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This paper integrates an estimation of distribution (EoD)-based update operator into decomposition-based multiobjective evolutionary algorithms for binary optimization. The probabilistic model in the update operator is a probability vector, which is adaptively learned from historical information of each subproblem. We show that this update operator can significantly enhance decomposition-based algorithms on a number of benchmark problems. Moreover, we apply the enhanced algorithms to the constrained optimal software product selection (OSPS) problem in the field of search-based software engineering. For this real-world problem, we give its formal definition and then develop a new repair operator based on satisfiability solvers. It is demonstrated by the experimental results that the algorithms equipped with the EoD operator are effective in dealing with this practical problem, particularly for large-scale instances. The interdisciplinary studies in this paper provide a new real-world application scenario for constrained multiobjective binary optimizers and also offer valuable techniques for software engineers in handling the OSPS problem. [ABSTRACT FROM AUTHOR]

This paper considers distribution systems with a high penetration of distributed, renewable generation and addresses the problem of incorporating the associated uncertainty into the optimal operation of these networks. Joint chance constraints, which satisfy multiple constraints simultaneously with a prescribed probability, are one way to incorporate uncertainty across sets of constraints, leading to a chance-constrained optimal power flow problem. Departing from the computationally heavy scenario-based approaches or approximations that transform the joint constraint into conservative deterministic constraints; this paper develops a scalable, data-driven approach which learns operational trends in a power network, eliminates zero-probability events (e.g., inactive constraints), and accurately and efficiently approximates bounds on the joint chance constraint iteratively. In particular, the proposed framework improves upon the classic methods based on the union bound (or Boole’s inequality) by generating a much less conservative set of single chance constraints that also guarantees the satisfaction of the original joint constraint. The proposed framework is evaluated numerically using the IEEE 37-node test feeder, focusing on the problem of voltage regulation in distribution grids. [ABSTRACT FROM AUTHOR]

This paper proves an impossibility result for stochastic network utility maximization for multi-user wireless systems, including multiple access and broadcast systems. Every time slot an access point observes the current channel states for each user and opportunistically selects a vector of transmission rates. Channel state vectors are assumed to be independent and identically distributed with an unknown probability distribution. The goal is to learn to make decisions over time that maximize a concave utility function of the running time average transmission rate of each user. Recently it was shown that a stochastic Frank-Wolfe algorithm converges to utility-optimality with an error of $O(\log (T)/T)$ , where $T$ is the time the algorithm has been running. An existing $\Omega (1/T)$ converse is known. The current paper improves the converse to $\Omega (\log (T)/T)$ , which matches the known achievability result. It does this by constructing a particular (simple) system for which no algorithm can achieve a better performance. The proof uses a novel reduction of the opportunistic scheduling problem to a problem of estimating a Bernoulli probability $p$ from independent and identically distributed samples. Along the way we refine a regret bound for Bernoulli estimation to show that, for any sequence of estimators, the set of values $p \in [{0,1}]$ under which the estimators perform poorly has measure at least 1/6. [ABSTRACT FROM AUTHOR]

The efficacy of the multimodel framework (MMF) in modeling and identification of complex, nonlinear, and uncertain systems has been widely recognized in the literature owing to its simplicity, transparency, and mathematical tractability, allowing the use of well-known modeling analysis and control design techniques. The approach proved to be effective in addressing some of the shortcomings of other modeling techniques such as those based on a single nonlinear autoregressive network with exogenous inputs model or neural networks. A great number of researchers have contributed to this active field. Due to the significant amount of contributions and the lack of a recent survey, the review of recent developments in this field is vital. In this two-part paper, we attempt to provide a comprehensive coverage of the multimodel approach for modeling and identification of complex systems. This paper contains a classification of different methods, the challenges encountered, as well as recent applications of MMF in various fields. In this part 2, the review of multimodel internal structures and parameter estimation as well as validity computation methods is presented. In addition, a multimodel application and future direction are covered. In this literature survey, our main focus is on the MMF where the final system’s representation and behavior is generated through the interpolation of several possible local models. This is of prime importance to control designers. All through this paper, different active research areas and open problems are discussed. [ABSTRACT FROM AUTHOR]

Plantar pressure analysis can be used for clinical diagnosis, exercise guidance and daily monitoring. In actual use, the CoP trajectory is an important parameter for dynamic analysis, which is generally obtained with an in-shoe system in outdoor and daily monitoring. Therefore, it is a critical issue to design the sensor placement to obtain accurate CoP estimation in a low-cost sensing insole. In this paper, a new sensor placement method, an improved genetic algorithm, was proposed, driven by a large amount of plantar pressure distribution data, with the objectives of reducing the trajectory estimation error and increasing the amount of information, and abstract the placement problem as a combinatorial optimization problem under multiple objectives. Through optimization iterations, a set of optimized sensor placements are determined and applied to practical use. Six subjects wore the optimal placement insoles and the mean absolute error was 3.81 mm (medial-lateral direction) and 8.61 mm (anterior-posterior direction) for comparison with the CoP trajectory provided by the measurement platform. Compared with previous results, the method proposed in this paper provides a more accurate CoP estimation with a 9.7% improvement. This study provides new guidelines for the selection of plantar pressure sensor placements and incorporates intelligent optimization algorithms into new ways to improve the accuracy of wearable device analysis. [ABSTRACT FROM AUTHOR]

Earth geomagnetic field provides very important information for autonomous navigation. However, in practice, magnetometer measurements are easily distorted by outer disturbances, as shown in the right diagram. In a recent study, the real-time magnetometer disturbance estimation problem has been solved via a constrained nonlinear programming for better underwater navigation accuracy. However, the employed interior-point optimizer will require considerable computational resources and cannot always guarantee optimality during optimization updates. This paper further investigates this problem and refines the solution by introducing the trust-region method. The challenge of the designed approach mainly falls into finding out the feasible regions of the possible solutions. Geometric analysis and algebraic elimination methods are taken into account to give globally optimal solutions to the trust-region solver. Experimentations are conducted to verify the correctness of the proposed method. Moreover, it is also confirmed that the trust-region technique can continuously and accurately propagate so that high-frequency dynamic magnetic disturbances can be efficiently estimated. [ABSTRACT FROM AUTHOR]

