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

The new power system effectively integrates a large number of distributed renewable energy sources, such as solar photovoltaic, wind energy, small hydropower, and biomass energy. This significantly reduces the reliance on fossil fuels and enhances the sustainability and environmental friendliness of energy supply. Compared to distribution networks in traditional power systems, the new-generation distribution network offers notable advantages in improving energy efficiency, reliability, environmental protection, and system flexibility, but it also faces a series of new challenges. These challenges include potential harmonic issues introduced by the widespread use of power electronic devices (such as inverters for renewable energy generation systems and electric vehicle charging stations) and the voltage fluctuations and flickering caused by the intermittency and uncertainty of renewable energy generation, which may affect the normal operation of electrical equipment. To address these challenges, this study proposes an optimization model aimed at minimizing network losses and voltage deviations, utilizing traditional capacitor adjustments and static var compensators (SVCs) as optimization measures. Furthermore, this study introduces an improved version of the multi-objective particle swarm optimization (MOPSO) algorithm, specifically enhanced to address the unique challenges of reactive power optimization in modern distribution networks. The test results show that this algorithm can effectively generate a large number of Pareto optimal solutions. The application of this algorithm on a 33-node network case study demonstrates its advantages in reactive power optimization. The optimization results highlight the effectiveness and feasibility of the proposed improved algorithm in the application of distribution network reactive power optimization, offering users a uniform and diverse range of reactive compensation solutions. [ABSTRACT FROM AUTHOR]

The security of workflow scheduling is a significant concern and even is one of the most important metrics of QoS (Quality of Service). This paper presents two approaches to provide a secure connection between users and servers and handle large and medium task size problems. Firstly, a multi-objective scheduling (MO-Ring-IC-NCCLA) algorithm for scientific workflow in the cloud environment is proposed. It tries to minimize workflow makespan and cost as well as increase the cost of attack from an invader. The proposed multi-objective is based on the New Caledonian Crow Learning Algorithm (NCCLA). However, this algorithm has a few drawbacks, including poor exploration activity and inability to balance exploration and exploitation. The social and asocial learning part of standard NCCLA has been modified to tackle these limitations, then a concept of ring topology is used to better Pareto optimal can be found. Secondly, the structure of virtual machines is modified so that the cost of attack from invaders increases. Experimental results based on various real-world workflows indicate the performance improvement of MO-Ring-IC-NCCLA over SBDE, NSGA-II, and MOHFHB algorithms in terms of FS-metric. According to the delta metric (i.e., diversity measures), the proposed algorithm is superior to 85% of the compared metaheuristics. In terms of Inverted Generational Distance (IGD) metric, it outperforms NSGAII and Multi-Objective Artificial Hummingbird Algorithm (MOAHA) for 95% and 80% of the cases, respectively. Based on experiments, makespan and cost improved by 23.12% and 18.43% over existing workflow algorithms. Compared to Multi-Objective Hybrid Fuzzy Hitchcock Bird (MOHFHB), Simulated-annealing Based Differential Evolution (SBDE), and non-dominated sorting genetic algorithm (NSGAII), it improves the FS-metric by 23.35% on average. [ABSTRACT FROM AUTHOR]

The new power system effectively integrates a large number of distributed renewable energy sources, such as solar photovoltaic, wind energy, small hydropower, and biomass energy. This significantly reduces the reliance on fossil fuels and enhances the sustainability and environmental friendliness of energy supply. Compared to distribution networks in traditional power systems, the new-generation distribution network offers notable advantages in improving energy efficiency, reliability, environmental protection, and system flexibility, but it also faces a series of new challenges. These challenges include potential harmonic issues introduced by the widespread use of power electronic devices (such as inverters for renewable energy generation systems and electric vehicle charging stations) and the voltage fluctuations and flickering caused by the intermittency and uncertainty of renewable energy generation, which may affect the normal operation of electrical equipment. To address these challenges, this study proposes an optimization model aimed at minimizing network losses and voltage deviations, utilizing traditional capacitor adjustments and static var compensators (SVCs) as optimization measures. Furthermore, this study introduces an improved version of the multi-objective particle swarm optimization (MOPSO) algorithm, specifically enhanced to address the unique challenges of reactive power optimization in modern distribution networks. The test results show that this algorithm can effectively generate a large number of Pareto optimal solutions. The application of this algorithm on a 33-node network case study demonstrates its advantages in reactive power optimization. The optimization results highlight the effectiveness and feasibility of the proposed improved algorithm in the application of distribution network reactive power optimization, offering users a uniform and diverse range of reactive compensation solutions.

