A Practical Parallel Algorithm for Propositional Knowledge Base Revision

Different methods for revising propositional knowledge base have been proposed recently by several researchers, but all methods are intractable in the general case. For practical application, this paper presents a revision method in special case, and gives a corresponding polynomial algorithm as well as its parallel version on CREW PRAM.

Improved Reduced Latency Soft-Cancellation Algorithm for Polar Decoding

Soft-cancellation(SCAN) is a soft output iterative algorithm widely used in polar decoding. This algorithm has better decoding performance than reduced latency soft-cancellation(RLSC) algorithm, which can effectively reduce the decoding delay of SCAN algorithm by 50% but has obvious performance loss. A modified reduced latency soft-cancellation(MRLSC) algorithm is presented in the paper. Compared with RLSC algorithm, LLR information storage required in MRLSC algorithm can be reduced by about 50%, and better decoding performance can be achieved with only a small increase in decoding delay. The simulation results show that MRLSC algorithm can achieve a maximum block error rate(BLER) performance gain of about 0.4 dB compared with RLSC algorithm when code length is 2048. At the same time, compared with the performance of several other algorithms under(1024, 512) polar codes, the results show that the throughput of proposed MRLSC algorithm has the advantage at the low and medium signal-to-noise ratio(SNR) and better BLER performance at the high SNR.

Optimizing Deep Learning Parameters Using Genetic Algorithm for Object Recognition and Robot Grasping

The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We propose a genetic algorithm(GA) based deep belief neural network(DBNN) method for robot object recognition and grasping purpose. This method optimizes the parameters of the DBNN method, such as the number of hidden units, the number of epochs, and the learning rates, which would reduce the error rate and the network training time of object recognition. After recognizing objects, the robot performs the pick-andplace operations. We build a database of six objects for experimental purpose. Experimental results demonstrate that our method outperforms on the optimized robot object recognition and grasping tasks.

Jointly-check iterative decoding algorithm for quantum sparse graph codes

For quantum sparse graph codes with stabilizer formalism, the unavoidable girth-four cycles in their Tanner graphs greatly degrade the iterative decoding performance with standard belief-propagation （BP） algorithm. In this paper, we present a jointly-check iterative algorithm suitable for decoding quantum sparse graph codes efficiently. Numerical simulations show that this modified method outperforms standard BP algorithm with an obvious performance improvement.

A Haze Feature Extraction and Pollution Level Identification Pre-Warning Algorithm

The prediction of particles less than 2.5 micrometers in diameter(PM2.5)in fog and haze has been paid more and more attention,but the prediction accuracy of the results is not ideal.Haze prediction algorithms based on traditional numerical and statistical prediction have poor effects on nonlinear data prediction of haze.In order to improve the effects of prediction,this paper proposes a haze feature extraction and pollution level identification pre-warning algorithm based on feature selection and integrated learning.Minimum Redundancy Maximum Relevance method is used to extract low-level features of haze,and deep confidence network is utilized to extract high-level features.eXtreme Gradient Boosting algorithm is adopted to fuse low-level and high-level features,as well as predict haze.Establish PM2.5 concentration pollution grade classification index,and grade the forecast data.The expert experience knowledge is utilized to assist the optimization of the pre-warning results.The experiment results show the presented algorithm can get better prediction effects than the results of Support Vector Machine(SVM)and Back Propagation(BP)widely used at present,the accuracy has greatly improved compared with SVM and BP.

A machine learning model for diagnosing acute pulmonary embolism and comparison with Wells score,revised Geneva score,and Years algorithm

