Search

The cardiac CT and MRI images depict the various structures of the heart, which are very valuable for analyzing heart function. However, due to the difference in the shape of the cardiac images and imaging techniques, automatic segmentation is challenging. To solve this challenge, in this paper, we propose a new constraint-based unsupervised domain adaptation network. This network first performs mutual translation of images between different domains, it can provide training data for the segmentation model, and ensure domain invariance at the image level. Then, we input the target domain into the source domain segmentation model to obtain pseudo-labels and introduce cross-domain self-supervised learning between the two segmentation models. Here, a new loss function is designed to ensure the accuracy of the pseudo-labels. In addition, a cross-domain consistency loss is also introduced. Finally, we construct a multi-level aggregation segmentation network to obtain more refined target domain information. We validate our method on the public whole heart image segmentation challenge dataset and obtain experimental results of 82.9% and 5.5 on dice and average symmetric surface distance (ASSD), respectively. These experimental results prove that our method can provide important assistance in the clinical evaluation of unannotated cardiac datasets.

Transient performance of aero-engine determines the maneuverability of aircraft. The optimal control schedule can tap the potential transient performance with subject to each constraint. Thus the transient time can be minimized with the optimal transient control schedule. This transient schedule is described by a strong constrained and nonlinear problem. It is therefore challenging to present an optimal method to achieve the best transient schedule. Motivated by solving this problem, a surrogate-assisted optimization framework is presented by using the learning capability of neural networks. It is achieved by presenting a sequential ensemble radial basis function (RBF) neural network-based optimization (SERO) algorithm. The advantage of the RBF neural network such as excellent prediction accuracy is taken and integrated into the surrogate-assisted optimization algorithm to improve the algorithm performance. With the application of the proposed scheme, the global optimization schedule is guaranteed for aero-engine transient control. Numerical validation is finally carried out by applying the presented optimization framework to a mixed-flow aero-engine transient control schedule. It is demonstrated that the SERO approach can minimize the transient process time despite any constraint at any time.

For deep networks, accurate image similarities cannot be well characterized with limited iterations, so the latent relationships between images can be embedded to enhance image retrieval performance. In this article, we propose a method named DFLLR to learn deep image features and accurate image similarities for remote sensing image retrieval (RSIR) simultaneously. First, the AlexNet is employed to extract high-level semantic features. Second, to obtain accurate image similarities, latent relationships between images are constructed with manifold learning and embedded in the AlexNet model with fully connected layers; in this way, the latent relationships and image features can be jointly learned. Third, to boost the RSIR performance further, the constraints of central and margin for jointly learning latent relationships and image features are integrated into our DFLLR. The central constraint is used to reduce the discrepancy of the latent relationships at the intraclass level and enhance the accuracies of image features. Moreover, the margin constraint is designed to enhance the accuracies of the latent relationships by maximizing the manifold margin between the latent relationships at the intraclass and interclass levels. To validate our method, we perform comprehensive experiments on three publicly available remote sensing image datasets, and the results demonstrate that it significantly outperforms other state-of-the-art methods.

Tomographic synthetic aperture radar (TomoSAR) is a 3-D imaging technology used to overcome the layover problem faced by traditional 2-D SAR systems. The quality of TomoSAR imaging capabilities relates closely to the number of acquisitions (NOAs). However, this number is often quite limited due to cost problems. The previous studies have shown some empirical requirements for the minimum NOAs. In this letter, an analytic constraint between the minimum NOAs and signal-to-noise ratio (SNR) is presented, and this constraint is more precise than the existing empirical one. The signal model is first analyzed, followed by the solution of the Fisher matrix, and finally leads to the constraint between the SNR and the minimum NOAs. The presented approach is evaluated via computer simulations.

The joint inversion of structural constraints is a new and rapidly developing detection technology in comprehensive geophysical interpretation. In this article, a new structural constraint 2-D multiphysical parameter joint inversion algorithm for magnetotelluric (MT), gravity, and magnetic data is developed. The structural constraint term is a combination of cosine dot-gradient (CDG) and joint total variation (JTV) constraints, which not only has characteristics of traditional dot product and cross-gradient structure constraints but also avoids the uncertainty of dot product constraints predicting the gradient direction of the model parameters, overcomes the need for high-order differential approximation of the cross-gradient constraint, ignores the influence of the gradient amplitude of different model parameters on the weight of the structural constraint of different regions, and enhances the reconstruction accuracy of the underground discontinuous interface. To more easily combine multiple optimization algorithms to improve the resolution and computational efficiency of joint inversion, an adaptive inexact structural resemblance (IESR) algorithm is developed to minimize numerical solutions to the objective function. Experimental results have demonstrated that the CDG constraint has a wider use range than the traditional structural constraint, the addition of the JTV constraint can recover the underground discontinuous interface, and an inversion result of higher resolution can be obtained using the adaptive IESR algorithm.

