Search results for: fuzzy inference systems

Authors: Mohammad Vahed, Ana Sadeghitohidi, Majid Vahed, Hiroki Takahashi

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In the genomics field, the huge amounts of data have produced by the next-generation sequencers (NGS). Data volumes are very rapidly growing, as it is postulated that more than one billion bases will be produced per year in 2020. The growth rate of produced data is much faster than Moore's law in computer technology. This makes it more difficult to deal with genomics data, such as storing data, searching information, and finding the hidden information. It is required to develop the analysis platform for genomics big data. Cloud computing newly developed enables us to deal with big data more efficiently. Hadoop is one of the frameworks distributed computing and relies upon the core of a Big Data as a Service (BDaaS). Although many services have adopted this technology, e.g. amazon, there are a few applications in the biology field. Here, we propose a new algorithm to more efficiently deal with the genomics big data, e.g. sequencing data. Our algorithm consists of two parts: First is that BDaaS is applied for handling the data more efficiently. Second is that the hybrid method of MapReduce and Fuzzy logic is applied for data processing. This step can be parallelized in implementation. Our algorithm has great potential in computational analysis of genomics big data, e.g. de novo genome assembly and sequence similarity search. We will discuss our algorithm and its feasibility.

Keywords: big data, fuzzy logic, MapReduce, Hadoop, cloud computing

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Authors: Jirarat Teeravaraprug, Usawadee Inklay

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This paper gave an attempt in prioritizing information technologies that organizations should give concentration. The case study was organizations in the automotive assembly industry in Thailand. Data were first collected to gather all information technologies known and used in the automotive assembly industry in Thailand. Five experts from the industries were surveyed based on the concept of fuzzy DEMATEL. The information technologies were categorized into six groups, which were communication, transaction, planning, organization management, warehouse management, and transportation. The cause groups of information technologies for each group were analysed and presented. Moreover, the relationship between the used and the significant information technologies was given. Discussions based on the used information technologies and the research results are given.

Keywords: information technology, automotive assembly industry, fuzzy DEMATEL

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Authors: Faith-Michael E. Uzoka, Boluwaji A. Akinnuwesi, Taiwo Amoo, Flora Aladi, Stephen Fashoto, Moses Olaniyan, Joseph Osuji

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The overarching aim of this study is to develop a soft-computing system for the differential diagnosis of tropical diseases. These conditions are of concern to health bodies, physicians, and the community at large because of their mortality rates, and difficulties in early diagnosis due to the fact that they present with symptoms that overlap, and thus become ‘confusable’. We report on the first phase of our study, which focuses on the development of a fuzzy cognitive map model for early differential diagnosis of tropical diseases. We used malaria as a case disease to show the effectiveness of the FCM technology as an aid to the medical practitioner in the diagnosis of tropical diseases. Our model takes cognizance of manifested symptoms and other non-clinical factors that could contribute to symptoms manifestations. Our model showed 85% accuracy in diagnosis, as against the physicians’ initial hypothesis, which stood at 55% accuracy. It is expected that the next stage of our study will provide a multi-disease, multi-symptom model that also improves efficiency by utilizing a decision support filter that works on an algorithm, which mimics the physician’s diagnosis process.

Keywords: medical diagnosis, tropical diseases, fuzzy cognitive map, decision support filters, malaria differential diagnosis

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Authors: A. Derrouazin, N. Mekkakia-M, R. Taleb, M. Helaimi, A. Benbouali

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Nowadays the use of different sources of renewable energy for the production of electricity is the concern of everyone, as, even impersonal domestic use of the electricity in isolated sites or in town. As the conventional sources of energy are shrinking, a need has arisen to look for alternative sources of energy with more emphasis on its optimal use. This paper presents design of a sustainable Hybrid Power System (PV-Wind-Storage) assisted by grid as supplementary sources applied to case study residential house, to meet its entire energy demand. A Fuzzy control system model has been developed to optimize and control flow of power from these sources. This energy requirement is mainly fulfilled from PV and Wind energy stored in batteries module for critical load of a residential house and supplemented by grid for base and peak load. The system has been developed for maximum daily households load energy of 3kWh and can be scaled to any higher value as per requirement of individual /community house ranging from 3kWh/day to 10kWh/day, as per the requirement. The simulation work, using intelligent energy management, has resulted in an optimal yield leading to average reduction in cost of electricity by 50% per day.

Keywords: photovoltaic (PV), wind turbine, battery, microcontroller, fuzzy control (FC), Matlab

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Authors: Alia Aldarmaki, Ahmad Elshennawy

