Stress-assisted corrosion mechanism of 3Ni steel by using gradient boosting decision tree machining learning method

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.

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.

Attribute Reduction Method Based on Sequential Three-Branch Decision Model

Attribute reduction is a research hotspot in rough set theory. Traditional heuristic attribute reduction methods add the most important attribute to the decision attribute set each time, resulting in multiple redundant attribute calculations, high time consumption, and low reduction efficiency. In this paper, based on the idea of sequential three-branch decision classification domain, attributes are treated as objects of three-branch division, and attributes are divided into core attributes, relatively necessary attributes, and unnecessary attributes using attribute importance and thresholds. Core attributes are added to the decision attribute set, unnecessary attributes are rejected from being added, and relatively necessary attributes are repeatedly divided until the reduction result is obtained. Experiments were conducted on 8 groups of UCI datasets, and the results show that, compared to traditional reduction methods, the method proposed in this paper can effectively reduce time consumption while ensuring classification performance.

Robustα-Fe_(2)O_(3)/Epoxy Resin Superhydrophobic Coatings for Anti-icing Property

α-Fe_(2)O_(3)/epoxy resin composite superhydrophobic coating was prepared withα-Fe_(2)O_(3) nanoparticles and epoxy resin by spin coating method.The coating without epoxy resin has higher contact angle(CA)and lower ice adhesion strength(IAS),but the mechanical properties are poor.Theα-Fe_(2)O_(3)/epoxy resin composite superhydrophobic coating exhibits good mechanical durability.In addition,compared with the bare aluminum substrate,the Ecorr of the composite coating is positive and the Jcorr is lower.The inhibition efficiency of the composite coating is as high as 99.98%in 3.5 wt%NaCl solution.The difference in the microstructure caused by the two preparation methods leads to the changes in mechanical properties and corrosion resistance of composite superhydrophobic coating.

Method for triangular fuzzy multiple attribute decision making based on two-dimensional density operator method

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.

Robustness Study and Superior Method Development and Validation for Analytical Assay Method of Atropine Sulfate in Pharmaceutical Ophthalmic Solution

Background: The robustness is a measurement of an analytical chemical method and its ability to contain unaffected by little with deliberate variation of analytical chemical method parameters. The analytical chemical method variation parameters are based on pH variability of buffer solution of mobile phase, organic ratio composition changes, stationary phase (column) manufacture, brand name and lot number variation;flow rate variation and temperature variation of chromatographic system. The analytical chemical method for assay of Atropine Sulfate conducted for robustness evaluation. The typical variation considered for mobile phase organic ratio change, change of pH, change of temperature, change of flow rate, change of column etc. Purpose: The aim of this study is to develop a cost effective, short run time and robust analytical chemical method for the assay quantification of Atropine in Pharmaceutical Ophthalmic Solution. This will help to make analytical decisions quickly for research and development scientists as well as will help with quality control product release for patient consumption. This analytical method will help to meet the market demand through quick quality control test of Atropine Ophthalmic Solution and it is very easy for maintaining (GDP) good documentation practices within the shortest period of time. Method: HPLC method has been selected for developing superior method to Compendial method. Both the compendial HPLC method and developed HPLC method was run into the same HPLC system to prove the superiority of developed method. Sensitivity, precision, reproducibility, accuracy parameters were considered for superiority of method. Mobile phase ratio change, pH of buffer solution, change of stationary phase temperature, change of flow rate and change of column were taken into consideration for robustness study of the developed method. Results: The limit of quantitation (LOQ) of developed method was much low than the compendial method. The % RSD for the six sample assay of developed method was 0.4% where the % RSD of the compendial method was 1.2%. The reproducibility between two analysts was 100.4% for developed method on the contrary the compendial method was 98.4%.

Robust Information Hiding Based on Neural Style Transfer with Artificial Intelligence

This paper proposes an artificial intelligence-based robust information hiding algorithm to address the issue of confidential information being susceptible to noise attacks during transmission.The algorithm we designed aims to mitigate the impact of various noise attacks on the integrity of secret information during transmission.The method we propose involves encoding secret images into stylized encrypted images and applies adversarial transfer to both the style and content features of the original and embedded data.This process effectively enhances the concealment and imperceptibility of confidential information,thereby improving the security of such information during transmission and reducing security risks.Furthermore,we have designed a specialized attack layer to simulate real-world attacks and common noise scenarios encountered in practical environments.Through adversarial training,the algorithm is strengthened to enhance its resilience against attacks and overall robustness,ensuring better protection against potential threats.Experimental results demonstrate that our proposed algorithm successfully enhances the concealment and unknowability of secret information while maintaining embedding capacity.Additionally,it ensures the quality and fidelity of the stego image.The method we propose not only improves the security and robustness of information hiding technology but also holds practical application value in protecting sensitive data and ensuring the invisibility of confidential information.

