Academic literature on the topic 'Fuzzy RPN'
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Journal articles on the topic "Fuzzy RPN"
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OLUWOLE, AYODELE OLUWASEGUN, BABATUNDE OMONIYI ODEDAIRO, and VICTOR OLUWASINA OLADOKUN. "MANAGING SUPPLY CHAIN RISKS: A FUZZY-FAILURE MODE AND EVALUATION APPROACH FOR RANKING THREATS." Journal of Engineering Studies and Research 27, no. 4 (December 15, 2021): 60–69. http://dx.doi.org/10.29081/jesr.v27i4.300.
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On the backdrop of lower transportation cost, outsourcing paved the way for borderless production activities and ushered in the era of Supply Chain Management (SCM). For many organizations, achieving the goals of their Supply Chain (SC) is constantly threatened by increased competition and disruption. In this study, the aim is to identify, and rank, SC threats in a developing country using Failure Mode and Effects Analysis (FMEA) with Fuzzy Logic (FL). FMEA parameters were derived for 44 supply chain threats (SCT1 – SCT44) and their Risk Priority Number (RPN) determined. Subsequently, the Mamdani Fuzzy Inference system was utilized to arrive at a Fuzzy-RPN with 125 rules using severity as a determining factor. The rules were ranked to prioritize SC threats. From the conventional FMEA, demand variation (SCT42) and long-distance sourcing (SCT27) had the highest and lowest RPN, respectively. After fuzzification and defuzzification, Fuzzy-RPN identified raw material delay (SCT1), government policy (SCT11), poor transport infrastructure (SCT18) and political instability (SCT19) as threats with the highest Fuzzy-RPN (210) and product recalls (SCT28) with the lowest Fuzzy-RPN (99). Based on these results, it is concluded that a Fuzzy-FMEA approach can identify and rank SC threats with the use of an RPN devoid of sentiments and inaccuracies.
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Wu, Xiaojun, and Jing Wu. "The Risk Priority Number Evaluation of FMEA Analysis Based on Random Uncertainty and Fuzzy Uncertainty." Complexity 2021 (February 26, 2021): 1–15. http://dx.doi.org/10.1155/2021/8817667.
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The risk priority number (RPN) calculation method is one of the critical subjects of failure mode and effects analysis (FMEA) research. Recently, RPN research under a fuzzy uncertainty environment has become a hot topic. Accordingly, increasing studies have ignored the important impact of the random sampling uncertainty in the FMEA assessment. In this study, a fuzzy beta-binomial RPN evaluation method is proposed by integrating fuzzy theory, Bayesian statistical inference, and the beta-binomial distribution. This model can effectively realize real-time, dynamic, and long-term evaluation of RPN under the condition of continuous knowledge accumulation. The major contribution of the proposed model is to use the random uncertainty and fuzzy uncertainty in an integrated model and provide a Markov Chain Monte Carlo (MCMC) method to solve the complex integrated model. The study presented a case study, which presented how to apply this model in practice and indicated the significant influence on the measurement error caused by ignoring the random uncertainty caused by expert evaluation in RPN calculations.
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Ofanson U, Tamunodukobipi D.T, and Nitonye S. "Failure mode effects and criticality analysis (FMECA) using fuzzy logic for ship dynamic positioning (DP) systems." Global Journal of Engineering and Technology Advances 13, no. 1 (October 30, 2022): 038–52. http://dx.doi.org/10.30574/gjeta.2022.13.1.0170.
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Predicting the failure modes effect and criticality analysis (FMECA) of a dynamic positioning (DP) system using fuzzy logic is the aim of this research. The identification of DP systems and subsystems, the classification of failure modes into critical and less-critical levels based on the Risk Priority Number (RPN) to depict the main root causes of failure in the DP system are some critical objectives in support of this goal. The analysis offers details on a number of issues, including the causes of failure modes and their effects on the functionality and dependability of equipment. Based on the information provided, it was determined that a number of failure modes produced identical RPN values, and that the ranking scale was erroneous. A new method was tested but could not really prioritize the failure modes with same RPN because of the few choices in the severity, occurrence and detection template. To compensate for this, excel ranking function was employed putting severity, occurrence and detection as key criteria for ranking. Due to the high severity and occurrence index, the RPN ranking results show that the faulty DP system component identified for the scenario SSTs (F1) is categorized as very critical. SSCs (F12), SSPr (F7), SSTs(F2), and SSPs(F15) are additional critical failures. In the study, data analysis and validation were done using a fuzzy rule system based on MATLAB. From the findings, it can be inferred that the failure modes F1, F2, F5, F6, F7, F8, F10, F11, and F15 have values of a similar type of RPN. According to the initial RPN risk level results; there are 19 failure modes in the medium risk level, 2 in the low risk level and 1 in the high risk level. In the final RPN-based risk level results, there are 18 failure scenarios in the low risk level and 4 in the medium risk level. In contrast, there are 5 failure modes in the fuzzy RPN low risk level and 17 failure scenarios in the medium risk level. Without fuzzy logic, the justification score on traditional FMECA can be given directly. This makes traditional FMECA ABS show greater risk than fuzzy FMECA. The failure modes with the highest RPN values were treated as critical parts, so it was recommended that the highest value of RPN be given special attention by making the necessary repairs or replacements in order to lengthen the equipment's lifespan.
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Jee, Tze Ling, Kai Meng Tay, and Chee Khoon Ng. "Enhancing a Fuzzy Failure Mode and Effect Analysis Methodology with an Analogical Reasoning Technique." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 9 (November 20, 2011): 1203–10. http://dx.doi.org/10.20965/jaciii.2011.p1203.
