618 research outputs found
An integrative functional genomics approach for discovering biomarkers in schizophrenia
No full textSchizophrenia (SZ) is a complex disorder resulting from both genetic and environmental causes with a lifetime prevalence world-wide of 1%; however, there are no specific, sensitive and validated biomarkers for SZ. A general unifying hypothesis has been put forward that disease-associated single nucleotide polymorphisms (SNPs) from genome-wide association study (GWAS) are more likely to be associated with gene expression quantitative trait loci (eQTL). We will describe this hypothesis and review primary methodology with refinements for testing this paradigmatic approach in SZ. We will describe biomarker studies of SZ and testing enrichment of SNPs that are associated both with eQTLs and existing GWAS of SZ. SZ-associated SNPs that overlap with eQTLs can be placed into gene-gene expression, protein-protein and protein-DNA interaction networks. Further, those networks can be tested by reducing/silencing the gene expression levels of critical nodes. We present pilot data to support these methods of investigation such as the use of eQTLs to annotate GWASs of SZ, which could be applied to the field of biomarker discovery. Those networks that have association with SNP markers, especially cis-regulated expression, might lead to a more clear understanding of important candidate genes that predispose to disease and alter expression. This method has general application to many complex disorders
Designing a Re-Usable Coordination Module for Cooperative Industrial Control Applications
No full textDistributed artificial intelligence (DAI) systems, in which multiple agents communicate and co-operate with one another to achieve their individual and collective goals, are a promising enabling technology for constructing large, real world industrial control applications. To facilitate the development of such systems a number of generic DAI frameworks have been devised. These frameworks typically aid the development process by providing a language, a set of structures, and/or some tools with which the necessary infrastructure and support mechanisms for interacting agents can be instantiated. The paper reports on one such framework, called ARCHON, which has been used to build DAI systems in the following industrial control domains: electricity distribution management, electricity transportation management, cement factory control, particle accelerator control and flexible assembly robotic cells. A distinguishing and novel feature of the ARCHON framework is that it extends the level of support offered to the system builder - it provides generic and reusable knowledge about the process of cooperation, in addition to the more standard development facilities. This generic knowledge is embedded in a domain-independent co-ordination module and it is the rationale, design, implementation and evaluation of this module which forms the major contribution of the paper
Um modelo de rede neuro-fuzzy baseada em funções de base radial capaz de inferir regras do tipo Mamdani
No full textDissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2015.Este trabalho tem como objetivo apresentar um novo sistema de inferência neuro-fuzzy, chamado RBFuzzy, capaz de extrair conhecimento a partir de dados e gerar regras fuzzy do tipo Mamdani com alta interpretabilidade. A RBFuzzy é um sistema de inferência neuro-fuzzy que aproveita o comportamento funcional de neurônios ativados por Funções de Base Radial (RBF) e sua relação com sistemas de inferência fuzzy. A arquitetura da rede RBFuzzy permite extrair um conjunto de regras linguísticas a partir da estrutura conexionista e dos pesos ajustados de uma rede neural. Uma extensão do algoritmo da otimização da colônia de formigas (ACO, do inglês ant colony optimization algorithm) é utilizada para ajustar os pesos de cada regra para gerar um conjunto de regras fuzzy acurado e interpretável. Tendo um conjunto de regras fuzzy um especialista pode adicionar regras novas para incorporar conhecimento novo ao modelo de previsão gerado e também corrigir regras que foram geradas por dados imprecisos.Abstract : This work presents a novel neuro-fuzzy inference system, called RBFuzzy, capable of knowledge extraction and generation of highly interpretable Mamdani-type fuzzy rules. RBFuzzy is a four layer neuro-fuzzy inference system that takes advantage of the functional behavior of Radial Basis Function (RBF) neurons and their relationship with fuzzy inference systems. Inputs are combined in the RBF neurons to compound the antecedents of fuzzy rules. The fuzzy rules consequents are determined by the third layer neurons where each neuron represents a Mamdani-type fuzzy output variable in the form of a linguistic term. The last layer weights each fuzzy rule and generates the crisp output. An extension of the ant-colony optimization (ACO) algorithm is used to adjust the weights of each rule in order to generate an accurate and interpretable fuzzy rule set. For benchmarking purposes some experiments with classic datasets were carried out to compare our proposal with the EFuNN neuro-fuzzy model. The RBFuzzy was also applied in a real world oil well-log database to model and forecast the Rate of Penetration (ROP) of a drill bit for a given oshore well drilling section. The obtained results show that our model can reach the same level of accuracy with fewer rules when compared to the EFuNN, which facilitates understandingthe operation of the system by a human expert
