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学者姓名：林瑞全
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Cognitive Internet of Vehicles (CIoV) adds the cognitive engine based on traditional Internet of Vehicles (IoV), which can improve spectrum utilization. However, spectrum sensing data falsification (SSDF) attacks pose a threat to CIoV network security. To ensure the full utilization of spectrum resources and protect primary users transmission, this article combines blockchain with CIoV to defend against SSDF attacks in the presence of vehicle users (VUs) entering and leaving the network. Specifically, this article introduces a virtual currency called Sencoins serve as credential for VUs to purchase transmission shares. And this article proposes a reward and punishment mechanism and a hybrid Proof-of-Stake (PoS) and Proof-of-Work (PoW) mining model to thwart the motivation of the VUs to launch SSDF attacks. On this basis, this article investigates the dynamics of SSDF attack strategy choice of VUs, and uses the largest Lyapunov exponent (LLE) to determine the critical value of Sencoins that avoids the system to exhibit chaotic behavior. To describe the uncertainty of the population proportion of VUs that choose different attack strategies due to high-speed movement and the VUs entering and leaving the CIoV network, this article introduces Gaussian white noise into the replication dynamics equation and builds the It & ocirc; stochastic evolutionary game model, and solves it according to the stability judgment theorem of stochastic differential equations and stochastic Taylor expansion. Finally, simulation results verify that the proposed method can quickly and effectively thwart SSDF attacks in the CIoV network. And compared with traditional methods, the proposed method can improve the efficiency of defending against SSDF attacks by 567% and the average throughput by 25%.
Keyword ：
Blockchain Blockchain Blockchains Blockchains Cognitive Internet of Vehicles (CIoV) Cognitive Internet of Vehicles (CIoV) Data models Data models Games Games Interference Interference Internet of Vehicles Internet of Vehicles Security Security Sensors Sensors spectrum sensing data falsification (SSDF) attack spectrum sensing data falsification (SSDF) attack stochastic evolutionary game stochastic evolutionary game Stochastic processes Stochastic processes Throughput Throughput Wireless sensor networks Wireless sensor networks
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| GB/T 7714 | Li, Fushuai , Lin, Ruiquan , Chen, Wencheng et al. Thwarting SSDF Attacks From High-Speed Movement VUs in the CIoV Network: Based on Blockchain and Stochastic Evolutionary Game [J]. | IEEE INTERNET OF THINGS JOURNAL , 2025 , 12 (2) : 2233-2250 . |
| MLA | Li, Fushuai et al. "Thwarting SSDF Attacks From High-Speed Movement VUs in the CIoV Network: Based on Blockchain and Stochastic Evolutionary Game" . | IEEE INTERNET OF THINGS JOURNAL 12 . 2 (2025) : 2233-2250 . |
| APA | Li, Fushuai , Lin, Ruiquan , Chen, Wencheng , Wang, Jun , Shu, Feng , Chen, Riqing . Thwarting SSDF Attacks From High-Speed Movement VUs in the CIoV Network: Based on Blockchain and Stochastic Evolutionary Game . | IEEE INTERNET OF THINGS JOURNAL , 2025 , 12 (2) , 2233-2250 . |
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Three-dimensional CAD reconstruction is a long-standing and important task in fields such as industrial manufacturing, architecture, medicine, film and television, research, and education. Reconstructing CAD models remains a persistent challenge in machine learning. There have been many studies on deep learning in the field of 3D reconstruction. In recent years, with the release of CAD datasets, there have been more and more studies on 3D CAD reconstruction using deep learning. With the continuous deepening of research, deep learning has significantly improved the performance of tasks in the field of CAD reconstruction. However, this task remains challenging due to data scarcity and labeling difficulties, model complexity, and lack of generality and adaptability. This paper reviews both classic and recent research results on 3D CAD reconstruction tasks based on deep learning. To the best of our knowledge, this is the first investigation focusing on the CAD reconstruction task in the field of deep learning. Since there are relatively few studies related to 3D CAD reconstruction, we also investigate the reconstruction and generation of 2D CAD sketches. According to the different input data, we divide all investigations into the following categories: point cloud input to 3D CAD models, sketch input to 3D CAD models, other input to 3D CAD models, reconstruction and generation of 2D sketches, characterization of CAD data, CAD datasets, and related evaluation indicators. Commonly used datasets are outlined in our taxonomy. We provide a brief overview of the current research background, challenges, and recent results. Finally, future research directions are discussed.
