Conventional Peak Research Articles

Machine learning-Assisted spiral fiber Bragg Grating-Based flexible dual-parameter sensing

The ability of flexible sensors to bend or fold freely offers great advantages in sensing ability and adaptability to harsh environments. Moreover, compared with typical electrical effect based flexible sensors, optical based fiber Bragg grating (FBG) flexible sensors offer much greater ease of networking and resistance to electromagnetic interference, making them suitable for distributed multi-point strain measurements in complex environments. In this paper, two FBGs with no overlapping reflective spectra are shallowly embedded in the surface layer of a flexible thin-cylinder substrate to form a dual-parameter flexible strain sensor. However, it is crucial for the changes in direction and curvature parameters of FBG strain sensors under deformation to be accurately understood to characterize the current deformation state of the flexible sensor. Moreover, conventional peak tracking demodulation methods often fail to account for distortion in the reflected spectrum of a spiral FBG under stress. Hence, a multi-output convolutional neural network learning model is constructed to simultaneously identify the bending direction and curvature radius of the flexible sensor using machine learning methods. Experimental results show that the flexible dual-parameter FBG sensor has precisely recognize angles to within 2° across a 360° range, with a curvature recognition accuracy of 99.1%, offering precision sensing performance suitable for highly demanding application scenarios such as bionic robots and flexible medical devices.

Achieving strength-ductility synergy in Mg-1.1Gd-0.6Zn-0.3Mn alloy by regulating precipitation behavior via stress aging strategy

Regulating the precipitation behavior of Mg alloys to overcome the strength-ductility trade-off puzzle is a long-thought pursuit in the materials community. With this purpose, external stress has been recently applied during aging and shows immense potential in affecting atomic diffusion, and regulating the coherence of the phase boundaries. In this study, elastic tensile (TSA) and compressive stress aging (CSA) of Mg-1.1Gd-0.6Zn-0.3Mn alloy are carried out and the competition of precipitation between multiple precipitates occurs during stress aging. A significant quantity of β’ precipitates primarily distribute along grain boundaries in conventional peak aging alloy. Whereas high density of γ’ phases rather than β’ phase precipitate in both TSA and CSA alloys. The first-principle calculations reveal that the application of external stress introduces shear strain, which decreases unstable stacking fault energies, and thereby promoting the precipitation of γ’ phase and impeding the precipitation of β’ phase. Furthermore, the sequential transformation from γ’ phase to Long Period Stacking Ordered (LPSO) phase occurs in CSA sample, due to the release of elastic local strain at phase boundaries. After subjected to TSA treatment, the sample possesses an ultimate tensile strength of 356 MPa, a yield strength of 294 MPa, and a total elongation of ∼14.3 %. The excellent strength-ductility synergy of TSA sample is primarily contributed to the profuse γ’ precipitates hindering the motion of large number of pyramidal 〈c + a〉 dislocations during tensile deformation. This study offers new insights on regulating the precipitation behavior of Mg alloys containing multiple types of precipitates through the application of external stress, and extends the potential window for obtaining an excellent strength-ductility synergy in age-hardenable Mg alloys.

Research on performance and potential of distributed heating system for peak shaving with multi-energy resource

Climate change and its negative effects are driving the global shift from fossil fuels to renewable energy sources. To tackle the dependency on traditional energy sources in harsh winter regions and improve heating quality during periods of thermal demand fluctuations, this paper proposes a new distributed heating peak shaving system (DHPS). The system combines municipal heat and clean energy within the secondary network while reducing the return water temperature in the primary network. It comprises solar collectors, electric thermal storage tanks (ETST), and absorption heat pump (AHP) units, integrated into conventional heat exchange stations. The system operates in two modes to manage peak and off-peak loads respectively, with TRNSYS simulation used to evaluate performance across a range of peak-shaving gradients. A multidimensional comprehensive assessment is conducted between the DHPS under optimal peak shaving coefficient (θ) conditions and conventional peak clipping boiler (PCB). Results indicate that DHPS achieves a high primary energy ratio (PER) of 1.251 at θ = 0.5, reducing combustion emissions by nearly 40%. The static payback period (PBP) of the system is 3.5 years. When the electricity price drops to 0.275 CNY, its operational costs are comparable to PCB. DHPS caters to the energy characteristics of cold regions where electricity supply exceeds demand. It enables flexible peak shaving while ensuring the complete utilization of clean energy and effectively utilizing waste heat from power plants.

