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Electric distribution grid operations typically rely on both centralized optimization and local non-optimal control techniques. As an alternative, distribution system operational practices can consider distributed optimization techniques that leverage communications among various neighboring agents to achieve optimal operation. With the rapidly increasing integration of distributed energy resources (DERs), distributed optimization algorithms are growing in importance due to their potential advantages in scalability, flexibility, privacy, and robustness relative to centralized optimization. Implementation of distributed optimization offers multiple challenges and also opportunities. This paper provides a comprehensive review of the recent advancements in distributed optimization for electric distribution systems and classifications using key attributes. Problem formulations and distributed optimization algorithms are provided for example use cases, including volt/var control, market clearing process, loss minimization, and conservation voltage reduction. Finally, this paper also presents future research needs for the applicability of distributed optimization algorithms in the distribution system. [ABSTRACT FROM AUTHOR]

The paper addresses a real-time optimization-based reference calculation integrated with a control structure for Modular Multilevel Converters (MMC) operating under normal and constrained situations (where it has reached current and/or voltage limitations, as it may occur during system faults). Firstly, a nonlinear optimization problem has been developed in which it prioritizes to satisfy the AC grid current set-points imposed by the transmission System Operator (TSO). The constrained nonlinear optimization problem is formulated based on the steady-state model of the MMC, whereby the prioritization is achieved through distinct weights defined in the Objective Function’s (OF) terms. The resultant optimization problem, however, is highly nonlinear requiring high computation burden to be solved in real-time. To cope with this issue, this paper applies a Linear Time-Varying (LTV) approximation, which permits to represent the nonlinear dynamics of the system as constant parameters, while a Linear Time-Invariant (LTI) system is used to formulate the optimization constraints. The converter’s current references are determined in real-time by solving a constrained linearized optimization problem at each control time step, which considers the TSO’s demands, the current MMC operating point and its physical limitations. Theoretical analyses comparing the responses of the linear and nonlinear optimization problems are performed to validate the accuracy of the LTV approximation. Finally, the linearized-optimization problem is integrated with the MMC controllers, evaluated under different AC and DC network conditions and compared with conventional control strategies, where it is shown that the presented method can be potentially employed to obtain the MMC current references for distinct network scenarios. [ABSTRACT FROM AUTHOR]

Superdirective arrays have been extensively studied because of their considerable potential accompanied, unfortunately, by a high sensitivity to random errors that affect the responses and positions of array elements. However, the statistics of their actual beam pattern (BP) has never been systematically investigated. This paper shows that the Rician probability density function (PDF), sometimes adopted to study the impact of errors in conventional arrays, is a valid approximation for superdirective BP statistics only where some mathematical terms are negligible. The paper also shows that this is the case for all linear end-fire arrays considered. A similar study is proposed concerning the correlation between BP lobes, showing that for the superdirective arrays considered the lobes, especially non-adjacent ones, are almost independent. Furthermore, knowledge of the PDF of the actual BP allows one to define quantile BP functions, whose probability of being exceeded, at any point, is fixed. Combining the lobes’ independence with quantile BP functions, an empirical equation for the probability that the entire actual BP will not exceed a quantile function over an interval larger than a given size is obtained. This new knowledge and these tools make it possible to devise new methods to design robust superdirective arrays via optimization goals with clearer and more relevant statistical meaning. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have attracted enormous attention in recent years due to their potentials in mitigating energy crisis and air pollutions. However, the long battery charging time and lack of sufficient charging infrastructure highly restrict the popularization of EVs. In this context, it is promising to establish battery charging and swapping systems (BCSSs) based on the concept of battery swapping services. To optimally achieve the combined operation of BCSSs, this paper proposes a hybrid swapped battery charging and logistics dispatch model in continuous time domain. Identifying the special structure of the mathematical models of the two problems, this paper innovatively formulated the swapped battery charging strategy as the rectangle packing problem and the battery logistics model as the vehicle routing problem. The two models are closely linked by the delivery time of transporting the well-charged batteries from battery charging stations to battery swapping stations. A hybrid optimal operation model of BCSSs is further formulated as a mixed-integer linear programming model by incorporating the interaction between the battery charging and battery logistics. Finally, case studies are conducted on several BCSSs and numerical results validate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

In the context of project management, the attention given to project portfolio management has increased in recent years. The use of mathematical programming for portfolio management is also on the rise, because it integrates the project interactions with the multiple objectives of portfolio management into a single model. Among the possible objectives, recent studies have paid special attention to the emerging objective of future preparedness, which has not yet been incorporated into the existing mathematical models. This paper presents a mixed-integer nonlinear optimization model for portfolio selection that considers four main performance measures for project management, namely, value maximization, strategic alignment, balance, and future preparedness. Given the importance of the last measure, the purpose of this paper is to provide a more complete model that provides the marginal contribution and the best combination of projects according to the needs of the company. The model is tested using real data from two companies, one in Brazil and one in Canada, and the results obtained are coherent with their respective practices. [ABSTRACT FROM AUTHOR]

The nonlinear and nonconvex nature of ac power flow challenges the analysis and optimization of unbalanced distribution networks. To tackle this issue, this paper proposes an online feedback-based linearized power flow model for unbalanced distribution networks with both wye-connected and delta-connected loads. The online feedback-based linearized model is grounded on the first-order Taylor expansion of the branch flow model (BFM), and updates the model parameters via online feedback by leveraging the instantaneous measurements of voltages and load consumption. Exploiting the connection structure of unbalanced radial distribution networks, we also provide a unified matrix-vector compact form of the model. The numerical tests on the IEEE 123-bus test system validate its accuracy and superiority compared with other offline BFM methods. Additionally, we apply the proposed model to the optimal power flow for voltage regulation, which further demonstrates its effectiveness. [ABSTRACT FROM AUTHOR]

