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The recycling bio-waste shells problem has grown more and more serious in recent years and many efforts have been made to solve this problem. One possible solution is to put these bio-shells into concrete and recycle them as building materials using the aggregate matrix concrete approach. To verify the engineering feasibility, the mechanical properties of bio-shells aggregated concrete were invested via gradient substitution rates at 10%, 30%, and 50% with a total of 78 groups of specimens in this paper. Our results show that the mechanical properties of the concrete were enhanced in maximum flexural strength and maximum compressive. Economic performance was also analyzed and found that the costs of frame-shear structure, frame structure, and tube-in-tube structure were reduced by 10.2%, 10%, and 10.3%. The carbon environmental assessment also shows superiority in the carbon reduction of a single specimen with various rates of the shell. In summary, compared with ordinary concrete materials, it is very possible to use waste bio-shells as a substitute for aggregates to develop the sustainable recycling development of concrete materials. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a Reduced-Dimension Multiple-Signal Classification (RD-MUSIC) technique via Higher-Order Orthogonal Iteration (HOOI), which facilitates the estimation of the target range and angle for Frequency-Diverse Array Multiple-Input–Multiple-Output (FDA-MIMO) radars in the unfolded coprime array with unfolded coprime frequency offsets (UCA-UCFO) structure. The received signal undergoes tensor decomposition by the HOOI algorithm to get the core and factor matrices, then the 2D spectral function is built. The Lagrange multiplier method is used to obtain a one-dimensional spectral function, reducing complexity for estimating the direction of arrival (DOA). The vector of the transmitter is obtained by the partial derivatives of the Lagrangian function, and its rotational invariance facilitates target range estimation. The method demonstrates improved operation speed and decreased computational complexity with respect to the classic Higher-Order Singular-Value Decomposition (HOSVD) technique, and its effectiveness and superiority are confirmed by numerical simulations. [ABSTRACT FROM AUTHOR]

As a new abstract computational model in evolutionary transfer optimization (ETO), single-objective to multi-objective optimization (SMO) is conducted at the macroscopic level rather than the intermediate level for specific algorithms or the microscopic level for specific operators; this method aims to develop systems with a profound grasp of evolutionary dynamic and learning mechanism similar to human intelligence via a "decomposition" style (in the abstract of the well-known "Transformer" article "Attention is All You Need", they use "attention" instead). To the best of our knowledge, it is the first work of SMO for discrete cases because we extend our conference paper and inherit its originality status. In this paper, by implementing the abstract SMO in specialized memetic algorithms, key knowledge from single-objective problems/tasks to the multi-objective core problem/task can be transferred or "gathered" for permutation flow shop scheduling problems, which will reduce the notorious complexity in combinatorial spaces for multi-objective settings in a straight method; this is because single-objective tasks are easier to complete than their multi-objective versions. Extensive experimental studies and theoretical results on benchmarks (1) emphasize our decomposition root in mathematical programming, such as Lagrangian relaxation and column generation; (2) provide two "where to go" strategies for both SMO and ETO; and (3) contribute to the mission of building safe and beneficial artificial general intelligence for manufacturing via evolutionary computation. [ABSTRACT FROM AUTHOR]

In the environment of unknown mutual coupling, many works on direction-of-arrival (DOA) estimation with sensor array are prone to performance degradation or even failure. Moreover, there are few literatures on off-grid direction finding using regularized sparse recovery technology. Therefore, the scenario of off-grid DOA estimation in sensor array with unknown mutual coupling is investigated, and then a reweighted off-grid Sparse Spectrum Fitting (Re-OGSpSF) approach is developed in this article. Inspired by the selection matrix, an undisturbed array output is formed to remove the unknown mutual coupling effect. Subsequently, a refined off-grid SpSF (OGSpSF) recovery model is structured by integrating the off-grid error term obtained from the first-order Taylor approximation of the higher-order term into the underlying on-grid sparse representation model. After that, a novel Re-OGSpSF framework is formulated to recover the sparse vectors, where a weighted matrix is developed by the MUSIC-like spectrum function to enhance the solution's sparsity. Ultimately, off-grid DOA estimation can be realized with the help of the recovered sparse vectors. Thanks to the off-grid representation and reweighted strategy, the proposed method can effectively and efficiently achieve high-precision continuous DOA estimation, making it favorable for real-time direction finding. The simulation results validate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have been shown to have the most advanced performance on image segmentation tasks. However, designing a reasonable convolutional neural network architecture requires a great deal of empirical knowledge, which can be expensive or unavailable in a variety of actual applications. To handle this problem, neural architecture search (NAS) has been developed by simultaneously optimizing the network architecture and hyperparameters, the main disadvantage of which is the requirement for extremely high computational resources. To accelerate the NAS of CNNs, a two-stage adaptive lightweight convolutional neural architecture search (AL-CNAS) method is proposed in this article based on an evolutionary algorithm. In this method, the original search space is divided into two parts based on feature grouping, and only one part is optimized at each stage to achieve an exponential decrease in the number of genotype combinations, which helps to improve search efficiency and reduce computational resource requirements. The proposed AL-CNAS is validated on the real-world segmentation of steel microstructure and two benchmark segmentation problems, and the computational results show that the proposed AL-CNAS has good segmentation performance and generality. [ABSTRACT FROM AUTHOR]

