Elliptical encirclement control capable of reinforcing performances for UAVs around a dynamic target

Most researches associated with target encircling control are focused on moving along a circular orbit under an ideal environment free from external disturbances.However,elliptical encirclement with a time-varying observation radius,may permit a more flexible and high-efficacy enclosing solution,whilst the non-orthogonal property between axial and tangential speed components,non-ignorable environmental perturbations,and strict assignment requirements empower elliptical encircling control to be more challenging,and the relevant investigations are still open.Following this line,an appointed-time elliptical encircling control rule capable of reinforcing circumnavigation performances is developed to enable Unmanned Aerial Vehicles(UAVs)to move along a specified elliptical path within a predetermined reaching time.The remarkable merits of the designed strategy are that the relative distance controlling error can be guaranteed to evolve within specified regions with a designer-specified convergence behavior.Meanwhile,wind perturbations can be online counteracted based on an unknown system dynamics estimator(USDE)with only one regulating parameter and high computational efficiency.Lyapunov tool demonstrates that all involved error variables are ultimately limited,and simulations are implemented to confirm the usability of the suggested control algorithm.

Field implementation of enzyme-induced carbonate precipitation technology for reinforcing a bedding layer beneath an underground cable duct

A suitable bearing capacity of foundation is critical for the safety of civil structures.Sometimes foundation reinforcement is necessary and an effective and environmentally friendly method would be the preferred choice.In this study,the potential application of enzyme-induced carbonate precipitation(EICP)was investigated for reinforcing a 0.6 m bedding layer on top of clay to improve the bearing capacity of the foundation underneath an underground cable duct.Laboratory experiments were conducted to determine the optimal operational parameters for the extraction of crude urease liquid and optimal grain size range of sea sands to be used to construct the bedding layer.Field tests were planned based on orthogonal experimental design to study the factors that would significantly affect the biocementation effect on site.The dynamic deformation modulus,calcium carbonate content and longterm ground stress variations were used to evaluate the bio-cementation effect and the long-term performance of the EICP-treated bedding layer.The laboratory test results showed that the optimal duration for the extraction of crude urease liquid is 1 h and the optimal usage of soybean husk powder in urease extraction solution is 100 g/L.The calcium carbonate production rate decreases significantly when the concentration of cementation solution exceeds 0.5 mol/L.The results of site trial showed that the number of EICP treatments has the most significant impact on the effectiveness of EICP treatment and the highest dynamic deformation modulus(Evd)of EICP-treated bedding layer reached 50.55 MPa.The area with better bio-cementation effect was found to take higher ground stress which validates that the EICP treatment could improve the bearing capacity of foundation by reinforcing the bedding layer.The field trial described and the analysis introduced in this paper can provide a practical basis for applying EICP technology to the reinforcement of bedding layer in poor ground conditions.

Assessment of Deviation in Quality of Steel Reinforcing Bars Used in Some Building Sites in Cameroon

The present work evaluated the deviations in the quality of steel reinforcing bars in terms of markings, diameter, yield strength and ductility in order to facilitate the drawing up of a yield strength value for the Cameroon National Annex to Eurocode 2. The methodology of the work started with the collection of steel samples from various active building project sites in four different towns viz: Bamenda, Douala, Maroua and Yaoundé and testing their tensile strength and elongation using a Universal Testing Machine and also carrying out the bending test. Results show that bars without marked manufacturer’s name fell all the tests. Other results show that 52% of all the steel had yield stresses below 400 Mpa and the highest deviation in the yield strengths was 22.50%. The study recommends that properly marked grade 500 steel bars should be adopted in the Cameroon national annex to Eurocode 2.

Machine learning applications in stroke medicine:advancements,challenges,and future prospectives

Stroke is a leading cause of disability and mortality worldwide,necessitating the development of advanced technologies to improve its diagnosis,treatment,and patient outcomes.In recent years,machine learning techniques have emerged as promising tools in stroke medicine,enabling efficient analysis of large-scale datasets and facilitating personalized and precision medicine approaches.This abstract provides a comprehensive overview of machine learning’s applications,challenges,and future directions in stroke medicine.Recently introduced machine learning algorithms have been extensively employed in all the fields of stroke medicine.Machine learning models have demonstrated remarkable accuracy in imaging analysis,diagnosing stroke subtypes,risk stratifications,guiding medical treatment,and predicting patient prognosis.Despite the tremendous potential of machine learning in stroke medicine,several challenges must be addressed.These include the need for standardized and interoperable data collection,robust model validation and generalization,and the ethical considerations surrounding privacy and bias.In addition,integrating machine learning models into clinical workflows and establishing regulatory frameworks are critical for ensuring their widespread adoption and impact in routine stroke care.Machine learning promises to revolutionize stroke medicine by enabling precise diagnosis,tailored treatment selection,and improved prognostication.Continued research and collaboration among clinicians,researchers,and technologists are essential for overcoming challenges and realizing the full potential of machine learning in stroke care,ultimately leading to enhanced patient outcomes and quality of life.This review aims to summarize all the current implications of machine learning in stroke diagnosis,treatment,and prognostic evaluation.At the same time,another purpose of this paper is to explore all the future perspectives these techniques can provide in combating this disabling disease.

Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.

A Comparative Study on the Post-Buckling Behavior of Reinforced Thermoplastic Pipes(RTPs)Under External Pressure Considering Progressive Failure

The collapse pressure is a key parameter when RTPs are applied in harsh deep-water environments.To investigate the collapse of RTPs,numerical simulations and hydrostatic pressure tests are conducted.For the numerical simulations,the eigenvalue analysis and Riks analysis are combined,in which the Hashin failure criterion and fracture energy stiffness degradation model are used to simulate the progressive failure of composites,and the“infinite”boundary conditions are applied to eliminate the boundary effects.As for the hydrostatic pressure tests,RTP specimens were placed in a hydrostatic chamber after filled with water.It has been observed that the cross-section of the middle part collapses when it reaches the maximum pressure.The collapse pressure obtained from the numerical simulations agrees well with that in the experiment.Meanwhile,the applicability of NASA SP-8007 formula on the collapse pressure prediction was also discussed.It has a relatively greater difference because of the ignorance of the progressive failure of composites.For the parametric study,it is found that RTPs have much higher first-ply-failure pressure when the winding angles are between 50°and 70°.Besides,the effect of debonding and initial ovality,and the contribution of the liner and coating are also discussed.

Development and application of novel high‐efficiency composite ultrafine cement grouts for roadway in fractured surrounding rocks

The fractured surrounding rocks of roadways pose major challenges to safe mining.Grouting has often been used to reinforce the surrounding rocks to mitigate the safety risks associated with fractured rocks.The aim of this study is to develop highly efficient composite ultrafine cement(CUC)grouts to reinforce the roadway in fractured surrounding rocks.The materials used are ultrafine cement(UC),ultrafine fly ash(UF),ultrafine slag(US),and additives(superplasticizer[SUP],aluminate ultrafine expansion agent[AUA],gypsum,and retarder).The fluidity,bleeding,shrinkage,setting time,chemical composition,microstructure,degree of hydration,and mechanical property of grouting materials were evaluated in this study.Also,a suitable and effective CUC grout mixture was used to reinforce the roadway in the fractured surrounding rock.The results have shown that the addition of UF and US reduces the plastic viscosity of CUC,and the best fluidity can be obtained by adding 40%UF and 10%US.Since UC and UF particles are small,the pozzolanic effect of UF promotes the hydration reaction,which is conductive to the stability of CUC grouts.In addition,fine particles of UC,UF,and US can effectively fill the pores,while the volumetric expansion of AUA and gypsum decreases the pores and thus affects the microstructure of the solidified grout.The compressive test results have shown that the addition of specific amounts of UF and US can ameliorate the mechanical properties of CUC grouts.Finally,the CUC22‐8 grout was used to reinforce the No.20322 belt roadway.The results of numerical simulation and field monitoring have indicated that grouting can efficaciously reinforce the surrounding rock of the roadway.In this research,high‐performance CUC grouts were developed for surrounding rock reinforcement of underground engineering by utilizing UC and some additives.

Mussel-inspired PTW@PDA composites for developing high-energy gun propellants with reduced erosion and enhanced mechanical strength

The severe erosion and inadequate mechanical strength are prominent challenges for high-energy gun propellants.To address it,novel PTW@PDA composites was prepared by polydopamine(PDA)-modifying onto potassium titanate whisker(PTW,K_(2)Ti_(6)O_(13)),and after was incorporated into gun propellant as erosion-reducing and mechanical-reinforcing fillers.The interfacial characterizations results indicated that as-prepared PTW@PDA composites exhibits an enhanced surface compatible with propellant matrix,thereby facilitating their dispersion into propellants more effectively than raw PTW materials.Compared to original propellants,PTW@PDA-modified propellants exhibited significant less erosion,with a Ti-Kbased protective coating being detected on the eroded steel.And 0.5 wt%and 1.0 wt%addition of PTW@PDA significantly improved impact,compressive and tensile strength of propellants.Despite the inevitably reduction in relative force,PTW@PDA slightly increase propellant burning rate while exerting little adverse impact on propellant dynamic activity.This strategy can provide a promising alternative to develop high-energy gun propellant with less erosion and more mechanical strength.