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.

Gates joint locally connected network for accurate and robust reconstruction in optical molecular tomography

Optical molecular tomography(OMT)is a potential pre-clinical molecular imaging technique with applications in a variety of biomedical areas,which can provide non-invasive quantitative three-dimensional(3D)information regarding tumor distribution in living animals.The construction of optical transmission models and the application of reconstruction algorithms in traditional model-based reconstruction processes have affected the reconstruction results,resulting in problems such as low accuracy,poor robustness,and long-time consumption.Here,a gates joint locally connected network(GLCN)method is proposed by establishing the mapping relationship between the inside source distribution and the photon density on surface directly,thus avoiding the extra time consumption caused by iteration and the reconstruction errors caused by model inaccuracy.Moreover,gates module was composed of the concatenation and multiplication operators of three different gates.It was embedded into the network aiming at remembering input surface photon density over a period and allowing the network to capture neurons connected to the true source selectively by controlling three different gates.To evaluate the performance of the proposed method,numerical simulations were conducted,whose results demonstrated good performance in terms of reconstruction positioning accuracy and robustness.

Image Hiding with High Robustness Based on Dynamic Region Attention in the Wavelet Domain

Hidden capacity,concealment,security,and robustness are essential indicators of hiding algorithms.Currently,hiding algorithms tend to focus on algorithmic capacity,concealment,and security but often overlook the robustness of the algorithms.In practical applications,the container can suffer from damage caused by noise,cropping,and other attacks during transmission,resulting in challenging or even impossible complete recovery of the secret image.An image hiding algorithm based on dynamic region attention in the multi-scale wavelet domain is proposed to address this issue and enhance the robustness of hiding algorithms.In this proposed algorithm,a secret image of size 256×256 is first decomposed using an eight-level Haar wavelet transform.The wavelet transform generates one coefficient in the approximation component and twenty-four detail bands,which are then embedded into the carrier image via a hiding network.During the recovery process,the container image is divided into four non-overlapping parts,each employed to reconstruct a low-resolution secret image.These lowresolution secret images are combined using densemodules to obtain a high-quality secret image.The experimental results showed that even under destructive attacks on the container image,the proposed algorithm is successful in recovering a high-quality secret image,indicating that the algorithm exhibits a high degree of robustness against various attacks.The proposed algorithm effectively addresses the robustness issue by incorporating both spatial and channel attention mechanisms in the multi-scale wavelet domain,making it suitable for practical applications.In conclusion,the image hiding algorithm introduced in this study offers significant improvements in robustness compared to existing algorithms.Its ability to recover high-quality secret images even in the presence of destructive attacksmakes it an attractive option for various applications.Further research and experimentation can explore the algorithm’s performance under different scenarios and expand its potential applications.

Physics-Constrained Robustness Enhancement for Tree Ensembles Applied in Smart Grid

With the widespread use of machine learning(ML)technology,the operational efficiency and responsiveness of power grids have been significantly enhanced,allowing smart grids to achieve high levels of automation and intelligence.However,tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks,making it urgent to enhance their robustness.To address this,we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree ensembles.Our algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws,ensuring training data accurately reflects possible attack scenarios while adhering to physical rules.In our experiments,the proposed method increased robustness against adversarial attacks by 100%when applied to real grid data under physical constraints.These results highlight the advantages of our method in maintaining efficient and secure operation of smart grids under adversarial conditions.

Robust optical mode converter based on topological waveguide arrays

Optical mode converters are essential for enhancing the capacity of optical communication systems. However, fabrication errors restrict the further improvement of conventional mode converters. To address this challenge, we have designed an on-chip TE0–TE1mode converter based on topologically protected waveguide arrays. The simulation results demonstrate that the converter exhibits a mode coupling efficiency of 93.5% near 1550 nm and can tolerate a relative fabrication error of 30%. Our design approach can be extended to enhance the robustness for other integrated photonic devices, beneficial for future development of optical network systems.

