Using Artificial Neural-Networks in Stochastic Differential Equations Based Software Reliability Growth Modeling

Due to high cost of fixing failures, safety concerns, and legal liabilities, organizations need to produce software that is highly reliable. Software reliability growth models have been developed by software developers in tracking and measuring the growth of reliability. Most of the Software Reliability Growth Models, which have been proposed, treat the event of software fault detection in the testing and operational phase as a counting process. Moreover, if the size of software system is large, the number of software faults detected during the testing phase becomes large, and the change of the number of faults which are detected and removed through debugging activities becomes sufficiently small compared with the initial fault content at the beginning of the testing phase. Therefore in such a situation, we can model the software fault detection process as a stochastic process with a continuous state space. Recently, Artificial Neural Networks (ANN) have been applied in software reliability growth prediction. In this paper, we propose an ANN based software reliability growth model based on Ito type of stochastic differential equation. The model has been validated, evaluated and compared with other existing NHPP model by applying it on actual failure/fault removal data sets cited from real software development projects. The proposed model integrated with the concept of stochastic differential equation performs comparatively better than the existing NHPP based model.

Application of Artificial Neural Network in Engineering

This paper mainly studies the dynamic direction of engineering valuation based on the artificial neural network methods,and seeks for a set of rapid,convenient and practical valuation models,for building construction projects.

Application of Artificial Neural Networks in Order to Predict Mahabad River Discharge

Estimating of river discharge is one of the more important parameters in the water resources management. In recent years, due to increasing population, increased water consumption in industrial, agricultural and health sections, thus water shortge becomes a global problem. Accurate estimation of the river discharge is one of the most important parameters in surface water resources management, especially in order to determine appropriate values in flood, drought, drinking, agricultural and industral topics. The case study in this research is Mahabad River that is located in west Azarbaijan province in west north of Iran. In this study, we used 70%, 15% and 15% data in order to train, validate and test, respectively. In this study, data of Kawtar and Baitas stations were used in order to determine Mahabad River discharge. In each ststion, several different networks were prepared using NeuroSolutions V.6.0 software. The neural models included Multilayer Perceptron (MLP), Generalized Feed Forward, Jordan/Elman, Radial Basis Functions (RBF) and Principle Component Analysis (PCA), and different transfer functions included Tanh, Sigmoid, Linear Tanh, Linear Sigmoid and the number of hidden layers of.The different number of nodesin layers with different learning algorithms (Momentum, Levenberg Marquardt, Quickprop, DeltaBarDelta, Conjugate Gradient) and different networks were compared. The results showed the artificial neural networks. They predicted the river discharge with 10.67 and 0.94 (m3/s)2 and the high value of correlation coefficient with 0.88 and 0.75 for Kawtar and Baitas stations respectivly.

Simulating the Inverse Kinematic Model of a Robot through Artificial Neural Networks： Complementing the Teaching of Robotics

Teaching robotics necessarily involves the study of the kinematic models of robot manipulators. In turn, the kinematics of a robot manipulator can be described by its forward and reverse models. The inverse kinematic model, which provides the status of the joints according to the desired position for the tool of the robot, is typically taught and described in robotics classes through an algebraic way. However, the algebraic representation of this model is often difficult to obtain. Thus, although it is unquestionable the need for the accurate determination of the inverse kinematic model of a robot, the use of ANNs （artificial neural networks） in the design stage can be very attractive, because it allows us to predict the behavior of the robot before the formal development of its model. In this way, this paper presents a relatively quick way to simulate the inverse kinematic model of a robot, thereby allowing the student to have an overview of the model, coming to identify points that should be corrected, or that can be optimized in the structure of a robot.

Application of artificial neural networks in analysis of CHF experimental data in round tubes

Artificial neural networks (ANNs) are applied successfully to analyze the critical heat flux (CHF) experimental data from some round tubes in this paper. A set of software adopting artificial neural network method for predicting CHF in round tube and a set of CHF database are gotten. Comparing with common CHF correlations and CHF look-up table, ANN method has stronger ability of allow-wrong and nice robustness. The CHF predicting software adopting artificial neural network technology can improve the predicting accuracy in a wider parameter range,and is easier to update and to use. The artificial neural nefwork method used in this paper can be applied to some similar physical problems.

Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines

Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.

