bluuebunny/crossref_metadata_2025 huggingface dataset:
Thermal induced deflection in atomic force microscopy cantilevers: analysis & solution
Authors: Mordue Christopher W , Weaver Jonathan M R , Dobson Phillip S
Published: August 2023

Atomic force microscopy (AFM) cantilevers are commonly made from two material layers: a reflective coating and structural substrate. Although effective, this can result in thermally induced cantilever deflection due to ambient and local temperature changes. While this has been previously documented, key aspects of this common phenomenon have been overlooked. This work explores the impact of thermally induced cantilever deflection when in- and out-of-contact, including the topographic scan artefacts produced. Scanning thermal microscopy probes were employed to provide direct cantilever temperature measurement from Peltier and microheater sources, whilst permitting cantilever deflection to be simultaneously monitored. Optical lever-based measurements of thermal deflection in the AFM were found to vary by up to 250% depending on the reflected laser spot location on the cantilever. This highlights AFM’s inherent inability to correctly measure and account for thermal induced cantilever deflection in its feedback system. This is particularly problematic when scanning a tip in-contact with the surface, when probe behaviour is closer mechanically to that of a bridge than a cantilever regarding thermal bending. In this case, measurements of cantilever deflection and inferred surface topography contained significant artefacts and varied from negative to positive for different optical lever laser locations on the cantilevers. These topographic errors were measured to be up to 600 nm for a small temperature change of 2 K. However, all cantilevers measured showed a point of consistent, complete thermal deflection insensitivity 55% to 60% along their lengths. Positioning the reflected laser at this location, AFM scans exhibited improvements of up-to 97% in thermal topographic artefacts relative to other laser positions.
Void fraction measurement of gas–liquid two-phase flow based on compressed sensing theory and capacitively coupled electrical resistance tomography system
Authors: Gao Heming , Ku Shuaichao , Jian Xiaohu
Published: August 2023

Capacitively coupled electrical resistance tomography (CCERT) is an innovative technique for electrical resistance tomography based on capacitively coupled contactless conductivity detection (C4D). Despite its potential, there are only a few studies on image reconstruction algorithms for CCERT. To address this, a CCERT measurement system is developed, and the compressed sensing (CS) theory is applied to the inverse problem imaging of CCERT to improve the image reconstruction quality and speed. Firstly, a mathematical CCERT image reconstruction model is constructed under CS theory and three algorithms under CS theory are employed to solve the convex optimization problem of the reconstruction model. Then a thresholding operation is applied to obtain the post-processed image and compared it with the classical linear back projection and Landweber algorithms. The simulation results demonstrate that the Barzilai–Borwein gradient projection for sparse reconstruction (GPSR-BB) algorithm yields more satisfactory imaging results than the other four algorithms. Additionally, three sensitivity matrix optimization methods for GPSR-BB algorithm are compared and find that the new method of screening the rows of the sensitivity matrix to zero is more effective than the other two common optimization methods in terms of reconstructed image quality. Finally, the static experiments of void fraction measurement are conducted using the developed CCERT system. The results indicated that the absolute error of void fraction measurement by GPSR-BB algorithm was less than 6.59% in the range of void fraction from 0.90% to 66.29%.
Machinery fault diagnosis using multi-scale feature focused network and adaptive cost-sensitive loss towards imbalanced dataset
Authors: Yang Jinsong , Min Zhishan , Han Songyu , Li Wei , Shao Haidong
Published: August 2023

The current deep learning based machinery fault diagnosis models still face challenges in effectively capturing rich multi-scale feature information and dynamically compensating training loss when dealing with imbalanced dataset. This paper presents a novel approach for machinery fault diagnosis using multi-scale feature focused network and adaptive cost-sensitive loss. Firstly, a multi-scale feature focused network is constructed with improved multi-scale CNN and point-wise attention mechanism module, in which the former can synthetically fuse the features at different scales to expand the coverage of the equivalent receptive field, and the latter can further refine fine-grained features and filter out irrelevant feature interference. Then, an adaptive cost-sensitive loss function is designed to adjust the cost matrix in the training process, dynamically assigning more loss weights for small samples that are difficult to distinguish. The experimental results of planetary gearbox fault diagnosis demonstrate that the proposed approach exhibits superior diagnostic performance compared to other existing methods.
A radiomics-incorporated deep ensemble learning model for multi-parametric MRI-based glioma segmentation
Authors: Chen Yang , Yang Zhenyu , Zhao Jingtong , Adamson Justus , Sheng Yang , Yin Fang-Fang , Wang Chunhao
Published: August 2023