Keyword ：
3D reconstruction 3D reconstruction CAD CAD deep learning deep learning literature survey literature survey
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| GB/T 7714 | Lin, Ruiquan , Ji, Yunwei , Ding, Wanting et al. A Survey on Deep Learning in 3D CAD Reconstruction [J]. | APPLIED SCIENCES-BASEL , 2025 , 15 (12) . |
| MLA | Lin, Ruiquan et al. "A Survey on Deep Learning in 3D CAD Reconstruction" . | APPLIED SCIENCES-BASEL 15 . 12 (2025) . |
| APA | Lin, Ruiquan , Ji, Yunwei , Ding, Wanting , Wu, Tianxiang , Zhu, Yaosheng , Jiang, Mengxi . A Survey on Deep Learning in 3D CAD Reconstruction . | APPLIED SCIENCES-BASEL , 2025 , 15 (12) . |
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Cognitive Radio (CR) and Energy Harvesting (EH) techniques have offered insights to mitigate issues related to inefficient spectrum utilization and limited energy storage capacity. In Cognitive Radio Networks, security threats, particularly from eavesdroppers, may result in information leakage. This study focuses on enhancing the Physical Layer Security (PLS) of multi-users with EH by employing cooperative jamming via a Autonomous Aerial Vehicle (AAV) to maximize the secure communication rate. In the AAV-assisted EH-CR system, Secondary Users (SUs) can utilize the licensed spectrum band occupied by a Primary User (PU) if the cooperative jamming power from SUs to the PU remains below a certain threshold. SUs can harvest and use Radio Frequency (RF) energy from the Primary Transmitter (PT). The AAV jammer disrupts the eavesdropper by transmitting jamming signals, thereby minimizing stolen information to optimize long-term secure communication performance. The paper formulates the problem of maximizing the average secure communication rate while considering system constraints and jointly optimizes the AAV trajectory, transmission power, and EH coefficient. As the problem is non-convex, it is reformulated as a Markov Decision Process (MDP). The paper employs the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm to address the problem, introduces counterfactual baselines to tackle the credit assignment problem in centralized learning, and integrates the Long Short-Term Memory (LSTM) network to enhance the learning capability of sequential sample data, thereby improving the training efficiency and effectiveness of the algorithm. Simulation results demonstrate the effectiveness and superiority of the proposed method in maximizing the system's secure communication rate.
Keyword ：
autonomous aerial vehicle (AAV) autonomous aerial vehicle (AAV) Autonomous aerial vehicles Autonomous aerial vehicles Cognitive radio (CR) Cognitive radio (CR) Communication system security Communication system security cooperative jamming cooperative jamming energy harvesting (EH) energy harvesting (EH) Interference Interference Jamming Jamming Optimization Optimization physical layer security (PLS) physical layer security (PLS) Radio frequency Radio frequency Relays Relays Resource management Resource management Security Security Trajectory Trajectory
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| GB/T 7714 | Wang, Jun , Wang, Rong , Zheng, Zibin et al. Physical Layer Security Enhancement in AAV-Assisted Cooperative Jamming for Cognitive Radio Networks: A MAPPO-LSTM Deep Reinforcement Learning Approach [J]. | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2025 , 74 (3) : 4713-4727 . |
| MLA | Wang, Jun et al. "Physical Layer Security Enhancement in AAV-Assisted Cooperative Jamming for Cognitive Radio Networks: A MAPPO-LSTM Deep Reinforcement Learning Approach" . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 74 . 3 (2025) : 4713-4727 . |
| APA | Wang, Jun , Wang, Rong , Zheng, Zibin , Lin, Ruiquan , Wu, Liang , Shu, Feng . Physical Layer Security Enhancement in AAV-Assisted Cooperative Jamming for Cognitive Radio Networks: A MAPPO-LSTM Deep Reinforcement Learning Approach . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2025 , 74 (3) , 4713-4727 . |
