Retraction and Generalized Extension of Computing with Words

Fuzzy automata, whose input alphabet is a set of numbers or symbols, are a formal model of computing with values. Motivated by Zadeh's paradigm of computing with words rather than numbers, Ying proposed a kind of fuzzy automata, whose input alphabet consists of all fuzzy subsets of a set of symbols, as a formal model of computing with all words. In this paper, we introduce a somewhat general formal model of computing with (some special) words. The new features of the model are that the input alphabet only comprises some (not necessarily all) fuzzy subsets of a set of symbols and the fuzzy transition function can be specified arbitrarily. By employing the methodology of fuzzy control, we establish a retraction principle from computing with words to computing with values for handling crisp inputs and a generalized extension principle from computing with words to computing with all words for handling fuzzy inputs. These principles show that computing with values and computing with all words can be respectively implemented by computing with words. Some algebraic properties of retractions and generalized extensions are addressed as well.Comment: 13 double column pages; 3 figures; to be published in the IEEE Transactions on Fuzzy System

Performances Evaluation and Comparison of Two Algorithms for Fuzzy Logic Rice Cooking System (MATLAB Fuzzy Logic Toolbox and FuzzyTECH)

This paper presents an evaluation of performances rice cooking system with using Intelligent Controller that is Fuzzy Logic Controller (FLC) to meet the special requirements and some limitations of the rice cooking system. A new inference scheme is given to estimate the amount of rice and water to be used, and the temperature will be controlled according to the amount of rice and the time while cooking. The FLC system is designed by using two types of simulation software which are MATLAB Fuzzy Logic Toolbox and FuzzyTECH. The results obtained from the both simulation software are given in this paper. The differences the between both simulation also will be discussed. MATLAB Toolbox gives more specific results compared FuzzyTECHsoftware. The both software meet the special requirements because is not much differ between each other

Evaluation of electrical load estimation in Diyala governorate (Baaquba city) based on fuzzy inference system

The evaluation of electrical load estimation is requisitely of any electrical power system. This manner is needed for system obligation, economical distribution and maintenance time of electrical system. In this paper, we propose electrical load estimation method based on fuzzy inference system which gives accurate results for estimated loads in Iraq (Diyala governorateBaaquba city). And it can assist the electrical generation and distribution system that depends on important parameters (temperature, humidity and the speed of the wind). By considering the parameters temperature, humidity and the speed of the wind. These parameters are applied as inputs to the fuzzy logic control system to obtain the normalize estimated load as output by electing membership functions. It is exceptionally valuable to form a choice by taking into consideration these assessed readings that come to from the proposed FIS that displayed in this paper with precision of 0.969 from the real stack request

Behaviour of a High Frequency Parallel Quasi Resonant Inverter Fitted Induction Heater with Different Switching Frequencies

This paper investigates the behavior of a high frequency parallel quasiresonantinverter fitted domestic induction heater with different switching frequencies. The power semiconductor switch Insulated Gate Bipolar Junction Transistor (IGBT) is incorporated in this high frequency inverter that can operate under ZVS and ZCS conditions during the switching operations at certain switching frequency to reduce switching losses. The proposed induction heating system responds to three different switching frequencies with providing different results. An Insulated Gate Bipolar Junction Transistor (IGBT) provides better efficiency and faster switching operations. After the complete study of the proposed induction heating system at the selected switching frequencies, the results are compared and it is decided that most reliable, efficient and effective operations from the proposed induction heater can be obtained if the switching frequency is selected slightly above the resonant frequency of the tank circuit of the resonant inverter. The proposed scheme is analyzed using Power SystemSimulator (PSIM) environment

Consumer Load Prediction and Theft Detection on Distribution Network Using Autoregressive Model

Load prediction is essential for the planning and management of electric power system and this has been an area of research interest recently. Various load forecasting techniques have been proposed to predict consumer load which represents the activities of the consumer on the distribution network. Commonly, these techniques use cumulative energy consumption data of various consumers connected to the power system to predict consumer load. However, this data fails to reveal the activities of individual consumers as related to energy consumption and stealing of electricity. A new approach of predicting consumer load and detecting electricity theft based on autoregressive model technique is proposed in this paper. The objective is to evaluate the relationship between the consumer load consumption vis-a-vis the model coefficients and model order selection. Such evaluation will facilitate effective monitoring of the individual consumer behaviour, which will be indicated in the changes in model parameters and invariably lead to detection of electricity theft on the part of the consumer. The study used the data acquired from consumer load prototype which represents a typical individual consumer connected to the distribution network. Average energy consumption obtained over 24 hours was used for the modelling and 5-minute step ahead load prediction based on model order 20 of minimum description length criterion technique was achieved. Electricity theft activities were detected whenever there are disparities in the model coefficients and consumer load data

