Analysis Of Large Systems Research Articles

Firefly artificial intelligence technique for model order reduction with substructure preservation

Model order reduction (MOR) is a process of finding a lower order model for the original high order system with reasonable accuracy in order to simplify analysis, design, modeling and simulation for large complex systems. It is desirable that the reduced order model preserves the fundamental properties of the original system. This paper presents a new MOR technique of multi-input multi-output systems utilizing the firefly algorithm (FA) as an artificial intelligence technique. The reduction operation is proposed to maintain the exact dominant dynamics in the reduced order model with the advantage of substructure preservation. This is mainly possible for systems that are characterized as multi-time scale systems. Obtaining the reduced order model is achieved by minimizing the fitness function that is related to the error between the full and reduced order models’ responses. The new approach is compared with recently published work on firefly optimization for MOR, in addition to three other artificial intelligence techniques; namely, invasive weed optimization, particle swarm optimization and genetic algorithm. As a result, simulations show the potential of the FA for the process of MOR.

Towards a multiscale vision of active particles

The aim of this paper is first to provide a presentation of the papers published in a special issue devoted to modeling, qualitative analysis, control and simulations of large systems of living entities, viewed as active particles, related to real-life applications, and subsequently to present some prospective ideas on possible future research programs for the development of mathematical tools to model living systems.

Asymptotic Analysis of RZF in Large-Scale MU-MIMO Systems Over Rician Channels

In this paper, we focus on the downlink ergodic sum rate of a single-cell large-scale multiuser MIMO system in which the base station employs N antennas to communicate with K single-antenna user equipments (TIEs). A regularized zero-forcing (RZF) scheme is used for precoding under the assumption that each TIE uses a specific power and each link forms a spatially correlated MIMO Rician fading channel. The analysis is conducted assuming that N and K grow large with a given ratio and perfect channel state information is available at the base station. New results from random matrix theory and large system analysis are used to compute an asymptotic expression of the signal-to-interference-plus-noise ratio as a function of system parameters, spatial correlation matrix, and Rician factor. Numerical results are used to validate the accuracy of asymptotic approximations in the finite system regime and to evaluate the performance under different operating conditions. It turns out that the asymptotic expressions provide accurate approximations even for relatively small values of N and K.

Analysis and further development of large telephone systems

The purpose of the article is the analysis and further development of large telephone systems from the disciplines of the department of specialized computer systems. The relevance of the material described in the article is due to the need for analysis of outdated technologies of automatic telephone exchanges in Ukraine and the suggestion of ways to improve them. The development of large telephone systems in our day consists of the creation and improvement of hardware and software systems. To implement the Internet telephone protocol we use the special program Cisco Packet Tracer. When creating telephony we use two types of automatic telephone exchanges. Hardware automatic telephone stations of the Internet protocol are usually supplied in the form of specialized equipment with installed software. In this particular project, we use the Yeastar S300 Internet hardware-based automatic telephone exchange, which has many advantages to reach our goal, namely support for more than 300 people, built-in voicemail functions with internal storage for messages, as well as the possibility of its expansion. A fairly easy installation and upgrade procedure.To meet the needs of a large variety of users, the station is equipped with a large number of built-in features ranging from auto attendant and ending with calls from the site or GSM numbers of mobile operators. As a result of the work, data analysis was carried out on large telephone systems and it was determined that in 2011 the subscriber base was about 11 million subscribers and gradually decreased from 3 to 5% each year. In 2017 In Ukraine, the subscriber base accounted for about 9 million users. These circumstances were conditioned by the gradual development and gaining popularity of mobile communication, as well as Internet protocol telephony. The development of automatic telephone stations of the Internet protocol is due to the fact that unlike the old telephone systems, new Internet protocol stations allow you to use a large number of additional features, and it is not difficult to switch from outdated coordinate telephone systems. To facilitate the use of the new system by subscribers, a portable Internet Protocol telephony program was developed. It is software that will be enough to download to your computer and go through a complicated registration. After that, you will only need to pass authentication and the user will be able to use all its capabilities. The results of this project work can be used to create Internet telephony protocols.

Massive MIMO With Antenna Selection: Fundamental Limits and Applications

Antenna selection is an effective means to address the cost and complexity issues in massive MIMO systems. This paper studies the performance limits of massive MIMO systems under practical antenna selection algorithms. A massive MIMO system is considered in which the transmitter employs only a fixed-size subset of the available antennas with the strongest channel gains. For this setup, the input-output mutual information of the system is shown to be well-approximated by a normal random variable when the number of transmit antennas is large. The mean of this random variable grows proportional to the number of antennas and its variance vanishes in the large-system limit. This behavior of the mutual information generalizes the well-known channel hardening property in massive MIMO systems to the cases with antenna selection. Our investigations show that 90% of the ergodic rate achieved by full antenna selection can be achieved by selecting less than 30% of the transmit antennas. Using large-system analysis, we drive an analytical expression for the number of selected antennas that maximizes the energy efficiency. This number is also derived for the case where a certain fraction of the totally achievable rate is aimed to be achieved. Our numerical investigations demonstrate a close match between the analytical and simulation results even for scenarios with not-so-large dimensions.

