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Topological data analysis (TDA) is a powerful mathematical framework that extracts valuable insights about the shape and structure of complex datasets by identifying and analyzing underlying topological features, including connected components, holes, voids, and higher-dimensional counterparts. TDA achieves this by constructing a simplicial complex from the data, comprising simple shapes like points, lines, triangles, and higher-dimensional counterparts. The Vietoris–Rips complex (VRC), a widely used method, constructs simplicial complexes by measuring the distances between data points in a metric space. While the link between Intuitionistic Fuzzy Sets (IFSs) and TDA is clear through intuitionistic fuzzy distance measures, the literature has not explored the application of TDA for extracting topological features from uncertain and vague datasets using IFSs. In this paper, we propose a novel approach that leverages Intuitionistic Fuzzy Distance Measures (IFDMs) to generate the Intuitionistic Fuzzy Vietoris–Rips Complex (IFVRC) of IFSs. Specifically, we investigate the persistence of invariant relations of topological features between IFVRCs constructed using different distances, including Atanassov's Euclidean distance (l 2 I F S), Boran and Akay's distance (D), and Liu's distance measure (d L), over artificially generated IFSs. Furthermore, we demonstrate that the generation of IFVRC can be viewed as a Natural Hierarchical Clustering Algorithm (NHCA) for IFSs. Finally, we apply the proposed IFVRC as a Natural Hierarchical Clustering Algorithm to cluster intuitionistic fuzzy datasets related to Car and Building materials. Moreover, we incorporate a relatively large dataset, the GTZAN dataset from the Kaggle datasets repository, to classify music genres based on their topological information. • Topological analysis of intuitionistic fuzzy distance measures (IFDMs) have been conducted. • Utilizing IFDMs, Intuition Fuzzy Vietoris Rips Complex (IFVRC) generated. • It is shown how to extract topological features from IFVRC. • The persistence relations of topological features between IFVRCs have been investigated. • Finally, IFVRC is treated as a natural hierarchical clustering algorithm. [ABSTRACT FROM AUTHOR]

Reinforcement Learning (RL) has gained significant popularity in recent years for its ability to solve complex control problems. However, most existing RL algorithms are designed to train policies for each environment in isolation, limiting their applicability to real-world scenarios with many related environments. Recently, many multitask optimization algorithms have been proposed and successfully applied to a wide range of optimization problems. However, existing studies mainly focus on solving multiple continuous function benchmarks and overlook the potential of tailoring towards RL. In this paper, we propose a simple multitask optimization algorithm called Multitask Augmented Random Search (MARS) that trains multiple RL agents together and exploits the performance surplus from highly correlated tasks. MARS is a modification of the simple random search Augmented Random Search (ARS) algorithm, which has been shown to outperform complicated methods in solving continuous control MuJoCo environments such as Soft Actor-Critic (SAC), Proximal Policy Optimization (PPO), and Trusted Region Policy Optimization (TRPO). The experimental results also demonstrate that our proposed algorithm is more consistent in solving different instances of MuJoCo benchmark than ARS, Multifactorial Evolution Algorithm (MFEA), and Adaptive MFEA RL (AMFEARL) within the same number of training episodes. • Propose a multitask extension of ARS for training related continuous control problems. • Derived an online data-driven measurement to preempt the transfer of solutions incomplementary. • Conduct experiments on synthetic benchmarks and in the multitask Mujoco environments. [ABSTRACT FROM AUTHOR]

A series of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to handle expensive multi-objective optimization problems (EMOPs). However, the surrogate of these SAEAs is underutilized to a large extent, which limits the search efficiency of these algorithms. To be specific, existing algorithms do not sufficiently exploit the estimated solution quality information from the surrogate models during offspring generation. To address this issue, this paper proposes an SAEA framework named EXO-SAEA (EXplanation Operator based Surrogate-Assisted Evolutionary Algorithm). First, it divides the current population into two populations according to the a priori knowledge from the surrogate model. Then, for each solution in the first population, EXO-SAEA employs the SHapley Additive exPlanations (SHAP) model to estimate the contribution of each decision variable to the fitness values. After that, the Shapley values are then normalized for the offspring generation of the first population, while the second population uses generic GA operators. Two representative surrogate-assisted evolutionary algorithms are used to instantiate the proposed framework. Experimental results on the synthetic benchmark problems and three real-world problems involving six state-of-the-art algorithms demonstrate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid bio-inspired search and optimization algorithm that combines the strengths of the PB3C (Parallel Big Bang Big Crunch) and 3PGA (3 Parent Genetic Algorithm) algorithms. The hybrid algorithm employs a single population-based evolutionary search coupled with multi-population parallel processing techniques to address optimization problems. The proposed algorithm is implemented in MATLAB software. We evaluate the performance of the proposed algorithm on the CEC2021 standard test bench suite. The performance of the proposed approach is compared with that of the other nine algorithms. The comparative analysis shows that the proposed hybrid PB3C and 3PGA algorithms performed better than the other nine optimization algorithms. Furthermore, this chapter proposes an HPB3C-3PGA-based approach to evolve the near-optimal architecture of CNN. The proposed plant image classification approach is implemented in Python and compared with 12 other approaches. The proposed approach achieved an accuracy of 98.96% on the Mendeley dataset and 98.97% on the CVIP100 dataset. The proposed approach outperforms all other approaches for the plant leaf classification problem. This research significantly contributes to overcoming limitations in existing approaches, providing a robust solution for optimization problems and image classification tasks. • Proposed hybrid algorithm combines PB3C and 3PGA strengths for optimization problems. • Tested on CEC2021 benchmark functions, outperformed nine existing algorithms. • Synergy of parallel processing and advanced genetic operators drives success. • Promising solution for real-world applications requiring efficient optimization techniques. • Achieved impressive accuracy of 98.96% in plant leaf image classification. [ABSTRACT FROM AUTHOR]

This paper examines the performance of three binary metaheuristic algorithms when applied to two distinct knapsack problems (0–1 knapsack problems (KP01) and multidimensional knapsack problems (MKP)). These binary algorithms are based on the classical mantis search algorithm (MSA), the classical quadratic interpolation optimization (QIO) method, and the well-known differential evolution (DE). Because these algorithms were designed for continuous optimization problems, they could not be used directly to solve binary knapsack problems. As a result, the V-shaped and S-shaped transfer functions are used to propose binary variants of these algorithms, such as binary differential evolution (BDE), binary quadratic interpolation optimization (BQIO), and binary mantis search algorithm (BMSA). These binary variants are evaluated using various high-dimensional KP01 examples and compared to several classical metaheuristic techniques to determine their efficacy. To enhance the performance of those binary algorithms, they are combined with repair operator 2 (RO2) to offer better hybrid variants, namely HMSA, HQIO, and HDE. Those hybrid algorithms are evaluated using several medium- and large-scale KP01 and MKP instances, as well as compared to other hybrid algorithms, to demonstrate their effectiveness. This comparison is conducted using three performance metrics: average fitness value, Friedman mean rank, and computational cost. The experimental findings demonstrate that HQIO is a strong alternative for solving KP01 and MKP. In addition, the proposed algorithms are applied to the Merkle-Hellman Knapsack Cryptosystem and the resource allocation problem in adaptive multimedia systems (AMS) to illustrate their effectiveness when applied to optimize those real applications. The experimental findings illustrate that the proposed HQIO is a strong alternative for handling various knapsack-based applications. [ABSTRACT FROM AUTHOR]

