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Study on the Impact Mechanism of Digital Economy on the Urban Function Level in the Yellow River Basin
WEI Wei;JIN Chenggong;ZHANG Wei;In order to quantify the impact of digital economy on urban function level, a multi-dimensional evaluation framework was established to assess both the digital economy and the urban function level. By applying the entropy weighting approach, the study quantitatively analyzed the digital economy and urban function level across the Yellow River Basin from 2012 to 2022. The fixed effects and mediation models were employed to explore the mechanism through which the digital economy influenced the urban function level in the Yellow River Basin. Additionally, the threshold effect model was employed to explore how the digital economy influences urban function level. The findings revealed that: 1) Both digital economy development and urban function level experienced a consistent upward trajectory over time in the Yellow River Basin. Moreover, the advancement of the digital economy contributed positively to the enhancement of urban function level. 2) The digital economy could indirectly promote the improvement of urban function level by enhancing the per capita scientific research funding level in cities. 3) The impact of the digital economy on urban function level was nonlinear.
Speech Depression Detection Based on Deep Depression Feature Encoding Network
LI Qi;JI Shengwen;ZHAO Di;WU Yan;XI Yang;MENG Tianyu;Aiming at the problem of feature redundancy in the speech depression dataset, a speech depression detection method based on the deep depression feature encoding network(D-DFENet) was proposed. Firstly, the Wav2vec2.0 pre-training model was utilized to extract the latent representations of speech. Secondly, a convolutional variational autoencoder module was designed. By introducing the variational autoencoder mechanism, the dimensionality reduction of the feature space was achieved, and convolutional neural networks were embedded layer by layer in the multi-layer structure of its encoder, to effectively filter out the redundant or interference information unrelated to the depressive state in the latent representations of speech. Finally, the performance of the model was evaluated on the DAIC-WOZ dataset. The experimental results showed that when the feature dimension of D-DFENet was reduced to 128, the detection accuracy reached 90%, which was superior to the existing methods in classification accuracy.
fNIRS Data Enhancement and Emotion Recognition Based on Bayesian Optimization WGAN-GP
LI Xiujun;GE Xiongxin;YANG Jingjing;Collecting large amounts of functional near-infrared spectroscopy(fNIRS) emotion data is a lengthy and tedious process. Limited data can affect training and accuracy of deep learning classification models. To address is issue of a method using Bayesian optimization with gradient penalty for Wasserstein generative adversarial networks(BO-WGAN-GP) was proposed for data augmentation. Extensive emotion classification experiments were conducted on data mixed from original and generated data using different classification models, and comparisons were made with other generative adversarial networks. The experimental results showed that data generated by the BO-WGAN-GP model performed best in fNIRS emotion recognition. The average classification accuracies for oxyhemoglobin(HbO2) and deoxyhemoglobin(HbR) reached 97.92% and 99.31% respectively.
An Interpretable Bearing Fault Diagnosis Method Based on Adaptive Wavelet Denoising Network
LIU Jing;WANG Zixuan;NIU Wei;JI Haipeng;WU Youxi;Deep learning-based bearing fault diagnosis methods often face challenges in extracting sufficient features from complex noisy operational data, and suffer from a lack of interpretability in their decision-making processes. To address these challenges, an interpretable DRWT-Trans method was introduced for bearing fault diagnosis, to overcomes challenges in feature extraction from noisy operational data and to enhance decision-making transparency. The DRWT-Trans module emploe discrete wavelet decomposition with soft thresholdto for noise reduction and feature extraction. An interpretable module was designed for model analysis increase the interpretability of the model decision. The method's validity was tested on the Case Western Reserve University dataset and a factory gearbox dataset.
Multi-strategy Harris Hawks Optimization Algorithm for Solving Steiner Tree Problems
WANG Xiaofeng;WANG Junxia;PENG Qingyuan;HUA Yingying;HE Fei;TANG Ao;To address the issues of uneven population distribution, imbalanced exploration and exploitation phases, and susceptibility to local optima in the traditional Harris hawks optimization algorithm when solving the Steiner tree problem of graph(GSTP), an improved Harris hawks optimization algorithm incorporating multiple strategies was proposed. Firstly, the algorithm was discretized using an S-shaped transfer function, and a Logistic-Sine hybrid chaotic mapping was introduced to optimize the population initialization process. Secondly, a dynamic adaptive weight strategy was designed to enhance the nonlinear expression of prey escape energy, thereby further balancing exploration and exploitation behaviors. Finally, adaptive Gaussian-Cauchy mixed mutation perturbation was applied to the optimal individuals during the later iterations to prevent the population from prematurely converging to local optima. Experiments were conducted on multiple GSTP instances, and the results showed that the proposed algorithm achieved higher solution accuracy and faster convergence speed.