Background:Acute pulmonary embolism(APE)is a fatal cardiovascular disease,yet missed diagnosis and misdiagnosis often occur due to non-specific symptoms and signs.A simple,objective technique will help clinicians make a quick and precise diagnosis.In population studies,machine learning(ML)plays a critical role in characterizing cardiovascular risks,predicting outcomes,and identifying biomarkers.This work sought to develop an ML model for helping APE diagnosis and compare it against current clinical probability assessment models.Methods:This is a single-center retrospective study.Patients with suspected APE were continuously enrolled and randomly divided into two groups including training and testing sets.A total of 8 ML models,including random forest(RF),Naïve Bayes,decision tree,K-nearest neighbors,logistic regression,multi-layer perceptron,support vector machine,and gradient boosting decision tree were developed based on the training set to diagnose APE.Thereafter,the model with the best diagnostic performance was selected and evaluated against the current clinical assessment strategies,including the Wells score,revised Geneva score,and Years algorithm.Eventually,the ML model was internally validated to assess the diagnostic performance using receiver operating characteristic(ROC)analysis.Results:The ML models were constructed using eight clinical features,including D-dimer,cardiac troponin T(cTNT),arterial oxygen saturation,heart rate,chest pain,lower limb pain,hemoptysis,and chronic heart failure.Among eight ML models,the RF model achieved the best performance with the highest area under the curve(AUC)(AUC=0.774).Compared to the current clinical assessment strategies,the RF model outperformed the Wells score(P=0.030)and was not inferior to any other clinical probability assessment strategy.The AUC of the RF model for diagnosing APE onset in internal validation set was 0.726.Conclusions:Based on RF algorithm,a novel prediction model was finally constructed for APE diagnosis.When compared to the current clinical assessment strategies,the RF model achieved better diagnostic efficacy and accuracy.Therefore,the ML algorithm can be a useful tool in assisting with the diagnosis of APE.

Chinese Adaptation and Psychometric Properties of the Belief in a Just World Scale for College Students

This study aims to revise the Belief in a Just World Scale(BJWS)for Chinese college students and test its reliability and validity(construct validity,convergent and divergent validity).Two samples of 546 and 595 college students were selected,respectively,using stratified cluster random sampling.Item analysis,exploratory factor analysis(EFA),confirmatory factor analysis(CFA),reliability analysis and convergent and divergent validity tests were carried out.The results showed that the 13 items of the BJWS have good item discrimination.The corrected item–total correlation in the general belief in a just world subscale was found to range from 0.464 to 0.655,and that in the personal belief in a just world subscale was 0.553 to 0.715.The internal consistency coefficients of the revised version of the BJWS and its subscales are good.The EFA and CFA results show that the structure and items of the revised scale are the same as those of the original scale.Belief in a just world was found to have significant positive correlations with gratitude and empathy,and has a significant negative correlation with anxiety,thereby exhibiting good convergent and divergent validity.Therefore,the Chinese revised version of the BJWS has good reliability and validity.

Belief Revision by Sets of Sentences

The aim of this paper is to extend the system of belief revision developed by Alchourron, Gardenfors and Makinson (AGM) to a more general framework.This extension enables a treatment of revision not only by single sentences but also by any sets of sentences, especially by infinite sets. The extended revision and contraction operators will be called general ones, respectively. A group of postulates for each operator is provided in such a way that it coincides with AGM's in the limit case. A notion of the nice-ordering partition is introduced to characterize the general contraction operation. A comp ut ation- orient ed ap-proach is provided for belief revision operations.

Fault Diagnosis of Photovoltaic Array Based on Deep Belief Network Optimized by Genetic Algorithm

When using deep belief networks(DBN)to establish a fault diagnosis model,the objective function easily falls into a local optimum during the learning and training process due to random initialization of the DBN network bias and weights,thereby affecting the computational efficiency.To address the problem,a fault diagnosis method based on a deep belief network optimized by genetic algorithm(GA-DBN)is proposed.The method uses the restricted Boltzmann machine reconstruction error to structure the fitness function,and uses the genetic algorithm to optimize the network bias and weight,thus improving the network accuracy and convergence speed.In the experiment,the performance of the model is analyzed from the aspects of reconstruction error,classification accuracy,and time-consuming size.The results are compared with those of back propagation optimized by the genetic algorithm,support vector machines,and DBN.It shows that the proposed method improves the generalization ability of traditional DBN,and has higher recognition accuracy of photovoltaic array faults.

Agent Belief with Credibility and Its Revision Mechanism

An approach to characterize the credibility of beliefs of an agent is proposed in this paper, which can define the uncertainty of beliefs, calculation rules and inference rules about credibility and a method for belief revision based on abductive reasoning is also given. When an agent receives some new information, if the new information is consistent with the current belief set, then incorporate this new information with an appropriate credibility, otherwise the choice will be different depending on the characters of agents, and the deliberated agents will choose the belief with a better explanation under the current belief set. Removing one belief may cause the removal of those beliefs that, together with others, logically entail the formula to be removed. A method based on abduction is proposed to solve these problems.