Forecasting of Software Reliability Using Neighborhood Fuzzy Particle Swarm Optimization Based Novel Neural Network

This paper proposes an artificial neural network(ANN) based software reliability model trained by novel particle swarm optimization(PSO) algorithm for enhanced forecasting of the reliability of software. The proposed ANN is developed considering the fault generation phenomenon during software testing with the fault complexity of different levels. We demonstrate the proposed model considering three types of faults residing in the software. We propose a neighborhood based fuzzy PSO algorithm for competent learning of the proposed ANN using software failure data. Fitting and prediction performances of the neighborhood fuzzy PSO based proposed neural network model are compared with the standard PSO based proposed neural network model and existing ANN based software reliability models in the literature through three real software failure data sets. We also compare the performance of the proposed PSO algorithm with the standard PSO algorithm through learning of the proposed ANN. Statistical analysis shows that the neighborhood fuzzy PSO based proposed neural network model has comparatively better fitting and predictive ability than the standard PSO based proposed neural network model and other ANN based software reliability models. Faster release of software is achievable by applying the proposed PSO based neural network model during the testing period.

Prediction for asphalt pavement water film thickness based on artificial neural network

In order to study the variation o f the asphalt pavement water film thickness influenced by multi-factors,anew method for predicting water film thickness was developed by the combination o f the artificial neural network(ANN)a d two-dimensional shallow water equations based on hydrodynamic theory.Multi-factors included the rainfall intensity,pavement width,cross slope,longitudinal slope a d pavement roughness coefficient.The two-dimensional hydrodynamic method was validated by a natural rainfall event.Based on the design scheme o f Shen-Sha expressway engineering project,the limited training data obtained by the two-dimensional hydrodynamic simulation model was used to predict water film thickness.Furthermore,the distribution of the water film thickness influenced by multi-factors on the pavement was analyzed.The accuracy o f the ANN model was verified by the18sets o f data with a precision o f0.991.The simulation results indicate that the water film thickness increases from the median strip to the edge o f the pavement.The water film thickness variation is obviously influenced by rainfall intensity.Under the condition that the pavement width is20m and t e rainfall intensity is3m m/h,t e water film thickness is below10mm in the fast lane and20mm in t e lateral lane.Athough there is fluctuation due to the amount oftraining data,compared with the calculation on the basis o f the existing criterion and theory,t e ANN model exhibits a better performance for depicting the macroscopic distribution of the asphalt pavement water film.

A study of using grey system theory and artificial neural network on the climbing ability of <i>Buergeria robusta</i>frog

Ecological engineering is an emerging study of integrating both ecology and engineering, concerned with the design, monitoring, and construction of ecosystems. In recent years, the threat to amphibian animals is becoming more and more serious. In particular, the loss of habitats caused by changes to the way land is used by human beings has hit amphibians particularly hard. Amphibians are known to be particularly vulnerable to human activities because they rely on both terrestrial and aquatic habitats for survival. With the increasing development of many areas in recent years, concrete structures are often installed along water bodies in order to increase the safety of local residents. The construction of concrete banks along rivers associated with human development has become a serious problem in Taiwan. Most ecosystems used by amphibians are lakes and stream banks, yet no related design solutions to accommodate the needs of amphibians. The need to develop the relevant design specification considering protecting the amphibian is imperative. Buergeria robusta, an endemic species in Taiwan, is tree frog widely distributed in lowland montane regions. Their breeding season is from April to September. They like to rest on trees or hide at caves during the daytime and move to the stream nearby in dusk for breeding. Males usually emit weak mating call while standing on stones. Sticky eggs are attached to undersides of rocks and stones. Tadpoles are found in slow flowing water of streams [1]. The goal of this study is to improve the understanding of the relationship between the climbing ability and the physical characteristics of amphibians. In this study, we use Artificial Neural Network to simulate the climbing ability of Buergeria robusta. Besides, Grey System Theory is also adopted to improve the performance of Artificial Neural Network. Artificial Neural Network (ANN) is a computing system that uses a large number of artificial neurons imitating natural neural ability to deal with an information network by computing system. The numerical results have show good agreement with the experimental results. The results can serve as a reference for technicians involved in future ecological engineering designs of banks throughout the world.

Artificial Neural Network for Websites Classification with Phishing Characteristics

Several threats are propagated by malicious websites largely classified as phishing. Its function is important information for users with the purpose of criminal practice. In summary, phishing is a technique used on the Internet by criminals for online fraud. The Artificial Neural Networks (ANN) are computational models inspired by the structure of the brain and aim to simu-late human behavior, such as learning, association, generalization and ab-straction when subjected to training. In this paper, an ANN Multilayer Per-ceptron (MLP) type was applied for websites classification with phishing cha-racteristics. The results obtained encourage the application of an ANN-MLP in the classification of websites with phishing characteristics.

