High Accuracy Testing Research Articles

Mixed compensation for the testing of large convex aspheres

Since the testing of convex mirrors often requires a compensator with a larger aperture than the mirror under test, this is undoubtedly a trade-off not worth pursuing. Moreover, for surfaces with higher levels of asphericity, it further escalates the challenges in testing. In this study, high-accuracy testing of large convex aspheric surfaces with rotary symmetry was achieved via a method that combines sub-aperture stitching and Computer-Generated Holograms (CGH) compensation testing. Applying the methodology outlined in this study, practical testing was conducted on a large-aperture convex aspheric mirror with a diameter of 538 mm. The outcomes demonstrated that the center testing data and the outer ring testing data were stitched by the stitching algorithm to obtain the surface error of the full-aperture. The results were compared to those of Luphoscan and compensation testing, with a residual Root Mean Square (RMS) less than λ/20 (λ = 632.8 nm). Finally, the error analysis of the entire testing process showed that the accuracy was better than λ/50. The results demonstrate that the method can achieve highly accurate test of large convex aspheric surfaces.

Experimental Analysis of the Magnetic Leakage Detection of a Corroded Steel Strand Due to Vibration.

The self-magnetic flux leakage (SMFL) detection technique has great potential in the corrosion detection of bridge stay cables due to its advantages of small testing equipment, high accuracy, and fast testing rate. However, the vibration effect in the cable's SMFL detection is unclear. To address this, the influence of vibration on the magnetic field distribution of cable structure is analyzed theoretically. According to the theoretical model, the effect of vibration on SMFL detection primarily manifests as displacement changes (displacement-added magnetic field) and defect shape changes (deformation-added magnetic field). SMFL detection experiments are conducted on steel strands. The results demonstrate that the displacement-added magnetic field exhibits statistical characteristics in the form of a normal distribution, fluctuating around the zero value. The impact of the deformation-added magnetic field on SMFL is linearly correlated with the corrosion ratio c. Moreover, a corrosion characterization index A was proposed and has an excellent linear fit with the corrosion ratio c. The index A effectively improves the accuracy of corrosion detection and provides early warning for the maintenance of cable structures.

Determination of echinococcosis IgG antibodies using magnetic bead-based chemiluminescence immunoassay

Echinococcosis is a kind of parasitic disease shared by humans and animals. The aim of this study was to establish a new detection method for echinococcosis screening using magnetic bead-based chemiluminescence immunoassay (CLIA). A magnetic bead-based CLIA to determine anti-echinococcosis IgG antibodies was optimized and established. The sensitivity, accuracy, precision and recovery rate were evaluated using the national reference serum, and the reference interval, specificity and comparison assays were performed using the clinical negative/positive echinococcosis serum samples. This study established a new CLIA to determine anti-echinococcosis IgG antibodies. The sensitivity of this CLIA method was higher than that of the registered ELISA kit and the national standard, the conformance rate of the negative/positive references was 100% (8/8), the CVs of the sensitivity reference were all below 5%, and the CVs of the precision reference were 5.7%. There was no obvious cross-reactivity with the common parasitic disease-positive serum and serum interferents. Clinical sample testing found that the cutoff value of this CLIA was 5537.15 (RLU), and there was no significant difference between the CLIA method and the registered ELISA kit. This study established a fully automated CLIA method with high sensitivity, specificity, accuracy, precision, recovery rate, and satisfactory clinical testing performance, which may provide a new choice for echinococcosis screening.

‘CoviSwiftTM’: A point-of-care RT-PCR device for SARS-CoV-2 and its variant detection

We present the point-of-care (POC) molecular diagnostic solution, evaluated during COVID-19 pandemic caused by SARS-CoV-2 (Dec 2021). The POC comprised of a complete platform from self-sampling to RT-PCR testing of SARS-CoV-2 and its variants on portable Compact Q Real time polymerase chain reaction system. The multiplex assay was designed to target S, ORF1, and N genes of SARS-CoV-2 genome in a single tube with RNaseP as endogenous internal control. The present POC enables high accuracy (>95%) and high-throughput testing with a turnaround time of 45 min. It provides a unique platform from self-sample collection to report generation with rapid protocol, pipette and expert-free operation, long shelf-life stability and room temperature storage which enable to increase the efficiency of molecular testing. This novel test named “CoviSwift™ COVID-19 S PLUS RAPID PCR KIT” has been approved by CDSCO, Indian National Regulatory Authority, India, and is in use for clinical settings in India.

High sensitivity CIP2A detection for oral cancer using a rapid transistor-based biosensor module.