A review of smart homes in healthcare

The technology of Smart Homes (SH), as an instance of ambient assisted living technologies, is designed to assist the homes’ residents accomplishing their daily-living activities and thus having a better quality of life while preserving their privacy. A SH system is usually equipped with a collection of inter-related software and hardware components to monitor the living space by capturing the behaviour of the resident and understanding his activities. By doing so the system can inform about risky situations and take actions on behalf of the resident to his satisfaction. The present survey will address technologies and analysis methods and bring examples of the state of the art research studies in order to provide background for the research community. In particular, the survey will expose infrastructure technologies such as sensors and communication platforms along with artificial intelligence techniques used for modeling and recognizing activities. A brief overview of approaches used to develop Human–Computer interfaces for SH systems is given. The survey also highlights the challenges and research trends in this area

Analysis of Power Quality Constrained Consumer-Friendly Demand Response in Low Voltage Distributions Network

Load management using demand response (DR) in a low voltage distribution network (LVDN) offers an economically profitable business platform with peak load management. However, the inconvenience caused to the consumer in depriving their devices and the low levels of associated incentive have contributed to lower consumer acceptance for DR programs in the community. However, with the increasing number of controllable consumer loads, a residential-level DR program is highly plausible in the short to medium term. Further, additional DR capabilities (including ancillary services) are likely to improve the remuneration potential for participants in DR. Considering the perspective of a distribution network operator (DNO), any service useful for maintaining the stable and secure operation of an LVDN will always be appreciated. Thus, in addition to DR\u27s peak load management potential, any further contribution in maintaining power quality (PQ) in the network considered as an ancillary service to DNO will create a profitable business opportunity. Firstly, primary PQ management tasks in an LVDN are maintaining voltage profile and reducing harmonics. With the advancement in the consumer electronics market, increased penetration of nonlinear low carbon technologies (LCTs) based loads at the consumer-side, will increases the harmonic content in the LVDN. While consumer devices may have non-threatening levels of harmonic components, they can still cause issues by accumulating at the main feeder when the additive nature of harmonics are considered. Further, and in respect to harmonics, total harmonic distortion (THD), as a universal indicator, may not be a deterministic measure of the impact of harmonics due to THD’s dependency on the magnitude of fundamental current. Moving to the voltage issue, in an electrical network, it is required to maintain the voltage level of all nodes in the network between regulated tolerance levels. However, during peak load hours, the voltage at the end of a radial feeder may drop below the tolerance level. The corollary is also an issue. A light loading scenario on the same feeder with a higher penetration of solar photovoltaic distributed generators (SPVDG) injecting active power can create a voltage rise scenario. While consumer loads/loading are responsible for these PQ issues in the network, there is no direct obligation on residential level consumers to manage them as long as they are individually operating within the regulation limits. However, a DR option can utilize PQ’s dependency on loads to provide additional service to DNO to mitigate any PQ violations. The DR program\u27s success is critically dependent on consumer participation. It also becomes essential to operate the program with a minimum level of consumer inconvenience. Therefore, a proposal for micromanaging consumer load on an LVDN while considering consumer inconvenience and attaining PQ objectives is thus the theme of this thesis. This research proposes a PQ constrained consumer-friendly DR (PQ-C-DR) program that can provide additional ancillary PQ management services along with conventional DR capabilities. Due consideration is given to minimize consumer inconvenience while operating DR to ensure social acceptability and equity. Harmonic levels in the network are essentially integrated as harmonic heating constraints to maintain stable levels of harmonics in LVDN. A DR in conjunction with a co-ordinated incremental and ‘fair’ curtailment algorithm is introduced to manage the voltage levels in the radial LVDN. A sensitivity study of the proposed algorithm is performed on an urban distribution network model under different operating scenarios. This thesis introduces a new algorithmic dimension in applications for load management to ancillary services (PQ management) using DR. The PQ-C-DR will favour consumer comfort while profiting all stakeholders involved, which essentially creates a win-win scenario for all network participants – essential in DNO/consumer negotiations to achieve wider DR engagement. Improving the profitability of DR by providing additional service(s) is beneficial to both customers and retailers. Furthermore, the DNO benefits from delaying additional peak and PQ management related investments, which could essentially improve the utilization factor of the network

Automatic Kidney Stone Detection Using Deep learning Method

Kidney stone disease is a common urological illness that affects millions of people worldwide. The identification of kidney stones early and accurately is critical for timely intervention and effective management of this illness. Deep learning approaches have showed promising results in a variety of medical image processing jobs in recent years. This paper describes a novel deep learning-based approach for automatic kidney stone diagnosis utilising medical imaging data. A convolutional neural network (CNN) architecture is used in the suggested method to identify and classify kidney stones in medical photographs. A huge collection of kidney stone images is first collected and preprocessed to ensure homogeneity and improve feature extraction capabilities. To optimise its performance, the CNN model is trained on this dataset using a large number of annotated samples. The trained CNN model distinguishes kidney stone presence from healthy regions in medical pictures with good accuracy and robustness. It detects kidney stones of various sizes and shapes while overcoming hurdles given by different stone compositions and human anatomy. Furthermore, the deep learning model has fast processing speeds, making it suited for real-time clinical applications. Extensive validation and testing on an independent dataset are performed to evaluate the model's performance. The results show that the proposed deep learning method is effective in autonomous kidney stone identification, with sensitivity, specificity, and accuracy metrics comparable to or exceeding those of existing classical methods

Intelligent Systems

This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier