Artificial Intelligence Prediction of One-Part Geopolymer Compressive Strength for Sustainable Concrete

Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological concrete.In terms of performance,fly ash and slag are preferredmaterials for precursors for developing a one-part geopolymer.However,determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely reported.Therefore,in this study,machine learning methods such as artificial neural networks(ANN)and gene expression programming(GEP)models were developed usingMATLAB and GeneXprotools,respectively,for the prediction of compressive strength under variable input materials and content for fly ash and slag-based one-part geopolymer.The database for this study contains 171 points extracted from literature with input parameters:fly ash concentration,slag content,calcium hydroxide content,sodium oxide dose,water binder ratio,and curing temperature.The performance of the two models was evaluated under various statistical indices,namely correlation coefficient(R),mean absolute error(MAE),and rootmean square error(RMSE).In terms of the strength prediction efficacy of a one-part geopolymer,ANN outperformed GEP.Sensitivity and parametric analysis were also performed to identify the significant contributor to strength.According to a sensitivity analysis,the activator and slag contents had the most effects on the compressive strength at 28 days.The water binder ratio was shown to be directly connected to activator percentage,slag percentage,and calcium hydroxide percentage and inversely related to compressive strength at 28 days and curing temperature.

Optimization of Coronavirus Pandemic Model Through Artificial Intelligence

Artificial intelligence is demonstrated by machines,unlike the natural intelligence displayed by animals,including humans.Artificial intelligence research has been defined as the field of study of intelligent agents,which refers to any system that perceives its environment and takes actions that maximize its chance of achieving its goals.The techniques of intelligent computing solve many applications of mathematical modeling.The researchworkwas designed via a particularmethod of artificial neural networks to solve the mathematical model of coronavirus.The representation of the mathematical model is made via systems of nonlinear ordinary differential equations.These differential equations are established by collecting the susceptible,the exposed,the symptomatic,super spreaders,infection with asymptomatic,hospitalized,recovery,and fatality classes.The generation of the coronavirus model’s dataset is exploited by the strength of the explicit Runge Kutta method for different countries like India,Pakistan,Italy,and many more.The generated dataset is approximately used for training,validation,and testing processes for each cyclic update in Bayesian Regularization Backpropagation for the numerical treatment of the dynamics of the desired model.The performance and effectiveness of the designed methodology are checked through mean squared error,error histograms,numerical solutions,absolute error,and regression analysis.

Trustworthy semi-supervised anomaly detection for online‐tooffline logistics business in merchant identification

The rise of online-to-offline(O2O)e-commerce business has brought tremendous opportunities to the logistics industry.In the online-to-offline logistics business,it is essential to detect anomaly merchants with fraudulent shipping behaviours,such as sending other merchants'packages for profit with their low discounts.This can help reduce the financial losses of platforms and ensure a healthy environment.Existing anomaly detection studies have mainly focused on online fraud behaviour detection,such as fraudulent purchase and comment behaviours in e-commerce.However,these methods are not suitable for anomaly merchant detection in logistics due to the more complex online and offline operation of package-sending behaviours and the interpretable requirements of offline deployment in logistics.MultiDet,a semi-supervised multiview fusion-based Anomaly Detection framework in online-to-offline logistics is proposed,which consists of a basic version SemiDet and an attention-enhanced multi-view fusion model.In SemiDet,pair-wise data augmentation is first conducted to promote model robustness and address the challenge of limited labelled anomaly instances.Then,SemiDet calculates the anomaly scoring of each merchant with an auto-encoder framework.Considering the multi-relationships among logistics merchants,a multi-view attention fusion-based anomaly detection network is further designed to capture merchants'mutual influences and improve the anomaly merchant detection performance.A post-hoc perturbation-based interpretation model is designed to output the importance of different views and ensure the trustworthiness of end-to-end anomaly detection.The framework based on an eight-month real-world dataset collected from one of the largest logistics platforms in China is evaluated,involving 6128 merchants and 16 million historical order consignor records in Beijing.Experimental results show that the proposed model outperforms other baselines in both AUC-ROC and AUC-PR metrics.

A topic-controllable keywords-to-text generator with knowledge base network

With the introduction of more recent deep learning models such as encoder-decoder,text generation frameworks have gained a lot of popularity.In Natural Language Generation(NLG),controlling the information and style of the output produced is a crucial and challenging task.The purpose of this paper is to develop informative and controllable text using social media language by incorporating topic knowledge into a keyword-to-text framework.A novel Topic-Controllable Key-to-Text(TC-K2T)generator that focuses on the issues of ignoring unordered keywords and utilising subject-controlled information from previous research is presented.TC-K2T is built on the framework of conditional language encoders.In order to guide the model to produce an informative and controllable language,the generator first inputs unordered keywords and uses subjects to simulate prior human knowledge.Using an additional probability term,the model in-creases the likelihood of topic words appearing in the generated text to bias the overall distribution.The proposed TC-K2T can produce more informative and controllable senescence,outperforming state-of-the-art models,according to empirical research on automatic evaluation metrics and human annotations.

