With the rapid development of Internet of things (IoT) and mobile communication technology, Internet of vehicles (IoV) has potential prospects in providing live streaming services and improving the quality of experience (QoE) of ground mobile vehicles (GMVs). However, when the communication link is blocked in emergency situations such as road congestion and base station (BS) paralysis, it is still an arduous task to provide realtime high-definition video for vehicle users. Fortunately, as the aerial mobile BS, unmanned aerial vehicle (UAV) is expected to be deployed for emergency communication recovery in extreme scenarios. But high mobility of GMVs and UAV leads to dynamic changes of network topology, how to establish a stable communication link between UAV and GMVs to ensure the QoE of vehicle users has become a huge challenge. Therefore, this study proposes a novel live video transmission scheme based on scalable video coding (SVC). By jointly optimizing the UAV trajectory, video layer selection, and bandwidth resource allocation, our objective is to maximize long-term QoE while reducing UAV’s flight energy consumption. To maximize QoE, time delay and bitrate switching at GMVs’ receiver are set as penalty items while maximizing the reward of video quality. The joint optimization problem is modeled as a Markov decision process (MDP), and is solved by the soft actor-critic (SAC) deep reinforcement learning (DRL) algorithm, which is based on the maximum entropy framework. Extensive simulation results based on real-world datasets show that compared with other DRL algorithms, the proposed scheme has excellent performance in all respects.

Aiming to solve the problem of the limited measuring range for angular motion parameters of high-speed rotating projectiles in the field of guidance and control, a self-adaptive measurement method for angular motion parameters based on the electromagnetic induction principle is proposed. First, a framework with type bent “I-shape” is used to design triaxial coils in a mutually orthogonal way. Under the condition of high rotational speed of a projectile, the induction signal of the projectile moving across a geomagnetic field is acquired by using coils. Second, the frequency of the pulse signal is adjusted self-adaptively. Angular velocity and angular displacement are calculated in the form of periodic pulse counting and pulse accumulation, respectively. Finally, on the basis of that principle prototype of the sensor is researched and developed, performance of measuring angular motion parameters are tested on the sensor by semi-physical and physical simulation experiments, respectively. Experimental results demonstrate that the sensor has a wide measuring range of angular velocity from 1 rps to 100 rps with a measurement error of less than 0.3%, and the angular displacement measurement error is lower than 0.2°. The proposed method satisfies measurement requirements for high-speed rotating projectiles with an extremely high dynamic range of rotational speed and high precision, and has definite value to engineering applications in the fields of attitude determination and geomagnetic navigation.

Shallow underground explosive source localization technology is a key technology in the field of underground space localization. The existing approaches mainly aim to improve the localization accuracy, but need to deploy enormous sensors in the monitoring area, and rely on a large number of back-end workstations to solve. These methods have the defects of considerable calculation and high time cost, and are hard to satisfy the precise and real-time requirements of onsite testing, ultimately resulting in slow localization speed and accurate localization failure. Fortunately, emerging deep reinforcement learning (DRL) can effectively solve the problem of slow search policy by modeling the source localication as a Markov decision process (MDP). Therefore, a Curiosity-Driven Deep Dueling Double Q-learning Network (C-D3QN) is subsequently proposed to solve the above MDP. The overestimation problem is solved by decoupling selection and evaluation of the bootstrap action, and the action difference is effectively increased by introducing the dueling network that separately represents state values and action advantages. Meanwhile, the exploration is jointly reinforced by an intrinsic reward outputted from the curiosity module and an extrinsic reward supplied by the environment, guaranteeing the convergence to global optimal. Finally, extensive simulation results based on the outfield experiment data show that compared with other algorithms, the proposed scheme can significantly improve exploration ability and learning speed as well as generalization and robustness. Additionally, compared to the baseline algorithm DQN, the C-D3QN algorithm can offer an improved localization accuracy as high as 99.62% and an increased localization speed of 66.23%.

Purpose: The purpose of this paper is to extract the angle information of direct P wave within multidimensional vibration signals obtained through the sensor array, and to realize high precision shallow burst point localization based on direct of angle (DOA).Design/methodology/approach: This paper presents a method which combines adaptive covariance matrix (ACM) algorithm with geometric constraint conditions for extracting the angle information of direct P wave by using its polarization characteristics. First, modify the obtained three-dimensional vibration data by using attitude rotation matrix and unify the coordinate system of vibration field. Next, construct the beam model of direct P wave by making use of ACM algorithm and extract its angle information. Finally, modify P wave beam model by taking advantage of the space geometric constraint relations between nodes.Findings: The results of numerical simulation show that this method not only can extract the angle information of direct P wave arriving at each node effectively, but also can evaluate the quality of extracted angle information of direct P wave. Meanwhile, the results of underground shallow explosion experiment show that this method can extract the angle information of direct P wave of each node significantly and can realize underground shallow explosion source localization based on DOA by using this information, the location error can be limited less than 50 cm and satisfies the location requirements of shallow burst point.Originality/value: This paper provides a method for various problems of underground localization based on the sensor array, such as directional demolition blasting, underground damage assessment, earth-penetrating projectile burst point positioning in weaponry industry testing plant, etc., and has definite value to engineering application in underground space positioning field.

To address the problem of slow speed and low accuracy for recognizing and locating the explosive source in complex shallow underground blind spaces, this paper proposes an energy-focusing-based scanning and localization method. First, the three-dimensional (3D) energy field formed by the source explosion is reconstructed using the energy-focusing properties of the steered response power (SRP) localization model, and the velocity field is calculated based on a multilayered stochastic medium model by considering the random statistical characteristics of the medium. Then, a power function factor is introduced to quantum particle swarm optimization (QPSO) to search for and solve the above energy field and to approach the real location of the energy focus point. Additionally, the initial population is constructed based on the logistic chaos model to realize global traversal. Finally, extensive simulation results based on the real-world dataset show that compared to the baseline algorithm, the focusing accuracy of the energy field of the proposed scheme is improved by 117.20%, the root mean square error (RMSE) is less than 0.0551 m, the triaxial relative error (RE) is within 0.2595%, and the average time cost is reduced by 98.40%. It has strong advantages in global search capability and fast convergence, as well as robustness and generalization.

To investigate the relationship between the structure-borne noise and the vibration of vertical concrete noise barriers sitting on the roadside of the railway, the time-frequency characteristics of the accelerations and sound pressure level of the barrier slab under train crossing events were analyzed by in situ vibration and noise testing. The results show that (1) the vertical vibration level in the mainline area is approximately 94.7 dB, while it is approximately 104.4 dB in the turnout area, and the average increase effect of the vibration level is approximately 9.7 dB. (2) The sound pressure level of the structure-borne noise in the mainline area is approximately 66.4 dB(A), while it is approximately 70.0 dB(A) in the turnout area, and the average increase effect of the sound pressure level is approximately 3.6 dB(A). (3) The vibration is amplified significantly at approximately 40-60 Hz. In summary, the structure-borne noise of the barrier slab is affected by the forced vibration amplification of the barrier slab, and the sound and vertical vibration amplified analysis of the noise barrier under ground motion excitation caused by train operation brought out the related relationship between the vertical vibration and the structure-borne noise.