bluuebunny/crossref_metadata_2025 huggingface dataset:
A Study of Resource Constrained Multi-Objective Scheduling Problem with Stochastic Duration of Activities
Authors: Chen Jie , Wang Lihua
Published: December 2023

This paper studies scheduling activities with stochastic duration of activities, and constructs a multi-objective optimization model, aiming at the balance between makespan and resource leveling. In this paper, a multi-objective optimization genetic algorithm based on fast non-dominated sorting (NSGA-II), is selected for solving the problem. In order to satisfy precedence constraints, an encoding scheme based on logical relationships, a crossover operator where sets are crossover units and mutation which is performed within the set are designed. Finally, the effectiveness of the proposed NSGA-II algorithm is illustrated by comparing the mutual coverage of the solution set of the improved with the original. The mutual coverage solution of sets refers to the mutual dominance between the Pareto optimal solutions produced by different algorithms.
Constrained K-Means Algorithm and Its Application in Distribution Center Location Problem
Authors: Zhan Jianjun , Xu Nan , Xu Yingqiang , Wang Jufeng , Zhang Xu , Gao Zhenyu
Published: December 2023

To solve the constrained clustering problem, this paper improves the K-means and proposes a constrained K-means algorithm (CK-means). CK-means algorithm takes into account both clustering analysis and constraints, and can effectively deal with clustering problems with constraints, such as distribution center location problem with warehouse capacity constraints, vehicle routing problem with capacity constraints, etc. It has higher practical value and a wider range of applications. There are two core innovations of the CK-means algorithm: firstly, incorporating constraints into the K-means. The second is a search strategy based on sample weights. In addition, this paper also applies the CK-means algorithm to the location problem of distribution stations at the end of JD Logistics’ supply chain. The experimental results show that the CK-means can solve the clustering problem with constraints with effect.
Teleoperation Formation Control of AUVs with State and Input Delays: A Board Learning-Based Solution
Authors: Gao Tianming , Yan Jing , Yang Xian , Guan Xinping
Published: December 2023

Formation control of autonomous underwater vehicles is regarded as a promising way in situ sensing and monitoring of marine activities. However, due to the harsh marine environment, the full autonomy is still unreachable to fulfill complex marine tasks. This letter develops a teleoperation formation control system toward human-on-the-loop for AUVs. A board learning (BL) based estimator is first designed to estimate the real-time states of master operator and slave AUVs, through which the BL-based formation controller is developed to steer AUVs to keep specific formation shape. Compared with the previous works, the BL-based estimator can capture the real-time states even with time delays, and meanwhile the BL-based formation controller can achieve bilateral teleoperation without model parameters. Simulation results are conducted to verify our solution.
Development of Automatic Plasma Gas Chromatography with Oil-Gas Separation and Gas Enrichment Functions
Authors: Liu Zien , Cao Jun , Ma Fengxiang , Zhao Yue , Wang Xiangke , Li Jianhao , Qian Ahui , Yuan Xiaofang , Song Yumei
Published: December 2023

With the continuous improvement of power grid voltage level, the requirement of equipment condition detection is also increasing. The existing detection technology of dissolved gas in oil is difficult to meet the early monitoring and warning of equipment failure of high voltage level. The technology of oil and gas separation and gas enrichment based on headspace degassing principle was studied. An automatic plasma gas chromatograph with nitrogen as carrier gas was developed. Through integrated design and intelligent control, the instrument realizes automatic analysis of transformer oil. The equipment can reduce the manual operation error and environmental interference in the detection process, with high detection precision and low detection limit. It has a good detection effect on oil samples with low concentration gas content, and has a good application prospect in the analysis and diagnosis of early faults of high voltage oil filling equipment.
Research on Autonomous Robot Navigation Algorithm in Indoor Environment
Authors: Dong Yujie , Wang Dashuai , Liu Jianhua
Published: December 2023

With the development of the times, in order to make robots more autonomous, this has led to research on autonomous navigation and other aspects. In the research of autonomous navigation, it is generally divided into two aspects. Firstly, the construction of the map requires the combination of sensors and algorithms to construct the map. During the construction process, many factors need to be considered to apply different algorithms in different scenarios and robot hardware facilities. When not applicable, it is easy to experience map misalignment and other phenomena. After building the map, the robot needs to plan its path based on the current environment to reach the desired location. For path planning, Ant colony optimization algorithms with high robustness is used to better adapt to the environment. Compared with other algorithms, Ant colony optimization algorithms has better effect in indoor small environment. On the basis of the traditional algorithm, Ant colony optimization algorithms is improved by adjusting the volatilization coefficient of Pheromone, which makes the path planning of Ant colony optimization algorithms more reasonable.
Equipment Spare Parts Demand Forecasting and Ordering Decision Based on Holt and MPG
Authors: Liu Min
Published: December 2023

By extracting the historical usage data of equipment spare parts in the equipment management platform, using Holt method to predict the demand of spare parts, and using MPG method, we can develop reasonable ordering strategy for the demand prediction of spare parts, and provide guarantee for the development of scientific spare parts reserve optimization strategy, and provide scientific decision basis for the improvement of equipment maintenance and guarantee effectiveness.
Research of Privacy-Preserving in Federated Learning
Authors: Yang Xiaojing , Min Weijuan
Published: December 2023

Federated learning is a mechanism for coordinating multiple participants to train models in response to the development of big data and artificial intelligence technologies. Federated learning not only introduces a large number of parameter exchange processes, but is also subject to attacks involving untrustworthy devices, so stronger privacy means are urgently needed to protect the data held by the parties. This paper briefly introduces the architecture and types of federated learning, analyzes the privacy risks and attack strategies, and summarizes the privacy protection technologies. By applying these technologies, this paper proposes a privacy protection scheme from three levels: center, local, local & central.
Feature Selection of High Value Patents Based on Random Forest
Authors: Li Zongze , Liu Songxian , Qiu Yihui , Yang Xin
Published: December 2023

In the process of high value patents evaluation, the important features of patents obtained through feature selection largely affect the performance of the classifier. The traditional feature selection method has the problem of single evaluation factor, and should combine multiple evaluation factors for a more comprehensive screening of features, so this paper proposes a feature selection of high value patents based on random forest, which obtains through the random forest algorithm The method obtains the influence factor, enhancement factor and importance factor of features by random forest algorithm, and combines the three factors for a more comprehensive screening of the original patent feature set to obtain the important features of high value patents. Through comparison experiments with the single factor method, it is proved that the method has better feature selection effect.
Personalized Television Program Recommendation Based on Item-Based Collaborative Filtering
Authors: Lei Chenghao , Tu Paomin , Lin Chengte
Published: December 2023

In light of the exponential growth of film and television programs, assisting viewers in discovering the desired TV programs from an overwhelming amount of information has become increasingly crucial for television service providers. This study addresses the computational limitations and personal information protection in the dataset by implementing a personalized TV program recommendation based on the item-based collaborative filtering algorithm. In addition to identifying the top-n popular programs, the research also adopts a recommendation strategy for these programs. The accuracy of successfully recommending a single program, which initially stood at 0.34, increases to 0.77 when recommending ten programs. Therefore, effective recommendations bring significant benefits to both television service providers and viewers.
A Novel Algorithm for Wireless Marine Internet of Things
Authors: Kang Jiabao , Zhang Shubo , Feng Ping , Ning Jiahong , Yang Tingting
Published: December 2023

The increasing use of wireless iot devices and edge servers in maritime environments has sparked interest in distributed edge computing research. Federated Learning (FL) has become a key solution to address communication resource consumption and data privacy issues in distributed edge computing. Nevertheless, current FL frameworks might not fully consider resource limitations in challenging marine environments. In this paper, we introduce a novel FL algorithm for signal overlapping in marine scenarios, focusing on maritime communication environment modeling, Wireless Federated Learning Overlapping (FedOverlap) algorithm design, and resource allocation optimization. We create a detailed wireless signal propagation model for maritime environments and develop an iterative FL algorithm to tackle the challenge of slow model convergence due to signal overlapping. Moreover, we suggest a resource scheduling and allocation strategy for efficient bandwidth, energy, and computation usage. Comprehensive experiments validate our FedOverlap algorithm’s properties and exhibit superior performance in accuracy, resource utilization, and convergence speed for practical network parameters and benchmark datasets in production-ready settings.
A Vulnerability Threat Assessment Model Based on Bayesian Networks
Authors: Liu Wenming , Zhai Rui , Zuo Fang
Published: December 2023

A The integration of information and network technology into industries has led to the widespread use of software. Unfortunately, these software designs may contain defects and vulnerabilities pose risks that could endanger safety. To ensure safety, it is essential to assess and analyze software vulnerabilities and implement adequate security measures. However, existing vulnerability assessment models cannot measure the severity of industrial software vulnerabilities which pose a significant challenge. To overcome this issue, a vulnerability threat assessment model for industrial software is proposed based on the Common Vulnerability Scoring System (CVSS 3.1). The proposed model is tailored to the specific characteristics of the scenarios of industrial software. The proposed model introduces three new indicator factors: device security, information security, and life safety which were inferred using a Bayesian network model. These factors are adjusted according to the lifecycle of industrial software vulnerabilities. The proposed model accurately scores vulnerabilities in industrial software examples and provides a basis for effective vulnerability repair and reinforcement in the industrial software field.
Research on Improved YOLOv7 Based Vehicle Speed and Traffic Flow Detection System
Authors: Ping An , Xue Yanfeng , Yao Bohao , Xiao Shihao , Zhang Xiang , Li Liming
Published: December 2023

