Accuracy Range Research Articles

Snapshot Multispectral Light‐Field Imaging Based on Nematic Liquid‐Crystal Microlens Array via Deep Learning

In this study, a multispectral light‐field imaging system is developed using nematic liquid‐crystal microlens arrays (LC‐MLAs). The system solves the spectral under‐sampling and complex system issues in existing light‐field and spectral cameras. The LC‐MLA is the core optical element of the snapshot imaging system. No dispersion or customized scanning machinery is required to obtain multispectral light fields. Deep learning technology is utilized to reconstruct spectral information from raw data. In the experiments, it is demonstrated that the proposed method efficiently reconstructs multispectral data with 10 nm accuracy in the visible wavelength range of 400–700 nm. The system offers high precision, simple equipment, and relatively low cost without additional dispersion and filtering devices.

Accurate Modeling, Design, and Load Estimation of LCC-S Based WPT System With a Wide Range of Load

Modeling of the wireless power transfer (WPT) system is the premise to design and control it. First-harmonic-analysis (FHA) is a simple and effective method to model the WPT system, but its accuracy may decrease with the variation of load in a wide range. In this paper, harmonics are analyzed in the primary side of an LCC-S compensation network based WPT system, depicting a waveform of the output current of the inverter much closer to the real one compared with FHA based model. In the secondary side, the time-domain analysis is conducted both in CCM&DCM of the rectifier, deriving an equivalent input impedance featuring the non-linear characteristics of the system. Based on the proposed model, the parameters of the LCC network are designed, proving to tune the ZVS condition better than the FHA based method. Finally, the load of the system is estimated, achieving a high accuracy (with the estimating error less than 5%) in a wide range with a simple sampling method.

Pre-event base flow as a useful indicator for flood forecasting

The hydrological cycle comprises several components, including precipitation, infiltration, evaporation, transpiration, and runoff, with the latter being a vital part of the cycle that connects precipitation to bodies of water. modelling is necessary due to limitations in standard hydrological measurement techniques, providing a better understanding of hydrological systems that were previously inaccessible, and making it essential for water resource management. Event-based rainfall-runoff modelling is a prevalent method due to its simplicity, but conventional models are limited in accounting for antecedent conditions. In order to tackle this problem, pre-event baseflow is proposed as an indicator for assessing catchment conditions for flood forecasting. This study employs a novel event-based rainfall-runoff equation that incorporates rainfall characteristics, improving hydrological modelling accuracy, and outperforms conventional models in predicting direct flow in most case studies. Overall, the study demonstrates the potential for the new equation to improve hydrological modelling accuracy and aid in a wide range of applications such as flood prediction, water resources management, and planning.

Approach toward the application of mobile robots in laser materials processing

Laser-based production systems have become more and more popular in recent years due to their potential to achieve high precision and accuracy in a wide range of different applications. However, the kinematic systems used for laser materials processing (LMP) are often inherited from other production technologies such as milling. The use of mobile robots (MRs) equipped with laser processing optics could disprove the current paradigm of adapted kinematic systems: scaling the size of the material processing system with the size of the components being processed and, thus, the resources used. The trend of autonomous MRs replacing classical kinematic systems in the field of material handling in industrial applications has been evident for years due to their higher flexibility, efficiency, and lower operating costs. In this paper, the prototype of a corresponding MR system is presented. In addition, the general design of the MR is presented. One challenge is the accuracy of an MR; for a common LMP such as laser cutting, the MR must be able to follow a predefined trajectory as accurately as possible. For this purpose, two different measurement systems are presented and compared. To demonstrate the potential of the mobile robot, an exemplary LMP process is also performed and evaluated. Finally, possibilities for improvement or further development, such as integration of scanner optics or the use of several autonomous MRs to increase productivity, are shown.

