Cooperative User-Scheduling and Resource Allocation Optimization for Intelligent Reflecting Surface Enhanced LEO Satellite Communication

Lower Earth Orbit(LEO) satellite becomes an important part of complementing terrestrial communication due to its lower orbital altitude and smaller propagation delay than Geostationary satellite. However, the LEO satellite communication system cannot meet the requirements of users when the satellite-terrestrial link is blocked by obstacles. To solve this problem, we introduce Intelligent reflect surface(IRS) for improving the achievable rate of terrestrial users in LEO satellite communication. We investigated joint IRS scheduling, user scheduling, power and bandwidth allocation(JIRPB) optimization algorithm for improving LEO satellite system throughput.The optimization problem of joint user scheduling and resource allocation is formulated as a non-convex optimization problem. To cope with this problem, the nonconvex optimization problem is divided into resource allocation optimization sub-problem and scheduling optimization sub-problem firstly. Second, we optimize the resource allocation sub-problem via alternating direction multiplier method(ADMM) and scheduling sub-problem via Lagrangian dual method repeatedly.Third, we prove that the proposed resource allocation algorithm based ADMM approaches sublinear convergence theoretically. Finally, we demonstrate that the proposed JIRPB optimization algorithm improves the LEO satellite communication system throughput.

Ensuring Secure Platooning of Constrained Intelligent and Connected Vehicles Against Byzantine Attacks:A Distributed MPC Framework

This study investigates resilient platoon control for constrained intelligent and connected vehicles(ICVs)against F-local Byzantine attacks.We introduce a resilient distributed model-predictive platooning control framework for such ICVs.This framework seamlessly integrates the predesigned optimal control with distributed model predictive control(DMPC)optimization and introduces a unique distributed attack detector to ensure the reliability of the transmitted information among vehicles.Notably,our strategy uses previously broadcasted information and a specialized convex set,termed the“resilience set”,to identify unreliable data.This approach significantly eases graph robustness prerequisites,requiring only an(F+1)-robust graph,in contrast to the established mean sequence reduced algorithms,which require a minimum(2F+1)-robust graph.Additionally,we introduce a verification algorithm to restore trust in vehicles under minor attacks,further reducing communication network robustness.Our analysis demonstrates the recursive feasibility of the DMPC optimization.Furthermore,the proposed method achieves exceptional control performance by minimizing the discrepancies between the DMPC control inputs and predesigned platoon control inputs,while ensuring constraint compliance and cybersecurity.Simulation results verify the effectiveness of our theoretical findings.

Intelligent diagnosis of retinal vein occlusion based on color fundus photographs

AIM:To develop an artificial intelligence(AI)diagnosis model based on deep learning(DL)algorithm to diagnose different types of retinal vein occlusion(RVO)by recognizing color fundus photographs(CFPs).METHODS:Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets,and used to train,verify and test the diagnostic model of RVO.All the images were divided into four categories[normal,central retinal vein occlusion(CRVO),branch retinal vein occlusion(BRVO),and macular retinal vein occlusion(MRVO)]by three fundus disease experts.Swin Transformer was used to build the RVO diagnosis model,and different types of RVO diagnosis experiments were conducted.The model’s performance was compared to that of the experts.RESULTS:The accuracy of the model in the diagnosis of normal,CRVO,BRVO,and MRVO reached 1.000,0.978,0.957,and 0.978;the specificity reached 1.000,0.986,0.982,and 0.976;the sensitivity reached 1.000,0.955,0.917,and 1.000;the F1-Sore reached 1.000,0.9550.943,and 0.887 respectively.In addition,the area under curve of normal,CRVO,BRVO,and MRVO diagnosed by the diagnostic model were 1.000,0.900,0.959 and 0.970,respectively.The diagnostic results were highly consistent with those of fundus disease experts,and the diagnostic performance was superior.CONCLUSION:The diagnostic model developed in this study can well diagnose different types of RVO,effectively relieve the work pressure of clinicians,and provide help for the follow-up clinical diagnosis and treatment of RVO patients.

Introduction to the Special Issue on Machine Learning-Guided Intelligent Modeling with Its Industrial Applications

With the advancement of Artificial Intelligence(AI)technology,traditional industrial systems are undergoing an intelligent transformation,bringing together advanced computing,communication and control technologies,Machine Learning(ML)-based intelligentmodelling has become a newparadigm for solving problems in the industrial domain[1–3].With numerous applications and diverse data types in the industrial domain,algorithmic and data-driven ML techniques can intelligently learn potential correlations between complex data and make efficient decisions while reducing human intervention.However,in real-world application scenarios,existing algorithms may have a variety of limitations,such as small data volumes,small detection targets,low efficiency,and algorithmic gaps in specific application domains[4].Therefore,many new algorithms and strategies have been proposed to address the challenges in industrial applications[5–8].