Weld lines are a common defect generated in injection molding, which apparently affects the performance of final products, but the available reports on carbon fiber-reinforced thermoplastics are still rather few. In this study, the effects of injection temperature, injection pressure, and fiber content on the mechanical properties of weld lines were studied for carbon fiber-reinforced nylon (PA-CF) composites. The weld line coefficient was also calculated by comparing specimens with and without weld lines. The tensile and flexural properties of PA-CF composites significantly increased with the rise of fiber content for specimens without weld lines, while injection temperature and pressure demonstrated slight influences on mechanical properties. However, the existence of weld lines had negative influences on the mechanical properties of PA-CF composites due to poor fiber orientation in weld line regions. The weld line coefficient of PA-CF composites decreased as fiber content increased, indicating that the damage of weld lines to mechanical properties increased. The microstructure analysis showed that there were a large number of fibers distributed vertically to flow direction in weld lines regions, which could not play a reinforcing role. In addition, increasing injection temperature and pressure facilitated fiber orientation, which improved the mechanical properties of composites with low fiber content, while weakening composites with high fiber content instead. This article provides practical information for product design containing weld lines, which helps to optimize the forming process and formula design of PA-CF composites with weld lines. [ABSTRACT FROM AUTHOR]

Since there is a frequency offset between each adjacent antenna of FDA radar, there exists angle-range two-dimensional dependence in the transmitter. For bistatic FDA-multiple input multiple output (MIMO) radar, range-direction of departure (DOD)-direction of arrival (DOA) information is coupled in transmitting the steering vector. How to decouple the three information has become the focus of research. Aiming at the issue of target parameter estimation of bistatic FDA-MIMO radar, a real-valued parameter estimation algorithm based on high-order-singular value decomposition (HOSVD) is developed. Firstly, for decoupling DOD and range in transmitter, it is necessary to divide the transmitter into subarrays. Then, the forward–backward averaging and unitary transformation techniques are utilized to convert complex-valued data into real-valued data. The signal subspace is obtained by HOSVD, and the two-dimensional spatial spectral function is constructed. Secondly, the dimension of spatial spectrum is reduced by the Lagrange algorithm, so that it is only related to DOA, and the DOA estimation is obtained. Then the frequency increment between subarrays is used to decouple the DOD and range information, and eliminate the phase ambiguity at the same time. Finally, the DOD and range estimation automatically matched with DOA estimation are obtained. The proposed algorithm uses the multidimensional structure of high-dimensional data to promote performance. Meanwhile, the proposed real-valued tensor-based method can effectively cut down the computing time. Simulation results verify the high efficiency of the developed method. [ABSTRACT FROM AUTHOR]

In this paper, the problem of direction-of-arrival (DOA) estimation for strictly noncircular sources under the condition of unknown mutual coupling is concerned, and then a robust real-valued weighted subspace fitting (WSF) algorithm is proposed via block sparse recovery. Inspired by noncircularity, the real-valued coupled extended array output with double array aperture is first structured via exploiting the real-valued conversion. Then, an efficient real-valued block extended sparse recovery model is constructed by performing the parameterized decoupling operation to avoid the unknown mutual coupling and noncircular phase effects. Thereafter, the WSF framework is investigated to recover the real-valued block sparse matrix, where the spectrum of real-valued NC MUSIC-like is utilized to design a weighted matrix for strengthening the solutions sparsity. Eventually, DOA estimation is achieved based on the support set of the reconstructed block sparse matrix. Owing to the combination of noncircularity, parametrized decoupling thought, and reweighted strategy, the proposed method not only effectively achieves high-precision estimation, but also efficiently reduces the computational complexity. Plenty of simulation results demonstrate the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Purpose. This study aims to observe the effect of optimized individualized nursing care applied to intensive care unit (ICU) patients with severe pneumonia (SP). Methods. 440 patients with SP admitted to the ICU of our hospital from January 2019 to June 2020 were provided with routine nursing care (group A), and 550 patients with SP admitted from July 2020 to December 2021 were provided with optimized individualized nursing care (group B). The blood lactate index and acute physiology and chronic health evaluation (APACHE II) scores before and after care were compared between the two groups. The WBC count recovery time, mechanical ventilation time, antipyretic time, and length of hospital stay of the two groups were recorded. The complication rate of the two groups during the nursing care period was compared. The prognosis effect of the two groups after 6 and 12 months of discharge was followed up with the Seattle angina pectoris questionnaire (SAQ). Results. After care, the lactate level and lactate clearance rate were higher in both groups than before care, and the lactate level in group B was lower than that in group A and the lactate clearance rate was higher than that in group A (P < 0.05). After care, APACHE II scores were lower in both groups than before care, and lower in group B than in group A (P < 0.05). After care, the WBC count recovery time, mechanical ventilation time, antipyretic time, and length of hospital stay were shorter in group B than in group A (P < 0.05). During the nursing care period, the complication rate was lower in group B (5.82%) than in group A (11.59%) (P < 0.05). 6 and 12 months after discharge, the SAQ scores were higher in group B than in group A (P < 0.05). Conclusion. Optimized individualized nursing care applied to ICU SP patients can effectively improve the patients' physiological indicators, reduce complications, improve the prognosis of quality of life, and have a positive effect on the patients' speedy recovery. [ABSTRACT FROM AUTHOR]

