Automated Research Articles

Multi-parameter coupling modeling method and hybrid mechanism of concrete-encased CFST hybrid structures

Concrete-encased concrete-filled steel tubular (CFST) hybrid structures consist of encased CFST components and reinforced concrete (RC) encasement, posing unique challenges related to their hybrid mechanisms and coupling effects. This study addresses these issues by proposing an automatic and efficient Finite Element (FE) modeling method, which accounts for multiple constitutive models of confined concrete, meticulous grid division and reasonable interaction. The FE modeling method was packaged with a user-friendly graphical interface in the software ‘Auto CECFST’, which has been shared on GitHub(https://github.com/CECFST/Auto-CECFST.git). The modeling method has been verified by test results on the strength, stiffness and deformation capacity. Utilizing the refined FE modeling method, 7776 FE models are generated based on an orthogonal combination of 7 critical parameters of concrete-encased CFST hybrid structures. To accurately define failure modes, stress ratio (φ) was proposed to achieve quantitative analysis on the failure mode, followed by a comprehensive investigation into the full-range analysis of the hybrid mechanism of two components. Moreover, further exploration focused on multi-parameter coupling effects of critical mechanism characteristics, including strength, stiffness, and deformation capacity, elucidating the hybrid mechanism and mechanical similarities between single and multi-chord structures. Based on the above analysis, the balance of strength and deformation ability could be achieved by certain parameter ranges. Finally, available strength and stiffness calculation methods are validated against experimental and FE results under three typical failure modes, revealing the advantages and limitations of calculation methods on the basis of mechanics and data statistics, which provides a basis for security structural design.

A Systematic Literature Review on Automatic Sexism Detection in Social Media

Sexist content has become increasingly prevalent on social media platforms, underscoring the critical need for the development of efficient Automatic Sexism Detection methods. Previous literature reviews have not encompassed the new advancements in Automatic Sexism Detection observed over the past three years. Hence, the present study conducted a Systematic Literature Review (SLR) that examined 48 primary studies published between 2014 and 17th Sept. 2024, retrieved from six bibliographic databases. This paper aims to present a comprehensive literature review on Automatic Sexism Detection, encompassing the datasets, preprocessing techniques, feature extraction methods, text representations, classification approaches, and evaluation models employed in Automatic Sexism Detection research. The paper includes a discussion of the findings, limitations, and future research directions of the chosen articles. Additionally, it provides an overview of the conclusions drawn from the conducted research. The performed analysis reveals a lack of corpus beyond the English and Spanish language encountered in datasets, with most of the latter being annotated for either misogyny or non-misogyny. Common preprocessing techniques analyzed in the current study include lowercase conversion, text removal, tokenization, stemming, and rewriting. Discrete representations, such as TF-IDF, N-grams, and BoW, are frequently utilized, while distributed representations, like Bert and GloVe, are prominent. Bert is the predominant classification model utilized while combining lexical features can enhance the results in the majority of the discussed scenarios. Accuracy (A) and F1 score (F1) are the most widely deployed evaluation metrics in this field.

Superpixel-based principal feature clustering annotation method for dual-phase microstructure segmentation

Metallographic analysis is one of the most commonly used techniques by materials scientists for studying metal materials. The deep learning methods, which have been widely applied in metallographic images analysis, demonstrate excellent performance in this task. However, the optimization of deep learning models often relies on a substantial amount of accurately labeled samples for effective supervision. To address this issue, this paper proposes a novel automatic annotation method based on brightness and spatial distribution which is suitable for deep learning-based segmentation of optical dual-phase metallographic microstructure. The proposed automatic annotated method includes the superpixel segmentation, principal feature extraction, and clustering algorithm therefore it is referred as the superpixel-based principal feature clustering annotation (SPFCA) method. SPFCA employs discriminative criteria similar to those used by metallurgists to differentiate between metallographic structures. Furthermore, it can mitigate the occasional errors inherent in manual annotation, leading to improved performance compared to models trained with expert annotations. Experimental validation was conducted using four self-built datasets with different image qualities to test the performance of models from different perspective. Initially, hyperparameter optimization for the SPFCA method tailored to our dataset was performed. Subsequently, SPFCA was utilized to guide the optimization of the convolutional neural network employed for segmentation. The results demonstrate that the segmentation model optimized with SPFCA guidance achieved an F1 score of 0.9226 in the single dataset without the need for manual labeling, surpassing the segmentation models optimized with expert annotations.

