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In this letter, we present an iterative algorithm for constructing high-dimensional incoherent Grassmannian Frames (GFs) which are sets of unit-norm vectors that are minimax optimal: the maximum absolute value of their inner products is a minimum. We formulate the GF design as a nonconvex optimization problem and solve it via an efficient iterative algorithm. The bulk of each iteration of the proposed algorithm is a Linear Program (LP) for which there are a host of efficient solvers. The proposed algorithm is based on the theoretically sound Minorization Maximization technique which, unlike some of the state-of-the-art design approaches, monotonically minimizes the design criterion and can construct GFs with low coherence values.

This paper is devoted to the numerical simulations of elastoplastic plates at small strains by using the non-incremental Brezis-Ekeland-Nayroles (BEN) principle. This space-time variational principle is based on the energy dissipation and transforms the boundary value problem into a null minimization one under constraints. Unlike the standard incremental procedure, the BEN principle characterizes the entire trajectory and delivers the mechanical fields at all time steps simultaneously. Two kinds of plates: the thin plate Love-Kirchhoff and the thick Reissner-Mindlin circular plates undergoing uniform pressure are studied respectively. The description of the dicretization of the principle using the mixed finite element method and the implementation are detailed. The obtained results are compared against analytical solutions provided in the literature and numerical predictions derived by the classical incremental procedure.

Peer-to-peer interconnection of households having on-site batteries, multi-port converters and solar panels to form a multi-port converter-enabled solar DC nano-grid is an emerging approach for providing affordable energy access in rural areas. Battery charge and discharge losses, distribution losses and converter losses are significant problem when operating such nano-grids. This paper presents a centralized control algorithm that can help address the power loss problem. The proposed algorithm uses a new problem formulation where the power loss problem is formulated as a two-stage convex optimization problem. The first stage of the optimization problem is an optimal battery dispatch problem for determining optimal battery charge and discharge currents. The second stage is an optimal current flow problem for determining optimal distribution voltages which corresponds to the optimal battery currents. Simulation results of the nano-grid show that the proposed algorithm can minimize the nano-grid power losses while facilitating the power exchange between the households. The proposed algorithm is suitable for small nano-grids where privacy of households is not a concern. In Part II of this paper we propose a distributed control algorithm that preserves the privacy of the households especially where the size of the nano-grid is large.

A new efficient technique for the best selection of the unknown variables in the Solid Oxide Fuel Cell (SOFC) stack models is proposed in this paper. The main concept in this paper is the minimization of the sum of squared error values between the empirical voltage and current profile and the obtained voltage and current profiles from the method. The minimization process is defined by a new improved metaheuristic, which is the Modified African Vulture Optimizer (MAVO). The MAVO algorithm is designed to modify the algorithm and achieve results with better effectiveness as concerns convergence and accuracy. For determining the system consistency, two scenarios based on pressure and temperature variations are investigated. The technique has been finally compared with several other techniques to verify its prominence. The results show the minimum value of the SSE under different temperatures, equal to 1.87 e−4, and the minimum value of the MSE under different temperatures, equal to 1.24 e−3. This indicates promising results for the proposed method as a proper identification system. Final achievements indicate that the suggested approach provides outstanding effectiveness toward the compared methods.

Hybrid renewable energy system (HRES) is a nexus of several renewable energy systems that concertedly overcomes the limitations of individual systems operating in isolation. This concept of HRES is extended in this study by considering the network of several such HRESs which further improves the flexibility in overcoming the individual limitations. However, operating such a network of HRESs involves complex decision-making and requires efficient optimization. In this study, a novel hierarchical two-tier optimization methodology for a network of HRESs is proposed when they trade power with each other and with the macro grid. In the proposed methodology, the operational optimization of each HRES is carried out by minimizing the operating cost of various sub-units in the first layer. In the second layer, three cooperative optimization strategies inspired from concepts of double-auction e-marketing are applied when HRESs are assumed to operate under the grand coalition. These three cooperative strategies try to achieve the following objectives (i) minimization of power loss during local trade, (ii) maximization of total market gain, and (iii) simultaneous power loss minimization and total market gain maximization, respectively. Simulation results depict that the cooperative optimization approach yields a reduction in power loss up to 7 % and improvement in market utility up to 29% relative to a scenario when they operate non-cooperatively.

It is well known that interposer-based 2.5-D integrated circuit (IC) designs have become one of the most promising solutions for providing yield improvement, enhancing system performance, decreasing power consumption, and supporting heterogeneous integration. In this article, given a set of nets including some inter-chip or through-silicon buses between chips and package, inside a silicon interposer, a via-avoidance-oriented routing algorithm can be proposed to minimize the number of the used layers by satisfying the noncrossing constraint between two nets and the constraint of using no via on the inter-chip sub-nets and no vertical detour on the through-silicon sub-nets in multiple-layer interposer routing. The routing process in our proposed algorithm can be divided into two sequential steps: iterative routing step and refinement step. In the iterative routing step, the routing process of all the given nets can be completed for layer minimization in a top-down layer-by-layer manner. In each iteration, based on the definition of the obstacle-aware routing pattern for the inter-chip sub-net on one given net, the assignment of the obstacle-aware routing patterns can be firstly obtained in single-layer routing. Furthermore, the routing paths of some inter-chip sub-nets and the partial or full routing paths of some through-silicon sub-nets can be assigned and routed onto the available layer by using a maze routing process under the detour constraints. In the refinement step, based on the routing result of the given nets on the used layers, the detoured inter-chip or through-silicon sub-nets can be firstly reassigned and rerouted for the detour reduction of the given nets. Furthermore, a set of zigzag paths can be inserted onto some nets inside the given inter-chip or through-silicon buses for skew minimization. Compared with the combination of the iterative routing using Cadence’s automatic router and the detouring-path insertion on the number of the used layers, the experimental results show that our proposed routing algorithm can use reasonable CPU time and shorter wirelength to decrease 19.3% of the number of the used layers for eight tested examples on the average.

