Dendritic Cell Algorithm with Bayesian Optimization Hyperband for Signal Fusion

The dendritic cell algorithm(DCA)is an excellent prototype for developing Machine Learning inspired by the function of the powerful natural immune system.Too many parameters increase complexity and lead to plenty of criticism in the signal fusion procedure of DCA.The loss function of DCA is ambiguous due to its complexity.To reduce the uncertainty,several researchers simplified the algorithm program;some introduced gradient descent to optimize parameters;some utilized searching methods to find the optimal parameter combination.However,these studies are either time-consuming or need to be revised in the case of non-convex functions.To overcome the problems,this study models the parameter optimization into a black-box optimization problem without knowing the information about its loss function.This study hybridizes bayesian optimization hyperband(BOHB)with DCA to propose a novel DCA version,BHDCA,for accomplishing parameter optimization in the signal fusion process.The BHDCA utilizes the bayesian optimization(BO)of BOHB to find promising parameter configurations and applies the hyperband of BOHB to allocate the suitable budget for each potential configuration.The experimental results show that the proposed algorithm has significant advantages over the otherDCAexpansion algorithms in terms of signal fusion.

Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study

Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams.One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance.In this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms.Our framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over time.We use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian networks.With regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC algorithm.By doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky dangers.Additionally,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost relevance.Our results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning attacks.Additionally,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.

A Bayesian Mixture Model Approach to Disparity Testing

The topic of this article is one-sided hypothesis testing for disparity, i.e., the mean of one group is larger than that of another when there is uncertainty as to which group a datum is drawn. For each datum, the uncertainty is captured with a given discrete probability distribution over the groups. Such situations arise, for example, in the use of Bayesian imputation methods to assess race and ethnicity disparities with certain insurance, health, and financial data. A widely used method to implement this assessment is the Bayesian Improved Surname Geocoding (BISG) method which assigns a discrete probability over six race/ethnicity groups to an individual given the individual’s surname and address location. Using a Bayesian framework and Markov Chain Monte Carlo sampling from the joint posterior distribution of the group means, the probability of a disparity hypothesis is estimated. Four methods are developed and compared with an illustrative data set. Three of these methods are implemented in an R-code and one method in WinBUGS. These methods are programed for any number of groups between two and six inclusive. All the codes are provided in the appendices.

基于Bayesian-Stacking模型的电影票房预测

本文构建了一种基于XGBoost的特征选取方法以及Bayesian-Stacking集成算法的票房预测模型。首先,构建XGBoost的影响力测量模型进行变量筛选,能够简化后期模型的输入和提高模型特征变量的可解释性;其次,分别构建了BP神经网络、XGBoost、Logistic Regression、LightGBM、GBDT以及Stacking模型,再利用贝叶斯优化算法实现上述模型超参数全局寻优后,对电影票房进行预测;最后,引入评价指标进行分析。结果表明:1)将贝叶斯优化算法与模型相结合,获得了相对于原模型更高的预测精度;2)Bayesian-Stacking模型的电影票房预测精度均优于其他模型。Bayesian-Stacking模型在电影上映期间预测最终票房具有较高的参考价值,可为有关部门提供决策参考。

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.

Reconstruction of Gene Regulatory Networks Based on Two-Stage Bayesian Network Structure Learning Algorithm

In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system,and it has been a challenging task in bioinformatics.The Bayesian network model has been used in reconstructing the gene regulatory network for its advantages,but how to determine the network structure and parameters is still important to be explored.This paper proposes a two-stage structure learning algorithm which integrates immune evolution algorithm to build a Bayesian network.The new algorithm is evaluated with the use of both simulated and yeast cell cycle data.The experimental results indicate that the proposed algorithm can find many of the known real regulatory relationships from literature and predict the others unknown with high validity and accuracy.