Handling non-linearities and pre-processing in multivariate calibration of vibrational spectra

Backgroundseveral automatic pre-processing methods are available for coping with scattering effects in vibrational spectra (near and mid infrared, Raman) when linear multivariate models are applied for calibration. In contrast, almost no methods have been developed for data sets showing a non-linear relationship between instrumental signals and analyte concentrations or target properties. Resultsseveral data sets are studied, both simulated and experimental, showing different degrees of non-linearity: very low, intermediate, and high. For each of them, three multivariate models have been applied: classical partial least-squares (PLS), sequential pre-processing through orthogonalization (SO-PLS) and Kernel partial least-squares (K-PLS). The success of these models depends on the degree of non-linearity presented by each data set. The results are discussed in terms of the model structures. Significancethe need of developing new automatic pre-processing techniques for non-linear vibrational data sets is highlighted, in order to reach a similar status as the linear counterparts.

An Automatic Optimization Method for Electrical and Thermal Performance of Stacked DBC Power Module

An Automatic Optimization Method for Electrical and Thermal Performance of Stacked DBC Power Module

A high-precision automatic diagnosis method of maize developmental stage based on ensemble deep learning with IoT devices

Accurately determining the stage of crop development holds significant importance for field crop management. With the advancement of smart agriculture, an increasing number of Internet of Things (IoT) devices are being integrated into agricultural production, enabling more efficient acquisition of high-precision crop images. Currently, research on detecting crop growth stages based on IoT device images remains relatively scarce. Most existing studies rely on a single network model for detection, often encountering issues such as low accuracy and overfitting. Therefore, in this study, we collected maize images using IoT devices and constructed an integrated deep learning model by utilizing four convolutional neural networks (CNNs) to detect the growth period of maize in real time. Additionally, we implemented several improvements on these four CNNs and subsequently tested the performance of the ensemble model on the maize dataset. Regarding the ensemble strategy for the ensemble model, we proposed a dynamic weighted voting method, building upon the original voting approach, which can mitigate model training fluctuations and expedite model convergence. Ultimately, we manually simulated various lighting conditions to assess their impact on the ensemble model. Experimental results demonstrate that the ensemble deep model proposed in this paper represents a robust method for detecting maize growth stages, achieving an accuracy rate of 0.976 on the maize dataset, effectively facilitating high-precision detection of maize growth stages in complex backgrounds.

An automatic defect detection and localization method using imaging geometric features for sewer pipes

Accurate longitudinal localization of defects in sewer pipes is crucial for efficient maintenance and renewal. However, traditional closed-circuit television (CCTV)-based localization methods have been inefficient and imprecise, impeding real-time inspection performance. In this paper, we firstly point out that the inaccuracy of CCTV-based localization methods stems from the oversight of monocular imaging geometry, which leads to a deviation in sewer defect localization. Then, we propose an automated framework for sewer defect detection and longitudinal localization based on a deep-learning algorithm and monocular imaging geometry. Specifically, structural defects are qualitatively analyzed to explore their intrinsic correlation with the pipe wall. Then, the imaging geometry is leveraged to establish the projection relationship between the defects and the pipe joints. The longitudinal localization correction model for structural defects is then developed using the pipe diameter as the actual size. Our experiments show that this framework enhances the mean average precision (mAP) for multi-defects by 7.2 % and achieves a theoretical mean absolute error of 0.2 m and a practical mean absolute error of 0.28 m for defect localization, surpassing current research. Finally, with the generated detection results and localization records, the proposed framework can promote the efficiency of the sewer investigation and accelerate the development of intelligent sewer systems.

A gradient-enhanced univariate dimension reduction method for uncertainty propagation

The univariate dimension reduction (UDR) method stands as a way to estimate the statistical moments of the output that is effective in a large class of uncertainty quantification (UQ) problems. UDR's fundamental strategy is to approximate the original function using univariate functions so that the UQ cost only scales linearly with the dimension of the problem. Nonetheless, UDR's effectiveness can diminish when uncertain inputs have high variance, particularly when assessing the output's second and higher-order statistical moments. This paper proposes a new method, gradient-enhanced univariate dimension reduction (GUDR), that enhances the accuracy of UDR by incorporating univariate gradient function terms into the UDR approximation function. Theoretical results indicate that the GUDR approximation is expected to be one order more accurate than UDR in approximating the original function, and it is expected to generate more accurate results in computing the output's second and higher-order statistical moments. Our proposed method uses a computational graph transformation strategy to efficiently evaluate the GUDR approximation function on tensor-grid quadrature inputs, and use the tensor-grid input-output data to compute the statistical moments of the output. With an efficient automatic differentiation method to compute the gradients, our method preserves UDR's linear scaling of computation time with problem dimension. Numerical results show that the GUDR is more accurate than UDR in estimating the standard deviation of the output and has a performance comparable to the method of moments using a third-order Taylor series expansion.

