bluuebunny/crossref_metadata_2025 huggingface dataset:
Large-area epitaxial growth of InAs nanowires and thin films on hexagonal boron nitride by metal organic chemical vapor deposition
Authors: Vilasam Aswani Gopakumar Saraswathy , Adhikari Sonachand , Gupta Bikesh , Balendhran Sivacarendran , Higashitarumizu Naoki , Tournet Julie , Li Lily , Javey Ali , Crozier Kenneth B , Karuturi Siva , Jagadish Chennupati , Tan Hark Hoe
Published: August 2023

Large-area epitaxial growth of III–V nanowires and thin films on van der Waals substrates is key to developing flexible optoelectronic devices. In our study, large-area InAs nanowires and planar structures are grown on hexagonal boron nitride templates using metal organic chemical vapor deposition method without any catalyst or pre-treatments. The effect of basic growth parameters on nanowire yield and thin film morphology is investigated. Under optimised growth conditions, a high nanowire density of 2.1

×

109 cm−2 is achieved. A novel growth strategy to achieve uniform InAs thin film on h-BN/SiO2/Si substrate is introduced. The approach involves controlling the growth process to suppress the nucleation and growth of InAs nanowires, while promoting the radial growth of nano-islands formed on the h-BN surface. A uniform polycrystalline InAs thin film is thus obtained over a large area with a dominant zinc-blende phase. The film exhibits near-band-edge emission at room temperature and a relatively high Hall mobility of 399 cm−2/(Vs). This work suggests a promising path for the direct growth of large-area, low-temperature III–V thin films on van der Waals substrates.
Tumour status prediction by means of carbon-ion beam irradiation: comparison of washout rates between in-beam PET and DCE-MRI in rats
Authors: Toramatsu Chie , Mohammadi Akram , Wakizaka Hidekatsu , Nitta Nobuhiro , Ikoma Yoko , Seki Chie , Kanno Iwao , Yamaya Taiga
Published: August 2023

Objective. Tumour response to radiation therapy appears as changes in tumour vascular condition. There are several methods for analysing tumour blood circulatory changes one of which is dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), but there is no method that can observe the tumour vascular condition and physiological changes at the site of radiation therapy. Positron emission tomography (PET) has been applied for treatment verification in charged particle therapy, which is based on the detection of positron emitters produced through nuclear fragmentation reactions in a patient’s body. However, the produced positron emitters are washed out biologically depending on the tumour vascular condition. This means that measuring the biological washout rate may allow evaluation of the tumour radiation response, in a similar manner to DCE-MRI. Therefore, this study compared the washout rates in rats between in-beam PET during 12C ion beam irradiation and DCE-MRI. Approach. Different vascular conditions of the tumour model were prepared for six nude rats. The tumour of each nude rat was irradiated by a 12C ion beam with simultaneous in-beam PET measurement. In 10–12 h, the DCE-MRI experiment was performed for the same six nude rats. The biological washout rate of the produced positron emitters (k
2,1st) and the MRI contrast agent (k
2a) were derived using the single tissue compartment model. Main results. A linear correlation was observed between k
2,1st and k
2a, and they were inversely related to fractional necrotic volume. Significance. This is the first animal study which confirmed the biological washout rate of in-beam PET correlates closely with tumour vascular condition measured with the MRI contrast agent administrated intravenously.
Evaluation of MR anatomically-guided PET reconstruction using a convolutional neural network in PSMA patients
Authors: Farag Adam , Huang Jin , Kohan Andres , Mirshahvalad Seyed Ali , Basso Dias Adriano , Fenchel Matthias , Metser Ur , Veit-Haibach Patrick
Published: August 2023

Background. Recently, approaches have utilized the superior anatomical information provided by magnetic resonance imaging (MRI) to guide the reconstruction of positron emission tomography (PET). One of those approaches is the Bowsher’s prior, which has been accelerated lately with a convolutional neural network (CNN) to reconstruct MR-guided PET in the imaging domain in routine clinical imaging. Two differently trained Bowsher-CNN methods (B-CNN0 and B-CNN) have been trained and tested on brain PET/MR images with non-PSMA tracers, but so far, have not been evaluated in other anatomical regions yet. Methods. A NEMA phantom with five of its six spheres filled with the same, calibrated concentration of 18F-DCFPyL-PSMA, and thirty-two patients (mean age 64 ± 7 years) with biopsy-confirmed PCa were used in this study. Reconstruction with either of the two available Bowsher-CNN methods were performed on the conventional MR-based attenuation correction (MRAC) and T1-MR images in the imaging domain. Detectable volume of the spheres and tumors, relative contrast recovery (CR), and background variation (BV) were measured for the MRAC and the Bowsher-CNN images, and qualitative assessment was conducted by ranking the image sharpness and quality by two experienced readers. Results. For the phantom study, the B-CNN produced 12.7% better CR compared to conventional reconstruction. The small sphere volume (<1.8 ml) detectability improved from MRAC to B-CNN by nearly 13%, while measured activity was higher than the ground-truth by 8%. The signal-to-noise ratio, CR, and BV were significantly improved (p < 0.05) in B-CNN images of the tumor. The qualitative analysis determined that tumor sharpness was excellent in 76% of the PET images reconstructed with the B-CNN method, compared to conventional reconstruction. Conclusions. Applying the MR-guided B-CNN in clinical prostate PET/MR imaging improves some quantitative, as well as qualitative imaging measures. The measured improvements in the phantom are also clearly translated into clinical application.
Rubidium fluoride additive for high-efficiency and low-hysteresis all-inorganic CsPbI3 perovskite solar cells
Authors: Zhang Dan , Chen Chunyang , Ren Mudan , Shi Kewang , Huang Jin
Published: August 2023