Real-time hybrid angular-interrogation surface plasmon resonance sensor in the near-infrared region for wide dynamic range refractive index sensing

Herein, we integrated angle-scanning surface plasmon resonance (SPR) and angle-fixed SPR as a hybrid angular-interrogation SPR to enhance the sensing performance. Galvanometer-mirror-based beam angle scanning achieves a 100-Hz acquisition rate of both the angular SPR reflectance spectrum and the angle-fixed SPR reflectance, whereas the use of near-infrared light enhances the refractive index (RI) sensitivity, range, and precision compared with visible light. Simultaneous measurement of the angular SPR reflectance spectrum and angle-fixed SPR reflectance boosts the RI change range, RI resolution, and RI accuracy to 10–1–10–6 RIU, 2.24 × 10−6 RIU, and 5.22 × 10−6 RIU, respectively. The proposed hybrid SPR is a powerful tool for wide-dynamic-range RI sensing with various applications.

Discrimination of Healthy and Cancerous Colon Cells Based on FTIR Spectroscopy and Machine Learning Algorithms

Colorectal cancer was one of the most frequent causes of death due to cancer in 2020. Current diagnostic methods, based on colonoscopy and histological analysis of biopsy specimens, are partly dependent on the operator’s skills and expertise. In this study, we used Fourier transform infrared (FTIR) spectroscopy and different machine learning algorithms to evaluate the performance of such method as a complementary tool to reliably diagnose colon cancer. We obtained FTIR spectra of FHC and CaCo-2 cell lines originating from healthy and cancerous colon tissue, respectively. The analysis, based on the intensity values of specific spectral structures, suggested differences mainly in the content of lipid and protein components, but it was not reliable enough to be proposed as diagnostic tool. Therefore, we built six machine learning algorithms able to classify the two different cell types: CN2 rule induction, logistic regression, classification tree, support vector machine, k nearest neighbours, and neural network. Such models achieved classification accuracy values ranging from 87% to 100%, sensitivity from 88.1% to 100%, and specificity from 82.9% to 100%. By comparing the experimental data, the neural network resulted to be the model with the best performance parameters, having excellent values of accuracy, sensitivity, and specificity both in the low-wavenumber range (1000–1760 cm−1) and in the high-wavenumber range (2700–3700 cm−1). These results are encouraging for the application of the FTIR technique, assisted by machine learning algorithms, as a complementary diagnostic tool for cancer detection.

Wildfire monitoring technologies of transmission line corridors based on Fengyun-3E satellite imaging

Facing towards the Carbon Neutrality goal by 2060, renewable energy sources are boosted in China in the past decade. Either these renewables or power systems are sensitive and vulnerable to natural disasters, such as wildfire and typhoon. In this paper, a fire spot identification algorithm is proposed for wildfire disaster occurred in transmission line corridors based on Fengyun-3E (FY-3E). It mitigates the effect of large observation of solar zenith angle, as well as changes of satellite views, on the infrared channel detection. And cloud information under complex atmospheric conditions is analyzed to extract the cloud pixel fire spot, which decreases false ratio of fire-spot alerts. According to the analysis of hybrid pixel linear spectrums, the fire-spot detection sensitivity of FY-3E is four times as that of geostationary meteorological satellites. The effectiveness of the proposed algorithm was confirmed by an application in the Shanxi province power grid of State Grid in China. It shows that and the FY-3E has a superiority in wildfire monitoring for power system in terms of fire detection sensitivity, spatial range accuracy, and positioning accuracy. Compared to geostationary meteorological satellites, the fire locating accuracy of FY-3E is increased by more than one-fold. This research can assist the operation and maintenance departments of transmission lines to rapidly detect wildfire, thereby reducing the damage of wildfires on the power grid.