Instance selection is an important preprocessing technology in data mining and machine learning. In this paper, we proposed a novel evolutionary based instance selection algorithm for big data. First, we defined a coarse granularity chromosome structure to reduce the size of search space and costs of chromosome operations(recombination and mutation, etc.). Then a stratified evolution strategy was proposed to remove the hyper parameter in classic fitness function and achieve precise control over the reduction ratio of instances. Finally, a sampling-based fitness function was proposed to reduce the time complexity. Experimental results shown that our new algorithm is efficient to complete the instance selection task on data set with millions of instances in minutes-level. The 10-fold cross-validation also proved that the selection results on many datasets have high nearest neighbor classification accuracy. • We proposed a distributed coarse granularity chromosome structure to reduce search space and ensure load balance. • We proposed a stratified evolution strategy to transform multi-objective to multiple single-objective tasks. • We proposed a sampling-based fitness function with less time complexity [ABSTRACT FROM AUTHOR]

This paper introduces a novel meta-heuristic optimization algorithm named Clustering Wavelet Opposition-based Marine Predators Algorithm (CWOMPA) to address some limitations present in the well-established Marine Predators Algorithm (MPA). CWOMPA incorporates three key strategies: a fuzzy clustering approach for escaping local optima, using wavelet basis function-based impact coefficient adjustment for elites to prevent premature convergence, and finally an adaptive opposition-based learning strategy for maintaining population diversity. Compared with some recent meta-heuristic algorithms, extensive evaluations conducted affirm that CWOMPA achieves the best Friedman rank, 4.30 and 1.95 respectively, on 23 benchmark functions and the CEC 2017 benchmark set. Not only does CWOMPA demonstrate significant effectiveness on six constrained problems from the CEC 2020 real-world benchmarks, but also when applied to 14 medical datasets, it gains superior Friedman ranks in terms of selected features, classification accuracy, F-Score, and objective function value, 2.5, 2.25, 2.96, 2.93 respectively, outperforming other common methods. Finally, CWOMPA exhibits the highest classification accuracy across all datasets and the best F-Score performance in nine datasets compared to traditional feature selection algorithms. The obtained results reveal that CWOMPA is a powerful and versatile optimization algorithm with significant potential in various real-world applications, including feature selection for medical datasets. [Display omitted] • An improved MPA called CWOMPA is proposed to improve the optimization performance. • CWOMPA incorporates FCM, wavelet basis function, and AOBL to enhance the MPA. • CWOMPA can be an effective tool in addressing feature selection challenges. [ABSTRACT FROM AUTHOR]

In recent years, there have been notable advancements in the theoretical research of existing meta-heuristic algorithms and in the development of novel algorithms. However, the research on the definition and construction methods of the so-called "mutation" operator, a concept widely used in existing meta-heuristic algorithms, remains notably insufficient. In addition, how to directly construct a meta-heuristic algorithm under the guidance of stability and invariance analysis continues to pose a challenge that has yet to resolve. This paper carefully investigates and rigorously defines the concepts of "mutation operator" and "mutation-driven meta-heuristic algorithm" as found in the literature. According to these definitions, a novel meta-heuristic algorithm, called Multi-direction guided Mutation-driven Stable swarm Intelligence algorithm with Translation and Rotation invariance (M2SITRI) is proposed, and the corresponding mutation operator is carefully designed based on the analysis of translation invariance, rotation invariance, order-1 stability, and order-2 stability. The stability and invariance properties of the M2SITRI algorithm are validated through numerical simulations, and its superior solving performance is demonstrated by benchmarking it against several other well-known existing meta-heuristic algorithms across a variety of test problems. • Mutation operator and mutation-driven meta-heuristic algorithm concepts are defined. • A novel translation and rotationally invariant meta-heuristic algorithm termed M2SITRI is proposed. • The invariance and stability properties and the excellent solving performance of M2SITRI algorithm are verified. [ABSTRACT FROM AUTHOR]

In this paper, we present the Generalized Packing Problem (GPF) in graphs in such a way that all other packing problems in graphs presented in the literature correspond to particular instances of it. We find the first class of graphs where one of these previously defined packing problems is tractable and another is NP-hard. We show that GPF remains linear-time solvable in strongly chordal graphs and, when reduced to instances with bounded neighborhood capacities (M-GPF), remains polynomial-time solvable in bounded clique-width graphs. To design specific algorithms that take advantage of the structural properties of certain subclasses of bounded clique-width graphs, we obtain technical results related to the behavior of the new packing parameter under several graph operations. In particular, we present a linear-time algorithm to solve GPF in block graphs and a polynomial-time algorithm to solve M-GPF in P 4 -tidy graphs. [ABSTRACT FROM AUTHOR]

Aiming at the existing problems that object tracking algorithm fails to track under the influence of occlusion conditions, the paper has improved the Kernel Correlation Filter algorithm. Firstly, the occlusion condition has been added to the Kernel Correlation Filter algorithm. If there is no occlusion, the Kernel Correlation Filter algorithm has used for object tracking. If there is occlusion, the improved algorithm based on Unscented Rauch--Tung--Striebel Smoother has been used. Secondly, the predicted position of the object has been feedback to the Kernel Correlation Filter algorithm. Finally, the combination of adaptive multi-model has been realized by combining the color histogram with the Kernel Correlation Filter algorithm, and the sparse representation method has been introduced into the training process to heighten the stability of the proposed object tracking algorithm. The experimental results using the proposed method on the OTB-2013 dataset can express that the proposed object tracking algorithm can reduce the occlusion interference in the object tracking process, and ameliorate the accuracy rate and success rate. [ABSTRACT FROM AUTHOR]

With the increase in digital communication, images have become indispensable media for carrying obscured communication these days. Unraveling the presence or details of obscured transmission also known as steganography is termed as steganalysis. Passive steganalysis focuses only on classification of object as clean or adulterated. Finding the length of payload and regions where the message is hidden, in a stego object is a challenging task and forms a part of Active or Forensic Steganalysis. Many researchers have worked towards finding the length of message, but not much work has been done on finding region where message is hidden. This motivated us to work towards finding the region of payload in stego images. Hence this paper proposes a novel technique for detection of payload regions in a stego image combining Denoising Autoencoder with Local Binary Pattern (LBP) Operator. Denoising Autoencoder is used to learn inter relationships between pixels of an image for estimating cover image from the given stego image. LBP operator capable of capturing local inter-relationships within neighborhood coordinates, have been used to compare corresponding blocks in input stego image and estimated cover image for unraveling payload locations. The major advantage of the proposed technique is that it is universal and it does not require cover image or knowledge about steganographic algorithm used. Accuracy of 96.5% and 97.7% respectively was achieved in the experimental study conducted with spatial and JPEG domain images from BOSSBase dataset. [ABSTRACT FROM AUTHOR]