All-inorganic CsPbI3 perovskite solar cells (PSCs) technology is gradually maturing because of its excellent photoelectric characteristics. However, the hysteresis phenomenon induced by ion migration in the perovskite film not only seriously affects the performance of the device, but also accelerates the degradation of the film, which limits the further improvement of power conversion efficiency (PCE) for CsPbI3 PSCs. Herein, in this paper, a new inorganic fluorine-containing additive rubidium fluoride (RbF) was introduced as a precursor additive. The incorporation of RbF effectively improved the crystallization kinetics of CsPbI3 perovskite film and effectively suppressed the occurrence of hysteresis. The defects on the CsPbI3 perovskite film are remarkably inhibited and the carrier dynamics process is greatly promoted with the incorporation of 0.03 mol% RbF. In addition, the non-radiative recombination is significantly suppressed, and the device stability is substantially improved. In particular, by doping 0.03 mol% RbF into the CsPbI3, the hysteresis index of PSCs decreases to 0.003. The introduction of RbF effectively improves the device performance, and the highest efficiency has reached to 17.21%. The environmental stability has also been significantly enhanced with the RbF doping.
Spectroscopy of the 85Rb 4
D3/2
state for hyperfine-structure determination
Authors: Duspayev A , Raithel G
Published: August 2023

We report a measurement of the hyperfine-structure constants of the 85Rb 4

D

3

/

2

state using two-photon optical spectroscopy of the 5

S

1

/

2

→

4

D

3

/

2

transition. The spectra are acquired by measuring the transmission of the low-power 795 nm lower-stage laser beam through a cold-atom sample as a function of laser frequency, with the frequency of the upper-stage, 1476 nm laser fixed. All 4 hyperfine components of the

4

D

3

/

2

state are well-resolved in the experimental data. The dominant systematic is the light shift from the 1476 nm laser, which is addressed by extrapolating line positions measured for a set of 1476 nm laser powers to zero laser power. The analysis of our experimental data yields both the magnetic-dipole and electric-quadrupole constants for the 85Rb 4

D

3

/

2

level, without using earlier hyperfine measurements of other atomic levels. The respective results,

A
=

7.419(35) MHz and

B
=

4.19(19) MHz, are discussed in context with previous works. Our investigation may be useful for optical atomic clocks for precision metrology and emerging atom-based quantum technologies, all-infrared excitation of Rb Rydberg levels, and molecular physics.
Brownian dynamics of Dirac fermions in twisted bilayer graphene
Authors: Yar Abdullah
Published: August 2023

Brownian dynamics of Dirac fermions in twisted bilayer graphene is investigated within the framework of semiclassical relativistic Langevin equations. We find that under the influence of orthogonal, commensurate ac drives in the periodic ratchet potential of a substrate, the charge carriers in the system exhibit pronounced random dynamics, tuned by the twist angle, making twisted bilayer graphene distinct from monolayer graphene. It is shown that as threshold twist angle matches the optimal angle, deterministic running states appear in the limit of weak thermal noise where the diffusion rate is enhanced significantly compared to bare thermal diffusion. Analysis of the real space trajectories and diffusion coefficient illustrates the significant role of thermal noise in the random motion of Dirac fermions. In addition, we find that the Brownian particle shows remarkable ratchet effect as a net current.
Griffiths phase, re-entrant spin-glass behaviour and schottky anomaly in anti-site disordered double perovskite Pr2MnNiO6
Authors: Kumar Rinku , Balasubramanian Padmanabhan , Anas M , Singh Ankita , Chandra Ramesh , Malik V K
Published: August 2023

In the present study, the effect of anti-site disorder on the magnetic properties of Pr2MnNiO6 is explored. Due to anti-site disorder, a reduced T
C preceded by a Griffith phase has been observed. At low temperatures, we also report the development of the spin glass phase in co-existence with the cluster-like ferromagnetic order. The signature of the re-entrant spin glass phase, where the material is ferromagnetic below T
c, and undergoes a transition to the spin glass state, is revealed by the frequency-dependent ac-susceptibility measurements. The spin glass behavior is also supported by the slow decay of thermo-remanent magnetization. The low temperature specific heat shows a small field dependent behaviour which may be attributed to Schottky effects.
Stacking effect on the orbital characters and transition probability of bilayer MoS2
Authors: Mansouri Masoume , Ghalambor Dezfuli Abdol-Mohammad , Salehi Hamdollah
Published: August 2023