The detection of speed and traffic flow is one of the important issues in the current smart city construction and intelligent transportation development process. The automatic detection of speed and traffic flow through video information in road traffic is conducive to optimising road traffic and transport management, strengthening urban management and improving the efficiency of urban transport and services. Therefore, the study of vehicle speed and traffic flow detection systems is of great practical value. To address the issue of urban road transport management, an improved YOLOv7-based vehicle speed and traffic flow detection system was designed and implemented based on traffic video data, and the system was validated and analysed based on the video data.
Accurate Detection of Third-Party Library Version of IoT Firmware Based on GNN
Authors: Guo Jingdong , Wang Ying , Wang Zhuo
Published: December 2023

Third-party libraries are widely used in IoT firmware, and different versions of libraries have different vulnerabilities. Therefore, extracting versions of third-party libraries is of great significance for discovering known vulnerabilities of IoT devices. However, identifying the version of third-party library in IoT firmware is very challenging due to cross-architecture, cross-compiler, and cross-optimization options issues. To address this challenge, we present FirmAd, a GNN-based system that accurately detects third-party libraries’ versions in IoT firmware. The system leverages a two-stage approach that calculates the similarity of different granularity features to obtain the final TPL version. We evaluate FirmAd on a large-scale dataset comprising 10,699 TPLs and achieve a version information identification accuracy of 92.68%, which is 8% higher than existing methods.
Fraud Detection Based on Graph Neural Network
Authors: Zhang Tao , Ye Haicheng
Published: December 2023

With the rapid development of digital and intelligent credit industry, credit fraud detection has become an important task to ensure financial digital intelligence. The traditional credit fraud detection model relies on artificial feature engineering and is built based on supervised learning algorithm, and its performance is greatly affected by data quality, sample distribution and other factors, and it is prone to prediction errors in the face of emerging fraud techniques. With the development of digitalization and fraud methods, these methods are often no longer applicable. There are rich information associations among users such as the users’ emergency contacts, home address, and social relationships between themselves, which make a large social network graph formed between users. In this regard, based on the Dgraph-Fin dataset, this paper uses the relationship network formed among users to better learn the weights between different edges through neighbor sampling and attention mechanism. Experimental results show that the accuracy and effectiveness are improved compared with the existing baseline.
Image Acquisition and Storage System of Binocular Camera Based on FPGA
Authors: Liu Bingkun , Wang Dashuai , Liu Jianhua
Published: December 2023

In order to better solve the problem that image processing technology is strict in real-time and stability, a binocular camera image acquisition and storage system based on FPGA is designed with the advantage of FPGA parallel processing. The system uses Xilinx ARTIX-7 XC7A35T as the master chip to control the CMOS camera as the image sensor for image acquisition, stores the acquired images in DDR3 SDRAM, and displays the images in the host computer by reading the memory data. The results show that the system has good real-time performance and stability, and meets the requirements of image transmission and processing.
Improved CBAM Rock Image Classification Based on Xception
Authors: Zhang Tingting , Ren Shuyan , Duan Hailong , Wen Shuoyi
Published: December 2023

To address the problems of traditional machine learning algorithms in rock recognition which are time-consuming, labor-intensive and low accuracy, a migration learning method using network weights for feature learning based on Xception network structure and incorporating an improved convolutional attention module (CBAM) to strengthen the important features of rocks, as well as adding a feature map perturbation structure to enhance the generalization ability of the model is proposed. Six types of manually collected rock sample images from five provinces, including Fujian and Zhejiang, are selected to construct the rock dataset and conduct experiments, and the learning rate is set at 0.001. Experiments show that the precision of the proposed method is 98.9% with 0.05 loss, which is higher than other mainstream recognition models with higher recognition accuracy and lower loss.
YOLO-UB Based Detection and Identification of Illegal Structures in the Ancient City
Authors: Ci Kang , Li Gang , Sun Jiaying , Wang Bao , Zhang Ling
Published: December 2023

To address the problem that current target detection algorithms do not work well in the task of detecting illegal building targets in the ancient city, a YOLO-UB-based algorithm for illegal building detection and recognition in the ancient city is proposed based on remote sensing image data taken by UAV. The algorithm incorporates the Coordinate attention (CA) attention mechanism to improve network robustness enhance the model’s detection ability for illegal building targets, and make target localization more accurate, and introduces the Swin Transformer V2 structure to use its own self-attention mechanism to deeply mine the target features, which enhances the global information capture capability and enables the network to better integrate multi-scale features. The algorithm is trained iteratively on a custom dataset and compared with other models. The results show that this algorithm achieves an average accuracy (mAP) of 96.8% in detecting illegal building targets, Compared to the algorithm with YOLOv7, the accuracy is improved by 3.1%, and the algorithm has better featureextraction, robustness and generalization than other target detection models.
Safety Situation Awareness Method for On-Site Safe Operation Based on Video and AI
Authors: Yang Chong , Wang Rongmin , Li Hualong , Yao Xinwei , Li Wei
Published: December 2023