Automatic registration of large-scale building point clouds with high outlier rates

Point cloud registration plays a crucial role in processing large-scale building point cloud data. However, existing registration algorithms face challenges in effectively handling outliers in descriptor-based correspondence. This paper presents an automatic registration method for large-scale building point clouds that is capable of achieving swift and accurate registration without the need for initial guessing. The method employs a two-step matching optimization approach: coarse (two-point)-to-fine (three-point), selecting matches based on two-point reliability and three-point consistency. Spatial transformation parameters are broken down into rotations and translations. A progressively optimized kernel function is proposed for estimating rotation, while a clustering confidence algorithm computes translation. Comprehensive experiments were conducted using real-world data. The results indicate that the approach swiftly and accurately estimates optimal outcomes when processing large-scale building point clouds with outlier rates up to 99%. Compared to six existing registration methods, the proposed approach reduces rotation error by 6.15% and translation error by 12.83%, while improving efficiency by 2.57%.

An Empirical Study on User Role Discovery Based on Clustering Algorithms and Optimizations in Location-Based Social Network

Location-Based Social Network (LBSN) has been widely used in social lives. Role is an important concept in user’s personalized analysis. Many automatic methods such as machine learning method and social network analysis method have been used in user role discovery in LBSN, however, the effectiveness of these methods has not been comprehensively analyzed. In this paper, firstly, the effectiveness of five clustering algorithms is comprehensively analyzed, including K-means algorithm, Bi-Kmeans algorithm, DBSCAN (Density-Based Spatial Clustering Application with Noise) algorithm, OPTICS (Ordering points to identify the clustering structure) algorithm and Agglomerate algorithms. Secondly, four strategies are designed to optimize the algorithm for user role discovery, namely GBK-means algorithm, RDK-means (Range and density k-means) algorithm, Canopy-based algorithm and reinforcement learning based algorithm. Thirdly, six data sets are used to validate the effectiveness of these algorithms, and the result shows that the optimization strategies are effective.

Automated MRI quantification of pediatric abdominal adipose tissue using convolutional neural networks and novel total intensity maps

ObjectiveChildhood obesity is a significant global health concern. Visceral adipose tissue (VAT) is more closely linked to the development of metabolic and cardiovascular diseases than abdominal subcutaneous adipose tissue (ASAT). This study aimed to develop a straightforward and automated method to quantify both ASAT and VAT in children from MRI. MethodsDixon-based 3 T-MRI scans were conducted on Mexican boys aged 7–9 years. Novel Total Intensity Maps were generated from these scans for input into simple 2D Convolutional Neural Networks (CNNs) for automatic prediction/quantification of ASAT and VAT. The results were compared with the commercial semi-automated quantification and validated method provided by AMRA® Researcher which is the gold standard. ResultsThe CNN-based method produced ASAT and VAT quantifications comparable to those obtained by AMRA® Researcher (ASAT, p = 0.1765; VAT, p = 0.7757), with a high correlation between the two methods for ASAT (R2 = 0.98, p < 10−14) and VAT (R2 = 0.95, p < 10−11). The reproducibility coefficients (RPC) were 0.32 L for ASAT and 0.17 L for VAT with our novel methodology. ConclusionThis study presents a simple automated method for quantifying ASAT and VAT in children from MRI data using 2D-CNN analysis of novel inputs, Total Intensity Maps. The new method produced measurements comparable to those obtained by AMRA® Researcher on the same dataset. SignificanceThe proposed automatic and straightforward quantification method, with minimal computational resource requirements, may serve as an accessible tool for researchers and clinicians in the diagnosis, follow-up, and prevention of childhood obesity.

