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This study proposes a real-time footstep and timing adaptation mechanism for humanoid robots that can be integrated into a conventional walking pattern generator and increase the robustness of walking against disturbances. In order to meet the strict real-time constraint of humanoid robot control, the proposed method computes viable capture basins in the design phase. This pre-computed data can be used at runtime to modify the foot placement, the timing of landing, and the center-of-mass movement in response to applied disturbances with small computation cost. The performance of the proposed method is evaluated in simulation experiments.

A novel distributed energy resources (DER) allocation method focused on grid constraints that avoids topological bias is proposed for distribution networks. A technology-agnostic approach is used, where a non-bias allocation of export capacity (NAEC) not specific to generation type is calculated. Moreover, the proposed NAEC is extended from an export capacity into a hosting capacity (HC) using a statistical approach. The methods are tested using the IEEE 33-bus distribution system, and two typical Irish distribution feeders -one urban, one rural- as case studies. Using a high-resolution year-long quasi-static time series simulation (QSTS) and three different generation profiles, the proposed NAEC method is validated against current practices and state of the art allocation methods in terms of active balancing, security of supply, interactions between users, operational concerns, and fairness. Results show that an equivalent or higher level of DER penetration is achieved using the proposed methodology. There are no additional constraint violations using the NAEC methodology, moreover, time slots with violations are reduced, improving security of supply. Furthermore, results suggest that avoiding topological bias makes the network accessible for more users, and prioritises self-consumption.

The rotating vectors of a matrix converter are uniquely featured by producing zero common-mode voltage, and control methods using them have the advantage of inherently achieving common-mode voltage minimization. However, the existing knowledge of their low voltage transfer ratio (VTR) has made it difficult for rotating vectors to get practical applications. This paper derives the theoretical limit of the VTR under the constraint of rotating vector. Firstly, the principle of selecting the voltage vector for maximum VTR is analyzed. Then the range of matrix converter input voltage phase angle is divided into intervals, and the phase angle of the selected rotating vector within each interval is determined. Finally, through integral and average calculations, the maximum VTR, 9/ $\pi ^{2}$ , is obtained, which is more than 80% higher than that of the linear modulation limit. Simulation and experiments are carried out to verify the correctness of the conclusion.

Discriminative correlation filter (DCF) has achieved promising performance in visual tracking for its high efficiency and high accuracy. However, DCF trackers usually suffer from some challenges, such as boundary effects and appearance changes. In this paper, we propose a novel correlation tracking method via spatial-temporal constraints and structured sparse regularization. Firstly, we introduce the background-aware selection strategy to extract real negative examples, and penalize the filter coefficients close to the boundary locations for spatial protection, both of which can alleviate the boundary effects. Secondly, we restrict the filters with structured sparse regularization to handle the local appearance changes, and exploit temporal consistent constraint on the filters to address the global appearance changes. Finally, we employ the alternative direction method of multipliers to optimize our correlation tracking model. In our optimization framework, we combine grayscale, color names, histogram of orientation gradient with deep features for appearance learning to improve the discrimination. Meanwhile, we penalize spatial constraint and structured sparse regularization alternatively based on occlusion detection to enhance processing efficiency. The qualitative and quantitative experiments are conducted on the OTB dataset. Experimental results demonstrate that the proposed tracker has better performance than other state-of-the-art trackers.

We study the problem of training an accurate deep learning mitosis detection model with only point annotations. To address this challenging label-efficient deep learning problem, we propose a novel contextual prior constraint mechanism and spatial area constrained loss to generate the reference ground truth for segmentation and to restrain incorrectly predicted pixels, respectively. The spatial area constraint mechanism is not strictly cast at the pixel-level and restrains the mitosis and non-mitosis areas as positive/negative bags under the framework of multiple instance learning. The experimental results show that our contextual prior mechanism with PSPNet as the segmentation baseline achieves state-of-the-art performance with an F-score of 69.92%, 56.22%, and 85.29% on the mitosis detection task of AMIDA 2013, ICPR MITOSIS 2014, and point-annotated ICPR MITOSIS 2012, respectively. Especially, using our spatial area constraint mechanism and reference ground truth, the detection result on point-annotated ICPR MITOSIS 2012 even outperforms the result using the same backbone network with pixel-level annotations. The experimental results demonstrate the advancement and effectiveness of our proposed method. In addition, they indicate that our work can definitely improve the performance of mitosis detection on point-annotated datasets and be extended to other medical image analysis tasks with limited annotations.

In this paper, we consider improving the secure performance of multiple-input single-output visible light communication channel in the presence of multiple eavesdroppers with multiple photodiodes. Our goal is to design an optimal artificial-noise (AN) aided transmission strategy to maximize the achievable secrecy rate subject to both sum power constraint and peak amplitude constraint. We consider a joint optimization of the transmit covariance and AN covariance for the non-convex secrecy rate maximization (SRM) problem. In order to solve it, the SRM problem is transformed into a series of single-variable semidefinite programming (SDP) problems without losing any optimality, and a one-dimensional search based algorithm is proposed to handle the converted problem, with polynomial complexity. By exploiting Karush-Kuhn-Tucker conditions of the problem, beamforming is found to be optimal for the confidential information transmission. Simulation results show the superior performance of the proposed AN-aided method compared with two other AN-aided methods and no AN-aided method.