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There is a global shift in healthcare systems’ emphasizing engaging different stakeholders in selecting quality improvement initiatives and incorporating their preferences to improve the healthcare efficiency and outcomes. Although experts bring scientific knowledge based on the scientific model and their personal experience, other stakeholders can bring new insights and information into the decision-making process. This study attempts to explore the impact of incorporating different stakeholders’ preference in identifying the most significant criteria that should be considered in healthcare for electing the improvement projects. A Framework based on a modified Fuzzy Delphi Method (FDM) was built. In addition to, the subject matter experts, doctors/physicians, nurses, administrators, and managers groups contribute to the selection process. The research identifies potential criteria for evaluating projects in healthcare, then utilizes FDM to capture expertise knowledge. The first round in FDM is intended to validate the identified list of criteria from experts; which includes collecting additional criteria from experts that the literature might have overlooked. When an acceptable level of consensus has been reached, a second round is conducted to obtain experts’ and other related stakeholders’ opinions on the appropriate weight of each criterion’s importance using linguistic variables. FDM analyses eliminate or retain the criteria to produce a final list of the critical criteria to select improvement projects in healthcare. Finally, reliability and validity were investigated using Cronbach’s alpha and factor analysis, respectively. Two case studies were carried out in a public hospital in the United Arab Emirates to test the framework. Both cases demonstrate that even though there were common criteria between the experts and the stakeholders, still stakeholders’ perceptions bring additional critical criteria into the evaluation process, which can impact the outcomes. Experts selected criteria related to strategical and managerial aspects, while the other participants preferred criteria related to social aspects such as health and safety and patients’ satisfaction. The health and safety criterion had the highest important weight in both cases. The analysis showed that Cronbach’s alpha value is 0.977 and all criteria have factor loading greater than 0.3. In conclusion, the inclusion of stakeholders’ perspectives is intended to enhance stakeholders’ engagement, improve transparency throughout the decision process, and take robust decisions.

Keywords: Fuzzy Delphi Method, fuzzy number, healthcare, stakeholders

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Authors: Anandita Mitro, Archana Srivastava, Bidisha Banerjee

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The present paper attempts to analyze the hierarchy and interconnection of factors responsible for the uptake of BCG vaccination in India. The study uses National Family Health Survey (NFHS-5) data which was conducted during 2019-21. The univariate logistic regression method is used to understand the univariate effects while the interconnection effects have been studied using the Categorical Inference Tree (CIT) which is a non-parametric Machine Learning (ML) model. The hierarchy of the factors is further established using Conditional Inference Forest which is an extension of the CIT approach. The results suggest that BCG vaccination coverage was influenced more by system-level factors and awareness than education or socio-economic status. Factors such as place of delivery, antenatal care, and postnatal care were crucial, with variations based on delivery location. Region-specific differences were also observed which could be explained by the factors. Awareness of the disease was less impactful along with the factor of wealth and urban or rural residence, although awareness did appear to substitute for inadequate ANC. Thus, from the policy point of view, it is revealed that certain subpopulations have less prevalence of vaccination which implies that there is a need for population-specific policy action to achieve a hundred percent coverage.

Keywords: vaccination, NFHS, machine learning, public health

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Authors: Chih-Chieh Yang, Chung-Neng Huang

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In power system, how to improve the fault diagnosis technology of transmission line has always been the primary goal of power grid operators. In recent years, due to the rise of green energy, the addition of all kinds of distributed power also has an impact on the stability of the power system. Because the smart meters are with the function of data recording and bidirectional transmission, the adaptive Fuzzy Neural inference system, ANFIS, as well as the artificial intelligence that has the characteristics of learning and estimation in artificial intelligence. For transmission network, in order to avoid misjudgment of the fault type and location due to the input of these unstable power sources, combined with the above advantages of smart meter and ANFIS, a method for identifying fault types and location of faults is proposed in this study. In ANFIS training, the bus voltage and current information collected by smart meters can be trained through the ANFIS tool in MATLAB to generate fault codes to identify different types of faults and the location of faults. In addition, due to the uncertainty of distributed generation, a wind power system is added to the transmission network to verify the diagnosis correctness of the study. Simulation results show that the method proposed in this study can correctly identify the fault type and location of fault with more efficiency, and can deal with the interference caused by the addition of unstable power sources.

Keywords: ANFIS, fault diagnosis, power system, smart meter

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Authors: Arjun Paul, Adrijit Goswami

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In our present work, an economic order quantity inventory model with shortages is developed where holding cost is expressed as linearly increasing function of time and demand rate is a ramp type function of time. The items considered in the model are deteriorating in nature so that a small fraction of the items is depleted with the passage of time. In order to consider a more realistic situation, the deterioration rate is assumed to follow a continuous uniform distribution with the parameters involved being triangular fuzzy numbers. The inventory manager offers his customer a discount in case he is willing to backorder his demand when there is a stock-out. The optimum ordering policy and the optimum discount offered for each backorder are determined by minimizing the total cost in a replenishment interval. For better illustration of our proposed model in both the crisp and fuzzy sense and for providing richer insights, a numerical example is cited to exemplify the policy and to analyze the sensitivity of the model parameters.

Keywords: fuzzy deterioration rate, price discount on backorder, ramp type demand, shortage, time varying holding cost

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Authors: Wanhyun Cho, Soonja Kang, Sangkyoon Kim, Soonyoung Park

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In this paper, we present the human action recognition method using the variational Bayesian HMM with the Dirichlet process mixture (DPM) of the Gaussian-Wishart emission model (GWEM). First, we define the Bayesian HMM based on the Dirichlet process, which allows an infinite number of Gaussian-Wishart components to support continuous emission observations. Second, we have considered an efficient variational Bayesian inference method that can be applied to drive the posterior distribution of hidden variables and model parameters for the proposed model based on training data. And then we have derived the predictive distribution that may be used to classify new action. Third, the paper proposes a process of extracting appropriate spatial-temporal feature vectors that can be used to recognize a wide range of human behaviors from input video image. Finally, we have conducted experiments that can evaluate the performance of the proposed method. The experimental results show that the method presented is more efficient with human action recognition than existing methods.

