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This paper focuses on the practical application of system architecture modeling and reference enabling in systems engineering, introducing the fundamental principles and proof of concept. The fundamental principles are defined by ISO/IEC 81346 standard series, which provides a common language for systems across all engineering disciplines through the Reference Designation System (RDS). RDS is used to create a reference model with unambiguous identifiers representing system breakdowns in different views called aspects, allowing for clear reference designations of system elements and their relationships, complying with the basic rules of ISO 704 – Terminology work. The practical use of this system reference model is demonstrated through a selected use case: the Digital Design, Manufacturing & Services (DDMS) transformation program at AIRBUS S.A.S. (Toulouse, France). This case includes practical experience and a brief introduction to tools that assist with the usage of ontologies and terminology work in daily life. [ABSTRACT FROM AUTHOR]

Metastasis is a multisequential process that allows tumor cells to migrate to tissues distant from the primary tumor. Only a small number of cells escape from the primary tumor; however, the metastases generated are responsible for more than 90% of cancer deaths. Many metastatic processes initially require the total or partial start‐up of a program for the transformation of tumor epithelial cells into mesenchymal cells (EMT). The launching of the EMT program is stimulated by cytokines and other elements produced by the diverse types of cells composing the tumor stroma. In parallel, a process of destabilization of the extracellular matrix (ECM) takes place by means of the synthesis of proteases of the matrix metalloproteinases (MMPs) family. EMC degradation allows the exportation of some tumor cells as mesenchymal cells to the circulatory system and their subsequent implantation in a tissue distant from the primary tumor. The blocking of these both processes appears as a hypothetical stop point in the metastatic mechanism. The present review deals with the different options to achieve the inhibition of MMPs, focusing on MMP7 as a target given its involvement in the metastatic processes of a wide variety of tumors. Significance statement: The simultaneous implantation of the epithelial–mesenchymal program and the synthesis and activation of matrix metalloproteinases during the first phases of the metastasis process is known. The inhibition of proteases could constitute a possible blockage of the process. The review describes the evolution of the different inhibition mechanisms that could inform applicable therapeutic mechanisms for the paralysis of the metastatic process. [ABSTRACT FROM AUTHOR]

Higher education in evolutionary anthropology involves providing students with in‐depth knowledge of biological and cultural heritage sites and collections that are frequently inaccessible. Indeed, most sites, fossils, and archaeological remains can be visited or manipulated only rarely and solely by specialists with extensive experience. Owing to the development of 3D and medical imaging techniques, this fragile heritage is now more widely accessible, and in a dynamic way. However, exclusive adoption of virtual teaching and learning has a negative impact on student engagement and, naturally, on exchanges with instructors, and thus cannot be used without some reservations. In the ITAP (Immersion dans les Terrains de l'Anthropologie biologique et de la Préhistoire) project of the higher education STEP (Soutien à la Transformation et à l'Expérimentation Pédagogiques) transformation program at the University of Bordeaux, we combine student‐active teaching with Master's students fully immersed in ongoing fieldwork, laboratory study, and dissemination of research results in order to develop more individually shaped learning curricula and to foster both professional and new interdisciplinary skills. Here, we present examples of experiments conducted in the ITAP project using both authentic and virtual collections of archaeological, experimental, and reference materials that help to break down the barriers between research activities and higher education, as well as providing a more general appraisal of the appropriate use of virtual tools in higher education by combining them with real‐life situations. [ABSTRACT FROM AUTHOR]

Unimodular waveforms with good correlation properties are desired for multiple‐input‐multiple‐output radar to achieve an increased transmitting/receiving virtual aperture. In this study, a new optimisation framework is introduced to design waveforms with good correlations. It is shown that many existing waveform design problems can be incorporated into this framework. Since the considered problem is in general highly non‐linear and non‐convex, an iterative optimisation method based on majorisation‐minimisation is developed. By carefully devising the surrogate function, the only requirement is to deal with a series of simple problems, which have closed‐form solutions. The proposed algorithm can be implemented via fast Fourier transform and hence is computationally efficient. In addition, the proposed algorithm is extended to deal with spectral constraints. Numerical results demonstrate the efficiency of the proposed algorithm compared with the state‐of‐the‐art techniques. [ABSTRACT FROM AUTHOR]

A convergence analysis of the alternating direction method of multipliers (ADMM) for linear model predictive control (MPC) problems with regularization terms is addressed here. Compared with conventional results, this paper focuses on the dynamical structure of the ADMM and derives the linear convergence of the state variables of the ADMM dynamics.In on‐line optimization problems associated with MPC problems, because the computation time is a significant issue, a hot‐start technique in which an initial point of the algorithm is set as the convergence point for the previous optimization problem is widely employed to improve the convergence property. Here, by utilizing the proposed convergence analysis framework, the effectiveness of the hot‐start is explored, and the upper bounds of the number of iterations to guarantee the required accuracy of an obtained iterative solution are deduced. The proposed hot‐start framework derives a criterion for choosing the penalty parameter of the ADMM in MPC problems and facilitates effective iteration, which is validated in a numerical example. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has gained wide popularity both in academic and industrial contexts. Unlike traditional embedded devices with specialized firmwares, modern IoT devices accommodate general‐purpose operating systems, allowing developers to run more sophisticated applications written in high‐level languages like JavaScript. Because IoT devices are subject to resource constraints like available battery power, we need to dynamically migrate a running process between different devices to prevent losing state. However, it is challenging to apply migration techniques using memory snapshots across the heterogeneous pool of IoT devices. We present ThingsMigrate, a middleware providing platform‐independent migration of JavaScript processes across IoT devices. Prior to execution, ThingsMigrate instruments the source code of a given program to expose its internal state. During run‐time, the transformed program produces on demand a JSON snapshot of its current state, from which new code is generated to resume execution. Thus, ThingsMigrate enables process migration entirely in the application space without any modifications to the underlying virtual machine (VM), providing VM‐independence. We present three versions of ThingsMigrate, each building on the previous to optimize for run‐time latency and memory consumption. We report on the experience of building each successive version and discuss the insights gained and the learning outcomes. We evaluated ThingsMigrate against standard benchmarks, over two IoT platforms and a cloud‐like environment. We show that it can migrate even highly CPU‐intensive applications, with average run‐time latency overhead of 33% and memory overhead of 78%. ThingsMigrate supports multiple subsequent migrations without introducing additional overhead over each subsequent migration. [ABSTRACT FROM AUTHOR]

