Fast Initiation Research Articles

A tightly-coupled PPP/visual positioning method for fast initialization and effective continuity

Precise Point Positioning (PPP) is one of the most widely used positioning methods, providing standalone positioning solutions with high accuracy. However, the algorithm usually requires tens of minutes to converge which makes the application restricted in many scenarios. The rapid developments of unmanned systems urge real-time positioning solutions with accuracy and continuity in complicated environments, where the GNSS observations get interrupted frequently. In view of this, we propose an integration positioning method tightly coupling PPP with visual localization. With the coordinates of the visual landmarks in the navigation frame measured priorly, the proposed method combines the observation equations of PPP and visual localizations providing accurate estimation of the position. Both simulation and real experiments are conducted to verify the effectiveness of the proposed method. With around five landmarks in sight simultaneously, the proposed method achieves its utmost performance to converge in seconds with a centimeter-level positioning accuracy, relatively better than the PPP-only method. The simulation based on real experiment data indicates that the proposed method keeps its output continuous and smooth when re-initialization happens once the observations of GNSS and visual get interrupted. The method offers positioning solutions with centimeter-level precision converging in 3–5 s, which indicates a promising application prospect in challenging environments in the future.

A Map-Aided Fast Initialization Method for the Magnetic Positioning of Vehicles

Magnetic positioning is a promising technique for vehicles in global navigation satellite system (GNSS)-denied scenarios. In general, a fixed-length magnetic sequence is required to provide an initial positioning result, which means that users need to wait a relatively long distance. To minimize this initialization distance, a map-aided fast initialization method, including magnetic database construction and magnetic positioning, is proposed in this paper. For magnetic database construction, a multisource fused database is established using a precise and effective strategy in which the positions of reference points (RPs) and the diverse information of paths are obtained from the map and the magnetic field is calculated using data collected during driving. For magnetic positioning, we innovatively propose a coarse–fine combination method that improves the positioning accuracy within a short distance. In the coarse map matching stage, by detecting the vehicle’s motion and utilizing the topological relationships between paths, the search range is precisely narrowed. In the fine magnetic localization stage, an improved mean absolute difference (MAD) metric and a derivative metric are combined to form a joint matching criterion to determine the positioning result. The experimental results illustrate the importance of each module in the proposed method, which improves the precision of the database up to 80% and significantly shortens the initialization distance up to 50%.

Toughness behavior and deformation mechanisms in FCC-based Fe45Co30Cr10V10Ni5-xMnx high-entropy alloys: Insights from instrumented Charpy impact tests

This study investigated toughness properties of FCC-based Fe45Co30Cr10V10Ni5-xMnx high entropy alloys (HEAs). Ductile-dimpled rupture mode in all alloys and test temperatures complicated precise explanations on Charpy impact energy, but the instrumented Charpy test provided a breakdown of total energy (ET) into initiation energy (EI) and propagation energy (EP), offering critical insights into Mn-content- and temperature-related energy variations. Pmax and TP, reflecting maximum flow stress and time, respectively, until initiation of crack propagation from the notch tip, involved a transition of deformation mechanisms from slip to TWIP, then to BCC-TRIP, thereby inducing increased strain hardening and delaying plastic instability at the notch tip. At 25 °C, an increase in Mn content prompted a transition in deformation behavior from slip to TWIP and subsequently to BCC-TRIP, correlating well with increased Pmax and TP and consequently ET due to enhanced strain hardenability. At −196 °C, predominant BCC-TRIP activity in all alloys led to increased formation of BCC martensite, intensifying strain hardening and requiring higher Pmax for crack initiation than at 25 °C. Due to minimal slip line field formation associated with reduced plastic deformation, however, cracks initiated directly from the notch-tip center, representing fast initiation of crack propagation and consequently reduction in EI and ET. Thus, utilizing parameters from instrumented Charpy tests, including Pmax, TP, EI, EP, and ET, provided insights into fracture phenomena and their interrelations in the present HEAs.

A Fast and Accurate Initialization Method for Mocap-Inertial Navigation System

A Fast and Accurate Initialization Method for Mocap-Inertial Navigation System

Fast initialization of the indirect optimization problem in the solar sail circle-to-circle orbit transfer

This Short Communication presents a procedure to approximate, in an analytical form, the solution of the optimization problem that gives the minimum-time trajectory of a low-performance solar sail, in a classical circle-to-circle heliocentric transfer. More precisely, the proposed procedure gives an accurate approximation of the solution of the two-point boundary value problem associated to the optimization process, when an indirect approach is exploited to obtain the optimal transfer trajectory and an ideal force model is used to describe the solar sail thrust vector. In particular, the analytical results illustrated in this work extend the model discussed in the recent literature, obtained with an ideal electric thruster, to the case of a flat, perfectly reflecting, solar sail-based scenario.