Keywords: human action recognition, Bayesian HMM, Dirichlet process mixture model, Gaussian-Wishart emission model, Variational Bayesian inference, prior distribution and approximate posterior distribution, KTH dataset

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Authors: Seada Hussen, Frie Ayalew

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Integrating different energy resources, like solar PV and hydro, is used to ensure reliable power to rural communities like Hara village in Ethiopia. Hybrid power system offers power supply for rural villages by providing an alternative supply for the intermittent nature of renewable energy resources. The intermittent nature of renewable energy resources is a challenge to electrifying rural communities in a sustainable manner with solar resources. Major rural villages in Ethiopia are suffering from a lack of electrification, that cause our people to suffer deforestation, travel for long distance to fetch water, and lack good services like clinic and school sufficiently. The main objective of this project is to provide a balanced, stable, reliable supply for Hara village, Ethiopia using solar power with a pumped hydro energy storage system. The design of this project starts by collecting data from villages and taking solar irradiance data from NASA. In addition to this, geographical arrangement and location are also taken into consideration. After collecting this, all data analysis and cost estimation or optimal sizing of the system and comparison of solar with pumped hydro and solar with battery storage system is done using Homer Software. And since solar power only works in the daytime and pumped hydro works at night time and also at night and morning, both load will share to cover the load demand; this need controller designed to control multiple switch and scheduling in this project fuzzy logic controller is used to control this scenario. The result of the simulation shows that solar with pumped hydro energy storage system achieves good results than with a battery storage system since the comparison is done considering storage reliability, cost, storage capacity, life span, and efficiency.

Keywords: pumped hydro storage, solar energy, solar PV, battery energy storage, fuzzy logic controller

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Authors: Fatemeh Torfi

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Estimation of stochastic demand in physical distribution in general and efficient transport routs management in particular is emerging as a crucial factor in urban planning domain. It is particularly important in some municipalities such as Tehran where a sound demand management calls for a realistic analysis of the routing system. The methodology involved critically investigating a fuzzy least-squares linear regression approach (FLLRs) to estimate the stochastic demands in the vehicle routing problem (VRP) bearing in mind the customer's preferences order. A FLLR method is proposed in solving the VRP with stochastic demands. Approximate-distance fuzzy least-squares (ADFL) estimator ADFL estimator is applied to original data taken from a case study. The SSR values of the ADFL estimator and real demand are obtained and then compared to SSR values of the nominal demand and real demand. Empirical results showed that the proposed methods can be viable in solving problems under circumstances of having vague and imprecise performance ratings. The results further proved that application of the ADFL was realistic and efficient estimator to face the stochastic demand challenges in vehicle routing system management and solve relevant problems.

Keywords: fuzzy least-squares, stochastic, location, routing problems

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Authors: Szymon Rutkowski, Tomasz Korbak

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We present a reinforcement-learning-based method of training neural machine translation models without parallel corpora. The standard encoder-decoder approach to machine translation suffers from two problems we aim to address. First, it needs parallel corpora, which are scarce, especially for low-resource languages. Second, it lacks psychological plausibility of learning procedure: learning a foreign language is about learning to communicate useful information, not merely learning to transduce from one language’s 'encoding' to another. We instead pose the problem of learning to translate as learning a policy in a communication game between two agents: the translator and the classifier. The classifier is trained beforehand on a natural language inference task (determining the entailment relation between a premise and a hypothesis) in the target language. The translator produces a sequence of actions that correspond to generating translations of both the hypothesis and premise, which are then passed to the classifier. The translator is rewarded for classifier’s performance on determining entailment between sentences translated by the translator to disciple’s native language. Translator’s performance thus reflects its ability to communicate useful information to the classifier. In effect, we train a machine translation model without the need for parallel corpora altogether. While similar reinforcement learning formulations for zero-shot translation were proposed before, there is a number of improvements we introduce. While prior research aimed at grounding the translation task in the physical world by evaluating agents on an image captioning task, we found that using a linguistic task is more sample-efficient. Natural language inference (also known as recognizing textual entailment) captures semantic properties of sentence pairs that are poorly correlated with semantic similarity, thus enforcing basic understanding of the role played by compositionality. It has been shown that models trained recognizing textual entailment produce high-quality general-purpose sentence embeddings transferrable to other tasks. We use stanford natural language inference (SNLI) dataset as well as its analogous datasets for French (XNLI) and Polish (CDSCorpus). Textual entailment corpora can be obtained relatively easily for any language, which makes our approach more extensible to low-resource languages than traditional approaches based on parallel corpora. We evaluated a number of reinforcement learning algorithms (including policy gradients and actor-critic) to solve the problem of translator’s policy optimization and found that our attempts yield some promising improvements over previous approaches to reinforcement-learning based zero-shot machine translation.