Summary: Multiple distributed stream queries, which are executed on stream processing systems, need to be fine‐tuned to the compute cluster in order to harness the full potential of the hardware they run on. In this paper, we describe an automatic technique for conducting such stream query optimization in the presence of multiple stream jobs. During this autotuning process, we identify the structure of each program and conduct automatic program transformation to generate optimized unified streaming jobs. The operators on the unified secondary sample application are grouped into processing elements considering their performance characteristics and the stream graph topology structure to produce high performance stream query network. We implemented this multiple stream query optimization technique on a mechanism called Tahitica. We demonstrate our approach's ability for producing optimized stream query performance by comparing it to naive deployments using two real‐world stream processing applications in the domains of health care and search advertising. Our stream query optimization approach reported 7.1% throughput performance improvement compared to a naive deployment. [ABSTRACT FROM AUTHOR]

This study presents the Wireless Charging Utility Maximization (WCUM) framework, which aims to maximize the utility of Wireless Charging Units (WCUs) for electric vehicle (EV) charging through the optimal WCU deployment at signalized intersections. Furthermore, the framework aims to minimize the control delay at all signalized intersections of the network. The framework consists of a two‐step optimization formulation, a dynamic traffic assignment model to calculate the user equilibrium, a traffic microsimulator to formulate the objective functions, and a global Mixed Integer Non‐Linear Programming (MINLP) optimization solver. An optimization problem is formulated for each intersection, and another for the entire network. The performance of the WCUM framework is tested using the Sioux Falls network. We perform a comparative study of 12 global MINLP solvers with a case study. Based on solution quality and computation time, we choose the Couenne solver for this framework. [ABSTRACT FROM AUTHOR]

The relationship between systems engineering and complex systems governance is not clear, especially given the field of ideas and methodologies supporting complex systems governance are in their early years of evolution. This paper looks to practice, drawing lessons from a case example about the role and application of systems engineering in a major reform program, especially in relation to complex systems governance, seeking to inform the evolution of both theory and practice in how these two fields of ideas work better together. The case study comes from the Australian defence maritime community. This community is transforming its approach to regulating and assuring the management of the seaworthiness of its maritime systems across multiple owners and operators and across their capability life cycles. The paper outlines the situation that created the case for change in the regulatory and management approach; and, examines the complication that caused the then Chief of Navy to change the approach half way through the six‐year transformation period. In making this change, 'traditional' systems engineering became less visible, and complex systems governance took precedence while still employing systems engineering principles in the design, build and implementation of the transformation program. The paper then considers three questions: what was the nature of the change, why did the change matter and what lessons do we draw for the relationship between complex systems governance and systems engineering? The authors' answers highlight the traps of not connecting the two fields of ideas. They illustrate the value of the viable systems model (VSM) as an architecture for good governance over the design, build, and implementation of change as well as its ongoing operation and evolution. They go so far as to suggest the lessons will add to the discussion of how the International Council of Systems Engineering (INCOSE) can implement the connections it has made between its vision and the United Nations' Sustainability Development Goals (SDG). [ABSTRACT FROM AUTHOR]

Software-defined networking that separates the control plane from the data plane is envisioned as a promising technology to enable resilient and flexible network management. Tolerating link failures is a fundamental problem in enhancing such network resilience in software-defined networking. Reactive and proactive fault tolerant schemes for conventional networks may not well balance the fault recovery time and network performance, since the proactive scheme typically underutilizes resources and the reactive scheme usually incurs a longer recovery time. In this paper, we propose a cooperative link failure recovery scheme to find a fine-grained trade-off between resource utilization and recovery time by combining reactive and proactive methods. We formalize the problem of link failure recovery as a multiobjective optimization problem and devise a 2-stage algorithm for it. The first stage of the algorithm guarantees connectivity restoration in an acceptable recovery interval based on fast failover feature supported in OpenFlow protocol, meanwhile it assigns virtual local area network tags to back up paths for achieving a lower memory consumption. The second stage of the algorithm guarantees the quality of service for different applications by adjusting the backup paths after rapid connectivity restoration. Extensive simulations highlight that cooperative link failure recovery scheme can satisfy both the carrier-grade recovery requirements and quality of service requirements in terms of delay and network bandwidth. [ABSTRACT FROM AUTHOR]

The Spiral Discovery Method (SDM) was originally proposed as a cognitive artifact for dealing with black-box models that are dependent on multiple inputs with nonlinear and/or multiplicative interaction effects. Besides directly helping to identify functional patterns in such systems, SDM also simplifies their control through its characteristic spiral structure. In this paper, a neural network-based formulation of SDM is proposed together with a set of automatic update rules that makes it suitable for both semiautomated and automated forms of optimization. The behavior of the generalized SDM model, referred to as the Spiral Discovery Network (SDN), and its applicability to nondifferentiable nonconvex optimization problems are elucidated through simulation. Based on the simulation, the case is made that its applicability would be worth investigating in all areas where the default approach of gradient-based backpropagation is used today. [ABSTRACT FROM AUTHOR]

