Errors In Field Research Articles

High dynamic range imaging with short- and long-exposures based on artificial remapping using multiscale exposure fusion

High dynamic range imaging (HDRI) is an excellent high-quality image acquisition technique, which can reflect real human visual characteristics from one (or several) captured low dynamic range (LDR) image. However, the input LDR image only provides partial information of the scene. Besides, in traditional HDRI methods that require multiple captured images as input, field of view errors can be induced, which will be difficult to apply it to the emerging image acquisition systems. Here, we propose a novel HDRI method that reconstructs an HDR image from only a pair of short- and long-exposure images based on artificial remapping using multi-scale exposure fusion. Firstly, we introduce a simulated exposure model called artificial remapping to synthesize a multi-exposure image sequence from the input LDR image pairs. Then, weighting maps of the sequence for fusion can be obtained according to the evaluation factors of contrast, saturation, as well as improved exposedness. Finally, we utilize the pyramid based multiscale exposure fusion framework to integrate them into an enhanced HDR image. Comparative experiments, fully implemented on some source images, have been demonstrated that better performance can be realized compared with some competing methods in qualitative and quantitative evaluation. Note that the operation of the proposed method is simple yet effective, which is easy to popularize. The method thus can be potentially applied to the emerging image acquisition systems where two images are captured simultaneously by two image sensors or by one image sensor with a pair of short- and long-exposure setting.

Effectiveness of data-driven wind turbine wake models developed by machine/deep learning with spatial-segmentation technique

In this paper, the effectiveness of three machine/deep learning algorithms, namely, the artificial neural network (ANN), convolutional neural network (CNN) and U-shape neural network (Unet), in constructing wind turbine wake modeling is investigated. In order to enhance the performance of different neural networks, the spatial-segmentation technique for wake flow field is adopted which aims to divide the original wake field configuration (4D × 50D, D is the rotor diameter) into several small pieces (each with 4D × 6.25D). This is followed by separately training the subdivided small piece of wake flow fields and the resultant sub-models are consolidated to predict the whole wake flow field. Both wake velocity field and turbulence intensity field are predicted by the wake model to facilitate its applications to alleviate both wind turbine power losses and fatigue loads caused by wake interactions. Through comparative study, it is found that by using the spatial-segmentation technique it can significantly reduce the prediction error of the wake velocity but not for the prediction of turbulence intensity. Among the three selected network structures, ANN has the best prediction performance yielding the wake model with the maximum error of 11.6 % near to the rotor place, while for other regions it is generally below 8 %. By dividing the wake flow field into pieces, the maximum error located right behind the rotor reduces to 7.2 % with others less than 6 %. Through further repetitive training analysis, it proves a better and robust wake model can be achieved by ANN with the spatial segmentation. In comparison, the prediction error of turbulence intensity field is higher, but still fairly accurate for the far wake prediction with the error less than 5 %.

Research on compensation method for registration error of large-scale measurement field based on multi-temperature sensors

PurposeThe size of the aircraft tooling structure is huge, and the ambient temperature is difficult to maintain a constant state. Aiming at the influence of current temperature, this paper aims to propose a compensation method for registration error of large-scale measurement fields based on multi-temperature sensors.Design/methodology/approachIn this method, an enhanced reference points (ERS)–temperature regression model is constructed from ERS and temperature data. The ERS offsets compensation model is established by solving the offset through the regression model, and the ERS offset compensation analysis is carried out.FindingsThe experimental results show that the proposed registration error compensation algorithm has obvious advantages over traditional methods in reducing the influence of ambient temperature and improving the measurement accuracy by reducing the registration error.Originality/valueThis method reduces registration error caused by the influence of ambient temperature and is used for aircraft measurements in different temperature environments.

