IET Intelligent Transport Systems最新文献_第9页

Q-EANet: Implicit social modeling for trajectory prediction via experience-anchored queries Q-EANet：通过经验锚定查询进行轨迹预测的内隐社会建模

IF 2.7 4区工程技术

IET Intelligent Transport Systems Pub Date : 2023-12-20 DOI: 10.1049/itr2.12477

Jiuyu Chen, Zhongli Wang, Jian Wang, Baigen Cai

{"title":"Q-EANet: Implicit social modeling for trajectory prediction via experience-anchored queries","authors":"Jiuyu Chen, Zhongli Wang, Jian Wang, Baigen Cai","doi":"10.1049/itr2.12477","DOIUrl":"10.1049/itr2.12477","url":null,"abstract":"Accurately predicting the future trajectory and behavior of traffic participants is crucial for the maneuvers of self-driving vehicles. Many existing works employed a learning-based “encoder-interactor-decoder” structure, but they often fail to clearly articulate the relationship between module selections and real-world interactions. As a result, these approaches tend to rely on a simplistic stacking of attention modules. To address this issue, a trajectory prediction network (Q-EANet) is presented in this study, which integrates GRU encoders, MLPs and attention modules. By introducing a new explanatory rule, it makes a contribution to interpretable modeling, models the entire trajectory prediction process via an implicit social modeling formula. Inspired by the anchoring effect in decision psychology, the prediction task is formulated as an information query process that occurs before traffic participants make decisions. Specifically, Q-EANet uses GRUs to encode features and utilizes attention modules to aggregates interaction information for generating the target trajectory anchors. Then, queries are introduced for further interaction. These queries, along with the trajectory anchors with added Gaussian noise, are then processed by a GRU-based decoder. The final prediction results are obtained through a Laplace MDN. Experimental results on the several benchmarks demonstrate the effectiveness of Q-EANet in trajectory prediction tasks. Compared to the existing works, the proposed method achieves state-of-the-art performance with only simple module design. The code for this work is publicly available at https://github.com/Jctrp/socialea.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12477","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138818047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A two-stage robust optimal traffic signal control with reversible lane for isolated intersections 隔离交叉口带可逆车道的两阶段稳健优化交通信号控制

IF 2.7 4区工程技术

IET Intelligent Transport Systems Pub Date : 2023-12-14 DOI: 10.1049/itr2.12465

Zhiyuan Sun, Zhicheng Wang, Xin Qi, Duo Wang, Yue Li, Huapu Lu

{"title":"A two-stage robust optimal traffic signal control with reversible lane for isolated intersections","authors":"Zhiyuan Sun, Zhicheng Wang, Xin Qi, Duo Wang, Yue Li, Huapu Lu","doi":"10.1049/itr2.12465","DOIUrl":"10.1049/itr2.12465","url":null,"abstract":"The integrated design of traffic signal control (TSC) and reversible lane (RL) is an effective way to solve the problem of tidal congestion with uncertainty at isolated intersections, because of its advantage in making full use of temporal-spatial transportation facilities. Considering the contradiction between the dynamic TSC scheme and the fixed RL scheme in one period, a two-stage optimization method based on improved mean-standard deviation (MSD) model for isolated intersections with historical and real-time uncertain traffic flow is proposed. In the first stage, applying the same-period historical data of multiple days, a robust optimal traffic signal control model with reversible lane based on MSD model (MSD-RTR model) is put forward to obtain the fixed RL scheme and the compatible initial TSC scheme. A double-layer nested genetic algorithm (DN-GA) is designed to solve this model. In the second stage, applying real-time period data and multi-day same-period historical data, a robust optimal dynamic traffic signal control model based on MSD model (MSD-RDT model) is put forward to obtain the dynamic TSC scheme. Three modes which reflect the different weights of historical period and real-time period in this MSD-RDT model are presented to improve the model stability, and a multi-mode genetic algorithm (MM-GA) is designed. Finally, a case study is presented to demonstrate the efficiency and applicability of the proposed models and algorithms.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12465","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138684915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hierarchical control strategy for reliable lane changes considering optimal path and lane-changing time point 考虑最佳路径和变道时间点的可靠变道分层控制策略