In the recent time indoor positioning becomes an extremely hot topic between researchers worldwide. This is mainly because of lack of available positioning solutions in the indoor environment and possibility to offer novel services based on location of users. Such services can also open new markets for service providers and thus increase their income. In this paper we will focus on application of optimization algorithms on performance of hybrid indoor positioning system, which utilize radio signals from both Global System for Mobile communication and Wi-Fi networks simultaneously. The system is based on fingerprinting framework, which is widely used for indoor positioning because of its performance in strong multipath environment. Tests of the optimization algorithms were performed via simulations that were performed using simulation model created in Matlab environment. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents an open-source, generic and efficient implementation of a very popular nonlinear optimization method: the Levenberg-Marquardt algorithm (LMA). This minimization algorithm is well known and hundreds of implementations have already been released. However, none of them offer at the same time a high level of genericity, a friendly syntax and a high computational performance. In this paper, we propose a solution to gather all those advantages in one library named LMA. The main challenge is to implement an efficient solver for every encounter problem. To overcome this difficulty, LMA uses compile time algorithms to design a code specific to the given optimization problem. The features of LMA are presented and the performances are compared with the state-of-the-art best alternatives through extensive benchmarks on different kind of problems. [ABSTRACT FROM AUTHOR]

SPIRAL is an autotuning, program generation, and code synthesis system that offers a fully automatic generation of highly optimized target codes, customized for the specific execution platform at hand. Initially, SPIRAL was targeted at problem domains in digital signal processing, later also at basic linear algebra. We open SPIRAL up to a new, practically relevant and challenging domain: multigrid solvers. SPIRAL is driven by algebraic transformation rules. We specify a set of such rules for a simple multigrid solver with a Richardson smoother for a discretized square 2D Poisson equation with Dirichlet boundary conditions. We present the target code that SPIRAL generates in static single-assignment form and discuss its performance. While this example required no changes of or extensions to the SPIRAL system, more complex multigrid solvers may require small adaptations. [ABSTRACT FROM AUTHOR]

The weighted stress function method is proposed here as a new way of identifying the best solution from a set of nondominated solutions according to the decision maker's preferences, expressed in terms of weights. The method was tested using several benchmark problems from the literature, and the results obtained were compared with those of other methods, namely, the reference point evolutionary multiobjective optimization (EMO), the weighted Tchebycheff metric, and a goal programming method. The weighted stress function method can be seen to exhibit a more direct correspondence between the weights set by the decision maker and the final solutions obtained than the other methods tested. [ABSTRACT FROM AUTHOR]

A set of restructurings to systematically normalize selective syntax in C++ is presented. The objective is to convert variations in syntax of specific portions of code into a single form to simplify the construction of large, complex program transformation rules. Current approaches to constructing transformations require developers to account for a large number of syntactic cases, many of which are syntactically different but semantically equivalent. The work identifies classes of such syntactic variations and presents normalizing restructurings to simplify each variation to a single, consistent syntactic form. The normalizing restructurings for C++ are presented and applied to two open source systems for evaluation. The evaluation uses the system's test cases to validate that the normalizing restructurings do not affect the systems' tested behavior. In addition, a set of example transformations that benefit from the prior application of normalizing restructurings are presented along with a small survey to assess the effect of the readability of the resultant code. [ABSTRACT FROM AUTHOR]

Network function virtualization and software-defined networking allow services consisting of virtual network functions to be designed and implemented with great flexibility by facilitating automatic deployments, migrations, and reconfigurations for services and their components. For extended flexibility, we go beyond seeing services as a fixed chain of functions. We define the service structure in a flexible way that enables changing the order of functions in case the functionality of the service is not influenced by this and propose a YANG data model for expressing this flexibility. Flexible structures allow the network orchestration system to choose the optimal composition of service components that, for example, gives the best results for placement of services in the network. When number of flexible services and number of components in each service increase, combinatorial explosion limits the practical use of this flexibility. In this paper, we describe a selection heuristic that gives a Pareto set of the possible compositions of a service as well as possible combinations of different services, with respect to different optimization objectives. Moreover, we present a heuristic algorithm for placement of a combination of services, which aims at placing service components along shortest paths that have enough capacity for accommodating the services. By applying these solutions, we show that allowing flexibility in the service structure is feasible. [ABSTRACT FROM AUTHOR]

