Integrating Higher-Order Dynamics and Roadway-Compliance into Constrained ILQR-based Trajectory Planning for Autonomous Vehicles

This paper addresses the advancements in on-road trajectory planning for Autonomous Passenger Vehicles (APV). Trajectory planning aims to produce a globally optimal route for APVs, considering various factors such as vehicle dynamics, constraints, and detected obstacles. Traditional techniques involve a combination of sampling methods followed by optimization algorithms, where the former ensures global awareness and the latter refines for local optima. Notably, the Constrained Iterative Linear Quadratic Regulator (CILQR) optimization algorithm has recently emerged, adapted for APV systems, emphasizing improved safety and comfort. However, existing implementations utilizing the vehicle bicycle kinematic model may not guarantee controllable trajectories. We augment this model by incorporating higher-order terms, including the first and second-order derivatives of curvature and longitudinal jerk. This inclusion facilitates a richer representation in our cost and constraint design. We also address roadway compliance, emphasizing adherence to lane boundaries and directions, which past work often overlooked. Lastly, we adopt a relaxed logarithmic barrier function to address the CILQR's dependency on feasible initial trajectories. The proposed methodology is then validated through simulation and real-world experiment driving scenes in real time.Comment: 6 pages, 3 figure

Design on the Winter Jujubes Harvesting and Sorting Device

According to the existing problems of winter jujube harvesting, such as the intensive labor of manual picking, damage to the surface of winter jujubes, a winter jujube harvesting and sorting device was developed. This device consisted of vibration mechanism, collection mechanism, and sorting mechanism. The eccentric vibration mechanism made the winter jujubes fall, and the umbrella collecting mechanism can collect winter jujube and avoid the impact of winter jujube on the ground, and the sorting mechanism removed jujube leaves and divided the jujube into two types, and the automatic leveling mechanism made the device run smoothly in the field. Through finite element analysis and BP (Back Propagation) neural network analysis, the results show that: The vibration displacement of jujube tree is related to the trunk diameter and vibration position; the impact force of winter jujubes falling is related to the elastic modulus of umbrella material; the collecting area can be increased four times for each additional step of the collection mechanism; jujube leaves can be effectively removed when blower wind speed reaches 45.64 m/s. According to the evaluation standard grades of the jujubes harvesting and sorting, the device has good effects and the excellent rate up to 90%, which has good practicability and economy

Induction of cytoprotective autophagy in PC-12 cells by cadmium

AbstractLaboratory data have demonstrated that cadmium (Cd) may induce neuronal apoptosis. However, little is known about the role of autophagy in neurons. In this study, cell viability decreased in a dose- and time-dependent manner after treatment with Cd in PC-12 cells. As cells were exposed to Cd, the levels of LC3-II proteins became elevated, specific punctate distribution of endogenous LC3-II increased, and numerous autophagosomes appeared, which suggest that Cd induced a high level of autophagy. In the late stages of autophagy, an increase in the apoptosis ratio was observed. Likewise, pre-treatment with chloroquine (an autophagic inhibitor) and rapamycin (an autophagic inducer) resulted in an increased and decreased percentage of apoptosis in contrast to other Cd-treated groups, respectively. The results indicate that autophagy delayed apoptosis in Cd-treated PC-12 cells. Furthermore, co-treatment of cells with chloroquine reduced autophagy and cell activity. However, rapamycin had an opposite effect on autophagy and cell activity. Moreover, class III PI3 K/beclin-1/Bcl-2 signaling pathways served a function in Cd-induced autophagy. The findings suggest that Cd can induce cytoprotective autophagy by activating class III PI3 K/beclin-1/Bcl-2 signaling pathways. In sum, this study strongly suggests that autophagy may serve a positive function in the reduction of Cd-induced cytotoxicity

110 cases unstented cases

110 cases unstented case

Research on Unbalanced Vibration Suppression Method for Coupled Cantilever Dual-Rotor System

The cantilever dual-rotor system is a typical structure of the blade output end of the open rotor engine and the coaxial output turboshaft engine. The excessive unbalanced vibration of a coupled cantilever dual-rotor system is one of the main factors limiting the application of the above engine type. In order to accurately describe the vibration coupling effect between the dual-rotor-intermediate bearings, the unbalanced response of the cantilever dual-rotor system is analyzed, and the self-sensitivity coefficient is proposed to guide the selection of measuring points and vibration suppression experiments for the dual-rotor system. On this basis, a new online automatic balance actuator applicable to this dual-rotor system is designed, and a feasibility experiment is carried out. The experimental results indicate that: (1) The self-sensitivity coefficient can be used as the basis for the actual vibration measuring point arrangement and unbalanced vibration suppression strategy of the dual-rotor system, and the proposed step-by-step vibration suppression strategy can reduce the vibration of the dual-rotor system by more than 80%. (2) The designed online automatic balance actuator can reduce the unbalanced vibration by 53% in 3.52 s. The proposed method in this study can provide guidance for the vibration suppression of the dual-rotor system

Research on Unbalanced Vibration Suppression Method for Coupled Cantilever Dual-Rotor System