Objective. To develop a deep ensemble learning (DEL) model with radiomics spatial encoding execution for improved glioma segmentation accuracy using multi-parametric magnetic resonance imaging (mp-MRI). Approach. This model was developed using 369 glioma patients with a four-modality mp-MRI protocol: T1, contrast-enhanced T1 (T1-Ce), T2, and FLAIR. In each modality volume, a 3D sliding kernel was implemented across the brain to capture image heterogeneity: 56 radiomic features were extracted within the kernel, resulting in a fourth-order tensor. Each radiomic feature can then be encoded as a 3D image volume, namely a radiomic feature map (RFM). For each patient, all RFMs extracted from all four modalities were processed using principal component analysis for dimension reduction, and the first four principal components (PCs) were selected. Next, a DEL model comprised of four U-Net sub-models was trained for the segmentation of a region-of-interest: each sub-model utilizes the mp-MRI and one of the four PCs as a five-channel input for 2D execution. Last, four softmax probability results given by the DEL model were superimposed and binarized using Otsu’s method as the segmentation results. Three DEL models were trained to segment the enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The segmentation results given by the proposed ensemble were compared to the mp-MRI-only U-Net results. Main Results. All three radiomics-incorporated DEL models were successfully implemented: compared to the mp-MRI-only U-net results, the dice coefficients of ET (0.777 → 0.817), TC (0.742 → 0.757), and WT (0.823 → 0.854) demonstrated improvement. The accuracy, sensitivity, and specificity results demonstrated similar patterns. Significance. The adopted radiomics spatial encoding execution enriches the image heterogeneity information that leads to the successful demonstration of the proposed DEL model, which offers a new tool for mp-MRI-based medical image segmentation.
CTG-Net: an efficient cascaded framework driven by terminal guidance mechanism for dilated pancreatic duct segmentation
Authors: Zou Liwen , Cai Zhenghua , Qiu Yudong , Gui Luying , Mao Liang , Yang Xiaoping
Published: August 2023

Pancreatic duct dilation indicates a high risk of various pancreatic diseases. Segmentation for dilated pancreatic duct (DPD) on computed tomography (CT) image shows the potential to assist the early diagnosis, surgical planning and prognosis. Because of the DPD’s tiny size, slender tubular structure and the surrounding distractions, most current researches on DPD segmentation achieve low accuracy and always have segmentation errors on the terminal DPD regions. To address these problems, we propose a cascaded terminal guidance network to efficiently improve the DPD segmentation performance. Firstly, a basic cascaded segmentation architecture is established to get the pancreas and coarse DPD segmentation, a DPD graph structure is build on the coarse DPD segmentation to locate the terminal DPD regions. Then, a terminal anatomy attention module is introduced for jointly learning the local intensity from the CT images, feature cues from the coarse DPD segmentation and global anatomy information from the designed pancreas anatomy-aware maps. Finally, a terminal distraction attention module which explicitly learns the distribution of the terminal distraction regions is proposed to reduce the false positive and false negative predictions. We also propose a new metric called tDice to measure the terminal segmentation accuracy for targets with tubular structures and two other metrics for segmentation error evaluation. We collect our dilated pancreatic duct segmentation dataset with 150 CT scans from patients with five types of pancreatic tumors. Experimental results on our dataset show that our proposed approach boosts DPD segmentation accuracy by nearly 20% compared with the existing results, and achieves more than 9% improvement for the terminal segmentation accuracy compared with the state-of-the-art methods.
Observation of impurity accumulation and its compatibility with high plasma performance in W7-X
Authors: Zhang D , Buttenschön B , Jablonski S , Kubkowska M , Ford O , Alcusón J A , Beidler C D , Burhenn R , Beurskens M N A , Langenberg A , Pablant N , Reimold F , Rahbarnia K , Smith H M , Wegner Th , Wurden G , Bozhenkov S A , Feng Y , Brunner K J , Fuchert G , Gao Y , Geiger J , Giannone L , Höfel U , Hirsch M , Huang Z , Knauer J , Kremeyer T , Krychowiak M , Kwak S , Laqua H P , Laube R , Neuner U , Pasch E , Pavone A , von Stechow A , Svensson J , Thomsen H
Published: August 2023

At the W7-X stellarator, the bolometer system has measured an intensive radiation zone in the inner plasma region (at a normalized radius ρ ∼ 0.3–0.4) in the hydrogen plasma generated by electron cyclotron resonance heating; it differs from the normal plasma radiation distribution with an edge-localized emission zone. Spectroscopic diagnostics have recorded high-Z elements such as iron. This phenomenon happens in the plasma phases after gas supply turn-off, which results in all impurity relevant diagnostic signals increasing for several seconds. Despite the enhancement of the core radiation, the plasma energy confinement is improved. A transport analysis shows that this impurity radiation behavior is associated with a low diffusion coefficient (D∼ 0.02 m2 s−1) and a reversal of the convection around the radial position of the emission peak, which, under normal conditions, separates the zones of outward convection in the central (|V| ∼ 0.1 m s−1) and inward convection in the outer region (|V| ∼ 0.3 m s−1). An impurity accumulation around this radial position has been identified. The transport coefficients obtained are comparable with the theoretical predictions of collisional impurity transport. In the plasma phases studied, both impurity and energy confinement are enhanced. The mechanism responsible for the improvement is believed to be a reduction of micro-instabilities associated with the observed steepening of the density profile, initiated by a low edge plasma density (<1.0 × 1019 m−3) after switching off the gas fueling. The normalized temperature and density gradients fulfil the condition for the suppression of ITG turbulence.
Experimental study on vibration suppression for robotic milling using an MRE absorber
Authors: Zhao Wei , Tian Wei , Liu Peng , Li Bo
Published: August 2023

