Surface Error Research Articles

Rigid-Foldable Doubly Curved Deployable Reflectors Based on Miura–Ori Pattern

This paper introduces a novel deployable reflector concept which achieves single degree-of-freedom folding of solid doubly curved surfaces. A set of folding kinematics is constructed using the Miura–Ori pattern and the Hoberman linkage to allow compact collision-free stowage of a fully rigid truss-supported solid-surface reflector structure. In this paper, the baseline kinematic architecture is introduced, and a design methodology using Bayesian optimization to systematically obtain optimally stowed geometries is detailed. An example design is carried out to demonstrate the methodology. Optimal parameters are produced, the design space is explored, and a detailed design based on the optimal parameters is made, along with its derivative variants. Finally, a prototype model and quantification of surface errors confirm the validity of resulting designs.

Comparison of ocular biometric measurements and intraocular lens power calculation using different methods in eyes with implantable collamer lenses

This prospective cohort study included 80 healthy candidates for Implantable Collamer Lens (ICL) implantation who underwent biometric assessments with Scheimpflug imaging (the Pentacam-AXL) and swept-source optical coherence tomography (SS-OCT; the IOLMaster-700), both before and 3 months after surgery. The main outcome measures were mean keratometry, anterior chamber depth, axial length, and various intraocular lens (IOL) calculation formulas (Haigis, SRK/T, Hoffer Q, Holladay 1, Barrett Universal 2, and Olsen). The interchangeability of the devices was assessed by generating 95% limits of agreement (95% LoA) and associated Bland-Altman plots. The average age of the participants was 31.5 ± 5.4 years (22–43), with 58 (72.5%) being female. Among the cases analyzed, 11 (13.4%) had incorrect anterior lens surface segmentation using the IOLMaster-700, and 1 case (1.2%) had inappropriate segmentation using the Pentacam-AXL. Postoperative IOL power calculation resulted in readings that were, on average, 0.15 to 0.30 D higher compared to preoperative measurements. The 95% LoAs could differ by up to 0.85 D higher after surgery, indicating weak agreement between pre- and postoperative measurements. There was poor agreement between the IOLMaster-700 and Pentacam-AXL in IOL power calculation for eyes with post-ICL implantation, with a difference of more than 1 D in the 95% LoAs. In conclusion, Scheimpflug imaging was found to be less susceptible than the SS-OCT technique to segmentation errors of the anterior lens surface after ICL implantation. Neither device showed interchangeable results for pre- versus postoperative IOL power calculation. The determination of IOL power by the IOLMaster-700 versus Pentacam-AXL was not interchangeable in eyes with ICL implantation.

Iterative correction of robotic grinding using spatial feedback for precision applications

Since the advent of robots, many tasks that were originally performed by humans have now been tasked to industrial robots. From a manufacturing standpoint, robots have primarily been used in pick-and-place or other non-machining operations that require high repeatability. However, with the increasing availability of CAD/CAM software and the development of high-precision metrology, comes the opportunity to integrate robots into a wider variety of manufacturing processes through the use of feedback control. One such machining operation that is being explored is precision grinding of metal parts. Most other work in this area has focused on force regulation to improve grind quality; however, this paper takes a different approach. In this work, an Iterative Learning Control (ILC) algorithm is implemented to correct the geometric error directly by altering the toolpath trajectory. Specifically, in this framework, a conservative initial cutting trajectory is implemented using a 6-DoF robotic grinding system, and the resulting part geometry is measured via a high-precision laser scanner. Based on the resultant geometric error, the toolpath is corrected and then rerun on the part. This process is then repeated iteratively until sufficient accuracy is achieved. Due to the inability to replace material in overground regions, the controller is designed with an emphasis on reducing overshoot which cannot be corrected. The controller is experimentally validated by grinding an elliptical pocket which meets FAA specifications for corrosion removal in aircraft. The results showed that within seven iterations the entire error surface could be brought to a tolerance of ±0.150 mm for the given geometry.

An Enhanced Symmetric Sand Cat Swarm Optimization with Multiple Strategies for Adaptive Infinite Impulse Response System Identification

An infinite impulse response (IIR) system might comprise a multimodal error surface and accurately discovering the appropriate filter parameters for system modeling remains complicated. The swarm intelligence algorithms facilitate the IIR filter’s parameters by exploring parameter domains and exploiting acceptable filter sets. This paper presents an enhanced symmetric sand cat swarm optimization with multiple strategies (MSSCSO) to achieve adaptive IIR system identification. The principal objective is to recognize the most appropriate regulating coefficients and to minimize the mean square error (MSE) between an unidentified system’s input and the IIR filter’s output. The MSSCSO with symmetric cooperative swarms integrates the ranking-based mutation operator, elite opposition-based learning strategy, and simplex method to capture supplementary advantages, disrupt regional extreme solutions, and identify the finest potential solutions. The MSSCSO not only receives extensive exploration and exploitation to refrain from precocious convergence and foster computational efficiency; it also endures robustness and reliability to facilitate demographic variability and elevate estimation precision. The experimental results manifest that the practicality and feasibility of the MSSCSO are superior to those of other methods in terms of convergence speed, calculation precision, detection efficiency, regulating coefficients, and MSE fitness value.