High spatial resolution (HSR) remote sensing images can reflect more subtle changes and more specific types of land use and land cover (LULC) due to the abundant spatial geometric information. In this article, a class-prior object-oriented conditional random field (COCRF) framework consisting of a binary change detection (CD) task and a multiclass CD task is proposed to fill the application gap. In the proposed framework, the class-prior knowledge is used to improve the construction of the unary potential in both the binary and multiclass CD tasks, to reduce the influence of spectral variability. The binary CD result provides a constraint to the multiclass CD result. As a result, both parts have effective interaction. The class posterior probability images of two dates can be obtained automatically with the class-prior knowledge by sample migration. Furthermore, an object constraint described by the class dispersion within the objects is added to improve the smoothness in local objects, while the pairwise potential improves the smoothness of the whole area by using the eight-neighborhood spectral information of the center pixel. By integrating the above approaches, the problems of error accumulation and the manual intervention required in the traditional multiclass CD methods can be relieved. An adaptive parameter estimation strategy is also adopted in the proposed framework, to save the time required for manual parameter setting. The proposed COCRF framework was validated on two HSR remote sensing image data sets, where it achieved a better performance than the other state-of-the-art CD methods.

This paper investigates covert communications with finite blocklength in the presence of multiple randomly distributed wardens, which are modeled as a Poisson point process (PPP). Specifically, the protection area, which is guarded by the transmitter against the wardens, is introduced in order to enhance the covert communication performance. First, the closed-form expressions of covert communication constraint are derived under both the non-colluding and colluding detection strategies over the channels suffering both path-loss and small scaling fading. Then, considering the constraint of maximum available blocklength, the transmit power and the blocklength are jointly optimized to maximize the average effective covert throughput (AECT). Differing from the existing literature with only one warden, it is demonstrated that the increasing of maximum available blocklength causes the improvement of maximum AECT only when the density of warden is small. In addition, our examinations show that the expansion of the protection area indeed improves the maximum AECT, and the improvement under the non-colluding detection strategy is more significant than that under the colluding detection strategy.

This paper studies the distributed convex optimization problem with a common decision variable, a global inequality constraint, and local constraint sets over a time-varying multi-agent network, the objective function of which is a sum of agents' local convex cost functions. To solve such problem, a penalty-based distributed continuous-time subgradient algorithm with time-varying gain is developed for each agent to seek the saddle point of the penalty Lagrangian function. It is shown that an exact primal optimal solution can be obtained with certain assumption on time-varying gain. Moreover, the proposed algorithm adopts fixed-time projection scheme to ensure that for any initial state value, each local state estimate converges to its convex constraint set within fixed time. Finally, numerical examples are provided to show the effectiveness of the theoretical results.

Existing human pose estimation methods have some problems, such as key-point representation quantization error, algorithm inconsistency, and easy-to-introduce unequal quantization error. Addressing these issues, this paper proposes a method of human key-point coordinate representation based on probability distribution perception on a depth convolution neural network. In this method, the perceived probability of the key-point coordinate distribution is extracted by a convolutional neural network, and the cross entropy is formed with the prediction probability distribution as the loss function. Under the constraint of minimizing the Kullback-Leibler (KL) distance between the prediction and the ground truth, the convolution neural network is optimized, and the coordinates of the heat map are finally located. The experimental results show that compared with the traditional method based on a heat map, the proposed method can accelerate the reasoning speed of the model in the COCO training set, and at the same time, it can improve the map by 1.7

Graph-based semisupervised hyperspectral image (HSI) classification methods have obtained extensive attention. In graph-based methods, a graph is first constructed, and then the label propagation is carried out on the constructed graph to obtain the labels for unknown samples. However, the results of label propagation may be unreliable, especially in the case of very limited labeled samples. To address the above problem, we propose dual sparse representation graph-based collaborative propagation (DSRG-CP) for HSI classification. Specifically, DSRG-CP adopts sparse representation (SR) to construct spectral and spatial graphs on spectral and spatial dimensions, respectively. Then, label propagation is performed on two graphs iteratively. In each iteration, only the samples with high classification confidence from one graph are added into another graph as labeled samples for the next label propagation. After several iterations, the labels of unlabeled samples are predicted by fusing the results of label propagation from two graphs. In addition, to make the spectral graph more discriminative, the regularizer of spectral statistical information is added into spectral SR model. To make the classification results more consistent in space, the superpixel block constraint is added into spatial graph model as regularizer. To evaluate the performance of the proposed method, DSRG-CP is compared with several graph-based methods and other state-of-the-art methods. Extensive experiments on real HSI data sets show that DSRG-CP can obtain competitive results for HSI classification.

In this paper, we propose a control framework for intelligent surgical robots under the Remote Center of Motion (RCM). The goal of a surgical robot is to assist surgeons in performing complex surgeries. RCM constraint implies that the surgical tip attached to the end-effector of the surgical robot does not slide away from the point of the incision while performing surgery. Implementation of a control algorithm to comply with RCM constraints is a complicated task because of the nonlinear model of the surgical robots and stringent conditions of accuracy imposed by the patient’s safety. This paper proposes an optimization-driven approach to perform the surgical maneuver under RCM constraints. We then applied a bio-inspired optimization algorithm to solve the problem efficiently. For testing the performance of ZNNBAS, we used MATLAB to simulate a surgical procedure. A 7-DOF surgical robot (KUKA LBR IIWA 7) was used as a test bench for running the simulations. The simulation results show that the ZNNBAS is comparable with BAS, PSO, and GA and efficiently and robustly performed the task commanded maneuvers while enforcing the RCM constraints.