Mutual information oriented deep skill chaining for multi‐agent reinforcement learning

Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual agents.However,in high‐dimensional continuous spaces,the non‐stationary environment can provide outdated experiences that hinder convergence,resulting in ineffective training performance for multi‐agent systems.To tackle this issue,a novel reinforcement learning scheme,Mutual Information Oriented Deep Skill Chaining(MioDSC),is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration efficiency.These rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment state.In addition,MioDSC can generate cooperative policies using the options framework,allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent learning.MioDSC was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty levels.The experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability.

Rockburst prediction using artificial intelligence techniques:A review

Rockburst is a phenomenon where sudden,catastrophic failure of the rock mass occurs in underground deep regions or areas with high tectonic stress during the excavation process.Rockburst disasters endanger the safety of people's lives and property,national energy security,and social interests,so it is very important to accurately predict rockburst.Traditional rockburst prediction has not been able to find an effective prediction method,and the study of the rockburst mechanism is facing a dilemma.With the development of artificial intelligence(AI)techniques in recent years,more and more experts and scholars have begun to introduce AI techniques into the study of the rockburst mechanism.In previous research,several scholars have attempted to summarize the application of AI techniques in rockburst prediction.However,these studies either are not specifically focused on reviews of the application of AI techniques in rockburst prediction,or they do not provide a comprehensive overview.Drawing on the advantages of extensive interdisciplinary research and a deep understanding of AI techniques,this paper conducts a comprehensive review of rockburst prediction methods leveraging AI tech-niques.Firstly,pertinent definitions of rockburst and its associated hazards are introduced.Subsequently,the applications of both traditional prediction methods and those rooted in AI techniques for rockburst prediction are summarized,with emphasis placed on the respective advantages and disadvantages of each approach.Finally,the strengths and weaknesses of prediction methods leveraging AI are summarized,alongside forecasting future research trends to address existing challenges,while simultaneously proposing directions for improvement to advance the field and meet emerging demands effectively.

Forecasting Monsoon Precipitation Using Artificial Neural Networks

This paper explores the application of Artificial Intelligent (AI) techniques for climate forecast. It presents a study on modelling the monsoon precipitation forecast by means of Artificial Neural Networks (ANNs). Using the historical data of the total amount of summer rainfall over the Delta Area of Yangtze River in China, three ANNs models have been developed to forecast the monsoon precipitation in the corresponding area one year, five-year, and ten-year forward respectively. Performances of the models have been validated using a 'new' data set that has not been exposed to the models during the processes of model development and test. The experiment results are promising, indicating that the proposed ANNs models have good quality in terms of the accuracy, stability and generalisation ability.

Manoeuvre decision-making of unmanned aerial vehicles in air combat based on an expert actor-based soft actor critic algorithm

The demand for autonomous motion control of unmanned aerial vehicles in air combat is boosted as taking the initiative in combat appears more and more crucial.Unmanned aerial vehicles inability to manoeuvre autonomously during air combat that features highly dynamic and uncertain manoeuvres of the enemy;however,limits their combat capabilities,which proves to be very challenging.To meet the challenge,this article proposes an autonomous manoeuvre decision model using an expert actor-based soft actor critic algorithm that reconstructs empirical replay buffer with expert experience.Specifically,the algorithm uses a small amount of expert experience to increase the diversity of the samples,which can largely improve the exploration and utilisation efficiency of deep reinforcement learning.And to simulate the complex battlefield environment,a one-toone air combat model is established and the concept of missile's attack region is introduced.The model enables the one-to-one air combat to be simulated under different initial battlefield situations.Simulation results show that the expert actor-based soft actor critic algorithm can find the most favourable policy for unmanned aerial vehicles to defeat the opponent faster,and converge more quickly,compared with the soft actor critic algorithm.