Performance prediction of ventilated building-integrated photovoltaic system with lightweight flexible crystalline silicon module based on experimental fitting method

The novel ventilated building-integrated photovoltaic system with lightweight flexible crystalline silicon modules (VL-BIPV) has a self-weight of only about 6 kg/m2, which helps to address weight-bearing challenges on low-capacity industrial building rooftops. However, the unique thermal dissipation features of the system pose challenges for the analysis of its photovoltaic performance and thermal processes. Therefore, a comparative experimental testing approach was employed, comparing the VL-BIPV system with a conventional rooftop photovoltaic system. Using the efficiency equations of the conventional photovoltaic system as a reference, the functional equations were fitted based on experimental results. Based on typical meteorological year data in Nanjing, annual power generation and PV cell temperature variations were evaluated, along with power generation and carbon reduction benefits over a 25-year lifetime. The results indicate that the VL-BIPV system achieves an annual power generation of 262.22 kWh/m2, a 6.52% increase compared to the conventional system. Additionally, the highest average and maximum cell temperatures in the VL-BIPV system during the year are 58.39 °C and 64.74 °C, respectively, both lower than the conventional system's peak at 68.34 °C. Over a 25-year lifespan, the VL-BIPV system reduces carbon emissions by 20.17 kgCO2/Wp, exceeding the conventional system's reduction by 1.25 kgCO2/Wp.

High-pressure mafic granulite and supracrustal rocks in the southern Hengshan area, North China Craton: Metamorphic P-T-t evolution and geotectonic significance

Amphibolite- to granulite-facies rocks are widely distributed in the Hengshan Complex, middle Trans-North China Orogen, and the high-pressure (HP) mafic granulite has been recently identified in the southern Hengshan area. The HP mafic granulite and amphibolite occur as rootless tectonic boudins/lenses within the TTG (tonalite–trondhjemite–granodiorite) gneiss/metapelite, indicative of typical “block-in-matrix” texture of metamorphic-tectonic mélange. Three to four generations of metamorphic mineral assemblages that correspond to the prograde (M1), peak (M2), and retrograde (M3-M4) stages, are recognized in these rocks. Conventional geothermobarometry and phase equilibrium modeling yield peak P–T conditions of 10.8–13.8 kbar/754–799 °C for the HP mafic granulite and 7.3–9.0 kbar/690–725 °C for the supracrustal rocks, respectively. A clockwise P–T path with near-isothermal decompression (ITD) and subsequent near-isobaric cooling (IBC) segments is reconstructed for the HP mafic granulite, indicating a dynamic subduction–collision–exhumation process that unfolded during an orogenic event. Secondary ion mass spectrometry (SIMS) U-Pb dating of zircon yields metamorphic ages of ca. 1.91–1.83 Ga, representing the long-lived tectono-metamorphic event caused by collision between the Eastern and Western Blocks along the Trans-North China Orogen. It is hypothesized that the tectono-metamorphic mélange in the area originated from the tectonically juxtaposition of metamorphic rocks that had diverse protoliths, different peak P–T conditions and discrepant metamorphic ages. This complexity may be a hallmark of Paleoproterozoic orogens, drawing intriguing parallels with the intricate characteristics observed in Phanerozoic orogenic belts.