Microstructural characterization,tribological and corrosion behavior of AA7075-TiC composites

Aluminum alloys are the potential materials in the automobile and aerospace sectors due to their lower density,easy forming and excellent corrosion resistance.The demand of high strength-to-weight ratio materials in structural applications needs the engineering industries to seek aluminum alloy with new versions of hard and brittle ceramic particles.The microstructure,hardness,wear and corrosion behaviors of AA7075 composites with 2.5wt.%and 5wt.%TiC particles were studied.Microscopic analysis is evident that the transformation of the strong dendritic morphology to non-dendritic morphology on the incorporation of TiC into AA7075.Furthermore,the precipitation of the second-phase compounds such as Al_(2)CuMg,Al_(2)Cu andFe-rich Al_6(Cu,Fe)/Al_(7)Cu_(2)Fe)is promoted by TiC particles at inter-and intra-dendritic regions.Accordingly,the hardness of composites is improved by grain boundary strengthening and particulate strengthening mechanisms.Both coefficient of friction and wear rate have an inverse relation with TiC concentration.The base alloy without TiC shows adhesive-type wear-induced deformation due to the formation of an oxide film,while composite samples exhibit a mechanically mixed layer and abrasive-type wear behavior.Composite samples shows a higher corrosion rate due to the presence of numerous precipitates which promote pitting corrosion.

Multi-UAV cooperative maneuver decision-making for pursuitevasion using improved MADRL

Aiming at the problem of multi-UAV pursuit-evasion confrontation, a UAV cooperative maneuver method based on an improved multi-agent deep reinforcement learning(MADRL) is proposed. In this method, an improved Comm Net network based on a communication mechanism is introduced into a deep reinforcement learning algorithm to solve the multi-agent problem. A layer of gated recurrent unit(GRU) is added to the actor-network structure to remember historical environmental states. Subsequently,another GRU is designed as a communication channel in the Comm Net core network layer to refine communication information between UAVs. Finally, the simulation results of the algorithm in two sets of scenarios are given, and the results show that the method has good effectiveness and applicability.

Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees

While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

Stability behavior of the Lanxi ancient flood control levee after reinforcement with upside-down hanging wells and grouting curtain

The stability of the ancient flood control levees is mainly influenced by water level fluctuations, groundwater concentration and rainfalls. This paper takes the Lanxi ancient levee as a research object to study the evolution laws of its seepage, displacement and stability before and after reinforcement with the upside-down hanging wells and grouting curtain through numerical simulation methods combined with experiments and observations. The study results indicate that the filled soil is less affected by water level fluctuations and groundwater concentration after reinforcement. A high groundwater level is detrimental to the levee's long-term stability, and the drainage issues need to be fully considered. The deformation of the reinforced levee is effectively controlled since the fill deformation is mainly borne by the upside-down hanging wells. The safety factors of the levee before reinforcement vary significantly with the water level. The minimum value of the safety factors is 0.886 during the water level decreasing period, indicating a very high risk of the instability. While it reached 1.478 after reinforcement, the stability of the ancient levee is improved by a large margin.

Distributed Graph Database Load Balancing Method Based on Deep Reinforcement Learning

This paper focuses on the scheduling problem of workflow tasks that exhibit interdependencies.Unlike indepen-dent batch tasks,workflows typically consist of multiple subtasks with intrinsic correlations and dependencies.It necessitates the distribution of various computational tasks to appropriate computing node resources in accor-dance with task dependencies to ensure the smooth completion of the entire workflow.Workflow scheduling must consider an array of factors,including task dependencies,availability of computational resources,and the schedulability of tasks.Therefore,this paper delves into the distributed graph database workflow task scheduling problem and proposes a workflow scheduling methodology based on deep reinforcement learning(DRL).The method optimizes the maximum completion time(makespan)and response time of workflow tasks,aiming to enhance the responsiveness of workflow tasks while ensuring the minimization of the makespan.The experimental results indicate that the Q-learning Deep Reinforcement Learning(Q-DRL)algorithm markedly diminishes the makespan and refines the average response time within distributed graph database environments.In quantifying makespan,Q-DRL achieves mean reductions of 12.4%and 11.9%over established First-fit and Random scheduling strategies,respectively.Additionally,Q-DRL surpasses the performance of both DRL-Cloud and Improved Deep Q-learning Network(IDQN)algorithms,with improvements standing at 4.4%and 2.6%,respectively.With reference to average response time,the Q-DRL approach exhibits a significantly enhanced performance in the scheduling of workflow tasks,decreasing the average by 2.27%and 4.71%when compared to IDQN and DRL-Cloud,respectively.The Q-DRL algorithm also demonstrates a notable increase in the efficiency of system resource utilization,reducing the average idle rate by 5.02%and 9.30%in comparison to IDQN and DRL-Cloud,respectively.These findings support the assertion that Q-DRL not only upholds a lower average idle rate but also effectively curtails the average response time,thereby substantially improving processing efficiency and optimizing resource utilization within distributed graph database systems.

