve productivity improvements. In particular, this study applied a dynamic scheduling method to determine the job sequence considering variability of processing time. A Markov decision process model was proposed for the job sequence problem, considering the trade-off relationship between two objective functions. Deep reinforcement learning was also used to learn the optimal scheduling policy. The developed algorithm was evaluated by comparing its performance with priority rules (SSPT, ATCS, MDD, COVERT rule) in test scenarios constructed by the sampling data. As a result, the proposed scheduling algorithms outperformed than the priority rules in terms of set-up ratio, tardiness, and makespan.

Evaluation of SMED Industrial Method to Increase the Crop Yield of Asparagus Based on Control of pH of Soil and Water in the Agricultural Industry of Mexicali Valley

In this scientific study was made an evaluation to analyze the process to make the setup change in the agricultural industries and improve the times of this change to have a better flow process. This investigation was made in an agricultural industry located in the Mexicali Valley, which is part of the Mexicali city that is located in the northwest of the Mexican Republic and is a border zone between Mexico and Unites States of America (USA) and is a commercial region with the California State of the USA. In this scientific study was evaluated the three principal types of variety of asparagus of agricultural machines as cutter, count and of package of agricultural products as asparagus in an agricultural industry located in the Mexicali Valley, which is necessary change the type of structures of each activity (cutter, count and of package), being the setup change, and make as soon as possible this action. Each structure to each type of asparagus evaluated in this investigation, have size and a time to installed, and to know if was installed the correct structure to each asparagus is necessary control by a computer system to avoid any damage to asparagus products and the agricultural machinery. The method used to evaluate the crop yielding of asparagus and with this the productivity and quality indices, was the Single Minute Exchange of Die (SMED) as an industrial engineering method in the agricultural industry. Also, in this scientific study was made an analysis of the occurrence of corrective, preventive and predictive maintenance and obtain data of the size of asparagus, time of cut and package and the weight and quantity of asparagus cropped, cut off and packaged. This data was obtained by electronic sensors, which are coupled to a computer systems and stored the data obtained to be evaluated with statistical methods with graphs and tables. This scientific study was made in 2022. This investigation was made in the Mexicali Valley, which is located in the northwest of the Mexican Republic.

PAINEIRA beamline at Sirius: an automated facility for polycrystalline XRD characterization

Herein we outline the design of the Paineira beamline – a beamline dedicated to powder X-ray diffraction at the new Brazilian synchrotron source (Sirius). The optical layout optimizes the high photon brightness of the fourth-generation synchrotron light source generated by an undulator. The heavy-duty diffractometer operating in Debye-Scherrer geometry has two detector setups: a multi-analyser crystal for high-resolution and an arc-shaped area detector covering 100° in scattering angle (2θ). The beamline is optimized by carrying out high-throughput measurements with the possibility of rapid setup changes to in-situ and operando measurement conditions. Paineira beamline is under construction and will open new opportunities for materials science, catalysis, energy materials, and geoscience research with synchrotron XRD in Brazil.

Event-Based Angular Speed Measurement and Movement Monitoring.

Computer vision techniques can monitor the rotational speed of rotating equipment or machines to understand their working conditions and prevent failures. Such techniques are highly precise, contactless, and potentially suitable for applications without massive setup changes. However, traditional vision sensors collect a significant amount of data to process and measure the rotation of high-speed systems, and they are susceptible to motion blur. This work proposes a new method for measuring rotational speed processing event-based data applied to high-speed systems using a neuromorphic sensor. This sensor produces event-based data and is designed to work with high temporal resolution and high dynamic range. The main advantages of the Event-based Angular Speed Measurement (EB-ASM) method are the high dynamic range, the absence of motion blurring, and the possibility of measuring multiple rotations simultaneously with a single device. The proposed method uses the time difference between spikes in a Kernel or Window selected in the sensor frame range. It is evaluated in two experimental scenarios by measuring a fan rotational speed and a Router Computer Numerical Control (CNC) spindle. The results compare measurements with a calibrated digital photo-tachometer. Based on the performed tests, the EB-ASM can measure the rotational speed with a mean absolute error of less than 0.2% for both scenarios.

