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    Complex Scenario Construction Method for Navigation Pilot Based on Natural Driving Behaviour
    WU Biao, REN Hongze, ZHENG Lianqing, ZHU Xichan, MA Zhixiong
    Journal of South China University of Technology(Natural Science Edition)    2025, 53 (2): 38-47.   DOI: 10.12141/j.issn.1000-565X.240346
    Abstract7940)   HTML7)    PDF(pc) (3325KB)(238)       Save

    As the focus of research and development of intelligent connected vehicles, the test and evaluation of autonomous driving systems must focus on the real performance of vehicles in complex weather and complex traffic flow scenarios. This research proposed a method for constructing complex scenarios based on weather complexity and traffic complexity to meet the testing requirements of intelligent driving systems in challenging traffic environments. Using natural driving data from China’s large-scale field operational test project (China-FOT), the study analyzed vehicle dynamics parameters such as speed, longitudinal acceleration, lateral acceleration, and yaw rate. By fitting safety boundary envelopes, driving behavior risk levels were defined, and hazardous scenarios in natural driving were identified. These scenarios help clarify the fundamental scene types related to the functional safety of navigation-based intelligent driving. A traffic interaction coupling method, incorporating multiple dynamic target features, was applied to construct complex scenario types. The quantified natural weather factors were used to construct influence indicators, such as light factor, rainfall factor, fog factor, which are employed to characterize the weather complexity through the distribution of natural driving behavior characteristics. The complexity parameters, including encounter angle, relative distance, relative speed, were constructed using the Support Vector Machines and K-fold cross validation methods to characterize the traffic state of the complex scenarios. In order to ascertain the complexity of the test scenario, a closed field vehicle test was conducted, during which the real test performance evaluation parameters were employed to verify the rationality of the complex scenario construction. This research indicates the necessity to construct a test scenario that can accurately portray the real-world complex traffic environment for the navigation pilot driving functions. This will facilitate the optimization and iteration of the autonomous driving system of intelligent connected vehicles.

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    Foggy Road Environment Perception Algorithm Based on an Improved CycleGAN and YOLOv8
    YUE Yongheng, LEI Wenpeng
    Journal of South China University of Technology(Natural Science Edition)    2025, 53 (2): 48-57.   DOI: 10.12141/j.issn.1000-565X.240225
    Abstract6948)   HTML20)    PDF(pc) (3298KB)(160)       Save

    In response to the issue of reduced road environment perception accuracy for intelligent vehicles under extreme haze conditions, this paper proposed a joint haze environment perception algorithm based on an improved CycleGAN and YOLOv8. Firstly, the CycleGAN algorithm was used as the framework for image defogging preprocessing. A self-attention mechanism was incorporated into the generator network to enhance the network’s feature extraction capability. Additionally, to minimize color discrepancies with real images, a self-regularized color loss function was introduced. Secondly, in the object detection phase, the lightweight GhostConv network was first used to replace the original backbone network, reducing computational complexity. Furthermore, the GAM attention mechanism was added to the neck network to effectively improve the network’s ability to interact with global information. Finally, the WIoU loss function was used to mitigate harmful gradients caused by low-quality samples, improving the model’s convergence speed. Experiments conducted on the RESIDE and BDD100k datasets validate the proposed algorithm. Results show that the structural similarity between dehazed and original images is 85%. Compared to the original CycleGAN algorithm and the AODNet algorithm, the proposed approach improves the peak signal-to-noise ratio (PSNR) by 2.24 dB and 2.5 dB, respectively, and the structural similarity index (SSIM) by 15.4% and 36.3%, respectively. Additionally, the improved YOLOv8 algorithm demonstrates enhancements over the original algorithm, with precision, recall, and mean average precision (mAP) increasing by 2.5%, 1.8%, and 1.1%, respectively. The experimental results confirm that the proposed algorithm outperforms traditional algorithms in terms of recall and detection accuracy, demonstrating its practical value

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    Customer Service Knowledge Recommendation Large Model Construction Driven by Intent Understanding
    MA Xiaoliang, GAO Jie, LIU Ying, PEI Qingqi, ZHAO Ruqiang, YANG Bangxing, DENG Congjian
    Journal of South China University of Technology(Natural Science Edition)    2025, 53 (3): 40-49.   DOI: 10.12141/j.issn.1000-565X.240191
    Abstract4779)   HTML8)    PDF(pc) (2265KB)(354)       Save