Average modeling and evaluation of 18-pulse autotransformer rectifier unit without interphase transformers

This paper presents an improved average model and evaluation of an 18-pulse autotransformer rectifier unit (ATRU) in differential delta configuration. Average models remove the switching behavior of diode rectifiers and high bandwidth transients, which not only facilitates simulation of power systems by reducing computational cost but also enables impedance-based stability analysis for large complex power systems. To experimentally validate the proposed average model, a 2-kW, 18-pulse ATRU has been tested and the results of the derived model are compared with those of the switching model and experimental prototype. Computed transfer function of load impedance from the proposed average model closely resembles those of the switching model and the experimentally measured results validate the modeling procedure. Furthermore, stability analysis of the 18-pulse ATRU may be executed based on the return ratio of source and load impedance.

Power Minimization-Based Joint Task Scheduling and Resource Allocation in Downlink C-RAN

In this paper, we consider the network power minimization problem in a downlink cloud radio access network (C-RAN), taking into account the power consumed at the baseband unit (BBU) for computation and the power consumed at the remote radio heads and fronthaul links for transmission. The power minimization problem for transmission is a fast time-scale issue, whereas the power minimization problem for computation is a slow time-scale issue. Therefore, the joint network power minimization problem is a mixed time-scale problem. To tackle the time-scale challenge, we introduce large system analysis to turn the original fast time-scale problem into a slow time-scale one that only depends on the statistical channel information. In addition, we propose a bound improving branch-and-bound algorithm and a combinational algorithm to find the optimal and suboptimal solutions to the power minimization problem for computation, respectively, and propose an iterative coordinate descent algorithm to find the solutions to the power minimization problem for transmission. Finally, a distributed algorithm based on hierarchical decomposition is proposed to solve the joint network power minimization problem. In summary, this paper provides a framework to investigate how execution efficiency and computing capability at BBU as well as delay constraint of tasks can affect the network power minimization problem in C-RANs.

A Simulation Modelling as a Tool for Design and Development Large-Scale Automated Systems Smart City Application in Terms of Lack Statistical Information

Design and development of large-scale automated systems (AS) is always associated with overcoming a large number of uncertainties, which structuring often through the using of practice standards and modeling at the Systems & Software Engineering. Methods of analysis of large systems (LS) involve their decomposition in the context of structural and/or functional paradigms, which allow us to consider the obtained components as objects for modelling: functional modelling, simulation etc. Design the automated control system (ACS) of region (SmartCity project) need to optimize the decisions. In this paper we propose the new methods of determining the parameters of project in terms of lack statistical information based on the simulation model.

Joint Optimization of Fronthaul Compression and Bandwidth Allocation in Uplink H-CRAN With Large System Analysis

In this paper, we consider an uplink heterogeneous cloud radio access network (H-CRAN), where a macro base station (BS) coexists with many remote radio heads (RRHs). For cost-savings, only the BS is connected to the baseband unit (BBU) pool via fiber links. The RRHs, however, are associated with the BBU pool through wireless fronthaul links, which share the spectrum resource with radio access networks. Due to the limited capacity of fronthaul, the compress-and-forward scheme is employed, such as point-to-point compression or Wyner-Ziv coding. Different decoding strategies are also considered. This work aims to maximize the uplink ergodic sum-rate (SR) by jointly optimizing quantization noise matrix and bandwidth allocation between radio access networks and fronthaul links, which is a mixed time-scale issue. To reduce computational complexity and communication overhead, we introduce an approximation problem of the joint optimization problem based on large-dimensional random matrix theory, which is a slow time-scale issue because it only depends on statistical channel information. Finally, an algorithm based on Dinkelbach's algorithm is proposed to find the optimal solution to the approximate problem. In summary, this work provides an economic solution to the challenge of constrained fronthaul capacity, and also provides a framework with less computational complexity to study how bandwidth allocation and fronthaul compression can affect the SR maximization problem.

