A knowledge-based expert system to assess power plant project cost overrun risks
یک سیستم خبره مبتنی بر دانش برای ارزیابی هزینه ریسک بیش ازحد پروژه نیروگاهی-2019
Preventing cost overruns of such infrastructure projects as power plants is a global project management problem. The existing risk assessment methods/models have limitations to address the complicated na- ture of these projects, incorporate the probabilistic causal relationships of the risks and probabilistic data for risk assessment, by taking into account the domain experts’ judgments, subjectivity, and un- certainty involved in their judgments in the decision making process. A knowledge-based expert system is presented to address this issue, using a fuzzy canonical model (FCM) that integrates the fuzzy group decision-making approach (FGDMA) and the Canonical model ( i.e. a modified Bayesian belief network model) . The FCM overcomes: (a) the subjectivity and uncertainty involved in domain experts’ judgment, (b) sig- nificantly reduces the time and effort needed for the domain experts in eliciting conditional probabilities of the risks involved in complex risk networks, and (c) reduces the model development tasks, which also reduces the computational load on the model. This approach advances the applications of fuzzy-Bayesian models for cost overrun risks assessment in a complex and uncertain project environment by addressing the major constraints associated with such models. A case study demonstrates and tests the application of the model for cost overrun risk assessment in the construction and commissioning phase of a power plant project, confirming its ability to pinpoint the most critical risks involved ̶ in this case, the complex- ity of the lifting and rigging heavy equipment, inadequate work inspection and testing plan, inadequate site/soil investigation, unavailability of the resources in the local market, and the contractor’s poor plan- ning and scheduling.
Keywords: Cost overruns | Risk assessment | Power plant projects | Fuzzy logic | Canonical model
Forensic psychiatric evaluations of defendants: Italy and the Netherlands compared
ارزیابی روانپزشکی پزشکی قانونی از متهمان: ایتالیا و هلند در مقایسه-2019
Background: Forensic psychiatric practices and provisions vary considerably across jurisdictions. The diversity provides the possibility to compare forensic psychiatric practices, as we will do in this paper regarding Italy and the Netherlands. Aim: We aim to perform a theoretical analysis of legislations dealing with the forensic psychiatric evaluation of defendants, including legal insanity and the management of mentally ill offenders deemed insane. This research is carried out not only to identify similarities and differences regarding the assessment of mentally ill offenders in Italy and the Netherlands, but, in addition, to identify strengths and weaknesses of the legislation and procedures used for the evaluation of the mentally ill offenders in the two countries. Results: Italy and the Netherlands share some basic characteristics of their criminal law systems. Yet, forensic psychiatric practices differ significantly, even if we consider only evaluations of defendants. A strong point of Italy concerns its test for legal insanity which defines the legal norm and enables a straightforward communication between the experts and the judges on this crucial matter. A strong point of the Netherlands concerns more standardized practices including guidelines and the use of risk assessment tools, which enable better comparisons and scientific research in this area. Conclusions: We argue that there appears to be room for improvement on both sides with regards to the evaluation of mentally ill offenders. More generally, a transnational approach to these issues, as applied in this paper, could help to advance forensic psychiatric services in different legal systems.
Keywords: Forensic psychiatry | Legal insanity | Italy | Netherlands | Risk assessment
تأمین امنیت اینترنت اشیاء: چالشها، تهدیدات و راهکارها
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 30 - تعداد صفحات فایل doc فارسی: 63
چکيده: اينترنت اشيا (IOT) يک جهش تکنولوژيکي بعدي است که باعث بهبود قابل توجهي در جنبه هاي مختلف محيط انسان مانند بهداشت، تجارت و حمل و نقل خواهد شد. با اين حال، با وجود اين واقعيت که ممکن است باعث ايجاد تغييرات اقتصادي و اجتماعي شود، امنيت و حفاظت از حريم خصوصي اشيا و کاربران يک چالش حياتي باقي مي ماند که بايد مورد توجه قرار گيرد. به طور خاص، در حال حاضر، اقدامات امنيتي بايد اقدامات کاربران و اشيا را تحت نظارت و کنترل قرار دهند. با اين حال ماهيت به هم پيوسته و مستقل اشيا علاوه بر قابليت هاي محدود آنها در رابطه با منابع محاسباتي، قابليت کاربرد مکانيزم هاي امنيتي مرسوم را غير ممکن مي سازد. علاوه بر اين، عدم تجانس فن آوري هاي مختلف که اينترنت اشيا را ترکيب مي کند پيچيدگي فرآيندهاي امنيتي را افزايش مي دهد، چرا که هر تکنولوژي با آسيب پذيري هاي مختلف مشخص مي شود. علاوه بر اين، مقادير عظيمي از داده ها که توسط تعاملات چندگانه بين کاربران و اشيا و يا بين اشيا ايجاد مي شود، مديريت آنها و عملکرد سيستم هاي کنترل دسترسي را سخت تر مي کند. در اين زمينه، اين مقاله قصد دارد يک تحليل جامع امنيتي از IoT را با بررسي و ارزيابي تهديدات بالقوه و اقدامات متقابل ارايه دهد. پس از مطالعه و تعيين الزامات امنيتي در زمينه IoT، ما يک آناليز ريسک کمي و کيفي را اجرا کرديم که در حال بررسي تهديدات امنيتي در هر لايه مي باشد. متعاقبا، براساس اين فرآيند ما اقدامات متقابل مناسب و محدوديت هاي آنها را شناسايي کرديم و توجه ويژه اي به پروتکل هاي اينترنت نموديم. در نهايت، دستورالعمل هاي تحقيق براي کار آينده را ارايه مي دهيم.