Vision based intelligent traffic light management system using Faster R‐CNN

Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

An intelligent prediction model of epidemic characters based on multi-feature

The epidemic characters of Omicron(e.g.large-scale transmission)are significantly different from the initial variants of COVID-19.The data generated by large-scale transmission is important to predict the trend of epidemic characters.However,the re-sults of current prediction models are inaccurate since they are not closely combined with the actual situation of Omicron transmission.In consequence,these inaccurate results have negative impacts on the process of the manufacturing and the service industry,for example,the production of masks and the recovery of the tourism industry.The authors have studied the epidemic characters in two ways,that is,investigation and prediction.First,a large amount of data is collected by utilising the Baidu index and conduct questionnaire survey concerning epidemic characters.Second,theβ-SEIDR model is established,where the population is classified as Susceptible,Exposed,Infected,Dead andβ-Recovered persons,to intelligently predict the epidemic characters of COVID-19.Note thatβ-Recovered persons denote that the Recovered persons may become Sus-ceptible persons with probabilityβ.The simulation results show that the model can accurately predict the epidemic characters.

Intelligent Transformation: General Intelligence Theory

This paper aims to formalize a general definition of intelligence beyond human intelligence. We accomplish this by re-imagining the concept of equality as a fundamental abstraction for relation. We discover that the concept of equality = limits the sensitivity of our mathematics to abstract relationships. We propose a new relation principle that does not rely on the concept of equality but is consistent with existing mathematical abstractions. In essence, this paper proposes a conceptual framework for general interaction and argues that this framework is also an abstraction that satisfies the definition of Intelligence. Hence, we define intelligence as a formalization of generality, represented by the abstraction ∆∞Ο, where each symbol represents the concepts infinitesimal, infinite, and finite respectively. In essence, this paper proposes a General Language Model (GLM), where the abstraction ∆∞Ο represents the foundational relationship of the model. This relation is colloquially termed “The theory of everything”.

Car-following strategy of intelligent connected vehicle using extended disturbance observer adjusted by reinforcement learning

Disturbance observer-based control method has achieved good results in the carfollowing scenario of intelligent and connected vehicle(ICV).However,the gain of conventional extended disturbance observer(EDO)-based control method is usually set manually rather than adjusted adaptively according to real time traffic conditions,thus declining the car-following performance.To solve this problem,a car-following strategy of ICV using EDO adjusted by reinforcement learning is proposed.Different from the conventional method,the gain of proposed strategy can be adjusted by reinforcement learning to improve its estimation accuracy.Since the“equivalent disturbance”can be compensated by EDO to a great extent,the disturbance rejection ability of the carfollowing method will be improved significantly.Both Lyapunov approach and numerical simulations are carried out to verify the effectiveness of the proposed method.

Intelligent reflecting surface for sum rate enhancement in MIMO systems

The research for the Intelligent Reflecting Surface(IRS)which has the advantages of cost and energy efficiency has been studied.Channel capacity can be effectively increased by appropriately setting the phase value of IRS elements according to the channel conditions.However,the problem of obtaining an appropriate phase value of IRs is difficult to solve due to the non-convex problem.This paper proposes an iterative algorithm for the alternating optimal solution in the Single User Multiple-Input-Multiple-Output(SU-MIMO)systems.The proposed iterative algorithm finds an alternating optimal solution that is the phase value of IRS one by one.The results show that the proposed method has better performance than that of the randomized IRS systems.The number of iterations for maximizing the performance of the proposed algorithm depends on the channel state between the IRS and the receiver.

CBA: multi source fusion model for fast and intelligent target intention identification

How to mine valuable information from massive multisource heterogeneous data and identify the intention of aerial targets is a major research focus at present. Aiming at the longterm dependence of air target intention recognition, this paper deeply explores the potential attribute features from the spatiotemporal sequence data of the target. First, we build an intelligent dynamic intention recognition framework, including a series of specific processes such as data source, data preprocessing,target space-time, convolutional neural networks-bidirectional gated recurrent unit-atteneion (CBA) model and intention recognition. Then, we analyze and reason the designed CBA model in detail. Finally, through comparison and analysis with other recognition model experiments, our proposed method can effectively improve the accuracy of air target intention recognition,and is of significance to the commanders’ operational command and situation prediction.