D^(2)PAM:Epileptic seizures prediction using adversarial deep dual patch attention mechanism

Epilepsy is considered as a serious brain disorder in which patients frequently experience seizures.The seizures are defined as the unexpected electrical changes in brain neural activity,which leads to unconsciousness.Existing researches made an intense effort for predicting the epileptic seizures using brain signal data.However,they faced difficulty in obtaining the patients'characteristics because the model's distribution turned to fake predictions,affecting the model's reliability.In addition,the existing prediction models have severe issues,such as overfitting and false positive rates.To overcome these existing issues,we propose a deep learning approach known as Deep dual‐patch attention mechanism(D^(2)PAM)for classifying the pre‐ictal signals of people with Epilepsy based on the brain signals.Deep neural network is integrated with D^(2)PAM,and it lowers the effect of differences between patients to predict ES.The multi‐network design enhances the trained model's generalisability and stability efficiently.Also,the proposed model for processing the brain signal is designed to transform the signals into data blocks,which is appropriate for pre‐ictal classification.The earlier warning of epilepsy with the proposed model obtains the auxiliary diagnosis.The data of real patients for the experiments provides the improved accuracy by D2PAM approximation compared to the existing techniques.To be more distinctive,the authors have analysed the performance of their work with five patients,and the accuracy comes out to be 95%,97%,99%,99%,and 99%respectively.Overall,the numerical results unveil that the proposed work outperforms the existing models.

Optimal fusion‐based localization method for tracking of smartphone user in tall complex buildings

In the event of a fire breaking out or in other complicated situations,a mobile computing solution combining the Internet of Things and wearable devices can actually assist tracking solutions for rescuing and evacuating people in multistory structures.Thus,it is crucial to increase the positioning technology's accuracy.The sequential Monte Carlo(SMC)approach is used in various applications such as target tracking and intelligent surveillance,which rely on smartphone‐based inertial data sequences.However,the SMC method has intrinsic flaws,such as sample impoverishment and particle degeneracy.A novel SMC approach is presented,which is built on the weighted differential evolution(WDE)algorithm.Sequential Monte Carlo approaches start with random particle placements and arrives at the desired distribution with a slower variance reduction,like in a high‐dimensional space,such as a multistory structure.Weighted differential evolution is included before the resampling procedure to guarantee the appropriate variety of the particle set,prevent the usage of an inadequate number of valid samples,and preserve smartphone user position accuracy.The values of the smartphone‐based sensors and BLE‐beacons are set as input to the SMC,which aids in fast approximating the posterior distributions,to speed up the particle congregation process in the proposed SMC‐based WDE approach.Lastly,the robustness and efficacy of the suggested technique more accurately reflect the actual situation of smartphone users.According to simulation findings,the suggested approach provides improved location estimation with reduced localization error and quick convergence.The results confirm that the proposed optimal fusion‐based SMC‐WDE scheme performs 9.92%better in terms of MAPE,15.24%for the case of MAE,and 0.031%when evaluating based on the R2 Score.

Online location of seismic damage to a water distribution system

As one of the most important urban lifeline systems,a water distribution system can be damaged under a strong earthquake,and the damage cannot easily be located,especially immediately after the event.This often causes tremendous difficulties to post-earthquake emergency response and recovery activities.This paper proposes a methodology to locate seismic damage to a water distribution system by monitoring water head online at some nodes in the water distribution system.An artificial neural network-based inverse analysis method is developed to estimate the water head variations at all nodes that are not monitored based on the water head variations at the nodes that are monitored.The methodology provides a quick,effective,and practical way to locate seismic damage to a water distribution system.

Parallel Optimization of Program Instructions Using Genetic Algorithms

This paper describes an efficient solution to parallelize softwareprogram instructions, regardless of the programming language in which theyare written. We solve the problem of the optimal distribution of a set ofinstructions on available processors. We propose a genetic algorithm to parallelize computations, using evolution to search the solution space. The stagesof our proposed genetic algorithm are: The choice of the initial populationand its representation in chromosomes, the crossover, and the mutation operations customized to the problem being dealt with. In this paper, geneticalgorithms are applied to the entire search space of the parallelization ofthe program instructions problem. This problem is NP-complete, so thereare no polynomial algorithms that can scan the solution space and solve theproblem. The genetic algorithm-based method is general and it is simple andefficient to implement because it can be scaled to a larger or smaller number ofinstructions that must be parallelized. The parallelization technique proposedin this paper was developed in the C# programming language, and our resultsconfirm the effectiveness of our parallelization method. Experimental resultsobtained and presented for different working scenarios confirm the theoreticalresults, and they provide insight on how to improve the exploration of a searchspace that is too large to be searched exhaustively.