Extracting relevant information from a large amount of data is a challenging task. Automatic text summarization is a potential solution for obtaining this information. In this paper, a nature inspired swarm intelligence-based algorithm viz. firefly algorithm for multi-document text summarization is proposed. A new fitness function consisting of three features viz. topic relation factor, cohesion factor and readability factor is utilized. The experiments are performed on datasets from Document Understanding Conference i.e. DUC-2002, DUC-2003 and DUC-2004. The performance of the algorithm has been evaluated using ROUGE score. The performance of the proposed algorithm is compared with some other nature inspired ones such as particle swarm optimization (PSO) and genetic algorithm (GA). The performance of the proposed algorithm outperforms the other adopted ones. [ABSTRACT FROM AUTHOR]

The paper is dedicated to the problem of computational urban science to estimate availability of urban services with limited capacity. The applied side of the problem is related to the need to assess the provision of citizens living in residential buildings in the city with a sufficient number of places of city services, such as kindergartens, schools, clinics, and others. This information is necessary for the qualitative management of the socio-economic and spatial development of the city. The computational complexity of the problem is associated with a great variety of factors that determine the variability of the citizens' preference function when accessing a particular service. It is further complicated by the presence of self-organization processes within this system, which lead to the actual final satisfaction of the citizens' service demand but with significant deviations from the required parameters of service availability established in regulatory documents or the optimal state of the system. Since validation of such models for large cities is difficult or impossible, this requires the development of new computational approaches for assessing the provision of the population with urban services based on the optimal state of the system to improve the efficiency of city management. A proposed algorithm is based on sequential aggregation of the demand from buildings for each service object and redistribution of unserved demand between other services objects with free capacity. This approach avoids high computational complexity which arises when using nonlinear programming to calculate buildings' provision of services in large cities. The article presents the algorithm for calculating the provision of the population and demonstrates its application on the data of the city of St. Petersburg. [ABSTRACT FROM AUTHOR]

Automatic Voltage Regulator (AVR) is fabricated to sustain the voltage level of a synchronous generator spontaneously. Several control strategies have been introduced into the AVR system with the aim of gaining a better dynamic response. One of the most universally utilized controllers is the Proportional–Integral–Derivative (PID) controller. Despite the PID controller having a relatively high dynamic response, there are still further possibilities to improve in order to obtain more appropriate responses. This paper designed a sigmoid-based PID (SPID) controller for the AVR system in order to allow for an accelerated settling to rated voltage, as well as increasing the control accuracy. In addition, the parameters of the proposed SPID controller are obtained using an enhanced self-tuning heuristic optimization method called Nonlinear Sine Cosine Algorithm (NSCA), for achieving a better dynamic response, particularly with regards to the steady-state errors and overshoot of the system. A time-response specifications index is used to validate the proposed SPID controller. The obtained simulation results revealed that the proposed method was not only highly effective but also greatly improved the AVR system transient response in comparison to those with the modern heuristic optimization based PID controllers. • A Sigmoid PID (SPID) controller according to Ateş et al. (2015) is first-time designed for the Automatic Voltage Regulator. • The proposed optimization method called Nonlinear Sine Cosine Algorithm (NSCA) is utilized to tune the SPID controller. • The proposed NSCA-SPID controller offers control dynamics and accuracy. • NSCA-SPID is also capable of handling the unpredicted disturbances or the uncertainties in the system. [ABSTRACT FROM AUTHOR]

To improve the performance of data fusion in wireless sensor networks, based on the suboptimal network powered deep learning model, a data fusion algorithm that combines a stacked autoencoder (SAE) and a clustering protocol is proposed. The fusion algorithm for sensor data is investigated by suboptimal network powered deep learning to analyse the fusion processing at the sensor measurement level and the fusion processing at the localization result level. This paper establishes a suboptimal network powered deep learning model and derives its weighted least squares solution algorithm based on this model, also, the best linear unbiased estimation method for fusion of vibration sensor and vision sensor localization results is adopted. First, to overcome the filter divergence caused by rounding error, reduce the computational complexity, ensure the non-negativity of the covariance, and improve the convergence speed of the filter, the target state volume after fusion using the proposed model is closer to the real state volume of the target compared to the state volume of each local sensor detection, verify the validity of the algorithm was increased. [ABSTRACT FROM AUTHOR]

The current study investigates the Intelligent Water Drops (IWD) metaheuristic algorithm to construct and produce good quality solutions for the university examination timetabling problem (UETP). The IWD is a population-based metaheuristic that simulates the dynamic of the river systems. The main motivations for investigating IWD algorithm for examination timetabling problem is the ability to explore the search space effectively. The main drawback of IWD algorithm is like other population-based algorithm in exploitation process where it is very efficient scanning several search space niches, but it is unable to drilling down in each niche to which it navigates. In this paper we propose a hybrid approach based on IWD and locale search algorithm to improve the exploitation of IWD algorithm. The experimental results demonstrated that the proposed algorithm (i.e., Hybrid IWD) obtained best results in three datasets when comparing with the best-known results performed by the swarm intelligent approaches. Finally, the proposed algorithm achieved one best results in comparison with the other metaheuristic approaches. [ABSTRACT FROM AUTHOR]

Decision tree induction is a simple, however powerful learning and classification tool to discover knowledge from the database. The volume of data in databases is growing to quite large sizes, both in the number of attributes and instances. Some important limitations of decision trees are instability, local decisions, and overfitting for this extensive data. The simple, effective and non-convergence nature of the African Buffalo Optimization (ABO) algorithm makes it suitable to solve complex optimization problems. In this paper, we propose the African Buffalo Optimized Decision Tree (ABODT) algorithm to create globally optimized decision trees using the intelligent and collective behaviour of African Buffalos. The modified African Buffalo optimization algorithm is used to create efficient and optimal decision trees. To evaluate the efficiency of the proposed African Buffalo Optimized Decision Tree algorithm, experiments are performed on 15 standard UCI learning repository datasets that are of various sizes and domains. Results show that the African Buffalo Optimized Decision Tree algorithm globally optimizes decision trees, increases accuracy and reduces the size of a decision tree. These optimized trees are stable and efficient than conventional decision trees. [ABSTRACT FROM AUTHOR]

One of the most important phases in text processing is stemming, whose aim is to aggregate all variations in a word into one group to aid natural language processing. The morphological structure of the Arabic language is more challenging than that of the English language; thus, it requires superior stemming algorithms for Arabic stemmers to be effective. One of the challenges is the irregular broken plural, which has been a problematic issue in Arabic natural language processing that affects the performance of Arabic information retrieval and other Arabic language engineering applications. Several studies have attempted to develop solutions to irregular plural problems, but the challenge remains, especially in extracting correct Arabic root words. In this paper, the broken plural rule (BPR) algorithm introduces new solutions to solve the problem in which an existing root-based method cannot extract correct roots by using their proposed rules. The BPR algorithm introduces several rules (main rules and subrules) to extract the correct roots of the Arabic irregular broken plural words. To evaluate the effectiveness of the BPR algorithm, we extracted roots from an Arabic standard dataset and applied the BPR algorithm as an enhancement to a root-based Arabic stemmer, ISRI. The obtained results from both evaluations showed encouraging results: (i) Only a few numbers of incorrect roots were stemmed on the large-sized Arabic word dataset. (ii) The enhanced root-based Arabic stemmer, ISRI + BPR, exhibited the best performance compared with the original ISRI stemmer and a well-known Arabic stemmer, ARLS 2. Thus, the proposed BPR algorithm has solved some of the irregular broken plural problems that eventually increase the performance of a root-based Arabic stemmer. [ABSTRACT FROM AUTHOR]