Keywords: agent-based language learning, low-resource translation, natural language inference, neural machine translation, reinforcement learning

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Authors: Donghwan Lee, Gichun Cha, Jooyoung Park, Junkyeong Kim, Seunghee Park

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3D shape models of the existing structure are required for many purposes such as safety and operation management. The traditional 3D modeling methods are based on manual or semi-automatic reconstruction from close-range images. It occasions great expense and time consuming. The Terrestrial Laser Scanner (TLS) is a common survey technique to measure quickly and accurately a 3D shape model. This TLS is used to a construction site and cultural heritage management. However there are many limits to process a TLS point cloud, because the raw point cloud is massive volume data. So the capability of carrying out useful analyses is also limited with unstructured 3-D point. Thus, segmentation becomes an essential step whenever grouping of points with common attributes is required. In this paper, members segmentation algorithm was presented to separate a raw point cloud which includes only 3D coordinates. This paper presents a clustering approach based on a fuzzy method for this objective. The Fuzzy C-Means (FCM) is reviewed and used in combination with a similarity-driven cluster merging method. It is applied to the point cloud acquired with Lecia Scan Station C10/C5 at the test bed. The test-bed was a bridge which connects between 1st and 2nd engineering building in Sungkyunkwan University in Korea. It is about 32m long and 2m wide. This bridge was used as pedestrian between two buildings. The 3D point cloud of the test-bed was constructed by a measurement of the TLS. This data was divided by segmentation algorithm for each member. Experimental analyses of the results from the proposed unsupervised segmentation process are shown to be promising. It can be processed to manage configuration each member, because of the segmentation process of point cloud.

Keywords: fuzzy c-means (FCM), point cloud, segmentation, terrestrial laser scanner (TLS)

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Authors: Sadık Ata, Kevser Dincer

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In this study, performance of proton exchange membrane (PEM) fuel cell was experimentally investigated and modelled with Rule-Based Mamdani-Type Fuzzy (RBMTF) modelling technique. Coating on the anode side of the PEM fuel cell was accomplished with the spin method by using Yttria-stabilized zirconia (YSZ). Input parameters voltage density (V/cm2), and current density (A/cm2), temperature (°C), time (s); output parameter power density (W/cm2) were described by RBMTF if-then rules. Numerical parameters of input and output variables were fuzzificated as linguistic variables: Very Very Low (L1), Very Low (L2), Low (L3), Negative Medium (L4), Medium (L5), Positive Medium (L6), High (L7), Very High (L8) and Very Very High (L9) linguistic classes. The comparison between experimental data and RBMTF is done by using statistical methods like absolute fraction of variance (R2). The actual values and RBMTF results indicated that RBMTF can be successfully used for the analysis of performance of PEM fuel cell.

Keywords: proton exchange membrane (PEM), fuel cell, rule-based Mamdani-type fuzzy (RMBTF) modeling, yttria-stabilized zirconia (YSZ)

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Authors: I. M. L. Nadeesha Jayaweera, Adao Alex Trindade

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In choosing a candidate model in likelihood-based modeling via an information criterion, the practitioner is often faced with the difficult task of deciding just how far up the ranked list to look. Motivated by this pragmatic necessity, we construct an uncertainty band for a generalized (model selection) information criterion (GIC), defined as a criterion for which the limit in probability is identical to that of the normalized log-likelihood. This includes common special cases such as AIC & BIC. The method starts from the asymptotic normality of the GIC for the joint distribution of the candidate models in an independent and identically distributed (IID) data framework and proceeds by deriving the (asymptotically) exact distribution of the minimum. The calculation of an upper quantile for its distribution then involves the computation of multivariate Gaussian integrals, which is amenable to efficient implementation via the R package "mvtnorm". The performance of the methodology is tested on simulated data by checking the coverage probability of nominal upper quantiles and compared to the bootstrap. Both methods give coverages close to nominal for large samples, but the bootstrap is two orders of magnitude slower. The methodology is subsequently extended to two other commonly used model structures: regression and time series. In the regression case, we derive the corresponding asymptotically exact distribution of the minimum GIC invoking Lindeberg-Feller type conditions for triangular arrays and are thus able to similarly calculate upper quantiles for its distribution via multivariate Gaussian integration. The bootstrap once again provides a default competing procedure, and we find that similar comparison performance metrics hold as for the IID case. The time series case is complicated by far more intricate asymptotic regime for the joint distribution of the model GIC statistics. Under a Gaussian likelihood, the default in most packages, one needs to derive the limiting distribution of a normalized quadratic form for a realization from a stationary series. Under conditions on the process satisfied by ARMA models, a multivariate normal limit is once again achieved. The bootstrap can, however, be employed for its computation, whence we are once again in the multivariate Gaussian integration paradigm for upper quantile evaluation. Comparisons of this bootstrap-aided semi-exact method with the full-blown bootstrap once again reveal a similar performance but faster computation speeds. One of the most difficult problems in contemporary statistical methodological research is to be able to account for the extra variability introduced by model selection uncertainty, the so-called post-model selection inference (PMSI). We explore ways in which the GIC uncertainty band can be inverted to make inferences on the parameters. This is being attempted in the IID case by pivoting the CDF of the asymptotically exact distribution of the minimum GIC. For inference one parameter at a time and a small number of candidate models, this works well, whence the attained PMSI confidence intervals are wider than the MLE-based Wald, as expected.

