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Research on Autonomous Grasping of a Humanoid Robot Based on Vision ZHANG Xuchong, YANG Jun Journal of South China University of Technology(Natural Science Edition)    2024, 52 (7): 53-61.   DOI: 10.12141/j.issn.1000-565X.230528 Abstract （8009）   HTML （20）    PDF（pc） （3170KB）（272）       Knowledge map    Save With the development of robotics technology, humanoid robots have shown application potential and value in multiple fields. Research on autonomous grasping of humanoid robots based on machine vision aims to improve their grasping adaptability and humanoid actions in natural environments. In terms of machine vision, the Realsense-D435 depth camera was adopted, and the YOLO (You Only Look Once) object detection model was used to achieve target object recognition, spatial positioning, depth map cropping, and target point cloud generation. The object’s posture was obtained based on the registration algorithm (ICP) between the target point cloud and the standard point cloud. The robot head was modeled using the D-H method, and the position and posture of the object were converted from the camera coordinate system to the robot coordinate system. In terms of motion planning, according to the grasping law of the human arm, the grasping process was divided into 9 basic actions: initial position, moving to the pre-grasping position, grasping the object, lifting the object, moving the object, moving to placement position placing the object, retreating position, and returning to the initial position. Corresponding grasping postures were determined for different objects to improve the success rate of grasping. Based on the grasping and placing points obtained visually, the remaining key points were calculated independently, and the spatial arc was used as the grasping trajectory. Through Matlab simulation, the rationality of the end movement trajectory and joint trajectory of the robotic arm during the grasping process was verified. Finally, an object grasping experiment was conducted, and the results showed that the humanoid robot can quickly and accurately recognize and locate different objects in the natural environment, and can successfully grasp and transport them with a success rate of over 80%. And it takes into account the imitation of human nature of the action, verifying the effectiveness of the proposed solution. This study can promote the application and popularization of humanoid robots in human daily life. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research Progress on Key Technologies in the Cooperative Vehicle Infrastructure System LIN Hongyi, LIU Yang, LI Shen, et al Journal of South China University of Technology(Natural Science Edition)    2023, 51 (10): 46-67.   DOI: 10.12141/j.issn.1000-565X.230200 Abstract （4938）   HTML （30）    PDF（pc） （1996KB）（427）       Knowledge map    Save With the steady growth of urban car ownership, the issue of traffic congestion is becoming increasingly prominent, bringing great pressure to urban development. To respond effectively to this challenge, it is critical to develop methods that can improve transport efficiency and reduce energy consumption. In current context, the Cooperative Vehicle Infrastructure System (CVIS), an ideal solution for realizing green and intelligent transportation systems, has become an important direction in both transportation research and practice. By integrating and optimizing various traffic resources, CVIS not only enhances traffic efficiency and reduces energy consumption but also provides key technical support for achieving “dual carbon” goals. This paper thoroughly analyzed the fundamental concepts, research methodologies and application scenarios of CVIS, and delved into its four core technological modules: fusion perception, driving cognition, autonomous decision-making, and cooperative control. The paper reviewed and summarized research achievements within these modules, ranging from traditional methods to the latest in deep reinforcement learning techniques. It also explored the potential applications of these technologies and methods for enhancing traffic efficiency, reducing energy consumption, and improving road safety. Finally, the paper scrutinized numerous challenges that CVIS may encounter in practical applications, including the security of information transmission, system stability, and environmental complexity. To overcome these challenges, the paper looked forward to the future development in four areas: developing datasets that integrate vehicle-side and roadside information, enhancing the fusion accuracy of multi-source perception information, improving the real-time performance and safety of CVIS, and optimizing multi-vehicle cooperative decision-making control methods under complex conditions. As a result, this paper not only has important reference value for the advancement of CVIS technology, but also provides important guidance for the future planning and construction of urban transportation systems. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Optimization Model of Bus Priority Control Considering Carbon Emissions with Stochastic Characteristics HU Xinghua, CHEN Xinghui, WANG Ran, et al Journal of South China University of Technology(Natural Science Edition)    2023, 51 (10): 160-170.   