In this paper, employing the van der Waals (vdW) corrected density functional method, the electronic and optical properties of bilayer MoS2are calculated for five different stacking patterns (AA,AA′,AB′,AB,andA′B). In this regard, the structural stability and the van der Waals interlayer distance are investigated. The minimum formation energy has indicated the overall trend of relative stability to be asAA′≅AB≅AB′≫AA≅A′B.This meansAA′,AB′,andABhave the most andAA,andA′Bhave the least stable structures. The band structure and probability distribution for finding electrons in atomic orbitals have also been calculated in the Brillouin zone. The results obtained from band structures and orbital characters show the same trend in the three most stable and a different but the same trend for the two least stable orders. In addition, the oscillator strength has been derived from the calculation of the momentum operator matrix elements using first-principles methods. The calculation of transition probabilities from matrix elements indicates thatAA′,AB,andAB′have higher transition probabilities than that ofAAandA′B.The study of transition probability also shows the y-polarized light is stronger than the x-polarized light for all stacking patterns.
Modelling the next-to-leading order matter three-point correlation function using FFTLog
Authors: Guidi M. , Veropalumbo A. , Branchini E. , Eggemeier A. , Carbone C.
Published: August 2023

The study of higher-order statistics, particularly 3-point statistics, of the Large Scale Structure (LSS) of the Universe
provides us with unique information on the biasing relation between luminous and dark matter and on deviations from primordial Gaussianity. As a result, much effort has been put into improving
measurement techniques as well as theoretical modelling,
especially in Fourier space. Comparatively, little progress has been made, instead, in configuration space analyses. This work represents a first step towards filling this gap by proposing a new strategy for modelling 3-point statistics at higher perturbative orders in configuration space. Starting from the next-to-leading order model for the matter bispectrum, we use 2D-FFTLog to generate its counterpart in configuration space. We calibrate the procedure using the leading order predictions for which an analytic model for the 3-point correlation function (3PCF) already exists. Then we assess the goodness of the 3PCF model by comparing its predictions with measurements performed on the matter distribution in collisionless cosmological N-body simulation (DEMNUni). We focus on two redshifts (z = 0.49 and z = 1.05) in the range spanned by current and future galaxy redshift surveys. The χ
2 analysis reveals that the next-to-leading order 3PCF models significantly improve over the leading order one for all triangle configurations in both redshifts, increasing the number of matched configurations at redshift z = 1.05 and z = 0.49, respectively. In particular, a significant improvement is also seen on the Baryonic Acoustic Oscillations (BAO) scale for triangle configurations whose smallest side length is well into the nonlinear regime. The computational cost of the model proposed here is high but not prohibitively large — order of five hours in a 48-core computation — and represents the first step towards a complete 3PC model for the galaxies.
Quasinormal modes of Bardeen black holes with a cloud of strings*
Authors: Liu 刘 Yunlong 云龙 , Zhang 张 Xiangdong 向东
Published: August 2023

We investigate the quasinormal mode and greybody factor of Bardeen black holes with a cloud of strings via the WKB approximation and verify them using the Prony algorithm. We find that the imaginary part of the quasinormal mode spectra is always negative, and the perturbation does not increase with time, indicating that the system is stable under scalar field perturbation. Moreover, the string parameter a has a dramatic impact on the frequency and decay rate of the waveforms. In addition, the greybody factor increases when a and λ increase and when q and l decrease. The parameters λ and l have a significant effect on the tails. In particular, when

, a de Sitter phase appears at the tail.
Highly efficient organic solar cells with improved stability enabled by ternary copolymers with antioxidant side chains
Authors: Song Ao , Huang Qiri , Zhang Chunyang , Tang Haoran , Zhang Kai , Liu Chunchen , Huang Fei , Cao Yong
Published: August 2023

The stability of organic solar cells (OSCs) remains a major concern for their ultimate industrialization due to the photo, oxygen, and water susceptibility of organic photoactive materials. Usually, antioxidant additives are blended as radical scavengers into the active layer. However, it will induce the intrinsic morphology instability and adversely affect the efficiency and long-term stability. Herein, the antioxidant dibutylhydroxytoluene (BHT) group has been covalently linked onto the side chain of benzothiadiazole (BT) unit, and a series of ternary copolymers D18-Cl-BTBHTx (x = 0, 0.05, 0.1, 0.2) with varied ratio of BHT-containing side chains have been synthesized. It was found that the introduction of BHT side chains would have a negligible effect on the photophysical properties and electronic levels, and the D18-Cl-BTBHT0.05: Y6-based OSC achieved the highest power conversion efficiency (PCE) of 17.6%, which is higher than those based active layer blended with BHT additives. More importantly, the unencapsulated device based on D18-Cl-BTBHTx (x = 0.05, 0.1, 0.2) retained approximately 50% of the initial PCE over 30 hours operation under ambient conditions, significantly outperforming the control device based on D18-Cl (90% degradation in PCE after 30 h). This work provides a new structural design strategy of copolymers for OSCs with simultaneously improved efficiency and stability.
High threshold voltage enhancement-mode GaN p-FET with Si-rich LPCVD SiN
x
gate insulator for high hole mobility
Authors: Zhu Liyang , Chen Kuangli , Ma Ying , Cai Yong , Zhou Chunhua , Li Zhaoji , Zhang Bo , Zhou Qi
Published: August 2023