Innovative automatic optimization method for ejectors in fuel cell vehicles based on a combined optimization strategy

Ejectors exhibit significant advantages in the field of fuel cell vehicles, playing a crucial role in promoting their development. Their fixed structure results in non-parasitic power consumption, yet this also poses greater challenges for optimizing their structural parameters across different application scenarios. However, most of the research focuses on the positions such as the nozzle and mixing section, and there is no effective method to determine the values of all parameters. In order to solve the above problems, an innovative automatic optimization method using a combined optimization strategy (COS) with a weight factor is proposed for achieving multi-objective optimization of ejectors. The COS combines parametric modeling, computational fluid dynamics, approximate modeling techniques, and multi-objective optimization to tune the full parameters. The results indicate that the COS achieves a high performance prediction accuracy with an R2 value of 0.9711 and a root mean square error of 9.23E-6. Furthermore, in the case of 300 computational samples, the computational time is reduced by 54.7%. The entrainment ratio has been increased to 4.17 times its pre-optimization level. The novel method not only ensures the simulation accuracy but also significantly enhances computational efficiency, making it a powerful tool for guiding the production and optimization of ejectors.

Global automatic detection method employing multi-level constraints for circular markers in high-speed videogrammetry

Global automatic detection method employing multi-level constraints for circular markers in high-speed videogrammetry

A Review on the Application of Automatic Text Summarization and Its Bias Analysis

Abstract. In the era of information explosion, the Internet produces a large number of text documents daily, providing rich information but also posing a challenge: how to swiftly extract key information. Traditional manual reading is time-consuming and inadequate for handling vast data. Thus, automatic text summarization technology emerges as a crucial solution. This paper reviews the application and deviation analysis of this technology across various fields, focusing on addressing shortcomings of traditional methods, such as initial cluster center selection and redundancy. An automatic text summarization method based on an improved TextRank algorithm and K-Means clustering is introduced. Existing methods often struggle with inaccurate initial clustering center selection and high summary redundancy, especially with long texts, resulting in summaries that fail to reflect core content accurately. Furthermore, the widespread use of pre-trained language models introduces potential biases that can propagate to downstream tasks, affecting summary accuracy and impartiality. To address these issues, this paper proposes an innovative automatic text summarization method that optimizes initial clustering center selection and clustering refinement strategies to enhance summary accuracy and readability. Additionally, it discusses name-nationality bias in pre-trained language models and its propagation in text summary tasks, offering a theoretical foundation and practical guidance for developing a more just and reliable Natural Language Processing (NLP) system.

Automatic Classification of Rotating Rectifier Faults in Brushless Synchronous Machines

This paper presents an advanced automatic fault classification method for detecting rotating rectifier faults in brushless synchronous machines (BSMs). The proposed approach employs a multilayer perceptron (MLP) neural network to classify the operational states of the rotating rectifier, including healthy conditions and common fault types: open-diode (OD), shorted-diode (SD), and open-phase (OP). Key machine measurements, available on an ordinary basis in the industry, such as active power (P), reactive power (Q), stator voltage (U), and excitation current (Ie), are used as inputs for this model, allowing for non-invasive, real-time fault detection. This model achieved an overall classification accuracy of 93.4%, with a precision of 94.9% for fault detection and strong recall performance across multiple fault types. The neural network’s robustness is enhanced by advanced data processing techniques, including Gaussian filtering and class balancing through the synthetic minority over-sampling technique (SMOTE). Experimental testing on a modified 5-kVA BSM setup, where rectifier faults were systematically induced, was used to train the network and validate the model’s performance. This method provides a promising tool for real-time condition monitoring of BSMs, improving machine reliability and minimizing downtime in industrial applications.

A Feature-based Shoe-last Design and its Automatic Model Construction Method in CAD Systems

A Feature-based Shoe-last Design and its Automatic Model Construction Method in CAD Systems

Research on the multi-area cooperative control method for novel power systems

As new energy sources increasingly penetrate novel power systems, the coordinated control of frequency regulation units has become more challenging, leading to the degraded performance of power grid control. The automatic generation control methods based on traditional reinforcement learning overly depend on exploration, making it difficult quickly obtain optimal solutions for multi-area cooperative control and resulting in suboptimal performance. To address this issue, this paper proposes a multi-area coordinated control method using a greedy actor-critic algorithm enhanced with expert experience replay. This method decouple the role of entropy for exploration and policy collapse, utilizing a proposal policy to secure high-value actions for policy updates. Thus the exploration and exploitation are balanced and the local optima is avoided. Additionally, the expert experience replay collects expert demonstration data with high-value to assist the learning of multi-agent. Therefore, the meaningless exploration in early training is reduced and the rapid attainment of optimal solutions is facilitated. This paper validates the proposed method through simulations on the model of the improved IEEE standard two-area load frequency control and the model of the Sichuan, Chongqing, and Hubei three-area. Compared with other various reinforcement learning methods, the proposed method is demonstrated with superior control performance.

