Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for st...Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.展开更多
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-lea...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.展开更多
Post-disaster reconstruction is a topic of global concern,and traditional villages have special heritage attributes and need to face more requirements and obstacles in post-disaster reconstruction.This paper summarize...Post-disaster reconstruction is a topic of global concern,and traditional villages have special heritage attributes and need to face more requirements and obstacles in post-disaster reconstruction.This paper summarizes four concepts based on the research on post-disaster reconstruction both domestically and internationally,as well as the recovery and reconstruction of cultural heritage.Through a field survey of traditional villages in the Ms 6.8 Luding earthquake-stricken area,it is found that there are problems such as insufficient awareness of heritage value,misalignment of scientific reconstruction technology,and insufficient protection of reconstruction elements during the reconstruction process.Traditional villages face the risk of declining or even loss of heritage value.In order to effectively protect traditional villages and inherit the carrier of regional culture,four targeted reconstruction response strategies are proposed,i.e.,to"establish special planning for traditional village preservation","emphasize recovery of the authenticity of village heritage","ensure elements for village heritage recovery"and"promote the activation and utilization of village heritage",based on the problems discovered during the survey and the four concepts summarized in the research on post-disaster reconstruction of traditional villages.The research results hope to provide useful reference for ancient cultural areas affected by earthquakes on how to protect cultural heritage during the post-disaster reconstruction process.展开更多
The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attr...The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.展开更多
BACKGROUND The incidence and mortality rates of primary hepatocellular carcinoma(HCC)are high,and the conventional treatment is radiofrequency ablation(RFA)with transcatheter arterial chemoembolization(TACE);however,t...BACKGROUND The incidence and mortality rates of primary hepatocellular carcinoma(HCC)are high,and the conventional treatment is radiofrequency ablation(RFA)with transcatheter arterial chemoembolization(TACE);however,the 3-year survival rate is still low.Further,there are no visual methods to effectively predict their prognosis.AIM To explore the factors influencing the prognosis of HCC after RFA and TACE and develop a nomogram prediction model.METHODS Clinical and follow-up information of 150 patients with HCC treated using RFA and TACE in the Hangzhou Linping Hospital of Traditional Chinese Medicine from May 2020 to December 2022 was retrospectively collected and recorded.We examined their prognostic factors using multivariate logistic regression and created a nomogram prognosis prediction model using the R software(version 4.1.2).Internal verification was performed using the bootstrapping technique.The prognostic efficacy of the nomogram prediction model was evaluated using the concordance index(CI),calibration curve,and receiver operating characteristic RESULTS Of the 150 patients treated with RFA and TACE,92(61.33%)developed recurrence and metastasis.Logistic regression analysis identified six variables,and a predictive model was created.The internal validation results of the model showed a CI of 0.882.The correction curve trend of the prognosis prediction model was always near the diagonal,and the mean absolute error before and after internal validation was 0.021.The area under the curve of the prediction model after internal verification was 0.882[95%confidence interval(95%CI):0.820-0.945],with a specificity of 0.828 and sensitivity of 0.656.According to the Hosmer-Lemeshow test,χ^(2)=3.552 and P=0.895.The predictive model demonstrated a satisfactory calibration,and the decision curve analysis demonstrated its clinical applicability.CONCLUSION The prognosis of patients with HCC after RFA and TACE is affected by several factors.The developed prediction model based on the influencing parameters shows a good prognosis predictive efficacy.展开更多
Aiming at the triangular fuzzy(TF)multi-attribute decision making(MADM)problem with a preference for the distribution density of attribute(DDA),a decision making method with TF number two-dimensional density(TFTD)oper...Aiming at the triangular fuzzy(TF)multi-attribute decision making(MADM)problem with a preference for the distribution density of attribute(DDA),a decision making method with TF number two-dimensional density(TFTD)operator is proposed based on the density operator theory for the decision maker(DM).Firstly,a simple TF vector clustering method is proposed,which considers the feature of TF number and the geometric distance of vectors.Secondly,the least deviation sum of squares method is used in the program model to obtain the density weight vector.Then,two TFTD operators are defined,and the MADM method based on the TFTD operator is proposed.Finally,a numerical example is given to illustrate the superiority of this method,which can not only solve the TF MADM problem with a preference for the DDA but also help the DM make an overall comparison.展开更多