|مقاله ترجمه شده|
On the application of machine learning techniques to derive seismic fragility curves
استفاده از روش های یادگیری ماشین برای استنتاج منحنی های شکنندگی لرزه ای-2019
Deriving the fragility curves is a key step in seismic risk assessment within the performance-based earthquake engineering framework. The objective of this study is to implement machine learning tools (i.e., classification-based tools in particular) for predicting the structural responses and the fragility curves. In this regard, ten different classification-based methods are explored: logistic regression, lasso regression, support vector machine, Naïve Bayes, decision tree, random forest, linear and quadratic discriminant analyses, neural networks, and K-nearest neighbors with the structural responses resulted from the multiple strip analyses. In addition, this study examines the impact of class imbalance in training dataset, which is typical among data of structural responses, when developing classification-based models for predicting structural responses. The statistical results using the implemented dataset demonstrate that among applied methods, random forest and quadratic discriminant analysis are, respectively, preferable with the imbalanced and balanced datasets since they show the highest efficiency in predicting the structural responses. Moreover, a detailed procedure is presented on how to derive the fragility curves based on the classification-based tools. Finally, the sensitivity of the applied machine learning methods to the size of employed dataset is investigated. The results explain that logistic regression, lasso regression, and Naïve Bayes are not sensitive to the size of dataset (i.e., the number of performed time history analyses); while the performance of discriminant analysis significantly depends on the size of applied dataset
Keywords: Fragility curve | Machine learning tools | Imbalanced dataset | Random forest | Support vector machine | Multiple strip analysis
The risks of risk: Regulating the use of machine learning for psychosis prediction
ریسک ریسک ها: تنظیم استفاده از یادگیری ماشین برای پیش بینی روان پریشی-2019
Recent advances in Machine Learning (ML) have the potential to revolutionise psychosis prediction and psychiatric assessment. This article has two objectives. First, it clarifies which aspects of English Law are relevant in order to regulate the use of ML in clinical research on psychosis prediction. It is argued that its lawful implementation will depend upon the legal requirements regarding the balance between potential harms and benefits, particularly with reference to: (i) any additional risks introduced by the use of ML for data analysis and outcome prediction; and (ii) the inclusion of vulnerable research populations such as minors or incapacitated adults. Second, this article investigates how clinical prediction via ML might affect the practice of risk assessment under mental health legislation, with reference to English Law. It is argued that there is a potential for virtuous applications of clinical prediction in psychiatry. However, reaffirming the distinction between psychosis risk and risk of harm is paramount. Establishing psychosis risk and assessing a persons risk of harm are discrete practices, and so should remain when using artificial intelligence for psychiatric assessment. Evaluating whether clinical prediction via ML might benefit individuals with psychosis will depend on which risk we try to assess and on what we try to predict, whether this is psychosis transition, a psychotic relapse, self-harm and suicidality, or harm to others.
Keywords: Psychosis | Machine learning | Risk | Prediction | Regulation
Loan delinquency in banking systems: How effective are credit reporting systems?
بزهکاری وام در سیستم های بانکی: سیستم های گزارش دهی اعتبار چقدر مؤثر هستند؟-2019
The role of credit reporting systems in influencing bank loan delinquency has received limited attention in the literature. However, better credit risk assessment can help mitigate some of the informational asymmetries involved in credit extension and thereby ease the flow of credit by addressing the bad loan problem. In this context, I empirically examine the efficacy of credit reporting systems in tackling the bank loan problems. Accordingly, I combine the staggered timing of credit reporting system reforms across countries of Middle East and North Africa with bank-level data in order to analyze the impact of such reforms on non-performing loans. The analysis suggests that credit reporting system reforms leads to a decline in such loans by roughly 40 percent. These effects are driven primarily by reforms of private credit bureau as compared with public credit registry. The analysis also points to a differential impact on NPLs across bank business models and across countries with differing banking structures. Finally, the results show that the efficacy of credit reporting systems is much less compelling during crises.