    With the deepening application of artificial intelligence technology in the field of customer service, telecommunications operators have raised higher standards for the accuracy of AI service knowledge recommendations. To enhance the efficiency and accuracy of knowledge recommendation in telecommunications operators’AI customer service systems, this paper proposed a large-scale customer service knowledge recommendation model driven by intent understanding. Firstly, the synonym and dialogue sequence keyword extraction model was employed to identify key terms in user queries. These keywords were then matched with questions in a standard question bank using semantic similarity comparison techniques to generate the most relevant standard questions. Additionally, a generative agent technology framework was utilized to construct and enrich the standard question bank, enabling the automatic generation of knowledge questions. The extracted standard questions were input into the ChatGLM2-6B large language model, which has been pre-trained and aligned with human preferences, further improving the accuracy of knowledge recommendations. The experimental results show that after the introduction of the standard question bank, the accuracy of the intelligent recommendation system in specific industry knowledge domains significantly increased from 74.8% to 85.9%. Multiple sets of comparative experimental results further validate the effectiveness of the strategy of establishing a standard question bank in improving accuracy. The large model discussed in this paper optimized the intelligent knowledge recommendation for operator AI customer service, providing new ideas and technical support for the knowledge recommendation in telecommunications operators’AI customer service systems. With this model, operators can more effectively understand and respond to customer inquiries, significantly enhancing the customer service experience.

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    Preparation and Performance Analysis of High Temperature Oxidation Cladding Coating on TC4 Surface
    ZHENG Lijuan, XIE Yinkai, ZHANG Kuo, FU Yuming
    Journal of South China University of Technology(Natural Science Edition)    2025, 53 (3): 97-104.   DOI: 10.12141/j.issn.1000-565X.230489
    Abstract3029)   HTML4)    PDF(pc) (2533KB)(60)       Save

    Due to the susceptibility of TC4 material to oxidation failure in high-temperature environments, its service life is significantly shortened under harsh conditions such as high temperatures and marine environments. To augment the high-temperature oxidation resistance of TC4 material surfaces, this paper employed laser cladding technology to prepare a high-temperature oxidation-resistant cladding coating with a gradient mass fraction of additive phases on the TC4 surface. The microstructure of the coating was observed using scanning electron microscopy (SEM), and the impact of the additive phase on the microstructure morphology of the cladding was analyzed. Subsequently, microhardness tests were performed to obtain the microhardness distribution of coatings with different material compositions, and the effect of additive phase content on the microhardness of the cladding was analyzed. Ultimately, macroscopic morphology observation, oxidation kinetics, SEM, and XRD methods were employed to evaluate the high-temperature oxidation resistance of the cladded samples after high-temperature oxidation tests. The effects of ceramic phase content and the high-temperature oxidation process on the microstructure and phase composition of the cladding layer were analyzed, and the oxidation resistance mechanism of the coating was explored. The experimental findings reveal that the incorporation of ceramic phase powders results in a marked improvement in the microhardness of the cladding layer, along with a refinement and densification of its microstructure. The dense oxide products formed during the high-temperature oxidation process effectively isolate the coating from the oxidizing environment, thereby substantially enhancing its resistance to high-temperature oxidation. The high-temperature oxidation product Ta2O5 formed on the surface of the cladding layer has a dense structure, strong high-temperature stability, and excellent oxidation resistance, which is the main reason for the improved high-temperature oxidation resistance of the ceramic phase-containing TC4 cladding layer.

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    Digital Twin Assisted Edge Computing Task Offloading and Resource Allocation Strategy in Industrial Internet of Things
    LI Song, LI Yiming, LI Shun
    Journal of South China University of Technology(Natural Science Edition)    2025, 53 (3): 88-96.   DOI: 10.12141/j.issn.1000-565X.240262
    Abstract2868)   HTML5)    PDF(pc) (1621KB)(151)       Save

    In the industrial Internet of Things, the reliability of mobile edge computing largely depends on the wireless channel conditions. In order to process the influence of imperfect channel state information to the system, this paper proposed a digital twin assisted mobile edge computing energy consumption optimization method. For the task offloading problem in industrial Internet of Things, a digital twin model of devices and channels in the edge computing system was established. Considering imperfect channel state information, the joint optimization of offloading decisions, transmission power, channel resources, and computational resources is performed with the aim of minimizing the total system energy consumption. To deal with the proposed nonlinear non convex problem of mixed integers, the probabilistic delay constraint was transformed and the original problem was decomposed into two sub-problems, and a joint optimization algorithm with the assistance of digital twins based on continuous convex approximation was proposed. Firstly, the original problem was relaxed to obtain resource allocation schemes and task offloading priorities. Then, the offloading priority of each terminal device was sorted in descending order. The complete task offloading scheme was obtained by solving the iterative optimization problem. Finally, simulation results show that, compared to other benchmark schemes, the proposed computational offloading optimization scheme significantly reduces the total energy consumption of the system.