In this work, the GaN p-MISFET with LPCVD-SiN
x
is studied as a gate dielectric to improve device performance. By changing the Si/N stoichiometry of SiN
x
, it is found that the channel hole mobility can be effectively enhanced with Si-rich SiN
x
gate dielectric, which leads to a respectably improved drive current of GaN p-FET. The record high channel mobility of 19.4 cm2/(V∙s) was achieved in the device featuring an Enhancement-mode channel. Benefiting from the significantly improved channel mobility, the fabricated E-mode GaN p-MISFET is capable of delivering a decent-high current of 1.6 mA/mm, while simultaneously featuring a negative threshold-voltage (V
TH) of –2.3 V (defining at a stringent criteria of 10 μA/mm). The device also exhibits a well pinch-off at 0 V with low leakage current of 1 nA/mm. This suggests that a decent E-mode operation of the fabricated p-FET is obtained. In addition, the V
TH shows excellent stability, while the threshold-voltage hysteresis ΔV
TH is as small as 0.1 V for a gate voltage swing up to –10 V, which is among the best results reported in the literature. The results indicate that optimizing the Si/N stoichiometry of LPCVD-SiN
x
is a promising approach to improve the device performance of GaN p-MISFET.
Terahertz phononic crystal in plasmonic nanocavity
Authors: Li Zhenyao , Chang Haonan , Lai Jia-Min , Song Feilong , Yao Qifeng , Liu Hanqing , Ni Haiqiao , Niu Zhichuan , Zhang Jun
Published: August 2023

Interaction between photons and phonons in cavity optomechanical systems provides a new toolbox for quantum information technologies. A GaAs/AlAs pillar multi-optical mode microcavity optomechanical structure can obtain phonons with ultra-high frequency (~THz). However, the optical field cannot be effectively restricted when the diameter of the GaAs/AlAs pillar microcavity decreases below the diffraction limit of light. Here, we design a system that combines Ag nanocavity with GaAs/AlAs phononic superlattices, where phonons with the frequency of 4.2 THz can be confined in a pillar with ~4 nm diameter. The Q
c/V reaches 0.22 nm−3, which is ~80 times that of the photonic crystal (PhC) nanobeam and ~100 times that of the hybrid point-defect PhC bowtie plasmonic nanocavity, where Q
c is optical quality factor and V is mode volume. The optomechanical single-photon coupling strength can reach 12 MHz, which is an order of magnitude larger than that of the PhC nanobeam. In addition, the mechanical zero-point fluctuation amplitude is 85 fm and the efficient mass is 0.27 zg, which is much smaller than the PhC nanobeam. The phononic superlattice-Ag nanocavity optomechanical devices hold great potential for applications in the field of integrated quantum optomechanics, quantum information, and terahertz-light transducer.
Propagation of conformity statements in compliance with the GUM and ISO 17025
Authors: Klauenberg Katy , Greenwood John , Foyer Gisa
Published: August 2023

According to ISO/IEC 17025:2017 statements of conformity must identify the decision rule applied, they must be risk-based and account for uncertainty. In legal metrology and often among testing and calibration laboratories, there is the need to reuse measurement-based conformity statements to disseminate acceptability of measurement results. In particular, decision rules are required that allow the statement of conformity for a linear combination of quantities for which, in turn, conformity statements are available. These decision rules should be simple and use information that is typically available, and they should comply with ISO/IEC 17025:2017, again by accounting for the level of risk and for uncertainty following the suite of documents of the GUM (the Guide to the Expression of Uncertainty in Measurement).

Existing guidance requires the input and evaluation of standard uncertainties, or even of distributions, to derive statements of conformity, and as such may be inapplicable, or the required effort may deter practitioners. After reviewing the existing guidance, this research will identify typical settings which lead to particularly simple decision rules for stating conformity for linear combinations of quantities. These new decision rules are based on the specification limits and on information implicitly available in the decision rules of each input quantity. The rules will be proven, they are generalizable, intended to comply with ISO/IEC 17025:2017 and the GUM documents, and suitable to easily state the risk of not conforming to the weighted sum of the input specifications.