This paper proposed a new topology of a symmetric single-phase multilevel inverter with the smaller number of semiconductor switches and optimized low-frequency control methods to optimize the Total Harmonic Distortion. A nine-level single phase output is obtained by eight number of active semiconductor switches, four diodes and four capacitors from two asymmetrical dc sources. The selected harmonic order in the output voltage is eliminated by the PWM (SHE-PWM) based on selective harmonic elimination. To optimize the switching angles, an ant colony optimization is introduced. The proposed SHE-PWM and ant optimization are implemented and tested for THD on the SIMULINK platform. The proposed approach offers less THD and is best suited to high-power applications with medium voltage.

The paper presents procedure for design optimization of a spur gear set with the objective of minimization of the gear weight, contact stress along path of contact and optimum film thickness at the...

This paper is devoted to solving the inverse problem of restoring the hydrometeor distribution function from radar measurements. We use the Tikhonov regularization algorithm based on the minimization of the residual functional by the conjugate gradient method.

The Reynolds number of turbine blades can be reduced by decreasing the surface roughness of the blades, which decreases energy loss in energy production systems. Due to the challenges of the polish...

We present a new algorithm for large-scale unconstrained minimization that, at each iteration, minimizes, approximately, a quadratic model of the objective function plus a regularization term, not necessarily based on a norm. We prove convergence assuming only gradient continuity and complexity results assuming Lipschitz conditions. For solving the subproblems in the case of regularizations based on the 3-norm, we introduce a new method that quickly obtains the approximate solutions required by the theory. We present numerical experiments.

Switching transients arises due to random switching of power apparatus results in degradation of thermal and dielectric health of equipment and system as a whole. Controlled switching or point on wave control has emerged as excellent solution for minimizing the charging transients and can aid the better condition monitoring of circuit breaker. Different switching strategies are in place based on different design and connection configurations. Each methodology has certain advantages and dis-advantages. The performance of control switching is dependent on the dielectric performance of insulating medium and circuit breaker performance. The effect of variations in insulation dielectric properties and breaker mechanical performance has been discussed in depth in this paper. Selection and performance of feedbacks used in controlled switching also play a major role in target evaluation and needs proper assessment. Review of different controlled switching methodologies in respect of transformer and shunt reactor have been discussed in paper augmented with field cases. Based on the review of available controlled switching methodologies and field experience, the acceptance criteria have been proposed.

Scheduling process arises naturally upon availability of resources through a systematic approach in which prior planning and decisions should be made. Two machine flow shop scheduling problem (FSSP) was solved by Johnson in the mid of 1954 with makespan minimization as objective. Earlier we proposed two algorithms for the makespan objective; in this paper we intend to investigate the same algorithms for the objective of Total Completion Time of all the jobs (TCT). Experimental results had shown that one of our algorithms gives better results than the other two when the machine order is reversed.

SIGNSGD is able to dramatically improve the performance of training large neural networks by transmitting the sign of each minibatch stochastic gradient, which achieves gradient communication compression and keeps standard stochastic gradient descent (SGD) level convergence rate. Meanwhile, the learning rate plays a vital role in training neural networks, but existing learning rate optimization strategies mainly face the following problems: (1) for learning rate decay method, small learning rates produced lead to converge slowly, and extra hyper-parameters are required except for the initial learning rate, causing more human participation. (2) Adaptive gradient algorithms have poor generalization performance and also utilize other hyper-parameters. (3) Generating learning rates via two-level optimization models is difficult and time-consuming in training. To this end, we propose a novel adaptive learning rate schedule for neural network training via SIGNSGD optimizer for the first time. In our method, based on the theoretical inspiration that the convergence rate’s upper bound has minimization with the current learning rate in each iteration, the current learning rate can be expressed by a mathematical expression that is merely related to historical learning rates. Then, given an initial value, learning rates in different training stages can be adaptively obtained. Our proposed method has following advantages: (1) it is a novel automatic method without additional hyper-parameters except for one initial value, thus reducing the manual participation. (2) It has faster convergence rate and outperforms the standard SGD. (3) It makes neural networks achieve better performance with fewer gradient communication bits. Three numerical simulations are conducted on different neural networks with three public datasets: MNIST, Cifar-10 and Cifar-100 datasets, and several numerical results are presented to demonstrate the efficiency of our proposed approach.

A theory of refining pure strategy efficient Nash equilibria in finite noncooperative games under uncertainty is outlined. The theory is based on guaranteeing the corresponding payoffs for the players by using maximultimin, which is an expanded version of maximin. If a product of the players’ maximultimin subsets contains more than one efficient Nash equilibrium, a superoptimality rule is attached wherein minimization is substituted with summation. The superoptimality rule stands like a backup plan, and it is involved if maximultimin fails to produce just a single refined efficient equilibrium (a metaequilibrium). The number of the refinement possible outcomes is 10. There are 3 single-metaequilibrium cases, 3 partial reduction cases, and 4 fail cases. Despite successfulness of refinement drops as the game gets bigger, pessimistic estimation of its part is above 54 % for games with no more than four players.

