bluuebunny/crossref_metadata_2025 huggingface dataset:
Reflection and transmission of an Airy beam in a dielectric slab
Authors: Yang Xiaojin , Qu Tan , Wu Zhensen , Li Haiying , Bai Lu , Gong Lei , Li Zhengjun
Published: January 2022

The reflection and transmission of a finite-power Airy beam incident on a dielectric slab are investigated by an analytical method. Based on the plane-wave angular spectrum expansion and Fresnel approximation, the analytical expressions of the reflected field, internal field as well as transmitted field in each region are obtained. Through numerical simulations, the intensity distributions of the incident beam, reflected beam, internal beam as well as transmitted beam are presented at oblique incidence. Besides, we also compare the intensity distributions of the geometrical-optics beam field, the first order beam mode field and the actual beam field, which indicates that the contribution of each order beam mode field to the actual beam field is related to the refractive index of the dielectric slab. Meanwhile, the reflection characteristics of the Airy beams in the special cases of Brewster incidence and total reflection are investigated. Finally, the effects of the optical thickness and refractive index of the dielectric slab on the peak intensity distributions and beam shifts of the reflected and transmitted beams are also discussed in detail. The analytical and numerical results will be useful to analyze the propagation dynamics of Airy beam in the dielectric slab and provide some theoretical supports to the design of optical film.
A 4H-SiC trench MOSFET structure with wrap N-type pillar for low oxide field and enhanced switching performance
Authors: Shen Pei , Wang Ying , Cao Fei
Published: January 2022

An optimized silicon carbide (SiC) trench metal-oxide-semiconductor field-effect transistor (MOSFET) structure with side-wall p-type pillar (p-pillar) and wrap n-type pillar (n-pillar) in the n-drain was investigated by utilizing Silvaco TCAD simulations. The optimized structure mainly includes a p+ buried region, a light n-type current spreading layer (CSL), a p-type pillar region, and a wrapping n-type pillar region at the right and bottom of the p-pillar. The improved structure is named as SNPPT-MOS. The side-wall p-pillar region could better relieve the high electric field around the p+ shielding region and the gate oxide in the off-state mode. The wrapping n-pillar region and CSL can also effectively reduce the specific on-resistance (R
on,sp). As a result, the SNPPT-MOS structure exhibits that the figure of merit (FoM) related to the breakdown voltage (V
BR) and R
on,sp (

V

BR

2

R

on
,
sp

) of the SNPPT-MOS is improved by 44.5%, in comparison to that of the conventional trench gate SJ MOSFET (full-SJ-MOS). In addition, the SNPPT-MOS structure achieves a much faster-witching speed than the full-SJ-MOS, and the result indicates an appreciable reduction in the switching energy loss.
Propagation and modulational instability of Rossby waves in stratified fluids
Authors: Yang Xiao-Qian , Fan En-Gui , Zhang Ning
Published: January 2022

Perturbation analysis and scale expansion are used to derive the (2+1)-dimensional coupled nonlinear Schrödinger (CNLS) equations that can describe interactions of two Rossby waves propagating in stratified fluids. The (2+1)-dimensional equations can reflect and describe the wave propagation more intuitively and accurately. The properties of the two waves in the process of propagation can be analyzed by the solution obtained from the equations using the Hirota bilinear method, and the influence factors of modulational instability are analyzed. The results suggest that, when two Rossby waves with slightly different wave numbers propagate in the stratified fluids, the intensity of bright soliton decreases with the increases of dark soliton coefficients. In addition, the size of modulational instable area is related to the amplitude and wave number in y direction.
Data-driven modeling of a four-dimensional stochastic projectile system
Authors: Huang Yong , Li Yang
Published: January 2022

The dynamical modeling of projectile systems with sufficient accuracy is of great difficulty due to high-dimensional space and various perturbations. With the rapid development of data science and scientific tools of measurement recently, there are numerous data-driven methods devoted to discovering governing laws from data. In this work, a data-driven method is employed to perform the modeling of the projectile based on the Kramers–Moyal formulas. More specifically, the four-dimensional projectile system is assumed as an Itô stochastic differential equation. Then the least square method and sparse learning are applied to identify the drift coefficient and diffusion matrix from sample path data, which agree well with the real system. The effectiveness of the data-driven method demonstrates that it will become a powerful tool in extracting governing equations and predicting complex dynamical behaviors of the projectile.
Laser fragmentation in liquid synthesis of novel palladium-sulfur compound nanoparticles as efficient electrocatalysts for hydrogen evolution reaction
Authors: Fu Guo-Shuai , Gao Hong-Zhi , Yang Guo-Wei , Yu Peng , Liu Pu
Published: January 2022