Single plant segmentation and growth parameters measurement of maize seedling stage based on point cloud intensity

Maize growth parameters are key pieces of information describing the growth, development and yield of plants, and they are of great significance for high quality and high yield maize breeding. Plants counting, row spacing, and plant spacing are closely related to maize sowing quality and yield. Plant height is closely related to maize photosynthetic rate and biomass. The main problem with measuring phenotypic parameters during the maize seedling stage is that maize plants are small, making it difficult to segment maize plant point clouds from ground point clouds, and there is a lack of automatic methods for measuring growth parameters. A point cloud intensity segmentation and improved Euclidean clustering method for single plant segmentation was proposed based on terrestrial laser scanning (TLS) technical, and an automatic measuring method of growth parameters of maize seedling stage was realized. First, TLS was used to scan maize plants at two planting densities at V1 (first leaf is fully expanded) and V2 (second leaf is fully expanded) stages. Second, the segmentation based on point cloud intensity method and the improved Euclidean clustering single plant segmentation method were used to achieve the segmentation of a single maize plant. Finally, the automatic measurement of maize plants counting, row spacing, plant spacing, and plant height were realized. When plants counting of maize, the percentage error was lower than 1.70%. In the measurement of row spacing, plant spacing and plant height of maize plants, the mean absolute percentage error (MAPE) were lower than 1.50%, 5.50% and 3.10%, respectively. These results demonstrate that the point cloud intensity segmentation and growth parameters measurement method is feasible for maize V1 and V2 stages and different planting densities. The automatic measurement values of the number of maize plants, row spacing, plant spacing and plant height has high measurement accuracy and small error. This study provides a rapid, automatic and accurate measurement method for researchers to measure maize growth parameters.

MugenNet: A Novel Combined Convolution Neural Network and Transformer Network with Application in Colonic Polyp Image Segmentation

Accurate polyp image segmentation is of great significance, because it can help in the detection of polyps. Convolutional neural network (CNN) is a common automatic segmentation method, but its main disadvantage is the long training time. Transformer is another method that can be adapted to the automatic segmentation method by employing a self-attention mechanism, which essentially assigns different importance weights to each piece of information, thus achieving high computational efficiency during segmentation. However, a potential drawback with Transformer is the risk of information loss. The study reported in this paper employed the well-known hybridization principle to propose a method to combine CNN and Transformer to retain the strengths of both. Specifically, this study applied this method to the early detection of colonic polyps and to implement a model called MugenNet for colonic polyp image segmentation. We conducted a comprehensive experiment to compare MugenNet with other CNN models on five publicly available datasets. An ablation experiment on MugenNet was conducted as well. The experimental results showed that MugenNet can achieve a mean Dice of 0.714 on the ETIS dataset, which is the optimal performance on this dataset compared to other models, with an inference speed of 56 FPS. The overall outcome of this study is a method to optimally combine two methods of machine learning which are complementary to each other.

The Application of National Security Algorithm in Wireless Networks for Industrial Automation Process Automation (WIA-PA) Network Data Security Transmission

Industrial wireless communication technology is the key to promoting the development of factory intelligence and automation. However, wireless network data security has hindered the development of industrial intelligence. Therefore, to solve the security issues of industrial wireless networks, a network data secure transmission technology based on the national security algorithm is proposed based on industrial wireless network standard protocols. Firstly, to address the vulnerability of network node identity authentication to attacks, the national security algorithm is used to construct a node authentication model. Secondly, a data security transmission model is constructed based on the improved national security algorithm, which can securely transmit industrial wireless networks by encrypting network application layer data. In network node identity authentication, when the step size was 50, the identity authentication average accuracy of the research mode reached over 98.95%, which was better than other models. When the step size was 50, the average accuracy of identity authentication in the research model was over 98.95%, showing the best performance among similar models. In the attack test of illegal client C, when the interaction history data was within 500, the node identity authentication was higher than 99.56%. However, when the interaction history data exceeded 500, the overall performance of the research model was still the best. In the comparison of node code storage and content usage overhead, when the code quantity was 15000, the storage overhead of the research model was 87356 bytes, with more node code storage and lower memory usage. The overall performance of the research model is better. From this, the research method performs the best in both identity recognition accuracy and security in network data security. The research content will provide technical support for the security management and data transmission of industrial wireless technology.