The aim of the paper is to define a limit performance of highly efficient battery electric quadricycles for urban mobility. The vehicle is employed for an energy efficiency competition in which urban concepts compete. A multi-physics (thermo-electro-mechanical) Tank To Wheels (TTW) model has been developed and validated. Given the information on the track route (track map and elevation) and on the throttle input command, the model computes the vehicle power demand and cruising speed. The model is validated by means of both indoor and outdoor experimental tests. The validated model is employed for the optimization of the transmission gear ratio and of the driving strategy to minimize the overall energy consumption on a given track. Design variables are related to the transmission gear ratio and to the throttle command profile. The algorithm aims to minimize the energy consumption, including constraints on the maximum current provided by the battery and on the maximum time available to complete the lap. Comparison with some common driving strategies confirmed the effectiveness of the proposed solution, with a foreseen 9% reduction of the vehicle overall energy demand. The single seater ultra-efficient electric vehicle, compared to other urban quadricycles, has a battery capacity ten times lower and a 50% longer driving range. [ABSTRACT FROM AUTHOR]

In order to reduce the dependence on accumulated experience that is difficult to replicate for the human dispatchers to make power generation scheduling more automatically, quickly, and intelligently, higher requirements are placed on the level of the auxiliary decision-making system. In this paper, a short-term scheduling problem was treated as a regression task that concentrates on how to learn a reliable statistic model that concludes the intrinsic logic of the dispatching policy from extensive historical dispatching experience. In this way, since Kullback-Leibler distance can better measure the distance between two distributions, we designed a novel GAN-based learning method for the scheduling task. Also, we proposed a feasible framework that combines the stage of learning, decision-making, and deployment to support the practical implementation of the proposed algorithm. In the experiment, a real case that takes short-term scheduling tasks on a regional large-scale power system is considered in our experiments. As a result, the comparison of several methods further shows the superiority of the proposed GAN-based method under our implementations. [ABSTRACT FROM AUTHOR]

This paper considers a novel algorithmic framework for the joint spectral sharing design of a colocated MIMO pulse radar and a MIMO communication system under signal direction mismatch, wherein both radar transceiver and communication transmitter designs are investigated, and the mutual information (MI) is maximized under a number of constraints to suppress the mutual interference of the coexistence system. The designed multi-variate formulation is non-convex, an iterative procedure is derived to compute a sequence of simpler sub-problems which admit a low complexity closed-form solution for each primal variable. The convergence of all devised algorithms is guaranteed. Finally, a thorough performance assessment is conducted to demonstrate the merits of the proposed solutions. [ABSTRACT FROM AUTHOR]

Objective: Computational modeling is increasingly used to design charging systems for implanted medical devices. The design of these systems must often satisfy conflicting requirements, such as charging speed, specific absorption rate (SAR) and coil size. Fast electromagnetic solvers are pivotal for enabling multi-criteria optimization. In this paper, we present an analytical model based on the quasi-static approximation as a fast, yet sufficiently accurate tool for optimizing inductive charging systems. Methods: The approximate model was benchmarked against full-wave simulations to validate accuracy and improvement in computation time. The coupling factor of two test coils was measured for lateral and axial displacements and the SAR was measured experimentally in a PAA phantom. Results: The approximate model takes only 11 seconds to compute a single iteration, while the full-wave model takes 5 hours to compute the same case. The maximum difference with full-wave simulations was less than 24% and the mean difference less than 2%. Adding a novel figure of merit into the multi-criterion optimization resulted in a 16% higher charging speed. The measured results of the SAR and coupling factor are within a 5 mm coil offset margin. Conclusion: The proposed approximate model succeeds as a rapid prototyping tool, enabling fast and sufficiently accurate optimization for wireless charging systems. Significance: The approximate model is the first of its kind to compute both the coupling factor and the SAR near conducting structures fast enough to enable optimization of charging speed. [ABSTRACT FROM AUTHOR]

Due to the increasing penetration of large-scale renewable energy generation at transmission level, hybrid AC/DC transmission expansion is becoming more relevant by the day, especially if the considered renewable generation is far away from existing AC grid infrastructure. This paper presents a multi-period hybrid AC/DC network expansion planning model that incorporates node voltages, reactive power and network losses. Since the operation of a high voltage DC link is influenced considerably by the converter stations, converter transformer, filter, and phase reactor are modeled in each converter station. Since the AC power flow equations render a non-convex problem whose solution algorithms do not always guarantee convergence or optimality, a second-order conic relaxation is utilized. The resulting model is a mixed-integer convex problem with a convergence guarantee that embodies a better approximation of system operation than its DC linear counterpart. Three case studies are considered to illustrate the proposed model. [ABSTRACT FROM AUTHOR]

State estimation plays a key role in the transition from the passive to the active operation of distribution systems, as it allows to monitor the networks and provides the necessary information to perform control actions. However, designing state estimators for distribution systems is challenging, due to the characteristics of the networks, such as limited measurement availability. Furthermore, the features of the distribution system present significant local variations, e.g., voltage level and number and type of customers, which makes it hard to design a “one-size-fits-all” state estimator. This paper introduces a unifying framework that allows to easily implement and compare diverse unbalanced static state estimation models. This is achieved by formulating state estimation as a general constrained optimization problem. The advantages of this approach are described and supported by numerical illustration on a large set of real distribution feeders. The framework is also implemented in software and made available open-source. [ABSTRACT FROM AUTHOR]

This paper proposes a new deep learning (DL) based model-free robust method for bulk system on-line load restoration with high penetration of wind power. Inspired by the iterative calculation of the two-stage robust load restoration model, the deep neural network (DNN) and deep convolutional neural network (CNN) are respectively designed to find the worst-case system condition of a load pickup decision and evaluate the corresponding security. In order to find the optimal result within a limited number of checks, a load pickup checklist generation (LPCG) algorithm is developed to ensure the optimality. Then, the fast robust load restoration strategy acquisition is achieved based on the designed one-line strategy generation (OSG) algorithm. The proposed method finds the optimal result in a model-free way, holds the robustness to handle uncertainties, and provides real-time computation. It can completely replace conventional robust optimization and supports on-line robust load restoration which better satisfies the changeable restoration process. The effectiveness of the proposed method is validated using the IEEE 30-bus system and the IEEE 118-bus system, showing high computational efficiency and considerable accuracy. [ABSTRACT FROM AUTHOR]

