Traditional Probability Research Articles

Estimation of construction waste composition based on bulk density: A big data-probability (BD-P) model

Estimating the composition of construction waste is crucial to the efficient operation of various waste management facilities, such as landfills, public fills, and sorting plants. However, this estimating task is often challenged by the desire of quickness and accuracy in real-life scenarios. By harnessing a valuable data set in Hong Kong, this research develops a big data-probability (BD-P) model to estimate construction waste composition based on bulk density. Using a saturated data set of 4.27 million truckloads of construction waste, the probability distribution of construction waste bulk density is derived, and then, based on the Law of Joint Probability, the BD-P model is developed. A validation experiment using 604 ground truth data entries indicates a model accuracy of 90.2%, Area Under Curve (AUC) of 0.8775, and speed of around 52 s per load in estimating the composition of each incoming construction waste load. The BD-P model also informed a linear model which can perform the estimation with an accuracy of 88.8% but consuming 0.4 s per case. The major novelty of this research is to harmonize big data analytics and traditional probability theories in improving the classic challenge of predictive analyses. In the practical sphere, it satisfactorily solves the construction waste estimation problem faced by many waste management facility operators. In the academic sphere, this research provides a vivid example that big data and theories are not adversaries, but allies.

Multisource evidence theory‐based fraud risk assessment of China's listed companies

AbstractFinancial fraud has great negative impact on capital markets. It misleads investors and the government and will hurt the development of the capital market in China. To detect corporate fraud, an artificial intelligence‐based method is proposed to evaluate the fraud risk. A new model for assessing the fraud risk of listed companies in China is put forward. The proposed approach collects multisource evidence from inside and outside listed companies. The internal evidence for fraud risk assessment is gathered by a machine‐learning method. The external evidence for fraud risk assessment is obtained by a web crawler. An evidence theory‐based is employed for integrating the multisource evidence. The Fraud Risk Assessment Model based on Multisource Evidence Theory is established. The proposed model is applied to assess the fraud risk of the listed companies in China. The results indicate that the proposed model is effective at evaluating the fraud risk of listed companies, and it also has a higher precision, recall, and F‐1 measure than the traditional probability model.

Renal Lesion Classification in Kidney CT Images by Seven-Layer Convolution Neural Network

AI techniques are pervading the medical field and facilitating the related educational applications, such as computer aided medical diagnosis, online surgery platforms and medical learning environments. Nowadays daily medical images and data come into being the big medical data need be processed as fast as possible. AI enables human to improve the accuracy and efficiency of diagnosis greatly relying on the techniques of radiological images analysis. In this paper one 7-layer deep Convolutional Neural Network (CNN) is designed to classify renal lesion in kidney Computed Tomography (CT) images. The CNN is trained on a middle-size dataset with 614 kidney CT images collected from real clinical data. Experiments show the mean and standard deviation of the overall accuracy of the binary classification reaches 90.36 ±1.02%. It has greatly better performance about 25% than the traditional Probability Neural Network (PNN) method with predefined features. The optimal structure of this CNN proves our method is rather promising to help doctors make medical diagnosis.

Identification of Network Topology Variations Based on Spectral Entropy

Based on the fact that the traditional probability distribution entropy describing a local feature of the system cannot effectively capture the global topology variations of the network, some indicators constructed by the network adjacency matrix and Laplacian matrix come into being. Specifically, these measures are based on the eigenvalues of the scaled Laplace matrix, the eigenvalues of the network communicability matrix, and the spectral entropy based on information diffusion that has been proposed recently, respectively. In this article, we systematically study the dependence of these measures on the topological structure of the network. We prove from various aspects that spectral entropy has a better ability to identify the global topology than the traditional distribution entropy. Furthermore, the indicator based on the eigenvalues of the network communicability matrix achieves good results in some aspects while, overall, the spectral entropy is able to identify network topology variations from a global perspective.

