Large engineering project risk management using a Bayesian belief network

This paper presents a scheme for large engineering project risk management using a Bayesian belief network and applies it to the Korean shipbuilding industry. Twenty-six different risks were deduced from expert interviews and a literature review. A survey analysis was conducted on 252 experts from 11 major Korean shipbuilding companies in April 2007. The overall major risks were design change, design manpower, and raw material supply as internal risks, and exchange rate as external risk in both large-scale and medium-sized shipbuilding companies. Differences of project performance risks between large-scale and medium-sized shipbuilding companies were identified. Exceeding time schedule and specification discontent were more important to large-scale shipbuilding companies, while exceeding budget and exceeding time schedule were more important to medium-sized shipbuilding companies. The change of project performance risks was measured by risk reduction activities of quality management, and strikes at headquarters and subcontractors, in both large-scale and medium-sized shipbuilding companies. The research results should be valuable in enabling industrial participants to manage their large engineering project risks and in extending our understanding of Korean shipbuilding risks.     

Public participation modelling using Bayesian networks in management of groundwater contamination

Negotiation and active involvement with participation of water managers, experts, stakeholders and representatives of the general public requires decision support tools (Environmental Decision Support Systems; EDSS) that build on transparency and flexibility in order to reach sound action plans and management instruments. One possible EDSS for active involvement of stakeholders is application of Bayesian networks (Bns). The paper gives an example of a case study (The Danish case) where farmers and hydrologists disputed the degree to which pesticide application affected the quality of deep groundwater. Instead of selecting one opinion or another, the decision was made to include both in the Bns. By adopting this approach, it was possible to view the results from either point of view, accepting the reality of the situation, not becoming mired in an insoluble conflict, and in this way laying the foundation for future compromises. The paper explores Bns as a tool for acting on and dealing with management of groundwater protection. Bns allow stakeholders' divergent values, interests and beliefs to be surfaced and negotiated in participatory processes for areas where conventional physically based groundwater models are insufficient due to lack of data, physical understanding, flexibility or lack of integration capability. In this way, the agency will be able to address the institutional arrangement influencing groundwater protection in all its complexity.     

Bayesian network modeling for evolutionary genetic structures

Evolutionary theory states that stronger genetic characteristics reflect the organism’s ability to adapt to its environment and to survive the harsh competition faced by every species. Evolution normally takes millions of generations to assess and measure changes in heredity. Determining the connections, which constrain genotypes and lead superior ones to survive is an interesting problem. In order to accelerate this process,we develop an artificial genetic dataset, based on an artificial life (AL) environment genetic expression (ALGAE). ALGAE can provide a useful and unique set of meaningful data, which can not only describe the characteristics of genetic data, but also simplify its complexity for later analysis.To explore the hidden dependencies among the variables, Bayesian Networks (BNs) are used to analyze genotype data derived from simulated evolutionary processes and provide a graphical model to describe various connections among genes. There are a number of models available for data analysis such as artificial neural networks, decision trees, factor analysis, BNs, and so on. Yet BNs have distinct advantages as analytical methods which can discern hidden relationships among variables. Two main approaches, constraint based and score based, have been used to learn the BN structure. However, both suit either sparse structures or dense structures. Firstly, we introduce a hybrid algorithm, called “the E-algorithm”, to complement the benefits and limitations in both approaches for BN structure learning. Testing E-algorithm against a standardized benchmark dataset ALARM, suggests valid and accurate results. BAyesian Network ANAlysis (BANANA) is then developed which incorporates the E-algorithm to analyze the genetic data from ALGAE. The resulting BN topological structure with conditional probabilistic distributions reveals the principles of how survivors adapt during evolution producing an optimal genetic profile for evolutionary fitness.     

基于贝叶斯信念网络的话题识别模型

针对贝叶斯信念网络应用于话题识别进行了研究, 提出了新的话题识别模型。模型的拓扑结构包括新报道、报道术语、事件术语、话题四层节点, 用弧标明索引关系。在贝叶斯概率和条件独立性假设的基础上, 模型运用条件概率计算新报道和已有话题簇的相似度, 从而实现话题识别。考虑到核心报道、核心事件的重要性, 对不同层次的权重计算进行了调整。实验采用DET曲线评测法对模型性能进行测试, 实验结果显示, 调整后的权重计算可在一定程度上提高新模型的性能, 与向量空间模型相比, 在相同阈值下新模型的漏报率与误报率有所降低。     

