bluuebunny/crossref_metadata_2025 huggingface dataset:
Electrospun Ti3C2T
x
MXene and silicon embedded in carbon nanofibers for lithium-ion batteries
Authors: Xu Huajun , Chen Gang , Du Fei , Wang Xin , Dall’Agnese Yohan , Gao Yu
Published: January 2022

In spite of outstanding theoretical specific capacity (∼3590 mAh g−1), huge volume change during lithiation/delithiation and poor conductivity hinder the practical applications of pure Si anodes for lithium ion batteries (LIBs), requiring conductive additives and porous architectures. In this work, flexible and freestanding composite films made of two-dimensional Ti3C2T
x
MXene nanosheets and silicon particles embedded in carbon nanofiber (denoted as Ti3C2T
x
-Si@CNF) were prepared via electrospinning and used as LIB anodes. This new electrode design provides a 3D network architecture and high conductivity favorable for good electrochemical performance and long cycle life. Ti3C2T
x
-Si@CNF electrodes, containing 28% silicon retained high specific capacity of ∼1580 mAh g−1 after 100 cycles at 0.1 A g−1 and 720 mAh g−1 after 1000 cycles at 1 A g−1, and delivered a great rate performance with 289 mAh g−1 at a current density of 5 A g−1. This is a significantly enhanced performance compared with Si particles embedded in CNF without Ti3C2T
x
(denoted Si@CNF). This work proposes a scalable manufacturing strategy for developing high-performance silicon-based anodes, overcoming the low conductivity and volume expansion issues.
Energy correlation of bottom quarks from decays of top quarks in electron–positron annihilation
Authors: Truten Ivan V , Korchin Alexander Yu
Published: January 2022

Joint energy distribution of the bottom quark and antiquark from decays of the top quark and antiquark produced in the reaction

e

+

e

−

→
t

t

¯

is studied. Main emphasis is put on CP-violation effects in the interaction of the photon and Z boson with the top quarks. Energy asymmetries of b and

b

¯

quarks, which give access to the CP-violating terms, are considered. To estimate the magnitude of these asymmetries, the CP-violating γtt and Ztt couplings are calculated in one-loop model with exchange of the Higgs boson with the mass of 125 GeV. Interaction of this boson with the top quarks is assumed to include scalar and pseudoscalar couplings. Values of these couplings are constrained from the recent CMS analysis. Energy dependence of the asymmetries of b and

b

¯

quarks is calculated up to

s

=
1.2

TeV and some interesting features of their behavior are observed. These observables can be of interest for future studies at electron–positron colliders CLIC and ILC.
Hot carrier dynamics in MoS2/WS2 heterostructure
Authors: Zhu Lang , Song Zongpeng , Li Ran , Zhu Haiou
Published: January 2022

TMDs based heterostructure have drawn much attention for its potential application in photoelectric devices benefiting from the rapid and effective carrier separation and ultra-long interlayer exciton lifetime. Recent studies on carrier dynamics of TMDs based heterostructures are mainly focused on the transfer process of photo-generated carriers across the interface and lifetime of interlayer exciton but little attention is paid on the dynamics of hot carriers. Here, the carrier dynamics of hot carriers in MoS2/WS2 heterostructure is investigated by transient absorption spectra. Rapid separation of electron and hole is observed. More importantly, hot carriers of C exciton, which contribute to the absorption of most of the visible light, could compensate for the carrier loss in the band edge exciton energy band through the intervalley transfer process. This re-injection process of hot carriers of C exciton could compensate for carrier depletion in photoelectric devices, thus may greatly improve the light utilization in optoelectronic devices.
Detection of fetal arrhythmias in non-invasive fetal ECG recordings using data-driven entropy profiling
Authors: Keenan Emerson , Karmakar Chandan , Udhayakumar Radhagayathri K , Brownfoot Fiona C , Lakhno Igor , Shulgin Vyacheslav , Behar Joachim A , Palaniswami Marimuthu
Published: January 2022

Objective. Fetal arrhythmias are a life-threatening disorder occurring in up to 2% of pregnancies. If identified, many fetal arrhythmias can be effectively treated using anti-arrhythmic therapies. In this paper, we present a novel method of detecting fetal arrhythmias in short length non-invasive fetal electrocardiography (NI-FECG) recordings. Approach. Our method consists of extracting a fetal heart rate time series from each NI-FECG recording and computing an entropy profile using a data-driven range of the entropy tolerance parameter r. To validate our approach, we apply our entropy profiling method to a large clinical data set of 318 NI-FECG recordings. Main Results. We demonstrate that our method (TotalSampEn) provides strong performance for classifying arrhythmic fetuses (AUC of 0.83) and outperforms entropy measures such as SampEn (AUC of 0.68) and FuzzyEn (AUC of 0.72). We also find that NI-FECG recordings incorrectly classified using the investigated entropy measures have significantly lower signal quality, and that excluding recordings of low signal quality (13.5% of recordings) increases the classification performance of TotalSampEn (AUC of 0.90). Significance. The superior performance of our approach enables automated detection of fetal arrhythmias and warrants further investigation in a prospective clinical trial.
Electric signals measured during plasma thin-film etching and their connection to the electron concentration and the properties of the treated surface
Authors: Dvořák P , Žemlička R , Přibyl R , Buršíková V
Published: January 2022

