Fuzzy Computation Research Articles

Analysis of C2C Website Competitiveness Based on Text Sentiment Analysis and Dynamic Fuzzy Computation

With the global step into the era of large data, online review data show an explosive growth trend, and the resulting information overload has affected the consumer’s decision making. In terms of consumers, to tap the purchase decision-making information from the vast amount of information is a must-do before the tedious process; how to find the best service from a variety of shopping sites and find satisfied products from thousands of Products and how to help companies improve themselves, has become the forefront of e-commerce research topics. In this paper, data acquisition, data filtering, theme analysis, correlation analysis, sentiment analysis and dynamic fuzzy statistics are introduced to the user-generated content (UGC) fuzzy comprehensive evaluation. Firstly, collect the data, we use the correlation analysis and the stopped words to reduce the dimensionality of the data, and generate the revised candidate sets of themes. Then, we use emotion extraction, emotion polarity judgment and comment analysis to divide the UGC into several evaluation grades; then we get the fuzzy comprehensive evaluation matrix by the fuzzy statistic method. Finally, the comprehensive evaluation model is constructed to give users product score and the C2C website ranking.

Complimentary Address

First of all it is my great pleasure to congratulate to the 20th anniversary of the Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII). The first volume of the journal was published in 1997 – and now twenty years later we reached the Vol. 21. Over these years JACIII was one of the forefront and determining journals of soft computing and intelligent informatics and focused to the most up-to-date topic despite the fast vary and evolvement of this research field through its regular and special sessions. One of the greatest merit of the journal is that it is able be serve as a common platform for the most relevant researchers and societies in order to expand the boundaries of many related fields including Fuzzy Logic, Neural Networks, Genetic and Evolutionary Computation, Biologically-Inspired Computation Systems and so on. In these days the most up-coming challenges are connected to the topics of JACIII including the current special issue as well. Self-driving cars, Wearables, Internet-of-Things and many other phenomena are imaginable with the advanced <em>Computational Intelligence</em> behind the rooftop. Expert- and decision-making systems and effective usage of big data request the highly developed <em>Fuzzy Inference Systems</em>. Monitoring of social media in order to get useful information regarding the habits of human users and artificial robots cannot be realized without <em>Web and Artificial Intelligence</em>. Soft-computing based methods and many other intelligent and automated solutions are needed for eligible <em>Data mining</em> – and for the further preparation of the gathered data. Moreover, the demands of future smart city concepts, developed green energy producing solutions and connected power grids present the scientists with specific challenges whereon <em>Smart Grid</em> concept can be the only answer. I would like to invite the Reader to an interesting reading which deals with the aforementioned challenges and scenarios. Furthermore, I hope that JACIII can continue its contribution to the leading-edge research and we can celebrate the 30th anniversary together as well.

Ambient Intelligence A Review of the Literature

Ambient intelligence stems from artificial intelligence and it can be defined as technology interacting and responding to humans. For an ambient intelligent environment to exist, it must contain three types of system: sensing, reasoning and acting. The primary sensors used in ambient intelligence are audiovisual, passive infrared, and radio frequency identification technology. To simulate a more natural living environment, a fuzzy computing system is used. Using a fuzzy computing system can also make it possible for machines to understand culture by analyzing people’s points of interests based on certain factors. Machines are also capable of generating and displaying creativity. Gaps in research and progression of ambient intelligence are still present due to the lack of trust and reliability these machines will possibly display.

Rule number and approximation of the hybrid fuzzy system based on binary tree hierarchy

To effectively avoid internal rule explosion of a fuzzy system or computer memory overflow caused by increased input variables, a hybrid fuzzy system is established by unifying the Takagi–Sugeno and the Mamdani fuzzy systems based on a binary tree hierarchical method. This method can greatly reduce the total number of rules within the system. Firstly, a calculation formula of the total number of rules for the hybrid fuzzy system is given, by comparing with other layered systems, the total number of rules based on the binary tree hierarchy has the largest decline. Secondly, a new K-integral norm is redefined by introducing a K-quasi-subtraction operator. Using the piecewise linear function the approximation capability of the hybrid fuzzy system after hierarchy to a kind of integrable functions is studied. Finally, the binary tree hierarchical structure expressions of the hybrid fuzzy system are given through two simulation examples.

Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement.

Ubiquitous health care (UHC) is beneficial for patients to ensure they complete therapeutic exercises by self-management at home. We designed a fuzzy computing model that enables recognizing assigned movements in UHC with privacy. The movements are measured by the self-developed body motion sensor, which combines both accelerometer and gyroscope chips to make an inertial sensing node compliant with a wireless sensor network (WSN). The fuzzy logic process was studied to calculate the sensor signals that would entail necessary features of static postures and dynamic motions. Combinations of the features were studied and the proper feature sets were chosen with compatible fuzzy rules. Then, a fuzzy inference system (FIS) can be generated to recognize the assigned movements based on the rules. We thus implemented both fuzzy and adaptive neuro-fuzzy inference systems in the model to distinguish static and dynamic movements. The proposed model can effectively reach the recognition scope of the assigned activity. Furthermore, two exercises of upper-limb flexion in physical therapy were applied for the model in which the recognition rate can stand for the passing rate of the assigned motions. Finally, a web-based interface was developed to help remotely measure movement in physical therapy for UHC.

A Fuzzy TOPSIS+Worst-Case Model for Personnel Evaluation Using Information Culture Criteria

Personnel evaluation process is aimed at choosing the best alternative to fill the defined vacancy in an organization. It determines the input quality of personnel and thus plays an important role in human resource management. The multi criteria nature and the presence of qualitative factors make it considerably more complex. This paper proposes a hybrid fuzzy MCDM model for personnel evaluation. It combines the fuzzy TOPSIS method with fuzzy worst-case (or entropy) method for linguistic reasoning under group decision making. Fuzzy worst-case and entropy methods are used to get weights of criteria, while fuzzy TOPSIS is utilized to rank the alternatives. The weights obtained from fuzzy worst-case and entropy methods are included in fuzzy TOPSIS computations and the alternatives are evaluated. The fuzzy MCDM for group decision making enables to aggregate subjective assessments of the decision-makers and thus offer an opportunity to perform more robust personnel evaluation procedures. To evaluate the alternatives the authors have formed an executive group consisting of five decision-makers. For evaluation the group has decided to consider five information culture criteria expressed in linguistic variables. A numerical example demonstrated the possibilities of application of the proposed method.

A unique fuzzy multivariate modeling approach for performance optimization of maintenance workshops with cognitive factors

This paper puts forward an integrated fuzzy simulation-fuzzy data envelopment analysis (FDEA)-fuzzy cognitive map (FCM) algorithm for performance optimization of maintenance workshops by incorporating cognitive and time-dependent factors. Due to severe ambiguousness associated with the collected data, the fuzzy set theory is incorporated into the approach. Fuzzy computer simulation is employed for modeling the workshop and providing time-dependent factors. FCM is used for extracting relations between cognitive factors. FDEA is used for ranking scenarios based on inputs and outputs of FCM and the developed computer simulation. Moreover, a recent possibilistic programming approach is used to convert the fuzzy DEA model to an equivalent crisp model. The sensitivity analysis show that α = 0.1 is the best α-cut level for interpreting data. The proposed algorithm is capable of modeling and optimizing the performance of maintenance workshops in uncertain and non-linear environments. The solution quality is inspected and shown through an actual maintenance workshop. This is the first study that presents an integrated non-crisp algorithm for performance optimization of maintenance activities with cognitive and time-dependent factors.