Enhancing gas chromatography-mass spectrometry resolution and pure analyte discovery using intra-chromatogram elution profile matching

Chemometric decomposition methods like multivariate curve resolution-alternating least squares (MCR-ALS) are often employed in gas chromatography-mass spectrometry (GC-MS) to improve analyte identification and quantitation. However, these methods can perform poorly for analytes with a low chromatographic resolution (Rs) and a high degree of spectral contamination from noise and background interferences. Thus, we propose a novel computational algorithm, termed mzCompare, to improve analyte identification and quantitation when coupled to MCR-ALS. The mzCompare method utilizes an underlying requirement that the retention time and peak shape between mass channels (m/z) of the same analyte should be similar. By discovering the selective m/z for a given analyte in a chromatogram, a pure elution profile can be generated and used as an equality constraint in MCR-ALS. The performance of the mzCompare methodology is demonstrated with both experimental and simulated chromatograms. Experimentally, unresolved analytes with a Rs as low as 0.05 could be confidently identified with mzCompare assisted MCR-ALS. Furthermore, application of the mzCompare algorithm to a complex aerospace fuel resulted in the discovery of 335 analytes, a 44 % increase compared to conventional peak detection methods. GC-MS simulations of target-interferent analyte pairs demonstrated that the performance of MCR-ALS deteriorated below a Rs of ∼0.25. However, mzCompare assisted MCR-ALS showed excellent identification and acceptable quantitative accuracy at a Rs of ∼0.02. These results show that the mzCompare algorithm can help analysts overcome modeling ambiguities resulting from the chemometric multiplex disadvantage.

Surface chemistry of ion beam modified native titania/Ti interfaces examined using X-ray photoelectron spectroscopy

It is often assumed in X-ray Photoelectron Spectroscopy that binding energy shifts are synonymous with changes in the chemical state of an atom and the chemical state can be described in terms of oxidation state and stoichiometry of the elements in a material that correlates with photoemission peaks. However, when an atom is bonded into a crystal lattice, the shapes and binding energy of photoemission may not match the expected stoichiometry when measured by XPS, even though shifts in binding energy suggest new oxidation states. In this work, a set of experiments is presented, in which Ar+ and He+ ions of different energies modify the native oxide on a titanium foil which yields XPS spectra that are not easily open to analysis by conventional peak models. In the course of analyzing these complex photoemission data by linear algebraic methods, the prospect emerged that XPS is suggesting that sputtering a native oxide may be providing insight into structural perturbation in addition to the stoichiometry-type changes to the native oxide.

PECULIARITIES OF NOISY IMAGE LOSSY COMPRESSION

This paper deals with lossy compression applied to grayscale noisy images using several coders. The case of additive white Gaussian noise is considered as the first step in studying the problem. A special attention is paid to quite new coders AVIF and HEIF for which, to the best of our knowledge, lossy compression of noisy images of different complexity has not been thoroughly considered yet. It is shown that optimal operation point is possible for all coders under certain conditions according to different quality metrics including conventional peak signal-to-noise ratio and visual quality metrics such as PSNR-HVS-M and MS-SSIM. The compression parameters (metrics) in optimal operation point significantly depend on image complexity and noise intensity where the existence of optimal operation point is more probable for simple structure images and/or intensive noise. Comparison of metric values in optimal operation point for the considered coders shows that slightly better characteristics are provided by better portable graphics (BPG) and advanced discrete cosine transform (ADCT) coders. The results for JPEG are significantly worse. The AVIF and HEIF encoders provide similar results and they outperform JPEG significantly. Optimal operation point for all studied coders is observed more rarely for visual quality metrics than for peak signal-to-noise ratio. General tendencies concerning dependence of optimal compression parameters on noise intensity are presented. It is shown that compression ratio in optimal operation point is often larger than 20 and can be as large as 60. The problem with AVIF and HEIF is that it is currently unclear how to choose quality factor for them to carry out lossy image compression in the neighborhood of the corresponding optimal operation point. Meanwhile, there is the tendency to optimal quality factor reduction if intensity of additive white Gaussian noise increases. The directions of further research are discussed.

Synthesis of virtual monoenergetic images from kilovoltage peak images using wavelet loss enhanced CycleGAN for improving radiomics features reproducibility.

