Abstract:With the widespread adoption of massive MIMO, phased arrays, and millimeter waves technologies, the distinction between communication and sensing is gradually diminished. The anticipation for the next generation mobile communication is to integrate sensing capabilities into cellular networks while minimizing any potential interference to communication. Given that the performance metrics for evaluating communication and sensing have traditionally differed, there is a need for a unified metric for the peformance tradeoff within an integrated sensing and communication (ISAC) system. Furthermore, the dynamic and complex co-channel interference can severely constrain the performance of communication and sensing when deploying a large-scale ISAC system. Therefore, a mono-static ISAC system is designed based on the multi-cell network. Firstly, the location of base stations and user equipments in this system is modeled using a homogeneous Poisson point process. Each base station generates multiple beams, and the main lobe is scheduled to determine whether to perform sensing or communication during the downlink slot. Secondly, the sensing success probability is defined from the perspective of mutual information, serving as a unified metric for evaluating both communication and sensing performance along with the downlink communication coverage probability. By employing the methodology of stochastic geometry, theoretical expressions for both the sensing success probability and the downlink communication coverage probability are derived. Additionally, simplified forms of these metrics under limiting conditions are presented. Finally, Monte Carlo simulation results are provided to verify the correctness of the derived expressions and analyze the impact of the path loss exponent on the performance of communication and sensing.

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	1. On the study of performance for a mono-static integrated sensing and communication system based on stochastic geometry.
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	Electrics, Communication and Autocontrol Technology 22 April 2024
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	Abstract:With the widespread adoption of massive MIMO, phased arrays, and millimeter waves technologies, the distinction between communication and sensing is gradually diminished. The anticipation for the next generation mobile communication is to integrate sensing capabilities into cellular networks while minimizing any potential interference to communication. Given that the performance metrics for evaluating communication and sensing have traditionally differed, there is a need for a unified metric for the peformance tradeoff within an integrated sensing and communication (ISAC) system. Furthermore, the dynamic and complex co-channel interference can severely constrain the performance of communication and sensing when deploying a large-scale ISAC system. Therefore, a mono-static ISAC system is designed based on the multi-cell network. Firstly, the location of base stations and user equipments in this system is modeled using a homogeneous Poisson point process. Each base station generates multiple beams, and the main lobe is scheduled to determine whether to perform sensing or communication during the downlink slot. Secondly, the sensing success probability is defined from the perspective of mutual information, serving as a unified metric for evaluating both communication and sensing performance along with the downlink communication coverage probability. By employing the methodology of stochastic geometry, theoretical expressions for both the sensing success probability and the downlink communication coverage probability are derived. Additionally, simplified forms of these metrics under limiting conditions are presented. Finally, Monte Carlo simulation results are provided to verify the correctness of the derived expressions and analyze the impact of the path loss exponent on the performance of communication and sensing.
	TO cite this article:,. On the study of performance for a mono-static integrated sensing and communication system based on stochastic geometry.[OL].[22 April 2024] http://en.paper.edu.cn/en_releasepaper/content/4763488


	2. Deep Reinforcement Learning-based Multi-Layer Cascaded Resilient Recovery for Cyber-Physical Systems
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	Computer Science and Technology 18 April 2024
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	Abstract:Cyber-physical systems (CPS) are complex systems comprised of physical and computational components, which are susceptible to various disturbances and attacks, leading to system failures and security breaches. In recent years, CPS resilience has garnered increasing attention, with some studies proposing CPS resilience methods. However, existing methods overlook the interdependence between different components of the information and physical layers in the CPS network, and exhibit limitations in scalability, adaptability, and efficiency. To address these issues, this paper introduces a multilayer cascade resilience recovery framework based on deep reinforcement learning. Firstly, the high degree of interaction between the information and physical layers in CPS resilience recovery is comprehensively synthesized, and this correlation relationship is modeled using an association matrix. Secondly, a hybrid resilience recovery strategy is proposed to segment the association matrix into horizontal and vertical slices, treating its resilience strategy solution as an optimization problem. Finally, a deep reinforcement learning algorithm centered on resilience prioritization is presented to solve the optimal policy for hybrid resilient recovery.
	TO cite this article:,,, et al. Deep Reinforcement Learning-based Multi-Layer Cascaded Resilient Recovery for Cyber-Physical Systems[OL].[18 April 2024] http://en.paper.edu.cn/en_releasepaper/content/4763397