Learning Bayesian networks using genetic algorithm

A new method to evaluate the fitness of the Bayesian networks according to the observed data is provided. The main advantage of this criterion is that it is suitable for both the complete and incomplete cases while the others not. Moreover it facilitates the computation greatly. In order to reduce the search space, the notation of equivalent class proposed by David Chickering is adopted. Instead of using the method directly, the novel criterion, variable ordering, and equivalent class are combined,moreover the proposed mthod avoids some problems caused by the previous one. Later, the genetic algorithm which allows global convergence, lack in the most of the methods searching for Bayesian network is applied to search for a good model in thisspace. To speed up the convergence, the genetic algorithm is combined with the greedy algorithm. Finally, the simulation shows the validity of the proposed approach.

Learning Bayesian network structure with immune algorithm

Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network （BN）, which is also necessary in promoting the application of BN. This pa- per proposes an immune algorithm based method （BN-IA） for the learning of the BN structure with the idea of vaccination. Further- more, the methods on how to extract the effective vaccines from local optimal structure and root nodes are also described in details. Finally, the simulation studies are implemented with the helicopter convertor BN model and the car start BN model. The comparison results show that the proposed vaccines and the BN-IA can learn the BN structure effectively and efficiently.

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.

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.

Self-Organizing Genetic Algorithm Based Method for Constructing Bayesian Networks from Databases

The typical characteristic of the topology of Bayesian networks (BNs) is the interdependence among different nodes (variables), which makes it impossible to optimize one variable independently of others, and the learning of BNs structures by general genetic algorithms is liable to converge to local extremum. To resolve efficiently this problem, a self-organizing genetic algorithm (SGA) based method for constructing BNs from databases is presented. This method makes use of a self-organizing mechanism to develop a genetic algorithm that extended the crossover operator from one to two, providing mutual competition between them, even adjusting the numbers of parents in recombination (crossover/recomposition) schemes. With the K2 algorithm, this method also optimizes the genetic operators, and utilizes adequately the domain knowledge. As a result, with this method it is able to find a global optimum of the topology of BNs, avoiding premature convergence to local extremum. The experimental results proved to be and the convergence of the SGA was discussed.

Building Bayesian Network(BN)-Based System Reliability Model by Dual Genetic Algorithm(DGA)

A system reliability model based on Bayesian network(BN)is built via an evolutionary strategy called dual genetic algorithm(DGA).BN is a probabilistic approach to analyze relationships between stochastic events.In contrast with traditional methods where BN model is built by professionals,DGA is proposed for the automatic analysis of historical data and construction of BN for the estimation of system reliability.The whole solution space of BN structures is searched by DGA and a more accurate BN model is obtained.Efficacy of the proposed method is shown by some literature examples.

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.

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.

基于Bayesian-MCMC算法的水利工程投标报价分布预测

投标是水利工程承包商获取项目的主要途径,投标报价的高低直接影响承包商能否获取项目的承建权,投标前对拟投水利工程投标报价分布进行预测可优化己方报价制定。为此,通过全局寻优的Bayesian-MCMC算法对投标报价分布模型进行反演,并通过数值分析模拟承包商投标行为。结果表明,Bayesian-MCMC算法无需考虑贝叶斯估计中先验分布与似然函数的共轭性,且模拟所需数据更少,得到的分布稳定性好且更加精确。

改进贝叶斯网络在变压器故障诊断中的应用

针对变压器故障诊断精度低的问题,文中提出一种基于改进黏菌优化算法(Improved Slime Mould Algorithm,ISMA)优化贝叶斯网络(Bayesian Network,BN)的变压器故障诊断方法。通过爬山算法对定向最大支撑树搜索得到贝叶斯网络初始结构即初始种群,在改进黏菌优化算法中引入反向学习策略,增加种群多样性。添加正弦-余弦算法(Sine Cosine Algorithm,SCA),更新解的位置以避免种群陷入局部最优。根据改良的无编码比值法选取变压器故障状态的特征,利用改进黏菌优化算法优化贝叶斯网络结构,提高基于贝叶斯网络的变压器故障诊断的准确率,并利用不同种类的测试函数验证了改进黏菌优化算法具有收敛速度快、收敛精度高的优良性能。仿真结果表明,ISMA-BN诊断模型的训练集和测试集准确率分别为98.2%和97.14%,具有一定的研究价值。