Maltagliati et al.1 recently highlighted the vital role of affective experiences in promoting physical activity(PA).The authors suggested that positive affective experiences,rather than health benefits,can tip the bal...Maltagliati et al.1 recently highlighted the vital role of affective experiences in promoting physical activity(PA).The authors suggested that positive affective experiences,rather than health benefits,can tip the balance in favor of PA over sedentary alternatives.The authors proposed a new formal decision model between PA and sedentary alternatives and reported that when health benefits are the unique reason to action,the costs of PA(e.g.,effort)and the subjective value(SV)of sedentary alternatives(V_(sed))are the main drivers of decision-making processes.展开更多
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...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.展开更多
Use of magnesium is the need of the hour due to its low density as well as its high strength-to-weight and stiffness-to-weight ratio etc.This study focuses on the effectiveness of liquid nitrogen(LN_(2))assisted cryog...Use of magnesium is the need of the hour due to its low density as well as its high strength-to-weight and stiffness-to-weight ratio etc.This study focuses on the effectiveness of liquid nitrogen(LN_(2))assisted cryogenic machining on the surface integrity(SI)characteristics of AZ91 magnesium alloy.Face milling using uncoated carbide inserts have been performed under liquid nitrogen(LN_(2))assisted cryogenic condition and compared with conventional(dry)milling.Experiments are performed using machining parameters in terms of cutting speeds of 325,475,625 m/min,feed rates of 0.05,0.1,0.15 mm/teeth and depth of cuts of 0.5,1,1.5 mm respectively.Most significant surface integrity characteristics such as surface roughness,microhardness,microstructure,and residual stresses have been investigated.Behaviour of SI characteristics with respect to milling parameters have been identified using statistical technique such as ANOVA and signal-to-noise(S/N)ratio plots.Additionally,the multi criteria decision making(MCDM)techniques such as additive ratio assessment method(ARAS)and complex proportional assessment(COPRAS)have been utilized to identify the optimal conditions for milling AZ91 magnesium alloy under both dry and cryogenic conditions.Use of LN_(2)during machining,resulted in reduction in machining temperature by upto 29%with a temperature drop from 251.2℃under dry condition to 178.5℃in cryogenic condition.Results showed the advantage of performing cryogenic milling in improving the surface integrity to a significant extent.Cryogenic machining considerably minimized the roughness by upto 28%and maximised the microhardness by upto 23%,when compared to dry machining.Cutting speed has caused significant impact on surface roughness(95.33%-dry,92.92%-cryogenic)and surface microhardness(80.33%-dry,82.15%-cryogenic).Due to the reduction in machining temperature,cryogenic condition resulted in compressive residual stresses(maximumσ║=-113 MPa)on the alloy surface.Results indicate no harm to alloy microstructure in both conditions,with no alterations to grain integrity and minimal reduction in the average grain sizes in the near machined area,when compared to before machined(base material)surface.The MCDM approach namely ARAS and COPRAS resulted in identical results,with the optimal condition being cutting speed of 625 m/min,a feed rate of 0.05 mm/teeth,and a depth of cut of 0.5 mm for both dry and cryogenic environments.展开更多
In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung n...In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this article.Adescription is provided of the scoring function,accuracy function and operational laws of the log q-rung VS.The algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming distance.Additionally,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify conceptualization.We chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile amnesia.Natural numbers q are used to express the results of the models.To demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.展开更多