One promising way to tune the physicochemical properties of materials and optimize their performance in various potential applications is to engineer material structures at the atomic level. As is well known, the performance of Pd-based catalysts has long been constrained by surface contamination and their single structure. Here, we employed an unadulterated top-down synthesis method, known as laser fragmentation in liquid (LFL), to modify pristine PdPS crystals and obtained a kind of metastable palladium-sulfur compound nanoparticles (LFL-PdS NPs) as a highly efficient electrocatalyst for hydrogen evolution reaction (HER). Laser fragmentation of the layered PdPS crystal led to a structural reorganization at the atomic level and resulted in the formation of uniform metastable LFL-PdS NPs. Noteworthy, the LFL-PdS NPs show excellent electrocatalytic HER performance and stability in acidic media, with an overpotential of –66 mV at 10 mA⋅cm−2, the Tafel slope of 42 mV⋅dec−1. The combined catalytic performances of our LFL-PdS NPs are comparable to the Pt/C catalyst for HER. This work provides a top-down synthesis strategy as a promising approach to design highly active metastable metal composite electrocatalysts for sustainable energy applications.
Oscillation properties of matter-wave bright solitons in harmonic potentials
Authors: Guan Shu-Wen , Meng Ling-Zheng , Zhao Li-Chen
Published: January 2022

We investigate the oscillation periods of bright soliton pair or vector bright soliton pair in harmonic potentials. We demonstrate that periods of low-speed solitons are greatly affected by the position shift during their collisions. The modified oscillation periods are described by defining a characterized speed, with the aid of asymptotic analysis on related exact analytic soliton solutions in integrable cases. The oscillation period can be used to distinguish the inter- and intra-species interactions between solitons. However, a bright soliton cannot oscillate in a harmonic trap, when it is coupled with a dark soliton (without any trapping potentials). Interestingly, it can oscillate in an anti-harmonic potential, and the oscillation behavior is explained by a quasi-particle theory. The modified period of two dark-bright solitons can be also described well by the characterized speed. These results address well the effects of position shift during soliton collision, which provides an important supplement for previous studies without considering phase shift effects.
Parity–time symmetric acoustic system constructed by piezoelectric composite plates with active external circuits
Authors: Zhou Yang , Yang Zhang-Zhao , Peng Yao-Yin , Zou Xin-Ye
Published: January 2022

Researches on parity-time (PT) symmetry in acoustic field can provide an efficient platform for controlling the travelling acoustic waves with balanced loss and gain. Here, we report a feasible design of PT-symmetric system constructed by piezoelectric composite plates with two different active external circuits. By judiciously adjusting the resistances and inductances in the external circuits, we obtain the exceptional point due to the spontaneous breaking of PT symmetry at the desired frequencies and can observe the unidirectional invisibility. Moreover, the system can be at PT exact phase or broken phase at the same frequency in the same structure by merely adjusting the external circuits, which represents the active control that makes the acoustic manipulation more convenient. Our study may provide a feasible way for manipulating acoustic waves and inspire the application of piezoelectric composite materials in acoustic structures.
Measurement of spatial fluence distribution of neutrons and gamma rays using MAGAT-type gel detector doped with LiCl for BNCT at Kyoto University Reactor
Authors: Tanaka Kenichi , Kajimoto Tsuyoshi , Mitsuyasu Aruma , Ito Yuto , Hayashi Shin-ichiro , Sakurai Yoshinori , Tanaka Hiroki , Takata Takushi , Bengua Gerard , Endo Satoru
Published: January 2022

The spatial distributions of neutrons and gamma rays in the epithermal mode of the Kyoto University Reactor were measured using a MAGAT-type polymer gel detector doped with LiCl at 6Li concentrations of 0, 10, 100 ppm. Reasonable distributions for thermal neutrons and gamma rays were obtained for 0 and 100ppm.
Simulated design optimization of a prototype solid tank optical CT scanner for 3D radiation dosimetry
Authors: Ogilvy A , Collins S , Hare W , Hilts M , Tuokko T , Deardon R , Jirasek A
Published: January 2022

Optical computed tomography (CT) is one of the leading modalities for imaging gel dosimeters. There exist many prototype designs, as well as some commercial optical CT scanners that have showcased the value that gel dosimeters can provide to improve 3D dose verification for radiation treatments. However, due to factors including image accuracy, scan time, or demanding setup and maintenance there is currently no single scanner that has become a ubiquitous staple in a clinical setting. In this work, a prototype solid tank optical CT scanner is proposed that minimizes the need for a refractive index bath commonly found in optical CT systems. In addition to the design proposal, a ray-path simulator was created to optimize the design such that the solid tank geometry improves light collection across the detector array, maximizes the volume of the dosimeter scanned, and maximizes the dynamic range of the scanner.
Rapid manufacture of patient-specific, elastomeric, three-dimensional dosimeters using the FlexyDos3D dosimeter
Authors: Wheatley M J , Y De Deene
Published: January 2022

Patient-specific dosimeters are ideal for quality assurance of radiotherapy treatments. Such dosimeters would ideally need to be manufactured in a short amount of time so that the time between initially imaging the patient, treating the patient-specific dosimeter, reading out the dosimeter, and then finally treating the patient, is minimised. 3D printing allows for rapid manufacture of complex 3D objects. The FlexyDos3D dosimeter is a silicone-based material that has enough strength for the dosimeter to hold its shape without a mould or other structure supporting it. A custom 3D printer has been built for making dosimeters from the FlexyDos3D material. Testing of the 3D printer is ongoing.
Study of the use of gel dosimetry in combination with 3D printing phantom for personalized pretreatment QA in radiotherapy
Authors: Colnot J , Chiavassa S , Delpon G , Huet C
Published: January 2022