For practitioners, a quick reference on feasible conformity statements for linear combinations of quantities is provided. The applicability of and need for the new decision rules are illustrated by two examples involving the legally regulated weighing of long vehicles and of net loads.
A Study of Chan-Vese Model with the Introduction of Edge Information
Authors: He Ruiying , Han Yajun , Tang Zumiao , Long Sha
Published: August 2023

In image segmentation, the image edge information and the selection of edge-stopping function have a extremely important influence on the segmentation effect. As a classical geometric active contour model, Chan-Vese model completely utilizes image global information for segmentation, which has the problems of inability to handle grayscale uneven images and slow curve evolution. This paper introduces edge information into the Chan-Vese model, chooses a new edge stopping function and simplifies the driving force term to build a new image segmentation model. The new model can detect the target fast, displaying better detection effect on the targets of images with uneven grayscale and the detection effect of the target in the uneven gray image is better
Anomaly Pixel Detection via Dual-Branch Uncertainty Metrics
Authors: Luo Peng , Guo Weizhong
Published: August 2023

Anomaly detection aims to detect abnormal samples from normal data. Existing methods mainly use synthesis-based approach and uncertainty-based approach. Moreover, the above-mentioned methods need to re-train part/all of the deep network, which limits its application scenarios especially real-time scenarios. We propose a novel retraining-free anomaly detection method, which includes a dual-branch anomaly detection mechanism to detect abnormal pixels from two views. In the pseudo-supervised branch, we use the Copy post method to transform the unsupervised anomaly detection task into a supervised binary detection task. In the prototype detection branch, we use prototypes to improve the accuracy of abnormal pixel detection. The anomaly results of the two branches are fused by means of soft voting to produce anomaly detection results with higher recall. Our experiments show that our method significantly outperforms other methods and achieves excellent results on different datasets, which also shows that our method has good generalization ability. Therefore, our proposed method can effectively improve the effectiveness and safety of anomaly detection in real-time scenarios, such as autonomous driving.
High Precision License Plate Recognition Algorithm in Open Scene
Authors: Shu Sen
Published: August 2023

At present, license plate recognition algorithm under restricted conditions is relatively mature and widely used in various license plate recognition system. Due to the influence of factors such as large differences in shooting angles and vehicle motion blur, Chinese license plate recognition is quite challenging. In response to the above problems, this research abandoned the single end-to-end deep learning license plate recognition method, and proposed a step-by-step license plate recognition algorithm that integrated detection and classification, and utilized a level-by-level object detection strategy combined with character classification to predict the characters of the license plate result. On the basis of the above, a multi-anchor character position regression algorithm was proposed to further accurately regress the local area position information of all license plate characters. At the same time, in order to meet the needs of character detection and character classification, as well as the imbalance of the existing license plate datasets, this study contributed a series of supporting license plate datasets. According to the published publications, this study contributed the first large-scale character-level annotated license plate dataset. Extensive experiments show that the method in this study can reach the current state-of-the-art on different datasets. If accepted, the dataset will be publicly available at https://gitee.com/wust30405/lpdataset.
The Research on Remote Sensing Image Change Detection Based on Deep Learning
Authors: Song Xu , Zhou Hongyu , Wu Liang
Published: August 2023

In the past, remote sensing image(RSI) change detection often ignored the importance of shallow information with high resolution and fine grained features, and there were problems of missing detection and rough detection boundary. This caused the uncertainty of the edge pixels of the target and the loss of information of the small target. To solve such problem, we construct a new network under the CDD data set for RSI change detection. In addition, in order to better integrate the information from the bottom and top layers, a channel attention module is introduced to conduct in-depth supervision. The testing results indicated that the accuracy of the evaluation index reaches 88%, the recall rate reaches 86%, and the F1 value reaches 83%, which better solves the problems of the uncertainty of the target edge pixel and the information missing of the small target.
Lunar Dome Detection Method Based on Few-Shot Object Detection
Authors: Sun Chen , Tian Xiaolin
Published: August 2023

Lunar domes have always been one of the important windows to understand lunar volcanic activities, but traditional geological dome identification methods are costly. This study attempts to establish an automatic identification method for lunar volcanic domes through FSOD (Few-Shot Object Detection). Since our previous research has been trying to automatically identify lunar volcanic hills using traditional recognition algorithms, our team will try Few-Shot Object Detection this time. In this study, the researchers first obtained the dome coordinates from the list of known lunar domes and intercepted the data this research need from the corresponding coordinates on the DEM moon images. Subsequently, this research used the existing data to train six traditional recognition algorithms and FSOD and compared their performance to verify the feasibility of this study. This research use AP50 and AP75 to evaluate the performance of the model. Finally, this research found that the AP50 of FSOD can reach 0.76, and the AP75 can reach 0.43. However, although FSOD does perform better than other traditional identification methods, this research believe its accuracy is still far from our expectations. Admittedly, compared with YOLO v5, the performance of FSOD has not been significantly improved, but this study still verifies that it is feasible to apply FSOD to the identification of lunar volcanic domes.
Environment Modeling Method for Intelligent Robots Based on Binocular Vision
Authors: Mi Zengzhen , Zhao Shanshan , Zhang Hongmin
Published: August 2023

