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ردیف | عنوان | نوع |
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1 |
Optimization based energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle considering fuel economy and fuel cell lifespan
استراتژی مدیریت انرژی مبتنی بر بهینه سازی موتور سوخت هیبریدی / باتری / ماورا بنفش با توجه به مصرف سوخت و طول عمر سلول سوختی-2020 Optimization of energy management strategy (EMS) for fuel cell/battery/ultracapacitor
hybrid electrical vehicle (FCHEV) is primarily aimed on reducing fuel consumption. However,
serious power fluctuation has effect on the durability of fuel cell, which still remains
one challenging barrier for FCHEVs. In this paper, we propose an optimized frequency
decoupling EMS using fuzzy control method to extend fuel cell lifespan and improve fuel
economy for FCHEV. In the proposed EMS, fuel cell, battery and ultracapacitor are
employed to supply low, middle and high-frequency components of required power,
respectively. For accurately adjusting membership functions of proposed fuzzy controllers,
genetic algorithm (GA) is adopted to optimize them considering multiple constraints on
fuel cell power fluctuation and hydrogen consumption. The proposed EMS is verified by
Advisor-Simulink and experiment bench. Simulation and experimental results confirm
that the proposed EMS can effectively reduce hydrogen consumption in three typical drive
cycles, limit fuel cell power fluctuation within 300 W/s and thus extend fuel cell lifespan. Keywords: Fuel cell electrical hybrid vehicle | Energy management strategy | Frequency decoupling | Fuzzy control | Genetic algorithm |
مقاله انگلیسی |
2 |
A Soft Computing Approach for group decision making: A supply chain management application
یک رویکرد نرم افزار محاسباتی برای تصمیم گیری گروهی: یک برنامه مدیریت زنجیره تأمین-2020 This paper presents a novel Soft Computing Approach called ‘‘Neuro-Fuzzy Analytical Network Process
(NFANP)’’ for the group decision-making problems based on the conventional Analytic Network
Process (ANP) method. The proposed approach deals with the interval values of judgments in a
fuzzy environment using mobile, not fixed, trapezoidal and triangular membership functions, as well
as the interval numerical ratio defined by alpha-cuts and the decision maker’s confidence levels.
The consistency problem of the fuzzy reciprocal matrices is addressed in the proposed paper by
allowing a certain tolerance deviation to be less than 0.20. Furthermore, trained Artificial Neural
Networks (ANNs) are included in the proposed approach to reduce the large number of computations
of the arithmetic operations required to correlate decision factors with the alternatives. In the
proposed implementation, the selection problem is defined into three main decision groups: Supplier
Characteristics, On-Going Performance, and Project Management Capabilities. The supplier alternatives
are classified by the decision makers corresponding to company size, quality system implementation,
and cost management. The application of the proposed approach shows a great accuracy in the final
utility values and a significant reduction in the calculation requirements. Keywords: Soft Computing | Neuro-Fuzzy Analytic Network Process | (NFANP) | Fuzzy judgments | Group decision-making | Supply chain management | ANNs |
مقاله انگلیسی |
3 |
Designing a general type-2 fuzzy expert system for diagnosis of depression
طراحی سیستم تخصصی فازی نوع 2 برای تشخیص افسردگی-2019 Depression is a common and important mental disorder that affects the quality of human life. Since
people with depression are not aware of their disorder and sometimes suffer from physical symptoms
such as chronic pain, refer to a physician instead of a psychologist. Hence, physician’s diagnosis is not
always correct in all patients. In the other words, misdiagnosis may occur by mislabeling their mental
disorder as physical diseases. Delay in depression diagnosis may have irrecoverable outcomes such
as suicide. Therefore, the most challenging aspect of depression diagnosis is to limit time loss and
preserve accuracy. In this paper, a novel general type-2 fuzzy expert system for depression diagnosis,
considering two main objectives, was developed. These objectives include accuracy of the system and