Global navigation satellite system (GNSS) and interferometric synthetic aperture radar (InSAR) data are integrated to extract the 3-D surface deformations, which are of great significance for studying geological hazards. In this study, two major problems are focused on integration. For one thing, we propose an iterated almost unbiased estimation (IAUE) method to estimate the variance components of GNSS and InSAR for the case where the estimation of variance components of multisource data by traditional variance component estimation methods may be negative and inaccurate. For another, considering that heterogeneous data errors may lead to unstable 3-D solutions, we propose adding the Laplacian smoothness constraint (LSC) to the function model, which can smooth the solutions by minimizing the second derivative of the displacements. These two methods are abbreviated as IAUE-LSC. In the simulation experiment, the performance of traditional Helmert variance component estimation is first compared with IAUE. IAUE can not only converge more quickly, but also avoid negative variances. Furthermore, we find that the excessively large relative error ratio between GNSS and InSAR is an essential factor leading to the instability of the 3-D solutions. The IAUE-LSC method is immune to this instability and can obtain more stable results. In addition, the 2018 Hawaii case demonstrates that IAUE achieves improvements of 2.58, 2.77, and 7.69 cm in the east, north, and up directions relative to the traditional weighted least-squares method, while the combined IAUE-LSC achieves improvements of 2.29, 0.32, and 1.68 cm compared to the IAUE alone.

We study the ability of recently developed variable-length constrained sequence codes to determine codeword boundaries in the received sequence upon initial receipt of the sequence and if errors in the received sequence cause synchronization to be lost. We first investigate construction of these codes based on the finite state machine description of a given constraint, and develop new construction criteria to achieve high synchronization probabilities. Given these criteria, we propose a guided partial extension algorithm to construct variable-length constrained sequence codes with high synchronization probabilities. With this algorithm we construct new codes and determine the number of codewords and coded bits that are needed to recover synchronization once synchronization is lost. We consider a large variety of constraints including the runlength limited (RLL) constraint, the DC-free constraint, the Pearson constraint and constraints for inter-cell interference mitigation in flash memories. Simulation results show that the codes we construct exhibit excellent synchronization properties, often resynchronizing within a few bits.

In this article, we propose an effective method for remote sensing image registration. Point features are robust to remote sensing images with low quality, small overlapping area, and local deformation. Therefore, we extract point features from remote sensing images and convert the problem of remote sensing image registration into the problem of feature point matching. A correspondence set constructed solely on the similar of features often contains many false correspondences or outliers, so our key idea is to remove the mismatches in the initial correspondence set and obtain a stable correspondence through a two-step strategy. First, we use two constraints to construct the optimization model which can solve in linear time. The first constraint is that the topology of the points and their neighbors can be maintained after the spatial transformation. Another constraint is that the feature distance of the correct matches are similar to the neighbors. Then, we design a strategy to increase the number of inliers and raise the precision by a global constraint calculated from the solution in the previous step. Experiments on a variety of remote sensing image datasets demonstrate that our method is more robust and accurate than state-of-the-art methods.

Metric-learning-based methods, which attempt to learn a deep embedding space on extremely large episodes, have been successfully applied to few-shot classification problems. In this paper, we propose the adoption of large margin nearest center (LMNC) loss during episodic training to enhance metric-learning-based few-shot classification methods. Loss functions (such as cross-entropy and mean square error) commonly used in episodic training strive to achieve the strict goal that differently labeled examples in the embedding space are separated by an infinite distance. However, the learned embedding space cannot guarantee that this goal will be achieved for every episode sampled from a large number of classes. Instead of an infinite distance, LMNC loss requires only that differently labeled examples be separated by a large margin, which can well relax the strict constraint of the traditional loss functions, easily leading to a discriminative embedding space. Moreover, considering the multilevel similarity between various classes, we alleviate the constraint of a fixed large margin and extend LMNC loss to weighted LMNC (WLMNC) loss, which can effectively take advantage of interclass information, achieving a more separable embedding space with adaptive interclass margins. Experiments on state-of-the-art benchmarks demonstrate that the adoption of LMNC and WLMNC losses can strongly improve the embedding learning performance and classification accuracy of metric-based few-shot classification methods for various few-shot scenarios. In particular, LMNC and WLMNC losses can obtain 1.86% and 2.46% gains in prototypical network on miniImageNet for 5-way 1-shot scenario, respectively.

In order to solve the problem of low spraying efficiency and excessive paint waste caused by overspray when the robot automatically sprays irregular plane, a method of spray trajectory planning with boundary-constrained considering spray gun changing posture is proposed. The static spraying model of the spray gun with variable posture, and the dynamic spraying model of the spray gun along the arc path are established separately, an optimization method for the symmetry of coating distribution based on the variable spray angle when the gun spraying along the arc path is proposed. Orthogonal experiment method based on numerical simulation is used to clarify the influence of boundary curve shape and spray gun posture on boundary constraint distance, the prediction of the value range of the boundary constraint distance at the boundary of different curvature radius is realized. Within the range of the value of the boundary constraint, combined with the equal division method of the plane intercept line and the distance search method, a boundary constraint spray path generation algorithm is established by setting a uniform boundary constraint distance value. Finally, a multivariable spraying parameter optimization algorithm based on PSO is established, the spraying velocity, spraying height and spraying angle in the dynamic spraying process are solved, and the coating uniformity is optimized. The effectiveness and feasibility of the proposed spray trajectory planning method is verified by robot spraying experiment for any irregular plane.

The generalized recursive circulant networking can be widely used in the design and implementation of interconnection networks. It consists of a series of processors, each is connected through bidirectional, point-to-point communication channels to different neighbors. In this work, we apply the shortest path routing concept to build independent spanning trees on the generalized recursive circulant graphs. The proposed strategy loosen the restricted conditions in previous research and extended the result to a more general vertex setting by design the specific algorithm to deal with the constraint issue.

A novel approach to Boolean matrix factorization (BMF) is presented. Instead of solving the BMF problem directly, this approach solves a nonnegative optimization problem with an additional constraint over an auxiliary matrix whose Boolean structure is identical to the initial Boolean data. This additional auxiliary matrix constraint forces the support of the NMF solution to adhere to that of a BMF solution. The solution of the nonnegative auxiliary optimization problem is thresholded to provide a solution for the BMF problem. We provide the proofs for the equivalencies of the two solution spaces under the existence of an exact solution. Moreover, the nonincreasing property of the algorithm is also proven. Experiments on synthetic and real datasets are conducted to show the effectiveness and complexity of the algorithm compared to other current methods.