Multiple solutions are often needed because of different kinds of uncertain failures in a plan execution process and scenarios for which precise mathematical models and constraints are difficult to obtain. This paper proposes an optimization strategy for multirobot task allocation (MRTA) problems and makes efforts on offering multiple solutions with same or similar quality for switching and selection. Since the mentioned problem can be regarded as a multimodal optimization one, this paper presents a niching immune-based optimization algorithm based on Softmax regression (sNIOA) to handle it. A prejudgment of population is done before entering an evaluation process to reduce the evaluation time and to avoid unnecessary computation. Furthermore, a guiding mutation (GM) operator inspired by the base pair in theory of gene mutation is introduced into sNIOA to strengthen its search ability. When a certain gene mutates, the others in the same gene group are more likely to mutate with a higher probability. Experimental results show the improvement of sNIOA on the aspect of accelerating computation speed with comparison to other heuristic algorithms. They also show the effectiveness of the proposed GM operator by comparing sNIOA with and without it. Two MRTA application cases are tested finally. [ABSTRACT FROM AUTHOR]

Federated learning (FL) has been considered as a promising paradigm for enabling distributed training/learning in many machine-learning services without revealing users’ local data. Driven by the growing interests in exploiting FL in wireless networks, this paper studies the Non-orthogonal Multiple Access (NOMA) assisted FL in which a group of end-devices (EDs) form a NOMA cluster to send their locally trained models to the cellular base station (BS) for model aggregation. In particular, we consider that the BS adopts wireless power transfer (WPT) to power the EDs (for their data transmission and local training) in each round of FL iteration, and formulate a joint optimization of the BS’s WPT for different EDs, the EDs’ NOMA-transmission for sending the local models to the BS, the BS’s broadcasting of the aggregated model to all EDs, the processing-rates of the BS and EDs, as well as the training-accuracy of the FL, with the objective of minimizing the system-wise cost accounting for the total energy consumption as well as the FL convergence latency. In spite of the strict non-convexity of the joint optimization problem, we analytically characterize the BS’s and all EDs’ optimal processing-rates, based on which we propose a layered algorithm for finding the optimal solutions for the joint optimization problem via exploiting monotonic optimization. Numerical results validate that our algorithm can achieve the optimal solution as LINGO’s global-solver (i.e., a commercial optimization package) while significantly reducing the computation-time. Moreover, the results also demonstrate that our NOMA assisted FL can reduce the system cost compared to the benchmark FL scheme with the fixed local training-accuracy by more than 70% and the conventional frequency division multiple access (FDMA) based FL by 78%. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth thermomechanical analysis of zero-power micromechanical photoswitches (MPs) aiming at aggressive performance enhancement for applications that require ultralow infrared (IR) detection threshold. An accurate analytical model was first derived and used to maximize the thermal sensitivity of the MPs through a loop-based optimization program. With specific pre-defined boundary conditions, such a program can be used to generate a set of device geometrical parameters leading to highest possible thermal sensitivity without sacrificing the structural strength of the device (i.e., spring constant), which otherwise will have to be done with time-consuming numerical simulations. The optimized MPs show more than 4 times improvement in thermal sensitivity compared to previous demonstrations, thanks to the high thermal resistance and temperature sensitivity achieved using the thermomechanical model. The demonstrated theoretical analysis and thermomechanical optimization methods for the MPs are critical for the development of the next-generation zero-power IR sensors specifically designed for applications that require ultra-sensitive IR detection capability without compromising their sensor size, weight and power consumption. [2021-0231] [ABSTRACT FROM AUTHOR]

In subway systems, increasing attention has been paid to improve energy efficiency due to remarkable energy consumption for rail transportation operation. The optimization of single-train driving control and multi-train operation synergy are two critical and closely related aspects for energy saving, but they are rarely solved in an integrative manner. The comprehensive study on these two aspects is a complex, large-scale, multi-variable and constrained task, which involves nested, coupled and mutual-input optimization processes. This paper proposes an energy-efficient integrated model, which includes optimizing single-train operation control in an inner model and multi-train operation synergy, committing to the utilization of regenerative braking energy (RBE) in an outer model. A parallelized particle swarm optimization based on Map-Reduce (PPSO-MR) algorithm is proposed to solve the integrated model to reduce the execution time. Two concepts of temporal-spatial record and time shift are introduced to derive the movement of a train at inter-stations for the operation adjustment of multiple trains on a subway line. Experimental results show that the proposed control model solved by the holistic optimization approach can achieve lower energy consumption with fast execution time. This study provides the novel model and algorithm to obtain energy-saving schedules serving the multi-train operation coordination. [ABSTRACT FROM AUTHOR]

Analytical expressions that have been derived to calculate ac resistance and losses of magnetic devices at high frequencies have primarily used a one-dimensional approximation, and have used Cartesian coordinates or approximations that are equivalent to Cartesian coordinates. The result is Dowell's equation. This paper examines the effect of winding curvature on the losses relative to Dowell's equation by solving the problem completely in cylindrical coordinates. The error in Dowell's equation relative to a full cylindrical solution is found to be as great as 33% for some designs. A mathematical model is derived for the relative error between the two solutions, which can be used to assess the accuracy of Dowell's equation for a particular design, as well as a correction factor for Dowell's equation. The corrected Dowell's equation using the model is validated using finite-element analysis simulations as well as previously published experimental data. This paper is accompanied by Python codes to generate all data and figures used in the paper, a Python object that implements the derived mathematical model, and Lua scripts to run the finite-element simulations using FEMM. [ABSTRACT FROM AUTHOR]