Gravity and magnetic inversion are important methods for comprehensive quantitative interpretation of data obtained in, e.g., mineral, oil and gas, and geothermal exploration. At present, the 3-D joint inversion technology of gravity and magnetic data is facing challenges from large-scale data exploration applications. In this letter, a new algorithm for 3-D joint inversion of gravity and magnetic data with high accuracy and low computational cost is presented. We use the geometric trellis method to perform fast forward calculations and then introduce the sparse constraint and adaptive sensitivity matrix into the model constraint terms. The inexact structural resemblance method is then used to add the cross-gradient constraint penalty term to the objective function. Finally, an algorithm (DS-TGN) combining data-space (DS) and truncated Gauss-Newton (TGN) methods is used to solve the joint inversion objective function. Numerical experiments with synthetic data show that the proposed algorithm can significantly reduce the computational cost and obtain high accuracy density and magnetization models with structural resemblance and sharp boundaries. We also apply the DS-TGN algorithm to data obtained in the area of Greater Khingan in northwestern Heilongjiang, China. The underground density and magnetization distribution results provide a high-resolution geological model for the detection of skarn-type deposits.

Cancer survivability prediction has been of great interest to health professionals and researchers. The task refers to the procedure of estimating the potential survivability according to an individual’s medical history. The difficulty is that raw data is usually subject to some noise, such as missing values. To address this issue, we propose a novel low-rank and sparse representation-based learning algorithm, which consists of two main stages of data self expressiveness and classification. Firstly, in the data self expressiveness stage, raw inputs have been decomposed into one dictionary (which is enforced with a low-rank constraint) and one coefficient matrix (which is sparsely coded), respectively. Secondly, this sparse coefficient matrix is paired with sample labels for training during the classification stage. We further integrate these two stages and formulate them into an optimization problem, which is then solved using an iterative computational strategy. Theoretically, we analyze the convergence of the proposed algorithm. The relationship between the proposed algorithm and existing approaches are also discussed. The efficiency of the proposed algorithm is experimentally verified using several benchmarking classification problems and a public longitudinal dataset. Experimental results demonstrate that the proposed algorithm achieves superior performance in terms of affordable computational complexity and high prediction accuracy, compared to state-of-the-art approaches.

This paper presents a control design method that achieves safety for systems with unmodeled dynamics at the plant input. The proposed method combines control barrier functions (CBFs) and integral quadratic constraints (IQCs). Simplified, low-order models are often used in the design of the controller. Parasitic, unmodeled dynamics (e.g. actuator dynamics, time delays, etc) can lead to safety violations. The proposed method bounds the input-output behavior of these unmodeled dynamics in the time-domain using an alpha-IQC. The alpha-IQC is then incorporated into the CBF constraint to ensure safety. The approach is demonstrated with a simple example.

This article considers the event-triggered control for the active seat suspension system with time-varying full-state constraints. Consider that communication resources may be limited, this article proposes a dynamic relative threshold strategy to reduce the communication burden of actuator and controller. Compared with the fixed value as a trigger condition, the dynamically changing thresholds as trigger conditions are more general and universal. The time-varying full-state constraint problem is solved by using the barrier Lyapunov function. In addition, the radial basis function neural networks are employed to approximate the unknown terms. Then, all signals in the resulted system are bounded, and the Zeno behavior can be avoided successfully. Moreover, all the system states satisfy their corresponding constraint condition. Finally, the feasibility and rationality of this method are proved by the simulation analysis of a real example of a seat suspension system.

The social presence theory in social psychology suggests that computer-mediated online interactions are inferior to face-to-face, in-person interactions. In this paper, we consider the scenarios of organizing in person friend-making social activities via online social networks (OSNs) and formulate a new research problem, namely, Hop-bounded Maximum Group Friending (HMGF), that takes into consideration both existing friendships and the likelihood of new friend making in organization of the targeted in person friend-making social activities. To find a set of attendees for such social activities, HMGF is unique and challenging due to the interplay of the group size, the constraint on existing friendships, and the objective of maximizing the likelihood of friend making. We propose an error-bounded approximation algorithm, MaxGF, to efficiently obtain the solutions very close to the optimal solutions. To boost the performance, we devise two graph-theoretical pruning strategies, namely Neighbor Pruning and Core Pruning, which can effectively avoid redundant graph explorations to improve the performance of HMGF. We conduct a user study to validate our problem formulation and perform extensive experiments on real datasets to demonstrate the efficiency and effectiveness of our proposed algorithm. The experimental results manifest that our proposed algorithms outperform the baselines, including the ILP formulation.

Priced timed automata (PTA) are discrete-event system models with temporal constraints and a cost function and are used to pose optimal scheduling and routing problems. To date, solutions to these problems have been found offline and executed open loop. \soutThe This open-loop control strategy makes it impossible to account for disturbances, i.e., changes in costs or scheduling constraints over time. To address this shortcoming, this work's first contribution is a closed-loop model predictive control (MPC) framework for PTA, enabling decision-making based on real-time model updates. To ensure the feasibility of an MPC problem, it is often desirable to soften constraints. However, the contemporary PTA theory does not consider soft constraints. Thus, this work's second contribution is to integrate constraint softening with PTA control by harnessing the capabilities of new solvers enabled by the recasting of the models and control problem into first-order logic by employing modified encoding schemes based on existing works. Finally, the proposed control framework and implementation are demonstrated in a simulation case study on the guidance of a product through a manufacturing system.