The accuracy and stability of navigation algorithms are crucial preconditions for underwater gravity matching aided navigation. To improve the matching accuracy and robustness of the matching algorithm, this paper presents a novel gravity matching navigation algorithm based on multiscale search and Hadamard transformed difference. The Hadamard transformation process was first time introduced in the new algorithm for the Walsh–Hadamard kernel function with the property of energy conservation. The gravity measurement sequences could be converted to the Hadamard domain; thus, the difference in numerical values, tendency, and spatial structure of the gravity measurement sequence were also a focus in the new algorithm, whereas only gravity statistical values were considered in classical matching algorithms. Therefore, with the proposed algorithm, the number of measurements necessary for matching can be effectively reduced, while improving the matching accuracy and success rate. In addition, a multiscale neighborhood search strategy based on contour constraints was designed to improve the matching efficiency, whereas a point-by-point global search was widely used in classical matching algorithms. Marine gravity maps of the South China Sea were used to construct the simulation tests. Simulation results show that under various conditions, the new algorithm has lower requirements for the number of measurements, measuring accuracy, and matching areas while exhibiting a higher navigation accuracy, matching success rate, and matching efficiency. Thus, the proposed algorithm could provide a new option for future practical applications of gravity matching aided navigation. • The Hadamard transformation was introduced in the gravity matching aided navigation. • A multiscale neighborhood search strategy based on contour constraints was designed. • The proposed method has better navigation accuracy, robustness, and efficiency in simulation tests. • The new algorithm has lower requirements for the number of measurements, measuring accuracy and matching areas. [ABSTRACT FROM AUTHOR]

In this paper, we propose a unified and simple trajectory planning-based control strategy for the n -link (n ≥ 2 ⋂ n ∈ Z) vertical underactuated manipulator (VUM) with an underactuated joint. The proposed method does not need to partition the entire motion space like the traditional methods, and can quickly realize the control objective of swinging the endpoint of the system up from the vertical downward starting position and stabilizing it at the vertical upward ending position. The trajectory planned for each active link includes two stages. The first stage is to make the first active link and passive link reach the intermediate states, and other active links reach the ending states. The second stage is to move the first active link and the passive link to the ending states, while other active links keep at the ending states. Considering the above targets of two stages, the trajectory of each active link is designed and the trajectory parameters are optimized via utilizing the differential evolution algorithm. Then, the tracking controllers and stabilization controllers are designed to track the two-stage trajectories and to stay the endpoint at the ending position. Finally, the numerical simulations are carried out to show the feasibility and superiority of the proposed method. • A unified method is proposed for a class of n-link underactuated manipulators. • The designed time switching condition is easier to be satisfied. • The proposed method has better control effect without partitioning the motion space. [ABSTRACT FROM AUTHOR]

One of the most important part of a stand-alone photovoltaic system is energy management. This part uses several sensors to measure solar radiations, load power and battery energy to determine operation of the system components. Reduction the number of sensors and optimization of energy management system are two key steps to improve the efficiency and reliability of the system without additional cost. Hence, in this paper, a comprehensive energy management algorithm along with a complementary algorithm to estimate radiation under uniform and partial shading conditions is proposed. According to the system state, the energy management algorithm commands to activate/ deactivate a part or entire of photovoltaic system which can increase the system lifetime and thus increase the system reliability. Another feature of the proposed algorithm is a new mode in the absence of radiation for parallelizing and sharing the battery's current to the load by unidirectional and bidirectional converters. Furthermore, a sensor-less complementary algorithm to determine the amount of radiation and also the partial shading condition is proposed. To prove the feasibility of the proposed algorithms, the system was simulated and constructed. The simulation and experimental results are found closely comparable and confirm the effectiveness of the proposed methods. • New energy management algorithm. • New sensorless approach. • New control strategy. [ABSTRACT FROM AUTHOR]

Visual tracking is one of the pre-eminent tasks in several computer vision applications. Particle filter (PF) is extensively used in visual tracking for intelligent surveillance system applications, hugely significant. But the re-sampling procedure of PF will result in sample impoverishment, which will affect the precision of tracking simultaneously. In this paper, a new tracking technique, called Trigonometric Particle Filter (TPF), based on PF optimized by Sine Cosine Algorithm (SCA), which contains trigonometric sine and cosine functions, is proposed. An enhanced method for improving the number of target particles used in a Sine Cosine Algorithm for trigonometric particle filter includes SCA ahead of the re-sampling step. This step ensures a more extensive particle set Achievement of the proposed TPF tracker is inspected and assessed on Visual Tracker Benchmark (VOT) databases. The proposed TPF tracker is compared with evolutionary-based methods like the Spider monkey optimization assisted PF (SMO-PF), Firefly algorithm-based PF (FAPF) method, Particle swarm optimization-based PF (PSO-PF) and Particle filter, recent four correlation filter-based trackers, and also with other ten state-of-the-art tracking methods. We demonstrate that visual tracking using TPF delivers additional consistent and proficient tracking outcomes than compared trackers. • A new tracking technique, called TPF, based on PF optimized by SCA, is proposed. • The SCA replaces the normal re-sampling process and improves the PF. • TPF compared with evolutionary-based approaches and 10 other leading-edge methods. • TPF visual tracking is more consistent and proficient than other trackers. [ABSTRACT FROM AUTHOR]

The multivariable modeling of a servo actuating system consisting of multi-field multi-armature direct current (MFMADC) machine is extracted and a novel discrete time nonlinear algorithm is proposed for the corresponding system. The proposed control algorithm demonstrates robustness against modeling uncertainty and by utilizing its novel mathematical structure, decouples the dynamical interactions of the connected motors. The main contribution of this paper is the proposition of a new decoupling control algorithm that in which, the driving (commanding) voltages of the connected driving motors are extracted separately and independently using the Lyapunov principle in discrete time. In fact, the obtained coupled stabilizing convex inequalities of the controlling voltages, resulting from the evaluation of the Lyapunov functions, are analytically decoupled using elementary matrix operations. Consequently, each motor now has the capability to perform its controlling task (position control or torque control) with asymptotic stability and robustness against uncertainty. To assess the performance of the proposed controlling algorithm and its verification, a MFMADC machine is attached to a harmonic drive reducer (HDR) whose flex spline and circular spline are fabricated using viscoelastic polyesters PLA and thermoplastic PLA, respectively. A number of experiments are conducted where in the first test, the MFMADC is controlled in only-position mode while in the second test, the MFMADC is controlled in simultaneous position-torque control mode. Comparative assessments confirm that the MFMADC technology is needed when a high precision tracking of position, under high frequency disturbances, is desired. • Offering a New Affordable Solution for Precision Servo Applications of PMDC Machines. • Proposing a New Nonlinear Decoupling Control for Simultaneous Position-Torque Control. • Analytical Robust Stability of Feedback System versus Broad-Band Impedance Disturbance. • Applicability of Proposed High-Precision Servo Actuator Using Regular Micro-Processors. [ABSTRACT FROM AUTHOR]