diagnosis time. The proposed system might be a helpful guideline for the physician to lead patients
toward psychologist by asking 15 questions from patients. The proposed general Type-2 expert system
has five steps. In the first step, we generate general type-2 membership function by using zSlices
method and interval agreement approach (IAA). Then fuzzy rules are extracted out of data gathered
from hospital and we extend Mendel method briefly in the second step. Approximate reasoning is
applied in the third step. In the fourth step, we solve a multi-objective problem to minimize time and
maximize accuracy by using MOEA/D method. Accordingly, in order to minimize time, feature selection
is applied. In this process, we use MIFS (Mutual Information Feature Selection) method and briefly, we
extend it. In the final step, we choose an appropriate solution from achieved Pareto Front (PF). The
proposed general type-2 expert system has been tested and evaluated to show its performance. This
Intelligent system is able to diagnose depression accurately at a suitable time. Keywords: Depression Computing with words (CWW) | General type-2 fuzzy sets | zSlices | MOEA/D algorithm | Feature selection | Beck Depression Inventory-II test (BDI-II) | Adaptive system | Expert system |
مقاله انگلیسی |
4 |
Land suitability assessments for yield prediction of cassava using geospatial fuzzy expert systems and remote sensing
ارزیابی تناسب اراضی برای پیش بینی عملکرد از این گونه گیاهان با استفاده از سیستم های خبره فازی جغرافیایی و سنجش از دور-2019 Cassava has the potential to be a promising crop that can adapt to changing climatic conditions in Indonesia due
to its low water requirement and drought tolerance. However, inappropriate land selection decisions limit
cassava yields and increase production-related costs to farmers. As a root crop, yield prediction using vegetation
indices and biophysical properties is essential to maximize the yield of cassava before harvesting. Therefore, the
purpose of this research was to develop a yield prediction model based on suitable areas that assess with land
suitability analysis (LSA). For LSA, the priority indicators were identified using a fuzzy expert system combined
with a multicriteria decision method including ecological categories. Furthermore, the yield prediction method
was developed using satellite remote sensing datasets. In this analysis, Sentinel-2 datasets were collected and
analyzed in SNAP® and ArcGIS® environments. The multisource database of ecological criteria for cassava
production was built using the fuzzy membership function. The results showed that 42.17% of the land area was
highly suitable for cassava production. Then, in the highly suitable area, the yield prediction model was developed
using the vegetation indices based on Sentinel-2 datasets with 10m resolution for the accuracy assessment.
The vegetation indices were used to predict cassava growth, biophysical condition, and phenology
over the growing seasons. The NDVI, SAVI, IRECI, LAI, and fAPAR were used to develop the model for predicting
cassava growth. The generated models were validated using regression analysis between observed and predicted
yield. As the vegetation indices, NDVI showed higher accuracy in the yield prediction model (R2=0.62)
compared to SAVI and IRECI. Meanwhile, LAI had a higher prediction accuracy (R2=0.70) than other biophysical
properties, fAPAR. The combined model using NDVI, SAVI, IRECI, LAI, and fAPAR reported the highest
accuracy (R2=0.77). The ground truth data were used for the evaluation of satellite remote sensing data in the
comparison between the observed and predicted yields. This developed integrated model could be implemented
for the management of land allocation and yield assessment in cassava production to ensure regional food
security in Indonesia. Keywords: Land suitability | Cassava | Yield prediction | Fuzzy expert systems | Remote sensing |
مقاله انگلیسی |
5 |
Survey on fuzzy TOPSIS state-of-the-art between 2007 and 2017