Detection of Polycyclic Aromatic Hydrocarbons in High Organic Carbon Ultrafine Particle Extracts by Electrospray Ionization Ultrahigh-Resolution Mass Spectrometry.

The detection of polycyclic aromatic hydrocarbons (PAHs) by electrospray ionization (ESI) without additional reagents or targeted setup changes to the ionization source was observed in ultrafine particle (UFP) extracts, with high organic carbon (OC) concentrations, generated by a combustion aerosol standard (CAST) soot generator. Particulate matter (PM) was collected on filters, extracted with methanol, and analyzed by ESI Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS). Next to oxygen-containing species, pure hydrocarbons were found to be one of the most abundant compound classes, detected as [M + Na]+ or [M + H]+ in ESI+ and mostly as [M - H]- in ESI-. The assigned hydrocarbon elemental compositions are identified as PAHs due to their high aromaticity index (AI > 0.67) and were additionally confirmed by MS/MS experiments as well as laser desorption ionization (LDI). Thus, despite the relatively low polarity, PAHs have to be considered in the molecular attribution of these model aerosols and/or fresh emissions with low salt content investigated by ESI.

Analyzing Porpoising on High Downforce Race Cars: Causes and Possible Setup Adjustments to Avoid It

The so-called porpoising is a well-known problem similar to bouncing that is affecting the dynamic behavior of basically all the field of 2022 Formula 1 racing cars. It is due to the extreme sensitivity of aerodynamic loads to ride height variations along a lap. Mid-way through the season race engineers are still struggling to cope with this phenomenon and its consequences, with regard to either physiological stress experienced by the drivers or to overall vehicle performance and stability. The paper introduces two kinds of models based on real-world chassis and aerodynamic data, where the above-mentioned downforce sensitivity has been arbitrarily recreated through the application of a decay function to aero maps. The first one is a quasi-static model, usually adopted as a trackside tool for controlling ride heights and aero balance, while the second, a fully dynamic model, recreates the interaction between oscillating aerodynamic loads and suspension dynamics resulting in a visible porpoising phenomenon. Basic setup changes have been tested, including significant static ride height variations. The paper should be seen as a proposal of guidelines in the search of a trade-off between aerodynamic stability and overall performance, without pretention of quantitative accuracy due to the highly confidential topic, which makes numerical validation impossible.

Quality-Enhancing Techniques for Model-Based Reconstruction in Magnetic Particle Imaging

Magnetic Particle Imaging is an imaging modality that exploits the non-linear magnetization response of superparamagnetic nanoparticles to a dynamic magnetic field. In the multivariate case, measurement-based reconstruction approaches are common and involve a system matrix whose acquisition is time consuming and needs to be repeated whenever the scanning setup changes. Our approach relies on reconstruction formulae derived from a mathematical model of the MPI signal encoding. A particular feature of the reconstruction formulae and the corresponding algorithms is that these are independent of the particular scanning trajectories. In this paper, we present basic ways of leveraging this independence property to enhance the quality of the reconstruction by merging data from different scans. In particular, we show how to combine scans of the same specimen under different rotation angles. We demonstrate the potential of the proposed techniques with numerical experiments.

Generic Chemometric Models for Metabolite Concentration Prediction Based on Raman Spectra.

Chemometric models for on-line process monitoring have become well established in pharmaceutical bioprocesses. The main drawback is the required calibration effort and the inflexibility regarding system or process changes. So, a recalibration is necessary whenever the process or the setup changes even slightly. With a large and diverse Raman dataset, however, it was possible to generate generic partial least squares regression models to reliably predict the concentrations of important metabolic compounds, such as glucose-, lactate-, and glutamine-indifferent CHO cell cultivations. The data for calibration were collected from various cell cultures from different sites in different companies using different Raman spectrophotometers. In testing, the developed “generic” models were capable of predicting the concentrations of said compounds from a dilution series in FMX-8 mod medium, as well as from an independent CHO cell culture. These spectra were taken with a completely different setup and with different Raman spectrometers, demonstrating the model flexibility. The prediction errors for the tests were mostly in an acceptable range (<10% relative error). This demonstrates that, under the right circumstances and by choosing the calibration data carefully, it is possible to create generic and reliable chemometric models that are transferrable from one process to another without recalibration.