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In the Energy-Harvesting (EH) Cognitive Internet of Things (EH-CIoT) network, due to the broadcast nature of wireless communication, the EH-CIoT network is susceptible to jamming attacks, which leads to a serious decrease in throughput. Therefore, this paper investigates an anti-jamming resource-allocation method, aiming to maximize the Long-Term Throughput (LTT) of the EH-CIoT network. Specifically, the resource-allocation problem is modeled as a Markov Decision Process (MDP) without prior knowledge. On this basis, this paper carefully designs a two-dimensional reward function that includes throughput and energy rewards. On the one hand, the Agent Base Station (ABS) intuitively evaluates the effectiveness of its actions through throughput rewards to maximize the LTT. On the other hand, considering the EH characteristics and battery capacity limitations, this paper proposes energy rewards to guide the ABS to reasonably allocate channels for Secondary Users (SUs) with insufficient power to harvest more energy for transmission, which can indirectly improve the LTT. In the case where the activity states of Primary Users (PUs), channel information and the jamming strategies of the jammer are not available in advance, this paper proposes a Linearly Weighted Deep Deterministic Policy Gradient (LWDDPG) algorithm to maximize the LTT. The LWDDPG is extended from DDPG to adapt to the design of the two-dimensional reward function, which enables the ABS to reasonably allocate transmission channels, continuous power and work modes to the SUs, and to let the SUs not only transmit on unjammed channels, but also harvest more RF energy to supplement the battery power. Finally, the simulation results demonstrate the validity and superiority of the proposed method compared with traditional methods under multiple jamming attacks.
Keyword ：
anti-jamming method anti-jamming method EH-CIoT network EH-CIoT network linearly weighted deep deterministic policy gradient linearly weighted deep deterministic policy gradient resource allocation resource allocation
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| GB/T 7714 | Li, Fushuai , Bao, Jiawang , Wang, Jun et al. Anti-Jamming Resource-Allocation Method in the EH-CIoT Network through LWDDPG Algorithm [J]. | SENSORS , 2024 , 24 (16) . |
| MLA | Li, Fushuai et al. "Anti-Jamming Resource-Allocation Method in the EH-CIoT Network through LWDDPG Algorithm" . | SENSORS 24 . 16 (2024) . |
| APA | Li, Fushuai , Bao, Jiawang , Wang, Jun , Liu, Da , Chen, Wencheng , Lin, Ruiquan . Anti-Jamming Resource-Allocation Method in the EH-CIoT Network through LWDDPG Algorithm . | SENSORS , 2024 , 24 (16) . |
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In Cognitive Radio (CR) networks combined with Energy Harvesting (EH) technology, Secondary Users (SUs) are vulnerable to jamming attacks when sensing idle channels. At the same time, they may encounter numerous jamming and eavesdropping attacks during the data transmission phase. This paper examines the scenario in which SUs are susceptible to malicious attacks and energy constraints in both the sensing and transmission phases. We propose a utility function applicable to a single time slot. The blockchain uses Smart Contract (SC) technology to set rewards and punishments for users' channel selection behavior and adjust mining difficulty. This method combines blockchain with spectrum sensing data fusion, abandons the decision-making mechanism of the traditional Cooperative Spectrum Sensing (CSS) Fusion Center (FC), and adopts a distributed structure to ensure the security and reliability of sensing data fusion. In addition, this paper uses the potential game and the Stackelberg game to study the optimal transmission channel and optimal time slot allocation strategy for SUs under malicious attacks. Considering the possible interference caused by channel switching and the greedy principle of Malicious User (MU), the proposed two-layer game method gradually optimizes the sensing detection probability and secure communication rate with time slot iteration. In order to further improve the secure communication rate, an iterative update formula for transmission power is given to make reasonable use of the remaining energy of each SU at the end of each time slot. Simulation results show that the proposed method is superior to traditional methods in both sensing performance and secure communication rate.