This paper focuses on the simultaneous extrinsic-odometric calibration of a mobile robot system equipped with odometric devices and a monocular camera. Most current approaches are based on either optimization or Gaussian filter, which depends on a manually provided initial guess. In this paper, we propose a two-step fully automatic calibration algorithm, which does not require any prior knowledge of the un-calibrated parameters. In the first step, both the odometric parameters and the extrinsic ones are estimated through a non-iterative auto initialization process. In the second step, a joint optimization problem is solved iteratively to obtain a refined calibration result. By exploiting the planar motion constraints of the landmark measurements, our auto initialization method outperforms a comparison approach in robustness against the image noise. Experiments are conducted with data sets collected from both simulation and an autonomous guided vehicle system, which validates the improvement. [ABSTRACT FROM PUBLISHER]

The wide variety of inverter control settings for solar photovoltaics (PV) causes the accurate knowledge of these settings to be difficult to obtain in practice. This paper addresses the problem of determining inverter reactive power control settings from net load advanced metering infrastructure (AMI) data. The estimation is first cast as fitting parameterized control curves. We argue for an intuitive and practical approach to preprocess the AMI data, which exposes the setting to be extracted. We then develop a more general approach with a data-driven reactive power disaggregation algorithm, reframing the problem as a maximum likelihood estimation for the native load reactive power. These methods form the first approach for reconstructing reactive power control settings of solar PV inverters from net load data. The constrained curve fitting algorithm is tested on 701 loads with behind-the-meter (BTM) PV systems with identical control settings. The settings are accurately reconstructed with mean absolute percentage errors between 0.425% and 2.870%. The disaggregation-based approach is then tested on 451 loads with variable BTM PV control settings. Different configurations of this algorithm reconstruct the PV inverter reactive power timeseries with root mean squared errors between 0.173 and 0.198 kVAR. [ABSTRACT FROM AUTHOR]

Can inefficiency be rational? Excess resources or slack may serve as a buffer against environmental shocks, help decouple organizations, ease planning and implementation, support innovation, and enable effective responses to competitors. Slack may however also be the result of inefficiency. In Bogetoft and Kerstens, Distinguishing useful and wasteful slack, we propose an approach to separate useful and wasteful slack. If an organization can maintain the same levels of output and slack at lower cost, there is wasteful or nonrationalizable spending. We develop ways to measure the extent to which total spending can be rationalized and show how to statistically estimate and test the usefulness of the available slack using bootstrapping. The literature on organization and strategic management suggests that slack in the form of excess resources may be useful. It may, for example, serve as a buffer against environmental shocks, help decouple organizations, ease planning and implementation, support innovation, and enable effective responses to competitors. In contrast, the economic literature tends to view slack as wasteful. When the same products and services can be produced with fewer resources and slack per se is not assigned any value, slack should be eliminated. The aim of this paper is to reconcile these two perspectives. We acknowledge that slack may be both useful and wasteful. The challenge is how to separate the two. Our approach relies on the simple Pareto idea. If an organization can maintain the same levels of output and slack at lower cost, there is wasteful or nonrationalizable spending. We develop ways to measure the extent to which total spending can be rationalized and show how to statistically estimate and test the usefulness of the available slack using bootstrapping. Funding: Financial support from Det Frie Forskningsråd [Grant 9038-00042A] is greatly appreciated. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2415. [ABSTRACT FROM AUTHOR]

This paper explores the non-convex composition optimization consisting of inner and outer finite-sum functions with a large number of component functions. This problem arises in important applications such as nonlinear embedding and reinforcement learning. Although existing approaches such as stochastic gradient descent (SGD) and stochastic variance reduced gradient (SVRG) descent can be applied to solve this problem, their query complexities tend to be high, especially when the number of inner component functions is large. Therefore, to significantly improve the query complexity of current approaches, we have devised the stochastic composition via variance reduction (SCVR). What's more, we analyze the query complexity under different numbers of inner function and outer function. Based on different kinds of estimation of inner component function, we also present the SCVRII algorithm, though the order of query complexities are the same with SCVR. Additionally, we propose an extension to handle the mini-batch cases, which improve the query complexity under the optimal mini-batch size. The experimental results validate our proposed algorithms and theoretical analyses. [ABSTRACT FROM AUTHOR]

Accurate, low-latency, and energy-efficient optical flow estimation is a fundamental kernel function to enable several real-time vision applications on mobile platforms. This paper presents neighbor-guided semi-global matching (NG-fSGM), a new low-complexity optical flow algorithm tailored for low-power mobile applications. NG-fSGM obtains high accuracy optical flow by aggregating local matching costs over a semi-global region, successfully resolving local ambiguity in texture-less and occluded regions. The proposed NG-fSGM aggressively prunes the search space based on neighboring pixels’ information to significantly lower the algorithm complexity from the original fSGM. As a result, NG-fSGM achieves $17.9{\times}$ reduction in the number of computations and $8.37{\times}$ reduction in memory space compared to the original fSGM without compromising its algorithm accuracy. A multicore architecture for NG-fSGM is implemented in hardware to quantify algorithm complexity and power consumption. The proposed architecture realizes NG-fSGM with overlapping blocks processed in parallel to enhance throughput and to lower power consumption. The eight-core architecture achieves 20 M pixel/s (66 frames/s for VGA) throughput with 9.6 mm2 area at 679.2-mW power consumption in 28-nm node. [ABSTRACT FROM AUTHOR]

The goal of state and input estimation is to simultaneously estimate both the unmeasured states and unknown input. Although this problem has been widely studied, existing techniques are confined to the case where the system is minimum phase. This paper introduces retrospective cost input estimation (RCIE), which is based on retrospective cost optimization. It is shown that RCIE automatically develops an internal model of the unknown input. This internal model provides an asymptotic estimate of the unknown input regardless of the location of the zeros of the plant, including the case of nonminimum-phase (NMP) dynamics. RCIE is applied to the NMP problem of estimating inertial acceleration of a maneuvering unmanned aerial vehicle using optical position data. [ABSTRACT FROM AUTHOR]

