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Space vector pulsewidth modulation (SVPWM) algorithms for cascaded H-bridge multilevel (CHB ML) inverter usually provide the possibility of using several combinations of active voltage vectors to generate the same output voltage vector. For preselected H-bridges, some of them may generate output voltages opposite to the assumed direction. This results in the change of the dc-link voltages of these H-bridges in the opposite direction to the assumed direction in the ordering algorithm. Consequently, these algorithms are characterized by undue constraints and narrow possibilities of dc-link voltage balancing. In the proposed control algorithm, CHB ML inverter is treated as groups of successively activated three-level inverters; depending on the length of the reference voltage vector. These three-level inverters consist of three H-bridges selected from each phase. The proposed extended selection method enables firm-grip control of the dc-link voltages. For a given direction of phase currents, the possibility of using H-bridges with lowest and highest dc-link voltages is simultaneously analyzed. Additionally, each of the three-level inverters is controlled by one of three proposed alternative modulation methods for which both the attainable output voltage vectors and unbalanced dc-link voltages are predicted. Simulation and experimental results confirm the correctness of the algorithm execution. [ABSTRACT FROM AUTHOR]

Visual analysis of relational information is vital in most real-life analytics applications. Automatic layout is a key requirement for effective visual display of such information. This article introduces a new layout algorithm named fCoSE for compound graphs showing varying levels of groupings or abstractions with support for user-specified placement constraints. fCoSE builds on a previous compound spring embedder layout algorithm and makes use of the spectral graph drawing technique for producing a quick draft layout, followed by phases where constraints are enforced and compound structures are properly shown while polishing the layout with respect to commonly accepted graph layout criteria. Experimental evaluation verifies that fCoSE produces quality layouts and is fast enough for interactive applications with small to medium-sized graphs by combining the speed of spectral graph drawing technique with the quality of force-directed layout algorithms while satisfying specified constraints and properly displaying compound structures. An implementation of fCoSE along with documentation and a demo page is freely available on GitHub at https://github.com/iVis-at-Bilkent/cytoscape.js-fcose. [ABSTRACT FROM AUTHOR]

The Transformer has been an indispensable staple in deep learning. However, for real-life applications, it is very challenging to deploy efficient Transformers due to the immense parameters and operations of models. To relieve this burden, exploiting sparsity is an effective approach to accelerate Transformers. Newly emerging Ampere graphics processing units (GPUs) leverage a 2:4 sparsity pattern to achieve model acceleration, while it can hardly meet the diverse algorithm and hardware constraints when deploying models. By contrast, we propose an algorithm–hardware co-optimized framework to flexibly and efficiently accelerate Transformers by utilizing general N:M sparsity patterns. First, from an algorithm perspective, we propose a sparsity inheritance mechanism along with inherited dynamic pruning (IDP) to obtain a series of N:M sparse candidate Transformers rapidly. A model compression scheme is further proposed to significantly reduce the storage requirement for deployment. Second, from a hardware perspective, we present a flexible and efficient hardware architecture, namely, STA, to achieve significant speedup when deploying N:M sparse Transformers. STA features not only a computing engine unifying both sparse–dense and dense–dense matrix multiplications with high computational efficiency but also a scalable softmax module eliminating the latency from intermediate off-chip data communication. Experimental results show that, compared to other methods, N:M sparse Transformers, generated using IDP, achieves an average of 6.7% improvement on accuracy with high training efficiency. Moreover, STA can achieve $14.47\times $ and $11.33\times $ speedups compared to Intel i9-9900X and NVIDIA RTX 2080 Ti, respectively, and perform $2.00 \,\,\sim 19.47 \times $ faster inference than the state-of-the-art field-programmable gate array (FPGA)-based accelerators for Transformers. [ABSTRACT FROM AUTHOR]

Thanks to the powerful learning capability, deep neural networks (DNNs) have acquired broad applications in single image reflection removal. The DNN-based algorithms relax the constraints of specific priors and learn to generate visually pleasant background layers from massive training data. However, most of them employ a single network structure to recover both the semantic information and local details of the background, which may lead to obvious reflection residue or even failure. To mitigate this deficiency, in this work, we propose a Multi-stage Curvature-guided De-Reflection Network (MCDRNet), which combines multiple network architectures in a unified framework to progressively reconstruct the background layer and refine the fine-grained details. Our framework consists of three stages, where the encoder-decoders are exploited in the first two stages to recover the semantic components of background layers with lower scales and a variant ResNet is applied in the last stage to refine the background details with the original input resolution. In the first two stages, to introduce the structural guidance for the reflection removal, we cascade another decoder branch to restore the curvature map of the background. In addition, at the end of the first two stages, instead of directly passing the intermediate estimates to the next stage, we propose a Non-local Attention Module (NAM) to augment and transmit the features from decoders. Extensive experimental results on several public datasets demonstrate that the proposed MCDRNet outperforms the state-of-the-art methods quantitatively and generates visually better reflection removal results. The source code and pre-trained models are available at https://github.com/NamecantbeNULL/MCDRNet. [ABSTRACT FROM AUTHOR]

Metal fill insertion has become an essential step in reducing dielectric thickness variation and improving pattern uniformity, which is important in mitigating process variations, thereby achieving better manufacturing yield. However, metal fills could induce coupling capacitance, which is not often considered in existing works that typically focus more on pattern density uniformity, incurring significant problems in timing closure. However, it is a great challenge to consider three types of capacitances (i.e., area, fringe, and lateral capacitances) with design rules and density constraints at the fill insertion stage simultaneously. This article presents an efficient timing-aware fill insertion algorithm for minimizing the total capacitance and fill amount, considering the density constraints. First, we present an initial metal fill insertion and design-rule-aware legalization to obtain an initial fill insertion solution quickly. Second, from critical conductors to powers/grounds in a circuit, we divide conductors into different equivalent paths and then construct a capacitance graph to reduce the capacitance of each equivalent path globally. Third, we propose a density-aware coupling capacitance optimization method and a fast Monte Carlo-based fill selection to further reduce the coupling capacitance between any pair of conductors. Finally, we present a density-aware fill deletion method to reduce the fill amount. We evaluate the performance of our algorithm on the benchmarks of the 2018 CAD Contest at ICCAD and its official contest evaluator. Compared with the first-place team of the contest and the state-of-the-artwork, experimental results show that our algorithm achieves the lowest total capacitance and the least fill amount in a comparable runtime. [ABSTRACT FROM AUTHOR]

