Radiation Signal Research Articles

Rock Burst Precursor Electromagnetic Radiation Signal Recognition Method and Early Warning Application Based on Recurrent Neural Networks

The fracture-induced electromagnetic radiation (FEMR) method is a promising geophysical method for the monitoring and early warning of coal-rock burst disasters. At present, the time-series characteristics of FEMR data are primarily used for rock burst monitoring and early warning. However, these rock burst precursor signal identification and hazard warning methods need further development. The progress made in the field of deep learning provides a new method for the identification of rock burst warning signals and the realization of intelligent early warning. In this paper, based on a deep learning algorithm, a method for identifying rock burst precursor FEMR signals is proposed. Based on bidirectional long short-term memory recurrent neural networks, this method trains and validates the model by analyzing a large number of normal FEMR signals and rock burst FEMR precursor signals. The input of the model is the FEMR data sequence, and the corresponding output is the hazard identification result. Upon the completion of training and validation, the model can perform automatic/intelligent shock hazard precursor signal recognition and quickly and accurately provide rock burst hazard early warning without requiring parameter adjustment and manual intervention. The results obtained showed that the rock burst precursor signal recognition method based on the recurrent neural network responds well to the rock burst hazard and can capture information regarding impact hazards in advance. Therefore, it is of great significance with regard to accurate rock burst monitoring and early warning.

Performance Characteristics of Mutational Signature Analysis in Targeted Panel Sequencing.

Mutational signatures have been described in the literature and a few centers have implemented pipelines for clinical reporting. To describe the performance of a mutational signature caller with clinical samples sequenced on a targeted next-generation sequencing panel with a small genomic footprint. One thousand six hundred eighty-two clinical samples were analyzed for the presence of mutational signatures using deconstructSigs on variant calls with at least 20 variant reads. Signature 10 (associated with POLe mutation) achieved separation of cases and controls in hypermutated samples. Signatures 4 (associated with tobacco smoking) and 7 (associated with ultraviolet radiation) as an indicator of pulmonary or cutaneous primary sites showed moderate sensitivity and high specificity at optimal cutpoints. Mutational signatures in malignancies with unknown primaries were somewhat consistent with the clinically suspected primary site, with an apparent dose-response relationship between the number of variants analyzed and the ability of mutational signature analysis to correctly suggest a primary site. Mutational signatures represent an opportunity for orthogonal testing of primary site, which may be particularly useful in supporting cutaneous or pulmonary sites in poorly differentiated neoplasms. Tobacco smoking, ultraviolet radiation, and POLe mutational signatures are the most appropriate signatures for implementation. Even relatively small numbers of variants appear capable of supporting a clinically suspected primary.

Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep Learning

Accurate high spatial resolution snow depth mapping in arid and semi-arid regions is of great importance for snow disaster assessment and hydrological modeling. However, due to the complex topography and low spatial-resolution microwave remote-sensing data, the existing snow depth datasets have large errors and uncertainty, and actual spatiotemporal heterogeneity of snow depth cannot be effectively detected. This paper proposed a deep learning approach based on downscaling snow depth retrieval by fusion of satellite remote-sensing data with multiple spatial scales and diverse characteristics. The (Fengyun-3 Microwave Radiation Imager) FY-3 MWRI data were downscaled to 500 m resolution to match Moderate-resolution Imaging Spectroradiometer (MODIS) snow cover, meteorological and geographic data. A deep neural network was constructed to capture detailed spectral and radiation signals and trained to retrieve the higher spatial resolution snow depth from the aforementioned input data and ground observation. Verified by in situ measurements, downscaled snow depth has the lowest root mean square error (RMSE) and mean absolute error (MAE) (8.16 cm, 4.73 cm respectively) among Environmental and Ecological Science Data Center for West China Snow Depth (WESTDC_SD, 9.38 cm and 5.36 cm), the Microwave Radiation Imager (MWRI) Ascend Snow Depth (MWRI_A_SD, 9.45 cm and 5.49 cm) and MWRI Descend Snow Depth (MWRI_D_SD, 10.55 cm and 6.13 cm) in the study area. Meanwhile, downscaled snow depth could provide more detailed information in spatial distribution, which has been used to analyze the decrease of retrieval accuracy by various topography factors.