Seismic engineering,a critical field with significant societal implications,often presents communication challenges due to the complexity of its concepts.This paper explores the role of Artificial Intelligence(AI),spe...Seismic engineering,a critical field with significant societal implications,often presents communication challenges due to the complexity of its concepts.This paper explores the role of Artificial Intelligence(AI),specifically OpenAI’s ChatGPT,in bridging these communication gaps.The study delves into how AI can simplify intricate seismic engineering terminologies and concepts,fostering enhanced understanding among students,professionals,and policymakers.It also presents several intuitive case studies to demonstrate the practical application of ChatGPT in seismic engineering.Further,the study contemplates the potential implications of AI,highlighting its potential to transform decision-making processes,augment education,and increase public engagement.While acknowledging the promising future of AI in seismic engineering,the study also considers the inherent challenges and limitations,including data privacy and potential oversimplification of content.It advocates for the collaborative efforts of AI researchers and seismic experts in overcoming these obstacles and enhancing the utility of AI in the field.This exploration provides an insightful perspective on the future of seismic engineering,which could be closely intertwined with the evolution of AI.展开更多
This work proposes a recorded recurrent twin delayed deep deterministic(RRTD3)policy gradient algorithm to solve the challenge of constructing guidance laws for intercepting endoatmospheric maneuvering missiles with u...This work proposes a recorded recurrent twin delayed deep deterministic(RRTD3)policy gradient algorithm to solve the challenge of constructing guidance laws for intercepting endoatmospheric maneuvering missiles with uncertainties and observation noise.The attack-defense engagement scenario is modeled as a partially observable Markov decision process(POMDP).Given the benefits of recurrent neural networks(RNNs)in processing sequence information,an RNN layer is incorporated into the agent’s policy network to alleviate the bottleneck of traditional deep reinforcement learning methods while dealing with POMDPs.The measurements from the interceptor’s seeker during each guidance cycle are combined into one sequence as the input to the policy network since the detection frequency of an interceptor is usually higher than its guidance frequency.During training,the hidden states of the RNN layer in the policy network are recorded to overcome the partially observable problem that this RNN layer causes inside the agent.The training curves show that the proposed RRTD3 successfully enhances data efficiency,training speed,and training stability.The test results confirm the advantages of the RRTD3-based guidance laws over some conventional guidance laws.展开更多
Edge caching has emerged as a promising application paradigm in 5G networks,and by building edge networks to cache content,it can alleviate the traffic load brought about by the rapid growth of Internet of Things(IoT)...Edge caching has emerged as a promising application paradigm in 5G networks,and by building edge networks to cache content,it can alleviate the traffic load brought about by the rapid growth of Internet of Things(IoT)services and applications.Due to the limitations of Edge Servers(ESs)and a large number of user demands,how to make the decision and utilize the resources of ESs are significant.In this paper,we aim to minimize the total system energy consumption in a heterogeneous network and formulate the content caching optimization problem as a Mixed Integer Non-Linear Programming(MINLP).To address the optimization problem,a Deep Q-Network(DQN)-based method is proposed to improve the overall performance of the system and reduce the backhaul traffic load.In addition,the DQN-based method can effectively solve the limitation of traditional reinforcement learning(RL)in complex scenarios.Simulation results show that the proposed DQN-based method can greatly outperform other benchmark methods,and significantly improve the cache hit rate and reduce the total system energy consumption in different scenarios.展开更多
A novel variable step-size modified super-exponential iteration(MSEI)decision feedback blind equalization(DFE)algorithm with second-order digital phase-locked loop is put forward to improve the convergence performance...A novel variable step-size modified super-exponential iteration(MSEI)decision feedback blind equalization(DFE)algorithm with second-order digital phase-locked loop is put forward to improve the convergence performance of super-exponential iteration DFE algorithm.Based on the MSEI-DFE algorithm,it is first proposed to develop an error function as an improvement to the error function of MSEI,which effectively achieves faster convergence speed of the algorithm.Subsequently,a hyperbolic tangent function variable step-size algorithm is developed considering the high variation rate of the hyperbolic tangent function around zero,so as to further improve the convergence speed of the algorithm.In the end,a second-order digital phase-locked loop is introduced into the decision feedback equalizer to track and compensate for the phase rotation of equalizer input signals.For the multipath underwater acoustic channel with mixed phase and phase rotation,quadrature phase shift keying(QPSK)and 16 quadrature amplitude modulation(16QAM)modulated signals are used in the computer simulation of the algorithm in terms of convergence and carrier recovery performance.The results show that the proposed algorithm can considerably improve convergence speed and steady-state error,make effective compensation for phase rotation,and efficiently facilitate carrier recovery.展开更多