With the development of technology, the demand for intelligent mobile robots is growing steadily. Environment perception and modeling, are the key elements to achieve robot intelligence. Therefore, binocular stereo vision sensor is used as the core device to perform operations such as binocular camera calibration, obtaining binocular camera internal/external parameters, and distortion correction. On this basis, a multi-dimensional and all-round environment perception system is constructed, and the dynamic target detection results and 3D scene map reconstruction results are displayed dynamically in real time. The experimental results of binocular camera calibration show that the re-projection error lies in the sub-pixel interval, and the image distortion caused by the camera is basically eliminated to meet the experimental expectation of distortion correction; the experimental results of environment modeling show that the mean value of deviation between the object representation value of 3D scene map and the actual object real value is -0.041m, among which the minimum deviation value is 0.001m and the maximum deviation value is 0.532m, in The experimental results are excellent in the tasks of 3D scene map reconstruction and feature point matching. In addition, the storage space of 3D scene map is significantly reduced, and the real-time performance of the system is significantly better than the traditional method.
Object Detection in Security Inspection Scenarios Based on YOLOv5s: Exploring Experiments
Authors: Wang Shuoyu , Zhang Chenhao , Xiao Zhiren
Published: August 2023

At present, there are many problems with the common methods of manual safety checks. Applying object detection technology to X-ray security inspection can effectively improve efficiency and reduce operating costs. The training parameters of deep neural networks directly affect the performance of the network. Based on YOLOv5s network model, this paper uses the method of control variables to explore the impact of training strategies, data enhancement strategies, and loss function selection on network performance. The experimental results show that a suitable batch size and learning rate can achieve significant performance improvements in the model without changing the network structure. With the increase in strength of the data enhancement strategy, the model performance shows a trend of first improving and then decreasing. The selection of the loss function has a great impact on the model’s performance. The research methods and ideas in this article can provide inspiration for subsequent related research.
Research on an Improved Adaptive Image Enhancement Algorithm
Authors: Chen Xinying , Qiu Yihui
Published: August 2023

Aiming at the image degradation problems that occur during industrial activities and the shortcomings of traditional fuzzy enhancement algorithms in dealing with image degradation problems with high algorithm complexity and poor enhancement results. Based on the tuned tri-threshold fuzzy intensification algorithm, this paper proposes an adaptive image enhancement algorithm, which combines three kinds of degraded images in reality (i.e., images in dusty, night and foggy environments) for simulation verification, and compares the visual effects of the enhanced images and makes objective quantitative evaluation. The experimental results show that: the proposed algorithm can not only effectively improve the contrast of the image, keep the detailed information of the image, and make the image present a more natural visual effect, but also improve the quality evaluation index of the image, and make the variance value is improved by more than 5% compared with other comparison algorithms.
Research on Energy Management of Pressurized Oxygen-Supplemented Buildings
Authors: Zhang Kun , Wang Kaiqiang , Zhou Hao , Lu Jianfeng , Liu Weijun , Zhou Jiyun , Karimzadeh Sina
Published: August 2023

The energy system of supercharged and oxygenated buildings is a hybrid energy system. The energy used is solar energy, electric energy, pressure energy, etc. In order to make the hybrid energy system more stable and controllable, and to achieve the purpose of energy saving under the condition of stable power consumption of supercharged and oxygenated buildings, a power consumption prediction and management model of supercharged and oxygenated buildings based on neural network is proposed. According to the power consumption system structure of the pressurized oxygenation building, this model establishes a power consumption prediction model based on neural network, and then verifies by simulation that the model can better meet the power consumption prediction conditions of the pressurized oxygenation building. Finally, according to the experiment, this power consumption prediction model can make the power consumption management of the pressurized oxygenation building more stable and controllable. When meeting the daily and stable power consumption requirements of the pressurized oxygenation building, the model can be used to predict the power consumption of the pressurized oxygenation building. Green energy is also reasonably used to save energy in pressurized and oxygen-supplemented buildings.
An Novel Technique for Wireless Power Transfer Systems Based on the Parametric Set
Authors: Du Juan , Lan Jianyu , Wei Qingxin , Xie Meng , Zhao Junli
Published: August 2023

High efficiency and high power level are important indicators of the Inductive Power Transfer (IPT) system. However, the problem of optimizing the efficiency of IPT has not been well solved, and improving control methods is one of the effective ways to solve this problem. In order to improve the existing secondary-side control scheme and achieve the goal of simultaneously achieving optimal transmission efficiency and stable output, this paper suggests a parameter-based control strategy for coordinating the primary and secondary sides in the IPT system. This paper suggests a parameter-based control strategy for coordinating the primary and secondary sides in the IPT system. The paper develops a model of the IPT system to find the best load, then analyzes the active rectifier, phase shift angle, and load relationship to create a parameter set for controlling quantities like α and β. From there, a new control strategy is proposed and its effectiveness is confirmed through simulations. The proposed strategy enhances transmission efficiency and output stability of the IPT system, making it highly applicable in areas like electric vehicle charging and wireless charging devices.
Design of a High Performance Vector Processor Based on RISIC-V Architecture
Authors: Han Xiaojing , Liu Liang , Zhang Zhe , Sun Yufeng , Zhou Jiahui , Cai Hao
Published: August 2023