Keyword ：
blockchain blockchain Blockchains Blockchains Eavesdropping Eavesdropping eavesdropping attack eavesdropping attack Energy harvesting Energy harvesting Full-duplex system Full-duplex system Games Games Jamming Jamming jamming attack jamming attack Power system reliability Power system reliability Probability Probability Security Security Sensors Sensors Throughput Throughput two-layer game two-layer game
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| GB/T 7714 | Hou, Haifeng , Lin, Ruiquan , Wang, Jun et al. Blockchain and Game Theory-Based Strategies for Anti-Jamming and Eavesdropping in EH-CR Networks [J]. | IEEE ACCESS , 2024 , 12 : 146996-147011 . |
| MLA | Hou, Haifeng et al. "Blockchain and Game Theory-Based Strategies for Anti-Jamming and Eavesdropping in EH-CR Networks" . | IEEE ACCESS 12 (2024) : 146996-147011 . |
| APA | Hou, Haifeng , Lin, Ruiquan , Wang, Jun , Li, Sheng , Chen, Wencheng . Blockchain and Game Theory-Based Strategies for Anti-Jamming and Eavesdropping in EH-CR Networks . | IEEE ACCESS , 2024 , 12 , 146996-147011 . |
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Cognitive radio (CR) is regarded as the key technology of the 6th-Generation (6G) wireless network. Because 6G CR networks are anticipated to offer worldwide coverage, increase cost efficiency, enhance spectrum utilization, and improve device intelligence and network safety. This article studies the secrecy communication in an energy-harvesting (EH)-enabled Cognitive Internet of Things (EH-CIoT) network with a cooperative jammer. The secondary transmitters (STs) and the jammer first harvest the energy from the received radio frequency (RF) signals in the EH phase. Then, in the subsequent wireless information transfer (WIT) phase, the STs transmit secrecy information to their intended receivers in the presence of eavesdroppers while the jammer sends the jamming signal to confuse the eavesdroppers. To evaluate the system secrecy performance, we derive the instantaneous secrecy rate and the closed-form expression of secrecy outage probability (SOP). Furthermore, we propose a deep reinforcement learning (DRL)-based framework for the joint EH time and transmission power allocation problems. Specifically, a pair of ST and jammer over each time block is modeled as an agent which is dynamically interacting with the environment by the state, action, and reward mechanisms. To better find the optimal solutions to the proposed problems, the long short-term memory (LSTM) network and the generative adversarial networks (GANs) are combined with the classical DRL algorithm. The simulation results show that our proposed method is highly effective in maximizing the secrecy rate while minimizing the SOP compared with other existing schemes.
Keyword ：
6G mobile communication 6G mobile communication Cognitive radio (CR) network Cognitive radio (CR) network Communication system security Communication system security deep reinforcement learning (DRL) deep reinforcement learning (DRL) energy harvesting (EH) energy harvesting (EH) Internet of Things Internet of Things Jamming Jamming Mobile handsets Mobile handsets physical-layer security (PLS) enhancement physical-layer security (PLS) enhancement Resource management Resource management Wireless communication Wireless communication
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| GB/T 7714 | Lin, Ruiquan , Qiu, Hangding , Wang, Jun et al. Physical-Layer Security Enhancement in Energy-Harvesting-Based Cognitive Internet of Things: A GAN-Powered Deep Reinforcement Learning Approach [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (3) : 4899-4913 . |
| MLA | Lin, Ruiquan et al. "Physical-Layer Security Enhancement in Energy-Harvesting-Based Cognitive Internet of Things: A GAN-Powered Deep Reinforcement Learning Approach" . | IEEE INTERNET OF THINGS JOURNAL 11 . 3 (2024) : 4899-4913 . |
| APA | Lin, Ruiquan , Qiu, Hangding , Wang, Jun , Zhang, Zaichen , Wu, Liang , Shu, Feng . Physical-Layer Security Enhancement in Energy-Harvesting-Based Cognitive Internet of Things: A GAN-Powered Deep Reinforcement Learning Approach . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (3) , 4899-4913 . |
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Rapid identification of peanut seed quality is crucial for public health. In this study, we present a terahertz wave imaging system using a convolutional neural network (CNN) machine learning approach. Terahertz waves are capable of penetrating the seed shell to identify the quality of peanuts without causing any damage to the seeds. The specificity of seed quality on terahertz wave images is investigated, and the image characteristics of five different qualities are summarized. Terahertz wave images are digitized and used for training and testing of convolutional neural networks, resulting in a high model accuracy of 98.7% in quality identification. The trained THz-CNNs system can accurately identify standard, mildewed, defective, dried and germinated seeds, with an average detection time of 2.2 s. This process does not require any sample preparation steps such as concentration or culture. Our method swiftly and accurately assesses shelled seed quality non-destructively.