Mountainous regions have disadvantages in economic development because of harsh physical and climatic conditions.However,winter tourism activities are one of the key components for supporting economic development in t...Mountainous regions have disadvantages in economic development because of harsh physical and climatic conditions.However,winter tourism activities are one of the key components for supporting economic development in the highlands.Establishing a ski resort area supports direct and indirect employment in a region,and it stops immigration from mountainous regions to other places.This research aimed to assess the potential ski areas using a multi criteria evaluation technique in the Van region which is located in the eastern part of Türkiye.In this context,snow cover duration,sun effect,slope,slope length,elevation,population density,distance from main roads and lake visibility were used as input factors in the decision making process.Each factor was standardized using a fuzzy technique based on existing well-known ski centers in Türkiye.The weight of inputs was defined by applying a survey to the professional skiers.The most important factors were detected as transportation opportunities and snow covers whereas,the least important factors were elevation and population density.Additionally,lake visibility was very important to make a difference from other existing facilities in the region.Therefore,it was included as constraints and lake visible areas were extracted at the final stage of the research.Potential ski areas were mapped in three levels as professional,intermediate and beginner skiers.One of the suitable areas was selected as a sample projection and for the 3D simulation of the ski investment area.Potential costs and benefits were discussed.It was found that a ski tourism area investment can be amortized in 3 years in the region.展开更多
The rapid development of Internet of Things(IoT)technology has led to a significant increase in the computational task load of Terminal Devices(TDs).TDs reduce response latency and energy consumption with the support ...The rapid development of Internet of Things(IoT)technology has led to a significant increase in the computational task load of Terminal Devices(TDs).TDs reduce response latency and energy consumption with the support of task-offloading in Multi-access Edge Computing(MEC).However,existing task-offloading optimization methods typically assume that MEC’s computing resources are unlimited,and there is a lack of research on the optimization of task-offloading when MEC resources are exhausted.In addition,existing solutions only decide whether to accept the offloaded task request based on the single decision result of the current time slot,but lack support for multiple retry in subsequent time slots.It is resulting in TD missing potential offloading opportunities in the future.To fill this gap,we propose a Two-Stage Offloading Decision-making Framework(TSODF)with request holding and dynamic eviction.Long Short-Term Memory(LSTM)-based task-offloading request prediction and MEC resource release estimation are integrated to infer the probability of a request being accepted in the subsequent time slot.The framework learns optimized decision-making experiences continuously to increase the success rate of task offloading based on deep learning technology.Simulation results show that TSODF reduces total TD’s energy consumption and delay for task execution and improves task offloading rate and system resource utilization compared to the benchmark method.展开更多
With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that consid...With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.展开更多
Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal ...Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper.The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated.We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes.展开更多
In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory...In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main f eatures of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.展开更多
基金supported by the National Nat-ural Science Foundation of China(No.52203376)the National Key Research and Development Program of China(No.2023YFB3813200).
文摘Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.
基金National Natural Science Foundation of China(61973037)National 173 Program Project(2019-JCJQ-ZD-324).
文摘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.
