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نتیجه جستجو - منطق فازی

تعداد مقالات یافته شده: 67
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 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
3 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
4 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
5 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
6 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
7 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
8 A text summarization method based on fuzzy rules and applicable to automated assessment
یک روش خلاصه سازی متن بر اساس قوانین فازی و قابل استفاده برای ارزیابی خودکار-2019
In the last two decades, the text summarization task has gained much importance because of the large amount of online data, and its potential to extract useful information and knowledge in a way that could be easily handled by humans and used for a myriad of purposes, including expert systems for text assess- ment. This paper presents an automatic process for text assessment that relies on fuzzy rules on a vari- ety of extracted features to find the most important information in the assessed texts. The automatically produced summaries of these texts are compared with reference summaries created by domain experts. Differently from other proposals in the literature, our method summarizes text by investigating correlated features to reduce dimensionality, and consequently the number of fuzzy rules used for text summariza- tion. Thus, the proposed approach for text summarization with a relatively small number of fuzzy rules can benefit development and use of future expert systems able to automatically assess writing. The pro- posed summarization method has been trained and tested in experiments using a dataset of Brazilian Portuguese texts provided by students in response to tasks assigned to them in a Virtual Learning En- vironment (VLE). The proposed approach was compared with other methods including a naive baseline, Score, Model and Sentence, using ROUGE measures. The results show that the proposal provides better f-measure (with 95% CI) than aforementioned methods.
Keywords: Text assessment | Computer-assisted assessment | Automatic text summarization | Fuzzy logic
مقاله انگلیسی
9 Big data driven graphical information based fuzzy multi criteria decision making
اطلاعات گرافیکی مبتنی بر داده های بزرگ بر اساس تصمیم گیری چند معیاره فازی-2018
Graphical information (visualized data, information, and knowledge generated from different investiga tions and experimentations) is a useful form of decision-relevant information in all fields of study. The usages of such information are expected to increase exponentially due to the advent of big data. Unfortu nately, there are no formal methods available for directly computing the graphical information generated from big data while making a decision. This study fills this gap and presents a fuzzy logic based method, as well as a decision support tool, to perform multiple criteria decision making by directly computing the graphical information generated from big data. The effectiveness of the proposed method and tool is demonstrated by conducting a case study. Further study can be carried out to see the implication of this study in making formal decisions aided by the big data.
Keywords: Big data ، Granular information ، Graphical information ، Multiple criteria decision making ، Fuzzy logic
مقاله انگلیسی
10 پیش بینی شاخص های پایداری و نسبت جذب سدیم در منابع آب آشامیدنی رودخانه لردگان در غرب ایران با استفاده از سیستم استنتاج عصبی فازی
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 11
بر اساس دستورالعمل سازمان بهداشت جهانی، کنترل خوردگی یکی از جنبه های مهم تامین آب آشامیدنی است. آب معمولا شامل مواد تشکیل دهنده، گازهای حل شده و مواد معلق است. اگر چه برخی از این مواد آب برای انسان ضروری است، ولی مقدار بیش از حد مجاز این عناصر ، می توانند سلامت انسان را به خطر بیندازند. هدف از این مطالعه ارزیابی پارامترهای فیزیکی و شیمیایی آب آشامیدنی در مناطق روستایی شهرستان لردگان، همچنین تعیین شاخص های خوردگی است. این مطالعه مقطعی در سال 2017 با 141 نمونه و 13 پارامتر انجام شده است که براساس روش استاندارد و برای ارزیابی شاخص های کیفیت آب آبهای زیرزمینی با استفاده از ANFIS انجام شد. همچنین نتایج این مقاله با استانداردهای آژانس حفاظت محیط زیست و استانداردهای ملی ایران مقایسه شده است. پنج شاخص، شاخص اشباع لانژليه (LSI)، شاخص پایداری ريزنار (RSI)، شاخص مقیاس پذیری پوكوريوس (PSI) ، شاخص لارسون-اسكولد(LS) و شاخص شدت خوردگي(AI) با استفاده از نرم افزار مایکروسافت اکسل محاسبه شد. با توجه به سادگی نرم افزار، این نرم افزار به راحتی توسط محققان و اپراتورها مورد استفاده قرار می گیرد. پارامترهایی چون سولفات، سدیم، کلرید و هدایت الکتریکی به ترتیب 13.5، 28، 10.5 و 15 درصد بیشتر از سطح استاندارد بود. مقدار نیترات در 98٪ موارد بیش از حد مجاز و حدود 2٪ بیشتر از سطح استاندارد بود. نتايج حاصل از تحقيق حاضر نشان مي دهد که طبق LSI، RSI، PSI، LS و AI، آب در 6/6 درصد، 4/89 درصد، 2/87 درصد، 6/60 درصد و 9/14 درصد مخازن آب آشاميدني خورنده است.
کليدواژه: آب آشاميدني | روستاهاي شهر لردگان | شاخص ثبات
مقاله ترجمه شده
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