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    Review of Multi-Level Urban Impacts of Shared Autonomous Vehicles
    ZHONG Shaopeng, LIU Ao, ZHAI Junnuo, FAN Meihan, LI Xiyao, LIN Yuan, LI Zhenhua
    Journal of South China University of Technology(Natural Science Edition)    2025, 53 (6): 104-118.   DOI: 10.12141/j.issn.1000-565X.240119
    Abstract2694)   HTML4)    PDF(pc) (1338KB)(39)       Save

    To gain a deeper understanding of the potential impacts of Shared Autonomous Vehicles (SAVs) on urban and promote the sustainable development of urban transportation systems, this paper conducted a comprehensive review and systematic analysis of the multi-level impacts of SAVs. The aim is to summarize the main contributions and shortcomings of previous studies and propose possible directions for future research. The review findings indicate that existing studies primarily focus on the short-term impacts of SAVs on the transportation system, including residents’ travel behavior and road traffic flow. However, there is relatively little research on the long-term impacts of SAVs, particularly concerning urban accessibility, environment, and energy. While some studies have revealed potential negative effects of SAVs, such as adverse impacts on the environment or accessibility, few have proposed targeted and effective development strategies. Additionally, in terms of methods, existing studies mainly rely on qualitative analysis or independent transportation demand models for projections and simulations, which have certain limitations regarding the reliability of the results. Future research should focus on developing integrated land use and transportation models combined with data-driven approaches to more precisely, comprehensively, and systematically characterize the long-term (negative) impacts of introducing SAVs on urban land use, the environment, and energy consumption. Additionally, targeted development strategies and responsive measures should be proposed to optimize the effectiveness of SAV deployment, mitigate potential adverse effects, and promote the evolution of urban transportation systems toward greater efficiency, intelligence, and sustainability.

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    Effect of Laccase Synergy with Ferulic Acid on the Rheological Properties of Wheat Dough and the Quality of Bread
    LI Bing, HE Min, HE Ni, PAN Zhiqin, ZHANG Xia, CHEN Xinran, LI Junyi, LI Lin
    Journal of South China University of Technology(Natural Science Edition)    2025, 53 (4): 113-124.   DOI: 10.12141/j.issn.1000-565X.240221
    Abstract2542)   HTML7)    PDF(pc) (5382KB)(266)       Save

    China is a large wheat-growing country, but the quality of domestic wheat flour can not fully meet the production demands of high-quality bread products. At present, research on the effects of laccase (LAC) and ferulic acid (FA) on wheat bread quality mainly focuses on their individual effects on bread baking performance, while the combined addition of LAC and FA on wheat bread baking performance remains unclear. Therefore, this study utilized various instruments, including a texture analyzer, rheometer, low-field nuclear magnetic resonance analyzer, scanning electron microscope, and gas-phase ion mobility spectrometry-flavor analysis coupled instrument, to investigate the effects of LAC and FA on the rheological properties, moisture migration characteristics, and microstructure of wheat dough.The results show that compared to the control and FA groups, the doughs in the LAC and LAC+FA groups exhibit lower weakening, with increased maximum stretching resistance and reduced extensibility, leading to dough hardening. Additionally, results from low-field nuclear magnetic resonance analysis showed that both individual and combined additions of LAC and FA could convert the immobile water from the T21b state to the T21a state. This indicates that LAC and FA, whether added alone or in combination, reduce the freedom of water, leading to a tighter binding of water with dough components, thus influencing moisture migration in the dough. Scanning electron microscopy (SEM) results of the dough show that the complex addition of LAC and FA results in a more complete and homogeneous gluten network. In terms of bread quality, both the LAC group and the LAC+FA group reduce the hardness of the bread crumb, with minimal impact on the composition and content of volatile compounds. Therefore, the addition of LAC and its combination with FA positively contribute to improving dough strength and bread quality.

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    Shelf Layout and Equipment Configuration Strategy of Four-Way Shuttle Stereoscopic Warehouse
    LI Jianguo, GONG Xincheng
    Journal of South China University of Technology(Natural Science Edition)    2025, 53 (3): 68-79.   DOI: 10.12141/j.issn.1000-565X.240327
    Abstract2510)   HTML13)    PDF(pc) (3498KB)(82)       Save

    Four-way Shuttle Automated Storage and Retrieval System (FWS-AS/RS) is a storage form widely used in industries such as e-commerce, pharmaceuticals, and food in recent years. It has the characteristics of flexible system configuration, high storage density, high efficiency, and high automation. To efficiently design the shelves layout and equipment configuration of FWS-AS/RS, this paper firstly considered the possibility of arranging shelf rows, columns, and layers under different storage scales and discussed the impact of changes in horizontal aisle layout position and quantity as well as different shelf depths on operational efficiency. Then, taking the different configurations of shuttle cars and lifts as well as the different placement positions of lifts and input/output (I/O) ports as variables, it established the motion models of shuttle cars and lifts considering acceleration, deceleration, no-load, load energy consumption, and energy recovery during braking; it used the total cost, transport distance, energy consumption, and space utilization rate as evaluation indicators. Through simulation experiments, regular design strategies were obtained for constructing four-way shuttle style stereoscopic warehouse shelves, including row, column, layer, depth, tunnel position, number and position of I/O ports, the ratio of four-way shuttle cars (FWS) to lifts, and the relationship between lifts and longitudinal tunnels. Finally, taking a storage capacity of 5 000 as an example, the study applied these strategies to design simulation. The simulation results show that the three optimization schemes reduced the transport distance, energy consumption, total cost, and floor area by an average of 43.30%, 57.69%, 11.17%, and 8.60%, while increasing the space utilization rate by an average of 5.66%, thus verifying the correctness of the design strategy. The design strategy can provide reference for the construction and operation of such stereoscopic warehouses.