基金funded by the National Natural Science Foundation of China under the project“Research on Urban Spatial Coupling Mechanism Between Urban Epidemic Spreading and Vulnerability and Planning Response in Chengdu-Chongqing Area”(Grant No.52078423)the Major Program of Sichuan Provincial Scientific Research under the Project“Research and Demonstration of Resilient Collaborative Planning and Design for Park Cities”(Grant No.2020YFS0054)the Sichuan Provincial Science and Technology Innovation Platform and Talent Plan"Research on the Construction and Development Strategies of Several Major Infrastructure Systems for New Smart Cities"(Grant No.2022JDR0356).
文摘Post-disaster reconstruction is a topic of global concern,and traditional villages have special heritage attributes and need to face more requirements and obstacles in post-disaster reconstruction.This paper summarizes four concepts based on the research on post-disaster reconstruction both domestically and internationally,as well as the recovery and reconstruction of cultural heritage.Through a field survey of traditional villages in the Ms 6.8 Luding earthquake-stricken area,it is found that there are problems such as insufficient awareness of heritage value,misalignment of scientific reconstruction technology,and insufficient protection of reconstruction elements during the reconstruction process.Traditional villages face the risk of declining or even loss of heritage value.In order to effectively protect traditional villages and inherit the carrier of regional culture,four targeted reconstruction response strategies are proposed,i.e.,to"establish special planning for traditional village preservation","emphasize recovery of the authenticity of village heritage","ensure elements for village heritage recovery"and"promote the activation and utilization of village heritage",based on the problems discovered during the survey and the four concepts summarized in the research on post-disaster reconstruction of traditional villages.The research results hope to provide useful reference for ancient cultural areas affected by earthquakes on how to protect cultural heritage during the post-disaster reconstruction process.
基金Anhui Province Natural Science Research Project of Colleges and Universities(2023AH040321)Excellent Scientific Research and Innovation Team of Anhui Colleges(2022AH010098).
文摘The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.
文摘BACKGROUND The incidence and mortality rates of primary hepatocellular carcinoma(HCC)are high,and the conventional treatment is radiofrequency ablation(RFA)with transcatheter arterial chemoembolization(TACE);however,the 3-year survival rate is still low.Further,there are no visual methods to effectively predict their prognosis.AIM To explore the factors influencing the prognosis of HCC after RFA and TACE and develop a nomogram prediction model.METHODS Clinical and follow-up information of 150 patients with HCC treated using RFA and TACE in the Hangzhou Linping Hospital of Traditional Chinese Medicine from May 2020 to December 2022 was retrospectively collected and recorded.We examined their prognostic factors using multivariate logistic regression and created a nomogram prognosis prediction model using the R software(version 4.1.2).Internal verification was performed using the bootstrapping technique.The prognostic efficacy of the nomogram prediction model was evaluated using the concordance index(CI),calibration curve,and receiver operating characteristic RESULTS Of the 150 patients treated with RFA and TACE,92(61.33%)developed recurrence and metastasis.Logistic regression analysis identified six variables,and a predictive model was created.The internal validation results of the model showed a CI of 0.882.The correction curve trend of the prognosis prediction model was always near the diagonal,and the mean absolute error before and after internal validation was 0.021.The area under the curve of the prediction model after internal verification was 0.882[95%confidence interval(95%CI):0.820-0.945],with a specificity of 0.828 and sensitivity of 0.656.According to the Hosmer-Lemeshow test,χ^(2)=3.552 and P=0.895.The predictive model demonstrated a satisfactory calibration,and the decision curve analysis demonstrated its clinical applicability.CONCLUSION The prognosis of patients with HCC after RFA and TACE is affected by several factors.The developed prediction model based on the influencing parameters shows a good prognosis predictive efficacy.