To enable the efficient division of labor in container yards, many large ports apply twin cranes, two identical automated stacking cranes each dedicated to one of the transfer zones on the seaside and landside. The use of a handshake area, a bay of containers that separates the dedicated areas of the two cranes, is a simple means to avoid crane interference. Inbound containers arriving in the transfer zone of one crane and dedicated to a stacking position of the other crane’s area are placed intermediately in the handshake area by the first crane and then taken over by the second crane, and vice versa for outbound containers. Existing research only evaluates simple heuristics and rule-based approaches for the coordination of twin cranes interconnected by a handshake area. For this setting, accounting for precedence constraints due to stacking containers in the handshake area, we derive exact solution procedures under a makespan minimization objective. In this way, a comprehensive computational evaluation is enabled, which benchmarks heuristics with optimal solutions and additionally compares alternative crane settings (i.e., without workload sharing and cooperation with flexible handover). We further provide insights into where to position the handshake area. Our results reveal that when planning is too simple (i.e., with a rule-based approach), optimality gaps become large, but with sophisticated optimization, the price of a simplified crane coordination via a handshake area is low.

In this paper, we address a variant of the identical parallel machines scheduling problem subject to common restrictive due windows. The performance measure adopted is the minimization of total weighted earliness and tardiness. Since the variant under study is an NP-hard problem for two or more machines, we develop a family of constructive heuristics, which are comprised of four phases. First, jobs are sequenced according to priority rules. Second, jobs are assigned to machines using a greedy strategy. Third, a local search is performed to find a better distribution of jobs into machines. Fourth, two heuristics are applied for individually sequencing jobs in each machine, namely RN-RGH and RN-SEA. In addition, we also propose an iterated greedy algorithm to improve the solutions of the best performing heuristic. The computational experiments were carried out to prove the ability of these heuristics to find high-quality solutions in acceptable CPU time. More specifically, the RN-SEA family of algorithms stands out as the most efficient for the problem, however, with a higher computational effort. We also confirm that the IG algorithm has the potential for improving existing solutions, specially for problems with two machines and instances with up to 100 jobs in size.

In recent years, low-rank tensor completion has been widely used in color image recovery. Tensor Train (TT), as a balanced tensor rank minimization method, has achieved good results in actual image recovery because of its ability to capture the hidden information of images. When processing the third-order tensor data of a color image, TT transforms it into a higher-order tensor through kat augmentation to exploit the local feature of the tensor effectively. However, this method actually destroys the original structure of the tensor, which leads to insufficient access to the global structure information. Therefore, the algorithm performs poorly when processing images with a significant amount of missing information. Aiming at this problem, a new tensor completion model is proposed, which combines Tucker rank and Tensor Train rank. Among them, Tucker is used to obtain the global structure information, and Tensor Train is used to capture the local hidden information. To tackle the proposed model, we develop an efficient alternating direction method based algorithm. The numerical experiments using various tensor data show the effectiveness of the model.

Made available in DSpace on 2022-05-01T09:31:06Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-02-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) In this work, a robust switched controller design using output feedback is proposed. The minimization of the output energy is taken into account as a design strategy to reduce the settling time and oscillations of the closed-loop system output. In the design of the controllers, physical limits of the system outputs are also covered, which is done by considering a convex set of initial conditions. Sufficient LMI conditions to solve the control design are presented in the form of theorems that require the choice of constant parameters. The search for the most adequate parameters was accomplished via the differential evolution algorithm. Moreover, the proposed switched controller design is compared with a constant output feedback gain controller and other methodologies proposed in the literature. Theoretical analyses, two numerical examples and a practical implementation in an active suspension system were carried out to show the advantages of the proposed methodology. São Paulo State University (UNESP) School of Engineering, Ilha Solteira, José Carlos Rossi Avenue, 1370 IFPR – Federal Institute of Education Science and Technology of Paraná Campus of Jacarezinho Jacarezinho IFMS – Federal Institute of Education Science and Technology of Mato Grosso do Sul Campus of Campo Grande São Paulo State University (UNESP) School of Engineering, Ilha Solteira, José Carlos Rossi Avenue, 1370 CAPES: 001 CNPq: 120545/2020-9 FAPESP: 2011/17610-0 CNPq: 301227/2017-9 CNPq: 309872/2018-9

The direction of arrival of multiple signals arriving on a non-uniform linear nested array in the presence of array errors due to gain and phase perturbations is considered in this paper. An $$ l $$ -2 norm minimization is proposed for gain and phase error calibration and the direction of signals is estimated using the spectral search method. The proposed method iteratively minimizes the error and reconstructs the covariance matrix subsequently reducing the effect of the error. The results obtained from simulations illustrate the capability of the proposed method in overcoming the performance deterioration due to errors in a nested array.