4D temperature measurements using tomographic two-color pyrometry.

This work presents a new approach for high-speed four-dimensional (3D + t) thermometry using only two high-speed cameras which are equipped with different band pass filters to capture thermal radiation signals at two narrow wavelength bands. With the help of a customized fiber bundle and a beam splitter, a total number of nine projections at each band were recorded, and the temperature distribution was evaluated by tomographic two-color pyrometry. In order to validate the effectiveness of this method, the 3D temperature distribution of a premixed steady flat flame was evaluated. The determined temperatures were compared to those of other studies, as well as to the results from inverse Abel transform and line-of-sight data. Further, the 3D temperature evolution of a weakly turbulent diffusion flame was observed at a repetition rate of 7.5 kHz. Such 4D temperature measurements are expected to be valuable in understanding turbulent combustion mechanisms especially of practical devices.

Production and Persistence of Extreme Two-temperature Plasmas in Radiative Relativistic Turbulence

Abstract Turbulence is a predominant process for energizing electrons and ions in collisionless astrophysical plasmas, and thus is responsible for shaping their radiative signatures (luminosity, spectra, and variability). To better understand the kinetic properties of a collisionless radiative plasma subject to externally driven turbulence, we investigate particle-in-cell simulations of relativistic plasma turbulence with external inverse Compton cooling acting on the electrons. We find that ions continuously heat up while electrons gradually cool down (due to the net effect of radiation), and hence the ion-to-electron temperature ratio T i /T e grows in time. We show that T i /T e is limited only by the size and duration of the simulations (reaching ), indicating that there are no efficient collisionless mechanisms of electron–ion thermal coupling. This result has implications for models of radiatively inefficient accretion flows, such as observed in the Galactic center and in M87, for which so-called two-temperature plasmas with have been invoked to explain their low luminosity. Additionally, we find that electrons acquire a quasi-thermal distribution (dictated by the competition of turbulent particle energization and radiative cooling), while ions undergo efficient nonthermal acceleration (acquiring a harder distribution than in equivalent nonradiative simulations). There is a modest nonthermal population of high-energy electrons that are beamed intermittently in space, time, and direction; these beamed electrons may explain rapid flares in certain high-energy astrophysical systems (e.g., in the Galactic center). These numerical results demonstrate that extreme two-temperature plasmas can be produced and maintained by relativistic radiative turbulence.

In This Issue

In This Issue

Study on the effect of water on electromagnetic radiation characteristics of fractured sandstone under load

A large number of electromagnetic radiation (EMR) signals are released during loading failure of rock mass, and these signals which contain complex and abundant fracture information can provides precursor of rock mass instability. In order to investigate the effect of water on the EMR characteristics of fractured sandstone, a uniaxial compression experiment was performed on the dried and saturated sandstone samples with prefabricated parallel double cracks. The EMR signals during loading failure of rock samples were acquired and their mechanical properties and EMR signal characteristics were analyzed. Besides, the precursor of rock sample instability was extracted based on the critical slowing down theory. The experimental results show that compared with the dried rock sample, the saturated rock sample has lower compressive strength, starts to crack earlier, and exhibits a more complicated failure mode. During the loading failure of rock samples, their EMR signals respond well to the fracturing and damage. The cumulative number of pulses of saturated rock sample grows uniformly, while that of dried rock sample show a “gentle-violent” rising mode. Moreover, the cumulative number of pulses of dried rock sample is greater than that of saturated rock sample. Water reduces the crack initiation stress level, and the saturated rock sample is the first to experience a sudden jump of EMR pulse. With the increase of the loading stress, the dominant frequency of EMR gradually transitions from low frequency to high frequency. Compared with dried rock samples, saturated rock samples have a lower proportion of high-frequency signals. Since the dominant mechanism of EMR varies with the change of failure stage, water first promotes the EMR signals of rock samples in the early stage of loading; then, it weakens the EMR signals in the middle and late stages of loading. Based on the critical slowing down theory, the EMR pulse signals were analyzed, which conduces to the extraction of potential precursor of rock sample failure and instability. Compared with dried rock samples, the precursory characteristic of saturated rock sample is more obvious. The research results provide some guidance for the stability of water-bearing fractured rock mass and the monitoring and early warning of related geological hazards.

