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A Stable and highly reliable DC link voltage represents an important factor for efficient power transfer in high voltage direct current (HVDC) networks. In this framework, this paper investigates the control and stability analysis of voltage source converter (VSC) for DC link voltage regulation. To separately achieve the independent active and reactive power control, the system voltages and currents are represented in the synchronous reference frame. In order to optimally design the parameters of proportional-integral (PI) controller, the inner and outer loops’ transfer functions are thoroughly derived/developed. In addition,/moreover, in order to attain satisfactory/certain system performance, modulus optimum, symmetrical optimum pole placement control approaches are studied and implemented for the purpose of tuning the voltage/current controller gain parameters. In particular for the symmetrical optimum control method, the gain parameters of the DC-bus voltage are determined under different values of network impedance. Furthermore, the impact/influence of changing gain parameters on the DC-link voltage and poles/zeros movement are investigated. MATLAB/Simulink model is built and simulation studies are carried out to verify the introduced concepts.

Stochastic nature of load demand has a great impact on the performance of electrical power system. As a result, planning of electrical power system considering load uncertainties became inevitable. This paper presents Monte Carlo simulation based different bio-inspired algorithms, grey wolf optimization (GWO), manta ray foraging optimization (MRFO), satin bower bird optimization (SBO) and whale optimization (WOA) to optimize locations of three DG units under load uncertainties considering 500 scenarios. Each scenario includes 50 iterations which means that for each run we have 25,000 iterations and 500 characteristics for different load value. Two objectives are achieved. Firstly, statistically finding the optimal probabilistic location of three DG units under load uncertainties in IEEE 33-bus and IEEE 69-bus radial distribution system based on Monte Carlo simulation integrated with different bio-inspired algorithms. Secondly, comparing between the performances of four different bio-inspired algorithms. Three objective functions are considered, minimizing active power loss, minimizing voltage deviation and maximizing voltage stability index. The active and reactive power demand are normally distributed using normal distribution function. The optimal probabilistic location is investigated considering two cases under load uncertainties, optimizing location of three DG units generally and optimizing location of one DG unit assuming two optimum locations for the other two units extracted from case I. The obtained results (after placing DG units) are compared to the base case (DG units are not connected) and compared to each other according to the optimization technique. The results show that, SBO algorithm superiors other algorithms almost in all cases. Comes next GWO which provide good results generally. However, the good performance obtained by MRFO, it consumes twice the time of other algorithms. WOA however fast convergence, it provides results worse than other algorithms. The system is applied to the well-known IEEE 33-bus and IEEE 69-bus radial distribution system.

Peer-to-Peer (P2P) networks are prominent in the Internet-of-things-assisted industrial environments for distributed computing and smart control systems. The problem arises with the independence and peer systems security due to anonymous access and security measures. In this paper, an innovative control-driven autonomous authentication scheme is proposed for improving the access security of P2P industrial systems. The proposed scheme provides authentication based on P2P system control requirements within its access time. The P2P control systems and their functionalities are provided with classified security measures for administering autonomous security. The advantage of offering autonomous protection is to prevent the sequence of security breaches and control sabotage. In this process, the control system requirements and authentications are paired by identifying the machines' operating time and access time. For identification and grouping-based classification, support vector machines are used. It learns the sabotage and control requirements based on access and control time for providing a rupture-less industrial process. It helps to leverage the detection of autonomous adversaries in P2P industrial control systems. Besides, a less complex and latent-free security measure is achievable using the proposed scheme.

The endless problem of energy supplies are always floating on the surface. As a result, there are a daily improvement to optimize power generators, networks and system configuration. Renewable distributed generators (RDG) are in the heart of these developments. The size of RDG is increasing daily so, it must be optimized to maximize benefits and eliminate drawbacks. Optimization algorithms are one of the fast growing techniques. In this study the Manta Ray Foraging optimization algorithm (MRFO) is applied to minimize power losses through sizing and allocation of DG type I integrated into radial distribution network (RDN). The proposed technique was tested on three different networks, IEEE 33, 69 and 85 test systems. Also, three cases were assumed to evaluate the effectiveness of MRFO algorithm. The results were compared to recent applied techniques.

