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The carbon trading mechanism is proposed to remit global warming and it can be considered in a microgrid. There is a lack of continuous-time methods in a microgrid, so a continuous-time model is proposed and solved by differential evolution (DE) in this work. This research aims to create effective methods to obtain some useful results in a microgrid. Batteries, microturbines, and the exchange with the main grid are considered. Considering carbon trading, the objective function is the sum of a quadratic function and an absolute value function. In addition, a composite electricity price model has been put forward to conclude the common kinds of electricity prices. DE is utilized to solve the constrained optimization problems (COPs) proposed in this paper. A modified DE is raised in this work, which uses multiple mutation and selection strategies. In the case study, the proposed algorithm is compared with the other seven algorithms and the outperforming one is selected to compare two different types of electricity prices. The results show the proposed algorithm performs best. The Wilcoxon Signed Rank Test is also used to verify its significant superiority. The other result is that time-of-use pricing (ToUP) is economic in the off-peak period while inclining block rates (IBRs) are economic in the peak and shoulder periods. The composite electricity price model can be applied in social production and life. In addition, the proposed algorithm puts forward a new variety of DE and enriches the theory of DE. [ABSTRACT FROM AUTHOR]

In this paper, the conceptualization of a control hardware architecture aimed to the implementation of integer- and fractional-order identification and control algorithms is presented. The proposed hardware architecture combines the capability of implementing PC-based control applications with embedded applications on microprocessor- and FPGA-based real-time targets. In this work, the potential advantages of this hardware architecture over other available alternatives are discussed from different perspectives. The experimental prototype that has been designed and built to evaluate the control hardware architecture proposed in this work is also described in detail. The thermal-based process taking place in the prototype is characterized for being reconfigurable and exhibiting fractional behaviour, which results in a suitable equipment for the purpose of fractional-order identification and control. In order to demonstrate the applicability and effectiveness of the proposed control hardware architecture, integer- and fractional-order identification and control algorithms implemented in various control technologies have been applied to the temperature-based experimental prototype described before. Detailed discussion about results and identification and control issues are provided. The main contribution of this work is to provide an efficient and practical hardware architecture for implementing fractional-order identification and control algorithms in different control technologies, helping to bridge the gap between real-time hardware solutions and software-based simulations of fractional-order systems and controllers. Finally, some conclusions and concluding remarks are offered in the industrial context. [ABSTRACT FROM AUTHOR]

The Internet of Things defines a global and comprehensive network whose task is to monitor and control the physical world by collecting, processing, and analyzing data sensed by IoT devices. This network has succeeded in various areas, and one of its most important applications is in smart agriculture because there are many demands for producing high-quality foodstuff in the world. These demands need new production schemes in the agriculture area. In IoT, communication security is essential due to the extensive heterogeneity of IoT devices. In this paper, a cluster-tree-based secure routing approach using the dragonfly algorithm (CTSRD) is proposed for IoT. The proposed scheme presents a distributed and lightweight trust mechanism called weighted trust (W-Trust). W-Trust reduces the trust value corresponding to malicious nodes based on a penalty coefficient to isolate this node in the network. Furthermore, it improves the trust value of honest IoT devices based on a reward coefficient. Additionally, CTSRD introduces a trust-based clustering process called T-Clustering. In this clustering process, cluster head nodes (CHs) are selected among honest IoT nodes. Finally, CTSRD establishes a routing tree based on the dragonfly algorithm (DA) between CHs. This tree is called DA-Tree. To evaluate the quality of the routing tree, a new fitness function is provided in CTSRD. DA-Tree finds a secure, stable, and optimal routing tree to balance the consumed energy and boost the network lifetime. CTSRD is compared with EEMSR and E-BEENISH with regard to the network lifetime, consumed energy, and packet delivery rate. This comparison shows that our scheme can uniformly distribute the consumed energy in IoT and improves the energy consumption and network lifetime. However, it has a slightly lower packet delivery rate than EEMSR. [ABSTRACT FROM AUTHOR]

Energy conservation, emission reduction, and green and low carbon are of great significance to sustainable development, and are also the theme of the transformation and upgrading of the manufacturing industry. This paper concentrates on studying the energy-efficient hybrid flowshop scheduling problem with consistent sublots (HFSP_ECS) with the objective of minimizing the energy consumption. To solve the problem, the HFSP_ECS is decomposed by the idea of "divide-and-conquer", resulting in three coupled subproblems, i.e., lot sequence, machine assignment, and lot split, which can be solved by using a cooperative methodology. Thus, an improved cooperative coevolutionary algorithm (vCCEA) is proposed by integrating the variable neighborhood descent (VND) strategy. In the vCCEA, considering the problem-specific characteristics, a two-layer encoding strategy is designed to represent the essential information, and a novel collaborative model is proposed to realize the interaction between subproblems. In addition, special neighborhood structures are designed for different subproblems, and two kinds of enhanced neighborhood structures are proposed to search for potential promising solutions. A collaborative population restart mechanism is established to ensure the population diversity. The computational results show that vCCEA can coordinate and solve each subproblem of HFSP_ECS effectively, and outperform the mathematical programming and the other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Missing data is a common and inevitable phenomenon. In practical applications, the datasets usually contain noises for various reasons. Most of the existing missing data imputing algorithms are affected by noises which reduce the accuracy of the imputation. This paper proposes a noise-aware missing data multiple imputation algorithm NPMI in static data. Different multiple imputation models are proposed according to the missing mechanism of data. Secondly, the method to determine the imputation order of multivariablesmissing is given. A random sampling consistency algorithm is proposed to estimate the initial values of the parameters of the multiple imputation model to reduce the influence of noise data and improve the algorithm's robustness. Experiments on two real datasets and two synthetic datasets verify the accuracy and efficiency of the proposed NPMI algorithm, and the results are analyzed. [ABSTRACT FROM AUTHOR]