Electron FLASH Delivery at Treatment Room Isocenter for Efficient Reversible Conversion of a Clinical LINAC

In this study, procedures were developed to achieve efficient reversible conversion of a clinical linear accelerator (LINAC) and deliver ultrahigh-dose-rate (UHDR) electron or conventional beams to the treatment room isocenter for FLASH radiation therapy. The LINAC was converted to deliver UHDR beam within 20 minutes by retracting the x-ray target from the beam's path, positioning the carousel on an empty port, and selecting 10 MV photon beam energy in the treatment console. Dose rate surface and depth dose profiles were measured in solid water phantom at different field sizes with Gafchromic film and an optically stimulated luminescent dosimeter (OSLD). A pulse controller counted the pulses via scattered radiation signal and gated the delivery for a preset pulse count. A fast photomultiplier tube-based Cherenkov detector measured the per pulse beam output at a 2-ns sampling rate. After conversion back to clinical mode, conventional beam output, flatness, symmetry, field size, and energy were measured for all clinically commissioned energies. The surface average dose rates at the isocenter for 1-cm diameter and 1.5-in diameter circular fields and for a jaws-wide-open field were 238 ± 5 Gy/s, 262 ± 5 Gy/s, and 290 ± 5 Gy/s, respectively. The radial symmetry of the beams was within 2.4%, 0.5%, and 0.2%, respectively. The doses from simultaneous irradiation of film and OSLD were within 1%. The photomultiplier tube showed the LINAC required ramp up time in the first 4 to 6 pulses before the output stabilized, after which its stability was within 3%. At the isocenter of the treatment room, 10 MeV UHDR beams were achieved. The beam output was reproducible but requires further investigation of the ramp up time, equivalent to ∼1 Gy, requiring dose monitoring. The UHDR beam can irradiate both small and large subjects to investigate potential FLASH radiobiological effects in minimally modified clinical settings, and the dose rate can be further increased by reducing the source-to-surface distance.

Mixed design of radar/infrared stealth for advanced fighter intake and exhaust system

With the continuous development and enhancement of anti-stealth technology, fighters will be increasingly threatened by joint detection of radar and infrared detectors. To improve the overall stealth performance of the aircraft's intake and exhaust systems, a mixed design approach (MDA) is presented to reduce the aircraft's radar cross-section (RCS) and suppress infrared signature. Full factorial design method is used to establish the aircraft model, taking into account the influencing factors of the air intake model, nozzle stages, exhaust port form, lower baffle, center baffle, lateral baffle and nozzle opening width. The physical optics and physical theory of diffraction are used to solve the electromagnetic scattering characteristics of the target, and the Monte Carlo and ray tracing method are used to solve the infrared radiation signature of the system. After comprehensive evaluation and selection of MDA, the RCS mean index and tail RCS index of the optimized aircraft model have been effectively reduced, and the infrared radiation index has also been greatly improved. MDA is effective and efficient in terms of radar/infrared integrated stealth design for the intake and exhaust systems of advanced fighters.

