bluuebunny/crossref_metadata_2025 huggingface dataset:
Hydrodynamic performance analysis of dual vertical axis turbine
Authors: Wu Aiping , Ma Tianli , Wang Shiming , Ding Chengling , Liu Boting
Published: August 2024

This article adopts a biomimetic approach and uses the profile of the fastest sailfish in the ocean as the airfoil profile of a water turbine. Integrating centrifugal and axial flow turbines on one drive shaft can effectively integrate wave and current energy (wave current integration). The wake of a centrifugal turbine flows into the flow field of an axial flow turbine, and based on the Kantar effect, the energy harvesting coefficient of the axial flow turbine is improved. The five blades of the axial flow turbine and centrifugal turbine are subjected to uneven forces, resulting in torsional and radial forces on the drive shaft, causing periodic series motion of the turbine and reducing the overall lifespan of the machine.
Numerical simulation study on the cold thread rolling forming of Ti-6Al-4V alloy fasteners with external threads and their defects.
Authors: Zhou Qiang , Zhang Jiawei
Published: August 2024

A three-dimensional numerical finite element model for cold rolling forming of external threads on Ti-6Al-4V alloy fasteners for aerospace applications was established using DEFORM-3D. This model was employed to investigate the characteristics of the cold rolling forming process and analyze the defects associated with the rolling operation. The stability of the ratio between torque and tangential load at the radius value of the rolling wheel (72.5) validates the accuracy of the model. The research results indicate that due to the unevenness of the extrusion force, the metal flow velocity is higher adjacent to the tooth-shaped surface compared to the central position. This causes the metal on both sides to accumulate and squeeze towards the middle position at the tooth top, ultimately resulting in tooth top folding defects.
Research and application of self-diagnosis and automatic intervention functions for turbine regulating valve malfunctions in nuclear power plants
Authors: Gan Changxian , Jiang Xiaolong , Li Min , Li Huahuan , Li Xianhai , Li Jiaxuan
Published: August 2024

The inlet regulating valve of the turbine is very important to the control of the turbine. If the control is abnormal, it will create some uncontrollable safety risks, which may cause the turbine to trip, even to over-speed. The study of the structure and control mode of the turbine regulating valve shows some defects in the design of the turbine regulating valve in a nuclear power plant. In this paper, through the analysis of the fault mode, the method of fault self-diagnosis is studied, verified, and improved so that it has perfect self-diagnosis and intervention means, improves the reliability of turbine valve control, and eliminates the hidden danger of tripping.
Rated flow coefficient of geometric compensation control valve
Authors: Li Zhong , Wang Wei , Ming You , Cheng Fengguang , Geng Shengtao , Yu Hongbin , Ye Xiaojie , Xu Tingting , Ni Yulong
Published: August 2024

Aiming at the large design error of the rated flow coefficient of existing control valves, it was difficult to meet the requirements of the equipment system. This study adopts the principle of infinite approximation to the existing flow coefficient calculation equation for geometric shape correction compensation, based on which the rated flow coefficient design method was proposed, and simulation and test verification were carried out. The results show that according to the design method, the rated flow coefficient of the control valve can be accurately designed. The variation rule of the simulated value and the test value under the two kinds of spool inner parts is consistent, and the maximum error is not more than 10%, which meets the requirements of industrial standards. The experiment verifies the accuracy and applicability of the design method.
Fault diagnosis and analysis of automobile capacitive touch display false triggering
Authors: Liu Tingting , Mao Ling , Chen Xiaoling , Li Zhifeng , Nie Jing
Published: August 2024

Nowadays, automobile handling is mainly concentrated in the central control display. The use of high-definition and large-size capacitive touch displays has become the mainstream trend. Driving information and entertainment information are displayed on it, and the operation of the electronic control function of the car is generally realized by touching the touch panel of the central control display. As the integration of the central control display becomes higher, the fault of the display needs to be paid more attention to. The common faults include display faults and touch faults. The display fault will affect the display of vehicle information, and the driver cannot obtain vehicle status information; the touch fault will affect the car operation and can not carry out human-machine interaction. Therefore, once the central control display fails, it will affect the safety driving of the car. This paper studies the false triggering failure which is a kind of touch fault of the vehicle’s 9-inch capacitive touch display. Firstly, we test the parasitic capacitance of faulty parts and repeat the touch failure. After testing and analyzing, it has been found that the mechanical structure design of the display and the air gap bonding process can change the value of parasitic capacitance which leads to false triggering. This paper proposes solutions to solve this failure which are changing the wideness of glue to avoid glue overflow and strengthening size management to improve the spacing between the touch panel and the TFT display screen. After the implementation of the solutions, the false triggering failure no longer occurs, which proves that the solutions are effective and feasible.
Fuzzy PID control based on genetic algorithm optimization inverted pendulum system
Authors: Li Tiehong , Li Jin , Jiang Junbang , Liu Xinyu
Published: August 2024