In this paper, a basic dynamical state-space system model with two state feedback controllers and tracking signal-to-interference-plus-noise (SINR) of secondary user (SU) and interference to primary user (PU) generated from the whole secondary user (SUs) is proposed based on the automatic control framework to guarantee the quality of service (QoS) both primary user (PU) and SU in underlay cognitive radio communication network, which considers the constraint of the average interference temperature. On the above foundation, a delay state-space model with the constraint of the weighted interference temperature is proposed under the time delay, multiple access interference (MAI), channel fading, measurement errors, and uncertain noise. Moreover, the robust mixed $$\text {H}_{2} / \text {H}_{\infty }$$ power tracking algorithm (RPCA) with adaptive step is proposed to obtain a robust optimal SU’s SINR tracking error under the minimization the worst-case and the minimum interference to PU tracking error, which can compensate the effects of the above factors. In comparison with the conventional power control algorithm (CPCA), the proposed mixed $$\text {H}_{2} / \text {H}_{\infty }$$ RPCA can ensure the communication quality of SUs and reduce the interference to PU from the all SUs.

The Almost 1.3 million casualties are reported round a calendar year due to road accidents. Advanced collision avoidance systems play major role in predicting the collision risk to avoid accidents. The existing deep learning algorithms are unable to predict the crash risk efficiently. In the existing system, Long Short Term Memory algorithm is used to predict the crash risk where weights are not optimized. The objective is to predict the rear end collision risk with optimized weight by combining Long Short Term Memory(LSTM) with Levenberg–Marquardt (LM) algorithms. The proposed algorithm predicts the collision risk considering vehicle, driver related factors, and temporal dependencies. Next Generation Simulation Project (NGSIM) dataset is used to evaluate the proposed model. The performance of the proposed system is compared with the performance of Long Short Term Memory and Back Propagation Neural Network. 95.6% of accuracy is achieved by LM-LSTM based Time series Deep Network Model. The prediction accuracy has been improved considerably than the existing algorithms. There is the drastic improvement in minimization of false alarm and missed alarm rate. The main advantage of the proposed system is that it will present warning at the time of high collision risk and it helps drivers to prevent from accident.

In recent times, transportation electrification has been recognized as one of the key solutions to accelerate global GHG emission reductions. As the electric vehicle industry grows faster, plug-in electric vehicles (PEV) are expected to be the most dominant load in the utility sector in less than a decade. Regular charging of the battery energy storage system (BESS) is a mandate for the continued operation of the vehicle, and the PEVs are connected to the utility to charge. Since PEVs are mobility loads, predicting the interconnection of these mobility loads in the utility network for recharging is a major challenge. The intermittent connection of mobility loads to the grid for charging leads to an unpredictable increase in electricity demand and other grid-related issues. Optimal scheduling of PEV charging would conquer the grid-related issues and provide financial benefits to the users. In this paper, an intelligent charge scheduling technique of PEV charging for both residential and commercial charging stations using the heuristic algorithm is proposed and discussed. The primary objective of the algorithm is to achieve the minimization of PEV charging costs by implementing an interrupted charging schedule. The proposed algorithm is tested by conducting exhaustive simulation studies under several conditions for PEVs with different power ratings for residential and commercial charging scenarios. The time-of-use pricing (ToUP) system is adopted as a tariff system in this paper. A detailed comparison of the unscheduled algorithm, the modified placement algorithm (MPA) and proposed heuristic technique-based charge scheduling is carried out through simulation studies. A detailed cost analysis for charging the PEVs with the selected charge scheduling techniques for various conditions is conducted and cost minimization by implementing the proposed charging scheme is validated.

Flexible optimization on video coding computational complexity is imperative to adapt to diverse terminals in video streaming, especially with high definition videos and increasingly comprehensive encoders. Until now, few efforts have been contributed to the encoder of multiview videos, which complicates its encoding structure with duplex frame display. To address this issue, we propose a flexible complexity optimization framework in this paper. In the first place, the proposed algorithm flexibly reduces the encoder complexity at different external-defined complexity constraints; furthermore, it is also committed to compression efficiency optimization at different complexity levels. The framework is achieved by a hybrid approach: complexity allocation , which allocates the external-defined complexity constraint to all views, hierarchical layers and frames based on linear programming; and complexity regulation , which dynamically adjusts local candidate partitions to fulfil the targeted local complexity constraint, with a probability-driven Alternate Partition Cost (APC) minimization. The overall algorithm is implemented on the popular multiview encoder, Multiview High Efficiency Video Coding (MV-HEVC), with promising Rate-Distortion (RD) performances at different complexity constraints, which are also superior to two recent complexity optimization algorithms.

This paper presents a real-time 3D LiDAR mapping framework based on global matching cost minimization. The proposed method constructs a factor graph that directly minimizes matching costs between frames over the entire map, unlike pose graph-based approaches that minimize errors in the pose space. For real-time global matching cost minimization, we use a voxel data association-based GICP matching cost factor that is able to fully leverage GPU parallel processing. The combination of the matching cost factor and GPU computation enables constraint of the relative pose between frames with a small overlap and creation of a densely connected factor graph. The mapping process is managed based on a voxel-based overlap metric that can quickly be evaluated on a GPU. We incorporate the proposed method with an external loop detection method in order to help the voxel-based matching cost factors to avoid convergence in a local solution. The experimental result on the KITTI dataset shows that the proposed approach improves the estimation accuracy of long trajectories., IEEE Robotics and Automation Letters, Video: https://youtu.be/TarRKF_Xd2E

This article proposes an integration method for interturn fault diagnosis and torque ripple minimization control of direct-torque-controlled surface-mounted permanent magnet synchronous machine (SPMSM) drive system without requiring the specific fault severity (short-circuited turn ratio, current). In this method, the interturn fault is detected and located based on the amplitude and initial phase angle of the zero-sequence voltage component (ZSVC). Both the torque injection and the improved flux observer are applied to reduce the torque ripple caused by the interturn fault. Furthermore, the torque injection and the improved flux observer are directly calculated by using the already obtained amplitude and initial phase angle of the ZSVC during the fault diagnosis process, without requiring the specific values of short-circuited turn ratio and fault current. The experimental results validate the effectiveness of the proposed integration method for the direct-torque-controlled SPMSM drive system.