Recently, due to the growth in machine learning and data mining, for scheduling applications in China's industrial intelligence, we are quite fortunate to witness a paradigm of evolutionary scheduling via learning, which includes a new tool of evolutionary transfer optimization (ETO). As a new subset in ETO, single-objective to multi-objective/many-objective optimization (SMO) acts as a powerful, abstract and general framework with wide industrial applications like shop scheduling and vehicle routing. In this paper, we focus on the general mechanism of selection that selects or gathers elite and high potential solutions towards gathering/transferring strength from single-objective problems, or gathering/transferring storms of knowledge from solved tasks. Extensive studies in vehicle routing problems with time windows (VRPTW) on well-studied benchmarks validate the great universality of the SMO framework. Our investigations (1) contribute to a deep understanding of SMO, (2) enrich the classical and fundamental theory of building blocks for genetic algorithms and memetic algorithms, and (3) provide a completive and potential solution for VRPTW. [ABSTRACT FROM AUTHOR]

In the iron and steel industry, the hardness of steel strips is one of the key performance indicators to evaluate strip quality and guide production for the continuous annealing production line (CAPL). However, the hardness cannot be measured online in the actual production process. Consequently, the precise prediction of the strip hardness based on practical data becomes one of the key tasks during production. In this article, a multiobjective sparse nonlinear ensemble learning with evolutionary feature selection (MOSNE-EFS) method is proposed, which is data-driven modeling of the soft sensor. The method mainly consists of two stages: 1) the construction of individual learners based on multiobjective feature selection learning (MOFSL) and 2) the selection and ensemble of individual learners based on sparse nonlinear ensemble learning via differential evolution (SNEL-DE). The final ensemble model obtained by SNEL-DE is used as the prediction model for strip hardness in CAPL. The proposed method is evaluated with industrial production data. Experimental results indicate that the two strategies, i.e., evolutionary feature selection and sparse nonlinear ensemble, are effective in improving the accuracy and robustness of the prediction model, and further comparison results demonstrate the superiority of the MOSNE-EFS model over the other existing methods. Note to Practitioners—Many quality metrics in the iron and steel industry cannot be online checked, which causes great difficulties in process monitoring, control, and operation optimization. The proposed multiobjective sparse nonlinear ensemble learning with evolutionary feature selection method can help practitioners to construct quality prediction models of many other similar production lines, such as hot rolling and cold rolling, and thus, better process monitoring, control, and optimization of product quality can be achieved. [ABSTRACT FROM AUTHOR]

In this paper, a fast sparse convex optimization algorithm based on a neural network is proposed to improve the direction of arrival estimation. First, a fast L 1 -sparse representation of the array covariance vector model based on the Hermitian Toeplitz structure of array covariance is established to reduce computational complexity in data dimension and variable number. Then, the estimation error upper bound problem is investigated, and a neural network-aided coefficient selection method is developed. The direction of arrival estimation problem is solved through spectral peak search. Finally, the algorithm is extended to the case of off-grid error. The algorithm's advantages in accuracy, calculation speed and robustness is verified by the simulations. [ABSTRACT FROM AUTHOR]

As a new radar system, FDA-MIMO radar has recently developed rapidly, as it has broad prospects in angle-range estimation. Unfortunately, the performance of existing algorithms for FDA-MIMO radar is greatly degrading or even failing under the condition of array gain-phase error. This paper proposes an innovative solution to the joint angle and range estimation of FDA-MIMO radar under the condition of array gain-phase error and an estimation algorithm is developed. Moreover, the corresponding Cramér-Rao bound (CRB) is derived to evaluate the algorithm. The parallel factor (PARAFAC) decomposition technique can be utilized to calculate transmitter and receiver direction matrices. Taking advantage of receiver direction matrix, the angle estimation can be obtained. The range estimation can be estimated by transmitter direction matrix and angle estimation. To eliminate the error accumulation effect of array gain-phase error, the gain error and phase error are obtained separately. In this algorithm, the impact of gain-phase error on parameter estimation is removed and so is the error accumulation effect. Therefore, the proposed algorithm can provide excellent performance of angle-range and gain-phase error estimation. Numerical experiments prove the validity and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we study the joint range and angle estimation problem based in monostatic frequency diverse-array multiple-input multiple-output (FDA-MIMO) radar, and propose a method for range and angle estimation base on compressed unitary parallel factor (PARAFAC). First, the received complex signal matrix is stacked into a third-order complex signal tensor. Then, we can transform the obtained third-order complex signal tensor into a third-order real-valued signal tensor by employing forward–backward and unitary transformation techniques. Next, a smaller third-order real-valued signal tensor is composed by using compressing the third-order real-valued signal tensor. After that, PARAFAC decomposition is applied to obtain the direction matrix. Lastly, the angle and range are estimated by employing the least square (LS) fitting. The estimation error of the proposed method is about 10 % lower than that of the traditional PARAFAC method under the low number of snapshots. When the number of snapshots is high, the performance of the two methods is close. Moreover, the computational complexity of the proposed method is nearly 96 % less than those of the traditional PARAFAC methods in the case of low snapshots, while the gap is larger in the case of high snapshots. The superiority and effectiveness of the method are proved by complexity analysis and simulation experiments. [ABSTRACT FROM AUTHOR]