In this paper, we present a hybrid visual inertial navigation algorithm for an autonomous and intelligent vehicle that combines the multi-state constraint Kalman filter (MSCKF) with the nonlinear visual-inertial graph optimization. The MSCKF is a well-known visual inertial odometry (VIO) method that performs the fusion between an inertial measurement unit (IMU) and the image measurements within a sliding window. The MSCKF computes the re-projection errors from the camera measurements and the states in the sliding window. During this process, the structure-only estimation is performed without exploiting the full information over the window, like the relative interstate motion constraints and their uncertainties. The key contribution of this paper is combination of the filtering and non-linear optimization method for VIO, and the design of a novel measurement model that exploits all of the measurements and information available within the sliding window. The local visual-inertial optimization is performed using pre-integrated IMU measurements and camera measurements. It infers the probabilistically optimal relative pose constraints. These local optimal constraints are used to estimate the global states under the MSCKF framework. The proposed local-optimal-multi-state constraint Kalman filter is validated using a simulation data set, as well as publicly available real-world data sets generated from real-world urban driving experiments. [ABSTRACT FROM AUTHOR]

This paper proposes and describes a method based on sparse optimization techniques for online estimation of missing data. The basic idea of the proposed method is to represent the noise-free signal of interest sparsely in a composite dictionary. The proposed algorithm offers three prime advantages: 1) handles nonlinear processes that are locally linearizable; 2) requires lower computational power than the existing algorithms; and 3) adapts to the changing process conditions by optimally selecting the most relevant feature for that window of time. Results show that the proposed algorithm outperforms the existing neural network-based method. An application of the proposed algorithm to classical (PID) control using measurements with missing data is presented. In this context, an added benefit of the proposed method is that it serves as a feature selection operator for the process, which can be useful in other applications. Numerical (simulation) studies are carried out to study the influence of user-defined parameter in the algorithm and two stochastic factors, namely, the random sampling scheme and noise, on the stability and performance of the closed-loop system. Results show that the closed-loop system with the proposed algorithm is stable and yields a satisfactory performance. [ABSTRACT FROM AUTHOR]

In this paper, an approach to synthesize compressor trees in high-level synthesis is proposed. We target the modern field-programmable gate arrays, which integrate carry chains and support fast ternary adders. Two main improvements are achieved in our approach: 1) based on the proposed modified bitmask analysis, we perform bit-level numerical optimizations to shrink the scale of generated compressor trees and achieve a better area-delay performance; 2) by estimating the arrival time of each multi-input addition operand, we combine the use of generalized parallel counters and ternary adders in compressor trees to further reduce the area while maintaining a similar delay performance. A series of experiments shows that our approach reduces the area significantly while maintaining similar delay performance, as compared to the existing approaches. [ABSTRACT FROM AUTHOR]

This paper deals with the compensation of phase miscalibration in the general context of tomographic synthetic aperture radar image focusing. Phase errors are typically independent of one acquisition to the other, thus leading to a spreading and defocusing in the multidimensional (3-D, 4-D, and 5-D) imaging space. Coping with this problem in presence of volumetric scattering is generally a complex issue. In this paper, we consider the approach for phase calibration characterized by the advantage, with respect to classical phase calibration algorithms, of not requiring either the identification of a reference target or specific assumptions about the unknown phase function, or a priori information about the terrain topography. The novelty of the proposed phase miscalibration estimation and compensation method is related to its ability to avoid unwanted and uncontrollable vertical shifts in the focused image. The estimation of the calibration phase is performed by optimizing the contrast or the entropy of the vertical profile with the constraint of a zero phase derivative. Such a constraint preserves the output height distribution. Experimental results of simulated and real data are included to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents nonlinear optimization-based position and speed estimation scheme for IPMSM drives with arbitrary signal injection generated by inherent switching ripples associated with finite control set model predictive control (FCSMPC). The existing standard sensorless techniques are not suitable for FCSMPC which applies voltage vectors directly to an electrical machine without a modulator. The proposed method optimizes the nonlinear cost function derived from the standard IPMSM model with respect to position and speed at every sampling interval. This method can be applied to any type of signal injection and, hence, an ideal candidate for sensorless FCSMPC. In this method, the signal injection is needed only to generate persistent excitation to maintain the observability at low speeds. A strong persistent excitation is always present with FCSMPC except at standstill where the control applies null vector when the reference currents are zero. This situation is overcome in this paper by introducing a small negative $d$ -axis current at standstill. Thus, the proposed method can estimate the position and speed over a wide speed range starting from standstill to the rated speed without a changeover or additional signal injection. This paper also presents detailed convergence analysis and proposes a compensator for standstill operation that prevents converging to saddle and symmetrical solutions, and therefore, also eliminates the well-known ambiguity of $\pi$ rad in position estimation. The performance of the proposed sensorless scheme is experimentally verified for a wide range of operating conditions. [ABSTRACT FROM AUTHOR]

This paper presents an innovative method to adaptively optimize traffic signal plans based on the estimation of traffic situation achieved from the information of various penetration rates of Connected Vehicles (CVs). The network-wide signal control problem is formulated as a linear optimization problem. Moreover, we develop a Kalman filter (KF) and Neural Network (NN) algorithms to predict and update the traffic situation under mixed non-connected and connected vehicles environment. To capture the dynamic of the traffic flow, we employ the cell transmission model synched with the Vissim traffic simulator. The methodology is tested using a challenging network of six intersections. We test our model for various Penetration Rates (PR) of the CV to provide a comparative analysis. The performance of the method is also compared with a conventional actuated-coordinated traffic signal plan. The results show that with a bare minimum PR (say more than 30%), our proposed methodology outperforms the actuated traffic signal plan. (note that the minimum PR is subject to further ongoing research in the literature, to the extent that lower PRs might be plausible). Though a 100% PR is highly desirable, our method can fetch the maximum benefit just by 60% PR. [ABSTRACT FROM AUTHOR]