While molecular discovery is critical for solving many scientific problems, the time and resource costs of experiments make it intractable to fully explore chemical space. Here, we present a generative modeling framework that proposes novel molecules that are 1) based on starting candidate structures and 2) optimized with respect to one or more objectives or constraints. We explore how this framework performs in an applied setting by focusing on the problem of optimizing molecules for aqueous solubility, using an experimental database containing data curated from the literature. The resulting model was capable of improving molecules with a range of starting solubilities. When synthetic feasibility was applied as a secondary optimization constraint (estimated using a combination of synthetic accessibility and retrosynthetic accessibility scores), the model generated synthetically feasible molecules 83.0% of the time (compared with 59.9% of the time without the constraint). To validate model performance experimentally, a set of candidate molecules was translated using the model and the solubilities of the candidate and generated molecules were verified experimentally. We additionally validated model performance via experimental measurements by holding out the top 100 most soluble molecules during training and showing that the model could rediscover 33 of those molecules. To determine the sensitivity of model performance to dataset size, we trained the model on different subsets of the initial training dataset. We found that model performance did not decrease significantly when the model was trained on a random 50% subset of the training data but did decrease when the model was trained on subsets containing only less soluble molecules (i.e., the bottom 50%). Overall, this framework serves as a tool for generating optimized, synthetically feasible molecules that can be applied to a range of problems in chemistry and chemical engineering.

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.

We propose an optimal currency hedging strategy for global equity investors using currency value, carry, and momentum to proxy for expected currency returns. A benchmark risk constraint ensures the overlay closely mimics a fully hedged portfolio. We compare this with naïve and alternative hedges in a demanding out-of-sample test, with transaction and rebalancing costs and margin requirements. Other hedging methods generally reduce risk but at a cost. Some tend to short currencies with high returns and all incur substantial costs with frictions, mostly margin requirements and equity rebalancing costs. The proposed strategy uses predictable returns to reduce this cost. It produces a statistically significant 17% gain in Sharpe ratio and an annualized Jensen-α of 0.93% versus a fully hedged benchmark. Notably, most of the implementation costs of the strategy would be incurred by the benchmark anyway. This reduces its marginal cost and highlights a specific synergy of integrating hedging with speculation. This paper was accepted by Gustavo Manso, finance.

For the problem of target localization under the multipath propagation environment, the existing methods are mainly restricted to the limited prior information of complex reflections, especially when the target is embedded in a mixed interference environment. They may suffer from performance degradation due to the shortage of target classification ability. To address this problem, we propose a target localization method based on iterative implementation with semi-unitary constraint and eigen-decomposition technique, where a practical propagation scenario based on the spherical earth model is considered. Compared to the previous works, the proposed method can automatically distinguish a real target from the mixed interference environment with improved localization accuracy. Neither additional decorrelation preprocessing nor prior information of the dynamic scenario is required. Both simulations and real data experiments validate the effectiveness and robustness of the proposed method.

Due to the rapid development of science and technology, the importance of imprecise, noisy, and uncertain data is increasing at an exponential rate. Thus, mining patterns in uncertain databases have drawn the attention of researchers. Moreover, frequent sequences of items from these databases need to be discovered for meaningful knowledge with great impact. In many real cases, weights of items and patterns are introduced to find interesting sequences as a measure of importance. Hence, a constraint of weight needs to be handled while mining sequential patterns. Besides, due to the dynamic nature of databases, mining important information has become more challenging. Instead of mining patterns from scratch after each increment, incremental mining algorithms utilize previously mined information to update the result immediately. Several algorithms exist to mine frequent patterns and weighted sequences from incremental databases. However, these algorithms are confined to mine the precise ones. Therefore, in this work, we develop an algorithm to mine frequent sequences in an uncertain database in this work. Furthermore, we propose two new techniques for mining when the database is incremental. Extensive experiments have been conducted for performance evaluation. The analysis showed the efficiency of our proposed framework.

The Takagi-Sugeno-Kang fuzzy system has wide applications across different areas, e.g., regression, classification and decision making, attributed to its high precision and interpretability . However, the existing Takagi-Sugeno-Kang fuzzy system is not an ideal solution to some special scenarios, particularly for those that are constrained monotonically. To this end, a monotonic relation-constrained Takagi-Sugeno-Kang fuzzy system classifier is proposed in this paper. The proposed method introduces a monotonic relation between the inputs and the outputs, where the objective function is expressed in a monotonically constrained form and a strategy for generating monotonicity constraint pairs is developed. Furthermore, to address the convexity loss caused by the increasing monotonicity constraints, the proposed method introduces the Tikhonov regularization strategy to ensure the uniqueness and boundedness of the solution. The results from extensive experiments show that the proposed method exhibits better classification performance than the original Takagi-Sugeno-Kang fuzzy system and state-of-the-art monotonic classification methods in handling monotonic datasets.

This letter proposes a set-theoretic Failure Mode and Effect Management (FMEM) strategy that handles stuck/jammed actuators and enforces pointwise-in-time state and control constraints. The approach exploits nesting between constraint admissible and recoverable sets to ensure the existence of a recovery sequence. A reference governor is applied to track reference commands while imposing constraint satisfaction using the remaining working actuators. Numerical results of an aircraft longitudinal flight application are reported.

This letter investigates the multi-input multi-output (MIMO) covert communication aided by intelligent reflecting surface (IRS) against a multi-antenna warden. We establish a design framework for IRS-assisted MIMO covert communication system, which aims to maximize covert rate by jointly optimizing the transmit covariance matrix at transmitter and phase shift matrix at IRS. Due to the unit modulus constraint of and the highly coupling of and , we propose an alternating optimization algorithm to solve it. Specifically, the sub-problem of optimizing for given is convex, and we can derive a closed-form solution. Then, we propose a minorization-maximization algorithm to tackle the sub-problem of optimizing for fixed , where we translate the problem to the maximization of a proper lower bound of the objective function which is then solved by the sequential rank-one constraint relaxation algorithm. Simulation results demonstrate the effectiveness and superiority of the proposed algorithm in terms of boosting covert rate under the surveillance of a multi-antenna warden.