Distributional reinforcement learning with the independent learners for flexible job shop scheduling problem with high variability

Abstract Multi-agent scheduling algorithm is a useful method for the flexible job shop scheduling problem (FJSP). Also, the variability of the target system has to be considered in the scheduling problem that includes the machine failure, the setup change, etc. This study proposes the scheduling method that combines the independent learners with the implicit quantile network by modeling of the FJSP with high variability to the form of the multi-agent. The proposed method demonstrates superior performance compared to the several known heuristic dispatching rules. In addition, the trained model exhibits superior performance compared to the reinforcement learning algorithms such as proximal policy optimization and deep Q-network.

Linear Response Modeling of High Luminous Flux Phosphor-Coated White LEDs for VLC

The widespread deployment of LEDs for illumination purposes has open the door to the use of these devices for visible light communications (VLC). Most lighting fixtures are mounted with phosphor-based white LEDs, and a driver connected to the LED is also required for VLC. This paper shows that the parasitic effects introduced by this setup change the frequency response of the intrinsic LED. A linear model to characterize the whole setup is proposed, as well as a methodology to extract its parameters. This methodology allows the designer to characterize the frequency response of LEDs without the additional difficulty of knowing the specific parasitic components introduced by the setup. The proposed model offers an accurate estimation of the slope of the LED frequency response in order to broaden the frequency range in which the model is useful to characterize and simulate VLC links. This was corroborated with the characterization of three commercial white LEDs whose measured and modeled frequency responses matched perfectly.

Plan quality assessment in clinical practice: Results of the 2020 ESTRO survey on plan complexity and robustness

PurposePlan complexity and robustness are two essential aspects of treatment plan quality but there is a great variability in their management in clinical practice. This study reports the results of the 2020 ESTRO survey on plan complexity and robustness to identify needs and guide future discussions and consensus. MethodsA survey was distributed online to ESTRO members. Plan complexity was defined as the modulation of machine parameters and increased uncertainty in dose calculation and delivery. Robustness was defined as a dose distribution’s sensitivity towards errors stemming from treatment uncertainties, patient setup, or anatomical changes. ResultsA total of 126 radiotherapy centres from 33 countries participated, 95 of them (75%) from Europe and Central Asia. The majority controlled and evaluated plan complexity using monitor units (56 centres) and aperture shapes (38 centres). To control robustness, 98 (97% of question responses) photon and 5 (50%) proton centres used PTV margins for plan optimization while 75 (94%) and 5 (50%), respectively, used margins for plan evaluation. Seventeen (21%) photon and 8 (80%) proton centres used robust optimisation, while 10 (13%) and 8 (80%), respectively, used robust evaluation. Primary uncertainties considered were patient setup (photons and protons) and range calculation uncertainties (protons). Participants expressed the need for improved commercial tools to control and evaluate plan complexity and robustness. ConclusionClinical implementation of methods to control and evaluate plan complexity and robustness is very heterogeneous. Better tools are needed to manage complexity and robustness in treatment planning systems. International guidelines may promote harmonization.

Overview of recent and upcoming activities at the BATMAN Upgrade test facility

The BATMAN Upgrade (BUG) test facility at IPP is contributing to the development of RF-driven H- sources towards the ITER neutral beam injection and beyond. BUG is equipped with an 1/8 size of the ITER NBI ion source and thus is highly flexible for setup changes or diagnostic access. The present strategy for BUG is aligned along two paths: (i) BUG is continuing to be upgraded upgrading for long pulse operation (up to 1 hour, both in H and D) to identify measures for the stabilization of long pulses. For pulses > 100 s a cooling of the plasma grid (first grid of the extraction system) is required in order to avoid the dominance of thermal Cs desorption. A Cs evaporation concept with evaporation close to the extraction system is being tested, showing a stabilization of co-extracted electrons in 100 s H pulses, (ii) BUG is used for investigations of whole beam and beamlet optics for which the beam diagnostics have been massively upgraded. The beam diagnostics have been used to characterize a newly installed MITICA-like extraction system including asymmetric deflection compensation magnets.