Optimized Generative Adversarial Networks for Adversarial Sample Generation

Detecting the anomalous entity in real-time network traffic is a popular area of research in recent times.Very few researches have focused on creating malware that fools the intrusion detection system and this paper focuses on this topic.We are using Deep Convolutional Generative Adversarial Networks(DCGAN)to trick the malware classifier to believe it is a normal entity.In this work,a new dataset is created to fool the Artificial Intelligence(AI)based malware detectors,and it consists of different types of attacks such as Denial of Service(DoS),scan 11,scan 44,botnet,spam,User Datagram Portal(UDP)scan,and ssh scan.The discriminator used in the DCGAN discriminates two different attack classes(anomaly and synthetic)and one normal class.The model collapse,instability,and vanishing gradient issues associated with the DCGAN are overcome using the proposed hybrid Aquila optimizer-based Mine blast harmony search algorithm(AO-MBHS).This algorithm helps the generator to create realistic malware samples to be undetected by the discriminator.The performance of the proposed methodology is evaluated using different performance metrics such as training time,detection rate,F-Score,loss function,Accuracy,False alarm rate,etc.The superiority of the hybrid AO-MBHS based DCGAN model is noticed when the detection rate is changed to 0 after the retraining method to make the defensive technique hard to be noticed by the malware detection system.The support vector machines(SVM)is used as the malicious traffic detection application and its True positive rate(TPR)goes from 80%to 0%after retraining the proposed model which shows the efficiency of the proposed model in hiding the samples.

Smart optimization in battery energy storage systems:An overview

The increasing drive towards eco-friendly environment motivates the generation of energy from renewable energy sources (RESs). The rising share of RESs in power generation poses potential challenges, including uncertainties in generation output, frequency fluctuations, and insufficient voltage regulation capabilities. As a solution to these challenges, energy storage systems (ESSs) play a crucial role in storing and releasing power as needed. Battery energy storage systems (BESSs) provide significant potential to maximize the energy efficiency of a distribution network and the benefits of different stakeholders. This can be achieved through optimizing placement, sizing, charge/discharge scheduling, and control, all of which contribute to enhancing the overall performance of the network. In this paper, we provide a comprehensive overview of BESS operation, optimization, and modeling in different applications, and how mathematical and artificial intelligence (AI)-based optimization techniques contribute to BESS charging and discharging scheduling. We also discuss some potential future opportunities and challenges of the BESS operation, AI in BESSs, and how emerging technologies, such as internet of things, AI, and big data impact the development of BESSs.

Artificial intelligence powered large-scale renewable integrations in multi-energy systems for carbon neutrality transition: Challenges and future perspectives

The vigorous expansion of renewable energy as a substitute for fossil energy is the predominant route of action to achieve worldwide carbon neutrality. However, clean energy supplies in multi-energy building districts are still at the preliminary stages for energy paradigm transitions. In particular, technologies and methodologies for large-scale renewable energy integrations are still not sufficiently sophisticated, in terms of intelligent control management. Artificial intelligent (AI) techniques powered renewable energy systems can learn from bioinspired lessons and provide power systems with intelligence. However, there are few in-depth dissections and deliberations on the roles of AI techniques for large-scale integrations of renewable energy and decarbonisation in multi-energy systems. This study summarizes the commonly used AI-related approaches and discusses their functional advantages when being applied in various renewable energy sectors, as well as their functional contribution to optimizing the operational control modalities of renewable energy and improving the overall operational effectiveness. This study also presents practical applications of various AI techniques in large-scale renewable energy integration systems, and analyzes their effectiveness through theoretical explanations and diverse case studies. In addition, this study introduces limitations and challenges associated with the large-scale renewable energy integrations for carbon neutrality transition using relevant AI techniques, and proposes further promising research perspectives and recommendations. This comprehensive review ignites advanced AI techniques for large-scale renewable integrations and provides valuable informational instructions and guidelines to different stakeholders (e.g., engineers, designers and scientists) for carbon neutrality transition.

Haar wavelet discretization method for free vibration study of laminated composite beam under generalized boundary conditions

Investigation on vibration of laminated composite beam(LCB)is an important issue owing to its wide use as fundamental component.In the present work,we study the free vibration of arbitrarily LCB with generalized elastic boundary condition(BC)by using Haar wavelet discretization method(HWDM).Timoshenko beam theory is utilized to model the free vibration of LCB.The LCB is first split into several segments,and then the displacement for each segment is obtained from the Haar wavelet series and their integral.Hamilton’s principle is applied to construct governing equations and the artificial spring boundary technique is adopted to obtain the general elastic boundary and continuity conditions at two ends of LCB.The vibration characteristics of beam with different fiber orientations and lamina numbers is investigated and its displacements are compared with those in previous works.Numerical results are shown graphically and demonstrate the validation of our method.