Multi-interval or dynamic economic dispatch (D-ED) is the core of various power system management functions. This optimization problem contains many constraints, a small subset of which is sufficient to enclose the D-ED feasible region. This paper presents a topology-aware learning-aided iterative constraint screening algorithm to identify a feasibility outlining subset of network and generating units ramp up/down constraints and create a truncated D-ED problem. We create a colorful image from nodal demand, thermal unit generation cost, and network topology information. Convolutional neural networks are trained for constraint status identification using colorful images corresponding to system operating conditions and transfer learning. Filtering inactive line flow and ramp up/down constraints reduces the optimization problem's size and computational burden, resulting in a reduction in solution time and memory usage. Dropping all inactive branch and ramp constraints may activate some of these originally inactive constraints upon solving the truncated D-ED. A loop is added to form a constraints coefficient matrix iteratively during training dataset preparation and algorithm utilization. This iterative loop guarantees truncated D-ED results feasibility and optimality. Numerical results show the proposed algorithm's effectiveness in constraint status prediction and reducing D-ED size and solution time. [ABSTRACT FROM AUTHOR]

Non-line-of-sight (NLOS) detection is an enduring topic as it provides a powerful tool to monitor visually blocked areas. Currently, NLOS detection requires precise prior knowledge of building layout, which limits its further applications in practice. In this article, we consider the problem of joint estimation of building layout and target location in the NLOS scenario by exploiting multipath returns. Specifically, first, the building layout is simplified into combined linear equations with unknown parameters. In this way, we establish a parametrized multipath propagation model in the multiple targets’ NLOS scenario for the multiple-input–multiple-output (MIMO) radar, which is used in the image reconstruction and layout estimation problem. Then, a shape remodeling group sparse constraint algorithm is proposed and combined with the particle swarm optimization method to simultaneously reconstruct the unknown layout and targets. Compared with the conventional compressed sensing-based methods, the proposed method integrates the basic structural characteristics and sparsity prior of the NLOS image to improve the stability of the solution. Finally, the performance of the proposed method is verified with numerical and experimental results. [ABSTRACT FROM AUTHOR]

One critical issue for wireless power transfer is to avoid human health impairments caused by electromagnetic radiation (EMR) exposure. The existing studies mainly focus on scheduling wireless chargers so that (expected) EMR at any point in the area does not exceed a threshold $R_t$ R t . Nevertheless, they overlook the EMR jitter that leads to exceeding of $R_t$ R t even if the expected EMR is no more than $R_t$ R t . This paper studies the fundamental problem of RObustly SafE charging for wireless power transfer (ROSE), that is, scheduling the power of chargers so that the charging utility for all rechargeable devices is maximized while the probability that EMR anywhere does not exceed $R_t$ R t is no less than a given confidence. We first build our empirical probabilistic charging model and EMR model. Then, we present EMR approximation and area discretization techniques to formulate ROSE into a Second-Order Cone Program. After that, we propose the first redundant second-order cone constraints reduction algorithm to reduce the computational cost, and therefore obtain a $(1-\epsilon)$ (1 - ε) -approximation centralized algorithm. Further, we propose a $(1-\epsilon)$ (1 - ε) -approximation fully distributed algorithm scalable with network size for ROSE. We conduct both simulation and field experiments, and the results show that our algorithms can outperform comparison algorithms by 480.19 percent. [ABSTRACT FROM AUTHOR]

This article proposes a second-order conic programming (SOCP) approach to solve distributionally robust two-stage linear programs over 1-Wasserstein balls. We start from the case with distribution uncertainty only in the objective function and then explore the case with distribution uncertainty only in constraints. The former program is exactly reformulated as a tractable SOCP problem, whereas the latter one is proved to be generally NP-hard as it involves a norm maximization problem over a polyhedron. However, it reduces to an SOCP problem if the extreme points of the polyhedron are given as a prior. This motivates the design of a constraint generation algorithm with provable convergence to approximately solve the NP-hard problem. Moreover, the least favorable distribution achieving the worst case cost is given as an “empirical” distribution by simply perturbing each original sample for both cases. Finally, experiments illustrate the advantages of the proposed model in terms of the out-of-sample performance and computational complexity. Note to Practitioners—The two-stage program with distribution uncertainty is an important decision problem in broad applications, e.g., two-stage schedule problems, facility location problems, and recourse allocation problems. To deal with the uncertainty, this work proposes a novel data-driven model over the 1-Wasserstein ball and develops an efficient second-order conic programming (SOCP)-based solution approach, where the sample data set can be easily exploited to reduce the distribution uncertainty. The good out-of-sample performance and computational complexity of the proposed model are validated by the experiments on the two-stage portfolio programs and material order programs. [ABSTRACT FROM AUTHOR]

We report a procedure to design fixed-structure strictly negative imaginary (SNI) controllers for collocated, highly resonant systems based on frequency response data. We formulate the problem in a two-input two-output (TITO) framework that includes robustness and stability constraints. The controller synthesis is cast as a convex optimization problem using convex approximation techniques. The measured frequency response of a two-degree-of-freedom (2-DOF) microelectromechanical system (MEMS) nanopositioner is used to compute the frequency response of the controller by minimizing the difference between the actual and desired closed-loop responses in an $H_{2}$ sense. By including the negative imaginary (NI) stability criterion as a constraint in the optimization algorithm, closed-loop stability is guaranteed. Performance of the synthesized controllers is verified in time and frequency domains through closed-loop experiments with the MEMS nanopositioner. [ABSTRACT FROM AUTHOR]