This paper focuses on a high-dimensional semi-parametric regression model in which a partially linear model is used for the parametric part and the B-spline basis function approach is used to estimate the unknown function for the non-parametric part. Within the framework of this model, the constrained least squares estimation is investigated, and the alternating-direction multiplier method (ADMM) is used to solve the model. The convergence is proved under certain conditions. Finally, numerical simulations are performed and applied to workers' wage data from CPS85. The results show that the ADMM algorithm is very effective in solving high-dimensional partially linear models. [ABSTRACT FROM AUTHOR]

Real life logical rule is not always satisfiable in nature due to the redundant variable that represents the logical formulation. Thus, the intelligence system must be optimally governed to ensure the system can behave according to non-satisfiable structure that finds practical applications particularly in knowledge discovery tasks. In this paper, we a propose non-satisfiability logical rule that combines two sub-logical rules, namely Maximum 2 Satisfiability and Random 2 Satisfiability, that play a vital role in creating explainable artificial intelligence. Interestingly, the combination will result in the negative logical outcome where the cost function of the proposed logic is always more than zero. The proposed logical rule is implemented into Discrete Hopfield Neural Network by computing the cost function associated with each variable in Random 2 Satisfiability. Since the proposed logical rule is difficult to be optimized during training phase of DHNN, Election Algorithm is implemented to find consistent interpretation that minimizes the cost function of the proposed logical rule. Election Algorithm has become the most popular optimization metaheuristic technique for resolving constraint optimization problems. The fundamental concepts of Election Algorithm are taken from socio-political phenomena which use new and efficient processes to produce the best outcome. The behavior of Random Maximum 2 Satisfiability in Discrete Hopfield Neural Network is investigated based on several performance metrics. The performance is compared between existing conventional methods with Genetic Algorithm and Election Algorithm. The results demonstrate that the proposed Random Maximum 2 Satisfiability can become the symbolic instruction in Discrete Hopfield Neural Network where Election Algorithm has performed as an effective training process of Discrete Hopfield Neural Network compared to Genetic Algorithm and Exhaustive Search. [ABSTRACT FROM AUTHOR]

Nonnegative Tucker decomposition (NTD) is an unsupervised method and has been extended in many applied fields. However, NTD does not make use of the label information of sample data, even though such label information is available. To remedy the defect, in this paper, we propose a label constraint NTD method, namely Discriminative NTD (DNTD), which considers a fraction of the label information of the sample data as a discriminative constraint. Differing from other label-based methods, the proposed method enforces the sample data, with the same label to be aligned on the same axis or line. Combining the NTD and the label-discriminative constraint term, DNTD can not only extract the part-based representation of the data tensor but also boost the discriminative ability of the NTD. An iterative updating algorithm is provided to solve the objective function of DNTD. Finally, the proposed DNTD method is applied to image clustering. Experimental results on ORL, COIL20, Yale datasets show the clustering accuracy of DNTD is improved by 8.47–32.17% and the normalized mutual information is improved by 10.43–29.64% compared with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are one of the devices used to collect big data as part of the artificial intelligence of things (AIoT). To reduce total energy consumption, most researchers focus on optimizing the number and the location of UAVs, but ignore the distribution of UAVs in relation to the AIoT devices. Therefore, this paper proposes a memetic algorithm based on isomorphic transcoding space (MA-IT) to optimize the deployment of UAVs, solving, in particular, the distribution of UAVs in energy-efficient AIoT data collection. First, a simplified encoding method is designed to reduce the search space. This method only uses the distribution to represent a solution, and the number and the location of UAVs can be greedily deduced through the distribution. Afterwards, a pseudo-random initialization is proposed to initialize a population randomly and greedily. Then, an isomorphic transcoding (isoTcode) method is proposed to identify solutions with the isomorphic relations and to represent these solutions in a practical way in the UAV deployment problem. Finally, a crossover and a local search based on the isoTcode method are proposed to increase the solution diversity and improve the solution quality. Comparative experiments are conducted in the randomly generated instances with three problem scales. The results show that MA-IT performs better than other algorithms for solving the deployment optimization of UAVs. [ABSTRACT FROM AUTHOR]

This paper deals with optimal consensus problems of a general heterogeneous nonlinear multi-agent system. A novel filter is proposed for each agent by integrating local gradients with neighboring output information. Using this filter and introducing an appropriate auxiliary variable, the event-triggered control algorithm is obtained within the framework of the prescribed performance control. One of the remarkable properties of the proposed algorithm is that it can save resources by updating control signals only when necessary rather than periodically while achieving optimal consensus. Theoretical and simulation verifications of the algorithm without the Zeno behavior are carefully studied. Instructions are also presented for control parameter selection to keep the residual errors as small as desired. [ABSTRACT FROM AUTHOR]

Feature selection (FS) methods play essential roles in different machine learning applications. Several FS methods have been developed; however, those FS methods that depend on metaheuristic (MH) algorithms showed impressive performance in various domains. Thus, in this paper, based on the recent advances in MH algorithms, we introduce a new FS technique to modify the performance of the Dwarf Mongoose Optimization (DMO) Algorithm using quantum-based optimization (QBO). The main idea is to utilize QBO as a local search of the traditional DMO to avoid its search limitations. So, the developed method, named DMOAQ, benefits from the advantages of the DMO and QBO. It is tested with well-known benchmark and high-dimensional datasets, with comprehensive comparisons to several optimization methods, including the original DMO. The evaluation outcomes verify that the DMOAQ has significantly enhanced the search capability of the traditional DMO and outperformed other compared methods in the evaluation experiments. [ABSTRACT FROM AUTHOR]

Many real-life problems that involve decisions under uncertainty are often sequentially repeated and can be approached iteratively. Knowledge Gradient (KG) formulates the decision-under-uncertainty problem into repeatedly estimating the value of information observed from each possible decisions and then committing to a decision with the highest estimated value. This paper aims to provide a multi-faceted overview of modern research on KG: firstly, on how the KG algorithm is formulated in the beginning with an example implementation of its most frequently used implementation; secondly, on how KG algorithms are related to other problems and iterative algorithms, in particular, Bayesian optimization; thirdly, on the significant trends found in modern theoretical research on KG; lastly, on the diverse examples of applications that use KG in their key decision-making step. [ABSTRACT FROM AUTHOR]

