Target distribution in cooperative combat based on Bayesian optimization algorithm

Target distribution in cooperative combat is a difficult and emphases. We build up the optimization model according to the rule of fire distribution. We have researched on the optimization model with BOA. The BOA can estimate the joint probability distribution of the variables with Bayesian network, and the new candidate solutions also can be generated by the joint distribution. The simulation example verified that the method could be used to solve the complex question, the operation was quickly and the solution was best.

Web multimedia information retrieval using improved Bayesian algorithm

The main thrust of this paper is application of a novel data mining approach on the log of user' s feedback to improve web multimedia information retrieval performance. A user space model was constructed based on data mining, and then integrated into the original information space model to improve the accuracy of the new information space model. It can remove clutter and irrelevant text information and help to eliminate mismatch between the page author' s expression and the user' s understanding and expectation. User spacemodel was also utilized to discover the relationship between high-level and low-level features for assigning weight. The authors proposed improved Bayesian algorithm for data mining. Experiment proved that the au-thors' proposed algorithm was efficient.

Well production optimization using streamline features-based objective function and Bayesian adaptive direct search algorithm

Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development.

Coordinated Bayesian optimal approach for the integrated decision between electronic countermeasure and firepower attack

The coordinated Bayesian optimization algorithm（CBOA） is proposed according to the characteristics of the function independence,conformity and supplementary between the electronic countermeasure（ECM） and the firepower attack systems.The selection criteria are combinations of probabilities of individual fitness and coordinated degree and can select choiceness individual to construct Bayesian network that manifest population evolution by producing the new chromosome.Thus the CBOA cannot only guarantee the effective pattern coordinated decision-making mechanism between the populations,but also maintain the population multiplicity,and enhance the algorithm performance.The simulation result confirms the algorithm validity.

Multi-sources information fusion algorithm in airborne detection systems

To aim at the multimode character of the data from the airplane detecting system, the paper combines Dempster- Shafer evidence theory and subjective Bayesian algorithm and makes to propose a mixed structure multimode data fusion algorithm. The algorithm adopts a prorated algorithm relate to the incertitude evaluation to convert the probability evaluation into the precognition probability in an identity frame, and ensures the adaptability of different data from different source to the mixed system. To guarantee real time fusion, a combination of time domain fusion and space domain fusion is established, this not only assure the fusion of data chain in different time of the same sensor, but also the data fusion from different sensors distributed in different platforms and the data fusion among different modes. The feasibility and practicability are approved through computer simulation.

Within-Project and Cross-Project Software Defect Prediction Based on Improved Transfer Naive Bayes Algorithm

With the continuous expansion of software scale,software update and maintenance have become more and more important.However,frequent software code updates will make the software more likely to introduce new defects.So how to predict the defects quickly and accurately on the software change has become an important problem for software developers.Current defect prediction methods often cannot reflect the feature information of the defect comprehensively,and the detection effect is not ideal enough.Therefore,we propose a novel defect prediction model named ITNB(Improved Transfer Naive Bayes)based on improved transfer Naive Bayesian algorithm in this paper,which mainly considers the following two aspects:(1)Considering that the edge data of the test set may affect the similarity calculation and final prediction result,we remove the edge data of the test set when calculating the data similarity between the training set and the test set;(2)Considering that each feature dimension has different effects on defect prediction,we construct the calculation formula of training data weight based on feature dimension weight and data gravity,and then calculate the prior probability and the conditional probability of training data from the weight information,so as to construct the weighted bayesian classifier for software defect prediction.To evaluate the performance of the ITNB model,we use six datasets from large open source projects,namely Bugzilla,Columba,Mozilla,JDT,Platform and PostgreSQL.We compare the ITNB model with the transfer Naive Bayesian(TNB)model.The experimental results show that our ITNB model can achieve better results than the TNB model in terms of accurary,precision and pd for within-project and cross-project defect prediction.

NARX neural network approach for the monthly prediction of groundwater levels in Sylhet Sadar, Bangladesh

Groundwater is important for managing the water supply in agricultural countries like Bangladesh. Therefore, the ability to predict the changes of groundwater level is necessary for jointly planning the uses of groundwater resources. In this study, a new nonlinear autoregressive with exogenous inputs(NARX) network has been applied to simulate monthly groundwater levels in a well of Sylhet Sadar at a local scale. The Levenberg-Marquardt(LM) and Bayesian Regularization(BR) algorithms were used to train the NARX network, and the results were compared to determine the best architecture for predicting monthly groundwater levels over time. The comparison between LM and BR showed that NARX-BR has advantages over predicting monthly levels based on the Mean Squared Error(MSE), coefficient of determination(R^2), and Nash-Sutcliffe coefficient of efficiency(NSE). The results show that BR is the most accurate method for predicting groundwater levels with an error of ± 0.35 m. This method is applied to the management of irrigation water source, which provides important information for the prediction of local groundwater fluctuation at local level during a short period.