Application of quantum-like Bayesian network and belief entropy for interference effect in multi-attribute decision making problem

Multi-attribute decision making (MADM) is a classical model that widely used in various fields. But most research about this topic often assume that the attributes are independent each other. In fact the attributes maybe interact or interference each other in many cases. There are researches on the interact relationships with Choquet integral, but there is no research on the interference effect by far to the authors. In this paper, a multi-attribute decision-making model is proposed by considering the interference effects between attributes with quantum-like Bayesian network (QLBN) and belief entropy. Quantum probability theory (QPT) is more in line with people's thinking by considering the interference relationships between attributes, which is represented as the interference value in quantum theory. Belief entropy, an index to calculate the uncertainty of probability, is introduced as a method to calculate the interference value in this paper. Finally, the model is applied to assessing the industrial economic benefit as well as the quantitative performance comparison between the traditional probability and the QLBN probability is presented. It shows that the combination of QLBN model and belief entropy has prediction ability compared with the traditional Bayesian network model.

Generating probability distributions on intervals and spheres with application to finite element method

This work aims to build a bridge between probability methods and finite element methods. It starts with considering probability distributions supported in an interval [a,b], which incorporate the traditional probability distributions defined on the whole real space as limit cases on the one hand, lead to a type of spherical probability models with wide potential applications on the other hand. This type of probability has scaling and symmetry feature, and sufficient conditions under which a density function can be generated through discrete polynomial spectrum are given in this work followed by concrete examples. The density function ρ obtained in this way has the advantage of being positive definite. Computer based numerical simulation shows that the theoretically verified criteria for probability distribution are almost optimal with respect to our testing examples. After the establishment of an approximation theorem in L1 space, we propose a probabilistic Galerkin scheme that can be either continuous or discontinuous, which is potentially useful to asymptotically solve some PDEs on the sphere locally and globally.

EcoQBNs: First Application of Ecological Modeling with Quantum Bayesian Networks.

A recent advancement in modeling was the development of quantum Bayesian networks (QBNs). QBNs generally differ from BNs by substituting traditional Bayes calculus in probability tables with the quantum amplification wave functions. QBNs can solve a variety of problems which are unsolvable by, or are too complex for, traditional BNs. These include problems with feedback loops and temporal expansions; problems with non-commutative dependencies in which the order of the specification of priors affects the posterior outcomes; problems with intransitive dependencies constituting the circular dominance of the outcomes; problems in which the input variables can affect each other, even if they are not causally linked (entanglement); problems in which there may be >1 dominant probability outcome dependent on small variations in inputs (superpositioning); and problems in which the outcomes are nonintuitive and defy traditional probability calculus (Parrondo’s paradox and the violation of the Sure Thing Principle). I present simple examples of these situations illustrating problems in prediction and diagnosis, and I demonstrate how BN solutions are infeasible, or at best require overly-complex latent variable structures. I then argue that many problems in ecology and evolution can be better depicted with ecological QBN (EcoQBN) modeling. The situations that fit these kinds of problems include noncommutative and intransitive ecosystems responding to suites of disturbance regimes with no specific or single climax condition, or that respond differently depending on the specific sequence of the disturbances (priors). Case examples are presented on the evaluation of habitat conditions for a bat species, representing state-transition models of a boreal forest under disturbance, and the entrainment of auditory signals among organisms. I argue that many current ecological analysis structures—such as state-and-transition models, predator–prey dynamics, the evolution of symbiotic relationships, ecological disturbance models, and much more—could greatly benefit from a QBN approach. I conclude by presenting EcoQBNs as a nascent field needing the further development of the quantum mathematical structures and, eventually, adjuncts to existing BN modeling shells or entirely new software programs to facilitate model development and application.