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In this paper, a fuzzy Failure Mode and Effect Analysis (FMEA) methodology incorporating an analogical reasoning technique is presented. FMEA methodology was introduced as a formal and systematic procedure for evaluation of risk associated with potential failure modes in the 1960s. Bowles and Peláez [1] proposed a Fuzzy Inference System (FIS)-based Risk Priority Number (RPN) model as an alternative to the conventional RPN model. For an FIS-based RPN (a three-input FIS model), a large set of fuzzy rules are required, and it is tedious to collect the full set of rules. With the grid partition strategy, the number of fuzzy rules required increases in an exponential manner, and this phenomenon is known as the “curse of dimensionality” or the combinatorial rule explosion problem. Hence, a rule selection and similarity reasoning technique, i.e., Approximate Analogical Reasoning Schema (AARS) technique are implemented in a fuzzy FMEA in order to solve the problem. The experiment was conducted using a set of data collected from a semiconductor manufacturing line, i.e., underfill dispensing process, and promising results were obtained.
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Widianti, Tri, and Himma Firdaus. "PENGUJIAN SUHU LEMARI ES DENGAN METODE TERINTEGRASI FUZZYFAILURE MODE AND EFFECT ANALYSIS (FUZZY-FMEA)." Jurnal Standardisasi 18, no. 1 (May 9, 2018): 9. http://dx.doi.org/10.31153/js.v18i1.693.
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Failure Mode and Effect Analysis (FMEA) banyak diimplementasikan untuk analisis risiko baik di bidang manufaktur maupun jasa. Permasalahan yang sering timbul pada implementasi FMEA yaitu sulitnya menentukan peringkat risiko karena kesamaan nilai RPN. Samanya nilai RPN menimbulkan kesulitan bagi pengambil keputusan untuk memprioritisasi risiko yang harus ditindaklanjuti. Logika fuzzy merupakan logika matematis yang dapat digunakan untuk memperbaiki kelemahan FMEA. Sehingga, tujuan penelitian ini adalah integrasi FMEA dengan logika fuzzy sebagai upaya perbaikan terhadap metode FMEA. Tujuan lainnya adalah implementasi integrasi Fuzzy-FMEA pada lingkup pengujian suhu lemari es. Implementasi Fuzzy-FMEA pada pengujian ini dilakukan sebagai tindakan pencegahan terhadap risiko kegagalan pada pengujian yang dipersyaratkan oleh SNI ISO/IEC 17025:2008. Studi kasus pengujian suhu pada lemari es ini dipilih karena lemari es merupakan salah satu produk yang diwajibkan untuk memperoleh Sertifikat Produk Penggunaan Tanda SNI (SPPT-SNI) yang mengacu pada standar SNI IEC 60335-2-7:2009. Selain itu, penerapan Fuzzy-FMEA pada konteks pengujian sampai saat ini belum ditemukan. Hasil analisis dengan Fuzzy-FMEA menunjukkan bahwa risiko kegagalan paling tinggi pada proses pengujian suhu lemari es paling tinggi terjadi pada mode kegagalan: power source tibatiba shut down dengan nilai RPN 5,8887.
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Rachieru, Nicoleta, Nadia Belu, and Daniel Constantin Anghel. "Evaluating the Risk of Failure on Injection Pump Using Fuzzy FMEA Method." Applied Mechanics and Materials 657 (October 2014): 976–80. http://dx.doi.org/10.4028/www.scientific.net/amm.657.976.
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This research is aimed at utilizing failure mode and effect analysis (FMEA) which is a reliability analysis method applicable to rotary injection pump design. In traditional FMEA, Risk Priority Number (RPN) ranking system is used to evaluate, the risk level of failures to rank failures and to prioritize actions. RPN is obtained by multiplying the scores of three risk factors like the Severity (S), Occurrence (O) and Detection (D) of each failure mode. RPN method can not emphasise the nature of the problem, which is multi-attributable and has a group of experts' opinions. Furthermore, attributes are subjective and have different importance levels. In this paper, a framework is proposed to overcome the shortcomings of the traditional method through the fuzzy set theory. Two case studies have been shown to demonstrate the methodology thus developed. It is illustrated a parallel between the results obtained by the traditional method and fuzzy logic for determining the RPNs. We expect that fuzzy FMEA model will assist FMEA team in assess and rank risks more precisely compared with risk assessment model of method.
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Godina, Radu, Beatriz Gomes Rolis Silva, and Pedro Espadinha-Cruz. "A DMAIC Integrated Fuzzy FMEA Model: A Case Study in the Automotive Industry." Applied Sciences 11, no. 8 (April 20, 2021): 3726. http://dx.doi.org/10.3390/app11083726.
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The growing competitiveness in the automotive industry and the strict standards to which it is subject, require high quality standards. For this, quality tools such as the failure mode and effects analysis (FMEA) are applied to quantify the risk of potential failure modes. However, for qualitative defects with subjectivity and associated uncertainty, and the lack of specialized technicians, it revealed the inefficiency of the visual inspection process, as well as the limitations of the FMEA that is applied to it. The fuzzy set theory allows dealing with the uncertainty and subjectivity of linguistic terms and, together with the expert systems, allows modeling of the knowledge involved in tasks that require human expertise. In response to the limitations of FMEA, a fuzzy FMEA system was proposed. Integrated in the design, measure, analyze, improve and control (DMAIC) cycle, the proposed system allows the representation of expert knowledge and improves the analysis of subjective failures, hardly detected by visual inspection, compared to FMEA. The fuzzy FMEA system was tested in a real case study at an industrial manufacturing unit. The identified potential failure modes were analyzed and a fuzzy risk priority number (RPN) resulted, which was compared with the classic RPN. The main results revealed several differences between both. The main differences between fuzzy FMEA and classical FMEA come from the non-linear relationship between the variables and in the attribution of an RPN classification that assigns linguistic terms to the results, thus allowing a strengthening of the decision-making regarding the mitigation actions of the most “important” failure modes.
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Bonato, Jasminka, Martina Badurina, and Julijan Dobrinić. "Parameters Assessment of the FMEA Method by Means of Fuzzy Logic." Journal of Maritime & Transportation Science 2, Special edition 2 (April 2018): 123–32. http://dx.doi.org/10.18048/2018-00.123.