Operational and Complete Approaches to Belief Revision

Two operational approaches to belief revision are presented in this paper. The rules of Rcalculus are modified in order to deduce all the maximal consistent subsets. Another set of rules is given in order to deduce all the minimal inconsistent subsets. Then a procedure, which can generate all the maximal consistent subsets, is presented. They are complete approaches, since all the maximal consistent subsets can be deduced or generated. In this paper, only the case of propositional logic is considered.

Default Reasoning and Belief Revision:A Syntax-Independent Approach

As an important variant of Relier's default logic, Poole (1988) developed a nonmonotonic reasoning framework in the classical first-order language. Brewka and Nebel extended Poole's approach in order to enable a representation of priorities between defaults. In this paper a general framework for default reasoning is presented, which can be viewed as a generalization of the three approaches above. It is proved that the syntax-independent default reasoning in this framework is identical to the general belief revision operation introduced by Zhang et al. (1997). This result provides a solution to the problem whether there is a correspondence between belief revision and default logic for the infinite case. As a by-product, an answer to the question, raised by Mankinson and Gardenfors (1991), is also given about whether there is a counterpart contraction in nonmonotonic logic.

Safety Assessment of Liquid Launch Vehicle Structures Based on Interpretable Belief Rule Base

A liquid launch vehicle is an important carrier in aviation,and its regular operation is essential to maintain space security.In the safety assessment of fluid launch vehicle body structure,it is necessary to ensure that the assessmentmodel can learn self-response rules from various uncertain data and not differently to provide a traceable and interpretable assessment process.Therefore,a belief rule base with interpretability(BRB-i)assessment method of liquid launch vehicle structure safety status combines data and knowledge.Moreover,an innovative whale optimization algorithm with interpretable constraints is proposed.The experiments are carried out based on the liquid launch vehicle safety experiment platform,and the information on the safety status of the liquid launch vehicle is obtained by monitoring the detection indicators under the simulation platform.The MSEs of the proposed model are 3.8000e-03,1.3000e-03,2.1000e-03,and 1.8936e-04 for 25%,45%,65%,and 84%of the training samples,respectively.It can be seen that the proposed model also shows a better ability to handle small sample data.Meanwhile,the belief distribution of the BRB-i model output has a high fitting trend with the belief distribution of the expert knowledge settings,which indicates the interpretability of the BRB-i model.Experimental results show that,compared with other methods,the BRB-i model guarantees the model’s interpretability and the high precision of experimental results.

Deep Belief Network for Lung Nodule Segmentation and Cancer Detection

Cancer disease is a deadliest disease cause more dangerous one.By identifying the disease through Artificial intelligence to getting the mage features directly from patients.This paper presents the lung knob division and disease characterization by proposing an enhancement calculation.Most of the machine learning techniques failed to observe the feature dimensions leads inaccuracy in feature selection and classification.This cause inaccuracy in sensitivity and specificity rate to reduce the identification accuracy.To resolve this problem,to propose a Chicken Sine Cosine Algorithm based Deep Belief Network to identify the disease factor.The general technique of the created approach includes four stages,such as pre-processing,segmentation,highlight extraction,and the order.From the outset,the Computerized Tomography(CT)image of the lung is taken care of to the division.When the division is done,the highlights are extricated through morphological factors for feature observation.By getting the features are analysed and the characterization is done dependent on the Deep Belief Network(DBN)which is prepared by utilizing the proposed Chicken-Sine Cosine Algorithm(CSCA)which distinguish the lung tumour,giving two classes in particular,knob or non-knob.The proposed system produce high performance as well compared to the other system.The presentation assessment of lung knob division and malignant growth grouping dependent on CSCA is figured utilizing three measurements to be specificity,precision,affectability,and the explicitness.

A Processor Performance Prediction Method Based on Interpretable Hierarchical Belief Rule Base and Sensitivity Analysis

The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can initially provide a solution to low prediction accuracy. However, theinterpretability of the model and the traceability of the results still warrantfurther investigation. Therefore, a processor performance prediction methodbased on interpretable hierarchical belief rule base (HBRB-I) and globalsensitivity analysis (GSA) is proposed. The method can yield more reliableprediction results. Evidence reasoning (ER) is firstly used to evaluate thehistorical data of the processor, followed by a performance prediction modelwith interpretability constraints that is constructed based on HBRB-I. Then,the whale optimization algorithm (WOA) is used to optimize the parameters.Furthermore, to test the interpretability of the performance predictionprocess, GSA is used to analyze the relationship between the input and thepredicted output indicators. Finally, based on the UCI database processordataset, the effectiveness and superiority of the method are verified. Accordingto our experiments, our prediction method generates more reliable andaccurate estimations than traditional models.