In modern radiotherapy, pretreatment patient-specific quality assurance (PSQA) generally consists in delivering the treatment plan to a phantom equipped with a detector and in comparing the measured dose and the dose calculated by the treatment planning system (TPS) in order to detect any gap between both dose distributions. Dosimetric gels have interesting properties for QA. In this work, the use of gel dosimetry together with a patient-based 3D printed phantom for personalized PSQA is investigated. CT images of a patient with a right mesencephalic brain tumor were used to generate a 3D printed phantom. Then it was filled with water and a radiochromic gel jar and irradiated according to the patient intracranial stereotactic plan using a Novalis TrueBeam STX accelerator. Measured dose distributions agree well with the calculated ones. Regarding 3D gamma-index (1 mm – 2%) estimated within the central 85% of the jar volume, 96.3% of points pass the test. In addition, 86.5% of the points pass the local 2D 3 mm-3% gamma-index. Results are promising but further work is needed to improve the protocol and investigate the possibility to extend it to end-to-end tests.
RSC: A 3D printed eyeball phantom for Sr-90 dosimetry measurements
Authors: Maxwell S K , Crowe S B , Simpson-Page E M , Cassim N , Kairn T
Published: January 2022

Strontium 90 (Sr-90) has been commonly used in the radiation treatment of pterygia of the eye. A radioactive plaque is placed in an applicator onto the surface of the eyeball for a specific length of time to achieve a desired dose. Dose is usually calculated using source activity and decay, as well as the distance from it to the surface of the eye. However, this assumes a flat eye surface. This investigation used 3D printing to produce an anthropomorphic eyeball phantom on which to perform dosimetry measurements for two different applicator sizes. These doses were compared to planar geometry measurements and a dose difference found. While planar geometry measurements are useful for routine quality assurance, measurements of the effects of the curved surface on dose calculations can provide valuable clinical information.
3D dose delivery QA using couch and gantry mounted cameras
Authors: Ramish Ashraf M. , Farwell Cedar , Alexander Daniel A , Zhang Rongxiao , Gladstone David J , Pogue Brian W , Bruza Petr
Published: January 2022

While 2D planar radiation therapy quality assurance techniques suffice for linac verification, a full 3D dosimetry measurement is ideal for robust treatment plan verification. In this study, an optical 3D dose reconstruction technique based on radioluminescence imaging is presented, using two orthogonal views provided by gantry and couch mounted cameras. Both small field and multi-target beam apertures were tested, mimicking typical 3D radiosurgery plans. Pass rates >94% for 3D gamma with 2%/2mm were achieved in all cases. Slice images were used for gamma analysis in each of transverse, sagittal and coronal planes, through the point of maximum dose. This proposed system can enable real-time imaging of beam delivery dynamics in a cylindrical water-equivalent phantom with high spatial resolution 3D reconstruction of the dose distribution, and verification of each beamlet used.
Real time 4D Radiation Gel Dosimetry on the Australian MRI-Linac
Authors: Deene Yves De , Wheatley Morgan
Published: January 2022

4D radiation dosimetry using a highly radiation-sensitive polymer gel dosimeter with real-time quantitative MRI readout is presented as a technique to acquire the accumulated radiation dose distribution during image guided radiotherapy (IGRT) on an MRI-Linac. Optimized T2 weighted TSE scans are converted into quantitative ΔR2 maps and subsequently to radiation dose maps. The potential of real-time 4D radiation dosimetry in a theragnostic MRI-Linac is demonstrated in test tubes, for a square beam in a cylindrical gel phantom, for a simple step-and-shoot irradiation in a head phantom and a dynamic arc treatment on a cylindrical gel phantom using a rotating couch. The optimal sequence parameters for maximal dose resolution in the dynamic MRI acquisition will be presented and the trade off between MRI scanning speed and dose resolution will be discussed. A further improvement in temporal resolution using a keyhole imaging approach is the focus of future research.
Reduce Wafer Remnants Through Pre-Assembly Process Optimization
Authors: Vijayakumaran P. , Ramli M. H. M. , Selvam M. , Hamid K. , Atiqah A. , Jalar A. , Bakar M. A.
Published: January 2022

Wafer fabrication technology has been seeing exponential growth in recent years. This development is driven by the demand for better reliability, superior performance, and lower cost of electronic consumer products. Generally, the standard soft-soldering wafer backside metallization multilayer stack is AuAs/Ag/Ni/Ag. The present backside metallization being developed is Ti/NiV/Ag as the cost of Ti is almost neglectable compared to Au. However, this new wafer backside metallization leads to increased remnants remaining on the dicing tape after die pickup. The reduced effective area of the die backside surface may cause adhesion issues in the subsequent die attach process. In realizing this essential phase, in pre-assembly process optimization has been carried out using some assessment on the process flow and data analysis through statistical analyses image and measurement of wafer skeleton remnants was taken showing a reduction from 70μm to 40μm in width. This method shows that skipping the baking process in the pre-assembly process will undoubtedly lessen the adhesion with the back metal with mounting tape. Without compromising the saw quality will indeed give a reduced wafer remnant. Henceforth, skipping baking in the pre-assembly process will significantly reduce wafer remnants and further improve die attach process.
The Microstructural and Bioactivity Behavior of Magnesium Alloy Filled with Bioglass For Biomedical Application
Authors: Adzali Nur Maizatul Shima , Dahlan Nur Farahin , Johar Banjuraizah , Baharom Nor Akmah
Published: January 2022