During the robotic milling process, vibration is one of the main factors that affect the machining accuracy and surface quality due to the low stiffness of the robot structure. Intelligent material, magneto-rheological elastomer (MRE), owns the features of adjustable stiffness, reversible and fast response, has been gradually used for vibration suppression. Therefore, to suppress the forced vibration for robotic milling, this paper used the principle of vibration absorption and combined it with MRE material to design a snubber. A series of excitation and milling experiments were carried out to evaluate its vibration attenuation characteristics. In the excitation experiment, sine signals with frequencies of 40 Hz, 42.02 Hz and 60 Hz with the same amplitude of 5 N were applied to the MRE absorber. In the milling experiment, the dominant frequencies under speeds of 800 RPM, 840 RPM and 1200 RPM were analyzed first. Then, the vibration mitigation characteristics of the MRE absorber during robotic milling of aluminum alloy were explored by adjusting the current. Experimental results showed that under the action of MRE absorber, the acceleration responses are suppressed effectively. In detail, after tuning the current, the amplitude and the root mean square of vibration acceleration in three directions of sinusoidal excitation could be suppressed by more than 19.23% and 7.35% correspondingly. The power spectral density value of the peak frequency could be reduced by more than 69.13%. In milling experiments, the MRE absorber also achieved certain vibration absorption effects. Furthermore, the surface roughness of the workpiece was also fallen by more than 16.51% which provides support for further engineering application in the field of robotic milling. Undoubtedly, the absorber needs further optimization to achieve better vibration suppression ability and a larger working bandwidth.
Nonlinear characteristics of Ti, Nb, and NbN superconducting resonators for parametric amplifiers
Authors: Zhao Songyuan , Withington S , Thomas C N
Published: August 2023

Superconducting resonators and parametric amplifiers are important components in scientific systems such as kinetic inductance detector arrays, frequency-domain multiplexers for other superconducting bolometers, spin-ensemble based memories, and circuit quantum electrodynamics demonstrators. In this paper, we report microwave measurements of superconducting Ti, Nb, and NbN resonators and their use as parametric amplifiers. These half-wave resonators were fabricated under near identical sputtering and lithographic conditions to ensure a like-for-like comparison of material properties. We report a wide range of properties and behaviours in terms of transition temperatures, resistivities, rate-limiting nonlinear response times, nonlinear dissipation, signs of the nonlinear inductances and their dependences on temperature and resonance harmonic. We have successfully operated Nb and NbN resonators as high gain parametric amplifiers, achieving greater than 20 dB of power amplification. We have shown that for a half-wave resonator, amplification can be realised not only in the fundamental resonance but also in the higher harmonic resonances. Further, for materials with high transition temperatures, e.g. Nb and NbN, amplification can be achieved at

∼

4

 K, i.e. a temperature maintained by a pulse tube cooler. Finally, in materials systems that have very fast response times, e.g. NbN, we have found that a cross-harmonic type of amplification can be achieved by placing pump tone in a different resonant mode as the signal and the idler. This wide range of observations will have important implications on the design and application of superconducting parametric amplifiers.
Spectral properties of two coupled Fibonacci chains
Authors: Moustaj Anouar , Röntgen Malte , Morfonios Christian V , Schmelcher Peter , Morais Smith Cristiane
Published: August 2023

The Fibonacci chain, i.e. a tight-binding model where couplings and/or on-site potentials can take only two different values distributed according to the Fibonacci word, is a classical example of a one-dimensional quasicrystal. With its many intriguing properties, such as a fractal eigenvalue spectrum, the Fibonacci chain offers a rich platform to investigate many of the effects that occur in three-dimensional quasicrystals. In this work, we study the eigenvalues and eigenstates of two identical Fibonacci chains coupled to each other in different ways. We find that this setup allows for a rich variety of effects. Depending on the coupling scheme used, the resulting system (i) possesses an eigenvalue spectrum featuring a richer hierarchical structure compared to the spectrum of a single Fibonacci chain, (ii) shows a coexistence of Bloch and critical eigenstates, or (iii) possesses a large number of degenerate eigenstates, each of which is perfectly localized on only four sites of the system. If additionally, the system is infinitely extended, the macroscopic number of perfectly localized eigenstates induces a perfectly flat quasi band. Especially the second case is interesting from an application perspective, since eigenstates that are of Bloch or of critical character feature largely different transport properties. At the same time, the proposed setup allows for an experimental realization, e.g. with evanescently coupled waveguides, electric circuits, or by patterning an anti-lattice with adatoms on a metallic substrate.
Commutation simulator for open quantum dynamics
Authors: Joo Jaewoo , Spiller Timothy P
Published: August 2023