A Comprehensive Framework for Long-Tailed Learning via Pretraining and Normalization.

Data in the visual world often present long-tailed distributions. However, learning high-quality representations and classifiers for imbalanced data is still challenging for data-driven deep learning models. In this work, we aim at improving the feature extractor and classifier for long-tailed recognition via contrastive pretraining and feature normalization, respectively. First, we carefully study the influence of contrastive pretraining under different conditions, showing that current self-supervised pretraining for long-tailed learning is still suboptimal in both performance and speed. We thus propose a new balanced contrastive loss and a fast contrastive initialization scheme to improve previous long-tailed pretraining. Second, based on the motivative analysis on the normalization for classifier, we propose a novel generalized normalization classifier that consists of generalized normalization and grouped learnable scaling. It outperforms traditional inner product classifier as well as cosine classifier. Both the two components proposed can improve recognition ability on tail classes without the expense of head classes. We finally build a unified framework that achieves competitive performance compared with state of the arts on several long-tailed recognition benchmarks and maintains high efficiency.

A fast and stable GNSS-LiDAR-inertial state estimator from coarse to fine by iterated error-state Kalman filter

Simultaneous localization and mapping (SLAM) aims to solve the problems of robot localization and mapping in unknown environments. Recent related research usually uses closed-loop correction or integrate GNSS (Global Navigation Satellite System) into the optimization framework to ensure the long-term system accuracy and stability at the cost of huge computational resources. To balance efficiency and accuracy, this paper presents a fast and stable GNSS-LiDAR-inertial state estimator: GNSS, LiDAR and IMU are fused to achieve state estimation from coarse to fine, thereby improving the system accuracy and stability; the overall framework based on iterated error-state Kalman filter makes our system faster than most multi-sensor fusion SLAM. We also design a fast GNSS online initialization method and a multi-layer outlier rejection mechanism for our system. In addition, we apply backward propagation for multi-sensor motion compensation to overcome the limitations of fast motion. Finally, comprehensive experiments demonstrate that our system achieves higher accuracy and computational efficiency than the state-of-the-art navigation systems on the latest challenging public datasets, and perform equally well in the real environment.

Comparison of gels synthesized by controlled radical copolymerization and free radical copolymerization: molecular dynamics simulation.

The structures of gels synthesized by controlled radical copolymerization (CRP) and conventional free radical copolymerization (FRP) were studied by a coarse-grained molecular dynamics simulation. It was confirmed that the CRP gel has a larger number of elastically effective chains and fewer cyclic structures and entanglements than the FRP gel, i.e., the network structure of the CRP gel is more uniform than that of the FRP gel. However, the difference in the shear modulus between the two gels was small due to the opposing changes in the number of elastically effective chains and that of entanglements. The relatively uniform structure of the CRP gel is attributed to the suppression of intramolecular cross-linking by the fast initiation and slow propagation, and the development of cross-linked structures in the post-gel region due to the limited termination. The effects of these CRP characteristics were studied in detail. From the results, it was found that all of these characteristics of CRP cooperatively act to improve the homogeneity of the structure of the CRP gel.

Fast monocular visual-inertial calibration and initialization with weak environmental control and motion control

Most existing visual-inertial systems require control of extrinsic environment and extrinsic motion to obtain calibration results and simplify the initialization model. The former limits the usage scenarios of the method, while the latter reduces the estimation accuracy of the state parameters of the navigation system. Therefore, a visual-inertial calibration and initialization method is necessary for weak environmental and motion control conditions. Firstly, to overcome the reliance on environmental control, we establish a motion constraint equation based on the relative consistency of visual-inertial sensing spatial motion and implement a visual-inertial online calibration method. Secondly, to eliminate the reliance on motion control, a dual graph optimization structure is be constructed. The initial optimization outcome serves as the foundation for achieving improved calibration results in the refined optimization. Thirdly, to improve initialization accuracy, a complete initial state model is considered to decouple and calculate the translation extrinsic parameters. These parameters are compensated into subsequent initialization, enhancing the robustness of the initialization process. The proposed method is validated on the EuRoC dataset, and experimental results demonstrate that it achieves more accurate calibration results at a faster rate than VINS-Mono in scenarios without calibration target and with limited vehicle motion. The navigation results of different scenarios demonstrate that our method can more effectively integrate visual-inertial data due to improved calibration and initialization estimates.