Reinforcement learning based edge computing in B5G

The development of communication technology will promote the application of Internet of Things,and Beyond 5G will become a new technology promoter.At the same time,Beyond 5G will become one of the important supports for the development of edge computing technology.This paper proposes a communication task allocation algorithm based on deep reinforcement learning for vehicle-to-pedestrian communication scenarios in edge computing.Through trial and error learning of agent,the optimal spectrum and power can be determined for transmission without global information,so as to balance the communication between vehicle-to-pedestrian and vehicle-to-infrastructure.The results show that the agent can effectively improve vehicle-to-infrastructure communication rate as well as meeting the delay constraints on the vehicle-to-pedestrian link.

Combining reinforcement learning with mathematical programming:An approach for optimal design of heat exchanger networks

Heat integration is important for energy-saving in the process industry.It is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly nonconvex nonlinear and combinatorial nature of the HEN problem,it is not easy to find solutions of high quality for large-scale problems.The reinforcement learning(RL)method,which learns strategies through ongoing exploration and exploitation,reveals advantages in such area.However,due to the complexity of the HEN design problem,the RL method for HEN should be dedicated and designed.A hybrid strategy combining RL with mathematical programming is proposed to take better advantage of both methods.An insightful state representation of the HEN structure as well as a customized reward function is introduced.A Q-learning algorithm is applied to update the HEN structure using theε-greedy strategy.Better results are obtained from three literature cases of different scales.

QoS Routing Optimization Based on Deep Reinforcement Learning in SDN

To enhance the efficiency and expediency of issuing e-licenses within the power sector, we must confront thechallenge of managing the surging demand for data traffic. Within this realm, the network imposes stringentQuality of Service (QoS) requirements, revealing the inadequacies of traditional routing allocation mechanismsin accommodating such extensive data flows. In response to the imperative of handling a substantial influx of datarequests promptly and alleviating the constraints of existing technologies and network congestion, we present anarchitecture forQoS routing optimizationwith in SoftwareDefinedNetwork (SDN), leveraging deep reinforcementlearning. This innovative approach entails the separation of SDN control and transmission functionalities, centralizingcontrol over data forwardingwhile integrating deep reinforcement learning for informed routing decisions. Byfactoring in considerations such as delay, bandwidth, jitter rate, and packet loss rate, we design a reward function toguide theDeepDeterministic PolicyGradient (DDPG) algorithmin learning the optimal routing strategy to furnishsuperior QoS provision. In our empirical investigations, we juxtapose the performance of Deep ReinforcementLearning (DRL) against that of Shortest Path (SP) algorithms in terms of data packet transmission delay. Theexperimental simulation results show that our proposed algorithm has significant efficacy in reducing networkdelay and improving the overall transmission efficiency, which is superior to the traditional methods.

Double DQN Method For Botnet Traffic Detection System

In the face of the increasingly severe Botnet problem on the Internet,how to effectively detect Botnet traffic in realtime has become a critical problem.Although the existing deepQnetwork(DQN)algorithminDeep reinforcement learning can solve the problem of real-time updating,its prediction results are always higher than the actual results.In Botnet traffic detection,although it performs well in the training set,the accuracy rate of predicting traffic is as high as%;however,in the test set,its accuracy has declined,and it is impossible to adjust its prediction strategy on time based on new data samples.However,in the new dataset,its accuracy has declined significantly.Therefore,this paper proposes a Botnet traffic detection system based on double-layer DQN(DDQN).Two Q-values are designed to adjust the model in policy and action,respectively,to achieve real-time model updates and improve the universality and robustness of the model under different data sets.Experiments show that compared with the DQN model,when using DDQN,the Q-value is not too high,and the detectionmodel has improved the accuracy and precision of Botnet traffic.Moreover,when using Botnet data sets other than the test set,the accuracy and precision of theDDQNmodel are still higher than DQN.

Cognitive interference decision method for air defense missile fuze based on reinforcement learning

To solve the problem of the low interference success rate of air defense missile radio fuzes due to the unified interference form of the traditional fuze interference system,an interference decision method based Q-learning algorithm is proposed.First,dividing the distance between the missile and the target into multiple states to increase the quantity of state spaces.Second,a multidimensional motion space is utilized,and the search range of which changes with the distance of the projectile,to select parameters and minimize the amount of ineffective interference parameters.The interference effect is determined by detecting whether the fuze signal disappears.Finally,a weighted reward function is used to determine the reward value based on the range state,output power,and parameter quantity information of the interference form.The effectiveness of the proposed method in selecting the range of motion space parameters and designing the discrimination degree of the reward function has been verified through offline experiments involving full-range missile rendezvous.The optimal interference form for each distance state has been obtained.Compared with the single-interference decision method,the proposed decision method can effectively improve the success rate of interference.