Optimal design of controllers without considering uncertainty in the plant dynamics can induce feedback instabilities and lead to obtaining infeasible controllers in practice. This paper presents a multi-objective evolutionary algorithm integrated with Monte Carlo simulations (MCS) to perform the optimal stochastic design of robust controllers for uncertain time-delay systems. Each potential optimal

Clustering is extensively realistic and considered in computer vision that follows unsupervised learning principles. In this, the performance of a clustering process mainly depends on the feature representation. Generally, the clustering process may have an error rate, and this affects the feature representation. To avoid this, unsupervised Learning (USL) provides an alternative path to obtain the

Software has made a profound influence in all walks of life. Developing quality software is a major challenge, and the consistency and completeness of the design has a prime role in the development of quality software. Many a times, the process of consistency checking in industries is manual. Artificial intelligence techniques can replace many of these manual efforts to make the development of software

This paper presents the stability analysis problem of fractional-order nonlinear systems with time-varying delay. After formulating the problem and selecting the nonlinear model as the system under study, stability analysis and expression of the sufficient conditions for fractional-order nonlinear systems with time-varying delay are obtained using two different methods. In these methods, sufficient

The article Adaptive ELM neural computing framework with fuzzy PI controller for speed regulation in permanent magnet synchronous motors, written by F. Vijay Amirtha Raj and V. Kamatchi Kannan, was originally published electronically on the publisher’s Internet portal (currently SpringerLink) on 06 May 2020 with open access.

The belief interval-valued soft set (BIVSS) combines soft set theory and belief interval value (Dempster–Shafer theory). In this study, we propose a generalized belief interval-valued soft set (GBIVSS) approach and explore the associated properties of this approach in decision-making applications. Using the score function, the scoring function and similarity measure used to compare the relationships

We present and discuss a formal approach for describing the quantum to classical crossover based on the group-theoretic construction of generalized coherent states. The method was originally introduced by Yaffe (Rev Mod Phys 54:407, 1982) in 1982 for tackling large-N quantum field theories and has been recently used for studying open quantum systems whose environment, while becoming macroscopic, may

Contrary to previous methods in production management, today’s approaches mainly focus on the whole supply chain parties’ considerations. Considering production planning and distribution, as the two main functions in supply chain (SC) management, in an integrated manner in order to enhance the SC advantages is one of today’s main dilemma. Here, we have firstly proposed and investigated the integrated

As an emerging technology, interval type-2 fuzzy logic systems (IT2 FLSs) have drawn great attentions in the past decade years. However, the computational intensive and time consuming type-reduction (TR) block may hinder the real applications IT2 FLSs. Unlike the most popular Karnik–Mendel (KM) iterative algorithms, the noniterative algorithms decrease the computational cost greatly. The comparison

Human factor is an inevitable element of safety-critical control room operations in nuclear power plants. Identifying human factors influencing operator error occurrence in such critical application is important to mitigate human errors. Conventional methods employed for human factor identification are often static, unable to deal with data and model uncertainty, and to consider independencies among

Thanks to the advent of powerful computers, searching for optimal solutions to engineering design problems becomes easier every day. Numerous researchers are still developing modern optimization algorithms, and the competition for “the most efficient optimization algorithm” continues apace. This study evaluates the performances of the Crow Search Algorithm (CSA) and a slightly modified variant (CSAM)

The purpose of this research study is to extend the multi-attribute group decision-making (MAGDM) methods to hesitant fuzzy soft set (HFS-set), hesitant fuzzy soft topology (HFS-topology) and HFS-Hausdorff spaces in group decision-making environment, as HFS-set is more superior tool to capture vagueness, hesitancy and incompleteness in individual evaluations. In order to obtain optimal decisions in

We will introduce a neutrosophic exponentially weighted moving average (NEWMA) statistic for the attribute data. We will use the proposed NEWMA to design an attribute control chart. We will introduce the neutrosophic Monte Carlo simulation to find the neutrosophic average run length (NARL). The comparative study shows the efficiency of the proposed NEWMA attribute. Two examples of having neutrosophic

Image classification has received extensive attention as an important technical means of acquiring image information. It has been widely used in various engineering fields. Although the existing traditional image classification methods have been widely applied in practical problems, there are some problems in the application process, such as unsatisfactory effects, low classification accuracy and weak

Regression analysis is a mathematical tool to estimate the relationship between explanatory variables and response variable. This paper defines a likelihood function in the sense of uncertain measure to represent the likelihood of unknown parameters. Furthermore, the method of maximum likelihood estimation is used for the parameter estimation of uncertain regression models, and the uncertainty distribution

While typesetting the article, the surname of the second author has been incorrectly published.