This paper describes a learning parallel constraint programming (CP) solver designed for solving CP problems with several instances on massively parallel computing platforms comprising multi-core parallel machines or Many Integrated Cores. The CP solver proposed in this work is based on a Portfolio parallelization that employs a linear reward inaction learning algorithm in order to obtain the best possible performance for a large set of instances of the same problem. The linear reward inaction algorithm enables the prediction of the number of cores to be assigned to each search strategy based on previous experiments, reducing the computing time required to solve constraint satisfaction and optimization problems. The underlying principle of the Portfolio approach is to run N sequential search strategies using N computing cores ( N To N Portfolio) where each core uses its own strategy in order to perform a search that is different from strategies used by the other cores. The first strategy that finds a solution stops all other strategies. The problem with the N To N Portfolio approach is that the number of search strategies is very small compared with the current number of computing cores used by the parallel machines. However, using an internal parallelization for each search strategy, it is possible to run N parallel search using P computing cores with P ≫ N ( N To P Portfolio). This N To P Portfolio performs suboptimally for solving different CP problems because many computing resources are wasted. To improve this Portfolio model, an adaptive N To P Portfolio was proposed, which tries to privilege the strategy that is most likely to find a solution first in order to give it more computing cores than the other strategies. However, the main problem with the adaptive Portfolio is that it loses all the learned information at the end of each search; it is designed to solve just one CP problem. Furthermore, many computational resources are wasted by both Portfolio solvers, the non-adaptive ( N To N and N To P) and the adaptive N To P Portfolio, when employed in some industrial projects, such as the PAJERO project, which always solves different instances of the same CP problem. To minimize the amount of wasted resources and to learn the most efficient search strategies, we propose a new learning Portfolio solver that uses a learning algorithm that configures automatically number of cores to each search. The performance obtained using the different Portfolio solvers is compared and illustrated by solving CP problems using as example the Google OR-Tools solver. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents a high-performance computing (HPC) framework for Monte Carlo (MC) simulations in the ocean color (OC) application domain. The objective is to optimize a parallel MC radiative transfer code named MOX, developed by the authors to create a virtual marine environment for investigating the quality of OC data products derived from in situ measurements of in-water radiometric quantities. A consolidated set of solutions for performance modeling, prediction, and optimization is implemented to enhance the efficiency of MC OC simulations on HPC run-time infrastructures. HPC, machine learning, and adaptive computing techniques are applied taking into account a clear separation and systematic treatment of accuracy and precision requirements for large-scale MC OC simulations. The added value of the work is the integration of computational methods and tools for MC OC simulations in the form of an HPC-oriented problem-solving environment specifically tailored to investigate data acquisition and reduction methods for OC field measurements. Study results highlight the benefit of close collaboration between HPC and application domain researchers to improve the efficiency and flexibility of computer simulations in the marine optics application domain. © 2016 The Authors. Concurrency and Computation: Practice and Experience Published by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Realistic applications of numerical modeling of acoustic wave dynamics usually demand high-performance computing because of the large size of study domains and demanding accuracy requirements on simulation results. Forward modeling of seismic motion on a given subsurface geological structure is by itself a good example of such applications, and when used as a component of seismic inversion tools or as a guide for the design of seismic surveys, its computational cost increases enormously. In the case of finite difference methods (or any other volumen-discretization scheme), memory and computing demands rise with grid refinement, which may be necessary to reduce errors on numerical wave patterns and better capture target physical devices. In this work, we present several implementations of a mimetic finite difference method for the simulation of acoustic wave propagation on highly dense staggered grids. These implementations evolve as different optimization strategies are employed starting from appropriate setting of compilation flags, code vectorization by using streaming SIMD extensions Advanced Vector Extensions (AVX), CPU parallelization by exploiting the Open Multi-Processing framework to the final code parallelization on graphics processing unit platforms. We present and discuss the increasing processing speed up of this mimetic scheme achieved by the gradual implementation and testing of all these performance optimizations. In terms of simulation times, the performance of our graphics processing unit parallel implementations is consistently better than the best CPU version. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Remote user authentication schemes allow an authorized user to access the resources of remote servers. A dynamic ID authentication scheme further provides the property of user anonymity, that is, information of user identification will not be compromised even if communicated messages are intercepted. When it comes to the mobile user authentication, the client-side processing capability is usually concerned the most. In this paper, the author proposes an efficient mobile dynamic ID authentication and key agreement scheme without trusted servers. For facilitating the application of mobile devices with limited processing capability, our scheme is optimized for the client-side computation. Moreover, compared with related works, the proposed scheme is also more secure. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A class of applications, such as home energy management and control and utility data acquisition, is emerging in recent times where smart meters, sensors, and appliances are networked together for intelligent management and coordination. Such applications rely on low data rate communication of monitoring and control information at large scale. For the underlying networking infrastructure to facilitate communication of the real-time and intermittent packet traffic expected, random access-based protocols are regarded as suitable medium access control solutions. A key challenge in this regard is that the random access protocols are prone to throughput degradation when the number of contending nodes grows, as expected with the infrastructures involved. Besides, provision for certain degree of criticality/priority is needed for some of the packets compared with the rest. With this background, this paper analytically determines the criterion for throughput-optimal operations in a network based on low-rate carrier sense multiple access protocol. In addition, ways to provide priority-wise access differentiation at arbitrary proportions without a negative impact on the achievable throughput is incorporated within a binary exponential backoff- based collision avoidance scheme. Discrete-event simulations are performed to validate the accuracy of the approximations made in analysis. [ABSTRACT FROM AUTHOR]

In network virtualization, it has been considered that virtual networks are constructed over a physical network where conventional data transmission services have been utilized. Here, virtual networks have to be constructed while keeping qualities of the conventional services. In this paper, we propose a new virtual network construction in order to construct many virtual networks while keeping the robustness of a physical network by using network resources effectively. The proposed method consists of three processes: K-shortest path algorithm and Prim's minimum spanning tree algorithm, path splitting, and path migration. In the proposed method, at first, multiple topologies are designed by using the K-shortest path algorithm and the Prim's MST algorithm according to the user's request. After the topology design is completed, an admission control with network robustness of the physical network is performed. Then, if one of the designed topologies can satisfy the construction conditions, a virtual network is constructed and provided with the user. Otherwise, the path splitting and path migration are performed. Here, the path splitting is utilized to design another topology of a virtual network and path migration is used to change the topologies of the virtual networks that have already been constructed. These processes are formulated as optimization problems and those are processed by solving the optimization problems. In numerical examples, we show that our proposed method can construct a higher number of virtual networks while keeping the robustness of a physical network by comparing with the conventional method where only the Kou-Markowsky-Berman algorithm is used. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

