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FPSblo:A Blockchain Network Transmission Model Utilizing Farthest Point Sampling

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Computers, Materials & Continua 2024年第2期78卷 2491-2509页

作者： Longle Cheng Xiru Li Shiyu Fang wansu pan He Zhao Haibo Tan Xiaofeng Li Hefei Institutes of Physical Science Chinese Academy of SciencesHefei230031China University of Science and Technology of China Hefei230026China

Peer-to-peer(P2P)overlay networks provide message transmission capabilities for blockchain systems.Improving data transmission efficiency in P2P networks can greatly enhance the performance of blockchain systems.However,traditional blockchain P2P networks face a common challenge where there is often a mismatch between the upper-layer traffic requirements and the underlying physical network topology.This mismatch results in redundant data transmission and inefficient routing,severely constraining the scalability of blockchain systems.To address these pressing issues,we propose FPSblo,an efficient transmission method for blockchain networks.Our inspiration for FPSblo stems from the Farthest Point Sampling(FPS)algorithm,a well-established technique widely utilized in point cloud image processing.In this work,we analogize blockchain nodes to points in a point cloud image and select a representative set of nodes to prioritize message forwarding so that messages reach the network edge quickly and are evenly distributed.Moreover,we compare our model with the Kadcast transmission model,which is a classic improvement model for blockchain P2P transmission networks,the experimental findings show that the FPSblo model reduces 34.8%of transmission redundancy and reduces the overload rate by 37.6%.By conducting experimental analysis,the FPS-BT model enhances the transmission capabilities of the P2P network in blockchain.

关键词： Blockchain P2P networks scalability farthest point sampling

A module based modeling framework for estimating lunar dust deposition due to human activities

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Chinese Journal of Aeronautics 2024年第2期37卷 501-510页

作者： Chunjian pan College of Automation Engineering Shanghai University of Electric PowerShanghai 200090China

Lunar dust is considered to be one of the top challenges for enabling humans to have extended stays on the moon.Human activities such as module landings and launches,walking,rover operation and construction activities will inevitably produce a significant amount of dust.Therefore,it is important to estimate the potential range and intensity of dust deposition caused by these activities to minimize dust accumulation over time and for maintenance planning and execution.A modular model that correlates the dust deposition distribution with initial mean dust particle velocity,its mean ejected angle and the total amount of ejected mass is developed for an elementary mechanical movement.This modular model is further employed to form a modeling framework to estimate dust deposition of a trajectory based activity of similar repeated movements such as the landing process of a lander,walking and rover operation.The model forms a unified modeling framework for different trajectory-based activities and is shown to predict consistent and physically meaningful ranges and intensities of dust deposition provided reliable data to calibrate the model parameters.

关键词： Lunar dust Dust deposition Lunar lander Lunar construction Model calibration

Aeroengine thrust estimation and embedded verification based on improved temporal convolutional network

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Chinese Journal of Aeronautics 2024年第1期37卷 106-117页

作者： Wanzhi MENG Zhuorui pan Sixin WEN pan QIN Ximing SUN Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of TechnologyDalian 116024China School of Control Science and Engineering Dalian University of TechnologyDalian 116024China

Thrust estimation is a significant part of aeroengine thrust control systems.The traditional estimation methods are either low in accuracy or large in computation.To further improve the estimation effect,a thrust estimator based on Multi-layer Residual Temporal Convolutional Network(M-RTCN)is proposed.To solve the problem of dead Rectified Linear Unit(ReLU),the proposed method uses the Gaussian Error Linear Unit(GELU)activation function instead of ReLU in residual block.Then the overall architecture of the multi-layer convolutional network is adjusted by using residual connections,so that the network thrust estimation effect and memory consumption are further improved.Moreover,the comparison with seven other methods shows that the proposed method has the advantages of higher estimation accuracy and faster convergence speed.Furthermore,six neural network models are deployed in the embedded controller of the micro-turbojet engine.The Hardware-in-the-Loop(HIL)testing results demonstrate the superiority of M-RTCN in terms of estimation accuracy,memory occupation and running time.Finally,an ignition verification is conducted to confirm the expected thrust estimation and real-time performance.