Sigmoidal learning rate optimizer for deep neural network training using a two-phase adaptation approach

The optimization of machine learning models is an open research question since an optimal procedure to change the learning rate throughout training is still unknown. Manually defining a learning rate schedule involves troublesome, time-consuming try-and-error procedures to determine hyperparameters, such as learning rate decay epochs and learning rate decay rates, in order to navigate the intricate landscape of loss functions. Although adaptive learning rate optimizers automatize this process, recent studies suggest they may produce overfitting and reduce performance compared to fine-tuned learning rate schedules. Considering that the new machine learning loss function approaches present landscapes with much more saddle points than local minima, we proposed the Training Aware Sigmoidal Optimizer (TASO). This automated two-phase learning rate adaptation mechanism significantly reduces the need for manual hyperparameter tuning. The first phase uses a high learning rate to quickly traverse the numerous saddle points in the error surface, while the second phase uses a low learning rate to gradually approach the center of the local minimum previously found. We compared the proposed approach with commonly used adaptive learning rate schedules such as Adam, RMSProp, and Adagrad. The validation experiments were performed in image and text datasets and showed that TASO outperformed all competing methods in optimal (i.e., performing hyperparameter validation) and suboptimal (i.e., using default hyperparameters) scenarios. In our benchmark tests, TASO demonstrated promising performance, achieving an 8.32% increase in accuracy and a significant 46.62% decrease in training loss on average across various datasets and models. This remarkable performance positions TASO ahead of well-established adaptive optimizers, suggesting higher effectiveness and consistency in performance.

A study on the precision of voxel- and surface-based mandibular superimposition.

To apply the more accurate technique for mandibular superimposition and provide a valuable reference for the assessment of mandibular tooth movement and condylar remodeling before and after orthodontic treatment. This retrospective study involved 38 adult patients who underwent two cone beam computer tomography (CBCT) scans at different stages of treatment at Fujian Provincial People's Hospital between September, 2020 and December, 2022. The software Dolphin was used for mandible segmentation, enabling voxel-based mandibular superimposition with the mandibular ramus as the reference region. The Geomagic Wrap software was employed to process surface-based mandibular superimposition with the mandibular ramus as a reference. Additionally, the voxel and surface-based methods were compared for precision, with the mandibular ramus being the reference. After voxel-based mandibular superimposition using the mandibular ramus as a reference, with all measurement errors (< 0.20 mm). In contrast, the results of surface-based mandibular superimposition with the same reference, and the measurement errors were all less than 0.10 mm. The Wilcoxon signed-rank test revealed statistically significant differences between AS1 and BS1, AS2 and BS2, AS3 and BS3, and AS4 and BS4 (all r< 0.05). Moreover, the absolute mean distances of AS1-AS4 were all greater than those of BS1-BS4. All mandibular superimposition procedures, including the voxel- and surface-based ones using the mandibular ramus as a reference, have acceptable surface errors (< 0.20 mm), indicating the good reliability of these techniques. Under the specified conditions, surface-based mandibular superimposition appears to yield a higher degree of precision compared with the voxel-based technique.

Prediction of dynamic behaviors of vibrational-powered electromagnetic generators: Synergies between analytical and artificial intelligence modelling