This paper is concerned with the exponential stability for a class of linear delay differential systems under logic impulsive control. Based on logic impulsive control, the strong constraint that the coefficient functions of this kind of linear delay differential system need to be non-negative, which is required in most of the previous studies, is reduced. By establishing the relationship between the logic impulsive system and a corresponding non-impulsive system, some exponential stability criteria for linear delay differential systems under logic impulsive control are presented in the following two cases: Case A–The coefficient functions are non-negative, Case B–The coefficient functions can be negative. Two numerical examples are discussed to verify the results.

The tracking performance of mobile robot is often affected by uncertainties from the deviation of initial conditions, external disturbances and varying loads, etc. An Udwadia-Kalaba based adaptive robust control is proposed for the trajectory tracking of an omnidirectional mobile robot in the presence of uncertainties. The proposed control includes nominal control part based on Udwadia-Kalaba theory and adaptive robust control part. The desired trajectory is considered as a virtual servo constraint applied to the robot system and converted into the second order standard form. So that the analytical form of constraint force could be obtained via Udwadia-Kalaba Fundamental Equation (UKFE). The system will precisely obey the given constraint (i.e., the desired trajectory) under the obtained constraint force in ideal cases. No auxiliary variables are required and it is effective whether the constraints are holonomic or nonholonomic. The designed adaptive law is in leakage type and the adaptive parameters are adjusted according to the performance of the system in order to compensate for the effect caused by uncertainty in the system. No extra information of uncertainty is needed except for the existence of uncertainty bound. Comparing with PID control, it can be found that the proposed control has better performance and can realize higher precision trajectory tracking control.

Automatic building boundariesextraction methods are important for urban planning, change monitoring, and smart city construction. In this article, we propose a regularized building boundaries extraction from remote sensing imagery based on augment feature pyramid network (AFPN) and morphological constraint. First, we build an AFPN, which can provide more accurate and dense global features for semantic segmentation tasks to avoid the loss of feature information. Second, the building shape is manually divided into linear and curved by analyzing the morphological characteristics. The extraction results are regularized to achieve the refined expression of contour according to different types of building shapes. Finally, we conduct experiments on the benchmark dataset to test the availability of the proposed approach. The results showed that the F1-score and intersection over union (IOU) reached 93.7% and 88.8%, respectively. Besides, our proposed approach is compared with some of the excellent research in recent years of models, such as PSPNet, Unet++, RefineNet, and DeconvNet. On the benchmark dataset, the proposed method increases the IOU by 0.9–2.7% and improves the F1-score by 0.2–2.5%. In addition, the results prove that our method considering morphological constraint can achieve better visual effects.

Constrained multi-objective optimization problems are common in practical engineering and are more difficult to handle than unconstrained problems. In general, it is necessary to find a balance between the convergence and diversity of solutions, as well as its feasibility. For the constrained multi/many-objective optimization problem, a particle swarm optimization algorithm based on a two-level balance strategy is proposed. In contrast to existing views, the first level of the proposed algorithmic framework emphasizes convergence, while diversity and feasibility are considered together as the second-level scheme. An ensemble fitness ranking was used to improve the convergence of the proposed algorithm. To balance the diversity and solution feasibility, the solutions are selected by combining the angles between the solutions using the constraint dominance principle. A penalty-based boundary-crossing approach is used as a utility function to calculate the fitness of the populations, which is compared with six state-of-the-art constrained multi/many-objective evolutionary optimization algorithms on multiple constrained test suites, and the experimental results show that the proposed algorithm is highly competitive in most test problems. Furthermore, to illustrate the effect of different utility functions on the performance of the algorithm, the Chebyshev decomposition method is employed and compared with the former, and the results show that different utility functions need to be chosen to cope with problems of different characteristics.

This paper presents a method to design an integrated chassis controller with four-wheel independent steering (4WIS) under the constraint on front slip angles for electric vehicles (EVs) adopting in-wheel motor (IWM) driving system. To improve lateral stability and maneuverability of a vehicle, direct yaw moment control strategy is adopted. A control allocation method is adopted to distribute control yaw moment into tire forces, generated by 4WIS. If corrective steering angles of 4WIS are added to front steering angles generated by a driver, it can deteriorate control performance because the lateral tire force of front wheels easily saturated and it causes loss of required yaw moment needed to stabilize a vehicle. To cope with the problem, it is necessary to impose constraints on front slip angles. To compensate the loss of control yaw moment caused by the constraint on front slip angles, a constrained control allocation method is presented. Simulation on driving simulation tool, CarSim®, shows that the proposed integrated chassis controller is capable of maintaining lateral stability and maneuverability without performance deterioration under the constraint on the front slip angles.

The differences in performance among binary-integer encodings in an Ising machine, which can solve combinatorial optimization problems, are investigated. Many combinatorial optimization problems can be mapped to find the lowest-energy (ground) state of an Ising model or its equivalent model, the Quadratic Unconstrained Binary Optimization (QUBO). Since the Ising model and QUBO consist of binary variables, they often express integers as binary when using Ising machines. A typical example is the combinatorial optimization problem under inequality constraints. Here, the quadratic knapsack problem is adopted as a prototypical problem with an inequality constraint. It is solved using typical binary-integer encodings: one-hot encoding, binary encoding, and unary encoding. Unary encoding shows the best performance for large-sized problems.