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The paper aims at presenting the FMEA method based on the fuzzy technique, representing a new approach to the failure analysis and its effects on the observed system. The FMEA (Failure Mode and Effect Analysis) method has assigned the risks a coefficient i.e. a numerical indicator that very clearly defines the degree of risk. The risk is calculated as a mathematical function of RPN which depends on the effects S, probability O that some case will lead to a failure and to a probability that a failure D can not be detected before its effects are realized. RPN = S O D. The FMEA method, based on the fuzzy logic, makes a more reliable evaluation of the observed system failures possible.
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Domán, László. "Fuzzy FMEA risk assessment approach for IFF system in military helicopters using Matlab R2022A." Katonai Logisztika 30, no. 1-2 (2022): 101–29. http://dx.doi.org/10.30583/2022-1-2-101.
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In this article, the author reviews the fuzzy Failure Mode and Effect Analysis (fuzzy FMEA) as one of the effective methods of risk assess- ment. Using the fuzzy logic toolbox of the MATLAB R2022a software, the Identification Friend or Foe (IFF) system of military helicopters is analysed in a case study, during which the fuzzy risk priority numbers (F-RPN) for this system of military helicopters is created. Finally, the author of this paper summarizes the results obtained.
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Karamoozian, Amirhossein, and Desheng Wu. "A hybrid risk prioritization approach in construction projects using failure mode and effective analysis." Engineering, Construction and Architectural Management 27, no. 9 (May 9, 2020): 2661–86. http://dx.doi.org/10.1108/ecam-10-2019-0535.
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PurposeConstruction projects involve with various risks during all phases of project lifecycle. Failure mode and effective analysis (FMEA) is a useful tool for identifying and eliminating possible risk of failure modes (FMs) and improving the reliability and safety of systems in a broad range of industries. The traditional FMEA method applies risk priority number method (RPN) to calculate risk of FMs. RPN method cannot consider the direct and indirect interdependencies between the FMs and is not appropriate for complex system with numerous components. The purpose of this study is to propose an approach to consider interdependencies between FMs and also using fuzzy theory to consider uncertainties in experts' judgments.Design/methodology/approachThe proposed approach consist of three stages: the first stage of hybrid model used fuzzy FMEA method to identify the failure mode risks and derive the RPN values. The second stage applied Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) method to determine the interdependencies between the FMs which are defined through fuzzy FMEA. Then, analytic network process (ANP) is applied in the third stage to calculate the weights of FMs based on the interdependencies that are generated through FDEMATEL method. Finally, weight of FMs through fuzzy FMEA and FDEMATEL–ANP are multiplied to generate the final weights for prioritization. Afterward, a case study for a commercial building project is introduced to illustrate proficiency of model.FindingsThe results showed that the suggested approach could reveal the important FMs and specify the interdependencies between them successfully. Overall, the suggested model can be considered as an efficient hybrid FMEA approach for risk prioritization.Originality/valueThe originality of approach comes from its ability to consider interdependencies between FMs and uncertainties of experts' judgments.
Dissertations / Theses on the topic "Fuzzy RPN"
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Umofia, Anietie Nnana. "Risk-based Reliability Assessment of Subsea Control module for Offshore Oil and Gas production." Thesis, Cranfield University, 2014. http://dspace.lib.cranfield.ac.uk/handle/1826/9256.
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Offshore oil and gas exploitation is principally conducted using dry or wet tree
systems, otherwise called the subsea Xmas tree system. Due to the shift to
deeper waters, subsea production system (SPS) has come to be a preferred
technology with attendant economic benefits. At the centre of the SPS is the
subsea control module (SCM), responsible for the proper functioning and
monitoring of the entire system. With increasing search for hydrocarbons in
deep and ultra-deepwaters, the SCM system faces important environmental,
safety and reliability challenges and little research has been done in this area.
Analysis of the SCM reliability then becomes very fundamental due to the huge
cost associated with failure. Several tools are available for this analysis, but the
FMECA stands out due to its ability to not only provide failure data, but also
showcase the system’s failure modes and mechanisms associated with the
subsystems and components being evaluated. However, the technique has
been heavily challenged in various literatures for several reasons. To close this
gap, a novel multi-criteria approach is developed for the analysis and ranking of
the SCM failures modes.
This research specifically focusses on subsea tree-mounted electro-hydraulic
(E-H) SCM responsible for the underwater control of oil and gas production. A
risk identification of the subsea control module is conducted using industry
experts. This is followed by a comprehensive component based FMECA
analysis of the SCM conducted with the conventional RPN technique, which
reveals the most critical failure modes for the SCM. A novel framework is
developed using multi-criteria fuzzy TOPSIS methodology and applied to the
most critical failure modes obtained from the FMECA evaluation using
unconventional parameters. Finally, a validation of these results is performed
using a stochastic input evaluation and SCM failure data obtained from the
offshore industry standard reliability database, OREDA.
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Mendes, Amanda dos Santos. "Gráficos de controle fuzzy para o monitoramento da média e amplitude de processos univariados /." Guaratinguetá, 2019. http://hdl.handle.net/11449/180937.
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Orientador: Marcela Aparecida Guerreiro Machado FreitasResumo: O controle de qualidade, principalmente por meio do uso de gráficos de controle, torna-se essencial na indústria para garantir um processo livre de causas especiais de variabilidade. Como os dados amostrais podem incluir incertezas advindas da subjetividade humana e dos sistemas de medição, a teoria dos conjuntos fuzzy pode ser aplicada aos gráficos de controle quando dados precisos não estiverem disponíveis. Para tal feito, os valores da característica de qualidade são fuzzificados a partir da inserção de incertezas e transformados em valores representativos para uma melhor comparação com o gráfico de controle tradicional. Este trabalho propõe o uso da lógica fuzzy aos gráficos de controle para monitorar a média e a amplitude de processos univariados, assim como dois gráficos de controle fuzzy baseados nas regras especiais de decisão: Synthetic e Side Sensitive Synthetic. O desempenho do gráfico de controle é medido pelo número médio de amostras até sinal (NMA). Verificou-se neste trabalho que os gráficos de controle fuzzy possuem maior eficiência que os gráficos de controle tradicionais para menores valores de α-cut, ou seja, maior incerteza inserida no processo e para cenários onde se tem uma maior diferença entre os limitantes de incerteza dos números fuzzy.