Artificial neural network based production forecasting for a hydrocarbon reservoir under water injection

As the conventional prediction methods for production of waterflooding reservoirs have some drawbacks, a production forecasting model based on artificial neural network was proposed, the simulation process by this method was presented, and some examples were illustrated. A workflow that involves a physics-based extraction of features was proposed for fluid production forecasting to improve the prediction effect. The Bayesian regularization algorithm was selected as the training algorithm of the model. This algorithm, although taking longer time, can better generalize oil, gas and water production data sets. The model was evaluated by calculating mean square error and determination coefficient, drawing error distribution histogram and the cross-plot between simulation data and verification data etc. The model structure was trained, validated and tested with 90% of the historical data, and blindly evaluated using the remaining. The predictive model consumes minimal information and computational cost and is capable of predicting fluid production rate with a coefficient of determination of more than 0.9, which has the simulation results consistent with the practical data.

Data-driven production optimization using particle swarm algorithm based on the ensemble-learning proxy model

Production optimization is of significance for carbonate reservoirs,directly affecting the sustainability and profitability of reservoir development.Traditional physics-based numerical simulations suffer from insufficient calculation accuracy and excessive time consumption when performing production optimization.We establish an ensemble proxy-model-assisted optimization framework combining the Bayesian random forest(BRF)with the particle swarm optimization algorithm(PSO).The BRF method is implemented to construct a proxy model of the injectioneproduction system that can accurately predict the dynamic parameters of producers based on injection data and production measures.With the help of proxy model,PSO is applied to search the optimal injection pattern integrating Pareto front analysis.After experimental testing,the proxy model not only boasts higher prediction accuracy compared to deep learning,but it also requires 8 times less time for training.In addition,the injection mode adjusted by the PSO algorithm can effectively reduce the gaseoil ratio and increase the oil production by more than 10% for carbonate reservoirs.The proposed proxy-model-assisted optimization protocol brings new perspectives on the multi-objective optimization problems in the petroleum industry,which can provide more options for the project decision-makers to balance the oil production and the gaseoil ratio considering physical and operational constraints.

Analysis and Prediction of New Media Information Dissemination of Police Microblog

This paper aims to analyze the microblog data published by the official account in a certain province of China,and finds out the rule of Weibo that is easier to be forwarded in the new police media perspective.In this paper,a new topic-based model is proposed.Firstly,the LDA topic clustering algorithm is used to extract the topic categories with forwarding heat from the microblogs with high forwarding numbers,then the Naive Bayesian algorithm is used to topic categories.The sample data is processed to predict the type of microblog forwarding.In order to evaluate this method,a large number of microblog online data is used to analysis.The experimental results show that the proposed method can accurately predict the forwarding of Weibo.

An Attempt to Analyze a Human Nervous System Algorithm for Sensing Earthquake Precursors

We statistically validate the 2011-2022 earthquake prediction records of Ada, the sixth finalist of the 2nd China AETA in 2021, who made 147 earthquake predictions (including 60% of magnitude 5.5 earthquakes) with a prediction accuracy higher than 70% and a confidence level of 95% over a 12-year period. Since the reliable earthquake precursor signals described by Ada and the characteristics of Alfvén waves match quite well, this paper proposes a hypothesis on how earthquakes are triggered based on the Alfvén (Q G) torsional wave model of Gillette et al. When the plume of the upper mantle column intrudes into the magma and lithosphere of the soft flow layer during the exchange of hot and cold molten material masses deep inside the Earth’s interior during ascent and descent, it is possible to form body and surface plasma sheets under certain conditions to form Alfven nonlinear isolated waves, and Alfven waves often perturb the geomagnetic field, releasing huge heat and kinetic energy thus triggering earthquakes. To explain the complex phenomenon of how Ada senses Alvfen waves and how to locate epicenters, we venture to speculate that special magnetosensory cells in a few human bodies can sense earthquake precursors and attempt to hypothesize an algorithm that analyzes how the human biological nervous system encodes and decodes earthquake precursors and explains how human magnetosensory cells can solve complex problems such as predicting earthquake magnitude and locating epicenters.

Recursive Bayesian Algorithm for Identification of Systems with Non-uniformly Sampled Input Data

To identify systems with non-uniformly sampled input data, a recursive Bayesian identification algorithm with covariance resetting is proposed. Using estimated noise transfer function as a dynamic filter, the system with colored noise is transformed into the system with white noise. In order to improve estimates, the estimated noise variance is employed as a weighting factor in the algorithm. Meanwhile, a modified covariance resetting method is also integrated in the proposed algorithm to increase the convergence rate. A numerical example and an industrial example validate the proposed algorithm.

Air Combat Assignment Problem Based on Bayesian Optimization Algorithm

In order to adapt to the changing battlefield situation and improve the combat effectiveness of air combat,the problem of air battle allocation based on Bayesian optimization algorithm(BOA)is studied.First,we discuss the number of fighters on both sides,and apply cluster analysis to divide our fighter into the same number of groups as the enemy.On this basis,we sort each of our fighters'different advantages to the enemy fighters,and obtain a series of target allocation schemes for enemy attacks by first in first serviced criteria.Finally,the maximum advantage function is used as the target,and the BOA is used to optimize the model.The simulation results show that the established model has certain decision-making ability,and the BOA can converge to the global optimal solution at a faster speed,which can effectively solve the air combat task assignment problem.