Radiation signatures from striped blazar jet

ABSTRACT Relativistic jets from supermassive black holes are among the most powerful and luminous astrophysical systems in Universe. We propose that the open magnetic field lines through the black hole, which drive a strongly magnetized jet, may have their polarity reversing over time scales related to the growth of the magnetorotational dynamo in the disc, resulting in dissipative structures in the jet characterized by reversing toroidal field polarities, referred to as ‘stripes’. The magnetic reconnection between the stripes dissipates the magnetic energy and powers jet acceleration. The striped jet model can explain the jet acceleration, large-scale jet emission, and blazar emission signatures consistently in a unified physical picture. Specifically, we find that the jet accelerates to the bulk Lorentz factor Γ ≳ 10 within 1-parsec distance from the central engine. The acceleration slows down but continues at larger distances, with intrinsic acceleration rate $\dot{\Gamma }/\Gamma$ between $0.0005$ and $0.005~\rm {yr^{-1}}$ at tens of parsecs, which is in very good agreement with recent radio observations. Magnetic reconnection continuously accelerates non-thermal particles over large distances from the central engine, resulting in the core-shift effect and overall flat-to-inverted synchrotron spectrum. The large-scale spectral luminosity peak νpeak is antiproportional to the location of the peak of the dissipation, which is set by the minimal stripe width lmin. The blazar zone is approximately at the same location. At this distance, the jet is moderately magnetized, with the comoving magnetic field strength and dissipation power consistent with typical leptonic blazar model parameters.

Applications of vibro-acoustic measurement and analysis in conjunction with tribological parameters to assess surface fatigue wear developed in the roller-bearing system

Rolling-element bearings are the most commonly used components in all rotating machinery. The variations in the operating conditions such as an increase in the number of operating cycles, load, speed, service temperature, and lubricant degradation result in the development of various defects such as pitting, spalling, scuffing, scoring, etc. The defects that appeared on rolling contact surfaces cause surface deterioration and change in the vibration and sound levels of the bearing system. The present experimental investigations are aimed at assessing the surface fatigue wear that appears on the contact surfaces of roller bearings. The studies considered the estimation of specific film thickness, analysis of surface fatigue wear developed on the rolling-element surfaces, surface roughness analysis, grease degradation analysis using Fourier transform infrared radiation, and vibration and sound signal measurement and analysis. The results obtained from the experimental investigation provide a good correlation between surface wear, vibration, and sound signals with a transition in the lubrication regimes in the Stribeck curve.

Investigation on Infrared Radiation Signatures of Under-Expanded Rocket Exhaust Plumes Due to Angle of Attack

A real flight trajectory varying from 5–60 km altitude was adopted for analyzing the effects of the angle of attack (AOA) on the IR signatures of rocket exhaust plumes. Three representative under-expanded states based on nozzle pressure ratio (NPR), including moderately under-expanded (NPR = 1.27), highly under-expanded (NPR = 12.5), and over-highly under-expanded (NPR = 120) states, were discussed. The results show that the appearance of the AOA shortens the plume scale size and afterburning region, but the peak temperature cannot significantly change. As an increase in the AOA, the high-temperature region and Mach cells are compressed to a small domain. The spectral radiant intensity is reduced and the decreasing rate is gradual for a large AOA. The peak radiance in the $2.7~\mu \text{m}$ band decreases, but that in the $4.3~\mu \text{m}$ band increases. The peak radiance curve shifted toward the increasing NPR as the AOA increases, and then the curve decreases monotonically from an initial “arched” shape. In addition, a group of band pairs can be observed by two intersecting in-band profiles as a function of altitude. The characteristic altitude of band pairs became more concentrated and moved toward low altitude with the increase in AOA.

Non-scanning wide range multi-wavelength imaging temperature measurement technology

Multi-wavelength temperature measurement technology is an advanced radiation temperature measurement technology, which assumes a functional relationship between emissivity and spectrum. The measured multi-wavelength radiation signals and the function relationship between emissivity and spectral is used to obtain the true temperature and emissivity of the target. Multi-wavelength imaging temperature measurement technology detects the multi-wavelength radiation image information of the target and inversely calculates the temperature field distribution of the target. Aiming at the inadequacy of color CCD-based multi-wavelength imaging temperature measurement, such as adaptability of the nonlinear spectral emissivity model, and the narrow dynamic range of temperature measurement, and so on, a four-wavelength non-scanning imaging temperature measurement method based on non-equal ratio filter color splitting at the diaphragm was proposed. This method could effectively compress the band imaging bandwidth to form four narrow band imaging detections, and was suitable for wide dynamic range temperature field measurement of nonlinear spectral emissivity model targets. A four-wavelength imaging thermometer was developed according to the proposed temperature measurement method. The thermometer was mainly composed of a window, a neutral optical attenuator, a four-color filter, an optical objective lens, a visible/near infrared integrated sensor, a measurement control unit, and software. The real temperature distribution of the target was obtained according to the multi-wavelength warming algorithm by the thermometer software. The target high-temperature field (800-2500 ℃) was tested under laser heating conditions. The comparison between the measurement result and the thermocouple data shows that the error is less than 1%, and the method has higher accuracy and better dynamic range adaptability.