Recent progress in quantum simulation and algorithms has demonstrated a rapid expansion in capabilities. The search continues for new techniques and applications to exploit quantum advantage. Here we propose an innovative method to investigate directly the properties of a time-dependent density operator

ρ
ˆ

(
t
)

. Using quantum commutation simulators, we can directly compute the expectation value of the commutation relation and thus of the rate of change of

ρ
ˆ

(
t
)

. The approach can be utilized as a quantum eigen-vector solver for the von Neumann equation and a decoherence investigator for the Lindblad equation, by using just the statistics of single-qubit measurements. A simple but important example is demonstrated in the single-qubit case and we discuss extension of the method for practical quantum simulation with many qubits, towards investigation of more realistic quantum systems.
Cooling microwave fields into general multimode Gaussian states
Authors: Yazdi Nahid , José García-Ripoll Juan , Porras Diego , Navarrete-Benlloch Carlos
Published: August 2023

We show that a collection of lossy multichromatic modulated qubits can be used to dissipatively engineer arbitrary Gaussian states of a set of bosonic modes. Our ideas are especially suited to superconducting-circuit architectures, where all the required ingredients are experimentally available. The generation of such multimode Gaussian states is necessary for many applications, most notably measurement-based quantum computation. We build upon some of our previous proposals, where we showed how to generate single-mode and two-mode squeezed states through cooling and lasing. Special care must be taken when extending these proposals to many bosonic modes, and we discuss here how to overcome all the limitations and hurdles that naturally appear. For the sake of illustration, we work out two examples of Gaussian-state families consisting of Greenberger–Horne–Zeilinger and cluster states, which allow us to show that it is possible to use a set of N lossy qubits to cool down a bosonic chain of N modes to any desired Gaussian state.
Comparative study of surface disorder in gold and silver nanoparticles obtained by molecular dynamics in cooling processes
Authors: Agudelo-Giraldo J D , Cantillo-Galindo N , López-Salguero J S , Reyes-Pineda H
Published: August 2023

This work presents structural analyses of gold and silver nanoparticles with a surface-to-volume aspect ratio of about 50%. The simulations were performed by molecular dynamics using the Sutton–Chen potential. The atoms were randomly placed in a box with periodic boundary conditions to realize a cooling process under the consideration of a canonical ensemble. The results of the potential energy as a function of temperature are presented, showing the transitions to the solidification phase. At low temperatures, the radial distribution for all nanoparticles follows patterns similar to those reported for metals with FCC structure. Coordination number and interatomic distance were established as parameters to analyze surface particularities. In general, a decrease in the inter-atomic distance was observed with respect to the bulk. In gold nanoparticles, the formation of surface planes is favored and the interatomic distances increase with the coordination number under a well-defined tendency. Interior atoms have an interatomic distances closer to the expected value. In silver nanoparticles, atoms with a coordination number equal 9, located in surface, are at distances closer to the expected value than the internal atoms, at the scale considered. However, the trend with the increments of particle size shows go to a similar gold tendency. Silver led to a more disordered surface with respect to Gold. Therefore, the properties that depend on the surface ordering will exhibit more abrupt transitions with particle size in Silver at the treated scale.
Exceptional strength-ductility synergy of Ni and Co–Mg–La ferrite nanoparticles reinforced Sn-1Ag-0.5Cu matrix composite
Authors: Eid N A M , El-Daly A A , Hammad A E , Ibrahiem A A
Published: August 2023

The use of innovative Co–Mg–La ferrite nanoparticles (ferrite) facilitates the production of superior Pb-free solder alloy materials that suffer from the strength-ductility trade-off (SDT). The current study introduces an original Pb-free Sn-1Ag-0.5Cu (SAC105) electrical connection alloy. It is fortified with cutting-edge ferrite and nickel providing a novel approach for improving SDT by avoiding early necking throughout different temperatures. The heterogeneous structures in Sn-1Ag-0.5Cu (SAC105) alloy are assembled by controlling a fraction of reinforcing ferrite and Ni, processed under cold-drawn and partial recrystallization. The optimal ferrite concentration in SAC105 composites is about 0.3 wt%, which possesses a significant strain hardening potential and the highest strength-ductility synergy. The excessive degree of heterogeneity of SAC105-0.3ferrite induces numerous strengthening mechanisms and dislocation accumulation, resulting in attaining high ultimate tensile strength (UTS) of 40.7 MPa with significant ductility of ∼47.7%, with ∼34.3% and 39% increasing when compared to SAC105. Once SAC105 solder was doped with 0.3 wt% ferrite or 0.05Ni with 0.3 wt% ferrite, the activation energy transferred from pipe diffusion to lattice diffusion. The solitary Ni addition, on the other hand, results in the SDT of plain SAC105.
Multi-frequency test of dark matter annihilation into long-lived particles in Sirius
Authors: Chen Yu-Xuan , Zu Lei , Xia Zi-Qing , Sming Tsai Yue-Lin , Fan Yi-Zhong
Published: August 2023