The electric efficiency of vibrational electromagnetic generators is highly dependent on their ability to ensure effective adaptability to uncertain and irregular dynamics of mechanical energy sources. Such adaptive ability demands a planning operation considering future information of the highly nonlinear dynamics of these generators and mechanical excitations patterns. High accurate energy predictions are then mandatory for high energy generation efficiencies. However, on the one hand, high prediction accuracy by analytical modelling from first principles requires high modelling complexity; on the other hand, artificial intelligence models ensuring high prediction accuracy have not yet been explored to enhance the performance of these generators, even though their pre-training holds potential to significantly reduce the energy production costs. We here provide a multifaceted study highlighting the synergies between analytical and artificial intelligence modelling for optimizing the efficiency of vibrational-powered electromagnetic generators. Two main innovations are introduced: (1) development and experimental validation of a time-series forecasting artificial intelligence model based on the deep deterministic policy gradient method; (2) validation of a pre-training scenario by analytical modelling ⟶ artificial intelligence modelling synergy. Both the analytical and artificial intelligence models were able to provide high prediction accuracies to periodic and random 3D motions combining translations and rotations. Moreover, the pre-training scenario, using simulation training data sets, ensures prediction accuracies within the ±20% absolute error surfaces, profiling approximately normal distributions centered at approximately null error. These are impacting results in the scope of vibrational electromagnetic generation, holding potential to be extended to innovative self-adaptive electromagnetic generators, including those with ability to absorb complex 6 DOF external mechanical excitations. Besides, it can support the implementation of high-performance AI modelling⟶analytical modelling synergies, aiming to re-parameterize the high complex analytical models throughout the EMG operation, such that a superior controllability of the adaptive systems can be achieved.

Accuracy of vestibular schwannoma segmentation using deep learning models - a systematic review & meta-analysis.

Following 2020 PRISMA guidelines, a search across four databases was conducted. Inclusion criteria focused on articles using DL for VS MR image segmentation. The primary metric was the Dice score, supplemented by relative volume error (RVE) and average symmetric surface distance (ASSD). The search process identified 752 articles, leading to 11 studies for meta-analysis. A QUADAS- 2 analysis revealed varying biases. The overall Dice score for 56 models was 0.89 (CI: 0.88-0.90), with high heterogeneity (I2 = 95.9%). Subgroup analyses based on DL architecture, MRI inputs, and testing set sizes revealed performance variations. 2.5D DL networks demonstrated comparable efficacy to 3D networks. Imaging input analyses highlighted the superiority of contrast-enhanced T1-weighted imaging and mixed MRI inputs. This study fills a gap in systematic review in the automated segmentation of VS using DL techniques. Despite promising results, limitations include publication bias and high heterogeneity. Future research should focus on standardized designs, larger testing sets, and addressing biases for more reliable results. DL have promising efficacy in VS diagnosis, however further validation and standardization is needed. In conclusion, this meta-analysis provides comprehensive review into the current landscape of automated VS segmentation using DL. The high Dice score indicates promising agreement in segmentation, yet challenges like bias and heterogeneity must be addressed in the future research.

K-filter-based distributed adaptive bipartite tracking for nonlinear multi-agent systems with unknown control direction

This paper investigates K-filter-based distributed adaptive output-feedback bipartite tracking problem for multi-agent systems with unknown control direction. The agents are partitioned into two groups, where agents belonging to the same group are cooperative while agents belonging to the distinct group are competitive. Firstly, the unmeasurable state variables of the system under unknown control direction are estimated through K-filters. Then, a neighbourhood error is introduced to transform a structurally balanced network into a cooperative network. Based on K-filters, neighbourhood error, Nussbaum-type functions and dynamic surface control, a distributed adaptive output-feedback control protocol is designed to achieve the bipartite tracking. It is shown that all signals in the resulting closed-loop systems are semi-global bounded and the output of each agent can track the reference signal provided that the network is structurally balanced and contains a directed spanning tree. Finally, a numerical example is given to verify the effectiveness of the proposed method.

Distributed resilient adaptive consensus tracking control of nonlinear multi-agent systems dealing with deception attacks via K-filters approach

It poses technical difficulty to achieve distributed consensus tracking control with input quantizations and deception attacks for nonlinear multi-agent systems subject to mismatched parametric uncertainties via backstepping design. The underlying problem becomes even more complicated because i) the system contains mismatched uncertainties; ii) the output becomes unavailable after attacks, making the conventional recursive control design strategy using traditional error surfaces unsuitable; iii) since only the compromised output signal is available, the impacts of nonlinear items related to unknown attack weights become difficult to be explicitly addressed in the existing available output feedback control results. In this paper, we present a solution to circumvent these obstacles by constructing a set of new K-filters using compromised output only, which, together with a coordinate transformation involving the attacked output and filter variables, allow a new form of backstepping based distributed resilient adaptive control protocol to be developed. Meanwhile, an extra compensation term is incorporated into the real controller to handle the effect of quantization on the system stability. It is shown that, with proper choices of Lyapunov functions, the global uniform boundedness of all the closed-loop signals and the asymptotically output consensus tracking can be achieved. A practice example is provided to verify the benefits of our scheme.

