Probability Distribution Model Research Articles

Analysing Cyber Threats: A Comprehensive Literature Review on Data-Driven Approaches

This literature review paper aims to examine and analyze the existing research on prediction models for different types of cyber-attacks. Four key research papers have been selected as the base for this review: "A Prediction Model of DoS Attack's Distribution Discrete Probability," "Apriori Viterbi Model for Prior Detection of Socio-Technical Attacks," "Cyber Attacks Prediction Model Based on Bayesian Network," and "Applying Data Science to Cybersecurity Network Attacks & Events.” An overview of the value of prediction models in cybersecurity and their function in reducing potential threats come first in the review. The methodology section outlines the search strategy used to identify relevant literature and the selection criteria for the base papers. The subsequent sections provide an overview of the field, highlighting the historical development and key theories or frameworks related to cyber-attack prediction. The themes or subtopics identified in the literature are discussed, focusing on the discrete probability distribution model of DoS attacks, the Apriori Viterbi model for detecting socio-technical attacks, the Bayesian network-based prediction model, and the application of data science in analyzing network attacks and events. The review critically evaluates the selected papers, analyzing their methodologies, findings, and limitations. It identifies gaps, controversies, and conflicting findings in the literature, paving the way for further research in the field. The synthesis and interpretation section integrates the findings from the different studies, compares various perspectives, and discusses the implications and significance of the literature for cyber-attack prediction. In conclusion, this literature review paper provides a comprehensive analysis of prediction models for cyber-attacks, based on the selected base papers. It highlights the strengths and weaknesses of existing approaches, identifies research gaps, and offers recommendations for future studies. This review contributes to the advancement of knowledge in the field of cybersecurity and aids in the development of more effective prediction models to combat evolving cyber threats.

An algorithm for automatically establishing a multi- parameter RVE model for z-pin composite laminates

Z-pinning is an effective method to improve the delamination resistance of composite laminates. The microstructure and manufacturing parameters have a great influence on the properties of z-pin composite laminates. In order to explore the influence of microscopic characteristic parameters on material properties and damage analysis, a parameterized RVE finite element model algorithm for automatic establishment of z-pin reinforced composites was proposed. The algorithm involves avoiding in-plane fibers to pin, generating fiber waviness, and assembling pin-fiber–matrix. Moreover, the statistical distribution of fiber misalignment angle can also be controlled. The developed program can generate RVE models with different parameters efficiently and quickly, such as fiber volume fraction, pin volume fraction, pin diameter, RVE size and fiber waviness. The statistical verification of the distribution of fiber misalignment angle can satisfy the probability distribution modeling of fiber angle. The generated model is divided into meshes, and finite element calculation can be realized. This work is of great significance to study the effects of microscopic characteristics on the properties and to optimize the design of z-pin reinforced composites.

A Nonparametric Bayesian Approach for Bridge Reliability Assessment Using Structural Health Monitoring Data

Integrating structural health monitoring (SHM) data into reliability assessment has increasingly been practiced in the condition evaluation of in-service bridges over the past decade. The selection of probability distribution models for load- and resistance-related random variables is a prerequisite for monitoring-based reliability assessment. However, the underlying probabilistic assumptions of the used models could be restrictive and unverifiable especially when dealing with real-world heterogeneous monitoring data, weakening the confidence on the estimated reliability index. This study aims to develop a nonparametric Bayesian model with the Dirichlet process prior for bridge reliability assessment, where the model order constraint can be released such that the complexity of the model adapts to the observed data. Reliability analysis via the nonparametric Bayesian model allows the aleatory uncertainty and the epistemic uncertainty arising from monitoring data to be concurrently accounted for in the formulated reliability index. A numerical example is presented to verify the effectiveness of the nonparametric Bayesian model for dealing with multimodal data. The feasibility of the proposed approach for reliability assessment is then demonstrated with one-year strain monitoring data acquired from a large-scale bridge instrumented with the SHM system.

Rising trend of childlessness in China: analysis of social and regional disparities with 2010 and 2020 census data

IntroductionIn recent decades, there has been a significant increase in childlessness. This paper analysed childlessness in China, specifically examining its socio and regional disparities.MethodsWith data from China’s 2020 population census,...