驾驶疲劳对危险化学品道路运输事故风险的影响规律

近年来,随着危险化学品使用量的急剧攀升,危险化学品道路运输事故率也呈现上升的趋势,且此类事故的发生往往会导致严重后果。为研究危险化学品道路运输事故动态风险变化规律,在修正贝叶斯网络模型基础上,利用2017—2021年历史数据进行机器学习,根据驾驶疲劳程度计算得到“驾驶人行为”动态节点的状态转移概率矩阵,建立基于动态贝叶斯网络(Dynamic Bayesian Network,DBN)的危险化学品道路运输动态风险预测模型并进行推理分析。研究显示:在驾驶3 h内,驾驶人“疲劳驾驶”发生概率随时间推移而增加,但增幅有所下降;在最常见情境下,随驾驶人“疲劳驾驶”概率增加,“侧翻”和“碰撞”事故类型的发生概率明显增加,进而导致“泄漏”事故后果的发生概率有所增加;驾驶人“疲劳驾驶”概率增加会导致“有伤亡事故”发生概率增加,即加重事故的严重程度;在驾驶3 h内,“侧翻”“碰撞”“泄漏”和“有伤亡事故”发生概率的变化趋势与驾驶人“疲劳驾驶”发生概率的变化趋势一致。

基于AESL-GA的BN球磨机滚动轴承故障诊断方法

针对基于知识的贝叶斯网络(BN)构建方法存在不完全和不精确的缺点,提出一种基于知识引导和数据挖掘的BN结构构建方法。针对单一信号故障诊断结果不精确的问题和故障信息中存在的不确定性问题,将电流信号与振动信号融合建立BN的特征节点,分别提取2种信号的故障特征参数,利用区分度指标法进行特征筛选,将其作为BN结构特征层的节点。将专家知识构建的初始BN结构结合自适应精英结构遗传算法(AESL-GA)进行结构优化,通过自适应限制进化过程中的搜索空间,减少自由参数的数量,提高其全局搜索能力,得到最优BN结构。通过MQY5585溢流型球磨机滚动轴承实测数据和Paderborn University轴承数据集对所提方法进行验证,结果证明了所提方法的有效性。

基于贝叶斯神经网络的船用惯导定位修正方法

船用惯性导航系统(inertial navigation system, INS)通常采用与全球卫星导航系统(global navigation satellite system, GNSS)组合导航的方式提高其长时间工作的定位精度。当GNSS失效时,其定位误差将随时间迅速发散。针对这一问题,设计了采用反向传播神经网络(back propagate neural network, BPNN)、根据INS原始输出数据拟合修正经纬度的定位修正方案,提出了基于Bayesian算法更新网络权重系数的方法,结合理论分析和试验研究确定了神经元个数与训练数据集的分配方案。实船试验结果表明,当GNSS失效时,在后续2 h,通过24 h历史数据训练得到的神经网络修正INS位置,相比INS独立工作时的定位误差,修正后误差均值下降了63%,误差最大值下降约50%,最小值下降至0。

基于BO-BiGRU-Attention短期电力负荷预测

电力系统的可靠供应对于工业、商业和居民的生活至关重要。为了满足电力需求并维持电力系统的稳定运行,提高短期电力负荷预测的准确性和可靠性尤为关键;针对负荷数据存在复杂的非线性特性,该文提出一种基于贝叶斯优化算法的双向门控循环单元和注意力机制(BO-BiGRU-Attention)的混合预测模型对短期电力负荷进行精准预测。首先,使用Min-Max Normalization方法对负荷数据进行归一化处理。其次,利用BiGRU网络捕获序列中的长期依赖关系和上下文信息,结合注意力机制,通过在输入序列的不同部分给予不同的权重,从而突出关键特征。最后,针对BiGRU-Attention模型的超参数难以选取最优解的问题,引入贝叶斯优化算法对BiGRU-Attention模型的超参数进行寻优,完成短期电力负荷的预测。采用印度北部某地区的电力负荷数据进行预测分析,仿真结果表明,BO-BiGRU-Attention网络表现优于其他模型,各误差评价指标最小,其中MAE、RMSE和MAPE分别为56.67,73.49和1.16%,预测精度达到了99.47%。