Keywords: model selection inference, generalized information criteria, post model selection, Asymptotic Theory

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Authors: Jui-Chen Huang, Shou-Hsiung Cheng

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This study examined their willingness to use telecare for people who have had experience falling in the last three months in Taiwan. This study adopted convenience sampling and a structural questionnaire to collect data. It was based on the definition and the constructs related to the Health Belief Model (HBM). HBM is comprised of seven constructs: perceived benefits (PBs), perceived disease threat (PDT), perceived barriers of taking action (PBTA), external cues to action (ECUE), internal cues to action (ICUE), attitude toward using (ATT), and behavioral intention to use (BI). This study adopted Fuzzy Neural Network (FNN) to put forward an effective method. It shows the dependence of ATT on PB, PDT, PBTA, ECUE, and ICUE. The training and testing data RMSE (root mean square error) are 0.028 and 0.166 in the FNN, respectively. The training and testing data RMSE are 0.828 and 0.578 in the regression model, respectively. On the other hand, as to the dependence of ATT on BI, as presented in the FNN, the training and testing data RMSE are 0.050 and 0.109, respectively. The training and testing data RMSE are 0.529 and 0.571 in the regression model, respectively. The results show that the FNN method is better than the regression analysis. It is an effective and viable good way.

Keywords: fall, fuzzy neural network, health belief model, telecare, willingness

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Authors: Ramin Javadzadeh

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The particle swarm optimization are Meta heuristic optimization method, which are used for clustering and pattern recognition applications are abundantly. These algorithms in multimodal optimization problems are more efficient than genetic algorithms. A major drawback in these algorithms is their slow convergence to global optimum and their weak stability can be considered in various running of these algorithms. In this paper, improved Particle swarm optimization is introduced for the first time to overcome its problems. The fuzzy cellular automata is used for improving the algorithm efficiently. The credibility of the proposed approach is evaluated by simulations, and it is shown that the proposed approach achieves better results can be achieved compared to the Particle swarm optimization algorithms.

Keywords: cellular automata, cellular learning automata, local search, optimization, particle swarm optimization

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Authors: Priyanka Verma, Vijaya Dixit, Rishabh Bajpai

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This research paper is aimed at selecting appropriate material handling equipment among the given choices so that the automation level in material handling can be enhanced. This work is a practical case scenario of material handling systems in consumer electronic appliances manufacturing organization. The choices of material handling equipment among which the decision has to be made are Automated Guided Vehicle’s (AGV), Autonomous Mobile Robots (AMR), Overhead Conveyer’s (OC) and Battery Operated Trucks/Vehicle’s (BOT). There is a need of attaining a certain level of automation in order to reduce human interventions in the organization. This requirement of achieving certain degree of automation can be attained by material handling equipment’s mentioned above. The main motive for selecting above equipment’s for study was solely based on corporate financial strategy of investment and return obtained through that investment made in stipulated time framework. Since the low cost automation with respect to material handling devices has to be achieved hence these equipment’s were selected. Investment to be done on each unit of this equipment is less than 20 lakh rupees (INR) and the recovery period is less than that of five years. Fuzzy analytic hierarchic process (FAHP) is applied here for selecting equipment where the four choices are evaluated on basis of four major criteria’s and 13 sub criteria’s, and are prioritized on the basis of weight obtained. The FAHP used here make use of triangular fuzzy numbers (TFN). The inability of the traditional AHP in order to deal with the subjectiveness and impreciseness in the pair-wise comparison process has been improved in the FAHP. The range of values for general rating purposes for all decision making parameters is kept between 0 and 1 on the basis of expert opinions captured on shop floor. These experts were familiar with operating environment and shop floor activity control. Instead of generating exact value the FAHP generates the ranges of values to accommodate the uncertainty in decision-making process. The four major criteria’s selected for the evaluation of choices of material handling equipment’s available are materials, technical capabilities, cost and other features. The thirteen sub criteria’s listed under these following four major criteria’s are weighing capacity, load per hour, material compatibility, capital cost, operating cost and maintenance cost, speed, distance moved, space required, frequency of trips, control required, safety and reliability issues. The key finding shows that among the four major criteria selected, cost is emerged as the most important criteria and is one of the key decision making aspect on the basis of which material equipment selection is based on. While further evaluating the choices of equipment available for each sub criteria it is found that AGV scores the highest weight in most of the sub-criteria’s. On carrying out complete analysis the research shows that AGV is the best material handling equipment suiting all decision criteria’s selected in FAHP and therefore it is beneficial for the organization to carry out automated material handling in the facility using AGV’s.

Keywords: fuzzy analytic hierarchy process (FAHP), material handling equipment, subjectiveness, triangular fuzzy number (TFN)

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Authors: Vladimir A. Todiras

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Spatio-temporal models provide new possibilities for real-time action in pest risk analysis. It should be noted that estimation of the possibility and probability of introduction of a pest and of its economic consequences involves many uncertainties. We present a new mapping technique that assesses pest invasion risk using online BioClass software. BioClass is a GIS tool designed to solve multiple-criteria classification and optimization problems based on fuzzy logic and level set methods. This research describes a method for predicting the potential establishment and spread of a plant pest into new areas using a case study: corn rootworm (Diabrotica spp.), tomato leaf miner (Tuta absoluta) and plum fruit moth (Grapholita funebrana). Our study demonstrated that in BioClass we can combine fuzzy logic and geographic information systems with knowledge of pest biology and environmental data to derive new information for decision making. Pests are sensitive to a warming climate, as temperature greatly affects their survival and reproductive rate and capacity. Changes have been observed in the distribution, frequency and severity of outbreaks of Helicoverpa armigera on tomato. BioClass has demonstrated to be a powerful tool for applying dynamic models and map the potential future distribution of a species, enable resource to make decisions about dangerous and invasive species management and control.

