Truck model recognition for an automatic overload detection system based on the improved MMAL-Net

Efficient and reliable transportation of goods through trucks is crucial for road logistics. However, the overloading of trucks poses serious challenges to road infrastructure and traffic safety. Detecting and preventing truck overloading is of utmost importance for maintaining road conditions and ensuring the safety of both road users and goods transported. This paper introduces a novel method for detecting truck overloading. The method utilizes the improved MMAL-Net for truck model recognition. Vehicle identification involves using frontal and side truck images, while APPM is applied for local segmentation of the side image to recognize individual parts. The proposed method analyzes the captured images to precisely identify the models of trucks passing through automatic weighing stations on the highway. The improved MMAL-Net achieved an accuracy of 95.03% on the competitive benchmark dataset, Stanford Cars, demonstrating its superiority over other established methods. Furthermore, our method also demonstrated outstanding performance on a small-scale dataset. In our experimental evaluation, our method achieved a recognition accuracy of 85% when the training set consisted of 20 sets of photos, and it reached 100% as the training set gradually increased to 50 sets of samples. Through the integration of this recognition system with weight data obtained from weighing stations and license plates information, the method enables real-time assessment of truck overloading. The implementation of the proposed method is of vital importance for multiple aspects related to road traffic safety

Transcriptomic evidence for involvement of reactive oxygen species in Rhizoctonia solani AG1 IA sclerotia maturation

Rhizoctonia solani AG1 IA is a soil-borne fungal phytopathogen that can significantly harm crops resulting in economic loss. This species overwinters in grass roots and diseased plants, and produces sclerotia that infect future crops. R. solani AG1 IA does not produce spores; therefore, understanding the molecular mechanism of sclerotia formation is important for crop disease control. To identify the genes involved in this process for the development of disease control targets, the transcriptomes of this species were determined at three important developmental stages (mycelium, sclerotial initiation, and sclerotial maturation) using an RNA-sequencing approach. A total of 5,016, 6,433, and 5,004 differentially expressed genes (DEGs) were identified in the sclerotial initiation vs. mycelial, sclerotial maturation vs. mycelial, and sclerotial maturation vs. sclerotial initiation stages, respectively. Moreover, gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analyses showed that these DEGs were enriched in diverse categories, including oxidoreductase activity, carbohydrate metabolic process, and oxidation-reduction processes. A total of 12 DEGs were further verified using reverse transcription quantitative PCR. Among the genes examined, NADPH oxidase 1 (NOX1) and superoxide dismutase (SOD) were highly induced in the stages of sclerotial initiation and maturation. In addition, the highest reactive oxygen species (ROS) production levels were detected during sclerotial initiation, and enzyme activities of NOX1, SOD, and catalase (CAT) matched with the gene expression profiles. To further evaluate the role of ROS in sclerotial formation, R. solani AG1 IA was treated with the CAT inhibitor aminotriazole and H2O2, resulting in the early differentiation of sclerotia. Taken together, this study provides useful information toward understanding the molecular basis of R. solani AG1 IA sclerotial formation and maturation, and identified the important role of ROS in these processes

The Overseeing Mother: Revisiting the Frontal-Pose Lady in the Wu Family Shrines in Second Century China

Located in present-day Jiaxiang in Shandong province, the Wu family shrines built during the second century in the Eastern Han dynasty (25–220) were among the best-known works in Chinese art history. Although for centuries scholars have exhaustively studied the pictorial programs, the frontal-pose female image situated on the second floor of the central pavilion carved at the rear wall of the shrines has remained a question. Beginning with the woman’s eyes, this article demonstrates that the image is more than a generic portrait (“hard motif ”), but rather represents “feminine overseeing from above” (“soft motif ”). This synthetic motif combines three different earlier motifs – the frontal-pose hostess enjoying entertainment, the elevated spectator, and the Queen Mother of the West. By creatively fusing the three motifs into one unity, the Jiaxiang artists lent to the frontal-pose lady a unique power: she not only dominated the center of the composition, but also, like a divine being, commanded a unified view of the surroundings on the lofty building, hence echoing the political reality of the empress mother’s “overseeing the court” in the second century during Eastern Han dynasty