Dynamic Bayesian Network-based Surface Roughness Accuracy Grade Prediction in Turning

A hybrid DBN model - Discrete and gaussian mixture hidden markov model with the weighted evidence fusion strategy (DGMWEFS), based on Discrete hidden Markov model (DHMM), Mixture of Gaussians hidden Markov model (MoGHMM), and DS evidence theory, is developed for surface roughness accuracy grade prediction. By analyzing the influence of tool vibration on the surface topography, the singular spectrum with wavelet analysis is proposed to extract the fusion feature (Ec). The ρ comparison is developed to overcome defect of the traditional probability comparison. For the multiple outputs of DHMM and MoGHMM, the weighted evidence fusion strategy based on the DS evidence theory is proposed for final decisions. Experiment of turning the workpiece with multi-material and hardness scale shows that, compared with the DHMM (prediction accuracy 85%) and MoGHMM(prediction accuracy 78%), the DGMWEFS proposed estimates the surface roughness with higher accuracy (prediction accuracy 93%).Therefore, the monitoring strategy proposed can be better used for supervising accuracy grade, which can be readily integrated into a computer integrated manufacturing environment.

Reliability analysis method for redundancy systems based on GO methodology and the universal generation function

In this study, Goal-Oriented (GO) methodology, as an important technique for reliability analysis, is used in multi-state redundancy systems. However, traditional GO operation is usually not sufficiently effective for such multi-state redundancy systems because of the complex computational problem. To solve this problem, an efficient UGF-based GO operation that combines the advantages of GO methodology and the universal generation function (UGF) is proposed. The algorithm can avoid the complex calculation process caused by the algorithm-based GO operation of traditional probability formulas, and can simplify the correction of the shared signal by regarding it as the system’s conditional probability. In addition, the further correction problem in which some dependent shared signals with a common failure are caused by other shared signals is also considered. Some application examples are presented to illustrate the proposed UGF-based GO methodology, as well as its effectiveness and practicability.

Mining Subsidence Prediction Parameter Inversion by Combining GNSS and DInSAR Deformation Measurements

Line of Sight (LOS) deformation based on Differential Interferometric Synthetic Aperture Radar (DInSAR) techniques cannot be used in traditional probability integration method (PIM) parameter inversion. To improve the accuracy of parameter inversion, a model based on 3D deformation was proposed. The model simulates 3D deformation using PIM directly. The inverse of the Sum of the Squared Errors (SSE) of the PIM results and the measured deformation results was used as a fitting function within the GA. Reliable PIM parameters can be obtained based on this GA model. To identify the surface movement law of the Jinfeng coal mine, 6 Global Navigation Satellite System (GNSS) monitor points were established over the 011207 and 011809 working panels. Due to the limited number of points and the large distance between the points, it is not sufficient to obtain reliable PIM parameters using GNSS only. As a complement, 83 Sentinel-1A images were analyzed with small baseline subset (SBAS) DInSAR, and the LOS direction deformation was obtained. The reliable PIM parameters were calculated with the 3D inversion model based on the combination of LOS direction deformation and GNSS-monitored deformation. Then, those parameters were used to predict the coal mine deformation of panels 011207 and 011809, which demonstrated that the prediction results coincide with the measured results. The model can be used to study the laws of mining subsidence combined with DInSAR and GNSS, which can reduce the requirements of the number of GNSS points and the impact of radar decoherence. This provides a new technical approach for studying the law of surface movement in mining subsidence research.

An event based topic learning pipeline for neuroimaging literature mining

Neuroimaging text mining extracts knowledge from neuroimaging texts and has received widespread attention. Topic learning is an important research focus of neuroimaging text mining. However, current neuroimaging topic learning researches mainly used traditional probability topic models to extract topics from literature and cannot obtain high-quality neuroimaging topics. The existing topic learning methods also cannot meet the requirements of topic learning oriented to full-text neuroimaging literature. In this paper, three types of neuroimaging research topic events are defined to describe the process and result of neuroimaging researches. An event based topic learning pipeline, called neuroimaging Event-BTM, is proposed to realize topic learning from full-text neuroimaging literature. The experimental results on the PLoS One data set show that the accuracy and completeness of the proposed method are significantly better than the existing main topic learning methods.

Research on prediction of residual deformation in goaf of steeply inclined extra-thick coal seam.