Bayesian belief network based broadcast sports video indexing

This paper presents a probabilistic Bayesian belief network (BBN) method for automatic indexing of excitement clips of sports video sequences. The excitement clips from sports video sequences are extracted using audio features. The excitement clips are comprised of multiple subclips corresponding to the events such as replay, field-view, close-ups of players, close-ups of referees/umpires, spectators, players’ gathering. The events are detected and classified using a hierarchical classification scheme. The BBN based on observed events is used to assign semantic concept-labels to the excitement clips, such as goals, saves, and card in soccer video, wicket and hit in cricket video sequences. The BBN based indexing results are compared with our previously proposed event-association based approach and found BBN is better than the event-association based approach. The proposed scheme provides a generalizable method for linking low-level video features with high-level semantic concepts. The generic nature of the proposed approach in the sports domain is validated by demonstrating successful indexing of soccer and cricket video excitement clips. The proposed scheme offers a general approach to the automatic tagging of large scale multimedia content with rich semantics. The collection of labeled excitement clips provide a video summary for highlight browsing, video skimming, indexing and retrieval.     

Intelligent fault inference for rotating flexible rotors using Bayesian belief network

Flexible rotor is a crucial mechanical component of a diverse range of rotating machineries and its condition monitoring and fault diagnosis are of particular importance to the modern industry. In this paper, Bayesian belief network (BBN) is applied to the fault inference for rotating flexible rotors with attempt to enhance the reasoning capacity under conditions of uncertainty. A generalized three-layer configuration of BBN for the fault inference of rotating machinery is developed by fully incorporating human experts’ knowledge, machine faults and fault symptoms as well as machine running conditions. Compared with the Naive diagnosis network, the proposed topological structure of causalities takes account of more practical and complete diagnostic information in fault diagnosis. The network tallies well with the practical thinking of field experts in the whole processes of machine fault diagnosis. The applications of the proposed BBN network in the uncertainty inference of rotating flexible rotors show good agreements with our knowledge and practical experience of diagnosis.     

一种基于Bayesian信念网络的客户行为预测方法

提出一种基于Bayesian信念网络（BN）的客户行为预测方法．通过知识学习构建客户行为Bayesian网络（CBN）,根据CBN对预实例计算联合分布概率,准确预测了一对一营销优化中的客户行为．CBN学习算法包括连线和定向部分,复杂度为O（N^4）条件相关测试．在零售行业一对一营销实际应用表明,CBN学习算法较现有BN学习算法更快构建CBN,预测精度高于朴素Bayesina分类法．     

Software effort estimation based on the optimal Bayesian belief network

In this paper, we present a model for software effort (person-month) estimation based on three levels Bayesian network and 15 components of COCOMO and software size. The Bayesian network works with discrete intervals for nodes. However, we consider the intervals of all nodes of network as fuzzy numbers. Also, we obtain the optimal updating coefficient of effort estimation based on the concept of optimal control using Genetic algorithm and Particle swarm optimization for the COCOMO NASA database. In the other words, estimated value of effort is modified by determining the optimal coefficient. Also, we estimate the software effort with considering software quality in terms of the number of defects which is detected and removed in three steps of requirements specification, design and coding. If the number of defects is more than the specified threshold then the model is returned to the current step and an additional effort is added to the estimated effort. The results of model indicate that optimal updating coefficient obtained by genetic algorithm increases the accuracy of estimation significantly. Also, results of comparing the proposed model with the other ones indicate that the accuracy of the model is more than the other models.     

An evolutionary hyper-heuristic to optimise deep belief networks for image reconstruction

Deep Belief Networks (DBN) have become a powerful tools to deal with a wide range of applications. On complex tasks like image reconstruction, DBN’s performance is highly sensitive to parameter settings. Manually trying out different parameters is tedious and time consuming however often required in practice as there are not many better options. This work proposes an evolutionary hyper-heuristic framework for automatic parameter optimisation of DBN. The hyper-heuristic framework introduced here is the first of its kind in this domain. It involves a high level strategy and a pool of evolutionary operators such as crossover and mutation to generates DBN parameter settings by perturbing or modifying the current setting of a DBN. Providing a large set of operators could be beneficial to form a more effective high level strategy, but in the same time would increase the search space hence make it more difficulty to form a good strategy. To address this issue, a non-parametric statistical test is introduced to identify a subset of effective operators for different phases of the hyper-heuristic search. Three well-known image reconstruction datasets were used to evaluate the performance of the proposed framework. The results reveal that the proposed hyper-heuristic framework is very competitive when compared to the state of art methods.     