Keyword ：
Aflatoxin Aflatoxin Machine learning Machine learning Peanut Peanut Quality identification Quality identification Terahertz imaging technology Terahertz imaging technology
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| GB/T 7714 | Jiang, Weibin , Wang, Jun , Lin, Ruiquan et al. Machine learning-based non-destructive terahertz detection of seed quality in peanut [J]. | FOOD CHEMISTRY-X , 2024 , 23 . |
| MLA | Jiang, Weibin et al. "Machine learning-based non-destructive terahertz detection of seed quality in peanut" . | FOOD CHEMISTRY-X 23 (2024) . |
| APA | Jiang, Weibin , Wang, Jun , Lin, Ruiquan , Chen, Riqing , Chen, Wencheng , Xie, Xin et al. Machine learning-based non-destructive terahertz detection of seed quality in peanut . | FOOD CHEMISTRY-X , 2024 , 23 . |
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The high-speed movement of Vehicle Users (VUs) in Cognitive Internet of Vehicles (CIoV) causes rapid changes in users location and path loss. In the case of imperfect control channels, the influence of high-speed movement increases the probability of error in sending local spectrum sensing decisions by VUs. On the other hand, Malicious Vehicle Users (MVUs) can launch Spectrum Sensing Data Falsification (SSDF) attacks to deteriorate the spectrum sensing decisions, mislead the final spectrum sensing decisions of Collaborative Spectrum Sensing (CSS), and bring serious security problems to the system. In addition, the high-speed movement can increases the concealment of the MVUs. In this paper, we study the scenario of VUs moving at high speeds, and data transmission in an imperfect control channel, and propose a blockchain-based method to defend against massive SSDF attacks in CIoV networks to prevennt independent and cooperative attacks from MVUs. The proposed method combines blockchain with spectrum sensing and spectrum access, abandons the decision-making mechanism of the Fusion Center (FC) in the traditional CSS, adopts distributed decision-making, and uses Prospect Theory (PT) modeling in the decision-making process, effectively improves the correct rate of final spectrum sensing decision in the case of multiple attacks. The local spectrum sensing decisions of VUs are packaged into blocks and uploaded after the final decision to achieve more accurate and secure spectrum sensing, and then identify MVUs by the reputation value. In addition, a smart contract that changes the mining difficulty of VUs based on their reputation values is proposed. It makes the mining difficulty of MVUs more difficult and effectively limits MVUs' access to the spectrum band. The final simulation results demonstrate the validity and superiority of the proposed method compared with traditional methods.