In this article, we propose an intelligent hybrid nonorthogonal multiple access (NOMA) relaying system for the next generation of millimeter-wave (mmWave) band end-edge-cloud vehicular networks, which mainly comprises the cloud high-throughput satellite (HTS), edge base station (BS), and end vehicle nodes (VNs). Specifically, by taking account of the movement of the end VNs in the edge BS, we investigate three typical scenarios due to the mobility of the end VNs during the downlink transmission, including quasistatic, intracell, and intercell scenarios, and formulate the optimal power allocation problem of the intelligent hybrid NOMA system for the throughput maximization and outage probability (OP) minimization. Concretely, we first present an iteration power allocation (IPA) algorithm to derive the optimal set of power coefficients for the NOMA transmission in the quasistatic scenario, and also design a power reallocation method based on the expectation–maximization (PREM) algorithm for the intracell and intercell scenarios. Simulation results validate that our proposed algorithms can approach to the exhaustive search method and outperform the existing optimal NOMA schemes. Further, we exploit the effects of the number of the moved end VNs, which can offer some useful guidelines for the design of the next-generation vehicular network.

As a critical component of coherent X-ray diffraction imaging (CDI), phase retrieval has been extensively applied in X-ray structural science to recover the 3D morphological information inside measured particles. Despite meeting all the oversampling requirements of Sayre and Shannon, current phase retrieval approaches still have trouble achieving a unique inversion of experimental data in the presence of noise. Here, we propose to overcome this limitation by incorporating a 3D Machine Learning (ML) model combining (optional) supervised learning with transfer learning. The trained ML model can rapidly provide an immediate result with high accuracy which could benefit real-time experiments, and the predicted result can be further refined with transfer learning. More significantly, the proposed ML model can be used without any prior training to learn the missing phases of an image based on minimization of an appropriate ‘loss function’ alone. We demonstrate significantly improved performance with experimental Bragg CDI data over traditional iterative phase retrieval algorithms.

Our objective in this article is to develop weighted extended B-spline (WEB-spline) approximation of the first eigenpair of p ( x ) -Laplacian problem. One of the reason for choosing WEB-spline based mesh-free method for solving p ( x ) -Laplacian problem is that, it has additional advantages compared to usual finite element method. For example, it does not require any mesh generation procedure and hence eliminates the difficult, time consuming preprocessing step in finite element method. Also, it combines the computational advantages of B-splines and geometric flexibility of standard mesh-based elements. To compute the first eigenpair of p ( x ) -Laplacian problem, we examine the problem numerically by applying an inverse power method with three different minimization techniques and we compare the results obtained. Finally, we study the asymptotic behaviour of the eigenvalue problem of p ( x ) -Laplacian and validate the results comparing with those given in the literature.

Adaptive beamforming with sidelobe-level control in the presence of signal steering vector uncertainty is investigated. Unlike the traditional multiconstrained optimization strategy using the interior point method, iterative optimization algorithms with the aid of the alternating direction method of multipliers (ADMM) framework are proposed. The uncertainty set constraint and the sidelobe constraint are formulated into two optimization subproblems and handled with the Lagrange multiplier method. By introducing matrix decomposition techniques, subproblem 1 is transformed into a polynomial root-finding problem that can be solved with low computational complexity. For subproblem 2, a closed-form solution can be obtained directly. Furthermore, for the continuously receiving snapshots case, iterative gradient minimization is introduced and embedded into the ADMM iterations to give an approximate solution free from matrix decompositions. Theoretical analyses and simulations verify the low complexities and performance advantages of the proposed algorithms in the low sample support, steering vector mismatch, and real-time snapshot update scenarios.

This article proposes an optimized zero-state replacement (ZSR) method for general bi-tri logic SPWM (BTSPWM) to deal with the common-mode voltage (CMV) issue. The BTSPWM can transform the well-developed modulation strategies for voltage source converters (VSCs) into current source converter (CSC) system through the logic translation, which can utilize the analysis concepts developed for VSC. With BTSPWM, all- off gating signals can be generated through the logic translation, which should be replaced with the redundant zero-states to avoid open-circuit, which provides the flexibility for ZSR. Existing ZSR strategies are mainly focused on switching time minimization while the CMV excited by the replaced zero-states is not considering. To address the CMV issue, optimized ZSR methods for general BTSPWM are proposed to address the CMV while not influence the regular PWM features. With the proposed methods, both the CMV peak value and the third-order component can be effectively reduced. Moreover, the proposed method can be easily applied to a parallel CSC system due to the inherent modularity. The performances of the proposed methods have been experimentally verified on both single and parallel CSC systems.