Subsurface lithofacies classification refers to the way of establishing a classifier on the interpreted well logging data to predict the lithofacies types corresponding to the uninterpreted ones. Such a task has become a research focus by applying machine learning technologies under the assumption of independent and identical distribution. However, due to the differences in, such as sedimentary environments, reservoir heterogeneity, and logging equipments, the same lithofacies might exhibit different logging characteristics between two different wells or even two different strata in one well. Therefore, motivated by the data drift issue and inspired by the domain adaptation methods, we propose the robust unilateral alignment (RUA) for lithofacies classification. The characteristics of the proposed RUA are as follows: 1) the projected maximum mean discrepancy (PMMD) is designed to reduce the marginal and conditional distribution discrepancy; 2) the random data mapping and target domain information preserving constraint is adopted to embody the data transformation model; and 3) the weighting mechanism and risk-aware constraint are introduced to solve the class imbalance and iterative risk problems. The experiments conducted on the data sets from Jiyang Depression, Bohai Bay Basin, verify the superior performances in accuracy and stability over the existing work.

We propose a novel constraint-handling technique for the covariance matrix adaptation evolution strategy (CMA-ES). The proposed technique is aimed at solving explicitly constrained black-box continuous optimization problems, in which the explicit constraint is a constraint whereby the computational time for the constraint violation and its (numerical) gradient are negligible compared to that for the objective function. This method is designed to realize two invariance properties: invariance to the affine transformation of the search space, and invariance to the increasing transformation of the objective and constraint functions. The CMA-ES is designed to possess these properties for handling difficulties that appear in black-box optimization problems, such as non-separability, ill-conditioning, ruggedness, and the different orders of magnitude in the objective. The proposed constraint-handling technique (CHT), known as ARCH, modifies the underlying CMA-ES only in terms of the ranking of the candidate solutions. It employs a repair operator and an adaptive ranking aggregation strategy to compute the ranking. We developed test problems to evaluate the effects of the invariance properties, and performed experiments to empirically verify the invariance of the algorithm. We compared the proposed method with other CHTs on the CEC 2006 constrained optimization benchmark suite to demonstrate its efficacy. Empirical studies reveal that ARCH is able to exploit the explicitness of the constraint functions effectively, sometimes even more efficiently than an existing box-constraint handling technique on box-constrained problems, while exhibiting the invariance properties. Moreover, ARCH overwhelmingly outperforms CHTs by not exploiting the explicit constraints in terms of the number of objective function calls., Accepted to Evolutionary Computation (MIT Press)

The dynamics of microorganisms in a bioreactor are described by population systems. The tracking of the biomass of a desired trajectory is ensured by means of a nonlinear control law, as long as the reference trajectory complies with certain naturally given inequality constraints. To generate such a reference trajectory, an optimal control problem based on a double integrator with nonlinear state inequality constraints is solved. The necessary optimality criteria are extracted and the differential equations are solved. Using these solutions and parameter assumptions, which are shown to be non-limiting, it is proven that at most one constraint at a time is active. In addition, it is shown that the intervals in which no constraint is active and those in which one constraint is active alternate.

To support the massive connectivity in Internet of Things (IoT), several promising techniques like cognitive radio (CR) and non-orthogonal multiple access (NOMA) enables the user to share spectrum resources. This work aims to achieve fairness among secondary users (SUs) in IoT cooperative NOMA-based CR transmission. We design a power allocation algorithm, an independent battery constraint at each node is considered, and power gap among transmissions of two NOMA users is applied for successive interference cancellation. The simulation results show that the proposed framework provides excellent performance and for sufficient available transmission power perfect fairness is achieved.

Accurate transient stability assessment (TSA) and effective preventive control are important for the stable operation of power systems. With the superiorities in precision and efficiency, data-driven methods are widely used in TSA nowadays. Data-driven TSA model can be adopted in the stability constraint of preventive control optimization, but existing methods are mostly iteration-based methods, which may result in low efficiency, sometimes even non-convergence. In this paper, an analytical representation method of data-driven transient stability constraint is proposed based on a nonparametric regression model built for TSA. Key feature extraction and dominant sample selection are proposed to reduce the scale of the TSA model, and bi-level linearization is applied to further modify it. Optimal preventive control model is then formulated as a Mixed-integer Linear Program (MILP) problem with the linearized analytical data-driven transient stability constraint, which can be solved without iterations. An overall procedure of data-driven TSA and preventive control is finally developed. Case studies show that the proposed method has high accuracy in TSA and can achieve effective preventive control of power system with high efficiency.

