bluuebunny/crossref_metadata_2025_split huggingface dataset:
Computational analysis of multivariant physical and mechanical experiments on laser hardening of gin disc saws
Authors: Abrorov Akbar , Kuvoncheva Matluba , Ismoyilov Furkat , Fayziev Sirojiddin , Mukhammadov Mukhsin 
Published: September 2023

Abstract
The article describes an original method for computational analysis of multivariate physical and mechanical experiments on laser hardening of gin saw blades. The laser processing method is one of the most effective physical and mechanical methods for improving the properties of resistance to fatigue, long-term strength, and corrosion cracking of circular saw blades that operate under conditions of elevated temperatures and cyclic loads. The stages of the analysis of multivariate experiments on hardening the teeth of a circular saw with laser radiation during grinding are described in detail. Based on theoretical studies three main factors have been selected that significantly affect the hardening of saw teeth. Considering these factors the developed technique makes it possible to increase the thinness of the teeth of circular saw blades significantly due to laser hardening, while maintaining their high hardness in thickness. It has been experimentally confirmed that the service life of gin saws has doubled.
Mathematical modeling of diagnostics of residual stresses in a layered medium
Authors: Lomazov V A , Lomazova V I , Akupiyan O A , Miroshnichenko I V , Petrosov D A 
Published: September 2023

Abstract
The work is devoted to the use of mathematical modeling in the framework of the problems of non-destructive testing of structural materials. A mathematical formulation of a three-dimensional problem of diagnostics for elastic layered medium is constructed, which makes it possible to determine the spatial distribution of residual stresses from the values of displacements measured on the surface of a layered half-space, which arose as a result of a specially initiated wave process. Under the assumption of weak inhomogeneity and anisotropy of the layers, a transition is made to a linearized (which is ill-posed) problem, the procedure for solving which is constructed using the method of stationary basic processes. As an example, the case of diagnosing a one-dimensional (depending only on the distance to the surface) distribution of residual stresses is considered.
Determination of regression coefficient for numerical models of adsorption isotherms
Authors: Kalistratova A A , Spiridonova I A , Kholzineva V O , Nikitin D V , Vavilov G D 
Published: September 2023

Abstract
The article considers a method for determining mathematical equations by describing experimental adsorption isotherms. These equations correspond to the main models of adsorption processes that are presented in this work. A method for selecting the regression coefficients of adsorption isotherms by the MathCad software is proposed. A mathematical adsorption equation for a pair of substances “ethyl alcohol - activated carbon” is found. To confirm the applicability of the obtained equation, a mathematical experiment was carried out in the Aspen Adsorption software. The result of the experiment included the values of the saturation time of the adsorbate (activated carbon) with the adsorbent (ethyl alcohol) under various conditions. An estimate of the experimental error relative to the conditions accepted in the calculation showed a satisfactory result.
A layer-based random SGD for distributed training of RNNs
Authors: Zhang Zhongyu 
Published: September 2023

Abstract
Since frequent data communication leads to increased time consumption in distributed training, compression of communication data is one of the effective ways to speed up the whole training process. Many existing compression methods introduce extra time overhead in data compression and decompression, for instance, Top-K and Random-k. There are also methods that can reduce the extra time overhead but suffer from the shortcoming of insufficient generalization. For example, the layer-based compression LR-SGD (Layer-based Random Stochastic Gradient Descent) cannot be applied to the training of recurrent neural networks. In this paper, an improved method based on LR-SGD is proposed. By being divided into virtual layers for compression, this improved method is able to train various networks efficiently. To validate the proposed method, experiments were conducted using different neural networks on a stimulative cluster. The results demonstrate that the proposed method applies well on different networks with low communication overhead and a nice convergence effect.
Researches advanced in fine-grained image classification based on deep learning
Authors: Zhang Qianwei 
Published: September 2023