New long-lived particles produced at the colliders may escape from conventional particle detectors.
Using satellites or ground telescopes, we can detect the photons
generated from the annihilation of the star-captured dark matter into a pair of long-lived particles.
When the propagation length of these long-lived particles surpasses the interplanetary distance
between the Sun and Jupiter, it becomes unfeasible to detect such dark matter signals originating from the Sun or Jupiter on Earth.
Our analysis of the dark matter-induced photons produced by prompt radiation, inverse Compton scattering, and synchrotron radiation mechanisms
reveals that a decay length of about 10-3 pc for long-lived particles is required for maximum detectability.
We investigate the parameters that allow the long-lived particle's lifetime to be consistent with Big Bang nucleosynthesis
while also allowing it to escape the confines of our solar system.
The Sirius system is proposed as a promising target for the indirect detection of such long-lived particles.
Utilizing the prompt, inverse Compton scattering, and synchrotron radiation,
upper limits on the dark matter-proton spin-independent and spin-dependent cross section are estimated with
the Fermi-LAT null-signal observation and the capabilities of the upcoming Square Kilometre Array radio telescope.
One-component fermion plasma on a sphere at finite temperature: the anisotropy of the path conformations
Authors: Fantoni Riccardo
Published: August 2023

In our previous work (Fantoni 2018 Int. J. Mod. Phys. C 29 1850064) we studied, through a computer experiment, a one-component fermion plasma on a sphere at finite, non-zero temperature. We extracted thermodynamic properties, such as the kinetic and internal energy per particle, and structural properties, such as the radial distribution function, and produced some snapshots of the paths to study their shapes. Here, we revisit this study, giving more theoretical details explaining the path shape anisotropic conformation due to the inhomogeneity in the polar angle of the variance of the random walk diffusion from the kinetic action.
Registration Method of Single-Line Point Clouds and Images Based on Depth Learning
Authors: Peng Saiqian
Published: August 2023

In high-definition map production, the point cloud and image data collected by the collection vehicle play a supportive role in high-definition map production. Point cloud data can provide accurate 3D spatial positioning, while image data can provide rich semantic information. The generation of high-definition maps depends on the fusion of multi-sensor data, and sensor registration is a requirement for multi-sensor fusion. The relative position between the LiDAR and the camera is largely resolved by the registration of single-line point clouds with images. Conventional manual registration techniques require a lot of labor and specialized gear for calibration, and they must be recalibrated if the distances between the sensors vary. This work suggests an online, unsupervised, and automatic deep learning-based registration approach for single-line point clouds and images. The technique was evaluated on an internal dataset, and it decreased the average displacement error to 33.22 cm and the average rotation error to 0.54° when the highest displacement error was 1.5 m and the maximum rotation error was 20°.
Quantum and Condor-Based Brainstorming Optimization Algorithm for NOx Prediction
Authors: Qin Hongwu , Fu Yu , Wang Lizheng , Yang Songhao , Liu Zhenqi , Sui Muxuan
Published: August 2023

This paper proposed a quantum bald eagle brainstorm (QBBSO) based on quantum initialization and combined with the bald eagle optimization algorithm in light of the original Brain Storm Optimization (BSO) algorithm’s strong local search ability, which would result in local optimization, a poor optimization effect, and difficult development. To accomplish the randomness of the number and increase the randomness of the population, we first modified the initialization method of the original brainstorming population, introduced the idea of quantum code, and then translated the binary numbers 0 and 1 to the decimal number. To achieve the best outcome, the original step size formula was employed for global selection, local search, and final selection using the vulture search method. The original BSO was then optimized to attract more global individuals. The algorithm versions were compared using the common benchmark function test. The findings demonstrated that QBBSO had a greater capacity for global search and a faster convergence speed. This research also applied the QBBSO algorithm to the long-term and short-term memory network (LSTM) to forecast the NOx concentration in the boiler, further demonstrating the algorithm’s superiority in real-world settings.
Support vector regression based on improved Harris Hawk optimization algorithm for power load forecasting
Authors: Liu Zilang , Chen Hongwei
Published: August 2023

Electricity load forecasting is an important part of power system operation, planning and management. Support vector regression (SVR) is a commonly used and efficient method for medium and long-term load forecasting. The hyperparameters of SVR have a very high influence on its performance and are not easily determined. In this paper, an improved Harris Hawk optimization algorithm (TAHHO) is proposed for optimizing the hyperparameters of SVR, and a TAHHO-SVR medium- and long-term prediction model is developed. TAHHHO enhances the convergence and stability of the original algorithm by introducing the survival of the fittest principle and the crossover operator of the artificial tree (AT) algorithm, which is validated in 13 benchmark test functions. The proposed TAHHO-SVR model is used for forecasting the UK National Grid dataset and demonstrates the feasibility and competitiveness of the model, which effectively improves the forecasting accuracy compared to HHO-SVR.
Image Recognition of Peach Pests Based on Improved Convnext
Authors: Lei Cheng Min , Shi Ai Jv , Cui En Quan , Wei Lv Hong , Min Mu Shao
Published: August 2023