Designing and Implementing a Multi-Agent Architecture for Business Process Management
No full textThis paper presents a general multi-agent architecture for the management of business processes, and an agent design that has been implemented within such a system. The autonomy of the agents involved in the system is considered paramount. Therefore, for agents to agree on the distribution of problem solving effort within the system they must negotiate. The knowledge sharing and negotiation functions of such an agent are focused on in this paper
ADEPT: Managing Business Processes Using Intelligent Agents
No full textThis paper describes work undertaken in the ADEPT (Advanced Decision Environment for Process Tasks) project towards developing an agent-based infrastructure for managing business processes. We describe how the key technology of negotiating, service providing, autonomous agents was realised and demonstrate how this was applied to the BT business process of providing a customer quote for network services. Issues of agent visualisation are also addressed
Analisis Perbandingan Metode Fuzzy Inferensi Sistem Tsukamoto dan Mamdani dalam Penentuan Estimasi Jumlah Produksi Gula
No full textPT Madu Baru Yogyakarta s a company that produces sugar. Many factors must be considered in determining the mount o f production generated annually. For that, in determining the mount of production per year required and analysis of decision support systems analysis will be done applying Method of Fuzzy Inference System Tsukamoto end Method Inference System Mamdani using the min as function o f its implications, the from each rule give nextplicitly (scrip) by the smallest degree of membership (Defuzzifikasi), the final result by applying a weighted average for Fuzzy Inference System defuzzyfikasi begins the composition rules between Max and the Method apllied taking the center point (z*) fuzzy area. With the comparison method of Fuzzy Inference System and Method of Fuzzy Inference System Mamdani, obtained the most appropriate methods and approaches in determining the estimated amount o f sugar production, in order to obtain an output that is the amount o f sugar per year.With statistical tests, it can be concluded that the method is relatively close to the calculation method of the factory is Tsukamoto, because the method o f Tsukamoto had a lower mean than the mean on the 7,960.42 Mamdani methods is 21710.14
Penentuan Server Kritis Dengan Menggunakan Fuzzy Mamdani Dan Fuzzy Sugeno Pada PT SAMUDERA INDONESIA TBK
No full textCRITICAL SERVER DETERMINATION USING FUZZY MAMDANI AND FUZZY SUGENO METHODS :CASE STUDY PT SAMUDERA INDONESIA TBKBy : Suhaemi (1611601392)PT Samudera Indonesia Tbk is a company engaged in the field of logistics. As a service company, prioritizes services especially in providing information services to the customer or client. However, in the common problems of the technology information system which is used in particular is happening is the server down. This is due to several factors could not be predicted. Plus the company is still dependent on the role of the IT personnel to wait for handling server down occurrences. The effort to know the level of critical server is one of the prevention or mitigation of interference that are at risk of fatal to the business processes of the company then needed a decision support system. In this study, the author compares the mamdani fuzzy logic method calculations with fuzzy sugeno method to measure the level of critical servers used PT Samudera Indonesia Tbk. This research resulted in a prototype that is built with MATLAB R2013a, can be used to calculate the critical level on the server. The prototype uses two methods of fuzzy mamdani and sugeno fuzzy. Accuracy of Mamdani fuzzy method has a 85,21% average. Of the two methods of calculation, the highest accuracy rating results will serve as a reference in determining a critical server.Keywords: SPK, Server, Fuzzy Mamdani, Fuzzy Sugeno, Matlab R2013aPT Samudera Indonesia Tbk merupakan perusahaan yang bergerak dalam bidang jasa yang berupa logistik. Sebagai perusahaan jasa, sangat mengutamakan pelayanan terutama dalam memberikan layanan informasi kepada customer atau client. Namun, dalam sering terjadi masalah teknologi sistem informasi yang digunakan khususnya yang terjadi adalah server down. Hal ini disebabkan beberapa faktor tidak bisa diprediksi. Ditambah lagi perusahaan ini masih bergantung kepada peranan personil IT untuk menunggu penanganan kejadian server down. Upaya mengetahui tingkat kritis server adalah salah satu pencegahan atau mitigasi terhadap gangguan yang beresiko fatal terhadap proses bisnis perusahaan maka dibutuhkan sistem pendukung keputusan. Dalam penelitian ini, penulis membandingkan perhitungan metode logika fuzzy mamdani dengan metode fuzzy sugeno untuk mengukur tingkat kritis server yang digunakan PT Samudera Indonesia Tbk. Penelitian ini menghasilkan sebuah Prototipe yang dibangun dengan Matlab R2013a, dapat digunakan untuk menghitung tingkat kritis pada server. Prototipe tersebut menggunakan dua metode fuzzy mamdani dan fuzzy sugeno. Metode fuzzy mamdani memiliki akurasi rata-rata 85,21%. Dari perhitungan kedua metode tersebut, hasil nilai akurasi yang tertinggi akan dijadikan sebagai acuan dalammenentukan server yang kritis.Kata Kunci: SPK, Server, fuzzy mamdani, fuzzy sugeno, Matlab R2013