In the era of ubiquitous computing, offloading certain computing tasks to the mobile ad hoc cloud (MAHC) could help mobile devices reduce execution time. However, if multiple resource demanders (RDs) offload tasks to the MAHC without a proper scheduling policy, it may cause unbalanced load distribution among resource providers (RPs), which will affect the overall quality of service (QoS). To this end, in this paper, we propose a multi-agent independent learning approach aiming to optimize the QoS of MAHC. Firstly, for the distributed MAHC, we formulate the QoS optimization model as a non-cooperative game, where each RD competes for maximizing its own utility. Secondly, based on the potential game theory, we prove the existence of Nash equilibrium. A multi-agent independent learning algorithm is then proposed to obtain the equilibrium points, and the convergence of this algorithm is analyzed. Simulation results confirm that the proposed approach helps balance the load distribution and enhances the QoS of MAHC. [ABSTRACT FROM AUTHOR]

This paper proposes a new decentralized data-driven load restoration (DDLR) scheme for transmission and distribution (TD) systems with high penetration of wind power. Robust DDLR models are constructed in order to handle uncertainties and ensure the feasibility of decentralized schemes. The Wasserstein metric is used to describe the ambiguity sets of probability distributions in order to build the complete DDLR model and realize computationally tractable formulation. A data-driven model-nested analytical target cascading (DATC) algorithm is developed to obtain the final load restoration result by iteratively solving small-scale mathematical models. The proposed DDLR scheme provides load restoration results with adjustable robustness, and performance efficiency is independent from the amount of data. The DDLR scheme makes full use of the available data while respecting information privacy requirements of independent operated systems, and ensures the feasibility of a decentralized load restoration strategy even in the worst-case condition. The effectiveness of the proposed method is validated using a small-scale TD system and a large-scale system with the IEEE 118-bus TS and thirty IEEE-33 DSs, showing high computational efficiency and superior restoration performance. [ABSTRACT FROM AUTHOR]

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application's behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems. [ABSTRACT FROM AUTHOR]

This paper presents a generic methodology to optimize constellations based on their geometrical shaping for bit-interleaved coded modulation (BICM) systems. While the method can be applicable to any wireless standard design it has been tailored to two delivery scenarios typical of broadcast systems: 1) robust multimedia delivery and 2) UHDTV quality bitrate services. The design process is based on maximizing the BICM channel capacity for a given power constraint. The major contribution of this paper is a low complexity optimization algorithm for the design of optimal constellation schemes. The proposal consists of a set of initial conditions for a particle swarm optimization algorithm, and afterward, a customized post processing procedure for further improving the constellation alphabet. According to the broadcast application cases, the sizes of the constellations proposed range from 16 to 4096 symbols. The BICM channel capacities and performance of the designed constellations are compared to conventional quadrature amplitude modulation constellations for different application scenarios. The results show a significant improvement in terms of system performance and BICM channel capacities under additive white Gaussian noise and Rayleigh independently and identically distributed channel conditions. [ABSTRACT FROM PUBLISHER]

Caching at mobile devices and leveraging device-to-device (D2D) communication are two promising approaches to support massive content delivery over wireless networks. The analysis of cache-enabled wireless networks is usually carried out by assuming that devices are uniformly distributed, however, in social networks, mobile devices are intrinsically grouped into disjoint clusters. In this regards, this paper proposes a spatiotemporal mathematical model that tracks the service requests arrivals and account for the clustered devices geometry. Two kinds of devices are assumed, particularly, content clients and content providers. Content providers are assumed to have a surplus memory which is exploited to proactively cache contents from a known library, following a random probabilistic caching scheme. Content clients can retrieve a requested content from the nearest content provider in their proximity (cluster), or, as a last resort, the base station (BS). The developed spatiotemporal model is leveraged to formulate a joint optimization problem of the content caching and spectrum partitioning in order to minimize the average service delay. Due to the high complexity of the optimization problem, the caching and spectrum partitioning problems are decoupled and solved iteratively using the block coordinate descent (BCD) optimization technique. To this end, an optimal and suboptimal solutions are obtained for the bandwidth partitioning and probabilistic caching subproblems, respectively. Numerical results highlight the superiority of the proposed scheme over conventional caching schemes under equal and optimized bandwidth allocations. Particularly, it is shown that the average service delay is reduced by nearly 100% and 350%, compared to the Zipf and uniform caching schemes under equal bandwidth allocations, respectively. [ABSTRACT FROM AUTHOR]

Bi-level optimization constitutes the most popular mathematical methodology for modeling the deregulated electricity market. However, state-of-the-art models neglect the physical non-convex operating characteristics of market participants, due to their inherent inability to capture binary decision variables in their representation of the market clearing process, rendering them problematic in modeling markets with complex bidding and unit commitment (UC) clearing mechanisms. This paper addresses this fundamental limitation by proposing a novel modeling approach enabling incorporation of these non-convexities into bi-level optimization market models, which is based on the relaxation and primal-dual reformulation of the original, non-convex lower level problem and the penalization of the associated duality gap. Case studies demonstrate the ability of the proposed approach to closely approximate the market clearing solution of the actual UC clearing algorithm and devise more profitable bidding decisions for strategic producers than the state-of-the-art bi-level optimization approach, and reveal the potential of strategic behavior in terms of misreporting non-convex operating characteristics. [ABSTRACT FROM AUTHOR]

The big video data size today imposes huge pressure on storage. The variation and aging induced memory failures significantly influence the video output quality. Recently, researchers have developed different memory designs for videos, deep learning, and other data-intensive applications, which enables better energy-quality tradeoffs with design constraints. Unfortunately, designing memory has been proven to be a very challenging problem due to: 1) various design constraints; 2) multiple memory bitcell design options; and 3) challenging layout integration and cost analysis using different memory technologies. In this paper, we develop novel mathematical models for optimizing embedded video memory design without applying a time-consuming and laborious ASIC design process. The problems are formulated as nonlinear programs and integer linear programs. Different SRAM designs and hybrid SRAM and DRAM designs are considered in our models. The results of the numerical studies show that by applying our proposed method the average mean square error of the video storage can be greatly reduced, even by more than 90% in many cases. [ABSTRACT FROM AUTHOR]