Hybrid Solar Forecasting Method Based on Empirical Mode Decomposition and Back Propagation Neural Network

In order to improve the accuracy of solar radiation prediction and optimize the energy management system. This study proposes a forecasting model based on empirical mode decomposition (EMD) and Back Propagation Neural Network (BPNN). Empirical mode of decomposition (EMD)-based ensemble methods with powerful predictive abilities have become relatively common in forecasting study. First, the existing solar radiation datasets are decomposed into an intrinsic mode function (IMF) and one residue produces fairly stationary sub-series that can easily be modeled on BPNN. Next, both components of the IMF and residue are applied to create the respective BPNN models. Then, the corresponding BPNN is used to predict some sub-series. Finally, the predictive values of the original solar radiation datasets are determined by the sum of each predicted sub-series. Compared with traditional models such as conventional neural network or ARIMA time series, the hybrid EMD-BPNN model shows great results in term of RMSE with 28.13 (W/m2). On the other hand, the result of BPNN and ARIMA was 83.28 (W/m2) and 108.88 (W/m2), respectively. that the non-stationary and non-linear of solar radiation signal has less effect on the accuracy of the prediction.

Counterfeit IC Detection Research Based on BP-AdaBoost Model and Genetic Algorithm

In view of the increasingly prominent problems exposed by chips in integrated circuits and the destructive problems of traditional chip detection methods, a BPAdaBoost neural network model based on genetic algorithm optimization was proposed and applied to chip detection and classification. By using electromagnetic probes to collect the electromagnetic signals generated by different chips in the same operating state and using the electromagnetic radiation signals as the basis for chip identification and classification, the signals are put into the BP-AdaBoost model optimized by genetic algorithm for learning and training. Experimental results show that this method has good effect in the application of chip recognition and classification.

Comparison and uncertainty of multivariate modeling techniques to characterize used nuclear fuel

The ability to characterize used nuclear fuel (UNF) is important for nuclear nonproliferation safeguards, criticality safety, and fuel storage. Multiple efforts have been made to estimate the burnup (BU), initial enrichment (IE), and cooling time (CT) based on multivariate models of isotopic concentrations and radiation signatures of the fuel. This work provides a comparison of multivariate modeling techniques and extends previous work by quantifying the uncertainty of the best model to predict each characteristic. Model inputs used are simulated gamma and neutron emissions from UNF of varying BU, IE, and CT. Modeling techniques explored include Ordinary Least Squares Regression (OLS), Principal Component Regression (PCR), and Partial Least Squares Regression (PLS). Multiple PCR and PLS models were built based on different variable selection methods, such as cross validation and Akaike Information Criteria. The OLS model predictions have a root mean square percent error (RMSPE) of less than 10%, but the models are very unstable. The PCR models exhibit a trade-off between accurate and stable predictions. The best performing PCR and PLS models have similar predictions errors, but the PLS models are favored due to their stability. The best model for each characteristic is a single output PLS model based on cross validation. The uncertainty of each of these models, based on their prediction variance and biases, is 0.220 GWd/MTU, 0.051% U-235, and 0.694 years for the BU, IE, and CT models, respectively. By building a 95% prediction interval based on the corresponding uncertainty of each characteristic, 1.97% of the BU predictions, 23.03% of the IE predictions, and 100% of the CT predictions lack 95% confidence that they are within the prescribed accuracy requirement for the characteristic.

