Fire and explosion analysis of filling station based on fuzzy mathematics and Bayesian network model

In order to explore the basic events and risk occurrence probability of fire and explosion accidents in CNG (Compressed Natural Gas) filling station, a corresponding Bayesian network risk model was established based on the fault tree of filling station. The prior probability was modified by introducing fuzzy mathematics in the process of transforming the fault tree into Bayesian network, and the posterior probability of the basic events of CNG filling station fire and explosion accidents was analyzed and calculated by GeNIe software. Finally, through case analysis, it is found out that the most dangerous factors that lead to the greatest risk of fire and explosion accidents in a filling station are: personnel misoperation, management defects, etc. After verifying the model, it shows that paying attention to the polymorphism of the base events and determining the rationality of the logical relationship between the base events can calculate the more accurate probability distribution of the base events, and at the same time provide reasonable suggestions for the accident prevention of the gas filling station.

Using Bayesian Updating to Improve Decisions under Uncertainty

Decision making requires managers to constantly estimate the probability of uncertain outcomes and update those estimates in light of new information. This article provides guidance to managers on how they can improve that process by more explicitly adopting a Bayesian approach. Clear understanding and application of the Bayesian approach leads to more accurate probability estimates, resulting in better informed decisions. More importantly, adopting a Bayesian approach, even informally, promises to improve the quality of managerial thinking, analysis, and decisions in a variety of additional ways.

Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data

This paper presents an innovative multisensor, multitemporal machine-learning approach using remote sensing big data for the detection of archaeological mounds in Cholistan (Pakistan). The Cholistan Desert presents one of the largest concentrations of Indus Civilization sites (fromca. 3300 to 1500 BC). Cholistan has figured prominently in theories about changes in water availability, the rise and decline of the Indus Civilization, and the transformation of fertile monsoonal alluvial plains into an extremely arid margin. This paper implements a multisensor, multitemporal machine-learning approach for the remote detection of archaeological mounds. A classifier algorithm that employs a large-scale collection of synthetic-aperture radar and multispectral images has been implemented in Google Earth Engine, resulting in an accurate probability map for mound-like signatures across an area that coversca. 36,000 km2. The results show that the area presents many more archaeological mounds than previously recorded, extending south and east into the desert, which has major implications for understanding the archaeological significance of the region. The detection of small (<5 ha) to large mounds (>30 ha) suggests that there were continuous shifts in settlement location. These shifts are likely to reflect responses to a dynamic and changing hydrological network and the influence of the progressive northward advance of the desert in a long-term process that culminated in the abandonment of much of the settled area during the Late Harappan period.

The Effect of Information Formats and Incidental Affect on Prior and Posterior Probability Judgments

Background. Interpreting medical test results involves judging probabilities, including making Bayesian inferences such as judging the positive and negative predictive values. Although prior work has shown that experience formats (e.g., slide shows of representative patient cases) produce more accurate Bayesian inferences than description formats (e.g., verbal statistical summaries), there are disadvantages of using the experience format for real-world medical decision making that may be solved by presenting relevant information in a 2 × 2 table format. Furthermore, medical decisions are often made in stressful contexts, yet little is known about the influence of acute stress on the accuracy of Bayesian inferences. This study aimed to a) replicate the description-experience format effect on probabilistic judgments; b) examine judgment accuracy across description, experience, and a new 2 × 2 table format; and c) assess the effect of acute stress on probability judgments. Method. The study employed a 2 (stress condition) × 3 (format) factorial between-subjects design. Participants ( N = 165) completed a Bayesian inference task in which information about a medical screening test was presented in 1 of 3 formats (description, experience, 2 × 2 table), following a laboratory stress induction or a no-stress control condition. Results. Overall, the 2 × 2 table format produced the most accurate probability judgments, including Bayesian inferences, compared with the description and experience formats. Stress had no effect on judgment accuracy. Discussion. Given its accuracy and practicality, a 2 × 2 table may be better suited than description or experience formats for communicating probabilistic information in medical contexts.