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    Preparation and CO2 Adsorption Performance of Ca-LTA Zeolite Derived From Titanium-Containing Slag
    HUANGFU Lin, HE Zhengqing, ZHAO Shimin, ZHOU Xintao, LUO Zhongqiu, ZU Yun, SHANG Bo, LI Fangyuan
    Journal of South China University of Technology(Natural Science Edition)    2025, 53 (3): 139-148.   DOI: 10.12141/j.issn.1000-565X.240417
    Abstract2456)   HTML6)    PDF(pc) (4961KB)(41)       Save

    LTA-type zeolites is considered a highly promising CO2 capture material due to their excellent pore structures and high selectivity for CO2 adsorption. This study presented a green and sustainable synthesis approach using industrial titanium-containing slag waste as a raw material to prepare Na-LTA zeolite precursors. Calcium loading was adjusted via a conventional liquid-phase ion exchange (LPIE) method to produce a series of xCa-LTA zeolites, specifically designed to enhance CO2 adsorption performance. The adsorption properties of the xCa-LTA zeolites were systematically evaluated through dynamic adsorption experiments. Results show that xCa-LTA zeolites not only significantly enhances CO2 capture capacity, but also exhibits excellent selectivity in CO2/N2 and CO2/CH4 separation processes, with the 0.05Ca-LTA sample demonstrating the most outstanding adsorption performance. Under conditions of 25 ℃ and 105 Pa, the CO2 adsorption rate of 0.05Ca-LTA is 4.95 times that of Na-LTA, with a maximum adsorption capacity of 4.02 mmol/g. Kinetic analysis indicates that the CO2 adsorption behavior of 0.05Ca-LTA follows a pseudo-second-order kinetic model, where the adsorption process is synergistically dominated by both physisorption and chemisorption. This synergy not only accelerated adsorption rates but also improved overall efficiency. After five adsorption/desorption cycles, 0.05Ca-LTA maintains highly efficient and stable adsorption performance, demonstrating excellent cyclic regeneration capability. This study follows the ecofriendly concept of “treating waste with waste” providing a new approach for the high-value utilization of solid waste while offering important theoretical and application potential for the synergistic optimization of CO2 capture and environmental pollution control.

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    Effect of QPQ Treatment on the Microstructure and Properties of Nickel-Aluminum-Bronze Alloy Coating of 27SiMn Alloy Steel
    SU Youliang, CUI Hao, GAO Xuenan, ZHENG Haobo
    Journal of South China University of Technology(Natural Science Edition)    2025, 53 (3): 105-115.   DOI: 10.12141/j.issn.1000-565X.240239
    Abstract2127)   HTML5)    PDF(pc) (7427KB)(85)       Save

    To address the unclear effects of quenching-polishing-quenching (QPQ) treatment on the performance of nickel-aluminum bronze alloy coatings welded onto 27SiMn alloy steel, this study investigated the geometric characteristics, microstructural changes, corrosion resistance, and hardness of the copper alloy coating. The influence of the QPQ treatment process on the coating’s microstructure and properties was analyzed to verify the rationality and feasibility of this composite anti-corrosion technology. The results indicate that after undergoing the two processes of carbonization/nitriding and oxidation, the copper alloy coating forms a dual-layer infiltration structure, with metal carbides distributed across both layers and copper oxides concentrated near the surface, providing corrosion protection. Before and after QPQ treatment, the microstructure of the copper alloy coatings primarily consists of the matrix phase α, the metastable phase, and various κ phases dispersed within the α phase. High-and medium-temperature tempering leads to the precipitation of a large amount of β' phase into the α phase, causing the matrix phase to coalesce and expand while reducing overall hardness. According to the protection rating representation method based on the proportion of substrate area affected by corrosion, the protection rating of the surface of the copper alloy coatings layer samples is 9, while the copper alloy samples after QPQ treatment is 10. The corrosion resistance of the copper alloy surface after QPQ treatment is not only maintained but also exceeds that of the untreated one. In light of this, the composite anti-corrosion technology can be applied to the maintenance and remanufacturing of hydraulic bracket cylinder barrel parts, which can enhance the corrosion resistance of the inner wall of the cylinder barrel as a whole, while also considering the corrosion resistance of other parts such as the joint holes and the outer surface of the cylinder body.