Keywords: Credit bureau | Credit registry | Loan delinquency | MENA | Banking
Predicting ground-level PM2:5 concentrations in the Beijing-Tianjin- Hebei region: A hybrid remote sensing and machine learning approach
پیش بینی غلظت PM2:5 در سطح زمین در منطقه پکن، Beijing-Tianjin- هبی: یک روش سنجش از دور و یادگیری ماشین هیبریدی-2019
An accurate estimation of PM2.5 (fine particulate matters with diameters 2.5 mm) concentration is critical for health risk assessment and generating air pollution control strategies. In this study, a hybrid remote sensing and machine learning approach, named RSRF model is proposed to estimate daily ground-level PM2.5 concentrations, which integrates Random Forest (RF), one of machine learning (ML) models, and aerosol optical depth (AOD), one of remote sensing (RS) products. The proposed RSRF model provides an opportunity for an adequate characterization of real-time spatiotemporal PM2.5 distributions at uninhabited places and complex surfaces. It also offers advantages in handling complicated non-linear relationships among a large number of meteorological, environmental and air pollutant factors, as well as ever-increasing environmental data sets. The applicability of the proposed RSRF model is tested in the Beijing-Tianjin-Hebei region (BTH region) during 2015e2017. Deep Blue (DB) AOD from Aqua-retrieved Collection 6.1 (C_61) aerosol products of Moderate Resolution Imaging Spectroradiometer (MODIS) is validated with Aerosol Robotic Network. The validation results indicate C_61 DB AOD has a high correlation with ground based AOD in the BTH region. The proposed RSRF model performed well in characterizing spatiotemporal variations of annual and seasonal PM2.5 concentrations. It not only is useful to quantify the relationships between PM2.5 and relevant factors such as DB AOD, meteorological and air pollutant variables, but also can provide decision support for air pollution control at a regional environment during haze periods.
Keywords: Remote sensing | Aerosol optical depth | Machine learning | PM2.5 | Random forest
A methodology for enhancing the reliability of expert system applications in probabilistic risk assessment
روشی برای افزایش قابلیت اطمینان برنامه های کاربردی سیستم خبره در ارزیابی ریسک احتمالی-2019
In highly complex industries, capturing and employing expert systems is significantly important to an organizations success considering the advantages of knowledge-based systems. The two most important issues within the expert system applications in risk and reliability analysis are the acquisition of domain experts professional knowledge and the reasoning and representation of the knowledge that might be expressed. The first issue can be correctly handled by employing a heterogeneous group of experts during the expert knowledge acquisition processes. The members of an expert panel regularly represent different experiences and knowledge. Subsequently, this diversity produces various sorts of information which may be known or unknown, accurate or inaccurate, and complete or incomplete based on its cross-functional and multidisciplinary nature. The second issue, as a promising tool for knowledge reasoning, still suffers from lack of deficiencies such as weight and certainty factor, and are insufficient to accurately represent complex rule-based expert systems. The outputs in current expert system applications in probabilistic risk assessment could not accurately represent the increasingly complex knowledge-based systems. The reason is the lack of certainty and self-assurance of experts when they are expressing their opinions. In this paper, a novel methodology is presented based on the concept of Znumbers to overcome this issue. A case study in a high-tech process industry is provided in detail to demonstrate the application and feasibility of the proposed methodology.
Keywords: Confidence level | Z-numbers | Fault tree analysis | Spherical hydrocarbon storage tank
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
Subjective data arrangement using clustering techniques for training expert systems
ترتیب داده های ذهنی با استفاده از تکنیک های خوشه بندی برای آموزش سیستم های خبره-2019
The evaluation of subjective data is a very demanding task. The classification of the information gath- ered from human evaluators and the possible high noise levels introduced are ones of the most difficult issues to deal with. This situation leads to adopt individuals who can be considered as experts in the specific application domain. Thus, the development of Expert Systems (ES) that consider the opinion of these individuals have been appeared to mitigate the problem. In this work an original methodology for the selection of subjective sequential data for the training of ES is presented. The system is based on the arrangement of knowledge acquired from a group of human experts. An original similarity measure between the subjective evaluations is proposed. Homogeneous groups of experts are produced using this similarity through a clustering algorithm. The methodology was applied to a practical case of the Intel- ligent Transportation Systems (ITS) domain for the training of ES for driving risk prediction. The results confirm the relevance of selecting homogeneous information (grouping similar opinions) when generating a ground truth (a reliable signal) for the training of ES. Further, the results show the need of consider- ing subjective sequential data when working with phenomena where a set of rules could not be easily learned from human experts, such as risk assessment.
Keywords: Subjective sequential data | Subjective data arrangement | Combination of similarities | Driving risk assessment | Driving risk prediction