The article On ε-soft topological semigroups, written by A. A. Bahredar and N. Kouhestani, was originally published electronically on the publisher’s internet portal

With the development of modern mobile communication, artificial intelligence (AI) has begun to enter people’s life, and it is also constantly changing the modern education mode. First, the structure of BPN (back-propagation network) is designed in this paper. On this basis, genetic algorithm is applied to optimize, which can accelerate the convergence speed and achieve the effect of global optimization

In this paper, we are reporting a system for detection and classification of road signs. This system consists of two parts. The first part detects the road signs in real time. The second part classifies the German traffic signs (GTSRB) dataset and makes the prediction using the road signs detected in the first part to test the effectiveness. We used HOG and SVM in the detection part to detect the road

The operational data of advanced process systems have met with explosive growth, but its fluctuations are so slight that the number of the extracted representative samples is quite limited, making it difficult to reflect the nature of the process and to establish prediction models. In this study, inspired by the process of fisherman repairing nets, a Kriging-based virtual sample generation (VSG) named

A simple jerk system with only one exponential nonlinearity is proposed and discussed. Dynamic analysis of the integer-order jerk system shows the existence of chaotic oscillations. A model for the fractional-order jerk system is derived. The Adomian decomposition method is used to analyse the fractional-order jerk system. Stability analysis of the fractional-order jerk system shows that chaotic oscillations

Type 2 intuitionistic fuzzy set (T2IFS) is one of the most important concepts to describe fuzzy information by providing an additional degree of freedom to the decision maker to decide the decision-making process. The fundamental superiority of the T2IFS, over the intuitionistic fuzzy set, has been its ability to capture the degrees of membership of relevant membership values, where the uncertainty

Over recent decades, several efficient constraint-handling methods have been proposed in the area of evolutionary computation, and the \(\varepsilon \) constraint method is considered as a state-of-the-art method for both single and multiobjective optimization. Still, very few attempts have been made to improve this method when applied to the differential evolution algorithm. This study proposes several

As a strategic resource for the region to participate in global economic cooperation and competition, the coordinated development of ports within the port group is of great practical significance to promote its own and regional economic development. In order to better promote the port’s rational development and resource allocation, this paper uses the rough set reduction method based on information

In this paper, a clustering algorithm based on emotional preference and migratory behavior (EPMC) is proposed for data clustering. The algorithm consists of four models: the migration model, the emotional preference model, the social group model and the inertial learning model. First, the migration model calculates the probability of individuals being learned, so that individuals can learn from the

There are two different perspectives to study single axioms for (S, T)-fuzzy rough approximation operators, that is, ordinary fuzzy operations and fuzzy product operations. However, it is too complex and tedious to characterize (S, T)-fuzzy rough approximation operators with ordinary fuzzy operations, such as intersection, union and so on. To remedy these defects, this paper further investigates single

In this paper, we propose a method to estimate the number and locations of change points and further estimate parameters of different regions for piecewise stationary vector autoregressive models. The procedure decomposes the problem of change points detection and parameter estimation along the component series. By reformulating the change point detection problem as a variable selection one, we apply

In this work, new implementations of the U-BRAIN (Uncertainty-managing Bach Relevance-Based Artificial Intelligence) supervised machine learning algorithm are described. The implementations, referred as SP-BRAIN (SP stands for Spark), aim to efficiently process large datasets. Given the iterative nature of the algorithm together with its dependence on in-memory data, a non-standard MapReduce paradigm

Process mining is an emerging discipline that aims to analyze business processes using event data logged by IT systems. In process mining, the focus is on how to effectively and efficiently predict the next process/trace to be activated among all the possible processes/traces that are available in the process schema (usually modeled as a graph). Most of the existing process mining techniques assume

In the literature, loop fusion is an effective optimization technique which tries to enhance parallelizing compilers’ performance via memory hierarchy management, and all its competing criteria create an NP-hard problem. This paper proposes an evolutionary algorithm that aims to achieve a profitable loop order which maximizes fusion taking into account register size, parallelism and data reuse advancement

Time series exist widely in either nature or society such that the research on analysis of time series has great significance. However, considering the nonlinearity and uncertainty, the prediction of time series is still an open problem. In this paper, by means of the intuitionistic fuzzy set theory, we proposed a novel time series prediction scheme based on intuitionistic fuzzy cognitive map. In the