关键词： Thrust estimation Temporal convolutional network Embedded deployment Hardware-in-the-loop testing Ignition verification

Cooperative Target Fencing of Multiple Vehicles for a General Target with Connectivity Preservation and Collision Avoidance

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Journal of Systems Science & Complexity 2024年第1期37卷 136-151页

作者： pan Zini CHEN Ben M Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina

In this paper,the authors study the cooperative target-fencing problem for n-dimensional systems and a target with a general trajectory.Without using the velocity of the vehicles,a position feedback control law is proposed to fence the general target into the convex hull formed by the vehicles.Specifically,the dynamics of each vehicle is described by a double-integrator system.Two potential functions are designed to guarantee connectivity preservation of the communication network and collision avoidance among the vehicles.The proposed approach can deal with a target whose trajectory is any twice continuously differentiable function of time.The effectiveness of the result is verified by a numerical example.

关键词： Multi-agent systems networked systems target fencing

Mechanisms of liver injuries caused by traditional Chinese medicines

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Hepatobiliary & pancreatic Diseases International 2024年第3期23卷 310-312页

作者： Shui-Fang Jin Qi pan Jin-Peng Zhou Xiao-Ping pan Intensive Care Unit The First Affiliated Hospital of Zhejiang Chinese Medical University(Zhejiang Provincial Hospital of Chinese Medicine)Hangzhou 310006China Institute of Liver Diseases College of Basic Medical SciencesZhejiang Chinese Medical UniversityHangzhou 310053China The Second Clinical Medical College of Zhejiang Chinese Medical University Hangzhou 310056China

Drug-induced liver injury(DILI)is a common adverse drug reaction,which can even result in liver failure[1,2].The Chinese Medical Association issued the Guidelines for the Diagnosis and Treatment of DILI based on the Roussel Uclaf Causality Assessment Method(RUCAM)in 2015[3].A previous study reported that traditional Chinese medicines(TCMs),herbal and dietary supplements,and antituberculosis drugs were the main causes of DILI in China[4].Herb-induced liver injury(HILI)refers to liver injury caused by TCMs,natural drugs,and their related preparations[5].

关键词： drugs tuberculosis TCM

A Time-Domain Numerical Simulation for Free Motion Responses of Two Ships Advancing in Head Waves

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China Ocean Engineering 2024年第3期38卷 519-530页

作者： pan Su-yong CHENG Yong School of Naval Architecture and Ocean Engineering Jiangsu University of Science and TechnologyZhenjiang 212003China

The constant panel method within the framework of potential flow theory in the time domain is developed for solving the hydrodynamic interactions between two parallel ships with forward speed.When solving problems within a time domain framework,the free water surface needs to simultaneously satisfy both the kinematic and dynamic boundary conditions of the free water surface.This provides conditions for adding artificial damping layers.Using the Runge−Kutta method to solve equations related to time.An upwind differential scheme is used in the present method to deal with the convection terms on the free surface to prevent waves upstream.Through the comparison with the available experimental data and other numerical methods,the present method is proved to have good mesh convergence,and satisfactory results can be obtained.The constant panel method is applied to calculate the hydrodynamic interaction responses of two parallel ships advancing in head waves.Numerical simulations are conducted on the effects of forward speed,different longitudinal and lateral distances on the motion response of two modified Wigley ships in head waves.Then further investigations are conducted on the effects of different ship types on the motion response.

关键词： ship motions time domain simulation forward speed different distances wave loads

FlowGANAnomaly: Flow-Based Anomaly Network Intrusion Detection with Adversarial Learning

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Chinese Journal of Electronics 2024年第1期33卷 58-71页

作者： Zeyi LI pan WANG Zixuan WANG School of Computer Science Nanjing University of Posts and Telecommunications School of Modern Posts Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing University of Posts and Telecommunications

In recent years, low recall rates and high dependencies on data labelling have become the biggest obstacle to developing deep anomaly detection(DAD) techniques. Inspired by the success of generative adversarial networks(GANs) in detecting anomalies in computer vision and imaging, we propose an anomaly detection model called Flow GANAnomaly for detecting anomalous traffic in network intrusion detection systems(NIDS). Unlike traditional GAN-based approaches, which are composed of a flow encoder, a convolutional encoder-decoder-encoder, a flow decoder and a convolutional encoder, the architecture of this model consists of a generator(G) and a discriminator(D).Flow GANAnomaly maps the different types of traffic feature data from separate datasets to a uniform feature space,thus can capture the normality of network traffic data more accurately in an adversarial manner to mitigate the problem of the high dependence on data labeling. Moreover, instead of simply detecting the anomalies by the output of D, we proposed a new anomaly scoring method that integrates the deviation between the output of two Gs’ convolutional encoders with the output of D as weighted scores to improve the low recall rate of anomaly detection. We conducted several experiments comparing existing machine learning algorithms and existing deep learning methods(Auto Encoder and VAE) on four public datasets(NSL-KDD, CIC-IDS2017, CIC-DDo S2019, and UNSW-NB15). The evaluation results show that Flow GANAnomaly can significantly improve the performance of anomaly-based NIDS.