For the first-order inverted pendulum control system, a fuzzy PID control system based on the optimization of the genetic algorithm is proposed. The traditional genetic algorithm has the problem that the difference in the fuzzy subset parameter leads to a decrease in the interpretative ability of the fuzzy system. The main problem of the current genetic algorithm is the complexity of the computation and the low efficiency. Based on this problem, this paper proposes an improved genetic algorithm, i.e., it adopts the variance operator and adaptive change of the variance index and elite retention strategy, which solves the premature and local convergence problems of the standard genetic algorithm, in order to optimize the fuzzy system. The experimental results show that the optimized genetic algorithm gives full play to the advantages of fuzzy control in terms of interpretability and robustness, and at the same time guarantees the prediction accuracy, which provides a new research idea in the field of artificial intelligence control.
DAT-Net: Filling of missing temperature values of meteorological stations by data augmentation attention neural network
Authors: Guo Xinshuai , Hou Tianrui , Wu Li
Published: August 2024

For a long time, filling in the missing temperature data from meteorological stations has been crucial for researchers in analyzing climate variation cases. In previous studies, people have attempted to solve this problem by using interpolation and deep learning methods. Through extensive case studies, it is observed that the data utilization rate of convolutional neural networks based on PConv is low at a high missing rate, which will result in the poor filling performance of each model at a high missing rate. To solve these problems, a Data Augmentation Attention Neural Network (DAT-Net) is presented. DAT Net uses encoder and decoder structures, which include a data augmentation training mechanism (DAM) to enhance model training. In addition, a time encoder (TED) has been developed to assist the model in learning the temporal dependencies of the data. To evaluate DAT-Net, 75% and 85% of experiments were performed, while comparisons were made with Linear, NLinear, DLinear, PatchTST, and GSTA-Net. The results showed that when the missing rate was 75%, DAT-Net decreased by 55.22%, 55.05%, 55.18%, 28.73%, and 12.35% on MAE and 54.08%, 53.88%, 54.08%, 35.48% and, 14.51% on RMSE,
R
2
increased by 3.80%, 3,75%, 3.68%, 0.55%, and 0.27%, respectively.
Path planning and collision avoidance approach for a multi-agent system in grid environments
Authors: Tian Delei , Zheng Bin
Published: August 2024

In multi-robot systems, ensuring efficient, safe, and conflict-free navigation for each robot is crucial, especially in scenarios requiring the coordination of complex tasks and interactions with the environment. Currently, most related research focuses on simplified grid environments, making it difficult to apply these findings to real-world scenarios. This paper introduces actual physical constraints into traditional grid environment path planning and designs a set of motion rules suitable for grid environments and a collision avoidance algorithm. We also propose a novel multi-agent path planning approach that integrates deep reinforcement learning with collision avoidance algorithms. Through a series of experiments, we have demonstrated that this method effectively guides agents in navigating from their starting points to their targets within grid environments that possess physical constraints, and significantly reduces the probability of collisions when used in conjunction with our collision avoidance algorithm. These findings not only showcase the efficacy of our approach but also open new avenues for deploying multi-agent systems in more complex real-world scenarios.
Power positioning control system for rake suction dredger
Authors: Zhao Ganyu , Yu Menghong
Published: August 2024

Rake suction dredgers are specialized vessels used for dredging projects in water bodies. Power positioning refers to the precise control of the vessel’s position in the water using power systems to complete operational tasks or maintain specific positions. This paper proposes a PID control algorithm based on reinforcement learning to address the accuracy and stability issues of power positioning in rake suction dredgers. The algorithm utilizes a deep Q-network as the core of reinforcement learning, combined with PID control principles, to enable the intelligent agent to control the vessel in real time. In algorithm design, the problem of overestimation is effectively addressed through a dual neural network structure, and an experience replay mechanism is introduced to enhance training efficiency and stability. In the reinforcement learning process, a reward function suitable for the characteristics of rake suction dredgers is designed, considering the balance between path-tracking accuracy and vessel stability. Experimental results demonstrate that the proposed algorithm can achieve power positioning control of rake suction dredgers under different sea conditions, with high path tracking accuracy and stability, providing an effective solution for the autonomous navigation and operation of rake suction dredgers.
Global and local complementary multi-path feature fusion network for the classification of crop remote sensing images
Authors: Zhang Keqian , Cui Tengfei , Wu Wei , Zhang Yichi , Zhang Ziqian , Si Qiliang , Song Xiangyang , Cheng Gang
Published: August 2024