Made available in DSpace on 2021-06-25T11:02:05Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-01-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) In this paper, new conditions for the stabilisation and transient performance improvement of linear parameter-varying (LPV) systems considering the gain-scheduling (GS) strategy are proposed. Our work is focused on dealing with LPV systems under the major practical constraint of incomplete state measurement. In that sense, we propose two new control design strategies based on linear matrix inequalities (LMI). First, for coping with the general case where only a subset of the state variables is measured, we propose a new static output feedback (SOF) strategy. Second, for dealing with the particular case where only accelerometers signals are available, we bring new synthesis conditions for the design of state derivative feedback (SDF) controllers. Further from stability, our proposed methods are able to induce better transient response by including pole placement LMI constraints in the control design. For illustrating our contribution efficacy, we present a couple of design examples. São Paulo State University (UNESP) School of Engineering Ilha Solteira - SP Brazil Department of Electrical Engineering, José Carlos Rossi Ave, 1370 São Paulo State University (UNESP) School of Engineering Ilha Solteira - SP Brazil Department of Electrical Engineering, José Carlos Rossi Ave, 1370 FAPESP: #2018/20839-9 CNPq: 301227/2017-9 CNPq: 309872/2018-9

In this paper, we consider the predefined-time distributed optimization problems of homogeneous and heterogeneous linear multi-agent systems under undirected and connected communication topologies, in which, all agents share coupled equality constraint. In order to make all agents converge to the optimal output at predefined-time cooperatively, we propose two distributed algorithms for homogeneous and heterogeneous multi-agent systems according to time-base generator technology and output feedback, therein, the optimal output can make the global cost function reach minimum. In the design of the algorithms, the control gains are not required, which can avoid the requirement of some global information in advance. Furthermore, all agents converge to the optimal output with exponential speed, and we can set the convergence time arbitrarily. Finally, we provide examples to illustrate the effectiveness of the proposed distributed algorithms.

This letter proposed a spatial and spectral anisotropic tensor total variation (SSATTV) driven adaptive pansharpening model for the fusion of low-resolution (LR) multispectral (MS) and panchromatic (Pan) images to the high-resolution (HR) MS images. Except for the local spectral consistency constraint-based fidelity term between HR and LR MS used for spectral preservation, the proposed model reformulated the adaptive linear constraint-based fidelity term between Pan and HR MS with the tensor-representation modeling, and particularly proposed a SSATTV prior term which imposed the spatial anisotropic TV prior between HR MS and Pan for spatial gradient feature preservation, and the spectral TV sparsity prior on HR MS for further spectral feature preservation. Furthermore, the proposed model was efficiently solved via the alternating direction method of multipliers (ADMM) scheme. Specifically, the experiments validated the superiority of proposed SSATTV method.

Complete complementary sequence (CCS) design has become an important topic in recent years due to its theoretically ideal zero sidelobe performance. However, the main obstacle for the widespread use of the CCS is its sensitivity to the Doppler shift. In this letter, a method for constructing the CCS with the desired matrix-valued ambiguity functions under a peak-to-average-power ratio (PAR) and an energy constraint is proposed. A gradient-based nonlinear iterative algorithm is used to solve the constrained sequence design problem. Numerical results are presented to show that, compared with the state-of-the-art methods, the proposed method can produce CCS with better correlation properties and the Doppler tolerance. Finally, indoor experiments are conducted to verify the superior performance of the designed CCS, and a peak sidelobe level under -45 dB is realized on the hardware system.

Only few studies have addressed how soccer goalkeepers adapt to both the spatial and temporal constraints while trying to stop a penalty kick. Moreover, research on penalty kicks has only involved male goalkeepers, even though the resultant constraint (i.e., the relation between the maximum action capabilities defining the time required and ball flight time and direction defining the time available) may or may not differ for female goalkeepers. We therefore compared penalty goalkeeping between male and female goalkeepers of similar skill level. The results showed that the resultant constraint was more stringent for female than male goalkeepers. In accordance with the affordance-based control theory, female and male goalkeepers both adapted to the resultant constraint, but did scale their diving action differently. Female goalkeepers initiated the lateral dive within the action boundaries set by the resultant constraint, while male goalkeepers tended to dive late, beyond the action boundaries. Owing to the early dive, female goalkeepers saved more penalties than male in non-deceptive penalties. Nevertheless, female goalkeepers were also more susceptible to deception by the penalty taker than male goalkeepers. These findings extend our knowledge of the affordance-based control theory in sports and contribute to the understanding of gender differences in soccer penalty goalkeeping.

Owing to easy acquisition and large coverage from the space, multispectral (MS) imaging has garnered growing interest in various applications of remote sensing. However, the limited spectral information of MS data, to a great extent, leads to difficulties in classifying the materials more accurately, particularly for those classes that have very similar visual appearances. To address this issue effectively, we attempt to enhance the spectral resolution of MS imagery, enabling the identification of materials at a more precise level by the means of richer spectral information. More specifically, we propose to learn locality-constrained sparse coding (LCSC) for short, on partially overlapped hyperspectral (HS)-MS pairs (i.e., dictionary). LCSC is capable of capturing neighboring relations well by enforcing the local constraint for each pixel. Such a strategy makes it possible to better reconstruct HS products from MS images and partially overlapped HS images. Reconstruction and unmixing are explored as potential applications to assess the performance of spectral enhancement. Extensive experiments are conducted on two HS-MS data sets in comparison with several state-of-the-art baselines, which demonstrate the effectiveness of the proposed LCSC algorithm in the task of spectral enhancement.

Directly grasping the tightly stacked objects may cause collisions and result in failures, degenerating the functionality of robotic arms. Inspired by the observation that first pushing objects to a state of mutual separation and then grasping them individually can effectively increase the success rate, we devise a novel deep Q-learning framework to achieve collaborative pushing and grasping. Specifically, an efficient non-maximum suppression policy (policyNMS) is proposed to dynamically evaluate pushing and grasping actions by enforcing a suppression constraint on unreasonable actions. Moreover, a novel data-driven pushing reward network called PR-Net is designed to effectively assess the degree of separation or aggregation between objects. To benchmark the proposed method, we establish a dataset containing common household items dataset (CHID) in both simulation and real scenarios. Although trained using simulation data only, experiment results validate that our method generalizes well to real scenarios and achieves a 97% grasp success rate at a fast speed for object separation in the real-world environment.