Surface error effect on reflectivity and focused spot of EUV tubular mirror machined by ultra-precision diamond turning

Surface error effect on reflectivity and focused spot of EUV tubular mirror machined by ultra-precision diamond turning

Lightweight hashing with contrastive self-cross XLNet and combinational similarity matching for content-based video retrieval

ABSTRACT Text-to-video retrieval systems enable simple and natural video searches, but existing methods often employ single queries with noisy annotation and low-quality descriptions. A novel lightweight hashing with Contrastive self-cross XLNet and Combinational similarity matching is proposed to provide high-quality descriptions and clarity in annotation. Issues in existing text and video processing approaches, such as redundancy in keyword generation and extracting frames without considering optical flow estimation of keyframes, are solved using a novel Textual Labelling and Automated key frame segmentation. The 3D Cuboid Net with Lightweight Hashing is proposed, which selects features and converts them into hash codes using Lightweight LSS-Net based hashing, reducing memory utilization. Additionally, a novel Combinational Attentive Text-Video Retrieval is proposed, which eliminates multiple narrow extrema matching error surfaces and increases retrieval validity. The model efficiently solves video retrieval problems with high accuracy, recall, and sensitivity. This novel approach addresses the limitations of existing text-to-video retrieval methods and improves the overall quality of the retrieval process.

Error-controlled adaptive machining of aeronautical cabin structures by laser triangulation on-machine measurement.

Laser Triangulation On-Machine Measurement (LTOMM) is being implemented increasingly to inspect aeronautical components accurately and efficiently, with its enhanced application in adaptive machining. This work proposes an error compensation and controlling method for measuring the typical features of steps, holes, and freeform surfaces to improve accuracy. Then, the global path to inspect the cabin's structures is planned by introducing optimization algorithms, thus providing an appropriate sequence to shorten the traveling length. After these, the test piece was designed, measured, and manufactured using the adaptive machining process that integrates the LTOMM. The results show that the measurement errors of steps, holes, and freeform surfaces are +0.0092, -0.006, and +0.0406mm, respectively, and further reduced to +0.0013, -0.0019, and +0.0083mm after error controlling. The cabin's freeform surface was fabricated with the maximum positive and minimum negative errors of +0.184 and -0.123mm, which is evaluated by the mechanical probe. The measured data-driven machining process can guarantee that the error satisfies the required tolerance, promoting the application of the LTOMM process in aeronautical intelligent manufacturing.

Compensation method of the spiral bevel gear tooth surface error based on eight tooth-surface fitting

Abstract The misalignment error between the gear theoretical coordinate system and actual gear coordinate system is illustrated, as well as its influence on the measurement of tooth surface deviation is discussed. On the analysis of the tooth convex and concave surface error measurement process, the measured points in the theoretical coordinate system are transformed from the actual measured points in the actual coordinate system. Then, an eight tooth-surface fitting method is utilized to calculate the misalignment error and the compensation method is given. Finally, actual measurements of three different spiral bevel gears are analyzed, and the misalignment error are calculated. Using the proposed compensation method, both the eccentricity and tilt are compensated to some extent. And the machining suggestions of each spiral bevel gear are also given according to its compensated tooth surface error distribution.

X-ray lens figure errors retrieved by deep learning from several beam intensity images.

The phase problem in the context of focusing synchrotron beams with X-ray lenses is addressed. The feasibility of retrieving the surface error of a lens system by using only the intensity of the propagated beam at several distances is demonstrated. A neural network, trained with a few thousand simulations using random errors, can predict accurately the lens error profile that accounts for all aberrations. It demonstrates the feasibility of routinely measuring the aberrations induced by an X-ray lens, or another optical system, using only a few intensity images.

Coherence sidelobe suppression of laser diode by white-noise-current modulation and external cavity feedback.

A coherence sidelobe suppression method for a laser diode (LD) is proposed, based on the white-noise-current modulation and external cavity feedback. The theoretical model of the coherence of the white-noise-current-modulated external cavity laser (WECL) is established, which details the spectrum broadening mechanism and the coherence sidelobe suppression. At the noise current modulation, the coherence sidelobe induced by the internal cavity is lowered by the external cavity feedback, and the position of the coherence sidelobe induced by the external cavity is controlled with the cavity length, which can extend the coherence range without the sidelobes. The experimental results demonstrate that the 98.4% sidelobe suppression ratio (SLSR) of the WECL is achieved in dynamic interferometry, which is about 20% higher than that of the laser diode only with the white-noise-current modulation. This new, to the best of our knowledge, method can avoid the stray fringe cross talk caused by the multiple coherence sidelobes in the surface error measurements of parallel plates.

Developing δ15N and δ13C isoscapes using whole blood from Magellanic penguins, Spheniscus magellanicus.