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    Analysis of the Influencing Factors of the Severity of Single-Vehicle Accidents Considering Temporal Stability
    NIU Shifeng, TAI Yinghao, CHANG Dongfeng, YU Pengcheng
    Journal of South China University of Technology(Natural Science Edition)    2025, 53 (2): 1-11.   DOI: 10.12141/j.issn.1000-565X.240189
    Abstract2054)   HTML12)    PDF(pc) (1657KB)(76)       Save

    Single-vehicle accidents often occur in time periods with low traffic volume and poor road conditions, and their fatality rate is significantly higher than the average mortality rate of traffic accidents. To explore the key factors affecting the accident severity degree of single-vehicle accidents, based on the single-vehicle traffic accident data from 2015 to 2019 in some regions of China, this paper selected 24 accident-influencing factors from aspects such as people, vehicles, roads, and the environment. The temporal stability of the accident data was tested by the log-likelihood ratio test which found that there was temporal instability in the accident data, so it should be divided into five years for separate modeling. Then, a random parameter Logit model considering the mean-variance heterogeneity was constructed. The marginal effects of accident factor variables were compared. The results show that the temporal stability models of the five different years all have good fitting effects. The model can effectively capture the unobserved heterogeneity, and the captured parameters under different time models also exhibit randomness. The results of model parameter estimation indicate that four accident factors, namely the type of intersection and road section, passenger cars, motorcycles, and accident liability, have temporal stability, while other accident-influencing factors only have significant effects in individual years. The accident factor of protective facilities also have temporal stability. Accident factor variables such as passenger cars, motorcycles, the state of vehicle headlights, vehicle safety conditions, hitting fixed objects, and accident liability will significantly increase the possibility of casualties in single-vehicle accidents. Variables such as the type of protective facilities and visibility greater than 100 meters will significantly reduce the severity of injuries.

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    Preparation and Thermal Property Regulation of Nitrates Based Phase Change Material for Low and Medium Temperature Thermal Energy Storage
    AN Zhoujian, LI Lu, MAO Shuai, LIU Ligong, DU Xiaoze, ZHANG Dong
    Journal of South China University of Technology(Natural Science Edition)    2025, 53 (3): 116-126.   DOI: 10.12141/j.issn.1000-565X.240256
    Abstract1930)   HTML7)    PDF(pc) (2731KB)(397)       Save

    The recovery, storage and reuse of low-temperature waste heat in industry by using phase change materials for heat storage is an important method to achieve the gradual utilisation of energy and improve the efficiency of energy utilisation. The physical properties of phase change materials are the key factors determining the performance of heat storage systems.Therefore, the development of phase change materials with an appropriate phase transition temperature and good thermal cycling stability is of great significance for achieving efficient waste heat recovery. Based on this, a new phase change material, NaNO3-KNO3-NaNO2-LiNO3, was synthesized using the static melting method. A series of characterizations were conducted to evaluate its thermal properties, including melting point, latent heat, specific heat capacity, and cyclic stability, using differential scanning calorimetry, thermogravimetric analysis, X-ray diffraction, and Fourier transform infrared spectroscopy. The optimal composition was identified as m(NaNO3)∶m(KNO3)∶m(NaNO2)∶m(LiNO3)=6.32∶47.83∶36.10∶9.75 which was selected as the final preferred salt. The experimental results demonstrate that the preferred salt has significant performance advantages, with a low melting point of 79.02 ℃ and a latent heat of phase transition of 176.71 J/g; the average specific heat capacities of the solid and liquid phases are 1.96 and 2.09 J/(g·℃), respectively; the decomposition temperature reaches more than 600 ℃, which demonstrates its wide applicability in terms of temperature; after 100 high and low temperature cycling tests, the preferred salt still exhibited good thermal cycling stability. This study provides a new type of phase change energy storage material for low and medium temperature waste heat recovery and heat storage system, which is of great significance for energy optimisation and energy saving and emission reduction in related fields.

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    Dissolution Mechanism and Kinetics Analysis of Fe From Copper Smelting Slag by Acid Leaching at Atmospheric Pressure
    YAN Cuirong, ZHANG Hao, ZHOU Xintao, LUO Zhongqiu, CAI Xiunan, GAO Zimeng, SHI Jinyu
    Journal of South China University of Technology(Natural Science Edition)    2025, 53 (3): 127-138.   DOI: 10.12141/j.issn.1000-565X.240354
    Abstract1929)   HTML12)    PDF(pc) (5250KB)(65)       Save

