دانلود و نمایش مقالات مرتبط با Fuzzy logic::صفحه 1
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نتیجه جستجو - Fuzzy logic

تعداد مقالات یافته شده: 74
ردیف عنوان نوع
1 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
مقاله انگلیسی
2 Detecting adverse drug reactions in discharge summaries of electronic medical records using Readpeer
بررسی عوارض جانبی دارویی در خلاصه تخلیه سوابق پزشکی الکترونیکی با استفاده از Readpeer-2019
Background: Hospital discharge summaries offer a potentially rich resource to enhance pharmacovigilance efforts to evaluate drug safety in real-world clinical practice. However, it is infeasible for experts to read through all discharge summaries to find cases of drug-adverse event (AE) relations. Purpose: The objective of this paper is to develop a natural language processing (NLP) framework to detect drug- AE relations from unstructured hospital discharge summaries. Basic procedures: An NLP algorithm was designed using customized dictionaries of drugs, adverse event (AE) terms, and rules based on trigger phrases, negations, fuzzy logic and word distances to recognize drug, AE terms and to detect drug-AE relations. Furthermore, a customized annotation tool was developed to facilitate expert review of discharge summaries from a tertiary hospital in Singapore in 2011. Main findings: A total of 33 trial sets with 50 to 100 records per set were evaluated (1620 discharge summaries) by our algorithm and reviewed by pharmacovigilance experts. After every 6 trial sets, drug and AE dictionaries were updated, and rules were modified to improve the system. Excellent performance was achieved for drug and AE entity recognition with over 92% precision and recall. On the final 6 sets of discharge summaries (600 records), our algorithm achieved 75% precision and 59% recall for identification of valid drug-AE relations. Principal conclusions: Adverse drug reactions are a significant contributor to health care costs and utilization. Our algorithm is not restricted to particular drugs, drug classes or specific medical specialties, which is an important attribute for a national regulatory authority to carry out comprehensive safety monitoring of drug products. Drug and AE dictionaries may be updated periodically to ensure that the tool remains relevant for performing surveillance activities. The development of the algorithm, and the ease of reviewing and correcting the results of the algorithm as part of an iterative machine learning process, is an important step towards use of hospital discharge summaries for an active pharmacovigilance program
Keywords: Pharmacovigilance | Text mining | Electronic medical records | Expert system | Adverse drug reaction
مقاله انگلیسی
3 Design and implementation of an expert system for periodic and emergency control under uncertainty: A case study of city gate stations
طراحی و اجرای سیستم خبره برای کنترل دوره ای و اضطراری تحت عدم اطمینان: مطالعه موردی ایستگاه های دروازه شهر-2019
Safety analysis is essential in the natural gas transmission industry to guarantee effective hazard identification and to prevent the failure of components in advance. The overall aim of this research is to introduce a new hybrid expert system and fuzzy logic to monitor city gate stations as one of the most vital installations of gas distribution networks. The proposed model utilizes fuzzy if-then rules based on multiple experts opinions to study the compound interrelations between the mechanical and physical elements of city gate stations. The presented expert system accounts for uncertainty associated with the experts’ judgments by fuzzy sets theory. The expert system is implemented in an object-oriented platform and is programmed with C#. The validity of the expert system is confirmed using simulation experiments through a case study of city gate stations.
Keywords: City gate stations | Fuzzy expert system | Gas industry | Decision tree Uncertainty | Inference chain
مقاله انگلیسی
4 GimmeHop: A recommender system for mobile devices using ontology reasoners and fuzzy logic
GimmeHop: یک سیستم توصیه گر برای دستگاه های همراه سیار با استفاده از استدلال هستی شناسی و منطق فازی-2019
This paper describes GimmeHop, a beer recommender system for Android mobile devices using fuzzy ontologies to represent the relevant knowledge and semantic reasoners to infer implicit knowledge. GimmeHop use fuzzy quantifiers to deal with incomplete data, fuzzy hedges to deal with the user context, and aggregation operators to manage user preferences. The results of our evaluation measure empirically the data traffic and the running time in the case of remote reasoning, the size of the ontologies that can be locally dealt with in a mobile device in the case of local reasoning, and the quality of the automatically computed linguistic values supported in the user queries
Keywords:Fuzzy ontologies | Aggregation | Fuzzy quantifiers | Recommender systems
مقاله انگلیسی
5 On constructing the largest and smallest uninorms on bounded lattices
ساخت بزرگترین و کوچکترین ناآرامی ها بر روی مشبک های محدود-2019
Uninorms on the unit interval are a common extension of triangular norms (t-norms) and triangular conorms (t-conorms). As important aggregation operators, uninorms play a very important role in fuzzy logic and expert systems. Recently, several researchers have studied constructions of uninorms on more general bounded lattices. In particular, Çaylı (2019) gave two methods for constructing uninorms on a bounded lattice L with e ∈ L {0, 1}, which is based on a t-norm Te on [0, e] and a t-conorms Se on [e, 1] that satisfy strict boundary conditions. In this paper, we propose two new methods for constructing uninorms on bounded lattices. Our constructed uninorms are indeed the largest and the smallest among all uninorms on L that have the same restrictions Te and Se on [0, e] and, respectively, [e, 1]. Moreover, our constructions does not require the boundary condition, and thus completely solved an open problem raised by Çaylı.