Dual-energy computed tomography (CT) can provide a range of image information beyond conventional CT through virtual monoenergetic images (VMIs). The purpose of this study was to investigate the impact of material decomposition in detector-based spectral CT on radiomics features and effectiveness of using deep learning-based image synthesis to improve the reproducibility of radiomics features. In this paper, spectral CT image data from 45 esophageal cancer patients were collected for investigation retrospectively. First, we computed the correlation coefficient of radiomics features between conventional kilovoltage peak (kVp) CT images and VMI. Then, a wavelet loss-enhanced CycleGAN (WLL-CycleGAN) with paired loss terms was developed to synthesize virtual monoenergetic CT images from the corresponding conventional single-energy CT (SECT) images for improving radiomics reproducibility. Finally, the radiomic features in 6 different categories, including gray-level co-occurrence matrix (GLCM), gray-level difference matrix (GLDM), gray-level run-length matrix (GLRLM), gray-level size-zone matrix (GLSZM), neighborhood gray-tone difference matrix (NGTDM), and wavelet, were extracted from the gross tumor volumes from conventional single energy CT, synthetic virtual monoenergetic CT images, and virtual monoenergetic CT images. Comparison between errors in the VMI and synthetic VMI (sVMI) suggested that the performance of our proposed deep learning method improved the radiomic feature accuracy. Material decomposition of dual-layer dual-energy CT (DECT) can substantially influence the reproducibility of the radiomic features, and the degree of impact is feature dependent. The average reduction of radiomics errors for 15 patients in testing sets was 96.9% for first-order, 12.1% for GLCM, 12.9% for GLDM, 15.7% for GLRLM, 50.3% for GLSZM, 53.4% for NGTDM, and 6% for wavelet features. The work revealed that material decomposition has a significant effect on the radiomic feature values. The deep learning-based method reduced the influence of material decomposition in VMIs and might improve the robustness and reproducibility of radiomic features in esophageal cancer. Quantitative results demonstrated that our proposed wavelet loss-enhanced paired CycleGAN outperforms the original CycleGAN.

Single high-energy arc proton therapy with Bragg peak boost (SHARP).

Because of the co-location of critical organs at risk, base of skull tumours require steep dose gradients to achieve the prescribed dosimetric criteria. When available, proton beam therapy (PBT) is often considered a desirable modality for these cases, but in many instances, compromises in target coverage are still required to achieve critical organ at risk (OAR) tolerance doses. A number of techniques have been proposed to further improve the penumbra of PBT. In the current study, we propose a novel, collimator-free treatment planning technique that combines high-energy shoot-through proton beams with conventional Bragg peak spot placement. The small spot size of the high-energy pencil beams provides a sharp penumbra at the target boundary, and the Bragg peak spots provide a higher linear energy transfer (LET) boost to the target centre. Three base of skull chordoma patients were retrospectively planned with three different PBT treatment planning techniques: (1) conventional intensity-modulated proton therapy (IMPT); (2) high-energy proton arc therapy (HE-PAT); and (3) the novel technique combining HE-PAT and IMPT, referred to as single high-energy arc with Bragg peak boost (SHARP). The Monaco 6 treatment planning system was used. SHARP was found to improve the PBT penumbra in the plane perpendicular to the HE-PAT beams. Minimal penumbra differences were observed in the plane of the HE-PAT beams. SHARP reduced dose-averaged LET to surrounding organs at risk. A novel PBT treatment planning technique was successfully implemented. Initial results indicate the potential for SHARP to improve the penumbra of PBT treatments for base of skull tumours.

Price-Based Demand Response: A Three-Stage Monthly Time-of-Use Tariff Optimization Model

In this research, we developed a three-stage monthly time-of-use (TOU) tariff optimization model to address the concerns of confusing time period division, illogical price setting, and incomplete seasonal element consideration in the previous TOU tariff design. The empirical investigation was conducted based on load, power generation, and electricity pricing data from a typical northwest region in China in 2022. The findings indicate the following: (1) In producing the typical net load curves, the employed K-means++ technique outperformed the standard models in terms of the clustering effect by 4.27–26.70%. (2) Following optimization, there was a decrease of 1900 MW in the maximum monthly abandonment of renewable energy, a decrease of 0.31–53.94% in the peak–valley difference, and a decrease of 2.03–17.27% in the monthly net load cost. (3) By taking extra critical peak and deep valley time periods into account, the average net load cost decreased by 10.36% compared with conventional peak–flat–valley time period division criteria.