A high-accuracy voltage reference with high-order curvature compensation and base-current-canceling techniques

This paper presents a low dropout bandgap voltage reference circuit that combines high output voltage accuracy and low temperature-coefficient in a wide temperature range. The low temperature-coefficient is achieved by an advanced high-order curvature compensation technique, which is implemented by a novel exponential piecewise compensation term in a high-temperature range. A supply noise self-regulation technique is proposed to realize an excellent power supply rejection ratio (PSRR) without using any amplifiers in this paper. The proposed high-accuracy voltage reference is designed in a 2[Formula: see text][Formula: see text]m bipolar process. The circuit generates a reference voltage of 3.3[Formula: see text]V and can operate under a minimum supply voltage of 3.8[Formula: see text]V, while consuming 44.8[Formula: see text][Formula: see text]A. The output voltage accuracy is ± 0.15%. The PSRR at 10[Formula: see text]Hz and 1[Formula: see text]MHz is [Formula: see text][Formula: see text]dB and [Formula: see text][Formula: see text]dB, respectively. The line regulation is 2.45[Formula: see text]ppm/V in a supply voltage range from 3.8[Formula: see text]V to 20[Formula: see text]V. The average temperature coefficient is 11.9[Formula: see text]ppm/∘C in a temperature range from [Formula: see text]C to +125∘C.

Predicting sub‐continental fuel hazard under future climate and rising atmospheric CO2 concentration

Abstract Bushfire fuel hazard is determined by the type, amount, density and three‐dimensional distribution of plant biomass and litter. The fuel hazard represents a biological control on fire danger and may change in the future with plant growth patterns. Rising atmospheric CO2 concentration (Ca) stimulates plant productivity (‘fertilisation effect’) but also alters climate, leading to a ‘climatic effect’. Both effects have impacts on future vegetation and thus fuel hazard. Quantifying these effects is an important component of predicting future fire regimes and evaluating fire management options. Here, we combined a machine learning algorithm that incorporates the power of large fine spatial resolution (i.e. 90 m) datasets with a novel optimality model that accounts for the climatic and fertilisation effects on vegetation cover. We demonstrated the usefulness and practicality of this framework by predicting fuel hazard across the state of Victoria in Australia. We fitted and evaluated the models with long‐term (i.e. 20 years), ground‐based fuel observations. The models achieved strong agreement with observations across the fuel hazard range (accuracy >65%). We found fuel hazard increased more in dry environments due to future climate and Ca. The contribution of the ‘fertilisation effect’ to future fuel hazard varied spatially by up to 12%. The predictions of future fuel hazard are directly useful to inform fire mitigation policies and as a reference for climate model projections to account for fire impacts. Synthesis and applications: Climate change and rising Ca have profound impacts on vegetation and thus fuel load. Operational fire management and future fire risk forecasts will benefit from our realistic fuel load prediction framework that incorporates plant responses and fine soil and terrain attributes.

Influence of the range Doppler coupling error on the characteristics of second-order filters

This paper mainly considers a growing-memory filter for tracking an object moving with constant acceleration and detected by chirp radar. Chirp radars employ linear frequency modulated (LFM) waveforms for object tracking which yields a displacement of measured position from a true range of moving objects. This effect may enable better track accuracy in range. The second order degree polynomial describes coordinates law for a constantly accelerating maneuvering object. The issues of lag errors, covariance matrix of the filtered state vectors, impulse responses and Kalman gains are discussed. For example, the growing-memory filter is most often used for initiating track. The growing-memory filter is derived in recursive and non-recursive form based on least-squares. It is shown that the growing-memory filter estimating a true object range (with the range-Doppler coupling coefficient) can be represented by the growing-memory filter estimating a biased object range (without the range-Doppler coupling coefficient) and compensating the estimated biased range by the estimated the range-Doppler coupling error. Expressions giving an interrelation between lag errors, covariance matrix of the filtered state vectors, impulse responses and Kalman gains of growing-memory filters estimating true and biased range of moving objects are derived.

A Highly Accurate and Sensitive mmWave Displacement-Sensing Doppler Radar With a Quadrature-Less Edge-Driven Phase Demodulator

A 110-mW 39-GHz Doppler radar front end in 65-nm CMOS for displacement and vibration sensing is proposed. Conventional Doppler radar suffers from detection nulls, at which the receiver detection gain drops to zero. Quadrature demodulation for either carrier frequency or intermediate frequency (IF) is necessary to alleviate nulls but still induces nonlinear detection gain that needs to be compensated through digital signal processing (DSP). In this article, a Doppler radar topology is proposed to achieve ultrahigh displacement range accuracy and sensitivity and to eliminate detection nulls without using quadrature demodulation. An edge-driven phase demodulator (EDPD) processes the rectified square-wave intermediate signal and converts displacement/vibration to a true-dc/baseband signal with a constant gain. Coherent demodulation and signal generation through common-referenced subsampling phase-locked loops (SSPLLs) allow the radar to achieve 4- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu$</tex-math> </inline-formula> m static range accuracy and 39-nm vibrational (at 10 kHz) range sensitivity in measurement.