In this paper, a Crank-Nicolson block-centered finite difference method is first developed and analyzed for the nonlinear regularized long wave equation. By using a cutoff technique, second-order convergences both in time and space are proved under a suitable time-space stepsize constraint condition. To further improve the computational efficiency, an efficient two-grid block-centered finite difference method is introduced and analyzed, in which a resulting small-scale nonlinear problem is first solved on a coarse grid space of size H, and then a resulting large-scale linear problem is solved on a fine grid space of size h. Under a rough time-space stepsize constraint condition Δ t = o ( H 1 / 4 ) , optimal-order error estimates for both the primal variable and its flux are derived on non-uniform spatial grids. Thus, the proposed method is competitive both in accuracy and efficiency compared with the fully nonlinear Crank-Nicolson block-centered finite difference scheme. Numerical experiments are presented to verify the theoretical analysis.

With the rapid development of mobile networks and e-commerce, clothing recommendation has achieved considerable success in recent years. Fashion outfit matching has become an essential component to users while shopping, which helps users to select and present items to individuals in a personalized fashion recommendation. Apparently, it is an arduous task to guide complementary clothing matching due to the complexity and subjectivity of fashion items. Some existing solutions have been presented in recent years, which are tending to discover a series of visual cues to establish the matching relations. However, it would be mismatched easily due to these methods being hard to represent all the potential semantic information from the appearance of clothes. To thoroughly make use of the visual characteristics of clothing products and the related description information, we propose a complementary clothing matching method with some compatibility knowledge, named it CCMCK shortly. For visual compatibility, we adopt the graph neural network to model the visual relationship between items. To generate an outfit that satisfies the requirement of fashion compatibility, we propose a matching way under the compatibility constraint and seek to recommend compatible items based on multi-modal compatibility. Finally, we performed a qualitative investigation on the fill-in-the-blank and fashion outfit compatibility tasks to evaluate the proposed method.

This work tackles the joint optimization of a unmanned aerial vehicle's (UAV's) three-dimensional (3D) placement and transmit power for covert communications with the objective to maximize the communication covertness subject to a constraint on communication quality. We analytically prove that the optimal horizontal location of the UAV must be on the straight line connecting the legitimate receiver and the warden, while its optimal height is the minimum allowable flying altitude. Subsequently, we derive closed-form expressions to characterize the UAV's optimal 3D location and its optimal transmit power, taking into account the impact of finite blocklength coding. Our numerical results show that the developed optimal design can achieve a better covertness performance compared with an optimal two-dimensional (2D) UAV deployment, especially when the warden is close to the receiver. Besides, we show that, for a given requirement on the communication quality, communication covertness does not always improve as the number of channel uses increases.

The university courses timetabling problem (UCTP) is a popular subject among institutions and academics because occurs every academic year. In general, UCTP is the distribution of events through slots time for each room based on the list of constraints for instance (hard constraint and soft constraint) supplied in one semester, intending to avoid conflicts in such assignments. Under no circumstances should hard constraints be broken while attempting to fulfill as many soft constraints as feasible. this article presented a modified best-nests cuckoo search (BNCS) algorithm depend on the base cuckoo search (CS) algorithm. BNSC algorithm was achieved by dividing the nests into two groups (best-nests and normal-nests). The BNCS algorithm selection was limited to the best-nests to generate new solutions. The comparison between BNCS and basic CS based on the experimental result is achieved. For performance evaluation, the BNCS has been tested on four variant-size datasets. It was observed that the BNCS has performed high performance and is faster at finding a solution from CS.

We investigated gaze behavior of expert goalkeepers during the prediction of penalty kicks in different spatiotemporal constraints: penalties taken from 11 and 6 m. From 11 m, goalkeepers were more successful in predicting ball direction, with longer movement time initiation and a visual strategy with more fixations and greater saccade rates than penalties from 6 m, where they exhibited fewer fixations with higher microsaccade rates. As long as the opponent’s distance is large and time pressure low, gaze can be frequently shifted between the kicker’s body and the ball, due to the low cost of saccades. Conversely, when the objects are close, there is increased reliance on foveal and parafoveal information. In conclusion, when the spatiotemporal constraint is less severe, goalkeepers adopt a visual strategy with more fixations and small saccades. When the spatiotemporal constraint is more severe, they rely on peripheral vision to monitor kickers’ movements through the use of microsaccades.

Efficient and stable operation (ESO) of the proton exchange membrane fuel cell (PEMFC) system is challenging because the nonlinearity of system not only aggravates the difficulty of performance analysis but also puts forward higher requirements for controller design. In this article, a hierarchical performance enhancement control strategy (HPECS) is proposed, which can deal with optimal trajectory seeking under multiobjective constraints and nonlinear problem of system separately. In upper optimization level, the multiobjective optimization based on dynamic adjustment of weighting factor is proposed to seek optimal trajectory for ESO of PEMFC system considering the harm of oxygen starvation, oxygen saturation to system, and contradiction characteristic between net power and efficiency; in lower control level, a data-driven constraint adaptive predictive control is proposed for optimal trajectory tracking with the nonlinearity of system. The proposed HPECS is implemented and validated on the experimental PEMFC system. The comparative studies show that the proposed HPECS is superior in performance enhancement, disturbance rejection, and tracking ability. The normalized multiobjective optimization function can be basically guaranteed to above 0.9, and a nearly 7.2% enhancement in average can be also observed, which shows that ESO of PEMFC system can be achieved in changeable working conditions under proposed HPECS.