Keywords: classification, model, pest, risk

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Authors: Omar M. Elmabrouk., Roaa Y. Taha., Najat M. Ebrahim, Sabbreen A. Mohammed

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Power transformers are the most crucial part of power electrical system, distribution and transmission grid. This part is maintained using predictive or condition-based maintenance approach. The diagnosis of power transformer condition is performed based on Dissolved Gas Analysis (DGA). There are five main methods utilized for analyzing these gases. These methods are International Electrotechnical Commission (IEC) gas ratio, Key Gas, Roger gas ratio, Doernenburg, and Duval Triangle. Moreover, due to the importance of the transformers, there is a need for an accurate technique to diagnose and hence predict the transformer condition. The main objective of this technique is to avoid the transformer faults and hence to maintain the power electrical system, distribution and transmission grid. In this paper, the DGA was utilized based on the data collected from the transformer records available in the General Electricity Company of Libya (GECOL) which is located in Benghazi-Libya. The Fuzzy Logic (FL) technique was implemented as a diagnostic approach based on IEC gas ratio method. The FL technique gave better results and approved to be used as an accurate prediction technique for power transformer faults. Also, this technique is approved to be a quite interesting for the readers and the concern researchers in the area of FL mathematics and power transformer.

Keywords: dissolved gas-in-oil analysis, fuzzy logic, power transformer, prediction

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Authors: Vinay P., Srinivas K. S.

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Low Light Image Enhancement is of utmost impor- tance in computer vision based tasks. Applications include vision systems for autonomous driving, night vision devices for defence systems, low light object detection tasks. Many of the existing deep learning methods are resource intensive during the inference step and take considerable time for processing. The algorithm should take considerably less than 41 milliseconds in order to process a real-time video feed with 24 frames per second and should be even less for a video with 30 or 60 frames per second. The paper presents a fast and efficient solution which has two main advantages, it has the potential to be used for a real-time video feed, and it can be used in low compute environments because of the lightweight nature. The proposed solution is a pipeline of three steps, the first one is the use of a simple function to map input RGB values to output RGB values, the second is to balance the colors and the final step is to adjust the contrast of the image. Hence a custom dataset is carefully prepared using images taken in low and bright lighting conditions. The preparation of the dataset, the proposed model, the processing time are discussed in detail and the quality of the enhanced images using different methods is shown.

Keywords: low light image enhancement, real-time video, computer vision, machine learning

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Authors: Imed Feki, Faouzi Msahli

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Selection of relevant parameters from a high dimensional process operation setting space is a problem frequently encountered in industrial process modelling. This paper presents a method for selecting the most relevant fabric physical parameters for each sensory quality feature. The proposed relevancy criterion has been developed using two approaches. The first utilizes a fuzzy sensitivity criterion by exploiting from experimental data the relationship between physical parameters and all the sensory quality features for each evaluator. Next an OWA aggregation procedure is applied to aggregate the ranking lists provided by different evaluators. In the second approach, another panel of experts provides their ranking lists of physical features according to their professional knowledge. Also by applying OWA and a fuzzy aggregation model, the data sensitivity-based ranking list and the knowledge-based ranking list are combined using our proposed percolation technique, to determine the final ranking list. The key issue of the proposed percolation technique is to filter automatically and objectively the relevant features by creating a gap between scores of relevant and irrelevant parameters. It permits to automatically generate threshold that can effectively reduce human subjectivity and arbitrariness when manually choosing thresholds. For a specific sensory descriptor, the threshold is defined systematically by iteratively aggregating (n times) the ranking lists generated by OWA and fuzzy models, according to a specific algorithm. Having applied the percolation technique on a real example, of a well known finished textile product especially the stonewashed denims, usually considered as the most important quality criteria in jeans’ evaluation, we separate the relevant physical features from irrelevant ones for each sensory descriptor. The originality and performance of the proposed relevant feature selection method can be shown by the variability in the number of physical features in the set of selected relevant parameters. Instead of selecting identical numbers of features with a predefined threshold, the proposed method can be adapted to the specific natures of the complex relations between sensory descriptors and physical features, in order to propose lists of relevant features of different sizes for different descriptors. In order to obtain more reliable results for selection of relevant physical features, the percolation technique has been applied for combining the fuzzy global relevancy and OWA global relevancy criteria in order to clearly distinguish scores of the relevant physical features from those of irrelevant ones.

Keywords: data sensitivity, feature selection, fuzzy logic, OWA operators, percolation technique

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Authors: Jia-Shiun Chen, Hsiu-Ying Hwang

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Hybrid electric vehicles can reduce pollution and improve fuel economy. Power-split hybrid electric vehicles (HEVs) provide two power paths between the internal combustion engine (ICE) and energy storage system (ESS) through the gears of an electrically variable transmission (EVT). EVT allows ICE to operate independently from vehicle speed all the time. Therefore, the ICE can operate in the efficient region of its characteristic brake specific fuel consumption (BSFC) map. The two-mode powertrain can operate in input-split or compound-split EVT modes and in four different fixed gear configurations. Power-split architecture is advantageous because it combines conventional series and parallel power paths. This research focuses on input-split and compound-split modes in the two-mode power-split powertrain. Fuzzy Logic Control (FLC) for an internal combustion engine (ICE) and PI control for electric machines (EMs) are derived for the urban driving cycle simulation. These control algorithms reduce vehicle fuel consumption and improve ICE efficiency while maintaining the state of charge (SOC) of the energy storage system in an efficient range.