A typical process route essentially represents the commonly used process planning-related knowledge and can be modified to generate new process routes easily. Hence, its quality directly affects the performance of newly generated process routes and thereby the goodness of products. To effectively discover typical process route knowledge, a reasonable similarity measure and a clustering method specifically for process routes are required. However, existing operation sequence similarity coefficients often assign coarse-grained similarities, which leads to inaccurate clustering results. For the clustering problem, most researchers have not considered the practical constraints during typical process route discovery. In this paper, an operation sequence similarity-based discovery method is presented. First, the characteristics and information requirements of the operation sequence similarity problem are analysed, and a novel comprehensive similarity coefficient combined with a modified pseudo-longest-common-subsequence (pseudo-LCS) and Jaccard similarity coefficient is proposed based on this analysis and principal component analysis (PCA). This coefficient considers the precedence relationship, the number of common operations, and the operation similarity simultaneously to handle all the potential similarity situations. Second, two soft constraints, namely, quantity constraint and size constraint, are introduced in the traditional process route clustering problem to ensure the quality and validity of the discovered typical process routes. To solve this more practical problem and achieve a balance between these two conflicting constraints, the K-medoids method is improved with an adjustment mechanism to generate valid results under these two soft constraints. Finally, numerical illustrations are presented to verify the effectiveness of the proposed methods. The results show that compared with existing similarity coefficients, the proposed comprehensive similarity coefficient is more sensitive and much better at distinguishing the tiny difference between the process routes. In addition, the modified K-medoids method can perform much better than existing methods on process route discovery data sets under two conflicting soft constraints.

Hyperspectral spectral mixture analysis (SMA), which intends to decompose mixed pixels into a collection of endmembers weighted by their corresponding fraction abundances, has been successfully used to tackle mixed-pixel problem in hyperspectral remote sensing applications. As an approach of decomposing a high-dimensional data matrix into the multiplication of two nonnegative matrices, nonnegative matrix factorization (NMF) has shown its advantages and been widely applied to SMA. Unfortunately, most of the NMF-based unmixing methods can easily lead to an unsuitable solution, due to inadequate mining of spatial and spectral information and the influence of outliers and noise. To overcome such limitations, a spatial constraint over abundance and a spectral constraint over endmembers are imposed over NMF-based unmixing model for spectral-spatial constrained unmixing. Specifically, a spatial neighborhood preserving constraint is proposed to preserve the local geometric structure of the hyperspectral data by assuming that pixels in a spatial neighborhood generally fall into a low-dimensional manifold, while a minimum spectral distance constraint is formulated to optimize endmember spectra as compact as possible. Furthermore, to handle non-Gaussian noises or outliers, an $ {L}_{2,1}$-norm based loss function is ultimately adopted for the proposed spectral-spatial constrained nonnegative matrix factorization model and a projected gradient based optimization algorithm is designed for optimization. Experimental results over both synthetic and real-world datasets demonstrate that the proposed spatial and spectral constraints can certainly improve the performance of NMF-based unmixing and outperform state-of-the-art NMF-based unmixing algorithms.

The traveling salesman problem (TSP), can be used as a typical combinatorial optimization problem, to describe a wide variety of practical engineering optimization problems in various fields. In this study, the problem of personnel and equipment utilization in the transportation industry was abstracted, a more general class of TSPs with replenishment arcs was proposed, an optimization model to minimize the total travel time was established, the ant colony optimization algorithm to solve the standard TSP was improved, and an improved ant colony algorithm based on dynamic heuristic information was designed. Simulation experiments showed that the algorithm can account for the cumulative mileage constraint and search for the shortest path, effectively solving the TSP with replenishment arcs.

The coupled electricity and heat system (CEHS) is considered one of the most efficient energy utilization schemes for the energy transition to solve energy crises and environmental problems. However, the individual data between the power system (PS) and heat system (HS) probably limit the high share and optimization operation. Moreover, the uncertain renewables and complex coupled network create challenges regarding the operation risk of CEHS. Given that the CEHS may be affiliated with different operation entities, this paper proposes two operation modes to achieve the solution of the optimal risk operation model (OROM), including the distributionally robust chance constraint (DRCC)-based centralized risk operation mode (CROM) and the DRCC-based alternating direction method of multipliers (ADMM) distributed risk operation mode (DROM). By formulating the moment-based ambiguity set estimated from historical data and introducing the operation risk constraints, a tractable reformulation of two-stage DRCC OROM problems is presented under CROM. Moreover, the DRCC-based ADMM distributed algorithm with the guarantee of convergence is developed to optimize the PS and HS independently under DROM considering the operation risk. The two operation modes achieve the minimum amount of information shared between the two systems regarding the underlying distribution. In the numerical case, the approximate OROM solution is obtained between CROM and DROM. The two proposed approaches based on different operation modes outperform the existing methods according to the risk level depicted by different forms of the violation probability and the risk cost compared with Gaussian chance constraint (GCC) under CROM, while the safety coefficient $\epsilon $ is set as 0.25. Then, the impact of the iteration number on the convergence is also discussed with comparison of the classical ADMM under DROM.

Appropriate region of interest (ROI) and lane structure restriction conditions can improve the ac-curacy of lane detection. Most previously reported approaches require human experience to set up the constraint conditions of the ROI and lane structure. Few of these studies involved quantitative calculation methods of the constraint conditions of the ROI and lane structure. To solve this problem, constant-speed cruise mode, which relies on lane recognition, was examined. A vehicle dynamics model with a charge-coupled device (CCD) camera was constructed based on the vehicle dynamics and camera projection. The projection of lane line feature points from the world coordinate system to the image coordinate system under different vehicle positions and postures was analyzed. A quantitative calculation method of the ROI lateral constraint equation and the lane structure constraint equation in constant-speed cruise mode is given. Actual road test data showed that the constraint condition calculation method not only agreed with the actual road test results but also improved the real-time accuracy.