MULTIPLE POSITIVE SOLUTIONS OF STRUM-LIOUVILLE EQUATIONS WITH SINGULARITIES

The existence of multiple positive solutions for Strum-Liouville boundary value problems with singularities is investigated. By applying a fixed point theorem of cone map, some existence and multiplicity results of positive solutions are derived. Our results improve and generalize those in some well-known results. Copyright © 2006 Zenggui Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1

Digital Twin-Driven Rear Axle Assembly Torque Prediction and Online Control

During the assembly process of the rear axle, the assembly quality and assembly efficiency decrease due to the accumulation errors of rear axle assembly torque. To deal with the problem, we proposed a rear axle assembly torque online control method based on digital twin. First, the gray wolf-based optimization variational modal decomposition and long short-term memory network (GWO-VMD-LSTM) algorithm was raised to predict the assembly torque of the rear axle, which solves the shortcomings of unpredictable non-stationarity and nonlinear assembly torque, and the prediction accuracy reaches 99.49% according to the experimental results. Next, the evaluation indexes of support vector machine (SVM), recurrent neural network (RNN), LSTM, and SVM, RNN, and LSTM based on gray wolf optimized variational modal decomposition (GWO-VMD) were compared, and the performance of the GWO-VMD-LSTM is the best. For the purpose of solving the insufficient information interaction capability problem of the assembly line, we developed a digital twin system for the rear axle assembly line to realize the visualization and monitoring of the assembly process. Finally, the assembly torque prediction model is coupled with the digital twin system to realize real-time prediction and online control of assembly torque, and the experimental testing manifests that the response time of the system is about 1 s. Consequently, the digital twin-based rear axle assembly torque prediction and online control method can significantly improve the assembly quality and assembly efficiency, which is of great significance to promote the construction of intelligent production line

An Improved Entropy-Weighted Topsis Method for Decision-Level Fusion Evaluation System of Multi-Source Data

Due to the rapid development of industrial internet technology, the traditional manufacturing industry is in urgent need of digital transformation, and one of the key technologies to achieve this is multi-source data fusion. For this problem, this paper proposes an improved entropy-weighted topsis method for a multi-source data fusion evaluation system. It adds a fusion evaluation system based on the decision-level fusion algorithm and proposes a dynamic fusion strategy. The fusion evaluation system effectively solves the problem of data scale inconsistency among multi-source data, which leads to difficulties in fusing models and low fusion accuracy, and obtains optimal fusion results. The paper then verifies the effectiveness of the fusion evaluation system through experiments on the multilayer feature fusion of single-source data and the decision-level fusion of multi-source data, respectively. The results of this paper can be used in intelligent production and assembly plants in the discrete industry and provide the corresponding management and decision support with a certain practical value

Digital Twin-Driven Adaptive Scheduling for Flexible Job Shops

The traditional shop floor scheduling problem mainly focuses on the static environment, which is unrealistic in actual production. To solve this problem, this paper proposes a digital twin-driven shop floor adaptive scheduling method. Firstly, a digital twin model of the actual production line is established to monitor the operation of the actual production line in real time and provide a real-time data source for subsequent scheduling; secondly, to address the problem that the solution quality and efficiency of the traditional genetic algorithm cannot meet the actual production demand, the key parameters in the genetic algorithm are dynamically adjusted using a reinforcement learning enhanced genetic algorithm to improve the solution efficiency and quality. Finally, the digital twin system captures dynamic events and issues warnings when dynamic events occur in the actual production process, and adaptively optimizes the initial scheduling scheme. The effectiveness of the proposed method is verified through the construction of the digital twin system, extensive dynamic scheduling experiments, and validation in a laboratory environment. It achieves real-time monitoring of the scheduling environment, accurately captures abnormal events in the production process, and combines with the scheduling algorithm to effectively solve a key problem in smart manufacturing