Highly sensitive, scalable, and rapid SARS-CoV-2 biosensor based on In2O3 nanoribbon transistors and phosphatase

Developing convenient and accurate SARS-CoV-2 antigen test and serology test is crucial in curbing the global COVID-19 pandemic. In this work, we report an improved indium oxide (In2O3) nanoribbon field-effect transistor (FET) biosensor platform detecting both SARS-CoV-2 antigen and antibody. Our FET biosensors, which were fabricated using a scalable and cost-efficient lithography-free process utilizing shadow masks, consist of an In2O3 channel and a newly developed stable enzyme reporter. During the biosensing process, the phosphatase enzymatic reaction generated pH change of the solution, which was then detected and converted to electrical signal by our In2O3 FETs. The biosensors applied phosphatase as enzyme reporter, which has a much better stability than the widely used urease in FET based biosensors. As proof-of-principle studies, we demonstrate the detection of SARS-CoV-2 spike protein in both phosphate-buffered saline (PBS) buffer and universal transport medium (UTM) (limit of detection [LoD]: 100 fg/mL). Following the SARS-CoV-2 antigen tests, we developed and characterized additional sensors aimed at SARS-CoV-2 IgG antibodies, which is important to trace past infection and vaccination. Our spike protein IgG antibody tests exhibit excellent detection limits in both PBS and human whole blood ((LoD): 1 pg/mL). Our biosensors display similar detection performance in different mediums, demonstrating that our biosensor approach is not limited by Debye screening from salts and can selectively detect biomarkers in physiological fluids. The newly selected enzyme for our platform performs much better performance and longer shelf life which will lead our biosensor platform to be capable for real clinical diagnosis usage.Electronic Supplementary MaterialSupplementary material (materials and methods for device fabrication, functionalization of In2O3 devices, photographs of the liquid gate measurement setup, mobilities of the nine devices labeled in Fig. 1(b), family curves of IDS-VDS with the liquid gate setup and current change after bubbling the substrate solution (current vs. time curve for S1 antigen detection)) is available in the online version of this article at 10.1007/s12274-022-4190-0.

3D-swivel-bending (SB³)

The trends in the evolution of the production structure of the automotive industry and the development of alternative drive concepts are accompanied by the fact that comparatively smaller batch sizes are produced for a large variety of models. These trends require production techniques to meet the demand of flexibility. The requirements for manufacturing processes, especially for forming processes, are low-tool production, fast set-up, and quick product changes. 3D-swivel-bending is a developed manufacturing technique to produce cross-section and load-adapted sheet metal parts and profile components. Such components are used in lightweight structures, often found in developments in automotive and aerospace industries.

Dynamic stochastic deep learning approaches for predicting geometric changes in head and neck cancer