Traffic awareness is the prerequisite of autonomous driving. Given the limitation of on-board sensors (e.g., precision and price), remote measurement from either infrastructure or other vehicles can improve traffic safety. However, the wireless communication carrying the measurement result undergoes fading, noise and interference and has a certain probability of outage. When the communication fails, the vehicle state can only be predicted by Bayesian filtering with a low precision. Higher communication resource utilization (e.g., transmission power) reduces the outage probability and hence results in an improved estimation precision. The power control subject to an estimate variance constraint is a difficult problem due to the complicated mapping from transmit power to vehicle-state estimate variance. In this paper, we develop an estimator consisting of several Kalman filters (KFs) or extended Kalman filters (EKFs) and an interacting multiple model (IMM) to estimate and predict the vehicle state. We propose to apply deep reinforcement learning (DRL) for the transmit power optimization. In particular, we consider an intersection and a lane-changing scenario and apply proximal policy optimization (PPO) and soft actor-critic (SAC) to train the DRL model. Testing results show satisfactory power control strategies confining estimate variances below given threshold. SAC achieves higher performance compared to PPO. [ABSTRACT FROM AUTHOR]

In man-made environments, most of the objects and structures are organized in the form of orthogonal and parallel planes. These planes can be approximated by an Atlanta world assumption, in which the normals of planes can be represented by Atlanta frames. The Atlanta world assumption has one vertical frame and multiple horizontal frames. Conventionally, given a set of inputs such as surface normals, the Atlanta frame estimation problem can be solved by a branch-and-bound (BnB) algorithm. However, the runtime of the BnB algorithm will increase greatly when the dimensionality (i.e., the number of horizontal frames) increases. In this paper, we estimate only the vertical direction, instead of all Atlanta frames at once. Accordingly, we propose a vertical direction estimation method by considering the relationship between the vertical frame and horizontal frames. Concretely, our approach employs a BnB algorithm to search the vertical direction, thereby guaranteeing global optimality without requiring prior knowledge of the number of Atlanta frames. In order to guarantee convergence, four novel bounds are investigated, by mapping a 3D hemisphere to a 2D region. We verify the feasibility of the proposed method using various challenging synthetic and real-world data. [ABSTRACT FROM AUTHOR]

This paper focuses on optimal time-of-arrival (TOA) sensor placement for multiple target localization simultaneously. In previous work, different solutions only using non-shared sensors to localize multiple targets have been developed. Those methods localize different targets one-by-one or use a large number of mobile sensors with many limitations, such as low effectiveness and high network complexity. In this paper, firstly, a novel optimization model for multi-target localization incorporating shared sensors is formulated. Secondly, the systematic theoretical results of the optimal sensor placement are derived and concluded using the A-optimality criterion, i.e., minimizing the trace of the inverse Fisher information matrix (FIM), based on rigorous geometrical derivations. The reachable optimal trace of Cramér-Rao lower bound (CRLB) is also derived. It can provide optimal conditions for many cases and even closed form solutions for some special cases. Thirdly, a novel numerical optimization algorithm to quickly find and calculate the (sub-)optimal placement and achievable lower bound is explored, when the model becomes complicated with more practical constraints. Then, a hybrid method for solving the most general situation, integrating both the analytical and numerical solutions, is proposed. Finally, the correctness and effectiveness of the proposed theoretical and mathematical methods are demonstrated by several simulation examples. [ABSTRACT FROM AUTHOR]

Visible light communication (VLC) is seen as a supplement for fifth-generation (5G) wireless communication in short-range high data rate communication applications. A reliable VLC system relies on an accurate estimate of the position and orientation of the receiver, which corresponds to the six-dimensional positioning problem mentioned in. In this paper, we investigate the simultaneous position and orientation estimation (SPO) problem using received signal strength (RSS), for a visible light system containing multiple LEDs and multiple photodiodes (PDs) (MLMP). Although in general, the position and orientation of the receiver can be represented by a vector and a rotation matrix, respectively, the constraints imposed by the rotation matrix make the numerical optimization in the estimation process cumbersome, e.g, the commonly used constrained optimization method is often very complex and non-robust. Therefore, in this paper, we design two SPO algorithms using the principle of optimization on manifolds, which alleviates the constraints from the rotation matrix. In addition, we propose an initialization algorithm, based on the direct linear transformation (DLT) principle, to obtain an initial estimate in closed-form for the iterative algorithms. To evaluate the performance of the proposed RSS-based SPO algorithms, we derive the Cramer-Rao bound (CRB). In particular, the orientation error component of the CRB corresponds to the intrinsic CRB or the CRB on manifolds, which measures the error in the estimated rotation matrix in a physically meaningful way. Finally, computer simulations show an asymptotic tightness between the performance of the proposed algorithms and the theoretical lower bound, demonstrating the effectiveness of the proposed solutions. [ABSTRACT FROM AUTHOR]

Information about power consumption patterns of devices, helps the residential consumers manage their energy usage. Nonintrusive load monitoring is an effective tool to extract the consumption patterns from the measured aggregated data at the meter. In this paper, an optimization-based method is proposed to disaggregate the total load, using low frequency data. The proposed algorithm is enhanced by enforcing the power profiles of appliances to be piecewise constant over specific time durations. Moreover, the state transitions of the appliances are determined and then employed as the optimization constraints to improve the estimation results. The proposed method is evaluated using almanac of minutely power data set (AMPds) and reference energy disaggregation data set (REDD) datasets by several performance metrics. Results indicate that the designed algorithm is able to recognize the frequently varying appliances in spite of piecewise constancy presumption. Furthermore, breaking down the optimization problem to the smaller parts enhances the ability of the algorithm to be operated in real time. [ABSTRACT FROM AUTHOR]

In this paper, an economic dispatch (ED) problem for a community microgrid is studied. In this microgrid, each agent pursues an ED for its personal resources. In addition, each agent is capable of trading electricity with other agents through a local energy market. In this paper, an energy market operating in the presence of the grid is introduced. The proposed market is mainly developed for an experimental community microgrid at the Missouri University of Science and Technology, Rolla, MO, USA, and can be applied to other distribution level microgrids. To develop the algorithm, first, the microgrid is modeled and a dynamic ED algorithm for each agent is developed. Afterwards, an algorithm for handling the market is introduced. Lastly, simulation results are provided to demonstrate the proposed community market, and show the effectiveness of the market in reducing the operation costs of passive and active agents. [ABSTRACT FROM PUBLISHER]