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	3. MU2MV: Efficient and Secure Task Offloading Framework in UAV-assisted Smart Networks
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	Computer Science and Technology 18 April 2024
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	Abstract:UAV technology has emerged as a promising solution, introducing a new approach to smart grid inspection. This paper explores the safe and efficient task offloading of UAV cruise systems within smart grids. Specifically, it proposes a multi-UAV to multi-vehicle fog computing node task offloading framework named MU2MV. Firstly, a novel reputation incentive mechanism based on the behavior of vehicle fog computing nodes is introduced. This mechanism provides incentives or penalties based on the behaviors of these nodes to select suitable target fog computing nodes and encourage newly joined nodes to actively provide computing services as service providers. Secondly, a two-tier collaborative non-cooperative game model is proposed to simulate the cooperative-competitive relationships among UAVs and vehicle fog computing nodes, as well as among the vehicle fog computing nodes themselves, the optimal strategy combinations are determined by solving the Nash equilibrium solution within the game model. Finally, recognizing the challenges posed by the dynamic movement of UAVs and vehicle fog computing nodes in real environments, a deep reinforcement learning algorithm is proposed to seek the optimal strategy combination for UAVs and vehicle fog computing nodes.
	TO cite this article:,,. MU2MV: Efficient and Secure Task Offloading Framework in UAV-assisted Smart Networks[OL].[18 April 2024] http://en.paper.edu.cn/en_releasepaper/content/4763398


	4. Research of License Plate Detection and Recognition in Complex Scene

	Computer Science and Technology 11 April 2024
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	Abstract:In The technology of license plate detection and recognition is very important in modern transportation system. However, in the real scene, bad weather and oblique shooting angles will affect the accuracy of detection and recognition. Therefore, this paper mainly studies license plate detection and recognition algorithms under complex conditions.The license plate detection in this article uses the RetinaNet detection algorithm. First, build a ResNet network and FPN combination for image feature extraction and feature fusion, and then use Focal Loss regression to get an accurate prediction frame. The data is the CCPD data, which includs photos of various scenes, and the number is large, which can make the detection model more adaptable to complex conditions.The license plate recognition uses the combination of STN and LPRNet. STN is a kind of image space change network, which can perform affine change processing on the image. In this paper, the STN network is added to the LPRNet network, so that the network can learn how to reduce Loss through the spatial change of the image, and then the network can correct the slanted license plate. LPRNet is an end-to-end network that can recognize license plate characters without cutting characters. It is mainly composed of convolutional neural networks and CTC Loss. Finally, the effectiveness of the methodology of this paper is demonstrated through experiments.
	TO cite this article:Research of License Plate Detection and Recognition in Complex Scene[OL].[11 April 2024] http://en.paper.edu.cn/en_releasepaper/content/4763301


	5. Sturdy Mo/MoO2 with Densely-Distributed Heterointerface as Highly Active and Stable HER Electrodes
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	Chemistry 11 April 2024
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	Abstract:The practical application of alkaline water electrolysis (AWE) has been hindered by the unsatisifactory activity and stability of HER electrodes. Here, Mo/MoO2 with densely-distributed heterointerface was constructed on nickel mesh via plasma spraying of metallic Mo powders as highly active and sturdy catalyst layer for HER electrodes. As a result, the electrode demonstrates a low overpotential of 48.7 mV@10 mA cm-2 and a prolonged stability of 500 h even under industrial conditions.
	TO cite this article:,. Sturdy Mo/MoO2 with Densely-Distributed Heterointerface as Highly Active and Stable HER Electrodes[OL].[11 April 2024] http://en.paper.edu.cn/en_releasepaper/content/4763290