We introduce new shape-constrained classes of distribution functions on R , the bi- s ∗ -concave classes. In parallel to results of Dumbgen et al. (2017) for what they called the class of bi-log-concave distribution functions, we show that every s -concave density f has a bi- s ∗ -concave distribution function F for s ∗ ≤ s ∕ ( s + 1 ) . Confidence bands building on existing nonparametric confidence bands, but accounting for the shape constraint of bi- s ∗ -concavity, are also considered. The new bands extend those developed by Dumbgen et al. (2017) for the constraint of bi-log-concavity. We also make connections between bi- s ∗ -concavity and finiteness of the Csorgő–Revesz constant of F which plays an important role in the theory of quantile processes.

In this letter, we study the robust beamforming design for the reconfigurable intelligent surface (RIS)-assisted communication systems from a multi-antenna access point (AP) to a single-antenna user under imperfect channel state information (CSI), where the RIS has only a finite number of phase shifts at each element. In particular, considering the cascaded AP-RIS-user channel estimation error, we aim to jointly optimize the transmit beamforming at the AP and discrete phase shifts of RIS to minimize the transmission power of AP, subject to a signal-to-noise ratio constraint at the user. To tackle the non-convex optimization problem, we decouple it into two subproblems and propose a novel alternative optimization framework. More specifically, the robust beamforming of AP is first obtained through S-Procedure and semidefinite relaxation. Then we recast the discrete phase shift constraint at RIS into an equivalent convex one, and propose an efficient algorithm to obtain near optimal solution. Finally, the two-subproblems are iteratively optimized to obtain the joint robust beamforming. Simulation results show that the proposed scheme can approach the performance of the perfect CSI counterpart, and substantially outperforms traditional non-robust methods.

Purpose Human-like musculoskeletal robots can fulfill flexible movement and manipulation with the help of multi joints and actuators. However, in general, sophisticated structures, accurate sensors and well-designed control are all necessary for a musculoskeletal robot to achieve high-precision movement. How to realize the reliable and accurate movement of the robot under the condition of limited sensing and control accuracy is still a bottleneck problem. This paper aims to improve the movement performance of musculoskeletal system by bio-inspired method. Design/methodology/approach Inspired by two kinds of natural constraints, the convergent force field found in neuroscience and attractive region in the environment found in information science, the authors proposed a structure transforming optimization algorithm for constructing constraint force field in musculoskeletal robots. Due to the characteristics of rigid-flexible coupling and variable structures, a constraint force field can be constructed in the task space of the musculoskeletal robot by optimizing the arrangement of muscles. Findings With the help of the constraint force field, the robot can complete precise and robust movement with constant control signals, which brings in the possibility to reduce the requirement of sensing feedback during the motion control of the robot. Experiments are conducted on a musculoskeletal model to evaluate the performance of the proposed method in movement accuracy, noise robustness and structure sensitivity. Originality/value A novel concept, constraint force field, is proposed to realize high-precision movements of musculoskeletal robots. It provides a new theoretical basis for improving the performance of robotic manipulation such as assembly and grasping under the condition that the accuracy of control and sensory are limited.

In this paper, the consensus problem is considered for multi-agent systems with input constraint under directed graphs, including leaderless and leader-following cases. Different from existing related works, the distinct feature of this paper is that both the amplitude and rate of the agents’ input are ensured in the given ranges. For the leaderless case, the saturation control strategy is designed and employed for multi-agent systems consensus with the aid of a novel saturation function. For the leader-following case, the saturation-function-based distributed observer as well as the observer-based saturation controller are proposed to achieve consensus. Finally, simulation results show the effectiveness of the designed methods.

The available literature on heat pumps shows that thermodynamic models lead to minimization of energy expense, very often in endoreversible configurations, sometimes irreversibles. The present paper purpose is to revisit and complete the heat pumps irreversible model by adding the coupling constraint between the cycle and the thermal reservoirs assumed to be thermostats. This constraint is in fact an essential condition for the cycle existence, practically always ignored. This modeling leads to a set of new results, including new expressions for the COP corresponding to minimum energy expense. The fundamental consequences of this new approach also show the limits of the equipartition theorem of heat exchange area, as well as of the entropy minimization method.

Many studies have been conducted on single lane detection, but multi-lane detection is rarely addressed. The latter is more advantageous for applications such as autonomous navigation, unmanned vehicles, departure warning, and cruise control. In this paper, we propose a novel and robust multiple lane detection algorithm based on the road structure information, which contains five complementary constraints: length constraint, parallel constraint, distribution constraint, pair constraint and uniform width constraint. All the five constraints are incorporated into a Hough transform (HT) based unified framework to select lane candidates. Nearly 99% of the false alarm candidates in HT space can be removed. Moreover, a dynamic programming strategy is proposed to find the most rational solutions among the remaining candidates. This strategy can effectively deal with combination complexity and interferences introduced by multi-lane detection. Experimental results on the benchmark dataset and other collected data demonstrate that the proposed method can outperform the state-of-the-art approaches in both accuracy and efficiency.