The anatomy of Boris type solvers and the Lie operator formalism for deriving large time-step magnetic field integrators

This work gives a Lie operator derivation of various Boris solvers via a detailed study of trajectory errors in a constant magnetic field. These errors in the gyrocenter location and the gyroradius are the foundational basis for why Boris solvers existed, independent of any finite-difference schemes. This work shows that there are two distinct ways of eliminating these errors so that the trajectory of a charged particle in a constant magnetic field is exactly on the cyclotron orbit. One way reproduces the known second-order symmetric Boris solver. The other yields a previously unknown, but also on-orbit solver, not derivable from finite-difference schemes. By revisiting some historical calculations, it is found that many publications do not distinguish the poorly behaved leap-frog Boris solver from the symmetric second-order Boris algorithm. This symmetric second-order Boris solver's trajectory is much more accurate and remains close to the exact orbit in a combined nonuniform electric and magnetic field at time-steps greater than the cyclotron period. Finally, this operator formalism showed that Buneman's cycloid fitting scheme is mathematically identical to Boris' on-orbit solver and that Boris' E-B splitting is unnecessary.

Improving SpellChecking: an effective Ad-Hoc probabilistic lexical measure for general typos

<p>Since the era of learning to write by human beings, mistakes made in typing words have occupied a privileged place in linguistic studies, integrating new disciplines into school curricula such as spelling and dictation. According to exhaustive studies that we have done in the field of spellchecking errors made in typing Arabic texts, very few research works that deal with typographical errors specifically caused by the insertion or missing of the blank-space in words. On the other hand, spelling correction software remains ineffective for handling this type of errors. Failure to process errors due to the insertion/missing of blankspace between and in words leads and brings us back to situations of ambiguity and incomprehension of the meaning of the typed text. To remedy this limitation of correction, we propose in this article an ad-hoc probabilistic method which is based jointly on two approaches. The first approach treats the errors due to deletion or missing of blank-space between or inside words, while the second puts emphasis in correcting space insertion errors in a word of course in addition to other kinds of elementary editing errors (addition, deletion, permutation of characters). Our new approach combines edit distance with n-gram language models to correct the errors already mentioned. Our new approach gave an accuracy rate that reaches 98,14% for missing blank-space errors (noted MBSE) and 89,5% for insertion blank-dpace errors (noted IBSE), which gives an average correction rate of around 95,26%. These results are very encouraging and show the interest and the importance of our approach.</p>

Ameliorated Automated Facial Fracture Detection System using CNN

The fracture of the bone is common issue in human body occurs when the pressure is applied on bone or minor accident and also due to osteoporosis and bone cancer. Therefore the accurate diagnosis of bone fracture is an important aspects in medical field. In this work X-ray/CT images are used for the bone fracture analysis. The main aim of the this project is to develop an image processing based efficient system for a quick and accurate classification of bone fractures based on the information gained from the x-ray / CT images of the skull. X- ray/CT scan images of the fractured bone are collected from the hospital and processing techniques like pre-processing method, segmentation method, edge detection and feature extraction methods are adopted. The images are tested out by considering the image slice of single slice and also grouping the slices of the patients. The patients CT scan/X-ray image was classified if bone is fractured then if two following slices were categorized with a probability fracture higher than 0.99. The results of the patient x-ray images show that the model accuracy of the maxillofacial fractures is contains 80%. Even the radiologist’s work is not replaced by the MFDS model system, it is useful only for the providing valuable assistive support, it reduces the human error in the medical field, preventing the harm for the patients by minimizing the diagnostic delays, and reducing the incongruous burden of hospitalization.