Oral squamous cell carcinoma (OSCC) is one of the most common lip and oral cavity cancer types. It requires early detection via various medical technologies to improve the survival rate. While most detection techniques for OSCC require testing in a centralized lab to confirm cancer type, a point of care detection technique is preferred for on-site use and quick result readout. The modular biological sensor utilizing transistor-based technology has been leveraged for testing CIP2A, and optimal transistor gate voltage and load resistance for sensing setup was investigated. Sensitivities of 1 × 10-15 g/ml have been obtained for both detections of pure CIP2A protein and HeLa cell lysate using identical test conditions via serial dilution. The superior time-saving and high accuracy testing provides opportunities for rapid clinical diagnosis in the medical space.

Accurate measurement of virtual image distance for near-eye displays based on auto-focusing.

Virtual reality (VR) and augmented reality (AR) are able to project virtual images to human eyes at a certain depth distance. This virtual image distance can be adjusted by controlling the diopter of the near-eye display. However, it is difficult to measure accurately and continuously since this virtual image distance spans a large range. In this work, we propose a method to accurately determine the virtual image distance of commercial VR/AR equipment. The measurement apparatus is built and calibrated to validate the feasibility. The focal distance of the focus-tunable lens can be automatically adjusted via a step motor by cooperating with the image sharpness analyzing program. Compared with other proposed methods, ours provides an effective means to achieve high accuracy, a wide and continuous testing range, and automatic evaluation of virtual image distance for compact near-eye displays.

High Accuracy Testing of MPPT Proteus Model Performance for Photovoltaic System

Abstract This study examines the performance of the MPPT Proteus model. The high performance of this model was proposed for a photovoltaic system and tested using the most commonly known MPPT techniques. The proposed Proteus model is a simulator of a maximum power point tracking system for a photovoltaic panel connected to the DC-DC converter with digital control. The Proteus software performs simulation and implementation of the photovoltaic panel model based on a one-diode model and a two-diode model with high accuracy. Both photovoltaic panel models were validated by experimental measurements. Simulation results for tracking voltage, tracking current and tracking power show that this model performs satisfactorily. The theoretical evaluation confirms the high performance of the MPPT Proteus model, which offers a high degree of control and planning.

COVID-19 Diagnosis: A Comprehensive Review of the RT-qPCR Method for Detection of SARS-CoV-2.

The world is grappling with the coronavirus disease 2019 (COVID-19) pandemic, the causative agent of which is severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 symptoms are similar to the common cold, including fever, sore throat, cough, muscle and chest pain, brain fog, dyspnoea, anosmia, ageusia, and headache. The manifestation of the disease can vary from being asymptomatic to severe life-threatening conditions warranting hospitalization and ventilation support. Furthermore, the emergence of mutecated variants of concern (VOCs) is paramount to the devastating effect of the pandemic. This highly contagious virus and its emergent variants challenge the available advanced viral diagnostic methods for high-accuracy testing with faster result yields. This review is to shed light on the natural history, pathology, molecular biology, and efficient diagnostic methods of COVID-19, detecting SARS-CoV-2 in collected samples. We reviewed the gold standard RT-qPCR method for COVID-19 diagnosis to confer a better understanding and application to combat the COVID-19 pandemic. This comprehensive review may further develop awareness about the management of the COVID-19 pandemic.

Background noise analysis and improvement for the water vapor and oxygen transmission rate test of free-standing films.

The background noise (BGN) of test equipment affects the lower detection limit of the measurement range, and achieving an ultralow BGN is identified as a formidable challenge for highly accurate testing of a free-standing film. In this paper, a new gas transmission rate test apparatus is designed by detecting the electrical resistance increase with calcium corrosion. Thanks to the new design strategy, the only permeable place of this apparatus is the clamping and holding site. Mathematical methods for calculating the molecular permeation behaviors and magnitude of BGN are devised. The results show that the properties and structures of the materials significantly affect the BGN value. By using a sealant with low permeability (2.25 × 10-15 g cm cm-2 s-1 Pa-1) and a clamping ring with small width (2.5 mm) and testing an ultrahigh barrier (WVTR: 10-6 g m-2 day-1), the BGN could reach a level of 10-11 g m-2 day-1. With theoretical guidance, after optimization, one sealing structure obtained an ultralow BGN value (∼10-6 g m-2 day-1) in the actual test. The electrical resistance test has many advantages over existing methods. Additionally, the computational method devised in this study to examine the effects of materials provides a useful tool to achieve high-accuracy testing.

COVID-19 in-vitro Diagnostics: State-of-the-Art and Challenges for Rapid, Scalable, and High-Accuracy Screening.