A Novel Idea of Implementing Birth-Death Process to Model SU Transmission in CRN Over MPC

This article proposes a solution to address spectrum scarcity matter by providing birth-death process dealing with a secondary user (SU) transmission over multiple primary channels (MPC) in a cognitive radio network (CRN). By taking advantage of the under-use of spectrum resources of licensed users, CR systems can develop the use of radio spectrum efficiently. The SU must remain in the interweaving performance process and find spectrum gaps before transmission. Furthermore, both expected slots and transmission slots comprise the extended delivery time (EDT) for the secondary user. In order to model the cognitive transmission of the SU on MPC, especially the author has made a birth-death model. This strategy is referred to as an accurate probability density function (PDF) and probability mass function (PMF) of EDT of the secondary transmission for both continuous and periodic sensing cases. In this research, the author also represents numerical and simulation results to demonstrate analysis and mathematical expression.

Trusted Transactions in Micro-Grid Based on Blockchain

In order to build a local electricity market (LEM), community members can trade electricity peer-to-peer (P2P) with their neighbors. This paper proposes a Hierarchical Bidding and Transaction Structure based on blockchain (HBTS). First, combined with the multi-agents, each microgrid corrects the estimated cost probability distribution of other microgrids by Bayesian theorem, making its probability closer to the accurate probability. Second, for maximize the benefits of the microgrid, this paper uses the Nash equilibrium in the Cournot model to find the optimal quotation and output of different bidding strategies for the microgrid under different power demand conditions. Then the exchange of electricity translates into an exchange of digital proof of electricity purchases and sales of electricity on the Hyperledger Fabric, ensuring the security of the transaction process and the irreparable modification of ledgers. Finally, we verify the effectiveness of the bidding strategy through experiments, and analyze the transaction process.

Comparing node degrees in probabilistic networks

Degree is a fundamental property of nodes in networks. However, computing the degree distribution of nodes in probabilistic networks is an expensive task for large networks. To overcome this difficulty, expected degree is commonly utilized in the literature. However, in this article, we show that in some cases expected degree does not allow us to evaluate the probability of two nodes having the same degree or one node having higher degree than another. This suggests that expected degree in probabilistic networks does not completely play the same role as degree in deterministic networks. For each node, we define a reference node with the same expected degree but the least possible variance, corresponding to the least uncertain degree distribution. Then, we show how the probability of a node’s degree being higher or equal to the degree of its reference node can be approximated by using variance and skewness of the degree distribution in addition to expected degree. Experimental results on a real dataset show that our approximation functions produce accurate probability estimations in linear computational complexity, while computing exact probabilities is polynomial with order of 3.

Development of Transmission Line Failure Rate Model using Polynomial Regression

Drastic climate change and more frequent occurrences of natural disaster which destruct power system infrastructure results in power delivery congestion at the transmission network. Heavily loaded transmission network that operates during adverse weather is very prone to outage, hence may trigger more critical problem such as voltage collapse. Research on risk of voltage collapse due to transmission line outage has been carried out by numerous researcher. Generally, this risk study involves two major parts; one is the assessment of voltage collapse impact due to the line outage and the other is the assessment of probability of line outage to occur. According to many literatures, precise probability estimation is very difficult to be evaluated since it is very unpredictable. Therefore, serious attention and studies have been focused in estimating the probability of transmission line outage prudently. The accuracy of probability assessed using Poisson distribution is very much dependent on its failure rate value. In this research, a weather-based transmission line failure rate model is developed using Ordinary Least Square (OLS) polynomial regression technique. To evaluate the effectiveness of the proposed method, comparative study with previous research which utilized robust MM-estimator technique is conducted. The results revealed that the proposed technique is more precise and the weather considered in the study has more significant impact compared to the preceding work. Thus, this finding contributes to more accurate probability estimation in risk of voltage collapse assessment.