基金supported by the Natural Science Foundation of Hunan Province(2023JJ50047,2023JJ40306)the Research Foundation of Education Bureau of Hunan Province(23A0494,20B260)the Key R&D Projects of Hunan Province(2019SK2331)。
文摘Aiming at the triangular fuzzy(TF)multi-attribute decision making(MADM)problem with a preference for the distribution density of attribute(DDA),a decision making method with TF number two-dimensional density(TFTD)operator is proposed based on the density operator theory for the decision maker(DM).Firstly,a simple TF vector clustering method is proposed,which considers the feature of TF number and the geometric distance of vectors.Secondly,the least deviation sum of squares method is used in the program model to obtain the density weight vector.Then,two TFTD operators are defined,and the MADM method based on the TFTD operator is proposed.Finally,a numerical example is given to illustrate the superiority of this method,which can not only solve the TF MADM problem with a preference for the DDA but also help the DM make an overall comparison.
文摘Maltagliati et al.1 recently highlighted the vital role of affective experiences in promoting physical activity(PA).The authors suggested that positive affective experiences,rather than health benefits,can tip the balance in favor of PA over sedentary alternatives.The authors proposed a new formal decision model between PA and sedentary alternatives and reported that when health benefits are the unique reason to action,the costs of PA(e.g.,effort)and the subjective value(SV)of sedentary alternatives(V_(sed))are the main drivers of decision-making processes.
基金supported in part by the National Key Laboratory of Air-based Information Perception and Fusion and the Aeronautical Science Foundation of China (Grant No. 20220001068001)National Natural Science Foundation of China (Grant No.61673327)+1 种基金Natural Science Basic Research Plan in Shaanxi Province,China (Grant No. 2023-JC-QN-0733)China IndustryUniversity-Research Innovation Foundation (Grant No. 2022IT188)。
文摘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.
基金supported by the ARDB,DRDO,New Delhi[Sanction Code:MSRB/TM/ARDB/GIA/19-20/044].
文摘Use of magnesium is the need of the hour due to its low density as well as its high strength-to-weight and stiffness-to-weight ratio etc.This study focuses on the effectiveness of liquid nitrogen(LN_(2))assisted cryogenic machining on the surface integrity(SI)characteristics of AZ91 magnesium alloy.Face milling using uncoated carbide inserts have been performed under liquid nitrogen(LN_(2))assisted cryogenic condition and compared with conventional(dry)milling.Experiments are performed using machining parameters in terms of cutting speeds of 325,475,625 m/min,feed rates of 0.05,0.1,0.15 mm/teeth and depth of cuts of 0.5,1,1.5 mm respectively.Most significant surface integrity characteristics such as surface roughness,microhardness,microstructure,and residual stresses have been investigated.Behaviour of SI characteristics with respect to milling parameters have been identified using statistical technique such as ANOVA and signal-to-noise(S/N)ratio plots.Additionally,the multi criteria decision making(MCDM)techniques such as additive ratio assessment method(ARAS)and complex proportional assessment(COPRAS)have been utilized to identify the optimal conditions for milling AZ91 magnesium alloy under both dry and cryogenic conditions.Use of LN_(2)during machining,resulted in reduction in machining temperature by upto 29%with a temperature drop from 251.2℃under dry condition to 178.5℃in cryogenic condition.Results showed the advantage of performing cryogenic milling in improving the surface integrity to a significant extent.Cryogenic machining considerably minimized the roughness by upto 28%and maximised the microhardness by upto 23%,when compared to dry machining.Cutting speed has caused significant impact on surface roughness(95.33%-dry,92.92%-cryogenic)and surface microhardness(80.33%-dry,82.15%-cryogenic).Due to the reduction in machining temperature,cryogenic condition resulted in compressive residual stresses(maximumσ║=-113 MPa)on the alloy surface.Results indicate no harm to alloy microstructure in both conditions,with no alterations to grain integrity and minimal reduction in the average grain sizes in the near machined area,when compared to before machined(base material)surface.The MCDM approach namely ARAS and COPRAS resulted in identical results,with the optimal condition being cutting speed of 625 m/min,a feed rate of 0.05 mm/teeth,and a depth of cut of 0.5 mm for both dry and cryogenic environments.