Objective. Modern radiotherapy stands to benefit from the ability to efficiently adapt plans during treatment in response to setup and geometric variations such as those caused by internal organ deformation or tumor shrinkage. A promising strategy is to develop a framework, which given an initial state defined by patient-attributes, can predict future states based on pre-learned patterns from a well-defined patient population. Approach. Here, we investigate the feasibility of predicting patient anatomical changes, defined as a joint state of volume and daily setup changes, across a fractionated treatment schedule using two approaches. The first is based on a new joint framework employing quantum mechanics in combination with deep recurrent neural networks, denoted QRNN. The second approach is developed based on a classical framework, which models patient changes as a Markov process, denoted MRNN. We evaluated the performance characteristics of these two approaches on a dataset of 125 head and neck cancer patients, which was supplemented by synthetic data generated using a generative adversarial network. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) scores. Main results. The MRNN framework had slightly better performance than the QRNN framework, with MRNN (QRNN) validation AUC scores of 0.742 0.021 (0.675 0.036), 0.709 0.026 (0.656 0.021), 0.724 0.036 (0.652 0.044), and 0.698 0.016 (0.605 0.035) for system state vector sizes of 4, 6, 8, and 10, respectively. Of these, only the results from the two higher order states had statistically significant differences A similar trend was also observed when the models were applied to an external testing dataset of 20 patients, yielding MRNN (QRNN) AUC scores of 0.707 (0.623), 0.687 (0.608), 0.723 (0.669), and 0.697 (0.609) for states vectors sizes of 4, 6, 8, and 10, respectively. Significance. These results suggest that both stochastic models have potential value in predicting patient changes during the course of adaptive radiotherapy.

Reconfigurability improvement in Industry 4.0: a hybrid genetic algorithm-based heuristic approach for a co-generation of setup and process plans in a reconfigurable environment

Reconfigurable manufacturing systems (RMS) are designed for adjustable production capabilities to cope with the fluctuating market demand. This adjustable capability and customised flexibility are offered by the modular Reconfigurable Machine Tools (RMTs), considered as the key component of an RMS. The main objective of this work is to develop a new approach to jointly consider the setup and process plan constraints. Indeed, based on the relationships between the operations to perform, a integrated setup and process plan is generated, minimising the total cost, including cost of processing, tolerance, setup change and tool module. The proposed new hybrid genetic algorithm-based approach is conducted in two stages. In the first stage, a heuristic is developed for the generation of setups and the assignments of fixtures to each set of operations. While in the second stage, a genetic algorithm is proposed to determine the best process plan to associate with the generated setup plan, under the economic cost consideration. A numerical experiment is performed to show the applicability and the efficiency of the developed approach. A test results highlight the economic gain of the simultaneous consideration of setup and process planning.

Sensitivity of Racing Tire Sliding Energy to Major Setup Changes: An Estimate Based on Standard Sensors

Understanding the amount of energy a tire is subjected to is one of the key elements to perform in motorsport competitions, especially in Formula 1 feeder categories, where the number of tires is limited over the race weekend to contain costs. This forces teams to use worn tires towards the end of the event. Therefore, tires are usually chosen only relying on their external shape or based on the kilometers traveled. Moreover, being aware of how a setup change impacts tires can be a breakthrough in tire management, especially in tracks where tire wear is limiting the overall performance. This paper provides a scientific method aimed at helping race engineers in tire management to maintain a high performance level through the entire race weekend.

Semi-active vibration control of a transmission system using a magneto-rheological variable stiffness and damping torsional vibration absorber

In this research effort, an innovative magneto-rheological variable stiffness and damping torsional vibration absorber (MR-VSDTVB) is proposed, and independent variable damping control and independent variable stiffness control are adopted to suppress the torsional vibration of the transmission system. MR-VSDTVB, based on semi-active control principle, exhibits a compact structure and integrates with magneto-rheological technology. First, the concept of MR-VSDTVB is discussed, and the output torque characteristic of MR-VSDTVB is analytically developed. Then, a prototype is fabricated and tested. A transmission system with MR-VSDTVB is proposed to verify the MR-VSDTVB's effectiveness. The structure and inherent characteristics of the transmission system are analyzed theoretically. Finally, an experimental setup of transmission system with MR-VSDTVB is built. Experimental results indicate that when torsional stiffness of MR-VSDTVB changes, a frequency shift phenomenon occurs; moreover, when torsional damping of MR-VSDTVB changes, the response amplitude of the experimental setup changes regularly; And finally, the on-off control test validates the effectiveness of semi-active control on the torsional vibration suppression of the transmission system. The above results verify the effectiveness of MR-VSDTVB in suppressing the torsional vibration of the transmission system. These findings are expected to expand the application of magneto-rheological technology and variable stiffness and variable damping technology in torsional vibration of transmission systems.