Keywords: hybrid electric vehicle, fuel economy, two-mode hybrid, fuzzy control

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Authors: M. N. Khajavi, S. Nasiri, E. Farokhi, M. R. Bavir

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Alcohol fuels are renewable, have low pollution and have high octane number; therefore, they are important as fuel in internal combustion engines. Percentage detection of these alcoholic fuels with gasoline is a complicated, time consuming, and expensive process. Nowadays, these processes are done in equipped laboratories, based on international standards. The aim of this research is to determine percentage detection of different fuels based on vibration analysis of engine block signals. By doing, so considerable saving in time and cost can be achieved. Five different fuels consisted of pure gasoline (G) as base fuel and combination of this fuel with different percent of ethanol and methanol are prepared. For example, volumetric combination of pure gasoline with 10 percent ethanol is called E10. By this convention, we made M10 (10% methanol plus 90% pure gasoline), E30 (30% ethanol plus 70% pure gasoline), and M30 (30% Methanol plus 70% pure gasoline) were prepared. To simulate real working condition for this experiment, the vehicle was mounted on a chassis dynamometer and run under 1900 rpm and 30 KW load. To measure the engine block vibration, a three axis accelerometer was mounted between cylinder 2 and 3. After acquisition of vibration signal, eight time feature of these signals were used as inputs to an Adaptive Neuro Fuzzy Inference System (ANFIS). The designed ANFIS was trained for classifying these five different fuels. The results show suitable classification ability of the designed ANFIS network with 96.3 percent of correct classification.

Keywords: internal combustion engine, vibration signal, fuel composition, classification, ANFIS

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Authors: Alexander Guzman Urbina, Atsushi Aoyama

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The sustainability of traditional technologies employed in energy and chemical infrastructure brings a big challenge for our society. Making decisions related with safety of industrial infrastructure, the values of accidental risk are becoming relevant points for discussion. However, the challenge is the reliability of the models employed to get the risk data. Such models usually involve large number of variables and with large amounts of uncertainty. The most efficient techniques to overcome those problems are built using Artificial Intelligence (AI), and more specifically using hybrid systems such as Neuro-Fuzzy algorithms. Therefore, this paper aims to introduce a hybrid algorithm for risk assessment trained using near-miss accident data. As mentioned above the sustainability of traditional technologies related with energy and chemical infrastructure constitutes one of the major challenges that today’s societies and firms are facing. Besides that, the adaptation of those technologies to the effects of the climate change in sensible environments represents a critical concern for safety and risk management. Regarding this issue argue that social consequences of catastrophic risks are increasing rapidly, due mainly to the concentration of people and energy infrastructure in hazard-prone areas, aggravated by the lack of knowledge about the risks. Additional to the social consequences described above, and considering the industrial sector as critical infrastructure due to its large impact to the economy in case of a failure the relevance of industrial safety has become a critical issue for the current society. Then, regarding the safety concern, pipeline operators and regulators have been performing risk assessments in attempts to evaluate accurately probabilities of failure of the infrastructure, and consequences associated with those failures. However, estimating accidental risks in critical infrastructure involves a substantial effort and costs due to number of variables involved, complexity and lack of information. Therefore, this paper aims to introduce a well trained algorithm for risk assessment using deep learning, which could be capable to deal efficiently with the complexity and uncertainty. The advantage point of the deep learning using near-miss accidents data is that it could be employed in risk assessment as an efficient engineering tool to treat the uncertainty of the risk values in complex environments. The basic idea of using a Near-Miss Deep Learning Approach for Neuro-Fuzzy Risk Assessment in Pipelines is focused in the objective of improve the validity of the risk values learning from near-miss accidents and imitating the human expertise scoring risks and setting tolerance levels. In summary, the method of Deep Learning for Neuro-Fuzzy Risk Assessment involves a regression analysis called group method of data handling (GMDH), which consists in the determination of the optimal configuration of the risk assessment model and its parameters employing polynomial theory.

Keywords: deep learning, risk assessment, neuro fuzzy, pipelines

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Authors: O. Poleshchuk, E. Komarov

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In this paper, semantic spaces with the properties of completeness and orthogonality (complete orthogonal semantic spaces) were chosen as models of expert evaluations. As the theoretical and practical studies have shown all the properties of complete orthogonal semantic spaces correspond to the thinking activity of experts that is why these semantic spaces were chosen for modeling. Two methods of construction such spaces were proposed. Models of comparative and fuzzy cluster analysis of expert evaluations were developed. The practical application of the developed methods has demonstrated their viability and validity.

