Achievable Accuracy Research Articles

Synchronization of Neurophysiological and Biomechanical Data in a Real-Time Virtual Gait Analysis System (GRAIL): A Proof-of-Principle Study.

The investigation of gait and its neuronal correlates under more ecologically valid conditions as well as real-time feedback visualization is becoming increasingly important in neuro-motor rehabilitation research. The Gait Real-time Analysis Interactive Lab (GRAIL) offers advanced opportunities for gait and gait-related research by creating more naturalistic yet controlled environments through immersive virtual reality. Investigating the neuronal aspects of gait requires parallel recording of brain activity, such as through mobile electroencephalography (EEG) and/or mobile functional near-infrared spectroscopy (fNIRS), which must be synchronized with the kinetic and /or kinematic data recorded while walking. This proof-of-concept study outlines the required setup by use of the lab streaming layer (LSL) ecosystem for real-time, simultaneous data collection of two independently operating multi-channel EEG and fNIRS measurement devices and gait kinetics. In this context, a customized approach using a photodiode to synchronize the systems is described. This study demonstrates the achievable temporal accuracy of synchronous data acquisition of neurophysiological and kinematic and kinetic data collection in the GRAIL. By using event-related cerebral hemodynamic activity and visually evoked potentials during a start-to-go task and a checkerboard test, we were able to confirm that our measurement system can replicate known physiological phenomena with latencies in the millisecond range and relate neurophysiological and kinetic data to each other with sufficient accuracy.

USNet: underwater image superpixel segmentation via multi-scale water-net

Underwater images commonly suffer from a variety of quality degradations, such as color casts, low contrast, blurring details, and limited visibility. Existing superpixel segmentation algorithms face challenges in achieving superior performance when directly applied to underwater images with quality degradation. In this paper, to alleviate the limitations of superpixel segmentation when applied to underwater scenes, we propose the first underwater superpixel segmentation network (USNet), specifically designed according to the intrinsic characteristics of underwater images. Considering the quality degradation, we propose a multi-scale water-net module (MWM) aimed at enhancing the quality of underwater images before superpixel segmentation. The degradation-aware attention (DA) mechanism is then created and incorporated into MWM to solve light scattering and absorption, which can decrease object visibility and cause blurred edges. By effectively directing the network to prioritize locations that exhibit a considerable decrease in quality, this method enhances the visibility of those specific areas. Additionally, we extract the deep spatial features using the coordinate attention method. Finally, these features are fused with the shallow spatial information using the dynamic spatiality embedding module to embed comprehensive spatial features. Training and testing were conducted on the SUIM dataset, the underwater change detection dataset, and UIEB dataset. Experimental results show that our method achieves the best scores in terms of achievable segmentation accuracy, undersegmentation error, and boundary recall evaluation metrics compared to other methods. Both quantitative and qualitative evaluations demonstrate that our method can handle complicated underwater scenes and outperform existing state-of-the-art segmentation methods.

Application of 3D laser scanning technology for mapping and accuracy assessment of the point cloud model for the Great Achievement Palace heritage building

Heritage buildings represent history and act as vessels of human civilisation. The significance of these architectural phenomena increases as society increases, which renders their preservation and ethical use increasingly crucial. Hence, preserving heritage buildings is necessary for societal benefit. The accuracy of three-dimensional (3D) point cloud models of heritage buildings is important to their digital conservation. Traditional mapping methods typically require more time and human resources. Furthermore, the outcome is subject to measurement omissions, errors, and other issues, which are contrary to the protection of the object measured. The complex elements of ancient Chinese architecture render it challenging for traditional measuring techniques to accurately capture spatial structural information. The 3D laser scanning technology is a novel technology to obtain 3D data rapidly. In this paper, the Great Achievement Palace of the Confucian Temple in Yuci District, Jinzhong City, Shanxi Province, was used as an example to examine the application of 3D laser scanning technology to acquire point cloud models of heritage buildings. Field data collection for architectural heritage using 3D laser scanning technology requires measuring station setting analysis to ensure effect accuracy while considering the elements of target location and quantity. For the 3D point cloud model of the Great Achievement Palace, error analysis and accuracy assessment were conducted on the quality elements of alignment accuracy between scanning stations, point cloud model reliability, point cloud data noise condition, and whether the point cloud data were stratified. The findings demonstrated that the improved method quickly and accurately acquired 3D point cloud model data and obtained realistic spatial data models of the heritage building.