Designing an adaptive and deep learning based control framework for modular production systems

AbstractIn today’s rapidly changing production landscape with increasingly complex manufacturing processes and shortening product life cycles, a company’s competitiveness depends on its ability to design flexible and resilient production processes. On the shop-floor, in particular, the production control plays a crucial role in coping with disruptions and maintaining system stability and resilience. To address challenges arising from volatile sales markets or other factors, deep learning algorithms have been increasingly applied in production to facilitate fast-paced operations. In particular deep reinforcement learning frequently surpassed conventional and intelligent approaches in terms of performance and computational efficiency and revealed high levels of control adaptability. However, existing approaches were often limited in scope and scenario-specific, which hinders a seamless transition to other control optimization problems. In this paper, we propose a flexible framework that integrates a deep learning based hyper-heuristic into modular production to optimize pre-defined performance indicators. The framework deploys a module recognition and agent experience sharing, enabling a fast initiation of multi-level production systems as well as resilient control strategies. To minimize computational and re-training efforts, a stack of trained policies is utilized to facilitate an efficient reuse of previously trained agents. Benchmark results reveal that our approach outperforms conventional rules in terms of multi-objective optimization. The simulation framework further encourages research in deep-learning-based control approaches to leverage explainability.

Impact of time-to-treatment on survival for early-stage non-small cell lung cancer in The Netherlands-a nationwide observational cohort study.

Varied outcomes on the relation between time-to-treatment and survival in early-stage non-small cell lung cancer (NSCLC) patients are reported. We examined this relation in a large multicentric retrospective cohort study and identified factors associated with extended time-to-treatment. We included 9,536 patients with clinical stage I-II NSCLC, diagnosed and treated in 2014-2019, from the Netherlands Cancer Registry that includes nation-wide data. Time-to-treatment was defined as the number of days between first outpatient visit for suspected lung cancer and start of treatment. The effect of extended time-to-treatment beyond the first quartile and survival was studied with Cox proportional hazard regression. Analyses were stratified for stage and type of therapy. Time-to-treatment was adjusted for multiple covariates including performance status and socioeconomic status. Factors associated with treatment delay were identified by multilevel logistic regression. Median time-to-treatment was 47 days [interquartile range (IQR): 34-65] for stage I and 46 days (IQR: 34-62) for stage II. The first quartile extended to 33 days for both stages. Risk of death increased significantly with extended time-to-treatment for surgical treatment of clinical stage II patients [adjusted hazard ratio (aHR) >33 days: 1.36, 95% confidence intervals (CI): 1.09-1.70], but not in stage II patients treated with radiotherapy or in stage I patients. Causes of prolonged time-to-treatment were multifactorial including diagnostic tests, such as endoscopic ultrasound (EUS) or endobronchial ultrasound (EBUS). Clinical stage II patients benefit from fast initiation of surgical treatment. Surprisingly this appears to be accounted for by patients who are clinically stage II but pathologically stage I. Further study is needed on characterizing these patients and the significance of lymph node- or distant micrometastasis in guiding time-to-treatment and treatment strategy.

Active reset of superconducting qubits using the electronics based on RF switches

Active reset of qubits is a crucial step in achieving quantum algorithms, particularly in fault-tolerant quantum computing, as it allows qubits to be quickly initialized to the ground state. In this paper, we demonstrate active reset of superconducting qubits using the feedback electronics based on RF switches. We are able to reset the qubit to the ground state in 11.412 µs with a success rate of 99.82%, which is better than the natural relaxation method in terms of both time consumption (180 µs) and success rate (99.61%). The demonstrated method facilitates fast and high-fidelity qubit initialization, holding practical significance for large-scale quantum computation.

COmbination of Targeted temperature management and Thrombectomy after acute Ischemic Stroke (COTTIS): a pilot study

BackgroundTo evaluate the feasibility and safety of a fast initiation of cooling to a target temperature of 35°C by means of transnasal cooling in patients with anterior circulation large vessel...