Schedule-based fieldbus is applied widely in industrial control. Building schedule table (ST) can affect the time-critical performance for communication greatly. Communication jitters can cause some unexpected results in bus traffic scheduling and longer jitters can even cause severe delay in time-critical application. Communication jitter and evenness in arrangement of variables to be scheduled in micro-cycles in ST were two important issues dealing with the performance. An algorithm to build schedule table based on numerical co-prime method was proposed in this paper. Communication jitter in ST was investigated. Concept dealing with rank of ST was introduced. Numerical co-prime method to evaluate rank of ST was proposed. The algorithm and procedure to build schedule table based on the proposed numerical co-prime method were presented and exemplified in detail. Performance analysis and comparison of the proposed algorithm to HCF/LCM method was presented. It was proved that the proposed algorithm can reduce communication jitter to its minimum value and the longest time consumed to schedule variable in one micro-cycle, and greatly reduce the possibility the time consumed to schedule variables exceeded micro-cycle. • Communication jitter in schedule table (ST) with its impact was investigated. • Concept dealing with rank of ST was introduced. • Numerical co-prime method to evaluate rank of ST was proposed. • The algorithm and procedure to build schedule table based on the proposed method was presented • Performance analysis and comparison of the proposed method was presented [ABSTRACT FROM AUTHOR]

The existing methodologies used for multilevel thresholding are not efficient in terms of both accuracy and computation time. Two-dimensional histogram-based techniques are better in terms of accuracy while they are computation intensive. The slime mould algorithm used for optimization mainly depends on the best leader and two randomly pooled slime moulds from the population, which leads to poor exploitation with more iteration to converge. These problems are solved in this paper by introducing a novel normalized square difference (NSD) based multilevel thresholding technique using a leader slime mould algorithm (LSMA). The contributions are many fold – i) a NSD based multilevel thresholding method is proposed using the gray level and normalized square difference (GLNSD) 2-D histogram with reduced computation; ii) LSMA is suggested; iii) 23 classical and 6 modern composition test functions from the IEEE CEC 2014 test suite are considered for evaluation of LSMA; iv) experiments on multispectral images are presented. The benefits are – i) reduces computations, ii) improves accuracy. The qualitative metrics used for analysis include – search history, trajectory, and average fitness history. Scalability analysis and statistical analysis (using Friedman's mean rank test) are presented. The proposal is compared with state-of-the-art techniques and found better. [ABSTRACT FROM AUTHOR]

The continuous improvement of sequencing technologies has been paralleled by the development of efficient algorithms and data structures for sequencing data analysis and processing. Suffix array is one of data structures that are used to construct the Burrows-Wheeler transform (BWT) for long length genomes. Building a suffix array itself is an expensive-resource process since the computations are dominant by sorting suffixes in a lexical order. Most of the suffix array construction algorithms consider the general and integer alphabets without utilizing special cases for fixed-size ones such as DNA alphabets. In this paper, we exploit the nature of four-sized DNA alphabets and utilize their predefined lexicographical ordering in order to construct suffix arrays for genomic data correctly and efficiently. The suffix array construction algorithm for DNA alphabets is evaluated using three real data sets with different lengths ranging from small E-coli genome to long length Homo sapiens GRCh38.p13 chromosomes. For long length genomes, their corresponding sequence is divided into parts (i.e. reads) with a minimum overlap length, the suffix array is computed for each part separately, and finally all partially computed arrays are merged together into a single one. We studied the effects of varying the reads/overlap lengths on the running time of the proposed suffix array construction algorithm and conclude that the minimum overlap length should be equal to the average length of the longest common prefix between the adjacent parts. [ABSTRACT FROM AUTHOR]

K-anonymity is the most widely used approach to privacy preserving microdata which is mainly based on generalization. Although generalization-based k-anonymity approaches can achieve the privacy protection objective, they suffer from information loss. Clustering-based approaches have been successfully adapted for k-anonymization as they enhance the data quality, however, the computational complexity of finding an optimal solution has shown as NP-hard. Nature-inspired optimization algorithms are effective in finding solutions to complex problems. We propose, in this paper, a novel algorithm based on a simple nature-inspired metaheuristic called Black Hole Algorithm (BHA), to address such limitations. Experiments on real data set show that data utility has been improved by our approach compared to k-anonymity, BHA-based k-anonymity and clustering-based k-anonymity approaches. [ABSTRACT FROM AUTHOR]

Data reduction has gained growing emphasis due to the rapidly unsystematic increase in digital data and has become a sensible approach to big data systems. Data deduplication is a technique to optimize the storage requirements and plays a vital role to eliminate redundancy in large-scale storage. Although it is robust in finding suitable chunk-level break-points for redundancy elimination, it faces key problems of (1) low chunking performance, which causes chunking stage bottleneck, (2) a large variation in chunk-size that reduces the efficiency of deduplication, and (3) hash computing overhead. To handle these challenges, this paper proposes a technique for finding proper cut-points among chunks using a set of commonly repeated patterns (CRP), it picks out the most frequent sequences of adjacent bytes (i.e., contiguous segments of bytes) as breakpoints. Besides to scalable lightweight triple-leveled hashing function (LT-LH) is proposed, to mitigate the cost of hashing function processing and storage overhead; the number of hash levels used in the tests was three, these numbers depend on the size of data to be de-duplicated. To evaluate the performance of the proposed technique, a set of tests was conducted to analyze the dataset characteristics in order to choose the near-optimal length of bytes used as divisors to produce chunks. Besides this, the performance assessment includes determining the proper system parameter values leading to an enhanced deduplication ratio and reduces the system resources needed for data deduplication. Since the conducted results demonstrated the effectiveness of the CRP algorithm is 15 times faster than the basic sliding window (BSW) and about 10 times faster than two thresholds two divisors (TTTD). The proposed LT-LH is faster five times than Secure Hash Algorithm 1 (SHA1) and Message-Digest Algorithm 5 (MD5) with better storage saving. [ABSTRACT FROM AUTHOR]

Cloud Computing is a robust model that allows users and organizations to purchase required services per their needs. The model offers many services such as storage, platforms for deployment, convenient access to web services, and so on. Load Balancing is a common issue in the cloud that makes it hard to maintain the performance of the applications adjacent to the Quality of Service (QoS) measurement and following the Service Level Agreement (SLA) document as required from the cloud providers to enterprises. Cloud providers struggle to distribute equal workload among the servers. An efficient LB technique should optimize and ensure high user satisfaction by utilizing the resources of VMs efficiently. This paper presents a comprehensive review of various Load Balancing techniques in a static, dynamic, and nature-inspired cloud environment to address the Data Center Response Time and overall performance. An analytical review of the algorithms is provided, and a research gap is concluded for the future research perspective in this domain. This research also provides a graphical representation of reviewed algorithms to highlight the operational flow. Additionally, this review presents a fault-tolerant framework and explores the other existing frameworks in the recent literature. [ABSTRACT FROM AUTHOR]