Purpose of this study is to investigate the problem of how typical (or, conversely, unique) is the phenomenon of explosive synchronization in networks of nonlinear oscillators with topologies of links such as “ring” and “small world”, and, in turn, how the partial frequencies of the interacting oscillators must correlate with each other for the phenomenon of explosive synchronization in these networks can be possible. Methods. In this paper, we use an analytical description of the synchronous behavior of networks of nonlinear elements with “ring” and “small world” link topologies. To confirm the obtained results the numerical simulation is used. Results. It is shown that in networks of nonlinear oscillators with topologies of links such as “ring” and “small world”, the phenomenon of explosive synchronization can be observed for the different distributions of partial frequencies of network oscillators. Conclusion. The paper considers an analytical description of the behavior of network oscillators with “ring” and “small world” topologies of links and shows that the phenomenon of explosive synchronization in such networks is atypical, but not unique.

Given a boolean predicate Π on labeled networks (e.g., proper coloring, leader election, etc.), a self-stabilizing algorithm for Π is a distributed algorithm that can start from any initial configuration of the network (i.e., every node has an arbitrary value assigned to each of its variables), and eventually converge to a configuration satisfying Π. It is known that leader election does not have a deterministic self-stabilizing algorithm using a constant-size register at each node, i.e., for some networks, some of their nodes must have registers whose sizes grow with the size n of the networks. On the other hand, it is also known that leader election can be solved by a deterministic self-stabilizing algorithm using registers of O(log log n) bits per node in any n-node bounded-degree network. We show that this latter space complexity is optimal. Specifically, we prove that every deterministic self-stabilizing algorithm solving leader election must use (log log n)-bit per node registers in some n-node networks. In addition, we show that our lower bounds go beyond leader election, and apply to all problems that cannot be solved by anonymous algorithms. [ABSTRACT FROM AUTHOR]

By leveraging deep learning technologies, data-driven-based approaches have reached great success with the rapid increase of data generated for medical applications. However, security and privacy concerns are obstacles for data providers in many sensitive data-driven scenarios, such as rehabilitation and 24 h on-the-go healthcare services. Although many federated learning (FL) approaches have been proposed with DNNs for medical applications, these works still suffer from low usability of data due to data incompleteness, low quality, insufficient quantity, sensitivity, etc. Therefore, we propose a ring-topology-based decentralized federated learning (RDFL) scheme for deep generative models (DGM), where DGM is a promising solution for solving the aforementioned data usability issues. Our RDFL schemes provide communication efficiency and maintain training performance to boost DGMs in target tasks compared with existing FL works. A novel ring FL topology and a map-reduce-based synchronizing method are designed in the proposed RDFL to improve the decentralized FL performance and bandwidth utilization. In addition, an inter-planetary file system (IPFS) is introduced to further improve communication efficiency and FL security. Extensive experiments have been taken to demonstrate the superiority of RDFL with either independent and identically distributed (IID) datasets or non-independent and identically distributed (Non-IID) datasets. [ABSTRACT FROM AUTHOR]

This paper presents a fault location technique for homogeneous ring-topology power transmission systems using synchrophasors sensed from the instruments in the power Internet of Things (IoT), such as Global Positioning System (GPS) based Phasor Measurement Units (PMUs) or Intelligent Electronic Devices (IEDs) with embedded GPS. Moreover, with a view to reducing the influence of measurement errors on fault location estimation, minimum demands for GPS-based equipment and Capacitor Voltage Transformer (CVT)/Current Transformer (CT) transducers are also considered when deriving the proposed algorithm. A unique fault locator is thus developed to recognize the faulted line branch as well as pinpoint the fault position at the same time. Not only can the proposed method avoid iterative operations, but it is also free from the problems of multiple solutions, fault type selections, and fault-path/source impedance assumptions. Last but not the least, extensive simulations considering 161 kV overhead-line parameters of Taiwan Power Company (Taipower) and actual non-ideal sensing/measurement phenomena are conducted to demonstrate the performance and effectiveness of the suggested idea. [ABSTRACT FROM AUTHOR]