Detailed analysis of the effective and intra-particle diffusion coefficient of proteins at elevated pressure in columns packed with wide-pore core-shell particles

To determine the efficiency that can be obtained in a packed-bed liquid-chromatography column for a particular analyte, a correct determination of the molecular and effective diffusion coefficients (Dm and Deff) of the analyte is required. The latter is usually obtained via peak parking experiments wherein the flow is stopped. As a result, the column pressure rapidly dissipates and the measurement is essentially conducted at ambient pressure. This is problematic for analytes whose retention depends on pressure, such as proteins and potentially other large (dipolar) molecules. In that case, a conventional peak parking experiment is expected to lead to large errors in Deff. To obtain a better estimate ofDeff, the present study reports on the use of a set-up enabling peak parking measurements under pressurized conditions. This approach allowed us to report, for the first time, Deff for proteins at elevated pressure under retained conditions. First, Deff was determined at a (average) pressure of about 105 bar for a set of proteins with varying size, namely: bradykinin, insulin, lysozyme, β-lactoglobulin, and carbonic anhydrase in a column packed with 400 Å core-shell particles. The obtained data were then compared to those of several small analytes: acetophenone, propiophenone, benzophenone, valerophenone, and hexanophenone. A clear trend between Deff and analyte size was observed. The set-up was then used to determine Deff of bradykinin and lysozyme at variable (average) pressures ranging from 28 bar to 430 bar. These experiments showed a decrease in intra-particle and surface diffusion with pressure, which was larger for lysozyme than bradykinin. The data show that pressurized peak parking experiments are vital to correctly determine Deff when the analyte retention varies significantly with pressure.

A multiline fitting method for measuring ethylene concentration based on WMS-2f/1f

Coal spontaneous combustion risk assessment is a global technical challenge for the sustainable development of deep mining technology, and C2H4 is a key indicator for early warning of coal spontaneous combustion. Tunable diode laser absorption spectroscopy (TDLAS) has the advantages of high selectivity, high sensitivity, high accuracy and real-time on-line measurement, and it can detect multiple gases simultaneously, so it has significant advantages in the accurate detection of coal spontaneous combustion indicator gases. To address the problem of cross-interference between the near-infrared absorption lines of CH4 and C2H4, which are the indicator gases of spontaneous combustion in coal, a multi-line fitting method was proposed in this study to calibrate the concentration of C2H4. The high-precision Environics2000 automatic standard gas dispenser from the United States, which has a built-in CPU computer control and data control and processing system, was used. Its gas concentration accuracy: ± 1.0%, gas flow accuracy: ± 1.0%, gas repeatability accuracy: ± 1.0%, flow linearity accuracy: ± 0.5%, and inlet operating pressure: minimum 10 psig (0.67 bar) ~ 75 psig (5.04 bar). The measured and simulated WMS-2f/1f signals were multilinearly fitted using a multilinear fitting algorithm and wavelength modulation spectroscopy (WMS), and the measurement of C2H4 concentration was achieved based on the extracted spectral line information. The results show that the maximum relative error of C2H4 concentration measurement is 2.40%, which is 54% lower than that of the conventional 2f peak measurement method, thus demonstrating the effectiveness of the multilinear fitting algorithm in the inversion of C2H4 concentration under the interference of absorption lines. In addition, this study has far-reaching implications for the application of TDLAS technology in the accurate detection of coal spontaneous combustion indicator gases.

Experimental Demonstration of Peak Wavelength Measurement of Multiplexing Fiber Bragg Gratings Using Convolutional Neural Network

We propose a peak detection method for measuring fiber Bragg gratings (FBGs) using convolutional neural network (CNN) to improve the performances of wavelength division multiplexing. In wavelength division multiplexing, each FBG occupies a certain wavelength range; therefore, the number of FBGs that can be installed is limited by the wavelength band of the light source. To address this issue, methods for overlapping multiple FBGs of the same wavelength within a single occupied wavelength range have been studied. This contributes to improving the limit of multipoint FBGs manifold. However, this method results in the complex overlapping of multiple FBG reflectance spectra making it difficult to accurately measure the peak wavelengths of individual FBGs using conventional peak detection methods. Therefore, we developed a peak detection process using CNN, which is suitable for identifying unique feature data. Each FBG of the same wavelength was characterized to have a unique spectral shape by assigning a different full width at half maximum to each. We introduced noise-additive learning, a well-known method of data augmentation that increases tolerance to variations in the experimental signal. As a result, the standard deviation for peak wavelength detection significantly improved to 2.8 pm and the strain measurements with three complex overlapping FBGs were successfully demonstrated. The CNN model is the first to solve the problem of three overlapping FBGs for arbitrary wavelength changes. Furthermore, the developed peak detection process was found to be applicable to measurements that combined multiplexing of FBGs of either identical or different wavelengths.