ABSTRACT Test and measurement devices (TMD) are the core tools for the engineering and technical education on the field of electrical and electronics. This study is mainly concentrated on the development of a low cost, multifunction original equipment manufacturer (OEM) product intended to be used in conventional TMD free educational training sets. An enhancement on the interactive learning is also demonstrated as a consequence of embedding computer interfaced TMD to the conventional sets. While the technical requirements for such an embedded system can be achieved by the use of high end microprocessors or FPGAs, this study is aimed to develop a cost effective solution with the low end microcontrollers which is only available with hardware and firmware optimization during the design cycle. © 2016 Wiley Periodicals, Inc. Comput Appl Eng Educ 24:905-913, 2016; View this article online at ; DOI [ABSTRACT FROM AUTHOR]

Multiuser cooperative communications represent a research area of great interest constituting a key technology for future wireless networks. In this study, the authors consider a scenario of multi-user, single-relay network, where each user employs a part of the relay's transmission power taking into account the constraint of the total transmission power of the relay. The problem of relay power allocation to individual users with a view to optimising the long-term network performance is investigated. An adequate utility function is defined, in terms of outage probability, evaluating the cooperative network's performance due to the relay's cooperation. Taking into consideration the distributed nature of cooperative networks along with the coupled constraint of relay's total transmission power, the specific problem is transformed from a network-level to a user-level optimisation problem using a modern game-theoretic tool. More specifically, the initial power allocation problem is formulated as a state-based potential game for which the corresponding Nash equilibrium exists. Finally, a gradient learning mechanism is proposed to achieve the stationary state Nash equilibrium of the game that constitutes also the global optimal solution of the initial problem. The performance of the new proposed scheme is investigated through numerical simulations for various network parameters. [ABSTRACT FROM AUTHOR]

In this study, the authors focus on the downlink resource allocation to optimise energy-efficiency (EE) of orthogonal frequency division multiplexing-based cognitive heterogeneous network (HetNet), where network service providers operate multi-radio access technologies. A more practical case is considered, in which imperfect channel state information (CSI) is available for wireless channels between secondary base stations and secondary users (SUs), primary users (PUs). Since imperfect CSI may result in outage in secondary networks and make collision occur in primary networks, a joint subcarrier and power allocation scheme for the cognitive HetNet is proposed to guarantee quality of service requirements for both SUs and PUs in probabilistic manners. Then the probabilistic EE optimisation scheme is approximated into a deterministic convex form, and the optimal solutions for the scheme are derived by double-loop iteration method. Afterwards, the authors develop a low-complexity algorithm in the presence of imperfect CSI with a small reduction of system performance. Simulation results are provided to show the impact of channel estimation error on the EE optimisation problem and to highlight the performance improvement from considering channel estimation error in the joint subcarrier and power allocation scheme for the cognitive HetNet system. [ABSTRACT FROM AUTHOR]

This article presents a novel neural network (NN) based on NCP function for solving nonconvex nonlinear optimization (NCNO) problem subject to nonlinear inequality constraints. We first apply the p-power convexification of the Lagrangian function in the NCNO problem. The proposed NN is a gradient model which is constructed by an NCP function and an unconstrained minimization problem. The main feature of this NN is that its equilibrium point coincides with the optimal solution of the original problem. Under a proper assumption and utilizing a suitable Lyapunov function, it is shown that the proposed NN is Lyapunov stable and convergent to an exact optimal solution of the original problem. Finally, simulation results on two numerical examples and two practical examples are given to show the effectiveness and applicability of the proposed NN. © 2015 Wiley Periodicals, Inc. Complexity 21: 130-141, 2016 [ABSTRACT FROM AUTHOR]

Streamgraphs were popularized in 2008 when The New York Times used them to visualize box office revenues for 7500 movies over 21 years. The aesthetics of a streamgraph is affected by three components: the ordering of the layers, the shape of the lowest curve of the drawing, known as the baseline, and the labels for the layers. As of today, the ordering and baseline computation algorithms proposed in the paper of Byron and Wattenberg are still considered the state of the art. However, their ordering algorithm exploits statistical properties of the movie revenue data that may not hold in other data. In addition, the baseline optimization is based on a definition of visual energy that in some cases results in considerable amount of visual distortion. We offer an ordering algorithm that works well regardless of the properties of the input data, and propose a 1-norm based definition of visual energy and the associated solution method that overcomes the limitation of the original baseline optimization procedure. Furthermore, we propose an efficient layer labeling algorithm that scales linearly to the data size in place of the brute-force algorithm adopted by Byron and Wattenberg. We demonstrate the advantage of our algorithms over existing techniques on a number of real world data sets. [ABSTRACT FROM AUTHOR]

Motivated by the significance of optimizing network performance with limited resources in real applications, we investigate the problem of achieving fast and desired consensus in a complex network via pinning control a fraction of nodes in the network. An optimization problem is proposed to select p optimal pinned nodes to guarantee fastest consensus, which is relaxed to a Mixed-Integer Semi-Definite Program. In the single pinned node case ( i.e., p = 1), we find that the optimal pinned node is the node with highest min-max distance centrality within the nodes with highest closeness centrality in the network. In the multiple pinned nodes case ( i.e., p > 1), we find that betweenness centrality could be used as a suboptimal index to select pinned nodes. [ABSTRACT FROM AUTHOR]