    The copper smelting slag, abundant in valuable elements such as Fe and Si, exhibits excellent secondary resource characteristics and can be utilized as a raw material for constructing high-value-added silicon-iron-based functional materials. Understanding the controllable release patterns of Si and Fe elements under acid leaching conditions and the effective separation mechanisms of mineral phases is crucial for their high-value resource utilization.This study employed HSC 6.0 to simulate the dominant species in the silicon-iron system under varying pH and potential conditions, investigating the dissolution conditions of iron-containing mineral phases in the slag and the controllable release patterns of Si and Fe elements under H2SO4 acid leaching conditions. The effects of acid leaching temperature, H2SO4 concentration, particle size, and stirring speed on Fe leaching rate were analyzed. The results indicate that acid leaching temperature and H2SO4 concentration have a positive impact on the Fe leaching rate, while particle size exerts a negative influence, and stirring speed has minimal effect. Under conditions of 2.0 mol/L H2SO4 concentration, 90 ℃ acid leaching temperature, and copper slag particle size ranging from (45, 88]μm, the iron leaching rate can reach 95.73% after 60 minutes of acid leaching. The shrinking unreacted core model was used to describe the leaching process. In the initial stage of the reaction, the reaction rate is primarily controlled by the chemical reaction process, with an activation energy of 40.99 kJ/mol, and subsequently shifts to internal diffusion control, with an activation energy of 8.70 kJ/mol. During the chemical reaction control stage, the influence indices for H2SO4 concentration and copper slag particle size were calculated to be 0.558 and -0.759, respectively, thereby establishing the macrokinetic equation for the atmospheric pressure leaching of copper smelting slag with H2SO4.

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    Fusion Transformer Model-Based Segmentation Algorithm for Laser Point Cloud of Distribution Lines
    DAI Zhou, LIU Yan, MAO Xianyin, GUO Tao, XU Lianggang, CHENG Guixian
    Journal of South China University of Technology(Natural Science Edition)    2025, 53 (5): 139-146.   DOI: 10.12141/j.issn.1000-565X.240542
    Abstract1859)   HTML12)    PDF(pc) (1833KB)(68)       Save

    As laser point cloud models are crucial for distribution line inspection and management, most distribution channels have constructed laser point cloud models at present. With the increase of the number of models, extracting key component locations (e.g., conductors, insulators) becomes vital. In order to enhance the accuracy and efficiency of segmenting key components such as lines, towers and insulators, this paper presents a segmentation algorithm for laser point cloud of distribution lines based on a fusion Transformer model. Given the need for detailed features in the point clouds of distribution lines, a dual-channel parallel feature extraction module is designed to capture high-frequency and low-frequency features. The low-frequency features are processed via average pooling and a fusion Transformer-based extractor, while the high-frequency features are handled through max pooling and a multi-layer perceptron (MLP) module with convolutional layers. The feature vectors from both channels are then fused to improve the ability of detail feature extraction. Additionally, the fused features are fed back into the MLP module for further refinement, achieving precise point cloud target segmentation. Extensive experiments demonstrate the accuracy and effectiveness of the proposed algorithm. It has potential advantages in many aspects, such as improving the inspection accuracy of unmanned aerial vehicles, enhancing the level of automation, improving the robustness, integrating multi-source data and reducing inspection costs.

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    Impact of Connected Collision Warning Information System on Freeway Driving Behavior in Foggy Conditions
    REN Wenhao, ZHAO Xiaohua, CHEN Chen, FU Qiang
    Journal of South China University of Technology(Natural Science Edition)    2025, 53 (2): 27-37.   DOI: 10.12141/j.issn.1000-565X.240122
    Abstract1847)   HTML6)    PDF(pc) (2653KB)(71)       Save

    Freeway driving in foggy conditions poses significant safety risks and accident hazards. The connected collision warning information system (CCWIS) offers a novel approach to addressing this challenge, and its impact on driving behavior is fundamental to demonstrating its effectiveness. This study employed driving simulation technology to create a CCWIS testing platform. Considering dense fog weather conditions, it designs two driving environments: a baseline traditional and a comparative connected. And a typical collision risk scenario involving a sudden braking maneuver by a slow-moving vehicle ahead was constructed. A total of 27 participants were recruited to conduct driving simulation experiments to yield fine-grained microscopic data on driving behavior. An evaluation indicator system was developed from multiple dimensions, including stability, safety, and compliance, to quantitatively describe and statistically analyze the effectiveness of CCWIS during different interaction stages with the lead vehicle (slow-speed car-following and the frout vehicle emergency braking stage). The results indicate that, regarding stability, collision warning information may lead to a decrease in driving stability for a period during the slow-speed car-following stage. After the vehicle in the slow-speed car-following stage has decelerated to a stable state and during the front vehicle emergency braking stage, it exhibits better stability compared to scenarios without collision warning information. In terms of safety, collision warning information significantly reduces risks in terms of time, acceleration, and distance across multiple dimensions during the driving process. Regarding compliance, collision warning information can improve the level of driver compliance with preceding vehicle information, reducing the differences in driver compliance with preceding vehicle information between the slow-speed car-following and the front vehicle emergency braking stages. The results of this study can provide platform and theoretical support for the test and application of connected collision warning information systems in foggy environments of freeways.