مرور وضعیت TOPSIS فازی بین سالهای 2007 و 2017-2019 A crucial topic in expert system and operations research is fuzzy multi-criteria decision making (FM- CDM), which is used in different fields. Existing options and gaps in this topic must be understood to prepare valuable knowledge on FMCDM environments and assist scholars. This study maps the research landscape to provide a clear taxonomy. The authors focus on searching for articles related to (i) technique for order of preference by similarity to ideal solution (TOPSIS); (ii) development; and (iii) fuzzy sets in four primary databases, namely, IEEE Xplore, Web of Science, Elsevier ScienceDirect and Springer. These databases include literature that focuses on FMCDM. The resulting final set after the filtering process in- cludes 170 articles, which are classified into four categories. The first, second, third and fourth categories include articles that used a type-1 fuzzy set with the TOPSIS method, a type-2 fuzzy set with the TOPSIS method, two fuzzy membership functions and a survey paper, respectively. The basic attributes of this topic include motivations for utilising FMCDM, open challenges and limitations that obstruct utilisation and recommendations to researchers for increasing the approval and application of FMCDM. Keywords: Multi-criteria decision making | Fuzzy set | FMCDM | Fuzzy-TOPSIS |
مقاله انگلیسی |
6 |
Providing Healthcare-as-a-Service Using Fuzzy Rule-Based Big Data Analytics in Cloud Computing
ارائه خدمات بهداشتی به عنوان یک سرویس با استفاده از تحلیل داده های زرگ مبتنی بر قانون فازی در محاسبات ابری-2018 With advancements in information and communication technology (ICT), there is steep increase in the remote
healthcare applications in which patients get treatment from the
remote places also. The data collected about the patients in remote
healthcare applications constitutes to big data because it varies
with respect to volume, velocity, variety, veracity, and value. To
process such a large collection of heterogeneous data is one of the
biggest challenges that needs a specialized approach. To address
this challenge, a new fuzzy rule-based classifier is presented in
this paper with an aim to provide Healthcare-as-a-Service (HaaS)1.
The proposed scheme is based upon the initial cluster formation,
retrieval, and processing of the big data in the cloud environment.
Then, a fuzzy rule-based classifier is designed for efficient decision
making about the data classification in the proposed scheme. To
perform inferencing from the collected data, membership functions
are designed for fuzzification and defuzzification processes. The
proposed scheme is evaluated on various evaluation metrics such
as-average response time, accuracy, computation cost, classification
time, and false positive ratio. The results obtained confirm the
effectiveness of the proposed scheme with respect to various
performance evaluation metrics in cloud computing environment.
Index Terms: Big data analytics, Cloud computing environment,Fuzzy rule-based classifier, Healthcare applications |
مقاله انگلیسی |
7 |
توسعه ماتریس ریسک و گسترش و تحلیل نگهداری مبتنی بر ریسک با منطق فازی
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 9 - تعداد صفحات فایل doc فارسی: 17 شکست های غیر منتظره، ماهش تولید و بالا بردن هزینه های نگهداری از مشکلات اصلی سیستم های تولید هستند. از آنجایی که، روش های مشخص سرمایه گذاری از قیبل: نگهداری باریسک به تعامل با این مسائل کمک میکند. یک عنصر مهم طرح ریزی نگهداری با ریسک، ارزیابی پیامدهای اقدام و اولویت بندی امور نگهداری برمبنای ریسک شکست های موجود است. هدف اصلی این دسته بندی، انتخاب صحیح برای طرح نگهداری، فواصل زمانی نگهداری و میزان مشخص لوازم یدکی در انبار است این متن، کاربرد منطق فازی برای کاهش دسته بندی های غیر بهینه را ارائه می کند و هم چنین یک سیستم استنباط نامعلوم برای غلبه بر چالش ذکر شده در بالا مطرح می کند. توابع عنصریت و مبنای قاعده گسترش یافت. ممکن است رویکرد مطرح شده با سیستم موجود مدیریت نگهداری با کمک کامپیوتر در یک شرکت تولیدی ادغام شود.
تخلفات کلیدی: دسته بندی | منطق فازی | سیستم های تولید کننده | نگهداری ریسک محور | ماتریس ریسک. |
مقاله ترجمه شده |
8 |
Big data analytics by automated generation of fuzzy rules for Network Forensics Readiness
تجزیه و تحلیل داده های بزرگ با تولید خودکار قوانین فازی برای آمادگی شبکه های جنایی-2017 Analysis of large-scale traffic dumps in Network Forensics can be a complex and non-trivial problem.
This is an important step in collecting evidences and making threat intelligence to foresee new ille
gal activities. Machine Learning comes into help to automatically support decision of forensics expert.