Abstract
In the recent past, the task of fine-grained image recognition has become a hot research topic for scholars. Due to its substantial intra-class and little inter-class variance, fine-grained image classification task is more difficult than traditional image classification task. From a deep learning standpoint, this paper first introduces representative algorithms for strongly supervised fine-grained image classification, consisting of the Part-based R-CNN algorithm, the Pose Normalized CNN algorithm, and the Mask-CNN model. Second, weakly supervised process in image classification is expounded from the representative methods of attention mechanism, end-to-end visual coding, zero-shot learning with adaptive domain decomposition. Third, in the experimental part, the most popular databases for precise picture categorization, the evaluation metrics and conclusion of the algorithms are listed. Finally, it summarizes and looks forward, points out the problems and deficiencies of current research, and gives guidance for possible solutions in the future.
Faster R-CNN and YOLOv3: a general analysis between popular object detection networks
Authors: Dong Wenbo 
Published: September 2023

Abstract
Computer vision is developing really fast in recent years, and object detection is now one of the hottest topics. faster region-based convolutional neural network (faster R-CNN) and You Only Look Once (YOLOv3) are two popular modules to solve object detection problems, but they are implemented in different ways and thus have different performance in practice. This article is going to introduce these two modern modules and make a few experiments to compare the performance of each module with various datasets. This analysis will focus on speed, accuracy and the performances in different situations. Two specific studies will be mentioned as two typical examples of object detection application: face mask detection and greenhouse detection from satellite images. At last, the article will draw out a conclusion, make some suggestions for the choice of faster R-CNN and YOLOv3 and make a prospect for the future. It turns out that generally faster R-CNN fits tasks that require high accuracy better and YOLOv3 can realize real-time detection tasks.
Performance analysis of robustness of BERT model under attack
Authors: Chen Xi 
Published: September 2023

Abstract
With the aim of testing the robustness of machine learning models, this paper tests the performance of five classification models based on IMDB datasets. Furthermore, in this work, two types of sentence embeddings generated by word2vec and BERT are added with the noise of the normal distribution with different intensities. They are fed into the Support Vector Machine for testing. The experimental results show that the performance of the model slowly decreases as the noise intensity is increased. BERT-based sentiment embedding reduces less than Word2vec-based sentiment embedding. This paper believes that this experimental phenomenon can be used as a basis to support the robustness of BERT representation learning. At the same time, this paper also noted that the previous work experimented with perturbating the BERT model by replacing the words in the original text of the IMDB dataset, causing the BERT model performance to drop sharply. However, the experiment in this paper tested the robustness of the BERT model by embedding tampering, and the experimental results were stable. In this paper, it is assumed that word substitution and embedding interference are possible situations when operating the model. Therefore, for the two different experimental phenomena, this paper plans to design more detailed experiments in the next work to check the robustness of the model.
Channel capacity on visible light communication
Authors: Wan Yukun 
Published: September 2023

Abstract
As a typical application scheme of OWC (Optical Wireless Communication), VLC (Visible Light Communication) plays a more vital role in our daily life. It is expected to be used in the fields of vehicle communication, 6G network, underwater wireless communication, etc. Although so many achievements on VLC have been obtained, researchers still have some obstacles to overcome. Channel capacity as an important quota to evaluate communication quality deserves researchers to study further. This paper not only discusses the basic theory and combines the results of some recent studies on visible light channel capacity, but also highlights a promising application prospect and advantages over other communication methods, including RF (radio frequency), and fiber optical. This paper draws a conclusion that VLC is mainly used in indoor and short-distance communication. There is still a long way to apply VLC in long-distance communication and overcome the fading problem.
The gap between theoretical computational theories and application models
Authors: Yang Chengge 
Published: September 2023

