Traffic signal control plays an essential role in the Intelligent Transportation systems (ITS). Due to the intrinsic uncertainty and the significant increase in travel demand, in many cases, a traffic system still has...
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ISBN:
(数字)9781728141497
ISBN:
(纸本)9781728141503
Traffic signal control plays an essential role in the Intelligent Transportation systems (ITS). Due to the intrinsic uncertainty and the significant increase in travel demand, in many cases, a traffic system still has to rely on human engineers to cope with the complicated and challenging traffic control and operation problem, which cannot be handled well by the traditional methods alone. Thus, imitating the good working experience of engineers to solve traffic signal control problems remains a practical, smart, and cost effective approach. In this paper, we construct a modelling framework to imitate how engineers cope with complex scenarios through learning from the historical record of manipulations by traffic operators. To extract spatial-temporal traffic demand features of the entire road network, a specially designed mask and a graph convolutional neural network (GCNN) are employed in this framework. The simulation experiments results showed that, compared with the original deployed control scheme, our method reduced the average waiting time, average time loss of vehicles, and vehicle throughput by 6.6%, 7.2%, and 6.85%, respectively.
Human emotion recognition is an important direction in the field of biometric and information forensics. However, most existing human emotion research are based on the single RGB view. In this paper, we introduce a RG...
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This paper tackles the problem of novel category discovery (NCD), which aims to discriminate unknown categories in large-scale image collections. The NCD task is challenging due to the closeness to the real world scen...
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With the remarkable development of the power grid, traditional manual maintenance modes cannot meet the requirement of efficiency and reliability. Robotic technology is applied for power line maintenance to bring down...
With the remarkable development of the power grid, traditional manual maintenance modes cannot meet the requirement of efficiency and reliability. Robotic technology is applied for power line maintenance to bring down risks and ensure inspectors' safety. Based on the power transmission line maintenance platform, a 5-DOF manipulator is designed to perform the maintenance tasks for the quad bundle conductors. Then the kinematics analysis of the manipulator is analyzed. The manipulator doesn't meet the Pieper criterion. So an improved adaptive particle swarm optimization (APSO) is proposed for figuring out the inverse kinematics. The weights of the pose and the constraint of the prismatic joint are introduced to the algorithm. What's more, half of the particles are initialized around the incipient value of joints and half of the particles are randomly initialized with the constraint of joints in this algorithm. Results show that the manipulator is able to work in the environment of quad bundle conductors and the proposed algorithm can improve the precision of inverse kinematics and prevent the leap of joints.
This paper presents an adaptive narrative game system that focuses on sequential logic design. The system adapts a random forest machine learning model to estimate a student's current level of domain knowledge rel...
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ISBN:
(数字)9781728169040
ISBN:
(纸本)9781728169057
This paper presents an adaptive narrative game system that focuses on sequential logic design. The system adapts a random forest machine learning model to estimate a student's current level of domain knowledge relative to the problem presented to him through his game-playing behavior data, such as time taken to find solutions, errors in solutions, and emotional indicators. Hints, prompts, and/or individualized lessons are then offered to the player to guide their learning in a positive and productive direction. Our preliminary pilot study demonstrates that the model can make accurate classifications, from which proper assistance can then be provided to individual students as they play.
This article studies the distributed estimation problem of a multi-agent system with bounded absolute and relative range measurements. Parts of the agents are with high-accuracy absolute measurements, which are consid...
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Wireless sensor networks consist of a large number of sensor nodes that have low power and limited transmission range and can be used in various scenario. The nodes can be deployed in the long and narrow region, such ...
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Wireless sensor networks consist of a large number of sensor nodes that have low power and limited transmission range and can be used in various scenario. The nodes can be deployed in the long and narrow region, such as road, bridge, tunnel and pipeline, to get some interesting information. The linear topology of these network application is different other application and have special feature, such as multi-hop, long delay, long distance and low reliability. This paper introduces the concept of linear wireless sensor networks and discusses the classification of topology and key issue of this network. The application of the linear wireless sensor network, such as road, bridge, tunnel and pipeline is presented. The research challenges are discussed at last in this paper.
It is widely acknowledged that verifying the safety of autonomous driving strategies requires a substantial body of simulation testing and road testing. In recent years, the formal safety methods represented by Respon...
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To tackle the problem of aquatic environment pollution, a vision-based autonomous underwater garbage cleaning robot has been developed in our laboratory. This paper proposes a garbage detection method based on a modif...
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To tackle the problem of aquatic environment pollution, a vision-based autonomous underwater garbage cleaning robot has been developed in our laboratory. This paper proposes a garbage detection method based on a modified YOLOv4,allowing high-speed and high-precision object detection. More specifically, the YOLOv4 algorithm is chosen as a basic neural network framework to perform object detection. With the purpose of further improvement on the detection speed, the channel pruning and layer pruning are implemented on the trained YOLOv4 model, while the fine-tuned mechanism assists the pruned model to restore accuracy. In virtue of the improved detection methods, the robot has the ability to collect garbage *** experimental results indicate that the pruned YOLOv4 detection method can still maintain the high performance even though the parameter amount is 7.062% of the original model.
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