In this paper, a robust DOA estimation scheme based on sparse Bayesian learning (SBL) for non-circular signals in impulse noise and mutual coupling (MC) is proposed. Firstly, the Toeplitz property of the MC matrix is used to eliminate the effect of array MC, and the array aperture is extended by using the properties of the non-circular signal. To eliminate the effect of impulse noise, the outlier part of the impulse noise is reconstructed together with the original signal in the signal matrix, and the DOA coarse estimation is obtained by balancing the accuracy and efficiency of parameter estimation using the alternating SBL update algorithm. Finally, a one-dimensional search is used in the vicinity of the searched spectral peaks to achieve a high-precision DOA estimation. The effectiveness and robustness of the algorithm for dealing with the above errors are demonstrated by extensive simulations. [ABSTRACT FROM AUTHOR]

Achieving simultaneous and precise prediction of multiple product performance indicators is crucial to ensure stable production in the continuous annealing process. However, the majority of existing quality prediction modeling methods concentrate on developing single-output regression models that are limited to single-indicator prediction scenarios. When dealing with the multi-indicator prediction scenarios, these methods usually require the construction of a separate model for each performance indicator, which leads to more computational and storage resources and more complex maintenance in practical applications. To address this issue, a dual-population multiobjective co-evolutionary matching ensemble learning method called DP-CEMEL is developed. It utilizes dual-population co-optimization to enable the joint learning of the algorithm on input–output relationships and inter-indicator correlations. More specifically, a two-stage learning based individual encoding and decoding method and a performance boosting degree (PBD) based individual evaluation strategy are designed to achieve co-optimization of the dual populations to evolve a set of individuals (i.e., base learners) with good accuracy and low complexity. Then, a PBD based matching ensemble method is applied to get the final multi-indicator prediction model by the optimal matching and ensemble of the evolved individuals. Experimental results on both benchmark data and practical strip production data demonstrate that the proposed DP-CEMEL attains competitive or better performance compared to other state-of-the-art learning methods. [ABSTRACT FROM AUTHOR]

The color-coated steel coil is a high value-added product for steel enterprises, and its production process is affected by multiple factors. How to provide operators with appropriate scheduling schemes is the key to improve the economic benefits of enterprises. In this article, for the scheduling of a single color-coating turn, we establish a multiobjective optimization model that minimizes the number of insertions of transition coils, the thickness jump penalty of adjacent coils, and the switching times of the backup rollers. To address this problem, we propose a piecewise coding approach to ensure that each individual meets the production constraints. Besides, a multiobjective evolutionary algorithm (MOEA) based on decomposition and dynamic local search (D-DLS) strategy is proposed (MOEA/D-DLS). More specifically, the color-coating multiobjective scheduling problem is decomposed into a series of single-objective subproblems and optimized simultaneously. Furthermore, based on the speed of evolution of these subproblems, local search is performed on partial subproblems dynamically. The proposed algorithm is used to solve eight multiobjective scheduling problem instances of color-coating with different scales, and the experimental results demonstrate that the proposed algorithm is very effective compared with four state-of-the-art algorithms. Note to Practitioners—Practical production scheduling problems in iron & steel industry generally need to optimize conflicting objectives simultaneously, which is very hard for practitioners to make appropriate decisions with manual experience. The decomposition-based multiobjective evolutionary algorithm (MOEA) can help practitioners of color-coating scheduling to achieve a set of Pareto optimal decisions with good distribution and tradeoff among three objectives. Since the scheduling of the other production lines shares many similarities with our problem, the proposed model and algorithm can also be applicable to these problems. [ABSTRACT FROM AUTHOR]

The issue of two-dimensional (2D) direction-of-departure and direction-of-arrival estimation for bistatic multiple-input multiple-output (MIMO) radar with a coprime electromagnetic vector sensor (EMVS) is addressed in this paper, and a tensor-based subspace algorithm is proposed. Firstly, the covariance measurement of the received data is arranged into a fourth-order tensor, which can maintain the multi-dimensional characteristic of the received data. Then, the higher-order singular value decomposition is followed to get an accurate signal subspace. By utilizing the uniformity of the subarrays in coprime EMVS–MIMO radar, the rotation invariant technique is adopted to achieve ambiguous elevation angle estimation. Thereafter, the unambiguous elevation angles are recovered by exploring the coprime characteristic of the subarrays. Finally, all azimuth angles are achieved by using the vector cross-product strategy. The tensor nature inherited from the array measurement is fully explored, and the coprime geometry enables EMVS–MIMO radar to achieve larger array aperture than the existing uniform linear configuration; thus, the proposed method offers better estimation performance than current state-of-the-art algorithms. Several computer simulations validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Differential evolution is one of the most powerful evolutionary algorithms for single objective optimization problems in the literature. Its application in the multiobjective optimization problems is also very successful, and many kinds of promising multiobjective differential evolution (MODE) algorithms have been proposed in the literature. This paper develops a new variant of MODE with two features. First, a set of personal archives are maintained to evolve the search process instead of a population with a fixed size, and a truncation procedure is used to enhance selection pressure. Second, multiple biased mutation operators incorporating the target solution quality are proposed, and an adaptive selection method is adopted to allocate the mutation operators to solutions. The proposed MODE is referred to as the MODE with personal archive and biased self-adaptive mutation selection (BiasMOSaDE). A set of 31 benchmark multiobjective problems selected from the literature are adopted to evaluate its performance. Computational results illustrate that the proposed BiasMOSaDE is competitive or even superior to several state-of-the-art MODEs in the literature. [ABSTRACT FROM AUTHOR]