Understanding the migration of marine animals is hindered by the limitations of traditional tracking methods. It is therefore crucial to develop alternative methods. Stable isotope-based tracking has proven useful for this task, although it requires detailed isoscapes in the focal area. Here, we present predator-based isoscapes of the coastal zone of the Patagonian Shelf Large Marine Ecosystem (PSLME), which offers a novel tool for geolocation. Whole-blood samples from breeding Magellanic penguins nesting at 11 colonies were used to create δ15N and δ13C isoscapes. Isotopic values were assigned to random positions inside their corresponding foraging area. Spatial analysis and data interpolation resulted in δ15N and δ13C isoscapes for the coastal zone of the PSLME, which were validated through cross-validation. The isoscapes mean standard error ranged from 0.05 to 0.41 for δ15N and from 0.07 to 0.3 for δ13C, similar to the error range of the mass spectrometer used for measuring isotope ratios. Predictive surfaces reflected the latitudinal trends, with δ13C and δ15N values increasing northwards. δ13C values showed a strong latitudinal gradient, while δ15N values had two distinct domains, with higher values in the north. The error surface indicated the highest certainty within 130 km from the shore and within the reported Magellanic penguin foraging areas. Both isoscapes revealed strong spatial variation. The δ13C isoscape showed a latitudinal gradient, consistent with patterns in other oceans. The δ15N isoscape clearly separated northern and southern colonies, likely influenced by nitrogen sources. The error obtained fell within the measurement error ranges, adding credibility to the models.

A method for assessing guide layout and error on implant accuracy

Functional and esthetic results require accurate implant placement. We aimed to develop a predictive method for assessing guide layout and error on implant accuracy. A mathematical model for position error analysis was constructed based on triangular mesh data. This model examines the relationship between the spatial shifts of multiple surfaces and the spatial shifts of specific points. It involves encasing these surfaces in a cuboid bounding box and expressing them in a local coordinate system. The influence of positioning surface error and layout of surgical guide were researched with a simulation test. The result shows that error in the implant site position is directly related to the error in the guide locating surface under the same layout. When the guide locating surface layout varies, as the length, width, and height of the minimum cuboid envelope increase, the maximum deviation in the implant site position decreases.

An Automatic Method for Elbow Joint Recognition, Segmentation and Reconstruction.

Elbow computerized tomography (CT) scans have been widely applied for describing elbow morphology. To enhance the objectivity and efficiency of clinical diagnosis, an automatic method to recognize, segment, and reconstruct elbow joint bones is proposed in this study. The method involves three steps: initially, the humerus, ulna, and radius are automatically recognized based on the anatomical features of the elbow joint, and the prompt boxes are generated. Subsequently, elbow MedSAM is obtained through transfer learning, which accurately segments the CT images by integrating the prompt boxes. After that, hole-filling and object reclassification steps are executed to refine the mask. Finally, three-dimensional (3D) reconstruction is conducted seamlessly using the marching cube algorithm. To validate the reliability and accuracy of the method, the images were compared to the masks labeled by senior surgeons. Quantitative evaluation of segmentation results revealed median intersection over union (IoU) values of 0.963, 0.959, and 0.950 for the humerus, ulna, and radius, respectively. Additionally, the reconstructed surface errors were measured at 1.127, 1.523, and 2.062 mm, respectively. Consequently, the automatic elbow reconstruction method demonstrates promising capabilities in clinical diagnosis, preoperative planning, and intraoperative navigation for elbow joint diseases.

COMPARISON OF MESSAGE PASSING SYSTEMS IN CONTEXT OF ADAPTIVE LOGGING METHOD

Computer software is an important part of technological progress. As it becomes more and more complex and sophisticated, so does the need to protect it. Apart from typical information security aspects of integrity, availability and confidentiality, the scale and complexity of modern computer systems require a high level of control and observability. The main goal of this research is to build upon the foundations laid by the general idea of an adaptive logging method and introduce the next iteration of its design in the form of an appropriate message passing system to be used to propagate required changes to corresponding implementation in an effective and performant manner. Four different message passing system models are introduced, based on different technologies such as RabbitMQ message broker, communication channels in PostgreSQL database management system, general web server architecture and Linux-based process signaling interface. For each of those an overview description and graphical model is presented. Finally, the resulting comparison is conducted, comparing aspects such as reliance on third-party software, communication medium, error surface increase and authentication related considerations. As a result, the design based on process signaling approach is determined to be the most suitable for adaptive logging method, as it does not introduce any third-party software (and as such affects error surface in a somewhat negligible manner), binds directly to an observed application, is built using low level concepts that should be present in multiple different platforms and programming languages and should be able to reuse authentication logic that is already used when accessing computational machine where observed program is executed.