Magnesium alloy have known as degradable implant material due to biodegradable properties. However, by developed Mg alloy matrix composites containing a bioceramic will produced more biodegradable and does not need second surgical to remove the implants in body. Powder metallurgy route was used to fabricate the AZ91/BG composite by mixing, compacting and sintering. Mg alloy (AZ91) was reinforced with 0, 3, 6, and 9 wt% of bioglass (BG) before compact and sintered at 400°C for 2 hours. The Vickers hardness, scanning electron microscope, and x-ray diffraction are used to investigate the effect of BG particles addition on the mechanical properties and microstructure of the composite AZ91/BG. Bioactivity behaviour was studied by immersion test of AZ91/BG composite into phosphate buffered saline (PBS) solution for 72 hours. The results showed that as the addition of BG increases from 3 to 9 wt%, the hardness of AZ91/BG decrease from 43.3HV to 27.9HV. The result also showed the formation of protective layers or apatite layer on the surface of AZ91/BG composite after immersed in PBS solution for 72 hours. Phase analysis by XRD shows the presence of peak Ca10(OH)2(PO4)6 as the BG increased from 3 to 9 wt%. As a conclusion, AZ91/3BG shows the optimum composite for biomedical application based on its properties and bioactivity behaviour.
Effects of electrolyte towards copper wire metallurgical interconnection in semiconductor
Authors: Abdul Hamid K. , Badarisman A. H. , Jalar A. , Abu Bakar M.
Published: January 2022

Copper (Cu) wire has been extensively used in the semiconductors industry to replace gold wire because of its cost-effectiveness and high performance. However, it has a massive challenge because of its high oxidation rate, high hardness, and high susceptibility to corrosion which is time-sensitive under environmental conditions. One of the attractive factors to investigate is the electrolytes used in the assembly process of copper wire. However, these electrolyte chemicals may potentially affect the quality of the copper wire bond’s metallurgical interconnection, manifesting as copper corrosion. Therefore, this paper will investigate the mechanism of the electrolyte reaction and the chemicals. In addition, the metallurgical morphology of the copper wire observes through electron microscopy. The results suggest that corrosion occurs with a specific time rate, electrolyte type-dependent and metallurgical interconnection system. The Copper wire bonded on silver (Ag) plated lead frame (Cu-Ag-Cu) interconnection experiences a significant morphological change in most compared with other electrolyte systems. Furthermore, since it is a bimetallic element (Cu and Ag) thus, the corrosion type is galvanic.
Synthesis of Kaolin Geopolymer as Ceramic Reinforcement in Lead-Free Solder
Authors: Mohamad Zaimi Nur Syahirah , Izwan Ramli Mohd Izrul , Mohd Salleh Mohd Arif Anuar , Bakri Abdullah Mohd Mustafa Al , Chaiprapa Jitrin , Abdul Aziz Mohd Shahrizal
Published: January 2022

This paper elucidates the fabrication of kaolin geopolymer as the ceramic material. The kaolin geopolymer ceramic (KGC) was used as a new potential reinforcement in the lead-free solder. The fabrication of KGC was started through combination of alkaline solution of NaOH and Na2SiO3 reaction with kaolin material. The mixture of kaolin geopolymer were prepared and the homogenised mixture were curing for 24 hours. The kaolin geopolymer is sintered at 1200°C and then crushed to produces a fine kaolin geopolymer. The KGC were then mixed with lead-free solder through powder metallurgy technique. The elemental distribution in the KGC was investigated by using Synchrotron Micro-XRF. Meanwhile, the phase analysis involved in KGC and composite solder with addition of KGC were investigated as well.
Investigation on the Effect of Printing Parameters on Flexural properties of 3D Printed Polymeric Scaffolds
Authors: Mohd Tamizi Nur Syahirah , Zainon Nooraizedfiza , Md Deros MohdAzaman , Basri Adi Azrif
Published: January 2022

Thermoplastic polymers (PLA) are used for bone scaffold reconstruction that helps facilitate the transportation of oxygen and nutrients, including cell activity such as migration, proliferation, attachment, and differentiation. Throughout evaluation of polymer scaffold of its mechanical properties that could heal human body injuries after implantation. However, these ideal parameters for polymeric scaffolds in terms of flexural characteristics are undefined in tissue engineering applications. The Taguchi approach was employed using an orthogonal array L9 to study the ideal print parameters for 3D printing and the elements that most influence flexural qualities, as well as to forecast the highest flexural strength that could be reached with the optimal printing parameter. Furthermore, the flexural test is an appropriate test to evaluate the mechanical properties of the scaffold. The Taguchi technique determined that a printing speed of 90 mm/s, a layer height of 0.2 mm, and a density of 60% infill was the optimal combination of parameters. Besides, Printing speed showed as the most significant factor contribution while the infill density is the lowest contributor. The maximum level of printing speed, the average percentage of infill, and the medium layer height are the best parameter combinations. Parameter optimization on the most influential contributor indicates the printing speed of the specimen. Thus, the parameter for the selected factor in scaffold fabrication was optimized with a significant contribution. The predicted flexural strength was 383.92 MPa, while actual test obtained was 360.221 MPa with an error of 6.57 %.
Influence of recycled wastes on ferrosilicon production in steel making applications: A short review
Authors: Ismail Anis Nadhirah , Ibrahim Mohd Hakim , Mohd Said Rita , Somidin Flora , Ismail Syarifah Aminah
Published: January 2022