The Hera Radio Science Experiment at Didymos

Hera represents the European Space Agency's inaugural planetary defense space mission and plays a pivotal role in the Asteroid Impact and Deflection Assessment international collaboration with NASA DART mission that performed the first asteroid deflection experiment using the kinetic impactor techniques. With the primary objective of conducting a detailed post-impact survey of the Didymos binary asteroid following the DART impact on its small moon called Dimorphos, Hera aims to comprehensively assess and characterize the feasibility of the kinetic impactor technique in asteroid deflection while conducting an in-depth investigation of the asteroid binary, including its physical and compositional properties as well as the effect of the impact on the surface and shape of Dimorphos. In this work, we describe the Hera radio science experiment, which will allow us to precisely estimate critical parameters, including the mass, which is required to determine the momentum enhancement resulting from the DART impact, mass distribution, rotational states, relative orbits, and dynamics of the asteroids Didymos and Dimorphos. Through a multi-arc covariance analysis, we present the achievable accuracy for these parameters, which consider the full expected asteroid phase and are based on ground radiometric, Hera optical images, and Hera to CubeSats InterSatellite Link radiometric measurements. The expected formal uncertainties for Didymos and Dimorphos GM are better than 0.01% and 0.1%, respectively, while their J2 formal uncertainties are better than 0.1% and 10%, respectively. Regarding their rotational state, the absolute spin pole orientations of the bodies can be recovered to better than 1°, and Dimorphos' spin rate to better than 10−3%. Dimorphos reconstructed relative orbit can be estimated at the sub-m level. Preliminary results, using a higher-fidelity dynamical model of the coupled motion between rotational and orbital dynamics, show uncertainties in the main parameters of interest that are comparable to those in standard radio science models. A first-order estimate of the expected uncertainty in the momentum transfer efficiency from DART's impact, obtainable with Hera, yields a value of about 0.25. This represents a significant improvement compared to current estimates. Overall, the retrieved values meet the Hera radio science requirements and goals, and remain valid under the condition that the system is determined to be in an excited but non-chaotic (or tumbling) state. The Hera radio science experiment will play an integral role in the exploration of the Didymos binary asteroid system and will provide unique scientific measurements, which, when combined with other observables such as optical images, altimetry measurements, and satellite-to-satellite tracking of the CubeSats, will support the mission's overarching goals in planetary defense and the deep understanding of binary asteroids.

Measurement of light absorption by chromophoric dissolved organic matter using a type-II liquid capillary waveguide: assessment of an achievable accuracy

Light absorption by chromophoric dissolved organic matter (CDOM) in the ocean is often measured using liquid waveguide capillary cells coupled to spectral array detectors. This type of optical setup is affected by several sources of uncertainties related to the waveguide and the detector. Uncertainties from the waveguide arise from errors in the effective path length and the effects of water salinity, while errors related to the detector are due to the non-linearity in the response, internal stray light, and wavelength accuracy. Here, uncertainties in the measurements of the spectral absorption coefficient of CDOM due to the optical setup itself were investigated in detail. The related systematic errors were very often significant (2–15%) and larger than expected from simple measurement uncertainty (±1%). However, they can be corrected by characterizing the detector’s response for non-linearity and stray light, regularly performing calibrations for the detector’s wavelength response, and routinely measuring the waveguide’s effective path length. Including such corrections and timely calibrations reduces the uncertainties related to the spectrophotometric measurements to about ±2%. Uncertainties related to the necessary handling of samples are not included here.

TONARI: Reactive Detection of Close Physical Contact Using Unlicensed LPWAN Signals

Recognizing if two objects are in close physical contact (CPC) is the basis of various Internet-of-Things services such as vehicle proximity alert and radiation exposure reduction. This is achieved traditionally through tailor-made proximity sensors that proactively transmit wireless signals and analyze the reflection from an object. Despite its feasibility, the past few years have witnessed the prosperity of reactive CPC detection techniques that do not need spontaneous signal transmission and merely exploit received wireless signals from a target. Unlike existing approaches entailing additional effort of multiple antennas, dedicated signal emitters, human intervention, or a back-end server, this article presents TONARI, an effortless CPC detection framework that performs in a reactive manner. TONARI is developed for the first time with LoRa, the representative of unlicensed low-power wide area network (LPWAN) technologies, as the wireless signal for CPC detection. At the heart of TONARI lies a novel feature arbitrator that decides whether two devices are in CPC or not by distinguishing different types of LoRa chirp-based additive sample magnitude sequences. Software-defined radio-based experiments are conducted to show that the achievable CPC detection accuracy via TONARI can reach 100% in most practical cases.