In this paper, optimization of the control of an electrical machine allowing a minimization of its total losses is described. It is based on the use of an iron loss model [loss surface (LS) model] coupled to the electromagnetic finite-element simulations of the machine. The LS model is a scalar and dynamic hysteresis model developed many years ago at G2Elab and tested for iron loss prediction in several cases of electric machines. It is first characterized and improved in this paper for M330-35A SiFe sheets. Then, it is associated with a finite-element analysis to compute, in a post-processor mode, the local and global magnetic losses in the machine. For electric vehicle application, the whole torque–speed variation should be investigated. To do that, a quick response surface is constructed from a small number of simulations and the iron loss is determined. Then, a suitable optimization algorithm is developed. This approach is then illustrated by a case study and compared with classical optimization in which only the copper losses in conductors are considered. Gains of up to 50% reduction in the total losses of the machine in certain operating areas are observed. [ABSTRACT FROM AUTHOR]

This paper studies a single beacon-based three-dimensional multirobot localization (MRL) problem. Unlike most of existing localization algorithms which use extended Kalman filter or maximum a posteriori, moving horizon estimation (MHE), and convex optimization are novelly designed to perform MRL with constraints and unknown initial poses. The main contribution of this paper is three-fold: 1) a constrained MHE-based localization algorithm, which can bound localization error, impose various constraints and compromise between computational complexity and estimator accuracy, is proposed to estimate robot poses; 2) constrained optimization is examined in the perspective of Fisher information matrix to analyze why and how multirobot information and constraints are able to reduce uncertainties; 3) a semidefinite programming-based initial pose estimation, which can efficiently converge to global optimum, is developed by using convex relaxation. Simulations and experiments are conducted to verify the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive channel estimation algorithm is proposed for the multi-user robust relay beamforming problem. We propose a norm-bounded channel uncertainty model for all of the channels. We employ the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) for joint estimation of channel coefficients and beamforming weights, and propose a Markov model for source-relay and relay-destination channels as well as the beamforming weights in the relays. The channel coefficients and bemforming weights are shown to be well-estimated in order to minimize the total relays power transmission subject to worst-case signal to interference and noise ratio (SINR) criterion at each receiver. As the main contribution of this paper, we propose an adaptive method for simultaneous estimation of the beamforming weights and channel states information, and solving the associated optimization problem by estimation tools. Furthermore, we show that our algorithm outperforms the interior point based methods for non-linear optimization. In comparison to our recent work, a sub-optimal solution to the non-convex robust relay beamforming problem was provided, the proposed method has superior performance and lower complexity. [ABSTRACT FROM AUTHOR]

The coexistence between radar and communications systems has received considerable attention from the research community in the past years. In this paper, a radar waveform design method for target parameter estimation is proposed. Target time delay parameter is used as an example. The case where the two systems are not colocated is considered. Radar waveform optimization is performed using statistical criteria associated with estimation performance, namely Fisher Information (FI) and Cramér–Rao Bound (CRB). Expressions for FI and CRB are analytically derived. Optimization of waveforms is performed by imposing constraints on the total transmitted radar power, constraints on the interference caused to the communications system, as well as constraints on the subcarrier power ratio (SPR) of the radar waveform. The frequency-domain SPR is different than the peak-to-average power ratio, which is computed in time domain. It is shown, using simulation results, that the proposed optimization strategies outperform other strategies in terms of estimation error. It is also shown that the SPR constraint reduces the delay domain ambiguities. [ABSTRACT FROM AUTHOR]

This paper studies the problem of global optimization of zero-delay source-channel codes that map between the source space and the channel space, under a given transmission power constraint and for the mean-square-error distortion. Particularly, we focus on two well-known network settings: the Wyner-Ziv setting where only a decoder has access to side information and the distributed setting where independent encoders transmit over independent channels to a central decoder. Prior work derived the necessary conditions for optimality of the encoder and decoder mappings, along with a greedy optimization algorithm that imposes these conditions iteratively, in conjunction with the heuristic noisy channel relaxation method to mitigate poor local minima. While noisy channel relaxation is arguably effective in simple settings, it fails to provide accurate global optimization in more complicated settings considered in this paper. We propose a powerful nonconvex optimization method based on the concept of deterministic annealing—which is derived from information theoretic principles and was successfully employed in several problems including vector quantization, classification, and regression. We present comparative numerical results that show strict superiority of the proposed method over greedy optimization methods as well as prior approaches in literature. [ABSTRACT FROM AUTHOR]

In this paper, the use of the Dual Kalman Filter for the identification of photovoltaic system parameters is presented. The system includes the photovoltaic source, the dc/dc converter and the battery/dc bus and both its states and parameters in the actual operating conditions are identified. In particular, the proposed approach gives the confidence interval for the system settling time, which is used for the real-time optimization of the perturbative maximum power point tracking algorithm. The proposed technique is implemented by using a Field-Programmable Gate Array and it is validated by means of both simulation and experimental results. [ABSTRACT FROM PUBLISHER]

A key challenge for enabling machine-to-machine (M2M) communications in long-term evolution (LTE) networks is the intolerably low access efficiency in the presence of massive access requests. To address this issue, a new analytical framework is proposed in this paper to optimize the random access performance of the M2M communications in LTE networks. Specifically, a novel double-queue model is established, which can both incorporate the queueing behavior of each machine-type device (MTD) and be scalable in the massive access scenarios. To evaluate the access efficiency, the network throughput is further characterized, and optimized by properly choosing the backoff parameters including the access class barring (ACB) factor and the uniform backoff (UB) window size. The analysis reveals that the maximum network throughput is solely determined by the number of preambles, and can be achieved by either tuning the ACB factor or the UB window size based on statistical information such as the traffic input rate of each MTD. Simulation results corroborate that with the optimal tuning of backoff parameters, the network throughput can remain at the highest level regardless of how many MTDs in the network, and is robust against feedback errors of the traffic input rate and burstiness of data arrivals. [ABSTRACT FROM PUBLISHER]

