The 18th International Conference on Mobility, Sensing and Networking (MSN 2022)
2nd International Workshop on Ubiquitous Electric Internet of Things
Session UEIoT
UEIoT
Conference
10:30 AM
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12:00 PM HKT
Local
Dec 13 Tue, 9:30 PM
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11:00 PM EST
Intelligent rush repair of unmanned distribution network based on deep reinforcement learning
Y ue Zhao, Y ang Chuan, Shi Pu, Xuwen Han, Shiyu Xia, Y anqi Xie
1
With the continuous expansion of China?€?s power grid scale and the increasing number of power users year by year, it is necessary to ensure the normal power supply of users. When a power failure occurs, it is particularly critical whether the emergency repair task can be completed quickly and scientifically. In this paper, an intelligent repair model of ?€?unmanned?€? distribution network based on deep reinforcement learning is proposed, which adopts speech recognition tech- nology and deep reinforcement learning algorithm to achieve the ?€?unmanned?€? of the whole system. Users can transmit the emergency repair information to the voice recognition module of the power supply emergency repair center by voice, SMS and IMS, and the module will get the emergency repair position and the amount of emergency repair tasks. Then, the resource allocation module is used to learn the emergency repair resource allocation strategy online, and the intelligent control of emergency repair in distribution network is realized. To verify the proposed algorithm, it is compared with two typical allocation strategies under the same settings. The results of the experiments demonstrate that the method based on deep reinforcement learning performs better in terms of emergency repair delay and intelligent emergency repair of the power supply in distribution networks.
Energy Minimization for IRS-assisted UAV-empowered Wireless Communications
Yangzhe Liao, Jiaying Liu, Yi Han, Quan Y u, Qingsong Ai, Quan Liu, Xiaojun Zhai
0
Non-terrestrial wireless communications have evolved into a technology enabler for seamless connectivity and ubiquitous computing services in the beyond fifth-generation (B5G) and sixth-generation (6G) networks, aiming to provision reliable and energy efficient communications among aerial platforms and ground mobile users. This paper considers intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV)-empowered wireless communication, which exploits both the high mobility of UAV and passive beamforming gain brought by IRS. The energy minimization of rotary-wing UAV is formulated by jointly considering numerous quality of service (QoS) constraints with intricately coupled variables. To tackle the formulated challenging problem, a heuristic algorithm is proposed. First, we decouple it into several subproblems. Moreover, we jointly investigate offloading decisions of Internet of Thing (IoT) devices by the proposed enhanced differential evolution algorithm. Then, minorization?€?maximization algorithm (MMA) is utilized to solve the optimization of IRS phase shift- vector. Moreover, ant colony optimization (ACO) algorithm is proposed to optimize UAV flight route indicator matrix. Numerical results validate the effectiveness of the proposed algorithm. The results show that the proposed solution can remarkably decrease UAV flight distance while improving the network energy efficiency in comparison with numerous advanced algorithms.
Trajectory Planning Model for Vehicle Platoons at Off-ramp
Xinyu Chen, Chen Mu, Yu Kong
0
Traffic delay and congestion frequently occur in off- ramp areas, but few researches focus on generating the microscopic trajectory plan for individual connected and automated vehicles (CAV) to instruct their real-time acceleration/deceleration rate and lane-changing maneuvers at off-ramps. This paper proposes a trajectory planning model for vehicle platoons at off-ramp based on mixed-integer nonlinear programming (MINLP). The model can generate systematical optimal trajectories for CAVs passing the off-ramp safely and efficiently. Aiming at optimizing the overall traffic efficiency, we developed a series of constraints to model the behavior of vehicles running in the trajectory area. Especially, the impact of the lane- changing of vehicles is carefully designed to further improve the efficiency and safety by coordinating the behavior of different vehicles on the main road into several vehicle platoons and simultaneously generates trajectories for all vehicles in a platoon. The experiments show that the model can improve the traffic efficiency of pure CAV flow on the off-ramp with safety and effectively solve traffic congestion at the off-ramp.