Cramér-Rao Lower Bound for Magnetic Field Localization around Elementary Structures.

The determination of a mobile terminal's position with high accuracy and ubiquitous coverage is still challenging. Global satellite navigation systems (GNSSs) provide sufficient accuracy in areas with a clear view to the sky. For GNSS-denied environments like indoors, complementary positioning technologies are required. A promising approach is to use the Earth's magnetic field for positioning. In open areas, the Earth's magnetic field is almost homogeneous, which makes it possible to determine the orientation of a mobile device using a compass. In more complex environments like indoors, ferromagnetic materials cause distortions of the Earth's magnetic field. A compass usually fails in such areas. However, these magnetic distortions are location dependent and therefore can be used for positioning. In this paper, we investigate the influence of elementary structures, in particular a sphere and a cylinder, on the achievable accuracy of magnetic positioning methods. In a first step, we analytically calculate the magnetic field around a sphere and a cylinder in an outer homogeneous magnetic field. Assuming a noisy magnetic field sensor, we investigate the achievable positioning accuracy when observing these resulting fields. For our analysis, we calculate the Cramér-Rao lower bound, which is a fundamental lower bound on the variance of an unbiased estimator. The results of our investigations show the dependency of the positioning error variance on the magnetic sensor properties, in particular the sensor noise variance and the material properties, i.e., the relative permeability of the sphere with respect to the cylinder and the location of the sensor relative to the sphere with respect to the cylinder. The insights provided in this work make it possible to evaluate experimental results from a theoretical perspective.

The effect of spacing grid in the precision solution of the inverse vertical electric sounding problem

Electrical methods for monitoring the stress-strain state of the Earth's crust involve measuring apparent resistivity, but changes in resistivity are of physical interest. During daily (for 12 years) measurements of the apparent resistivity of the Earth's crust using a stationary 32-electrode vertical electrical sounding installation in a highly seismic region of Tajikistan and precision equipment, a vertical electrical sounding profile of a special type was obtained for which the sounding date changes from picket to picket. To invert field data, a special version of the IPI program was developed taking into account the features of the measuring setup. Initially, the direct and inverse problems of vertical electrical sounding were solved on a standard logarithmic spacing grid which significantly increased the speed of calculations. The actual data were interpolated to this grid. However, testing the algorithm on synthetic data showed that in this case the maximum achievable accuracy is limited by a special type of equivalence – the effect of resistance buildup. The precision version of the IPI program implements several algorithmic techniques aimed at reducing the error in solving the inverse problem. When describing the effects that depend on the choice of one or another spacing grid, the results of solving the inverse problem for synthetic profiles in two versions of the IPI program were compared and the beneficial effect of switching to the actual spacing grid was determined. When comparing the results of inversion of the experimental vertical electrical sounding profile obtained as a result of long-term observations, the amplitude of errors arising when using a logarithmic spacing grid was assessed using indirect evidence.

Explainable Classification of Patients with Primary Hyperparathyroidism Using Highly Imbalanced Clinical Data Derived from Imaging and Biochemical Procedures

Primary hyperthyroidism (PHPT) is a common endocrine disorder characterized by hypercalcemia and elevated parathyroid hormone (PTH) levels. The most common cause is a single parathyroid adenoma, though the rest of the cases are due to multiglandular disease [double adenoma/hyperplasia]. The main focus driving this work is to develop a computer-aided classification model relying on clinical data to classify PHPT instances and, at the same time, offer explainability for the classification process. A highly imbalanced dataset was created using biometric and clinical data from 134 patients (six total features, 20.2% multiglandular instances). The features used by the current study are age, sex, max diameter index, number of deficiencies, Wisconsin index, and the reference variable indicating the type of PHPT. State-of-the-art machine learning (ML) classification algorithms were used in order to create trained prediction models and give predicted classifications based on all features/indexes. Of the ML models considered (Support Vector Machines, CatBoost, LightGBM, and AdaBoost), LightGBM was able to procure the best performing prediction model. Given the highly imbalanced nature of the particular dataset, oversampling was opted for, so as to increase prediction robustness for both classes. The ML model’s performance was then evaluated using common metrics and stratified ten-fold validation. The significance of this work is rooted in two axes: firstly, in the incorporation of oversampling to smooth out the highly imbalanced dataset and offer good prediction accuracy for both classes, and secondly, in offering an explainability aspect to an otherwise black-box ML prediction model. The maximum achievable accuracy for adenoma is 86.9% and for multigland disease 81.5%. Summarizing the above, this study demonstrates the potential for an ML approach to improve the diagnosis of PHPT and also highlights the importance of explainable artificial intelligence (AI).