3-D inversion for gravity data is of great significance in geophysical quantitative interpretation. However, due to the complexity of the density distribution on the subsurface and nonuniqueness of inverse problems, the classical inversion still has many shortages. The smooth constrained inversion results in redundant structures, whereas the results of the compact inversion are too focused, and excessively rely on the choice of target density parameters. In view of the above issues, we develop a 3-D combined compact and smooth inversion for gravity and gravity gradient data. The objective function is built by the combination of the minimization of field source volume and the misfit between observed and predicted data. The prior information is treated as the “target density” in the inversion process. Furthermore, in order to solve the problem of overreliance on “target density,” we improve a 3-D gravity inversion algorithm where the model constraints include the combination of compact and smooth factors. Model tests show the method can better recover the source distribution when the parameter of target density is not given. Also, we make a comparative analysis of the inversion results of different gradient components and conclude that the inversion results of the gradient data $g_{xy}$ are better than those of other gradient components. Finally, we apply the method to the real gravity data. The inferred results of the anomalous rock mass are consistent with the previous geologic knowledge. [ABSTRACT FROM AUTHOR]

Extracting the power lines and pylons automatically and accurately from airborne LiDAR data is a critical step in inspecting the routine power line, especially in the remote mountainous areas. However, challenges arise in using existing methods to extract the targets from large scenarios of remote mountainous areas since the terrain is undulating, and the features are difficult to distinguish. In this article, to overcome these challenges, we propose a graph convolutional network (GCN)-based method to extract power lines and pylons from Airborne LiDAR point clouds. First, data augmentation and near-ground filtering methods are developed to overcome the problems of insufficient and imbalanced samples in the LiDAR data. Then, a GCN-based framework is proposed to extract the power lines and pylons, which consist of two main modules, i.e., the neighborhood dimension information (NDI) module and the neighborhood geometry information aggregation (NGIA) module. These two modules are designed to strengthen the model’s ability to portray local geometric details. Besides, an attention fusion module is investigated to further improve the NDI and NGIA features. Finally, a line structure constraint algorithm is proposed to identify individual power lines, where the power corridor is reconstructed using a polynomial-based algorithm. Numerical experiments are conducted based on two different power line scenarios acquired in mountainous areas. The results demonstrate the superior performances of the proposed method over several existing algorithms, where the $F_{1}$ score and quality of the power line are 99.3% and 98.6%, and the results of the pylon are 96% and 92.4%, respectively. The identification rate of power line identification is above 98%. [ABSTRACT FROM AUTHOR]

The deep network has shown its superiority to extract discriminative features for hyperspectral image (HSI) classification. However, most existing methods only exploit label information of land classes to supervise the learning of deep features, and the learning process is totally automatic. Considering the complex spectral–spatial characteristic in real HSI, it is reasonable to utilize the manifold structure of input data to provide complementary information for precise classification. This article proposes a structure-preserving spectral–spatial network (SPSSN) to exploit the manifold structure information during the feature learning process, which can extract discriminative deep structure-preserving spectral–spatial features. Specifically, a basic spectral–spatial network (SSN), comprising a spectral module and a spatial module connected in series, is first built to learn spectral–spatial features from HSI cubes. Second, a novel structure-preserving constraint is introduced to transfer the intrinsic manifold structure of the input data into the learned deep spectral–spatial features, so as to enhance the discrimination. Furthermore, a feasible algorithm of imposing the constraint is designed to make it compatible with the deep learning framework, which results in a structure-preserving loss. Finally, the SSN is combined with the structure-preserving loss to obtain the SPSSN. Experiments on real HSI datasets verify the effectiveness and superiority of the SPSSN compared with several state-of-the-art methods in HSI classification. [ABSTRACT FROM AUTHOR]

Nowadays, the availability of accurate vehicle position becomes more and more indispensable. The GNSS/INS (Global Navigation Satellite Systems/Inertial Navigation System) is currently the most widely-used integrated navigation scheme for land vehicles, which is capable of provide high-accuracy and continuous positioning results in the open-sky environments. However, under the GNSS-denied conditions, the existing GNSS/INS integrated system often fails to provide reliable positioning results due to various and nonlinear errors contained in the MEMS (Micro-Electro-Mechanical System) IMU (Inertial Measurement Unit) measurements. To improve the positioning accuracy during GNSS outage, deep learning has been introduced into the GNSS/INS integrated system in recent years. In this paper, we propose a residual attention network-based confidence (i.e., measurement noise covariance) estimation algorithm for non-holonomic constraint in GNSS/INS integrated navigation system, which adopts a residual attention network to dynamically estimate the noise covariance of the pseudo-observation (i.e., non-holonomic constraint) for optimal Kalman filtering (KF) fusion. To emphasize the more representative features with larger weights for accurate noise covariance estimation, we introduce an attention mechanism to automatically assign proper weights to the learned features according to their contributions. We evaluate our proposed method on three practical road datasets and compare it with other seven methods including the traditional KF, Pure INS, KF with three deep learning networks, K-means, and the Input-Delayed Neural Networks based method. Extensive experimental results demonstrate that our proposed RA-NHC bounds the errors associated with velocities and achieves reasonable accuracy improvement in position and velocity estimation. [ABSTRACT FROM AUTHOR]