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    Research on the Joint Optimization of Shared Bikes and Demand-Responsive Connector
    XU Hang, LI Xin, YUAN Yun
    Journal of South China University of Technology(Natural Science Edition)    2025, 53 (8): 20-28.   DOI: 10.12141/j.issn.1000-565X.240455
    Abstract1823)   HTML5)    PDF(pc) (2642KB)(41)       Save

    Demand-Responsive Connector (DRC), as a flexible public transportation mode, can provide personalized bus services according to passengers’ needs and has been widely applied in urban areas both domestically and internationally. However, in actual operation, it faces the dilemma of balancing service efficiency and operational costs, as well as the challenge of achieving “door-to-door” services. To address these issues, a joint travel mode combining shared-bike transfer and DRC was proposed. By integrating the advantages of shared bikes and DRC, the coupling optimization of the two transportation modes can be realized, thereby improving the overall service efficiency and service level of public transportation. To this end, based on the continuous approximation method, discrete demand points and shared bicycle deployment locations were continuousized. The operating costs of the transit system, shared bicycle costs, and passenger travel time costs were derived and calculated. By minimizing the total system cost, the joint mobility system was optimized. With the goal of minimizing the total system cost, the coupling optimization of shared bicycles and demand-responsive buses was realized. To verify the effectiveness of the proposed joint travel system, an empirical study was conducted using the university town area of Chongqing as a case. The operation of the joint travel system under different scenarios was simulated and compared with the traditional DRC system without shared-bikes. The results show that the joint travel system can effectively address the operational problems of DRC. Compared with the traditional DRC system, the joint travel system can reduce the total system cost by up to 14.8%, the travel time saving by 15.2%, and the detouring saving of DRC vehicles by 29%. It is demonstrated that introducing shared bicycles as a first- and last-mile connection tool in demand-responsive transit systems can significantly reduce transit operating costs and passenger travel times. At the same time, it minimizes unnecessary detours by transit vehicles, optimizes transit routes, and greatly improves the efficiency and quality of public transportation services.

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    Dynamic Scheduling of Demand Responsive Transit Based on Model Predictive Control
    JIN Wenzhou, ZHANG Yong, SUN Jie
    Journal of South China University of Technology(Natural Science Edition)    2025, 53 (6): 77-90.   DOI: 10.12141/j.issn.1000-565X.240331
    Abstract1797)   HTML4)    PDF(pc) (3833KB)(73)       Save

    As a typical representative of the new mode of shared public transport, demand responsive transit (DRT) systems are facing the challenge of efficiently processing travel demand and real-time planning of vehicle routes. Traditional dynamic scheduling methods for DRT primarily focus on adjusting vehicle routes after demand has been realized, which often limits their ability to effectively respond to dynamic fluctuations in travel demand. Therefore, this study introduced a Model Predictive Control (MPC) approach and develops a dynamic scheduling model for DRT based on a multi-period rolling optimization framework. The model used potential future stage passenger flow information to optimize current stage scheduling decisions and timely re-planning according to the latest disclosed information to cope with the uncertainty and dynamic changes of demand. In terms of solution methods, this study integrated the adaptive large neighborhood search (ALNS) strategy to design the MPC-ALNS algorithm. It iteratively optimized the vehicle scheduling sequence through a two-phase heuristic approach. Numerical experimental results demonstrate that in ideal scenarios without prediction deviation, compared to traditional dynamic scheduling methods, the proposed method significantly reduces the total cost of the system by 14.54%. Even in a pessimistic scenario with a 30% prediction deviation, it still achieves a cost optimization of 5.27%, and various passenger service indicators show superior performance, indicating strong universal applicability in different stochastic environments. At the same time, the experiment further verified the stable optimization performance of the method in dealing with different orders and vehicle scales, and analyzed the sensitivity of the rejection cost and proposed the setting idea of the optimal rejection cost suitable for different operating scenarios.

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    Carbon Emission Prediction in Transportation Industry Based on SD-ISSA-DALSTM
    WANG Qingrong, WANG Junjie, ZHU Changfeng, HAO Fule
    Journal of South China University of Technology(Natural Science Edition)    2025, 53 (5): 66-81.   DOI: 10.12141/j.issn.1000-565X.240356
    Abstract1782)   HTML7)    PDF(pc) (3678KB)(58)       Save