Generative Adversarial Networks (GANs) are a powerful subclass of generative models. Yet, how to effectively train them to reach Nash equilibrium is a challenge. A number of experiments have indicated that one possible solution is to bound the function space of the discriminator. In practice, when optimizing the standard loss function without limiting the discriminator’s output, the discriminator may suffer from lack of convergence. To be able to reach the Nash equilibrium in a faster way during training and obtain better generative data, we propose constrained generative adversarial networks, GAN-C, where a constraint on the discriminator’s output is introduced. We theoretically prove that our proposed loss function shares the same Nash equilibrium as the standard one, and our experiments on mixture of Gaussians, MNIST, CIFAR-10, STL-10, FFHQ, and CAT datasets show that our loss function can better stabilize training and yield even better high-quality images.

This paper considers the novel problem of deriving a Time Division Multiple Access (TDMA) link schedule for rechargeable wireless sensor networks (rWSNs). Unlike past works, it considers: (i) the energy harvesting time of nodes, (ii) a battery cycle constraint that is used to overcome so called memory effects , and (iii) battery imperfections, i.e., leakage. This paper shows analytically that the battery cycle constraint and leaking batteries lead to unscheduled links. Further, it presents a greedy heuristic that schedules links according to when their corresponding nodes have sufficient energy. Our simulations show that enforcing the battery cycle constraint increases the link schedule by up to 1.71 (0.31) times for nodes equipped with a leaking (leak-free) battery. When nodes have a leaking battery, the derived schedules are on average 1.05 times longer than the case where nodes have a leak-free battery. Finally, the battery cycle constraint reduces the number of charge/discharge cycles by up to 47.41% (45.67)% when nodes have a leak (leak-free) battery. Between leak-free and leak battery scenarios, using the former produces up to 51.46% fewer cycles than the latter.

Whereas Deep Neural Network(DNN) shows wonderful performance on large scale data, lacking interpretability limits their usage in scenarios relevant to security. To make visual explanations less noisy and more class-discriminative, in this work, we propose a visual explanation method of DNN, named Exclusive Feature Constrained Class Activation Mapping(EFC-CAM). A new exclusive feature constraint is introduced to optimize the weight calculated from Grad-CAM or initialized from a constant vector. To better measure visual explanation methods, we design an effective evaluation metric which does not need bounding boxes as auxiliary information. Extensive quantitative experiments and visual inspection on ImageNet and Fashion validation set show the effectiveness of the proposed method.

Modern communication systems of high data capacity incorporate circular polarization (CP) as the preferred antenna radiation field configuration. In many applications, integration of the system circuitry with antennas imposes size limitations on CP radiators, which makes their development process a challenging endeavor. This can be mitigated by means of simulation-driven design, specifically, constrained numerical optimization. Majority of the performance-related constraints are expensive to evaluate, i.e. require full-wave electromagnetic (EM) analysis of the system. Their practical handling can be realized using a penalty function approach, where the primary objective (antenna size reduction) is complemented by contributions proportional to properly quantified constraint violations. The coefficients determining the contribution of the penalty terms are normally set up using designer’s experience, which is unlikely to render their optimum values in terms of the achievable miniaturization rates as well as constraint satisfaction. This paper proposes a procedure for automated penalty factor adjustment in the course of the optimization process. Our methodology seeks for the most suitable coefficient levels based on the detected constraint violations and feasibility status of the design. It is validated using two CP antenna structures. The results demonstrate a possibility of a precise constraint control as well as superior miniaturization rates as compared to the manual penalty term setup.

The traditional graph Laplacian model has been widely used in many computer vision tasks. The small target detection technique is one of the most challenging computer vision tasks in various practical applications. This article presents a small target detection method by developing a modified graph Laplacian model with additional constraints. The proposed method is designed based on specific characteristics of small target: Global rarity, local contrast, and contrast consistency. First, we analyze the primal graph Laplacian model, and exploit its ability to describe global rarity, and isolate outliers from nonoutliers. Next, indicators measuring local contrast and contrast consistency are constructed to delineate local characteristics of small targets. Then, we integrate the indicators with the primal graph Laplacian model, and propose a modified graph Laplacian model for small target detection. In the confidence maps obtained by the proposed model, small targets are well enhanced, while backgrounds are significantly suppressed. Finally, a small target detection method is proposed based on the graph model. Extensive experiments on various real datasets demonstrate the effectiveness and superiority of the proposed method in detecting small targets.

Despite the promising progress made in recent years, person re-identification (Re-ID) remains a challenging task due to the intra-class variations. Most of the current studies used the traditional Softmax loss for solutions, but its discriminative capability encounters a bottleneck. Therefore, how to improve person Re-ID performance is still a challenging task. To address this problem, we proposed a novel loss function, namely additive distance constraint with similar labels loss (ADCSLL). Specifically, we reformulated the Softmax loss by adding a distance constraint to the ground truth label, based on which similar labels were introduced to enhance the learned features to be much more stable and centralized. Experimental evaluations were conducted on two popular datasets (Market-1501 and DukeMTMC-reID) to examine the effectiveness of our proposed method. The results showed that our proposed ADCSLL was more discriminative than most of the other compared state-of-the-art methods. The rank-1 accuracy and the mAP on Market-1501 were 95.0% and 87.0%, respectively. The numbers were 88.6% and 77.2% on DukeMTMC-reID, respectively.

Constrained optimization problems (COPs) are common in many fields, and the search algorithm and constraint handling technique play important roles in the constrained evolution algorithms. In this article, we propose a new optimization algorithm named CETDE based on ensemble differential evolution (DE) and a two-level epsilon-constrained method. In the ensemble DE variant, some promising parameters and mutation strategies constitute the candidate pool, and each element in the pool coexists throughout the search process and competes to generate new solutions. The two-level epsilon method is proposed by incorporating a generation and a population comparison level to retain more promising solutions without degrading the solution quality. Moreover, a diversity promotion scheme is developed to improve the population distribution when the search becomes trapped in a small region. The superior performance of CETDE is validated by comparison with some state-of-the-art COEAs over two sets of artificial benchmarks and five real-world problems. The competitive results show that CETDE is an effective method for solving COPs.