Risk Assessment Method for UAV’s Sense and Avoid System Based on Multi-Parameter Quantification and Monte Carlo Simulation

The rise in Unmanned Aerial Vehicle (UAV) usage has opened exciting possibilities but has also introduced risks, particularly in aviation, with instances of UAVs flying dangerously close to commercial airplanes. The potential for accidents underscores the urgent need for effective measures to mitigate mid-air collision risks. This research aims to assess the effectiveness of the Sense and Avoid (SAA) system during operation by providing a rating system to quantify its parameters and operational risk, ultimately enabling authorities, developers, and operators to make informed decisions to reach a certain level of safety. Seven parameters are quantified in this research: the SAA’s detection range, field of view, sensor accuracy, measurement rate, system integration, and the intruder’s range and closing speed. While prior studies have addressed these parameter quantifications separately, this research’s main contribution is the comprehensive method that integrates them all within a simple five-level risk rating system. This quantification is complemented by a risk assessment simulator capable of testing a UAV’s risk rating within a large sample of arbitrary flight traffic in a Monte Carlo simulation setup, which ultimately derives its maximum risk rating. The simulation results demonstrated safety improvements using the SAA system, shown by the combined maximum risk rating value. Among the contributing factors, the detection range and sensor accuracy of the SAA system stand out as the primary drivers of this improvement. This conclusion is consistent even in more regulated air traffic imposed with five or three mandatory routes. Interestingly, increasing the number of intruders to 50 does not alter the results, as the intruders’ probability of being detected remains almost the same. On the other hand, improving SAA radar capability has a more significant effect on risk rating than enforcing regulations or limiting intruders.

On-line preconcentration of fluorescein derivatives of essential amino acids using sweeping in a nonionic micellar system

A rapid and effective method of nonionic micellar electrokinetic chromatography with laser-induced fluorescence detection and sweeping as an online preconcentration method was developed for the simultaneous determination of free essential amino acids in supplement formulations. The analytes were derivatized using fluorescein isothiocyanate isomer I dissolved in dimethyl sulfoxide. Optimal separation conditions were achieved using an uncoated fused silica capillary (40.0 cm effective length, 50.2 cm total length, 50 µm internal diameter), 20 mM Na2B4O7, (pH 9.3), 50 mM Brij-35, and 15% (v v-1) methanol as running buffer. Complete separation of all analytes was achieved within 15 min. The optimized method exhibited good linearity (r2 > 0.997), acceptable precision (1.3 – 12.4%), and accuracy (88 – 112%) in concentration range 20 – 1000 ng mL−1. The limit of detection was 6 ng mL−1. The method successfully determined the quantity of free essential amino acids in a dietary supplement containing spirulina.

Investigations of the scan characteristics with special focus on multi-target capability for the 2D laser scanner RIEGL miniVUX-2UAV

Abstract Due to recent improvements in sensor technology, UAV-based laser scanning is nowadays used in more and more applications like topographic surveying or forestry. The quality of the scanning result, a georeferenced 3D point cloud, mainly depends on errors coming from the trajectory estimation, the system calibration and the laser scanner itself. Due to the combined propagation of errors into the point cloud, the individual contribution is difficult to assess. Therefore, we propose an entire investigation of the scan characteristics of a 2D laser scanner without the use of the other sensors included in the system. The derived parameters include the range precision, the rangefinder offset as part of the range accuracy, the angular resolution capability and the multi-target capability of the RIEGL miniVUX-2UAV. The range precision is derived from amplitude values by a stochastic model, with observations fitting a theoretical model very well. The resolution capability in the angular direction is about twice the laser beam footprint size and therefore increases linearly for larger distances. Further, a new approach with the corresponding methodology for the investigation of multi-target capability is presented. The minimum distance between two targets to appear as separated echoes within a single laser beam is about 1.6 m and inliers within the measurement precision occur from 1.9 m separation distance. The scan attributes amplitude and deviation, which are computed during the online waveform processing, show a clear systematic relation to the range precision, also in cases of multiple echoes.