Embedding and cross-sectioning as a sample preparation procedure for accurate and representative size and shape measurement of nanopowders

Reliable measurement of the size of polydisperse, complex-shaped commercial nanopowders is a difficult but necessary task, e.g., for regulatory requirements and toxicity risk assessment. Suitable methods exist for the accurate characterization of the size of non-aggregated, stabilized, spherical and monodisperse nanoparticles. In contrast, industrial nanoscale powders usually require dedicated sample preparation procedures developed for the analysis method of choice. These nano-powders tend to agglomerate and/or aggregate, a behavior which in combination with an innate broad particle size distribution and irregular shape often significantly alters the achievable accuracy of the measured size parameters. The present study systematically tests two commercially available nanoscale powders using different sample preparation methods for correlative analysis by scanning electron microscopy, dynamic light scattering, Brunauer–Emmet–Teller method and differential mobility analysis. One focus was set on the sample preparation by embedding nanoparticles in carbon-based hot-mounting resin. Literature on this topic is scarce and the accuracy of the data extracted from cross sections of these particles is unclearly stated. In this paper systematic simulations on the deviation of the size parameters of well-defined series of nanoparticles with different shapes from the nominal value were carried out and the contributing factors are discussed.

Cold Atom Gravimeter Based on an Atomic Fountain and a Microwave Transition

A method based on measuring the shift of Ramsey spectral line in an atomic fountain in the gravitational field has been proposed to develop an atomic gravimeter involving the atomic fountain on ultracold atoms. The accuracy of the measurement of the gravitational acceleration with a fountain microwave frequency standard on Cs atoms is \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\delta g = 2 \ imes {{10}^{{ - 6}}}g{\ ext{/}}\\sqrt {{{\ au }_{{\ ext{a}}}}} $$\\end{document}. The achievable accuracy at the integration time τa = 10 000 s is \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\delta g \\approx 2 \ imes {{10}^{{ - 8}}}g \\approx 20{\\kern 1pt} $$\\end{document} μGal.

Determination of the Invariant Spin Axis in a COSY model using Bmad

The matter-antimatter asymmetry might be understood by investigating the EDM (Electric Dipole Moment) of elementary particles. A permanent EDM of a subatomic particle violates time reversal and parity symmetry at the same time and would be, with the currently achievable experimental accuracy, an indication for further CP violation than established in the SM (Standard Model of Particle Physics). The JEDI-Collaboration (Jülich Electric Dipole moment Investigations) in Jülich has performed a direct EDM measurement for deuterons with the so-called precursor experiments at the storage ring COSY (COoler SYnchrotron) by measuring the orientation of the ISA (Invariant Spin Axis). In order to interpret the measured data and to disentangle a potential EDM signal from systematic effects, spin tracking simulations in an accurate simulation model of COSY are needed. Therefore a model of COSY was implemented using the software library Bmad. Systematic effects were considered by including element misalignments, effective dipole shortening, longitudinal fields and steerer kicks. These effects rotate the ISA in addition to the EDM and have to be analyzed and understood. The most recent spin tracking results as well as the methods to find the ISA are presented in this paper.

Combining low-cost electronic structure theory and low-cost parallel computing architecture.

The computational efficiency of low-cost electronic structure methods can be further improved by leveraging heterogenous computing architectures. The software package TeraChem has been developed since 2008 to make use of graphical processing units (GPUs), particularly their strong single-precision performance, for the acceleration of quantum chemical calculations. Here, we present the implementation of three low-cost methods, namely HF-3c, PBEh-3c, and the recently introduced ωB97X-3c. We show that these can benefit in terms of performance when combined with "consumer grade" GPUs by leveraging the mixed precision integral handling in TeraChem. The current limitation of the latter's GPU integral library is that Gaussian integrals only for functions with angular momentum l < 3 can be computed, which generally restricts the achievable accuracy in terms of the one-particle basis set. Particularly, the implementation of the ωB97X-3c method now enables higher accuracy with this setting which, in turn, provides the most efficient implementation accessible with consumer-grade hardware. We furthermore show that the implemented 3c methods can be combined with the hh-TDA formalism. This gives new and efficient low-cost multi-configurational excited states methods, which are benchmarked for the description of lowest vertical excitation energies in this work. All in all, the combination of these efficient electronic structure theory methods with affordable highly parallelized computing hardware provides an optimal computational and monetary cost to accuracy ratio.