Abstract 3450: Impact of model inaccuracy on dose estimation in TTFields therapy

Abstract Introduction: Tumor Treating Fields (TTFields) is a modality for treating Glioblastoma (GBM). A recent study combining clinical data and simulations demonstrated that the simulation-based dose estimation at the tumor level is directly correlated with patient survival (Ballo M, et al. Int J Radiat Oncol Biol Phys. 2019;104(5):1106-1113). This study highlights the importance of computer-modelling based TTFields treatment planning in the clinic. Performing such planning in a meaningful manner requires an understanding about how inaccuracies in the patient model influence calculated TTFields distributions. In this study we show the effect of local perturbations in the model, errors in tumor segmentation, and inaccuracy in the patient head model on TTFields dose estimation. Methods: Computational studies were performed with Sim4Life software. 1) Local model uncertainty outside of tumor region: To create defects in the models, conductive spheres with varying conductivities and radii were placed into the model’s brains at different distances from the tumor. Virtual transducer arrays were placed on the models, and delivery of TTFields numerically simulated. The error in the electric field induced by the defects as a function of defect conductivity, radius, and distance to tumor was investigated. 2) Tumor segmentation uncertainty: For illustrating segmentation errors in tumors and necrotic cores, layered spheres with various conductivities and radii were placed in different head locations. Tumor conductivity uncertainty was defined as (σ-σref)/σref where σref = 0.24 S/m is the standard tumor conductivity and σ is the varied conductivity over the tumor. The normalized tumor radius was calculated when each of the varied radii was normalized to the results obtained by all the other combinations. Results: The results show that that when a defect of radius R is placed at a distance, d, from the tumor that is larger than seven times R, the error is below 1% regardless of the defect conductivity. Conclusions: Our models and results show the impact of uncertainty in segmentation of the tumor or other tissue distant from it on the TTFields dose estimation. These results could serve as a guideline for model creation and tissue segmentation that would lead to optimal dose estimation in TTFields treatment planning. Citation Format: Tal Marciano, Oshrit Zeevi, Zeev Bomzon. Impact of model inaccuracy on dose estimation in TTFields therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3450.

<italic>Operando</italic> monitoring microstructures and defects in laser fusion additive manufacturing of metals

<p indent="0mm">Laser fusion additive manufacturing of metals has been growing tremendously over the past decade. Despite its unrivaled capability to directly fabricate complex geometries, the accuracy, repeatability, and reliability of the build remain a major concern. From the micro perspective, it is the unfavorable microstructures and defects/anomalies that deteriorate the service performances such as fatigue life and corrosion resistance. The microstructures here involve not only melt pool and keyhole but also grain morphology and size, crystal orientation, interface structure, composition homogeneity, phase stability, etc. The defects/anomalies mainly include porosity, balling, lack of fusion, spattering, element loss, oxidation, micro residual stress and deformation, microcracking, and rough surface. Accurate detection and characterization of these features are critical for their understanding and control. Traditionally, we use optical microscope, electron microscope, X-ray tomography and diffraction, and so forth, to perform postmortem analysis and track the thermal traces the local material has experienced. However, such information and evidence are typically static and limited. This poses difficulties in recreating and interpreting the microstructural formation. In this review, we summarize recent progress on<italic> in situ</italic>/<italic>operando</italic> monitoring the laser fusion additive manufacturing of metals. It mainly covers high-speed visible light and thermal imaging, laboratory X-ray imaging, ultrasonic measurement, and high-speed synchrotron X-ray imaging and diffraction. Beyond traditional postmortem characterizations, the <italic>in situ</italic>/<italic>operando</italic> techniques have demonstrated the capability to capture the transient and dynamic formation and evolution processes of various microstructures and defects. They are revolutionizing the research mode in the field of metal additive manufacturing, from trial and error to mechanism oriented. In the former, we continue to optimize the objective function (e.g., grain morphology and size) until it arrives at a satisfactory solution, largely based on limited experience and knowledge and costly iterations. In the latter, we quest for the ultimate solution to manufacturing defect-free and perfect structures through direct exploration of the physical origins of the aforementioned features. Among the various techniques, we address high-speed synchrotron X-ray imaging and diffraction. It is an emerging approach in this field and is more suitable to monitor the internal microstructural evolution of metals, e.g., melt pool and keyhole dynamics, pore formation, cracking, rapid solidification, and phase transformation, because of the micrometer spatial resolution, sub-nanosecond temporal resolution, megahertz frame rate, and millimeter penetration capacity. Nevertheless, other techniques like high-speed visible light and thermal imaging have irreplaceable advantages and application scenarios. They can provide complementary information (e.g., thermal distribution on the melt pool surface, vapor plume dynamics, and three-dimensional spatter morphologies) for comprehensive understanding of the fundamental mechanisms in laser melting. Looking ahead to the next decade, the integration of multiple <italic>in situ</italic>/<italic>operando</italic> techniques and the translation among the resultant multi-dimensional signals, as well as big data computing and high precision modeling, will bridge understanding of mechanisms on the microscale and tailoring of properties on the macroscale. This will promote sustainable growth of the additive manufacturing industry.