We study notions of conditional probability and stochastic dependence/independence in an upgraded probability model in which the space of events is modeled by a full Łukasiewicz tribe of all measurable functions from some measurable space into [0, 1]. Our study is based on properties of joint experiments and the notion of stochastic channel, a construct equivalent to the notion of Markov kernel between

Effective data management is a crucial problem in distributed systems such as data grid and cloud. This can be achieved by replicating file in a wise manner, which reduces data access time, increases data availability, reliability and system load balancing. Determining a reasonable number and appropriate location of replicas is essential decision in cloud computing. In this paper, a new dynamic replication

New energy products (NEPs) are important driving forces for promoting economic transformation and green development. The current trend of NEPs development is propelled by consumers, spurred by governmental regulations, and implemented by manufacturers. However, the previous studies rarely discussed the behavioral strategies of consumers. The relationships among consumers, manufacturers, and governments

In the current scenario, mechatronics-based new product development requires a multi-disciplinary approach for the interaction of mechanics, electronics and software requirements in order to enhance the system effectiveness. This work aims in developing a strategic methodology for making decisions during mechatronics-based new product development. This methodology integrates six different stages of

Based on relational exchange theory and transaction cost theory, a conceptual model for the relationships among supply chain partnership, inter-organizational knowledge trading and enterprise innovation performance is proposed and empirically tested using the data collected from 256 Chinese manufacturing enterprises in project-based supply chain with the structural equation model. The dimension of

In this paper, we introduce e-right, e-left, e-semi and \(\varepsilon \)-soft topological semigroups and examine the way these are related to each other. To do so, we need to define \(\bigtriangleup \)-soft and point open soft topologies, which are defined in the third and fourth sections, respectively. Also, soft separation axioms on these soft topologies will be studied.

We consider pseudofinite MV-algebras. As a main result, we show that an infinite MV-algebra is pseudofinite if and only if it is definably well founded, improving a result of a previous paper. Moreover, we show that the theory of pseudofinite MV-algebras has a partial form of elimination of quantifiers. Further, we show that the class of pseudofinite MV-chains and the class of pseudofinite MV-algebras

In 1988, given a complete Brouwerian lattice \({\mathbb {L}}:=(L;~\wedge ,~\vee ;~0,~1)\) and a ring \({\mathcal {A}}:=(A;~+,~\cdot ;~-;~0)\) with unity 1, Swamy and Swamy (J Math Anal Appl 134:94–103, 1988) built a lattice structure, on the set of L-fuzzy ideals of \({\mathcal {A}}\), and investigated some of its arithmetic properties. Since the residuation theory is richer than the lattice theory

Abstract Failure mode and effect analysis (FMEA), a bottom-up method, is one of risk assessment tools to eliminate or reduce failures in design and process. It has been applied to many industries due to its flexibility and effectiveness. However, the conventional FMEA considers less about the subjectivity and vagueness in the process of risk assessment and assumes that three risk factors’ importance

Abstract Feature selection is a critical preprocess for constructing model in computer vision and machine learning, yet it is difficult to simultaneously satisfy both reducing features’ number and maintaining classification accuracy. Toward this problem, we propose dividing-based many-objective evolutionary algorithm for large-scale feature selection (DMEA-FS). Firstly, four novel objectives are established

Abstract In this paper, an urban hazmat transportation problem considering multiple factors that tangle with real-world applications (i.e., weather conditions, traffic conditions, population density, time window, link closure and half link closure) is investigated. Based on multiple depot capacitated vehicle routing problem, we provide a multi-level programming formulation for urban hazmat transportation

Abstract This paper focuses on the reliability of intelligent logistics for fresh food e-commerce. Based on the development of fresh food e-commerce, this paper analyses the factors that influence the reliability of fresh food logistics from the aspects of information technology, facilities and equipment, personnel operation and external environment. A Bayesian network is used to analyse the influence

Abstract This paper presents a new multi-objective discreet learnable evolution model (MODLEM) to address the vehicle routing problem with time windows (VRPTW). Learnable evolution model (LEM) includes a machine learning algorithm, like the decision trees, that can discover the correct directions of the evolution leading to significant improvements in the fitness of the individuals. We incorporate

Abstract In this paper, the lifetimes of system components are assumed to have independent and nonidentical uncertainty distributions with uncertain parameters. The reliability functions and mean time to failure of the general systems are investigated according to the uncertainty theory. Basic models of the general systems with bi-uncertain variables are established and analyzed, including series,