Educational data classification has become an effective tool for exploring the hidden pattern or relationship in educational data and predicting students' performance or teachers' competency. This study proposes a new method based on machine learning algorithms to predict the technology-integrated competency of pre-service mathematics teachers. In this paper, we modified the inertial subgradient extragradient algorithm for pseudomonotone equilibrium and proved the weak convergence theorem under some suitable conditions in Hilbert spaces. We then applied to solve data classification by extreme learning machine using the dataset comprised of the technology-integrated competency of 954 pre-service mathematics teachers in a university in northern Thailand, longitudinally collected for five years. The flexibility of our algorithm was shown by comparisons of the choice of different parameters. The performance was calculated and compared with the existing algorithms to be implemented for prediction. The results show that the proposed method achieved a classification accuracy of 81.06%. The predictions were implemented using ten attributes, including demographic information, skills, and knowledge relating to technology developed throughout the teacher education program. Such data driven studies are significant for establishing a prospective teacher competency analysis framework in teacher education and contributing to decision-making for policy design. [ABSTRACT FROM AUTHOR]

In this paper, a relaxed variable metric primal-dual fixed-point algorithm is proposed for solving the convex optimization problem involving the sum of two convex functions where one is differentiable with the Lipschitz continuous gradient while the other is composed of a linear operator. Based on the preconditioned forward–backward splitting algorithm, the convergence of the proposed algorithm is proved. At the same time, we show that some existing algorithms are special cases of the proposed algorithm. Furthermore, the ergodic convergence and linear convergence rates of the proposed algorithm are established under relaxed parameters. Numerical experiments on the image deblurring problems demonstrate that the proposed algorithm outperforms some existing algorithms in terms of the number of iterations. [ABSTRACT FROM AUTHOR]

A huge amount of construction waste is generated in construction sites every day that needs to be transported by vehicle to disposal facilities for processing. Unlike in most typical transportation problems, once these vehicles are loaded with construction waste, they must travel directly to the disposal facility. Moreover, there are different types of construction waste that may require handling by different disposal facilities. In this paper, we develop a model and algorithm for identifying the optimal transportation routes specific to construction waste transportation. Our results can not only minimize the overall costs for both the logistics company and the contractor but also minimize the distance traveled, thus reducing urban traffic emissions. [ABSTRACT FROM AUTHOR]

There is a constant rise in the amount of data being copied or plagiarized because of the abundance of content and information freely available across the internet. Even though the systems try to check documents for the plagiarism, there have been trials to overcome these system checks. In this paper, the concept of character injection is used to trick plagiarism checker is presented. It is also showcased that how does the similarity check algorithms based on k-grams fail to detect the character injection. In order to eradicate the problem or error in similarity rates caused due to the problem of character injection, image processing based approach of multiple histogram projections are used. An application is developed to detect the character injection in the document and produce the accurate similarity rate. The results are shown with some test documents and the proposed method eliminates any kind of character injected in the document that tricks plagiarism. The proposed method has addressed the problem of character injection with image processing based changes in the existing methods of document-similarity check algorithms using k-grams. The proposed method can detect 100% injected character be it any alphabet of any language, The processing time for conversion, histogram projections and applying winnowing algorithm takes 1.2 sec per page on average when experimented on multiple types of document varying in size from 2 KB to 10 MB. [ABSTRACT FROM AUTHOR]

The survival prediction of children undergoing hematopoietic stem-cell transplantation is essential for successful transplantation. However, the performance of current algorithms for predicting mortality in this patient group has not improved over recent decades. This paper proposes a new feature selection technique for survival prediction problems using the Mud Ring Algorithm (MRA). Experiments and tests were initially performed on 13 real datasets with varying occurrences to compare the suggested algorithm with other algorithms. After that, the constructed model classification performance was compared to other techniques using the bone marrow transplant children's dataset. Modern techniques were used to acquire their classification results, which were then compared to the suggested outcomes using a variety of well-known metrics, graphical tools, and diagnostic analysis. This investigation has demonstrated that our suggested approach is comparable and outperformed other methods in terms of results. In addition, the results showed that the constructed model enhanced prediction accuracy by up to 82.6% for test cases. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is applied to reduce data dimensions while retaining much information. Many optimization methods have been applied to enhance the efficiency of FS algorithms. These approaches reduce the processing time and improve the accuracy of the learning models. In this paper, a developed method called MPAO based on the marine predators algorithm (MPA) and the "narrowed exploration" strategy of the Aquila optimizer (AO) is proposed to handle FS, global optimization, and engineering problems. This modification enhances the exploration behavior of the MPA to update and explore the search space. Therefore, the narrowed exploration of the AO increases the searchability of the MPA, thereby improving its ability to obtain optimal or near-optimal results, which effectively helps the original MPA overcome the local optima issues in the problem domain. The performance of the proposed MPAO method is evaluated on solving FS and global optimization problems using some evaluation criteria, including the maximum value (Max), minimum value (Min), and standard deviation (Std) of the fitness function. Furthermore, the results are compared to some meta-heuristic methods over four engineering problems. Experimental results confirm the efficiency of the proposed MPAO method in solving FS, global optimization, and engineering problems. [ABSTRACT FROM AUTHOR]

This paper addresses an integrated scheduling problem of batch manufacturing and delivery processes with a single batch machine and direct-shipping trucks. In the manufacturing process, some jobs in the same family are simultaneously processed as a production batch in a single machine. The batch production time depends only on the family type assigned to the production batch and it is dynamically adjusted by batch deterioration and rate-modifying activities. Each job after the batch manufacturing is reassigned to delivery batches. In the delivery process, each delivery batch is directly shipped to the corresponding customer. The delivery time of delivery batches is determined by the distance between the manufacturing site and customer location. The total volume of jobs in each production or delivery batch must not exceed the machine or truck capacity. The objective function is to minimize the total tardiness of jobs delivered to customers with different due dates. To solve the problem, a mixed-integer linear programming model to find the optimal solution for small problem instances is formulated and meta-heuristic algorithms to find effective solutions for large problem instances are presented. Sensitivity analyses are conducted to find the effect of problem parameters on the manufacturing and delivery time. [ABSTRACT FROM AUTHOR]