Abstract: Quality control, mainly through the use of control charts, becomes essential in the industry to ensure a process free from special causes of variability. As sample data may include uncertainties arising from human subjectivity and measurement systems, fuzzy set theory can be applied to control charts when accurate data is not available. For this purpose, the quality characteristic values are fuzzified from the insertion of uncertainties and transformed into representative values for a better comparison with the traditional control chart. This work proposes the use of fuzzy logic to control charts to monitor the mean and range of univariate processes, as well as two fuzzy control charts based on the special run rules: Synthetic and Side Sensitive Syntehtic. The performance of the control chart is measured by the average run length (ARL). It was verified in this work that the fuzzy control charts have higher efficiency than the traditional control charts for lower values of α-cut, that is, greater uncertainty inserted in the process and for scenarios where there is a greater difference between the limiting uncertainties of fuzzy numbers.
Mestre
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Kamgueu, Patrick Olivier. "Configuration dynamique et routage pour l'internet des objets." Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0241/document.
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L’intérêt croissant de la communauté scientifique et industrielle ces dernières années pour les réseaux de capteurs sans fil (RCSF), a conduit à la définition de nouveaux protocoles normalisés prenant en compte les spécificités matérielles des nœuds utilisés. Dans la couche réseau, le protocole RPL (de l’acronyme anglais IPv6 Routing Protocol for Low-power and Lossy Network) a été proposé en 2012 par l’IETF, comme standard de routage pour les réseaux dont les nœuds sont de type "LLN" (Low-power and Lossy Network), i.e. caractérisés par une faible autonomie énergique et transmettant sur des liens radios dotés d’un taux de perte de données élevé. Dans cette thèse, nous nous intéressons à l’optimisation du routage dans ces réseaux (notamment ceux utilisant la pile protocolaire TCP/IP), ainsi qu’à leur interconnexion efficace à Internet à des coûts soutenables. Tout d’abord, nous proposons deux fonctions d’objectif organisant le routage avec RPL. La première se sert de l’unique critère énergétique, avec comme objectif principal la maximisation de la durée de vie du réseau. Pour ce faire, nous avons implémenté un modèle d’estimation d’énergie, intégré par la suite aux nœuds pour leur permettre d’estimer en temps réel leur énergie résiduelle. La deuxième fonction d’objectif proposée, vise à combiner plusieurs critères pour la prise en compte de la qualité de service durant le routage. Nous développons un modèle à base de la logique floue pour mettre en œuvre la combinaison. En effet, elle nous permet d’obtenir un bon compromis entre les différentes entrées et requiert une empreinte mémoire faible. Dans la dernière partie de cette thèse, nous concevons et implémentons une architecture d’activation de passerelles permettant d’assurer une connexion Internet efficace de divers RCSFs utilisant RPL, pour la réalisation de la vision de l’Internet des ObjetsIn recent years, the growing interest of scientific and industrial community has led to the standardization of new protocols that consider the unique requirements of Wireless Sensor Networks (WSN) nodes. At network layer, RPL (IPv6 Routing Protocol for Low-power and Lossy Network) has been proposed by IETF as the routing standard for network that uses LLN nodes, namely, those where both nodes and their interconnects are constrained. They operate on low-power embedded batteries and use lossy links, making communications unreliable and lead to a significant data loss rates. This thesis aims to optimize the routing in WSNs (especially those using TCP/IP protocol stack), as well as their efficient and cost-effective connection to the Internet. First, we have proposed two new RPL objective functions. The first uses as unique routing criterion, the node remaining energy with the goal of maximizing the network lifetime. An energy model that allows the nodes to dynamically estimate their remaining energy at runtime has been implemented and integrate to the protocol. The second objective function uses fuzzy logic reasoning to combine several criteria to take Quality of Service into account. Indeed, this scheme provides a good trade-off on several inputs and requires a low memory footprint. In the last part of this thesis, we designed and implemented an architecture that enable an efficient integration of several RPL based WSNs to the Internet to achieve the Internet of Things vision
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RUPENDER. "DEVELOPMENT OF A INTEGRATED FTA AND FMEA APPROACH FOR FAILURE ANALYSIS OF SOLAR PHOTOVOLTAIC SYSTEMS." Thesis, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19749.
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There is a need of an alternate source of energy that is both reliable and affordable.
Solar PV system is one of the source which is reliable but it has higher cost. As
technology advances, the cost of harvesting energy decreases, but their lifespan is
determined by several process fault/factors and their mitigation which is true in case
of solar photovoltaic systems. To mitigate the faults, industries use various fault
analysis techniques such as FTA, FMEA, RBD, etc. FMEA is the most commonly
used tools. The results of FMEA are dependent on the knowledge and experience of
the team carrying out the process, which can change the ranking of the faults due to
the vagueness of the ideas of a different team member. In the proposed methodology,
fuzzy logic is used to counter the vagueness of the ideas of a different team member.
The suggested methodology combines the FTA and FMEA approaches as these are
both time-consuming and inefficient when used individually. An integrated FTA and
fuzzy FMEA strategy uses the FTA data as input to the FMEA process, then applies
fuzzy logic to generate the fuzzy RPN value to prioritize the sequence of failure
modes to mitigate first. Twelve failure modes are considered in this work. The results
produced utilizing the integrated technique differed significantly from the standard
method for intermediate failure modes. The proposed methodology can be applied to
any industry, including manufacturing, automobiles, and pharmaceuticals.
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SHIH, YU-CHU, and 施語筑. "QoS routing mechanism based on RPL with fuzzy logic in wireless sensor network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/x2teqb.
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Sigwele, Tshiamo, Prashant Pillai, and Yim Fun Hu. "Elastic call admission control using fuzzy logic in virtualized cloud radio base stations." 2015. http://hdl.handle.net/10454/11129.