A ConvNeXt-based image recognition model for peach tree pests is proposed to address the problem of low accuracy of pest image recognition due to the large intra-class morphological variation of peach tree pests in natural scenes. First, ConvNeXt is used as the backbone network and the model structure is optimized by introducing the visual perceptual field module to extract discriminative features of peach tree pests. Secondly, a joint loss function is used to make the model continuously learn the features that minimize the intra-class distance to accelerate the model convergence and improve the recognition accuracy. Finally, two tandem fully connected layers are used to replace the original fully connected layer, and the feature vectors input to the fully connected layer are visualized to show the pest classification effect. The experimental results show that compared with 10 networks such as RegNet, the model in this paper effectively improves the recognition accuracy of peach tree pest images, and the accuracy is improved by 4.8% compared with the standard ConvNeXt, which verifies the effectiveness of the model in this paper for accurate recognition of peach tree pest images in natural scenes.
Improved MobileViT: A More Efficient Light-weight Convolution and Vision Transformer Hybrid Model
Authors: Liu Jing , Chen Huiyang , Zhou Weimin
Published: August 2023

Convolutional neural networks (CNNs) utilize spatial induction bias to learn local features of images, while vision transformers (ViTs) can learn global representations of visual information through self-attention. However, due to ViT’s large number of parameters, it is challenging to apply them to mobile vision tasks. Based on Conv-ViT light-weight hybrid model MobileViT, this paper proposed the improved backbone network and transformer block to improve the applicability. Firstly, the MV2 block in MobileViT is substituted with an improved depth separable convolution and linear activation function. Secondly, we proposed a novel self-attention to replace the original self-attention of the transformer block in MobileViT, which reduces computational load and enhances global representation by enabling information interaction between different windows. Experimental results demonstrate that the improved model’s accuracy in image classification is 2.2% higher than that of MobileViT on the CIFAR-100 dataset.
Natural scene text detection algorithm based on the regional proposal
Authors: Wu Guangyue , Zeng Qingtao , Zhao Jiefeng , Yang Zibo
Published: August 2023

Text detection in natural scenes is widely used in various fields. The combination of deep learning models and text detection improves the accuracy and efficiency of detection. Its algorithm is also endless, with continuous optimization and improvement. On this basis, some algorithms for text detection in natural scenes are summarized from the two aspects of the Faster R-CNN method and SSD method based on regional suggestion. These algorithms and experimental data are analyzed and compared to further summarize the challenges faced by text detection in natural scenes and the direction of improvement.
Crankshaft defect detection based on machine vision
Authors: Guo Kunxiang , Sun Yuan
Published: August 2023

As a core component of the Marine engine, the crankshaft crank is required to have a high surface quality to ensure that the ship does not fail during the voyage. After analysing the defect characteristics, aiming at the problems such as the inaccurate detection of single defect feature in the crankshaft bend defect, which leads to poor detection effect, a crankshaft bend defect detection classification method based on machine vision was proposed. The recognition accuracy can reach 97.8%. Compared with the defect identification by texture and geometric features alone, the classification and recognition accuracy can be improved, and the requirements of industrial detection can be better met.
Numerical Detection of Concrete Slump by Fusion of Target Segmentation and Image Classification Network
Authors: Gao Xiao , Yan Hairong
Published: August 2023

In construction, concrete compatibility is an important comprehensive index to ensure the construction, and the concrete slump is an important criterion to judge concrete compatibility in the actual construction process. In this study, we propose to extract new data sets from concrete mixing video sequences and correlate the image features characterized in the concrete mixing and transportation process with the concrete performance features, starting from the concrete transportation process [1]. First, the UNet network model for semantic segmentation is used to identify and locate the concrete regions, and the localized concrete regions are zoomed in using interpolation; then the ResNet image classification network is used to determine the slump category of the processed concrete regions; finally, the results of the semantic segmentation network and the image classification network are fused to obtain the final concrete slump detection results. The experimental results demonstrate that the proposed method can guarantee real-time concrete slump detection with improved accuracy.
SSFN: a novel Spatial-Spectral FusionNet for hyperspectral image classification
Authors: Yan Chenyang , Jia Hong
Published: August 2023