As the generalization of fuzzy systems, the belief rule base (BRB) expert system is transparent and interpretable. However, the interpretability of BRB has almost been ignored recently and leads to the decrease of model credibility. The main reason is the lack of unified guidelines for establishing an interpretable BRB expert system. In this article, the interpretability characteristics of BRB are summarized systematically, which can be used as the guideline of BRB establishment. Four interpretability criteria are proposed to ensure the interpretability of BRB in the optimization. A modified optimization algorithm with the interpretability constraints transformed from the interpretability criteria is further developed. As such, an interpretable BRB can be established. A case study for health state evaluation of the aerospace relay is conducted to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

False data injection (FDI), could cause severe uneconomic system operation and even large blackout, which is further compounded by the increasingly integrated fluctuating renewable generation. As a commonly conducted type of FDI, load redistribution (LR) attack is judiciously manipulated by attackers to alter the load measurement on power buses and affect the normal operation of power systems. In particular, LR attacks have been proved to easily bypass the detection of state estimation. This paper presents a novel distributionally robust optimization (DRO) for operating transmission systems against cyber-attacks while considering the uncertainty of renewable generation. The FDI imposed by an adversary aims to maximally alter system parameters and mislead system operations while the proposed optimization method is used to reduce the risks caused by FDI. Unlike the worst-case-oriented robust optimization, DRO neglects the extremely low-probability case and thus weakens the conservatism, resulting in more economical operation schemes. To obtain computational tractability, a semidefinite programming problem is reformulated and a constraint generation algorithm is utilized to efficiently solve the original problem in a hierarchical master and sub-problem framework. The proposed method can produce more secure and economic operation for the system of rich renewable under LR attacks, reducing load shedding and operation cost to benefit end customers, network operators, and renewable generation. [ABSTRACT FROM AUTHOR]

The inertial-based pedestrian dead reckoning (PDR) system assisted by zero-velocity update (ZUPT) algorithm has been widely researched, as it can independently provide effective pedestrian’s position in indoor environment. However, in the realistic test, the system still often suffers from drift, due to the ZUPT algorithm’s poor observation of system’s heading. In this article, based on full analysis of the existing maximum range constraint algorithm between both-side feet, first, a constraint method based on the maximum distance of one-side foot has been proposed. It mainly uses the principle that the distance between the position of one-side foot at current time and that of the previous stationary instant should be within a maximum range. Second, a one-side foot and both-side feet cooperative maximum range constraint algorithm has been proposed. It mainly uses the one-side foot maximum range and the both-side feet maximum range to intersect the most likely position of pedestrian’s current moving foot and then uses this position to constrain the heading divergence. Finally, to further improve the heading constraint accuracy, a maximum range decoupling algorithm has been proposed, which decouples the maximum ranges of one-side foot and both-side feet into the distance in the forward direction. Then, using this distance, the heading divergence can be constrained more effectively. [ABSTRACT FROM AUTHOR]

Volt-VAR optimization (VVO) has been investigated extensively in power systems. However, under the era of integrated energy systems (IES), the growing interdependencies between different energy systems complicate traditional VVO. This is further hardened by incurred gas quality problems due to the hydrogen injection in IES, produced by widely applied power-to-gas (P2G) facilitates that couple between power and gas systems. This paper develops a two-stage volt-VAR-pressure optimization (VVPO) model for PV-penetrated IES to manage the variation of system voltages while managing gas quality indices. In addition to the traditional voltage regulating devices, P2G facilities, which can mitigate fluctuating PV output via converting the surplus generation into hydrogen, are also used for voltage management. A two-stage distributionally robust optimization (DRO) based on moment information is utilized to model PV uncertainty. A semidefinite programming model is formulated and finally solved by the constraint generation algorithm. A 33-bus-20-node IES is used to verify the effectiveness of the proposed VVPO on voltage management, ensured gas quality with high economic efficiency. The proposed VVPO is applicable to injecting other green gases into gas systems while ensuring power quality and enable system operators to provide low-cost but high-quality multi-energy to customers. [ABSTRACT FROM AUTHOR]

The optimal power flow (OPF) problem contains many constraints. However, equality constraints and a limited set of inequality constraints encompass sufficient information to determine the problem feasible space. This article presents a hybrid supervised regression-classification learning-based algorithm to predict active and inactive inequality constraints before solving AC OPF solely based on nodal power demand information. The proposed algorithm is structured using a mixture of classifiers and regression learners. Instead of directly mapping OPF results from demand, the proposed algorithm removes inactive constraints to construct a truncated AC OPF. This truncated optimization problem can be solved faster than the original problem with less computational resources. Numerical results on several test systems show the proposed algorithm's effectiveness for predicting active and inactive constraints and constructing a truncated AC OPF. [ABSTRACT FROM AUTHOR]

A novel dimming control scheme, termed as generalized dimming control (GDC), is proposed for visible light communication (VLC) systems. The basic idea of GDC is to exploit adaptively time, spatial and signal domain resources for enhancing communication performance under practical illumination constraints. In particular, to satisfy the illumination constraints, an incremental algorithm for index mapping is first proposed to achieve target optical power and uniform illumination. Next, GDC having the optimal activation pattern is investigated to improve the bit-error rate (BER) performance of VLC. In particular, the BER performance of GDC is analyzed using the union bound technique. Based on the analytical BER bound, the optimal activation pattern of GDC scheme having the minimum BER criterion (GDC-MBER) is obtained by exhaustively searching all conditional pairwise error probabilities. However, since GDC-MBER requires high search complexity, two low-complexity GDC schemes having the maximum free distance criterion (GDC-MFD) are proposed. The first low-complexity GDC-MFD scheme, termed as GDC-MFD1, is derived by a lower bound of the free distance using the Rayleigh-Ritz theorem. The second low-complexity GDC-MFD scheme, termed as GDC-MFD2, is proposed to reduce further the computation by exploiting the time-invariance characteristics of the VLC channel. Simulation and numerical results show that GDC-MBER, GDC-MFD1 and GDC-MFD2 have similar BER performance, and they can achieve more than 3 dB SNR gains compared with conventional dimming control schemes for a BER of ${10^{ - 4}}$ and a dimming level of 50%. [ABSTRACT FROM AUTHOR]