The accuracy and efficiency of crop distribution information extraction are pivotal in ensuring global food security. In long-time-series optical satellite data, most existing methods focus on extracting spatial features using Convolutional Neural Networks (CNNs), which do not adequately mine and model the spatial-temporal information. The development of the attention mechanism allows for the extraction of global features in remote-sensing images of long temporal sequences. To extract global attentional features with complementary features in crop remote sensing images, we propose a Global and Local Complementary Multi-path Feature Fusion Network (GLMP), which is capable of extracting global features from remote sensing images of long temporal sequence, that enhances the local characteristics of crop images derived from CNNs, thus obtaining more effective multi-scale complementary features. This extraction of features enhances the comprehension of crop images, thereby boosting the performance of associated tasks. Within GLMP, we introduce two pivotal modules: the Hybrid Attention and Convolutional Paths Module (HACM) and the Multi-path Feature Fusion Module (MPFM). These modules synergistically converge multi-path features, yielding more discriminative feature information. Experimental results on the ZueriCrop dataset show that the proposed GLMP technique is effective; it performs promisingly having a total accuracy of 90.2% and an F1 value of 62.5%. Furthermore, the ablation study verifies the substantial improvement in classification accuracy for remote sensing crop images of long-time series in nature, specifically attributed to the HACM and MPFM modules.
Traffic light detection algorithm based on improved YOLOXs
Authors: Nie Zhiling , Jiang Shaochen , Wang Liejun
Published: August 2024

The detection and recognition of traffic lights play a vital role in driverless and assisted driving technology, given the rapid advancements in autonomous driving. Vehicles can receive crucial information about traffic signals by precisely locating and identifying traffic lights to ensure safety. To make the traffic light detection model more lightweight and improve the detection speed, this paper proposes an improved YOLOXs traffic light detection algorithm. Firstly, the Ghost module is used to enhance the backbone model of YOLOXs in lightweight, reduce the parameters quantity of the network, and enhance the detection speed. Then, the CBAM module is added to the YOLOX neck model to direct the model’s focus toward the target area and ensure the accuracy of traffic light detection. Many experiments on the S2TLD public datasets display that the enhanced model has fewer parameters and achieves accelerated detection speed, up to 112 FPS, which can achieve real-time detection effects. At the same time, in contrast to the primary network, the accuracy of the enhanced network is improved by 1%, reaching 97%. The recall rate increased by 1.2% to 96.2%; average accuracy increased by 0.3% to 97.1%.
3DMMF: 3D object detection network based on multi-layer and multi-modal fusion
Authors: Zhou Jia , Xu Limei , Ma Wenhao , Zhou Zhiguo , Zhou Xuehua , Shi Yonggang
Published: August 2024

Cameras and LiDAR are important sensors in autonomous driving systems that can provide complementary information to each other. However, most of the LiDAR-only methods outperform the fusion method on the main benchmark datasets. Current studies attribute the reasons to the misalignment of views and difficulty in matching heterogeneous features. In particular, the single-stage fusion method makes it difficult to fully fuse the features of the image and point cloud. In this work, we propose 3DMMF: a 3D object detection network based on multi-layer and multi-modal fusion methods. 3DMMF works by painting and encoding point clouds in the frustum proposed by the 2D object detection network. Then the painted point cloud is fed to the LiDAR-only object detection network which has expanded channels and a self-attention mechanism module. Finally, CLOCs are used to match the geometric direction features and category semantic features of 2D and 3D detection results. Experiments on KITTI datasets show that this fusion method has a significant improvement over the baseline of the LiDAR-only method, with an average mAP improvement of 6.3%.
Multi-source data fusion for power outage warning based on transformer model
Authors: He Jindong , Lei Shanshan , Wu Lijin
Published: August 2024

In this study, we propose a method that uses the Transformer model to enhance power outage alerts by integrating data from multiple sources. By integrating data from various sources, including operational data from the local distribution network and meteorological data, we have constructed a comprehensive multi-source data framework for power outage warnings. The Transformer model, known for its ability to capture complex dependencies and patterns, has been employed to extract features and make accurate predictions. Results on actual power system data have shown that our approach significantly boosts the accuracy and stability of predictions. The fusion of multi-source data has enabled timely maintenance and protection measures, reducing the duration and impact of power outages. The findings from this study have provided valuable insights for power outage warnings and future research on multi-source data fusion.
An improved container-based deep forest model for predicting groundwater recharge
Authors: Liu Bao , Sun Yaohua , Gao Lei
Published: August 2024

This paper proposes ICDF, an improved container-based deep forest model, for effectively modeling and predicting groundwater recharge. The model consists of four points: the construction and expansion of the container module, the assignment of weights to the base model, the growth of the cascade layer, and the decision output. First, container modules are created and a maximization objective function is assigned to each container to control its growth. Next, different weights are assigned to each base model based on its contribution to container prediction. Cascade layers are built using container modules until the model prediction decreases. Finally, the average prediction vectors of the last cascade layer are output. The model’s performance is evaluated and compared with DF and its base models (random forest, adaptive boosting, and extreme gradient boosting) using a case study of 1549 bores in New South Wales, Australia. Remarkably, compared to DF, ICDF has improved prediction accuracy by 6.66%. Moreover, it outperformed the RF, AdaBoost, and XGBoost by 2.94%, 5.85%, and 5.3% in prediction performance, respectively. The ICDF exhibited superior capabilities in predicting groundwater recharge, offering significant improvement over existing models. Practitioners are encouraged to consider adopting ICDF for groundwater management.
Smith GA-PID feedback control of hot strip final rolling temperature
Authors: Wang Saisai , Tian Hai , Tian Jiaming , Wu Zijing , Li Chao , Tao Zhen
Published: August 2024