IF 2.7 4区工程技术

IET Intelligent Transport Systems Pub Date : 2023-12-13 DOI: 10.1049/itr2.12460

Jiayu Fan, Yinxiao Zhan, Jun Liang

{"title":"A hierarchical control strategy for reliable lane changes considering optimal path and lane-changing time point","authors":"Jiayu Fan, Yinxiao Zhan, Jun Liang","doi":"10.1049/itr2.12460","DOIUrl":"10.1049/itr2.12460","url":null,"abstract":"Implementing reliable lane changes is crucial for reducing collisions and enhancing traffic safety. However, existing research lacks comprehensive investigation into the optimal path for maintaining driving quality, and little attention has been given to determining the appropriate lane changing time point. This paper addresses these gaps by presenting a novel hierarchical strategy. First, a synthesized safety distance for lane changing, which considers variable execution duration, is designed to reduce collision risk. Next, a hierarchy of optimization control strategies is proposed to obtain the optimal path. An upper neural network-fuzzy control algorithm is established to identify an appropriate lane-changing time point. Additionally, a lower neural network-improved firefly algorithm is formulated to optimize the preliminary safety path based on multiple driving criteria. Furthermore, the dynamics characteristics of the vehicle are incorporated into the model predictive control algorithm to ensure the vehicle follows the optimal path. Finally, the feasibility of the proposed hierarchical control strategy is validated through typical lane-changing scenarios conducted on the Carsim–Simulink platform.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12460","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138580848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy-efficiency optimization and control for electric vehicle platooning with regenerating braking 带再生制动功能的电动汽车排队行驶的能效优化与控制

IF 2.7 4区工程技术

IET Intelligent Transport Systems Pub Date : 2023-12-08 DOI: 10.1049/itr2.12445

Zhicheng Li, Yang Wang

{"title":"Energy-efficiency optimization and control for electric vehicle platooning with regenerating braking","authors":"Zhicheng Li, Yang Wang","doi":"10.1049/itr2.12445","DOIUrl":"10.1049/itr2.12445","url":null,"abstract":"It is a critical problem to improve energy efficiency for electric vehicle platooning systems. Moreover, different from internal combustion engine vehicles, the electric engine has higher efficiency, and further regenerating braking is widely used to recycle part of the energy in the electric vehicle when it is braking. What is more, if vehicles take a formation to drive, they can save more energy. Combining all the favorable factors, this paper presents a two-layer energy-efficiency optimization strategy for electric vehicle platooning. The upper layer presents an optimization method to find the optimal velocities and distances between vehicles under different road conditions during the cruise status of the electric vehicle platooning. Due to the nonconvex cost function and considering regenerative braking, the optimization problem is addressed by the dynamic programming method combined with the successive convex approximation method. Further, the lower layer presents a real-time Model Predictive Control (MPC) strategy, and it directly introduces the battery pack state of charge consumption as the input, which not only finishes the control mission but also consumes minimal energy. Finally, simulation results are provided to verify the effectiveness and advantages of the proposed methods.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12445","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138563504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lane-level short-term travel speed prediction for urban expressways: An attentive spatio-temporal deep learning approach 城市快速路的车道级短期行驶速度预测：贴心的时空深度学习方法

IF 2.7 4区工程技术

IET Intelligent Transport Systems Pub Date : 2023-12-08 DOI: 10.1049/itr2.12464

Keshuang Tang, Siqu Chen, Yumin Cao, Di Zang, Jian Sun

{"title":"Lane-level short-term travel speed prediction for urban expressways: An attentive spatio-temporal deep learning approach","authors":"Keshuang Tang, Siqu Chen, Yumin Cao, Di Zang, Jian Sun","doi":"10.1049/itr2.12464","DOIUrl":"10.1049/itr2.12464","url":null,"abstract":"Numerous efforts have been made to address the section-level travel speed prediction problem. However, section-level predictions can hardly be used for fine-grained applications, such as lane management and lane-level navigation. The main reason for this is that significant speed heterogeneity exists among the lanes within one section. Thus, this study proposes a three-dimensional (3D) dual attention convolution-based deep learning model for predicting the lane-level travel speed. 3D convolutions are designed to learn high-dimensional spatiotemporal traffic flow features, that is, the relationships between different sections, lanes, and periods. Dual attention modules are used to focus on the traffic flow propagation patterns and to explain the model's mechanisms. To evaluate the proposed model, an indicator is introduced to assess the spatio-temporal learning ability, based on targeting the lane-level case. Evaluation experiments are conducted based on loop detector data in Shanghai, China. The results show that high accuracy is obtained by the proposed model, with a 2.9 km/h mean absolute error, thereby outperforming several existing methods. Finally, an in-depth analysis is provided regarding the attention coefficients and interpretation of real-world lane-level traffic flow propagation patterns, so as to gain insights into the model's mechanism when capturing dynamic lane-level traffic flow.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12464","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138558393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Two-objective train operation optimization based on eco-driving and timetabling 基于生态驾驶和时刻表的双目标列车运行优化