Conventional field safety operation safety situation awareness methods mainly use UWB (Ultra Wide Band) tags for safety situation awareness, which is vulnerable to noise interference, resulting in high deviation of safety situation awareness prediction indicators. Therefore, a new field safety operation safety situation awareness method needs to be designed based on fusion video and AI. That is to say, using fusion video and AI to build a cloud center for on-site safe work safety situational awareness, and design an effective algorithm for operational safety situational awareness, so as to achieve on-site safe work safety situational awareness. The experimental results show that the safety situation awareness prediction indicators generated by the designed safety situation awareness method at different operation points are more consistent with the actual safety situation awareness indicators, which proves that the designed safety situation awareness method has good perception effect, reliability, and certain application value, and has made certain contributions to reducing the subsequent safety risks of on-site operations.
Knowledge Graph-Based Approach to Unstructured Multi-Source Data Fusion
Authors: Mao Zhengxiong , Chuan Tao , Zhou Jing , Su Wenwei , He Yingjun , Mei Donghui , Li Chenglin
Published: December 2023

The current unstructured multi-source data fusion has insufficient application of data attributes, which leads to unsatisfactory data fusion results. To this end, an unstructured multi-source data fusion method based on knowledge graph is proposed. Building a data tracking model to integrate unstructured multi-source datasets. Processing unstructured multi-source datasets. Associating data based on knowledge graphs. Alignment and complementary processing of data attributes to correlate unstructured multi-source data attribute fusion. The experiments show that the average fusion of the method reaches 0.92152. The fusion of unstructured multi-source data is substantially improved and has high practical application value.
SGC-GCN: Constructing Stronger Feature Fusion Ability Graph Convolution for Skeleton-Based Action Recognition
Authors: Hu Qibin , Wang Xiaotian , Wang Qian
Published: December 2023

Skeleton-based action recognition algorithms have made extensive use of graph topologies to model the connections between human skeletal joints. Constructing graph topology models with greater representational power is key to obtaining powerful feature extractors. However, existing methods are not as effective in achieving stronger modelling of associations between physically connected joints in the human skeleton. To tackle these concerns, we put forward an innovative approach involving a distinct graph topology - a graph convolutional neural network (SGC-GCN) that amalgamates a dynamic, refined graph topology with a static, shared graph topology. The dynamic partial feature extractor (CTR-GC) and the static partial feature extractor (SGC-GC) are used in conjunction with each other to obtain a stronger feature aggregation capability in the form of static reinforcement of dynamic weaker features and to achieve the objective of strengthening the correlation modelling between interconnected joints within the human skeletal structure. The combined use of the two also introduces only a small number of additional parameters, ensuring that the refined features are not affected by the noise of statically shared features. Combining SGC-GC with the temporal modelling module has resulted in the development of SGC-GCN, a graphical convolutional network with even greater feature aggregation capability. Our network demonstrates a remarkable performance surpassing existing advanced methods on the dataset(NTU RGB+D), yielding substantial advancements in action recognition accuracy.
Facial Expression Recognition in Classroom Environment Based on Attention Mechanism
Authors: Wu Yue
Published: December 2023

In view of the lack of facial expression data set in the classroom environment, the classroom expression data set was constructed, including the acquisition and preprocessing of students’ face pictures, the selection of students’ emotional categories in the classroom environment and the labeling of pictures. Based on the Resnet50 network model, a network structure with attention module is proposed, so that it can focus on the feature parts that clearly represent the target emotion in facial images, so as to enhance the accuracy of facial emotion recognition. In order to verify the effect of the model presented in this paper, training tests were carried out on the common data set of expression Fer2013 and the classroom data set constructed in this article. The results show that the structural model presented in this paper has better recognition effect and can effectively enhance the accuracy of expression recognition.
A New Remote Reading Method for Alarm Information Suitable for Power Automation System
Authors: Wang Jingyan , Ding Jie , Jiang Xiaolong
Published: December 2023