Classification Method of Medical Insurance Fraud Suspects Based on Data Augmentation
MA Zongchen;DING Weilong;CAI Ruihao;SHAO Jingcheng;HE Haoyang;ZHAO Zhuofeng;To address the low accuracy in classifying fraud suspects due to imbalanced samples and difficulty in obtaining global information, a data augmentation method for classifying medical insurance fraud suspects was proposed. Firstly, a table of script vocabulary was constructed by extracting WeChat chat records, and the retrieval-augmented generation technique was employed to expand the minority categories.Key information semantically similar to suspect instances was generated and converted into a graph structure for storage in a graph database. Secondly, a graph attention network model was introduced in the graph classification stage. Combined with a mask matrix, the training process was optimized to enhance the model′s ability to extract key features, thereby achieving accurate node classification. Experimental results on real-world datasets demonstrated that compared to the graphSAGE and ChatGLM4 methods, the macro-average F1 scores of the proposed method increased by 3. 3 and 12. 9 percentage points respectively, significantly improving the classification accuracy.
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Adaptive Multiple Undersampling for Imbalanced Data Ensemble Classification
ZHANG Zhen;GUO Yujie;TIAN Hongpeng;WANG Wenjuan;An adaptive multiple undersampling ensemble classification method was proposed to address the limitations of underfitting and information loss caused by fixed sampling ratios and random sample deletion in traditional undersampling techniques when dealing with imbalanced data. Firstly, an adaptive dynamic undersampling strategy was designed in the sampling stage, which automatically determined the optimal imbalance ratio through an iterative process and generated multiple diverse training subsets, thereby overcoming the limitation of a fixed sampling ratio while retaining key sample information. Secondly, in the classification stage, an evidence fusion strategy based on conflict discrimination and dynamic weighting was proposed to collaboratively train and integrate the base classifier ensemble. Both global and local weights were comprehensively evaluated to achieve accurate sample classification. Comparative experiments conducted on multiple real-world datasets demonstrated that the proposed method achieved significant improvements in key metrics such as accuracy, precision and Matthews correlation coefficient, thereby confirming its effectiveness and superiority.
[Downloads: 30 ] [Citations: 0 ] [Reads: 12 ] HTML PDF Cite this article
Intelligent Recognition for Petroleum Drilling Conditions Based on Association Rule Mining and Dynamics Graph
SHAN Xin;YANG Zhongguo;ZHAO Zhuofeng;A drilling condition recognition method based on association rule mining and system dynamics graph construction was proposed to address the problems that multisource sensor time-series data in petroleum drilling were easily interfered by noise, that the existing deep learning methods had poor interpretability, and that traditional association rule mining methods were difficult to capture parameter mutations and dynamic associations. Firstly, the adaptive optimal smoothing window algorithm was introduced to denoise the original high-dimensional time-series data. Secondly, a correlation mining algorithm based on symbolization and mutation perception was proposed, and dynamic rules were extracted from the symbol sequences. Finally, a system dynamics graph was constructed based on the mined results to reveal the logical relationship chains of drilling parameters under different conditions. The experimental results showed that the proposed method could effectively identify the association patterns of key parameters in the drilling process, generate interpretable rule sets, and provide reliable support for intelligent monitoring and analysis in petroleum engineering.
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Weak Termination Analysis of Microservice Systems Based on Model Checking
YANG Li;ZHANG Chu;LIU Guoxi;LI Lecheng;DAI Fei;To address the automatic detection of minimum requirements for reliable interaction in microservice systems, a weak termination analysis method for microservice systems was proposed based on model checking. Firstly, microservices were modeled as labelled transition systems, and under asynchronous communication( mailbox communication) based on first-in-first-out buffers, the microservice system was formed by asynchronous composition of microservices. Secondly, in order to describe both narrow termination and generalized termination simultaneously, the traditional terminal state set was extended to a weak terminal state set. From the perspectives of deadlock-freedom and well-formedness, weak termination was then defined to formally characterize the minimum requirements for reliable interaction in microservice systems. Model checking was used to automatically check whether the microservice system satisfied weak termination on the classical microservice system case dataset. Experimental results showed that the proposed method could effectively check weak termination in microservice systems and thus ensure reliable interactions among microservice systems.