In intelligent transportation systems (ITSs), the communication between the base station (BS) and the vehicles can form a virtual multiple-input multiple-out put (VMIMO) vehicle communication system. This paper addresses the issue of channel estimation in a virtual MIMO system that simultaneously adopt a smart antenna (SA). First, based on quaternion theory, a novel algorithm named quaternion noncircular MUSIC (QNC-MUSIC) is proposed, and direction of arrival (DOA) estimation is achieved for circular and noncircular signals. Then, a dimension reduction procedure is performed for the parameter search by employing the partial derivative of the spectrum function with respect to the noncircular phase; thus, a novel dimensional reduction noncircular MUSIC (DRNC-MUSIC) algorithm with a low computational complexity is proposed for DOA and polarization estimation of circular signals and noncircular signals. Finally, numerical examples are provided to demonstrate the parameter estimation performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The operation optimization problem of furnace process (OOPFP) is to obtain an optimal setting of furnace temperatures to make the reheated slabs have suitable temperature distribution with minimum energy consumption and oxide loss. Since the furnace process is complex, dynamic, and nonlinear, it is difficult to establish a precise process model for the OOPFP. In this paper, firstly, a novel hybrid modeling method combining temperature mechanism with data analytics is proposed to map the relationship of the temperature variables in the furnace process more accurately. The main idea of the hybrid modeling is to compensate for the deviation of original temperature mechanism model through the least squares support vector machine. Then, an improved differential evolution (DE) with feasible region dynamic adjustment strategy and population size gradual shrinking strategy is proposed to solve the OOPFP under hybrid model. Finally, extensive numerical experiments are carried out to demonstrate the effectiveness and accuracy of the hybrid model, and evaluate the performance of the improved DE on solving OOPFP. Experimental results show that the hybrid model is more precise than the original mechanism one, and the proposed DE outperforms other representative algorithms, respectively. [ABSTRACT FROM AUTHOR]

The paper proposes a fast method for the multidimensional parameter estimation of a polarization-sensitive array. Compared with conventional methods (e.g., MUSIC algorithm), the proposed method applies an iterative approach based on Newton's method to obtain joint estimation results instead of a spectral search and dimension reduction. It also extends the original Newton method to the 4D scale using the Hessian matrix. To reduce the complexity of establishing the aim function, Nystrom's method is applied to process the covariance matrix. A new threshold is also proposed to select the results, which can accomplish the parameter estimation with a small number of iterations while guaranteeing a high estimation accuracy. Finally, the proposed algorithm is analyzed in detail and the numerical simulations of various algorithms are compared to verify its effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, the direction of arrival (DOA) estimation for noncircular sources in multiple-input multiple-output (MIMO) radar is dealt with by a novel nuclear norm minimization (NNM) framework. The proposed method exploits the noncircular property of signals to extend the data model for doubling the array aperture. Then a block sparse model of the extended data is formulated without the influence of the unknown noncircularity phase, and a novel signal reconstruction algorithm based on nuclear norm minimization is proposed to recover the block-sparse matrix. In addition, a weight matrix based on the reduced dimensional noncircular Capon (RD NC-Capon) spectrum is designed to reweight the nuclear norm minimization for enhancing the sparsity of solution. Finally, the DOA is estimated from the non-zero blocks of the reconstructed matrix. Due to exploiting the extended array aperture and block-sparse information, the proposed method provides superior DOA estimation performance and higher angular resolution. Furthermore, the proposed method has a low sensitivity to the priori information on the number of sources. Simulation results are presented to verify the effectiveness and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

A reweighted l1‐norm minimisation algorithm for high‐resolution direction‐of‐arrival (DOA) estimation under unknown mutual coupling is proposed in this Letter. First, the proposed method forms a new block representation model by parameterising the steering vector. Then, a reweighted l1‐norm constraint based on the new data model is proposed, in which the principle of a novel multiple signals classification (MUSIC)‐like algorithm is used to construct a weighted matrix. Finally, the DOAs can be achieved by the recovered sparse matrix. Owing to the use of the whole received data and reweighted procedure, the performance of the proposed method is better than the state‐of‐the‐art methods. Extensive simulation experiments confirm that the above inference is correct. [ABSTRACT FROM AUTHOR]

High-efficient heat dissipation has already become a key issue challenging the further development of smart and flexible electronic devices. In this work, a three-dimensional (3D) modified graphene-carbon fiber (MGCF) hybridized skeleton/polydimethylsiloxane (PDMS) composite is prepared. The polyimide (PI) fibers are coated by polyamide acid salt (PAAS)-modified graphene oxide (GO), then freeze-drying technique is used to construct a 3D hybridized structure, followed by high-temperature annealing. Finally, PDMS is impregnated into 3D MGCF hybridized skeletons to generate MGCF/PDMS composites. It is found that GO sheets are covalently welded by PAAS into larger-size GO assemblies, which effectively improves the interfacial interactions and synergistic graphitization between PI molecules and GO sheets. The larger-size graphene assemblies wind and interconnect on the surface of PI-derived carbon fibers to construct a dual-channel 3D thermally conductive network. Such a special structure provides MGCF/PDMS composite with a high thermal conductivity of 1.569 W m−1 K−1 at 2 wt% loading, which is 636% higher than that of pure PDMS. More importantly, MGCF/PDMS composite still displays good mechanical properties with 94.8% of elongation at break and 9.35 MPa of compressive modulus. The outstanding comprehensive properties provide MGCF/PDMS composites with a promising application prospect in lightweight and flexible thermal interface materials (TIMs). 3D MGCF hybridized skeletons are effectively prepared by in situ polymerization, freeze-drying and thermal annealing. PAAS-modification significantly increases the lateral size of modified graphene assemblies to construct a complete and continuous 3D two-channel thermally conductive network, which provides MGCF/PDMS composites with excellent heat conduction capabilities as TIMs. [Display omitted] • 3D MGCF hybridized skeletons were fabricated by wrapping graphene on carbon fibers. • PAAS-modification effectively improves the graphitization degree of 3D skeletons. • MGCF/PDMS composites exhibit high thermal conductivity (1.569 W m−1 K−1) at 2wt% loading.. [ABSTRACT FROM AUTHOR]