At present, the hot strip finishing temperature control system generally adopts a computer model or mechanism model to predict the strip’s final rolling temperature, which is open-loop control. To realize the precise control of the final rolling temperature of high-performance steel, this paper introduces the PID feedback control strategy at the end of the finishing zone, to realize the high-precision temperature control of the strip steel. The Smith compensator is used to predict the compensation for the large hysteresis problem of the final rolling temperature, and various intelligent algorithms are used to optimize the three parameters of the PID controller, eliminate the nonlinear problem, and obtain better dynamic performance. After the experiment, it is concluded that the Smith GA-PID optimization control strategy system has a small overshoot, the peak time and regulation time are the shortest, and the dynamic response is the fastest. Compared with the traditional PID control method, the overshoot is reduced by 97.6%, and the required regulation time is reduced by 7.85 s.
Fault detection of industrial processes using attention-based gated recurrent unit autoencoder with skip connection
Authors: Wang Yanan , Jin Qibing , Li Dazi , Wang Bin , Zhang Yang , Zhu Qian
Published: August 2024

With the continuous evolution of modern industrial technology, industrial production has grown progressively complex, necessitating the use of various sensors to measure multiple process variables. However, intricate temporal dependencies and nonlinear relationships between data presented by multivariate sequences pose significant challenges to process fault detection. In response to these challenges, this paper proposes an attention-based gated recurrent unit autoencoder with skip connection (SAGRU-AE) model for monitoring large-scale, nonlinear, and multivariate industrial process faults. SAGRU-AE combines gated recurrent units, multi-head self-attention, and autoencoder to extract features from multivariable time series data efficiently. Concurrently, feature reuse is achieved through the skip connection structure, which improves the accuracy of data reconstruction. Based on the implementation of process data feature extraction and input reconstruction in SAGRU-AE, two statistics have been developed, namely the H2 statistic and square prediction error (SPE) statistic, for fault detection tasks. Ultimately, the feasibility and effectiveness of the proposed algorithms are validated through experimentation on the TE process.
Machine anomalous sound detection based on audio synthesis generative adversarial network
Authors: Cheng Tianyao , Guo Fabin
Published: August 2024

In the pursuit of implementing safety monitoring within Industry 4.0 smart production, deep learning methodologies are employed to harness the audio signals emanating from the operation of machinery and equipment, facilitating anomaly detection. Aiming to address the challenge of the few-shot samples for anomalous sound detection in an unsupervised setting, this paper introduces a machine anomalous sound detection method using an Audio Synthesis Adversarial Generative Network (ASGAN). The augmentation of classifiers within the network’s discriminator is proposed to execute a multi-classification task, thereby transforming the unsupervised training milieu into a semi-supervised one and enhancing the discriminator’s accuracy. To address the oversight of audio frequency domain features by conventional generative adversarial networks, the squeeze-and-excitation network is applied to concentrate on crucial frequency bands within the frequency domain. Experimental validation is conducted by using the DCASE Challenge Task2 dataset, revealing that the performance of the anomaly detection method leveraging audio synthesis generative adversarial networks surpasses that of the baseline system.
Improvement of defect detection for steel based on YOLOv7 algorithm
Authors: Zhu Guiwei , Zheng Guanghai
Published: August 2024

Working on the conundrum of low efficiency of strip surface defect detection, a surface defect detection algorithm for boards and strips leveraging YOLOV7 is introduced. First, the global attention mechanism is set up to enhance global information interaction and expression capabilities, and improve detection performance; in second place, the C2f component is integrated into the feature pyramid network to the lightweight pattern; ultimately, Focal-EIoU is exerted to replace loss function of the YOLOv7 pattern to solve the divination box and the problem of wrong amplification of length and width, improving the accuracy of defect classification and positioning. The outcomes reveal that the precision of the elevated algorithm has been boosted by about 4.3%, the recall rate has reached 78.3%, and mAP increased by 3.4%. This technique exhibits high accuracy and efficiency in detecting surface defects on the tape.
Research on PCB tiny displacement estimation method for ORB feature-guided FREAK descriptor enhancement
Authors: Yang Duo , Su Yang , Wu Hongwei , Yi Pengfei
Published: August 2024