Bayesian belief network for box-office performance: A case study on Korean movies

Due to their definition as experience goods with short product lifetime cycles, it is difficult to forecast the demand for motion pictures. Nevertheless, producers and distributors of new movies need to forecast box-office results in an attempt to reduce the uncertainty in the motion picture business. Previous research demonstrated the ability of certain movie attributes such as early box-office data and release season to forecast box-office revenues. However, no previous research has focused on the causal relationship among various movie attributes, which have the potential to increase the accuracy of box-office predictions. In this paper a Bayesian belief network (BBN), which is known as a causal belief network, is constructed to investigate the causal relationship among various movie attributes in the performance prediction of box-office success. Subsequently, sensitivity analysis is conducted to determine those attributes most critically related to box-office performance. Finally, the probability of a movie’s box-office success is computed using the BBN model based on the domain knowledge from the value chain of theoretical motion pictures. The results confirm the improved forecasting accuracy of the BBN model compared to artificial neural network and decision tree.     

A novel approach for detection of APT malware using multi-dimensional hybrid Bayesian belief network

International Journal of Information Security - Due to the continuous evolution of adversary tactics, strategies, and processes, the contemporary digital universe is confronted with new obstacles...     

An optimum time scale for discrete Laguerre network

Any discrete-time stable transfer function can be expressed by a discrete-time Laguerre series with a chosen time scale. An optimum time scale such that an index is minimized is derived. This index ensures that the coefficients of higher-order Laguerre functions go toward zero quickly. The solution derived requires the knowledge of the impulse response of the discrete plant. Cases of first-order plants, second-order underdamped plants, and plants with multiunit delay are also discussed     

Fusing multimodal biometrics with quality estimates via a Bayesian belief network

Biometric systems for today's high security applications must meet stringent performance requirements; fusing multiple biometrics can help lower system error rates. Fusion methods include processing biometric modalities sequentially until an acceptable match is obtained, using logical (AND/OR) operations, or summing similarity scores. More sophisticated methods combine scores from separate classifiers for each modality. This paper develops a novel fusion architecture based on Bayesian belief networks. Although Bayesian update methods have been used before, our approach more fully exploits the graphical structure of Bayes nets to define and explicitly model statistical dependencies between relevant variables: per sample measurements, such as match scores and corresponding quality estimates, and global decision variables. These statistical dependencies are in the form of conditional distributions which we model as Gaussian, gamma, log-normal or beta, each of which is determined by its mean and variance, thus significantly reducing training data requirements. Moreover, by conditioning decision variables on quality as well as match score, we can extract information from lower quality measurements rather than rejecting them out of hand. Another feature of our method is a global quality measure designed to be used as a confidence estimate supporting decision making. Preliminary studies using the architecture to fuse fingerprints and voice are reported.     

A Bayesian belief network analysis of factors influencing wildfire occurrence in Swaziland

The impacts of wildfires on ecosystems and the factors contributing to their occurrence are increasingly receiving global attention. Advances in satellite remote sensing and information technology provide an opportunity to study these complex interrelationships. A Bayesian belief network (BBN) model was developed from a set of 12 biotic, abiotic and human variables to determine factors that influence wildfire activity in Swaziland using wildfire data from the Terra and Aqua satellites' Moderate Resolution Imaging Spectroradiometer (MODIS) for the period 2001–2007. These were geospatially integrated in the geographic information system (GIS) software ArcView and input into the software Netica for BBN analyses. Land cover, elevation, and climate (mean annual rainfall and mean annual temperature) were found to be strong predictors of wildfire occurrence, while aspect had the least influence on the wildfire occurrence. The model had a high predictive accuracy with an error rate of 9.62%, and an area under the receiver-operating characteristic (ROC) curve of 0.961. The study demonstrates how domain or field knowledge and limited empirical and GIS data can be combined within a BBN model to assist in determining key fire management interventions and lays the foundation for the future development of advanced and dynamic models.     

Decision field theory extensions for behavior modeling in dynamic environment using Bayesian belief network

Decision field theory (DFT), widely known in the field of mathematical psychology, provides a mathematical model for the evolution of the preferences among options of a human decision-maker. The evolution is based on the subjective evaluation for the options and his/her attention on an attribute (interest). In this paper, we extend DFT to cope with the dynamically changing environment. The proposed extended DFT (EDFT) updates the subjective evaluation for the options and the attention on the attribute, where Bayesian belief network (BBN) is employed to infer these updates under the dynamic environment. Four important theorems are derived regarding the extension, which enhance the usability of EDFT by providing the minimum time steps required to obtain the stabilized results before running the simulation (under certain assumptions). A human-in-the-loop experiment is conducted for the virtual stock market to illustrate and validate the proposed EDFT. The preliminary result is quite promising.     