DOI: 10.12141/j.issn.1000-565X.230178 Abstract （4577）   HTML （3）    PDF（pc） （2474KB）（104）       Knowledge map    Save In the context of the construction of a country with a strong transportation network, vigorously developing urban public transportation and promoting sustainable urban development has become an inevitable requirement for urban transportation development. Transit signal priority control, as an active priority strategy, can effectively reduce the carbon emissions and delays generated by buses at signal intersections, and improve the quality of bus service. A bus speed probability density function was introduced to study the effect of bus priority control strategy on traffic carbon emission, based on the speed stochastic characteristics of intersection. The effect of main parameters such as delay, stopping times, and speed on traffic carbon emission was analyzed. A bi-level optimization model of single-intersection bus priority control was established using the combination strategy of speed guidance and green extension. The model took the optimal carbon emission reduction of buses and cars with different fuel types in the upstream section of the intersection and the intersection control area as the upper-level objective, the optimal total people delay reduction as the lower-level objective, and the guidance speed as well as the compressed green time of the non-bus-priority phases as the decision variables. The Gauss-Seidel iterative algorithm was used to solve the model. Finally, the established model was applied to the calculation cases for analysis, and the results indicated that under the guidance acceleration and green extension strategy, the overall carbon emission and total passenger delay reduction of the intersection could reach 25.63% and 36.27%, respectively. The model effectively reduced carbon emissions and total passenger delays in the upstream sections of the intersection and the intersection control area, and optimized the overall traffic benefit of the intersection while promoting sustainable development. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Trajectory Friction Compensation Algorithm for Robots Based on Velocity Control YE Bosheng, LI Siao, TAN Shuai, et al Journal of South China University of Technology(Natural Science Edition)    2024, 52 (4): 51-58.   DOI: 10.12141/j.issn.1000-565X.230368 Abstract （3260）   HTML （4）    PDF（pc） （2377KB）（51）       Knowledge map    Save Currently, robots are extensively utilized in industrial manufacturing. However, due to the influence of joint friction and other factors in the robot system, the robot trajectory tracking accuracy is difficult to meet the requirements of high-precision production. In this study, a friction compensation control algorithm in speed mode was proposed to mitigate the impact of non-linear friction factors in the mechanical structure and unmodelled disturbances on the robot’s operational stability and machining precision. The optimal excitation trajectory was designed by a combination of Fourier series and fifth-order polynomial. Dynamic parameters were then pre-identified by the least squares method and iteratively optimized through the Levenberg-Marquardt method to establish a more precise robot dynamic model. Subsequently, the Lyapunov method was adopted to design the trajectory tracking control algorithm, and the joint angles collected in the steepest discrete tracking differentiator were fed into the control algorithm to calculate the real-time compensation. The compensation value was then applied in the robot, which effectively achieving friction compensation. The proposed algorithm was validated by employing a six-degree-of-freedom serial robot as an experimental subject. The results demonstrate that the trajectory tracking error is reduced by approximately 35%, as comparing with that under the non-compensation conditions, which confirms the efficacy of the algorithm in the realm of robot friction compensation. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Robot Collision Detection Based on External Torque Observer ZHANG Tie, CHEN Yijie, ZOU Yanbiao Journal of South China University of Technology(Natural Science Edition)    2024, 52 (3): 84-92.   DOI: 10.12141/j.issn.1000-565X.230086 Abstract （3192）   HTML （2）    PDF（pc） （2873KB）（40）       Knowledge map    Save Collision detection technology can reduce the probability of equipment damage and personal injury and plays an important role in modern human-robot collaborative production. To realize the collision detection without external torque sensor, it is necessary to accurately estimate the external torque of industrial robots. However, the accuracy of external torque estimation can be affected by parameters identification error of dynamic model and measurement error of motor current. To solve these problems, this paper designed a disturbance Kalman filter external torque observer based on the disturbance principle. The observer takes the equivalent external torque of external collision as the disturbance term, defines the joint disturbance model, and introduces the generalized momentum of the robot to construct the state-space equation. Considering the parameters identification error of the dynamic model and the measurement error of the motor current, it carried out an iterative estimation based on Kalman filter algorithm to obtain the optimal external torque. In order to improve the sensitivity of collision detection, a time-varying symmetric threshold function which varies with joint velocity was proposed for collision detection. The proposed method can adjust the threshold according to the change of joint velocity to adapt to the observed values of external torques at different working speeds. Experimental results show that compared with the generalized momentum observer, the accuracy of external torque estimation of the proposed observer is improved by 52.03%. In order to verify the effectiveness of the proposed method, this paper used a 6-DOF series joint industrial robot to conduct collision detection experiments. The experimental results show that compared with the static threshold, the time-varying threshold method reduces the detection delay by 58.06%, which can improve the sensitivity of collision detection and is more conducive to the safe operation and collision protection of industrial robots. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Travel Carbon Emission Prediction Model Based on Resident Attribute Data SU Yuejiang, WEN Huiying, YUAN Minxian, WU Dexin, ZHOU Lulu, QI Weiwei Journal of South China University of Technology(Natural Science Edition)    2024, 52 (8): 23-33.   