To intercept maneuvering target considering impact angle constraint and actuator faults, a novel three-dimensional (3D) guidance law is proposed in this paper. To guarantee the interception, the multi-variable super-twisting-algorithm-like (STA-like) is adopted in the proposed guidance law, so as to drive the line-of-sight (LOS) angle to the desired value and its rate to zero in finite time in both pitch and azimuth directions. However, it is usually a difficult task for STA-like to select the proper design parameters, and the necessary disturbance gradient for STA-like is also not clearly known, owing to realistic actuator faults and the independence between missile and target. Moreover, the actuator faults in this paper are formulated as disturbances in the control scheme, and the necessary disturbance gradient for STA-like is not clearly known as well. To solve these problems, a multi-variable dual-layer adaptive scheme is employed to adjust the control gains and guarantees its precision. The theoretical analysis and numerical simulation results demonstrate the effectiveness of the proposed guidance law. The combination of STA-like and adaptive theory in the presented guidance law for the first time can guarantee the successful interception and can generate precise and robust control signal simultaneously with impact angle constraint and actuator faults consideration.

With the increasing concern over environment protection, Economic Emission Dispatch (EED) problem has received much attention. It is essentially a Multi-objective Optimization Problem, which minimizes both fuel cost and emission pollution simultaneously, as well as meets some system limits. This study transforms EED problem to a single-objective problem with weighted sum method, and then use Newton method to solve the equality constraint iteratively and introduce a common penalty function to deal with the inequality constraint. Moreover, this study tries to propose a new meta-heuristic algorithm inspired by kernel tricks to solve EED problem with no hyper parameters to be tuned. The new algorithm can map a non-linear objective function into a linear one with higher-dimension. Thus the optimization process could be transformed into a linear process, which is more likely to get the optimum solution. When applied in the 3 real-world EED cases with valve point, the new algorithm achieved a better performance compared with other algorithms in the literature.

The iterative algorithms of computed tomography (CT) reconstruction derived from the dictionary learning (DL) regularization have been developed to make high quality recovery from the under-sampled data acquired by a low dose protocol. However, when they are applied to noisy data with low sampling rate, streaking artifacts and bias tends to appear in early iteration results. Since the dictionary is over-complete, the artifacts and bias can also be represented well by the dictionary, resulting in the reservation of these unexpected structures in the final image. We proposed a smoothed L0 norm-constraint dictionary learning (SL0-DL) algorithm to deal with these unexpected structures. For the proposed algorithm, we introduce smoothed-L0 norm regularization to the objective function. In each iteration process, the intermediate image generated by the DL representation will be smoothed using SL0 norm, and then the smoothed image is used to update the output of this iteration. The raw data from both the numerical simulation and actual CT acquisition are used to test the performances of the proposed SL0-DL method. Experimental results demonstrate that the proposed method performs better than other competing algorithms with better noise and artifacts suppression performance while preserving image texture details. And the results show a significant improvement in the quality of the reconstructed image, which demonstrates that the proposed algorithm is really effective.

Predicting new user behavior has always been a challenging issue in intelligent recommender systems. This challenge is mainly due to the extreme asymmetry of information between new users and old users. Existing factorization models can efficiently process and map asymmetric information, but they are not good at mining deep relationships between contexts when compressing high-dimensional data. In contrast, neural network methods can deeply exploit the relationship between contexts; however, their training cost is much larger than factorization approaches. Therefore, this paper proposes a scalable and efficient recommender to solve the new user problem by filling the gap between factorization models and neural networks. The scalable part is a neural network that can jointly encode, compress, and fuse various types of contexts. The efficient part is a factorization model based on a correlation constraint mechanism and a projection strategy, which enables an asymmetric mapping of information between old and new users. The entire recommender fuses the two parts so that the factorization model and the neural network can complement each other. The experimental results show that our approach can achieve a good balance between performance and training efficiency compared to state-of-the-art methods.

Most of the existing cross-modal retrieval methods make use of labeled data to learn projection matrices for different modal data. These methods usually learn the original semantic space to bridge the heterogeneous gap, ignoring the rich semantic information contained in unlabeled data. Accordingly, a semantic consistency cross-modal retrieval with semi-supervised graph regularization (SCCMR) algorithm is proposed, which integrates the prediction of labels and the optimization of projection matrices into a unified framework to ensure that the solution obtained is globally optimal. At the same time, the method uses graph embedding to consider the nearest neighbors in the potential subspace of paired images and texts as well as images and texts with the same semantics. ${l_{21}}$ -norm constraint is applied to the projection matrices to select the discriminative features for different modal data. The results show that our method outperforms several advanced methods on four commonly used cross-modal retrieval datasets.

The traditional human behavior recognition algorithm is easy to ignore the spatial constraint problem of feature blocks, which leads to poor recognition effect and low correct rate. Therefore, we proposed a human motion target recognition algorithm based on Convolution Neural Network (referred to as the “CNN”) and global constraint block matching. First, key frames of the human motion video were extracted, second, the local feature and global feature of key frames were analyzed, and CNN was used to perform feature fusion, then, according to the result of the feature fusion, a feature block was formed and the closest matching feature block is obtained, using the definition of spatial constraint, we considered the spatial data of human motion in the vertical direction, calculates the spatial constraint weight, and further completes the matching. Finally, the score of matching block and the spatial constraint weight were calculated, and the human motion targets are recognized based on the cumulative score. The experimental results show that the proposed algorithm has a high key frame extraction accuracy of more than 90% and less time consumed in feature fusion, high matching accuracy of feature blocks of more than 80%, and high feature blocks, the F-measure of human behavior recognition is 0.95 on average, and the overall recognition performance is good.

