Search

In this article, a new energy-efficient reference model has been established for a plastic injection molding plant. However, the proposed model handled difficulties due to the lack of robust and complete data, such as production mix and cooling degree-days. In addition, the proposed model applies three distinct enhanced modeling methodologies, including regression modeling, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Furthermore, these performance parameters were established to assess the accuracy of each model in this work. Moreover, the numerical results show that among the methodologies used in this work, the ANN demonstrated effective performance despite uncertainties in the measured input variables. The ANN numerical results in this paper highlight the ability to accurately assess baseline consumption in the industrial sector, providing a practical tool for decision-makers to improve energy efficiency. [ABSTRACT FROM AUTHOR]

The sugar industry is facing challenges in increasing productivity to meet consumer demand. One opportunity for productivity improvement lies in ensuring sugar content. This study proposes a hybrid model to predict sugar content by considering uncertainty factors. A hybrid model combining fuzzy subtractive clustering, and a fuzzy inference system is proposed to predict sugar content. The clustering results using silhouette and fuzzy subtractive clustering successfully identified 6 cluster centres from 2225 datasets collected in a sugar industry in East Java Province. The hybrid inference engine model is designed with fuzzy rules derived from the clustered data. Two inference models are developed: triangular and Gaussian fuzzy numbers. The testing results indicate that the hybrid model with triangular fuzzy numbers shows the smallest error with an R2 value of 0.95. This model is possible to applied in the sugar industry for decision makers in improving productivity with taking attention into uncertain factors influencing sugar content. [ABSTRACT FROM AUTHOR]

Sixth-generation (6G) applications require ultra-speed and large-capacity wireless communication services. Millimeter wave technology can be used to satisfy these requirements, especially at 28 GHz. This paper study used the Ansys® high-frequency structure simulator (HFSS) to design and simulate rectangular and slotted rectangular microstrip patch antennas (MSPAs) at 28 GHz. The proposed designs contained a Rogers RT/Duroid® 5,880 substrate with a dielectric constant (er) of 2.2 and a loss tangent of 0.0009. The performance of both the proposed antennas was compared to determine which was more efficient. This present study also used an adaptive network-based fuzzy inference system (ANFIS) to determine the optimal frequency and gain. The main objective of the manuscript is to use artificial intelligence (AI) to obtain the best design results for MSPA. The results indicated, with the use of AI, the gain of the rectangular and slotted antennas, was 6.3943 and 6.3094 dB at an efficiency of 98.338% and 98.651%, respectively. [ABSTRACT FROM AUTHOR]

Constraint discovery in relational databases aims to find constraints that express dependency relationships among a set of attributes and has witnessed remarkable success in the applications of data cleaning, detecting data errors, and enhancing police and security operations. In this paper, we propose a new type of constraint, called distributional constraints (DCs), which leverages the attribute value distribution feature for intelligent auditing and security analysis. The constraint, which specifies the range of attribute values that most data follow, enables financial auditors, law enforcement, and security analysts to identify data with anomalous distributions and explain the reasons for such data anomalies. In the context of police and security applications, distributional constraints can help detect potential criminal activities, fraud, and other security threats by identifying unusual patterns in data. To efficiently discover distributional constraints, we propose an inference system to find the minimum coverage of a set of DCs. The efficient optimization technique BitVector indexing is also proposed to further speed up the distributional constraint discovery. We conduct experiments on 12 real datasets such as medical bills and credit card statements to validate the efficiency and effectiveness of our solution. We show the performance of the discovery DCs and the effectiveness of using DCs for detecting abnormal data in different audit datasets. [ABSTRACT FROM AUTHOR]

The World health organization (WHO) reported that cardiovascular disease is the leading cause of death worldwide, particularly in developing countries. But while diagnosing cardiovascular disease, medical practitioners might have differences of opinions and faced challenging when there is inadequate information and uncertainty of the problem. Therefore, to resolve ambiguity and vagueness in diagnosing disease, a perfect decision-making model is required to assist medical practitioners in detecting the disease at an early stage. Thus, this study designs a fuzzy analytic hierarchy process (FAHP) point-factored inference system to detect cardiovascular disease. The attributes are selected and classified into sub-attributes and point factor scale using the clinical data, medical practitioners, and literature review. Fuzzy AHP is used in calculating the attribute weights, the strings are generated using the Mamdani fuzzy inference system, and the strength of each set of fuzzy rules is calculated by multiplying the attribute weights with the point factor scale. The string weights determine the output ranges of cardiovascular disease. Moreover, the results are validated using sensitivity analysis, and comparative analysis is performed with AHP techniques. The results show that the proposed method outperforms other methods, which are elucidated by the case study. [ABSTRACT FROM AUTHOR]