The potential transforming the waste materials into an alternative source was found in iron and steelmaking application that also would solved the world’s most problematic waste stream. Generally, converting the waste materials into auxiliary source only is accessible to certain wastes industries due to its difficulty to recycle hence generally landfilled. Recycling waste materials used in ferrosilicon production as carbon and silica source to control the reduction reaction with iron oxide. The present paper reviews the phase transformation and morphology in the production of ferrosilicon at temperature 1550°C on graphite and plastic waste (bakelite) as the potential carbon materials and silica powder and glass waste (automotive glass - windshield, window glass) as alternatives silica source in production of ferrosilicon. The utilization of carbon material from plastic waste and silica source from glass waste (automotive glass) can be used for ferrosilicon synthesis and CO gas concentration comparable with conventional carbon source (graphite) typical silica source (silica powder). The utilization of recycled wasted is efficient due to enhancement in the reaction with iron oxide thus potentially replacing the conventional materials in ferrosilicon synthesis as well as minimizing the landfill wastes.
Isothermal Oxidation Behaviour of 800H Nickel-Based Alloy
Authors: Parimin Noraziana , Hamzah Esah , Ahad Nor Azwin , Nazree Derman Mohd
Published: January 2022

The behaviour of 800H nickel (Ni)-based alloy under isothermal oxidation condition was study in this paper. The isothermal oxidation behaviour was examined in terms of fine grained and coarse grained 800H Ni-based alloy obtained from heat treatment operation at 1000 °C and 1150 °C, respectively. The heat treated samples were undergo an isothermal oxidation attempt at 500 °C in laboratory air for 500 hours exposure duration. The effects on the oxidation kinetics was examined in terms of weight change measurement. In addition, the effects on the oxide scale growth was observed in terms of phase analysis using XRD technique and surface morphology analysis using FESEM outfitted with EDX spectrometer. As a results, both fine grained and coarse grained 800H Ni-based alloy were obey a parabolic rate law showing that the oxide growth rate was followed a diffusion controlled mechanism. Additionally, fine grained 800H Ni-based alloy sample exhibited a inferior oxidation rate compared to coarse grained sample. The XRD analysis exhibited that the oxides scale composed of Cr2O3, TiO2 and MnCr2O4. The observation on the oxide scale morphology indicate that uniform oxide scales were formed on the alloy surface of both samples. However, coarse grained 800H Ni-based alloy recorded an oxide exfoliation which indicate poor oxidation protection.
Mixed Assembly of Lead-free Solder Joint: A Short Review
Authors: Said Rita Mohd , Salleh Mohd Arif Anuar Mohd , Ramli Mohd Izrul Izwan , Saud Norainiza , Somidin Flora , Razak Nurul Razliana Abdul , Ismail Anis Nadhirah
Published: January 2022

The transition from lead (Pb) to Pb-free solder has arisen the need for the development of the reliability of mixed assemblies solder joint research. Mixed assemblies are defined as solder joints that joint together with different compositions or solder forms for example Ball Grid Array (BGA) and solder paste. During the transition period of solder materials, mixed assemblies are still used in electronic packaging. In addition, Pb-free manufacturing has been forced to release some of the product categories since legislation banning the use of lead solder in electronic assemblies. This phenomenon causes health and environmental concern of the Pb solder used in electronic assembly. Hence, some electronic assemblies will continue to use traditional eutectic Sn–Pb solder paste while others will use Pb-free solder paste. This situation indicates that the use of mixed assemblies in electronics manufacturing is still inevitable. This paper presents a projection of the reliability of mixed assembly’s Pb-free solder joint.
Automatic Information Extraction for Financial Events by Integrating BiGRU and Attention Mechanism
Authors: Lu Jiaheng , Liu Weirong
Published: January 2022

In this paper, an information extraction method for financial events written in Chinese is proposed. The core entities of the causes and results, as well as the verbs and conditions are extracted from the financial events reported by websites. The method takes the original words and the part-of-speech of words as two inputs. BERT encoder is utilized to transform the original sentences to word-embedding vectors, which then are send to BiGRU to extract the sematic features. And a full-connected network is overlapped on BiGRU to reduce the impact of “covariate shift”. For the second input, the original sentences are cut by Chinese cut-word tool ‘jieba’ to get the part-of-speech of words, which are then transformed by self-attention mechanism to get global dependencies. The two outputs for word-embedding vectors and part-of-speech of words are combined and then decoded by CRF. Finally, the Viterbi algorithm is utilized to get the best sequences. The experiment results validate the effectiveness of the proposed method.
Satellite Segmentation with Pre-trained CNN Models
Authors: Huang Yefei , Xu Tianlai , Zhang Zexu , Cui Hutao , Su Yu
Published: January 2022