Recent research has presented a sufficient condition for the existence of a practical power flow solution in distribution networks, and a fixed-point linear approximation in complex coordinates. By employing Wirtinger calculus, this paper shows that the fixed-point linear approximation can be obtained via a complex-variable first-order Taylor series expansion around the no-load operating point; it then presents new simplified formulas for second- and third-order Taylor series expansions. The high-order power flow solutions are shown to be highly accurate on standard distribution networks available in the literature, and significantly more precise than the existing linear approximation. The proposed power flow approximations are useful in circular arithmetic methods, which extend the modeling of parameter uncertainty from the real line to the complex plane. The application of circular arithmetic is finally illustrated for the solution of power flow based Volt/VAr control under power injection uncertainty. The results show the capacity of the approach in mitigating voltage violations whenever injections deviate from their nominal values. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of reliability and makespan optimization of hardware task graphs in reconfigurable platforms by applying fault tolerance (FT) techniques to the running tasks based on the exploration of the Pareto set of solutions. In the presented solution, in contrast to the existing approaches in the literature, task graph scheduling, tasks parallelism, reconfiguration delay, and FT requirements are taken into account altogether. This paper first presents a model for hardware task graphs, task prefetch and scheduling, reconfigurable computer, and a fault model for reliability. Then, a mathematical model of an integer nonlinear multi-objective optimization problem is presented for improving the FT of hardware task graphs, scheduled in partially reconfigurable platforms. Experimental results show the positive impacts of choosing the FT techniques selected by the proposed solution, which is named Pareto-based. Thus, in comparison to nonfault-tolerant designs or other state-of-the-art FT approaches, without increasing makespan, about 850% mean time to failure (MTTF) improvement is achieved and, without degrading reliability, makespan is improved by 25%. In addition, experiments in fault-varying environments have demonstrated that the presented approach outperforms the existing state-of-the-art adaptive FT techniques in terms of both MTTF and makespan. [ABSTRACT FROM PUBLISHER]

Cell range expansion (CRE) is an effective technique in the ultradense network (UDN) to enlarge small cells' ranges and promote network utility such as system throughput, number of users lower than a rate threshold, and proportional fairness. Due to the coupled relationship of user association and scheduling in rate-related utility optimization, optimal cell-specific CRE bias is difficult to achieve. This paper first proposes a centralized CRE bias adjusting algorithm based on Gibbs sampling to achieve the optimal solution of cell-specific CRE bias based on global information. After that, a decentralized Gibbs-sampling-based CRE bias adjusting algorithm without the need for the entire knowledge of global channel gains is designed to deal with the computational complexity and message exchange overhead problem caused by scale expansion of UDN. Finally, to further reduce the increasing computational complexity, message exchange overhead, and time complexity caused by scale expansion of UDN, this paper constructs a neighbor graph based on the mutual bias influence among cells, develops a graph-coloring-based clustering algorithm to classify cells into groups, and proposes a central-aided distributed CRE bias adjusting algorithm to obtain the optimal solution to the rate-related utility optimization problem based on local information. In the central-aided distributed CRE bias adjusting algorithm, a central macrocell is used to collect the information from the small cells, and the small cells distributively determine their CRE bias based on shared central information. The optimality and complexity of the proposed algorithms are proven and analyzed. Numerical results show that, compared with existing schemes, the proposed Gibbs-sampling-based algorithms can achieve a larger utility function with less iteration time. [ABSTRACT FROM PUBLISHER]

Joint optimal resource allocation and probabilistic caching design is conceived for device-to-device (D2D) communications in a heterogeneous wireless network (HetNet) relying on full-duplex (FD) relays. In particular, popular contents can be cached at user devices and at relays that are located close to users. A user may request the contents of interest from another user via D2D communications and also from a nearby relay equipped with FD radios. If the requested contents are not found in the buffers of other users/relays within the coverage range, users may opt for connecting to the macro base station (MBS) via a relay by using an FD communication. Furthermore, we propose a beneficial mobility-aware coded caching philosophy for D2D communications in the HetNet considered. Especially, we model the mobility pattern of users as discrete random jumps and exploit coded caching for improving the throughput attained. Subsequently, we develop mathematical models for analyzing the throughput in the presence of edge caching, where both the system-level co-channel interference and the FD self-interference are considered. We circumvent the high complexity of stochastic optimization by developing low-complexity optimization. Finally, numerical results are presented to illustrate the theoretical findings developed in this paper and quantify the throughput gains attained. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of learning a shallow artificial neural network that best fits a training data set. We study this problem in the over-parameterized regime where the numbers of observations are fewer than the number of parameters in the model. We show that with the quadratic activations, the optimization landscape of training, such shallow neural networks, has certain favorable characteristics that allow globally optimal models to be found efficiently using a variety of local search heuristics. This result holds for an arbitrary training data of input/output pairs. For differentiable activation functions, we also show that gradient descent, when suitably initialized, converges at a linear rate to a globally optimal model. This result focuses on a realizable model where the inputs are chosen i.i.d. from a Gaussian distribution and the labels are generated according to planted weight coefficients. [ABSTRACT FROM AUTHOR]

Compensated pulsed alternator (compulsator) plays a significant role in the field of pulsed power supply. Different kinds of compulsators have been used to drive high-energy weapons. In this paper, the mathematical model of a two-phase four-pole air-core compulsator is established, taking the current coupling, the change of rotor speed and changing load characteristics into account. Both the self-excitation process and the discharge process are modeled. Simulation results indicate that our model has high accuracy in comparison with the results of the co-simulation method using finite-element method and circuit principle. Moreover, the usage of the mathematical model can improve the simulation efficiency and flexibility. The current pulse requirements of electromagnetic rail gun (EMRG), flash lamp, and electro-thermal-chemical gun (ETCG) are analyzed, respectively, and the pulse shape optimization problem is studied based on the intelligent optimization algorithm. For EMRG and flash lamp, the term “acceleration ratio” is introduced to identify whether a pulse is flat or spiked. For ETCG, the conception of “shape variance” is proposed to evaluate the fitness of its pulse shape. With the help of suitable objection function and intelligent optimization algorithm, the optimal discharge pulse for specific load can be obtained easily. [ABSTRACT FROM AUTHOR]