Zero MAC Latency Sensor Networking for Cyber-Physical Systems

In this paper, we consider a cyber-physical system, where an unstable dynamic plant is monitored by multiple distributed sensors over a wireless network with a shared common spectrum. We propose a novel zero-latency MAC protocol for cyber-physical systems. The proposed scheme exploits interferences and collisions among active sensors in the wireless channels to enhance the performance of remote state estimation. Using the Lyapunov drift analysis approach, we further establish closed-form necessary and sufficient requirements on the communication resources needed to achieve stabilization of the cyber-physical system. Large system analysis demonstrates that the proposed scheme has superior scalability with respect to a large number of sensors. The proposed scheme is also compared with various state-of-the-art baselines and we show that significant performance gains can be achieved.

OpenCL-Accelerated Probabilistic Power Flow for Active Distribution Networks

Open computing language (OpenCL) is an open standard developed for general purpose parallel programming of heterogeneous computing devices. OpenCL can be used to exploit the massively parallel architecture of graphics processing units (GPU) in performing non graphic tasks. This paper presents an OpenCL parallel implementation of Monte-Carlo simulation based probabilistic power flow analysis for large active distribution systems. The proposed implementation adopts single precision arithmetic and is independent of third-party linear algebra libraries, which makes it suitable for standalone implementation and use on any mainstream GPU. Moreover, given that it adopts the OpenCL programing model, the proposed approach may also be used on multicore CPUs. The accuracy and speedup of the proposed implementation is validated using several test systems, of sizes ranging from 32 to 10476 buses. The results show that the proposed parallelized implementation can achieve substantial speedups without requiring high performance computing purposed hardware.

The Study of Simulation Architecture in Meta-Synthetic Hall

The meta-synthetic methodology is the scientific and effective way to solve the problems of the complicated large system. Through the meta-synthetic Hall, we combined the expert, repository, and simulation test together, building an environment to realize the Meta-Synthesis methodology. In this article, we put forward simulation architecture for the Meta-Synthesis Hall. With the architecture, we can build the simulation system and do experiment for the Hall, supporting expert discussion and repository accumulation. We can solve the system experiment and data collection problems for complicated large system analysis, enforces system improve and perfect.

Compositionality in scenario-aware dataflow: a rendezvous perspective

Finite-state machine-based scenario-aware dataflow (FSM-SADF) is a dynamic dataflow model of computation that combines streaming data and finite-state control. For the most part, it preserves the determinism of its underlying synchronous dataflow (SDF) concurrency model and only when necessary introduces the non-deterministic variation in terms of scenarios that are represented by SDF graphs. This puts FSM-SADF in a sweet spot in the trade-off space between expressiveness and analyzability. However, FSM-SADF supports no notion of compositionality, which hampers its usability in modeling and consequent analysis of large systems. In this work we propose a compositional semantics for FSM-SADF that overcomes this problem. We base the semantics of the composition on standard composition of processes with rendezvous communication in the style of CCS or CSP at the control level and the parallel, serial and feedback composition of SDF graphs at the dataflow level. We evaluate the approach on a case study from the multimedia domain.

Guest Editorial Special Issue on Search-Based Software Engineering

It is our pleasure to introduce this Special Issue on Search-Based Software Engineering (SBSE) focusing on the application of evolutionary computation to solve real-world software engineering problems. Evolutionary computation (EC) methods have now become integral part of software engineering. New advancements in EC, such as multi- and many-objective optimization, uncertainty handling for robust and reliable solutions, knowledge discovery and knowledge-augmented EC, dynamic EC, have a great deal of applications in software engineering. Many applications in software engineering have emerged based on the usage of EC for the automation of all phases of the software development process, including the analysis, design, implementation, testing, and maintenance of large software systems. A total of 26 papers were submitted to the Special Issue. Each was subjected to at least three reviews and finally six were accepted for publication as described in the following.

Large System Analysis of Two-Stage Beamforming With Limited Feedback in FDD Massive MIMO Systems

Two-stage beamforming is a transmit strategy that uses two types of beamformers to reduce the feedback overhead of frequency-division-duplexing massive multiple-input multiple-output systems that are spatially correlated. In this paper, we present large system analysis of two-stage beamforming when a transmitter has limited channel information from feedback and when regularized-zero-forcing (RZF) is used as a second-stage beamformer. We consider two random-vector-quantization-based feedback schemes that can be respectively applied when users have perfect channel information or perfect effective channel information. For each feedback method, we analyze both expected signal-to-interference-plus-noise ratio (SINR) and expected rate loss and then characterize their bounds as a function of the number of feedback bits. From the characterization, we reveal that the sum rate of two-stage beamforming is very sensitive to the regularization parameter of RZF, especially when the number of feedback bits is limited. Motivated by this, we derive the optimal regularization parameter that maximizes the SINR of two-stage beamforming with limited feedback. Using simulations, we verify the tightness of the characterized bounds and quantify how the use of the optimal regularization parameter improves the sum rate of two-stage beamforming.