The cantilever dual-rotor system is a typical structure of the blade output end of the open rotor engine and the coaxial output turboshaft engine. The excessive unbalanced vibration of a coupled cantilever dual-rotor system is one of the main factors limiting the application of the above engine type. In order to accurately describe the vibration coupling effect between the dual-rotor-intermediate bearings, the unbalanced response of the cantilever dual-rotor system is analyzed, and the self-sensitivity coefficient is proposed to guide the selection of measuring points and vibration suppression experiments for the dual-rotor system. On this basis, a new online automatic balance actuator applicable to this dual-rotor system is designed, and a feasibility experiment is carried out. The experimental results indicate that: (1) The self-sensitivity coefficient can be used as the basis for the actual vibration measuring point arrangement and unbalanced vibration suppression strategy of the dual-rotor system, and the proposed step-by-step vibration suppression strategy can reduce the vibration of the dual-rotor system by more than 80%. (2) The designed online automatic balance actuator can reduce the unbalanced vibration by 53% in 3.52 s. The proposed method in this study can provide guidance for the vibration suppression of the dual-rotor system

Thermal Defect Detection for Substation Equipment Based on Infrared Image Using Convolutional Neural Network

Thermal defects of substation equipment have a great impact on the stability of power systems. Temperature is crucial for thermal defect detection in infrared images. The traditional detection methods, which have low efficiency and poor accuracy, record the temperature of infrared images manually. In this study, a thermal defect detection method based on infrared images using a convolutional neural network (CNN) is proposed. Firstly, the improved pre-processing method is applied to reduce background information, and the region of interest is located according to the contour and position information, hence improving the quality of images. Then, the temperature values are segmented to establish the dataset (T-IR11), which contains 11 labels. Finally, the CNN model is constructed to extract features, and the support vector machine is trained for classification. To verify the effectiveness of the proposed method, precision, recall, and F1 score are adopted and 10-fold cross-validation is employed on the T-IR11 dataset. The results demonstrate that the accuracy of the proposed method is 99.50%, and the performance is superior to that of previous methods in terms of infrared images. The proposed method can realize automatic temperature recognition and equipment with thermal defects can be recorded systematically, which has significant practical value for defect detection in substation equipment

Surface defect detection of steel strips based on classification priority YOLOv3-dense network

The steel strip is one of the essential raw materials in the machinery industry. Besides, the defects on the surface of the steel strip directly determine its performance. To achieve rapid and effective detection of surface defects on steel strips, a CP-YOLOv3-dense (classification priority YOLOv3 DenseNet) deep convolutional neural network was proposed in the present study. The model used YOLOv3 as the basic network, implemented priority classification on the target images, and then replaced the two residual network modules in the YOLOv3 network with two dense network modules. Therefore, the model can receive the multi-layer convolution features output by the dense connection block before making predictions, consequently enhancing the reuse and fusion of features. Finally, the six kinds of surface defects of steel strips were detected by the improved network model, and the results were compared with other deep learning networks. According to the results, the recognition precision of the CP-YOLOv3-dense network model is 85.7%, the recall rate is 82.3%, the mean average precision is 82.73%, and the detection time of each image is 9.68ms. The mean average precision is 6.65% higher than the original YOLO network and 10.6% higher than the DNN network. In addition, the detection speed is 1.77 times faster than the Faster RCNN network. The proposed CP-YOLOv3-dense network has stronger robustness and higher detection precision, which can be used for the identification of various steel strip surface defects

The Influence of Temperature on the Capacity of Lithium Ion Batteries with Different Anodes

Temperature is considered to be an important indicator that affects the capacity of a lithium ion batteries. Therefore, it is of great significance to study the relationship between the capacity and temperature of lithium ion batteries with different anodes. In this study, the single battery is used as the research object to simulate the temperature environment during the actual use of the power battery, and conduct a charge and discharge comparison test for lithium iron phosphate battery, lithium manganate battery and lithium cobalt oxide battery. In the test of capacity characteristics of lithium ion batteries of three different cathode materials at different temperatures, the optimal operating temperature range of the lithium ion battery is extracted from the discharge efficiencies obtained. According to the research results, the discharge capacity of a lithium ion battery can be approximated by a cubic polynomial of temperature. The optimal operating temperature of lithium ion battery is 20–50 °C within 1 s, as time increases, the direct current (DC) internal resistance of the battery increases and the slope becomes smaller. Between 1 s and 10 s, the DC internal resistance of the battery basically shows a linear relationship with time. In the charge and discharge process, when state of charge (SOC) 0% and SOC 100%, the internal resistance of the battery is the largest. The SOC has the greatest impact on the polarization internal resistance, and the smallest impact on the ohmic internal resistance

The Influence of Temperature on the Capacity of Lithium Ion Batteries with Different Anodes

Temperature is considered to be an important indicator that affects the capacity of a lithium ion batteries. Therefore, it is of great significance to study the relationship between the capacity and temperature of lithium ion batteries with different anodes. In this study, the single battery is used as the research object to simulate the temperature environment during the actual use of the power battery, and conduct a charge and discharge comparison test for lithium iron phosphate battery, lithium manganate battery and lithium cobalt oxide battery. In the test of capacity characteristics of lithium ion batteries of three different cathode materials at different temperatures, the optimal operating temperature range of the lithium ion battery is extracted from the discharge efficiencies obtained. According to the research results, the discharge capacity of a lithium ion battery can be approximated by a cubic polynomial of temperature. The optimal operating temperature of lithium ion battery is 20–50 °C within 1 s, as time increases, the direct current (DC) internal resistance of the battery increases and the slope becomes smaller. Between 1 s and 10 s, the DC internal resistance of the battery basically shows a linear relationship with time. In the charge and discharge process, when state of charge (SOC) 0% and SOC 100%, the internal resistance of the battery is the largest. The SOC has the greatest impact on the polarization internal resistance, and the smallest impact on the ohmic internal resistance