Keywords: Bounded lattices | Aggregation operators | Uninorms | Neutral elements
مقاله انگلیسی
6 Expert system based on a fuzzy logic model for the analysis of the sustainable livestock production dynamic system
سیستم خبره مبتنی بر یک مدل منطق فازی برای تجزیه و تحلیل سیستم پویا تولید دام پایدار-2019
This essay documents the development of an “Expert System” based on a Fuzzy Logic model, designed to analyze the outcome a number of variables have on the performance of livestock production (milk and meat) in the Huasteca region of Veracruz in order to support the decision-making of a Sustainable Livestock Production Dynamic System (SLPDS). The Expert System takes into consideration the following input variables: Temperature (T), Rain (RA), Breed (B), Health Plan Implementation (HP), Feeding Plan (FP) and Production System (PS). The aforementioned variables then have an impact on three output variables: Lactation Days (LD), Daily Milk Production (DMP) and Intervals between Births (IBB). Once the Fuzzy Logic model has been developed, an assessment of the variables is made through the Response Surface technique, which allows verifying how the variables behave in the system under study, and their impact on the output variables; as well as, testing different scenarios in order to validate the model and to identify the Livestock Production Systeḿs behavioral patterns. Through the application of Fuzzy Logic regarding the modeling of the 6 variables that impact the performance of livestock production, it is possible to capitalize on the knowledge and experience that producers have and what they have learned based on the observation and practice of many years. Therefore, being able to obtain reliable results that can be shared with agricultural producers and technicians for the improvement of the livestock productivity of the Huasteca region in the state of Veracruz. The Expert System is efficient showing an 86.67% reliability by comparing its results with a panel of specialists in livestock production. The test of the different scenarios shows interesting results when exposing the application of good livestock practices in certain conditions (temperature and rainfall) that maximize the milk and meat production of the region under study.
Keyword: Sustainable livestock production | Expert system | Fuzzy logic | Response surface
مقاله انگلیسی
7 یک الگوریتم ترکیبی بهبود یافته فازی کلنی مورچه ای به کار رفته برای متعادل سازی بار در محیط محاسبه ابری
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 18
این مقاله یک الگوریتم ترکیبی نوین را برمبنای منطق فازی و مفاهیم بهینه سازی کلنی مورچه ای (ACO) جهت بهبود متعدل سازی بار در محیط ابری توضیح می دهد. متاسفانه تعداد زیاد درخواست های پردازش شده و نیز سرورهای دردسترس در هر نمونه t، الگوریتم های سنتی متعادل سازی بار را غیرمفید می کند. الگوریتم پیشنهادی متعادل سازی بار و اهداف زمان پاسخ را در ابر مدنظر قرار می دهد. به علاوه، عملکرد الگوریتم ACO شدیدا" با مقادیر پارامترهای ACO همبسته است. دیدگاه معرفی شده (1) از طراحی تجربی تاگوچی برای شناسایی بهترین مقدار پارامترهای ACO استفاده می کند (2) و یک مدول فازی برای ارزیابی مقدار فرومون به منظور بهبود مدت زمان محاسبه تعریف می کند. شبیه سازی های به دست آمده ازطریق بستر تحلیلگر ابری نشانگر مفید بودن الگوریتم فازی ترکیبی ACO در مقایسه با سایر الگوریتم های متعادل سازی بار هستند.