A Stochastic Framework for Reliability and Sensitivity Analysis of Large Scale Water Distribution Networks

The hydraulic reliability and sensitivity analysis of large scale water distribution systems in presence of uncertainty is considered in this work. The assessment of the network reliability and sensitivity is performed by an efficient Markov chain Monte Carlo method, namely Subset simulation. Prescribed nodal heads of storage tanks, nodal demands and pipe roughness coefficients are modeled as uncertain parameters and described in a probabilistic manner. Failure is assumed to occur when the minimum nodal head in the network is lower than a minimum allowable value. The efficiency of the proposed method is demonstrated with the analysis of a real water distribution network consisting of a large number of nodes and pipes (of the order of thousands). The corresponding reliability problem represents a high dimensional problem. The approach gives an important insight into the performance, reliability and sensitivity of a class of complex utility networks.

An efficient, large-scale, non-lattice-detection algorithm for exhaustive structural auditing of biomedical ontologies

One of the basic challenges in developing structural methods for systematic audition on the quality of biomedical ontologies is the computational cost usually involved in exhaustive sub-graph analysis. We introduce ANT-LCA, a new algorithm for computing all non-trivial lowest common ancestors (LCA) of each pair of concepts in the hierarchical order induced by an ontology. The computation of LCA is a fundamental step for non-lattice approach for ontology quality assurance. Distinct from existing approaches, ANT-LCA only computes LCAs for non-trivial pairs, those having at least one common ancestor. To skip all trivial pairs that may be of no practical interest, ANT-LCA employs a simple but innovative algorithmic strategy combining topological order and dynamic programming to keep track of non-trivial pairs. We provide correctness proofs and demonstrate a substantial reduction in computational time for two largest biomedical ontologies: SNOMED CT and Gene Ontology (GO). ANT-LCA achieved an average computation time of 30 and 3 sec per version for SNOMED CT and GO, respectively, about 2 orders of magnitude faster than the best known approaches. Our algorithm overcomes a fundamental computational barrier in sub-graph based structural analysis of large ontological systems. It enables the implementation of a new breed of structural auditing methods that not only identifies potential problematic areas, but also automatically suggests changes to fix the issues. Such structural auditing methods can lead to more effective tools supporting ontology quality assurance work.

Multiset Analysis of Consequences of Natural Disasters Impacts on Large-Scale Industrial Systems

Paper is dedicated to the new approach to distributed industrial systems (IS) sustainability/vulnerability assessment. This approach is based on the unitary multiset grammars (UMG) as a flexible and convenient tool designed specially for large systems analysis and optimization. UMG description of IS technological base as well as multiset representation of order completed by the IS, its resource base and impact on the IS are presented. Criterion for recognition of IS sustainability to the impact is formulated. UMG extension for natural disasters impacts (NDI) representation is introduced, and criterion for recognition of IS sustainability to the NDI is also presented. The solution of the reverse problem, concerning part of the order, which may be completed by the affected IS, is described. Implementation issues are considered.

Performance evaluation of two-machine lines with generalized thresholds

An analytical model for evaluating the performance of two-machine continuous flow systems with finite buffer capacity, multiple up and down states and generalised thresholds is presented in this paper. The idea is that the machines can behave differently above or below certain buffer levels named thresholds. In addition, when the buffer level crosses one of the thresholds, certain changes of state of the machines can happen. With the method presented in the paper it is possible to consider all the different types of two machine lines which can be modelled with multiple up and down states, like for example cases with machines having phase-type failure and repair time distributions, serial/parallel machines and quality control machines. The proposed approach, allows to include system control by means of thresholds in the system model and provides a way to analyse the performance of a wide range of two-machine systems. Some of these cases are proposed in the numerical examples at the end of the paper. Moreover, the proposed two-machine line can be used as a building block for the analysis of larger systems, including systems with loops.

Large System Analysis of Heterogeneous Cellular Networks With Interference Alignment

In this paper, we conduct large system analysis of heterogeneous cellular networks (HCNs) which are modeled as a two-tier cellular network consisting of one macro base station (MaBS) and multiple micro base stations (MiBSs) distributed according to a Poisson point process. Interference alignment is performed via rank constrained rank minimization (RCRM) to design the transmit precoding and receive beamforming matrices of the users in HCNs for the suppression of interference signal. We then analyse the average achievable rate for both macro users (MaUEs) and micro users (MiUEs) with an objective of obtaining a closed-form expression. It is shown that the MaUE and MiUE rate expressions are functions of the transmit power, noise power, MiBS density, and the transmit distance. Extensive simulations show that the rate expressions derived through large system analysis provide accurate estimates of the average achievable rates of HCNs with different system parameters.