Given the hurdles posed by existing techniques for identifying microscopic offsets in miniature printed circuit boards (PCBs), including their low accuracy and high iteration count, this study presents an innovative approach that measures micrometer offsets by utilizing the ORB feature-guided FREAK descriptor enhancement. It improves matching outcomes with a brute-force matcher and RANSAC via ORB feature extraction to guide FREAK descriptors for better feature uniqueness. A PCB small offset detection algorithm is developed with a homography matrix and a theoretical offset model. A novel distance threshold performance evaluation method for this suggested matching algorithm that objectively evaluates matching results is also proposed in this article. The proposed matching method outperforms alternative algorithms in precision and recall, according to its experiments. This research conducts simulation detection of PCB calibration bare boards and instance verification of real PCB boards by constructing a PCB tiny offset detection experimental system. The results show that micron-level detection precision can be achieved without iteration.
A data augmentation method for multiaxial fatigue life prediction based on physics-informed tabular generative adversarial network
Authors: He Gaoyuan , Zhao Yongxiang , Yan Chuliang
Published: August 2024

Currently, in the multiaxial fatigue life prediction problem, machine learning (ML) algorithms are still limited by small samples. For ML tasks requiring large amounts of data, this paper proposes a Conditional Tabular Generative Adversarial Network (CTGAN) model using a physical equation as a generation constraint. The proposed method can make synthetic samples satisfy specific physical knowledge. The method is evaluated using two datasets of different materials. ML-based algorithms verify the feasibility of using synthetic datasets for life prediction. Verification analysis shows that the proposed method successfully synthesizes high-quality multiaxial fatigue datasets and effectively improves the prediction accuracy of ML algorithms.
Identification of emitting times based on improved CRNN model
Authors: Liang Cuiping , Li Yang , Wei Yuyun , Zhang Dan , Wang Ying , Jiang Suiping
Published: August 2024

The number of times that equipment can emit is limited, and exceeding the usage limit will affect its shooting accuracy. Existing research mainly focuses on the classification and detection of emitting times, with few studies focusing on the recognition of emitting times. This paper combines multi-scale feature extraction with the CRNN model and proposes an improved CRNN emission frequency recognition model. After experimental verification, the improved model has a high prediction accuracy on high-quality emitted sound data, with an accuracy of over 95.2%. The accuracy of the data in the presence of strong interference is 88.6%. The experimental results show that the method proposed in this paper is effective.
Time-optimal trajectory planning for manipulator based on an improved sparrow search algorithm
Authors: Xu Hanwen , Gao Wengen , Zhu Jiaming , Zhang Guoqing
Published: August 2024

With the development of industrial automation, it is crucial to use manipulators for efficient painting. To solve the problem of inefficient trajectory planning of manipulators in the point-to-point painting process, this paper proposes a time-optimal trajectory planning method based on an improved sparrow search algorithm. Initially, the standard D-H parameter method is used to establish the kinematic model of the manipulator. To ensure the continuity of each joint in the manipulator, a 3-5-3 polynomial interpolation function is utilized to construct the motion trajectory in joint space. Subsequently, the improved sparrow search algorithm is utilized to optimize the point-to-point motion trajectory of the manipulator with a constraint on motion time. Finally, simulations carried out in MATLAB demonstrated that the manipulator, which was optimized by using the improved sparrow search algorithm, exhibited a significant increase in motion efficiency compared to its pre-optimization state. Additionally, the movements of the manipulator’s joints were smooth.
Research on dynamic bottleneck prediction methods in manufacturing workshops
Authors: Xia Maoyu , Lin Shuo , Han Zhonghua
Published: August 2024

To address the challenge of identifying bottleneck responsibilities when multiple bottlenecks arise in process workshops and predicting bottleneck trends before their occurrence, a novel dynamic prediction method based on the Markov chain is introduced. This approach effectively identifies all bottlenecks in a multi-bottleneck process shop and identifies the primary bottleneck unit with the highest level of responsibility, enabling anticipation of bottleneck shifts prior to their occurrence. Initially, the concept of bottleneck is elaborated, accounting for the intricate interplay among manufacturing processes, leading to the development of a static mathematical model for multi-bottleneck prediction incorporating parameters such as time, cost, and quality. This model evaluates the bottleneck severity of each manufacturing unit, facilitating the identification of all bottleneck units and the assignment of bottleneck responsibilities, thereby establishing primary and secondary bottlenecks. Subsequently, a bottleneck state transition matrix is formed based on historical bottleneck processes, initializing the state matrix through the process with the highest bottleneck responsibility in the static setting. Leveraging the Markov chain prediction technique, the method anticipates the location of bottleneck transitions preceding the occurrence of bottleneck drift. Finally, simulations were conducted in an authentic production line environment to validate the method’s efficacy. Results indicate a 9.1% increase in the main bottleneck coincidence rate compared to single-bottleneck prediction models. Moreover, the Markov chain prediction accurately forecasts bottleneck trends prior to drift occurrences, boasting an 86.4% accuracy rate. These findings underscore the applicability and guidance potential of the proposed method.
Hybrid soil strength prediction model for geotechnical ground investigation using convolutional neural network and ensemble learning
Authors: Zhang Xuqun , Li Zhili , Sui Yaohua , Liu Chengjun , Li Zhaofeng
Published: August 2024

