An Approach Based on Bayesian Network for Improving ProjectManagement Maturity: An Application to Reduce Cost Overrun Risksin Engineering Projects
رویکرد مبتنی بر شبکه بیزی برای بهبود بلوغ مدیریت پروژه: برنامه ای برای کاهش هزینه های پیشی گرفتن از پروژه های مهندسی ریسکین-2020
The project management field has the imperative to increase the success probability of projects. Expertshave developed several Project Management Maturity (PMM) models to assess project managementpractices and improve the project outcome. However, the current literature lacks models that allowexperts to correlate the measured maturity with the expected probability of success. The present paperdevelops a general framework and a method to estimate the impact of PMM on project performance. Ituses Bayesian networks to formalize project management experts’ knowledge and to extract knowledgefrom a database of past projects. An industrial case concerning large projects in the oil and gas industryis used to illustrate the application of the method to reduce the risk of project cost (or budget) overruns.
Keywords:Bayesian Networks | Cost Overrun | Knowledge Modeling | Maturity Model | Project Management
Bayesian networks + reinforcement learning: Controlling group emotion from sensory stimuli
شبکه های بیزی + یادگیری تقویتی : کنترل احساسات گروهی از محرک های حسی-2020
As communication technology develops, various sensory stimuli can be collected in service spaces. To enhance the service effectiveness, it is important to determine the optimal stimuli to induce group emo- tion in the service space to the target emotion. In this paper, we propose a stimuli control system to adjust the group emotion. It is a stand-alone system that can determine optimal stimuli by utility ta- ble and modular tree-structured Bayesian networks designed for emotion prediction model proposed in the previous study. To verify the proposed system, we collected data using several scenarios at a kinder- garten and a senior welfare center. Each space is equipped with sensors for collection and equipment for controlling stimuli. As a result, the system shows a performance of 78% in the kindergarten and 80% in the senior welfare center. The proposed method shows much better performance than other classifica- tion methods with lower complexity. Also, reinforcement learning is applied to improving the accuracy of stimuli decision for a positive effect on system performance.
Keywords: Adjusting emotion | Group emotion | Bayesian networks | Reinforcement Learning | IoT
A knowledge-based reasoning model for crime reconstruction and investigation
یک مدل استدلال دانش بنیان برای بازسازی و تحقیقات جرم-2020
Artificial intelligence has been successfully applied in many areas including forensic sciences. Perhaps all forensic works can be regarded as helping reconstruct crimes, i.e. clarify and sequence the events that took place in the commission of a crime through evidence. However, there are few researches on the crime reconstruction using artificial intelligence methods. In this paper, we present a model based on Bayesian networks to help solve crimes. The model, which is termed ‘case-type based model’, is based on the knowledge of a type of crimes. We use Bayesian networks to represent the knowledge and conduct the uncertainty reasoning. We propose a growth algorithm of Bayesian networks to adapt the model to different cases. The model was tested through a real case, and the results indicate that the model can provide effective investigation suggestions and achieve the crime reconstruction.
Keywords: Artificial intelligence | Forensic science | Bayesian networks | Criminal investigation | Uncertainty reasoning | Evidence
Bayesian networks and dissonant items of evidence: A case study
شبکه های بیزی و شواهد متفرق: یک مطالعه موردی-2020
The assessment of different items of evidence is a challenging process in forensic science, particularly when the relevant elements support different inferential directions. In this study, a model is developed to assess the joint probative value of three different analyses related to some biological material retrieved on an object of interest in a criminal case. The study shows the ability of probabilistic graphical models, say Bayesian networks, to deal with complex situations, those that one expects to face in real cases. The results obtained by the model show the importance of a conflict measure as an indication of inconsistencies in the model itself. A contamination event alleged by the defense is also introduced in the model to explain and solve the conflict. The study aims to give an insight in the application of a probabilistic model to real criminal cases.
Keywords: DNA evidence | Activity level interpretation | Bayesian networks | Conflict measure
Analytical games for knowledge engineering of expert systems in support to Situational Awareness: The Reliability Game case study
بازی های تحلیلی برای مهندسی دانش سیستم های خبره در حمایت از آگاهی وضعیتی: مطالعه موردی بازی اطمینان-2019
Knowledge Acquisition (KA) methods are of paramount importance in the design of intelligent systems. Research is ongoing to improve their effectiveness and efficiency. Analytical games appear to be a promis- ing tool to support KA. In fact, in this paper we describe how analytical games could be used for Knowl- edge Engineering of Bayesian networks, through the presentation of the case study of the Reliability Game. This game has been developed with the aim of collecting data on the impact of meta-knowledge about sources of information upon human Situational Assessment in a maritime context. In this paper we describe the computational model obtained from the dataset and how the card positions, which reflect a player belief, can be easily converted in subjective probabilities and used to learn latent constructs, such as the source reliability, by applying the Expectation-Maximisation algorithm.
Keywords: Source reliability | Expert knowledge | Knowledge acquisition | Bayesian networks | Parameter learning | Analytical game
Knowledge representation using non-parametric Bayesian networks for tunneling risk analysis
نمایش دانش با استفاده از شبکه های بیزی غیر پارامتری برای تجزیه و تحلیل ریسک تونل زنی-2019
Knowledge capture and reuse are critical in the risk management of tunneling works. Bayesian networks (BNs) are promising for knowledge representation due to their ability to integrate domain knowledge, encode causal relationships, and update models when evidence is available. However, the model development based on classic BNs is challenging when expert opinions are solicited due to the discretization of variables and quantification of large conditional probability tables. This study applies non-parametric BNs, which only require the elicitation of the marginal distribution corresponding to each node and correlation coefficient associated with each edge, to develop a knowledge-based expert system for tunneling risk analysis. In particular, we propose to use the pairwise Pearsons linear correlations to parameterize the model because the assessment is intuitive and experts in the engineering domain are more familiar and comfortable with this notion. However, when Spearmans rank correlation is given, the method can also be used by modification of the marginals. The method is illustrated with a tunnel case in the Wuhan metro project. The expert knowledge of risk assessment for common failures in shield tunneling is integrated and visualized. The developed model is validated by real documented accidents. Potential applications of the model are also explored, such as decision support for risk-based design.