The electric characteristics of a discharge are usually changed when a thin film is deposited on or etched from a discharge electrode or a substrate. The electric characteristics include the plasma potential, discharge voltage and discharge current, including higher harmonic frequencies of these quantities. This fact can be used for the monitoring of various plasma processes, but the mechanism by which the thin film influences the electric characteristics of the discharge has not been fully clarified. Our study of diamond-like carbon (DLC) film etching verified that variations of electric discharge parameters are caused by variations of electron concentration, which is caused by a difference in the electron emission yield between the DLC film and its substrate.
Multiple bottle beams based on metasurface optical field modulation and their capture of multiple atoms
Authors: Zhang Xichun , Fu Wensheng , Lv Jinguang , Zhang Chong , Zhao Xin , Li Weiyan , Zhang He
Published: January 2022

Compared to conventional devices, metasurfaces offer the advantages of being lightweight, with planarization and tuning flexibility. This provides a new way to integrate and miniaturize optical systems. In this paper, a metasurface capable of generating multiple bottle beams was designed. Based on the Pancharatnam–Berry (P–B) phase principle, the metasurface lens can accurately control the wavefront by adjusting the aspect ratio of the titanium dioxide nanopillars and the rotation angle. When irradiated by left-handed circularly polarized light with a wavelength of 632.8 nm, the optical system can produce multiple micron bottle beams. Taking two bottle beams as examples, the longitudinal full widths at half-maximum of the optical tweezers can reach 0.85 μm and 1.12 μm, respectively, and the transverse full widths at half-maximum can reach 0.46 μm and 0.6 μm. Also, the number of generated bottle beams can be varied by controlling the size of the annular obstacle. By changing the x-component of the unit rotation angle, the metasurface can also change the shape of the bottle beam — the beam cross-section can be changed from circular to elliptical. This paper also analyzes the trapping of ytterbium atoms by the multi bottle beam acting as optical tweezers. It is found that the multi bottle beam can cool and trap multiple ytterbium atoms.
Definition and expression of non-symmetric physical properties in space for uniaxial crystals
Authors: Guo Xiaojie , Chen Lijuan , Gao Zeliang , Yin Xin , Tao Xutang
Published: January 2022

The anisotropic physical property is the most noteworthy feature of crystals. In this paper, the subscript change method is used to analyze the sign changes of different tensors describing physical properties in uniaxial crystals. The distribution of some physical properties in special point groups exhibits non-symmetry in eight quadrants, which should attract the attention of crystal research. The difference between the crystallographic and physical coordinate systems and the lack of crystal symmetry operations are considered to be the origins of the non-symmetry. To avoid ambiguities and difficulties in characterizing and applying crystal physical properties, eight quadrants in space should be clarified. Hence, we proposed the use of piezoelectric properties to define the positive direction of the optical coordinate axis prior to the research and applications of optical properties.
Thermodynamic properties of two-dimensional charged spin-1/2 Fermi gases
Authors: Yang Jia-Ying , Liu Xu , Qin Ji-Hong , Guo Huai-Ming
Published: January 2022

Based on the mean-field theory, we investigate the thermodynamic properties of the two-dimensional (2D) charged spin-1/2 Fermi gas. Landé factor g is introduced to measure the strength of the paramagnetic effect. There is a competition between diamagnetism and paramagnetism in the system. The larger the Landé factor, the smaller the entropy and specific heat. Diamagnetism tends to increase the entropy, while paramagnetism leads to the decrease of the entropy. We find that there exists a critical value of Landé factor for the transition point due to the competition. The entropy of the system increases with the magnetic field when g < 0.58. With the growth of paramagnetism, when g > 0.58, the entropy first decreases with the magnetic field, then reaches a minimum value, and finally increases again. Both the entropy and specific heat increase with the temperature, and no phase transition occurs. The specific heat tends to a constant value at the high-temperature limit, and it approaches to zero at very low temperatures, which have been proved by the analytical calculation.
Development of an electronic stopping power model based on deep learning and its application in ion range prediction
Authors: Guo Xun , Wang Hao , Li Changkai , Zhao Shijun , Jin Ke , Xue Jianming
Published: January 2022

Deep learning algorithm emerges as a new method to take the raw features from large dataset and mine their deep implicit relations, which is promising for solving traditional physical challenges. A particularly intricate and difficult challenge is the energy loss mechanism of energetic ions in solid, where accurate prediction of stopping power is a long-time problem. In this work, we develop a deep-learning-based stopping power model with high overall accuracy, and overcome the long-standing deficiency of the existing classical models by improving the predictive accuracy of stopping power for ultra-heavy ion with low energy, and the corresponding projected range. This electronic stopping power model, based on deep learning algorithm, could be hopefully applied for the study of ion-solid interaction mechanism and enormous relevant applications.
Wind Environment Analysis of Ground-based Optical Observatory
Authors: Li Tao-Ran , Jiang Xiao-Jun
Published: January 2022