The world continues to grapple with the devastating effects of the current COVID-19 pandemic. The highly contagious nature of this respiratory disease challenges advanced viral diagnostic technologies for rapid, scalable, affordable, and high accuracy testing. Molecular assays have been the gold standard for direct detection of the presence of the viral RNA in suspected individuals, while immunoassays have been used in the surveillance of individuals by detecting antibodies against SARS-CoV-2. Unlike molecular testing, immunoassays are indirect testing of the viral infection. More than 140 diagnostic assays have been developed as of this date and have received the Food and Drug Administration (FDA) emergency use authorization (EUA). Given the differences in assasy format and/or design as well as the lack of rigorous verification studies, the performance and accuracy of these testing modalities remain unclear. In this review, we aim to carefully examine commercialized and FDA approved molecular-based and serology-based diagnostic assays, analyze their performance characteristics and shed the light on their utility and limitations in dealing with the COVID-19 global public health crisis.

A Complementary Filter-Based All-Parameters Estimation for Triaxis Gyroscopes and Optical Angular Encoders With Intrinsic Eccentricity

Outfield tests for inertial sensors have drawn growing attention in recent years since it will provide more practical application conditions. The traditional field tests carried out with simple ancillary equipment cannot maintain high accuracy testing results. In this paper, we implemented the outfield tests on an outfield testing platform with a high-accuracy turntable. However, the platform’s movement and deformation in mechanical structure will induce an unknown intrinsic eccentricity to the optical angular encoders (OAE) in the turntable, which will have a negative impact on the testing accuracy. To estimate the unknown eccentricity, the limitation brought by the outfield condition is the difficulty in acquiring and applying the additional standard sensors. Therefore, we propose an all-parameters estimation method is to identify the eccentricity with a triaxis gyroscope under tests synchronously based on a designed complementary filter with the Gauss-Newton. This method will reduce the dependence of additional references. Then, several rules are discussed to design rotation schemes. Finally, both simulations and experiments are performed to evaluate the proposed method. Experiments result show that the method can improve the accuracy of the optical angular encoder and gyroscopes.

Network intrusion detection based on deep learning model optimized with rule-based hybrid feature selection

ABSTRACT Network Intrusion Detection System (NIDS) is often used to classify network traffic in an attempt to protect computer systems from various network attacks. A major component for building an efficient intrusion detection system is the preprocessing of network traffic and identification of essential features which is essential for building robust classifier. In this study, a NIDS based on deep learning model optimized with rule-based hybrid feature selection is proposed. The architecture is divided into three phases namely: hybrid feature selection, rule evaluation and detection. Several search methods and attribute evaluators were combined for features selection to enhance experimentation and comparison. The results obtained showed that the number of selected features will not affect the detection accuracy of the feature selection algorithms, but directly proportional to the performance of the base classifier. Results from the performance comparison proved that the proposed method outperforms other related methods with reduction of false alarm rate, high accuracy rate, reduced training and testing time of 1.2%, 98.8%, 7.17s and 3.11s, respectively. Finally, the simulation experiments on standard evaluation metrics showed that the proposed method is suitable for attack classification in NIDS.

A cost analysis of machine learning using dynamic runtime opcodes for malware detection

The ongoing battle between malware distributors and those seeking to prevent the onslaught of malicious code has, so far, favored the former. Anti-virus methods are faltering with the rapid evolution and distribution of new malware, with obfuscation and detection evasion techniques exacerbating the issue. Recent research has monitored low-level opcodes to detect malware. Such dynamic analysis reveals the code at runtime, allowing the true behaviour to be examined. While previous research uses machine learning techniques to accurately detect malware using dynamic runtime opcodes, underpinning datasets have been poorly sampled and inadequate in size. Further, the datasets are always fixed size and no attempt, to our knowledge, has been made to examine the cost of retraining malware classification models on datasets which grow continually. In the literature, researchers discuss the explosion of malware, yet opcode analyses have used fixed-size datasets, with no deference to how this model will cope with retraining on escalating datasets. The research presented here examines this problem, and makes several novel contributions to the current body of knowledge.First, the performance of 23 machine learning algorithms are investigated with respect to the largest run trace dataset in the literature. Second, following an extensive hyperparameter selection process, the performance of each classifier is compared, on both accuracy and computational costs (CPU time). Lastly, the cost of retraining and testing updatable and non-updatable classifiers, both parallelized and non-parallelized, is examined with simulated escalating datasets. This provides insight into how implemented malware classifiers would perform, given simulated dataset escalation. We find that parallelized RandomForest, using 4 cores, provides the optimal performance, with high accuracy and low training and testing times.