	6. Granularized Dialogue Model of The Knowledge Graph
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	Computer Science and Technology 10 April 2024
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	Abstract:This paper designs a dialogue model based on the granulated knowledge graph (Granulation Knowledge aware Dialogue Model, GKDM). Through the method of structural granulation, the knowledge graph is divided into two modules: local knowledge graph and non-local knowledge graph, respectively capturing the local semantics adjacent to the knowledge entity and the overall semantic representation of the located knowledge sub-graph, which solves the problem of the division of the knowledge graph. This paper designs the static graph attention mechanism and the hierarchical dynamic graph attention mechanism to participate in the process of encoding and decoding in the model respectively, and aggregates the semantics of knowledge entities through multi-level attention weights, and fuses them into the text vector, which solves the problem of the fusion of multi-layer knowledge information. Based on the above two innovations, the model proposed in this paper alleviates the problems of the current knowledge graph-based dialogue model that cannot capture the reasoning meaning of the multi-hop paths of the knowledge graph and the inconsistent theme of the reply context.
	TO cite this article:,. Granularized Dialogue Model of The Knowledge Graph[OL].[10 April 2024] http://en.paper.edu.cn/en_releasepaper/content/4763289


	7. Research on Lightweight Based on HiFi-GAN Model
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	Electrics, Communication and Autocontrol Technology 08 April 2024
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	Abstract:The HiFi-GAN model combines audio with high efficiency and high fidelity in speech synthesis, but its huge model parameters and calculations make it difficult to store, deploy, and perform real-time computing on limited-resource devices. There are relatively few studies on the compression and acceleration of HiFi-GAN models, while the need for real-time speech synthesis on mobile devices and edge devices is very extensive. This paper studies the lightweighting method of HiFi-GAN model to realize the network compression and deploy on the hardware platform. The main research and results of the paper are as follows: Aiming at the problems of high computing and storage resource consumption and complex structure in the inference stage of HiFi-GAN, this paper proposes a compression method combining knowledge distillation and architecture search according to its network structure. In this method, the resBlock layer of the HiFi-GAN model is convolutional decomposed to obtain a relatively compact student model, and the designed training objective scheme is used for distillation learning. After that, this student network is used as a "one-time" network for architecture search, and finally the compact optimal sub-student model is obtained. Experiments show that this method significantly compreses the size of the HiFi-GAN model and reduces the amount of computation, and generates good speech quality PESQ values on both the single speaker LJSpeech dataset and the unknown speaker VCTK dataset.
	TO cite this article:,. Research on Lightweight Based on HiFi-GAN Model[OL].[ 8 April 2024] http://en.paper.edu.cn/en_releasepaper/content/4763318


	8. Study on Optimization of Zinc Anode Performance with 2-mercaptosuccinic Acid Electrolyte Additive
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	Chemical Engineering 08 April 2024
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	Abstract:Adding additives to the electrolyte is an effective and simple method to improve the electrochemical performance of zinc anodes. In this paper, 2-mercaptosuccinic acid (MA) is introduced into the ZnSO4 electrolyte as an additive to improve the zinc anode performance. Electrochemical tests show that the addition of MA additive can reduce the charge transfer resistance of the interface, reduce the occurrence of side reactions such as hydrogen evolution and corrosion, reduce the nucleation overpotential, which is beneficial to the uniform nucleation of zinc, constrain the diffusion of zinc ions and accelerate the migration of zinc ions, which is beneficial to the uniform deposition of zinc and inhibits dendrite growth. Through XRD and SEM characterization, it was found that there was no obvious by-product generation on the surface of the zinc anode, and the zinc foil was protected. The Zn\|\|Zn batteries containing MA additive electrolyte shows excellent long-term cycle performance. It can cycle stably for 1574 h under the conditions of 5 mA/cm2, 1 mAh/cm2, and has a large current density and area of 8 mA/cm2, 5 mAh/cm2. It can cycle stably for 546 hours even if the capacity is high. The polarization voltage during the cycle is low, showing excellent cycle stability. This shows that it is feasible to choose 2-mercaptosuccinic acid (MA) as an electrolyte additive to improve the stability of the zinc anode.
	TO cite this article:,. Study on Optimization of Zinc Anode Performance with 2-mercaptosuccinic Acid Electrolyte Additive[OL].[ 8 April 2024] http://en.paper.edu.cn/en_releasepaper/content/4763269

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