基金supported by the National Research Foundation of Korea(NRF)Grant funded by the Korea government(MSIT)(No.RS-2023-00218176)Korea Institute for Advancement of Technology(KIAT)Grant funded by the Korea government(MOTIE)(P0012724)The Competency Development Program for Industry Specialist)and the Soonchunhyang University Research Fund.
文摘In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this article.Adescription is provided of the scoring function,accuracy function and operational laws of the log q-rung VS.The algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming distance.Additionally,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify conceptualization.We chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile amnesia.Natural numbers q are used to express the results of the models.To demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.
文摘Seismic engineering,a critical field with significant societal implications,often presents communication challenges due to the complexity of its concepts.This paper explores the role of Artificial Intelligence(AI),specifically OpenAI’s ChatGPT,in bridging these communication gaps.The study delves into how AI can simplify intricate seismic engineering terminologies and concepts,fostering enhanced understanding among students,professionals,and policymakers.It also presents several intuitive case studies to demonstrate the practical application of ChatGPT in seismic engineering.Further,the study contemplates the potential implications of AI,highlighting its potential to transform decision-making processes,augment education,and increase public engagement.While acknowledging the promising future of AI in seismic engineering,the study also considers the inherent challenges and limitations,including data privacy and potential oversimplification of content.It advocates for the collaborative efforts of AI researchers and seismic experts in overcoming these obstacles and enhancing the utility of AI in the field.This exploration provides an insightful perspective on the future of seismic engineering,which could be closely intertwined with the evolution of AI.
基金supported by the National Natural Science Foundation of China(Grant No.12072090)。
文摘This work proposes a recorded recurrent twin delayed deep deterministic(RRTD3)policy gradient algorithm to solve the challenge of constructing guidance laws for intercepting endoatmospheric maneuvering missiles with uncertainties and observation noise.The attack-defense engagement scenario is modeled as a partially observable Markov decision process(POMDP).Given the benefits of recurrent neural networks(RNNs)in processing sequence information,an RNN layer is incorporated into the agent’s policy network to alleviate the bottleneck of traditional deep reinforcement learning methods while dealing with POMDPs.The measurements from the interceptor’s seeker during each guidance cycle are combined into one sequence as the input to the policy network since the detection frequency of an interceptor is usually higher than its guidance frequency.During training,the hidden states of the RNN layer in the policy network are recorded to overcome the partially observable problem that this RNN layer causes inside the agent.The training curves show that the proposed RRTD3 successfully enhances data efficiency,training speed,and training stability.The test results confirm the advantages of the RRTD3-based guidance laws over some conventional guidance laws.
基金supported in part by the National Natural Science Foundation of China under Grant 62172255in part by the Outstanding Youth Program of Hubei Natural Science Foundation under Grant 2022CFA080the Wuhan AI Innovation Program(2022010702040056)。
文摘Edge caching has emerged as a promising application paradigm in 5G networks,and by building edge networks to cache content,it can alleviate the traffic load brought about by the rapid growth of Internet of Things(IoT)services and applications.Due to the limitations of Edge Servers(ESs)and a large number of user demands,how to make the decision and utilize the resources of ESs are significant.In this paper,we aim to minimize the total system energy consumption in a heterogeneous network and formulate the content caching optimization problem as a Mixed Integer Non-Linear Programming(MINLP).To address the optimization problem,a Deep Q-Network(DQN)-based method is proposed to improve the overall performance of the system and reduce the backhaul traffic load.In addition,the DQN-based method can effectively solve the limitation of traditional reinforcement learning(RL)in complex scenarios.Simulation results show that the proposed DQN-based method can greatly outperform other benchmark methods,and significantly improve the cache hit rate and reduce the total system energy consumption in different scenarios.