Abstract
This paper examined two key generalization assumptions in the theoretical and computational fields and discussed three dominant pre-training models from multiple computer science concentrations. Then, this paper illustrated the increasingly severe gap between theoretical research and model applications for current machine learning studies. This paper discussed four advanced concepts in academia regarding possible solutions for the gap. The researchers developed these concepts to redirect future machine learning work by helping the academia re-understand the concept of “learning” and its appropriate implementation in the machine learning field. Finally, one possible reason for such a gap to exist is explained, and based on the interpretation of advanced concepts, possible solutions the academia can try to implement are also discussed.
The classification of human brain tumors with machine learning
Authors: Ma Zihan , Lin Ziwei 
Published: September 2023

Abstract
This paper aims to report human brain tumor classification based on machine learning. Brain tumors have long been regarded as one of the deadliest tumors. Conventionally, without the assistance of automation tools, brain tumor diagnosis is a skill-demanding work for the doctors and a time-consuming burden on the medic system. Applying machine learning to clinical medicine and classifying brain tumors enables physicians to increase their diagnosis correctness and allows the health system to treat more patients. This paper covers the methods used in the research: the dataset, network models, algorithm, and implementation. The experiment results and discussion section follow this. Focusing on comparing and discussing convolutional neural networks’ performance and resource consumption under different network settings and network models. In the experiment, by feeding the MRI images to the network, the network automates the results indicating whether a tumor is detected in the image and classifies the underlying tumor. The experiment results indicate Machine Learning’s great accuracy of 93.38 percent in classifying brain tumors and the significant correlations between the model’s performance and resource cost, and network settings as well as model complexity.
A time-dependent SEIRS model for COVID-19 in China (mainland)
Authors: Huang Zeyan 
Published: September 2023

Abstract
In the spring of 2022, two years after the onset of the coronavirus pandemic, a second worldwide outbreak is underway, with the number of patients infected with COVID-19 much higher than the peak during the first outbreak. Moreover, the second outbreak was not prevented in mainland China by a series of management measures with few local infections and mandatory quarantine of incoming persons. In this paper, we construct a compartmental model that includes exposed and isolated individuals to address the fact that the COVID-19 virus cannot be permanently immunized and to consider the isolation and treatment process in China. The model is used to analyze which factors have an impact on secondary outbreaks and uses the basic reproduction number to represent the outbreak. We obtained the expression for the basic reproduction number by calculating the basic reproduction number of the model. We used MATLAB to perform sensitivity analysis on each variable included in the expression for the basic reproduction number. In the sensitivity analysis, the increase in the basic reproduction number indicates that the rate of virus transmission and the rate of antibody failure are more critical to the pandemic. Reducing these rates can be effective in mitigating the epidemic. Also, the sensitivity analysis showed that increasing the vaccination rate does not help to reduce the basic reproduction number effectively. Therefore, the Chinese government should urge people to continue wearing masks to reduce the route and likelihood of the COVID-19 virus transmission and could appropriately reduce funding for vaccine promotion until a long-term effective vaccine is invented.
A 3D UAV path planning model based on improved A* algorithm and DEM data
Authors: Liu Bingqi , Lyu Yecheng 
Published: September 2023

Abstract
Unmanned aerial vehicles (UAVs) have been widely used in different tasks. Path planning is the most important factor in the operation of UAVs. This paper proposes a 3D path planning method based on an improved A* algorithm to train the UAV to fly in a real-world environment. The terrain data is first processed with a digital elevation model (DEM). Then the cost function, which balances route efficiency and environmental factors such as weather threats, is built. The available action space for the UAV is reduced considering the manoeuvrability of the vehicle. An improved A* algorithm is used to solve the search model, and the efficiency is enhanced by optimizing the data structure and building a weighted A* algorithm. Simulation results show that the method performed well in planning an optimal path in a 3D environment, and the algorithm is simple and efficient. This work contributes to path planning for UAVs in different missions.
Kernels with peripheral vison: weights of different significance
Authors: He Zixuan 
Published: September 2023