IF 2.7 4区工程技术

IET Intelligent Transport Systems Pub Date : 2023-12-07 DOI: 10.1049/itr2.12456

Xiaowen Wang, Xiaoyun Feng, Pengfei Sun, Qingyuan Wang

{"title":"Two-objective train operation optimization based on eco-driving and timetabling","authors":"Xiaowen Wang, Xiaoyun Feng, Pengfei Sun, Qingyuan Wang","doi":"10.1049/itr2.12456","DOIUrl":"10.1049/itr2.12456","url":null,"abstract":"In urban railway systems, the timetable guides the section operation of the single train and the arrangement of the train group to meet the dual needs of cost and passengers. This paper proposes a two-objective train operation optimization based on eco-driving and timetabling to restore a more realistic scene, including a method level and an objective level. For the method level, the speed curve optimization of the single train and the timetable optimization of the train group are adopted jointly. For the objective level, both the total energy consumption of the train group and the consuming time of passengers are considered. A hybrid solution strategy based on quadratic programming and improved artificial bee colony algorithm is proposed. A hardware-in-the-loop platform is built to carry out validation experiments. Both the cases in general hours and special hours are verified based on the actual data from Beijing Metro Line 15. The results show that both the energy consumption and the passenger consuming time are reduced simultaneously. Correspondingly, the speed curve and the time distribution of the timetable are individually optimized based on the fluctuating passenger flow.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12456","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138545605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Extracting long-term spatiotemporal characteristics of traffic flow using attention-based convolutional transformer 利用注意力卷积变换器提取交通流的长期时空特征

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2023-12-07 DOI: 10.1049/itr2.12468

Ali Reza Sattarzadeh, Pubudu N. Pathirana, Ronny Kutadinata, Van Thanh Huynh

{"title":"Extracting long-term spatiotemporal characteristics of traffic flow using attention-based convolutional transformer","authors":"Ali Reza Sattarzadeh, Pubudu N. Pathirana, Ronny Kutadinata, Van Thanh Huynh","doi":"10.1049/itr2.12468","DOIUrl":"10.1049/itr2.12468","url":null,"abstract":"Predicting traffic flow is vital for optimizing transportation efficiency, reducing fuel consumption, and minimizing commute times. While artificial intelligence tools have been effective in addressing this, there have been some difficulties in processing spatial and temporal data. Current transformer-based methods, although cutting-edge for traffic prediction, encounter challenges with handling long sequences and capturing temporal relations effectively. Addressing these, the research introduces a model combining multi-scale attention modules within transformer layers. This model employs spatio-temporal transformer blocks, enriched with multi-scale convolutional attention mechanisms, allowing for a deeper understanding of temporal and spatial traffic patterns. This unique attention mechanism enhances data feature interpretation, leading to heightened prediction precision. The tests on extensive traffic datasets showcase the model's prowess in capturing both local and global traffic features, resulting in superior traffic status predictions. In summary, the innovative model offers an efficacious approach to long-sequence traffic data learning and temporal relationship extraction, setting a new benchmark in traffic flow prediction accuracy.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12468","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138545670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed non-linear model predictive control with Gaussian process dynamics for two-dimensional motion of vehicle platoon 车辆排二维运动的分布式非线性模型预测控制与高斯过程动力学