Hyperspectral image classification is the process of identifying ground objects within hyperspectral images at the pixel level. While many CNN based methods have been successfully used in this field, they often struggle to effectively extract inter-spectral information due to the high spectral dimensions of hyperspectral images. Recently, the transformer model has been introduced to capture the interdependence between spectral data, but it may lose the ability to capture local context features. In order to address the limitations of CNN and Transformer models, we propose a novel approach that integrates the advantages of both models. Specifically, we use CNN to extract spatial information and Transformer to extract spectral information, which is then fused before being fed into the MLP framework for classification. Additionally, we introduce a sparse strategy to eliminate the impact of redundant frequency bands on the transformer’s performance. Our method fully utilizes the spatial and spectral information in hyperspectral image data and has achieved excellent performance on hyperspectral datasets. By leveraging both CNN and transformer models, we can effectively capture both spatial and spectral features, providing a powerful tool for hyperspectral image classification.
Multi-feature Fusion Based on RV Correlation Coefficient for Facial Expression Recognition
Authors: Wang Yan , Lu Yuming , Wan Xing
Published: August 2023

Extracting effective features of expressions becomes a hot research topic, and a single feature pattern cannot reflect the diversity of expressions. Therefore, to obtain rich information feature data and raise the expression recognition performance, we propose a feature fusion model of multiple feature selection by the measure of the RV correlation coefficient. In the proposed feature fusion model, the feature patterns are firstly selected by RV correlation coefficient from various expression texture features. And then according to rank the values of the RV correlation coefficient, we build a CCA subspace and PCA subspace respectively to fuse selected features. Finally, a new facial expression feature presentation is constructed through weighting and combining the two fusion features from the subspaces. The new features are fed to SVM classifier for expression recognition. Experimental verification shows that our proposed model has a superior performance than the existing algorithms.
Design of an Intelligent High-Temperature Infrared Temperature Measurement System
Authors: Jiang Han , Chen Cai , Wu Conglin , Ren Zhi , Yang Jintang
Published: August 2023

The system is designed to address the problem of untimely transmission of temperature measurement signals in the sampling process at the LF refinery location, as well as the subjective involvement of the operator in the traditional temperature measurement method, i.e. the technology and experience of the operation, which results in a certain systematic error in the temperature measurement results. In this paper, a steel plant is used as an experimental object to test the performance of the device and the practicality of the system in combination with the on-site operating environment; and the system differs from the traditional contact temperature measurement by visualizing the steel image and generating an intelligent temperature profile while measuring the temperature with the help of an infrared camera. The experimental results show that the system can be remotely controlled to achieve intelligent temperature measurement, and the percentage of temperature measurement error is controlled within 1.5%, which is of practical significance to the automation and intelligence of the LF refining furnace temperature measurement in the iron and steel metallurgical industry.
Target detection based on multi-scale feature fusion and cross-channel interactive attention mechanism
Authors: Zhao Chenyang , Song Yong , Yang Xin , Zhou Ya , Yang Jinqi
Published: August 2023

Aiming at the problems of complex background, target scale change and small target in aerial image detection, we propose a YOLOv5 target detection algorithm based on multi-scale feature fusion and cross-channel interactive attention mechanism. Including: M-PPM (Multi-scale pyramid pooling module) is designed as a replacement for the SPP (Spatial Pyramid Pooling) structure in YOLOv5, so as to make full use of different scale features to fuse global feature information; CCA (Cross-channel interactive attention mechanism) is designed to realize cross-channel information interaction and utilization, and enhance the network’s capability to generalize and fusion efficiency of small target features. Bi-directional Feature Pyramid Network (BiFPN) is utilized to solve scale difference problem in multi-target detection. The proposed algorithm’s experimental results is 2.3 % and 1.8 % higher than YOLOv5 on the VisDrone and UAVDT aerial data sets, respectively.
Fabric Defect Detection Using a One-class Classification Based on Depthwise Separable Convolution Autoencoder
Authors: Chen Chun , Deng Xiaoyan , Yu Zhuliang , Wu Zhengtao
Published: August 2023

Fabric defect detection is anomaly detection, which is widely studied in the textile industry. Like most anomaly detection tasks, there are some problems hindering detection results, such as class imbalance, defective sample scarcity, and feature selection. This paper proposes a method applying depthwise separable convolution autoencoder on dimensionality reduction and one-class classifier support vector data description (SVDD) to detect fabric defects. A depthwise separable convolution autoencoder can effectively extract sample features with less computation and fewer parameters than the regular convolution, which will be easily used in industrial production. SVDD can only use non-defective samples to train the classifier and solve the difficulty and heavy cost of collecting negative samples (defective samples). In this paper, we will demonstrate the effectiveness of the method on polyester fibers by using accuracy and AUC as evaluation criteria.
Research on Eye Movement Classification Based on the Deep Learning Model
Authors: Xie Shuangqiang , Guo Chunyong , Qiao Xiang
Published: August 2023