Abstract
The visual field of humankind which is generally composed of central and peripheral vison has different acuity at different spots, while in a convolution layer in convolution neural networks, different weights in a kernel are of the same significance. In order to attach weights with decreasing significance from the center of the kernel to its edge, this paper proposes kernels with peripheral vison, imitating the variable acuity of the human’s visual field. It is achieved by adding a module to the loss function, and a comparation between its implements in Residual Network (ResNet) and GoogLeNet on CIFAR100 is provided in this paper. With this method, kernels whose weights have different sensitivity of the input data are created. It will consume insignificant energy, since it is only functioned when training. The improvements in valid accuracy of ResNet and GoogLeNet are 0.6 percent and 2 percent. It can be easily attached to most structures, improving the accuracy of these structures.
A novel markov random field segmentation method for SAR images
Authors: Zhang Jiangxue , Sun Lujie 
Published: September 2023

Abstract
The synthetic aperture radar(SAR) images is difficult to be segmented by traditional Hidden Markow Random Field(HMRF)-based segmentation method, a novel HMRF-based segmentation method for SAR images is proposed in this article. The method utilizes more spacial information of SAR image model by involving the intensity differences between pixels in the potential function. An initial segmentation result is obtained by K-means method. Then accurate segmentation is carried out by the novel MRF-based segmentation method. The result showed that this method is better than the traditional HMRF one both in noise filtering and miss classification ratio.
Research on compression pruning methods based on deep learning
Authors: Cai Mingzhuo , Su Yihong , Wang Binyu , Zhang Tianyu 
Published: September 2023

Abstract
In the past decade or so, deep neural networks have continuously refreshed the best performance in tasks such as computer vision and natural language processing, and have become the most concerned research directions. Although deep neural networks have significant performance, they are difficult to deploy on hardware-constrained embedded and mobile devices due to their huge number of parameters, computational and storage costs. Therefore, in recent years, the lightweight design of network structure has gradually become a cutting-edge and popular direction, improving the operation speed and optimizing the storage space under the premise of maintaining the accuracy of the neural network. In this paper, the results achieved by domestic and foreign scholars in deep learning model compression are sorted and classified, and the methods of network pruning, model quantification, knowledge distillation, and lightweight network design are summarized, and the compression effect of related methods on known public models is summarized. Finally, the possible directions, application directions and development trends of future neural network model lightweight research are prospected.
Mechanical characteristics and stretch-bend failure analysis on ultra high frequency pulsed gas tungsten arc welded thin FSS 409/430 dissimilar joints
Authors: Nagarajan Anandharajan , Marimuthu Balakrishnan 
Published: September 2023

Abstract
The Mechanical and Stretch-Bend Failure studies on Ultra High Frequency Pulsed Gas Tungsten Arc Welded dissimilar joints of AISI409-AISI430 Ferritic Stainless Steels were conducted. Welding was conducted with 5 ultra high frequencies (50 Hz, 150 Hz, 250 Hz, 350 Hz, 450 Hz). Mechanical characteristics evaluation on the joints included tensile strength, microhardness variations across the welds and creep. Microstructural and metallurgical investigations included weld cross section evaluation, comparing grain variations in high, medium and low thermal heat affected zones, weld zones and base material region. Stretch bend failure studies included studies on angular distortion, fracture limit strain, and coefficient of friction. Tests revealed that joints welded at 350 Hz was better, compared to other joints. Dissimilar AISI409-AISI430 joint fabricated at 350 Hz exhibited 267 ± 3 MPa as yield and 409 ± 6 MPa and as ultimate tensile strength. Its creep fracture duration was 72.7 min (highest among the joints). Microstructural studies revealed grain growth, partially coarse and partially fine grains in heat affected zones. Depending on the difference in grain sizes, on both sides of the welds, heat affected regions were identified as three distinct zones. In AISI430 side; high temperature austenitic, martensitic, delta ferrites and in AISI409 side; needle like martensitic structures, mixture of ferritic-austenitic, δ-ferrite with carbide precipitation were found in high, medium and low thermal heat affected zones, respectively. On increasing the ultra high frequency pulses, angular distortion increased, fractures changed from tensile/shear type to mixed type. In shear bend tests, on increasing the ratio of radius: thickness, fracture limit strain on outer surface, across sheet thickness, due to stretching increased.
Application of lever law in dual-cluster formulas model to interpret eutectic and bulk metallic glass
Authors: Chen Jixiang , Li Shilong , Sha Xiaosong , Dou Yuehui , Wang Shuo , Cheng Yi 
Published: September 2023