A hybrid model of soil strength prediction from the image data and feature data of soil was proposed, which is aimed at providing an efficient solution in geotechnical ground investigation by leveraging the power of AI. In this model, CNN purely with the encoder was used to establish the relationship between image data and strength parameters of soil, while ensemble learning stacked of three base learners, i.e., KNN, LASSO, and LSVM, was applied to the feature data. Then, the CNN part and the ensemble learning part were integrated into the loss function. Results show that the convergence of the hybrid model was slower than that of the ensemble learner. However, with the aid of soil image which has more features of soil in essence, the predicted soil strengths by the hybrid model better matched with the actual ones, compared with the ensemble learner and base learners. This hybrid model offers an effective framework of multi-modal data fusion for geotechnical engineering, by leveraging the synergy of the high-dimensional feature extraction capabilities of CNN and the generalization abilities of ensemble learning.
A study on civil engineering construction simulation technology based on artificial intelligence for civil buildings
Authors: Yi Jiachen , Liu Qian , Fan Liwei , Zhang Jiahao , Sun Haoyun
Published: August 2024

This research delves into utilizing Artificial Intelligence (AI) in civil engineering, specifically construction simulations. The primary objective is to reconcile the discrepancies between conventional structural analysis techniques and the actual performance of structures. By integrating AI technologies—neural networks, cellular automata, and support vector machines— with data mining, this research proposes a novel approach for simulating civil building constructions. Traditional methods, often constrained by underlying assumptions, fail to capture the complex behaviors of engineering structures accurately. The developed AI simulation framework utilizes experimental and on-site data to construct digital models, enabling precise prediction of structural behaviors without the limitations of traditional assumptions. Results indicate that AI-enhanced simulations can significantly improve the accuracy of structural analysis, demonstrating potential for wider application in civil engineering. This advancement promises to enhance the predictability, efficiency, and safety of civil constructions, marking a significant step forward in the field.
Stability analysis and anti-windup design of saturated switching systems based on multiple Lyapunov functions
Authors: Guo Rui , Zhang Xinquan
Published: August 2024

In this paper, employing advanced multiple Lyapunov functions (MLF) technology, we conduct a thorough investigation into the fault-tolerant characteristics of actuator saturation-based systems, particularly those prone to failures. Based on this comprehensive analysis, we carefully design an anti-windup (AW) controller, incorporating an efficient AW compensator and precisely calibrated gains for the dynamic state feedback controller, tailored to meet the specific needs of practical control systems. To further demonstrate the effectiveness of our approach, we also provide a detailed numerical example.
Detection of pedestrians and vehicles on foggy roads based on improved YOLOv7
Authors: Geng Shuai , Niu Datian , Qin Changjie , Bai Caiyun , Hao Zeran
Published: August 2024

Nowadays, object detection technology plays an indispensable role in the direction of automatic vehicle driving. However, inclement weather, such as fog and haze, poses severe challenges to the ability to understand visual perception and scene understanding of automatic driving technology, making it extremely difficult. Therefore, this paper proposes an improved YOLOv7 algorithm. Firstly, Gaussian blur and Gaussian noise are used to process the original data set, reducing the sensitivity of our model to image information, improving the ability to adapt to changing environments and tasks, and enhancing the generalization performance. Then, using the SIOU loss function, we optimize the algorithm to accelerate the convergence speed while considering the vector angle problem. Add Coordinate Attention integrates accurate position information into the coding network, optimizes the recognition accuracy of the model for a specific position, significantly improves its overall feature extraction ability, and captures key features more accurately, thereby improving the overall performance, capturing key information, and reducing the possibility of missed detection. The introduction of CoordConv into ELAN in the backbone allows for a more sensitive perception of the position of objects during image processing, thus improving the accuracy of its recognition and positioning. The RepGhostNet structure is introduced into the neck to realize the implicit reuse of features. According to the experiments in this paper, our improved YOLOv7 algorithm achieves 87.3% map in the RTTS data set and partial synthetic fog KITTI data set, which improves by 3.9% based on the original model.
Research on named entity recognition method for high-speed railway technology transformation project text
Authors: Lian Xiaoqin , Bao Kexin , Gao Chao , Gong Yonggang , Wu Yanhua , Cheng Zhibo
Published: August 2024