In a generic satellite relative pose estimation pipeline, finding sufficient features in objects is quite essential to build the correct matching relationship and then solve the relative movement. However, for low-earth-orbit (LEO) satellites, since the earth background contains much more texture than objects, an object segmentation process is necessary to provide a prior range for feature extraction. In this work, we address this task with the pre-trained Deeplabv3 and fully convolutional network (FCN). Unlike the fine-tuning or transfer learning processes in other researches, we obtain probabilistic maps from the high-dimensional output of the above-mentioned CNN models and achieve a rough satellite extraction. Our method makes Deeplabv3 and FCN models work in a totally unfamiliar LEO scene and still achieves 0.2927 and 0.2122 in average intersection over union (IoU) respectively.
Software Defect Prediction: An Ensemble Learning Approach
Authors: Yang Zhenyu , Jin Chufeng , Zhang Yue , Wang Jingjie , Yuan Bingchang , Li Heng
Published: January 2022

Software defect prediction plays an increasingly critical role in emerging software systems. However, existing software defect prediction approaches typically suffer from low accuracy due to the under/over fitting problems. To address this problem, we propose an ensemble learning approach to achieve the accurate defect prediction, where various machine learning algorithms, i.e., artificial neural network, random forest, k-nearest neighbour methods are integrated together. The proposed software defect prediction workflow is introduced. Experiments are conducted to verify the effectiveness of the proposed method. Extensive experiment results verify that our proposed method can improve the defect prediction accuracy when compared with existing methods.
Design and Implementation of Intelligent Speech Recognition System Based on FPGA
Authors: Hu Huaixiang , Li Jiatong , Wu Chunchun , Li Xueyang , Chen Yuping
Published: January 2022

Under certain circumstances, where GPU is not available, the computing power of the CPU alone may not be sufficient enough to run speech recognition system efficiently. As a reconfigurable heterogeneous device, FPGA has the characteristics of high parallelism, low power consumption, and high flexibility. It can support high-performance computing acceleration for speech recognition applications. In order to implement speech recognition system on an specified computing platform, a speech recognition system architecture is proposed, which is based on software and hardware cooperative computing technology that focus on FPGA. First, a system architecture based on gated convolutional neural network is designed, with the simplification of the traditional speech recognition system architecture. Then a modular design idea is used to map the gated convolutional neural network operator to the FPGA hardware logic. Further through the state machine to schedule and control the data stream and operators, to achieve the flexible acceleration of the voice recognition network model. AISHELL data set is used to carry out the test, where presents remarkable improvement on computing performance, compared with Feiteng 1500A processor, the efficiency is improved by 18.1 times.
Research on 3D Defect Reconstruction in Plate with Phased Array Ultrasonic Test (PAUT)
Authors: Zheng Gangfeng , Wu Bolin , Li Ze , Liu Songfeng , Dong Hao , Zhang Haotian
Published: January 2022

In order to clarify the distribution and shape information of the defects inside the workpiece, it has become a new industrial testing requirement to display the defects more intuitively in three dimensions(3D). In this paper, by introducing 3D volume rendering method, the expansion of PAUT from 2D to 3D is realized. The method is used to reconstruct specific “gu-shaped [[CHECK_DOUBLEQUOT_ENT]] (“Gu” is the Pinyin in Chinese characters, and at the same time, “Gu” is the shape of the defect in the plate discussed in the paper.) defects, and the results of reconstruction are compared with actual defects. By using ultrasonic phased array focusing to detect defect C-scan images of different depths, the collected C-scan images were preprocessed and reconstructed according to the defect contour by volume rendering. The results show that the defect morphology with different depths can be well obtained by focused detection, and the reconstruction effect is good, which is consistent with the actual defect.
Research of Text Classification Based on TF-IDF and CNN-LSTM
Authors: Zhou Hai
Published: January 2022

With the rapid development of deep learning, many deep learning models have been widely used in Natural Language Processing(NLP). The Long-Short Term memory network(LSTM) model and convolutional neural network(CNN) model can achieve high classification accuracy in text classification tasks. However, the high input dimension of text features and the need to train a large number of parameters in the deep learning model often take a lot of time. This paper uses Term Frequency-inverse Document Frequency(TF-IDF) to remove features with lower weights, extract key features in the text, extract the corresponding word vector through the Word2Vec model, and then input it into the CNN-LSTM model. We compared the model with CNN, LSTM, and LSTM-attention methods and found that the model can significantly reduce model parameters and training time in short and long text data sets. The model hardly loses accuracy in the long text, but the model will lose a certain amount of accuracy in short texts. This paper also proposes fusing original text features to make up for the accuracy loss caused by the TF-IDF feature extraction method.
Large-scale Text Multiclass Classification Using Spark ML Packages
Authors: Wang Shuang , Luo Jian , Luo Liangfu
Published: January 2022