Numerical investigation on the D-SPR-PQF — High Refractive Index and temperature sensor for transport fuel adulteration

This study presents a Surface Plasmonic Resonance based D-shape oval open loop Photonic Quasi-Crystal Fiber (SPR-PQF). There is necessary to concurrently measure the changes in transport fuels temperature and Refractive index for onsite fuel quality measurements. However, the sensing methods and material selectivity significantly impact the sensor performance. The Finite Element Method (FEM) is a good choice for solving the propagation characteristics of the modes using Maxwell equations. The composite coating of Silver (Ag) and Graphene Oxide (GO) in the open loop D-PQF-structure effectively tunes the SPR condition in the external sensing approach. This FEM-based numerical study reaches the high-temperature sensitivity (22 nm/ ∘C), wavelength Sensitivity (150000 nm/ RIU), amplitude Sensitivity (-6000/ RIU), Figure of Merit (FoM) (1457.85), and detection accuracy (0.66 mg/L, 1.4 mg/L) in the RI range of 1.4185 to 1.448. This sensitivity response is six times larger than the existing SPR PCF sensor in the literature. Hence, this sensor has enormous potential in the petrochemical sensing applications.

An enhanced cyclostationary method and its application on the incipient fault diagnosis of induction motors

The cyclostationary analysis techniques have been extensively explored for the purpose of fault detection in rotating machinery. However, there are still huge challenges because of both limited detection frequency range and low fault identification accuracy. This paper proposes an improved cyclostationary method to enhance incipient fault features. Firstly, the continuous wavelet transform is used to accurately locate important frequency bands, and the fault modulation mechanism or fast kurtogram can be adopted to design the optimal wavelet transform scale factor. Secondly, the Teager-Kaiser energy operator is improved to be used in the frequency domain for the weak fault feature enhancement. Finally, fault features are presented in the cyclic frequency domain through spectral coherence and enhanced envelope spectrum. The proposed method is verified through both numerical simulation and experiments, including incipient half-broken rotor bar, and rolling bearing outer race faults in induction motors.

Enhancing Object Detection for VIPs Using YOLOv4_Resnet101 and Text-to-Speech Conversion Model

Vision impairment affects an individual’s quality of life, posing challenges for visually impaired people (VIPs) in various aspects such as object recognition and daily tasks. Previous research has focused on developing visual navigation systems to assist VIPs, but there is a need for further improvements in accuracy, speed, and inclusion of a wider range of object categories that may obstruct VIPs’ daily lives. This study presents a modified version of YOLOv4_Resnet101 as backbone networks trained on multiple object classes to assist VIPs in navigating their surroundings. In comparison to the Darknet, with a backbone utilized in YOLOv4, the ResNet-101 backbone in YOLOv4_Resnet101 offers a deeper and more powerful feature extraction network. The ResNet-101’s greater capacity enables better representation of complex visual patterns, which increases the accuracy of object detection. The proposed model is validated using the Microsoft Common Objects in Context (MS COCO) dataset. Image pre-processing techniques are employed to enhance the training process, and manual annotation ensures accurate labeling of all images. The module incorporates text-to-speech conversion, providing VIPs with auditory information to assist in obstacle recognition. The model achieves an accuracy of 96.34% on the test images obtained from the dataset after 4000 iterations of training, with a loss error rate of 0.073%.