Abstract Hollywood enjoys the position of being the biggest movie producers when it comes to global recognition among movie-making industries. Despite being the biggest movie producer, it has been facing high revenue losses lately since most of the films that it has created have failed to capture viewer’s attention in the first few weeks of its release resulting in a box-office flop. It has been observed

Abstract Airport bus is an important public transportation mode for large international airport. To improve the bus station coverage, passenger demand compatibility and the scheduling flexibility of Beijing International Airport bus line, a dynamic line generation and vehicle scheduling method is proposed in this paper. Firstly, based on multi-source big data from the airport (including data from taxi

Abstract Clustering analysis has been widely used in image segmentation, face recognition, protein identification, intrusion detection, document clustering and so on. Most of the previous clustering algorithms are not suitable for complex situations with manifold structure and large variations in density. Clustering by density core (DCore) turns out to be a very effective clustering method for complex

The incremental learning methods based on rough set theory are effective in acquiring knowledge in dynamically changing information tables. In this paper, we focus on the effective acquisition of decision rules by incrementally updating approximations when an incomplete information table changes. First of all, we present a four-step model to obtain three-way decision rules in an incomplete information

Abstract In order to improve the traffic efficiency of official vehicles in the traffic road network, a backpressure routing control strategy for multi-commodity flow (official traffic flow) using official vehicle network environmental data information is proposed. Firstly, the road network composed of official service vehicle-mounted wireless network nodes is used to collect information on road conditions

Abstract The effective identification of critical failure modes of individual equipment components or processes and the development of plans for improvement are crucial for the manufacturing industry. Recently, the failure modes and effects analysis (FMEA) approach based on multiple criteria decision making (MCDM) has been utilized effectively for the assessment of primary failure modes and risks.

Abstract Twist-structure representation theorems are established for De Morgan and Kleene lattices. While the former result relies essentially on the quasivariety of De Morgan lattices being finitely generated, the representation for Kleene lattices does not and can be extended to more general algebras. In particular, one can drop the double negation identity (involutivity). The resulting class of

Abstract Growing rates of innovation and consumer demand resulted in rapid accumulation of waste of electrical and electronic equipment or electronic waste (e-waste). In order to build and sustain green cities, efficient management of e-waste rises as a viable response to this accumulation. Accurate e-waste predictions that municipalities can utilize to build appropriate reverse logistics infrastructures

Abstract Clustering problems widely exist in machine learning, pattern recognition, image analysis and information sciences, etc. Although many clustering algorithms have been proposed, it is unpractical to find a clustering algorithm suitable for all types of datasets. Fuzzy c-means (FCM) is one of the most frequently-used fuzzy clustering algorithm for the reason that it is efficient, straightforward

Abstract Granular computing is a widely used computational paradigm nowadays. Particularly, within the rough set theory, granular computing plays a key role. In this paper, we propose a generic approach of rough sets, the granular extended multigranular sets (GEMS) for dealing with both mixed and incomplete information systems. Not only our proposal does use the traditional optimistic and pessimistic

Abstract High-accuracy traffic flow forecasting is vital to the development of intelligent city transportation systems. Recently, traffic flow forecasting models based on the kernel method have been widely applied due to their great generalization capability. The aim of this article is twofold: A novel kernel learning method, relevance vector machine, is employed to short-term traffic flow forecasting

Abstract Many location-based services are supported by the moving k-nearest neighbour (k-NN) query, which continuously returns the k-nearest data objects for a query point. Most of existing approaches to this problem have focused on a centralized setting, which show poor scalability to work around massive-scale and distributed data sets. In this paper, we propose an efficient distributed solution for

Abstract In the evolutionary computing community, differential evolution (DE) is well appreciated as a simple yet versatile population-based, non-convex optimizer designed for continuous optimization problems. A simple two-phase DE algorithm is presented in this article, which aims to identify promising basins of attraction on a non-convex functional landscape in the first phase, and starting from

Abstract In this paper, a new optimization algorithm called the Hitchcock bird-inspired algorithm (HBIA) is proposed. It is inspired by the aggressive bird behavior portrayed by Alfred Hitchcock in the 1963 thriller “The Birds.” It is noteworthy to emphasize that the bird’s behavior as shown in the movie is itself inspired by a considered natural birds behavior when faced with extreme conditions. HBIA

Abstract With the rapid development of the Internet of Things, the massive amount of big data generated by the Internet of Things terminals and the real-time processing requirements have brought enormous challenges. A two-tier computing model consisting solely of two entities, cloud and user, will not be sufficient to support processing large numbers of concurrent data requests. Therefore, fog computing