Space-Air-Ground-Aqua Integrated Intelligent Network: Vision, and Potential Techniques
Jinhui Huang, Junsong Yin, Shuangshuang Wang
1
The space-air-ground-aqua integrated network will become the basic form of the next generation network. Various technologies, including artificial intelligence, big data, cloud computing, edge computing, etc., will be deeply integrated into the network to form an integrated intelligent network of land, sea, air and space. In this article, we will present the vision for the development of the space-air-ground- aqua integrated intelligent network and describe its main features. We put forward a network architecture which integrated sub-networks of space, air, land and sea while emphasizing network interconnection, resources sharing, cooperative control and service reuse. We also discussed several promising technologies, including the THz, free space optical communication, software defined network, network function virtualization, edge intelligent, digital twins, physical layer security and blockchains.
Fast Detection of Multi-Direction Remote Sensing Ship Object Based on Scale Space Pyramid
Ziying Song, Li Wang, Guoxin Zhang, Caiyan Jia, Jiangfeng Bi, Haiyue Wei, Yongchao Xia,Chao Zhang,Lijun Zhao
1
Ships in remote sensing images are usually arranged in arbitrary direction, small in size, and densely arranged. As a result, existing object detection algorithms cannot detect ships quickly and accurately. In order to solve the above problems, a lightweight object detection network for fast detection of ships is proposed. The network is composed of backbone network, four-scale fusion network and rotation branch. First, a lightweight network unit S-LeanNet is designed and used to build a low-computing and accurate backbone network. Then, a four-scale feature fusion module is designed to generate a four-scale feature pyramid, which contains more features such as ship shape and texture, and at the same time is conducive to the detection of small ships. Finally, a novel rotation branch module is designed, using balance L1 loss function and R-NMS for post-processing, to realize the precise positioning and regression of the rotating bounding box in one step. Experimental results show that the detection precision of our method in the DOTA remote sensing data set is compared with the latest SCRDet detection method, the precision is increased by 1.1%, and the operating speed is increased by 8 times, which can meet the fast detection requirements of ships.
Fire Detection Scheme in Tunnels Based on Multi-source Information Fusion
Tianyu Zhang, Yi Liu, Weidong Fang, Gentuan Jia, Yunzhou Qiu
0
Multi-sensor information fusion technology is an effective method for fire detection. However, in the underground road scenario, due to the closed environment and dispersed sensor layout, common fire detection data fusion methods have defects of poor detection timeliness and low accuracy. Therefore, this paper proposes a new fire detection scheme combining BP neural network and D-S evidence theory, and further puts forward a evidence correction method based on exponential entropy. We compare this method with common methods, and the experimental results show that the new method can detect the fire at the earliest in both open fire and smoldering fire scenes of underground roads, which improves the real-time performance and accuracy of fire detection.
Improving Imbalanced Text Classification with Dynamic Curriculum Learning
Xulong Zhang, Jianzong Wang, Ning Cheng, Jing Xiao
1
Recent advances in pre-trained language models have improved the performance for text classification tasks. However, little attention is paid to the priority scheduling strategy on the samples during training. Humans acquire knowledge gradually from easy to complex concepts, and the difficulty of the same material can also vary significantly in different learning stages. Inspired by this insights, we proposed a novel self-paced dynamic curriculum learning (SPDCL) method for imbalanced text classification, which evaluates the sample difficulty by both linguistic character and model capacity. Meanwhile, rather than using static curriculum learning as in the existing research, our SPDCL can reorder and resample training data by difficulty criterion with an adaptive from easy to hard pace. The extensive experiments on several classification tasks show the effectiveness of SPDCL strategy, especially for the imbalanced dataset.
Intelligent optimization and allocation strategy of emergency repair resources based on big data
Jiangdong Liu, Yue Zhao, Bo Wang, Jie Gao, Li Xu and Ying Ma
0
An important task in managing production and power supply is emergency distribution network maintenance. The effectiveness of emergency repair command can be in?creased in part by the optimization of emergency repair resources. In the power user information collection system, the public transformer?€?s power outage data are examined in this article. The data on outages?€? relationship to actual line faults is built using data mining techniques. The genuine fault detection model, which is founded upon this comprehensive outage data of the common transmitter, can quickly and correctly identify the true issue, making it easy for maintenance staff to act promptly. At the same time, on the basis of analyzing the allocation level of emergency resources and the estimation of working time of emergency repair team, a general model is proposed to optimize the estimation of emergency working time. By allocating emergency maintenance resources to repair, and then optimize the allocation of emergency repair resources.
Session Chair
Ying Ma, Harbin Institute of Technology, China
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