Effects of Auditory Pre-Stimulation on Cognitive Task Performance in a Noisy Environment

The accident rate due to human errors in industrial fields has been consistently high over the past few decades, and noise has been emerging as one of the main causes of human errors. In recent years, auditory pre-stimulation has been considered as a means of preventing human errors by improving workers’ cognitive task performance. However, most previous studies demonstrated the effectiveness of the auditory pre-stimulation in a quiet environment. Accordingly, studies on the effects of pre-stimulation in a noisy environment are still lacking. Therefore, this study aimed to empirically investigate: (1) the effects of noisy environments on the performances of cognitive tasks related to different functions of working memory and (2) the effects of auditory pre-stimulation on the performances of cognitive tasks in a field-noise environment. To accomplish these research objectives, two major experiments were conducted. In the first experiment, a total of 24 participants performed each of three basic short-term/working memory (STM/WM) tasks under two different experimental conditions (quiet-noise environment and field-noise environment) depending on the presence or absence of field noise. In the second experiment, the participants performed each of the three basic STM/WM tasks in a field-noise environment after they were provided with one of four different auditory pre-stimulations (quiet noise, white noise, field noise, and mixed (white and field) noise). The three STM/WM tasks were the Corsi block-tapping, Digit span, and 3-back tasks, corresponding to the visuospatial sketchpad, the phonological loop, and the central executive of WM, respectively. The major findings were that: (1) the field-noise environment did not affect the scores of the Corsi block-tapping and 3-back tasks, significantly affecting only the Digit span task score (decreased by 15.2%, p &lt; 0.01); and (2) the Digit span task performance in the field-noise environment was improved by 17.9% (p &lt; 0.05) when mixed noise was provided as a type of auditory pre-stimulation. These findings may be useful for the work-space designs that prevent/minimize human errors and industrial accidents by improving the cognitive task performance of workers in field-noise environments.

Integrated optical system design for large-field-of-view and broad-spectrum laser warning.

We introduce an integrated large-field-of-view optical system, including a diffraction grating and a multiband narrowband filter, for laser detection. Simulation of the system in the ZEMAX optical design software package indicated that it was capable of detecting laser radiation in the visible light to near-infrared spectral range (500-1700 nm) from azimuth angles up to 120°, and pitch angles up to 96°. In practical experiments conducted at typical laser wavelengths (532 nm, 1064 nm, 1550 nm), the angle error in the full field of view was better than 0.5°, while the laser center wavelength detection accuracy was better than 7 nm, confirming the system's ability to identify the angle of incidence and wavelength of an incoming laser beam.

La Plata Basin Hydroclimate Response to Solar Radiation Modification With Stratospheric Aerosol Injection

This study analyses the La Plata Basin (LPB) hydroclimate response to Solar Radiation Modification (SRM) with Stratospheric Aerosol Injection (SAI) in terms of changes in mean and extreme temperature, rainfall, and river flows simulations from the Geoengineering Large Ensemble (GLENS). The GLENS data used consists of three members simulations of a single model under the high anthropogenic emission scenario RCP8.5 with and without sulfur injection in the tropical stratosphere. The response of such an intervention is compared to a historical period (1980–2010) and to projections under the RCP8.5 scenario for two time slices: 2021–2050 and 2051–2080. The analysis of changes in extreme temperature and precipitation focuses on a subset of climate indices from the Expert Team of Climate Change Detection and Indices (ETCCDI). To determine the possible effects of SRM in the LPB water cycle, we use the distributed Variable Infiltration Capacity (VIC) hydrologic model in combination with daily extreme temperature and precipitation outputs from GLENS. Systematic errors in both fields were corrected by the application of a statistical bias correction method. Results show robust impacts on the water cycle of the LPB due to SRM implementation, particularly associated with increased precipitation over the northern part of the region and a general reduced warming compared to the RCP8.5 scenario. These variations would lead to regional-dependent responses in the river flows, mostly related to reductions in the severity of the extremely low flow conditions. Additionally, under the SRM scenario a reduction in extreme precipitation and a cooling effect on extreme temperatures could be expected over the LPB.