Mixture models of probability distributions applied to rainfall in the state of Pernambuco, Brazil

The Brazilian semi-arid region is recurrently affected by the scarcity of water that marks the landscape as it prints periods of severe drought. Therefore, rainfall in this region greatly influences plant growth in regional hydrological processes that affect droughts or floods. It is of practical interest to assess how changes in rainfall patterns occur to anticipate hydrological dynamics. However, this is not easy as climate change reshapes global hydrology. Thus, assertive forecasting has become rare and imputed estimates of a reasonable degree of uncertainty. The objective of this work was to verify from the mixture of exponential, gamma, beta, log-normal, Weibull, normal, log-logistic, and exponentiated log-logistic distributions, which best fits the monthly rainfall of the state of Pernambuco, Brazil. The data used came from 133 monthly rainfall series (1950 to 2012) distributed over the state of Pernambuco. The Maximum Likelihood Method estimated all parameters. The Kolmogorov-Smirnov adherence test was applied at 5% probability to assess the adjustments. The least rejected distributions in the adherence test were Weibull, gamma, and beta; October presented the smallest number of distributions considered adequate to model monthly rainfall. More than 99% of the rain gauge stations had some adequate probabilistic distribution to model monthly rainfall in March. For most months, except for March, the Weibull distribution was the most suitable for modeling the monthly rainfall in the vast majority of rain gauge stations of Pernambuco.

A Novel Operating State Evaluation Method for Photovoltaic Strings Based on TOPSIS and Its Application

PV strings are essential for energy conversion in large-scale photovoltaic (PV) power plants. The operating state of PV strings directly affects the power generation efficiency and economic benefits of PV power plants. In the process of evaluating PV arrays, a reference array needs to be identified. By comparing PV arrays with the reference array, the operational status of the PV arrays can be evaluated. However, in the actual operation of PV power stations, it is difficult to directly determine the reference state of a PV array due to random fluctuations in the PV power output. In order to solve the problems mentioned above, this paper proposes a method to select the reference state and perform a grading evaluation of PV strings. Additionally, the proposed method is based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm, which is used to rank the performance of PV arrays to determine their status. In order to solve the problem of random fluctuations in PV power generation, a probability distribution model of the PV string conversion efficiency was built by using the kernel density estimation method. Then, the characteristic indicator of the PV string’s operating state was described by the output power of the PV string and its probability distribution model. Then, based on the operating characteristic indicator, the reference state of the PV string was determined using the TOPSIS method, and the grading evaluation of the operating state of the PV string was realized. Finally, the effectiveness of the proposed method was verified using the actual data of a PV power station.

Comparison of Wind Speed Probability Distribution Models for Accurate Evaluation of Wind Energy Potential: A Case Study from Kerala, India

Comparison of Wind Speed Probability Distribution Models for Accurate Evaluation of Wind Energy Potential: A Case Study from Kerala, India

Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm

Unmanned autonomous helicopter (UAH) path planning problem is an important component of the UAH mission planning system. Aiming to reduce the influence of non-complete ground threat information on UAH path planning, a ground threat prediction-based path planning method is proposed based on artificial bee colony (ABC) algorithm by collaborative thinking strategy. Firstly, a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats. The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time. Then, a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats. By adding the collision warning mechanism to the path planning model, the flight path could be dynamically adjusted according to changing no-fly zones. Furthermore, a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy. The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution, and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy, which makes the optimization performance of ABC algorithm more controllable and efficient. Finally, simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method.