Power loss is a bottleneck in every power system and it has been in focus of majority of the researchers and industry. This paper proposes a new method for determining the power loss in wind-solar power system based on deep learning. The main idea of the proposed scheme is to freeze the feature extraction layer of the deep Boltzmann network and deploy deep learning training model as the source model. The sample data with closer distribution with the data under consideration is selected by defining the maximum mean discrepancy contribution coefficient. The power loss calculation model is developed by configuring the deep neural network through the sample data. The deep learning model is deployed to simulate the non-linear mapping relationship between the load data, power supply data, bus voltage data and the grid loss rate during power grid operation. The proposed algorithm is applied to an actual power grid to evaluate its effectiveness. Simulation results show that the proposed algorithm effectively improved the system performance in terms of accuracy, fault tolerance, nonlinear fitting and timeliness as compared with existing schemes.

This paper is focused on Intel Advanced Vector Extension (AVX) which has been borne of the modern developments in AMD processors and Intel itself. Said prescript processes a chunk of data both individually and altogether. AVX is supporting variety of applications such as image processing. Our goal is to accelerate and optimize square single-precision matrix multiplication from 2080 to 4512, i.e. big size ranges. Our optimization is designed by using AVX instruction sets, OpenMP parallelization, and memory access optimization to overcome bandwidth limitations. This paper is different from other papers by concentrating on several main technique and the results therein. Making parallel implementation guidelines of said algorithms, where the target architecture’s characteristics need to be taken into consideration when said algorithms are applied are presented. This work has a comparative study of using most popular compilers: Intel C++ compiler 17.0 over Microsoft Visual Studio C++ compiler 2015. Additionally, a comparative study between single-core and multicore platforms has been examined. The obtained results of the proposed optimized algorithms are achieved a performance improvement of 71%, 59%, and 56% for C = A.B, C = A.BT, and C = AT.B separately compared with results that are achieved by implementing the latest Intel Math Kernel Library 2017 SGEMV subroutines.

Recently, an explosive growth in the potential use of natural metaphors in modelling and solving large-scale non-linear optimization problems. Artificial neural network (ANN) is a potent gadget broadly utilized in many data classification tasks. Fundamentally, nature-inspired algorithms have demonstrated their effectiveness and ability over traditional algorithms for generating the optimal ANN parameters, rules and topology that provide the best classification performance with regarding to the quality of the solution, computational cost and avoiding local minima. The literature is vast and growing. This study provides a review on the basic theories and main recent algorithms for optimizing the ANN. Different types of nature-inspired meta-heuristic algorithms are presented; outlining the concepts and components that are used in order to give a summary and ease of the state-of-the-arts to find suitable methods in real world applications for the readers. Additionally, this survey covers the most used type of neural networks, feed-forward neural network (FFNN) in several optimized applications. The performances of FFNNs designed by nature-inspired algorithms have been explored in single and multi-dimensional optimization space; highlighting their models, features, objectives, constraints, etc. to analyse their differences and similarities. A comprehensive survey of the earliest works and recent modified in the last decade in addition to expect approaches has been investigated in details.

Data mining optimization received much attention in the last decades due to introducing new optimization techniques, which were applied successfully to solve such stochastic mining problems. This paper addresses implementation of evolutionary optimization algorithms (EOAs) for mining two famous data sets in machine learning by implementing four different optimization techniques. The selected data sets used for evaluating the proposed optimization algorithms are Iris dataset and Breast Cancer dataset. In the classification problem of this paper, the neural network (NN) is used with four optimization techniques, which are whale optimization algorithm (WOA), dragonfly algorithm (DA), multiverse optimization (MVA), and grey wolf optimization (GWO). Different control parameters were considered for accurate judgments of the suggested optimization techniques. The comparitive study proves that, the GWO, and MVO provide accurate results over both WO, and DA in terms of convergence, runtime, classification rate, and MSE.

Maximizing the classification accuracy and minimizing the number of selected features are the two main incompatible objectives for using feature selection to overcome the curse of dimensionality. “Classification accuracy highly dependents on the nature of the features in a dataset which may contain irrelevant or redundant data. The main aim of feature selection is to eliminate these types of features to enhance the classification accuracy.” This work presents a new meta-heuristic optimization approach, called Parasitism-Predation Algorithm (PPA), which mimics the interaction between the predator (cats), the parasite (cuckoos) and the host (crows) in the crow–cuckoo–cat system model to overcome the problems of low convergence and the curse of dimensionality of large data. The proposed hybrid framework combines the relative advantages of cat swarm optimization (CSO), cuckoo search (CS) and crow search algorithm (CSA) to attain a combinatorial set of features to boost up the classification accuracy. Nesting, parasitism, and predation phases are supposed to help exploration ability and balance in the context of solving classification problems. In addition, Levy flight distribution is applied to help better diversity of conventional CSA and improve ability of exploration. Meanwhile, an effective fitness function is utilized to enable the proposed PPA-based feature selector using K-Nearest Neighbors algorithm (KNN) to attain a combinatorial set of features. The proposed PPA and four standard heuristic search algorithms are looked at to gauge how efficient the proposed option is. Additionally, eighteen classification datasets are deployed to gauges its efficacy. The results highlight that the algorithm proposed is both effective and competitive in terms of performance of classification and dimensionality reduction as opposed to other heuristic options.