In order to achieve efficient integration and optimization of alarm information in the dispatching /distribution integration monitoring mode, it is necessary to solve the problem of remote access of alarm information between independent control and monitoring systems. This paper proposes a remote access method of the alarm information based on load balancing, type filtering, fault takeover, and safe box mechanism. Through this method, the synchronous transmission of data can be reduced, transmission efficiency can be improved, and transmission reliability can be improved, It can still ensure that alarm information is not lost in the event of some node failures. This method can provide technical support for achieving the dispatching /distribution integration monitoring mode.
Short-Term Sequential Pattern for Temporal Knowledge Graph Reasoning
Authors: Li Hongyang , Zhu Cui , Zhu Wenjun , Zhou Yunfeng , Zhang Xinyu
Published: December 2023

Temporal knowledge graphs (TKGs) have been widely used in various fields, and predicting missing knowledge graph inference has been widely explored. However, the task of reasoning about potential future facts on TKGs is more challenging and has attracted the attention of researchers. As the unknowability of future events complicates inference, a thorough study of the characteristics of historical facts becomes crucial. The study of the concurrency of historical events and the underlying common patterns of relation-ships facilitates reasoning about future facts. In this paper, we propose a novel representation learning model based on Short-Term Sequential Patterns for TKG reasoning, namely STSP. By modeling TKG sequences recurrently and learning representations of entities and relations. Specifically, the STSP encoder uses three main modules. Concurrent facts for each timestamp are modeled using a convolution-based relation-aware GCN. The entity-aware attention module is used to integrate the entity representation of the current timestamp and the previous timestamp. The sliding window mechanism is used to learn different relations sequentially. The entity and relation representations are then handed over to a translation-based decoder for final reasoning. We use four benchmark datasets to evaluate the proposed approach. The experimental results show that STSP out-performs state-of-the-art TKG reasoning methods and obtains substantial performance.
A Multi-Body Structure Optimization and Fully Coupled Hydrodynamic Simulation Method for Marine Semi-Submersible Equipment
Authors: Mao Hongyong , Jing Manjun , Fan Zhuo , Lu Jingjing , Xiao Xin , Fang Zhen , Liu Zihan , Chen Minghao , Jiang Yong
Published: December 2023

For marine semi-submersible equipment, based on the AQWA platform, the intelligent optimization design of key hydrodynamic parameters is realized by integrating Matlab algorithm development and data interaction module. The general complex kinematic pair model is constructed by the equivalent model to realize the modeling and hydrodynamic calculation of complex relative motion mechanism. Through the secondary development of the data interface, the full coupling simulation calculation of multidisciplinary and multidisciplinary fields is realized.
Evaluation and Research on Cut Stem Uniformity Based on Principal Component Analysis
Authors: Chen Jiaojiao , Li Zijuan , Sun Shuo , Lv Xuan , Wu Yue , Zhao Haiyang , Ma Yanling
Published: December 2023

In order to improve the stability and uniformity of cut stem module quality in the process of blending cut tobacco, 15 influencing factors were selected from the two dimensions of cut stem morphology and cut stem structure, and the principal component analysis was used to establish a comprehensive evaluation model for cut stem uniformity, and the cut stem structure was characterized and evaluated by combining the cut stem width distribution interval. Five principal components are extracted according to the principle that the cumulative contribution rate is greater than 80%, and the model score coefficient is calculated according to the eigenvalue. The results showed that the principal component comprehensive score of cut stem uniformity of G01 and G05 batches was lower than that of the other three batches, and the proportion of cut stem with cut stem width>1.8mm reached more than 65%. The shape of cut stem was flake, and the cut stem uniformity was poor. The characteristic width of G02 and G03 batches of cut stem filaments is about 1.5mm, and the morphology of cut stem is filiform. The comprehensive score of 5 batches of cut stem is consistent with the analysis result of the proportion of cut stem width distribution, which indicates that the principal component analysis method can quickly and accurately evaluate the uniformity of cut stem, providing a new idea for qualitative analysis of cut stem quality.
Solution and Decision of Multi-Objective Integrated Process Planning and Scheduling Problem
Authors: Ou Li , Yin Ruixue , Yang Qingbin
Published: December 2023

Effective integration of process planning and shop scheduling is the main way to solve the problems of low equipment utilization and poor production efficiency when process planning and shop scheduling are optimized separately. In order to achieve green and low-carbon manufacturing, this paper firstly takes the minimization of total carbon emissions, the maximum time of completion and delay time in the manufacturing process as the optimization goals. On account of the principle of nonlinear process planning, a multi-objective integrated process planning is established. Then, the NSGA-II genetic algorithm is used to solve the problem, and the Pareto optimal solution set is obtained by optimization; and the TOPSIS decision method based on the entropy weight method is proposed to select the optimal process route and scheduling scheme from the Pareto optimal solution set. Finally, combined with cases, the validity of the model and the solution process is verified and also provides a basis for the decision of scheduling scheme in the actual production process.
A Recommendation Model Fusing Knowledge Graph Convolutional Networks and Social Relationships
Authors: Wu Jie , Shen Lan , Jing Yanhong , Gao Chengxi
Published: December 2023