In this paper, the event-triggered adaptive fault tolerant tracking control for the strict-feedback nonlinear system with mismatched unknown parameters, external disturbances and actuator faults is addressed. A novel adaptive fault tolerant mechanism is presented to overcome the difficulty arising from the actuator faults and mismatched parameters, where the parameters estimators are generated from discrete state governed by the state dependent event-triggered mechanism. Furthermore, by introducing the auxiliary dynamic, it is shown that all the signals of the system are ultimately bounded and the asymptotical output tracking error is within a small residual set including origin which can be modified arbitrarily small by tuning the control coefficients. Based on the presented adaptive fault tolerant mechanism, the robust event-triggered mechanism and adaptive algorithm are further developed and the boundedness of all the variables of the system is shown. What is more, it is proved that both of the presented event-triggered mechanisms are Zeno-free. Finally, the control effectiveness of the proposed fault tolerant strategy is discussed and verified by a numerical example and the single-link manipulator. • The fault tolerant scheme over the event-triggered network is proposed. • The adaptive estimators for mismatched parameters are continuous and event-triggered. • The auxiliary dynamic is proposed to compensate the measurement error without ISS assumption. [ABSTRACT FROM AUTHOR]

Traffic sign detection is of great importance to the development of intelligent vehicles. In this paper, we propose a modified SSD algorithm called FFSSD to the traffic environment. Firstly,we integrate low and high level features together in order to enhance the accuracy of tiny instances. Besides, we insert a feature enhancement module into it and the original SSD loss was altered by focal loss, which is designed to solve the problem of uneven sample distribution. We evaluate the algorithm on GTSDB (The German Traffic Sign Detection Benchmark dataset). The experimental result demonstrates that our algorithm has a great achievement. Compared with some up-of-date methods, it also achieves higher correctness in the traffic field. [ABSTRACT FROM AUTHOR]

This study present a method to calculate first and second order displacement sensitivity based on Epsilon algorithm and improved Neumann series. In this paper, analytical expressions of first and second-order sensitivity are derived. They are then decomposed into vectors using the improved Newman series method. Finally, Epsilon is used to calculate the result. The method which is suitable for complex structure has the character of fast calculation and high efficiency. The high precision and fast convergence of the proposed method are proved by two numerical examples. The study show that the proposed method has engineering application value. [ABSTRACT FROM AUTHOR]

This paper studies a projective contraction algorithm for solving semidefinite programming problems. The interior point algorithm is a classic algorithm for solving semidefinite programming problems. It can be divided into feasible and infeasible algorithms according to whether the initial point is feasible or not. The feasible interior point algorithm needs to use a self-dual strategy to find strictly feasible points. Although the infeasible interior point algorithm does not require the initial point to be strictly feasible, the theoretical analysis of the algorithm is very complicated. Compared with the interior point algorithm, the projection contraction algorithm has no requirement on the initial point, and the algorithm analysis is much simpler. The optimal condition of the semidefinite programming problem can be rewritten as a projection equation, which is then solved by the projection contraction algorithm. We propose a new projection contraction algorithm based on different descent directions to solve the semidefinite programming problem. Further, under the framework of the projection contraction algorithm, an improved algorithm is proposed, that is, changing the descending direction by solving the linear equation system, thus avoiding the high-dimensional inversion operation so that the algorithm can solve the large-scale semidefinite programming problem. Numerical experiments show that the algorithm is feasible and the improved algorithm is competitive with the interior point algorithm. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless communication technology, new wireless applications and services are exploding. These new applications and services usually need wider bandwidth and higher speed. The voice of people calling for a fast and convenient network is getting stronger and stronger, and the wireless network must be the main development direction of the network in the future world. In order to make the development of wireless communication technology not limited by limited resources, resource optimization design has always been a research hotspot of scientists. Among them, the optimal resource algorithm can get the theoretical global optimal solution, while the sub-optimal resource allocation algorithm, as a supplement to the former, can be used to reduce costs and improve efficiency when the system structure is too complex. In this paper, a resource optimization method based on sparse clustering algorithm is proposed, and the matching relationship between spectrum resources and the services carried by cognitive terminals is established, so that the effectiveness of collaboration and cognitive systems is verified. [ABSTRACT FROM AUTHOR]

Text classification is a technique that automatically classifies and labels text according to certain rules, and is widely used in sentiment analysis, intelligent recommendation systems and intelligent question and answer systems. Deep learning-based text classification methods can automatically identify and extract features in text that are useful for classification, so that it can analyse the text content directly, saving a lot of labour costs required for manual feature extraction. In this paper, the TF-IDF algorithm and the input structure of bidirectional LSTM was modified. Specifically, we rewrote the TF-IDF formula and processed the texts with a sliding window. The word2vec vector was used as the word embedding layer, and the text features were extracted using a combination of bidirectional LSTM and Text-CNN for classification prediction. Bidirectional LSTM treats texts as sequences to grasp information as a whole, while Text-CNN can extract local important features at the sentence level. As a result, good accuracy was achieved. [ABSTRACT FROM AUTHOR]

Collaborative filtering is the most widely used and successful recommendation technology in recommendation systems. In view of the problem that the effect of collaborative filtering technology depends on an accurate similarity measurement method and its poor scalability can not deal with big data, in the information age of computer development, all undertakings are developing rapidly. English has long been an important tool for people to communicate with each other, but due to the limitation of learning mode and environment, the level of Chinese oral English is generally low. The cloud computation-based oral English learning system is designed. Users can learn oral English knowledge, simulate oral English testing and establish exclusive dictionaries through mobile terminals. According to the specific learning situation of Chinese English learners, a flexible and expandable virtual interactive platform is constructed through virtual technology and artificial intelligence, and multi-dimensional virtual scenes and intelligent AI roles are designed to realize the communication and learning of AI roles in different scenarios and improve learners' oral ability and language sense knowledge. However, compared with the traditional self-help learning method, there are huge changes. Our modern English self-help learning has basically got rid of the limitation of paper books, and most of it is carried out through the computer network. [ABSTRACT FROM AUTHOR]

With the improvement of our modernization level and the development of urbanization, the phenomenon of large crowds gathering is very common, which brings great hidden danger to public safety. Crowd counting plays a very important role in the field of intelligent video surveillance. It can estimate the total number of people according to the image and provide real-time warning, which can effectively avoid the occurrence of safety accidents. In this paper, in order to build an efficient lightweight crowd density estimation model, we proposed a dilated and depthwise separable convolution network. In the network, we build a dilated and depthwise separable convolution module, which improves the receptive field of the convolution kernel without increasing network parameters. Meanwhile, sparse convolutional kernels are used to extract sparse features, and compact convolutional kernels are used to extract more dense features, thus improving the efficiency of the convolutional kernels. Through experimental comparison on SHT Part A data set, the accuracy of the proposed method is 2.6% higher than that of MCNN, and the parameters are 7.6% less. [ABSTRACT FROM AUTHOR]