Measuring the population diversity in metaheuristics has become a common practice for adaptive approaches, aiming mainly to address the issue of premature convergence. Understanding the processes leading to a diversity loss in a metaheuristic algorithm is crucial for designing successful adaptive approaches. In this study, we focus on the relation of the neighborhood size and the rate of diversity loss in the Particle Swarm Optimization algorithm with local topology (also known as LPSO). We argue that the neighborhood size is an important input to consider when designing any adaptive approach based on the change of population diversity. We used the extensive benchmark suite of the IEEE CEC 2014 competition for experiments.

A wideband down conversion ring mixer is proposed for multi band orthogonal frequency division multiplexing (MB-OFDM) system in 180 nm CMOS technology. The mixer is essentially used in a heterodyne wireless receiver to enhance the selectivity of the system. Being a nonlinear system, the mixer dominates the overall performance of the system. The design of down conversion mixer is the most challenging part of a receive chain. Wideband impedance matching always remains a challenge in any radio frequency integrated circuit design. This paper presents the design of a ring mixer with high linearity, wideband impedance matching using differential resistive impedance matching and without using any DC bias. The proposed mixer is tuned for a frequency of 3.432 GHz of band 1 of the MB-OFDM system. Mixer core is based on the FET ring mixer topology. The mixer is implemented in 180 nm CMOS technology. The mixer achieves the minimum conversion loss of 10.49 dB, 1 dB compression point (P1) of 12.40 dBm, third order input intercept point (IIP3) of 12.01 dBm, a minimum SSB noise figure of 8.99 dB, and S11 of less than -10 dB over the frequency range of 0 to 13.61 GHz. The layout of the mixer records an active area of 183.75 μm2. [ABSTRACT FROM AUTHOR]

The stability and convergence time become an essential factor in network availability performance. Multiprotocol Label Switching (MPLS) is one of the Virtual Private Network (VPN) technologies that can support the quality of communication media on the high-speed backbone network. Therefore, it is necessary to determine the proper protocol routing in espousing VPN technology based on MPLS supported by direct-link backup to improve network availability in the Data Center. The purpose of this study is comparing the convergence time and Quality of Service (QoS) among the three IGP protocols routing, namely Routing Information Protocol version 2 (RIP), Open Shortest Path First (OSPF), and Enhanced Interior Gateway Routing Protocol (EIGRP) based on two autonomous system number using Ring topology design between Data Center and DRC. Network scenario is created using the Graphic Network Simulator (GNS3) application to measure convergence time and QoS parameters of the three protocols routing and the use of MPLS-TE and RR in enhancing MPLS backbone performance. The results revealed that QoS in the three protocols routing has a good quality level according to TIPHON’s standard with the number of indexes up to 3.25 (Good). On the other hand, the fastest convergence time when interruption on the main link (VPN) is EIGRP with convergence time for about 15 seconds.

This study exploits the modeling and behavioral analysis of electrical coupling medium between neurons. A new electrical coupling model that represents two resistor, inductor and capacitor (RLC) circuits for a single link between two neurons is introduced by employing the biological evidences and electrical circuit theory knowledge. Herein, a model consisting of a network of multiple FitzHugh–Nagumo (FHN) neurons in a ring topology is also proposed. The presented model follows direction-dependent transmission based on resistive, inductive, and capacitive inter-neuronal electrical coupling medium for generalization purposes. The coupling medium, attributable to the direction-dependent information transmittance, admitting resistive, inductive and capacitive nature, reveals the astonished results such as the existence of two PID controllers for a single link among two neurons. Furthermore, the effects of difference in PID parameters by fixing the parametric difference and by increasing the parametric difference on synchronization are studied in order to explore the behavior of electrical coupling medium. Astoundingly, such behavioral analysis reveals that the coupling medium truly tracks the well-known proportional integral derivative (PID) characteristics curve.