This paper is devoted to the construction and analysis of a novel hybrid optimization model of the l 0 minimization and the l 1 minimization with a given support estimate. With the help of the Moreau envelop of the l 1 norm, we provide reasonable explanation for the claim that the capped- l 1 penalty is one of the continuous relaxation to the l 0 -norm penalty and thus develop the scale iteratively reweighed l 1 -minimization (SIRL1) aiming to achieve fast reconstruction and a reduced requirement on the number of measurements compared to the l 1 minimization approach. To illustrate the theoretical results, some numerical experiments are presented to demonstrate the effectiveness and flexibility of the proposed SIRL1 algorithm.

Caching popular contents at the network edge has been considered as a promising enabler to relieve the pressure on networks due to the fact that a substantial portion of global data traffic is repeatedly requested by many subscribers and thus redundantly generated. Recommendation, on the other hand, has attracted spiraling attention for its capability of reshaping users’ contents demand patterns. In this paper, we examine the practicability of recommendation in boosting the gains of edge caching with uncharted users’ feature information. To this end, we first characterize the average system cost for a generic network model, disclosing its dependence on the recommendation and caching strategies. Then, we formulate the joint caching and recommendation decision oriented cost minimization problem, taking the constraints on each content provider’s cache capacity budget, each individual user’s recommendation size and recommendation quality into account. However, the implicit information regarding users’ preference makes the problem inextricable. To address this issue, a versatile long short term memory (LSTM) network assisted prediction paradigm is proposed to attain the preference schema of users with the assistance of their historical behavior data. Based on that, we rigorously prove the NP-hardness of obtaining the optimal recommendation and caching policies that jointly minimize the system cost. Therewith, an iterative suboptimal algorithm is developed, which has provable polynomial time complexity and convergence guarantee. Extensive simulation results validate the effectiveness of our proposed LSTM enabled feature information prediction approach and the convergence performance of the devised joint decision making methodology. In addition, it is shown that the proposed scheme outperforms numerous benchmarks significantly.

Planes ubiquitously exist in the indoor environment. This letter presents a real-time and low-drift LiDAR SLAM system using planes as the landmark for the indoor environment. Our algorithm includes three components: localization, local mapping and global mapping. The localization component performs real-time and global registration, instead of the scan-to-scan registration adopted in the state-of-the-art LiDAR odometry and mapping (LOAM) framework that yields lower fidelity poses. The local mapping component optimizes poses of the keyframes within a sliding window and parameters of the planes observed by these keyframes. The global mapping component conducts global plane adjustment (GPA) that jointly refines plane parameters and keyframe poses. The GPA is triggered when planes are revisited, rather than a place is revisited. This can establish constraints among remote places, and correct the drift without having to go back to a previously visited place. We adopt the point-to-plane distance to construct the cost functions of all the three components. Although this distance results in a large-scale least-squares problem that seems not suitable for real-time applications, we propose efficient algorithms to solve the resulting minimization problems by exploiting the special structure of the point-to-plane distance. Experimental results show that our algorithm achieves real-time performance and outperforms state-of-the-art LiDAR SLAM algorithms.

The optimal placement and the sizing of various distribution system components have gained extra significance in the recent past as the concepts of smarter distribution systems, distributed generation, and microgrids blossomed. This paper presents two novel techniques for sizing the Distributed Generators (DGs) in a large-sized radial distribution network. The primary strategy is proposed by considering the bi-directional progression of current in the parts of the circuit that occurs because of the presence of newly installed DGs. The subsequent strategy utilizes the active power sensitivity calculations for sizing the DGs. These strategies are applied on the chosen radial distribution systems to size numerous DGs all at once, with the main objective being the improvement of efficiency, by accomplishing minimization of losses and improvement in nodal voltages. Then, the maximum number of self-adequate and highly reliable microgrids is formed in the distribution network utilizing the “Reverse Current Flow” procedure cited in the literature, for the proposed sizing methods. Finally, a sensitivity analysis is performed by varying the rates of infiltration of the DGs to throw light on the suitability of the newly introduced methods of sizing. This analysis gives a set of operational rules for the controller of the smart grid.

Intelligent mechanical fault diagnosis is a crucial measure to ensure the safe operation of equipment. To address the issue of model collapse in domain adversarial training and the problem posed by different training samples having different transferability not considered in transfer tasks, this article proposes a weighted entropy minimization based deep conditional adversarial diagnosis approach of rotating machines under variable working conditions. First, the features of vibration signals in the source domain and target domain are extracted by a weight-sharing one-dimensional deep convolution neural network. The feature vectors and category prediction vectors are then fused by multilinear mapping to carry out adversarial training in domain adaptation. The entropy of the output of the domain discrimination model provides the index by which to measure the transferability of training samples. The transferability weights of samples are applied to the entropy minimization loss to eliminate the influence of these samples that are hard to transfer in adversarial domain adaptation. Experimental datasets under variable working conditions support the value of our approach.

Triangulation is a fundamental module in vision based navigation system, which reconstructs the observed 3D point using cameras with known intrinsic and poses. In this article, we adopt the minimization of the angular residual as the criterion of triangulation and a novel structure of multiple-view triangulation is proposed. A multiple-view triangulation problem is divided into several two-view triangulation problems and a linear algorithm is derived to find the explicit closed form solutions of these two-view triangulation problems. Then, the Cramer–Rao lower bound (CRLB) is employed to calculate their covariances, according to which these solutions are fused to obtain the final result of the multiple-view triangulation. Our proposed algorithm is proved to be efficient based on the theoretical analysis, as it is linear and non-iterative. The simulations on both synthetic and real data show that the proposed algorithm can achieve competitive accuracy within less processing time, comparing with the most state-of-art algorithms.