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NoConventional Call Admission Control (CAC) schemes are based on stand-alone Radio Access Networks (RAN) Base Station (BS) architectures which have their independent and fixed spectral and computing resources, which are not shared with other BSs to address their varied traffic needs, causing poor resource utilization, and high call blocking and dropping probabilities. It is envisaged that in future communication systems like 5G, Cloud RAN (C-RAN) will be adopted in order to share this spectrum and computing resources between BSs in order to further improve the Quality of Service (QoS) and network utilization. In this paper, an intelligent Elastic CAC scheme using Fuzzy Logic in C-RAN is proposed. In the proposed scheme, the BS resources are consolidated to the cloud using virtualization technology and dynamically provisioned using the elasticity concept of cloud computing in accordance to traffic demands. Simulations shows that the proposed CAC algorithm has high call acceptance rate compared to conventional CAC.
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Sigwele, Tshiamo, Prashant Pillai, Atm S. Alam, and Yim Fun Hu. "Fuzzy-Logic Based Call Admission Control in 5G Cloud Radio Access Networks with Pre-emption." 2017. http://hdl.handle.net/10454/13203.
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YesFifth generation (5G) cellular networks will be comprised of millions of connected devices like wearable devices, Androids, iPhones, tablets and the Internet of Things (IoT) with a plethora of applications generating requests to the network. The 5G cellular networks need to cope with such sky-rocketing tra c requests from these devices to avoid network congestion. As such, cloud radio access networks (C-RAN) has been considered as a paradigm shift for 5G in which requests from mobile devices are processed in the cloud with shared baseband processing. Despite call admission control (CAC) being one of radio resource management techniques to avoid the network congestion, it has recently been overlooked by the community. The CAC technique in 5G C-RAN has a direct impact on the quality of service (QoS) for individual connections and overall system e ciency. In this paper, a novel Fuzzy-Logic based CAC scheme with pre-emption in C-RAN is proposed. In this scheme, cloud bursting technique is proposed to be used during congestion, where some delay tolerant low-priority connections are pre-empted and outsourced to a public cloud with a penalty charge. Simulation results show that the proposed scheme has low blocking probability below 5%, high throughput, low energy consumption and up to 95% of return on revenue.
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Sobral, José Victor Vasconcelos. "Performance Assessment of Routing Protocols for IoT/6LoWPAN Networks." Doctoral thesis, 2020. http://hdl.handle.net/10400.6/11158.
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The Internet of Things (IoT) proposes a disruptive communication paradigm that allows
smart objects to exchange data among themselves to reach a common goal. IoT application
scenarios are multiple and can range from a simple smart home lighting system to fully controlled
automated manufacturing chains. In the majority of IoT deployments, things are equipped with
small devices that can suffer from severe hardware and energy restrictions that are responsible
for performing data processing and wireless communication tasks. Thus, due to their features,
communication networks that are used by these devices are generally categorized as Low Power
and Lossy Networks (LLNs).
The considerable variation in IoT applications represents a critical issue to LLN networks,
which should offer support to different requirements as well as keeping reasonable
quality-of-service (QoS) levels. Based on this challenge, routing protocols represent a key issue
in IoT scenarios deployment. Routing protocols are responsible for creating paths among devices
and their interactions. Hence, network performance and features are highly dependent
on protocol behavior. Also, based on the adopted protocol, the support for some specific requirements
of IoT applications may or may not be provided. Thus, a routing protocol should be
projected to attend the needs of the applications considering the limitations of the device that
will execute them.
Looking to attend the demand of routing protocols for LLNs and, consequently, for IoT
networks, the Internet Engineering Task Force (IETF) has designed and standardized the IPv6
Routing Protocol for Low Power and Lossy Networks (RPL). This protocol, although being robust
and offering features to fulfill the need of several applications, still presents several faults and
weaknesses (mainly related to its high complexity and memory requirement), which limits its
adoption in IoT scenarios. An alternative to RPL, the Lightweight On-demand Ad Hoc Distancevector
Routing Protocol – Next Generation (LOADng) has emerged as a less complicated routing
solution for LLNs. However, the cost of its simplicity is paid for with the absence of adequate
support for a critical set of features required for many IoT environments. Thus, based on the
challenging open issues related to routing in IoT networks, this thesis aims to study and propose
contributions to better attend the network requirements of IoT scenarios. A comprehensive survey,
reviewing state-of-the-art routing protocols adopted for IoT, identified the strengths and
weaknesses of current solutions available in the literature. Based on the identified limitations,
a set of improvements is designed to overcome these issues and enhance IoT network performance.
The novel solutions are proposed to include reliable and efficient support to attend
the needs of IoT applications, such as mobility, heterogeneity, and different traffic patterns.
Moreover, mechanisms to improve the network performance in IoT scenarios, which integrate
devices with different communication technologies, are introduced.