IF 2.7 4区工程技术

IET Intelligent Transport Systems Pub Date : 2023-12-07 DOI: 10.1049/itr2.12418

Xiaorong Hu, Yao Shi, Lei Xie, Hongye Su

{"title":"Distributed non-linear model predictive control with Gaussian process dynamics for two-dimensional motion of vehicle platoon","authors":"Xiaorong Hu, Yao Shi, Lei Xie, Hongye Su","doi":"10.1049/itr2.12418","DOIUrl":"10.1049/itr2.12418","url":null,"abstract":"The platoon control of connected and automated vehicles is an important topic in transportation research. The characteristics of non-linearities, external disturbances, and strong coupling are non-negligible in two-dimensional motion control. An integrated longitudinal and lateral vehicle dynamics is required. A Gaussian Process-based Distributed Stochastic Model Predictive Control (GP-DSMPC) for two-dimensional motion is proposed. It achieves global longitudinal stability and lateral error suppression. Gaussian process (GP) regression is employed to approximate the unknown model error. For the two-norm chance constraints, over-approximating the confidence ellipse to an outer polyhedron is an effective way to reduce the conservativeness and coupling effect in longitudinal and lateral motion. A neighbour-average target trajectory is designed with an upper-level optimization for adjustable target coefficients. The sum of the local cost functions is used as a Lyapunov candidate to achieve the global stability of the longitudinal motion. Some conditions on penalty weights and target coefficients among subsystems, and terminal outputs are derived. Simulation results reveal that the proposed method is effective for disturbance attenuation and performs better than distributed non-linear model predictive control without GP estimation under low-friction road conditions.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12418","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138546434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Auxiliaries’ power and energy demand prediction of battery electric vehicles using system identification and deep learning 利用系统识别和深度学习预测电池电动汽车的辅助动力和能源需求

IF 2.7 4区工程技术

IET Intelligent Transport Systems Pub Date : 2023-12-06 DOI: 10.1049/itr2.12467

Lukas Schäfers, Kai Franke, Rene Savelsberg, Stefan Pischinger

{"title":"Auxiliaries’ power and energy demand prediction of battery electric vehicles using system identification and deep learning","authors":"Lukas Schäfers, Kai Franke, Rene Savelsberg, Stefan Pischinger","doi":"10.1049/itr2.12467","DOIUrl":"10.1049/itr2.12467","url":null,"abstract":"The energy demand of the auxiliaries of battery electric vehicles can account for a significant share of the total energy demand of a trip and must be taken into account for the prediction of the vehicle's remaining driving range or the implementation of predictive driving functions. This paper investigates a method that uses system identification and neural networks with bidirectional long short-term memory layers to predict the power requirements of the auxiliaries depending on information that is known prior to the trip. By using a self-learning, data-driven approach as well as data that can be measured without additional instrumentation, a prediction is made possible without the need to design detailed physical models in advance. Additionally, a rule-based allocation of the training data based on environmental conditions is implemented, which serves to adapt individual models to different climatic modes of the thermal system. The potential of the method is demonstrated for three different systems showing a prediction accuracy of on average 3% to 8% in terms of energy, while the deviation of the predicted power consumption is on average about 500 watts. Due to the complete automation of the process, a further increase in prediction accuracy can be expected.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12467","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138545526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Risk analysis of autonomous vehicle test scenarios using a novel analytic hierarchy process method 使用新型层次分析法对自动驾驶汽车测试场景进行风险分析

IF 2.7 4区工程技术

IET Intelligent Transport Systems Pub Date : 2023-12-05 DOI: 10.1049/itr2.12466

Shengpeng Zhang, Taeoh Tak

{"title":"Risk analysis of autonomous vehicle test scenarios using a novel analytic hierarchy process method","authors":"Shengpeng Zhang, Taeoh Tak","doi":"10.1049/itr2.12466","DOIUrl":"10.1049/itr2.12466","url":null,"abstract":"Scenario-based test methods are employed to assess the safety and performance of autonomous vehicles. The analytic hierarchy process (AHP) method is a common assessment method for determining the criticality of test scenarios. However, the AHP method is subjective and less reproducible when performed by different persons, as the elements of pairwise comparison values that are directly linked to the outcome must be assigned by the person involved. This paper proposes a novel AHP method that automatically generates pairwise comparison values by optimizing the correlation between performance metrics and risk of test scenarios by simulation. Performance metrics are defined as the minimum relative distances and corresponding relative velocities between vehicles, and the risk of the test scenario is determined by the pairwise comparison values of AHP. The novel AHP method was evaluated using a cut-in scenario. The results showed that the minimum relative distance and the risk determined by the novel AHP method achieved a better correlation coefficient of −0.96, which is better than the conventional AHP of −0.828 and Fuzzy AHP of −0.824. These results suggest that the criticality of the test scenarios determined by the novel AHP method can more accurately reflect real-world driving environments.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12466","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138545527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0