Keyword ：
blockchain blockchain Blockchains Blockchains Cognitive Internet of Vehicles (CIoV) Cognitive Internet of Vehicles (CIoV) Data communication Data communication Decision making Decision making History History Internet of Vehicles Internet of Vehicles prospect theory (PT) prospect theory (PT) Sensors Sensors smart contract smart contract Smart contracts Smart contracts spectrum sensing data falsification (SSDF) attack spectrum sensing data falsification (SSDF) attack
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| GB/T 7714 | Lin, Ruiquan , Li, Fushuai , Wang, Jun et al. A Blockchain-Based Method to Defend Against Massive SSDF Attacks in Cognitive Internet of Vehicles [J]. | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2024 , 73 (5) : 6954-6967 . |
| MLA | Lin, Ruiquan et al. "A Blockchain-Based Method to Defend Against Massive SSDF Attacks in Cognitive Internet of Vehicles" . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 73 . 5 (2024) : 6954-6967 . |
| APA | Lin, Ruiquan , Li, Fushuai , Wang, Jun , Hu, Jinsong , Zhang, Zaichen , Wu, Liang . A Blockchain-Based Method to Defend Against Massive SSDF Attacks in Cognitive Internet of Vehicles . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2024 , 73 (5) , 6954-6967 . |
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This article proposes an incomplete information Bayesian Stackelberg game, which is adapted to the Cognitive Internet of Vehicles (CIoVs) network to defend against spectrum sensing data falsification (SSDF) attacks from malicious vehicle users (MVUs). Specifically, this article considers the random appearance of MVUs caused by mobility, intelligent SSDF attacks of MVUs, and the different spectrum sensing performances among vehicle users (VUs). In the game, the fusion center (FC) as the leader aims to improve the global detection performance while effectively identifying the identities of different VUs by optimizing the global decision threshold and the reputation threshold. On the other hand, this article models the random appearance of MVUs as a Poisson random process, and the MVUs are the intelligent followers; they optimize the attack probabilities according to the FC's strategies to evade detection and increase the chance of selfish transmission and the damage to the CIoV network. To solve the MVUs' nonconvex optimization problem, this article uses the successive convex approximation (SCA) technique to obtain MVUs' optimal attack probabilities. For the FC, this article proposes the method combining alternating optimization and SCA to solve the nonconvex optimization problem of the FC and obtain its optimal defense strategies. This article also proves the convergence of the proposed method and the existence of the Stackelberg equilibrium (SE). The simulation results demonstrate the validity and superiority of the proposed method compared with traditional methods.
Keyword ：
Bayes methods Bayes methods Cognitive Internet of Vehicles (CIoVs) Cognitive Internet of Vehicles (CIoVs) Games Games game theory game theory Intelligent sensors Intelligent sensors Internet of Vehicles Internet of Vehicles Optimization Optimization physical layer security physical layer security Random processes Random processes Sensors Sensors spectrum sensing data falsification (SSDF) attacks spectrum sensing data falsification (SSDF) attacks
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| GB/T 7714 | Li, Fushuai , Lin, Ruiquan , Chen, Wencheng et al. Defending Against SSDF Attacks From Randomly Appearing Intelligent Malicious Vehicle Users in the CIoV Network by Bayesian Stackelberg Game [J]. | IEEE SENSORS JOURNAL , 2024 , 24 (19) : 31310-31323 . |
| MLA | Li, Fushuai et al. "Defending Against SSDF Attacks From Randomly Appearing Intelligent Malicious Vehicle Users in the CIoV Network by Bayesian Stackelberg Game" . | IEEE SENSORS JOURNAL 24 . 19 (2024) : 31310-31323 . |
| APA | Li, Fushuai , Lin, Ruiquan , Chen, Wencheng , Wang, Jun , Hu, Jinsong , Shu, Feng . Defending Against SSDF Attacks From Randomly Appearing Intelligent Malicious Vehicle Users in the CIoV Network by Bayesian Stackelberg Game . | IEEE SENSORS JOURNAL , 2024 , 24 (19) , 31310-31323 . |
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| GB/T 7714 | Liao, Jianbin , Yu, Hongliang , Jiang, Weibin et al. Optimal resource allocation method for energy harvesting based underlay Cognitive Radio networks (vol 18, e0279886, 2023) [J]. | PLOS ONE , 2024 , 19 (12) . |
| MLA | Liao, Jianbin et al. "Optimal resource allocation method for energy harvesting based underlay Cognitive Radio networks (vol 18, e0279886, 2023)" . | PLOS ONE 19 . 12 (2024) . |
| APA | Liao, Jianbin , Yu, Hongliang , Jiang, Weibin , Lin, Ruiquan , Wang, Jun . Optimal resource allocation method for energy harvesting based underlay Cognitive Radio networks (vol 18, e0279886, 2023) . | PLOS ONE , 2024 , 19 (12) . |
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