Wi-Fi fingerprinting is a well-known technique used for indoor positioning. It relies on a pattern recognition method that compares the captured operational fingerprint with a set of previously collected reference samples (radio map) using a similarity function. The matching algorithms suffer from a scalability problem in large deployments with a huge density of fingerprints, where the number of reference samples in the radio map is prohibitively large. This paper presents a comprehensive comparative study of existing methods to reduce the complexity and size of the radio map used at the operational stage. Our empirical results show that most of the methods reduce the computational burden at the expense of a degraded accuracy. Among the studied methods, only $k$ k -means, affinity propagation, and the rules based on the strongest access point properly balance the positioning accuracy and computational time. In addition to the comparative results, this paper also introduces a new evaluation framework with multiple datasets, aiming at getting more general results and contributing to a better reproducibility of new proposed solutions in the future. [ABSTRACT FROM AUTHOR]

Resting-state functional magnetic resonance imaging (rs-fMRI) has become a popular non-invasive way of diagnosing neurological disorders or their early stages by probing functional connectivity between different brain regions of interest (ROIs) across subjects. In the past decades, researchers have proposed many methods to estimate brain functional networks (BFNs) based on blood-oxygen-level-dependent (BOLD) signals captured by rs-fMRI. However, most of the existing methods estimate BFNs under the assumption that signals are independently sampled, which ignores the temporal dependency and sequential order of different time points (or volumes). To address this problem, in this paper, we first propose a novel BFN estimation model by introducing a latent variable to control the sequence of volumes for encoding the temporal dependency and sequential information of signals into the estimated BFNs. Then, we develop an efficient learning algorithm to solve the proposed model by the alternating optimization scheme. To verify the effectiveness of the proposed method, the estimated BFNs are used to identify subjects with mild cognitive impairment (MCIs) from normal controls (NCs). Experimental results show that our method outperforms the baseline methods in the terms of classification performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose multi-input multi-output (MIMO) beamforming designs towards joint radar sensing and multi-user communications. We employ the Cramér-Rao bound (CRB) as a performance metric of target estimation, under both point and extended target scenarios. We then propose minimizing the CRB of radar sensing while guaranteeing a pre-defined level of signal-to-interference-plus-noise ratio (SINR) for each communication user. For the single-user scenario, we derive a closed form for the optimal solution for both cases of point and extended targets. For the multi-user scenario, we show that both problems can be relaxed into semidefinite programming by using the semidefinite relaxation approach, and prove that the global optimum can be generally obtained. Finally, we demonstrate numerically that the globally optimal solutions are reachable via the proposed methods, which provide significant gains in target estimation performance over state-of-the-art benchmarks. [ABSTRACT FROM AUTHOR]

Demand response (DR) is an important technique to explore the demand-side flexibility. The wide deployment of smart meters makes it possible to quantify the baseline load. As an intermediate agent, demand response aggregator needs to obtain the aggregated baseline load (ABL) for the DR event. Previous studies about the household level estimation focus on the estimation method. However, for ABL estimation, customer division is an important issue. A major limitation is the mismatch between the objectives of segmentation and estimation. Therefore, this paper proposes a new closed-loop method for estimating the ABL, which utilizes the contextual bandit with policy gradient to link the segmentation with the estimation. As such, the ABL estimation accuracy can guide the segmentation to divide the customers. The segmentation and estimation optimize collaboratively to improve the ABL estimation accuracy. An ensemble method for combining network’s weights during the training process is proposed. Moreover, a pre- and post-event adjustment method is developed to further improve the estimation accuracy. Comprehensive comparisons demonstrate the proposed method can achieve the best estimation performance with regard to the MAPE and RMSE. It improves the estimation accuracy by 7% in terms of MAPE, and 11% in terms of RMSE. [ABSTRACT FROM AUTHOR]

Over-the-air computation (AirComp) is a disruptive technique for fast wireless data aggregation in Internet of Things (IoT) networks via exploiting the waveform superposition property of multiple-access channels. However, the performance of AirComp is bottlenecked by the worst channel condition among all links between the IoT devices and the access point. In this paper, a reconfigurable intelligent surface (RIS) assisted AirComp system is proposed to boost the received signal power and thus mitigate the performance bottleneck by reconfiguring the propagation channels. With an objective to minimize the AirComp distortion, we propose a joint design of AirComp transceivers and RIS phase-shifts, which however turns out to be a highly intractable non-convex programming problem. To this end, we develop a novel alternating minimization framework in conjunction with the successive convex approximation technique, which is proved to converge monotonically. To reduce the computational complexity, we transform the subproblem in each alternation as a smooth convex-concave saddle point problem, which is then tackled by proposing a Mirror-Prox method that only involves a sequence of closed-form updates. Simulations show that the computation time of the proposed algorithm can be two orders of magnitude smaller than that of the state-of-the-art algorithms, while achieving a similar distortion performance. [ABSTRACT FROM AUTHOR]

Caching high-frequency reuse contents at the edge servers in the mobile edge computing (MEC) network omits the part of backhaul transmission and further releases the pressure of data traffic. However, how to efficiently decide the caching contents for edge servers is still an open problem, which refers to the cache capacity of edge servers, the popularity of each content, and the wireless channel quality during transmission. In this paper, we discuss the influence of unknown user density and popularity of content on the cache placement solution at the edge server. Specifically, towards the implementation of the cache placement solution in the practical network, there are two problems needing to be solved. First, the estimation of unknown users’ preference needs a huge amount of records of users’ previous requests. Second, the overlapping serving regions among edge servers cause the wrong estimation of users’ preference, which hinders the individual decision of caching placement. To address the first issue, we propose a learning-based solution to adaptively optimize the cache placement policy without any previous knowledge of the user density and the popularity of the contents. We develop the extended multi-armed bandit (Extended MAB), which combines the generalized global bandit (GGB) and Standard Multi-armed bandit (MAB), to iteratively estimate both a global parameter, i.e., the user density, and individual parameters, i.e., the popularity of each content. For the second problem, a multi-agent Extended MAB based solution is presented to avoid the mis-estimation of parameters and achieve the decentralized cache placement policy. The proposed solution determines the primary time slot and secondary time slot for each edge server. The edge servers estimate expected satisfied user number of caching a content with the overlap information and determine the cache placement solution. The proposed strategies are proven to achieve the bounded regret according to the mathematical analysis. Extensive simulations verify the optimality of the proposed strategies when comparing with baselines. [ABSTRACT FROM AUTHOR]