The residual deformation of a goaf is studied to improve the foundation stability assessment for metro lines passing through the subsidence area of steeply inclined extra-thick coal seams. The variable mining influence propagation angle is introduced to describe the special form of the rock movement. Based on the modified parameters in the traditional probability integral model, a subsidence prediction model is established. Then, based on the idea of an equivalent mining thickness, Kelvin model is introduced to analyze the creep characteristics of the old goaf, and the dynamic prediction function of the residual subsidence is constructed to realize the dynamic analysis of the residual deformation. Moreover, a case study is used to evaluate the predictive effectiveness of the prediction model, and the results are compared with the monitoring data and numerical simulation results. The results show that the values with a relative error between the predicted value and measured value are in the range of ±7%, indicating that the prediction model based on the mining influence propagation angle is feasible. Thus, the residual deformation prediction model based on the mining influence propagation angle is considered to be suitable for predicting the subsidence of engineering projects crossing a goaf.

Extraction of Irregularly Shaped Coal Mining Area Induced Ground Subsidence Prediction Based on Probability Integral Method

Underground coal mining-induced ground subsidence (or major ground vertical settlement) is a major concern to the mining industry, government and people affected. Based on the probability integral method, this paper presents a new ground subsidence prediction method for predicting irregularly shaped coal mining area extraction-induced ground subsidence. Firstly, the Delaunay triangulation method is used to divide the irregularly shaped mining area into a series of triangular extraction elements. Then, the extraction elements within the calculation area are selected. Finally, the Monte Carlo method is used to calculate extraction element-induced ground subsidence. The proposed method was tested by two experimental data sets: the simulation data set and direct leveling-based subsidence observations. The simulation results show that the prediction error of the proposed method is proportional to mesh size and inversely proportional to the amount of generated random points within the auxiliary domain. In addition, when the mesh size is smaller than 0.5 times the minimum deviation of the inflection point of the mining area, and the amount of random points within an auxiliary domain is greater than 800 times the area of the extraction element, the difference between the proposed method-based subsidence predictions and the traditional probability integral method-based subsidence predictions is marginal. The measurement results show that the root-mean-square error of the proposed method-based subsidence predictions is smaller than 3 cm, the average of absolute deviations of the proposed method-based subsidence predictions is 2.49 cm, and the maximum absolute deviation is 4.05 cm, which is equal to 0.75% of the maximum direct leveling-based subsidence observation.

Detection Strategy Against Restricted SSDF Attack With Potential Interaction Assistance

This paper investigates the malicious users (MUs) detection problem against restricted spectrum sensing data falsification (RSSDF) attack in cooperative spectrum sensing (CSS) network. We consider a CSS network with non-ideal reporting channels, in which there are two kinds of secondary users (SUs): interactive SUs (ISUs) and non-interactive SUs (NSUs). We simplify the interaction process among ISUs with proper assumptions and formulate the impact of non-ideal reporting channels on falsified data. Based on the traditional probability SSDF attack on M-ary quantized data, we generalize the attack model by considering the restriction on attack magnitude. To detect ISUs, we construct a detection architecture with ISUs partitioned into groups of two and perform distributed detection by proposed interaction-based detection algorithm. For the detection of NSUs and undetected ISUs, we further propose a semi-supervised fuzzy c-means-based (SSFCM-based) detection algorithm with potential interaction assistance. Simulation results demonstrate the validity of the proposed theorems. In addition, we illustrate the detection performance of the proposed strategy in different conditions and show the superiority of the proposed strategy over other detection algorithms.

Research on an advanced intelligence implementation system for engineering process in industrial field under big data

To develop an advanced CBR system to well adapt to the intelligence implementation of new engineering process in the big data environment, Bayesian network (BN) model is introduced to CBR system for knowledge reasoning. However, as engineering application is becoming more and more complicated, the number of parameters used to define engineering application grows larger and larger, leading to the seriously reduced efficiency as well as the accuracy of the integrated model. For the problem of reduced efficiency, this paper proposes In-External (IE) algorithm to perform the assignment of big data distribution for parallel data processing, which can fully utilize the capacity of Hadoop system and attain the best efficiency of knowledge reasoning. For the problem of reduced accuracy, in view of the fact that traditional probability learning methods are unfit for the proposed CBR system, this paper proposes Discount Exponential Coefficients of Multivariate Beta Distribution (DECMBD) algorithm to conduct the probability learning of proposed system. In DECMBD algorithm, a discount ratio is given to each exponential coefficient of multivariate Beta distribution to improve the occurrence times counting of all problem features and then gain better effect of probability learning. Finally, lots of experiments are performed to validate the effectiveness of the proposed advanced CBR system.