The accomplishment of Exascale systems in the next few years will pose the need for deep hierarchical parallelism and a high degree of heterogeneity, in addition to the expected component failures. Moreover, it will be constrained by energy limitation and power consumption. As a result, Exascale scheduling algorithms need to be robust in balancing the workload consistently, reliably and efficiently amongst the processors. It is also required to achieve the lowest overhead complexity, and, at the same time, decrease the communication costs as low as possible. In this paper, a task scheduler framework is introduced. The framework consists of three parts, the first of which is the resource monitor, the second the task scheduler and the third the dispatcher. The resource monitor explores the resources in the system dynamically, collects the computing resources metadata, and updates the metadata in a continuous manner. The task-scheduler schedules the tasks based on an improved version of the Heterogeneous Earliest Finish Time (HEFT) heuristic, a directed acyclic graph (DAG) scheduling algorithm. The dispatcher receives tasks list from scheduler to allocate tasks on a resources based on HEFT insertion method and constrained. The HEFT algorithm is compatible efficiently for heterogeneous systems and it improves operations without increasing the time complexity. In this paper, our improvement for HEFT algorithm is introduced. The improved algorithm is based on two steps: a static step which uses various schemes to prioritize the nodes (tasks) and a dynamic one. The dynamic step has the ability to allocates tasks that are scheduled statically and is able to receive and allocate new tasks dynamically. The algorithm we named: Dynamic Variant Rank HEFT algorithm (DVR HEFT), is implemented. A Random job generator is implemented to conduct a comparison study to evaluate DVR-HEFT algorithm against some of the state-of-the-art algorithms, specifically HEFT, PEFT, and Lookahead. Our results indicate that DVR-HEFT is able to reduce the scheduler's makespan in HEFT algorithm without increasing the algorithm's time complicity. [ABSTRACT FROM AUTHOR]

Medical images constitute important information that clinicians need to diagnose and make the suitable treatment decisions. The diagnostic process extremely involves the image visual perception. Unfortunately, the possibility of error existence in perception is not acceptable as it mainly affects the patients' lives. Image enhancement improves the visual quality of image, helps the clinician in his decision and thus saves the patients' lives. Histogram is a common tool for improving contrast in medical imaging. It recovers the lost contrast by redistributing the image brightness values that unfortunately may generate undesirable artifacts. Therefore, researchers developed the histogram-based algorithms to overcome this problem. This paper presents a comprehensive study of many histogram-based algorithms. We utilized the powerful MATLAB package to analyze the enhancement performance of these histogram-based algorithms. Moreover, this paper quantitatively compares the results and thus evaluates their performance by three metric parameters, which are the mean square error, standard deviation, and the peak signal to noise ratio. [ABSTRACT FROM AUTHOR]

Course timetabling problems are challenging, laborious and repetitive work in the universities. However, in many universities this repetitive works was still carried out manually. It is because there are so many constraints that must be considered either from the students and lecturers' requirement or the infrastructure such as room availability. Therefore, to automate the process of timetabling is hard problem. Scientifically, in the literature, course timetabling optimization is one of non-deterministic polynomial problems, usually abbreviated as NP-hard problem. For NP-hard problem, there is not any exact algorithm known could solve the problem within polynomial-time. The state-of-the-art methods for solving the problem are approximation algorithms that are mainly meta-heuristics. This paper presents a new approach, namely, hyper-heuristics as opposed to meta-heuristics, to cope with the need of intensive problem specific parameter tuning in meta-heuristics approach. The algorithms employed within hyper-heuristic approach presented in this paper are tabu search hybridize with variable neighborhood search. Tested over two real-world course timetabling problem datasets, the computational results from the experiments showed that the proposed algorithm could automate the process of timetabling. Furthermore, compared to the timetable produced manually, in term of soft constraint violation penalties, the proposed algorithm could improve by 1855 and 1110 respectively. In addition to new approach, the main contribution of this paper is two real-world course timetabling problems available for public to encourage further research as future works. [ABSTRACT FROM AUTHOR]

Performance of dynamic clouds depends on the efficiency of its load balancing and resource allocation. This paper is an exploratory study on the predictive approach for dynamic resource distribution of cloud services. Efficient cloud resource management can be achieved by simulating cloud services based on the predictions of incoming workloads, which can be more efficient than static allocation methods. This paper introduces a rule-based workload-balancing algorithm based on the predictions of an end-to-end system called Cicada. A simulation of cloud services can be achieved by a cloud service simulator called CloudSim and it will be used to achieve an algorithm with lower computational demand and a faster workload balancing. The final result will demonstrate the effectiveness of a predictive workload balancing approach that can achieve faster workload balancing with a lower computational power usage. [ABSTRACT FROM AUTHOR]

Abstract Regression problems try estimating a continuous variable from a number of characteristics or predictors. Several proposals have been made for regression models based on the use of fuzzy rules; however, all these proposals make use of rule models in which the irrelevance of the input variables in relation to the variable to be approximated is not taken into account. Regression problems share with the ordinal classification the existence of an explicit relationship of order between the values of the variable to be predicted. In a recent paper, the authors have proposed an ordinal classification algorithm that takes into account the detection of the irrelevance of input variables. This algorithm extracts a set of fuzzy rules from an example set, using as the basic model a sequential covering strategy along with a genetic algorithm. In this paper, a proposal for a regression algorithm based on this ordinal classification algorithm is presented. The proposed model can be interpreted as a multiclassifier and multilevel system that learns at each stage using the knowledge gained in previous stages. Due to similarities between regression and ordinal problems as well as the use of a set of ordinal algorithms, an error interval can be returned with the regression output value. Experimental results show the good behavior of the proposal as well as the results of the error interval. Highlights • We present a new fuzzy rule learning algorithm for regression problems. • The algorithm is based on an ordinal one which uses the sequential covering strategy with a genetic algorithm as the search mechanism. • The final regression algorithm is a multiclassifier and multilevel system which learns in an incremental way by using the learnt knowledge in previous stages. • An error interval can be returned with the regression output value. • The results show a good behavior compared with the same category algorithms, so as, other category algorithms, for regression problems. [ABSTRACT FROM AUTHOR]

• We propose new measures of the impact of research based on fractional counted citations, called individual citations. • By applying the PageRank algorithm to individual citations we define a PaperRank for citations among papers and an AuthorRank for citations among authors. • We show that self- and circular-citations can be eliminated by defining a closed market of Citation-coins. • We apply these metrics to the InSpire database that covers fundamental physics, to rank papers, authors, journals, institutes, towns, and countries, for all times and in recent time periods. Counting of number of papers, of citations and the h -index are the simplest bibliometric indices of the impact of research. We discuss some improvements. First, we replace citations with individual citations , fractionally shared among co-authors, to take into account that different papers and different fields have largely different average number of co-authors and of references. Next, we improve on citation counting applying the PageRank algorithm to citations among papers. Being time-ordered, this reduces to a weighted counting of citation descendants that we call PaperRank. We compute a related AuthorRank applying the PageRank algorithm to citations among authors. These metrics quantify the impact of an author or paper taking into account the impact of those authors that cite it. Finally, we show how self- and circular-citations can be eliminated by defining a closed market of Citation-coins. We apply these metrics to the InSpire database that covers fundamental physics, presenting results for papers, authors, journals, institutes, towns, countries for all-time and in recent time periods. [ABSTRACT FROM AUTHOR]