Reliability evaluation of repairable systems using various fuzzy-based methods – A substation automation case study

This paper focuses on material that is required for the practical application of fuzzy reliability; impacts of repair and data uncertainty. For this purpose, a holistic solution is presented to measure and manage the degree of uncertainty in the reliability indices of repairable systems. First, a flexible form of Markov Processes (MPs) namely Equivalent Transition Rate (ETR) is introduced in order to obtain an analytical formalism. Second, we delve deeply into the issues of fuzzy arithmetic by providing an intuitive, methodic and computational discussion of feasible approaches; Standard Fuzzy Arithmetic (SFA), Intermediate Variables (IMVs) and Taylor Series (TS). Specific emphasis is then placed upon Fuzzy Transformation Method (FTM). Third, we evaluate the performance of proposed techniques in case of a reliability problem involving fuzzy complexity. Consequently, the reader is given useful insights into the nature of fuzzy computations and practical impediments when measuring uncertain data. Given materials and methods, current paper provides a broad perspective by evaluating the reliability characteristics of a repairable Substation Automation System (SAS) that is one of the fundamental elements in Smart Grids (SGs). In this regard, quantitative results and diagrammatic outputs are supported by a detailed analysis under different assumptions. Our findings demonstrate that fuzzy outputs are computationally tractable and properly estimated. Besides, this investigation shows how the objective SAS can respond to the variations in reliability data. The framework proposed here encourages the continuation of the work towards more applications in electric power components and systems. As far as we know, there are no studies on these techniques, results and remarks.

Applying FQFD and Utility Representative Functions Under Fuzzy Environment for FMCDM

Abstract In this paper, we apply fuzzy quality function deployment (FQFD) and utility representative functions under fuzzy environment for fuzzy multi-criteria decision-making (FMCDM). In the FMCDM, criteria weights can present both consumers’ opinions and experts’ opinions. Consumers’ opinions are expressed by fuzzy importance levels of customer needs, experts’ opinions are presented by fuzzy relationship strength ratings for customer needs on skill needs, and the two opinions are integrated by FQFD. In addition to importance levels of customer needs and relationship strength ratings for customer needs on skill needs being fuzzy numbers, performance criteria ratings of alternatives are also displayed by fuzzy numbers in FMCDM. Thus, multiplying fuzzy numbers and aggregating several pooled fuzzy numbers formed by multiplying fuzzy numbers are critical on computation. Practically, the fuzzy computations are hard. In this paper, we use utility representative functions of multiplication and aggregation on fuzzy numbers to resolve the computation ties, especially for multiplying three fuzzy numbers. In addition, we compare the proposed method with other FMCDM methods to demonstrate our method’s feasibility and rationality. Therefore, FMCDM can be finished easily and efficiently through associating FQFD with the utility representative functions of fuzzy numbers applied.

Finite element modeling and control of a high-power SRM for hybrid electric vehicle

In this study, a new modified fuzzy-PI (MFPI) controller is designed to control a high-power Switched Reluctance Motor (SRM) modeled by means of the finite element method (FEM) for hybrid electric vehicle (HEV) applications. Finite element analysis has been carried out via the Maxwell 2D software package. The motor flux, inductance and torque characteristics obtained from the analyses have been presented. The obtained numerical analysis data was transferred to the Matlab/Simulink environment, and a non-linear dynamic SR motor model was created. The motor speed has been controlled by conventional fuzzy-PI (FPI) and new modified fuzzy-PI (MFPI) controller after designing the nonlinear model of the SRM.The controller performances are compared in terms of maximum percentage overshoot (Os), rise time (tr), steady-state error (ess), rate of torque ripple (Td), and rate of ripple in speed (ωd). The comparisons for the SRM's different operation situations show that MFPI generally yields the better performance in terms of the rise-time, the steady-state error, and the maximum percentage overshoot criteria. In addition, the high-power SR motor simulation computation time (fuzzy computation) for the MFPI controller appears to be shorter than the value for the conventional FPI controller.