Intelligent diagnostic model for pterygium by combining attention mechanism and MobileNetV2

AIM:To evaluate the application of an intelligent diagnostic model for pterygium.METHODS:For intelligent diagnosis of pterygium,the attention mechanisms—SENet,ECANet,CBAM,and Self-Attention—were fused with the lightweight MobileNetV2 model structure to construct a tri-classification model.The study used 1220 images of three types of anterior ocular segments of the pterygium provided by the Eye Hospital of Nanjing Medical University.Conventional classification models—VGG16,ResNet50,MobileNetV2,and EfficientNetB7—were trained on the same dataset for comparison.To evaluate model performance in terms of accuracy,Kappa value,test time,sensitivity,specificity,the area under curve(AUC),and visual heat map,470 test images of the anterior segment of the pterygium were used.RESULTS:The accuracy of the MobileNetV2+Self-Attention model with 281 MB in model size was 92.77%,and the Kappa value of the model was 88.92%.The testing time using the model was 9ms/image in the server and 138ms/image in the local computer.The sensitivity,specificity,and AUC for the diagnosis of pterygium using normal anterior segment images were 99.47%,100%,and 100%,respectively;using anterior segment images in the observation period were 88.30%,95.32%,and 96.70%,respectively;and using the anterior segment images in the surgery period were 88.18%,94.44%,and 97.30%,respectively.CONCLUSION:The developed model is lightweight and can be used not only for detection but also for assessing the severity of pterygium.

Privacy-Preserving Large-Scale AI Models for Intelligent Railway Transportation Systems:Hierarchical Poisoning Attacks and Defenses in Federated Learning

The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency.Federated learning offers a promising solution by allowing multiple clients to train models collaboratively without sharing private data.However,despite its privacy benefits,federated learning systems are vulnerable to poisoning attacks,where adversaries alter local model parameters on compromised clients and send malicious updates to the server,potentially compromising the global model’s accuracy.In this study,we introduce PMM(Perturbation coefficient Multiplied by Maximum value),a new poisoning attack method that perturbs model updates layer by layer,demonstrating the threat of poisoning attacks faced by federated learning.Extensive experiments across three distinct datasets have demonstrated PMM’s ability to significantly reduce the global model’s accuracy.Additionally,we propose an effective defense method,namely CLBL(Cluster Layer By Layer).Experiment results on three datasets have confirmed CLBL’s effectiveness.

Joint Active and Passive Beamforming Design in Intelligent Reflecting Surface(IRS)-Assisted Covert Communications:A Multi-Agent DRL Approach

Intelligent Reflecting Surface(IRS),with the potential capability to reconstruct the electromagnetic propagation environment,evolves a new IRSassisted covert communications paradigm to eliminate the negligible detection of malicious eavesdroppers by coherently beaming the scattered signals and suppressing the signals leakage.However,when multiple IRSs are involved,accurate channel estimation is still a challenge due to the extra hardware complexity and communication overhead.Besides the crossinterference caused by massive reflecting paths,it is hard to obtain the close-formed solution for the optimization of covert communications.On this basis,the paper improves a heterogeneous multi-agent deep deterministic policy gradient(MADDPG)approach for the joint active and passive beamforming(Joint A&P BF)optimization without the channel estimation,where the base station(BS)and multiple IRSs are taken as different types of agents and learn to enhance the covert spectrum efficiency(CSE)cooperatively.Thanks to the‘centralized training and distributed execution’feature of MADDPG,each agent can execute the active or passive beamforming independently based on its partial observation without referring to others.Numeral results demonstrate that the proposed deep reinforcement learning(DRL)approach could not only obtain a preferable CSE of legitimate users and a low detection of probability(LPD)of warden,but also alleviate the communication overhead and simplify the IRSs deployment.

Method for Detecting Industrial Defects in Intelligent Manufacturing Using Deep Learning

With the advent of Industry 4.0,marked by a surge in intelligent manufacturing,advanced sensors embedded in smart factories now enable extensive data collection on equipment operation.The analysis of such data is pivotal for ensuring production safety,a critical factor in monitoring the health status of manufacturing apparatus.Conventional defect detection techniques,typically limited to specific scenarios,often require manual feature extraction,leading to inefficiencies and limited versatility in the overall process.Our research presents an intelligent defect detection methodology that leverages deep learning techniques to automate feature extraction and defect localization processes.Our proposed approach encompasses a suite of components:the high-level feature learning block(HLFLB),the multi-scale feature learning block(MSFLB),and a dynamic adaptive fusion block(DAFB),working in tandem to extract meticulously and synergistically aggregate defect-related characteristics across various scales and hierarchical levels.We have conducted validation of the proposed method using datasets derived from gearbox and bearing assessments.The empirical outcomes underscore the superior defect detection capability of our approach.It demonstrates consistently high performance across diverse datasets and possesses the accuracy required to categorize defects,taking into account their specific locations and the extent of damage,proving the method’s effectiveness and reliability in identifying defects in industrial components.