基金supported by the National Natural Science Foundation of China(61671461)。
文摘A novel variable step-size modified super-exponential iteration(MSEI)decision feedback blind equalization(DFE)algorithm with second-order digital phase-locked loop is put forward to improve the convergence performance of super-exponential iteration DFE algorithm.Based on the MSEI-DFE algorithm,it is first proposed to develop an error function as an improvement to the error function of MSEI,which effectively achieves faster convergence speed of the algorithm.Subsequently,a hyperbolic tangent function variable step-size algorithm is developed considering the high variation rate of the hyperbolic tangent function around zero,so as to further improve the convergence speed of the algorithm.In the end,a second-order digital phase-locked loop is introduced into the decision feedback equalizer to track and compensate for the phase rotation of equalizer input signals.For the multipath underwater acoustic channel with mixed phase and phase rotation,quadrature phase shift keying(QPSK)and 16 quadrature amplitude modulation(16QAM)modulated signals are used in the computer simulation of the algorithm in terms of convergence and carrier recovery performance.The results show that the proposed algorithm can considerably improve convergence speed and steady-state error,make effective compensation for phase rotation,and efficiently facilitate carrier recovery.
文摘Mountainous regions have disadvantages in economic development because of harsh physical and climatic conditions.However,winter tourism activities are one of the key components for supporting economic development in the highlands.Establishing a ski resort area supports direct and indirect employment in a region,and it stops immigration from mountainous regions to other places.This research aimed to assess the potential ski areas using a multi criteria evaluation technique in the Van region which is located in the eastern part of Türkiye.In this context,snow cover duration,sun effect,slope,slope length,elevation,population density,distance from main roads and lake visibility were used as input factors in the decision making process.Each factor was standardized using a fuzzy technique based on existing well-known ski centers in Türkiye.The weight of inputs was defined by applying a survey to the professional skiers.The most important factors were detected as transportation opportunities and snow covers whereas,the least important factors were elevation and population density.Additionally,lake visibility was very important to make a difference from other existing facilities in the region.Therefore,it was included as constraints and lake visible areas were extracted at the final stage of the research.Potential ski areas were mapped in three levels as professional,intermediate and beginner skiers.One of the suitable areas was selected as a sample projection and for the 3D simulation of the ski investment area.Potential costs and benefits were discussed.It was found that a ski tourism area investment can be amortized in 3 years in the region.
文摘The rapid development of Internet of Things(IoT)technology has led to a significant increase in the computational task load of Terminal Devices(TDs).TDs reduce response latency and energy consumption with the support of task-offloading in Multi-access Edge Computing(MEC).However,existing task-offloading optimization methods typically assume that MEC’s computing resources are unlimited,and there is a lack of research on the optimization of task-offloading when MEC resources are exhausted.In addition,existing solutions only decide whether to accept the offloaded task request based on the single decision result of the current time slot,but lack support for multiple retry in subsequent time slots.It is resulting in TD missing potential offloading opportunities in the future.To fill this gap,we propose a Two-Stage Offloading Decision-making Framework(TSODF)with request holding and dynamic eviction.Long Short-Term Memory(LSTM)-based task-offloading request prediction and MEC resource release estimation are integrated to infer the probability of a request being accepted in the subsequent time slot.The framework learns optimized decision-making experiences continuously to increase the success rate of task offloading based on deep learning technology.Simulation results show that TSODF reduces total TD’s energy consumption and delay for task execution and improves task offloading rate and system resource utilization compared to the benchmark method.
基金The work was supported by Humanities and Social Sciences Fund of the Ministry of Education(No.22YJA630119)the National Natural Science Foundation of China(No.71971051)Natural Science Foundation of Hebei Province(No.G2021501004).
文摘With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.
基金supported by National Key Research and Development Program of China(2018YFC1504502).
文摘Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper.The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated.We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes.
文摘In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main f eatures of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.