Consider the following setup: Through a joint design, multiple observations of a remote data source shall be locally compressed before getting transmitted via several error-prone , rate-limited forward links to a (distant) processing unit. For addressing this specific instance of multiterminal Joint Source-Channel Coding problem, in this article, the foundational principle of the Information Bottleneck method is fully extended to obtain purely statistical design approaches, enjoying the Mutual Information as their fidelity criterion. Specifically, the forms of stationary points for two types of distributed compression schemes are characterized here. Subsequently, those acquired solutions are utilized as the centerpiece of the proposed generic, iterative algorithm, termed the Multiterminal Forward-Aware Vector Information Bottleneck (M-FAVIB) , for addressing the design optimizations. Leveraging an unfolding trick, it will be proven that both distributed compression schemes fall into the category of Successive Upper-Bound Minimization , ensuring their convergence to a stationary point. Eventually, the effectiveness of the proposed compression schemes will be substantiated as well by means of numerical investigations over some typical transmission scenarios.

Convolutional neural networks (CNNs) are a promising tool for solving real-world problems. However, successful CNNs often require a large number of parameters, which leads to a significant amount of memory and a higher computational cost. This may produce some undesirable phenomena, notably the overfitting. Indeed, in CNNs, many kernels are usually redundant and can be eliminated from the network while preserving the performance. In this work, we propose a new optimization model for kernels redundancy reduction in CNN named KRR-CNN. It consists of minimization and optimization phases. In the first one, a dataset is used to train a specific CNN generating a learned CNN with optimal parameters. These later are combined with a decision optimization model to reduce kernels that have not contributed to the first task. The optimization phase is carried out by the evolutionary genetic algorithm. Efficiency of KRR-CNN has been demonstrated by several experiments. In fact, the suggested model allows reducing the kernels redundancy and improving the classification performance comparable to the state-of-the-art CNNs.

In this study, we propose a reinforcement learning (RL) approach for minimizing the number of work overload situations in the mixed model sequencing (MMS) problem with stochastic processing times. The learning environment simulates stochastic processing times and penalizes work overloads with negative rewards. To account for the stochastic component of the problem, we implement a state representation that specifies whether work overloads will occur if the processing times are equal to their respective 25%, 50%, and 75% probability quantiles. Thereby, the RL agent is guided toward minimizing the number of overload situations while being provided with statistical information about how fluctuations in processing times affect the solution quality. To the best of our knowledge, this study is the first to consider the stochastic problem variation with a minimization of overload situations.

Optimization problems with orthogonality constraints appear widely in applications from science and engineering. We address these types of problems from a numerical approach. Our new framework combines the steepest gradient descent, using implicit information, with a projection operator in order to construct a feasible sequence of points. In addition, we adopt an adaptive Barzilai–Borwein steplength mixed with a globalization technique in order to speed-up the convergence of our procedure. The global convergence, and some theoretical related to our algorithm are proved. The effectiveness of our proposed algorithm is demonstrated on a variety of problems including Rayleigh quotient maximization, heterogeneous quadratics minimization, weighted orthogonal procrustes problems and total energy minimization. Numerical results show that the new procedure can outperform some state of the art solvers on some practically problems.

The similarity of the switching mechanisms of compact vacuum spark breakers and spark breakers with laser ignition is established at a comparable level of energy flux density in the ignition node–ionization of the residual gas by a stream of short-wave radiation and fast electrons from the cathode spot plasma or laser plasma. This mechanism allows you to effectively reduce the delay in triggering the spark gap by increasing the ignition energy. An experimental study of the advantages of using an ignition circuit with increased energy for controlling small-sized vacuum spark breakers is carried out. There is a steady decrease in the delay time of the spark gap and an increase in the level of delay stability. From the point of view of minimization and stability of the delay time of the spark gap, the energy investment in the formation of the initiating plasma occurs most effectively at the spark stage of the auxiliary discharge along the surface of the dielectric in the ignition node.

As transit agencies expand, they may outgrow their existing bus storage and service facilities. When selecting a site for an additional facility, an important consideration is the change in bus deadhead time, which affects the agency’s operating costs. Minimizing bus deadhead time is the subject of many studies, though agencies may lack the necessary software or programming skill to implement those methods. This study presents a flexible tool for determination of bus facility location. Using the R dodgr package, it evaluates each candidate site based on a given bus network and existing depots and calculates the network minimum deadhead time for each potential set of facilities. Importantly, the tool could be used by any transit agency, no matter its resources. It runs on open-source software and uses only General Transit Feed Specification (GTFS) and data inputs readily available to transit agencies in the U.S.A., filling the accessibility gap identified in the literature. The tool is demonstrated through a case study with the Metropolitan Atlanta Rapid Transit Authority (MARTA), which is considering a new bus depot as it builds its bus rapid transit network. The case study used current MARTA bus GTFS data, existing depot locations, and vacant properties from Fulton County, Georgia. The tool evaluated 17 candidate sites and found that the winning site would save 29.7 deadhead hours on a typical weekday, which translates to more than $12,000 daily based on operating cost assumptions. The output provides important guidance to transit agencies evaluating sites for a new bus depot.