High-speed railway technology transformation projects are crucial to the layout of high-speed rail transportation production, high-speed rail safety, and high-speed rail operation and management. However, the data of high-speed railway technology transformation projects is mainly stored in the form of unstructured text, and the relevant personnel of high-speed railroad often need to manually go through the form to extract the key information of the project data and enter it into the system, with a low degree of automation and intelligence. Therefore, to improve the efficiency and quality of processing the data of high-speed railway technology transformation projects, and help the intelligent development of high-speed railways, this paper constructs the RoBERTa-BiLSTM-CRF fusion model to automate the identification of key information entities in the data of high-speed railway technology transformation project. The experimental data were selected as technology transformation project documents of a railway bureau from 2013 to 2022, and six defined entity types were identified. Experimental results show that the constructed model can effectively identify six types of defined entities, and the F1 value of each type of entity is above 90%. In addition, compared with other commonly used models, the F1 value has been significantly improved. The fusion model proposed in this paper can be used as a method to automatically extract key information for high-speed rail technology transformation projects.
Experiment and path optimization on Large-Space Multi-Area crowd evacuation
Authors: Chen Peizhu , Zeng Li , Zhong Yaojun
Published: August 2024

In large spaces where there are multiple evacuation zones, the flow patterns during evacuation are intricate, leading to congestion in emergency scenarios, and affecting evacuation efficiency. To address this, we proposed an optimization method for crowd evacuation paths in large-space multi-area environment settings under fire scenarios by utilizing multi-agent simulation. Using Pathfinder software, disorganized evacuation simulations are conducted within expansive multi-zonal settings to pinpoint areas with inadequate evacuation capabilities. Subsequent evacuation experiments are performed for verification, facilitating adjustments to agent-based model parameters for evacuees in fire scenarios, and carrying out organized evacuation simulations. Through meticulous simulation analysis, we aim to optimize the most efficient evacuation paths under organized responses in large-space multi-area settings. Results indicate that in emergency conditions due to fire, the evacuation speeds reach 1.8 m/s, and significant reductions in total evacuation time are achieved through simulation optimization, with the time required for the slowest individuals to evacuate decreasing from 240.32 s to 209.32 s. Furthermore, the flow rate per second at congested exits diminishes from 2.01 persons/s to 1.39 persons/s, highlighting the efficacy of the proposed path optimization method.
Research on key algorithm optimization strategy of oscillographic blood pressure measurement
Authors: Wang Long , Luo Yiqun , Tang Zhenghua
Published: August 2024

In this paper, adaptive filtering and wavelet de-baseline drift algorithms are proposed for the signal preprocessing of the oscillographic blood pressure measurement algorithm. On feature extraction, fuzzy logic classification and dynamic programming tracking methods are designed. The blood pressure model of support vector machine and adaptive neural network is constructed. Active learning and cross-domain modeling are introduced to correct individual differences. This study comprehensively optimizes the key algorithm of oscillographic blood pressure measurement, and significantly improves the measurement accuracy, stability, and individual adaptability of the system.
A Human Activity Recognition model based on CNN and Transformer
Authors: Wang Man , Liu Rutong , Xiong Yong
Published: August 2024

This study aims to utilize data from built-in sensors in smartphones for human activity recognition. By analyzing the three-dimensional accelerometer and gyroscope data in user behavior, accurate classification of eight common activity states is achieved, including walking, standing, sitting, squatting, going up stairs, going down stairs, climbing ladders, and descending ladders. To enhance the model’s generalization capability, a method combining Transformer neural networks with one-dimensional Convolutional Neural Networks (CNNs) is employed, along with data sample augmentation. Experimental results demonstrate a significant improvement in recognition accuracy compared to traditional models, indicating the potential for real-time application on smartphones and other devices. This approach provides essential technical support for predictive human-computer interaction on smart devices and holds extensive application prospects.
Speech emotion recognition method based on time-aware bidirectional multi-scale network
Authors: Zhang Liyan , Du Jiaxin , Li Jiayan , Wang Xinyu
Published: August 2024

In response to the difficulty of traditional speech emotion recognition models in capturing long-distance dependencies in speech signals and the impact of changes in speaker pronunciation speed and pause time, this paper proposes a new time emotion modeling method called Time Perceived Bidirectional Multi-scale Network (TIM-Net), which is used to learn Multi-scale contextual emotion expression in different time scales. TIM-Net starts by acquiring temporal emotional representations using time-aware blocks. Subsequently, information from different time points is combined to enhance contextual understanding of emotional expression. Finally, it consolidates various Timescale features to better accommodate emotional fluctuations. The experiment shows that the network can focus useful information on features, and the WAR and UAR of TIM-Net are significantly better than other models on RAVDESS, EMO-DB, and EMOVO datasets.
Drone inspection path planning with self-organizing neural network of elastic Hebbian learning rule
Authors: Li Rui , Zhang Lei , Yi Chenying , Lu Yufeng , Chen Liangyuan , Su Yi , Huang Wei
Published: August 2024