The studies conducted to assess the performance of the proposed solutions showed
the high potential of the proposed solutions. When the approaches presented in this thesis
were compared with others available in the literature, they presented very promising results
considering the metrics related to the Quality of Service (QoS), network and energy efficiency,
and memory usage as well as adding new features to the base protocols. Hence, it is believed
that the proposed improvements contribute to the state-of-the-art of routing solutions for IoT
networks, increasing the performance and adoption of enhanced protocols.A Internet das Coisas, do inglês Internet of Things (IoT), propõe um paradigma de comunicação disruptivo para possibilitar que dispositivos, que podem ser dotados de comportamentos autónomos ou inteligentes, troquem dados entre eles buscando alcançar um objetivo comum. Os cenários de aplicação do IoT são muito variados e podem abranger desde um simples sistema de iluminação para casa até o controle total de uma linha de produção industrial. Na maioria das instalações IoT, as “coisas” são equipadas com um pequeno dispositivo, responsável por realizar as tarefas de comunicação e processamento de dados, que pode sofrer com severas restrições de hardware e energia. Assim, devido às suas características, a rede de comunicação criada por esses dispositivos é geralmente categorizada como uma Low Power and Lossy Network (LLN). A grande variedade de cenários IoT representam uma questão crucial para as LLNs, que devem oferecer suporte aos diferentes requisitos das aplicações, além de manter níveis de qualidade de serviço, do inglês Quality of Service (QoS), adequados. Baseado neste desafio, os protocolos de encaminhamento constituem um aspecto chave na implementação de cenários IoT. Os protocolos de encaminhamento são responsáveis por criar os caminhos entre os dispositivos e permitir suas interações. Assim, o desempenho e as características da rede são altamente dependentes do comportamento destes protocolos. Adicionalmente, com base no protocolo adotado, o suporte a alguns requisitos específicos das aplicações de IoT podem ou não ser fornecidos. Portanto, estes protocolos devem ser projetados para atender as necessidades das aplicações assim como considerando as limitações do hardware no qual serão executados. Procurando atender às necessidades dos protocolos de encaminhamento em LLNs e, consequentemente, das redes IoT, a Internet Engineering Task Force (IETF) desenvolveu e padronizou o IPv6 Routing Protocol for Low Power and Lossy Networks (RPL). O protocolo, embora seja robusto e ofereça recursos para atender às necessidades de diferentes aplicações, apresenta algumas falhas e fraquezas (principalmente relacionadas com a sua alta complexidade e necessidade de memória) que limitam sua adoção em cenários IoT. Em alternativa ao RPL, o Lightweight On-demand Ad hoc Distance-vector Routing Protocol – Next Generation (LOADng) emergiu como uma solução de encaminhamento menos complexa para as LLNs. Contudo, o preço da simplicidade é pago com a falta de suporte adequado para um conjunto de recursos essenciais necessários em muitos ambientes IoT. Assim, inspirado pelas desafiadoras questões ainda em aberto relacionadas com o encaminhamento em redes IoT, esta tese tem como objetivo estudar e propor contribuições para melhor atender os requisitos de rede em cenários IoT. Uma profunda e abrangente revisão do estado da arte sobre os protocolos de encaminhamento adotados em IoT identificou os pontos fortes e limitações das soluções atuais. Com base nas debilidades encontradas, um conjunto de soluções de melhoria é proposto para superar carências existentes e melhorar o desempenho das redes IoT. As novas soluções são propostas para incluir um suporte confiável e eficiente capaz atender às necessidades das aplicações IoT relacionadas com suporte à mobilidade, heterogeneidade dos dispositivos e diferentes padrões de tráfego. Além disso, são introduzidos mecanismos para melhorar o desempenho da rede em cenários IoT que integram dispositivos com diferentes tecnologias de comunicação. Os vários estudos realizados para mensurar o desempenho das soluções propostas mostraram o grande potencial do conjunto de melhorias introduzidas. Quando comparadas com outras abordagens existentes na literatura, as soluções propostas nesta tese demonstraram um aumento do desempenho consistente para métricas relacionadas a qualidade de serviço, uso de memória, eficiência energética e de rede, além de adicionar novas funcionalidades aos protocolos base. Portanto, acredita-se que as melhorias propostas contribuiem para o avanço do estado da arte em soluções de encaminhamento para redes IoT e aumentar a adoção e utilização dos protocolos estudados.
Books on the topic "Fuzzy RPN"
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Yamamoto, Kōji. Faji kanri kaikei shisutemu-ron. Sakai-shi: Ōsaka Furitsu Daigaku Keizai Gakubu, 1992.
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Han yu ci lei de ren zhi yan jiu he mo hu hua fen: A cognitive investigation and fuzzy classification of word-class in mandarin Chinese. Shanghai: Shanghai jiao yu chu ban she, 2010.
Find full textBook chapters on the topic "Fuzzy RPN"
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Yan, Chun, Meixuan Li, and Wei Liu. "Power Transformer Fault Diagnosis Based on Improved Bat Algorithms to Optimize RNN." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 531–38. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32456-8_58.
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Kiraz, Murat Ugur, and Atinc Yilmaz. "Comparison of ML Algorithms to Detect Vulnerabilities of RPL-Based IoT Devices in Intelligent and Fuzzy Systems." In Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation, 254–62. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85577-2_30.
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wunder, june, Arthur Azevedo de Amorim, Patrick Baillot, and Marco Gaboardi. "Bunched Fuzz: Sensitivity for Vector Metrics." In Programming Languages and Systems, 451–78. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30044-8_17.
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AbstractProgram sensitivity measures the distance between the outputs of a program when run on two related inputs. This notion, which plays a key role in areas such as data privacy and optimization, has been the focus of several program analysis techniques introduced in recent years. Among the most successful ones, we can highlight type systems inspired by linear logic, as pioneered by Reed and Pierce in the Fuzz programming language. In Fuzz, each type is equipped with its own distance, and sensitivity analysis boils down to type checking. In particular, Fuzz features two product types, corresponding to two different notions of distance: the tensor product combines the distances of each component by adding them, while the with product takes their maximum.In this work, we show that these products can be generalized to arbitrary $$L^p$$ L p distances, metrics that are often used in privacy and optimization. The original Fuzz products, tensor and with, correspond to the special cases $$L^1$$ L 1 and $$L^\infty $$ L ∞ . To ease the handling of such products, we extend the Fuzz type system with bunches—as in the logic of bunched implications—where the distances of different groups of variables can be combined using different $$L^p$$ L p distances. We show that our extension can be used to reason about quantitative properties of probabilistic programs.
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Kabadayi, Nihan. "Fuzzy Hybrid FMEA for Risk Assessment in Service Industry." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 43–75. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7564-2.ch003.
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Service products are mostly produced and consumed simultaneously through interaction between customer and service providers. To prevent external failures in service operations, it is important to identify potential risks and take relevant actions to eliminate or reduce the occurrence. Therefore, risk assessment is vital to customer satisfaction in any service organization. Failure mode and effects analysis (FMEA) is an effective and useful tool for risk assessment. Although FMEA has been extensively studied in the manufacturing literature, there are a limited number of studies considering the application of FMEA in the hospitality industry. In traditional FMEA, the risk priority of failure modes is determined by generating a crisp risk priority number (RPN). However, it has been claimed in the literature that crisp RPN doesn't have a good performance in reflecting real-life situations. To overcome this shortcoming, a fuzzy hybrid FMEA method is developed. The proposed method has been tested on a case study in a five-star hotel to assess its applicability and benefits.