[Downloads: 8 ] [Citations: 0 ] [Reads: 9 ] HTML PDF Cite this article
Federated Prediction of Remaining Useful Life for Critical Components of Autonomous Vehicles in Multiple Ports
MA Chao;ZHANG Chengcheng;GUAN Zhibo;ZHANG Kaiqi;HUANG Hai;To address the challenge of remaining useful life prediction caused by data silos and privacy concerns among ports, a federated learning method based on long short-term memory( LSTM) was proposed. Firstly, autoencoders were locally deployed at each port to extract key features and construct a federated LSTM model. Secondly, a feature similarity-driven adaptive client grouping and weighted aggregation strategy was developed. In this strategy, clients with similar degradation patterns were grouped, and model aggregation was performed within each group based on validation performance to enhance global performance under non-independent and identically distributed conditions. Experiments conducted on a real-world port battery dataset demonstrated that the proposed method effectively mitigated model drift and significantly improved remaining useful life prediction accuracy.
[Downloads: 28 ] [Citations: 0 ] [Reads: 7 ] HTML PDF Cite this article
Second Development for Fore Treatment of ABAQUS Using Python Language
ZHONG Tong-sheng~1,WEI Feng~1,WANG Zhi~2,ZHI You-hai~1 (1.Department of Mechanical Engineering,Northwestern Polytechnical University, Xi'an 710072,China;2.Department of Civill Engineering and Architecture, Northwestern Polytechnical University,Xi'an 710072,China)The fore treatment modular of ABAQUS software is developed using Python language.The function and calling procedure of the scenarios ports and target models in the second treatment of ABAQUS are discussed.The model building and ploting grid course of ABAQUS are controlled by the Python scenarios program.Therefore,it improves the fore treatment efficiency and solves the fussy problem that apprears during the manual model building of the model which includes a lot of repeated members.
A New Extreme Learning Machine Optimized by PSO
WANG Jie,BI Hao-yang(School of Electrical Engineering,Zhengzhou University,Zhengzhou 450001,China)Extreme learning machine(ELM) was a new type of feedforward neural network.Compared with traditional single hidden layer feedforward neural networks,ELM possessed higher training speed and smaller error.Due to random input weights and hidden biases,ELM might need numerous hidden neurons to achieve a reasonable accuracy.A new ELM learning algorithm,which was optimized by the particle swarm optimization(PSO),was proposed.PSO algorithm was used to select the input weights and bias of hidden layer,then the output weights could be calculated.To test the validity of proposed method,two simulation experiments were drawn on the approximation curves of the Sinc function.Experimental results showed that the proposed algorithm achieved better performance with less hidden neurons than other similar methods.
A Review on Image Mosaicing Techniques
PEI Hongxing;LIU Jinda;GE Jialong;ZHANG Bin;School of Physics and Engineering,Zhengzhou University;Image mosaicing was analyzed and summarized comprehensively. Based on the development of this technology the background and applications were described generally. The definition and steps of image mosaicing were indicated initially,and the algorithms of image registration were examined by classifying them into several groups. For each algorithm, especially the scale-invariant feature transform( SIFT),the basic theory and characteristics were demonstrated in detail. In the last part,the classifications of image fusion were illustrated and methods of this stage,specially the weighted average,were explained briefly. In addition,the challenges and tendencies of image mosaicing were also pointed out.
Study on the Stability of Aqueous Ferrate(VI) Solution
Jia Handong Ma Ning Sun Hongbin Liu Tongliang Yang Yong (Department of Chemistry, Zhengzhou University, Zhenzhou 450052)The catalytic behaviour of Fe(OH) 3 for the hydrolysis reaction of ferrate(VI) is reported.The effect of temperature, concentration and alkalinity on stability of aqueous ferrate(VI) solutions was studied. It has been descovered that the stability of FeO 2- 4 decreased with increasing amount of Fe(Ⅲ) and with increasing concentration of FeO 2- 4, that the solubility of FeO 2- 4 was not affected by alkalinity at the concentration of OH - below 1.0 mol/L, and that dilute solution of FeO 2- 4 is stable at the normal temperature . These important laws have been used to the synthesis of potassium ferrate(VI) with satisfactory results.