Additive manufacturing of dental ceramics in prosthodontics: The status quo and the future.

This review aims to summarize the available technologies, material categories, and prosthodontic applications of additive manufacturing (AM) dental ceramics, evaluate the achievable accuracy and mechanical properties in comparison with current mainstream computer-aided design/computer-aided manufacturing (CAD/CAM) subtractive manufacturing (SM) methods, and discuss future prospects and directions. This paper is based on the latest reviews, state-of-the-art research, and existing ISO standards on AM technologies and prosthodontic applications of dental ceramics. PubMed, Web of Science, and ScienceDirect were amongst the sources searched for narrative reviews. Relatively few AM technologies are available and their applications are limited to crowns and fixed partial dentures. Although the accuracy and strength of AM dental ceramics are comparable to those of SM, they have the limitations of relatively inferior curved surface accuracy and low strength reliability. Furthermore, functionally graded additive manufacturing (FGAM), a potential direction for AM, enables the realization of biomimetic structures, such as natural teeth; however, specific studies are currently lacking. AM dental ceramics are not sufficiently developed for large-scale clinical applications. However, with additional research, it may be possible for AM to replace SM as the mainstream manufacturing technology for ceramic restorations.

Convolutional neural network hyperparameters for face emotion recognition using genetic algorithm

The development of artificial intelligence in facial emotion recognition (FER) is rapidly growing and has been widely applied in various fields. Deep learning (DL) techniques with evolutionary algorithms have become the preferred choice for solving various security, health, gaming, and other related problems. This research proposes the use of a genetic algorithm (GA) as the main method to optimize hyperparameters in the convolutional neural network (CNN) model for FER. The required computation time is approximately 37 hours 57 minutes 55 seconds, with generation 3 taking the longest time at around 16 hours 45 minutes 4 seconds. However, generation 3 achieved an accuracy of 76.11%, which is the highest compared to other generations. The results indicate that the more generations are involved, the higher the achievable accuracy. Furthermore, the proposed CNN-GA model in this study outperforms previous models that have been examined. Thus, this study makes a significant contribution to improving the understanding of using GAs to optimize the performance of CNN models for FER.

Evaluasi Program Kartu Pedagang Produktif Bagi Usaha Mikro Kecil dan Menengah di Kabupaten Bojonegoro

The Productive Merchant Card Program is an MSME empowerment program created by the Bojonegoro Regency Government. This program is made so that people who work as traders can prosper with the facilities provided by this program. However, in its implementation, there are problems, among others, that this program should provide capital loans with low interest without collateral, it turns out that there is a guarantee, the nominal liquid loan is reduced from the amount proposed, and it turns out that there are still people who do not understand how to use the card. The purpose of this study is to assess how far the Productive Merchant Card Program is realized for MSME actors and to find out what factors affect the implementation of the program. This study uses William N. Dunn's Evaluation theory which contains six achievement indicators, namely, Effectiveness, Efficiency, Adequacy, Equity, Responsiveness, and Accuracy. The method used is a descriptive qualitative method with a case study strategy. The results of this study found that the Productive Merchant Card Program has not fully achieved the expected target so in its realization it has not fully benefited MSME actors or traders.

Critical heat flux for downward flows in vertical round pipes

Pressurized thermal shock (PTS) is one of the causes of pressure vessel failure during nuclear reactor accidents. Critical heat flux (CHF) for downward flow is an essential thermohydraulic parameter for predicting PTS using nuclear reactor system codes. While many experimental data have been accumulated and many correlations have been proposed for CHF for upward flow, only a few studies exist on downward flow. This study reviewed the literature that measured CHF for downward flow in round pipes and arranged the proposed correlations. Then, we extracted the parameter values that determine experimental conditions and CHF values from the literature and compared the CHF values predicted from the correlation in each study and the measured CHF values under those conditions across the literature. Each correlation shows relatively good prediction accuracy (average prediction accuracy of 10%–30%) for experimental data from their literature, but the accuracies sometimes decrease for experimental data from other literature. No correlation accurately predicts all the experimental data of the literature, indicating an issue in extrapolating existing correlations. Therefore, we developed a correlation that can accurately predict the experimental data of the collected literature. First, we used a neural network to select the essential dimensionless quantities that comprise the correlation. Then, we regarded the prediction accuracy when all candidate dimensionless quantities extracted from the literature were used for the input variables of the network as the achievable limit prediction accuracy and searched for the minimum combination of dimensionless quantities required to achieve it. The results showed that only the dimensionless mass flux (G*) and the ratio of the heating length to the channel diameter (L/D) are the essential parameters to achieve it. We developed a correlation equation using these two dimensionless quantities and achieved 17.6% of the average prediction accuracy. This result considerably improved existing correlation equations with 25%–40% average prediction accuracy for the same experimental data. Furthermore, unlike many existing correlations, it has excellent practical features, such as applicability over wide parameter ranges and no need to separate cases.