Thermo-mechanical numerical analyses in support of fire endurance assessment of ordinary soda-lime structural glass elements

PurposeGlass material is largely used for load-bearing components in buildings. For this reason, standardized calculation methods can be used in support of safe structural design in common loading and boundary conditions. Differing from earlier literature efforts, the present study elaborates on the load-bearing capacity, failure time and fire endurance of ordinary glass elements under fire exposure and sustained mechanical loads, with evidence of major trends in terms of loading condition and cross-sectional layout. Traditional verification approaches for glass in cold conditions (i.e. stress peak check) and fire endurance of load-bearing members (i.e. deflection and deflection rate limits) are assessed based on parametric numerical simulations.Design/methodology/approachThe mechanical performance of structural glass elements in fire still represents an open challenge for design and vulnerability assessment. Often, special fire-resisting glass solutions are used for limited practical applications only, and ordinary soda-lime silica glass prevails in design applications for load-bearing members. Moreover, conventional recommendations and testing protocols in use for load-bearing members composed of traditional constructional materials are not already addressed for glass members. This paper elaborates on the fire endurance and failure detection methods for structural glass beams that are subjected to standard ISO time–temperature for fire exposure and in-plane bending mechanical loads. Fire endurance assessment methods are discussed with the support of Finite Element (FE) numerical analyses.FindingsBased on extended parametric FE analyses, multiple loading, geometrical and thermo-mechanical configurations are taken into account for the analysis of simple glass elements under in-plane bending setup and fire exposure. The comparative results show that – in most of cases – thermal effects due to fire exposure have major effects on the actual load-bearing capacity of these members. Moreover, the conventional stress peak verification approach needs specific elaborations, compared to traditional calculations carried out in cold conditions.Originality/valueThe presented numerical results confirm that the fire endurance analysis of ordinary structural glass elements is a rather complex issue, due to combination of multiple aspects and influencing parameters. Besides, FE simulations can provide useful support for a local and global analysis of major degradation and damage phenomena, and thus support the definition of simple and realistic verification procedures for fire exposed glass members.

Experimental Study on Creep Characteristics and Long–Term Strength of Anthracite

Coal pillars from old mines undergo creep, a type of time−dependent deformation. Research on underground engineering stability of coal mines has increasingly focused on long−term strength as an important mechanical index of the creep characteristics of coal pillars. This study performed conventional triaxial compression and triaxial creep tests of anthracite under different confining pressures. The creep law of anthracite and the long−term strength of anthracite was studied according to the test results. The test results demonstrated the following: (1) The conventional elastic modulus and peak strain of anthracite increased exponentially with the confining pressure. (2) Under low stress levels, anthracite exhibited only instantaneous deformation and attenuation creep. In contrast, anthracite exhibited instantaneous deformation, attenuation creep, and steady creep under high stress levels; accelerated creep occurred until failure when the stress reached a particular value. (3) By fitting the steady−state creep rate of anthracite under high stress levels, the functional relationship between axial stress and steady−state creep rate was established, and the threshold of the steady−state creep rate in high−stress−level areas was suggested as the optimum long−term strength of anthracite. (4) The ratio of the long−term strength to instantaneous strength under various confining pressure grades ranged from 70% to 91%.