Review on Infrared Dim and Small Target Detection Technology
REN Xiangyang;WANG Jie;MA Tianlei;ZHU Xiaodong;BAI Ke;WANG Jiaqi;With the continuous development of infrared detection technology, the requirements for detection distance were increasingly high. The infrared dim and small target detection technology became the focus of infrared detection. The background and significance of infrared dim and small target detection were reviewed at first. Then the current research status and latest progress of various typical methods in the field of infrared dim and small target detection were trained. After that, experimental comparisons of several different types of infrared dim and small target detection methods were presented. Finally, the research on infrared dim and small target detection technology was summarized and prospected.
Second Development for Fore Treatment of ABAQUS Using Python Language
ZHONG Tong-sheng~1,WEI Feng~1,WANG Zhi~2,ZHI You-hai~1 (1.Department of Mechanical Engineering,Northwestern Polytechnical University, Xi'an 710072,China;2.Department of Civill Engineering and Architecture, Northwestern Polytechnical University,Xi'an 710072,China)The fore treatment modular of ABAQUS software is developed using Python language.The function and calling procedure of the scenarios ports and target models in the second treatment of ABAQUS are discussed.The model building and ploting grid course of ABAQUS are controlled by the Python scenarios program.Therefore,it improves the fore treatment efficiency and solves the fussy problem that apprears during the manual model building of the model which includes a lot of repeated members.
A Review on Image Mosaicing Techniques
PEI Hongxing;LIU Jinda;GE Jialong;ZHANG Bin;School of Physics and Engineering,Zhengzhou University;Image mosaicing was analyzed and summarized comprehensively. Based on the development of this technology the background and applications were described generally. The definition and steps of image mosaicing were indicated initially,and the algorithms of image registration were examined by classifying them into several groups. For each algorithm, especially the scale-invariant feature transform( SIFT),the basic theory and characteristics were demonstrated in detail. In the last part,the classifications of image fusion were illustrated and methods of this stage,specially the weighted average,were explained briefly. In addition,the challenges and tendencies of image mosaicing were also pointed out.
Research on Trusted Cloud Computing Technologies
ZHANG Liqiang;LYU Jianrong;YAN Fei;XIONG Yunfei;With the advantages of high performance, servitization, elastic scale and environmental-friendliness, as a new IT infrastructure, cloud computing has been widely used. Because of its feature of resource outsourcing and resource renting, security and privacy requirements were of great importance. Traditional security technologies were unable to meet the requirements of security in cloud computing. So in recent years, vast security improvements and innovations were proposed in academia and industry. These schemes were used to solve various security problems in cloud computing bottom-up, and built a trusted cloud system architecture in order to achieve a secure and reliable cloud computing. Based on the security threats to cloud computing, the implementations and key technologies of trusted cloud computing were discussed. The advantages and drawbacks of the related works were summarized, and the developing directions of trusted cloud computing were discussed.
Synthesis and Characterization of Mesoporous Molecular Sieves SBA-15
ZHENG Xiu-cheng,YUAN Cheng-yuan,ZHAO Wen-ping,YANG Chun-yan,YE Wen-hao,WANG Xiang-yu(Department of Chemistry,Zhengzhou University,Zhengzhou 450001,China)Mesoporous molecular sieves SBA-15 are synthesized by conventional hydrothermal process using tetraethylorthosilicate(TEOS) as silicon source and triblock copolymer P123 as template.The synthesized samples are characterized by N2 adsorption-desorption,X-ray diffraction (XRD) and transmission electron microscopy(TEM).The results show that with SiO2/P123 ratio increasing,the pore volume and pore size of SBA-15 decrease.With the increasing of reaction solution acidity,the surface area does not change observably but the pore volume and pore size increase.The addition of co-solvents can increase the pore volume and pore size to a certain extent and PhMe3 is the best one.
A Survey on the Construction and Application of Knowledge Graphs Driven by Large Language Models
ZHANG Kunli;WANG Ying;FU Wenhui;ZHU Yongqi;ZHANG Yanli;ZAN Hongying;With the support of large language models, knowledge graphs, characterized by their structured and semantically rich features, enhanced data association and interpretability. This provided new research directions and application potential for complex knowledge reasoning and intelligent decision-making. Therefore, from the perspective of knowledge graphs, the latest progress in the construction and application of knowledge graphs driven by large language models was summarized. Firstly, the new methods for building knowledge graphs were explored from the perspectives of knowledge modeling, information extraction, knowledge integration, and knowledge graph completion. Secondly, the applications of knowledge graphs in augmenting large language models, improving retrieval capabilities, and achieving mutual enhancement with large language models were investigated. Finally, the future directions of combining large language models with knowledge graphs were discussed.
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Journal of Jilin University(Science Edition)
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