کلیدواژه ها: بهینه سازی کلنی مورچه ای | منطق فازی | محاسبه ابری | متعادل سازی بار | تاگوچی | زمان پاسخ
مقاله ترجمه شده
8 A fuzzy decision support system for managing maintenance activities of critical components in manufacturing systems
یک سیستم پشتیبانی تصمیم گیری فازی برای مدیریت فعالیت های نگهداری از قطعات مهم در سیستم های تولید-2019
Management of critical components in manufacturing systems aims at managing components with very low reliability or the highest risk which can cause disruptions in manufacturing. This study presents a method for identifying critical components and a decision support tool for managing maintenance activities of critical components in manufacturing systems. Unlike the traditional reliability function, the proposed method uses the duty cycle, utilization rate of capacity, safety stock effect, and redundancy effect. It also has the ability to calculate some of the costs associated with the reliability and maintenance. In addition to the proposed method, an expert system as a decision support tool has also been proposed to assist in managing maintenance activities of critical components. The proposed method and the developed decision support system have been tested with a real data set taken from an industrial company and a randomly generated data set. The results have shown that the proposed method has a more realistic impact on component reliability compared to the traditional reliability function. The experimental results have validated the credibility of the proposed decision support system to manage maintenance activities of critical components. Besides, two comparison tables have shown that the proposed decision support system outperforms some approaches such as ANN, FMEA, FMECA, and AHP.
Keywords: Critical component management | Maintenance management | Reliability | Fuzzy logic | Decision support sys
مقاله انگلیسی
9 Artificial intelligence SF6 circuit breaker health assessment
ارزیابی سلامت سلامت قطع کننده مدار SF6 هوش مصنوعی-2019
This paper presents advance artificial intelligence (AI) methods for health assessment of high voltage SF6 circuit breakers. Paper presents an overview of monitoring and diagnostics of most important indicators of the state of high voltage SF6 circuit breakers. Special attention is devoted to identifying and determining indicator limit values which can be used by AI in order to create new health assessment. Fuzzy logic as a part of AI was applied to define fuzzy expert systems which will make decisions about the maintenance of circuit breakers. Three fuzzy expert systems are created to indicate the state of: contacts, the fluid for extinguishing the electric arc and the drive mechanism. Unsupervised machine learning (UML) was applied through the k-means cluster method and cluster tree for classifying and dividing the examined high-voltage circuit breakers into groups with similar state and probability of failure. Artificial neural network (ANN) as part of supervised machine learning (SML) is created in order to predict end-life and accelerated aging of tested circuit breakers. The presented AI methods can be used to improve health assessment of high-voltage SF6 circuit breakers
Keywords: Circuit breaker | Diagnostics | Artificial intelligence | Fuzzy logic | Machine learning
مقاله انگلیسی
10 Application of fuzzy decision tree in EOR screening assessment
کاربرد درخت تصمیم فازی در ارزیابی غربالگری EOR-2019
Ranking of best possible Enhanced Oil Recovery (EOR) technics for implementing on a target field is one of the most important questions that should be answered by reservoir engineers. EOR screening can be considered as a tool for recommending the most appropriate EOR methods. Although for each candidate reservoir, the applicability of EOR processes must be investigated specifically, EOR screening can be used as an indicator before economic evaluations or reservoir descriptions are done and executive decisions are made. Implementing an EOR project for predictions that pass this screening is the next step. In this study, the fuzzy decision tree method (with the ability to rank and classify EOR methods simultaneously) is introduced for EOR screening. Basic features for this study are permeability, viscosity, depth, temperature, saturation, and API. Using a fuzzy decision tree enables us to design an expert system which generates EOR rules automatically. This is one of the noticeable features of this study which reduces the importance of a human expert role while designing the system and making it as expert as possible. Here, the fuzzy decision tree method is implemented on a dataset consisting of 548 observations related to 10 different EOR techniques. Predictions made by this method which are ranked from the most applicable EOR method to the least one include the EOR method mentioned in the dataset for every observation in both training and test set. Moreover, using the procedure introduced here for training the trees enables the expert system to be adaptive whenever the dataset is updated
Keywords: Data mining | Expert system | Artificial intelligence algorithms | EOR screening | Fuzzy logic | Fuzzy decision tree | Automatic rule generation
مقاله انگلیسی
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