Accuracy Evaluation of Standardized Precipitation Index (SPI) Estimation under Conventional Assumption in Yeşilırmak, Kızılırmak, and Konya Closed Basins, Turkey

The doubt in the calculation algorithm of the standardized precipitation index (SPI), which is widely preferred in the evaluation and monitoring of drought, still remains up-to-date because its calculation process is performed in the form of standardization or normalization with a default probability distribution. Therefore, the success of this index is directly affected by the choice of the probability distribution model. This study is based on the effect of three different parameter estimation methods on the calculation process, as well as the comparison of the SPI results calculated based on the default Gamma distribution and the distribution with the best ability to represent the 3-and 12-month consecutive summed rainfall data among the 15 candidate distributions namely Gamma (GAM), Generalized Extreme Value (GEV), Pearson Type III (P III), Log Pearson Type III (LP III), two-parameter Lognormal (LN2), three-parameter Lognormal (LN3), Generalized Logistic (GLOG), Extreme Value Type I (EVI), Generalized Pareto (GPAR), Weilbul (W), Normal (N), Exponential (EXP), Logistic (LOG), four-parameter Wakeby (WK4), and five-parameter Wakeby (WK5) distributions. Approximately 68.4% and 18.4% of the 3-month data considered had the best fit to the Weibull and Pearson III distribution, while approximately 24% and 18% of the 12-month data had the best fit to the Weibull and Logistic distribution. On the other hand, it was found that the default Gamma distribution calculated the extreme drought categories significantly more than the best-fit distribution model. In terms of parameter estimation methods, L-moments for 3-month series and maximum likelihood approaches for 12-month series were most dominant.

Wind Power Potential Assessment at Different Locations in Lebanon: Best–Fit Probability Distribution Model and Techno-Economic Feasibility

The objective of the current paper is to evaluate Lebanon's wind energy generation potential as an alternative solution to the electricity supply to households and to enhance sustainable technological development. Firstly, the paper aims to investigate the appropriateness of 44 distribution function models for the evaluation of wind speed characteristics and compared them with popular models at 12 locations in Lebanon for the first time. The results showed that Wakeby and Beta distribution functions gave the best fit to the actual data for most locations. Secondly, the techno-economic and environmental feasibility assessment for 10MW grid-connected wind farms was developed based on variations in financial parameters using RETScreen Experts software. The findings demonstrate that the proposed power plant is both technically and financially feasible. It was found that Ain ed Dabaa is the most viable location for the installation of a wind farm.

Comparative analysis and applicability of corrosion test methods for construction steel components

In this paper, a systematic study of different methods of manufacturing corroded components and their post-corrosion morphology has been carried out to verify the effectiveness of different accelerated corrosion testing methods. Corrosion tests were performed on specimens of construction steel Q345 by three corrosion methods, i.e., atmospheric exposure corrosion, salt spray accelerated corrosion, and accelerated electrochemical corrosion. The surface morphology of the specimens after corrosion was measured, the characteristics of the corrosion morphology development of the various corrosion methods of the components were analyzed, and the corresponding probability model of the corrosion morphology distribution was developed. Accordingly, a random corrosion surface modeling technique was developed based on the fast Fourier transform and two-dimensional digital filtering techniques, and the mechanical analysis of axially pressed components with different corrosion surfaces was carried out. The results indicate a clear difference in the undulation and variation pattern of the corroded surface under different corrosion methods. However, the disparity in the axial compression load capacity of the specimens under different surface conditions were minimal, was less than 1%. Therefore, the axial compression performance of corroded steel members can be studied using electrochemical corrosion and smooth corrosion surface simulation in the future study.

Performance assessment of recycled aggregate concrete and its variability

AbstractThe use of recycled aggregate improves waste management and is a crucial step toward the sustainable development of the concrete industry. Hence, this paper presents the experimental investigation to determine the mechanical properties of concrete with recycled concrete aggregates as the selective substitution for natural aggregates. The slump was observed to decrease by replacing natural coarse aggregates with recycled concrete aggregates. Compressive strength of about 40 MPa was achieved after 56 days of curing with the utilization of 100% recycled concrete aggregates. Besides, a reduction in the splitting tensile strength of 14% was observed due to the incorporation of recycled concrete aggregates. The statistical techniques use various probability distribution models such as Gamma, Lognormal, Weibull, and Normal distributions. Amongst the considered functions, lognormal and Weibull distributions best represent the experimentally obtained compressive strength of concrete with 100% natural aggregates and 50% recycled concrete aggregates, respectively. However, for concrete with 100% recycled aggregates, Weibull distribution represent compressive strength after 28 days of curing, and lognormal distribution is given the best fit for the compressive after 56 days of curing. In the case of split tensile strength, lognormal distribution provided the best representation after 56 days of curing for all specimens.

Assessment of process operating performance with supervised probabilistic slow feature analysis

Assessment of operating performance, which is adopted to assess optimal degree for complex industrial process, has been increasing concerned in the last few years. In this study, a supervised probabilistic slow feature analysis (SPSFA) is proposed to assess the optimal degree of process performance by deep mining process variables information on the basis of the probability distribution model. First, the slow features (SFs) are modeled in state–space form as a serial correlation feature to effectively represent the potential variation of performance, and the serial correlation hiding in the performance features reveals the potential changes of performance, which helps improve cognitive depth and assessment accuracy of performance. Then, multi-order dynamic information of SFs is further combined to design the multi-order dynamic index, which accurately and timely depict the dynamic changes of the process. Hence, the proposed method provides an explicit representation of underlying driving forces of performance variations and meaningful physical interpretation of each stage from optimal to non-optimal. In addition, a sparse learning-based non-optimal factor identification method is proposed, which identifies non-optimal factor variables by reconstructing the online estimation of the Kalman filter and using Group Lasso. The feasibility and effectiveness of the proposed method is validated by an actual dense medium coal preparation process and a typical Tennessee Eastman process.

Single-Location and Multi-Locations Scenarios Generation for Wind Power Based On WGAN-GP

To address the randomness of renewable energy, scenario generation can simulate the random process of renewable energy. Still, most of the previous scenario generation algorithms are based on assumed probability distribution models, which are difficult to grasp the dynamic characteristics of renewable energy accurately. Based on an improved generative adversarial network algorithm, this paper generates single-site and multi-site scenarios for wind power generation data. The temporal and spatial correlations of the generated data were judged based on the maximum mean difference and the Pearson coefficient. Finally, multiple sets of wind power generation data are used to verify that the proposed method can reflect not only the volatility of renewable energy but also ensure the temporal and spatial correlation between the data.

Reliability-based fatigue life assessment using random road load condition and local damage criteria

This study presents a fatigue reliability assessment of a vehicle coil spring based on random road load conditions for fatigue life characterisation. Fatigue life data characterisation was carried out to accelerate the durability analysis by retaining the higher amplitude cycles in the original strain signals and thus indicate the fatigue damage. To collect experimental data, road testing was conducted using various road profiles, which contributed significantly to the fatigue failure performance of the vehicle coil spring. Fatigue reliability analysis was performed to assess the failure behaviour of the coil spring utilising the probabilistic Gumbel distribution model to forecast the probability distribution of the fatigue life data. Furthermore, the highest and lowest mean-cycles-to-failure (McTF) of 5.44 × 107 and 2.46 × 105 blocks were estimated for the Coffin-Manson and Smith-Watson-Topper models from the highway and rural road profiles. The findings demonstrate that the lowest hazard rate corresponded to the highest McTF, making this an acceptable model to be implemented in reliability assessments of road profiles. Hence, the fatigue reliability assessment provided a hazard rate based on the fatigue life predictions for risk assessments when characterising the fatigue life data.

Machine Learning Classification and Prediction of Wind Estimation Using Artificial Intelligence Techniques and Normal PDF

Estimating wind energy at a specific wind site depends on how well the real wind data in that area can be represented using an appropriate distribution function. In fact, wind sites differ in the extent to which their wind data can be represented from one region to another, despite the widespread use of the Weibull function in representing the wind speed in various wind locations in the world. In this study, a new probability distribution model (normal PDF) was tested to implement wind speed at several wind locations in Jordan. The results show high compatibility between this model and the wind resources in Jordan. Therefore, this model was used to estimate the values of the wind energy and the extracted energy of wind turbines compared to those obtained by the Weibull PDF. Several artificial intelligence techniques were used (GA, BFOA, SA, and a neuro-fuzzy method) to estimate and predict the parameters of both the normal and Weibull PDFs that were reflected in conjunction with the actual observed data of wind probabilities. Afterward, the goodness of fit was decided with the aid of two performance indicators (RMSE and MAE). Surprisingly, in this study, the normal probability distribution function (PDF) outstripped the Weibull PDF, and interestingly, BFOA and SA were the most accurate methods. In the last stage, machine learning was used to classify and predict the error level between the actual probability and the estimated probability based on the trained and tested data of the PDF parameters. The proposed novel methodology aims to predict the most accurate parameters, as the subsequent energy calculation phases of wind depend on the proper selection of these parameters. Hence, 24 classifier algorithms were used in this study. The medium tree classifier shows the best performance from the accuracy and training time points of view, while the ensemble-boosted trees classifier shows poor performance regarding providing correct predictions.

SMLSOM: The shrinking maximum likelihood self-organizing map

Determining the number of clusters in a dataset is a fundamental issue in data clustering. Many methods have been proposed to solve the problem of selecting the number of clusters, considering it to be a problem with regard to model selection. This paper proposes an efficient algorithm that automatically selects a suitable number of clusters based on a probability distribution model framework. The algorithm includes the following two components. First, a generalization of Kohonen's self-organizing map (SOM) is introduced. In Kohonen's SOM, clusters are modeled as mean vectors. In the generalized SOM, each cluster is modeled as a probabilistic distribution and constructed by samples classified based on the likelihood. Second, the dynamically updating method of the SOM structure is introduced. In Kohonen's SOM, each cluster is tied to a node of a fixed two-dimensional lattice space and learned using neighborhood relations between nodes based on Euclidean distance. The extended SOM defines a graph with clusters as vertices and neighborhood relations as links and updates the graph structure by cutting weakly connected and unnecessary vertex deletions. The weakness of a link is measured using the Kullback–Leibler divergence, and the redundancy of a vertex is measured using the minimum description length. Those extensions make it efficient to determine the appropriate number of clusters. Compared with existing methods, the proposed method is computationally efficient and can accurately select the number of clusters.

Capacity Assessment of a Transmission Tower Considering the Wind-Ice Probability Distributions

Extreme weather, such as wind and ice, threatens the safety and stability of transmission networks. In this paper, the joint probability distribution model of wind speed and ice thickness are established, based on which the bearing capacity of a transmission tower under combined wind and ice loads are evaluated. The results show that the developed probability distribution models can well characterize the dependence structure of wind speed and ice thickness, and the safety of the transmission tower under combined wind-ice loads can be guaranteed within its design life.

Dual Stream Conditional Generative Adversarial Network Fusion for Video Abnormal Behavior Detection

Deep learning has been successfully applied to video anomaly detection. However, the way that deep network learn spatio-temporal features autonomously will ignore the specificity of different pattern features. Therefore, this paper focuses on how to efficiently learn deep appearance feature, introduces the idea of learning appearance information by predicting future frame, and proposes dual stream conditional generative adversarial network fusion for video abnormal behavior detection. The video frame and its corresponding optical flow image are transferred to the conditional generative adversarial network to learn the motion feature representation. In addition, inputting the video frame and its corresponding future frame to the network to generate the appearance representation complementary to motion feature. The model is only trained with normal events, therefore it is not able to generate abnormal events accurately. During the test, for the foreground moving targets, the images generated by the model are compared with the ground truth to obtain a two-stream anomaly probability distribution model based on the mean square error used to achieve the purpose of region anomaly detection. Experiments on the public datasets show that the proposed method can effectively detect and locate abnormal behaviors in the video.

Study of the large scale structure through modified gravity theory using statistical mechanics

We discuss the galaxy clustering based on thermodynamics and statistical mechanics in the expanding universe in a modified theory of gravity. The modified general relativity (MGR) is developed using the regular line element field to construct a symmetric tensor that represents the energy momentum of the gravitational field. This in turn provides a modified gravitational potential with terms that represent dark matter and dark energy effects without actually invoking the two. Based on the modified gravitational potential we calculate the distribution function of the galaxies. We also calculate various thermodynamic equations of state. We make a data analysis of the data obtained through the SDSS-III survey and check the feasibility of the theoretical model of probability distribution of galaxies in the universe.