An evolutionary neural network approach for module orientationproblems

A novel neural network approach called “Evolutionary Neural Network (ENN)” is presented for the module orientation problem. The goal of this NP-complete problem is to minimize the total wire length by flipping circuit modules with respect to their vertical and/or horizontal axes of symmetry. In order to achieve high quality VLSI systems, it is strongly desired to solve the problem as quickly as possible in the design cycle. Based on the concept of the genetic algorithm, the evolutionary initialization scheme on neuron states is introduced so as to provide a high quality solution within a very short time. The performance of ENN is compared with three heuristic algorithms through simulations on 20 examples with up to 500 modules. The results show that ENN can find the best solutions in the shortest time     

An evolutionary design methodology of printed circuit boards for high-speed VLSIs

As operation frequencies of the printed circuit boards (PCBs) increase in keeping with VLSI frequencies in the GHz domain, two independent serious problems occur in the PCB design. One is waveform distortion problem, or signal integrity (SI) degradation problem, in PCB traces. And the other is power-supply drop problem, or power integrity (PI) degradation problem, in PCB power planes. Those problems are barely able to be overcome on case-by-case empirical designs conventionally. In this paper we newly propose a design approach for each problem, both of which are based on the genetic algorithm. And we obtained improvement ratios of more than double compared with the both conventional designs for SI and PI degradations, respectively.     

An optimization methodology for intermodal terminal management

A solution to the problems of resource allocation and scheduling of loading and unloading operations in a container terminal is presented. The two problems are formulated and solved hierarchically. First, the solution of the resource allocation problem returns, over a number of work shifts, a set of quay cranes used to load and unload containers from the moored ships and the set of yard cranes to store those containers on the yard. Then, a scheduling problem is formulated to compute the loading and unloading lists of containers for each allocated crane. The feasibility of the solution is verified against a detailed, discrete-event based, simulation model of the terminal. The simulation results show that the optimized resource allocation, which reduces the costs by [frac13], can be effectively adopted in combination with the optimized loading and unloading list. Moreover, the simulation shows that the optimized lists reduce the number of crane conflicts on the yard and the average length of the truck queues in the terminal.     

Bayesian network model of maritime safety management

This paper presents a model of maritime safety management and its subareas. Furthermore, the paper links the safety management to the maritime traffic safety indicated by accident involvement, incidents reported by Vessel Traffic Service and the results from Port State Control inspections. Bayesian belief networks are applied as the modeling technique and the model parameters are based on expert elicitation and learning from historical data. The results from this new application domain of a Bayesian network based expert system suggest that, although several its subareas are functioning properly, the current status of the safety management on vessels navigating in the Finnish waters has room for improvement; the probability of zero poor safety management subareas is only 0.13. Furthermore, according to the model a good IT system for the safety management is the strongest safety-management related signal of an adequate overall safety management level. If no deficiencies have been discovered during a Port State Control inspection, the adequacy of the safety management is almost twice as probable as without knowledge on the inspection history. The resulted model could be applied to performing several safety management related queries and it thus provides support for maritime safety related decision making.     

Bayesian belief network learning algorithms for modeling contextual relationships in natural imagery: a comparative study

This paper presents a comparative study of Bayesian belief network structure learning algorithms with a view to identify a suitable algorithm for modeling the contextual relations among objects typically found in natural imagery. Four popular structure learning algorithms are compared: two constraint-based algorithms (PC proposed by Spirtes and Glymour and Fast Incremental Association Markov Blanket proposed by Yaramakala and Margaritis), a score-based algorithm (Hill Climbing as implemented by Daly), and a hybrid algorithm (Max-Min Hill Climbing proposed by Tsamardinos et al.). Contrary to the belief regarding the superiority of constraint-based approaches, our empirical results show that a score-based approach performs better on our context dataset in terms of prediction power and learning time. The hybrid algorithm could achieve similar prediction performance as the score-based approach, but requires longer time to learn the desired network. Another interesting fact the study has revealed is the existence of strong correspondence between the linear correlation pattern within the dataset and the edges found in the learned networks.