One key challenge in the point cloud segmentation is the detection and split of overlapping regions between different planes. The existing methods depend on the similarity and the dissimilarity in neighbor regions without a global constraint, which brings the ‘over-’ and ‘under-’ segmentation in the results. Hence, this paper presents a pipeline of the accurate plane segmentation for point clouds to address the shortcoming in the local optimization. There are two phases included in the proposed segmentation process. One is a local phase to calculate connectivity scores between different planes based on local variations of surface normals. In this phase, a new optimal-vector-field is formulated to detect the plane intersections. The optimal-vector-field is large in magnitude at plane intersections and vanishing at other regions. The other one is a global phase to smooth local segmentation cues to mimic leading eigenvector computation in the graph-cut. Evaluation of two datasets shows that the achieved precision and recall is 94.50 percent and 90.81 percent on the collected mobile LiDAR data and obtains an average accuracy of 75.4 percent on an open benchmark, which outperforms the state-of-the-art methods in terms of completeness and correctness. [ABSTRACT FROM AUTHOR]

Renewable power plants must comply with certain codes and requirements to be connected to the grid, being the ramp-rate compliance one of the most challenging requirements, especially for photovoltaic or wind energy generation plants. Battery-based energy storage systems represent a promising solution due to the fast dynamics of electrochemical storage systems, besides their scalability and flexibility. However, large-scale battery energy storage systems are still too expensive to be a mass market solution for the renewable energy resources integration. Thus, in order to make battery investment economically viable, the use of second life batteries is investigated in the paper. This paper proposes a method to determine the optimal sizing of a second life battery energy storage system (SLBESS). SLBESS performance is also validated and, as an ultimate step, the power exchanged with the batteries is calculated during one-year operation. The power profile obtained is further used to define the cycling patterns for laboratory testing of second life batteries and to study their ageing evolution when used for the power smoothing renewable integration application. Real photovoltaic energy generation data from a Spanish plant were used for the study. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to review the scientific literature on direct load control (DLC) technology in the context of a smart grid (SG). This study synthesized and compared various articles from the literature with the aim of conducting a comprehensive analysis. This study aims to provide a detailed understanding of the landscape, recent advancements, and the relationships among various aspects within this field. The evaluation is conducted through a systematic literature review, examining recent advancements in the field. A survey of over 200 papers published between 1983 and 2022 is classified based on appliance information, aim/objective, constraint, management method and tool, implementation, and publication status. In the DLC application, the study identifies the type and population of appliances used in and the aim/objective and constraint considered. It also determines whether forecast, prediction, and estimation structures, optimization and control algorithms, and specific structures are employed. The practical aspect of the study examines various perspectives, including the modeling and presentation of the research. This involves identifying the validation methods, time period for implementation, solver, program, software, toolbox, and platform structures used during validation. Additionally, the characteristics of the database used for implementation and the location of the implementation are detailed. The publication year and the countries of the authors are also provided. Comparative relationships between these elements are presented based on the aforementioned information.

This paper proposes a novel proximal-gradient algorithm for a decentralized optimization problem with a composite objective containing smooth and nonsmooth terms. Specifically, the smooth and nonsmooth terms are dealt with by gradient and proximal updates, respectively. The proposed algorithm is closely related to a previous algorithm, PG-EXTRA (W. Shi, Q. Ling, G. Wu, and W. Yin, “A proximal gradient algorithm for decentralized composite optimization,” IEEE Trans. Signal Process., vol. 63, no. 22, pp. 6013–6023, 2015), but has a few advantages. First of all, agents use uncoordinated step-sizes, and the stable upper bounds on step-sizes are independent of network topologies. The step-sizes depend on local objective functions, and they can be as large as those of the gradient descent. Second, for the special case without nonsmooth terms, linear convergence can be achieved under the strong convexity assumption. The dependence of the convergence rate on the objective functions and the network are separated, and the convergence rate of the new algorithm is as good as one of the two convergence rates that match the typical rates for the general gradient descent and the consensus averaging. We provide numerical experiments to demonstrate the efficacy of the introduced algorithm and validate our theoretical discoveries. [ABSTRACT FROM AUTHOR]

The surface normal estimation from photometric stereo becomes less reliable when the surface reflectance deviates from the Lambertian assumption. The non-Lambertian effect can be explicitly addressed by physics modeling to the reflectance function, at the cost of introducing highly nonlinear optimization. This paper proposes a numerical compensation scheme that attempts to minimize the angular error to address the non-Lambertian photometric stereo problem. Due to the multifaceted influence in the modeling of non-Lambertian reflectance in photometric stereo, directly minimizing the angular errors of surface normal is a highly complex problem. We introduce an alternating strategy, in which the estimated reflectance can be temporarily regarded as a known variable, to simplify the formulation of angular error. To reduce the impact of inaccurately estimated reflectance in this simplification, we propose a numerical compensation scheme whose compensation weight is formulated to reflect the reliability of estimated reflectance. Finally, the solution for the proposed numerical compensation scheme is efficiently computed by using cosine difference to approximate the angular difference. The experimental results show that our method can significantly improve the performance of the state-of-the-art methods on both synthetic data and real data with small additive costs. Moreover, our method initialized by results from the baseline method (least-square-based) achieves the state-of-the-art performance on both synthetic data and real data with significantly smaller overall computation, i.e., about eight times faster compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Focusing on generalized sensor combinations, this paper deals with the attitude estimation problem using a linear complementary filter (CF). The quaternion observation model is obtained via a gradient descent algorithm. An additive measurement model is then established according to derived results. The filter is named as the generalized CF where the observation model is simplified as a linear one that is quite different from previous-reported brute-force nonlinear results. Moreover, we prove that representative derivative-based optimization algorithms are essentially equivalent to each other. Derivations are given to establish the state model based on the quaternion kinematic equation. The proposed algorithm is validated under several experimental conditions involving the free-living environment, harsh external field disturbances, and aerial flight test aided by robotic vision. Using the specially designed experimental devices, data acquisition and algorithm computations are performed to give comparisons on accuracy, robustness, time-consumption, and so on with representative methods. The results show that not only the proposed filter can give fast, accurate, and stable estimates in terms of various sensor combinations but also produces robust attitude estimation in the scenario of harsh situations, e.g., irregular magnetic distortion. Note to Practitioners—Multisensor attitude estimation is a crucial technique in robotic devices. Many existing methods focus on the orientation fusion of specific sensor combinations. In this paper, we make the problem more concise. The results given in this paper are very general and can significantly decrease the space consumption and computation burden without losing the original estimation accuracy. Such performance will be of benefit to robotic platforms requiring flexible and easy-to-tune attitude estimation in the future. [ABSTRACT FROM AUTHOR]