The knowledge graph’s rich semantic information can solve some of the issues with conventional recommendation. Most current knowledge graph-based recommendation models solely take into account the prospective characteristics of items, ignoring the supporting function of user potential features for recommendation systems, despite approaches like data sparsity and cold start. We suggest a recommendation model that incorporates social relationships and knowledge graph convolutional networks to solve this issue. Once more, the KGCN model is utilized to get the item feature matrix. Next, the multilayer perceptron is fed with the user feature vector, user embedding representation, and item feature vector to determine projected scores and provide user recommendations. The proposed recommendation model outperforms other benchmark models, according to experimental results that were done on the publicly accessible datasets CiaoDVD, Epinions, and FilmTrust to verify the model performance.
Target Detection of Hyperspectral Images
Authors: Zhou Jian , Wang Shuijie , Cheng Qianqian
Published: December 2023

Hyperspectral imaging can detect targets that cannot be detected in broadband remote sensing, greatly improving the ability to describe and distinguish ground object categories. However, the increase in band dimensionality also brings many problems, such as insufficient samples, high dimensionality, and a large amount of redundant information, which poses a huge challenge to feature extraction in hyperspectral images. This article reviews hyperspectral image feature extraction algorithms from four aspects: dimensionality reduction, feature extraction, feature matching, and image synthesis. Elaborated on the advantages and disadvantages of various algorithms. This article reviews classification algorithms for remote sensing images from two aspects: feature space method and spectral matching method. Elaborated on the iteration and comparison of traditional algorithms and new technologies. At the same time, from the perspective of the drone industry, the difficulties faced by existing algorithms and the development trend of hyperspectral images were elaborated. It is particularly critical to select accurate methods based on actual data in specific applications.
Research on Student Classroom Attention Detection Based on Multi-Feature Fusion
Authors: Sun Jihu , Ma Long , He Song , Xi Qiushi , Hu Jun
Published: December 2023

With the proposal of the concept of “AI+education”, the research on learner attention has received great attention from scholars. Student classroom attention is an important aspect of educational research, which directly affects their learning status and efficiency in class, and also provides objective data support for teachers to adjust classroom rhythms and teaching methods. This paper proposes a multi-feature fusion method for attention detection based on artificial intelligence, which applies multiple features such as eye, mouth, and facial expression to determine whether students are in a state of focused learning. Compared with only detecting body posture or single facial feature detection, this method improves the accuracy of detection.
Design and Implementation of Speech Signal Feature Extraction Algorithm Based on MATLAB
Authors: Yang Jianli , Yang Long , Chen Yin
Published: December 2023

Speech signal is a widely-used type of information, with the development of artificial intelligence, the processing algorithms of speech signals is also rapidly evolving. The key to speech signal processing is to extract the feature information of speech signals. This paper extracts the fundamental period and LPC parameters of speech, and detects the resonance peak information of speech signals by focusing on real-time collected speech signals and related preprocessing. Based on understanding the principles of typical algorithms, the programming implementation and algorithmic results are provided, which can be applied to many places where speech signals are used, such as speech compression, speech recognition, and speech synthesis.
Linear Regression Predicts Future Sales and Selling Prices
Authors: Xin Jiheng
Published: December 2023

linear regression is a common technique in machine learning, in the process of our use of machine learning, we will inevitably use linear regression for statistics, this time we used the correlation model of linear regression to study part of the public data set of Olist store Brazilian e-commerce in 2016-2018. The OLS method is mainly adopted, and the relevant sales volume and total sales price are predicted according to the existing data, and the relevant data in the future are predicted. In solving this problem, we have encountered many problems, including the classification of data, and which specific model can be selected to make the fitting effect better is a problem that we need to repeatedly consider.
Tracking and Positioning of Operation and Maintenance Personnel in Distribution Substations and 3D Reconstruction of Their Joint Points Based on Multiple-View Stereo
Authors: Cheng Wenzhong , Hu Yuexing , Zhang Lizhen , Zhao Lixun , Li Zhihua
Published: December 2023

It is fundamental to ensure the safety of distribution substations that the operation and maintenance personnel complete the standardized actions in the specified position. In this paper, a method of three-dimensional (3D) tracking and positioning the operation and maintenance personnel is proposed by collecting two-dimensional (2D) images, extracting 2D joint data, and fusing them to produce 3D joint data. Firstly, the calibration target is used to calibrate the multiple cameras installed in the distribution substation. Secondly, the YOLO-Pose model is adopted to extract the joint coordinates of multiple human 2D skeletons. Thirdly, the matching of different operation and maintenance personnel in different cameras is realized based on the epipolar constraint relationship of binocular cameras. The combination of the distance error obtained by the epipolar constraint relationship and the confidence level of 2D joint coordinates is used for the selection of two cameras to reconstruct 3D joint points of operation and maintenance personnel based on the principle of multiple-view stereo. The study will lay a foundation for the behavior identification and evaluation, behavior inversion of operation and maintenance personnel, and the establishment of electronic fences in distribution substations.
The Broad Category Asset Investment Forecast Solutions Based on LSTM Algorithm and Macroeconomic Development
Authors: Zhang Hongtao , Chen Yuantong , Zhang Wanying , Li Yefeng , Zeng Qiting
Published: December 2023