To date, increasing number of entities on the smart grid begin to establish their local energy generator for ensuring reliability and resilience of power supply. These 'microgrids' can either connect to the power grid or isolate themselves from the grid by consuming their locally generated or stored energy. In reality, some microgrids may have excessive energy while the others may have to request extra energy from the main grid. To better balance the demand and supply of the distributed smart microgrids, it is desired to develop peer-to-peer (P2P) energy exchange models that enable microgrids to interactively exchange their local energy instead of consuming energy delivered from the main grid. However, in this scenario, all the microgrids have to disclose their private information (e.g., demand load and energy storage amount) to each other in the exchange. To tackle these issues, in this paper, we first formulate several novel energy exchange optimization problems that minimize the global energy loss during the exchange in different scenarios, and then develop an efficient and privacy-preserving scheme to solve the energy exchange optimization problems without private information disclosure. We also extend the privacy-preserving scheme to a collusion-resistant scheme in which all the microgrids cannot learn any additional information through colluding with each other. The performance of our proposed approaches is experimentally validated on real microgrid data. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A multihead weigher machine (MWM) is a computer-controlled machine used to fill a package with small products or parts with a given target weight. MWMs are widespread in the food and non-food industries because of their high performance in terms of variety of products to be packed, high throughput and quality of the packaged items. Despite the popularity of these machines, no studies to date have addressed how to set up the operating parameters of these machines. In industrial practice, operators currently use ad hoc rules to setup an MWM, but the initial settings can have a dramatic effect on the machine performance. The aim of this paper is to formalize the setup problem and its main variables. Then an optimization technique is proposed to solve the problem. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In order to accomplish exploration missions in complex environments, a new type of robot has been designed. By analyzing the characteristics of typical moving systems, a new mobile system which is named wheel-tracked moving system (WTMS) has been presented. Then by virtual prototype simulation, the new system’s ability to adapt complex environments has been verified. As the curve of centroid acceleration changes in large amplitude in this simulation, ride performance of this robot has been studied. Firstly, a simplified dynamic model has been established, and then by affecting factors analysis on ride performance, an optimization model for suspension parameters has been presented. Using NSGA-II method, a set of nondominated solutions for suspension parameters has been gotten, and by weighing the importance of the objective function, an optimal solution has been selected to be applied on suspension design. As the wheel-tracked exploration robot has been designed and manufactured, the property test has been conducted. By testing on physical prototype, the robot’s ability to surmount complex terrain has been verified. Design of the wheel-tracked robot will provide a stable platform for field exploration tasks, and in addition, the certain configuration and suspension parameters optimization method will provide reference to other robot designs. [ABSTRACT FROM AUTHOR]

One heuristic evolutionary algorithm recently proposed is the grey wolf optimizer (GWO), inspired by the leadership hierarchy and hunting mechanism of grey wolves in nature. This paper presents an extended GWO algorithm based on Powell local optimization method, and we call it PGWO. PGWO algorithm significantly improves the original GWO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique. Hence, the PGWO could be applied in solving clustering problems. In this study, first the PGWO algorithm is tested on seven benchmark functions. Second, the PGWO algorithm is used for data clustering on nine data sets. Compared to other state-of-the-art evolutionary algorithms, the results of benchmark and data clustering demonstrate the superior performance of PGWO algorithm. [ABSTRACT FROM AUTHOR]

Computer experiments are used frequently for the study and improvement of a process under study. Optimizing such process based on a computer model is costly, so an approximation of the computer model, or metamodel, is used. Efficient global optimization (EGO) is a sequential optimization method for computer experiments based on a Gaussian process model approximation to the computer model response. A long-standing problem in EGO is that it does not consider the uncertainty in the parameter estimates of the Gaussian process. Treating these estimates as if they are the true parameters leads to an improper assessment of the precision of the approximation, a precision that is crucial to assess not only in optimization but in metamodeling in general. One way to account for these uncertainties is to use bootstrapping, studied by previous authors. Alternatively, some other authors have mentioned how a Bayesian approach may be the best way to incorporate the parameter uncertainty in the optimization, but no fully Bayesian approach for EGO has been implemented in practice. In this paper, we present a fully Bayesian implementation of the EGO method. The proposed Bayesian EGO algorithm is validated through simulation of noisy nonlinear functions and compared with the standard EGO method and the bootstrapped EGO. We also apply the Bayesian EGO algorithm to the optimization of a stochastic computer model. It is shown how a Bayesian approach to EGO allows one to optimize any function of the posterior predictive density. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A hybrid optimization methodology is presented for the probabilistic finite element model updating of structural systems. The model updating process is formulated as an inverse problem, analyzed by Bayesian inference, and solved using a hybrid optimization algorithm. The proposed hybrid approach is a combination of a modified artificial bee colony (MABC) algorithm and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. The MABC includes four modifications compared to the standard ABC algorithm, which basically improve the global convergence of ABC in the solution phases of initialization, updating, selection, and rebirth. The BFGS is inserted to improve the finer solution search ability aiming at a higher solution accuracy. In brief, a probabilistic framework based on Bayesian inference is first derived so to get a regularized objective function for optimization. Then the proposed MABC-BFGS algorithm is applied to determine the unknown system parameters by minimizing the newly defined objective function. System parameters as well as the prediction error covariance are updated iteratively in the optimization process. Posterior distributions of the identified system parameters are determined using a weighted sum of Gaussian distributions. Finally, the effectiveness of the proposed approach is illustrated by the numerical data sets of the Phase I IASC-ASCE benchmark model and the experimental data sets of a three-storey frame structure (from the Los Alamos National Laboratory (LANL), New Mexico, United States). [ABSTRACT FROM AUTHOR]