In this two-part paper, we present a novel framework and methodology to analyze data from certain image-based biochemical assays, e.g., ELISPOT and Fluorospot assays. In this first part, we start by presenting a physical partial differential equations (PDE) model up to image acquisition for these biochemical assays. Then, we use the PDEs’ Green function to derive a novel parameterization of the acquired images. This parameterization allows us to propose a functional optimization problem to address inverse diffusion. In particular, we propose a nonnegative group-sparsity regularized optimization problem with the goal of localizing and characterizing the biological cells involved in the said assays. We continue by proposing a suitable discretization scheme that enables both the generation of synthetic data and implementable algorithms to address inverse diffusion. We end Part I by providing a preliminary comparison between the results of our methodology and an expert human labeler on real data. Part II is devoted to providing an accelerated proximal gradient algorithm to solve the proposed problem and to the empirical validation of our methodology. [ABSTRACT FROM AUTHOR]

In the framework of maintenance optimization of multicomponent systems, dynamic grouping maintenance based on rolling horizon has developed and became an interesting approach. However, most existing dynamic grouping models assume that the maintenance duration is negligible. This assumption may be not always relevant and limits the application of these models in many real situations. The first objective of this paper is to develop a dynamic grouping model in the presence of both corrective and preventive maintenance duration for complex structure systems. A new practical maintenance cost model is proposed allowing to take into consideration of maintenance duration and system structure as well as economic dependence between components. Taking into maintenance duration and system structure leads, however, to a complex grouping model, which is efficiently solved by the proposed analytical methods. The second objective is to investigate the impacts of maintenance duration on grouping modeling and grouping optimization. Both theoretical and experimental studies give a complete vision about maintenance duration impacts and some recommendations in real applications. The uses and advantages of the proposed grouping maintenance approach are illustrated through a numerical example of a six-component system. [ABSTRACT FROM AUTHOR]

Following a study on the optimal write head design for perpendicular magnetic recording, this paper aims at optimizing the design of write head for shingled magnetic record by applying the Taguchi method and the response surface methodology. The criteria for the evaluation of the write head design are to maximize the signal-to-noise ratio (SNR) and minimize the bit error rate (BER) in the shingled magnetic recording (SMR). In this paper, the magnetic fields of the write head designs are modeled using finite-element analysis, the SMRs are simulated by applying micromagnetic modeling based on Landau–Lifshitz–Gilbert equation, and the SNR and the BER are characterized using the grain flipping probability model. An optimal design of the write head is obtained to achieve the maximum SNR and the minimum BER. A discussion on the erasure band width is also carried out in this paper. Furthermore, the SNR and the BER at different bits and track densities in SMR are compared and discussed. [ABSTRACT FROM PUBLISHER]

This paper proposes a robust frequency-domain method to tune the d–q decoupled control system used in Modular Multilevel Converter-type Voltage Source Converter (MMC-VSC) systems. A linearized state-space model of the MMC-VSC system is developed and used to calculate the stability-related frequency-domain attributes. The controller design problem is formulated as an optimization problem. In this paper, the simulated annealing optimization technique is applied to find the proportional-integral (PI) controller parameters that give desired damping for the oscillatory modes and desired values for decaying exponential modes. The efficacy of this method is tested on the electromagnetic transient model of a two-terminal MMC-VSC system on the real-time digital simulators, and the results are provided in this paper. Finally, tuned controller parameters for different ac system strengths are discussed and it is shown that this mathematical model is suitable to tune the PI-controller parameters for MMC-VSC systems connected to strong as well as weak ac networks. [ABSTRACT FROM AUTHOR]

Smart scheduling can be used to reduce infrastructure-to-vehicle energy costs in delay-tolerant vehicular networks. In this paper, we show that, by combining this with vehicle-to-vehicle (V2V) forwarding, downlink (DL) traffic schedules can be generated, whose energy costs are lower than that in the single-hop case. This is accomplished by having the roadside units (RSUs) dynamically forward packets through vehicles, which are in energy-favorable locations. This paper considers both constant bit rate (CBR) and variable bit rate (VBR) air-interface options. We first derive offline schedulers for the DL RSU energy usage when V2V forwarding is added to RSU-to-vehicle communication. Both in-channel and off-channel forwarding cases are considered. The CBR and VBR cases are obtained using integer linear programming (ILP) and time-expanded graph (TEG) formulations, respectively. These schedulers provide lower bounds on energy performance and are used for comparisons with a variety of proposed online scheduling algorithms. The first algorithm is based on a greedy local optimization (GLOA). A version of this algorithm, which uses a minimum-cost flow graph (MCFG) scheduler, is also introduced. A more sophisticated algorithm is then proposed, which is based on a finite-window group optimization (FWGO). Results from various experiments show that the proposed algorithms can generate traffic schedules with much improved DL energy requirements compared with the case where V2V packet forwarding is not used. The performance improvements are particularly strong when under heavy loading conditions and when the variation in vehicle communication requirements or vehicle speed is high. Results that compare the proposed algorithms with conventional nonenergy-aware schedulers are also presented. [ABSTRACT FROM PUBLISHER]

In this paper, a new systematic technique to design manifold-coupled multiplexers in waveguide technology is proposed. The new technique uses generalized low-order electromagnetic (EM) distributed models, which constitute a half-way point between the fast, but imprecise, analytical models, and the more accurate, but costly, full-wave EM models. The method can be applied to contiguous and noncontiguous channel multiplexers, in both E-plane or H-plane configurations. This paper covers the complete design procedure for manifold multiplexers, starting from the required specifications and finishing with the physical dimensions. After explaining the general design technique for multiplexers with channel filters based on transmission-line resonators, the method is specialized for the design of classic multiplexers with circular-waveguide dual-mode filters. As a further application example, the same methodology is also used for the design of a nonconventional tuningless wideband multiplexer with a new type of rectangular waveguide filters. This paper is concluded with a comparison between simulation and measurements for the wideband multiplexer. Excellent agreement between measurements and simulations are demonstrated, thereby validating the complete design procedure. [ABSTRACT FROM AUTHOR]