Globally, category asset allocation as the core investment method has been the consensus of the industry. Macroeconomic index data and market data of various asset indexes reflect different economic states of each economic cycle and can calculate the risk and return characteristics of a certain type of asset, and construct passive investment portfolio. However, for China’s investors and investment environment, we need a more suitable macroeconomic and broad asset category forecast model. In order to find high-frequency and effective macroeconomic indicators, we divided China’s macroeconomic operation status from 2001 to 2021 into different economic states. In this paper, macroeconomic environment such as economic growth, inflation and interest rate of China in the next five years is simulated through macroeconomic models or other mathematical models. According to the above calculated GDP growth rate, the consumer price index (CPI) is used to calculate the inflation rate of money and commodities, and the monthly interest rate of demand deposit is calculated to simulate and forecast the macroeconomic development situation of China in the next five years. LSTM algorithm is used for sliding window forecast of economic growth, inflation and interest rates in the next five years. The risk-return characteristics, including expected return, standard deviation of return and Sharpe ratio, of major asset indexes in the data set under various economic conditions were calculated by using the four economic conditions divided by the Merrill Lynch clock frame. The indexes listed in the data set were arranged and combined, and the risk-return characteristics of the recent five years were selected for prediction, so as to obtain the investment portfolio with relatively suitable recovery stage and high return in the next five years.
Particle Swarm Optimization Algorithm Based on Levy Flight
Authors: Su Juan , Xue Xinxin , Wen Xuhui
Published: December 2023

On account of the shortcomings of particle swarm optimization algorithm(PSO), such as poor global search capability and low convergence accuracy, levy flight [1] and Gaussian mutation [2] are used to propose particle swarm optimization algorithm based on levy flight. In iteration, the particle aggregation degree [3] is calculated, and the corresponding probability is selected to carry out levy flight [4] according to the particle aggregation degree, it strengthen the global optimization ability. Gaussian variation is carried out on particle positions of each iteration, and particles with better fitness are selected for iteration, which enhances the local search capability. At the same time, adaptive perturbation is carried out to the global optimal position of each iteration to increase the optimization precision of the optimal value. The test shows that the convergence rate of the improved algorithm is better than that of PSO, and the convergence accuracy is higher.
Optimal Allocation of Resources for Innovation and Entrepreneurship Education in Universities Based on PSO Algorithm
Authors: Ye Ping , Cheok Mui Yee
Published: December 2023

Innovation and entrepreneurship (IAE) education in universities is an important way to promote IAE among college students. In order to improve the quality of IAE education in universities, it is necessary to optimize the allocation of its resources. Through the analysis of IAE education resources in colleges and universities, this paper combined PSO (Particle Swarm Optimization) to optimize the allocation of resources, and achieved certain results in practical applications. This paper studied the resource allocation model, defined the optimization objective function, and used PSO for optimization calculation. Experimental data showed that when the number of iterations was 100, it took the longest time, but the optimization effect was the best. The optimization scheme of PSO can effectively improve the effect of IAE practice in colleges and universities, thus promoting the cultivation of students’ innovation spirit and entrepreneurial ability, and providing an effective optimization idea for colleges and universities.
Research on the Application of Marine Multi-Modal Big Data in Maritime Law Enforcement
Authors: Liu Jinchao , Zhu Li , Du Zhihong , Yu Chao
Published: December 2023

This article first elaborates on the characteristics and classification of big data for maritime law enforcement, and analyzes the current application status of big data in the field of maritime law enforcement. Subsequently, based on the research situation of big data application in the marine field both domestically and internationally, discussions were conducted on the direction of big data application towards maritime law enforcement. Finally, in response to the shortcomings in the application of big data for maritime law enforcement, and drawing on existing research experience at home and abroad, a method based on the Apriori algorithm for data mining of maritime illegal activities is proposed.
Application of Hybrid Particle Swarm Optimization Algorithm Based on Entropy in Flexible Job Shop Scheduling
Authors: Huang Yingjie
Published: December 2023