In this paper, a covariance vector sparsity-aware DOA estimation method is proposed for monostatic multiple-input multiple-output (MIMO) radar with unknown mutual coupling. The new method firstly utilizes the banded symmetric Toeplitz structure of the mutual coupling matrix (MCM) in both of the transmit and receive arrays to eliminate the unknown mutual coupling. Then a sparse representation framework of the array covariance vector is formulated for obtaining the coarse DOA estimation. Finally, a refined maximum likelihood estimation procedure is introduced to estimate the DOA based on the recovered result. Compared with conventional algorithms, the proposed method provides higher angular resolution and better angle estimation performance. Furthermore, the computational complexity of the proposed method is reasonable, because it only involves single measurement vector (SMV) problem and does not require a dense discretized sampling grid for the recovered procedure. Simulation results are used to verify the effectiveness and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a tensor-based real-valued subspace approach for joint direction of departure (DOD) and direction of arrival (DOA) estimation in bistatic MIMO radar with unknown mutual coupling is proposed. Exploiting the inherent multidimensional structure of received data after matched filtering, a third-order measurement tensor signal model is formulated. For eliminating the effect of the unknown mutual coupling, a sub-tensor can be extracted from the third-order measurement tensor by taking advantage of the banded symmetric Toeplitz structure of the mutual coupling matrix (MCM). Then the sub-tensor can be turned into a real-valued one by forward–backward averaging and unitary transformation, and a real-valued signal subspace is constructed to estimate the DOD and DOA by the higher-order singular value decomposition (HOSVD). Owing to utilize the multidimensional structure of received data and forward–backward averaging technique, the proposed method has better angle estimation performance than MUSIC-Like and ESPRIT-Like algorithms. Furthermore, the proposed method is suitable for coherent targets. Simulation results verify the effectiveness and advantage of the proposed method. [ABSTRACT FROM AUTHOR]

We study the problem of angle estimation for non-circular sources in bistatic multiple-input multiple-output (MIMO) radar system and propose a low-complexity ESPRIT-Root-MUSIC algorithm for joint direction of departure (DOD) and direction of arrival (DOA) estimation. By taking the non-circularity characteristics of the impinging signals, the received data can be extended, which corresponds to double the number of elements in MIMO radar. Then a novel signal subspace, based on the multistage wiener filter, can be determined directly from the extended data, which does not involve the estimation of covariance matrix and its eigenvalue decomposition (EVD), thus implying that the proposed method is computationally efficient. Finally, the DOD and DOA are estimated by ESPRIT and Root-MUSIC, respectively. Compared with EVD-based algorithms, the proposed method provides the comparable angle estimation performance with lower computational complexity. Some simulation results verify the validity of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the problem of direction of arrival (DOA) estimation for monostatic multiple-input multiple-output (MIMO) radar is addressed, and a sparse representation scheme for DOA estimation is proposed. Firstly, the reduced-dimensional transformation matrix and SVD-technique are utilized to reduce the computational complexity of the sparse signal reconstruction. Then the coefficients of the reduced-dimensional Capon (RD-Capon) spectrum are exploited to design a weight matrix for reweighting l 1 norm constraint minimization to enhance the sparsity of the solution, and the DOA can be estimated by finding the non-zero rows in the recovered matrix. The angle estimation performance of the proposed method is better than RD-ESPRIT and RD-Capon algorithms. Furthermore, the proposed method works well for coherence targets without any decorrelation procedure, and has low sensitivity to the priori information of the target number. Simulation results verify the effectiveness of the proposed method. [Copyright &y& Elsevier]

Theoperation optimization problem (OOP) is an important issuein production process control and optimization in process industries,because the desired solution of OOP is the optimal setting for controlvariables, and this setting affects the product quality to a greatextent. In this paper, the OOP in the hot-rolling production processof iron and steel industry is investigated. The OOP lies between theproduction scheduling layer and the process control layer in the integratedautomation system in iron and steel industry, and its main task isto set the optimal values for control variables (i.e., rolling force,exit thickness, etc.) based on the production process constraints.These values are then set as the targets for the process control layer.Different from previous operation optimization of hot rolling processconsidered in the literature, the mathematical model constructed inthis paper considers more practical process constraints such as theramping constraints, to get high-quality products. To efficientlysolve this problem, a hybrid self-adaptive genetic algorithm (HSaGA)is presented. The main feature of the HSaGA is that it utilizes fourdifferent crossover operators and can self-adaptively select the operatorsthat are most appropriate for the current problem. Such a strategycan help to improve the convergence speed and robustness of the geneticalgorithm. Besides this strategy, a solution selection strategy isalso proposed to select parent solutions to perform the crossoveroperation. This strategy divides the entire population into two partsonewith good quality in the objective function value and the other onewith good quality in diversityand then the parent solutionsare selected from them, to improve the search diversity during evolutionand, thus, avoid being trapped in a local optimum. Experiments arecarried out based on benchmark test problems and practical OOP ofthe hot-rolling production process. The computational results showthat the proposed HSaGA is superior to many state-of-the-art evolutionaryalgorithms in the literature for benchmark problems and can obtainbetter results than the empirical method for the OOP of the hot-rollingprocess. [ABSTRACT FROM AUTHOR]