Machine learning is widely used in the field of natural language processing. Text classification is an important branch of natural language processing, mainly utilized to determine the topic of news information and judge the emotional tendency reflected by social data. However, with the expansion of data scale, traditional machine learning algorithms will encounter various performance bottlenecks due to the limitation of single node resource. The machine learning library ML provided by the distributed memory computing framework spark can efficiently process large-scale datasets. Based on Spark ML, this paper uses logistic regression to classify the preprocessed data originated from standard dataset 20newsgroups. The classification accuracy and F1-score can reach 89.64% and 89.64% respectively. Meanwhile, 5-fold cross validation experiments prove that the result is reliable and procedure design is feasible.
Incorporating Siamese Network Structure into Graph Neural Network
Authors: Zhang Yinan , Chen Wenyu
Published: January 2022

Siamese network plays an important role in many artificial intelligence domains, but there requires more exploration of applying Siamese structure to graph neural network. This paper proposes a novel framework that incorporates Siamese network structure into Graph Neural Network (Siam-GNN). We use DropEdge as graph augmentation technique to generate new graphs. Besides, the strategy of constructing Siamese network’s paired inputs is also studied in our work. Notably, stopping gradient backpropagation one side in Siam-GNN is an important factor affecting the performance of model. We equip some graph neural networks with Siamese structure and evaluate these Siam-GNNs on several standard semi-supervised node classification datasets and achieve surprising improvement on almost every original graph neural network.
UniformLIME: A Uniformly Perturbed Local Interpretable Model-Agnostic Explanations Approach for Aerodynamics
Authors: Jiang Enshuo
Published: January 2022

Machine learning and deep learning are widely used in the field of aerodynamics. But most models are often seen as black boxes due to lack of interpretability. Local Interpretable Model-agnostic Explanations (LIME) is a popular method that uses a local surrogate model to explain a single instance of machine learning. Its main disadvantages are the instability of the explanations and low local fidelity. In this paper, we propose an original modification to LIME by employing a new perturbed sample generation method for aerodynamic tabular data in regression model, which makes the differences between perturbed samples and the input instance vary in a larger range. We make several comparisons with three subtasks and show that our proposed method results in better metrics.
An Efficient Evaluation Method for Automobile Shells Design Based on Semi-supervised Machine Learning Strategy
Authors: Chen Wenxing , Dai Shuyang , Zheng Baojuan , Lin Hao
Published: January 2022

Automobile is one of the important modes of transportation for human travel in today’s society. Batch production in various countries in the world has also promoted the transformation of production concepts. At present, the development of the automobile industry is developing towards the trend of intelligence, personalizat-ion and sharing. Car appearance in a variety of ways, not every design is reasonable. Therefore, the main purpose of this article is to establish a scientific evaluation standard in order to large-scale test the quality of a variety of car shells design. The scientific nature is mainly reflected in combination the fluid-solid coupling knowledge and machine learning in this article, which can analyze the force of different shells in the flow field, and put out the cloud map information such as the stress, pressure and velocity of the shell. At last, analyze the best test samples and store them in the database, and then using semi-supervised heuristic algorithm to perform the sample training, the ultimate goal is to make the evaluation system more robust. The trained model can correctly evaluate each personalized car shape and give a reasonable score, which is convenient for car manufacturers to make best decision with personalized demand and scientific production.
Improving Word Alignment by Adding Gromov-Wasserstein into Attention Neural Network
Authors: Huang Yan , Zhang Tianyuan , Zhu Huidong
Published: January 2022

Statistical machine translation systems usually break the translation task into two or more subtasks and an important one is finding word alignments over a parallel sentence bilingual corpus. We address the problem of introducing word alignment for language pairs by developing a novel neural network model that can applied to other generative alignment models. We use Multi-layer attention model and multi-layer model with multi-head-attention mechanism on each layer provides superior translation quality. It can be trained on bilingual data without relying on word alignment. In this paper, we cast the correspondence problem directly as an optimal distance problem. We use the Gromov-Wasserstein distance to calculated how similarities between word pairs are related across languages. The resulting alignments dramatically outperform the GIZA++ and FastAlign approach, these alignments are comparable on public data sets.
Research on Low Resource Neural Machine Translation Based on Non-autoregressive Model
Authors: Xinlu Zhang , Hongguan Wu , Beijiao Ma , Zhengang Zhai
Published: January 2022

The autoregressive model can’t make full use of context information because of its single direction of generation, and the autoregressive method can’t perform parallel computation in decoding, which affects the efficiency of translation generation. Therefore, we explore a non-autoregressive translation generation method based on insertion and deletion in low-resource languages, which decomposes translation generation into three steps: deletion-insertion-generation. Therefore, the dynamic editing of the translation can be realized in the iterative updating process. At the same time, each step can be calculated in parallel, which improves the decoding efficiency. In order to reduce the complexity of data sets in non-autoregressive model training, we have trained Uyghur-Chinese training data with sequence-level knowledge distillation. Experiments on Uyghur-Chinese, English-Romanian distilled data sets and standard data sets verify the effectiveness of the non-autoregressive method.
Semi-supervised LDA and Multi-distance Metric Learning for Person Re-identification
Authors: Li Bin , Ding Haoyang , Zhou Mengjing
Published: January 2022