This paper proposes a high performance Vector processor based on the high performance Embedded Core which is named TS800. The TS800 is a 4-core processor based on RISC-V architecture, implements IMAFDV instruction set, supports L2 Cache, branch prediction, sequential pipeline, and dual-issue structure. The traditional CPU mainly supports Scalar calculations, or only supports Vector calculations. For applications such as image and signal processing, there are a large number of data parallel computing operations. To solve the problem of low performance of parallel data calculations in industrial power applications, it is proposed to add VPU hardware implementation in the TS800. The TS800 can support FFT algorithm, adaptive controllers Reinforcement learning and learning-based underlying algorithm requirements. In this paper, the module and data flow between each processing unit and the control circuit, that is, the hardware realization of VPU module are proposed. Large-area units such as float arithmetic, multiplication and division are multiplexed with the Scalar operator in the CPU, while the control circuit is placed in the VPU-ALU, and the area is small. Units such as arithmetic and logic operation instructions, shift operation instructions, comparison operation instructions, and permutation instruction are implemented through the VPU-ALU, which makes the overall design area smaller and the performance better. At the same time, through the fir, fft, conv, matrix, Signal Converge and variance test, it is proved that while executing the same program, the running time of the cpu only with Scalar is 1.44 to 9.55 times that of the CPU with Vector module, which can support the underlying algorithm of the adaptive controller.
Automatic Relay Protection Calibration Device and System Design for New Intelligent Power Grid
Authors: Dai Yang , Li Dongmei , Chen Yuxin , Tang Mengqin
Published: August 2023

Maintaining the protection device and eliminating the abnormal and fault defects of the device are important tasks for the maintenance of the power system. In general, relay protection equipment managers need to regularly check the relay protection device. According to the usual practice, relay protection professionals use the relay protection tester to manually connect. The traditional relay protection verification method has complicated operation, high professional requirements for testers. In this paper, a set of intelligent relay protection verification device with high degree of automation and harmonious human-computer interaction is developed to realize the communication between the host computer and the protection device and the tester. The host computer software and database were developed. The thermal overload protection in the SPAM150C motor protection device of ABB company is verified, and the reliability of the device is verified. The device can improve the efficiency of relay protection equipment inspection, reduce the technical threshold of operators, and reduce the probability of human error. It realizes the process and standardized control of the operation process of relay protection verification which has strong practical application value.
Design and Development of Ground Wire Repair Robot for Transmission Line
Authors: Zhao Hanghang , Fan Min , Pei Shaotong , Zheng Wulue , Fang Jing , Liang Weixin
Published: August 2023

To ensure the normal and stable operation of the power system, it is important to repair broken and loose strand lines in a timely manner. In view of the limitations and shortcomings in repairing broken and loose strands of overhead transmission lines, this paper designs a set of robots that can automatically repair overhead transmission lines online. First, research and design for the mechanical system of the ground wire repair device for overhead transmission lines, including the walking system and the pre-twisted wire winding system; then develop corresponding software and hardware control system, including the data, image transmitting, receiving modules and the operating system of the device, and develop corresponding control devices, mobile phone software and other wireless terminal control systems. The ground wire repair device of the overhead transmission line developed by the project meets the basic requirements for the repair of broken and loose strands of the line, solved a series of problems caused by current manual repair methods, improves the automation of the operation, and has the significance of promotion and application.
Assessment of Oil Chromatography Sensor Failure for Transformer Based on Multi-dimensional Data-driven
Authors: Sha Weiyan , He Ninghui , Zhang Fangbo
Published: August 2023

Online monitoring and analysis of transformer oil chromatography is of great significance for the assessment of internal insulation with transformer operation. The existence of numerous invalid monitoring sensors in the field makes the quality of online monitoring data degraded, which makes the online monitoring system unable to monitor the operating status of transformers in a timely and accurate manner. This paper propose a data-driven method for evaluating the effectiveness of oil chromatography monitoring sensors by selecting a fixed-length feature data set from online data for evaluation, and then using the distribution of abnormal values, the distribution of consecutive identical values, the variation of coefficient of variation and the variation of gas production rate in the feature data set as criteria to judge the feature data set and obtain the corresponding discriminant values. Afterwards, the discriminant values are assigned weights according to the focus of each criterion to obtain the sensor status values, which are compared with the preset tolerance level to obtain the sensor evaluation results. The method proposed evaluates the effectiveness of online monitoring sensors from several aspects, which can detect faulty sensors in time and is of great significance for improving the accuracy of online monitoring data and the reliability of monitoring device.
Research on Indoor Wireless Positioning Method based on Center-of-mass Algorithm
Authors: Wang Yutang
Published: August 2023