In this article, an intelligent predictor–corrector entry guidance approach for lifting hypersonic vehicles is proposed to achieve real-time and safe control of entry flights by leveraging the deep neural network (DNN) and constraint management techniques. First, the entry trajectory planning problem is formulated as a univariate root-finding problem based on a compound bank angle corridor, and two constraint management algorithms are presented to enforce the satisfaction of both path and terminal constraints. Second, a DNN is developed to learn the mapping relationship between the flight states and ranges, and experiments are conducted to verify its high approximation accuracy. Based on the DNN-based range predictor, an intelligent, multiconstrained predictor–corrector guidance algorithm is developed to achieve real-time trajectory correction and lateral heading control with a determined number of bank reversals. Simulations are conducted through comparing with the state-of-the-art predictor-corrector algorithms, and the results demonstrate that the proposed DNN-based entry guidance can achieve the trajectory correction with an update frequency of 20 Hz and is capable of providing high-precision, safe, and robust entry guidance for hypersonic vehicles. [ABSTRACT FROM AUTHOR]

The time-optimal trajectory planning problem involves minimizing the time required to follow a path defined in space, subject to kinematic and dynamic constraints. Here, we introduce a novel technique, called the bisection algorithm (BA), which is fully implemented in C++ and extends dynamic programming approaches to the problem. These approaches, which rely on dividing the global problem into a series of simpler subproblems, become increasingly advantageous compared to direct transcription methods as the number of problem constraints increases. In contrast to nearly all other dynamic programming approaches, BA does not rely on finding a maximum-velocity curve or explicitly finding acceleration switching points during the trajectory planning process. Additionally, only one forward and one backward integration are used, during which all constraints are imposed. This approach is made feasible through careful control of the numerical integration process and the use of a bisection algorithm to resolve constraint violations during integration. BA is shown to be significantly simpler, faster, and more robust than recently proposed algorithms: a direct comparison is made for a series of paths to be followed by a serial manipulator, subject to kinematic constraints. The wide applicability of BA is then established by solving the time-optimal problem for a parallel manipulator following a complex path, subject to both kinematic and dynamic constraints. [ABSTRACT FROM AUTHOR]

Partial Label Learning (PLL) aims to learn from the data where each training example is associated with a set of candidate labels, among which only one is correct. The key to deal with such problem is to disambiguate the candidate label sets and obtain the correct assignments between instances and their candidate labels. In this paper, we interpret such assignments as instance-to-label matchings, and reformulate the task of PLL as a matching selection problem. To model such problem, we propose a novel Graph Matching based Partial Label Learning (GM-PLL) framework, where Graph Matching (GM) scheme is incorporated owing to its excellent capability of exploiting the instance and label relationship. Meanwhile, since conventional one-to-one GM algorithm does not satisfy the constraint of PLL problem that multiple instances may correspond to the same label, we extend a traditional one-to-one probabilistic matching algorithm to the many-to-one constraint, and make the proposed framework accommodate to the PLL problem. Moreover, we also propose a relaxed matching prediction model, which can improve the prediction accuracy via GM strategy. Extensive experiments on both artificial and real-world data sets demonstrate that the proposed method can achieve superior or comparable performance against the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this article, a control of solar photovoltaic (PV) array, and wind energy conversion system (WECS) based charging station using an adaptive frequency-fixed second-order generalized integrator with dc offset rejection capability (AFF-SOGI-DRC), is presented for charging the electric vehicles (EVs) and improving the power quality of the grid. In comparison to SOGI-based algorithm, wherein a frequency feedback loop reduces the stability margin and FF-SOGI-based algorithm, where a double frequency oscillation and offset error constraint the algorithm capability, the presented algorithm precisely estimates the fundamental EV current. Moreover, the AFF-SOGI-DRC-based positive sequence extractor helps in generating the sinusoidal in-quadrature unit vectors under distorted and unbalanced grid voltages; therefore, the reference grid currents become harmonic free. The charging station is operated in the grid-connected mode (GCM) and an islanded mode (IM) to utilize the renewable energy maximally. The control of the charging station also includes the voltage synchronizing strategy that ensures the smooth transition between the GCM and IM. Moreover, a unified controller is designed to achieve four-quadrant operation vehicle to grid/grid to vehicle (V2G/G2V), vehicle to home power transfer, reactive power compensation, and active filtering, etc. The operation of the charging station complies with the IEEE 1547 standard, and the operation of the charging station is validated through the laboratory prototype. [ABSTRACT FROM AUTHOR]

Modern analog placement techniques require consideration of current path and symmetry constraints. The symmetry pairs can be efficiently packed using the symmetry island configurations, but not all these configurations result in minimum gate interconnection, which can impact the overall circuit routing and performance. This article proposes the first work that reformulates this problem considering all of them together in the form of parallel current path (PCP) constraints. PCP constraints, in addition to monotonic current paths, also consider partial-monotonic current paths to generate a more compact placement. We use a novel two-step approach to detect symmetry-feasible sequence-pairs (SFSPs) without doing placement construction by using representative sequence-pair (RSP). Then a placement algorithm satisfying these constraints is formulated to reduce a vast search space via efficient sequence pair manipulation. The experimental results show that this formulation and algorithm can generate multiple placement solutions that satisfy all the constraints in a more tightly packed configuration, resulting in smaller wirelength, reduced parasitics, and thus better post-layout performance. [ABSTRACT FROM AUTHOR]