To improve the recognition accuracy of eye movement direction based on EOGM, the Emotiv Epoc, and EEG acquisition headset was used to collect the EEG signals of the subjects in real time. After the 14-channel signals were filtered by wavelet, the short-time energy calculation method was used to select the two channels most sensitive to eye movement. The long and short-term memory network (LSTM) was then used to classify the eye movement signals. On the same data set, the experimental results show that the classification rates in the upper, lower, left, and right directions are up to 91%, 90%, 83%, and 75%, respectively, and the average classification rate is up to 84%. Compared with the existing classification methods, the classification accuracy of the proposed method is higher and the implementation process of the classification algorithm is simpler, which further verifies the feasibility and effectiveness of using EEG to identify the direction of eye movement.
Parallel transient response topology optimization based on PETSc
Authors: Wang Sen , Lou Nengchuang , Kang Zhan
Published: August 2023

Improving the dynamic properties and reducing the vibration levels is extremely important for the design of many engineering structures. In this paper, we develop a dynamic topology optimization program based on parallel computing with the help of the PETSc Portable Extension Toolkit for Scientific Computing and the MPI communication protocol. The SIMP model is used as the topology optimization model, and the sensitivity analysis schemes of the dynamic compliance and displacement response as opposed to the density design variables are given in the paper. Using this optimization formulation, we developed a parallel topology optimization program for minimizing the transient response. The convergence performance of the program is shown with a relatively large-scale numerical example.
A Rotation-Invariance Face Detector Based on RetinaNet
Authors: Xiong Yuyang , Meng Wei , Yan Junwei , Yang Jun
Published: August 2023

The use of deep convolutional neural networks has greatly improved the performance of general face detection. For detecting rotated faces, the mainstream approach is to use multi-stage detectors to gradually adjust the rotated face to a vertical orientation for detection, which increases the complexity of training as multiple networks are involved. In this study, we propose a new method for rotation-invariant face detection, which abandons the previously used cascaded architecture with multiple stages and instead uses a single-stage detector to achieve end-to-end detection of face classification, face box regression, and facial landmark regression. Extensive experiments on FDDB in multiple orientations have shown the effectiveness of our method. The results demonstrate that our method achieves good detection performance and the detection accuracy of our method even exceeds that of other rotated face detectors on the front-facing FDDB dataset.
3D Human Pose Estimation Based on Transformer
Authors: Yin He , Lv Chang , Shao Yeqin
Published: August 2023

Currently, 3D human pose estimation has gradually been a well-liked subject. Although various models based on the deep neural network have produced an excellent performance, they still suffer from the ignorance of multiple feasible pose solutions and the problem of the relatively-fixed input length. To solve these issues, a coordinate transformer encoder based on a 2D pose is constructed to generate multiple feasible pose solutions, and multi-to-one pose mapping is employed to generate a reliable pose. A temporal transformer encoder is used to exploit the temporal dependencies of consecutive pose sequences, which avoids the issue of relatively-fixed input length caused by temporal dilated convolution. Adequate experiments indicate that our model achieves a promising performance.
Inversion studies of CO2 emission sources in industrial parks
Authors: Chen Yuan , Wang Yingxiang , Zhou Sixuan , Lei He , Li Siwu , Deng Guangyu
Published: August 2023

Industrial parks are a significant contributor to global CO2 emissions, making low-carbon development essential in mitigating climate change. In this study, we focus on the Hubei province industrial park as a case study and investigate the inversion of critical information on CO2 emission sources. Firstly, we establish a forward model of CO2 diffusion within the industrial park by employing atmospheric monitoring data and the AERMOD model system. Subsequently, we use a single back propagation (BP) neural network with the particle swarm optimization (PSO), whale optimization algorithm (WOA), and pelican optimization algorithm (POA) for inverse calculation of CO2 emission source locations and emission intensity. Our results show that the POA-BP inversion model has high inversion accuracy and stability, enabling us to locate the CO2 emission sources within the industrial park. This information can provide crucial decision support for the industrial park to tackle climate change and promote green transformation.
Prediction Model of Horizontal Drilling Trajectory of Anti-collision Drilling Robot
Authors: Kang Mingxia , Wu Honglin , Hua Dezheng
Published: August 2023

The horizontal drilling trajectory of the anti-collision drilling robot is taken as the research object and its drilling trajectory prediction model is established in this paper. The drilling trajectory is affected by many factors, such as the structural parameters of the auger, the drill bit, the drilling process parameters, and the geological characteristics of the coal seam, etc., which results in the existence of multi-level time-scale structure and localization characteristics of the drilling trajectory data in the time domain. The wavelet analysis method can decompose the relatively complex non-stationary time series problem into low-frequency decomposition signals representing trend items and high-frequency decomposition signals representing periodicity and randomness. It can not only predict the main trend of drilling trajectory changes but also effectively predict sudden load changes and short-term changes. Based on this, a new prediction method is proposed in this paper. Firstly, the data is decomposed into relatively simple component signals using wavelet decomposition technology, then the prediction models are established according to the characteristics of each component signal. Finally, the drilling trajectory prediction model is established by synthesizing the prediction results. The anti-impact drilling robot drilling test bed is set up and the drilling experiments are carried out to verify the effectiveness of the model, which can lay the foundation for the anti-impact drilling robot drilling deflection control.