As the scale of the power grid continues to expand, inspection scenarios become increasingly complex, and inspection and maintenance work in the power industry faces huge challenges. The traditional manual inspection mode has problems such as high cost, low efficiency, and poor safety. Using electric drones to inspect all equipment nodes in the area can effectively reduce operation and maintenance costs and ensure personnel safety. As a key technology to realize autonomous power inspection, UAV path planning needs to overcome difficulties such as long solution time and intersection of inspection paths. Therefore, this article conducts research on UAV path planning algorithms for large-scale power inspections, which is of great significance to improving the efficiency of power inspections. To avoid the occurrence of path intersections and improve the path planning effect, this paper uses the convex hull property in graph theory to design elastic Hebbian learning rules and proposes a self-organizing neural network algorithm based on elastic Hebbian learning rules.
Land morphology analysis with LiDAR technology to increase oil palm production
Authors: Wijayanti S I , Hadi I P , Tanjung A A , Islami J D , Adilah A H A , Pulungan N A H J
Published: August 2024

The morphological condition of the land plays an important role in determining the quality of growth of oil palm plants. Integrated management of oil palm plantations is a key factor in increasing productivity. Analysis of land morphology in oil palm plantations is a crucial first step. The objectives of this study were (1) to characterize land morphology with LiDAR implementation, (2) to explain the LiDAR mechanism in the NDVI class classification used for determining the oil palm Vegetation Index, and (3) to provide ideas to optimize oil palm productivity. The study was conducted using the image interpretation method from the acquisition of LiDAR data which has a resolution of 3 x 3 cm in the form of DEM and orthophoto to be able to perform land surface morphology analysis (MPL) and NDVI flatfoot. The data needed in this study is Orthophoto, which is used to crosscheck field conditions, plant canopy conditions, and populations of plants per block. NDVI processing is used to determine the Vegetation Index to interpret the health of oil palm plants. The results showed that LiDAR technology can be used to determine the health of oil palm plants. The overall accuracy and reliability value of NDVI reached 88.33% and 88.13%, respectively. This shows that the value of NDVI can predict the health of oil palm plants and can be used to monitor them effectively.
Ecological, Socioeconomic, and Economic Factors Shaping Vegetable Oil Production in the Batak People Community of North Sumatera
Authors: Bernadtua Simanjuntak Marudut , Sawitri Wulandari Retno , Barasa Larsen , Soritua Sijabat Panderaja , Mudakir  , Gunawan Malau April
Published: August 2024

This research delves into the intricate interplay between ecological, socioeconomic, and economic factors in the production of vegetable oils, with a specific focus on the Batak People community in Tapanuli-Toba Samosir, North Sumatra. The research highlights the profound importance of considering these multifaceted aspects within the context of farmers’ labour rights and community empowerment, thereby illuminating the industry’s broader implications. The study is structured into three principal dimensions: ecological consideration, socioeconomic dimensions, and economic viability. The study assesses farmers’ working conditions and wages, their engagement with national and international labour standards, and the extent to which the vegetable oil industry influences the socioeconomic well-being of the Batak People community. It analyses market dynamics, value chain processes, and the influence of government policies and subsidies on the industry’s economic prospects. By doing so, the research aims to discern how the vegetable oil industry can contribute to the region’s broader economic development. To foster knowledge exchange and collaborative problem-solving, an international seminar is proposed. This seminar will bring together academics, government researchers, industry experts, and community representatives. The event’s objectives include sharing research findings, addressing industry challenges, expanding networks, and crafting a sustainable framework for the vegetable oil sector. This research, alongside the international seminar, seeks to enhance our comprehension of encouraging sustainable practices and formulating policies that can serve as models for similar regions.
Socio-economic analysis of oil palm farmers in Kuantan Singingi District, Riau
Authors: Marbun Firdaus
Published: August 2024

Oil Palm is one of the most important sources of vegetable oil today. It has become more critical when utilised as a renewable energy source. These advantages make it a commodity of choice for both farmers and corporations. Despite controversies such as deforestation, oil palm expansion is unstoppable because more and more people invest and convert their agricultural land into oil palm plantations. One of the cases happened in Kuantan Singingi District, where many farmers have converted their agricultural land into oil palm plantations. This study explains the factors influencing farmers to convert their plantation land into oil palm plantations. This study also demonstrates the impact of the conversion on improving their economy. The expectation of high profits in oil palm yields influences the diversion of the land. However, the conversion to oil palm is not always paralleled with improving farmers’ welfare. Through a qualitative approach using FGDs and interviews with oil palm farmers in two villages in Kuantan Singingi District, Land conversion to oil palm plantations carried out by Kuantan Singingi farmers is predominantly influenced by technical factors, market access and prices. The transformation that occurred has not been able to improve their economy. Some indications of this failure are a lack of business capital, limited land ownership, and limited knowledge of oil palm cultivation.