Multi-Criteria Decision Analysis Approaches for Selecting and Evaluating Digital Learning Objects

The wide spread of internet resources has emerged new paradigms of learning and knowledge delivery to facilitate and enhance learning particularly in e-learning systems. The important milestones of these new paradigms are known as digital learning objects (DLO) which are smallest packed bits for learning. The abundance of these DLOs raises an important question on how to select effectively high quality reusable learning objects. There exist many soft computing approaches such as; fuzzy computing, neural networks, evolutionary computing, support vector machines, machine learning and probabilistic reasoning. In this paper, fuzzy multi-criteria decision-making methods in choosing suitable DLOs were reviewed. This paper discusses recent variety of soft computing methods used in selecting and evaluating digital learning objects through multi criteria decision analysis approaches used; for selecting metrics like basic topical, course similarity, internal topical, basic and user similarity personal, and context similarity situational relevance and for evaluation; scalarization method, employing triangular, trapezoidal and distance based similarity (adapted from TOPSIS techniques). As DLOs continue to evolve it is inevitable to utilize multi-criteria techniques for selecting and or improving quality. The abundance of DLOs has increased the need for applying practical soft computing techniques to retrieve high quality, reusable DLOs.

Energy management in a hybrid PV/wind/battery system using a type-1 fuzzy logic computer algorithm

This paper presents a computer algorithm based on fuzzy logic control to manage the flow of energy in a stand-alone PV/wind/battery hybrid system. The system is composed of 16 photovoltaic PV solar...

Energy management in a hybrid PV/wind/battery system using a type-1 fuzzy logic computer algorithm

This paper presents a computer algorithm based on fuzzy logic control to manage the flow of energy in a stand-alone PV/wind/battery hybrid system. The system is composed of 16 photovoltaic PV solar panels, one wind turbine and two batteries. The solar and wind energies were combined together to increase system's efficiency. Batteries were used to ensure the availability of power on demand and improve the dynamic behaviour of the system. The proposed algorithm uses fuzzy logic to control the energy between production and consumption and ensure the availability of power on demand for an isolated house in the city of Essaouira in Morocco. The algorithm was tested on real weather data taken for the region of Essaouira in the month of April and was able to determine the status of the battery for each hour. The batteries state of charge was maintained between 30% and 80%.

Robotics & artificial intelligence : The future of surgeons & surgery

Robotics and Artificial Intelligence complement surgical technical expertise and judgement. A robot’s accuracy in the programmed performance of predesignated tasks is exquisite. Historically, with the evolution of microsurgery, senior surgeons designated delicate suture placement to more steady handed, sharper eyed young fellows or even nurses. Tactile feedback and ultrasensitive pressure velocity controls make robotics a viable but expensive option to fine surgical assistance. Autonomous surgical robots are however a distinct paradigm. Myriad unfathomed mysteries of human pathophysiology and anatomy confer upon surgical procedures complexities which defy stratification and simplification into sets of preordained tasks. In the current scenario where fuzzy logic, neural networks and intuitive computing are still in early evolution, robots replacing master surgeons seem as improbable as Google self driving cars in formula one racing. Robots have evolved as dextrous, fatigue and tremor free surgical tools. The data crunching capability of computers is improving in speed and in capability for machine learning. Human surgical maturity on the other hand is attained and matures through phases of information assimilation, knowledge consolidation and attainment of surgical wisdom. Human surgeons at the helm will, in this decade harness robotic capabilities and information template paradigms to fine tune many procedures and to augment surgical reach. Quantum leaps and paradigm shifts towards robotic surgical autonomy may be neither desirable nor practical.

A Synergistic Effect in the Measurement of Neuro-Fuzzy System

We consider a new type of hybrid neuro-fuzzy system based on fuzzy and neural computing in hierarchical sequential structure, the total effect exceeds the effect of each component separately. The proposed system can be applied to multi-criteria analysis, automatic classification on signs and obtain evidence-based estimates of the efficiency of scientific and technical solutions and technologies, engineering and robotics. An example of a neuro-fuzzy system measuring the intensity of the emotions of a robot, with the extraction of diagnostic decision rules “If & then”.

Dynamic Stability Improvement of Multimachine Power Systems using ANFIS-based Power System Stabilizer

Modern power system are very vurnerable to against load fluctuation during their operation. Load fluctuation identified as small disturbance is important to test dynamic (small signal) stability. This research is focused on improvement of multimachine dynamic stability by using ANFIS-based power system stabilizer (proposed PSS). ANFIS method is proposed because the ANFIS computation is more efective than Mamdani fuzzy computation. Simulation results show that the proposed PSS is able to maintain the dynamic stability by decreasing peak overshoot (Po) to the value -3,37´10 -5 pu/pu and accelerating settling time (St) to the time 4.01 s for rotor speed deviation Machine-2. Also, the Po is decreased to the value -3,37´10 -5 pu/pu and the St is accelerated to the time 3.98 s for rotor speed deviation Machine-3.