Direction of arrival (DOA) estimation is an important issue for monostatic MIMO radar. A DOA estimation method for monostatic MIMO radar based on unitary root-MUSIC is presented in this article. In the presented method, a reduced-dimension matrix is first utilised to transform the high dimension of received signal data into low dimension one. Then, a low-dimension real-value covariance matrix is obtained by forward–backward (FB) averaging and unitary transformation. The DOA of targets can be achieved by unitary root-MUSIC. Due to the FB averaging of received signal data and the eigendecomposition of the real-valued matrix covariance, the proposed method owns better angle estimation performance and lower computational complexity. The simulation results of the proposed method are presented and the performances are investigated and discussed. [ABSTRACT FROM AUTHOR]

A steam cracking furnace plays animportant role in the naphthapyrolysis process that produces ethylene and propylene. These twoproducts are the key monomers in the petrochemical industry. Thisarticle investigates the multiobjective operation optimization ofthe naphtha pyrolysis process to maximize the yields of ethylene andpropylene. To solve this problem efficiently, multiobjective paralleldifferential evolution with competitive evolution strategies (MOPDE-CES)is employed. In MOPDE-CES, the population is divided into four parts,each part having its own evolution strategy. During evolution, thefour parts of the population compete with each other to survive andevolve. At the same time, they share search information with eachother by communicating with an external archive that stores the obtainednondominated solutions. The MOPDE-CES approach is compared to otherpowerful multiobjective algorithms, and the computational resultsshow that MOPDE-CES is superior based on benchmarking and practicalproblems. In addition, the computational results of MOPDE-CES indicatethat the operation of an ethylene plant can be improved by increasingthe yields of ethylene and propylene. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a novel conjugate ESPRIT (C-ESPRIT) method for direction of arrival (DOA) estimation in monostatic MIMO radar is proposed. Firstly, a reduced-dimensional matrix is employed to transform the data matrix into a low dimensional space. Then the properties of noncircular signals are utilized to construct a new virtual array, whose elements are twice that of the monostatic MIMO radar virtual array with distinct elements. The rotational invariance properties of the new virtual array are figured out to estimate DOA through ESPRIT. Compared with the reduced-dimensional ESPRIT (RD-ESPRIT), the proposed method improves the angular estimation accuracy significantly and detects more targets. Simulation results verify the effectiveness of the proposed method. [Copyright &y& Elsevier]

Frequency diverse array (FDA) radar has attracted much attention due to the angle and range dependence of the beam pattern. Multiple-input-multiple-output (MIMO) radar has high degrees of freedom (DOF) and spatial resolution. The FDA-MIMO radar, a hybrid of FDA and MIMO radar, can be used for target parameter estimation. This paper investigates a tensor-based reduced-dimension multiple signal classification (MUSIC) method, which is used for target parameter estimation in the FDA-MIMO radar. The existing subspace methods deteriorate quickly in performance with small samples and a low signal-to-noise ratio (SNR). To deal with the deterioration difficulty, the sparse estimation method is then proposed. However, the sparse algorithm has high computation complexity and poor stability, making it difficult to apply in practice. Therefore, we use tensor to capture the multi-dimensional structure of the received signal, which can optimize the effectiveness and stability of parameter estimation, reduce computation complexity and overcome performance degradation in small samples or low SNR simultaneously. In our work, we first obtain the tensor-based subspace by the high-order-singular value decomposition (HOSVD) and establish a two-dimensional spectrum function. Then the Lagrange multiplier method is applied to realize a one-dimensional spectrum function, estimate the direction of arrival (DOA) and reduce computation complexity. The transmitting steering vector is obtained by the partial derivative of the Lagrange function, and automatic pairing of target parameters is then realized. Finally, the range can be obtained by using the least square method to process the phase of transmitting steering vector. Method analysis and simulation results prove the superiority and reliability of the proposed method. [ABSTRACT FROM AUTHOR]

• The data model with the bistatic FDA-MIMO radar based on the subarray scheme is presented. • The real-valued subspace method for parameter estimation is provided. • The problem of false range estimation caused by phase ambiguity is analyzed and solved. • An original method is proposed to match the 3D parameter for multi-objective case. The frequency diverse array (FDA) is mainly applied to achieving target localization under the complicated electromagnetic jamming condition. The bistatic multiple-input-multiple-output (MIMO) radar with FDA has gained comprehensive attention in recent years. An innovative method covering the transmitting subarray scheme and the unitary estimation of signal parameters via rotational invariance technology (ESPRIT) is proposed for joint direction of departure (DOD), direction of arrival (DOA), and range estimation. First, a non-overlapping subarray scheme is designed for the transmitting array to decouple DOD and range. For the purpose of enhancing the estimation accuracy and reducing the computational complexity, the real-valued rotational invariance matrix is obtained via applying the unitary transformation to the extended data. The removal method is proposed to avoid the periodic phase ambiguity. The pairing method is put forward to achieving the match of three-dimensional parameter (DOD, DOA and range) for multiple targets. The computational complexity of the proposed algorithm and conventional ESPRIT algorithm are also provided. Finally, extensive experiment results indicate that the proposed algorithm shows the superior performance than the ESPRIT algorithm. [ABSTRACT FROM AUTHOR]