Influenced by the economic globalization, the distributed manufacturing has been a common production mode. This paper considers a multiobjective distributed no-wait flow-shop scheduling problem with sequence-dependent setup time (MDNWFSP-SDST). This scheduling problem exists in many real productions such as baker production, parallel computer system, and surgery scheduling. The performance criteria are the makespan and the total weight tardiness. In the MDNWFSP-SDST, several identical factories are considered with the related flow-shop scheduling problem with no-wait constraints. For solving the MDNWFSP-SDST, a Pareto-based estimation of distribution algorithm (PEDA) is presented. Three probabilistic models including the probability of jobs in empty factory, two jobs in the same factory, and the adjacent jobs are constructed. The PWQ heuristic is extended to the distributed environment to generate initial individuals. A sampling method with the referenced template is presented to generate offspring individuals. Several multiobjective neighborhood search methods are developed to optimize the quality of solutions. The comparison results show that the PEDA obviously outperforms other considered multiobjective optimization algorithms for addressing MDNWFSP-SDST. Note to Practitioners—This paper is motivated by the process cycles in multiproduction factories (or lines) of baker production, surgery scheduling, and parallel computer systems. In these process cycles, jobs are assigned to multiproduction factories (or lines), and no interruption exists between consecutive operations. This paper models this process as a multiobjective distributed no-wait flow-shop scheduling with SDST. Scheduling becomes more challenging when facing distributed factories. This paper provides an estimation of distributed algorithm with Pareto dominate concept which uses a probabilistic model to generate offspring. Experiment results suggest that the proposed algorithm can find superior solutions of large-scale instances. This scheduling model can be extended to practical problems by considering other constraints, such as assembly process, mixed no-wait, and transporting times. Besides, the proposed algorithm can be applied to solve other distributed scheduling problems and industrial cases, once their constraints are known, i.e., the processing time of operations, the setup time of machines. [ABSTRACT FROM AUTHOR]

This paper studies linear distributed estimation of an unknown random parameter vector in a bandwidth-constrained multisensor network. To meet the bandwidth limitations, each sensor converts its observation into a low-dimensional datum via a suitable linear transformation. Then, the fusion center estimates the parameter vector by linearly combining all the received low-dimensional data, aiming at minimizing the estimation mean square error. The main purpose of this paper is to jointly determine the compression dimension of each sensor (referred to as dimension assignment) and design the corresponding compression matrix when the total compression dimensions is limited. Such a joint design problem can be formulated as a rank-constrained optimization problem and it is shown to be NP-hard for the first time. In addition, successive quadratic upper-bound minimization (SQUM), SQUM-block coordinate descent (SQUM-BCD) and nuclear norm regularization (NNR) methods are developed to solve it approximately. Furthermore, we show that any accumulation point of the sequence generated by the SQUM method satisfies the Karush-Kuhn-Tucker conditions of the rank-constrained optimization problem, and the Phase II algorithm of the SQUM-BCD and NNR methods (both are two-phase algorithms and have the same Phase II algorithm) guarantees convergence at least to a stationary point. Numerical experiments illustrate the advantages of the proposed methods compared with the existing method. [ABSTRACT FROM AUTHOR]

Battery and energy management methodologies have been proposed to address the design challenges of driving range and battery lifetime in electric vehicles (EVs). However, the driving behavior is a major factor which has been neglected in these methodologies. In this paper, we propose a novel context-aware methodology to estimate the driving behavior in terms of future vehicle speeds and integrate this capability into EV energy management. We implement a driving behavior model using a variation of artificial neural networks called nonlinear autoregressive model with eXogenous inputs (NARX). We train our novel context-aware NARX model based on historical behavior of real drivers, their recent driving reactions, and route average speed retrieved from Google Maps in order to enable driver-specific and self-adaptive driving behavior modeling and long-term estimation. We analyze the estimation error of our methodology and its impact on a battery lifetime-aware automotive climate control, comparing to the state-of-the-art methodologies for various estimation window sizes. Our methodology shows only 12% error for up to 30-s speed prediction which is an improvement of 27% compared to the state-of-the-art. Therefore, the higher accuracy helps the controller to achieve up to 82% of the maximum energy saving and battery lifetime improvement achievable in ideal methodology where the future vehicle speeds are known. [ABSTRACT FROM AUTHOR]

Finite control set-model predictive torque control (FCS-MPTC) has a fast dynamic response because this algorithm directly selects the optimal voltage vector by its cost function for induction machine drives fed by voltage source inverter (VSI). However, belonging to open-loop control paradigm, the FCS-MPTC has torque tracking error due to inevitable load disturbance and mismatched model parameters in reality. In traditional FCS-MPTC, the outer loop, i.e., speed loop, adopts a classic proportional integral (PI) controller, abbreviated as PI-MPTC. The lumped disturbance is only suppressed by a PI controller. However, pole placement of the PI controller is usually designed by cut-and-trial, which is difficult to simultaneously achieve optimal dynamic performance and optimal suppression of lumped disturbance. In this paper, the FCS-MPTC with mismatched parameters is first analyzed. Second, the deficiencies of the traditional PI controller are introduced. Third, disturbance feedforward compensation-based-model predictive torque control (DFCB-MPTC) of induction machine is proposed to compensate lumped disturbance and improve the performance of the system. Furthermore, a simplified stator flux observer is proposed, whose gain matrix is independent of rotor speed. Experimental results verify the feasibility of the proposed DFCB-MPTC. Compared with traditional PI-MPTC, the proposed DFCB-MPTC has better dynamic performance, steady performance, and stronger robustness. [ABSTRACT FROM AUTHOR]