The total heat exchange factor is one of the most important thermal physical parameters in the heat transfer model for a reheating furnace machine. In this paper, a novel general strategy, which is combined with the first-optimize-then-discretize (FOTD) approach and an improved hybrid conjugate gradient (IHCG) algorithm, is proposed to identify the total heat exchange factor by solving a nonlinear inverse heat conduction problem (IHCP). Firstly, a nonlinear IHCP with the Dirichlet-type boundary condition T m (t) = T (0 , t) is built to determine the unknown total heat exchange factor w (t) . Secondly, the analysis of the Fréchet gradient of the cost functional is given and the gradient is proved as Lipschitz continuous by the FOTD approach. Thirdly, based on the gradient information by FOTD, a new IHCG algorithm, whose global convergence is proved by us, is proposed for fast solving of the optimization problem. Finally, simulation experiments are given to verify the effectiveness of the proposed strategy. Compared with the first-discretize-then-optimize (FDTO) approach, the FOTD approach can reduce running time and iteration number. Compared with other CG algorithms, the proposed IHCG algorithm has better convergence performance. The experimental data by the thermocouples experiments from a reheating furnace are also given to identify the total heat exchange factor. [ABSTRACT FROM AUTHOR]

Job Shop Scheduling Problem (JSSP) is a well-known NP-hard combinatorial optimization problem. In recent years, many scholars have proposed various metaheuristic algorithms to solve JSSP, playing an important role in solving small-scale JSSP. However, when the size of the problem increases, the algorithms usually take too much time to converge. In this paper, we propose a hybrid algorithm, namely EOSMA, which mixes the update strategy of Equilibrium Optimizer (EO) into Slime Mould Algorithm (SMA), adding Centroid Opposition-based Computation (COBC) in some iterations. The hybridization of EO with SMA makes a better balance between exploration and exploitation. The addition of COBC strengthens the exploration and exploitation, increases the diversity of the population, improves the convergence speed and convergence accuracy, and avoids falling into local optimum. In order to solve discrete problems efficiently, a Sort-Order-Index (SOI)-based coding method is proposed. In order to solve JSSP more efficiently, a neighbor search strategy based on a two-point exchange is added to the iterative process of EOSMA to improve the exploitation capability of EOSMA to solve JSSP. Then, it is utilized to solve 82 JSSP benchmark instances; its performance is evaluated compared to that of EO, Marine Predators Algorithm (MPA), Aquila Optimizer (AO), Bald Eagle Search (BES), and SMA. The experimental results and statistical analysis show that the proposed EOSMA outperforms other competing algorithms. [ABSTRACT FROM AUTHOR]

This paper studies the robust stabilization of impulsive Boolean control networks (IBCNs) with function perturbation. A Boolean control network (BCN) with a state-dependent impulsive sequence is converted to an equivalent BCN by the semi-tensor product method. Based on the equivalence of stabilization between the IBCN and the corresponding BCN, several criteria are proposed for the robust stabilization of IBCNs. Furthermore, when the IBCN is not robustly stabilizable after the function perturbation, an algorithm is presented to modify the control or the impulse-triggered set. Finally, an example is given to verify the obtained results. [ABSTRACT FROM AUTHOR]

This work addresses the robust counterpart of the shortest path problem (RSPP) with a correlated uncertainty set. Because this problem is difficult, a heuristic approach, based on Frank–Wolfe's algorithm named discrete Frank–Wolfe (DFW), has recently been proposed. The aim of this paper is to propose a semi-definite programming relaxation for the RSPP that provides a lower bound to validate approaches such as the DFW algorithm. The relaxed problem is a semi-definite programming (SDP) problem that results from a bidualization that is done through a reformulation of the RSPP into a quadratic problem. Then, the relaxed problem is solved by using a sparse version of Pierra's decomposition in a product space method. This validation method is suitable for large-size problems. The numerical experiments show that the gap between the solutions obtained with the relaxed and the heuristic approaches is relatively small. [ABSTRACT FROM AUTHOR]

LoRa (Long Range), a wireless communication technology for low power wide area networks (LPWANs), enables a wide range of IoT applications and inter-device communication, due to its openness and flexible network deployment. In the actual deployment and operation of LoRa networks, the static link transmission scheme does not make full use of the channel resources in the time-varying channel environment, resulting in a poor network performance. In this paper, we propose a more effective adaptive data rate (ADR) algorithm for low-cost gateways, we firstly analyze the impact of the different hardware parameters (RSSI, SNR) on the link quality and classify the link quality using the fuzzy support vector machine (FSVM). Secondly, we establish an end device (ED) throughput model and energy consumption model and design different adaptive rate algorithms, according to the different link quality considering both the link-level performance and the MAC layer performance. The proposed algorithm uses machine learning to classify the link quality, which can accurately classify the link quality using a small amount of data, compared to other adaptive rate algorithms, and the link parameter adaptation algorithm can maximize the throughput while ensuring the link stability, by considering the link-level performance and the MAC layer performance, compared to other algorithms. The results show that it outperforms the standard LoRaWAN ADR algorithm in both the single ED and the multi ED scenarios, in terms of the packets reception rate (PRR) and the network throughput. Compared to the LoRaWAN ADR in 32 multi-ED scenarios, the proposed algorithm improves the throughput by 34.12% and packets the reception rate by 26%, significantly improving the network throughput and the packets reception rate. [ABSTRACT FROM AUTHOR]