An Operational Approach to Belief Revision

A deduction system, called RE-proof system, is constructed for generating the revisions of first order belief sets. When a belief set is rejected by a given fact, all maximal subsets of the belief set consistent with the fact can be deduced from the proof system. The soundness and completeness of the RE-proof system are proved, which imply that there exists a resolution method to decide whether a revision retains a mtalmal subset of a belief set.

Performance and Complexity Trade-Off between Short-Length Regular and Irregular LDPC

In this paper, both the high-complexity near-ML list decoding and the low-complexity belief propagation decoding are tested for some well-known regular and irregular LDPC codes. The complexity and performance trade-off is shown clearly and demonstrated with the paradigm of hybrid decoding. For regular LDPC code, the SNR-threshold performance and error-floor performance could be improved to the optimal level of ML decoding if the decoding complexity is progressively increased, usually corresponding to the near-ML decoding with progressively increased size of list. For irregular LDPC code, the SNR-threshold performance and error-floor performance could only be improved to a bottle-neck even with unlimited decoding complexity. However, with the technique of CRC-aided hybrid decoding, the ML performance could be greatly improved and approached with reasonable complexity thanks to the improved code-weight distribution from the concatenation of CRC and irregular LDPC code. Finally, CRC-aided 5GNR-LDPC code is evaluated and the capacity-approaching capability is shown.

基于DBN和BES-LSSVM的矿用压风机异常状态识别方法

针对矿用压风机这类分布式系统的异常类别复杂、识别精度低等问题,提出了一种基于深度置信网络(DBN)和最小二乘支持向量机(LSSVM)的异常状态识别方法。首先,分析压风机组成系统及其运行机理,确定常见的异常状态类型;其次,采用DBN无监督学习方式充分挖掘监测数据中异常特征并快速提取;然后,利用秃鹰搜索算法(BES)优化LSSVM的超参数,构建最优的BES-LSSVM分类模型;最后,将DBN提取的异常特征作为BES-LSSVM模型的输入,对矿用压风机异常状态进行识别。试验验证与对比分析结果表明,相较于GA,PSO,GWO算法,BES算法的求解精度和收敛速度均有所提高,同时DBN-BES-LSSVM模型在测试集上平均识别精度达到94.65%,较PCA-LSSVM模型、DBN模型和DBN-LSSVM模型的识别精度分别提高了10.53%,5.84%和3.76%,验证了DBN-BES-LSSVM模型在矿用压风机异常特征提取以及特征识别方面的优越性。

基于深度置信网络的光伏发电阵列的故障诊断方法

为了对光伏发电阵列的故障进行及时准确的诊断,首先以在Matlab/Simulink中搭建的光伏阵列输出特性仿真模型为基础,采用深度置信网络作为光伏阵列故障诊断算法,并通过蝙蝠算法对网络各隐含层神经元的数量进行优化;然后,以蝙蝠算法优化后的深度置信网络(bat algorithm-deep belief network,BA-DBN)作为故障诊断模型,分别采集不同运行工况下的光伏阵列输出特性四参数,并将其归一化后作为特征样本输入BA-DBN故障诊断模型,实现了对光伏阵列的故障诊断。仿真结果表明:所提出的BA-DBN算法在光伏阵列故障诊断应用中的准确率显著高于KNN、BPNN和原始DBN算法,更加适用于光伏阵列故障诊断,具有更优的分类效果。

结合遗传算法的RF-DBN入侵检测方法

针对目前不平衡数据集少数类攻击样本识别率较低的问题,提出一种BorderlineSMOTE、随机森林和遗传算法(genetic algorithm,GA)-深度信念网络(deep belief network,DBN)相结合的入侵检测方法。首先采用BorderlineSMOTE对少数类样本进行过采样,减少数据集的不平衡度;然后使用随机森林算法实现正异常数据分类,筛选出异常数据;最后采用经GA优化的DBN网络对异常数据进行进一步分类。使用网络安全数据集CICIDS2017进行验证,该方法的准确率达到了99.85%,而且少数类样本的识别精度也有明显提高。