The main drawback of reluctance machines is a high torque ripple, due to the interaction between the stator magnetomotive force and the rotor structure. Adopting a rotor configuration characterized by several flux barriers per pole, there is a high influence of the rotor geometry on the machine performance in terms of both average torque and ripple. An optimization is often required to determine the optimal rotor geometry so as to achieve a high and smooth torque. Then, the geometry determined earlier should guarantee good performance for various operating points (i.e., changing the current amplitude and phase), as well as for small variations of the geometry. This paper investigates this aspect, showing the results of optimizations carried out on various machines. The impact of the geometry parameters is taken into account, and the sensitivity of the optimal solution to the geometry variation is pointed out. This paper highlights the difficulty to get a robust geometry as far as the torque ripple reduction is concerned. Finally, a few experimental results on a synchronous reluctance motor prototype will be presented, compared with finite-element analysis simulations for validation. [ABSTRACT FROM AUTHOR]

This paper presents an optimization approach for the placement of geomagnetically induced current (GIC) blocking devices (BDs) on power system transformers to mitigate the adverse effects of a geomagnetic disturbance (GMD). Solar storms lead to GMDs which, in turn, drive GICs along transmission lines and through transformer windings. GICs cause half-cycle saturation in power transformers, increase their shunt reactive power loss, lead to a significant lack of power system reactive power support, and, as a result, create voltage instability and, potentially, large-scale voltage collapse. To mitigate these detrimental effects, a long-term remedy is to install GIC BDs at the neutral point of transformers. Since these devices are fairly costly, their placement should be performed in an optimal manner. The optimization problem proposed in this paper minimizes the cost of BD placement while satisfying power system voltage and generator maximum reactive power limits. The numerical results demonstrate the effectiveness of the proposed method in maintaining an acceptable voltage profile for the power system in case of GMDs with any degree of severity. [ABSTRACT FROM AUTHOR]

This paper presents a new reliability-based incremental phasor measurement unit (PMU) placement method via an enumeration-based integer linear programming optimization approach. By evaluating the proposed reliability indices for node and system observability, incremental PMUs are sequentially placed on the locations where the reliability of entire system's observability can be best improved at each step. As a result, a pre-set desired reliability of the system's observability can be achieved while using the minimal number of PMUs. The proposed method is generic and flexible in a way that different kinds of failures that impact the reliability of system observability can be easily included in the method. The 1st-order and 2nd-order failures of PMUs and lines are considered in this paper. The proposed method is validated using a 4-node system, the IEEE 14-node system and the IEEE RTS 96 test system. Simulation results show that the proposed method is effective and efficient in enhancing the reliability of observability for any system regardless of what the initial PMU placement of the system may be. [ABSTRACT FROM AUTHOR]

This paper studies the trajectory tracking control problem of an autonomous underwater vehicle (AUV). We develop a novel Lyapunov-based model predictive control (LMPC) framework for the AUV to utilize computational resource (online optimization) to improve the trajectory tracking performance. Within the LMPC framework, the practical constraints, such as actuator saturation, can be explicitly considered. Also, the thrust allocation subproblem can be addressed simultaneously with the LMPC controller design. Taking advantage of a nonlinear backstepping tracking control law, we construct the contraction constraint in the formulated LMPC problem so that the closed-loop stability is theoretically guaranteed. Sufficient conditions that ensure the recursive feasibility, and hence the closed-loop stability, are provided analytically. A guaranteed region of attraction is explicitly characterized. In the meantime, the robustness of the tracking control can be improved by the receding horizon implementation that is adopted in the LMPC control algorithm. Simulation results on the Saab SeaEye Falcon model AUV demonstrate the significantly enhance trajectory tracking control performance via the proposed LMPC method. [ABSTRACT FROM PUBLISHER]

Since the hot metal silicon content simultaneously reflects the product quality and the thermal state of the blast furnace, its modeling is crucial and representative. In order to facilitate the realization of control, this paper proposes a Bayesian block structure sparse based Takagi–Sugeno (T–S) fuzzy modeling method, with which the main important fuzzy rules and the corresponding pivotal consequent parameters can be selected automatically to obtain a compact fuzzy model with good generalization performance. For being conjugate to the Gaussian likelihood that would lead to the associated Bayesian inference to be performed in closed form, a hierarchy of block structure sparse priori is adopted, and the variational Bayesian inference is used to solve it. The screening of model inputs and data processing appropriately consider the characteristics of the blast furnace process. The applicability and performance of the proposed method are demonstrated on no. 2 blast furnace of Liuzhou Steel in China. [ABSTRACT FROM PUBLISHER]

The heterogeneous networks (HetNets) performance is highly dependent on the interference coordination among cells since both macrocells and small cells share the same spectrum. This paper focuses on the configuration of eICIC parameters: almost blank subframe ratio and cell range expansion bias defined for LTE-A. We propose an online learning mechanism based on a big data-driven framework and multiarmed bandit algorithms that retrieves data from the network to learn efficient configurations of eICIC parameters without requiring any previous knowledge about the network (e.g., traffic load, topology, scheduling algorithm). Our numerical results show that our approach attains a significant improvement with respect to the state of the art of online learning algorithms in networks under stationary and variable conditions (e.g., number of small cells, traffic load). [ABSTRACT FROM AUTHOR]