Energy management becomes essential when distributed energy sources such as solar, wind, and fuel cell are connected in a micro-grid. In this paper, using a combination of two powerful neural network tools and fuzzy logic, intelligentization, and adaptation of droop control along with voltage and current control as one of the most common methods of decentralized control is done. One of the essential features of this method is its fast performance and the need for telecommunication infrastructure. In this paper, we provide a comprehensive control system that enables proper operation in both upstream and island network modes for both AC and DC microgrids. The proposed method is simulated to evaluate its effectiveness. The proposed structure, by adapting the control system by ANFIS structure, can properly distribute power between distributed products in a brilliant way and without the need for operators and costly telecommunications infrastructure in the face of severe disturbances such as change. The state between the connection and the island or fault occurrence ensures the stability of both the AC and DC parts of the microgrid. The results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Environment and climate change have caused a rise in a wide range of diseases and infections. In countries where overpopulation is a problem, many infections spread severely. The main focus of this paper is the detection and identification of blood diseases. An automated system that examines all potential diseases using patient information and data is needed to deal with unpredictable circumstances. Having an automated and intelligent system that evaluates the reports and counsels doctors in any other area or nation is a demand of the time. The same solutions can be identified by the proposed system. To apply the adaptive neuro-fuzzy inference system (ANFIS) and related techniques to predict chronic diseases early, the authors have gone through various existing models and case studies on diabetics and other patients. The proposed approach, called S-ANFIS which is using the hybrid approach, is based on ANFIS and includes content curation and intelligence analysis in addition to comparison with current models. As a result, the suggested model outperforms other approaches in terms of disease prediction accuracy, with a score of 88.6%. [ABSTRACT FROM AUTHOR]

Freeway networks, despite being built to handle the transportation needs of large traffic volumes, have suffered in recent years from an increase in demand that is rarely resolvable through infrastructure improvements. Therefore, the implementation of particular control methods constitutes, in many instances, the only viable solution for enhancing the performance of freeway traffic systems. The topic is fraught with ambiguity, and there is no tool for understanding the entire system mathematically; hence, a fuzzy suggested algorithm seems not just appropriate but essential. In this study, a fuzzy cognitive map-based model and a fuzzy rule-based system are proposed as tools to analyze freeway traffic data with the objective of traffic flow modeling at a macroscopic level in order to address congestion-related issues as the primary goal of the traffic control strategies. In addition to presenting a framework of fuzzy system-based controllers in freeway traffic, the results of this study demonstrated that a fuzzy inference system and fuzzy cognitive maps are capable of congestion level prediction, traffic flow simulation, and scenario analysis, thereby enhancing the performance of the traffic control strategies involving the implementation of ramp management policies, controlling vehicle movement within the freeway by mainstream control, and routing control. [ABSTRACT FROM AUTHOR]

A power grid in a nation or continent integrates utilities by generating stations and loads in order to meet power requirements. When such colossal power systems are involved in the transfer of power, the voltage stability issue typically crops up. Voltage collapse may lead to blackouts. Blackouts can only be reduced by focusing on the steady-state and dynamic operation of power systems. A possible solution may be to increase the power system infrastructure in order to endure these blackouts. Therefore, we need to distinguish the weak and less reliable buses or lines as soon as possible in the operation of the power system so that necessary actions can be taken for smooth operation or maintaining steady-state equilibrium. The weak elements may suffer outages, and that may lead to cascading outages. In this study, contingency ranking is carried out using two evaluation approaches, namely a pre-developed Fast Voltage Stability Index (FVSI) and a new Reduced Fast Voltage Stability Index (RFVSI) or the classical approach and the soft computing approach based on Fuzzy Logic (FL). The methods have been used for different bus systems such as IEEE-14, IEEE-30, and a practical Indian 62 bus system. The RFVSI is a reduced and faster version of FVSI. The outcomes of the two approaches were compared. It is found that the Fuzzy Logic (FL) approach computes the contingency ranking faster compared to the classical approaches, whereas the classical approach based on the RFVSI method calculates more correctly the contingencies and reliable results are produced. [ABSTRACT FROM AUTHOR]