In this paper, by using subsuper solutions method, we study the existence of weak positive solutions for a new class of p,q Laplacian nonlinear elliptic system in bounded domains, when ax, bx,αx, and βx are sign-changing functions that maybe negative near the boundary, without assuming sign conditions on f0,g0,h0, and γ0.

The current paper introduces a realistic solution for energy demand in Makadi Bay, Red-Sea, Hurgada, Egypt using energy system crossbred of Renewable Wind Energy System (WES) and Photovoltaic System (PVS) in the presence of Battery Energy Storage (BES). A real measurement for wind speed was recorded through a year of 2017. Also, the sun irradiance and temperature were recorded through the same period, to be considered for the output power calculations from the proposed crossbred renewable energy system. The demand load data for the city was recoded as well as through the same period for evaluating the feasibility of the system if it can cover the city loads. Linear TORSCHE optimization technique has utilized to reach an optimum solution of the proposed crossbred renewable energy system. Individual configuration of PVS & WES in presence of BES have been studied and compared with the hybrid PV/WT. Furthermore, economic analysis has presented to prove the best economical system. The obtained results show that installing such hybrid system consists of WES, PVS and BES is cheaper than installing each one individually.

A vehicular ad-hoc network (VANET) was employed in commercial, road-safety, and entertainment applications due to its accessibility. Applications and services were shared with the service providers (SP) over mobile nodes to any destination without special infrastructure. The mobility pattern of the nodes was independent, and the acceleration remained unpredictable, which led to service failures and information drop-outs. Resuming communication requires the flooding of additional control messages, which exploits the network resource in a shorter period. This paper introduces the neighbor predictive adaptive handoff (NPAH) algorithm for ensuring seamless communication, regardless of the application service time. NPAH discovers weak communication links in the service, which persist through the least resource dependent distance-based neighbor discovery. The selected neighbors are characterized by distance and minimum cost exploitation using the Q-learning technique. The process of learning decides the handoff of a vehicle based on storage utilization and cost factors. The results demonstrated the effectiveness of the NPAH algorithm by achieving less packet loss, shorter outage times, and improving the delivery factor.

This article proposes an information security management system (ISMS) model for automated data processing systems of critical applications (ADPS-CAs). The model allows users to carry out an assessment of the risk level for information security (IS) purposes and provides support for decision-making related to countering unauthorized access to the information circulating in an ADPS-CA. Further, this article analyzes the effect of a control parameter on the probability of launching an information integrity monitoring service (IMS) during a procedure that launches a typical ADPS-CA and its IS subsystem. The results present system quality indicators for protecting information from unauthorized access. The simulation was performed in relation to automated workstations as part of the ADPS-CA of a large enterprise.

The main purpose of this study is to investigate the effects of integration of online learning and assessment in synchronization form (OLASF) on students’ learning performance. The study seeks to evaluate how the synchronization content with immediate assessment can affect the knowledge performance of the students. An experimental design with digital formative assessment tools was used to fulfill the research purpose. Participants were 64 undergraduate students enrolled in is introduction to computer and programming (CSC 101) at Qassim University. The students from level five were divided into two-treatment groups: online learning & assessment instruction in Synchronization form using Nearpod, and PowerPoint instruction with traditional form. The experiment was completed within 6 weeks. Formative assessment test was used to access students learning performance after class. Descriptive statistics and a t-test were used to analyze the data. Our findings revealed that the use of OLASF was an intrinsic motivation and could be a promising way of enhancing students’ learning performance.