We consider power distribution networks containing source nodes producing electricity and nodes representing electricity consumers. These sources and these consumers are interconnected by a switched network. Configuring this network consists in deciding which switches are activated and the orientation of the links between these switches, so as to obtain a directed acyclic graph (DAG) from the producer nodes to the consumer nodes. This DAG is valid if the electric flow it induces satisfies the demand of each consumer without exceeding the production capacity of each source and the flow capacity of each switch. We show that the problem of deciding if such a valid DAG exists is NP-complete. In the case where such a valid DAG exists, we study the problem of determining a valid DAG that balances the ratio between the amount of electricity produced and the maximum production capacity for each source. We show that this minimization problem is also NP-complete in the general case but that it becomes polynomial in the case of ring network topologies. • We balance the ratio between effective and maximum production of electrical sources. • All the input flows of a node should be equal. The flow satisfies capacity constraints. • The flow is indirectly imposed by activating switches and directing electrical links. • The problem is NP-Hard but polynomial for ring networks. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is an evolutionary algorithm that is well known for its simplicity and effectiveness. It usually has strong global search capability but has the drawback of being easily trapped by local optima. A scaling mutation strategy and an elitist learning strategy are presented in this paper. Based on these strategies, an improved PSO variant (LSERPSO) is developed through a local search and ring topology strategy. The new scaling mutation strategy involved an exploration and exploitation balance focusing on mutation operation. A collection of elite individuals is maintained such that an array of current particles can learn from them. A ring topology-based neighborhood structure is adopted to maintain the population diversity and to reduce the possibility of particles being trapped in local optima. Finally, a quasi-Newton-based local search is incorporated to enhance the fine-grained capability. The effects of these proposed strategies and their cooperation are verified step by step. The performance of LSERPSO is comprehensively studied using IEEE CEC2015 benchmark functions.

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

High-resolution direct position determination (DPD) using a single moving uniform linear array is considered. In this paper, we firstly propose an improved DPD model based on eigenspace. This model exploits both signal subspaces and noise subspaces which results in higher resolution than those utilizing minimum variance distortionless response, multiple signal classification or subspace fitting. In order to achieve rapid and high precision localization, a hybrid calculation algorithm which combines particle swarm optimization using a ring topology and Broyden–Fletcher–Goldfarb–Shanno is proposed. This algorithm can extract multiple emitter positions with less computational complexity. We combine the improved DPD model and the hybrid calculation algorithm and examine its performance via numerical simulations. The results show that the proposed method can reach Cramer–Rao lower bound when the signal-to-noise ratio is moderate. [ABSTRACT FROM AUTHOR]

Hub location problems (HLPs) are applied in a variety of fields including public transportation and telecommunication systems. The subgraph induced by the hubs can configure different structures (e.g., tree, star, path or ring). This paper presents a new mixed-integer linear programming model for an HLP with a ring-structured hub network considering congestion in hubs under uncertainty. Hubs are considered as M/G/c queue systems to analyze the waiting time of flow units at hubs. Since the parameters (i.e., demand, transportation cost and fixed costs of establishing links and hub facilities) are tainted with a great degree of epistemic uncertainty, a robust possibilistic-programming approach is utilized. To study the behavior of the robustness of the proposed model, computational experiments are considered for a number of test problems. A comparative analysis is carried out to show the performance and superiority of the robust approach over the basic possibilistic chance constrained programming approach. [ABSTRACT FROM AUTHOR]

Distance adaptive spectrum allocation exploits the tradeoff between spectrum width and reach to improve resource utilisation by tailoring the modulation format to the level of impairments along the path. The authors first show that the distance‐adaptive routing and spectrum assignment (DA‐RSA) problem is a special case of a multiprocessor scheduling problem. The authors then develop a suite of efficient and effective DA‐RSA algorithms for ring networks, that build upon list scheduling concepts. This work explores the tradeoffs involved in DA‐RSA algorithm design, and opens up new research directions that may leverage the vast literature in scheduling theory.