Novel multi-responsive soft actuator assembled with a graphene oxide nanoribbons doped strain hydrogel sensor with high sensitive and NIR-triggered performances

Effectively integrating multiple properties of hydrogel materials for expanding the application field of soft materials is highly desirable. In this work, a fast initiation system was used to prepare hydrogels. Based on the dynamic hydrogen bonding interactions between two-dimensional graphene oxide nanoribbons (GONRs), polymer chains and metal ions, the hydrogel exhibited excellent mechanical properties, great sensing performance and great self-healing property. Especially, the hydrogel exhibited sensitive response performance to near-infrared (NIR) light. A bilayer hydrogel actuator was assembled by combining the hydrogel with the temperature-sensitive Poly-N-isopropylacrylamide (PNIPAM) hydrogel. The bilayer hydrogel actuator exhibited stable responses to temperature, pH, and NIR light. The bilayer hydrogel could achieve bending close to 360° with good cycling stability in these three environments. As a proof-of-concept, the bilayer hydrogel was used as an identifiable fluid valve with selective passage which released fluids of different properties into corresponding containers. At the same time, the self-healing properties of the hydrogel and the actuation performance of the bilayer hydrogel were successfully used to simulate the wagging motion of the tail of puppy. The developed self-healing and sensing hydrogel and the assembled bilayer hydrogel actuator are expected to open new application fields in the health monitoring and soft robotics.

C-Coupler3.0: an integrated coupler infrastructure for Earth system modelling

Abstract. The community coupler (C-Coupler) for Earth system modelling is a coupler family that was developed in China in 2010. C-Coupler3.0, the latest version, is fully compatible with the previous version, C-Coupler2, and is an integrated infrastructure with new features, i.e. a series of parallel-optimization technologies for accelerating coupling initialization and reducing memory usage, a common halo-exchange library for developing a parallel version of a model, a common module-integration framework for integrating a software module (e.g. a flux algorithm, a parameterization scheme, and a data assimilation method), a common framework for conveniently developing a weakly coupled ensemble data assimilation system, and a common framework for flexibly inputting and outputting fields in parallel. Specifically, C-Coupler3.0 is able to handle coupling under much finer resolutions (e.g. more than 100 million horizontal grid cells) with fast coupling initialization and successful generation of remapping-weight files.

Calibration of Compact Resist Model Through CNN Training

We notice that the compact resist model can be mapped to a simple CNN (convolutional neural network): convolutional layer corresponds to convolutions between input images and resist kernels, and a fully connected layer can model the formation of weighted sum of convolutions followed by the comparison to threshold to determine the development. Resist kernels correspond to convolution filters, so they can be obtained through CNN training, which is a key motivation. A number of challenges are identified and solutions are proposed: (1) We demonstrate a CNN structure that can be mapped to a resist model. (2) Convolution filters are large images and cannot be trained with standard methods. Adaptive learning rate and gradient clipping are applied. (3) Convolution filters may easily be overfitted if training data is not enough. We apply gradient descent for fast initialization of filters. (4) CNN is trained with printability of image pixels rather than CD values. Extraction of pixels and their sampling are addressed. The number of kernels and the number of convolutions can greatly be reduced through the proposed method: 22 standard kernel functions are reduced to only 4 optimized ones, which contributes to 44% faster lithography simulation yet accuracy is improved by 10%.

Numerical geometric acoustics: An eikonal-based approach for modeling sound propagation in 3D environments

We present algorithms for solving high-frequency acoustic scattering problems in complex domains. The eikonal and transport partial differential equations from the WKB/geometric optic approximation of the Helmholtz equation are solved recursively to generate boundary conditions for a tree of eikonal/transport equation pairs, describing the phase and amplitude of a geometric optic wave propagating in a complicated domain, including reflection and diffraction. Edge diffraction is modeled using the uniform theory of diffraction. For simplicity, we limit our attention to domains with piecewise linear boundaries and a constant speed of sound. The domain is discretized into a conforming tetrahedron mesh. For the eikonal equation, we extend the jet marching method to tetrahedron meshes. Hermite interpolation enables second order accuracy for the eikonal and its gradient and first order accuracy for its Hessian, computed using cell averaging. To march the eikonal on an unstructured mesh, we introduce a new method of rejecting unphysical updates by considering Lagrange multipliers and local visibility. To handle accuracy degradation near caustics, we introduce several fast Lagrangian initialization algorithms. We store the dynamic programming plan uncovered by the marcher in order to propagate auxiliary quantities along characteristics. We introduce an approximate origin function which is computed using the dynamic programming plan, and whose 1/2-level set approximates the geometric optic shadow and reflection boundaries. We also use it to propagate geometric spreading factors and unit tangent vector fields needed to compute the amplitude and evaluate the high-frequency edge diffraction coefficient. We conduct numerical tests on a semi-infinite planar wedge to evaluate the accuracy of our method. We also show an example with a more realistic building model with challenging architectural features. Finally, we demonstrate a simple approach to extending the method to handle nonconstant speeds of sound by modifying the semi-Lagrangian updates to account for a varying speed.