Coarse-Grained Reconfigurable Architectures (CGRA) is a promising solution for accelerating computation intensive tasks due to its good trade-off in energy efficiency and flexibility. One of the challenging research topic is how to effectively deploy loops onto CGRAs within acceptable compilation time. Modulo scheduling (MS) has shown to be efficient on deploying loops onto CGRAs. Existing CGRA MS algorithms still suffer from the challenge of mapping loop with higher performance under acceptable compilation time, especially mapping large and irregular loops onto CGRAs with limited computational and routing resources. This is mainly due to the under utilization of the available buffer resources on CGRA, unawareness of critical mapping constraints and time consuming method of solving temporal and spatial mapping. This article focus on improving the performance and compilation robustness of the modulo scheduling mapping algorithm for CGRAs. We decomposes the CGRA MS problem into the temporal and spatial mapping problem and reorganize the processes inside these two problems. For the temporal mapping problem, we provide a comprehensive and systematic mapping flow that includes a powerful buffer allocation algorithm, and efficient interconnection & computational constraints solving algorithms. For the spatial mapping problem, we develop a fast and stable spatial mapping algorithm with backtracking and reordering mechanism. Our MS mapping algorithm is able to map loops onto CGRA with higher performance and faster compilation time. Experiment results show that given the same compilation time budget, our mapping algorithm generates higher compilation success rate. Among the successfully compiled loops, our approach can improve 5.4 to 14.2 percent performance and takes x24 to x1099 less compilation time in average comparing with state-of-the-art CGRA mapping algorithms. [ABSTRACT FROM AUTHOR]

Methods for cleaning dirty data typically employ additional information about the data such as user-provided constraints specifying when data is dirty, e.g., domain restrictions, illegal value combinations, or logical rules. However, real-world scenarios usually only have dirty data available, without known constraints. In such settings, constraints are automatically discovered on dirty data and discovered constraints are used to detect and repair errors. Typical repairing processes stop there. Yet, when constraint discovery algorithms are re-run on the repaired data (assumed to be clean), new constraints and thus errors are often found. The repairing process then introduces new constraint violations. We present a different type of repairing method, which prevents introducing new constraint violations, according to a discovery algorithm. Summarily, our repairs guarantee that all errors identified by constraints discovered on the dirty data are fixed; and the constraint discovery process cannot identify new constraint violations. We do this for a new kind of constraints, called forbidden itemsets (FBIs), capturing unlikely value co-occurrences. We show that FBIs detect errors with high precision. Evaluation on real-world data shows that our repair method obtains high-quality repairs without introducing new FBIs. Optional user interaction is readily integrated, with users deciding how much effort to invest. [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]

In this paper, we propose an image segmentation model that incorporates convexity shape priori using level set representations. In the past decade, several discrete and continuous methods have been developed to solve this problem. Our method comes from the observation that the signed distance function of a convex region must be a convex function. Based on this observation, we transfer the complicated geometrical convexity shape priori into some simple constraints on the signed distance function. We propose a simple algorithm to keep these constraints exactly. The proposed method could be easily applied to level set based segmentation models, such as the well-known Chan-Vese mode and the active contour models. By setting some good initial curves, the proposed method can easily segment convex objects from images with complicated background. We demonstrate the performance of the proposed methods on both synthetic images and real images, as well as the comparison to some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The joint intelligence ranking of intelligent systems like autonomous driving is of great importance for building a more general, extensive, and universally accepted intelligence evaluation scheme. However, due to issues such as privacy security and industry or area competition, the integration of isolated test results may face large unimaginable difficulty in information security and encrypted model training. To address this, we derive the federated multiplicative update (FMU) algorithm with boundary constraints to solve the nonnegative matrix factorization based joint intelligence ranking. The encrypted learning process is developed to alternate original computation steps in multiplicative update algorithms. Owning feasible property for the fast convergence and secure exchange of variables, the proposed framework outperforms the previous work on both real and simulated data. Further experimental analysis reveals that the introduced federated mechanism does not harm the overall time efficiency. [ABSTRACT FROM AUTHOR]

Optimal power flow (OPF) is a fundamental problem in power system operating and planning. The need for distributed solutions is rapidly growing in distribution systems with radial networks, discrete equipments and many distributed generation units. Typically the model is difficult to solve due to the highly non-convexity and even non-continuity of the arising OPF problem. In this paper, we address these challenges by developing a penalty dual decomposition (PDD) based algorithm to obtain distributed solutions. The proposed PDD based algorithm mainly consists of two loops: in the outer loop we update the dual variables and the penalty parameter according to the constraint violation, while in the inner loop we divide the primal variables into several blocks and employ the block successive upper-bound minimization (BSUM) method to iteratively optimize each block variables with fixed penalty parameter and dual variables. Each subproblem in the proposed PDD based algorithm can be solved either in closed-form or by the bisection method and every limit point generated by the proposed algorithm is guaranteed to be a stationary point of the OPF problem. Simulation results, and comparison with centralized PDD and concave-convex procedure (CCCP) based algorithms are presented to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

We present a method for fabricating general models with multi-directional 3-D printing systems by printing different model regions along with different directions. The core of our method is a support-effective volume decomposition algorithm that minimizes the area of the regions with large overhangs. A beam-guided searching algorithm with manufacturing constraints determines the optimal volume decomposition, which is represented by a sequence of clipping planes. While current approaches require manually assembling separate components into a final model, our algorithm allows for directly printing the final model in a single pass. It can also be applied to models with loops and handles. A supplementary algorithm generates special supporting structures for models where supporting structures for large overhangs cannot be eliminated. We verify the effectiveness of our method using two hardware systems: a Cartesian-motion-based system and an angular-motion-based system. A variety of 3-D models have been successfully fabricated on these systems. Note to Practitioners—In conventional planar-layer-based 3-D printing systems, supporting structures need to be added at the bottom of large overhanging regions to prevent material collapse. Supporting structures used in single-material 3-D printing technologies have three major problems: being difficult to remove, introducing surface damage, and wasting material. This article introduces a method to improve 3-D printing by adding rotation during the manufacturing process. To keep the hardware system relatively inexpensive, the hardware, called a multi-directional 3-D printing system, only needs to provide unsynchronized rotations. In this system, models are subdivided into different regions, and then, the regions are printed in different directions. We develop a general volume decomposition algorithm for effectively reducing the area that needs supporting structures. When supporting structures cannot be eliminated, we provide a supplementary algorithm for generating supports compatible with multi-directional 3-D printing. Our method can speed up the process of 3-D printing by saving time in producing and removing supports. [ABSTRACT FROM AUTHOR]