In this paper, a combined structure of algorithms based on field-oriented control (FOC) and direct torque control (DTC) is proposed for improving the performance of a three-phase induction motor (IM) over a wide range of torque loads. The objective of this study is to investigate the state-of-the-art research performed on the resilient intersection control management between FOC and DTC algorithms for IM drive. Three algorithms are employed in the proposed scheme: Direct Torque Stator Flux Control (DTSFC), Direct Torque Rotor Flux Control (DTRFC), and the FOC algorithm. The main aim of the control law is to launch the drive system by selecting DTSFC or DTRFC during the transient state while FOC is switched smoothly to the steady-state. The proposed method had several advantages, including the ability to produce torque beyond the pull-out torque range at 340%, high torque dynamics during transient state within 0.5 ms, low chattering up to 0.05 N.m, and fixed frequency at steady-state operation. Furthermore, a comprehensive comparison study has been conducted, including the most recent research, to verify the effectiveness and superiority of the proposed technique. Results were obtained using both MATLAB/Simulink environment and also were experimentally performed by using a dSPACE DS1103 digital signal processing board to verify the concept of the proposed control scheme. [ABSTRACT FROM AUTHOR]

For analyzing multiple events data, the illness death model is often used to investigate the covariate–response association for its easy and direct interpretation as well as the flexibility to accommodate the within-subject dependence. The resulting estimation and inferential procedures often depend on the subjective specification of the parametric frailty distribution. For certain frailty distributions, the computation can be challenging as the estimation involves both the nonparametric component and the parametric component. In this paper, we develop efficient computational methods for analyzing semi-competing risks data in the illness death model with the general frailty, where the Minorization–Maximization (MM) principle is employed for yielding accurate estimation and inferential procedures. Simulation studies are conducted to assess the finite-sample performance of the proposed method. An application to a real data is also provided for illustration. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) allows the integration of the physical world with network devices for proper privacy and security in a healthcare system. IoT in a healthcare system is vulnerable to spoofing attacks that can easily represent themselves as a legal entity of the network. It is a passive attack and can access the Medium Access Control address of some valid users in the network to continue malicious activities. In this paper, an algorithm is proposed for detecting spoofing attacks in IoT using Received Signal Strength (RSS) and Number of Connected Neighbors (NCN). Firstly, the spoofing attack is detected, located and eliminated through Received Signal Strength (RSS) in an inter-cluster network. However, the RSS is not useful against intra-cluster spoofing attacks and therefore the NCN is introduced to detect, identify and eliminate the intra-cluster spoofing attack. The proposed model is implemented in Network Simulator 2 (NS-2) to compare the performance of the proposed algorithm in the presence and absence of spoofing attacks. The result is that the proposed model increases the detection and prevention of spoofing. [ABSTRACT FROM AUTHOR]

In order to solve the problem in which structurally incoherent low-rank non-negative matrix decomposition (SILR-NMF) algorithms only consider the non-negativity of the data and do not consider the manifold distribution of high-dimensional space data, a new structurally incoherent low rank two-dimensional local discriminant graph embedding (SILR-2DLDGE) is proposed in this paper. The algorithm consists of the following three parts. Firstly, it is vital to keep the intrinsic relationship between data points. By the token, we introduced the graph embedding (GE) framework to preserve locality information. Secondly, the algorithm alleviates the impact of noise and corruption uses the L1 norm as a constraint by low-rank learning. Finally, the algorithm improves the discriminant ability by encrypting the structurally incoherent parts of the data. In the meantime, we capture the theoretical basis of the algorithm and analyze the computational cost and convergence. The experimental results and discussions on several image databases show that the proposed algorithm is more effective than the SILR-NMF algorithm. [ABSTRACT FROM AUTHOR]

Optimization problems are prevalent in almost all areas and hence optimization algorithms are crucial for a myriad of real-world applications. Deterministic optimization algorithms tend to be computationally costly and time-consuming. Hence, heuristic and metaheuristic algorithms are more favoured as they provide near-optimal solutions in an acceptable amount of time. Swarm intelligence algorithms are being increasingly used for optimization problems owing to their simplicity and good performance. The Dragonfly Algorithm (DA) is one which is inspired by the swarming behaviours of dragonflies, and it has been proven to have a superior performance than other algorithms in multiple applications. Hence, it is worth considering its application to the traveling salesman problem which is a predominant discrete optimization problem. The original DA is only suitable for solving continuous optimization problems and, although there is a binary version of the algorithm, it is not easily adapted for solving discrete optimization problems like TSP. We have previously proposed a discrete adapted DA algorithm suitable for TSP. However, it has low effectiveness, and it has not been used for large TSP problems. In this paper, we propose an optimized discrete adapted DA by using the steepest ascent hill climbing algorithm as a local search. The algorithm is applied to a TSP problem modelling a package delivery system in the Kuala Lumpur area and to benchmark TSP problems, and it is found to have a higher effectiveness than the discrete adapted DA and some other swarm intelligence algorithms. It also has a higher efficiency than the discrete adapted DA. [ABSTRACT FROM AUTHOR]

The Internet of Things is widely used, which results in the collection of enormous amounts of data with numerous redundant, irrelevant, and noisy features. In addition, many of these features need to be managed. Consequently, developing an effective feature selection (FS) strategy becomes a difficult goal. Many FS techniques, based on bioinspired metaheuristic methods, have been developed to tackle this problem. However, these methods still suffer from limitations; so, in this paper, we developed an alternative FS technique, based on integrating operators of the chameleon swarm algorithm (Cham) with the quantum-based optimization (QBO) technique. With the use of eighteen datasets from various real-world applications, we proposed that QCham is investigated and compared to well-known FS methods. The comparisons demonstrate the benefits of including a QBO operator in the Cham because the proposed QCham can efficiently and accurately detect the most crucial features. Whereas the QCham achieves nearly 92.6%, with CPU time(s) nearly 1.7 overall the tested datasets. This indicates the advantages of QCham among comparative algorithms and high efficiency of integrating the QBO with the operators of Cham algorithm that used to enhance the process of balancing between exploration and exploitation. [ABSTRACT FROM AUTHOR]