Detectivity analysis of infrared photodetector devices under nonuniform distribution of quantum well and wire

This manuscript is proposed for overcoming the nonuniformity issues of quantum well (QW) and quantum wire (QR) bases in infrared photodetectors. Spectral detectivity of quantum well infrared photodetector (QWIP) and quantum wire infrared photodetector (QRIP) devices is analyzed that compared in presence of uniformity and nonuniformity QWs/QRs. Besides, optimizations of the behaviors of QWIP/QRIP instruments are realized through suggested implicit and explicit models. VisSim environment models are proposed for devising the QWIP/QRIP characteristics. These graphical user interface leads to ease of realization and fast experimentation with optional parameters values such as intensity of incident radiation and spacing between QWs/QRs. These parameters are tuned in order to enhance the radiative signal characteristics of such devices. The demonstrated implicit and explicit results are validated through comparison with literature results. A higher and fixed detectivity is observed for QWIP/QRIP under uniformity distribution of QWs/QRs compared to nonuniformity of QWs/QRs. However, linear and Gaussian spectral detectivity are attained respectively for uniformity and nonuniformity (peaked around 2 × 1013 cm2 QR density) distribution of QRs. The implemented models lead to an improvement in the processing of the received signal by these detectors.

Infrared signature of NEPE, HTPB rocket plume under varying flight conditions and motor size

In this study, radiation signatures of rocket plumes based on HTPB and NEPE propellants are simulated under varying altitudes, flight Mach numbers, and motor size. The flow field of plume is calculated by computational fluid dynamics, and the infrared signature is predicted using a Ludwig model. To verify the numerical method, experiments were conducted using small rocket motors and radiometric FTIR. The acceptable agreement between numerical calculation and the experiment was obtained in 2–5 μm wavelength region. The total radiance decreases with increasing altitude whether afterburning exists or not. A decreasing rate in the radiance of the plume with strong afterburning is larger than that of the plume with weak afterburning. The change in total intensity according to altitude depends on the afterburning and is directly affected by the radiating area. As the flight Mach number increases, total radiance, total intensity, and radiating area decrease. The motor size makes radiation intensity increase exponentially; exponent value is from 2.6 to 2.7. As the motor size increases, the effect of altitude increases, while the effect of flight Mach number decreases.

Laser synthesis of thin MnxSi1-x films (x ~ 0.5) on c- and r-Al2O3 substrates at different laser energy densities at the target

Abstract Thin films of MnxSi1-x (x ~ 0.5) were obtained by pulsed laser deposition in droplet-free mode on c- and r-Al2O3 substrates at different laser energy densities E at the target. Their structural, electrical, and magnetic properties were studied depending on the value of E and the orientation of the substrate. The films are X-ray amorphous on the substrates of both types at E 5.5 J/cm2 the high-temperature ferromagnetic phase shows in the films, and at E ≅ 4 - 5 J/cm2, the low-temperature ferromagnetic phase predominates and there is no influence of the sapphire substrate orientation. The highest Curie temperature TC reached was 330 K at E ≅ 7.4 J/cm2 for the MnxSi1-x films obtained on c- and r-Al2O3. The correlated behavior of the magnetization and the signal of diffuse X-ray radiation scattering confirms the existence of e-MnSi nanocrystallites.

Advances in Wearable Sensors: Signalling the Provenance of Garments Using Radio Frequency Watermarks.

There is a significant nascent market for ethically produced products with enormous commercial potential around the world. A reliable method to signal the provenance of products is therefore critical for industry, given that competition based on price is not a viable strategy. The ability to trace and signal ethical treatment of animals is also of significant value to textiles manufactures. The efficacy of such a method can be measured with respect to the cost of implementation, scalability, and the difficulty of counterfeiting. The key to traceability is to win the trust of the consumer about the veracity of this information. Wearable sensors make it possible to monitor and improve the management of traceability and/or provenance. In this paper, we introduce a method for signalling the provenance of garments using radio frequency watermarks. The proposed model consists of two levels of authentication that are easy to use by legitimate vendors, but extremely difficult to imitate or hack, because the watermark is built-in and based on the radiation signature of electroactive materials.