Although high efficiency video coding (HEVC) has achieved high coding performance, it has been shown that dependent rate-distortion optimization (RDO) can still further improve the coding performance at the encoder side. Inspired by our previous work on temporal-dependent RDO, this paper proposes a rate control method at coding tree unit (CTU) level to obtain both higher rate-distortion (R-D) performance and lower bitrate errors. We first formulate the global optimization problem in hybrid video coding and present a general solution framework based on a weighted Lagrange multiplier, where the weighted coefficient is related to the strength of temporal dependency among coding units, and a propagation factor is introduced for quantitative measurement of the temporal dependency. Second, an estimation method of CTU level R-D model is proposed to characterize R-D relationship more accurately. Finally, under the constraint of frame level bit budget, a formulation combining inter-block dependency and R-D characteristic is developed for optimal bit allocation at the CTU level. In the process of encoding a frame, a relaxed constraint algorithm (RCA) and a strict constraint algorithm (SCA) are employed to reduce the difference between numbers of actual and target bits for the current frame, respectively. Experimental results demonstrate that, on the HEVC test model (HM-16.7), our methods including RCA and SCA achieve considerable R-D performance improvement with lower bitrate errors, outperforming the state-of-the-art schemes. It is worth mentioning that even compared with HEVC without rate control, the RCA can still achieve bitrate savings of 5.4% and 5.1% on average under configurations of low delay P and B frames, respectively. [ABSTRACT FROM AUTHOR]

We present an optimal control method for distance-based formation control of multiagent systems with energy constraints. We combine the rigid graph theory and state-dependent Riccati equation method to develop a multiagent formation control scheme. We defined a normalized rigidity matrix and use it for the rigorous stability analysis. The proposed method asymptotically minimizes weighted cost functional that includes formation and energy consumption costs. Furthermore, we propose a solution for the global asymptotic stability and collision avoidance. Simulation results illustrate the effectiveness of the proposed method in two- and three-dimensional spaces. [ABSTRACT FROM AUTHOR]

In non-contact heart rate (HR) monitoring via Doppler radar, the disturbance from respiration and/or body motion is treated as a key problem on the estimation of HR. This paper proposes a blind source separation (BSS) approach to mitigate the noise effect in the received radar signal, and incorporates the sparse spectrum reconstruction to achieve a high-resolution of heartbeat spectrum. The proposed BSS decomposes the spectrogram of mixture signal into original sources, including heartbeat, using non-negative matrix factorization (NMF) algorithms, through learning the complete basis spectra (BS) by a hierarchical clustering. In particular, to exploit the temporal sparsity of heartbeat component, two variants of NMF algorithms with sparseness constraints are applied as well, namely sparse NMF and weighted sparse NMF. Compared with usual BSS, our proposed BSS has three advantages: 1) clustering-induced unsupervised manner; 2) compact demixing architecture; and 3) merely requiring single-channel input data. In addition, the HR estimation method using our proposal delivers more satisfactory precision and robustness over other existing methods, which is demonstrated through the measurements of distinguishing people's activities, gaining both smallest absolute errors of HR estimation for sitting still and typewriting. [ABSTRACT FROM AUTHOR]

In this paper, we propose a distributed voltage control in power distribution networks through reactive power compensation. The proposed control can operate in a distributed fashion where each bus makes its decision based on local voltage measurements and communication with neighboring buses, always satisfy the reactive power capacity constraint, drive the voltage magnitude into an acceptable range, and minimize an operational cost. We also perform various numerical case studies to demonstrate the effectiveness and robustness of the controller using the nonlinear power flow model. [ABSTRACT FROM AUTHOR]

In this paper, a novel and fast memetic evolutionary algorithm is presented which can solve fully constrained generic inverse kinematics with multiple end effectors and goal objectives, leaving high flexibility for the design of custom cost functions. The algorithm utilizes a hybridization of evolutionary and swarm optimization, combined with the limited-memory-Broyden–Fletcher–Goldfarb–Shanno with bound constraints algorithm for gradient-based optimization. Accurate solutions can be found in real-time and suboptimal extrema are robustly avoided, scaling well even for greatly higher degree of freedom. The algorithm provides a general framework for bounded continuous optimization which only requires two parameters for the number of individuals and elites to be set, and supports adding additional goals and constraints for inverse kinematics, such as minimal displacement between solutions, collision avoidance, or functional joint relations. Experimental results on several industrial and anthropomorphic robots as well as on virtual characters demonstrate the algorithm to be applicable for solving complex kinematic postures for different challenging tasks in robotics, human-robot interaction and character animation, including dexterous object manipulation, collision-free full-body motion, as well as animation post-processing for video games and films. Implementations are made available for Unity3D and robot operating system. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms have shown their promise in coping with many-objective optimization problems. However, the strategies of balancing convergence and diversity and the effectiveness of handling problems with irregular Pareto fronts (PFs) are still far from perfect. To address these issues, this paper proposes an adaptive sorting-based evolutionary algorithm based on the idea of decomposition. First, we propose an adaptive sorting-based environmental selection strategy. Solutions in each subpopulation (partitioned by reference vectors) are sorted based on their convergence. Those with better convergence are further sorted based on their diversity, then being selected according to their sorting levels. Second, we provide an adaptive promising subpopulation sorting-based environmental selection strategy for problems which may have irregular PFs. This strategy provides additional sorting-based selection effort on promising subpopulations after the general environmental selection process. Third, we extend the algorithm to handle constraints. Finally, we conduct an extensive experimental study on the proposed algorithm by comparing with start-of-the-state algorithms. Results demonstrate the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper describes a control algorithm for a battery energy storage system (BESS) to deliver a charge/discharge power output in response to changes in the grid frequency constrained by the National Grid Electricity Transmission (NGET)—the primary electricity transmission network operator in the U.K.—while managing the state of charge of the BESS to optimize the availability of the system. Furthermore, this paper investigates using the BESS in order to maximize triad avoidance benefit revenues while layering other services. Simulation using a 2 MW/1 MWh lithium–titanate BESS validated model is carried out to explore possible scenarios using the proposed algorithms. Finally, experimental results of the 2 MW/1 MWh Willenhall Energy Storage System verify the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) can locally provide energies in distribution network and thus help to reduce the negative impact caused by large-scale electric vehicle (EV) charging demand. This paper proposes a two-stage robust DG investment planning model capable of accommodating uncertainty of EV charging demand and renewable generations. The DG investment location and size are optimized in the first stage and the distribution network operation feasibility in the worst case scenario is checked in the second stage to avoid constraint violations. The EV charging demand uncertainty is modeled by sampling from real-life scenarios of travel behavior. Finally, the column-and-constraint generation algorithm is adopted to solve the proposed problem. Simulations on modified IEEE 123-node distribution network demonstrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