Constructive Robust Steganography Algorithm Based on Style Transfer

Traditional information hiding techniques achieve information hiding by modifying carrier data,which can easily leave detectable traces that may be detected by steganalysis tools.Especially in image transmission,both geometric and non-geometric attacks can cause subtle changes in the pixels of the image during transmission.To overcome these challenges,we propose a constructive robust image steganography technique based on style transformation.Unlike traditional steganography,our algorithm does not involve any direct modifications to the carrier data.In this study,we constructed a mapping dictionary by setting the correspondence between binary codes and image categories and then used the mapping dictionary to map secret information to secret images.Through image semantic segmentation and style transfer techniques,we combined the style of secret images with the content of public images to generate stego images.This type of stego image can resist interference during public channel transmission,ensuring the secure transmission of information.At the receiving end,we input the stego image into a trained secret image reconstruction network,which can effectively reconstruct the original secret image and further recover the secret information through a mapping dictionary to ensure the security,accuracy,and efficient decoding of the information.The experimental results show that this constructive information hiding method based on style transfer improves the security of information hiding,enhances the robustness of the algorithm to various attacks,and ensures information security.

Tjong:A transformer‐based Mahjong AI via hierarchical decision‐making and fan backward

Mahjong,a complex game with hidden information and sparse rewards,poses significant challenges.Existing Mahjong AIs require substantial hardware resources and extensive datasets to enhance AI capabilities.The authors propose a transformer‐based Mahjong AI(Tjong)via hierarchical decision‐making.By utilising self‐attention mechanisms,Tjong effectively captures tile patterns and game dynamics,and it decouples the decision pro-cess into two distinct stages:action decision and tile decision.This design reduces de-cision complexity considerably.Additionally,a fan backward technique is proposed to address the sparse rewards by allocating reversed rewards for actions based on winning hands.Tjong consists of 15M parameters and is trained using approximately 0.5 M data over 7 days of supervised learning on a single server with 2 GPUs.The action decision achieved an accuracy of 94.63%,while the claim decision attained 98.55%and the discard decision reached 81.51%.In a tournament format,Tjong outperformed AIs(CNN,MLP,RNN,ResNet,VIT),achieving scores up to 230%higher than its opponents.Further-more,after 3 days of reinforcement learning training,it ranked within the top 1%on the leaderboard on the Botzone platform.

Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

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.

Nonlinear robust adaptive control for bidirectional stabilization system of all-electric tank with unknown actuator backlash compensation and disturbance estimation

Since backlash nonlinearity is inevitably existing in actuators for bidirectional stabilization system of allelectric tank,it behaves more drastically in high maneuvering environments.In this work,the accurate tracking control for bidirectional stabilization system of moving all-electric tank with actuator backlash and unmodeled disturbance is solved.By utilizing the smooth adaptive backlash inverse model,a nonlinear robust adaptive feedback control scheme is presented.The unknown parameters and unmodelled disturbance are addressed separately through the derived parametric adaptive function and the continuous nonlinear robust term.Because the unknown backlash parameters are updated via adaptive function and the backlash effect can be suppressed successfully by inverse operation,which ensures the system stability.Meanwhile,the system disturbance in the high maneuverable environment can be estimated with the constructed adaptive law online improving the engineering practicality.Finally,Lyapunov-based analysis proves that the developed controller can ensure the tracking error asymptotically converges to zero even with unmodeled disturbance and unknown actuator backlash.Contrast co-simulations and experiments illustrate the advantages of the proposed approach.

Constructing high-toughness polyimide binder with robust polarity and ion-conductive mechanisms ensuring long-term operational stability of silicon-based anodes

Silicon-based materials have demonstrated remarkable potential in high-energy-density batteries owing to their high theoretical capacity.However,the significant volume expansion of silicon seriously hinders its utilization as a lithium-ion anode.Herein,a functionalized high-toughness polyimide(PDMI) is synthesized by copolymerizing the 4,4'-Oxydiphthalic anhydride(ODPA) with 4,4'-oxydianiline(ODA),2,3-diaminobenzoic acid(DABA),and 1,3-bis(3-aminopropyl)-tetramethyl disiloxane(DMS).The combination of rigid benzene rings and flexible oxygen groups(-O-) in the PDMI molecular chain via a rigidness/softness coupling mechanism contributes to high toughness.The plentiful polar carboxyl(-COOH) groups establish robust bonding strength.Rapid ionic transport is achieved by incorporating the flexible siloxane segment(Si-O-Si),which imparts high molecular chain motility and augments free volume holes to facilitate lithium-ion transport(9.8 × 10^(-10) cm^(2) s^(-1) vs.16 × 10^(-10) cm^(2) s~(-1)).As expected,the SiO_x@PDMI-1.5 electrode delivers brilliant long-term cycle performance with a remarkable capacity retention of 85% over 500 cycles at 1.3 A g^(-1).The well-designed functionalized polyimide also significantly enhances the electrochemical properties of Si nanoparticles electrode.Meanwhile,the assembled SiO_x@PDMI-1.5/NCM811 full cell delivers a high retention of 80% after 100 cycles.The perspective of the binder design strategy based on polyimide modification delivers a novel path toward high-capacity electrodes for high-energy-density batteries.