DOI: 10.12141/j.issn.1000-565X.230355 Abstract （3138）   HTML （7）    PDF（pc） （5415KB）（216）       Knowledge map    Save It is an important basis for precise formulation of transportation emission reduction measures to accurately analyze the importance of factors influencing residents’ travel mode and the sensitivity of carbon emissions. According to the comprehensive analysis of the influencing factors such as family attributes, personal attributes, travel attributes and environmental attributes of the residents’ travel survey, the prediction model of residents’ travel mode was constructed based on LightGBM (Light Gradient Boosting Machine) and verified. Combined with the travel activity level, the carbon emission coefficient of various energy types, the standard coal coefficient and other parameters, the travel carbon emission prediction model based on the resident attribute data was constructed. Finally, taking Guangzhou as an example, the carbon emission intensity and total amount of residents’ travel mode were predicted, and importance of factors influencing travel mode and sensitivity was analyzed.The results indicate that the carbon emission prediction model constructed based on the attribute data of residents can more accurately predict the carbon emission of various modes of travel, better analyze the importance and sensitivity of the influencing factors of carbon emission, and comprehensively reveal the relationship between travel behavior, travel mode and travel carbon emission. Among them, the distance between the start and the end and the nearest bus station or the distance from the nearest subway station, the cost of self-driving and travel distance are important factors affecting the choice of residents’ travel mode. The competitiveness of subway travel increases significantly with the decrease of distance when the distance between the starting and the end point and the nearest subway station drops by 55%. In the area with high density of bus stops, the distance between the start and the nearest bus station is not sensitive to residents’ travel mode choice. It is the turning point of the residents’ travel mode and carbon emission when the carbon emission cost increases by 400%. After passing the turning point, the car travel mode is difficult to transfer. The carbon emissions fell the fastest, with a maximum reduction of 90.4% when the reduction in travel distance was within 90%. Reference | Related Articles | Metrics | Comments（0）

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Resilience Evolution of Multi-mode Transportation Network in Urban Agglomeration Based on Risk Diffusion MA Shuhong, YANG Lei, CHEN Xifang Journal of South China University of Technology(Natural Science Edition)    2023, 51 (6): 42-51.   DOI: 10.12141/j.issn.1000-565X.220486 Abstract （3019）   HTML （4）    PDF（pc） （4048KB）（106）       Knowledge map    Save It can help improve the risk resilience of urban agglomerations to carry out in-depth analysis of the dynamic resilience evolution characteristics of multi-modal transport networks in urban agglomerations during the risk diffusion phase. Based on complex network theory and its extension theory, this study constructed a multi-modal and multi-level transportation network model for urban agglomerations based on road, railway and air networks. It matched node degree and node betweenness with accessibility, and analyzed their static topological characteristics under risk diffusion. Based on the theory of cascading failure dynamics, it considered the initial risk level, risk warning threshold and risk resilience of nodes at different stages, and constructed a risk dynamic diffusion model based on the nodes and connected edge measures affecting risk diffusion. Considering the characteristics of structural and functional changes under risk shocks, the paper constructed a network structural and functional resilience measurement model, and the resilience performance of multi-modal transport networks was represented by their coupling values. Using the multi-modal transport network of the Guan-Zhong Plain urban agglomeration as the research object, the Python Networkx and Matlab network analysis tools were used to simulate and analyze the dynamic resilience evolution of the multi-modal transport network of the urban agglomeration during the risk diffusion phase with respect to different risk diffusion methods, network reliability, redundancy, robustness and node risk handling capacity coefficients. The results show that the model results are consistent with reality. The network resilience can be effectively enhanced by improving network reliability, redundancy, robustness and node risk handling. Compared to the risk diffusion approach based on the node measure, the risk diffusion approach based on the connected edge measure has greater impact on resilience performance, suggesting that the distribution of route hierarchy levels has a greater impact on the resilience performance of multimodal transport networks in urban agglomerations compared to the number of routes. The overall resilience of a multi-modal, multi-level transport network performs better than that of a single transport network. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Calculation and Spatial Distribution Characteristics of Carbon Emissions from New Energy Vehicles on Expressways WEN Huiying, HE Ziqi, HU Yuqing, et al Journal of South China University of Technology(Natural Science Edition)    2024, 52 (8): 1-13.   