A novel receding horizon control scheme is presented in this paper to manage networked leader–follower formation control problems in the presence of time-varying delay and data loss (measurement and feedback packet dropouts) occurrences on the communication and feedback channels. One of the main novelties of the proposed paradigm relies on the use of reachability analysis concepts involving the capability to combine sequences of predecessor sets with polyhedral piecewise state-space partitions. The predecessor sets are here derived offline by taking into account the prescribed state and input constraints, the interconnected nature of the system and the time delay induced by the network latency. They are used online to compute admissible and optimal control actions via a distributed receding horizon computational paradigm. Feasibility, uniformly ultimate boundedness and constraints satisfaction are formally proved regardless of any bounded time delays. [ABSTRACT FROM AUTHOR]

The main contribution of this correspondence paper consists in a linear algebraic characterization of the admissible marking set relative to a Petri net with uncontrollable transitions, subject to a linear constraint. In more detail, given a linear constraint that limits the number of tokens in one place, an algorithm is proposed to compute an approximation of the admissible marking set in terms of a disjunction of transformed linear constraints. The optimality of the solution is guaranteed provided that certain conditions are satisfied during the intermediate steps of the iterative approach. In all the other cases, the set of markings described by the transformed constraints could be surely contained in the admissible marking set. [ABSTRACT FROM AUTHOR]

This paper investigates the physical layer security in a multiple-input-single-output multicast channel, where a common message is transmitted to multiple receivers and only statistical channel state information of the eavesdropper is available. We consider transmitting some artificial noise (AN) along with the information-bearing signal to improve secrecy. To find the optimal AN design, we formulate a secrecy outage probability (SOP) constrained secrecy rate maximization problem. For solving this problem, we adopt a novel transformation of the intractable SOP constraint and establish an efficient algorithm which solves the transformed problem to global optimality. We also propose a low-complexity refinement method to reduce the conservatism incurred by the transformation and an efficient algorithm to find a suboptimal solution when the information-bearing signal is transmitted using single-stream beamforming. Our solution is found to be a valuable generalization of the traditional null-space AN scheme. According to our simulation results, when the number of receivers is less than that of transmit antennas, distributing AN uniformly in the null space of the legitimate channel vectors is near-optimal. When there is no valid null space, transmitting AN will interfere with some legitimate receivers, and the secrecy performance can also be improved with carefully designed AN. [ABSTRACT FROM PUBLISHER]

We consider distributed multitask learning problems over a network of agents where each agent is interested in estimating its own parameter vector, also called task, and where the tasks at neighboring agents are related according to a set of linear equality constraints. Each agent possesses its own convex cost function of its parameter vector and a set of linear equality constraints involving its own parameter vector and the parameter vectors of its neighboring agents. We propose an adaptive stochastic algorithm based on the projection gradient method and diffusion strategies in order to allow the network to optimize the individual costs subject to all constraints. Although the derivation is carried out for linear equality constraints, the technique can be applied to other forms of convex constraints. We conduct a detailed mean-square-error analysis of the proposed algorithm and derive closed-form expressions to predict its learning behavior. We provide simulations to illustrate the theoretical findings. Finally, the algorithm is employed for solving two problems in a distributed manner: A minimum-cost flow problem over a network and a space–time varying field reconstruction problem. [ABSTRACT FROM PUBLISHER]

A methodology based on a new uncertainty quantization formulation and an improved wind driven optimization (WDO) algorithm is proposed for robust optimizations of electromagnetic devices under interval uncertainties. In the proposed methodology, the robust performances are enforced as the constraint functions, and the objective function is selected as the biasing force to evolve the iteration procedures. The WDO method is also improved to guarantee a good balance between the exploration and exploitation searches. Numerical results on a case study are reported to showcase the feasibility and merit of the proposed methodology in solving practical engineering design problems. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of robust tracking control of networked control systems with communication constraints (network-induced delay, packet dropouts, and packet disorder) and external disturbance. A novel networked predictive control algorithm based on k-order adaptive discrete-time sliding-mode control (k -ADSMC) is proposed. The ADSMC, which can self-adapting adjust sliding-mode parameters and ensure faster convergence, is designed based on the error and error difference of tracking output. Furthermore, k -ADSMC is designed, which uses the estimated disturbance as weight factors and synthesizes the sliding- mode values of all k times, in order to improve the robust of tracking control. Finally, the stability of the system is proved, and a dc motor servo system is utilized to verify the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]