This paper contains two main parts, Part I and Part II, which discuss the local and global minimization problems, respectively. In Part I, a fresh conjugate gradient (CG) technique is suggested and then combined with a line-search technique to obtain a globally convergent algorithm. The finite difference approximations approach is used to compute the approximate values of the first derivative of the function f. The convergence analysis of the suggested method is established. The comparisons between the performance of the new CG method and the performance of four other CG methods demonstrate that the proposed CG method is promising and competitive for finding a local optimum point. In Part II, three formulas are designed by which a group of solutions are generated. This set of random formulas is hybridized with the globally convergent CG algorithm to obtain a hybrid stochastic conjugate gradient algorithm denoted by HSSZH. The HSSZH algorithm finds the approximate value of the global solution of a global optimization problem. Five combined stochastic conjugate gradient algorithms are constructed. The performance profiles are used to assess and compare the rendition of the family of hybrid stochastic conjugate gradient algorithms. The comparison results between our proposed HSSZH algorithm and four other hybrid stochastic conjugate gradient techniques demonstrate that the suggested HSSZH method is competitive with, and in all cases superior to, the four algorithms in terms of the efficiency, reliability and effectiveness to find the approximate solution of the global optimization problem that contains a non-convex function. [ABSTRACT FROM AUTHOR]

One of the common power quality (PQ) problems in transmission and distribution systems is the voltage sag that affects the sensitive loads. Losses and problems caused by the voltage sag in the power system can be reduced by correctly determining the relative location of the voltage sag. This paper proposes a novel algorithm to classify voltage sag relative location and fault type, which is the main root cause of voltage sag, based on the actual voltage and current data before and during the voltage sag. The performance of the algorithm is investigated by performing a numerical simulation utilizing MATLAB/Simulink. Moreover, the proposed algorithm is integrated into the power quality monitoring system (PQMS) of the real distribution system and tested. The results show that the performance of the proposed method is satisfactory. [ABSTRACT FROM AUTHOR]

The functioning of complex systems relies on subsystems (modules) that in turn are composed of multiple units. In this paper, we focus on modular systems that might fail due to wear on their units or environmental conditions (shocks). The lifetimes of the units follow a phase-type distribution, while shocks follow a Markovian Arrival Process. The use of Matrix-Analytic methods and a bottom-up approach for constructing the system generator is proposed. The use of modular structures, as well as its implementation by the Modular Matrix-Analytic (MoMA) algorithm, make our methodology flexible in adapting to physical changes in the system, e.g., incorporation of new modules into the current model. After the model for the system is built, the modules are seen as a 'black box', i.e., only the contribution of the module as a whole to system performance is considered. However, if required, our method is able to keep track of the events within the module, making it possible to identify the state of individual units. Compact expressions for different reliability measures are obtained with the proposed description, optimal maintenance strategies based on critical operative states are suggested, and a numerical application based on a k-out-of-n structure is developed. [ABSTRACT FROM AUTHOR]

This paper studies the problem of target control and how a virtual ellipsoid can avoid the static obstacle. During the motion to the target set, the virtual ellipsoid can achieve a motion under collision avoidance by keeping the distance between the ellipsoid and obstacle. We present solutions to this problem in the class of closed-loop (feedback) controls based on Hamilton–Jacobi–Bellman (HJB) equation. Simulation results verify the validity and effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

Detecting transformer faults is critical to avoid the undesirable loss of transformers from service and ensure utility service continuity. Transformer faults diagnosis can be determined based on dissolved gas analysis (DGA). The DGA traditional techniques, such as Duval triangle, Key gas, Rogers' ratio, Dornenburg, and IEC code 60599, suffer from poor transformer faults diagnosis. Therefore, recent research has been developed to diagnose transformer fault and the diagnostic accuracy using combined traditional methods of DGA with artificial intelligence and optimization methods. This paper used a novel meta-heuristic technique, based on Gravitational Search and Dipper Throated Optimization Algorithms (GSDTO), to enhance the transformer faults' diagnostic accuracy, which was considered a novelty in this work to reduce the misinterpretation of the transformer faults. The robustness of the constructed GSDTO-based model was addressed by the statistical study using Wilcoxon's rank-sum and ANOVA tests. The results revealed that the constructed model enhanced the diagnostic accuracy up to 98.26% for all test cases. [ABSTRACT FROM AUTHOR]

In this paper, we focus on several ideas associated with linear Diophantine fuzzy soft sets (LDFSSs) along with its algebraic structure. We provide operations on LDFSSs and their specific features, elaborating them with real-world examples and statistical depictions to construct an inflow of linguistic variables based on linear Diophantine fuzzy soft (LDFSS) information. We offer a study of LDFSSs to the multi-criteria decision-making (MCDM) process of university determination, together with new algorithms and flowcharts. We construct LDFSS-TOPSIS, LDFSS-VIKOR and the LDFSS-AO techniques as robust extensions of TOPSIS (a technique for order preferences through the ideal solution), VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje) and AO (aggregation operator). We use the LDFSS-TOPSIS, LDFSS-VIKOR and LDFSS-AO techniques to solve a real-world agricultural problem. Moreover, we present a small-sized robotic agri-farming to support the proposed technique. A comparison analysis is also performed to examine the symmetry of optimal decision and to analyze the efficiency of the suggested algorithms. [ABSTRACT FROM AUTHOR]

Narrowband Internet of Things (NB-IoT) is one of the low-power wide-area network technologies that aim to support enormous connection, deep coverage, low power consumption, and low cost. Therefore, low cost of implementation and maintenance is one of the key challenges of NB-IoT terminals. This paper presents a low-complexity formulation for narrowband secondary synchronization signal (NSSS) detection in the NB-IoT system, supported by a coherent algorithm that requires a priori knowledge of the channel. By exploiting a symmetric conjugate feature of the NSSS sequence, a joint physical cell ID and radio frame number detection method with low complexity is proposed for coherent detection. The probability of erroneous detection of the presented NSSS detection method is computed, and the analytical model is verified by means of simulation. Numerical experiments will demonstrate that the proposed detection scheme remarkably reduces the computational complexity with almost the same detection ability compared to the existing detection scheme. [ABSTRACT FROM AUTHOR]