The telescopes and the infrastructures may alter the local wind environment around the observatory and further affect the observing environment. After the completion of site testing, it is necessary to analyze the wind environment of the entire site and plan the telescope layout to make use of the excellent conditions scientifically and rationally. Taking a typical observatory as an example, the effect of topographical features on the wind environment and the mutual interference between telescope enclosures are analyzed by using the Computational Fluid Dynamics (CFD) method. The CFD simulations are compared with the seeing data from the Differential Image Motion Monitor, and the results are in good agreement, which verifies the effectiveness of the CFD method. The results of wind environment analysis can provide reasonable suggestions for site layout and construction, reducing the interference effects and improving the observing environment.
Development of dosimetric procedures for experimental ultra-high dose rate irradiation at a clinical linear accelerator
Authors: Konradsson E , Petersson K , Adrian G , Lempart M , Blad B , Ceberg S , Knöös T , Bäck S Å J , Ceberg C
Published: January 2022

As radiotherapy using ultra-high dose rates has gained new interest, the dosimetric challenges arising at these conditions needs to be addressed. Ionization chambers suffer from a large decrease in ion collection efficiency due to ion recombination, making on-line dosimetry difficult. In this work we present experimental setups and dosimetric procedures for FLASH irradiation of cells, zebrafish embryos and small animals using a 10 MeV electron beam at a modified clinical linear accelerator, and describe the dosimetric steps required to initiate clinical trials. The dosimetric equipment used for our pre-clinical experiments consisted of radiochromic film, thermoluminescent dosimeters, a Farmer-type ionization chamber and phantom material mimicking the experimental setup for irradiation. In preparation for small animal irradiation, dose profiles and depth dose curves were measured for all collimator sizes. The average dose rates were ≥620 Gy/s, ≥640 Gy/s and ≥400 Gy/s for cells, zebrafish embryos and small animals, respectively.
Dose rate effects in radiochromic leuco crystal violet dosimeters
Authors: Jordan Kevin
Published: January 2022

Radiochromic dosimeters based on leuco crystal violet (LCV) in aqueous solutions and gelatin hydrogels were evaluated for dose and dose rate performance. Optical transmission measurements were performed on samples in custom 10 cm long cuvettes in order to have measurable signals below 10 Gy. Standard gel formulations with 0.7 mM concentration of anionic surfactant sodium dodecyl sulfate (SDS) had a dose rate effect of increasing sensitivity with increasing dose rate of nearly 15% from 1 to 10 Gy/min at 20 Gy. Solutions of LCV and trichloracetic acid also showed similar dose rate effects. Adding LCV increased dose rate effect. Adding SDS, lowered sensitivity but maintained dose rate effect. Adding 0.4% gelatin also lowered sensitivity but reversed dose rate effect. For gels, lowering the pH reduced both sensitivity and dose rate effect. These trends are consistent with LCV solubility contributing to dose rate effects. These data indicate there are several factors related to dose rate effects in leuco crystal violet dosimeters and formulations designed to maximize sensitivity may not minimize dose rate effects.
On the feasibility of using an optical fiber Bragg grating array for multi-point dose measurements in radiation therapy
Authors: Lebel-Cormier Marie-Anne , Boilard Tommy , Bernier Martin , Beaulieu Luc
Published: January 2022

Fiber Bragg gratings (FBGs) have proven to be a valuable dosimeter in nuclear environment where radiation doses reach up to a hundred of kiloGray (kGy). Multiple FBGs can be written in a single fiber to allow multi-point detection which would prove very useful for radiotherapy dosimetry. The purpose here is to adapt this already existing technology to provide a novel dosimeter for radiotherapy measurements. The proposed real-time dosimeter consists of twenty 4 mm-long FBGs, equally distributed over 20 cm. FBGs are written through the coating of a standard polyimide-coated silica fiber with the phase-mask technique and femtosecond pulses. The wavelength dependant variation of each FBG is recorded at 1 kHz with a commercially available interrogator. The use of gamma radiation (clinical radiotherapy accelerator) induces a linear shift (0.070 ± 0.006 pm/Gy) of the FBG’s reflected wavelength, which is independent of the dose rates (2.8-11.6 Gy/min) and the energy (6-23 MV). A statistical error of 0.03 pm is obtained on data points therefore limiting the detectable dose to 0.4 Gy. A dose profile of 6 and 23 MV radiotherapy accelerator is also measured. The presented FBGs dosimeter allows for real-time dose measurement in 2D and the small size of its detector makes it a versatile tool. The length and spacing of FBGs can be easily modified to increase both the spatial resolution and the amount of dose point.
Three-dimensional reproducibility assessment of radiochromic ClearView™ gel in custom vessel
Authors: Jordan Kevin , Xu Lu , Barnett Rob
Published: January 2022