Exploiting Bivariate Dependencies to Speedup Structure Learning in Bayesian Optimization Algorithm

Bayesian optimization algorithm （BOA） is one of the successful and widely used estimation of distribution algorithms （EDAs） which have been employed to solve different optimization problems. In EDAs, a model is learned from the selected population that encodes interactions among problem variables. New individuals are generated by sampling the model and incorporated into the population. Different probabilistic models have been used in EDAs to learn interactions. Bayesian network （BN） is a well-known graphical model which is used in BOA. Learning a propel model in EDAs and particularly in BOA is distinguished as a computationally expensive task. Different methods have been proposed in the literature to improve the complexity of model building in EDAs. This paper employs bivariate dependencies to learn accurate BNs in BOA efficiently. The proposed approach extracts the bivariate dependencies using an appropriate pairwise interaction-detection metric. Due to the static structure of the underlying problems, these dependencies are used in each generation of BOA to learn an accurate network. By using this approach, the computational cost of model building is reduced dramatically. Various optimization problems are selected to be solved by the algorithm. The experimental results show that the proposed approach successfully finds the optimum in problems with different types of interactions efficiently. Significant speedups are observed in the model building procedure as well.

Extensive game analysis and improvement strategy of DPOS consensus mechanism

Delegated proof-of-stake(DPOS) consensus mechanism is widely adopted in blockchain platforms, but problems exist in its current applications. In order to explore the security risks in the voting attack of the DPOS consensus mechanism, an extensive game model between nodes was constructed, and it was concluded that the DPOS consensus mechanism relies too much on tokens, and the possibility of node attacks is very high. In order to solve the problems of frequent changes of DPOS consensus mechanism nodes, inactive node voting, excessive reliance on tokens, and malicious nodes, a dynamic, credible, and attack-evading DPOS consensus mechanism was proposed. In addition, the Python simulation results show that the improved Bayesian voting algorithm is effective in calculating node scores.

Multi-objective optimization of environmental tax for mitigating air pollution and greenhouse gas

Government macro-control through various policies is an important way to mitigate air pollution and greenhouse gases.Therefore,environmental tax is used worldwide as an important measure.However,few studies have considered the interaction between carbon and environmental protection taxes.Additionally,different sectors differ in their energy structure,pollution emission intensity,and economic status,and previous studies rarely proposed differentiated environmental tax rates based at the sectoral level.A model framework combining the computable general equilibrium(CGE)model and Bayesian optimization(BO)algorithm is proposed to maximize GDP,meet environmental planning objectives,and explore the optimal environmental taxation scheme to realize the multi-objective optimization of the economy and environment.Meanwhile,this study compares the different impact mechanisms of environmental protection tax and carbon tax.It discusses the impacts of differentiated environmental tax rates in different sectors on the environment and economy.For example,the results show that the coordinated implementation of environmental protection and carbon tax policies and the sectoral differentiated environmental tax rates in China could better balance economic development and environmental governance.Additionally,the optimal taxation scheme could mitigate air pollution and greenhouse gases,promote economic growth,and realize sustainable economic and environmental development.Furthermore,the optimized taxation scheme positively affects the energy and industrial structures.

Web-Based Biomedical Literature Mining

With an upsurge in biomedical literature,using data-mining method to search new knowledge from literature has drawing more attention of scholars.In this study,taking the mining of non-coding gene literature from the network database of PubMed as an example,we first preprocessed the abstract data,next applied the term occurrence frequency(TF) and inverse document frequency(IDF)(TF-IDF) method to select features,and then established a biomedical literature data-mining model based on Bayesian algorithm.Finally,we assessed the model through area under the receiver operating characteristic curve(AUC),accuracy,specificity,sensitivity,precision rate and recall rate.When 1 000 features are selected,AUC,specificity,sensitivity,accuracy rate,precision rate and recall rate are 0.868 3,84.63%,89.02%,86.83%,89.02% and 98.14%,respectively.These results indicate that our method can identify the targeted literature related to a particular topic effectively.

Adaptive Electric Load Forecaster

In this paper, a methodology, Self-Developing and Self-Adaptive Fuzzy Neural Networks using Type-2 Fuzzy Bayesian Ying-Yang Learning （SDSA-FNN-T2FBYYL） algorithm and multi-objective optimization is proposed. The features of this methodology are as follows： （1） A Bayesian Ying-Yang Learning （BYYL） algorithm is used to construct a compact but high-performance system automatically. （2） A novel multi-objective T2FBYYL is presented that integrates the T2 fuzzy theory with BYYL to automatically construct its best structure and better tackle various data uncertainty problems simultaneously. （3） The weighted sum multi-objective optimization technique with combinations of different weightings is implemented to achieve the best trade-off among multiple objectives in the T2FBYYL. The proposed methods are applied to electric load forecast using a real operational dataset collected from Macao electric utility. The test results reveal that the proposed method is superior to other existing relevant techniques.