Indoor mobile positioning is widely used in shopping malls, energy saving in buildings, medical care and emergency rescue. Range-free algorithms typically use the connectivity of a wireless sensor network system to estimate the location of target nodes, typically through Dv-hop, the prime method, ap-proximate point-in-triangulation test, or APIT for short. -free algorithms can be obtained by changing the distribution density of reference nodes or by changing the blind node communication radius to obtain higher accuracy. Due to constraints such as complex indoor environments, current indoor positioning methods are often less accurate and more expensive. To address these issues, this paper improves on the traditional center-of-mass algorithm by using a six-point center-of-mass algorithm for indoor positioning to further improve the accuracy of the indoor positioning algorithm. To this end, the following work is accomplished in this paper: 1. introduced the algorithms currently used for indoor positioning and their shortcomings, laying the theoretical foundation for the improved approach proposed in the following paper. 2. introduced the basic working principle of the center-of-mass algorithm and proposed an improved system using the six-point center-of-mass algorithm. 3. established the best possible model through experiments, then input test data and evaluated it based on expert judgement. The experimental results show that the model proposed in this study has excellent accuracy in indoor wireless positioning.
A Strategy for Predicting Residual Service Life of Batteries Based on Impedance Spectrum
Authors: Sun Dexing , Jian Zhichao , Li Xiaosha , Li Shaoliang , Ouyang Ling , Liu Jianda , Lyu Chao , Chen Yeh-Cheng
Published: August 2023

With the wide application of lithium batteries in electric vehicles, energy storage and other fields, accurate life prediction of lithium ion batteries becomes particularly important. As a fast and non-destructive electrochemical technology, impedance spectroscopy can reflect the internal characteristics of Li-ion batteries, and it is widely used in battery management. A method based on the health characteristic parameters extracted from the impedance spectrum of Li-ion battery is proposed in this paper to predict the battery life. Firstly, the equivalent circuit model is used to fit the impedance spectrum of the battery and screen out the characteristic quantity which can reflect the aging state of the battery. Then, BP neural network was used to establish the relationship model between the two. Finally, particle filter algorithm was used to predict the remaining service life of the battery. At the end of this paper, the measured results are compared with the predicted results to verify the effectiveness of the proposed method.
Research on Thermal Infrared Imaging and Positioning Technology for Emergency Rescue at Sea in Bad Weather
Authors: Wang Yilin , Zhang Shouxiang , Lu Chuanfa , Sha Jinzhao , Zhang Hangran
Published: August 2023

In bad weather, the visibility at sea is low, and it is difficult to rescue people and ships in distress. Existing sensors such as visible light cameras and LIDAR will fail in bad weather, so this paper proposes a design scheme for maritime emergency rescue based on thermal infrared cameras. Infrared thermal imaging is a technology that converts infrared thermal radiation into electrical signals and displays them through the screen, which is unaffected in low visibility environments such as bad weather and night and still works normally, and is a great aid to maritime emergency rescue. The accurate identification of maritime vessels is achieved by selecting the YOLOV5 target detection algorithm with high accuracy, the accurate ranging of maritime vessels by monocular ranging algorithm, and finally the whole project is deployed to NVIDIA Jetson AGX Xavier high-performance edge computing device for inference acceleration using TensorRT framework. To verify the effect of different sizes of network structures on the training of the dataset, experiments were conducted on YOLOV5’s s-model, m-model, l-model and x-model, respectively, and the results showed that the detection results of these four models were comparable, however, the inference frame rates did vary greatly. In windows the inference frame rate is 19fps for the s-model, 15fps for the m-model, 9fps for the l-model and 4fps for the x-model. The s-model was chosen to be used in the subsequent study. The s-model was accelerated by TensorRT to increase the frame rate from 19fps to 30fps, enabling real-time detection and ranging functions. The experimental analysis shows that the scheme of this paper is feasible and reliable.
Vehicle Detection Using the Light Weight Model Based on YOLOv5
Authors: Lyu Aihong
Published: August 2023

Vehicle detection is important for the administration because it’s an important part of the intelligent traffic management system. It can help to make the best decision for the government and provide the data for intelligent roadside equipment. And can reduce road congestion and emissions, reduce the incidence of traffic accidents, improve road safety and reduce damage caused by accidents. The current method of object detection needs large computing resources and makes the roadside equipment costly or unable to meet the computing resources. So, this paper proposed a light weight detection model based on YOLOv5. This model reduces the parameter and FLOPs by using the deepthwise separable convolution CBAM to improve the performance of vehicle detection. And this paper reduces the input image size and halves the feature channel to reduce the amount of computation. The experiment result shows that the proposed model is 8 times smaller than YOLOv5-s while the mAP only decreases 6% on the UA-DETRAC dataset, significantly outperforming the other methods. This paper provided an idea for implementing a light weight model and a way to reduce the application cost of roadside smart devices.