The problem of person re-identification has attracted a lot of attention in the field of machine vision. In practice, the non-overlapping sample images change drastically and the sample size is small, which makes the metric model overfitting phenomenon. In this paper, based on the k-NN and the sample normality property, we propose a resampling linear discriminant analysis (LDA) algorithm to suppress the local constraints caused by small samples, then train it to obtain the person re-identification metric learning model. A semi-supervised LDA algorithm with semi-supervised characteristics is developed by optimizing the inter-class scatter for weighting. A joint distance metric-based approach is also proposed to learn both the Mahalanobis distance and Euclidean distance. The improved algorithm is tested on the VIPeR and CUHK01 datasets, and the results indicate that, despite the change in the total number of training samples, the algorithm in this paper shows high recognition accuracy.
Intelligent LoRa NoC Applied in the Environmental Protection for Mu Us Desert
Authors: Liu Lu , Wang Huhu , Yang Yanfei
Published: January 2022

Intelligent NoC can be applied in the filed of environmental protection, since routing algorithm and network architecture possess advantages of speed and capacity. In this paper, wireless LoRa network is attempted to build for the environment of Mu Us Desert on the borders of Shaanxi and Inner Mongolia. Environmental sensors connected to LoRa network collects the environmental information, while intelligent routing algorithm helps to propagate and decide the environmental status. The intelligent NoC can contribute to complex data decision and build the NoC through multicast mapping.
Natural Scene Chinese Recognition Based on Deep Learning
Authors: Liu Xiaofei , Tong Pan , Yu Tao , Dong Andi , Yong Cuo
Published: January 2022

Text recognition is one of the research focuses in the field of computer vision. In recent years, many researchers have proposed recognition methods based on codec frameworks, such as CRNN, but most of these methods are based on English, and relatively few are based on Chinese. Chinese language, because of its large number of character types and contains a large number of similar characters, only using CNN to extract features will lose a large amount of information. Meanwhile, the method of giving combined attention is an emerging direction. In this paper, we study the recognition of the Chinese language and introduce attention to BiLSTM to obtain more contextual information. Compared with the original algorithm, the performance of this algorithm is improved on the synthetic dataset.
Research on Operating System Migration Method Based on Domestic Embedded Devices
Authors: Li Yan , Zhang Shiran , Li Jian , Xi Haikuo , Dong Tianying , Du Zexu
Published: January 2022

At present, Google’s Android system has the characteristics of open source, rich software functions, and good user experience, and has been highly recognized by a wide range of applications and the market. If the Loongson platform wants to make a difference in mobile terminals and other fields, it needs to support the Android system. However, although the native Android system supports multiple architectures, it only performs a lot of optimizations for the ARM architecture. For other architectures, Google only provides a generic version. This brings a huge challenge to the Loongson processor based on the MIPS architecture. Based on the analysis of the Android system, this paper studies the Android transplantation method of the Loongson 2K1000 platform in detail realizes the transplantation of the Android Lollipop system on the platform and has passed the verification of the Loongson 2K1000 platform.
Finger Vein Image ROI Extraction Based on Active Contour Method
Authors: Wang Yifan , Lu Huimin , Gao Ruoran , Wang Guizeng
Published: January 2022

Under the background of the novel coronavirus pneumonia outbreak in the world, unrestricted and contactless finger vein collection devices have significantly improved public health safety. However, due to the unfixed position of the finger and the open or semi-open characteristics of the acquisition device, it is inevitable to introduce plenty of factors that affect the recognition performance, such as low contrast, uneven illumination and edge disappearance. In view of these practical problems, we propose a method for ROI extraction of finger vein images that combines active contour method and morphological post-processing operations. This method starts from the local segmentation, and finally completes the acquisition of finger masks at the global level, and then combines some morphological operations to achieve precise extraction of finger masks. We designed and conducted plenty of comparison experiments on the proposed algorithm and the current mainstream finger vein image ROI extraction methods on three public available finger vein datasets. Experimental results show that our method accurately extracts the complete finger region mask and achieves the best matching accuracy on all datasets.
Research on Machine Vision Detection Method of Ship Sulfur Emission Based on Convolutional Neural Network
Authors: Han Yang , Li Guangzheng , Qin Qi , Wang Shipeng , Li Yuanhao
Published: January 2022

The traditional method of ship exhaust gas detection has the shortcomings of poor timeliness, blindness and hysteresis, which cannot meet the needs of marine supervision departments for comprehensive, efficient and real-time supervision of ship sulfur emissions. To solve this problem, a convolution neural network(CNN) based machine vision detection method for ship sulfur emission is introduced, and the plume characteristics of heavy sulfur black smoke ship exhaust are adaptively extracted by convolution operation. An improved LeNet-5 network model structure is proposed.By changing the activation function and increasing the depth of the model, the batch normalization operation is introduced to improve the accuracy and generalization ability of the model.The experimental results show that the detection accuracy of the model for heavy sulfur black smoke ships can reach 94.52%, which is better than the ordinary CNN network model and the VGG-16 network model.This method can quickly screen suspicious ships with excessive sulfur content, broaden the way for maritime supervision departments to detect ships under the background of “sulfur limit order”, and effectively improve the supervision efficiency of maritime personnel.