Investigating Tropical Versus Extratropical Influences on the Southern Hemisphere Tropical Edge in the Unified Model

AbstractSince the late 1970s, observations have shown a widening of the tropical Hadley cell (HC) circulation. State‐of‐the‐art climate models reproduce the general trend along with a projected continuous expansion. Discrepancies in expansion rates of observation‐ and model‐based studies have been attributed to differences in applied methods, natural variability and model shortcomings. Furthermore, the driving influence of tropical or extratropical processes on these changes is not well understood. All of this highlights the dynamical mechanisms and the region of origin controlling the tropical width are still insufficiently understood. Here we examine the influence of systematic model biases of the atmosphere‐only Unified Model (UM) onto the simulation of the Southern Hemisphere (SH) tropical edge. We utilize nudged experiments with prescribed sea surface temperatures, where potential temperature and horizontal winds are relaxed back to ERA‐Interim reanalysis for a 20‐year period in selected regions. Correcting model biases in the tropics and extratropics separately allows us to dissect the dominant remote impacts of present model errors onto the SH tropical edge simulation. The experiments are applied to established tropical width metrics ranging from near‐surface to upper‐level metrics capturing the poleward flank of the HC. We find both regions work remotely to reduce errors in the UM fields and location of the tropical edge. Surprisingly, correcting the extratropical biases, south of 45°S, more consistently improves the tropical width across the metrics and seasons than nudging the tropics (10°N–10°S). These findings demonstrate the substantial role of extratropical influences in locating the SH tropical edge.

Correction of Three Sensors in a Magnetic Field Gradiometer

A magnetic sensor array is usually used for weak magnetic target detection. This paper presents a vector second-order gradiometer that is arranged in a vertical structure. The measurement accuracy of the gradiometer is affected by the error of each three-axis magnetometer (TAM) and the misalignment error between TAMs. By analyzing the error mechanism of the magnetic gradiometer test system, the corresponding error model for the correction of the gradient of the component is established. The error parameters of the gradiometer were estimated by the proposed method, and the gradient error of the magnetic field and the component gradient error of the magnetic field were corrected. After correction, the gradiometer is used to localize the magnetic target. Simulation results illustrate that the localization error of magnetic targets is reduced by two orders. Thus, this proposed correction method can effectively improve the locating performance of gradiometer.

Bias error in measurement of complex flow regime with ultrasonic flowmeter

Abstract Ultrasonic flowmeters are widely used for flow measurement. We proposed a theoretical model to quantify the measurement error caused by the deviation of ultrasonic propagation trajectory in complex flow regimes. The three-dimensional ultrasonic propagation model is based on the assumption of ray acoustics. The results show that the trajectory offset of ultrasonic waves and velocity measurement errors in a three-dimensional non-uniform flow field is of the first order of the Mach number. Unlike gas flow, the Mach number of open channel flow is small, and the velocity measurement error of open channel ultrasonic flowmeter can be safely ignored.

Diagnostic Modalities in Primary Immunodeficiency.

As the field of inborn errors of immunity expands, providers continually update and fine-tune their diagnostic approach and selection of testing modalities to increase diagnostic accuracy. Here, we first describe a mechanistic consideration of laboratory testing, highlighting both benefits and drawbacks of currently clinically available testing modalities. Next, we provide methods in evaluation of patients presenting with concern for inborn errors of immunity as defined by the International Union of Immunological Societies 2019 phenotypic categories: primary antibody deficiencies, cellular and humoral immune deficiency, disorders of the innate immune system, and syndrome-associated and primary immune regulation disorders (PIRDs). Using the suggested approach in this paper as a roadmap highlights the importance of thorough history taking and physical examination as the foundation to guide further diagnostic tests. This is followed by enumeration and functional testing. Finally, to determine the underlying molecular etiology-specific genetic panels, chromosomal microarrays, and broad genetic testing (whole exome sequencing or whole genome sequencing) are available.