Complete convergence theorem for negatively dependent random variables under sub-linear expectations

Under the condition that the Choquet integral exists, we study the complete convergence theorem for negatively dependent random variables under sub-linear expectation space. Two general complete convergence theorems under sub-linear expectation space are obtained, where the coefficient of weighted sum is the general function. This paper not only extends the complete convergence theorem in the traditional probability space to the sub-linear expectation space, but also extends the coefficient of weighted sum as a general function.

Resilient Supplier Selection in Electronic Components Procurement: An Integration of Evidence Theory and Rule-Based Transformation into TOPSIS to Tackle Uncertain and Incomplete Information

The experience of disruptive events causing supply chain vulnerability and business downturns has motivated manufacturing purchasers to consider resilience capability when selecting suppliers. However, this problem is complex, mainly due to difficulties in obtaining precise data on supplier performance. Disruptions are viewed as low-possibility events, leading to incomplete or insufficient evidence to support assessment. A literature review presented in this paper identifies a list of prospective criteria for resilient supplier selection, within the electronics market, considering both quantitative and qualitative aspects in a symmetrical way. A new hybrid methodology, able to handle various forms of uncertain and incomplete data, is proposed to facilitate the supplier selection process. Evidence theory, which suggests the assignment of degrees of belief, instead of traditional probabilities, to expected results, is adopted to construct a decision matrix. The rule-based transformation technique is then employed to transform various forms of the assessment results into a unified format before further aggregation by the modified Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The proposed methodology is tested with a case of resilient supplier selection in a company producing computer hardware components. The proposed decision-making methodology can be applied not only by electronics purchasers but also by practitioners in other industries to logically and straightforwardly model the uncertainty and incompleteness of the available information.

The logic of uncertainty in law and life

Abstract My central interest is decision making in the presence of epistemic uncertainty. A method appropriate for both specialized inquiries and everyday reasoning is based on credal logic, which employs multivalent degrees of belief rather than traditional probability theory. It accounts for epistemic uncertainty as unallocated belief. It holds that, when facing real uncertainty, if a person believes a and believes b, then the person believes a and b together. This brand of multivalent logic underlies and justifies how legal decision makers and the rest of us find facts in a world infused with epistemic uncertainty. Indeed, this article closes by showing the equivalence of multivalent logic and inference to the best explanation. By demonstrating this similarity in reasoning, I aim to shore up our faith in the logic of traditional legal reasoning.

Precise Asymptotics for Complete Integral Convergence under Sublinear Expectations

The aim of this paper is to study and establish the precise asymptotics for complete integral convergence theorems under a sublinear expectation space. As applications, the precise asymptotics for p0≤p≤2 order complete integral convergence theorems have been generalized to the sublinear expectation space context. We extend some precise asymptotics for complete moment convergence theorems from the traditional probability space to the sublinear expectation space. Our results generalize corresponding results obtained by Liu and Lin (2006). There is no report on the precise asymptotics under sublinear expectation, and we provide the method to study this subject.

Logic in Law and Science

My central interest is decisionmaking in the presence of epistemic uncertainty. A method appropriate for both specialized inquiries and everyday reasoning is based on credal logic, which employs multivalent degrees of belief rather than traditional probability theory. It accounts for epistemic uncertainty as unallocated belief. It holds that, when facing real uncertainty, if a person believes a and believes b, then the person believes a and b together. This brand of multivalent logic underlies and justifies how legal decisionmakers and the rest of us find facts in a world infused with epistemic uncertainty. Indeed, this Article closes by showing the equivalence of multivalent logic and inference to the best explanation. By demonstrating this similarity in reasoning, I aim to shore up our faith in the logic of traditional legal reasoning.