Keywords: Non-parametric Bayesian networks | Structured expert judgment | Expert system | Risk analysis | Tunneling
A Bayesian network model to explore practice change by smallholder rice farmers in Lao PDR
مدل شبکه های بیزی برای کشف تغییر عمل توسط کشاورزان برنج ; کاران در لائوس-2018
A Bayesian Network model has been developed that synthesizes findings from concurrent multi-disciplinary research activities. The model describes the many factors that impact on the chances of a smallholder farmer adopting a proposed change to farming practices. The model, when applied to four different proposed tech nologies, generated insights into the factors that have the greatest influence on adoption rates. Behavioural motivations for change are highly dependent on farmers individual viewpoints and are also technology de pendent. The model provides a boundary object that provides an opportunity to engage experts and other sta keholders in discussions about their assessment of the technology adoption process, and the opportunities, barriers and constraints faced by smallholder farmers when considering whether to adopt a technology.
Keywords: Innovation diffusion ، Bayesian networks ، Small-holder farmers ، Rice agriculture ، Laos ، Lao PDR
An activity-based defect management framework for product development
چارچوب مدیریت نقص مبتنی بر فعالیت برای توسعه محصول-2018
As competition intensifies, development of complicated hardware products and the decrease in development cycle lead to increasing design defect risk in hardware products, resulting in all kinds of problems such as unsafe product, product development failure and so on. Therefore, it is important to manage design defect during all stages of product development to improve product design quality and product development success rate. Factors influencing design defects injection vary according to the different attributes of a product development, in cluding the product complexity, the experience of the developers, the development cycle and tool. The most significant challenge in design defect management is to identify design activities that are likely to cause defects. This paper proposes a design defect management framework based on design activities that assess and identify design defects. First, the product development process is decomposed by using a work breakdown structure (WBS) to obtain design activities. Subsequently, a Bayesian network is adapted to construct defect assessment model using design activities as network nodes. Finally, the defect control activities such as review, verification, and validation are used to identify design defect. The proposed risk management framework enables an product development to be focused on the key defect activities in which the most serious defect risk exists and provides a more effective way to assess, identify defect risk along the product development cycle. A case study on medical syringes is presented to validate the capability of the proposed approach in providing low residual defect in delivered products.
Keywords: Design activity ، Work breakdown structure ، Defect management ، Bayesian networks ، Defect assessment ، Defect identification
A new reasoning and learning model for Cognitive Wireless Sensor Networks based on Bayesian networks and learning automata cooperation
یک مدل استدلال و یادگیری جدید برای شبکه های حسگر بی سیم شناختی مبتنی بر شبکه های بیزی و همکاری اتوماسیون یادگیری-2017
Adding cognition to existing Wireless Sensor Networks (WSNs) with a cognitive networking approach, which deals with using cognition to the entire network protocol stack to achieve end-to-end goals, brings about many benefits. However cognitive networking may be confused with cognitive radio or cross-layer design, it is a different concept; cognitive radios applies cognition only at the physical layer to overcome the problem of spectrum scarcity, and cross layer design usually focuses on linking at least two non consecutive specific layers, to achieve a particular goal. Indeed, it can be said that the cognitive radio and the cross layer design are two effective methods in cognitive networking. To the best of our knowledge, almost all of the existing researches on the Cognitive Wireless Sensor Networks (CWSNs) have focused on spectrum allocation and interference reduction in the physical layer. In this paper, we propose a new reasoning and learning model for CWSNs, in which firstly, a team of learning automata is employed to construct a Bayesian Network (BN) model of the parameters of the network protocol stack, and then the constructed BN is used to tune the controllable parameters. The BN represents the dependency relation ships between the parameters of the network protocol stack, and the BN-based reasoning is an efficient tool for cross-layer optimization, in order to maximize the perceived network performance. Simulations have been done to evaluate the performance of the proposed model. The results of the simulations show that the proposed model successively adds cognition to a WSN and improves the performance of the communication network.
Keywords: Bayesian Networks | Cognitive networks | Learning automata | Reasoning | Wireless Sensor Network
درباره استفاده از سیستم های چندعاملی برای نظارت بر سیستم های صنعتی
سال انتشار: 2016 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 17
هدف مقاله حاضر، ارائه سیستم هوشمند برای نظارت بر فرایند پیچیده، مبتنی بر تکنولوژی هوش مصنوعی است. هدف این سیستم، تحقق موفق وظایف نظارت بر فرایند پیچیده ای است که عبارتند از: آشکارسازی، تشخیص، شناسایی و پیکر بندی مجدد. بدین منظور، توسعه سیستم چندعاملی که چندین هوش را ترکیب کند همچون: نمودارهای کنترل چند متغیره، شبکه های عصبی، شبکه های بیزی و سیستم های خبره، یک ضرورت بوده است. سیستم پیشنهادی از نظر نظارت بر فرایند پیچیده Tennessee Eastman ارزیابی می شود.
واژه های کلیدی: فرایند چندمتغیری | نمودار کنترل هتلینگ T2 | سیستم چندعاملی | شبکه بیزی | شبکه عصبی.
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