The elastic net is among the most widely used types of regularization algorithms, commonly associated with the problem of supervised generalized linear model estimation via penalized maximum likelihood. Its attractive properties, originated from a combination of ℓ 1 and ℓ 2 norms, endow this method with the ability to select variables, taking into account the correlations between them. In the last few years, semi-supervised approaches that use both labeled and unlabeled data have become an important component in statistical research. Despite this interest, few researchers have investigated semi-supervised elastic net extensions. This paper introduces a novel solution for semi-supervised learning of sparse features in the context of generalized linear model estimation: the generalized semi-supervised elastic net (s2net), which extends the supervised elastic net method, with a general mathematical formulation that covers, but is not limited to, both regression and classification problems. In addition, a flexible and fast implementation for s2net is provided. Its advantages are illustrated in different experiments using real and synthetic data sets. They show how s2net improves the performance of other techniques that have been proposed for both supervised and semi-supervised learning. [ABSTRACT FROM AUTHOR]

The manta ray foraging optimizer (MRFO) is a novel nature-inspired optimization algorithm that simulates the foraging strategy and behavior of manta ray groups, i.e., chain, spiral, and somersault foraging. Although the native MRFO has revealed good competitive capability with popular meta-heuristic algorithms, it still falls into local optima and slows the convergence rate in dealing with some complex problems. In order to ameliorate these deficiencies of the MRFO, a new elite chaotic MRFO, termed the CMRFO algorithm, integrated with chaotic initialization of population and an opposition-based learning strategy, is developed in this paper. Fourteen kinds of chaotic maps with different properties are used to initialize the population. Thereby, the chaotic map with the best effect is selected; meanwhile, the sensitivity analysis of an elite selection ratio in an elite chaotic searching strategy to the CMRFO is discussed. These strategies collaborate to enhance the MRFO in accelerating overall performance. In addition, the superiority of the presented CMRFO is comprehensively demonstrated by comparing it with a native MRFO, a modified MRFO, and several state-of-the-art algorithms using (1) 23 benchmark test functions, (2) the well-known IEEE CEC 2020 test suite, and (3) three optimization problems in the engineering field, respectively. Furthermore, the practicability of the CMRFO is illustrated by solving a real-world application of shape optimization of cubic generalized Ball (CG-Ball) curves. By minimizing the curvature variation in these curves, the shape optimization model of CG-Ball ones is established. Then, the CMRFO algorithm is applied to handle the established model compared with some advanced meta-heuristic algorithms. The experimental results demonstrate that the CMRFO is a powerful and attractive alternative for solving engineering optimization problems. [ABSTRACT FROM AUTHOR]

In this paper, in order to solve systems of nonlinear equations, a new class of frozen Jacobian multi-step iterative methods is presented. Our proposed algorithms are characterized by a highly convergent order and an excellent efficiency index. The theoretical analysis is presented in detail. Finally, numerical experiments are presented for showing the performance of the proposed methods, when compared with known algorithms taken from the literature. [ABSTRACT FROM AUTHOR]

In this paper, fast high-order finite difference algorithms for solving the electromagnetic scattering from the finite array of two-dimensional rectangular cavities are proposed in TE polarization. The scattering problem from the cavity array is described as coupled Helmholtz equations with transparent boundary conditions on open apertures. Second-order and fourth-order schemes for solving the coupled systems are developed in TE polarization respectively. A special technique is applied to construct a fourth-order scheme for transparent boundary conditions. Further, we propose fast algorithms which can simplify the larger global system to a small linear interface system on the apertures of cavities. Numerical experiments show the validity and efficiency of the proposed fast algorithms for solving the scattering problem with high wave numbers. [ABSTRACT FROM AUTHOR]

To schedule material supply intelligently and meet the production demand, studies concerning the material logistics planning problem are essential. In this paper, we consider the problem based on the scenario that more than one vehicle may visit each station in batches. The primary objective is to satisfy the demands in the time windows, followed by logistics planning with the minimum vehicles and travel time as the optimization objective. We construct a multi-objective mixed-integer programming model for the scenario of discrete material supply in workshops. First, we propose a hybrid heuristic algorithm combining NSGA-II and variable neighborhood search. This proposed algorithm combines the global search capability of NSGA-II and the strong local search capability, which can balance intensification and diversification well. Second, to maintain the diversity of population, we design the population diversity strategy and various neighborhood operators. We verify the effectiveness of the hybrid algorithm by comparing with other algorithms. To test the validity of the proposed problem, we have carried out research and application in a construction machinery enterprise. [ABSTRACT FROM AUTHOR]

In this paper, applying some properties of matrix inequality and Schur complement, we give new upper and lower bounds of the solution for the unified algebraic Lyapunov equation that generalize the forms of discrete and continuous Lyapunov matrix equations. We show that its positive definite solution exists and is unique under certain conditions. Meanwhile, we present three numerical algorithms, including fixed point iterative method, the acceleration fixed point method and the alternating direction implicit method, to solve the unified algebraic Lyapunov equation. The convergence analysis of these algorithms is discussed. Finally, some numerical examples are presented to verify the feasibility of the derived upper and lower bounds, and numerical algorithms. [ABSTRACT FROM AUTHOR]