关键词： Anomaly detection Unsupervised learning Generative adversarial network Intrusion detection system

Polynomial-Time Key-Recovery Attacks Against NTRURe Encrypt from ASIACCS'15

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Journal of Systems Science & Complexity 2024年第3期37卷 1308-1325页

作者： LIU Zhen pan Yanbin ZHENG Jinwei School of Cyber Science and Technology Hubei Key Laboratory of Applied MathematicsHubei UniversityWuhan 430062China Key Laboratory of Mathematics Mechanization Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100190China

In ASIACCS 2015, Nu nez, et al. proposed a proxy re-encryption scheme, named NTRURe Encrypt, based on NTRU, which allows a proxy to translate ciphertext under the delegator’s public key into a re-encrypted ciphertext that can be decrypted correctly by delegatee’s private key. Because of the potential resistance to quantum algorithm, high efficiency and various applications in real life,NTRURe Encrypt has drawn lots of attention and its security has been widely discussed and analyzed.In PQCrypto2019, Liu, et al. proposed two key recovery attacks against it. However, their first attack heavily relies on a weaken decryption oracle, and the second attack needs to collect about 260ciphertexts from the same message by theoretical analysis, which makes both of the attacks unrealistic. In this paper, inspired by the broadcast attack against NTRU, the authors find out that for NTRURe Encrypt the delegator and the delegatee can efficiently recover each other’s private key in polynomial time without any unrealistic assumptions. In addition, the authors also show how to fix NTRURe Encrypt to resist the proposed attacks. As a by-product, the authors also show how to commit broadcast attacks against NTRU 2001 with even dg, which was thought infeasible before.

关键词： Broadcast attack key recovery NTRU NTRUReEncrypt

Depth-Guided Vision Transformer With Normalizing Flows for Monocular 3D Object Detection

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 673-689页

作者： Cong pan Junran Peng Zhaoxiang Zhang the Center for Research on Intelligent Perception and Computing(CRIPAC) National Laboratory of Pattern Recognition(NLPR)Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190 the School of Future Technology University of Chinese Academy of Sciences(UCAS)Beijing 100049China the Huawei Inc. Beijing 100085China IEEE the Institute of Automation Chinese Academy of Sciences(CASIA)Beijing 100190 the University of Chinese Academy of Sciences(UCAS) Beijing 100049 the Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science&Innovation Chinese Academy of Sciences(HKISI CAS)Hong Kong 999077China

Monocular 3D object detection is challenging due to the lack of accurate depth information.Some methods estimate the pixel-wise depth maps from off-the-shelf depth estimators and then use them as an additional input to augment the RGB images.Depth-based methods attempt to convert estimated depth maps to pseudo-LiDAR and then use LiDAR-based object detectors or focus on the perspective of image and depth fusion learning.However,they demonstrate limited performance and efficiency as a result of depth inaccuracy and complex fusion mode with convolutions.Different from these approaches,our proposed depth-guided vision transformer with a normalizing flows(NF-DVT)network uses normalizing flows to build priors in depth maps to achieve more accurate depth information.Then we develop a novel Swin-Transformer-based backbone with a fusion module to process RGB image patches and depth map patches with two separate branches and fuse them using cross-attention to exchange information with each other.Furthermore,with the help of pixel-wise relative depth values in depth maps,we develop new relative position embeddings in the cross-attention mechanism to capture more accurate sequence ordering of input tokens.Our method is the first Swin-Transformer-based backbone architecture for monocular 3D object detection.The experimental results on the KITTI and the challenging Waymo Open datasets show the effectiveness of our proposed method and superior performance over previous counterparts.

关键词： Monocular 3D object detection normalizing flows Swin Transformer