Due to grid division, the existing target localization algorithms based on sparse signal recovery for the frequency diverse array multiple-input multiple-output (FDA-MIMO) radar not only suffer from high computational complexity but also encounter significant estimation performance degradation caused by off-grid gaps. To tackle the aforementioned problems, an effective off-grid Sparse Bayesian Learning (SBL) method is proposed in this paper, which enables the calculation the direction of arrival (DOA) and range estimates. First of all, the angle-dependent component is split by reconstructing the received data and contributes to immediately extract rough DOA estimates with the root SBL algorithm, which, subsequently, are utilized to obtain the paired rough range estimates. Furthermore, a discrete grid is constructed by the rough DOA and range estimates, and the 2D-SBL model is proposed to optimize the rough DOA and range estimates. Moreover, the expectation-maximization (EM) algorithm is utilized to update the grid points iteratively to further eliminate the errors caused by the off-grid model. Finally, theoretical analyses and numerical simulations illustrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the hot rolling production scheduling problem in the steel industry and proposes a new mixed integer programming model for this problem based on the monolithic modeling strategy that integrates batching and scheduling. Using this strategy, the new model can simultaneously schedule multiple turns and simultaneously determine the production timetable of these turns in a global optimal view. A long-term tabu search heuristic using frequency-based memory is developed to obtain near-optimal solutions for this problem. Three kinds of speed-up strategies are developed to accelerate the search procedure of the proposed tabu search. A practical scheduling system combining the proposed mathematical model and the tabu search heuristic has been developed and tested on instances collected from practical production data. The experimental results show that the proposed mathematical model and the tabu search heuristic outperform both the current manual scheduling method and the traditional serial method. [ABSTRACT FROM AUTHOR]

The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to the increasing demand for real-time radar imaging. In this paper, a fast joint direction-of-arrival (DOA) and range estimation framework for target localization is proposed; it utilizes a very deep super-resolution (VDSR) neural network (NN) framework to accelerate the imaging process while ensuring estimation accuracy. Firstly, we propose a fast low-resolution imaging algorithm based on the Nystrom method. The approximate signal subspace matrix is obtained from partial data, and low-resolution imaging is performed on a low-density grid. Then, the bicubic interpolation algorithm is used to expand the low-resolution image to the desired dimensions. Next, the deep SR network is used to obtain the high-resolution image, and the final joint DOA and range estimation is achieved based on the reconstructed image. Simulations and experiments were carried out to validate the computational efficiency and effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

A reweighted nuclear norm minimisation algorithm by considering the inherent rank sparsity of the submatrix block for direction‐of‐arrival (DOA) estimation with unknown mutual coupling in a uniform linear array (ULA) is proposed. A novel block overcomplete dictionary is first derived by parameterising the steering vector to avoid the unknown mutual coupling effect. Then, in order to take advantage of the inherent rank sparsity of the submatrix block, a reweighted nuclear norm minimisation algorithm is introduced, and the weighted matrix can be designed by the Capon spectrum for enhancing the sparsity of scheme. Finally, the DOA estimates can be obtained by the spatial spectrum of the reconstructed matrix. Simulation results verify that the proposed method has distinct advantages over state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

A novel unitary estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, for the joint direction of arrival (DOA) and range estimation in a monostatic multiple-input multiple-output (MIMO) radar with a frequency diverse array (FDA), is proposed. Firstly, by utilizing the property of Centro-Hermitian of the received data, the extended real-valued data is constructed to improve estimation accuracy and reduce computational complexity via unitary transformation. Then, to avoid the coupling between the angle and range in the transmitting array steering vector, the DOA is estimated by using the rotation invariance of the receiving subarrays. Thereafter, an automatic pairing method is applied to estimate the range of the target. Since phase ambiguity is caused by the phase periodicity of the transmitting array steering vector, a removal method of phase ambiguity is proposed. Finally, the expression of Cramér–Rao Bound (CRB) is derived and the computational complexity of the proposed algorithm is compared with the ESPRIT algorithm. The effectiveness of the proposed algorithm is verified by simulation results. [ABSTRACT FROM AUTHOR]

• Multitask neural network is introduced to predict mechanical properties of steels. • Multitask neural network achieves the highest prediction accuracy among algorithms. • Only one model is built to predict multiple properties simultaneously. • The influence of various chemical compositions on steel performances is analyzed. An effective multitask neural network was carried out to predict the mechanical properties of steels based on their chemical compositions. The multitask neural network model outperforms other neural networks, and conventional algorithms. It achieved high prediction accuracy for both tensile strength and elongation, with R2 values of 0.9204 and 0.9409, respectively. Benefiting from the strong inter-task relationships, the multitask neural network enhances performance and parameter efficiency by sharing a potent representation across tasks. Additionally, we analyzed the influence of chemical composition on mechanical properties using the model's parameters, providing valuable insights into the relationship between different chemical compositions and the mechanical properties of steels. [ABSTRACT FROM AUTHOR]