ECG-based Mental Stress Assessment Using Fuzzy Computing and Associative Petri Net

Medical reports suggest that long-term stress can directly or indirectly lead to mental disorders and cardiovascular disease. However, many people frequently ignore their stress symptoms and fail to take remedial action before developing serious psychological or physiological health problems. Many previous studies have used electrocardiograms (ECG) to evaluate mental stress. However, ECG pattern recognition presents difficulties because the time-varying morphology is subject to physiological conditions and the presence of noise. Therefore, how to achieve effective noise reduction and accurately determine an individual’s exposure to mental stress under various conditions is a recurring issue in engineering and medical research. Heart rate variability is assessed using time- and frequency-domain analyses. To accurately detect mental stress, this research adopts both time and frequency domains, two aspects of physiological characteristics. In this study, a rule-based reasoning model is created for mental stress assessment by combining fuzzy and associative Petri net methodologies. It can serve as a basis for clinical diagnosis and quickly measure the subject’s mental condition and reduce subjective errors. Performance evaluation using Physionet’s stress recognition database shows that the proposed approach compares well with data mining methods and other proposed methods. Moreover, a prototype mental stress assessment system is proposed to help users understand their mental condition.

Comments on some theories of fuzzy computation

In classical computability theory, there are several (equivalent) definitions of computable function, decidable subset and semi-decidable subset. This paper is devoted to the discussion of some proposals for extending these definitions to the framework of fuzzy set theory. The paper mainly focuses on the notions of fuzzy Turing machine and fuzzy computability by limit processes. The basic idea of this paper is that the presence of real numbers in the interval [0,1] forces us to refer to endless approximation processes (as in recursive analysis) and not to processes terminating after a finite number of steps and giving the exact output (as in recursive arithmetic). In accordance with such a point of view, an extension of the famous Church thesis is proposed.

User preferences-aware recommendation for trustworthy cloud services based on fuzzy clustering

The cloud computing has been growing over the past few years, and service providers are creating an intense competitive world of business. This proliferation makes it hard for new users to select a proper service among a large amount of service candidates. A novel user preferences-aware recommendation approach for trustworthy services is presented. For describing the requirements of new users in different application scenarios, user preferences are identified by usage preference, trust preference and cost preference. According to the similarity analysis of usage preference between consumers and new users, the candidates are selected, and these data about service trust provided by them are calculated as the fuzzy comprehensive evaluations. In accordance with the trust and cost preferences of new users, the dynamic fuzzy clusters are generated based on the fuzzy similarity computation. Then, the most suitable services can be selected to recommend to new users. The experiments show that this approach is effective and feasible, and can improve the quality of services recommendation meeting the requirements of new users in different scenario.

A review of fuzzy methods in automotive engineering applications

The paper addresses evolution of fuzzy systems for core applications of automotive engineering. The presented study is based on the analysis of bibliography dedicated to fuzzy sets and fuzzy control for ground vehicles. A special attention is given to fuzzy approaches used in the following domains of automotive engineering: vehicle dynamics control systems, driver and driving environment identification, ride comfort control, and energy management of electric vehicles. The bibliographical analysis, supplemented with statistics of relevant research publications, has allowed to allocate the most important fuzzy application cases for each domain. In particular, it concerns driver identification, human-machine interface, recognition of road conditions, and controllers of vehicle chassis and powertrain systems. It is found out that fuzzy methods have the primary use most of all for tasks requiring identification and forecasting procedures, especially in conditions of limited informational space. Additional observation that can be also derived from the presented survey points to reasonable integration of fuzzy technique with other control engineering methods to improve the performance of automotive control systems. In the aggregate the performed review indicates that fuzzy computing can be considered as a versatile tool for automotive engineering applications of different nature.