Samples of the radiochromic hydrogel ClearView™ were characterized for 3D dosimeter performance in custom vessels and read with a modified Vista16 optical cone beam scanner as part of a small field dosimetry study. Uniform dose responses and small field depth doses revealed each sample was unique. As the gels aged over several weeks at 4 °C, the dose sensitivity decreased. Reconstructions of uniform dose distributions revealed unique features near the vessel interface. The central sub-volumes ranged from uniformity to within 2% throughout the sub-volume to a 12% gradient from base to top of gel. Comparisons of small field depth doses revealed that if the central volume had a uniform dose response, no correction was required. However, if a gradient in sensitivity from bottom to top was measured a correction may or may not be required. It is suspected that the filling protocol of the custom vessels is a factor in the observed variations in 3D dose response.
Marker trajectory assessment in optical cone beam computed tomography scanner geometry
Authors: Marks Gareth , Jaryd Christie , Jordan Kevin
Published: January 2022

Measured trajectories of markers on vessel wall and beads on a plumb are compared with calculated trajectories for transmission images obtained with Vista16™ optical cone beam scanner. Effective pixel size was 0.11 mm. An algorithm was developed to measure trajectories for the plumb line phantom and a semi-automatic version was required for the gel filled vessels due to overlap of markers and seam projections at maximum lateral positions. It was found that for 15 cm diameter vessels, the vessel edge was not visible due to divergence in cone beam geometry. Accounting for this effect was necessary in order to remove artefacts from calculated vessel trajectory. Displacement differences were found to be less than 0.1 mm for the plumb line, 0.3 mm for a beads and 1 mm for markers on gel filled vessel. Minimization of the vessel marker trajectory deviation will provide a more accurate approach to refractive index optimization. No optical aberrations were detected from the trajectory analysis and manufacturer scanner alignment and geometrical calibrations were independently verified.
Development of a novel proton CT system using a 3D scintillator detector
Authors: Beddar S , Darne C D , Robertson D G
Published: January 2022

The goal of this work is to develop a novel proton computed tomography (CT) system using a 3-dimensional (3D) scintillator detector. The detector design of the pCT system consists of a 3D scintillator that generates scintillation light in response to the incident proton beams. A scientific charge-coupled device (CCD) camera placed along the beam axis captures a 2D projection or radiograph of the resulting 3D scintillation light signal. A set of CCD cameras placed laterally to the beam axis capture two unique depth dose distributions of the beam. These additional beam images can be used to improve the resolution of the proton radiograph. In this study, we demonstrate the feasibility of this unique imaging system design for proton radiography using a 3D liquid scintillator detector.
Development of photonic detector system for ultra-fast beam diagnostics in proton radiotherapy: the proof of concept
Authors: Iakovenko Viktor , Jaffray David A.
Published: January 2022

A concept of a photonic detector system for proton beam and Bragg peak position measurements in proton radiation therapy is presented. An approach of using scintillator plates with ultra-fast timing characteristics to detect the temporal fine structure of the beam is described. A detector module is made of a 10 × 10 cm2 plastic scintillator plate with 1mm thickness. The light is collected on the corners of a plate by the optical fibers of pre-defined length, which introduce various known time delays. Using the Anger algorithm, the lateral position of the proton pencil beam traversing scintillator plate is reconstructed. We propose two applications of the system: thin single-plate beam position monitor and multi-plate stack quality control device to measure lateral beam position and relative position of the Bragg peak.
Utilizing Pencil Beam Scan Dynamics and a Scintillation Screen to produce 3D Dose Distribution of Proton Beams
Authors: Rahman Mahbubur , Bruza Petr , Gladstone David J. , Zhang Rongxiao , Pogue Brian W.
Published: January 2022

This study describes a method for producing volumetric dose profiles of proton beams from 2D slices of optical scintillation images. The method relies on a high frame rate camera acquisition (100 frames per second), the spot scanning capabilities of current proton pencil beam scanning systems, and a water equivalent scintillation screen. The acquired slices are corrected for optical blurring and ionization quenching and stacked to produce volumetric dose distribution. The volumetric optical dose profile had a pass rate of 98.3% for 2%/2mm local gamma analysis, suggesting the method can accurately measure dose profiles. The method can potentially image all clinical proton beams for pencil beam scanning systems and can extend to imaging patient plans, with further verification.
Effects of Organic Solvent Doping on the Structural and Conductivity Properties of PEDOT: PSS Fabric
Authors: Ayub Ayu Natasha , Ismail Nur Atikah , Aizamddin Muhammad Faiz , Bonnia Noor Najmi , Sulaiman Noor Safina , Nadzri Nur Izzati Muhammad , Mahat Mohd Muzamir
Published: January 2022