A Hybrid Model for Improving Software Cost Estimation in Global Software Development

Accurate software cost estimation in Global Software Development(GSD)remains challenging due to reliance on historical data and expert judgments.Traditional models,such as the Constructive Cost Model(COCOMO II),rely heavily on historical and accurate data.In addition,expert judgment is required to set many input parameters,which can introduce subjectivity and variability in the estimation process.Consequently,there is a need to improve the current GSD models to mitigate reliance on historical data,subjectivity in expert judgment,inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns.This study introduces a novel hybrid model that synergizes the COCOMO II with Artificial Neural Networks(ANN)to address these challenges.The proposed hybrid model integrates additional GSD-based cost drivers identified through a systematic literature review and further vetted by industry experts.This article compares the effectiveness of the proposedmodelwith state-of-the-artmachine learning-basedmodels for software cost estimation.Evaluating the NASA 93 dataset by adopting twenty-six GSD-based cost drivers reveals that our hybrid model achieves superior accuracy,outperforming existing state-of-the-artmodels.The findings indicate the potential of combining COCOMO II,ANN,and additional GSD-based cost drivers to transform cost estimation in GSD.

Research and applications of artificial neural network in pavement engineering:A state-of-the-art review

Given the great advancements in soft computing and data science,artificial neural network(ANN)has been explored and applied to handle complicated problems in the field of pavement engineering.This study conducted a state-of-the-art review for surveying the recent progress of ANN application at different stages of pavement engineering,including pavement design,construction,inspection and monitoring,and maintenance.This study focused on the papers published over the last three decades,especially the studies conducted since 2013.Through literature retrieval,a total of 683 papers in this field were identified,among which 143 papers were selected for an in-depth review.The ANN architectures used in these studies mainly included multi-layer perceptron neural network(MLPNN),convolutional neural network(CNN)and recurrent neural network(RNN)for processing one-dimensional data,two-dimensional data and time-series data.CNN-based pavement health inspection and monitoring attracted the largest research interest due to its potential to replace human labor.While ANN has been proved to be an effective tool for pavement material design,cost analysis,defect detection and maintenance planning,it is facing huge challenges in terms of data collection,parameter optimization,model transferability and low-cost data annotation.More attention should be paid to bring multidisciplinary techniques into pavement engineering to tackle existing challenges and widen future opportunities.

CAM-BRAIN"ATR's ARTIFICIAL BRAIN PROJECT A Progress Report

This paper reports on progress made in the first 3 years of.ATR's 'CAM-Brain'Project, which aims to use 'evolutionary e.gi...,i.gi' techniques to build/grow/evolve a RAM-and-cellular-automata based artificial brain consisting of thousands of interconnected neural network modules inside special hardware such as MITs Cellular Automata Machine 'CAM-8,i, or NTT's Content Addressable Memory System 'CAM-System'. The states of a billion (later a trillion) 3D cellular automata cells, and edlions of cellular automata rules which govern their state changes, can be stored relatively cheaply in giga(tera)bytes of RAM. After 3 years work, the CA rules are almost ready. MITt,,'CAM-8' (essentially a serial device) can update 200,000,000 CA cells a second. It is possible that NTT's 'CAM-System' (essentially a massively parallel device) may be able to update a trillion CA cells a second. Hence all the ingredients will soon be ready to create a revolutionary new technology which will allow thousands of evolved neural network modules to be assembled into artificial brains. This in turn will probably create not only a new research field, but hopefully a whole new industry,namely 'brain building'. Building artificial brains with a billion neurons is the aim of ATR's 8 year i,CAM-B,ai.,' research project, ending in 2001.

A Comparative Study of Three Machine Learning Methods for Software Fault Prediction

The contribution of this paper is comparing three popular machine learning methods for software fault prediction. They are classification tree, neural network and case-based reasoning. First, three different classifiers are built based on these three different approaches. Second, the three different classifiers utilize the same product metrics as predictor variables to identify the fault-prone components. Third, the predicting results are compared on two aspects, how good prediction capabilities these models are, and how the models support understanding a process represented by the data.

PWM VLSI Neural Network for Fault Diagnosis

An improved pulse width modulation (PWM) neural network VLSI circuit for fault diagnosis is presented, which differs from the software-based fault diagnosis approach and exploits the merits of neural network VLSI circuit. A simple synapse multiplier is introduced, which has high precision, large linear range and less switching noise effects. A voltage-mode sigmoid circuit with adjustable gain is introduced for realization of different neuron activation functions. A voltage-pulse conversion circuit required for PWM is also introduced, which has high conversion precision and linearity. These 3 circuits are used to design a PWM VLSI neural network circuit to solve noise fault diagnosis for a main bearing. It can classify the fault samples directly. After signal processing, feature extraction and neural network computation for the analog noise signals including fault information,each output capacitor voltage value of VLSI circuit can be obtained, which represents Euclid distance between the corresponding fault signal template and the diagnosing signal, The real-time online recognition of noise fault signal can also be realized.