In this paper, we formalize the defect-prediction problem as a multiobjective optimization problem. Specifically, we propose an approach, coined as multiobjective defect predictor (MODEP), based on multiobjective forms of machine learning techniques-logistic regression and decision trees specifically-trained using a genetic algorithm. The multiobjective approach allows software engineers to choose predictors achieving a specific compromise between the number of likely defect-prone classes or the number of defects that the analysis would likely discover (effectiveness), and lines of code to be analysed/tested (which can be considered as a proxy of the cost of code inspection). Results of an empirical evaluation on 10 datasets from the PROMISE repository indicate the quantitative superiority of MODEP with respect to single-objective predictors, and with respect to trivial baseline ranking classes by size in ascending or descending order. Also, MODEP outperforms an alternative approach for cross-project prediction, based on local prediction upon clusters of similar classes. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY MapReduce frameworks allow programmers to write distributed, data-parallel programs that operate on multisets. These frameworks offer considerable flexibility to support various kinds of programs and data. To understand the essence of the programming model better and to provide a rigorous foundation for optimizations, we present an abstract, functional model of MapReduce along with a number of customization options. We demonstrate that the MapReduce programming model can also represent programs that operate on lists, which differ from multisets in that the order of elements matters. Along with the functional model, we offer a cost model that allows programmers to estimate and compare the performance of MapReduce programs. Based on the cost model, we introduce two transformation rules aiming at performance optimization of MapReduce programs, which also demonstrates the usefulness of our model. In an exploratory study, we assess the impact of applying these rules to two applications. The functional model and the cost model provide insights at a proper level of abstraction into why the optimization works. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY Domain-specific languages (DSLs) are well-recognized to ease programming and improve robustness for a specific domain, by providing high-level domain-specific notations and checks of domain-specific properties. The compiler of a DSL, however, is often difficult to develop and maintain, because of the need to define a specific treatment for a large and potentially increasing number of language constructs. To address this issue, we propose an approach for specifying a DSL compiler using control-flow sensitive concrete-syntax based matching rules. These rules either collect information about the source code to carry out checks or perform transformations to carry out compilation. Because rules only mention the relevant constructs, using their concrete syntax, and hide the complexity of control-flow graph traversal, it is easy to understand the purpose of each rule. Furthermore, new compilation steps can be added using only a small number of lines of code. We explore this approach in the context of the z2z DSL for network gateway development and show that it is beneficial to implement the core of its compiler in this manner.Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Marine reserve design often involves a tradeoff between meeting conservation goals (persistence of fished populations) and minimizing economic costs (lost fishing grounds). Optimization tools such as Marxan navigate that tradeoff by finding reserve configurations that minimize economic costs while protecting some minimum fraction of fish habitat. However, typical Marxan implementations do not account for patterns of larval connectivity among reserves, a factor known to be the key to population dynamics. We show how connectivity information (self-recruitment and network centrality) can be incorporated into the optimization. We then used a spatially explicit population model to compare the performance of reserves designed using habitat information alone or including connectivity. Incorporating connectivity information improved reserve performance for some species but not others. We conclude that including connectivity information can improve reserve design algorithms, but it is essential to evaluate species' population dynamics to determine which species will benefit from a given reserve network. [ABSTRACT FROM AUTHOR]

ABSTRACT We study the problem of intruder tracking with non-stealthy sensors, i.e., sensors whose ON/OFF state can be detected by an intruder, sometimes in advance. The sensor field is assumed to be operating with a sleep schedule to conserve energy. Both motion sensors and presence sensors are considered. We provide a rigorous basis for the study of this scenario by defining a new intruder model, the Ideal Intruder, that knows the entire sleep schedule of all the sensors in the field. More realistic intruders that have spatially and temporally limited knowledge of the sensor states are also defined. We study the well-known Random Independent Sleep (RIS) scheduling scheme with a single parameter p, giving mathematical bounds for the ideal intruder's crossing time in the motion sensor case and showing that the crossing probability in the presence sensor case undergoes a sharp transition as p increases. Further, we show that non-ideal intruders perform almost as well as the ideal intruder against RIS. Motivated by this finding and by the comparison between Barrier Coverage and RIS, we define a new sleep scheduling scheme, Spotlight, that is more robust to faults than Barrier Coverage and more effective than RIS. But more than that, Spotlight is shown to be specifically suited to the non-stealthy case because a non-ideal intruder, that is, one with limited information performs significantly worse against it than the ideal intruder. Spotlight, therefore, apart from being a novel and interesting sleep scheduling scheme in its own right, also illustrates the power of the analytical framework we introduce in this paper. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

ABSTRACT This study focuses on the false feedback of spectrum information and the malicious behavior that forcibly occupies spectrum resources in cognitive radio networks. A credible spectrum sensing and allocation model based on fuzzy theory is proposed. The spectrum sensing behavior and spectrum utilization behavior are taken as two evaluation factors. On the basis of subjectivity and uncertainty of trust, this study adopts the fuzzy comprehensive evaluation method to construct the trust evaluation of nodes. In cooperative spectrum sensing, comprehensive evaluation results can be used to identify malicious nodes and eliminate false feedback information. In spectrum allocation, the degree of lattice closeness among fuzzy sets is used to define and calculate the difference between the actual comprehensive evaluation set and the ideal comprehensive evaluation. The difference is calculated to quantify the credibility of non-malicious nodes and to determine the allocation of spectrum resources to nodes using the multi-objective optimization algorithm. These techniques can control the malicious behaviors of nodes and encourage cooperative behavior among nodes. Consequently, the joint design of spectrum sensing at the physical layer and spectrum allocation at the media access control layer is realized. Simulation results and analysis indicate that under malicious attacks, the proposed model has sound performance in terms of system sensing, throughput, and fair spectrum allocation compared with existing models. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