Furthermore, application in live systems may bring additional obstacles related to forensics readiness
and knowledge discovery. We believe that it can be mitigated by means of Neuro-Fuzzy, a fusion of
human-understandable model and automated data analytic. This method includes optimal unsupervised
grouping of samples with so-called Self-Organizing Features Map and fuzzy rules tuning by Artificial
Neural Network. In this work we propose improvements of the methods that makes it possible to extract
fewer fuzzy rules in a faster manner. The new method has two advantages in comparison to existing.
First, we improve the estimation of fuzzy patches. Second, parameterization that represents the data by
incorporating additional ellipse compactness information. By using ellipse rotation and flattering infor
mation, the membership functions can be derived. To even further enhance the generalization of the
method, the bootstrap aggregation was tested during the grouping phase. Finally, the method has been
assessed on the intrusion detection dataset with a five millions samples with classification accuracy 94%
using only 12 rules.
Keywords:Big data|Soft Computing|Neuro-Fuzzy|Intrusion detection|Self-organizing feature maps|Computational forensics |
مقاله انگلیسی |
9 |
Big data analytics by automated generation of fuzzy rules for Network Forensics Readiness
تجزیه و تحلیل داده های بزرگ توسط نسل خودکار از قوانین فازی برای شبکه پزشکی قانونی-2017 Analysis of large-scale traffic dumps in Network Forensics can be a complex and non-trivial problem.
This is an important step in collecting evidences and making threat intelligence to foresee new ille
gal activities. Machine Learning comes into help to automatically support decision of forensics expert.
Furthermore, application in live systems may bring additional obstacles related to forensics readiness
and knowledge discovery. We believe that it can be mitigated by means of Neuro-Fuzzy, a fusion of
human-understandable model and automated data analytic. This method includes optimal unsupervised
grouping of samples with so-called Self-Organizing Features Map and fuzzy rules tuning by Artificial
Neural Network. In this work we propose improvements of the methods that makes it possible to extract
fewer fuzzy rules in a faster manner. The new method has two advantages in comparison to existing.
First, we improve the estimation of fuzzy patches. Second, parameterization that represents the data by
incorporating additional ellipse compactness information. By using ellipse rotation and flattering infor
mation, the membership functions can be derived. To even further enhance the generalization of the
method, the bootstrap aggregation was tested during the grouping phase. Finally, the method has been
assessed on the intrusion detection dataset with a five millions samples with classification accuracy 94%
using only 12 rules.
Keywords:Big data|Soft Computing|Neuro-Fuzzy|Intrusion detection|Self-organizing feature maps|Computational forensics |
مقاله انگلیسی |
10 |
A Mapreduce Fuzzy Techniques of Big Data Classification
تکنیک های فازی Mapreduce طبقه بندی داده های بزرگ-2016 Due to the huge increase in the size of the data it
becomes troublesome to perform efficient analysis using the
current traditional techniques. Big data put forward a lot of
challenges due to its several characteristics like volume, velocity,
variety, variability, value and complexity. Today there is not only
a necessity for efficient data mining techniques to process large
volume of data but in addition a need for a means to meet the
computational requirements to process such huge volume of
data. The objective of this research is to implement a map reduce
paradigm using fuzzy and crisp techniques, and to provide a
comparative study between the results of the proposed systems
and the methods reviewed in the literature. In this paper four
proposed system is implemented using the map reduce paradigm
to process on big data. First, in the mapper there are two
techniques used; the fuzzy k-nearest neighbor method as a fuzzy
technique and the support vector machine as non-fuzzy
technique. Second, in the reducer there are three techniques
used; the mode, the fuzzy soft labels and Gaussian fuzzy
membership function. The first proposed system is using the
fuzzy KNN in the mapper and the mode in the reducer, the
second proposed system is using the SVM in the mapper and the
mode in the reducer, the third proposed system is using the SVM
in the mapper and the soft labels in the reducer, and the fourth
proposed system is using the SVM in the mapper and fuzzy
Gaussian membership function in the reducer. Results on
different data sets show that the fuzzy proposed methods
outperform a better performance than the crisp proposed
method and the method reviewed in the literature.
Keywords: Big data | Classification | Fuzzy k-nearest neighbor | Support vector machine | Hadoop | MapReduce |
مقاله انگلیسی |