AEg̅IS latest results

The validity of the Weak Equivalence Principle (WEP) as predicted by General Relativity has been tested up to astounding precision using ordinary matter. The lack hitherto of a stable source of a probe being at the same time electrically neutral, cold and stable enough to be measured has prevented highaccuracy testing of the WEP on anti-matter. The AEg̅IS (Antimatter Experiment: Gravity, Interferometry, Spectroscopy) experiment located at CERN's AD (Antiproton Decelerator) facility aims at producing such a probe in the form of a pulsed beam of cold anti-hydrogen, and at measuring by means of a moiré deflectometer the gravitational force that Earth's mass exerts on it. Low temperature and abundance of the H̅ are paramount to attain a high precision measurement. A technique employing a charge-exchange reaction between antiprotons coming from the AD and excited positronium atoms is being developed at AEg̅IS and will be presented hereafter, alongside an overview of the experimental apparatus and the current status of the experiment

Swing arm profilometer: high accuracy testing for large reaction-bonded silicon carbide optics with a capacitive probe

A feasible way to improve the manufacturing efficiency of large reaction-bonded silicon carbide optics is to increase the processing accuracy in the ground stage before polishing, which requires high accuracy metrology. A swing arm profilometer (SAP) has been used to measure large optics during the ground stage. A method has been developed for improving the measurement accuracy of SAP using a capacitive probe and implementing calibrations. The experimental result compared with the interferometer test shows the accuracy of 0.068 μm in root-mean-square (RMS) and maps in 37 low-order Zernike terms show accuracy of 0.048 μm RMS, which shows a powerful capability to provide a major input in high-precision grinding.

Improved Accuracy On-Ground Testing of Spacecraft Antennas Using Numerical Calibration of Standard Gain Horn [EM Programmer's Notebook

High accuracy testing of spacecraft antennas is one of the primary tasks in the space industry. The gain transfer method is a widely used IEEE standard method for gain measurement. Standard gain horns (SGHs) are usually provided with theoretical gain curves such as from the U.S. Naval Research Lab (NRL) with a typical gain accuracy of 0.3 dB, which is the major contributor in the overall measurement uncertainty of a facility. However, modern spaceborne applications require gain accuracy of the order of 0.1 dB or better. For such demanding applications, SGHs need to be calibrated in a standard laboratory such as the National Institute of Standards and Technology.

Computer-aided high-accuracy testing of reflective surface with reverse Hartmann test

The deflectometry provides a feasible way for surface testing with a high dynamic range, and the calibration is a key issue in the testing. A computer-aided testing method based on reverse Hartmann test, a fringe-illumination deflectometry, is proposed for high-accuracy testing of reflective surfaces. The virtual "null" testing of surface error is achieved based on ray tracing of the modeled test system. Due to the off-axis configuration in the test system, it places ultra-high requirement on the calibration of system geometry. The system modeling error can introduce significant residual systematic error in the testing results, especially in the cases of convex surface and small working distance. A calibration method based on the computer-aided reverse optimization with iterative ray tracing is proposed for the high-accuracy testing of reflective surface. Both the computer simulation and experiments have been carried out to demonstrate the feasibility of the proposed measurement method, and good measurement accuracy has been achieved. The proposed method can achieve the measurement accuracy comparable to the interferometric method, even with the large system geometry calibration error, providing a feasible way to address the uncertainty on the calibration of system geometry.

Defect Detecting Technology Based on Machine Vision of Industrial Parts

In order to reach and test the surface defects on industrial parts, based on Machine Vision this paper put forward a defective parts detection method. The method of median filter was adopted to eliminate the noise of image. The Ostu-method was used for the segmenting threshold. Pixel level and level edge detection were used to complete the precise components defects detection. Experiments show that this scheme is feasible, and can achieve high accuracy and shorter testing time.

The Development and Application of Slug Test in Hydrogeology

The slug test is a field-testing method to evaluate hydrogeological parameters by recording the instantaneous change of the underground water level. Basing on abroad development of slug test, this paper tried to measure some parameters of aquifer with middle and high permeability using high accuracy testing. According to field tests and existing pumping data, discussed difference of several theoretic model, analyzed and computed the hydrogeological parameters of a certain spot in Chang Chun, China by classical Cooper Model. At last, comparing with pumping test, the accuracy of the test was verified, which supplied the theory foundation for its application and generalization.

High resolution low-energy dielectronic recombination rate coefficients of beryllium-like germanium: QED test bench for two-valence-electron systems

Low-energy dielectronic recombination resonances of beryllium-like Ge28+ have been studied by electron collision spectroscopy in the heavy-ion storage ring TSR using a twin-electron beam technique combined with an ultracold photoelectron source. Rich resonance structure below 1.7 eV is observed which mainly arises from resonant capture of the free electron to 91 Rydberg states accompanied by excitation of inner valence electrons from the 2s2(1S0) ground state to 2s2p(1P1). The positions of resonances below 0.3 eV are measured with an accuracy better than 0.5 meV. Accurate calculations of Rydberg binding energies for these resonances are still required in order to derive a precise value for the 1S0 – 1P1 transition energy, which may give the opportunity for high-accuracy testing of QED corrections to atomic energy levels for a two valence electron system.