DOI: 10.12141/j.issn.1000-565X.230293 Abstract （2435）   HTML （12）    PDF（pc） （25804KB）（223）       Knowledge map    Save In order to overcome the difficulty in estimating the indirect carbon emissions from new energy vehicles due to the energy consumption in a large-scale expressway network, this paper proposes a new carbon emission calculation method of new energy vehicles based on the toll data in expressway ETC network. Firstly, the traffic flow data of new energy vehicles on expressways were cleansed and processed. Then, according to the distribution of toll gantries, road sections were defined and segmented. On this basis, the energy consumption of different types of new energy vehicles was analyzed. Furthermore, a carbon emission quantification model of new energy vehicles in the operational stage was established. Finally, by taking Guangdong Province as an example, the carbon emissions of new energy vehicles on expressways were calculated, with its spatial distribution characteristics being also analyzed. The results show that (1) Category 1 pure electric passenger cars are the main source of carbon emissions from new energy vehicles on expressways, accounting for 74.20% of the total carbon emissions of new energy vehicles, followed by Category 1 pure electric trucks, whose carbon emissions account for 14.18% of the total, and Category 1 plug-in hybrid passenger cars contribute 11.62% of the total; (2) the cities in the Pearl River Delta urban agglomeration are the main contributors to high carbon emissions from new energy vehicles, and the less developed regions in eastern, western and northern Guangdong account for less than 13% of the total carbon emissions from new energy vehicles; (3) the road segments with high carbon emissions from new energy vehicles mainly locate in the transportation hubs and inner-city expressway rings of developed cities, and Guangzhou metropolitan area as well as Shenzhen metropolitan area as the center has a radiating effect on the expressway network of surrounding cities; and (4) in less developed areas, due to the low density of expressway network and relatively lagging infrastructure, the penetration rate of new energy vehicles is low, the corresponding carbon emissions from new energy vehicles are generally low. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Precise Calculation Method of Traffic Carbon Emission in Expressway Segment Integrating Multi-Source Data LIN Peiqun, ZHANG Yang, LUO Zhiqing, et al Journal of South China University of Technology(Natural Science Edition)    2023, 51 (7): 100-108.   DOI: 10.12141/j.issn.1000-565X.220627 Abstract （2175）   HTML （10）    PDF（pc） （2488KB）（107）       Knowledge map    Save In the context of “dual-carbon” policy, the task of carbon emission reduction in transportation industry is arduous, but the vehicle carbon emission currently cannot be measured accurately at present. In order to realize the fine calculation of carbon emission, this paper proposes a precise calculation method of traffic carbon emission in expressway segment based on multi-source data. Firstly, KD-tree algorithm is used to match the GPS data of operational vehicles with the road points, thus implementing the real-time monitoring of dynamic vehicles. Then, the calculation model of vehicle carbon emission in road segment is established, and the relevant calculation process is designed. Finally, the main section of Humen Bridge is taken as an example to calculate the carbon emission of the section. Through VISSIM simulation and relative comparison experiments, the science and reliability of the proposed algorithm are verified. The results show that, for different vehicle types, the carbon emission of minibus is the highest, accounting for 74.36%; and that, for different fuel types, the carbon emission of gasoline automobile is the highest, accounting for 80.50%. The new energy vehicles in operation account for 12.60% of the total vehicles but the corresponding carbon emission only accounts for 4.27%, which means that energetically developing new energy trucks is the key to the carbon emission reduction of expressways. It is also found that, when the traffic saturation is controlled at 0.32~0.38, the average carbon emission of equivalent standard vehicle is lower; while when the traffic saturation is greater than 0.62, the average carbon emission of standard vehicle increases significantly. These conclusions provide theoretical basis for traffic management departments to formulate relevant strategies. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Experimental Study on Failure Mechanism of RC Frame Structures Based on Performance Design Method LING Yuhong, HUANG Qianyi, ZHOU Jing, et al Journal of South China University of Technology(Natural Science Edition)    2024, 52 (11): 9-20.   DOI: 10.12141/j.issn.1000-565X.230648 Abstract （2150）   HTML （0）    PDF（pc） （5218KB）（24）       Knowledge map    Save To verify the rationality, reliability and fault tolerance of the “two-level and two-stage” seismic performance-based design method of Guangdong standard DBJ/T 15-92—2021 “Technical specification for concrete structures of high-rise buildings”, this study designed two batches of 1∶4 scale plane RC frame structure specimens with the same seismic structure grade of first-level, second-level and third-level. During loading, iron counterweights were arranged on each floor to simulate the distributed load, and the influence of floor and floor load on the failure mechanism of frame structure was considered. The test adopted displacement-controlled single-point loading. The loading point is located at the elevation of the three-story floor beam. Before the longitudinal reinforcement of the column reaches the yield strain, it is single-cycle loading, and after the yield, it is three-cycle loading. Through the pseudo-static test, the seismic failure mode and failure mechanism of the structure were investigated, and the evolution law of seismic performance indexes such as hysteresis curves, ductility, stiffness and energy dissipation was analyzed. The test results show that the plastic hinge development paths of the specimen damage are basically the same, which conforms to the failure mechanism of the plastic hinge ductility mechanism at the beam end. The specimen has no obvious shear failure characteristics, and the bearing capacity utilization coefficient ξ can meet the seismic design requirements of “strong shear and weak bending”. The hysteresis curves of the six frame structure specimens are full, and the seismic ductility coefficient ranges from 4.36 to 6.10. The maximum value range of equivalent viscous damping coefficient is 0.125~0.165, which shows good seismic energy dissipation performance. The floor slab improves the stiffness and bearing capacity of the frame beam, which has a significant impact on the seismic failure mechanism of the specimen. The specimen maintains the seismic failure characteristics of “strong column and weak beam”, and the component importance coefficient η can ensure the seismic design requirements of “strong column and weak beam”. The failure characteristics of the specimens are random, but the overall regularity of the failure mechanism is strong, and the gradient characteristics of the specimens with different seismic structural grades are obvious. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Lane-Changing Trajectory Planning Strategy for Autonomous Vehicles on Superhighways HE Yongming, XING Wanyu, WEI Kun, et al Journal of South China University of Technology(Natural Science Edition)    2024, 52 (4): 104-113.   DOI: 10.12141/j.issn.1000-565X.230339 Abstract （2019）   HTML （10）    PDF（pc） （2783KB）（87）       Knowledge map    Save To improve the driving safety of autonomous vehicles on superhighways, this paper proposed a lane-changing trajectory planning strategy. Firstly, five polynomials were used to generate general lane-changing trajectory clusters, and the trajectory planning problem was quantified as the duration of solving lane-changing behavior with the limit of vehicle dynamics and surrounding traffic vehicles. Then, considering the constraints of vehicle dynamics, the vehicle dynamics model and Brush tire model were established. Based on the tire lateral force data of the established vehicle model, the tire lateral stiffness was solved, and the magic tire model was used to verify the tire lateral stiffness. Next, the phase plane of sideslip angle and yaw rate was introduced to obtain the safe driving envelope of high-speed vehicle. CarSim simulation training was carried out on given multiple groups of vehicle speeds and adhesion coefficients to determine the shortest lane-changing time that meets the vehicle dynamics constraints. Finally, considering the collision avoidance constraints with surrounding traffic vehicles, three typical lane-changing scenarios were analyzed. The shortest and longest lane-changing durations satisfying the collision avoidance requirements were determined based on the position of single obstacle vehicle, and the threshold model of lane-changing duration satisfying the safe lane-changing requirements was established. The multi-parameter safety lane-changing domain test shows that the established vehicle safety lane-changing duration boundary model can solve the safe and feasible lane-changing trajectory under the given parameters, provide trajectory reference for the superhighway lane-changing behavior, and improve the safety of the superhighway lane-changing behavior. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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A Small Sample Rolling Bearing Fault Diagnosis Method Based on Gramian Angular Difference Field and Generative Adversarial Network QIANG Ruiru, ZHAO Xiaoqiang Journal of South China University of Technology(Natural Science Edition)    2024, 52 (10): 64-75.   DOI: 10.12141/j.issn.1000-565X.240021 Abstract （2013）   HTML （13）    PDF（pc） （4151KB）（136）       Knowledge map    Save Aiming at the problem that deep learning-based rolling bearing fault diagnosis algorithms need to learn from a large amount of labeled data and face poor diagnosis effect when the number of samples is limited, this paper proposed a small-sample rolling bearing fault diagnosis method based on the Gramian angular difference field (GADF) and generative adversarial networks (GAN). Firstly, a data enhancement method based on GADF transform was proposed, and it converts a few 1D vibration signals into 2D GADF images by GADF transform. GADF subgraphs are obtained by cropping to obtain a large number of image samples. Then, a conditional generative adversarial network (CGAN) was combined with Wasserstein GAN with gradient penalty (WGAN-GP) to construct a novel generative adversarial network, which enhances the model training stability by conditional auxiliary information with gradient penalty and designs dynamic coordinate attention mechanism to enhance the spatial perception of the model, so as to generate high-quality samples. Finally, the generative samples were used to train the classifier, and the diagnosis results were obtained on the validation set. Two sets of bearing fault diagnosis experiments in a small sample environment were conducted using the Southeast University dataset and the Case Western Reserve University dataset, respectively. The results show that, compared with traditional generative adversarial networks as well as advanced small-sample fault diagnosis methods, the proposed method can obtain the best results in five fault diagnosis metrics, including accuracy and precision, and can accurately diagnose the type of bearing faults under small-sample conditions. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Study on Sodium β-glycerophosphate in Concrete Affects the Inhibited Behavior of Steel Bar WANG Xiaoxian, LIU Jiaping, MU Song, et al Journal of South China University of Technology(Natural Science Edition)    2024, 52 (3): 28-40.   DOI: 10.12141/j.issn.1000-565X.230030 Abstract （1987）   HTML （0）    PDF（pc） （5481KB）（15）       Knowledge map    Save In the high alkaline environment of concrete, in order to improve the resistance of steel bar in concrete to chloride ion erosion, this study adopted a new environmentally friendly organic rust inhibitor-β-glycerophosphate sodium to protect the steel bar and achieve the purpose of extending the service life of reinforced concrete structures. In this study, the electrochemical measurements were used to monitor the evolution properties of the steel embedded in concrete in real time. The corresponding key parameters were obtained to explore the relationship between sodium β-glycerophosphate and steel passive film in the passivation period, as well as the relationship among sodium β-glycerophosphate, steel passive film and chloride ions during the maintenance passivation period, and then the rust resistance mechanism of this kind of organic matter was revealed. The results obtained by OCP, LPR and EIS electrochemical testing methods show that: β-sodium glycerophosphate forms a more density protective film through physicochemically