Keywords: expert evaluation, comparative analysis, fuzzy cluster analysis, theoretical and practical studies

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Authors: Arup K. Sarma, Jayshree Hazarika

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The North-eastern part of India, which receives heavier rainfall than other parts of the subcontinent, is of great concern now-a-days with regard to climate change. High intensity rainfall for short duration and longer dry spell, occurring due to impact of climate change, affects river morphology too. In the present study, an attempt is made to delineate the North-Eastern region of India into some homogeneous clusters based on the Fuzzy Clustering concept and to compare the resulting clusters obtained by using conventional methods and non conventional methods of clustering. The concept of clustering is adapted in view of the fact that, impact of climate change can be studied in a homogeneous region without much variation, which can be helpful in studies related to water resources planning and management. 10 IMD (Indian Meteorological Department) stations, situated in various regions of the North-east, have been selected for making the clusters. The results of the Fuzzy C-Means (FCM) analysis show different clustering patterns for different conditions. From the analysis and comparison it can be concluded that non conventional method of using GCM data is somehow giving better results than the others. However, further analysis can be done by taking daily data instead of monthly means to reduce the effect of standardization.

Keywords: climate change, conventional and nonconventional methods of clustering, FCM analysis, homogeneous regions

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Authors: Jialin Wu, Zhiwei Lian, Jieyu Tang, Jingyun Shen

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Tornado is a serious and unpredictable natural disaster, which has an important impact on people's production and life. The probability of being hit by tornadoes in China was analyzed considering the principles of tornado formation. Then some suggestions on layout and shapes for newly-built buildings were provided combined with the characteristics of tornado wind fields. Fuzzy clustering and inverse closeness methods were used to evaluate the probability levels of tornado risks in various provinces based on classification and ranking. GIS was adopted to display the results. Finally, wind field single-vortex tornado was studied to discuss the optimized design of rural low-rise houses in Yancheng, Jiangsu as an example. This paper may provide enough data to support building and urban design in some specific regions.

Keywords: tornado probability, computational fluid dynamics, fuzzy mathematics, optimal design

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Authors: Mareike Dornhoefer, Madjid Fathi

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Water supply and water quality are key problems of mankind at the moment and - due to increasing population - in the future. Management disciplines like water, environment and quality management therefore need to closely interact, to establish a high level of water quality and to guarantee water supply in all parts of the world. Groundwater remediation is one aspect in this process. From a knowledge management perspective it is only possible to solve complex ecological or environmental problems if different factors, expert knowledge of various stakeholders and formal regulations regarding water, waste or chemical management are interconnected in form of a knowledge base. In general knowledge management focuses the processes of gathering and representing existing and new knowledge in a way, which allows for inference or deduction of knowledge for e.g. a situation where a problem solution or decision support are required. A knowledge base is no sole data repository, but a key element in a knowledge based system, thus providing or allowing for inference mechanisms to deduct further knowledge from existing facts. In consequence this knowledge provides decision support. The given paper introduces an environmental knowledge system in water management. The proposed environmental knowledge system is part of a research concept called Green Knowledge Management. It applies semantic technologies or concepts such as ontology or linked open data to interconnect different data and information sources about environmental aspects, in this case, water quality, as well as background material enriching an established knowledge base. Examples for the aforementioned ecological or environmental factors threatening water quality are among others industrial pollution (e.g. leakage of chemicals), environmental changes (e.g. rise in temperature) or floods, where all kinds of waste are merged and transferred into natural water environments. Water quality is usually determined with the help of measuring different indicators (e.g. chemical or biological), which are gathered with the help of laboratory testing, continuous monitoring equipment or other measuring processes. During all of these processes data are gathered and stored in different databases. Meanwhile the knowledge base needs to be established through interconnecting data of these different data sources and enriching its semantics. Experts may add their knowledge or experiences of previous incidents or influencing factors. In consequence querying or inference mechanisms are applied for the deduction of coherence between indicators, predictive developments or environmental threats. Relevant processes or steps of action may be modeled in form of a rule based approach. Overall the environmental knowledge system supports the interconnection of information and adding semantics to create environmental knowledge about water environment, supply chain as well as quality. The proposed concept itself is a holistic approach, which links to associated disciplines like environmental and quality management. Quality indicators and quality management steps need to be considered e.g. for the process and inference layers of the environmental knowledge system, thus integrating the aforementioned management disciplines in one water management application.

Keywords: water quality, environmental knowledge system, green knowledge management, semantic technologies, quality management

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Authors: Fatéma Zahra Benchara, Mohamed Youssfi, Omar Bouattane, Hassan Ouajji, Mohamed Ouadi Bensalah

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The aim of this paper is to present a distributed implementation of the Type-2 Fuzzy algorithm in a parallel and distributed computing environment based on mobile agents. The proposed algorithm is assigned to be implemented on a SPMD (Single Program Multiple Data) architecture which is based on cooperative mobile agents as AVPE (Agent Virtual Processing Element) model in order to improve the processing resources needed for performing the big data image segmentation. In this work we focused on the application of this algorithm in order to process the big data MRI (Magnetic Resonance Images) image of size (n x m). It is encapsulated on the Mobile agent team leader in order to be split into (m x n) pixels one per AVPE. Each AVPE perform and exchange the segmentation results and maintain asynchronous communication with their team leader until the convergence of this algorithm. Some interesting experimental results are obtained in terms of accuracy and efficiency analysis of the proposed implementation, thanks to the mobile agents several interesting skills introduced in this distributed computational model.

Keywords: distributed type-2 fuzzy algorithm, image processing, mobile agents, parallel and distributed computing

Procedia PDF Downloads 403