Timing resilient designs can remove variation margins by adding error detecting logic (EDL) that detects timing errors when execution completes within a resiliency window. Speeding up near-critical-paths during logic synthesis can reduce the amount of EDL needed but at the cost of increasing logic area. This creates a logic optimization strategy called resynthesis. This paper proposes four alternatives to optimize resilient designs through resynthesis. The first is a brute force approach that explores speeding up all combinations of near-critical paths and produces good results but is computationally impractical for complex circuits. The second is a naive brute-force (BF) approach in which near-critical paths are sped up one end-point at a time. It is much faster than the BF approach because it does not explore the benefits of speeding up multiple end-points simultaneously and thus provides a quick-and-dirty lower bound for the benefits of resynthesis. The third is a geometric program-based iterative algorithm (GPIA) that achieves area reductions that compare favorably across all four approaches. The GPIA algorithm completes within 24 h for all examples and the average area reduction is up to 16%. Because the run-time required to solve this mathematical model can still be long; however, we propose a fourth approach that involves creating a virtual resynthesis cell library that tries to trick the synthesis tool to understand EDL overhead and optimize total area quickly and automatically. This approach obtains an average of approximately two-third of the area reductions of the GPIA approach with fast run times associated with only a single synthesis run. [ABSTRACT FROM PUBLISHER]

The increasing impacts of natural disasters have led to concerns regarding predisaster plans and post-disaster responses. During post-disaster responses, emergency transportation is the most important part of disaster relief supply chain operations, and its optimal planning differs from traditional transportation problems in the objective function and complex constraints. In disaster scenarios, fairness and effectiveness are two important aspects. This paper investigates emergency transportation in real-life disasters scenarios and formulates the problem as an integer linear programming model (called cum-MDVRP), which combines cumulative vehicle routing problem and multidepot vehicle routing problem. The cum-MDVRP is NP-hard. To solve it, a novel hybrid ant colony optimization-based algorithm is proposed by combining both saving algorithms and a simple two-step 2-opt algorithm. The proposed algorithm allows ants to go in and out the depots for multiple rounds, so we abbreviate it as ACOMR. Moreover, we present a smart design of the ants’ tabus, which helps to simplify the solution constructing process. The ACOMR could yield good solutions quickly, then the decision makers for emergency responses could do expert planning at the earliest time. Computational results on standard benchmarking data sets show that the proposed cum-MDVRP model performs well, and the ACOMR algorithm is more effective and stable than the existing algorithms. [ABSTRACT FROM PUBLISHER]

Inhomogeneities in the transmit radio frequency magnetic field ( \textB1^{+} ) reduce the quality of magnetic resonance (MR) images. This quality can be improved by using high-permittivity pads that tailor the \textB1^{+} fields. The design of an optimal pad is application-specific and not straightforward and would therefore benefit from a systematic optimization approach. In this paper, we propose such a method to efficiently design dielectric pads. To this end, a projection-based model order reduction technique is used that significantly decreases the dimension of the design problem. Subsequently, the resulting reduced-order model is incorporated in an optimization method in which a desired field in a region of interest can be set. The method is validated by designing a pad for imaging the cerebellum at 7 T. The optimal pad that is found is used in an MR measurement to demonstrate its effectiveness in improving the image quality. [ABSTRACT FROM PUBLISHER]

Hybrid zero dynamics (HZD) has emerged as a popular framework for dynamic walking but has significant implementation difficulties when applied to the high degrees of freedom humanoids. The primary impediment is the process of gait design—it is difficult for optimizers to converge on a viable set of virtual constraints defining a gait. This paper presents a methodology that allows for fast and reliable generation of dynamic robotic walking gaits through the HZD framework, even in the presence of underactuation. Specifically, we describe an optimization formulation that builds upon the novel combination of HZD and direct collocation methods. Furthermore, achieving a scalable implementation required developing a defect-variable substitution formulation to simplify expressions, which ultimately allows us to generate compact analytic Jacobians of the constraints. We experimentally validate our methodology on an underactuated humanoid, DURUS, a spring-legged machine designed to facilitate energy-economical walking. We show that the optimization approach, in concert with the HZD framework, yields dynamic and stable walking gaits in hardware with a total electrical cost of transport of 1.33. [ABSTRACT FROM PUBLISHER]

This paper presents a new method called optimal shift reassignment (OSR), used for reconfigurable multiplication circuits. These circuits consist of adders, subtractors, shifts, and multiplexers (MUXs). They calculate the multiplication of an input number by one out of several constants which can be selected dynamically during run-time. The OSR method is based on the idea that shifts can be placed at different positions along the circuit, while the calculated output constant stays the same. This differs from previous approaches, which were limited by the fact that all constants within the constant multiplier were forced to be odd. The OSR method subsequently releases this restriction. As a result, the number of required MUXs in the circuit can be reduced. This happens when the shift reassignment aligns the shift values of different inputs of an MUX. Experimental results show MUX savings of up to 50% and average savings between 11% and 16% using the OSR method compared to previous approaches. [ABSTRACT FROM PUBLISHER]

Radio-wave propagation in tunnels and mines can be qualitatively understood by representing these environments as overmoded waveguides. Waveguide models are fast and intuitive, but the accurate prediction of the propagation characteristics of realistic geometries is beyond their scope. Such predictions can be derived by computationally intensive techniques, such as ray-tracing and vector parabolic equation (VPE) methods. This paper combines the two approaches, to extract surrogate models of arched tunnels in the form of rectangular waveguides. The parameters of the latter are optimized to match their propagation characteristics to those of the original arched tunnel, as predicted by the VPE method. The accuracy and limitations of these surrogate models are thoroughly evaluated. Their practical usefulness is demonstrated in an application where they are iteratively used, in lieu of the original computationally costly arched tunnel model, to optimally distribute the access points of an in-tunnel wireless communication system. [ABSTRACT FROM PUBLISHER]

The objective of this paper is to present an original study for optimizing the size of the longitudinal-flux double-sided linear switched reluctance motor (LSRM) under thermal and weight constraints. The performance is evaluated taken into account duty cycle operating conditions and thermal restrictions. The proposed approach couples finite element analysis for magnetic propulsion force computation and lumped-parameter thermal network for thermal transient analysis. The LSRMs design parameters are characterized by the number of phases and by their size denoted by the pole stroke. The operating conditions are the current density, the duty cycle and the admissible temperature rise of the insulation system. The grid search algorithm is used for solving the optimization problem. From the results, with the help of a novel multivariable optimization chart, a set of optimal configurations regarding to miniaturizations and downsizing of LSRMs is provided. [ABSTRACT FROM PUBLISHER]