ABSTRACT In this paper, we address the optimization problems of downlink coordinated beamforming over the K-user multiple-input single-output interference channel with confidential messages. Our design objective is to minimise the total transmit power subject to the constraints of individual signal-to-interference plus noise ratio and equivocation rates. For the case with perfect channel state information (CSI), by deriving the equivalent forms for the signal-to-interference plus noise ratio and the secrecy constraints, the optimization problem is reformulated as a second-order cone programme problem. Then, an optimal distributed algorithm using the partial Lagrangian duality is discussed. In addition we extend the analysis to the imperfect CSI scenario with bounded ellipsoidal errors, the centralised algorithm has been proposed by solving a semi-definite programme with the help of rank relaxation, whereas the distributed robust algorithm is devised by using ADMM. Finally, the simulation results have shown that with perfect CSI, the centralised and distributed algorithms have nearly the same performance, but in the case of imperfect CSI, the centralised algorithm shows a slightly better performance. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, the problem of the data and model management in the lifecycle of a process plant is addressed by presenting the database structure of the modeling and optimization platform MOSAIC. The first step of data integration is done by combining models and plant data into one central database. This has advantages for both plant design and plant optimization. Basic engineering workflows can then be improved. Several database configurations are presented to meet the different requirements with respect to security and open access. [ABSTRACT FROM AUTHOR]

ABSTRACT In this paper, the joint source-relay power allocation issue in green dual-hop single-antenna relaying systems is investigated from the energy efficiency point of view. By considering a practical power consumption model, the optimal transmit power of the source and that of the relay are derived in the form of analytical expressions based on the maximisation of energy efficiency with a guarantee of the required spectral efficiency. The novelty of the work lies in that the highly complicated optimisation problem is solved by deliberately manipulating the Lagrangian function using the properties of the Lambert function. It is shown that the optimal relay-to-source power ratio is adaptive to the ratio of the instantaneous two-hop channel gains. Numerical simulations have illustrated the effectiveness of our proposed scheme. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A robust multi-response optimization framework is proposed for simultaneously optimizing multiple conflicting quality characteristics. Unlike prior methods, the proposed approach is insensitive to subjective inputs like target specifications and improves optimization process for correlated responses. The effectiveness of the proposed approach is demonstrated and compared with existing methods considering two examples from the literature. The proposed method yields similar results consistently for different assigned target values demonstrating repeatability of the model, hence demonstrating insensitivity to assigned subjective target values. Furthermore, the study also considers multiple correlated design characteristics issue to achieve better trade-off during design optimization. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) are flexible computing tools that have been applied to a wide range of domains with a notable level of accuracy. However, there are multiple choices of ANNs classifiers in the literature that produce dissimilar results. As a consequence of this, the selection of this classifier is crucial for the overall performance of the system. In this work, an integral framework is proposed for the optimization of different ANN classifiers based on statistical hypothesis testing. The framework is tested in a real ballistic scenario. The new quality measures introduced, based on the Student t-test, and employed throughout the framework, ensure the validity of results from a statistical standpoint; they reduce the appearance of experimental errors or the appearance of possible randomness. Results show the relevance of this framework, proving that its application improves the performance and efficiency of multiple classifiers. [ABSTRACT FROM AUTHOR]

To speed up data-intensive programs, two complementary techniques, namely nested loops parallelization and data locality optimization, should be considered. Effective parallelization techniques distribute the computation and necessary data across different processors, whereas data locality places data on the same processor. Therefore, locality and parallelization may demand different loop transformations. As such, an integrated approach that combines these two can generate much better results than each individual approach. This paper proposes a unified approach that integrates these two techniques to obtain an appropriate loop transformation. Applying this transformation results in coarse grain parallelism through exploiting the largest possible groups of outer permutable loops in addition to data locality through dependence satisfaction at inner loops. These groups can be further tiled to improve data locality through exploiting data reuse in multiple dimensions. [ABSTRACT FROM AUTHOR]

Proliferation of mutually exclusive applications on circuits and the higher cost of silicon make the resource sharing more and more important. The state-of-the-art synthesis tools may often be unsatisfactory. Their efficiency may depend on the hardware description style. Nevertheless, today, different applications in a circuit can be developed by different developers. This paper proposes an efficient method to improve resource sharing between mutually exclusive applications with no dependence on the coding style. It takes the advantage of the possibility of resource sharing as done in FPGA and of predefined multiple functions as in ASIC. [ABSTRACT FROM AUTHOR]

We present a method for accelerating the convergence of continuous non-linear shape optimization algorithms. We start with a general method for constructing gradient vector fields on a manifold, and we analyse this method from a signal processing viewpoint. This analysis reveals that we can construct various filters using the Laplace-Beltrami operator of the shape that can effectively separate the components of the gradient at different scales. We use this idea to adaptively change the scale of features being optimized to arrive at a solution that is optimal across multiple scales. This is in contrast to traditional descent-based methods, for which the rate of convergence often stalls early once the high frequency components have been optimized. We demonstrate how our method can be easily integrated into existing non-linear optimization frameworks such as gradient descent, Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the non-linear conjugate gradient method. We show significant performance improvement for shape optimization in variational shape modelling and parameterization, and we also demonstrate the use of our method for efficient physical simulation. [ABSTRACT FROM AUTHOR]

The field of parallel metaheuristics is continuously evolving as a result of new technologies and needs that researchers have been encountering. In the last decade, new models of algorithms, new hardware for parallel execution/communication, and new challenges in solving complex problems have been making advances in a fast manner. We aim to discuss here on the state of the art, in a summarized manner, to provide a solution to deal with some of the growing topics. These topics include the utilization of classic parallel models in recent platforms (such as grid/cloud architectures and GPU/APU). However, porting existing algorithms to new hardware is not enough as a scientific goal, therefore researchers are looking for new parallel optimization and learning models that are targeted to these new architectures. Also, parallel metaheuristics, such as dynamic optimization and multiobjective problem resolution, have been applied to solve new problem domains in past years. In this article, we review these recent research areas in connection to parallel metaheuristics, as well as we identify future trends and possible open research lines for groups and PhD students. [ABSTRACT FROM AUTHOR]