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Panchal, Dilbagh, Prasenjit Chatterjee, Morteza Yazdani, and Shankar Chakraborty. "A Hybrid MCDM Approach-Based Framework for Operational Sustainability of Process Industry." In Advances in Environmental Engineering and Green Technologies, 1–13. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8579-4.ch001.
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The aim of this chapter is to develop a hybrid decision-making framework for studying the risk issues related to failure of an industrial system. On the basis of plant expert's knowledge, failure mode effect analysis (FMEA) sheet has been generated and various failure causes associated with the sub-systems were listed. On the basis of three risk factors, namely probability of occurrence of failure, severity and non-detection (, and ), Risk Priority Numbers (RPN) for each failure cause has been tabulated. The demerits of FMEA approach in prioritizing the failure causes has been overcome by implementing fuzzy rule-based tool. The consistency and heftiness of the ranking results have been tested by implementing grey relation analysis (GRA) approach. Comparison of ranking results has been done for effective decision making of ranking results. The accuracy of decision results would be highly useful in developing a planned maintenance policy for the plant. The proposed framework has been tested with its application on a cooling tower system of a thermal power plant located in the northern part of India.
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Balluff, Stefan, Jörg Bendfeld, and Stefan Krauter. "Meteorological Data Forecast using RNN." In Deep Learning and Neural Networks, 905–20. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0414-7.ch050.
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Gathering knowledge not only of the current but also the upcoming wind speed is getting more and more important as the experience of operating and maintaining wind turbines is increasing. Not only with regards to operation and maintenance tasks such as gearbox and generator checks but moreover due to the fact that energy providers have to sell the right amount of their converted energy at the European energy markets, the knowledge of the wind and hence electrical power of the next day is of key importance. Selling more energy as has been offered is penalized as well as offering less energy as contractually promised. In addition to that the price per offered kWh decreases in case of a surplus of energy. Achieving a forecast there are various methods in computer science: fuzzy logic, linear prediction or neural networks. This paper presents current results of wind speed forecasts using recurrent neural networks (RNN) and the gradient descent method plus a backpropagation learning algorithm. Data used has been extracted from NASA's Modern Era-Retrospective analysis for Research and Applications (MERRA) which is calculated by a GEOS-5 Earth System Modeling and Data Assimilation system. The presented results show that wind speed data can be forecasted using historical data for training the RNN. Nevertheless, the current set up system lacks robustness and can be improved further with regards to accuracy.
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Xing, Hao, Zhike Han, and Yichen Shen. "ClothNet: A Neural Network Based Recommender System." In Fuzzy Systems and Data Mining VI. IOS Press, 2020. http://dx.doi.org/10.3233/faia200706.
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The traditional collaborative filtering recommendation systems have many deficiencies, which make them incompetent in the domain of clothing recommendation; we proposed a new ClothNet model based on CNN, RNN, collaborative filtering and the characteristics of the fashion industry. The accuracy and generalization performance of this model are improved compared with traditional systems. The visual information integrated into the ClothNet model enables the recommendation system to alleviate the cold start problem, and new clothes can be added to the recommendation list faster through the visual information. The addition of temporal information enables ClothNet sharply capturing the impact of seasonal and time changes on user preferences. However, because RNN and CNN have the disadvantage of requiring a large amount of data, combining RNN and CNN will make the model more difficult to converge, so we have adopted the LearningToRank training mode and obtained good results.
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Mhetre, Nalini A., Arvind V. Deshpande, and Parikshit Narendra Mahalle. "Trust Management Model based on Fuzzy Approach for Ubiquitous Computing." In Securing the Internet of Things, 398–412. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9866-4.ch022.
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The current state of ubiquitous computing has been greatly influenced by emerging networking developments like Internet of Things (IoT), Future Internet etc. Adequate trust management is crucial to provide security. The entities involved in communication must be trusted for specific purposes depending on their role. Using trust model, devices can run trust computations and guide their behaviors. To this effect, a method is needed to evaluate the level of trust between devices. Trust models investigated so far discusses that devices face problems when communicating as transforming trust relationships from real to virtual world requires the negotiation of trust based on the security properties of devices. However, these models are developed in limited devices. This paper proposes a distributed trust model for device-to-device communication in ubiquitous computing. Mathematical model based on fuzzy rules to establish trust is presented. Fuzzy simulation of the model is presented to validate the findings. Simulation results show that proposed model calculates fuzzy trust values reliably.
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Borgeest, Kai, and Peter Josef Schneider. "Comparison of Control Strategies by the Example of the Cooling Fan Control of a Mobile Machine." In Advances in Computational Intelligence and Robotics, 500–526. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-7387-8.ch017.
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In order to compare different control strategies, the cooling system of a mobile machine has been chosen. The example control problem was to run the cooling system for m control variables and with n=m correction variables in a way to minimize power in order to save energy and to reduce fan noise while maintaining sufficient cooling. The plant is nonlinear. Three different kinds of controllers have been investigated in several variations (i.e. fuzzy control, PI[D], and Model Predictive Control [MPC]). Fourteen different criteria have been used in this chapter for evaluation. In many respects, a linear controller with fuzzy prediction proved best, in particular the prediction model can handle nonlinear properties of the plant.
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Toapanta Toapanta, Segundo Moisés, Yaritza Julieth Terán Terranova, Bertha Alice Naranjo Sánchez, and Luis Enrique Mafla Gallegos. "Security and Privacy in Information Management in a Distributed Environment for Public Organizations." In Fuzzy Systems and Data Mining VI. IOS Press, 2020. http://dx.doi.org/10.3233/faia200716.