interacting with Fe oxides/hydroxides on the steel surface, so as to make the surface of the steel bar form a protective film with more inhibited behavior, which also improves the resistance of the steel bar under chloride ion erosion. The resistance of the steel bar in each of the four solutions is: NaOH + 0.1 mol/L sodium β-glycerophosphate > saturated clarified Ca(OH)2 > NaOH > saturated clarified Ca(OH)2 + 0.1 mol/L sodium β-glycerophosphate. Among them, the critical chloride ion concentrations (ccrit) of steel bars in NaOH, NaOH + 0.1 mol/L sodium β-glycerophosphate and saturated clarified Ca(OH)2 solutions are: 0.02 mol/L, 0.07 mol/L, and 0.04 mol/L, respectively, while no effective passive film is generated on the steel bar in saturated clarified Ca(OH)2 + 0.1 mol/L sodium β-glycerophosphate. In addition, the addition of sodium β-glycerophosphate to Na+ solutions can promote the formation of a more dense passive film on the steel bar surface with a faster passivation rate. That is, more than 80% passivation film can be formed in 72 h, and the rust inhibitor rate is as high as 99.80%. Furthermore, further comparative analyses of the effects the Na+ and Ca2+ solutions themselves on the resistance of the steel bar under chloride ions erosion show that Ca2+ solution is more conducive to the resistance to chloride ion erosion ability, and the corrosion inhibition efficiency is more than 90%. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Electric Bus Scheduling Method Considering Differences in the State of Health of Batteries BIE Yiming, ZHU Aoze, CONG Yuan Journal of South China University of Technology(Natural Science Edition)    2023, 51 (10): 11-21.   DOI: 10.12141/j.issn.1000-565X.230279 Abstract （1927）   HTML （18）    PDF（pc） （1868KB）（311）       Knowledge map    Save Electric buses (EBs) have the advantages of zero-emission and low energy consumption in operation. The electrification of urban buses is being vigorously promoted in many countries to reduce carbon emissions and promote the realization of the “Carbon peaking and Carbon neutrality” goals. However, due to financial constraints and the fact that fuel buses have not yet reached the end of life and bus companies usually replace fuel buses with EBs in batches, there are differences in the battery health degree and driving range of each bus on the line, which makes the optimization of the vehicle scheduling scheme more complicated. Considering the impact of battery differences in the state of health and time-of-use tariff, this paper proposed an optimized scheduling model for single-route, with the objective of minimizing average daily charging costs, EB acquisition costs, and battery loss costs. Then, the model was transformed into two sub-problems, the vehicle scheduling problem and the charging scheduling problem. In the outer layer, the vehicle scheduling problem was solved by the improved simulated annealing algorithm (ISAA), whose perturbation strategy is designed with the operating intensity differences among EBs. And Gurobi was employed to solve the charging scheduling problem in the inner layer. Finally, an actual EB route was taken as an example to verify the effectiveness of the method, and the method was compared with the simulated annealing algorithm in the perturbation strategy which does not consider differences in vehicle operating intensity. Results show that the ISAA can increase the convergence speed by 31.8% and achieve high-quality solutions in a short time. Moreover, the generated scheduling scheme can not only arrange EBs to be charged preferentially in the off-peak period of electricity prices but also reduce the EB fleet size. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Small-Sample Fault Diagnosis Method Based on Multi-Head Convolution and Differential Self-Attention CHEN Xindu, FU Zhisen, WU Zhiheng, et al. Journal of South China University of Technology(Natural Science Edition)    2023, 51 (7): 21-33.   DOI: 10.12141/j.issn.1000-565X.220626 Abstract （1900）   HTML （3）    PDF（pc） （4158KB）（68）       Knowledge map    Save Bearing is one of the most widely used rotating parts in industrial equipment. If the bearing runs in fault condition for a long time, it will cause huge economic loss and threaten personal safety, so that the investigation of bearing fault diagnosis is of great significance. Fault diagnosis technology based on deep learning is becoming more and more mature, but there are problems such as over-fitting, unstable effect and low accuracy in the case of small samples. In order to solve these problems, this paper proposes a Transformer variant model MDT (Multi-Head Convolution and Differential Self-Attention Transformer) to realize end-to-end few-shot fault diagnosis. This model combines the new data embedding algorithm of MC (Multi-Head Convolution) and the DSA (Differential Self-Attention) mechanism. The MC algorithm performs multi-path one-dimension convolution on the sample, extends the sample from one dimension to two dimensions by multi-channel output, and extracts rich fault information in each frequency domain in the original sample through multiple convolution kernel sizes. As compared with the original dot product self-attention in Transformer, the DSA mechanism obtains the corresponding attention weight vector for each feature through the difference, so as to extract deeper fault features from the sample. MDT inherits the powerful ability of Transformer to process sequence data, which can extract richer fault information from time-domain signals and avoid the overfitting problem common in small-sample models. Experimental results show that the proposed method can stably obtain more than 99% test accuracy in the bearing fault diagnosis task with only 100 training samples per fault type, and has strong anti-overfitting ability and strong robustness. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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User Portrait Method of Freeway Freight Car for Risk Identification of Freight Transportation LIN Peiqun, GONG Minping, ZHOU Chuhao Journal of South China University of Technology(Natural Science Edition)    2023, 51 (6): 1-9.   