    Aiming at the low accuracy of carbon emission prediction caused by the high volatility and nonlinearity of the carbon emission data series in transportation industry, a transportation carbon emission prediction model combining the secondary decomposition, dual attention mechanism, improved sparrow search algorithm (ISSA) and long short-term memory (LSTM) network is proposed. First, complete ensemble empirical mode decomposition with adaptive noise is introduced to decompose the transportation carbon emission data series into modal components with different frequencies, then sample entropy is used to quantify the complexity of each component, and secondary decomposition is performed on the component with the highest entropy value via variational mode decomposition, which further weakens the volatility and nonlinearity of the transportation carbon emission data series. Next, in order to explore the correlation between transportation carbon emission and its influencing factors, a double attention mechanism-optimized LSTM (DALSTM) model is constructed, in which a feature attention mechanism is added to the input side of the LSTM to highlight the key input features. Meanwhile, a temporal attention mechanism is added to the output side to extract the key historical moments. Finally, the SSA algorithm is improved by combining the Circle chaotic mapping, the dynamic inertia weight factor and the mixed variance operator strategies, ISSA-DALSTM models are established for each component separately, and the predicted values of each component are reconstructed. By measuring the carbon emission data of China’s transportation industry from 1990 to 2019, it is found that the root mean square error, mean square error, and mean absolute percentage error of the proposed model are respectively 5.308 8, 3.566 1 and 0.443 9, which are better than those of other comparative models, thus verifying the validity of the proposed model.

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    Lane Line Detection Algorithm Based on Deep Learning
    YUE Yongheng, ZHAO Zhihao
    Journal of South China University of Technology(Natural Science Edition)    2025, 53 (9): 22-30.   DOI: 10.12141/j.issn.1000-565X.240609
    Abstract1697)   HTML31)    PDF(pc) (2888KB)(140)       Save

    Aiming at the problem of lane detection accuracy of intelligent vehicles in complex scenes, this paper proposed a lane line detection algorithm which incorporates a multi-scale spatial attention mechanism and a path aggregation network (PANet). The algorithm first introduced the pre-anchored frame UFLD lane detection model and incorporated a feature pyramid enhancement module PANet with depthwise separable convolution to achieve multi-scale feature extraction of images. Next, a multi-scale spatial attention module was designed in the network framework and a SimAM lightweight attention mechanism was introduced to enhance the focusing ability on target features. Then, an adaptive feature fusion module was designed to perform cross-scale fusion of feature maps output from PANet by intelligently adjusting the fusion weights of feature maps at different scales, so as to effectively enhance the network’s ability to extract complex features. Finally, the application of TuSimple dataset detection proves that the proposed algorithm achieves a detection accuracy of 96.84%, representing a 1.02 percentage point improvement over the original algorithm, and outperforms conventional mainstream algorithms. Experimental results on the CULane dataset demonstrate that the proposed algorithm achieves an F1 score of 72.74%, outperfor-ming conventional mainstream methods with a 4.34 percentage point improvement over the baseline. Notably, it exhibits significant performance gains in extreme scenarios (e.g., strong illumination and shadows), confirming its superior detection capability in complex environments. In addition, the real-time test shows that the model infe-rence speed reaches 118 f/s, which meets the real-time demand of intelligent vehicles.

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    Study on the Impact Characteristics of Horizontal Curve Elements on Carbon Emissions from Passenger Car Operation
    WANG Xiaofei, LUO Zhen, WANG Shaohua, PAN Ling, ZENG Qiang
    Journal of South China University of Technology(Natural Science Edition)    2025, 53 (2): 68-79.   DOI: 10.12141/j.issn.1000-565X.240047
    Abstract1565)   HTML8)    PDF(pc) (2493KB)(41)       Save

    China’s “14th Five-Year Plan” places higher demands on green transportation development, with emissions from traffic operations being the primary source of carbon emissions in the transportation sector. To investigate the factors influencing carbon emissions of passenger cars on highway curved segments, this study conducted on-site driving tests using OBD-equipped vehicles to collect driving data from typical curved road segments in Guangdong Province, and obtains carbon emission data through the IPCC carbon emission accounting method. Re-levant evaluation indicators influencing passenger car emissions were selected based on road alignment, and gray relational analysis was used to calculate the correlations between these indicators. The results indicate that among the geometric alignment elements of horizontal curve sections, indicators such as the proportion of transition curve length and transition curve parameters are significantly correlated with the segmental carbon emission rate. The radius of the circular curve is also significantly correlated within a specific range. For non-geometric factors, indicators such as the standard deviation and mean of acceleration show significant correlations with carbon emissions, and these two indicators are further associated with geometric factors like transition curve parameters and the proportion of transition curve length. Based on the results of the grey relational analysis, eight correlated indicators were selected, and a grey GM(1, N) model was developed to predict the total carbon emissions of passenger cars on horizontal curve sections. The prediction results show an average relative error of 5.10% compared to the actual values. The predictive performance of the model surpasses that of traditional multiple regression models, demonstrating outstanding performance and reliability in scenarios with limited data.The findings of this study can identify key design and operational parameters significantly influencing carbon emissions, providing a theoretical basis for the low-carbon optimization and management of highway horizontal curve sections.

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