This work introduces a new population-based stochastic search technique, named multi-variant differential evolution (MVDE) algorithm for solving fifteen well-known real world problems from UCI repository and compared to four popular optimization methods. The MVDE proposes a new self-adaptive scaling factor based on cosine and logistic distributions as an almost factor-free optimization technique. For more updated chances, this factor is binary-mapped by incorporating an adaptive crossover operator. During the evolution, both greedy and less-greedy variants are managed by adjusting and incorporating the binary scaling factor and elite identification mechanism into a new multi-mutation crossover process through a number of sequentially evolutionary phases. Feature selection decreases the number of features by eliminating irrelevant or misleading, noisy and redundant data which can accelerate the process of classification. In this paper, a new feature selection algorithm based on the MVDE method and artificial neural network is presented which enabled MVDE to get a combination features’ set, accelerate the accuracy of the classification, and optimize both the structure and weights of Artificial Neural Network (ANN) simultaneously. The experimental results show the encouraging behavior of the proposed algorithm in terms of the classification accuracies and optimal number of feature selection.

In this paper, the performance of different optimization techniques namely, multi-objective dragonfly algorithm (MODA) and multi-objective differential evolution (MODE) are presented and compared. The uncertainty effect of a wind turbine (WT) on the performance of the distribution system is taken into account. The point estimate method (PEM) is used to model the uncertainty in wind power. Optimization methods are applied to determine the multi-objective optimal allocation of distributed generation (DG) in radial distribution systems at a different load level (light, normal, heavy load level). The multi-objective function is expressed to minimize the total power loss, total operating cost, and improve the voltage stability index of the radial distribution system (RDS). Multi-objective proposed algorithms are used to generate the Pareto optimal solutions, and a fuzzy decision-making function is used to produce a hybrid function for obtaining the best compromise solution. The proposed algorithms are carried out on 33-bus and IEEE-69-bus power systems. The simulation results show the effectiveness of installing the proper size of DG at the suitable location based on different techniques.

This paper aims to assess methods that may help to improve the efficiency of task scheduling, to enhance the overall functionality and performance of grid systems. The study will examine and analyze the integrated approach, which could help to enhance task scheduling within grid systems. It is recognized that modifying the scheduling algorithm can play a significant role in improving the functionality and performance of task scheduling and therefore the veracity of the grid system. It is important to note that this study also includes appropriate simulations along with analyses to establish the reliability and authenticity of the proposed technique.

© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. For university websites to be successful and to increase the chance of converting a prospective student into a current student, it is necessary to increase the visibility and accessibility of all related content so that a student can achieve their desired task in the fastest possible time. The criteria for evaluating university websites are very vague and are usually unknown to most developers, which adversely impacts the user-experience of the students visiting such websites. To solve this problem, we devised a usability metric and examined the leading university websites to analyze whether these websites were able to meet the requirements of students. In this research, we applied qualitative and quantitative approaches by considering 300 students and evaluating 86 university websites (26 from Canada, 30 from the United States, and 30 from Europe) based on a six-attribute metric comprising navigation, organization, ease of use (simplicity), design (layout), communication and content. From the evaluation results, we find that the 88% of the students are satisfied with our proposed usability attributes, but that most universities fail to meet basic standards of usability as desired by the students. The findings also show that the usability evaluation score for each usability feature varies from country to country, such as for (1) multiple language support − 23% of the Canadian websites, 63% of the European websites and none of the USA websites has the feature; for (2) Scholarships/Funding/Financial Aid link − 24% of the Canadian websites, 80% of the European and the USA websites has the feature; for (3) admission link − 88% of the Canadian websites, 20% of the European websites and 90% of the USA websites has the feature. In addition, from the evaluative result we find that our proposed approach will not only increase the usability of academic websites but will also provide an easiest way to covert prospective student inquiries into enrollment opportunities.

This work introduces multi-objective water cycle algorithm (MOWCA) to find the accurate location and size of distributed energy resource (DERs) considering different load models for two seasons (winter, and summer). The impact of uncertainties produced from load and renewable energy resource (RES) such as wind turbine (WT) and photovoltaic (PV) on the performance of the radial distribution system (RDS) are covered as this is closer to the real operation condition. The point estimate method (PEM) is applied for modeling the RES uncertainties. An optimization technique is implemented to find the multi-objective optimal allocation of RESs in RDSs considering uncertainty effect. The main objectives of the work are to maximize the technical, economic and environmental benefits by minimizing different objective functions such as the dissipated power, the voltage deviation, DG cost and total emissions. The proposed multi-objective model is solved by using multi-objective water cycle algorithm (MOWCA), considering the Pareto criterion with nonlinear sorting based on fuzzy mechanism. The proposed algorithm is carried out on different IEEE power systems with various cases., 論文