The development of highly conductive fabrics for application in electronic textiles on non-rigid substrates has gained much interest owing to their potential for realizing next-generation wearable conductive fabrics. Besides, it could be used as a portable device in the energy and healthcare industries. However, pristine PEDOT: PSS has been reported to have low conductivity values due to excessive PSS chains that wrapped up around the PEDOT conductive chain structure. In this context, one of the most successful techniques to include dopant is to use ethylene glycol (EG) to improve the conductivity of poly (3,4-ethylenedioxythiophene): poly (styrene sulfonate) (PEDOT: PSS). Immersing or coating of fabrics with PEDOT: PSS is one of the facile methods used for obtaining functional and smart properties. Our studies prepared conductive polymer; PEDOT: PSS with ethylene glycol (EG) dopant) at different concentrations on polyester (PES) fabric substrates by a facile immersion process. The effect of the different concentrations of EG on the conductivity is shown when Electrochemical Impedance Spectroscopy (EIS) is conducted. It shows that 6% v/v of EG gives the optimum conductivity value up to 4.06 × 10-3 Scm-1. Meanwhile, Scanning Electron Microscopy (SEM) imaging is focusing on the morphology of the immersed fabric. The improvement in crystallinity of the doped PEDOT: PSS was revealed and evaluated using X-ray Diffraction (XRD). As a result, it has been demonstrated that EG is an excellent dopant because it efficiently increases the electrical conductivity and crystallinity of PEDOT: PSS fabric.
Effect of pre-twinning on microstructure and texture evolution in room and cryogenically deformed Mg-0.5Ca alloy
Authors: Chaudry Umer Masood , Noh Yeonju , Han Gukin , Hamad Kotiba , Jun Tea-Sung
Published: January 2022

Introducing {10-12} tension twin lamellas has proved to be the most effective strategy to enhance the performance of Mg alloys. Twins can provide the preferential nucleation sites for recrystallization, hence twinning induced grain refinement can result in higher strength material based on Hall-Petch Effect. In addition, twinning can also modify the crystallographic c-axis distribution in wrought Mg alloys resulting in lower plastic anisotropy. Accordingly, in the present work, the twinning evolution in the Mg-0.5Ca (wt.%) alloy at room and cryogenic temperature was critically investigated. The samples were subjected to pre-compression (5%) at room temperature and -150 °C at the strain rate of 10-3 s-1. The microstructural and textural characterization was carried out using OM, SEM and EBSD to emphasize the twinning evolution during compression at room and low temperatures. The local lattice distortion and the localized deformation in terms of Kernel average misorientation (KAM) was also studied. The results revealed the significant grain refinement (13.7 μm 5.61 μm and 6.02 μm of uncompressed and RT, - 150 °C pre-compressed, respectively), where slightly enhanced twinning fraction in the sample compressed at RT was observed. Finally, remarkable increase in the yield strength was noticed in the pre-twinned samples, which was attributed to the twinning-induced grain refinement based on the Hall-Petch relationship.
Influence of Fly Ash Geopolymer Ceramic Powder Addition on Sn-3.0Ag-0.5Cu Solder Joints
Authors: Somidin Flora , Natasha Ilias Nur Fatin , Mohamad Zaimi Nur Syahirah , Izwan Ramli Mohd Izrul , Abdul Razak Nurul Razliana
Published: January 2022

This paper reports the effect of 1.0 wt.% fly ash (FA) geopolymer ceramic powder addition in Sn-3.0Ag-0.5Cu (SAC305, in wt.%) solder joint. Powder metallurgy route was used to fabricate the new composite solder. Solder balls were formed from the new composite solder and reflowed on Cu substrate. The effect of FA as ceramic reinforcement on the bulk microstructure and the interfacial intermetallic compound layer formation of solder joints were investigated under optical microscope. Microstructure observation showed that the β-Sn dendrite size was refined in SAC305-1.0FA/Cu bulk solder joint sample than that in the non-reinforced sample. The addition of FA geopolymer ceramic powder into the solder matrix also produced a thinner intermetallic compound layer.
Preparation and Characterization of Irradiated Bioplastic from Cassava Peel – A Review
Authors: Nee Fong Chui , Othman Siti Amira
Published: January 2022

The conventional plastic bears unavoidable responsibility for the massive scale generation of garbage. The bulk of the market is mostly made up of fossil-based plastics and the worldwide environmental pollution generated by them is growing increasingly problematic. Agro-waste product is probably a good way to make bioplastic. In fact, biodegradable plastic is indeed a good alternative to replace the petroleum-based plastics. Besides, making good use of agriculture waste can assist in diminishing waste accumulation. Cassava peel is a type of waste from cultivating activities that has the potential in making bio-based plastics. This research will focus on the preparation and characterization of bioplastic from cassava peel. Several related characterization methods such as XRD, SEM, FTIR, TGA will be discussed. The main purpose of this review is to fabricate and produce bioplastic samples from cassava peel with the addition of different ratios of sorbitol and chitosan.
Fast Recognition of Pig Faces Based on Improved Yolov3
Authors: Li Guangbo , Jiao Jun , Shi Guolong , Ma Huimin , Gu Lichuan , Tao Liang
Published: January 2022