In order to better solve flexible shop scheduling problems of various scales, a hybrid particle swarm optimization algorithm based on entropy is designed by combining particle swarm optimization with simulated annealing algorithm and adaptively adjusting inertia coefficient according to population entropy. The simulation results prove that this algorithm can effectively deal with flexible operation shop scheduling problem, and it has obvious advantages compared with the traditional optimization algorithm.
A Novel Heuristics for the Strong Generalized Minimum Label Spanning Tree Problem
Authors: Long Yinyan , Li Xiaoxia , Long Zhuming , Wu Fuying , Long Hao
Published: December 2023

The strong generalized minimum label spanning tree problem (SGMLSTP) is to search the minimum label spanning tree (MLST) from an Edge-labeled graph (ELG), in which each edge is associated with one or more labels. SGMLSTP is commonly existed in reality and proven NP-hard. In recent years, researchers have proposed some algorithms; however, high computational costs are still severe obstacle, especially for large size graphs. In this paper, we propose a novel heuristics to solve SGMLSTP. We decompose the problem into two sub-problems, one is to search a connected subgraph with minimum labels from the original graph, the other is to search a spanning tree from the subgraph. As the latter sub-problem is solved, we focus on the former sub-problem and propose a community-based zigzag piloting algorithm: Firstly a label graph is derived from the original edge-labeled graph; then the label graph is partitioned and some label community (or community combinations) is chosen to form an initial solution; finally, the zigzag piloting process is applied to refine the initial solution. Label partition finds the initial solution quickly, the zigzag piloting process improves solution refinement. Experimental results on typical benchmark datasets show better effectiveness and performance of our algorithm than that of the state-of-the-art algorithms.
Fast Algorithm of Brightness and Contrast Enhancement Based on HSV
Authors: Zou Yuxiang , Zou Xiangqun
Published: December 2023

By analyzing RGB-to-HSV conversion algorithms, this paper proposes a fast algorithm for enhancing brightness and contrast based on HSV. Unlike traditional algorithm that convert RGB to HSV, enhance the brightness V, and then convert back to RGB, our approach allows for enhancing brightness and contrast directly in HSV without any conversion to RGB. Avoiding using the floating point multiplication and division, it used integer operations, shift operator and look-up table methods in this algorithm, so that improved the procession suggest admit the look-up table methods. Finally, the experiments show that the fast algorithm can save 70% of the computing time than the traditional algorithm. It can process the 720p video images real time.
An Improved Coati Optimization Algorithm Based on Multi-Strategy
Authors: Ru Xingwang
Published: December 2023

To solve the problems of the original Coati Optimization Algorithm with low exploration ability, insufficient exploitation ability and easy to fall into local optimum, an improved coati optimization algorithm based multi-strategy is proposed. Firstly, the initial solution traversal is improved through Circle chaotic mapping to lay the foundation for global search; secondly, the local optimum is jumped out through Lévy flight to improve the global search ability of the algorithm; finally, the performance of the proposed algorithm is evaluated using numerical analysis and convergence analysis in comparison with four algorithms such as PSO and WOA. The experimental results show that the algorithm in this paper has better search accuracy and convergence speed and has better performance in solving high-dimensional problems.
Research on Safety Assessment of Urban Rail Transit Station Simulation Operation Based on Multi-Source Data
Authors: Zhou Lin , Lu Yuan , He Nan , Xue Fei
Published: December 2023

In order to improve the operation safety management efficiency of urban rail transit stations, a study on simulation operation safety evaluation of urban rail transit stations based on multi-source data is put forward. Firstly, this paper starts with the construction principle of evaluation system, fully considers the characteristics of each region of the station, selects evaluation indicators for each region, analyzes the indicators and divides them into standard grades, and constructs a safety state evaluation model for station simulation operation. Secondly, taking a transfer station as an example, combined with the specific scene, the parameters of the evaluation model are estimated, and the passenger transport organization scheme of the station is simulated, and the existing problems are analyzed and the optimization scheme is put forward, which provides strong support for the station operation safety and optimization decision. Finally, a line area of a transfer station is selected for example verification and test analysis. The station is a two-line transfer station, in which the line area is an underground station with an east-west layout, and the platform is a side platform. Two lines are transferred by escalators, straight ladders and passages in the transfer building. By connecting the real data of the station to the simulation system, the analysis and evaluation of the safety state of the station operation driven by data can be realized, and the rationality and efficiency of the station operation organization scheme can be effectively improved, thus improving the digitalization and informatization level of the station.
A Comparative Study of Machine Learning Interpretability Methods in Computer Vision
Authors: Duanmu Bowen
Published: December 2023

Machine learning models have achieved great success in computer vision tasks, but the complicated architecture made it hard to explain the prediction result and put trust in its application in real life. This paper studies the machine learning interpretability methods in computer vision and summarizes its progress by classifying reference papers based on the research objects, methods, and experiments analysis. At last, this paper summarizes current research progress and gives suggestions for future research directions in the field.