The article proposes an optimized algorithm that allows the use of a fuzzy inference system with a large number of inference rules (about 10 million) in computing systems limited by RAM and CPU power. Optimization is achieved by redistributing the membership functions of input variables and the dynamic formation of inference rules, which allows fuzzy system to store only inference rules conclusions, without using a complete search of the rules in the process of fuzzy inference. [ABSTRACT FROM AUTHOR]

Medical Internet of Things (MIoT) plays a vital role in improving people's health, protection, and treatment. Rather than going to the hospital, it is possible to remotely track patients' health-related criteria and move them to medical data centers using cloud storage. There is also an exponential rise in the amount of data handled by MIoT devices. This means higher disclosure of confidential data; consequently, data protection and privacy obtained from MIoT devices are major unresolved concerns. As classification systems are growing rapidly, the need to apply machine learning algorithms to large-scale industrial data is also expanding. In this paper, we classify medical data into disease-free individuals from affected ones. To securely store this information in the cloud, we introduce a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) for data protection to improve and determine the degree of security like breaches, data integrity, etc. Results indicate that with the 6.2506e−06 training error and 0.3429 testing error, ANFIS performs extremely well in improving the degree of security parameters for a secure path to the cloud. [ABSTRACT FROM AUTHOR]

In relational databases, it is essential to know all minimal keys since the concept of database normalization is based on keys and functional dependencies of a relation schema. Existing algorithms for determining keys or computing the closure of arbitrary sets of attributes are generally time-consuming. In this paper we present an efficient algorithm, called KeyFinder, for solving the key-finding problem. We also propose a more direct method for computing the closure of a set of attributes. KeyFinder is based on a powerful proof procedure for finding keys called tableaux. Experimental results show that KeyFinder outperforms its predecessors in terms of search space and execution time. [ABSTRACT FROM AUTHOR]

Freeway networks, despite being built to handle the transportation needs of large traffic volumes, have suffered in recent years from an increase in demand that is rarely resolvable through infrastructure improvements. Therefore, the implementation of particular control methods constitutes, in many instances, the only viable solution for enhancing the performance of freeway traffic systems. The topic is fraught with ambiguity, and there is no tool for understanding the entire system mathematically; hence, a fuzzy suggested algorithm seems not just appropriate but essential. In this study, a fuzzy cognitive map-based model and a fuzzy rule-based system are proposed as tools to analyze freeway traffic data with the objective of traffic flow modeling at a macroscopic level in order to address congestion-related issues as the primary goal of the traffic control strategies. In addition to presenting a framework of fuzzy system-based controllers in freeway traffic, the results of this study demonstrated that a fuzzy inference system and fuzzy cognitive maps are capable of congestion level prediction, traffic flow simulation, and scenario analysis, thereby enhancing the performance of the traffic control strategies involving the implementation of ramp management policies, controlling vehicle movement within the freeway by mainstream control, and routing control.

An inference system using gated Schottky diode (GSD) is proposed for highly reliable hardware-based neural networks (HNNs). We explain the characteristics of the GSD and present circuits that take into account the characteristics of the device. The reverse current of the GSD, which is the synaptic current, is saturated with respect to input voltage, which results in immunity of input and output noise and overcoming the IR drop problem in metal wire. In order to take advantages of this saturated I-V characteristics, pulse-width modulation (PWM) of input data instead of amplitude modulation is proposed. In addition, by applying identical pulses to the bottom gate, the synaptic current of the GSD increases linearly, which makes it easy to transfer the calculated weights to the conductance of GSDs. By considering these characteristics, electronic circuits for PWM, current sum, and activation function are designed. Through SPICE simulation, we evaluate the inference accuracy of a 2-layer neural network. The classification accuracy rate of 100 images of MNIST test sets is 94% accuracy obtained with software.