Nowadays, determining the exact position of sensor nodes in WSNs is an important factor in many applications. As the need of the localization accuracy varies between the applications, many localization techniques are used in different applications. Hence, node localization becomes one of the fundamental challenges in WSNs. Localization is categorized in two groups: range free and range based. In the range free techniques, localization is related between nodes and topological information of sensor nodes. On the other hand, in range based techniques, it is required to calculate distance between nodes. The scope of this paper is on range free localization. We survey different range free localization techniques and discuss some localization-based applications where the location of these sensor nodes is vital and sensitive. On the second part of the paper, we describe five algorithms namely: Centroid, Amorphous, APIT, DV-Hop and DV-HopMax algorithms. We simulate these algorithms using MATLAB based on different setups. Moreover, we make a comparative study between these localization algorithms based on different performance metrics showing their pros and cons. [ABSTRACT FROM AUTHOR]

In future wireless networks, network functions virtualization lays the foundations for establishing a new dynamic resource management framework to efficiently utilize network resources. The main problem discussed in the paper is to minimize the total latency for a network and how to do it efficiently. In this paper, a model of users, Virtual Network Functions (vNFs) and hosting devices has been taken, and it has been used to find the minimum latency using the Integer Linear Programming (ILP) which is an NP-hard problem and takes exponential time, but this is the optimal solution. We apply the stable matching based algorithm to solve the problem in polynomial time. From extensive performance evaluation, it is found that our proposed algorithm is very close to the optimal in terms of latency. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a speed sensorless control scheme named as finite control set-model predictive current control (FCS-MPCC) using a modified fictitious ohmic quantity (R) based model reference adaptive system (MRAS) for grid-connected doubly-fed induction machine (DFIM) drive. The variables of the reference model of this speed sensorless scheme (R-MRAS) are represented in stationary reference frame while those for the adaptive model are denoted in synchronously rotating reference frame. The sensorless formulation thus obtained is completely independent of any stator/rotor resistance terms. The scheme is also devoid of any stator/rotor flux estimation. Moreover, the intuitiveness of FCS-MPCC brings in additional flexibility in comparison to the conventional control techniques like field oriented control (FOC) and direct torque control (DTC). The overall scheme demonstrates faster execution time than FOC/DTC based control of DFIM drive. The proposed control algorithm is simulated and tested for limited speed range application in MATLAB/Simulink. The validation of simulation results are further done by experimentation on a dSPACE-1103 based DFIM laboratory setup. Highlights • In this paper, MRAS based sensorless speed estimation of DFIM drive by FCS-MPCC is reported. • The estimation scheme insensitive to the changes in machine parameters. • The overall scheme demonstrates faster execution time than FOC/DTC based control of DFIM drive. [ABSTRACT FROM AUTHOR]

Abstract To improve the concurrency of leaders' formation and followers' containment, a difficult problem of designing the formation controller and the containment controller simultaneously should be addressed for networked systems. Motivated by this, this paper presents an even-triggered control framework for networked Euler–Lagrange systems to achieve formation-containment control even in the presence of uncertain parameters. An event-triggered formation controller is firstly designed for leaders to achieve the desired configuration. An event-triggered containment control law is then developed to guarantee that all the followers can converge to the convex hull formed by leaders. The key feature of the containment control law is that it does not necessitate any relative velocity information with respect to neighbor followers. Each controller's gains are adaptively tuned using only local information. The parametric uncertainties are accommodated by using the adaptive updating law. Zeno behaviors of the triggering time sequences are also excluded. As a result, the communication burden of formation-containment system can be reduced. Numerical simulation is finally presented to verify the effectiveness of the proposed event-triggered formation-containment control framework. Highlights • To improve the concurrency of leaders' formation and followers' containment, this paper presents an even-triggered control framework for networked Euler-Lagrange systems with parametric uncertainties to achieve the formation-containment control. • An event-triggered formation controller is firstly designed for leaders to achieve the desired formation configuration. An event-triggered containment control law is then developed to guarantee that all the followers can converge to the convex hull formed by leaders. The key feature of the containment control law is that it does not necessitate any relative velocity information with respect to neighbor followers. • Each designed controller's gains are adaptively tuned using only local information. The parametric uncertainties are accommodated by using the adaptive updating law. The communication burden of the formation-containment system is reduced. [ABSTRACT FROM AUTHOR]

Nowadays, a provision of the computational process fault-tolerance in Grid is a relevant issue. In the paper, we address a fault-tolerance improvement in solving large-scale scientific and applied problems that are implemented through modular programming in heterogeneous distributed computing environments. We describe a computational process by an abstract program (problem-solving scheme) that correlates to a workflow. The problem-solving scheme specifies modules (applied software) and their relations with each other. This paper proposes a new multi-agent algorithm for re-allocating Grid-resources when the computational process fails. The algorithm execution involves forming a residual problem-solving scheme using methods of the abstract program specialization and reallocating its modules between agents that represent computational resources. In comparison to the known algorithms for the same purpose, the proposed algorithm implements an adaptive multi-scenario solving this issue and therefore increases a degree of computational process fault-tolerance. Extensive modeling and practical experiments demonstrate the practicability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a novel decomposition method based on user-preference and developed a variation of the decomposition based multi-objective optimization algorithm (MOEA/D) targeting only solutions in a small region of the Pareto-front defined by the preference information supplied by the decision maker (DM). This is particularly advantageous for solving multi-objective optimization problems (MOPs) with more than 3 objectives, i.e., many-objective optimization problems (MaOPs). As the number of objectives increases, the ability of an EMO algorithm to approximate the entire Pareto front (PF) is rapidly diminishing. In this paper, we first propose a novel scalarizing function making use of a series of new reference points derived from a reference point specified by the DM in the preference model. Based on this scalarizing function, we then develop a user-preference-based EMO algorithm, namely R-MOEA/D. One key merit of R-MOEA/D is that it does not rely on an estimation of the ideal point, which may impact significantly the performances of state-of-the-art decomposition based EMO algorithms. Our experimental results on multi-objective and many-objective benchmark problems have shown that R-MOEA/D provides a more direct and efficient search towards the preferred PF region, resulting in competitive performances. In an interactive setting when the DM changes the reference point during optimization, R-MOEA/D has a faster response speed and performance than the compared algorithms, showing its robustness and adaptability to changes of the preference model. Furthermore, the effectiveness of R-MOEA/D is verified on a real-world problem of reservoir flood control operations. [ABSTRACT FROM AUTHOR]