Improving Clustering Accuracy of K-Means and Random Swap by an Evolutionary Technique Based on Careful Seeding

K-Means is a “de facto” standard clustering algorithm due to its simplicity and efficiency. K-Means, though, strongly depends on the initialization of the centroids (seeding method) and often gets stuck in a local sub-optimal solution. K-Means, in fact, mainly acts as a local refiner of the centroids, and it is unable to move centroids all over the data space. Random Swap was defined to go beyond K-Means, and its modus operandi integrates K-Means in a global strategy of centroids management, which can often generate a clustering solution close to the global optimum. This paper proposes an approach which extends both K-Means and Random Swap and improves the clustering accuracy through an evolutionary technique and careful seeding. Two new algorithms are proposed: the Population-Based K-Means (PB-KM) and the Population-Based Random Swap (PB-RS). Both algorithms consist of two steps: first, a population of J candidate solutions is built, and then the candidate centroids are repeatedly recombined toward a final accurate solution. The paper motivates the design of PB-KM and PB-RS, outlines their current implementation in Java based on parallel streams, and demonstrates the achievable clustering accuracy using both synthetic and real-world datasets.

Applied machine learning for stroke differentiation by electrical impedance tomography with realistic numerical models

Electrical impedance tomography (EIT) may have potential to overcome existing limitations in stroke differentiation, enabling low-cost, rapid, and mobile data collection. Combining bioimpedance measurement technologies such as EIT with machine learning classifiers to support decision-making can avoid commonly faced reconstruction challenges due to the nonlinear and ill-posed nature of EIT imaging. Therefore, in this work, we advance this field through a study integrating realistic head models with clinically relevant test scenarios, and a robust architecture consisting of nested cross-validation and principal component analysis. Specifically, realistic head models are designed which incorporate the highly conductive layers of cerebrospinal fluid in the subarachnoid space and ventricles. In total, 135 unique models are created to represent a large patient population, with normal, haemorrhagic, and ischemic brains. Simulated EIT voltage data generated from these models are used to assess the classification performance of support vector machines. Parameters explored include driving frequency, signal-to-noise ratio, kernel function, and composition of binary classes. Classifier accuracy at 60 dB signal-to-noise ratio, reported as mean and standard deviation, are (79.92% ± 10.82%) for lesion differentiation, (74.78% ± 3.79%) for lesion detection, (77.49% ± 15.90%) for bleed detection, and (60.31% ± 3.98%) for ischemia detection (after ruling out bleed). The results for each method were obtained with statistics from 3 independent runs with 17,280 observations, polynomial kernel functions, and feature reduction of 76% by PCA (from 208 to 50 features). While results of this study show promise for stroke differentiation using EIT data, our findings indicate that the achievable accuracy is highly dependent on the classification scenario and application-specific classifiers may be necessary to achieve acceptable accuracy.

Research on the grinding rounding mechanism and law of the cylindrical grinding system supported all by hydrostatic spindles

The maximum achievable journal roundness accuracy range by the conventional “Rolling bearing supported spindle and fixed center clamping” grinding solution is about 0.2–0.5 μm, which barricades the grinding roundness improvement. To further improve the grinding roundness, this study proposes a novel “Fully hydrostatic supported spindle and movable center clamping” grinding solution, which adopts hydrostatic supported grinding wheel spindle, and hydrostatic supported movable clamping centers. Firstly, based on the interaction between wheel and workpiece, a coupling material removal deformation compatibility model is built, and the rounding process of external cylindrical grinding is quantitatively simulated. Secondly, a fully hydrostatic supported grinding system is developed and tested. The optimal roundness error in the experiment reached 0.12 μm, the maximum relative error between the simulated results and the measured average results is 18.5 %, which validates the proposed solution and the algorithm. Thirdly, from the perspective of grinding parameters, the grinding rounding law of the system is studied. Finally, the proposed grinding solution is compared with the conventional grinding solution, the novel grinding solution has better grinding efficiency and grinding roundness. The proposed grinding solution may provide a new path for grinding accuracy improvement.