Genetics of inborn errors of immunity

In the last 10 years there has been enormous progress in the field of inborn errors of immunity (IEI). The number of newly discovered diseases is growing exponentially, including not only rare but also frequent genetic defects. The spectrum of clinical phenotypes ascribed to IEI is also rapidly expanding. There is every reason to assume that this is only the tip of the iceberg and in the near future further IEI will be discovered with the help of genetic and immunological studies. Patients will benefit from the timely diagnostics as well as from the individualized treatment.

Clustering-Correcting Codes

A new family of codes, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">clustering-correcting codes</i> , is presented in this paper. This family of codes is motivated by the special structure of the data that is stored in DNA-based storage systems. The data stored in these systems has the form of unordered sequences, also called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">strands</i> , and every strand is synthesized thousands to millions of times, where some of these copies are read back during sequencing. Due to the unordered structure of the strands, an important task in the decoding process is to place them in their correct order. This is usually accomplished by allocating part of the strand for an index. However, in the presence of errors in the index field, important information on the order of the strands may be lost. Clustering-correcting codes ensure that if the distance between the index fields of two strands is small, their data fields have large distance. It is shown how this property enables to place the strands together in their correct clusters even in the presence of errors. We present lower and upper bounds on the size of clustering-correcting codes and an explicit construction of these codes which uses only a single symbol of redundancy. The results are first presented for the Hamming metric and are then extended for the edit distance.

ANALYSIS OF ERROR IN INDONESIAN LANGUAGE AT THE MORPHOLOGICAL LEVEL IN THE DISCUSSION OF BUYING ONLINE ON INSTAGRAM

This research is based on the results of direct observations and experiences of researchers through Instragam which is an online-based communication (in a network), thus freeing the general public to comment on one another. Therefore, the researcher analyzed Indonesian language errors at the morphological level in the discourse of online buying and selling on Instagram. The problem of this research is how the form of errors in Indonesian in the discourse of buying and selling online on Instagram at the morphological level. The purpose of this study is to find out the form of language errors in the morphology field in the discourse of online buying and selling on Instagram. This study uses a qualitative approach. This study uses observation and documentation methods with direct observation media. 3) incorrect use of capital letters

Usability evaluation algorithm of sea area landmark

The landmarks encountered by a flight vehicle in its scene matching navigation are insufficient and distributed unevenly, thus being unable to effectively assist in its inertial navigation system (INS). Therefore, this paper proposes a novel algorithm for sea area landmark usability evaluation based on the probability model. The algorithm defines three types of sea area landmarks and gives their matching method and strategy. The visible range of each sea area landmark is determined according to its relative relations between each position and all landmarks in flight area, flight altitude, flight speed, camera field of view angle and INS drift error. The matching probability of different types of landmarks at different flight altitudes is calculated. Then the observable probability of each landmark is reckoned to give the probability cloud of the sea area landmark usability. The simulation results verify the effectiveness of the algorithm. The sea area landmark usability evaluation results can provide bases for INS to effectively utilize landmarks to perform route planning and realize long-time flight and high-precision navigation.

Robustly decorrelating errors with mixed quantum gates

Coherent errors in quantum operations are ubiquitous. Whether arising from spurious environmental couplings or errors in control fields, such errors can accumulate rapidly and degrade the performance of a quantum circuit significantly more than an average gate fidelity may indicate. As Hastings (2017 Quantum Inf. Comput. 17 488) and Campbell (2017 Phys. Rev. A 95 042306) have recently shown, by replacing the deterministic implementation of a quantum gate with a randomized ensemble of implementations, one can dramatically suppress coherent errors. Our work begins by reformulating the results of Hastings and Campbell as a quantum optimal control problem. We then discuss a family of convex programs able to solve this problem, as well as a set of secondary objectives designed to improve the performance, implementability, and robustness of the resulting mixed quantum gates. Finally, we implement these mixed quantum gates on a superconducting qubit and discuss randomized benchmarking results consistent with a marked reduction in the coherent error.