Detection, characterization, and persistence of Campylobacter hepaticus, the cause of spotty liver disease in layer hens

A Campylobacter species was first described as the etiological agent of Spotty Liver Disease (SLD) in 2015 and subsequently named as Campylobacter hepaticus in 2016. The bacterium predominantly affects barn and/or free-range hens at peak lay, is fastidious and difficult to isolate, which has impeded elucidation of its sources, means of persistence and transmission. Ten farms from South-Eastern Australia, of which 7 were free range entities participated in the study. A total of 1,404 specimens from layers and 201 from environmental sources, were examined for the presence of C. hepaticus. In this study, our principal findings included the continuing detection of C. hepaticus infection in a flock following an outbreak, indicating a possible transition of infected hens to asymptomatic carriers, that was also characterized by no further occurrence of SLD in the flock. We also report that the first outbreaks of SLD on newly commissioned free-range farms affected layers ranging from 23 to 74 wk of age, while subsequent outbreaks in replacement flocks on these farms occurred during the more conventional peak lay period (23-32 wk of age). Finally, we report that in the on-farm environment, C. hepaticus DNA was detected in layer feces, inert elements such as stormwater, mud, soil, as well as in fauna such as flies, red mites, Darkling beetles, and rats. While in off-farm locations, the bacterium was detected in feces from a variety of wild birds and a canine.

High-throughput XPS spectrum modeling with autonomous background subtraction for 3 d 5/2 peak mapping of SnS

We propose a fitting model that automatically conducts the background subtraction during high-throughput peak fitting. The fitting model consists of the pseudo-Voigt mixture model and the ramp-sum background model, and each model represents the peak and background component, respectively. The optimization of the fitting model is performed by the spectrum adapted ECM algorithm that enables us to perform the peak fitting and background subtraction simultaneously through the high-throughput calculation. Application of the proposed model to the synthetic spectral data showed appropriate decomposition of the peak and background component. We also applied the proposed model to 3721 spectral data collected from the SnS sheet by X-ray photoelectron spectroscopy. The spectral data from the SnS sheet were successfully decomposed to the component of Sn 3d3/2 peak, Sn 3d5/2 peak and background, respectively. As the spectrum adapted ECM algorithm can efficiently analyze a large amount of spectral data, we can obtain the color map showing spatial distribution of Sn(II) and Sn(IV) using the parameter of Sn 3d5/2 peak. The proposed model supports us to obtain the insightful spatial distribution of peak component that has been difficult to obtain by conventional peak fitting.

Fiber grating loop ring-down strain sensors using overlap spectrum demodulation and machine learning algorithm

The fiber grating loop ring-down (FGLRD) can be used in strain sensors to reduce costs and enhance system sensitivity. Introducing the overlap spectrum demodulation technology in the FGLRD can improve the system's linearity and expand the measurement range. However, there is always strong amplified spontaneous emission (ASE) noise owing to erbium-doped fiber amplifiers (EDFAs) in the FGLRD structure. The noise increases the difficulty of detecting the output ring-down spectrum and reduces the detection accuracy. This study proposes a machine learning algorithm for data processing of chirped FGLRD strain sensors based on overlap spectrum demodulation. It uses a global average pooling one-dimensional convolutional neural network algorithm to construct a data recognition model. The experimental results show that the method can improve the recognition accuracy and reduce the recognition errors caused by ASE noise compared to the conventional ring-down spectral peak detection method. The model accuracy was 100 % for the training and test sets and 98 % for the validation set. The loss value was close to 0.06, and the recognition time was approximately 0.5 ms.

High-Fidelity Compressive Heart Rate Tracking

Recently, photoplethysmography sensors in smart watches have been frequently used to monitor heart rate. Because a photoplethysmography sensor flashes light on the skin and measures the reflected light, the rate of flashing and sampling is directly related to its energy consumption. It is necessary to reduce the rate to extend the battery life of a smart watch. In this study, to satisfy sub-Nyquist sampling, real-time, and high accuracy requirements, we propose a novel heart rate tracking method that consists of online compressive covariance sensing, signal subspace tracking, and spectral peak tracking. The proposed method (average sampling rate: 1.4 Hz) showed better frequency tracking performance than the conventional spectral peak tracking algorithm (sampling rate: 10 Hz) in time-varying heart rate simulations, and a statistically significant difference between them was not observed in the real photoplethysmogram data. The trimmed average absolute error was 1.04 beats/min. As a result, the proposed real-time heart rate tracking method with sub-Nyquist sampling showed high accuracy.