A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm

Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.

Intelligent Reflecting Surface-Aided Secure and Covert Communications

This work employs intelligent reflecting surface(IRS)to enhance secure and covert communication performance.We formulate an optimization problem to jointly design both the reflection beamformer at IRS and transmit power at transmitter Alice in order to optimize the achievable secrecy rate at Bob subject to a covertness constraint.We first develop a Dinkelbach-based algorithm to achieve an upper bound performance and a high-quality solution.For reducing the overhead and computational complexity of the Dinkelbach-based scheme,we further conceive a low-complexity algorithm in which analytical expression for the IRS reflection beamforming is derived at each iteration.Examination result shows that the devised low-complexity algorithm is able to achieve similar secrecy rate performance as the Dinkelbach-based algorithm.Our examination also shows that introducing an IRS into the considered system can significantly improve the secure and covert communication performance relative to the scheme without IRS.

Artificial Intelligence Based Multi-Scenario mmWave Channel Modeling for Intelligent High-Speed Train Communications

A large amount of mobile data from growing high-speed train(HST)users makes intelligent HST communications enter the era of big data.The corresponding artificial intelligence(AI)based HST channel modeling becomes a trend.This paper provides AI based channel characteristic prediction and scenario classification model for millimeter wave(mmWave)HST communications.Firstly,the ray tracing method verified by measurement data is applied to reconstruct four representative HST scenarios.By setting the positions of transmitter(Tx),receiver(Rx),and other parameters,the multi-scenarios wireless channel big data is acquired.Then,based on the obtained channel database,radial basis function neural network(RBF-NN)and back propagation neural network(BP-NN)are trained for channel characteristic prediction and scenario classification.Finally,the channel characteristic prediction and scenario classification capabilities of the network are evaluated by calculating the root mean square error(RMSE).The results show that RBF-NN can generally achieve better performance than BP-NN,and is more applicable to prediction of HST scenarios.

Innovative Practice of Intelligent Business Models in the Field of Communication

As a service format to meet spiritual needs, fashion culture industry is often synchronized in its creation, production, dissemination, display and consumption. In order to explore how artificial intelligence provides technical means, platforms, channels and space for intelligent formats in the field of communication, and how to provide intelligent services for cultural creation, communication, display and consumption. This paper discusses the application and innovation of artificial intelligence in all aspects of communication field, analyzes the problems and puts forward corresponding countermeasures and suggestions.

MADDPG-D2: An Intelligent Dynamic Task Allocation Algorithm Based on Multi-Agent Architecture Driven by Prior Knowledge

Aiming at the problems of low solution accuracy and high decision pressure when facing large-scale dynamic task allocation(DTA)and high-dimensional decision space with single agent,this paper combines the deep reinforce-ment learning(DRL)theory and an improved Multi-Agent Deep Deterministic Policy Gradient(MADDPG-D2)algorithm with a dual experience replay pool and a dual noise based on multi-agent architecture is proposed to improve the efficiency of DTA.The algorithm is based on the traditional Multi-Agent Deep Deterministic Policy Gradient(MADDPG)algorithm,and considers the introduction of a double noise mechanism to increase the action exploration space in the early stage of the algorithm,and the introduction of a double experience pool to improve the data utilization rate;at the same time,in order to accelerate the training speed and efficiency of the agents,and to solve the cold-start problem of the training,the a priori knowledge technology is applied to the training of the algorithm.Finally,the MADDPG-D2 algorithm is compared and analyzed based on the digital battlefield of ground and air confrontation.The experimental results show that the agents trained by the MADDPG-D2 algorithm have higher win rates and average rewards,can utilize the resources more reasonably,and better solve the problem of the traditional single agent algorithms facing the difficulty of solving the problem in the high-dimensional decision space.The MADDPG-D2 algorithm based on multi-agent architecture proposed in this paper has certain superiority and rationality in DTA.

The Extreme Machine Learning Actuarial Intelligent Agricultural Insurance Based Automated Underwriting Model

The paper presents an innovative approach towards agricultural insurance underwriting and risk pricing through the development of an Extreme Machine Learning (ELM) Actuarial Intelligent Model. This model integrates diverse datasets, including climate change scenarios, crop types, farm sizes, and various risk factors, to automate underwriting decisions and estimate loss reserves in agricultural insurance. The study conducts extensive exploratory data analysis, model building, feature engineering, and validation to demonstrate the effectiveness of the proposed approach. Additionally, the paper discusses the application of robust tests, stress tests, and scenario tests to assess the model’s resilience and adaptability to changing market conditions. Overall, the research contributes to advancing actuarial science in agricultural insurance by leveraging advanced machine learning techniques for enhanced risk management and decision-making.