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A bottleneck of laboratory analysis in process industries including steelmaking plants is the low sampling rate. Inference models using only variables measured online have then been used to made such information available in advance. This study develops predictive models for key mechanical properties of seamless steel tubes, by strength, ultimate tensile strength and hardness. A plant in Brazil was used as the case study. The sample sizes of some steel tube families given namely, yield a particular property are discrepant and sometimes very small. To overcome this sample imbalance and lack of representativeness, committees of predictive neural network models based on bagging predictors, a type of ensemble method, were adopted. As a result, all steel families for all properties have been satisfactorily described showing the correlations between targets and model estimates close to 99%. These results were compared to multiple linear regression, support vector machine and a simpler neural network. Such information available in advance favors corrective actions before complete tube production mitigating rework costs in general. [ABSTRACT FROM AUTHOR]

Fuzzy cognitive maps (FCMs) as a kind of knowledge-based tools are widely applied to model complex dynamical systems using causal relations. Besides the representation and reasoning of systems behaviors, how to control the given systems into a desirable target by causal objects established by FCMs is also an open problem. Although, so far, there are some existing works about the applications of FCMs on the control-related problems, it is still a lack of the theoretical analysis in this domain. In this paper, the controllability of k-valued FCMs is studied. To improve the universality of models, a temporal extension of generalized FCMs is implemented. By means of semitensor product, the algebraic representation of k-valued FCMs with controls is established and a generalized formula of control-depending network transition matrices is achieved. A necessary and sufficient condition is proved to determine the control-depending fixed points of k-valued FCMs with temporalization. By utilizing three kinds of controls, the controllability of the discrete FCMs is discussed, respectively. The reachability condition of a specific target state from a given initial state at time s is studied, and the reachable set along with the corresponding reachable probability are also provided by analytic formula. Results provide a way to make FCMs evolving into the designed states by controls, which can further conduct the behaviors of the modeled systems in reality. Examples are shown to demonstrate the effectiveness and feasibility of the proposed scheme. [ABSTRACT FROM AUTHOR]

The use of Multi Agents Systems (MAS), Cloud Computing (CC) and Fuzzy Inference System (FIS) in e-commerce has increased in recent years. The purpose of these systems is to enable users of electronic markets to make transactions in the best conditions and to help them in their decisions. The design and implementation is often characterized by the constant manipulation of information, many of which are imperfect. The use of the multi-agent paradigm for the realization of these systems implies the need to integrate mechanisms that take into account the processing of fuzzy information. This makes it necessary to design multi-agent systems (MAS) with fuzzy characteristics. For the modeling and realization of this system, we chose to use the FMAS model. This paper deals with the presentation of the use of the Fuzzy MAS model for the development of a management and decision support application in a virtual market with high availability. After the presentation of the system to be realized in the first section, we describe in the second section the application of the model FMAS for the design and the realization of this system. We then specify the JADE implementation platform and how the fuzzy agents of our model (Expert, Choice and Query) can be implemented using this platform. [ABSTRACT FROM AUTHOR]

In Internet of Things (IoT) environment, a number of sensors and devices exist and they have various and heterogeneous characteristics. Applications which provide a variety of services based on the sensors also have different service requirements. Therefore, a middleware that is located between sensors and application systems is needed for integrating two layers. This paper proposes a general purpose middleware for providing intelligent services based on heterogeneous sensors existing in IoT environment. The proposed middleware acquires and manages sensing data in real time. The middleware stores and manages heterogeneous sensors, node, and network metadata. Especially, the middleware in this paper includes a component for providing intelligent services based on inferencing the situation based on ontologies and rules. [ABSTRACT FROM AUTHOR]

Tuberculosis is a social, re-emerging infectious disease with medical implications throughout the globe. Despite efforts, the coverage of tuberculosis disease (with HIV prevalence) in Nigeria rose from 2.2% in 1991 to 22% in 2013 and the orthodox diagnosis methods available for Tuberculosis diagnosis were been faced with a number of challenges which can, if measure not taken, increase the spread rate; hence, there is a need for aid in diagnosis of the disease. This study proposes a technique for intelligent diagnosis of TB using Genetic-Neuro-Fuzzy Inferential method to provide a decision support platform that can assist medical practitioners in administering accurate, timely, and cost effective diagnosis of Tuberculosis. Performance evaluation observed, using a case study of 10 patients from St. Francis Catholic Hospital Okpara-In-Land (Delta State, Nigeria), shows sensitivity and accuracy results of 60% and 70% respectively which are within the acceptable range of predefined by domain experts.