A component-based back-propagation reliability model with low complexity for complex software systems

Since most of the available component-based software reliability models consume high computational cost and suffer from the evaluating complexity for the software system with complex structures,a component-based back-propagation reliability model(CBPRM)with low complexity for the complex software system reliability evaluation is presented in this paper.The proposed model is based on the artificial neural networks and the component reliability sensitivity analyses.These analyses are performed dynamically and assigned to the neurons to optimize the reliability evaluation.CBPRM has a linear increasing complexity and outperforms the state-based and the path-based reliability models.Another advantage of CBPRM over others is its robustness.CBPRM depends on the component reliabilities and the correlative sensitivities,which are independent from the software system structure.Based on the theory analysis and experiment results,it shows that the complexity of CBPRM is evidently lower than the contrast models and the reliability evaluating accuracy is acceptable when the software system structure is complex.

Social Engineering Attack Classifications on Social Media Using Deep Learning

In defense-in-depth,humans have always been the weakest link in cybersecurity.However,unlike common threats,social engineering poses vulnerabilities not directly quantifiable in penetration testing.Most skilled social engineers trick users into giving up information voluntarily through attacks like phishing and adware.Social Engineering(SE)in social media is structurally similar to regular posts but contains malicious intrinsic meaning within the sentence semantic.In this paper,a novel SE model is trained using a Recurrent Neural Network Long Short Term Memory(RNN-LSTM)to identify well-disguised SE threats in social media posts.We use a custom dataset crawled from hundreds of corporate and personal Facebook posts.First,the social engineering attack detection pipeline(SEAD)is designed to filter out social posts with malicious intents using domain heuristics.Next,each social media post is tokenized into sentences and then analyzed with a sentiment analyzer before being labelled as an anomaly or normal training data.Then,we train an RNN-LSTM model to detect five types of social engineering attacks that potentially contain signs of information gathering.The experimental result showed that the Social Engineering Attack(SEA)model achieves 0.84 in classification precision and 0.81 in recall compared to the ground truth labeled by network experts.The experimental results showed that the semantics and linguistics similarities are an effective indicator for early detection of SEA.

A Neuro-Based Software Fault Prediction with Box-Cox Power Transformation

Software fault prediction is one of the most fundamental but significant management techniques in software dependability assessment. In this paper we concern the software fault prediction using a multilayer-perceptron neural network, where the underlying software fault count data are transformed to the Gaussian data, by means of the well-known Box-Cox power transformation. More specially, we investigate the long-term behavior of software fault counts by the neural network, and perform the multi-stage look ahead prediction of the cumulative number of software faults detected in the future software testing. In numerical examples with two actual software fault data sets, we compare our neural network approach with the existing software reliability growth models based on nonhomogeneous Poisson process, in terms of predictive performance with average relative error, and show that the data transformation employed in this paper leads to an improvement in prediction accuracy.

Application of neural network to speed-up equilibrium calculations in compositional reservoir simulation

Compositional reservoir simulation is an important tool to model fluid flow in oil and gas reservoirs.Important investment decisions regarding oil recovery methods are based on simulation results,where hundred or even thousand of different runs are performed.In this work,a new methodology using artificial intelligence to learn the thermodynamic equilibrium is proposed.This algorithm is used to replace the classical equilibrium workflow in reservoir simulation.The new method avoids the stability test for single-phase cells in most cases and provides an accurate two-phase flash initial estimate.The classical and the new workflow are compared for a gas-oil mixing case,showing a simulation time speed-up of approximately 50%.The new method can be used in compositional reservoir simulations.

Large eddy simulation of spray combustion using flamelet generated manifolds combined with artificial neural networks

In the present work,artificial neural networks(ANN)technique combined with flamelet generated manifolds(FGM)is proposed to mitigate the memory issue of FGM models.A set of ANN models is firstly trained using a 68-species mass fractions in mixture fraction-progress variable space.The ANN prediction accuracy is examined in large eddy simulation(LES)and Reynolds averaged Navier-Stokes(RANS)simulations of spray combustion.It is shown that the present ANN models can properly replicate the FGM table for most of the species mass fractions.The network models with relative error less than 5%are considered in RANS and LES to simulate the Engine Combustion Network(ECN)Spray H flames.Validation of the method is firstly conducted in the framework of RANS.Both non-reacting and reacting cases show the present method predicts very well the trend of spray and combustion process under different ambient temperatures.The results show that FGM-ANN can replicate the ignition delay time(IDT)and lift-off length(LOL)precisely as the conventional FGM method,and the results agree very well with the experiments.With the help of ANN,it is possible to achieve high efficiency and accuracy,with a significantly reduced memory requirement of the FGM models.LES with FGM-ANN is then applied to explore the detailed spray combustion process.Chemical explosive mode analysis(CEMA)approach is used to identify the local combustion modes.It is found that before the spray flame is developed to the steady-state,the high CH_(2)O zone is always associated with ignition mode.However,high CH_(2)O zone together with high OH zone is dominated by the burned mode after the steady-state.The lift-off position is dominated mainly by the diffusion mode.