As the power systems in some large developing and developed countries are getting bigger, solving large-scale unit commitment (UC) is an urgent need and significant task to ensure their economic operation and contribute green energy consummation to society. In this article optimization models covering economy and environmental protection are established, and an improved binary artificial fish swarm algorithm (IBAFSA) is presented to solve the large-scale UC problems. The parameters of IBAFSA are improved by Lévy flight and adaptive average visual distance to search space more actively, and a double threshold selection strategy is used to enhance the effectiveness of population evolution in the optimization. Meanwhile, a heuristic greedy search algorithm among the best individuals of all generations in the iterative process of the optimization is proposed, which is beneficial to improve computation convergence and reach the optimum solution. A fast constraints processing mechanism based on the heuristic modifying strategy of unit violation is established to handle the coupling between system spinning reserve constraint and unit minimum up and down time constraint. The effectiveness of the proposed approach is verified by the UC simulations of test systems of 10-1000 units, the IEEE 118-bus system, and a large-scale power system of 270 units. The numerical simulating results show that the proposed UC solution method can achieve the near-optimal solutions in a reasonable time, improve the economic and environmental benefits of a large-scale power system, and is a general method to adapt to the changes of the objective function and constraints of a UC optimization.

In the recent past, Wireless endoscopy (WE) helps physicians to study the digestive tract at the cost of a wide range of images without surgery. The major challenge arises from the complication of robust image characterization in computer-aided WE pictorial diagnostics. The purpose of the research is to provide a biased definition of WE images and to enable medical professionals to automatically identify polyp images. In this paper, the learning feature approach called Deep Stacked Auto Encoder with Constraint Image (DSAECI) for recognizing polyps in WE images has been proposed. This DSAECI differs from the Traditional Method of Auto Encoder (TMAE) due to the introduction of constraint image, created by the nearest neighbor's image and describing inherent object structures. The multiple limitations of images force users to keep images in the same category far away, which share similar learned characteristics and images in various categories and utilized the Flexible medical sensor platform for data analysis. The learned characteristics thus retain large inter-variances and small intra-images. The average total accuracy (OA) of our WE images method is 98.00%. The full results showed that the proposed DSAECI can correctly identify polyps in a WE-image and provide definition characterization for web images. In clinical trials, this approach could be further used to help doctors interpret repetitive images.

This paper proposed a distributed sensor management method in the constraint of target losing probability for maneuvering multi-target tracking. Three main contributions existed in this paper, including adding the emission interception risk to the sensor management objective function, analyzing actual working modes of radars and regarding the target losing probability as one of constraints in sensor management. Two kind of radar working modes were introduced. They were the single target tracking mode and the multiple target tracking mode, leading to different kinds of sensor management methods. Then two sensor management models, containing sensor management in a large defending scope and in a small defending scope, were built. After that, the distributed sensor management process was introduced. Finally, simulations were conducted to show the effectiveness and efficiency of the proposed sensor management methods. By applying the proposed sensor management method, targets couldn't be easily lost and radar could be well protected at the same time.

As the demand for more functions and capabilities in the system increases, the application of multiprocessors has brought advantages in many ways. Many systems now have multiprocessors, and safety-critical systems with real-time properties are no exception. In these systems where the satisfaction of real-time properties is directly linked to the safety of life, the predictability of the behavior is very important, and the behavior of the system can be predicted using the schedulability analysis. In this paper, we propose the schedulability analysis of a real-time system in a homogeneous multiprocessor environment through constraint solving approach. First, the constraints that must be satisfied in order for the system to function properly were derived. These include the constraints of the task behavior, the scheduling behavior, and the operating conditions of a homogeneous multiprocessor environment. Once all the constraints were identified, they were encoded in the form of first-order logic expressions. The encoded constraints are then entered into a constraint solver along with a set of tasks. Finally, the solver provides a schedulable answer if the set of tasks satisfies all the constraints.

This paper proposes the models to solve the optimal generator dispatching problem in an islanded Micro grid with different uncertainties in the constraint and in the objective coefficient. The optimal problem with interval in the power balance constraint is considered as a linear parametric optimization problem, focusing on optimal solutions based on the lower and upper ends of this interval. When the coefficients of cost per power unit caused by load shedding are imprecise and expressed as intervals, the proposed model will be based on the two ends of interval and the problem is converted to a two-objective problem. With uncertainties in both constraint and objective coefficient, the problem will be treated as a four-objective one, considering the lower and upper ends of all intervals. All models are expressed in the linear forms and the linearization is carried out by Max-Affine method. To solve this multi-objective problem, the Bellman-Zadeh approach and Particle Swarm Optimization (PSO) algorithm are applied. The rationality of the proposed models is confirmed in the case study with one low voltage Micro Grid.

Due to high performance and low power consumption, heterogeneous processors are widely used in many real-time systems. In these systems, if tasks are not completed before deadline, it will cause disastrous consequences, and thus it is important to provide fault-tolerance. This paper proposes a novel, adaptive and transient fault-tolerant scheduling algorithm to solve the fault-tolerant problem in heterogeneous real-time systems, aiming to improve system reliability within a given deadline. Since task replication is efficient in improving system reliability, the proposed algorithm supports multiple replicas for each primary task and allows the primary tasks and their replicas to be scheduled on the same processor to increase reliability and lower latency. Also, the algorithm can dynamically adjust the number of replicas for each task to accommodate the deadline and ensure higher reliability. Simulated results show that the proposed algorithm can achieve higher reliability in comparison with existing and related fault-tolerant algorithms. To be specific, the proposed algorithm can obtain the reliability of 89.37% whereas the two existing algorithms DB-FTSA and FTSA obtain the reliability of 47.05% and 84.75% for the benchmark of sixty tasks, respectively, to be detailed in Fig. 4 in experiment.