Data-driven Wasserstein distributionally robust chance-constrained optimization for crude oil scheduling under uncertainty

Crude oil scheduling optimization is an effective method to enhance the economic benefits of oil refining.But uncertainties,including uncertain demands of crude distillation units(CDUs),might make the production plans made by the traditional deterministic optimization models infeasible.A data-driven Wasserstein distributionally robust chance-constrained(WDRCC)optimization approach is proposed in this paper to deal with demand uncertainty in crude oil scheduling.First,a new deterministic crude oil scheduling optimization model is developed as the basis of this approach.The Wasserstein distance is then used to build ambiguity sets from historical data to describe the possible realizations of probability distributions of uncertain demands.A cross-validation method is advanced to choose suitable radii for these ambiguity sets.The deterministic model is reformulated as a WDRCC optimization model for crude oil scheduling to guarantee the demand constraints hold with a desired high probability even in the worst situation in ambiguity sets.The proposed WDRCC model is transferred into an equivalent conditional value-at-risk representation and further derived as a mixed-integer nonlinear programming counterpart.Industrial case studies from a real-world refinery are conducted to show the effectiveness of the proposed method.Out-of-sample tests demonstrate that the solution of the WDRCC model is more robust than those of the deterministic model and the chance-constrained model.

MarkINeRV: A Robust Watermarking Scheme for Neural Representation for Videos Based on Invertible Neural Networks

Recent research advances in implicit neural representation have shown that a wide range of video data distributions are achieved by sharing model weights for Neural Representation for Videos(NeRV).While explicit methods exist for accurately embedding ownership or copyright information in video data,the nascent NeRV framework has yet to address this issue comprehensively.In response,this paper introduces MarkINeRV,a scheme designed to embed watermarking information into video frames using an invertible neural network watermarking approach to protect the copyright of NeRV,which models the embedding and extraction of watermarks as a pair of inverse processes of a reversible network and employs the same network to achieve embedding and extraction of watermarks.It is just that the information flow is in the opposite direction.Additionally,a video frame quality enhancement module is incorporated to mitigate watermarking information losses in the rendering process and the possibility ofmalicious attacks during transmission,ensuring the accurate extraction of watermarking information through the invertible network’s inverse process.This paper evaluates the accuracy,robustness,and invisibility of MarkINeRV through multiple video datasets.The results demonstrate its efficacy in extracting watermarking information for copyright protection of NeRV.MarkINeRV represents a pioneering investigation into copyright issues surrounding NeRV.

Hierarchical Optimization Method for Federated Learning with Feature Alignment and Decision Fusion

In the realm of data privacy protection,federated learning aims to collaboratively train a global model.However,heterogeneous data between clients presents challenges,often resulting in slow convergence and inadequate accuracy of the global model.Utilizing shared feature representations alongside customized classifiers for individual clients emerges as a promising personalized solution.Nonetheless,previous research has frequently neglected the integration of global knowledge into local representation learning and the synergy between global and local classifiers,thereby limiting model performance.To tackle these issues,this study proposes a hierarchical optimization method for federated learning with feature alignment and the fusion of classification decisions(FedFCD).FedFCD regularizes the relationship between global and local feature representations to achieve alignment and incorporates decision information from the global classifier,facilitating the late fusion of decision outputs from both global and local classifiers.Additionally,FedFCD employs a hierarchical optimization strategy to flexibly optimize model parameters.Through experiments on the Fashion-MNIST,CIFAR-10 and CIFAR-100 datasets,we demonstrate the effectiveness and superiority of FedFCD.For instance,on the CIFAR-100 dataset,FedFCD exhibited a significant improvement in average test accuracy by 6.83%compared to four outstanding personalized federated learning approaches.Furthermore,extended experiments confirm the robustness of FedFCD across various hyperparameter values.