One of the key tools in an organization's performance management is the goal tree, which is used for solving both direct and inverse problems. This research deals with goal setting based on a model of the future by presenting the goal and subgoal in the form of concrete quantitative and qualitative characteristics and stepwise formation of factors. A stepwise solution to a factor generation problem is considered on the basis of mathematical symmetry. This paper displays an algorithm for solving hierarchical inverse problems with constraints, which is based on recursively traversing the vertices that constitute the separate characteristics. Iterative methods, modified for the case of nonlinear models and the calculation of constraints, were used to generate solutions to the subproblems. To realize the algorithm, the object-oriented architecture, which simplifies the creation and modification of software, was elaborated. Computational experiments with five types of models were conducted, and the solution to a problem related to fast-food restaurant profit generation was reviewed. The metrics of remoteness from set values and t-statistics were calculated for the purpose of testing the received results, and solutions to the subproblems, with the help of a mathematical package using optimization models and a method of inverse calculations, were also provided. The results of computational experiments speak to the compliance of the received results with set constraints and the solution of separate subproblems with the usage of the mathematical package. The cases with the highest solution accuracy reached are specified. [ABSTRACT FROM AUTHOR]

Component Labeling, as a fundamental preprocessing task in image understanding and pattern recognition, is an indispensable task in digital image processing. It has been proved that it is one of the most time-consuming tasks within pattern recognition. In this paper, a fast quantum image component labeling algorithm is proposed, which is the quantum counterpart of classical local-operator technique. A binary image is represented by the modified novel enhanced quantum image representation (NEQR) and a quantum parallel-shrink operator and quantum propagate operator are executed in succession, to finally obtain the component label. The time complexity of the proposed quantum image component labeling algorithm is O (n 2) , and the spatial complexity of the quantum circuits designed is O (c n) . Simulation verifies the correctness of results. [ABSTRACT FROM AUTHOR]

In a complex environment, although the artificial potential field (APF) method of improving the repulsion function solves the defect of local minimum, the planned path has an oscillation phenomenon which cannot meet the vehicle motion. In order to improve the efficiency of path planning and solve the oscillation phenomenon existing in the improved artificial potential field method planning path. This paper proposes to combine the improved artificial potential field method with the rapidly exploring random tree (RRT) algorithm to plan the path. First, the improved artificial potential field method is combined with the RRT algorithm, and the obstacle avoidance method of the RRT algorithm is used to solve the path oscillation; The vehicle kinematics model is then established, and under the premise of ensuring the safety of the vehicle, a model predictive control (MPC) trajectory tracking controller with constraints is designed to verify whether the path planned by the combination of the two algorithms is optimal and conforms to the vehicle motion. Finally, the feasibility of the method is verified in simulation. The simulation results show that the method can effectively solve the problem of path oscillation and can plan the optimal path according to different environments and vehicle motion. [ABSTRACT FROM AUTHOR]

Data that have not been modeled cannot be correctly predicted. Under this assumption, this research studies how k-fold cross-validation can introduce dataset shift in regression problems. This fact implies data distributions in the training and test sets to be different and, therefore, a deterioration of the model performance estimation. Even though the stratification of the output variable is widely used in the field of classification to reduce the impacts of dataset shift induced by cross-validation, its use in regression is not widespread in the literature. This paper analyzes the consequences for dataset shift of including different regressand stratification schemes in cross-validation with regression data. The results obtained show that these allow for creating more similar training and test sets, reducing the presence of dataset shift related to cross-validation. The bias and deviation of the performance estimation results obtained by regression algorithms are improved using the highest amounts of strata, as are the number of cross-validation repetitions necessary to obtain these better results. [ABSTRACT FROM AUTHOR]

One of the main problems encountered by social networks is the cold start problem. The term "cold start problem" refers to the difficulty in predicting new users' friendships due to the limited number of links those users have with existing nodes. To fill the gap, this paper proposes a Fully Integrated Link Prediction Algorithm (FILPA) that describes the social distance of nodes by using "betweenness centrality," and develops a Social Distance Index (SDI) based on micro- and macro-network structure according to social distance. With the aim of constructing adaptive SDIs that are suitable for the characteristics of a network, a naive Bayes (NB) method is firstly adopted to select appropriate SDIs according to the density and social distance characteristics of common neighbors in the local network. To avoid the risk of algorithm accuracy reduction caused by blind combination of SDIs, the AdaBoost meta-learning strategy is applied to develop a Fully Integrated Social Distance Index (FISDI) composed of the best SDIs screened by NB. The possible friendships among nodes will then be comprehensively presented using high performance FISDI. Finally, in order to realize the "products rapidly attracting users" in new user marketing, FILPA is used to predict the possible friendship between new users in an online brand community and others in different product circles. [ABSTRACT FROM AUTHOR]

This paper proposes a multicriteria decision-making model based on a three-layer virtual internet of things (IoT) algorithm to automatically track and evaluate professional football players' performance over the Internet. The three layers were respectively related to (1) automated data reading, (2) the players' comprehensive grey relational degree calculation, and (3) the players' classification. The methodology was applied in the context of the COVID-19 pandemic to investigate the performance of the top 10 defenders (according to The Sun, an internationally renowned sports website) in the European leagues, participating in the knockout phase of the 2019–20 UEFA Champions League. The results indicate that Virgil van Dijk of Liverpool FC was the best defender, followed by Harry Maguire of Manchester United, and Sergio Ramos of Real Madrid in the second and third positions, respectively. However, this ranking contradicted that of The Sun's, which ranked these defenders in the seventh, tenth, and eighth positions, respectively. These results can help club management, coaches, and teams negotiate price positioning and future contract renewals or player transfers. [ABSTRACT FROM AUTHOR]

It is known that, to more adequately describe expert knowledge, it is necessary to go from the traditional (type-1) fuzzy techniques to higher-order ones: type-2, probably type-3 and even higher. Until recently, only type-1 and type-2 fuzzy sets were used in practical applications. However, lately, it turned out that type-3 fuzzy sets are also useful in some applications. Because of this practical importance, it is necessary to design efficient algorithms for data processing under such type-3 (and higher-order) fuzzy uncertainty. In this paper, we show how we can combine known efficient algorithms for processing type-1 and type-2 uncertainty to come up with a new algorithm for the type-3 case. [ABSTRACT FROM AUTHOR]