Automated reasoning is becoming crucial for information systems. Building one uniform decision support system has become too complicated. The natural approach is to divide the task and combine the results from different subsystems into one uniform answer. It is the basic idea behind the system approach, where one solution is a composition of multiple subsystems. In this paper, the main emphasis is on establishing the theoretical framework that combines various reasoning methods into a collective system. The system’s formal abstraction uses graph theory and provides a discussion on possible aggregation function definitions. The proposed framework is a tool for building and testing specific approaches rather than the solution itself.

Considering fuzziness, randomness, and the association between them, cloud model-based control is a new way to address uncertainty in the inference system. Similar to fuzzy control theory, this method includes an important step of dealing with the logic concept "and", which is defined as the operation of soft-and between several antecedents and has not been scientifically solved in the current literatures. The traditional method of realizing soft-and is to use multi-dimensional cloud model theory, which lacks a theoretical basis. Based on the fuzzy and random theory, this paper proposes a novel approach using numeric simulation to calculate the soft-and in the cloud control system. In this method, the theory to determine the distribution of the minimum value between two random variables is applied. Compared with the traditional method, the considered approach is more reliable and reasonable, and its result is also in accordance with the standard fuzzy inference system. [ABSTRACT FROM AUTHOR]

Highlights • Fuzzy inference system for dynamic delivery of language learning material. • Knowledge inference relationships among learning objects for personalized learning. • Hybrid model for misconception diagnosis and identification using machine learning. • Framework of machine learning and fuzzy logic for individualized language learning. • Implementation of expert system and evaluation using an established framework. Abstract Adaptive e-learning employs algorithmic mechanisms in order to orchestrate the pace of instruction and provide tailored learning objects to support the unique educational experience of each learner. Taking this into consideration, this research work presents a fully operating and evaluated adaptive and intelligent e-learning system for second language acquisition. This system uses a hybrid model for misconception detection and identification (MDI) and an inference system for the dynamic delivery of the learning objects tailored to learners' needs. More specifically, the MDI mechanism incorporates the Fuzzy String Searching and The String Interpreting Resemblance algorithms in order to reason between possible learners' misconceptions. Furthermore, the inference system utilizes the knowledge inference relationship between the learning objects and creates a personalized learning environment for each student. The paper presents examples of operation and the system is evaluated using an evaluation model. The results are very encouraging and promising since they reveal that the hybrid model for misconception detection and identification and the inference system operate collaboratively and enhance the adaptivity of the students' needs and preferences. [ABSTRACT FROM AUTHOR]

Poor voice quality in Voice over Internet Protocol (VoIP) models during communication is a common occurrence that VoIP users experience, and can be frustrating when users cannot communicate efficiently. Most people find it difficult when they make calls and there is an echo. In addition time, effort and energy are wasted without any compensation. Users are also frustrated by not receiving, not transmitting or misunderstanding voice messages correctly. This study aims to reduce the threat of bad calls and improve the quality of voice calls. The work is designed to solve the need of the community of users by helping them to reduce communication costs and at the same time enabling them to communicate with greater clarity of signal using a unified communication system such as phones, computers, and tablets with one another. However, when the service requirement is greater than or equal to the threshold, it is segmented into less than or equal to the threshold value in smaller packets with a maximum service requirement. Besides this, if this node is vacant or has a single packet in the service unit, a packet is not saved at an intermediate node. These findings show the validity of hybrid switching that includes three fundamental computer network switching methods: packet, signal, and circuit switching. The results show that the maximum value of the limit is always available, which minimizes the network delay for nearly all traffic concentrations. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy clustering system is presented to display the flow field changes in the rotor outlet of centrifugal turbomachinery. What is important in the research done in the field of turbomachinery is the need for all the fields to properly understand the phenomena of flow inside the turbomachines, which has complexities. For this reason, the most advanced laboratory equipment is used in this regard, which is associated with issues such as time consuming, high costs, and a large number of required tests, and doubles the importance of simulating and observing current phenomena through artificial intelligence algorithms. The present system operates on the basis of fuzzy clustering so that the spatial data (from the PIV measurement system) by the number of specific clusters to the field display in the initial time; then, by applying changes to the cluster related to the time series (from the system measurement of LDA) that contains the recorded changes of the current during the time of the data mining, the new field data are obtained at a new time step and the clustering of the data shows the variation of the flow field in the fuzzy environment. In this paper, the flow field was investigated for 6 successive steps, and the results of the system output showed the variation of the flow field from the rotor rotation at different angles. [ABSTRACT FROM AUTHOR]