The traditional ear labels are easy to get off and cause infection in the intelligent managements of live pigs. Therefore, a noninvasive pig face recognition method based on improved YOLOv3 was designed to carry out simultaneous recognition of multiple live pigs.The YOLOv3 model was used to introduce the dense network into the Darknet53 feature extractor, forming a new backbone network in combination with down-sampling, and the improved SPP unit was added to the YOLOv3 model, constructing the YOLOv3_DB_SPP model. The pig face data set used in the test was divided into 10 categories. After data enhancement, the number of samples was 8 512, and the ratio of training set to test set was about 9:1. The test results showed that: 1) Under different classification probability thresholds, the mean average precision of the YOLOv3_DB_SPP model for detecting pig face data set was higher than that of the YOLOv3 model; 2) When the IOU threshold was 0.5 and the classification probability threshold was 0.1, the mean average precision of YOLOv3_DB_SPP model was 9.87% higher than that of the YOLOv3 model; 3) When detecting long-distance covered small target samples, the mean average precision was also higher in the YOLOv3_DB_SPP model. Thus, the YOLOv3_DB_SPP model improved the feature extraction ability of the basic feature extractor and the precision of the detector.
Research on Safety Helmet Detection Algorithm of Power Workers Based on Improved YOLOv5
Authors: Fu Desu , Gao Lin , Hu Tao , Wang Shukun , Liu Wei
Published: January 2022

The traditional helmet detection algorithm in power industry has low precision and poor robustness. In response to this problem, the helmet detection algorithm based on improved YOLOv5 (You only look once) is put forward in this paper. Firstly, the YOLOv5 network structure is improved. By increasing the size of the feature map, one scale is added to the original three scales, and the added 160*160 feature map can be used for the detection of small targets; Secondly, the K-means is used for re-clustering the helmet data set to get more suitable priori anchor boxes. The experimental results illustrate that the average accuracy of the improved YOLOv5 algorithm is increased by 2.9% and reaching 95% compared with the initial model, and the accuracy of helmet recognition is increased by 2.4% and reaching 94.6%. This algorithm reduces the rates of missing detection and misdetection of small target detection in original network, and has strong practicability and advanced nature. It can satisfy the requirements of real-time detection and has a certain role in promoting the safety of power industry.
Research on Facial Expression Recognition Based on Improved Inception Network
Authors: Yang Chao , Gong Weibin
Published: January 2022

Facial expression recognition is one of the hot research fields in computer vision. Deep learning method is widely used due to its excellent performance in feature characterization. However, convolutional neural network, which is commonly used in the field of image classification, is prone to challenges such as too many parameters, difficulty in training and too many network layers. We proposed an improved inception network to solve the above. In this model, small-scale filter was used to replace large-scale one and multi-channel features were extracted. In addition, a smaller stochastic gradient value introduced in the ReLU makes the weights of some neurons be able to update. AM-softmax is adopted as the loss function with respect of the issue that the large intra-class distance about expression features. The recognition rates of the proposed model on CK+ and FER2013 are 98.32% and 71.12%, respectively. The experimental results verify the effectiveness of the proposed algorithm.
Similarity-Based Echo State Network for Remaining Useful Life Prediction
Authors: Wang Hao , Chen Yuzhi , Zhao Hongming , Cui Shitong , Zhao Xudong
Published: January 2022

The health state of aero engines will affect economic benefits, availability, and safety of aircrafts. Fault prognosis is considered to be one of useful methods to evaluate the health state of aero engines and avoid unscheduled down time. Prognosis is the ability to forecast the evolution of engine deterioration. The two major objectives of prognosis are: forecasting the impending failures and predicting the engines’ remaining useful life (RUL). This paper proposes a method that combines echo state network (ESN) with similarity-based methods for RUL prediction. First, principal component analysis (PCA) is used to pre-process data to obtain degradation trajectories, which handles the fusion problem of multiple data. Second, in order to use historical information accurately, the Euclidean distance is applied to calculate the similarity among different degradation trajectories. Third, the trajectory with the highest similarity is used in the ESN training process. Then, a numerical simulation on the NASA C-MAPSS dataset verifies the accuracy of the method. Finally, the comparison with other methods shows the superiority of the method.
Few-Shot Image Classification Based on Ensemble Metric Learning
Authors: Wang Hang , Chen Duanbing
Published: January 2022

In the case of few labelled image data samples, image classification is a difficult challenge, which is called few-shot image classification. Recently, many methods based on metric learning have been proposed. Most of these methods mainly focus on the representations of global image-level features or local feature-level descriptors. However, these methods calculate similarity from a single metric learning perspective. Motivated by ensemble learning, a novel Ensemble Metric Learning (EML) method for few-shot image classification is proposed, which not only utilizes label propagation, but also considers image-level and local feature-level descriptor metrics. The experimental results show that the proposed method can effectively improve the classification accuracy by ensemble learning.
A Light Model for Super-Resolution of Remote Sensing Images
Authors: Tingting Song
Published: January 2022

Remote sensing image super-resolution is an important research topic, which helps to improve the quality of remote sensing images. However, because remote sensing images are usually quite large while the satellite equipment often have low computing capacity and small memory, it is appealing to develop lightweight and fast models to perform the super-resolution task for the remote sensing images. In this paper, we empirically study the effectiveness of conventional super-resolution approaches for remote sensing images, and propose an effective way to reduce the model parameters and computational cost. Specifically, motivated by Res2Net, we design a new multi-scale hierarchy residual block to replace the ResBlock in EDSR to provide a more diverse receptive field for each residual block. The proposed modified method has fewer parameters and faster speed compared with the original EDSR. Moreover, we also build two benchmark super-resolution datasets (i.e., DOTA-SR and LEVIR-SR) from DOTA and LEVIR-CD, respectively, for experimental evaluation. We perform experiments on the two datasets, and results show that our method is light and have comparable performance over the existing super-resolution baselines.
Teacher-Student Based Domain Adaptation for Person Re-identification
Authors: Ding Jianpeng
Published: January 2022

In traditional deep Convolutional Neural Network (CNN) based person re-identification (Re-ID) methods, there must be thousands of training samples with annotated information under the same data distribution, in other words, the data used to train the CNN model is collected from the same scene. However, if we need to deploy a trained Re-ID model at the airport, but the data for training the model is collected on campus, the traditional methods will suffer considerable performance degradation because the data come from different distributions. It is a non-trivial task to straightforwardly re-collect sufficient annotations from the new scene, due to the high cost of time and human labor. Therefore, the domain adaptation paradigm is used to resolve the data distribution problem. Specifically, an efficacious method for domain adaptive person Re-ID based on the teacher-student framework is proposed in our research. Our method has been evaluated by numerous experiments and proved its effectiveness.
Fine-Graine Visual Classification with Aggregated Object Localization and Salient Feature Suppression
Authors: Qu Haicheng , Zhao Siqi , Liu Wanjun
Published: January 2022

Fine-grained visual classification (FGVC) is desired to classify sub-classes of objects in the same super-class. For the FGVC tasks, it is necessary to find subtle yet discriminative information from local areas. However, traditional FGVC approaches tended to extract strong discriminative features, and overlook some subtle yet useful features. Besides, current methods ignore the influence of background noises on feature extraction. Therefore, aggregated object localization combined with salient feature suppression are proposed, which is a stacked network. First, the feature maps extracted by the coarse network are fed into aggregated object localization to obtain complete foreground object in an image. Secondly, the refined features obtained through zooming in complete foreground object are fed into fine network. Finally, through finer network processing, the feature maps are fed into salient feature suppression module to find more valuable region discriminative features for classification. Experiment results on two datasets show that our proposed method can get superior result compared with state-of-the-art methods.
Extracting Parallel Sentences from Low-Resource Language Pairs with Minimal Supervision
Authors: Shi Xiayang , Liu Xinyi , Yu Zhenqiang , Cheng Pei , Xu Chun
Published: January 2022

At present, machine translation in the market depends on parallel sentence corpus, and the number of parallel sentences will affect the performance of machine translation, especially in low resource corpus. In recent years, the use of non parallel corpora to learn cross language word representation as low resources and less supervision to obtain bilingual sentence pairs provides a new idea. In this paper, we propose a new method. First, we create cross domain mappings in a small number of single languages. Then a classifier is constructed to extract bilingual parallel sentence pairs. Finally, we prove the effectiveness of our method in Uygur Chinese low resource language by using machine translation, and achieve good results.
Runet: Convolutional Networks for Crack Detection
Authors: Hu Lin , Yang Fuping
Published: January 2022

In daily life, crack is a common phenomenon. Such as road cracks, stone cracks. These cracks are usually not easy to be quickly and intuitively identified, because these cracks are characterized by poor continuity and low contrast. Among the existing crack detection methods based on deep learning, the model is too large to be directly used in life. We design an end-to-end neural network, its name is Runet, which greatly reduces the parameters of the model by depthwise separable convolution, increases the receptive field of the network by hole convolution. At the same time, we introduce the trainingtime and inferencetime architecture to increase the running speed and precision.
Attention Based Data Augmentation for Knowledge Distillation with Few Data
Authors: Tian Shengzhao , Chen Duanbing
Published: January 2022

Knowledge distillation has attracted great attentions from computer vision researchers in recent years. However, the performance of student model will suffer from the absence of the complete dataset, which is used to train the teacher model. Especially for conducting knowledge distillation between heterogeneous models, it is difficult for student model to learn and receive guidance with few data. In this paper, a data augmentation method is proposed based on the attentional response of teacher model. The proposed method utilizes the knowledge in teacher model without requiring homogeneous architecture between teacher model and student model. Experimental results demonstrate that combining the proposed data augmentation method with different knowledge distillation methods, the performance of student model can be improved in knowledge distillation with few data.
Transformer Model for Remaining Useful Life Prediction of Aeroengine
Authors: Li Qinghua , Yang Ying
Published: January 2022

Accurate aeroengine remaining useful life (RUL) prediction plays a vital role in ensuring safe operation and reducing maintenance losses. In order to improve the accuracy of aeroengine RUL prediction, an aeroengine RUL prediction method based on the Transformer model is proposed, which gives greater weight to the characteristics of important time steps through self attention mechanism, and solves the memory degradation problem caused by too long sequence in engine RUL prediction, and excavates the complex mapping relationship between input features and aeroengine RUL. Experiments on the C-MAPSS data set show that the Transformer model can better predict the aeroengine RUL based on the aeroengine degradation data. Compared with the long short term memory network model, the root mean square error of the two sub data sets is reduced by 6.57% and 5.63% respectively.
Based on Improved CNN Bearing Fault Detection
Authors: Xu Yaning , Yang Lei
Published: January 2022