Cooperative quantum information erasure

We demonstrate an information erasure protocol that resets N qubits at once. The method displays exceptional performances in terms of energy cost (it operates nearly at Landauer energy cost kTln⁡2), time duration (∼μs) and erasure success rate (∼99,9%). The method departs from the standard algorithmic cooling paradigm by exploiting cooperative effects associated to the mechanism of spontaneous symmetry breaking which are amplified by quantum tunnelling phenomena. Such cooperative quantum erasure protocol is experimentally demonstrated on a commercial quantum annealer and could be readily applied in next generation hybrid gate-based/quantum-annealing quantum computers, for fast, effective, and energy efficient initialisation of quantum processing units.

The potential of eHealth for cancer patients-does COVID-19 pandemic change the attitude towards use of telemedicine services?

Internet penetration worldwide has increased rapidly over the recent years. With this growth, modern information and communication technologies (ICT) have become increasingly important. They do not only change daily life but also patient-physician interaction and health related information search, which can be summarized as electronic Health (eHealth). eHealth was already known before the emergence of the coronavirus disease 2019 (COVID-19), but this pandemic substantially challenged health systems, physicians and hospitals so profoundly that new services and methods of patient-physician interaction had to be implemented rapidly. This study investigates the attitude of cancer patients towards eHealth and the potential impact of COVID-19 on its use. The study was a multicentered study carried out at the university hospitals Bonn and Aachen. Patients were asked to answer a structured questionnaire in the time span between September 2019 and February 2021. Due to the COVID-19 pandemic, no patients were addressed between March 2020 and July 2020. The questionnaire focused on socio-demographic data, the dissemination of internet-enabled devices, the patients' attitude towards eHealth and the use of modern ICT in daily life and for health-related information search. In total, 280 patients have filled the questionnaire of which 48% were female and 52% were male. Men have a slightly more positive attitude towards the overall potential of eHealth than women which was shown by a significant influence for receiving medical information via e-mail. Hematological-oncological patients with a higher education level reported a significantly higher willingness to send personal health information to their physician and health insurance. A frequency of medical consultation of more than 5 times during the previous year has a significantly positive impact regarding the use of online communication, online video consultation and treatment quality. Younger patients have more concerns about data security than older patients. The study shows a different attitude towards the influence of eHealth on the patient-physician relationship in different therapy situations. While there were no significant changes in patients' attitude towards eHealth after the start of the COVID-19 pandemic, there was a trend towards an increasingly embracing attitude in patients, who answered the questionnaire during COVID-19 pandemic situation. Overall, cancer patients had a positive attitude towards eHealth and the dissemination of internet-enabled devices was high. The study shows that the potential of eHealth is high among hematological-oncological patients. Further eHealth technologies and especially telemedically supported care processes should be implemented to improve patient-physician interaction and cross-sectoral care. COVID-19 pandemic led to a fast initiation and acceleration of new structures and routines for physicians, hospitals and patients. These new processes should be used to promote digitalization in hematological and oncological telemedicine. To successfully implement new eHealth technologies, future research should focus on patients' concerns about data privacy and data availability especially in the context of exchange of medical information in cross sectoral and interdisciplinary care processes.

[Electroencephalogram characteristics of 26 children with febrile infection-related epilepsy syndrome].

To investigate the electroencephalogram (EEG) characteristics and progression of febrile infection-related epilepsy syndrome (FIRES) in children, aiming to enhance diagnosis and treatment approaches. A retrospective analysis was conducted on 26 children with FIRES between May 2017 and December 2021. All 26 children (100%) presented with fever at the onset, followed by frequent convulsions that rapidly progressed into convulsive status. Ventilator support was required for 22 cases (85%). During the acute phase, EEG features demonstrated the disappearance of background activity and physiological sleep cycles in all children. Diffuse slow waves and multifocal slow spike slow waves were observed as abnormal waves during the interictal period. A characteristic pattern of focal low amplitude fast wave initiation was detected in all children during seizure episodes. In the chronic phase, the background EEG activity gradually recovered, and the presence of abnormal waves was relatively limited. The characteristic pattern of focal slow wave rhythm initiation was evident during seizure episodes. Additionally, extreme δ brushes were observed in four cases (15%). These findings suggest that EEG manifestations in children with FIRES exhibit distinctive patterns during the acute and chronic stages, providing significant value for early diagnosis and clinical staging. Extreme δ brushes may be one of the distinctive markers of children with FIRES.