The shearer positioning method with an inertial navigation system (INS) is feasible in a longwall mining face. However, INS error greatly increases with time, which reduces the shearer positioning accuracy. This study aims to improve the shearer positioning accuracy using the shearer motion constraints. Firstly, according to the longwall mining method, two constraints on the shearer velocity and position were obtained. Then, velocity constraint information and position constraint information were modeled to obtain the observation equations in the filter. In order to improve the shearer positioning accuracy, an information filter was utilized to integrate the velocity constraint information and position constraint information. Finally, an experiment was performed to validate the effectiveness of the proposed algorithm. The result showed that the shearer positioning accuracy improved by 56% in the east and 54% in the north.

We describe properties and constructions of constraint-based codes for DNA-based data storage which account for the maximum repetition length and AT/GC balance. Generating functions and approximations are presented for computing the number of sequences with maximum repetition length and AT/GC balance constraint. We describe routines for translating binary runlength limited and/or balanced strings into DNA strands, and compute the efficiency of such routines. Expressions for the redundancy of codes that account for both the maximum repetition length and AT/GC balance are derived.

This paper designs non-adaptive querying policies (NQPs) for the noisy 20 questions game based on error-correction codes. The querying accuracy of a specific NQP is upper bounded by a function of the minimum distance among its codewords. As a result, the row-column constraint is put on codewords of NQPs for scenarios with limited detection to enlarge their minimum distance for improving the querying accuracy, where the limited detection means that only a small number of intervals can be detected at each querying round. Then, it is used to protect the least significant bits in unequal error protection NQPs with linear codes for unlimited detection scenarios. In particular, these structures allow us to deterministically optimize parameters for better querying accuracy. Simulation results show that our methods achieve quantized mean squared error up to several magnitudes compared with the NQPs based on random block coding.

This paper presents an integrated guidance and control (IGC) law for the strapdown homing missile with consideration of the field-of-view (FOV) constraint and actuator saturation. Given that the commonly-required guidance information, such as the inertial line-of-sight (LOS) angle and/or its angular rate, cannot be measured by the strapdown seeker, a detailed IGC model considering the gravity and time-varying missile velocity is first derived based on the only measurable information, the body-LOS (BLOS) angle. Then a novel IGC controller is designed for this model by means of the integral-type Barrier Lyapunov Function (iBLF) based dynamic surface control technique. This IGC controller following the pure tracking principle is capable of forcing the BLOS angle to track the negative angle of attack while satisfying the FOV constraint and actuator saturation in an integrated manner, thereby guaranteeing a precise attack on a stationary ground target. The stability of closed-loop system and the boundedness of constrained BLOS angle are both proved strictly, and the performance of proposed IGC controller is thoroughly testified by method comparisons and Monte-Carlo analysis.

Research and development of Li-Fi (Light Fidelity) in Internet-of-things (IoT) has created significant interest among researchers. The low-cost version of standard color shift keying (CSK) modulation scheme having single photodetector (PD) at the receiver can become an alternative to on-off keying (OOK) in IoT sensor networks as an M -ary CSK modulation scheme provides log2M times more data rate than that of OOK. This work revolves around optimizing the constellation points of single PD based CSK (CSK-1PD) to achieve a more power-efficient modulation scheme. The optimization has been done with and without average white tone constraint for uniform and non-uniform (exponential, Maxwell-Boltzmann, Pareto) source distributions. The constellation design strategy is based on maximizing the minimum distance among the differently distributed symbols. In the case of non-uniform distributions, the constellations points have been optimized by geometrically shaping the points based on their probabilistic shaping (PS). The optimized constellation points (OCPs) of CSK-1PD without white tone constraint provide higher signal-to-noise ratio (SNR) gain at forward error correction (FEC) limit symbol error rate (SER) as compared with strict white tone constraint. Further, the strict white tone constraint has been relaxed by considering the whole white light region in the CIE 1931 chromaticity diagram as white tone. The OCPs for uniform data with relaxed white tone constraint achieves SNR gain similar to without white tone constraint. However, for probabilistically shaped symbols, the SNR gain achieved from OCPs improves from strict white tone constraint but remains less than the without white tone constraint. A novel method to utilize an additional RGB LED at the transmitter side has been proposed and designed to maintain any white tone light in the white light region without degrading the maximum achieved SNR gain.

This article proposes a novel discrete grey wolf optimization for the packing problem, called the two-dimensional strip packing (2DSP) problem without guillotine constraint. The 2DSP involves cutting pieces from a stock sheet with the objective of minimizing waste. To solve the 2DSP problem by the discrete grey wolf algorithm, many strategies are originally proposed. The searching and attacking operators in the algorithm are redesigned to guarantee coding effectiveness. A novel approach to measure the distance between the wolves is presented. In addition, an improved best-fit strategy is developed to solve this packing problem. The best-fit strategy divides the situation into five cases based on the width and length of the rectangle. Computational results on widely used benchmark instances show that the novel discrete grey wolf algorithm can solve the 2DSP problem effectively, and surpasses most of the previously reported meta-heuristic algorithms.

Metasurface based positional modulation design has been introduced recently, where a given modulation pattern can only be received at certain desired positions by the proposed method. However, the magnitude of weight coefficient of each element on metasurface varies from one other, representing an attenuation of the amplitude of incoming signal in different degrees. In this paper, a constant reflection attenuation constraint for incoming signals is proposed for the first time, and the proposed method can be extended with a post-processing process to the ideal case where there is no reflection attenuation.