DOI: 10.12141/j.issn.1000-565X.220525 Abstract （1885）   HTML （6）    PDF（pc） （1833KB）（209）       Knowledge map    Save At present, the freight car overload phenomenon is coming from bad to worse, in order to improve the efficiency of freight car control on the highway and the level of safety in the freight transport, a freight transport risk level identification model based on user portrait of freight risk was proposed. Firstly, based on highway toll data, taking freight car as the research object, a user portrait system for freight transport risk identification was developed from the aspects of driving behavior and operation status. Then，the sample data was cleaned and the label index was extracted and analyzed. Then, K-means++ algorithm was applied to obtain the classification results of freight transport risk feature portraits. Next, the entropy weight method was used to score the freight risk of all kinds of freight car to determine the risk level of all kinds of freight car. Finally, by combining with the relevant indicators of various types of vehicles, the vehicle portrait was completed. Based on the trucking toll data of the entire highway network in Guangdong Province from March to May 2022, the proposed model was used to divide the trucking vehicles into five categories. Among them, “the freight car of high risk and high workload” accounted for 5.42%, the freight car of higher risk and night-driving and overloaded ”accounted for 19.12%, “the freight car of medium-risk and overspeed” accounted for 12.85%, “ the freight car of low risk and low-frequency” accounted for 37.00%, and “ the freight car of low risk and high-frequency ” accounted for 25.61%. The validity of the model was verified by the data of an accident database in Guangdong Province in the same period. The data showed that the relative risk coefficient of high risk vehicles is much higher than that of low risk vehicles. The research shows that the proposed model can effectively identify trucks with high freight risk characteristics. Based on the results of risk grade identification, traffic management departments can carry out high-risk vehicle identification, key inspection of overload and over-limit, and specific message push to guide vehicle driving safety, so as to improve the safety management level of the industry. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Vehicle Trajectory Tracking at Intersections Based on Millimeter Wave Radar Point Cloud LIN Yongjie, CHEN Ning, LU Kai Journal of South China University of Technology(Natural Science Edition)    2023, 51 (10): 110-125.   DOI: 10.12141/j.issn.1000-565X.230100 Abstract （1880）   HTML （10）    PDF（pc） （5925KB）（276）       Knowledge map    Save As an emergent traffic detection device, millimeter wave radar is little affected by environmental factors (e.g., light and weather) and can provide reliable data support for road traffic sensing, safety control and signal timing optimization. Vehicle trajectory data collected by millimeter wave radar contains rich traffic information, reflecting spatial-temporal characterization of vehicle motion, which is critical in traffic parameter extraction, abnormal detection, driving behavior analysis, signal timing optimization, etc. Aiming at solving the problems such as trajectory fragmentation and poor valid tracking rate caused by the vehicle data loss and easy occlusion of vehicles detected by millimeter wave radar in the intersection, this paper proposed a continuous tracking method of vehicle trajectory based on short trajectory fragment associations. Firstly, the 2D point cloud data with high frequency collected by millimeter wave radar at the intersection was acquired and cleaned to obtain valid target information. Secondly, short track fragments were extracted from 2D point clouds by inter-frame association, and multiple movement sequence feature was used for track fragment correction to reject split trajectories. Thirdly, the fuzzy correlation function was constructed based on the motion characteristics of the spatiotemporal dimension to describe the correlation among multiple short track fragments. Hungarian algorithm was employed to solve the set of target short track with the highest correlation. Finally, the missing trajectory points in the vehicle tracklet set were repaired based on the piecewise cubic Hermite interpolation, which derived complete trajectories and achieved continuous tracking. The experiments were conducted using 6 627 frames of 2D point cloud data collected at the real intersection. The results indicate that the proposed method achieves better tracking performance under different traffic densities, monitoring directions, and occlusions than the traditional trajectory tracking algorithm. Specifically, the trajectory tracking accuracy is 92.4%, the number of fragmentations is 4.5, and the accuracy of estimated vehicle volume is significantly improved. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Multi-Task Assisted Driving Policy Learning Method for Autonomous Driving LUO Yutao, XUE Zhicheng Journal of South China University of Technology(Natural Science Edition)    2024, 52 (10): 31-40.   DOI: 10.12141/j.issn.1000-565X.230503 Abstract （1866）   HTML （14）    PDF（pc） （3027KB）（212）       Knowledge map    Save With the development of autonomous driving technology, deep reinforcement learning has become an important means to realize the efficient driving policy learning. However, the implementation of autonomous driving is faced with the challenges brought by the complex and changeable traffic scenes, and the existing deep reinforcement learning methods have the problems of single scene adaptation ability and slow convergence speed. To address these issues and to improve the scene adaptability and policy learning efficiency of autonomous vehicles, this paper proposed a multi-task assisted driving policy learning method. Firstly, this method constructed the encoder-multi-task decoder module based on the deep residual network, squeezing high-dimensional driving scenes into low-dimensional representations, and adopted multi-task-assisted learning of semantic segmentation, depth estimation and speed prediction to improve the scene information richness of low-dimensional representations. Then, the low-dimensional representation was used as the state input to build a decision network based on reinforcement learning, and the multi-constraint reward function was designed to guide the learning of driving strategies. Finally, simulation experiments were conducted in CARLA. The experimental results show that, compared to classic methods such as DDPG and TD3, the proposed method improves the training process through multi-task assistance and learns better driving policies. It achieves higher task success rates and driving scores in several typical urban driving scenarios such as roundabouts and intersections, demonstrating excellent decision-making capabilities and scene adaptability. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Review of Research on Road Traffic Detectors and Its Optimized Deployment Methods XU Zhihang, YAO Xinpeng, XU Zhigang, et al Journal of South China University of Technology(Natural Science Edition)    2023, 51 (10): 68-88.   DOI: 10.12141/j.issn.1000-565X.230223 Abstract （1852）   HTML （15）    PDF（pc） （3824KB）（431）       Knowledge map    Save A widely studied and concerned problem in the traffic network research is how to optimize the location and number of road traffic detectors, so as to obtain real-time and accurate diversified traffic situation information and provide a comprehensive information basis for traffic control departments and as a basis for reasonable decision-making. The key to this problem is to select a suitable detector type and build a decision model according to the research purpose. At the same time, considering the constraints such as the investment cost limit and the number of road sections, appropriate heuristic algorithm should be used to solve the model to get the best number and location of detectors. This paper summarized the optimal layout of road traffic detectors from the types of road traffic detectors, application scenarios, data acquisition indexes and research objectives of various optimization layout studies. Firstly, the detector was divided into two categories according to the installation mode: stationary traffic detector and mobile detector, and the principle, characteristics, advantages and disadvantages of each type of detector were described in detail. Secondly, the application of various types of road traffic detectors in different scenarios and the corresponding data acquisition indicators are given. Then, according to the research purpose of optimization layout methods in the research literature, the optimization layout problems of road traffic detectors were divided into three types: user-oriented travel time estimation, traffic flow observation/estimation, and traffic event detection. And this paper discussed the development course, development direction, problem research model constructed, problem solving methods, and existing shortcomings of these studies. Finally, it summarized a large number of existing studies. And it pointed out that in the complex situation of large traffic network scale, prominent traffic uncertainty and rapid development of wisdom, future research should take the diversity of traffic information detection as the leading factor, fully consider the combination arrangement of different types of traffic detectors, various uncertainties in the traffic network and various scenarios, etc., so as to build a complete optimization model to solve the optimization arrangement of road traffic detectors. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Location and Capacity Optimization Model of Battery-Swapped Electric Bus Charging Station ZHANG Wenhui, SU Jiaqi, HA Zihong, et al Journal of South China University of Technology(Natural Science Edition)    2023, 51 (10): 126-134.   DOI: 10.12141/j.issn.1000-565X.230198 Abstract （1835）   HTML （9）    PDF（pc） （1750KB）（154）       Knowledge map    Save In order to improve the electricity exchange efficiency of urban pure electric bus and reduce the construction and operation costs of charging stations, this paper studied the optimization of urban battery exchange pure electric bus charging station siting and capacity selection. Firstly, considering the operation guarantee capacity of pure electric bus exchange stations, the study established a model of the number of exchange facilities and battery reserve capacity and obtained the number of exchange stations and optimal battery configuration. Then, based on the pure electric public exchange demand, the charging station operation conditions were modeled by using queuing theory, and penalty factors were set to ensure the service quality of charging stations. A site selection and capacity model with service radius, service intensity and supply and demand balance as constraints and minimum annual total cost as the object was established, and GA, PSO and PSO-GA algorithms were applied to solve it. Finally, a sensitivity analysis of the siting and capacity model was performed to obtain the effects of parameters such as charging rate and rated driving range on the siting results. The results of the case application show that the PSO-GA algorithm is better than the GA and PSO algorithms in terms of objective function and convergence speed, and the optimal number of charging stations is 30, the number of charging piles is 943, and the lowest total cost is 12 151 429 000 RMB. The rated driving range of pure electric buses is negatively correlated with the number of stations, transportation cost and construction cost; the charging rate is negatively correlated with the number of stations and construction cost and positively correlated with transportation cost, and increasing the charging rate will accelerate battery aging and reduce battery life. The research results can provide theoretical basis for the reasonable planning and operation of urban pure electric bus charging stations. Table and Figures | Reference | Related Articles | Metrics | Comments（0）