Abstract
It is well known that the structure of bulk metallic glass has a close relationship with that of its crystalline eutectic phase, but how this is manifested on the atomic scale is not yet clear. The present paper further develops and optimizes dual-cluster formulas model to describe the eutectic and bulk metallic glass, as C1[(principal cluster)1(glue atom)1 or 3] + C2[(principal cluster)2(glue atom)1 or 3]. C1, C2 are the proportions governed by the lever law, and the two principal clusters which have the highest atomic close-packing efficiency and large degree of isolation are derived from the eutectic-related phases. Cu-Zr binary system are studied using this model and the results match well with the experiments. According to dual-cluster formulas and valence electrons, the correlation between deep eutectic and bulk metallic glass may be disclosed, and a detailed step towards composition design of bulk metallic glass is established as well.
Meta-learning-based estimation of the barrier layer thickness in the tropical Indian Ocean
Authors: Qi Jifeng , Qu Tangdong , Yin Baoshu 
Published: September 2023

Abstract
Accurately estimating the barrier layer thickness (BLT) is crucial for enhancing our understanding of the ocean’s role in climate variability on both regional and global scales. Here, we propose a meta-learning-based ensemble model to estimate the BLT using satellite observations in the tropical Indian Ocean. The results show that the meta-learning-based ensemble model outperforms the three individual models in terms of spatial distribution and accuracy, with significantly reduced root mean square errors in the Southeast Arabian Sea, Bay of Bengal, and eastern equatorial Indian Ocean. Furthermore, we found that sea surface salinity plays the most significant role in the estimation of BLT, highlighting the dominant influence of salinity stratification. These preliminary results provide an insight into the feasibility of predicting the BLT using satellite observations and have implications for studying the upper ocean dynamics using machine learning techniques.
Multidecadal variability of dust activity in Gobi desert and its connection with the pacific decadal oscillation
Authors: Huo Yanfeng , Bai Dongping , Liu Lin , Dong Zizhen , Xun Shangpei , He Binfang , Wu Wenyu , Chen Xintong , Zhang Hongqun 
Published: September 2023

AbstractThe multidecadal changes of dust column mass density (DCMD) in Gobi desert (GD) in spring are investigated based on the Modern-Era Retrospective analysis for Research and Applications version 2 dataset. In addition, the possible effects of the atmospheric circulation and sea surface temperature (SST) forcing on the multidecadal changes are analyzed. Results show that the dust aerosol over GD experienced a decadal change in 1999 with about 30% higher dust loading during 2000–2013 in comparison to that during 1987–1999. Further analysis indicates that the decadal change of dust aerosol over GD is attributed to the more strengthened northwesterly wind anomaly extending from lower to middle troposphere and the anticyclonic anomaly in middle troposphere over GD during the latter epoch, which is favorable to the increase of local dust activities. Furthermore, the decadal change of DCMD in GD is associated with the switch of Pacific Decadal Oscillation (PDO) phase. From 2000 to 2013, the PDO was in the negative phase, which induced to a positive potential height anomaly and northwesterly wind anomalies in the middle troposphere over GD. The dry and cold air brought by the anomalous northwesterly wind associated with the negative PDO phase reduces the relative humidity in the lower troposphere further amplify the effect of strengthened wind speed, being favorable for the increase of local dust loading and the resultant increase of DCMD there.