DETECTING CLEAN WATER AND CLOUDY WATER BASED ON IOT USING FUZZY LOGIC SUGENO AND MAMDANI METHOD
No full textWater is a very important need for humans on a daily basis, so humans need good water quality, poor water quality occurs because of pollution and contamination. To determine the quality of a water, there are several parameters determined by the Regulation of the Minister of Health Number 416/MENKES/PER/IX/1990 concerning water quality requirements, including turbidity parameters and dissolved solids (TDS) parameters used in this study, and by using the Sugeno method fuzzy logic and then comparing it with the Mamdani method to produce water quality output in accordance with the reference in the World Health Organization on drinking water quality guidelines.
In solving this problem, a tool is designed to help calculate water quality, with the turbidity parameter the author uses an LDR sensor that works to calculate the scattering of light that penetrates the water and hits the sensor, then on the TDS parameter the author uses a TDS sensor that works to calculate the amount of dissolved substances in the water. The Sugeno method and the Mamdani method used in this study are tasked with determining whether the water quality includes water of decent, medium, or unfeasible quality. This method has stages such as fuzzification, inference, and defuzzification, where in this defuzzification produces the output of the fuzzification and inference process.
The final result in this study, it was found that these two sensors can be used as a tool to calculate the quality of a water, and these two methods can also determine the quality of a water with the same accuracy of 80%. From 10 samples of water, it was found that the output of 2 samples of water in the Sugeno and Mamdani methods was not in accordance with the WHO reference and using a tool other than a sensor that calculates water quality, namely the TDS EC Meter. Then the results from the sensors and fuzzy calculations are sent to the database, and from the database it will be sent to the websit
Sistem Pendukung Keputusan Penentuan Program Studi Di Perguruan Tinggi Menggunakan Fuzzy Mamdani
No full textA decision support system is a system that can be used to help make
decisions based on existing criteria, basically decision making is motivated by the
existence of a problem or problem that occurs in the decision making process. The
author wants to create a decision support system for determining software
engineering study programs at the Bengkalis State Polytechnic, to help
recommend study programs that suit the abilities of these students based on
existing criteria. In addition, there is a chat feature created directly by the admin
which is intended for students and the student affairs section of the Bengkalis State
Polytechnic. By using the mamdani fuzzy method, fuzzy logic is able to model very
complex non-linear functions. Based on this research that was passed, the author
concluded that by applying the fuzzy mamdani algorithm in building a decision
support system for determining the study program, this study program can help
users in the process of determining the software engineering study program with
the assessment criteria that have been determined in the syste
Comparing Mamdani Sugeno fuzzy logic and RBF ANN network for PV fault detection
No full textThis work proposes a new fault detection algorithm for photovoltaic (PV) systems based on artificial neural networks (ANN) and fuzzy logic system interface. There are few instances of machine learning techniques deployed in fault detection algorithms in PV systems, therefore, the main focus of this paper is to create a system capable to detect possible faults in PV systems using radial basis function (RBF) ANN network and both Mamdani, Sugeno fuzzy logic systems interface.
The obtained results indicate that the fault detection algorithm can detect and locate accurately different types of faults such as, faulty PV module, two faulty PV modules and partial shading conditions affecting the PV system. In order to achieve high rate of detection accuracy, four various ANN networks have been tested. The maximum detection accuracy is equal to 92.1%. Furthermore, both examined fuzzy logic systems show approximately the same output during the experiments. However, there are slightly difference in developing each type of the fuzzy systems such as the output membership functions and the rules applied for detecting the type of the fault occurring in the PV plant
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