In recent years, the problem about the fault detection of rolling bearings in mechanical equipment has gradually become an important research direction.Among them, the diagnostic method based on vibration signal analysis is widely used in the fault detection of rolling bearings.Since the one-dimensional convolutional neural network (1D-CNN) has certain limitations on the processing of vibration signal data, the solution to this problem in this paper is to integrate the attention mechanism and bi-directional long and short-term memory neural network (BiLSTM) on the basis of the one-dimensional convolutional neural network, using the attention mechanism to give different weights to different feature dimensions in the sample data and extract key and important information, thus the sample data can be further optimized.On the other hand, BiLSTM can automatically extract the deep information of the bearing vibration signal, which makes up for the deficiency of artificial extraction features to a certain extent, and strengthens the discriminative property of high-level features.Subsequently, the improved CNN bearing fault detection model was experimentally validated using the Case Western Reserves University bearing dataset, and it was concluded that the attention mechanism acting on the model obtained by adding BiLSTM to the 1D-CNN could achieve a fault identification accuracy of about 98.9% and the loss degree was reduced to about 0.17%, thus achieving an effective diagnosis of the fault state.
Reference Context Guided Vector to Achieve Multimodal Machine Translation
Authors: Shi Xiayang , Yuan Jiaqi , Huang Yuanyuan , Yu Zhenqiang , Cheng Pei , Liu Xinyi
Published: January 2022

Traditional machine translation mainly realizes the introduction of static images from other modal information to improve translation quality. In processing, a variety of methods are combined to improve the data and features, so that the translation result is close to the upper limit, and some even need to rely on the sensitivity of the sample distance algorithm to the data. At the same time, multi-modal MT will cause problems such as lack of semantic interaction in the attention mechanism in the same corpus, or excessive encoding of the same text image information and corpus irrelevant information, resulting in excessive noise. In order to solve these problems, this article proposes a new input port that adds visual image processing to the decoder. The core idea is to combine visual image information with traditional attention mechanisms at each time step specific to decoding. The dynamic router extracts the relevant visual features, integrates the multi-modal visual features into the decoder, and predicts the target word by introducing the visual image process. At the same time, experiments were carried out on more than 30K datasets translated in the United Kingdom, France and the Czech Republic, which proved the superiority of adding visual images to the decoder to extract features.
Management status of blue swimming crab (Portunus pelagicus Linnaeus, 1758) based on EAFM in the coastal of Pangkep Regency, South Sulawesi
Authors: Yanti N D , Kurnia R , Mashar A , Mardyani Y , Lindawati L
Published: January 2022

The Blue Swimming Crab (BSC) is the main export commodity of Indonesia and one of the BSC agribusinesses in the coastal waters of the Pangkep Regency. However, in recent years, BSC agribusiness in this region has been estimated to be unsustainable. The purpose of this study is to formulate the management of BSC (Portunus pelagicus) resources with the Ecosystem Approach to Fisheries Management (EAFM) in the coastal of the Pangkep Regency. The average value score in the BSC management domain is in the medium category (resource, habitat, economic, and institutional domains); the fishing techniques domain is in the good category, while the social domain is in the poor category. In general, the status of BSC fisheries management is categorized as medium (value = 194.25), therefore it can still be developed and improved. To repair the BSC fisheries management for gradually, tactical decisions were made to enhance the indicator conditions. To make the fishery activities sustainable, recommendations to improve the condition of the BSC fisheries in the Pangkep Regency include strengthening the supervision, improving the seagrass cover, improving coordination among institutions on the BSC fisheries management, and increasing stakeholder participation in the effort to manage the BSC fisheries.
Fisheries forecasting, physical approach comparison between regression and autoregressive integrated moving average (ARIMA)
Authors: Ahmad T E , Rais A , Azhari D R , Rosalia A A , Hutapea R Y F
Published: January 2022

Empowerment of fishery resources requires analysis of forecasting results as an effort to maintain sustainability and human needs. Forecasting is an approach to predicting based on past facts, which is expected to be used as a decision support system. However, there are problems with the accuracy. In this study, we compare the regression method with ARIMA to find out which method can be used as the most appropriate choice in fisheries forecasting with physical benchmarks (seasonal and climate). We conduct a systematic literature study on various studies with the theme of fisheries forecasting. The search focuses on studies with the main criteria in the form of an explicit discussion of the basic forecasting methods and literature on marine physical influences. Then define a search method by combining fishery stocks or landings against forecasting with marine physical features optionally using PRISMA. The results show that the ARIMA models have a better fair value and accuracy than the regression because the ARIMA model captures the history of data autocorrelation and extrapolates it to the forecasting framework that will be carried out. Hence, it is most suitable for use with additional marine physical variability.