Research on Switch Machine Fault Prediction Based on CHMM

Objective As an important component of urban rail transit signal equipment, once the switch machine malfunctions, the operation will be seriously affected. Monitoring and predicting its health status is particularly important. Method A fault prediction method for switch machine based on CHMM (continuous hidden Markov model) is proposed. The features of the switch machine degradation state are extracted, and the original input data dimension is reduced based on t-SNE algorithm to reduce the redundant features. Spectral clustering algorithm is used to determine the optimal number of degradation states, make clustering segmentation and analyze the degradation state features of the switch machine action power curve. Based on CHMM model and fault diagnosis model, the switch machine fault prediction is realized by constructing degradation state identification model and fault identification model. The fault prediction method for switch machine based on CHMM is verified through measured data. Result & Conclusion With the normal operation power curve of the switch machine as the research object, the above method taps the monitored data deeply, and the extracted degradation state features have good expressive ability. According to the matching results between the curve model in severely degraded state and the normal curve model, the fault types of switch machine can be predicted when the power of the switch machine fluctuates abnormally

Fault Diagnosis of Intelligent Production Line Based on Digital Twin and Improved Random Forest

Digital twin (DT) is a key technology for realizing the interconnection and intelligent operation of the physical world and the world of information and provides a new paradigm for fault diagnosis. Traditional machine learning algorithms require a balanced dataset. Training and testing sets must have the same distribution. Training a good generalization model is difficult in an actual production line operation process. Fault diagnosis technology based on the digital twin uses its ultrarealistic, multisystem, and high-precision characteristics to simulate fault data that are difficult to obtain in an actual production line to train a reliable fault diagnosis model. In this article, we first propose an improved random forest (IRF) algorithm, which reselects decision trees with high accuracy and large differences through hierarchical clustering and gives them weights. Digital twin technology is used to simulate a large number of balanced datasets to train the model, and the trained model can be transferred to a physical production line through transfer learning for fault diagnosis. Finally, the feasibility of our proposed algorithm is verified through a case study of an automobile rear axle assembly line, for which the accuracy of the proposed algorithm reaches 97.8%. The traditional machine learning plus digital twin fault diagnosis method proposed in this paper involves some generalization, and thus has practical value when extended to other fields

The Effects of <i>Trichoderma viride</i> T23 on Rhizosphere Soil Microbial Communities and the Metabolomics of Muskmelon under Continuous Cropping

The continuous cropping can restrict the large scale and intensive cultivation of muskmelon, and the use of Trichoderma preparation to alleviate the negative effects is an effective mean. Although the impact on rhizosphere soil microbial communities and metabolites after applying Trichoderma are still unclear. In this study, we applied the fermentation broth of Trichoderma viride T23 to muskmelon under continuous cropping, collected rhizosphere soil samples at 60 days after transplantation, and investigated the changes in the microbial communities and metabolites of muskmelon by using high−throughput sequencing and metabolomic analysis, respectively. The results showed that T. viride T23 could effectively reduce the disease index of muskmelon wilt (65.86 to 18) and significantly increase the soil pH value (6.06 to 6.40). Trichoderma viride T23 induced drastic shifts in the richness, structure, and composition of rhizosphere microbial communities, and Proteobacteria, Bacteroidetes, and Actinobacteria were the dominant bacterial phyla. Bioactive substances such as scopoletin, erythronic acid, and palmitic acid were significantly upregulated in the rhizosphere soil, which enhanced soil activity. Overall, T. viride T23 resolves the continuous cropping limitation in muskmelon by improving soil physicochemical properties, elevating the biomass and diversity of soil microbial communities, and stimulating the production of soil active substances