Adaptive Fault Locator for High-Voltage Transmission Lines Based on the Estimation of Power System Model Parameters

Abstract This paper presents an adaptive transmission line fault location method, which incorporates fault location devices at both line ends and utilises data import from a supervisory control and data acquisition system without strictly requiring data synchronisation. The developed method aims at achieving a higher degree of robustness, adaptiveness and accuracy. The adaptiveness is achieved by dynamic updating of mathematical models used on the basis of network-wide information, such as data on the state of circuit-breakers and apparent power at load and generation nodes. The robustness and accuracy are enhanced by incorporating two stages of identification of model parameters with the goal of reducing the decision variable space for the stage identifying fault parameters. Furthermore, in addition to utilisation of all measurements available at a particular substation, the developed method partially employs the measurements from the other end of the line by means of result cross-checks, but does not require a full data set, unlike deterministic-model-based methods. An optimisation-based approach, redundancy on the basis of extended measurement set, and cross-checks reduce the risk of fault location errors due to measurement errors or “voids” in the data available. Testing of the developed method demonstrates its accuracy and robustness in a wide range of pre-fault and fault regime scenarios, even when considering various pre-fault contingencies.

Origin identification of Poria cocos based on hyperspectral imaging technology

To realize the non-destructive and rapid origin discrimination of Poria cocos in batches, this study established the P. cocos origin recognition model based on hyperspectral imaging combined with machine learning. P. cocos samples from Anhui, Fujian, Guangxi, Hubei, Hunan, Henan and Yunnan were used as the research objects. Hyperspectral data were collected in the visible and near infrared band(V-band, 410-990 nm) and shortwave infrared band(S-band, 950-2 500 nm). The original spectral data were divided into S-band, V-band and full-band. With the original data(RD) of different bands, multiplicative scatter correction(MSC), standard normal variation(SNV), S-G smoothing(SGS), first derivative(FD), second derivative(SD) and other pretreatments were carried out. Then the data were classified according to three different types of producing areas: province, county and batch. The origin identification model was established by partial least squares discriminant analysis(PLS-DA) and linear support vector machine(LinearSVC). Finally, confusion matrix was employed to evaluate the optimal model, with F1 score as the evaluation standard. The results revealed that the origin identification model established by FD combined with LinearSVC had the highest prediction accuracy in full-band range classified by province, V-band range by county and full-band range by batch, which were 99.28%, 98.55% and 97.45%, respectively, and the overall F1 scores of these three models were 99.16%, 98.59% and 97.58%, respectively, indicating excellent performance of these models. Therefore, hyperspectral imaging combined with LinearSVC can realize the non-destructive, accurate and rapid identification of P. cocos from different producing areas in batches, which is conducive to the directional research and production of P. cocos.

Hemoglobin Derivatives in Beef Irradiated with Accelerated Electrons.

The efficiency of food irradiation depends on the accuracy of the irradiation dose range that is sufficient for inhibiting microbiological growth without causing an irreversible change to the physical and chemical properties of foods. This study suggests that the concentration of hemoglobin derivatives can be used as a criterion for establishing the limit for chilled beef irradiation at which irradiation-induced oxidation becomes irreversible. The express spectrophotometry method for estimating the hemoglobin derivative concentration shows a nonlinear increase in methemoglobin concentration from 15% to 50% in beef irradiated by accelerated electrons with the doses ranging from 250 Gy to 10,000 Gy. The monitoring of the hemoglobin derivative concentration for three days after irradiation shows nonmonotonous dependencies of methemoglobin concentration in beef in the storage time since the oxidation of hemoglobin occur as a result of irradiation and biochemical processes in beef during storage. The proposed method based on the quantitative analysis of the hemoglobin derivative concentration can be used to estimate the oxidation level for irradiation of foods containing red blood cells. The study proposes a model that describes the change in hemoglobin derivative concentration in beef after irradiation considering that oxidation of hemoglobin can be triggered by the direct ionization caused by accelerated electrons, biochemical processes as a result of bacterial activity, and reactive oxygen species appearing during irradiation and storage. This research throws light on the mechanisms behind food irradiation during storage that should be taken into account for selecting the optimal parameters of irradiation.