This paper formulates a new strategy for designing a robust anti-windup (AW) controller for nonlinear delayed system with parametric uncertainties, time delays, input nonlinearity, and exogenous perturbations. A novel robust decoupling framework is developed for uncertain nonlinear delayed systems subject to actuator nonlinearity to deal with the modeling uncertainties. A delay-range-dependent criteria based on free-matrix-based inequality is employed to derive conditions for finding the anti-windup gains by using Lyapunov–Krasovskii function, sector condition, Lipschitz condition, $$L_2$$ gain minimization, and bound of delay. Finally, an application to nonlinear one-link flexible robot and Hopfield neural network are provided to validate the effectiveness of the proposed robust AW to mitigate the saturation effect in the uncertain nonlinear delayed system.

Capacitated two-stage time minimization transportation problem is an important optimization problem arising in industries. In literature, there is only one approach to solving this problem, but it has a deficiency of resulting in memory overflow in implementation on computer for large scale instances. In this paper, this problem is reduced to a series of finding the feasible flow in a network with lower and upper arc capacities, and two iterative algorithms are proposed as more robust solution method for this problem as compared to the existing approach. It is proved that both iterative algorithms find the optimal solution to this problem in a polynomial time. Due to fully utilizing the network structure characteristics inherent to this problem, both iterative algorithms have the advantage of easy implementation on computer and high computational efficiency, and successfully overcome the deficiency of existing approach. Computational experiments validate that as compared to the existing approach, both iterative algorithms are efficient and more robust method to solve this problem, where one iterative algorithm significantly outperforms the other in terms of computational time, especially for large scale instances.

Статья посвящена синтезу многослойных диэлектрических отражательных дифракционных решеток, с высокой эффективностью обеспечивающих спектральное сложение пучков с различной длиной волны в заданном дифракционном порядке. Приводятся результаты решения задачи синтеза многослойных диэлектрических дифракционных решеток, обеспечивающих спектральное сложение в первом или минус первом порядке дифракции. Кроме того, решается задача синтеза для таких решеток с учетом возможных технологических ограничений на высоту профиля (глубину травления). Решение задачи синтеза проводится путем минимизации зависящего от параметров решетки целевого функционала методом Нелдера-Мида. Решение прямой задачи на каждом шаге минимизации осуществляется при помощи комбинации неполного метода Галеркина и метода матриц рассеяния. The paper is devoted to the synthesis of multilayer dielectric reflection diffraction gratings providing high-efficiency spectral combining of the beams with different wavelengths in a given diffraction order. The results are presented for solving the synthesis problems for multilayer dielectric diffraction gratings providing spectral combining in the first or minus first diffraction order. Besides, the synthesis problem for such gratings is solved with account taken of possible technological constraints imposed by the height of the grating profile (etch depth). The solution of the synthesis problem is obtained by means of Nelder-Mead minimization of the merit function depending on the grating parameters. At each minimization step the direct problem is solved using a combination of the incomplete Galerkin method and scattering matrix method.

This paper studies the minimization of a broad class of nonsmooth nonconvex objective functions subject to nonlinear functional equality constraints, where the gradients of the differentiable parts in the objective and the constraints are only locally Lipschitz continuous. We propose a specific proximal linearized alternating direction method of multipliers in which the proximal parameter is generated dynamically, and we design an explicit and tractable backtracking procedure to generate it. We prove subsequent convergence of the method to a critical point of the problem, and global convergence when the problem’s data are semialgebraic. These results are obtained with no dependency on the explicit manner in which the proximal parameter is generated. As a byproduct of our analysis, we also obtain global convergence guarantees for the proximal gradient method with a dynamic proximal parameter under local Lipschitz continuity of the gradient of the smooth part of the nonlinear sum composite minimization model.

In this paper monitor lizard optimization algorithm based on class room learning algorithm (MLCL) is designed to solve the optimization problem. Key objectives are power loss reduction, voltage stability enhancement and voltage deviation minimization. When power loss reduction has been done in the electrical transmission network then it will economically improves the whole system. Equally system stability is based on the voltage stability amplification and voltage deviation minimization. In monitor lizard optimization algorithm (MLA) movement of the prey and environmental issues are considered in the modeling. The monitor lizard does the Brownian movement and Victim (prey) performs the levy movement. Population has been divided for exploration and exploitation. Monitor lizard is accountable for exploration and Victim (prey) is in charge for exploitation. Natural issues will have impact over the action of preying and that has been included in the modeling of the algorithm. Action of species (AS) has been considered and included as a factor in the modeling of the algorithm. Then class room learning algorithm (CLA) is designed which based on how the learner gains the knowledge form tutor and co learner. The designed algorithm possesses two segments—Tutor segment and learner segment. Mutation and crossover of differential evolution is added in the hybridized MLCL algorithm. At first the tutor segment is intermingled in the procedure such that learning about the victim (prey) information will be enhanced. By considering L (voltage stability)—index MLCL verified in IEEE 30-bus system. Then without L-index MLCL optimization algorithm is appraised in 30 bus test systems. Monitor lizard optimization algorithm based on class room based learning algorithm condensed the power loss competently with augmentation in voltage stability and minimization of voltage deviation.