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Security and privacy problems in information management are evident in public organizations. The objective of this research is the analisys risks that these organizations run, since computer attacks have increased along with both internal and external threats. Causing information and database thefts, there are risk analysis methodologies which are oriented to the objective for the preservation of guaranteeing the security and privacy of the information. Were used the deductive method and exploratory research to analyze the articles in the references and in the information available online and MAGERIT methodology what protects the information in its integrity, confidentiality and availability guaranteeing the security of the system and processes of public organizations. It turned out a Control of Security and Privacy factors, Threat Probability, Risk Assessment Formula, Prototype of Risk Management for Public Organizations and Privacy and security factor formula. It was concluded that MAGERIT is an alternative what allow mitigate the vulnerabilitys, threat and risks its processes in public organizations for protecting their information.
Conference papers on the topic "Fuzzy RPN"
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Sankar, Nune Ravi, and Bantwal S. Prabhu. "A Revised Matrix FMEA Technique Based on Fuzzy Logic." In ASME 2000 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/detc2000/rsafp-14472.
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Abstract A methodology combining the benefits of matrix FMEA and fuzzy logic is presented in this paper. The matrix approach is improved to develop a pictorial representation retaining all relevant qualitative and quantitative information of a several FMEA element relationships, which can be described as many-to-many. For example, one failure mode may result in several effects, and one effect may result from several failure modes. The methodology presented also extends the risk prioritization beyond the conventional Risk Priority Number (RPN) method. Fuzzy logic is used for prioritizing failures for corrective actions in FMEA. In RPN method, the criticality assessment is based on the severity, frequency of occurrence and detectability of failure. However, these parameters are here represented as members of a fuzzy set, combined by matching them against rules in a rule base, evaluated with min-max inferencing, and then defuzzified to assess the riskness of the failure. The fundamental problem with RPN technique is that it attempts to quantify risk without adequately quantifying the factors that contribute to risk. In particular cases, RPNs can be misleading. This deficiency can be eliminated by introducing the new technique to calculate criticality rank based on fuzzy logic. The methodology presented is demonstrated by application to an illustrative example.
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Ludwig, Simone A. "Comparison of Time Series Approaches applied to Greenhouse Gas Analysis: ANFIS, RNN, and LSTM." In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2019. http://dx.doi.org/10.1109/fuzz-ieee.2019.8859013.
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Lewallen, Colby. "Wavelet-Based Time-Frequency Control of a Flywheel Energy Storage System." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67682.
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The objective of this paper was to implement a novel controller called “wavelet-based time-frequency control” (WFXLMS) in a computer simulation of a FES system with six degrees-of-freedom and compare its dynamic stability and active power consumption with the following conventional controllers: PID and fuzzy-logic. Specifically, all three controllers were applied to a FES system operating at 100,000 rpm, and the amplitude of vibration, rate of convergence, and current draw were compared. As of writing this paper, the PID and fuzzy-logic controllers have converged but the WFXLMS controller has not. The parameter values for the WFXLMS controller need further tweaking for a more comprehensive analysis. Despite this setback, both the fuzzy-logic and PID controllers did demonstrate convergence at 100,000 rpm. The fuzzy-logic converged immediately and the PID converged around 0.8 seconds. The PID demonstrated a periodic motion about the z axis while the fuzzy-logic settled at a constant displacement. Finally, the PID controller had a smaller maximum and average current draw over the fuzzy-logic. In conclusion, the PID controller provided sufficient control of the system with the least amount of current draw, but the fuzzy-logic controller provided the steadiest response.
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Zernov, M. M., and V. V. Mladov. "Associative methods of fuzzy operations implementation." In 2017 Second Russia and Pacific Conference on Computer Technology and Applications (RPC). IEEE, 2017. http://dx.doi.org/10.1109/rpc.2017.8168098.
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Li, Xiaojun, Alan Palazzolo, and Zhiyang Wang. "Rotating Machinery Monitoring and Fault Diagnosis With Neural Network Enhanced Fuzzy Logic Expert System." In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/gt2016-58102.
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This paper presents an intelligent monitoring and fault diagnosis approach for rotating machinery by utilizing artificial neural networks and fuzzy logic expert systems (FLES). A recurrent neural network (RNN) is introduced to filter the input signal before they are forwarded to the expert system. The RNN is trained based on existing operational data so that it can adapt to a specific machine’s configurations and conditions. The RNN is able to generate proper baseline signal even if the machine is not under the exact same condition. A fuzzy logical expert system is then used for diagnosis based on the residual signal generated by the RNN. The system is incorporated into an existing comprehensive roto-dynamics software package named LVTRC.
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Popova, Daria. "Neuro-Fuzzy Modeling of Compressor Unit Performance." In 2018 3rd Russian-Pacific Conference on Computer Technology and Applications (RPC). IEEE, 2018. http://dx.doi.org/10.1109/rpc.2018.8482214.
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Fedulov, Yaroslav Alexandrovich, Vadim Vladimirovich Borisov, and Alexander Sergeevich Fedulov. "Fuzzy Model and Method of Rating University Evaluation." In 2018 3rd Russian-Pacific Conference on Computer Technology and Applications (RPC). IEEE, 2018. http://dx.doi.org/10.1109/rpc.2018.8482176.
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Korshunova, Kseniya P. "A Convolutional Fuzzy Neural Network for Image Classification." In 2018 3rd Russian-Pacific Conference on Computer Technology and Applications (RPC). IEEE, 2018. http://dx.doi.org/10.1109/rpc.2018.8482211.
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Lv Chong, Pang Yong-Jie, and Li Ye. "Fuzzy neural network controller for AUV based on RAN." In 2009 Chinese Control and Decision Conference (CCDC). IEEE, 2009. http://dx.doi.org/10.1109/ccdc.2009.5195220.
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Jenckel, Martin, Sourabh Sarvotham Parkala, Syed Saqib Bukhari, and Andreas Dengel. "Impact of Training LSTM-RNN with Fuzzy Ground Truth." In 7th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006592703880393.
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